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--- title: Twelve-Month Outcomes in Patients with Obesity Following Bariatric Surgery—A Single Centre Experience authors: - Radu Petru Soroceanu - Daniel Vasile Timofte - Madalina Maxim - Razvan Liviu Platon - Vlad Vlasceanu - Bogdan Mihnea Ciuntu - Alin Constantin Pinzariu - Andreea Clim - Andreea Soroceanu - Ioana Silistraru - Doina Azoicai journal: Nutrients year: 2023 pmcid: PMC10005116 doi: 10.3390/nu15051134 license: CC BY 4.0 --- # Twelve-Month Outcomes in Patients with Obesity Following Bariatric Surgery—A Single Centre Experience ## Abstract Background: As obesity rates continue to rise worldwide, many surgeons consider bariatric procedures as a possible cure for the upcoming obesity pandemic. Excess weight represents a risk factor for multiple metabolic disorders, especially for type 2 diabetes mellitus (T2DM). There is a strong correlation between the two pathologies. The aim of this study is to highlight the safety and short-term results of laparoscopic sleeve gastrectomy (LSG), Roux-en-Y gastric bypass (RYGB, laparoscopic gastric plication (LGP) and intragastric balloon (IGB) as methods used in the treatment of obesity. We followed the remission or amelioration of comorbidities, tracked metabolic parameters, weight loss curves and hoped to outline the profile of the obese patient in Romania. Methods: The target population of this study was represented by patients ($$n = 488$$) with severe obesity who qualified for the metabolic surgery criteria. Starting from 2013 to 2019, patients underwent four types of bariatric procedures and were subsequently monitored over the course of 12 months in the 3rd Surgical Clinic at “Sf. Spiridon” Emergency Hospital Iași. Descriptive evaluation indicators, as well as those of analytical evaluation were used as statistical processing methods. Results: A significant decrease in body weight was recorded during monitoring and was more pronounced for patients who underwent LSG and RYGB. T2DM was identified in $24.6\%$ of patients. Partial remission of T2DM was present in $25.3\%$ of cases, and total remission was identified in $61.4\%$ of patients. Mean blood glucose levels, triglycerides, LDL and total cholesterol levels decreased significantly during monitoring. Vitamin D increased significantly regardless of the type of surgery performed, while mean levels of vitamin B12 decreased significantly during monitoring. Post-operative intraperitoneal bleeding occurred in 6 cases ($1.22\%$) and a reintervention for haemostasis was required. Conclusions: All procedures performed were safe and effective methods of weight loss and improved associated comorbidities and metabolic parameters. ## 1. Introduction As the prevalence of obesity nearly tripled during the last three decades, it has become a global public health issue. Obesity is associated with considerable morbidity and mortality rates and could reverse the current trend of increasing life expectancy [1]. Prevention efforts are difficult due to the multifactorial nature of the disease and treatment is always challenging. Obesity is a result of the complex interaction between human behaviour, environmental factors and genetic predisposition. It represents the most important risk factor for multiple chronic diseases, such as T2DM, cardiovascular, pulmonary, renal and oncological pathologies [2,3,4]. The World Health Organization reported more than 1.9 billion overweight and 650 million obese adults in 2016 [5]. It already represents a burden to the economy with massive healthcare costs. Therefore, obesity prevention and treatment demand the attention of researchers and the scientific community [6]. Obesity is classified with the aid of the body mass index (BMI). Available treatment for obesity includes lifestyle changes through diet and physical exercise, medication and invasive procedures known as bariatric or metabolic surgery. Numerous studies report that the surgical approach has superior results compared to non-surgical controls [7,8,9]. To date, bariatric surgery is the most effective treatment both in terms of weight loss and weight loss maintenance. Literature data also show amelioration or remission of weight associated comorbidities [2,3,4]. Bariatric surgery is considered complex due to the anatomical and physiological changes that develop in the gastrointestinal tract. The morbid profile of the obese patient is also a challenge. Technical advances, especially the introduction of laparoscopy, have substantially reduced the risks associated with these procedures [10]. Although bariatric surgery is very efficient, there is still controversy regarding what the optimal type of procedure is. For patients with severe clinical obesity and high anaesthetic risk, Douglas Hess described biliopancreatic diversion with duodenal switch (BPD-DS). This procedure was developed as an improvement to BPD. By preserving the pylorus, Hess has eliminated some common shortcomings such as dumping syndrome, biliary reflux and anastomotic ulcerations [11]. The surgery was performed in two steps in order to minimize perioperative risk related to technical complexity and long operative time. A longitudinal sleeve gastrectomy (LSG) that removed over $80\%$ of the stomach was initially performed. Satisfied by the results, both in weight loss and comorbidity improvement, many of the patients never came back and LSG became a standalone procedure. It has become one of the most performed procedures worldwide due to reduced technical difficulty and optimal results. It is a purely restrictive procedure and can be easily converted to Roux-en-Y gastric bypass (RYGB) if results are inadequate or complications arise [12]. LSG is not recommended in patients with gastroesophageal reflux disease (GERD) or hiatal hernias (HH) because it can worsen their symptoms. RYGB was initially described in the 1970s based on observations regarding unintentional weight loss in patients with peptic ulcer disease who underwent gastric resections. It combines the principles of restriction and malabsorption and is, to this day, one of the most efficient procedures both in terms of weight loss and remission rates of associated comorbidities [13]. Laparoscopic gastric plication was introduced in 2007 as a more conservative, reversible and cheaper alternative to LSG. It is a restrictive procedure in which a double plication of the greater curvature of the stomach is performed. It is preferred by patients because it does not alter the physiology of the stomach and does not require a resection. However, long-term results are debatable [14]. Endoscopic treatment is the least invasive option. The intragastric balloon (IGB) is a temporary restrictive procedure that occupies part of the stomach and induces early satiety. Aimed to fill the gap between surgery and medication, it is the preferred method for patients with low grade obesity or for those reluctant to undergo more invasive procedures [15]. Depending on the BMI and associated comorbidities, the surgeon together with the patient will choose the most appropriate procedure. Consulting a multidisciplinary team in a specialized centre before surgery can help to manage the patient’s modifiable risk factors, reduce perioperative complications and optimize outcomes. ## 2. Materials and Methods The target population of this study consisted of patients with obesity who failed to lose weight through diet and physical exercise. They underwent four types of bariatric surgical procedures from 2013 to 2019 and were subsequently monitored over the course of 12 months in “St. Spiridon” Emergency Clinical Hospital Iași, 3rd Surgical Clinic. The subject selection was performed by non-random sampling and data collection was performed retrospectively, longitudinally, based on the information contained in the medical documents of each patient. We included 488 patients who underwent bariatric surgery during the study period. They were followed up at 1, 3, 6 and 12 months after the procedure. LSG was performed on 443 patients, RYGB on 30 patients, 7 patients opted for LGP and 8 patients for IGB (Figure 1). At the time of the study, eligibility conditions for bariatric procedures were in accordance with the 1991 National Institutes of Health (NIH) Consensus Statement recommendations, as follows [16]:patients with a BMI > 40 kg/m2 with or without coexisting medical conditions and who do not present a high anaesthetic-surgical risk;patients with a BMI > 34.9 kg/m2 with one or more obesity-related comorbidities or with a significant impairment in quality of life (T2DM, essential hypertension, dyslipidaemia, sleep apnoea syndrome or non-alcoholic fatty liver disease—NAFLD);patients over 18 years old. The procedures performed were LSG ($$n = 443$$), RYGB ($$n = 30$$), LGP ($$n = 7$$) and IGB ($$n = 8$$). During the study period, we monitored the main demographic and morphological characteristics, the evolution of comorbidities associated with obesity and the biological parameters in each of the surgical groups. The data were extracted from medical charts, then uploaded and processed using the statistical functions in SPSS 18.0. Due to the longitudinal data collection, all patient data protection provisions were enforced, as the medical team in the teaching hospital were well informed on data and patients’ rights protection. In calculating the differences between two or more groups at the $95\%$ significance threshold, depending on the distribution of the value series, the t-Student test, the F test (ANOVA), the χ2 test, the Pearson correlation coefficient (r) and the ROC curve were applied to the quantitative variables. ## 3.1. Patient Distribution In our centre, the first LSG procedures were performed in 2013. Since then, the number of procedures performed annually has followed an increasing trend (Figure 2). Most of the interventions were performed in 2019 ($$n = 106$$), until all elective surgeries were prohibited at the beginning of 2020 due to the global COVID-19 pandemic. ## 3.2. Demographic Characteristics In the study group, $63.9\%$ of the patients were female, with a sex ratio of F/$M = 1.8$/1. The age of the patients ranged from 18 to 70 years, with a mean of 40.81 years, close to the median of 41 years, suggesting a normal distribution of the series of values. The highest frequency was registered in the fourth decade of life ($31.4\%$). ## 3.3. Distribution by Type of Procedure Performed The most common procedure was LSG, with a proportion of $90.8\%$ (443 patients). RYGB represented $6.14\%$ of the interventions (30 patients), $1.4\%$ were LGP (7 patients) and $1.6\%$ were IGB (8 patients). The average post-operative length of stay was 2.79 days. ## 3.4. Preoperative Comorbidities and Morphologic Characteristics Patient weights ranged from 77 to 216 kg, with an overall average of 132.33 ± 23.07 kg. Preoperative BMI ranged from 29.11 to 66.48 kg/m2, with a mean of 45.69 ± 10.81 kg/m2 in patients treated with RYGB and 45.01 ± 6.84 kg/m2 in patients treated with LSG. These values were significantly higher than those recorded in patients treated with LGP (35.20 ± 1.92 kg/m2) or IGB (32.13 ± 1.70 kg/m2) ($$p \leq 0.001$$). Essential hypertension was identified in $30.53\%$ of patients. Of them, $27.66\%$ of patients had stage I hypertension, $1.84\%$ had stage II and $1.02\%$ had stage III. All patients were under chronic antihypertensive medication. Dyslipidaemia was present in $48.77\%$ of patients, hepatomegaly in $66.8\%$ of cases and NAFLD in $81.76\%$ of patients. Vitamin D deficiency was identified in $53.07\%$ of the cases, and 30 patients ($6.14\%$) presented gastroesophageal reflux disease (GERD) symptoms. T2DM was identified in $\frac{1}{4}$ of the patients in the study group ($24.6\%$), with slightly higher frequencies in the group of patients who underwent IGB ($37.5\%$) and in the patients who underwent LSG ($25.3\%$). The differences were not statistically significant ($$p \leq 0.313$$). Sleep apnoea syndrome (SAS) was identified in $88.1\%$ of the study group, with $69.9\%$ of patients having a severe form. It occurred more frequently in women ($62.6\%$ vs. $37.4\%$; $$p \leq 0.078$$) in the over 40 age group ($46\%$ vs. $54\%$; $$p \leq 0.571$$) and in those with urban residence ($61.6\%$ vs. $38.4\%$; $$p \leq 0.564$$). ## 3.5. Outcomes Regardless of the type of procedure performed and the age and gender of the patient, the mean BMI has decreased from one follow-up to another during the 12 months ($p \leq 0.001$): from 45.01 ± 6.84 kg/m2 to 31.27 ± 4.46 kg/m2 for LSG, from 48.69 ± 10.81 kg/m2 to 31.33 ± 4.63 kg/m2 for RYGB, from 35.20 ± 1.92 kg/m2 to 26.04 ± 3.18 kg/m2 for LGP and from 32.13 ± 1.70 kg/m2 to 25.58 ± 1.30 kg/m2 for IGB (Figure 3). Partial remission of T2DM was present in $25.3\%$ of the study group and total remission was identified in $61.4\%$ of patients. Plotting the ROC curve, it is highlighted that neither the preoperative weight (G0-AUC = 0.493; $95\%$ CI: 0.433–0.553; $$p \leq 0.819$$) nor the excess weight (EW-AUC = 0.527; $95\%$ CI: 0.469–0.586; $$p \leq 0.369$$) were good predictors for partial or total remission of T2DM (Figure 4). Fasting glucose levels at 12 months in the presence or absence of glucose-lowering pharmacologic treatment were used to define total or partial remission. The mean preoperative weight was significantly higher in patients with apnoea than in those without apnoea (133.55 vs. 123.31 kg; $$p \leq 0.001$$). Lower follow-up weight loss is also identified throughout the monitoring. One year after the procedure, the average weight remained significantly higher in patients with apnoea syndrome (92.41 vs. 86.45 kg; $$p \leq 0.003$$), but the percentage of weight lost at 12 months (%EWL12) did not differ significantly compared to patients without apnoea syndrome ($p \leq 0.05$). Plotting the ROC curve highlights that both the preoperative weight (G0-AUC = 0.623; $95\%$ CI: 0.551–0.696; $$p \leq 0.002$$) and the excess weight (EW-AUC = 0.615; $95\%$ CI: 0.542–0.687; $$p \leq 0.004$$) were good predictors of the presence of sleep apnoea syndrome (Figure 5). Total cholesterol and LDL cholesterol levels decreased significantly during monitoring, from an average of 248.51 ± 46.04 mg/dL to 193.43 ± 41.11 mg/dL ($$p \leq 0.001$$) and from 227.85 ± 77.63 mg/dL to 97.42 ± 17.52 mg/dL, respectively ($$p \leq 0.001$$), and did not correlate with any specific type of intervention. Triglyceride levels decreased significantly during monitoring, from a mean value of 350.40 ± 160.77 mg/dL to 212.81 ± 136.73 mg/dL ($$p \leq 0.001$$) at the end of the follow-up. Mean blood glucose levels decreased significantly during monitoring, from an average of 112.53 ± 60.88 mg/dL to 91.38 ± 15.77 mg/dL ($$p \leq 0.001$$). Vitamin D was dosed before surgery and at 3, 6 and 12 months. During monitoring, values increased significantly regardless of the type of surgery applied from 21.43 ± 11.36 ng/dL to 35.31 ± 11.39 ng/dL ($p \leq 0.001$). At the same time, the mean vitamin B12 levels decreased significantly during monitoring, from an average of 497.60 ± 215.83 pg/dL to 463.81 ± 211.41 pg/dL ($$p \leq 0.001$$). All monitored metabolic parameters were centralised in the adjacent table (Table 1). We consider it necessary to define the parameters used to describe weight loss: Body Mass Index (BMI) = Weight (kg)/Height2 (m);Ideal Body Weight (IBW) = 50 + [0.91 × (height in cm − 152.4)] in men;Ideal Body Weight (IBW) = 45.5 + [0.91 × (height in cm − 152.4)] in women;Excess Weight (EW) = Actual weight − IBW;Percentage of Weight Loss (%EWL) = (postoperative weight loss)/(preoperative EW) × 100. ## 4. Discussion The aim of the study is to report clinically relevant data and to highlight the results of bariatric surgical procedures regarding weight loss and the subsequent effect on ameliorating comorbidities and metabolic parameters. The validation of our results by comparison is challenging, as populations have different social, economic, and cultural backgrounds and eating habits. We thus hope to outline the metabolic profile and morphological characteristics of the patient with obesity in a middle-income Eastern European country. The first bariatric procedures were performed in 2013 and were exclusively LSG for the first two years. This procedure was also performed the most during the span of the study ($90.8\%$). The number of procedures performed increased annually. LSG is currently the most practiced procedure worldwide. This is due to the safety profile of the intervention, with a steeper learning curve and lower rates of intraoperative and postoperative complications. Comparable efficiency and results as RYGB are proven in numerous studies [17,18,19]. As the experience of the surgical team grew, more complex bariatric procedures were performed. In addition, patients became more aware that obesity is a serious health problem and started asking for a surgical solution. LSG does not require gastro-intestinal anastomoses and there are fewer short- and long-term complications associated with the procedure. Initially described by E. Mason in 1967, perfected and standardized by Griffin et al. in 1977, RYGB remains the cornerstone of metabolic surgery to this day, combining both the principles of restriction and malabsorption. RYGB was gradually introduced in our centre and performed more often alongside some redo procedures, surgical management of complications (migrated adjustable gastric bands) and 14 Single Anastomosis Duodeno-Ileal Switch (SADIS), but these patients were not included in the current study. Analysing the demographic characteristics of the patients in this study, especially gender distribution, we can observe that $63.9\%$ of patients were female, with a sex ratio of F/$M = 1.8$/1. Other studies also confirm a higher prevalence of obesity among the female population: 42.48 vs. $33.85\%$ [20,21]. One cross-sectional study carried out on a cohort of 3361 patients follows the association between BMI and depressive disorder. It emphasizes a U-shaped relationship between the two pathologies. They tend to be more strongly associated in underweight male patients and in female patients with a higher BMI. This may be due to the social promotion of a more muscular body type among men, while a leaner body type is more desirable among females. This may contribute to a higher addressability to bariatric surgery for females [22]. A closer analysis pertaining to the age of our patients highlights that almost $60\%$ are between the ages of 30 and 49, a socio-economically active population, thus emphasizing the importance of finding the optimal solution. The mean weight at the time of surgery is higher among patients who underwent RYGB. This is in line with the literature data, as this type of intervention is especially indicated for patients with higher levels of obesity and those for whom LSG is not recommended. Contraindications for LSG include addictive behaviour towards sweet foods with a high caloric index, symptomatic GERD or hiatal hernias. In these cases, LSG would aggravate the symptoms [23,24]. In addition, while LSG is exclusively a restrictive procedure, RYGB also adds malabsorption to the equation. This is achieved by altering the anatomy of the digestive tract, shortening the path that food has to travel and implicitly lowering the amount of nutrients that are being absorbed [25]. Although the long-term results are comparable between LSG and RYGB, the associated comorbidities and the particularities of each patient must be considered. Regardless of the type of surgery chosen, the best results will be obtained by patients who understand the risks, benefits and assume responsibility for their diet and regular post-operative clinical and biological follow-ups [26]. The most important parameters that describe the dynamics of the weight curve are represented by BMI and %EWL. In this study, there were no significant differences gender-wise. The BMI values had a favourable trend at all timepoints, regardless of the type of intervention performed. This uniform variation can be attributed to the fact that patients with LGP and IGB had lower mean weight values at the time of surgery than those with LSG and RYGB and associated fewer comorbidities. Similar results are reported in some long-term studies [27,28]. The role of bariatric surgery in the partial or total remission of T2DM is already well established and some guidelines even recommend extending surgical indications to patients with class I obesity in whom glycaemic control under optimal drug treatment is inadequate [2,7]. In our study group, T2DM was identified in $24.6\%$ of patients, with a slightly higher frequency in the LSG group. For these patients, the mean preoperative weight was similar to that of non-diabetic patients, but %EWL12 was slightly higher in the T2DM group. Plotting the ROC curve highlighted that neither preoperative weight nor excess weight were good predictors of partial or total remission of T2DM. Remission criteria were defined as follows [29]:complete T2DM remission—fasting plasma glucose < 100 mg/dL and/or HbA1c < $6\%$ for at least 1 year after surgery in the absence of glucose-lowering pharmacologic treatment;partial T2DM remission—fasting plasma glucose < 126 mg/dL and/or HbA1c < $6.5\%$ without antidiabetic medication for at least 1 year. Complete remission spanning over 5 years or more is considered curative [29]. A randomized clinical trial reports a remission rate of $66.7\%$ at two years after surgery, similar to our investigation [30]. However, some patients relapsed during the 10-year monitoring period, but glycaemic control remained satisfactory. It seems that one important factor leading to remission is the hypocaloric state induced by the prolonged caloric deficit following bariatric procedures. Some studies on patients with T2DM highlighted the immediate improvement of insulin sensitivity similar to their surgical counterparts just by having a caloric restriction, similar to that in the first 10 to 20 days after bariatric surgery [31]. This effect was mainly attributed to the amelioration of the hepatic insulin sensitivity. The favourable effects of insulin on skeletal muscles were observed later on and were more weight loss dependent. Incretin and insulin levels are both severely altered in obese patients with T2DM. However, they seem to return to normal values shortly after bariatric surgery, especially in those who underwent RYGB [32]. The factors and action mechanisms leading to remission are still unclear. It seems that patients with a more substantial weight loss have higher chances than others. In addition, the duration of T2DM prior to surgery, poor glycaemic control and intensive use of insulin seem to negatively influence remission rates. These patients also have a higher chance of relapse [33,34]. Lack of remission should not be considered as a failure of bariatric surgery. Long term control of all metabolic comorbidities is equally as important and the International Diabetes Federation even advocates for the complementary use of medication to prolong and enhance the effects of surgical procedures [34]. As of November 2022, the American Society of Metabolic and Bariatric Surgery (ASMBS) and the International Federation for the Surgery of Obesity and Metabolic Disorders (IFSO) updated the criteria for bariatric surgical procedures as follows:patients with a BMI > 35 kg/m2 with or without coexisting medical conditions and who do not present a high anaesthetic-surgical risk;patients with a BMI of 30–34.9 kg/m2 with one or more obesity-related comorbidities or with a significant impairment in quality of life (T2DM, essential hypertension, dyslipidaemia, sleep apnoea syndrome or NAFLD). Due to numerous studies that demonstrate the efficacy, long-term results and safety, bariatric surgery may now be considered for adolescents and patients over 70 years that are appropriately selected. These indications are widely accepted and the evidence indicates superior results compared to non-surgical interventions in terms of both weight loss and glycaemic control [35]. All interventions had a favourable outcome on total and LDL cholesterol at all time-points. The lipid profile improved or normalized during the 12-month period of monitoring. In our study group, obstructive sleep apnoea (OSA) was the main factor that delayed the surgical procedure. As seen in the data presented above, severe forms were diagnosed in patients unaware of this pathology. They required continuous positive airway pressure (CPAP) treatment and re-evaluation from the pneumonologist in order to assess the improvement in the apnoea–hypopnea index (AHI). Poor oxygenation may be responsible for ischaemia in sutured, resected or anastomosed tissue which is a predisposing factor to anastomotic leaks. Undiagnosed patients present higher risks of postoperative cardiac events and respiratory failure [36]. These are the main reasons bariatric candidates require preoperative screening, treatment, postoperative monitoring and extensive follow-ups. Polysomnography is the gold standard for diagnosing OSA [36]. Although few studies focus on sleep apnoea improvement in patients who underwent bariatric procedures, they demonstrate the superior outcomes in the surgical group compared to the common medical care group [37,38]. At the end of the monitoring period, some patients in the control group had an AHI increased by five and a higher BMI than the baseline values. In contrast, RYGB had a significant impact on OSA remission or improvement in moderate and severe forms [38]. Regarding our patients, the ROC curve highlights that both the preoperative and the excess weight were good predictors of the presence of OSA. The obstructive aetiology of apnoea syndrome may explain this result. In patients with severe obesity, excess fat in the cervical extremity may cause compression of the upper airways when in the supine position. A randomized, double-blind clinical study among patients with obesity, altered basal blood glucose and hypovitaminosis D observed that correcting vitamin D deficiency improves insulin resistance and reduces the risk of progression to T2DM [39]. Furthermore, our patients’ serum levels of vitamin D show a significant increase during monitoring due to the initiation of nutritional supplementation aimed to correct hypovitaminosis and protein deficiency starting with the first month postoperatively. The role of vitamin D in modulating the immune system has been proven in numerous studies. The low vitamin D levels among the European general population represent a public health problem. They have been associated with a predisposition to infections and chronic diseases [40]. A recent study concludes that individuals with low vitamin D levels are $80\%$ more likely to acquire a COVID-19 infection compared to a control group with normal levels. Moreover, obesity and overweight are positively associated with higher rates of mortality in SARS-CoV-2 infections. A strong association is also underlined in previous MERS and SARS epidemics [41]. Vitamin D can be obtained from exogenous sources (nutrition or supplementation) or synthetized in the presence of UV-B light, but the levels vary depending on season and latitude of residence [42]. Some studies report that the effects of vitamin D deficiency could be reversible. Supplementation proved a favourable outcome increasing both the size and number of aged skeletal muscle fibres. It also improved muscle strength and balance in laboratory animals [43]. Among several murine animal models, some studies mention the beneficial effects of prolonged vitamin D administration on adipose tissue remodelling. The histological findings suggest that it can regulate adiposity, decrease lipid accumulation and prevent sarcopenia [44,45]. Micronutrient deficiencies are common after bariatric surgery. Therefore, many patients need routine vitamin and mineral supplementation. These changes can also be seen in our study, especially in the case of vitamin B12 found only in exogenous sources. These deficiencies could be explained by reduced dietary intake and anatomical and physiological changes in the gastrointestinal tract, especially in the case of malabsorptive procedures. However, the incidence of vitamin and mineral deficiencies following LSG compared to RYGB have yet to be reported [46]. At the end of the study, the levels of vitamin B12 decreased significantly, thus confirming the necessity of long-term supplementation with iron and vitamin B12 in addition to multivitamins and general minerals. Unmonitored patients may develop anaemia caused by altered gastrointestinal absorption. Short term complications consisted of six post-operative intraperitoneal bleedings in patients with LSG. Laparoscopic reintervention during post-operative day 1 for definitive haemostasis was required in all cases. Bleeding occurred either from the trocar site, divided gastro-colic ligament or at the level of the remaining stomach due to imperfect stapling. Pertaining long-term complications, we could mention a case of small bowel volvulus caused by an entero-parietal flange and a case of common bile duct obstruction due to migrated gall stones. Both patients had underwent RYGB and the latter was treated through a laparoscopic gastrotomy in the excluded part of the stomach to allow access to the duodenum and to endoscopically remove the gall stones. This was caused by the anatomical changes in the GI tract that occur as a consequence of RYGB. Limitations of the study include incomplete data availability. Although the patient pool was higher, some were not compliant with the follow-up program and could not be included in the study. Some patients had their residency abroad or in other regions. Long term results are not reported. Although our recommendation for post-bariatric follow-up is every 6 months after the first year, few patients presented long-term. A more procedure-oriented comparison could not be performed as the vast majority of our patients underwent LSG. Strengths include the large sample size, the close monitoring of the main metabolic parameters throughout the first 12 months, and outlining the profile of the patient with obesity in a developing Eastern European country. This study is also among the first reports of bariatric surgery outcomes in Romania. We hope to continue following our patients further in order to gain a better understanding of their needs and expectations. ## 5. Conclusions At 12 months after surgery, the weight loss percentage was not significantly correlated with a lower or higher preoperative weight. All bariatric procedures were effective methods of weight loss and improved weight associated comorbidities and laboratory parameters. The outcomes of LSG and RYGB were comparable and both procedures had better outcomes in terms of weight loss than LGP and IGB. 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--- title: 'Characterizing Dietary Advanced Glycation End-Product (dAGE) Exposure and the Relationship to Colorectal Adenoma Recurrence: A Secondary Analysis' authors: - Maren Sfeir - Elizabeth T. Jacobs - Lindsay N. Kohler - Susan E. Steck - Angela K. Yung - Cynthia A. Thomson journal: Nutrients year: 2023 pmcid: PMC10005122 doi: 10.3390/nu15051126 license: CC BY 4.0 --- # Characterizing Dietary Advanced Glycation End-Product (dAGE) Exposure and the Relationship to Colorectal Adenoma Recurrence: A Secondary Analysis ## Abstract Limited studies have evaluated the association between dietary advanced glycation end-product AGE (dAGEs) intake and cancer risk; however, no studies have addressed adenoma risk or recurrence. The objective of this study was to determine an association between dietary AGEs and adenoma recurrence. A secondary analysis was conducted using an existing dataset from a pooled sample of participants in two adenoma prevention trials. Participants completed a baseline Arizona Food Frequency Questionnaire (AFFQ) to estimate AGE exposure. NƐ- carboxymethyl-lysine (CML)-AGE values were assigned to quantify foods in the AFFQ using a published AGE database, and participants’ exposure was evaluated as a CML-AGE (kU/1000 kcal) intake. Regression models were run to determine the relationship between CML-AGE intake and adenoma recurrence. The sample included 1976 adults with a mean age of 67.2 y ± 7.34. The average CML-AGE intake was 5251.1 ± 1633.1 (kU/1000 kcal), ranging between 4960 and 17032.4 (kU/1000 kcal). A higher intake of CML-AGE had no significant association with the odds of adenoma recurrence [OR($95\%$ CI) = 1.02 (0.71,1.48)] compared to participants with a lower intake. In this sample, CML-AGE intake was not associated with adenoma recurrence. Future research is needed and should be expanded to examine the intake of different types of dAGEs with consideration for the direct measurement of AGE. ## 1. Introduction Colorectal cancer (CRC) is the third most commonly diagnosed cancer in the US and the second leading cause of cancer mortality [1]. It is estimated that in 2022 in the US, there will be 151,030 new colorectal cancer cases and 52,850 estimated deaths as a result of this disease [2]. It is recommended that men and women over the age of 45 years undergo regular screening for colorectal cancer 1. A proportion of individuals will require colonoscopy-based screening, including higher-risk groups, such as individuals with a prior diagnosis of gastrointestinal polyps [3]. There are different types of polyps that can develop asymptomatically along the inner lining of the colon over long periods of time. Adenomatous polyps (adenomas) are the most common type of polyps and are most prone to becoming cancerous, and their removal is a primary prevention strategy to reduce CRC incidence. About half of the adult screened population will be diagnosed with this specific type of polyp over their lifetime. Efforts to reduce the risk of adenomas have been widely explored [4], with a significant interest in modifiable behavioral approaches, including dietary modification [5,6]. Diet is associated with CRC risk [7,8], with evidence of a positive association between the regular consumption of relatively higher amounts of red meat, sugar, saturated fat, alcohol, and adenoma development and CRC risk [9,10]. Higher body adiposity and physical inactivity are also significant drivers of CRC [11,12], while consuming greater amounts of higher-fiber foods, fruits, and vegetables is associated with a decreased risk for CRC [13,14]. There are several postulated mechanisms by which diet may promote adenoma development, including effects on inflammation, DNA damage, oxidative stress, alterations in gut mucosal integrity, changes in the gut microbiota, and exposure to carcinogens [15,16,17,18,19,20]. One dietary exposure that has been sparsely evaluated to date is advanced glycation end-products (AGEs). AGEs are heterogenous compounds that are formed endogenously by nonenzymatic reactions or exist in select foods consumed in the human diet [21]. These compounds are known to promote inflammation and reactive oxygen species that, in turn, promote cancer [22,23]. Within the gut, AGEs can promote inflammation by downregulating antioxidative pathways or promoting dysbiosis related to the gut microbiota [22]. The AGE receptor, RAGE, has been identified in cancer-related pathways suggesting that the intake of these molecules may be associated with adenoma development [24,25]. Foods with high amounts of AGEs include animal-derived products, highly processed foods, and foods that are high in saturated fat [26]. The limited studies to date have suggested an association between dietary AGE (dAGE) intake and cancer risk, specifically breast, pancreatic, and colorectal cancer [27,28,29]. However, there are no data robustly evaluating the role of dAGEs and the risk of adenomas. Identifying a dietary modifiable risk factor that drives precancerous lesions such as adenomas could inform primary prevention strategies for CRC. The objective of this study was to determine whether dAGEs were associated with the risk of precursor lesions to CRC among adults who previously underwent screening for adenoma and CRC. Specifically, we aimed to develop an AGE database for the Arizona Food Frequency Questionnaire to estimate dAGE intake and exposure among participants in the Wheat Bran Fiber (WBF) and Ursodeoxycholic Acid (UDCA) study cohorts [30,31]. Further, we described food groups associated with greater AGE exposure in this study population to inform future exposure assessments. Lastly, we sought to explore if these relationships varied by age, sex, and body mass index (BMI). We hypothesized that higher dAGE intake among adults participating in these two colorectal polyp prevention trials would be associated with greater odds for adenoma recurrence. ## 2.1. Study Design & Parent Studies A secondary analysis was conducted using data from a pooled sample of participants from two double-blind, randomized controlled trials at The University of Arizona Cancer Center. Participants were recruited between 1990–1995 and 1995–1999 for the WBF and UDCA studies, respectively. All participants were undergoing follow-up screening colonoscopy at gastrointestinal practices located in Phoenix, AZ, and Tucson, AZ. To qualify for this study, participants had at least one adenoma detected (>3 mm) and were removed via colonoscopy within 6 months prior to study enrollment. Inclusion criteria across the trials included adults (male and female) between the ages of 40 and 80 years. The primary outcomes in each study were adjudicated adenoma status at an average of 3.1 years of follow-up. WBF participants were randomized to either a daily wheat bran fiber supplement (13.5 g/day) or a low-fiber supplement (2.0 g/day) [32]. UDCA participants were randomized into either a treatment group (8–10 mg UDCA per kilogram of body weight) or the placebo group [31]. ## 2.2. Study Sample The present analysis was conducted using data collected from 1976 participants, including baseline characteristics, dietary intake, and follow- up for the evaluation of recurrent adenomas or CRC. Additionally, for this pooled analytical sample, we excluded participants under the age of 50 years ($$n = 139$$), the recommended CRC screening age at the time, to reduce bias being introduced by including individuals undergoing diagnostic screening before age 50 years, as there was likely an underlying condition or symptom that led the participant to seek a colonoscopy. We also excluded participants with any missing data (Figure 1). Both trials were previously approved by The University of Arizona Human Subjects Committee. ## 2.3. Data Collection Participants in both trials were requested to complete a baseline questionnaire at the enrollment clinic visit. The baseline questionnaire included questions about participant demographics, educational status, marital status, health history, smoking status, NSAIDs use, and history of CRC and/or adenomas. Dietary intake was estimated using the validated Arizona Food Frequency questionnaire (AFFQ). The 175-item food frequency questionnaire assessed food intake over a period of 12 months based on times per day, week, or month [33]. The participant’s physical activity level measured in MET-hours/week (in the past four weeks) was self-reported using the validated 59-item Arizona Activity Frequency questionnaire [34]. ## 2.4. Data Availability Statement The data generated in this study are available upon request from the corresponding author. Data will be shared according to the University of Arizona data sharing policy. ## 2.5. Dietary AGE intake Assessment Uribarri et al. developed an AGE food composition database item [26] by estimating NƐ-carboxymethyl-lysine (CML)-AGE levels in over 500 food items using an enzyme-linked immunosorbent assay (ELISA) based on a monoclonal anti-CML antibody [35]. CML-AGE is one of many identified AGEs and has been the most commonly measured and studied to date [36]. To estimate dietary AGE intake in our sample, we applied an FFQ food mapping approach similar to that used in other studies [27]. Using the published AGE database [26], CML-AGE values were assigned and quantified to foods in the AFFQ from both trials to estimate dAGE exposure (Figure 2). This mapping process was completed by two independent investigators (M.S. and A.Y.), and discrepancies were resolved with additional input from L.K. AFFQ food items not found in the AGE database were assigned average CML-AGE values created with the existing food items within the same food group. For example, since the AFFQ food item “onions” does not exist in the AGE database, it was assigned an average CML-AGE value of vegetables included in the database. Combination/mixed dishes were assigned CML-AGE average values that were created by assigning CML-AGE values to the individual food components that comprised the recipe of the combination/mixed dish. Recipes for that food item were obtained from the Nutrition Data Systems for Research (NDS-R) software [37] which contains information about nutrient intake, ratios, and serving size. Food items from the AFFQ with different options for cooking methods were assigned an average CML -AGE value across cooking methods. ## 2.6. Outcome Variable: Adenoma Recurrence The primary outcome variable for this analysis was colonoscopy-detected adenoma recurrence. To collect recurrent adenoma or adenocarcinoma characteristics, medical records, and pathology reports were reviewed by pathologists in both trials. Adenocarcinoma recurrence was classified as a binary outcome: yes or no as to whether a recurrent adenoma was present during a follow-up colonoscopy. Additional characteristics of recurrent adenomas that were collected for evaluation included the average of the largest adenoma size (mm), adenoma size (>1 cm), number of adenomas, location, histology, and any advanced adenoma recurrence [30,31]. New CRC cases ($$n = 14$$) after screening adenoma resection were classified as advanced lesions/recurrent adenomas for these analyses and were given a small number. ## 2.7. Statistical Analysis Descriptive statistics included the means and standard deviation (SD), which were estimated for continuous variables, while frequencies and percentages were used for categorical variables related to demographics, health history, and lifestyle factors (e.g., diet, physical activity, BMI, tobacco use, and aspirin use). All foods contributing to AGE in the diet were examined to determine the percent of CML-AGE contribution across food categories. For example, fruits, vegetables, and nuts were grouped based on having low CML-AGE amounts. AGE exposure was evaluated in relation to the outcome of the presence of at least one adenoma on repeat colonoscopy and controlling for literature-based confounders in relation to factors that drive adenoma risk or AGE exposure. The following covariates were included in the statistical models: age, sex, trial participation (WBF or UDCA), educational status, race, ethnicity, history of polyps previous to qualifying colonoscopy, BMI, family history of CRC, energy intake (kcal/d), tobacco use, and health history, including diabetes. To determine the association between dAGE intake and adenoma recurrence in participants, a series of logistic regression models were fitted with the binary outcome of adenoma recurrence (yes or no). dAGEs were categorized into quartiles, with the first quartile serving as the referent group. Three models were examined, an adjusted model, a simple model adjusting only for age and sex, and a multivariable model additionally adjusting for BMI, energy intake, study and trial arm, having had a previous polyp prior to study entry, years of formal education, race, smoking history, a family history of CRC, and diabetes. As a sensitivity analysis, physical activity (METs), which was recorded for only 1479 participants, was added to the multivariable model. To explore whether the association differed by age, sex, or BMI categories, interaction terms were added to the multivariable model. Interactions were retained if they met a threshold of p ≤ 0.20 [38]. In all other analyses, a type I error rate of 0.05 was used, and all tests were two-sided. Data management and analyses were conducted with SAS 9.4 (SAS Institute, Cary, NC, USA) and Stata 17 (Stata Corp, College Station, TX, USA). ## 3. Results The mean age of the study population was 67.2 ± 7.3 y. Most participants were male ($68.1\%$), white ($95.4\%$), married/cohabitating ($83.9\%$), and had completed at least one year of college ($59.3\%$). Almost half of the population had a history of colorectal polyps prior to qualifying for colonoscopy ($45.2\%$) and had a BMI within the overweight range (25.0–29.9 (kg/m2) ($44.0\%$). The mean CML-AGE intake was 5251.1 ± 1633.1 (kU/1000 kcal). The baseline characteristics of 1976 participants were summarized by a quartile of CML-AGE intake (Table 1). CML-AGE intake ranged between 4960 and 17,032.4 (kU/1000 kcal). The majority of male participants were in the highest quartile ($88.1\%$), while the majority of female participants were in the lowest quartile ($54.9\%$) of dAGE exposure. Participants in the lowest quartile of dAGE intake were older (68.9 ± 6.9 y) compared to participants in the highest intake quartile (65.2 ± 7.6 y). Current and prior tobacco use was higher, with a greater reported AGE intake. Participants in the highest quartile of dAGE intake had a higher average daily energy intake (2729 ± 770.4) which correlated with a higher intake of carbohydrates, protein, total fat, polyunsaturated fat, monounsaturated fat, total meat, red meat, processed meat, fruits, vegetables, fiber, alcohol, and added sugar. Higher dAGE intake was observed among participants who were overweight or obese based on BMI. The food groups that contributed most to CML-AGE intake included mixed dishes, red meat, fat/oil, poultry, and dairy (Figure 3). A series of unconditional logistic regression models were conducted to determine the association between CML-AGE intake and adenoma recurrence in the sample (Table 2). At trial completion, there were a total of 940 recurrent adenoma cases. There were no significant associations between higher CML-AGE intake and adenoma recurrence in the unadjusted model or simple model adjusting only for age and sex. After adjusting for multiple covariates, there was no significant association between a higher intake of CML-AGE and adenoma recurrence with an OR of 1.02 ($95\%$ confidence interval 0.71, 1.48) for participants in the highest quartile compared to the lowest quartile. We examined the interaction between dAGE exposure and adenoma risk by age, sex, and BMI and observed no significant association. Lastly, we ran a sensitivity analysis adding the physical activity levels (METs) of 1479 participants to the multivariable model and observed no significant association between higher dAGE intake and adenoma recurrence. ## 4. Discussion We developed a CML-AGE database for the AFFQ to estimate the dietary intake of CML-AGE in a sample of CRC screening-age adults who participated in the WBF and UDCA cohorts. We found no association between self-reported CML-AGE intake and adenoma recurrence after an average of 3.1 years of follow-up. Using a published AGE database, we applied a standardized protocol to assign CML-AGE values to food items in the AFFQ and estimated dietary exposure based on the participant’s frequency and quantity of intake at the baseline time point for each randomized controlled trial. In our sample, the consumption of ‘mixed dishes’ contributed most to total dAGE intake, followed by red meat and fat/oil. This may have been influenced by the region in which participants live. Our category for ‘mixed dishes’ predominantly included southwestern food items such as tamales, enchiladas, and burritos. The ingredients that make up these dishes are food items that are high in AGEs, such as meat, animal fat, and dairy. Foods that contributed least to total dAGE intake were fruits, vegetables, and carbohydrates, as has been described in other studies [26]. Overall, our mean reported CML-AGE intake (5251.31 ± 1633.06 (kU/1000 kcal) was less than the average intake reported by Omofuma et al. [ 27] and similar to the mean intake for women reported by Jiao et al. [ 29]. These results are the first to evaluate dAGE in relation to adenoma recurrence: a precursor and risk factor for CRC. A few studies have examined the association between dAGE intake and cancer risk overall or CRC. In an analysis of the prospective NIH-AARP Diet and Health study, a positive association between dAGE intake and the risk of pancreatic cancer in men was observed [29]. Omofuma et al. also observed a positive association between dAGE intake and breast cancer risk in prostate, lung, colorectal and ovarian cancer screening trials (PLCO) [27]. Recently, Omofuma et al. examined the association between post-diagnosis CML-AGE intake and mortality among breast cancer patients and observed an association between higher dAGE intake with a higher risk of mortality [39]. Aglago et al. examined the dietary intake of three major AGEs [CML, N€- carboxyethyllysine (CEL), and NGamma-(5-hydro-5-methyl-4-imidazolon-2-yl)-ornithine (MG-H1)] and the CRC risk in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Unexpectedly, modest inverse associations between CML and MG-HI intake and CRC risk were observed, while a null association with CEL was reported [28]. Additionally, Córdova et al. assessed the dietary intake of CML, CEL, and MGH1 and the overall risk for specific cancer types in the European Prospective Investigation into Cancer and Nutrition (EPIC) study and reported that these three AGE types were not associated with an overall risk for cancer [40]. The null findings related to CML-AGE intake and risk for precancerous adenoma formation in the present study could reflect the fact that only certain types of AGEs are involved in adenoma development. The binding of AGEs to RAGEs can induce oxidative stress and inflammation, both of which are characteristics of an adenoma-promoting environment [16,41], but this varies by AGE type. We quantified CML-AGE, a prevalent AGE in vivo [42]. CML-AGEs are considered protein-bound AGEs, which tend to have a higher molecular weight [22,43]. High-molecular weight AGEs need to be broken down within the gut to be absorbed. AGEs with a larger structure, such as CML-AGE, are broken down into two subgroups. The involvement of these subgroups in different mechanisms within the gut is not well understood. However, it may be possible that the interaction between the subunits of CML-AGE and RAGE could be impacted by different factors, such as the gastrointestinal environment. Studies have suggested that AGEs may alter gut microbiota and promote the adenoma development environment [44,45]. Our null findings may also reflect the insufficient sample size to detect the relatively modest effects of AGE on adenoma recurrence. Future studies should evaluate this association using an even larger pooled sample and expand to other dietary AGEs, perhaps including the potential interaction with the gut microbiota. An additional explanation for the null findings may be that other risk factors dominate in promoting recurrent adenomas, thus attenuating the risk associations observed with dAGE intake in this study population. For example, the population that had previously detected adenomas was older, the majority were male, and the number of adenoma recurrences was high (approximately $48\%$). Lastly, adenomas are initially benign unless they are advanced, in which case, they are more likely to become cancer if not removed in time. It is possible that AGEs may not be involved in the stage of progression from adenoma to cancer but may be involved during initiation, adenoma development, or progression to a more advanced adenoma. A strength of this study is the availability and access to complete dietary exposure and adjudicated adenoma outcome data from two cohort trials with a relatively large, combined study sample. These data included robust information on relevant covariates, including baseline demographics, overall dietary intake, physical activity, and health status. Our dataset expanded the earlier food database by providing the estimated AGE levels in combined food items (e.g., burritos, enchiladas, tamales) that were not included in the dataset developed by Uribarri et al. Furthermore, we applied a literature-informed protocol to create an AGE dataset for the AFFQ, which could be used for future analyses. However, limitations exist, including the self-reported nature of dAGE exposure. There is a lack of valid biomarkers of dAGE at this time. The enzyme-linked immunosorbent assay (ELISA) is a method to measure serum levels of endogenous AGEs but was not available in our studies [46]. We also recognize that the lifestyle and behavior of participants at the baseline may not be an accurate representation of risk exposures over the longer time course of adenoma and/or CRC development, including during the average 3.1 years of follow-up herein. Further, the mean follow-up of 3.1 years may not be long enough to detect an impact from dAGE. Finally, as mentioned above, risk factors for colorectal adenoma and CRC may differ, and conclusions about the relationship between AGEs and CRC cannot be drawn from this study. ## 5. Conclusions Our findings do not support our initial hypothesis that dAGE intake among this pooled sample of adults with previous adenomas is associated with adenoma recurrence. Future studies should consider larger samples with longer-term follow-up and possibly biomarkers of dietary exposure to AGEs. In addition, future studies should consider evaluating younger participants as AGEs may be involved in the initiation of adenoma development. Further, exploring additional dietary AGEs could shed light on whether other AGEs could be involved in precancerous adenoma development. ## References 1. 1. Colorectal Cancer Facts & Figures 2020–2022 Available online: https://www.cancer.org/research/cancer-facts-statistics/colorectal-cancer-facts-figures.html(accessed on 13 March 2022) 2. 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--- title: Efficiency of Different Solvents in the Extraction of Bioactive Compounds from Plinia cauliflora and Syzygium cumini Fruits as Evaluated by Paper Spray Mass Spectrometry authors: - Vinícius Tadeu da Veiga Correia - Viviane Dias Medeiros Silva - Henrique de Oliveira Prata Mendonça - Ana Luiza Coeli Cruz Ramos - Mauro Ramalho Silva - Rodinei Augusti - Ana Cardoso Clemente Filha Ferreira de Paula - Ricardo Manuel de Seixas Boavida Ferreira - Júlio Onésio Ferreira Melo - Camila Argenta Fante journal: Molecules year: 2023 pmcid: PMC10005132 doi: 10.3390/molecules28052359 license: CC BY 4.0 --- # Efficiency of Different Solvents in the Extraction of Bioactive Compounds from Plinia cauliflora and Syzygium cumini Fruits as Evaluated by Paper Spray Mass Spectrometry ## Abstract Jabuticaba (Plinia cauliflora) and jambolan (Syzygium cumini) fruits are rich in phenolic compounds with antioxidant properties, mostly concentrated in the peel, pulp, and seeds. Among the techniques for identifying these constituents, paper spray mass spectrometry (PS-MS) stands out as a method of ambient ionization of samples for the direct analysis of raw materials. This study aimed to determine the chemical profiles of the peel, pulp, and seeds of jabuticaba and jambolan fruits, as well as to assess the efficiency of using different solvents (water and methanol) in obtaining metabolite fingerprints of different parts of the fruits. Overall, 63 compounds were tentatively identified in the aqueous and methanolic extracts of jabuticaba and jambolan, 28 being in the positive ionization mode and 35 in the negative ionization mode. Flavonoids ($40\%$), followed by benzoic acid derivatives ($13\%$), fatty acids ($13\%$), carotenoids ($6\%$), phenylpropanoids ($6\%$), and tannins ($5\%$) were the groups of substances found in greater numbers, producing different fingerprints according to the parts of the fruit and the different extracting solvents used. Therefore, compounds present in jabuticaba and jambolan reinforce the nutritional and bioactive potential attributed to these fruits, due to the potentially positive effects performed by these metabolites in human health and nutrition. ## 1. Introduction Jabuticabeira (Plinia cauliflora) and jambolan (Syzygium cumini) are plants that belong to the Myrtaceae family, whose fruits have a sweet and astringent flavor, and a purplish color due to the presence of anthocyanins [1,2]. Studies have reported the presence of several phenolic compounds in different parts of the fruit, such as flavonoids, tannins, gallic and ellagic acids, and quercetin, among others. These phytochemicals possess several bioactivities, responsible for many beneficial activities on human health and nutrition, such as antioxidant, antimicrobial, antimutagenic, and anti-inflammatory activities [3,4,5,6,7,8]. The fruits of Plinia cauliflora and Syzygium cumini have been processed and used to develop new products in different food industry sectors and in the pharmaceutical area, such as jams, desserts, wines, teas, and microcapsules [9,10]. The fruit processing chain generates a considerable number of residual co-products, mainly represented by their peel and seeds, usually discarded and underexplored [10]. Different techniques have been used to identify bioactive compounds in plant materials. High-performance liquid chromatography with an evaporative light-scattering detector and electrophoresis in polyacrylamide gel are examples of these methodologies, however, they require extensive laboratory preparation, time-consuming analyses, and high operational costs, limiting the potential of several studies [11]. Paper-spray mass spectrometry (PS-MS) has been shown to be efficient in overcoming these limitations, as it allows the rapid acquisition of fingerprints of different matrices at a considerably accessible analytical cost and without the generation of chemical residue [12,13,14,15]. Advantages of paper-spray mass spectrometry range from higher replicability to shorter data acquisition time to stronger signal stability [12]. In this method, the compounds present in the raw material are extracted by a solvent, with the drag of the extracted analytes recorded on paper and a spray ionization utilized due to the high voltage applied [16]. The factors that influence an extraction process are diverse and may be related to the conditions and time elapsed, the temperature used during the process, and the sample–solvent ratio, mainly associated with the polarity of the metabolites [17]. Therefore, the objective of this study was to determine the chemical profiles of jabuticaba and jambolan fruits by paper spray mass spectrometry (PS-MS) and to verify the influence of using different solvents (water and methanol) in obtaining fingerprints of their parts, such as the peel, pulp, and seeds. ## 2.1. Plant Material and Chemicals Ripe jabuticaba and jambolan fruits were collected in Paraopeba—Minas Gerais, Brazil (latitude 19° 16′ 54″ S, longitude 44° 24′ 32″ W, altitude 741 m), between August and December 2020. The fruits were harvested manually in the morning from three different matrices. After harvest, they were packed in polyethylene bags, labeled, and kept in thermal boxes until transport to the Organic Chemical Laboratory of the University Federal de São João del Rei—Campus Sete Lagoas. They were then sanitized with sodium hypochlorite and fractionated into their constituent parts: peel, pulp, and seeds (the seeds were ground in a batch mill, IKA A11 basic). The samples were stored separately in a freezer at −18 °C until analysis. Analytical grade methanol was acquired from Neon (São Paulo, Brazil), and the ultrapure water used was Milli-Q quality. ## 2.2. Physicochemical Characterization For the physical analyses, 150 fruits (50 from each of the three matrices) of each species were evaluated. The fruit weight (g) was determined using a digital scale (Diamond, model 500). The longitudinal diameter (mm) and the transversal diameter (mm) were obtained with the aid of a digital caliper (Insize, digital caliper 0–300 mm/0–12″) with a sensitivity of 0.01 mm. To determine the soluble solids (SS) and pH of jabuticaba and jambolan pulps, a benchtop refractometer (AAKER, model: Q767BOV-OW) and pH meter (MS Tecnopon), respectively, were used [18]. ## 2.3. Obtaining Extracts The peel, pulp, and seeds of the fruits were used to prepare samples of 1.0 g each, which were added to 10 mL, in proportion 1:10 (m/v), of the different solvents analyzed: water and methanol, separately. The samples were stirred for 30 s in a vortex mixer and then kept at rest for 60 min in the dark at room temperature (±25 °C). They were centrifuged at 3500 rpm for 15 min in a bench centrifuge, and the supernatant subsequently transferred to Eppendorf tubes and kept at −18 °C in a cold chamber until further evaluations. ## 2.4. Mass Spectrometry with Paper Spray Ionization The chemical profiles of the peel, pulp, and seeds of the fruits under study were analyzed using an LCQ mass spectrometer (Thermo Scientific, San Jose, CA, USA) equipped with a paper spray ionization source. For analysis, 2 μL of samples and 40 μL of methanol were applied on chromatographic paper and cut into a triangular shape (equilateral 1.5 cm) coupled to the equipment. The instrumental conditions of the PS-MS analysis were source voltage equal to −3.0 kV (negative mode) and +4.0 kV (positive mode), a capillary voltage of 40 V, a tube lens voltage of 120 V, transfer tube temperature of 275 °C, and mass range of 100 to 1000 for positive and negative ionization modes. A comparison was made between the mass/charge ratios (m/z) obtained in the study with those found in literature, through fragmentation by sequential mass spectrometry to identify the compounds under analysis. The collision energy used to fragment the compounds ranged from 15 to 40 V [11,18,19]. ## 2.5. Statistical Analysis The Xcalibur software version 2.1 (Thermo Fisher Scientific Inc., San Jose, CA, USA) processed the mass spectra obtained and tabulated in both ionization modes in Excel 2016 spreadsheets. The fingerprints of the samples in the positive and negative ionization modes were arranged in an X/Y matrix, with data centered on the mean, and the analysis of the principal components was performed in the MatLab software with the aid of the PLS Toolbox. ## 3.1. Physicochemical Characterization Table 1 presents the mean values of the physicochemical characteristics analyzed in the fruits of jabuticaba and jambolan. The results showed that jabuticaba fruits presented an average pH equal to 3.12, quite similar to that of the jambolan sample (pH 3.47). According to Costa et al. [ 20], the soluble solids content is an extremely important parameter for estimating fruit quality. It is typically associated with the contents of sugars, organic acids, and other microconstituents of plant samples. This characteristic is also related to in natura consumption of these raw materials by the population, and their industrialization, due to the concentrations of nectars produced by the amount of pulp. The ratio between the diameters was calculated (LD/TD), and the results (1.004 for jabuticaba and 1.409 for jambolan) indicated a spherical shape for jabuticaba and an ellipsoidal or oval shape for jambolan (LD/TD > 1) [18]. The mean fruit weights in this analysis corroborate the values found in other studies involving these materials, such as that of Zerbielli et al. [ 21] when evaluating jabuticaba and Steiner et al. [ 22] when studying jambolan fruit samples. ## 3.2. Chemical Profile The analyses of the spectra of aqueous and methanolic extracts of jabuticaba and jambolan fruits in ionization modes allowed the identification of 63 compounds, comprising flavonoids ($40\%$, $$n = 25$$), benzoic acid derivatives ($13\%$, $$n = 8$$), fatty acids ($13\%$, $$n = 8$$), carotenoids ($6\%$, $$n = 4$$), phenylpropanoids ($6\%$, $$n = 4$$), tannins ($5\%$, $$n = 3$$), terpenes ($5\%$, $$n = 3$$), sugars ($5\%$, $$n = 3$$), organic acids ($3\%$, $$n = 2$$), amino acids ($3\%$, $$n = 2$$), and esters ($1\%$, $$n = 1$$), 28 being compounds tentatively identified in the positive ionization mode and 35 in the negative ionization mode. According to the information base, the compounds found were provisionally identified through fragmentation and comparison with data already reported in the scientific literature. The attempt to identify compounds using PS-MS in the positive ionization mode (+) distinguished eight chemical classes: flavonoids [15], carotenoids [4], sugars [2], amino acids [2], fatty acids [2], one phenylpropanoid [1], one ester [1], and one benzoic acid derivative [1], verified in the subsequent constituent fractions of peel, pulp, and seeds. Table 2 shows that all 28 compounds identified in the positive ionization mode were present in jabuticaba, while 27 substances were found in jambolan: 5-pyranopelargonidin-3-O-glucoside was not detected in jambolan. In the positive mode, some flavonoids maintain an isomeric relationship and evidently could not be differentiated by their exact mass; examples are delphinidin and peonidin (m/z 303), and some carotenoids, such as 9-cis-β-carotene, all-trans-β-carotene and 13-cis-β-carotene (m/z 537), all-trans-zeaxanthin, all-trans-lutein and cis-lutein (m/z 569), and cis-neoxanthin and cis-violaxanthin (m/z 601). Ripe fruits contain high levels of carbohydrates, such as glucose and sucrose, which can be identified by PS-MS. These nutrients were also found in several other fruits, such as cagaita [24], cambuí [18], ripe banana peel [19], grumixama [25], pequi [13] and cerrado pear [35], using identical methodologies. In both fruits under study, the presence of flavonoids stands out, mainly derived from glycosidic conjugates, a form that confers better performance to plants, such as protection against UV radiation and microbial pathogens [36,37]. This group of secondary metabolites is responsible for several beneficial health properties, such as antioxidant, antimicrobial, antimutagenic, antihypertensive, antidiabetic, and anti-inflammatory activities [3,8,12]. Among the class of flavonoids, the anthocyanins represented by the ions at m/z 287, 303, 317, 331, 433, 625, 627, 641, and 655 stand out, totaling nine tentatively identified compounds. According to Minighin et al. [ 38], anthocyanins are natural pigments belonging to the large class phenolic compounds that can vary from bright red to violet/blue, being water-soluble compounds, which may explain why they are all found in the aqueous extracts evaluated. Anthocyanins are located mainly in the peel and seeds of jabuticaba and jambolan fruits. However, they were also tentatively identified in the pulp of these materials, which have a colorless hue. This is due to the migration of these constituents, possibly as a result of fruit ripening or during sample preparation [30]. The results obtained in the present study reinforce the potential for using jabuticaba peel and other dark-colored fruits, such as jambolan, in the development of natural dyes, due to the presence of anthocyanins as the main class of flavonoids tentatively identified. Tavares et al. [ 30] identified nine anthocyanins, mainly derived from delphinidin, petunidin, and malvidin, in the edible parts of jambolan. The same number was obtained in another study by Tavares et al. [ 8] in jambolan fruit, juice, and powder. The species of the Myrtaceae family are generally considered as good sources of anthocyanins, and the higher their content, the greater the intensity of the peel color [30]. Carotenoids were also significantly represented in jabuticaba and jambolan samples in the positive ionization mode. These substances play an important role in plant metabolism and have beneficial effects at the level on human health [39]. The carotenoids all-trans-β-cryptoxanthin, 13-cis-β-carotene, all-trans-β-carotene, and 9-cis-β-carotene, tentatively identified in jabuticaba and jambolan, were also present in Syzygium cumini in the study reported by Faria et al. [ 32]. The frozen jabuticaba peel and pulp (−18 °C) were characterized by Dessimoni-Pinto et al. [ 40]. According to the authors, it was possible to observe that the highest concentrations of nutrients were found in the peel of this fruit, with significant levels of natural pigments, such as certain flavonoids, fibers, and carbohydrates, represented mainly by sugars. In terms of the nutritional potential of seeds, Fidelis et al. [ 3] and Khan et al. [ 5] reinforced the activities of these constituent fractions of jabuticaba and jambolan, respectively, by observing the influence of compounds identified in these materials, with promising antimutagenic and DNA-protective properties for S. cumini seeds, and antioxidant, antimicrobial, anti-hyperglycemic, anti-inflammatory, and antihypertensive activities for jabuticaba seeds. The jabuticaba contains all 35 compounds tentatively identified in the negative ionization mode, whereas for jambolan, 33 compounds were found (gallic and abscisic acids were not detected; Table 3). In the negative mode, some flavonoids also show an isomeric relationship and evidently could not be differentiated by their exact mass; examples are provided by epicatechin and catechin (m/z 289), as well as some tannins, such as castalagin and vescalagin (m/z 933). Table 3 shows the compounds possibly identified in the negative mode [-], which can be grouped into eight chemical classes, namely flavonoids [10], benzoic acid derivatives [7], fatty acids [6], tannins [3], phenylpropanoids [3], terpenes [3], organic acids [2], and one sugar [1], with a differential distribution among the fruit fractions under study. The flavonoid at m/z 447, cyanidin-3-O-glucoside, is the most abundant anthocyanin in dark-skinned fruits [50] and was detected in both jabuticaba and jambolan, as well as in the study by Lequiste et al. [ 51] in freeze-dried jabuticaba peel and the aqueous extract of jabuticaba peel, by Quatrin et al. [ 7] in powdered jabuticaba peel, by Fidelis et al. [ 3] in jabuticaba seeds, and by Tavares et al. [ 8] in jambolan (fruit, juice, and powder). Another group of compounds tentatively identified in the present study was that of fatty acids, with six representative compounds comprising saturated (palmitic, stearic, and lignoceric acids), monounsaturated (oleic acid), and polyunsaturated (α-linolenic and linoleic acids) fatty acids. Of these, oleic and linoleic acids play an important role in human nutrition [35]. Using PS-MS, Mariano et al. [ 35] also identified the presence of oleic acid in cerrado pear (Eugenia klotzschiana Berg), another fruit of the Myrtaceae family. The tentatively identified phenylpropanoids are also highlighted. This group of substances has demonstrated several pharmacological activities, such as anti-inflammatory, antitumor, antibacterial, and antifungal action [18]. In addition, it is reported in the literature that many chronic non-communicable diseases, such as cardiovascular diseases, type II diabetes, and various types of cancer, have their development reduced by the preventive action of phenylpropanoids [52]. When studying the chemical profile of cambuí pulp, a fruit of the Myrtaceae family, García et al. [ 18] observed that benzoic acid derivatives were also one of the most predominant classes. In terms of comparing the solvents used, fatty acids, carotenoids, terpenes, and tannins were not detected in aqueous extracts. Fatty acids and terpenes have low affinity for water; carotenoids are fat-soluble molecules and are soluble in organic solvents such as petroleum ether, methanol, and acetone [53]. According to Três et al. [ 54], knowledge of the solubility of carotenoids in organic solvents is of fundamental importance for the understanding of recrystallization processes using solvents. According to Bazykina et al. [ 55], alcohol is more effective in extracting tannins and the anthocyanins have a comparatively higher affinity for water [56]. However, some scientific studies report that it is possible to improve the solubility of anthocyanins and other phenolic compounds by using a mixture of solvents, using aqueous solutions and organic solvents (e.g., hydroethanolic mixtures) [57,58,59]. Quatrin et al. [ 7] also identified castalagin/vescalagin and pedunculagin in powdered jabuticaba peel. According to these authors, hydrolysable tannins play an important role in the antioxidant capacity of jabuticaba. Furthermore, as Tavares et al. [ 30] reported, these compounds can cause the astringent sensation of jambolan. Preferential solubility in each solvent is a peculiar characteristic of certain phytochemical classes, such as flavonoids, fatty acids, sugars, and other phenolics, which explains, for example, the lack of a universal extraction procedure. Studies report that high polarity solvents are more effective in extracting phenolic compounds [60]. ## 3.3. Principal Component Analysis (PCA) In addition to the attempt to identify the chemical constituents of jabuticaba and jambolan, a PCA was performed among the samples. A data matrix was generated using the presence or absence of compounds found in the peel, pulp, and seeds of the fruits, when performing a fusion of the results obtained in the two ionization modes evaluated. The models were built by selecting two main components of analysis (PC1 and PC2), which explained $61.18\%$ (PC1 $35.87\%$ and PC2 $25.31\%$) of the total variability of the data related to the effect of the use of different solvents and evaluation of the different parts of the fruits (Figure 1). Through the loads PC1 and PC2, it is possible to observe that, depending on the solvent used, the jabuticaba and jambolan samples were grouped according to their constituent fractions, which leads to the realization that there was a significant difference in terms of the use of water or methanol in extracting substances. PC1 showed the discrimination of the fruits through its positive scores, where all the methanolic fractions were found, both from jabuticaba and jambolan. The compounds responsible for this separation were hydroxybenzoic acid-O-hexoside, the fatty acids palmitic, α-linolenic, linoleic, oleic, octadecanoic, licanic, stearic and lignoceric, the tannins pedunculagin, castalagin/vescalagin and potentilin, the terpenes annurcoic acid and guavenoic acid, p-hydroxybenzoic acid, galloyl-glucose ester, and the carotenoids 9-cis-β-carotene/all-trans-β-carotene/13-cis-β-carotene, all-trans-β-cryptoxanthin, all-trans-zeaxanthin/all-trans-lutein/cis-lutein and cis-neoxanthin/cis-violaxanthin (positive score), and taxifolin, caffeic acid, tryptophan and apigenin (negative score), as shown in Figure 2. In turn, PC2 shows the distinction between the constituent fractions, mainly the peel of the two fruits (positive) and the seeds (negative) of jambolan. The following compounds responsible for this differentiation are: salicylic acid, syringic acid, hydroxybenzoic acid-O-hexoside, kaempferol, epicatechin/catechin, taxifolin, gallocatechin, myricetin, cyanidin-3-O-glycoside, p-hydroxybenzoic, tryptophan, caffeic acid, apigenin, cyanidin, diosmetin, delphinidin/peonidin, perlagonidin-3-O-glucoside, peonidin-diglucoside, delphinidin-3,5-O-diglucoside, quercetin-3-7-diglucoside, dihydromyricetin diglucoside and cis-neoxanthin/cis-violaxanthin (positive scores), and palmitic, linoleic, oleic and galloyl-glucose ester acids (negative scores). ## 4. Conclusions The use of mass spectrometry in the ambient ionization mode by paper spray provided comprehensive information associated with the chemical composition of jabuticaba and jambolan fruits. 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--- title: Spectral and Redox Properties of a Recombinant Mouse Cytochrome b561 Protein Suggest Transmembrane Electron Transfer Function authors: - Alajos Bérczi - Zsuzsanna Márton - Krisztina Laskay - András Tóth - Gábor Rákhely - Ágnes Duzs - Krisztina Sebők-Nagy - Tibor Páli - László Zimányi journal: Molecules year: 2023 pmcid: PMC10005133 doi: 10.3390/molecules28052261 license: CC BY 4.0 --- # Spectral and Redox Properties of a Recombinant Mouse Cytochrome b561 Protein Suggest Transmembrane Electron Transfer Function ## Abstract Cytochrome b561 proteins (CYB561s) are integral membrane proteins with six trans-membrane domains, two heme-b redox centers, one on each side of the host membrane. The major characteristics of these proteins are their ascorbate reducibility and trans-membrane electron transferring capability. More than one CYB561 can be found in a wide range of animal and plant phyla and they are localized in membranes different from the membranes participating in bioenergization. Two homologous proteins, both in humans and rodents, are thought to participate—via yet unidentified way—in cancer pathology. The recombinant forms of the human tumor suppressor 101F6 protein (Hs_CYB561D2) and its mouse ortholog (Mm_CYB561D2) have already been studied in some detail. However, nothing has yet been published about the physical-chemical properties of their homologues (Hs_CYB561D1 in humans and Mm_CYB561D1 in mice). In this paper we present optical, redox and structural properties of the recombinant Mm_CYB561D1, obtained based on various spectroscopic methods and homology modeling. The results are discussed in comparison to similar properties of the other members of the CYB561 protein family. ## 1. Introduction Cytochrome b561 proteins (CYB561s) are integral membrane proteins with six trans-membrane domains, two heme-b redox centers, and trans-membrane electron transporting properties [1,2]. These proteins came into the focus of interest about 25 years ago when it was shown that more than one CYB561 can be found in a wide range of animal and plant phyla. The CYB561s are localized in membranes different from the membranes participating in bioenergization. The two heme-b chromophores, one on each side of the membrane housing the CYB561 in question, are coordinated by four highly conserved His residues localized in the central four trans-membrane helices [3]. The major characteristics of these proteins are their ascorbate (ASC) reducibility and trans-membrane electron transferring capability [4]. CYB561s have been classified into seven groups based on primary structural similarities [1]. Five out of the seven groups contain CYB561s with only the core six trans-membrane domains [2] that show significant similarity to the structure of the bovine adrenal gland chromaffin granule cytochrome b561 protein (Bt_CYB561A1). This latter protein was the first and is the denominator member of the CYB561 protein family [1]. In spite of the rather intense studies in the past two decades, the atomic structure has been resolved for only two members of the CYB561 protein family, one from Arabidopsis thaliana (At_CYB561B2) and the human duodenal protein (Hs_CYB561A2) [5,6]. Two CYB561s that might be involved in cancerous phenomena have been experimentally characterized so far. The recombinant forms of the human tumor suppressor 101F6 protein (Hs_CYB561D2 or human Cyb561d2 protein) and its mouse ortholog (Mm_CYB561D2 or mouse Cyb561d2 protein) have been expressed in yeast and their optical and redox properties established [7,8,9,10,11]. Recently, the ferric reductase activity of detergent purified and lipid nanodisc embedded Hs_CYB561D2 was directly demonstrated [12]. Tsubaki et al. [ 1] predicted the presence of another homologue CYB561 protein both in humans (Hs_CYB561D1; known also as human Cyb561d1 protein) and in mouse (Mm_CYB561D1 or mouse Cyb561d1 protein), however very little is known about these two proteins. The primary structure of both the human and the mouse protein has 229 amino acids and sequence alignment shows $90\%$ identity and $95\%$ similarity between the two sequences. It is assumed that they are also involved in tumor suppression and have physical-chemical characteristics very similar to those of the CYB561D2s, however, a detailed study has not yet been published about these proteins. The human Cyb561d1 gene product has been detected in many tissues (https://www.proteinatlas.org/ENSG00000174151-CYB561D1/tissue, accessed on 27 February 2023) and the gene activity was evident in a wide variety of biological processes, such as in different tumorous processes [13,14], in blocking mitosis of both U20S and HeLa cells [15], in upregulation of expression of Retinoid X receptors in pancreatic β-cells [16], in aging [17], in type 2 diabetes [18] and in cognitive function [19]. The expression of the mouse Cyb561d1 gene was highest in thymus, spleen, colon, and large intestine (https://www.ncbi.nlm.nih.gov/gene/72023, accessed on 27 February 2023). To obtain a deeper insight into the possible biological function of these proteins, we expressed the recombinant form of the mouse protein in yeast (Saccharomyces cerevisiae) cells, purified partially from the yeast membranes, and determined the basic optical, redox and electron paramagnetic properties of Mm_CYB561D1. No electronic interaction between the two heme-b centers was observed by circular dichroism spectroscopy. The binding constants of the two putative ascorbate binding sites and the midpoint reduction potentials of the two heme-b centers were comparable to other members of the CYB561 protein family. No spectral differences were discernable for the two hemes, in contrast to several other CYB561 proteins. The redox titration experiments were analyzed in the framework of a complex model and the consequences of this model are discussed. The two hemes showed a highly asymmetric low-spin (HALS) character. We have also generated the putative 3D structure of the protein by homology modelling, and we discuss the likely transmembrane electron transfer pathways. ## 2. Results Since more than $50\%$ of the amino acids of Mm_CYB561D1 are predicted to be located in the transmembrane domains in a hydrophobic milieu, some well-known and frequently used nonionic detergents with c.m.c. value less than 0.5 mM were tested for solubilization efficacy. As shown in Table S1 (Supplementary material) the most efficient solubilizing agent proved to be the dodecyl-β-D-maltoside (DDM). The amount of ascorbate-reducible Mm_CYB561D1 in the solubilized fractions was determined from the ascorbate-reduced minus ferricianide-oxidized difference spectrum of the solubilized fractions. Although C12E8 and SB3-14 as well as SMA(2:1) polyelectrolite—which has recently been used in many cases for solubilizing integral membrane proteins [20,21,22,23,24]—solubilized about the same amount of proteins as DDM did, the specific content of ascorbate-reducible Mm_CYB561D1 in the solubilized fractions was much lower than that in the DDM-solubilized fraction. Thus, for affinity chromatography, only DDM-solubilized membrane fractions were collected and further processed. The Mm_CYB561D1 could not be purified to homogeneity by His6-tag affinity chromatography, thus the final sample we used in our spectral and electrochemical characterization contained partially purified Mm_CYB561D1. Since the aromatic amino acid content of Mm_CYB561D1 is comparable to that of other CYB561 proteins [1], only the presence of contaminating proteins could explain the rather high absorbance at 280 nm in the affinity-purified final fraction (Figure 1A) and it was the reason that the A(280 nm)-to-A(Soret peak) ratio—that is below 0.4 for other, highly purified CYB561 proteins [25,26]—was higher than 1.1 in our final samples. Since the yeast membranes we used do not contain ascorbate-reducible cytochromes, only a minor amount of dithionite-reducible ones (Figure 1B), it is safe to assume that protein contamination did not influence the results obtained by the ascorbate and redox titration of the His6-tagged, affinity-purified, recombinant Mm_CYB561D1. Figure 2 shows the circular dichroism (CD) spectra of oxidized and reduced Mm_CYB561D1 in 50 mM phosphate buffer (pH 7.0) in the presence of 0.5 mM DDM between 380 and 600 nm. Except for a minor sign in the Soret-band of the dithionite-reduced spectrum, no exciton splitting can be seen in either band, that would indicate electronic interaction between the two heme-b chromophores either in the oxidized or the reduced state. As it was predicted by Tsubaki et al. [ 1], the Mm_CYB561D1 was an ascorbate reducible b-type cytochrome. When partially purified Mm_CYB561D1 was reduced by increasing the concentration of ascorbate at pH 7 (Figure 3A), the pattern was similar to that obtained for other CYB561 proteins earlier [27,28,29]. Difference spectra obtained by subtracting the fully oxidized spectrum from all others resulted in the spectral matrix D, shown in the Supplementary material (Figure S1A). Singular value decomposition (SVD) (Equation [9], see Materials and Methods) yielded two significant eigenvector pairs, i.e., matrix D had a rank of 2. The significant titration eigenvectors and their successful fit by Equation [10] (Materials and Methods) is plotted in Figure 3B and in 2D plot in Figure S1B. The two characteristic ascorbate concentration values for the Mm_CYB561D1 were around K1 = 0.045 ± 0.007 mM and K2 = 2.34 ± 0.50 mM. These two values (a) may be interpreted as the ascorbate concentrations where half of either of the two heme-b centers in the protein is already reduced and (b) are close to the values obtained for other CYB561 proteins [4]. A calculation based on Equation [11] (Materials and Methods) revealed the presence of two major components with identical Soret and rather similar α-bands (Figure 3C). The oxidized (black), high ASC affinity (low ASC concentration) reduced (red) and low ASC affinity (high ASC concentration) reduced (yellow) spectra in Figure 3C correspond to the black, red and yellow squares in Figure S1B. The yellow spectrum contains a minor oxidized contribution, and the subtraction of this oxidized “contamination” resulted in the fully reduced blue spectrum. The near identity of the two spectra is at variance with the results obtained for the purified, recombinant, His6-tagged At_CYB561A1 [30] and Mm_CYB561D2 [11] proteins where the calculated spectra for the low and high ASC concentration-reduced hemes were distinctly different in the α-band. Redox titration of the fully oxidized Mm_CYB561D1 by dithionite under anaerobic conditions yielded the set of absorption spectra in Figure 4A. The difference spectra in matrix D, obtained from this set (not shown), were analyzed based on the reaction scheme depicted in Figure 4B and the Nernst equation as follows. D = P CT = U S VT = U VST = (U R) (R−1 VST) [1] where Pmxr and Cnxr are the spectra and potential dependent amounts of the species titrated in two steps (see Figure 4B), Umxr and Vnxr are the SVD eigenvectors, Srxr is the diagonal matrix of the singular values, VS = V ST, and Rrxr is the transformation connecting the physical and abstract spectra and the physical and abstract titration patterns. The rank, r, in the present experiments is 2. The relative amounts for the four species shown in the scheme in Figure 4B are obtained as functions of the potential E, in mV, as follows (N is the number of electrons transferred in each elementary step, assumed to be 1):[2]cred/ox=cox/ox 10(Em,1−E)N60cox/red=α cred/ox, with α=10(Em,3−Em,1)N60cred/red=cox/ox 10(Em,1+Em,2−2E)N60cox/ox=1/[1+(1+α)10(Em,1−E)N60+10(Em,1+Em,2−2E)N60] From Equation [1] it follows for the titration eigenvectors:VS = C RT[3] where C = [(cred/ox + cox/red),cred/red] Null model: With the arbitrary choice α = 0 one assumes that only the upper branch of the scheme in Figure 4B is operational, meaning that the second heme can only be reduced after the first one. Strictly speaking, this is only possible if the reduction potential difference between the two heme-b centers is infinite. In this case the concentration matrix used for fitting the V eigenvectors is [4]C=[10(Em,1−E)N60, 10(Em,1+Em,2−2E)N60]/(1+10(Em,1−E)N60+10(Em,1+Em,2−2E)N60) Simultaneous nonlinear least squares fit of the VS matrix according to Equation [3] provides N, 0Em,1, 0Em,2 and the elements of R. The result of this fit is shown as lines in Figure 4B, together with the obtained null model midpoint potentials, 144 ± 7 mV and −19 ± 4 mV. The high midpoint reduction potential is within the range usually obtained for other CYB561 proteins, but the low midpoint potential is somewhat lower than usual (see Table 1). Thus, the difference between the two reduction potentials is higher than that obtained usually for the other CYB561 proteins, although there have been publications with midpoint reduction potential differences as high as ~150 mV [4]. In earlier work this model, termed here as “null” was always considered [10,11,31,32,33]. Realistic model: It can be shown that allowing any arbitrary 0 < α ≤ 1 the following midpoint reduction potential values would provide identical fit to the vs. matrix: Em,1 = 0Em,1 − 60 log(1 + α)/NEm,2 = 0Em,2 + 60 log(1 + α)/NEm,3 = Em,1 + 60 log(α)/N[5] Therefore, the true midpoint reduction potentials of the two hemes cannot be determined without further assumptions. Here we can consider another choice. ( i) The midpoint reduction potential of either heme-b center is independent of the oxidation state of the other heme-b center (simple model) or (ii) each heme has two midpoint reduction potential values corresponding to the oxidation status of the other heme (coupled model). In the simple model the Em,3 = Em,2 restriction applies, and this results in a further constraint, allowing the determination of α and hence the true reduction potentials:(1 + α)2 = β α, with log(β) = (0Em,1 − 0Em,2)N/60[6] With the accurate values from the fit in Figure 4B, 0Em,1 = 144.11 mV and 0Em,2 = −19.00 mV we obtain for the simple model β = 324, α = 3.1 × 10−3, Em,1 = Em,4 = 144.02 mV and Em,2 = Em,3 = −18.92 mV. Considering the coupled model, if we select α = 0.01, allowing ~$1\%$ “flow” through the bottom branch of the scheme, the obtained midpoint reduction potentials would be Em,1 = 143.83 mV, Em,2 = −18.761 mV, Em,3 = 13.857 mV and Em,4 = 111.21 mV. In this case the midpoint reduction potential of the low potential heme would be lowered from 13.857 mV to −18.761 mV as a result of the reduction of the high potential heme. Electron paramagnetic resonance (EPR) spectra of oxidized and ~$50\%$ reduced Mm_CYB561D1 are shown in Figure 5 at temperatures 12.5 K and 20 K (panels A and B, respectively). These spectra are very similar to those obtained for the oxidized Hs_CYB561D2 at different temperatures [26] and, with one exception, the observed spectral peaks correspond well to previously assigned ones for other members of the di-heme cytochrome b561 protein family [30,32,34]. The characteristic g-values are marked and are shown with vertical lines. As expected, all spectral peaks are more intense at 12.5 K than at 20 K (which means a factor of 1.6 in kT between these temperatures). The peak at gz = 4.30 has been assigned to non-heme iron and it is frequently observed in biological samples. The peak at gz = 6.02 shows the presence of high spin ferric iron, and it has been associated with protein degradation. Protein degradation may be the result of the fact that the samples were stored at −80 oC before the measurements. Despite its apparent intensity, this peak represents a minor component because of the spin-quantum-number-dependence of the EPR intensity [30]. The gz = 4.30 and gz = 6.02 peaks are of no interest for further considerations. However, the peak at gz = 3.69 is rather prominent. At both temperatures, it loses most of its intensity but does not fully vanish upon ~$50\%$ reduction of Mm_CYB561D1. No significant EPR peak is present around gz = 3.16, a signal usually assigned to rhombic heme environments in other cytochrome b561 proteins. A relatively broad and very weak peak might be present around gz = 2.98, which vanishes or changes upon ~$50\%$ reduction, but this peak is not sufficiently clear. It has also been assigned earlier to protein degradation [30]. These observations suggest that in Mm_CYB561D1 both hemes have a highly asymmetric low-spin (HALS) character [9]. Table 1 \| CYB561 \| Expression System \| Localization \| High-Affinity Asc Binding Site (mM) \| Low-Affinity Asc Binding Site (mM) \| Reduction Potentials (mV) \| Reduction Potentials (mV).1 \| EPR Signal (g Value) \| EPR Signal (g Value).1 \| Ref. \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| CYB561 \| Expression System \| Localization \| High-Affinity Asc Binding Site (mM) \| Low-Affinity Asc Binding Site (mM) \| Ehigh \| Elow \| g high-field \| g low-field \| \| \| Bt_CYB561A1 \| none \| adrenal gland \| ? \| ? \| 150 \| 60 \| 3.69 \| 3.13 \| [35] \| \| Bt_CYB561A1 \| E.coli \| adrenal gland \| 0.0053 \| 0.394 \| 171 \| 81 \| 3.72 \| 3.15 \| [33,36] \| \| Mm_CYB561A1 \| S.cerevisiae \| adrenal gland \| 0.016 \| 1.24 \| 160 \| 20 \| 3.71 \| 3.27 \| [27] \| \| At_CYB561B1 \| S.cerevisiae \| vacuolum \| 0.0054 \| 0.336 \| 165 \| 57 \| 3.63 \| 3.16 \| [10,31], \| \| Mm_CYB561D2 \| S.cerevisiae \| ? \| 0.268 \| 7.3 \| 141 \| 43 \| 3.61 \| 2.96 \| [10,11] \| \| Hs_CYB561D2 \| P.pastoris \| ? \| ? \| ? \| 109 \| 26 \| 3.75 \| 3.65 \| [8,26] \| \| Mm_CYB561D1 \| S.cerevisiae \| ? \| 0.045 \| 2.34 \| 144 \| −19 \| 3.69 \| 3.69 \| present \| Our result supports also the unpublished observation by Asada et al. ( see [26]) according to which study on the purified recombinant Hs_CYB561D2 expressed in P. pastoris cells, the EPR spectra might show two HALS-type heme signals and they overlapped around gz = 3.6, but neither the rhombic EPR signal around gz = 3.14, that would be indicative of a cytochrome b5-type alignment of the axial His ligands, nor a peak at gz = 2.96 were recorded. Since the overlap of the two HALS-type heme signals is not unprecedented [9], it is likely that the difference in the reduction potential of the two hemes originates from structural differences relatively remote from the unpaired electron of the hemes. ## 3. Discussion Spectral and physicochemical properties of the recombinant Mm_CYB561D1 are rather similar to those obtained earlier for other recombinant CYB561 proteins (Figure 1A and Table 1) and to those of the protein purified from bovine adrenal chromaffin granule vesicles [34]. There are, however, two minor differences; (a) the lower midpoint reduction potential of the recombinant Mm_CYB561D1 is below 0 mV and (b) only one EPR signal can be detected. Unfortunately, due to the lack of CD spectra of other CYB561 proteins we cannot compare our CD spectra to published ones. Our result suggests that there seems to be only minor electronic interaction between the two heme-b centers in the recombinant Mm_CYB561D1. As demonstrated in Figure 3A,C, and as always observed for other CYB561 proteins [10,11,30,35,37,38], ascorbate cannot fully reduce both heme-b centers, whereas dithionite can. ASC at low concentrations first binds to the high affinity site. Full occupancy of the high affinity binding site can result in apparently $50\%$ reduction of the protein by single electron donation. Depending on the midpoint reduction potentials of the two heme-b centers either exclusively one of them will be reduced or they will distribute the single electron. The second ASC binding site will be saturated at the highest concentrations. Apparently, the low potential heme cannot be completely reduced by the second electron due to the relative electron affinities of this heme and the ASC molecule docked in the low affinity binding site. In other words, an equilibrium is developed between the reduction of the low potential heme by ASC and the re-oxidation of the heme by the oxidized form of ascorbate. The spectral similarity of the two consecutive reduction products in case of Mm_CYB561D1 (Figure 3C) suggests that in this protein the two heme-b centers are in a very similar environment. Alternatively, it is also possible that in both steps the received electron is distributed almost equally between the two heme centers. This would then be different for certain other CYB561 proteins where there is a spectral difference between the consecutive reduction products. Redox titration—similarly to other CYB561 proteins—also showed a bi-phasic pattern. This is readily explained by the presence of a low and a high potential heme (note that in this case no particular binding sites for the electron donor(s) are assumed). The null model (Equations [6], [7] and Figure 4B) can yield two definite values for the midpoint reduction potentials. However, the realistic model can yield the exact same fit as the null model with a whole range of possible midpoint reduction potential values. The assumption of no interaction yields two extreme potential values, while allowing a mutual effect of the redox state of either heme on the reduction potential of the other heme (coupling model) allows a whole range of less distant reduction potentials. For At_CYB561B1 it was possible to express and purify site directed double mutants with one pair of axial ligand His residues replaced, leaving only the low-potential heme in the protein [30]. The two measured reduction potentials were 178 and 20 mV for WT, whereas for the H83A/H156A and the H83L/H156L double mutants the reduction potential turned out to be 46 and 21 mV, respectively. Although the former, increased value (46 vs. 20 mV) may be explained by our realistic, coupled model, it appears that conformation alterations introduced by replacing the pair of His residues with different residues (e.g., Ala vs. Leu) and, consequently, the absence of the corresponding heme may also significantly affect the reduction potential of the remaining heme. The putative structure of Mm_CYB561D1 was calculated by homology modeling (Figure 6). The structure highly overlaps with the two existing experimental structures. The docked ASC ligands as well as structural water molecules were also included in the modeling. The obtained Mm_CYB561D1 protein surface shows similar troughs to those in the crystal structures, serving as potential substrate binding sites. The overall pattern of the surface charge around the ASC binding sites on both sides of the protein are rather similar in the two experimental and the model structures (Figures S2 and S3). Interestingly, out of the three residues that contributed to hydrogen bonding the ASC on the cytoplasmic side, K77[79], K81[83] and R150[152] for At_CYB561B2 (Hs_CYB561A2), only R86, corresponding to K77, is involved in hydrogen bonding of ASC in our modelled structure. In place of K81 the model contains isoleucine (I90) and, although there is an arginine, R158, corresponding to R150 in At_CYB561B2, its side chain points in the wrong direction due to an amino acid insertion in the sequence between this position and the strictly conserved heme ligand, H166. The rate coefficient of non-adiabatic electron transfer, according to the Marcus theory [39,40] is:[7]$k = 1013$TAD2exp(−(ΔG+λ)24λkBT) sec−1 where kB is Boltzman’s constant, T is absolute temperature, ΔG is the midpoint redox potential difference between the electron donor and acceptor pairs, λ is the reorganization energy and TAD is the donor-acceptor electronic coupling term. TAD is an exponential function of the distance (geometric distance or connectivity) between the donor and acceptor:[8]TAD=exp(−½β(r−r0)) or → TAD=∏iεi In the first, packing density model, β = 0.9ρ + 2.8(1 − ρ), with ρ being the packing density of the medium spanning the space between the electron donor and acceptor and r0 is their contact distance, usually taken as 3.6 Å [41]. In the second, pathway model εi is the decay factor for the ith step whose usual value is 0.6 for a covalent bond, 0.36 exp (−1.7(r − 2.8)) for a hydrogen bond, where r is the heteroatom distance in Å and 0.6 exp (−1.7(r − 1.4)) for a through space jump [42,43]. The most efficient electron transfer pathway from one ASC to the first heme, between the two hemes, and from the second heme to the other ASC was calculated by the program HARLEM. In these calculations, the aromatic rings of the hemes were taken as redox centers. As already pointed out by Ganasen et al. [ 6], there are no conserved amino acid residues and, therefore, no single best pathways in the two crystal structures between the hemes, and the best pathway in our model structure is also different from the others. The program also yields estimates for the packing density, the distance decay constant and the maximal theoretical electron transfer rate between the redox centers. The parameters of the packing density model for the three structures are listed in Table 2. The parameters for the model structure of Mm_CYB561D1 are rather close to those calculated from the two crystal structures, corroborating the conclusion that it is the sufficient packing of the medium between the redox centers rather than any specific amino acid residues that determine the efficiency of electron transfer in the cytochrome b561 proteins. ## 4.1. Plasmid Construct, Yeast Transformation The DNA fragments encoding the Mm_CYB561D1 (GenBank protein entry NP_001074789) and Mm_CYB561D1 fused to C-terminal thrombin cleavage site and His6 tag with the addition of 5′ and 3′ flanking BamHI, EcoRI and NotI, SalI restriction sites were codon-optimized for expression in Saccharomyces cerevisiae and chemically synthesized (GenScript, Piscataway NJ). The synthetic genes were inserted into pUC57 vector at EcoRV site and transformed into E. coli XL-1 Blue MRF’ competent cells. The plasmids were isolated with GenElute Plasmid Miniprep Kit (Sigma-Aldrich, St- Louis, MI). To clone the gene of recombinant Mm_CYB561D1 into expression vector the OWTCBD1CTH6 fragment was cut out from pUC59 with EcoRI/NotI digestion, isolated with GeneJET Gel Extraction Kit (Thermo Scientific, Waltham, MA) and ligated into the pESC-His plasmid (Agilent Technologies, La Jolla, CA) at EcoRI and NotI restriction sites, downstream of the GAL10 galactose-inducible promoter, to produce vector pMMCBD1H6G10. All recombinant plasmid constructs were verified by DNA sequencing. The pESC-His expression vectors were transformed into S. cerevisiae cells (BMS1 overexpressing yTHCBMS1 strain [44]). For transformation yeast cells were grown in complex medium (YPAD broth, Agilent Technologies, La Jolla, CA). Competent cell preparation and transformation were performed according to manufacturer instructions (Agilent Technologies, La Jolla, CA). Transformed yeast cell lines were selected and maintained on synthetic dextrose minimal medium lacking histidine (SD-His). ## 4.2. Cell Growth and Membrane Preparation Transformed yeast cells were grown in 250 mL portions in 1000 mL Erlenmeyer flasks in a temperature controlled incubator shaker at 150 rpm and 30 °C in growth medium described by Bonander et al. [ 44]. The final growth medium contained 20 mg/mL galactose as carbon source and 1 μg/mL doxycycline. Cell growth was terminated when the carbon source was used up completely. Cells were harvested by low-speed centrifugation (at 4000 gmax and 10 °C for 10 min), washed twice in ice cold phosphate buffer (25 mM KH2PO4, 100 mM NaCl, pH 7), and suspended finally in ice cold homogenization buffer (50 mM MES-KOH, pH 7.0, 5 mM EDTA, 150 mM KCl, 200 mM sucrose) supplemented with $0.1\%$ (w/v) Na-ASC and cysteine as well as with freshly prepared protease inhibitors. Cells were broken in a Bead Beater (Biospec Products, Bartlesville, OK, USA) with four 30 s cycles, with 2 min cooling intervals, using 0.5 mm glass beads. The unbroken cells and the cell debris were spun down (at 4000 gmax and 5 °C for 10 min). The yeast microsomal membrane fraction (YMMF) was obtained after high-speed centrifugation of the 4000 gmax supernatant (at 75,000 gmax and 4 °C for 60 min). The pellet was suspended in 50 mM phosphate buffer (pH 7.0) and either used immediately for solubilization or stored at −80 °C in the presence of $10\%$ (w/v) glycerol until use. ## 4.3. Solubilization and Protein Purification Solubilization of integral membrane proteins in the YMMF was performed in cold room (at ~6 °C) under continuous stirring (at ~150 rpm) for 90–120 min. Different detergents were tested (see the Results section) under slightly different experimental conditions. Since the dodecyl-β-D-maltoside (DDM) proved to be the best solubilizing agent among the tested ones, DDM was only used for solubilization throughout the further works. Insoluble material was pelleted by high-speed centrifugation (at 75,000 gmax and 4 °C for 60 min). The supernatant containing the DDM-solubilized proteins was concentrated by low-speed centrifugation using centrifugal filter units (Amicon Ultra-15 centrifugal filters with 50 kDa cut-off). Concentrated, DDM-solubilized proteins were stored in a deep-freezer (at ~−80 °C) in the solubilization buffer complemented with $10\%$ (w/v) glycerol until use. His6-tagged Mm_CYB561D1 was purified as detailed earlier [7] with little modifications. In this work (a) 0.5 mM DDM was present in all buffers after solubilization, (b) only 400 mM NaCl and $1\%$ (w/v) glycerol was present in the affinity binding buffer, (c) 300 mM imidazole was present in the affinity elution buffer, and (d) Ni Sepharose High Performance resin (GE Healthcare Bio-Sciences AB, Uppsala, Sweden) was used as His6-tag binding resin. The affinity-purified protein was stored in deep-freezer (at ~−80 °C) until use in 50 mM phosphate buffer (pH 7.0), 0.5 mM DDM and $10\%$ (w/v) glycerol. Protein concentration in the YMMFs and the detergent-solubilized fractions were determined according to Markwell et al. [ 45] with BSA (Sigma, A4503) as protein standard. Total protein concentration in affinity purified fractions were not determined. ## 4.4. Optical Spectroscopy All UV/Vis spectra were recorded with a Unicam UV4 spectrophotometer (in split-beam mode, in cuvettes with 1 cm optical path and at room temperature (at ~22 °C)). For calculation of the concentration of ascorbate- or dithionite-reduced CYB561 proteins the reduced-minus-oxidized difference spectrum and a differential molar extinction coefficient of ɛ(429–411 nm) = 222 mM−1cm−1 [46] was used. Circular dichroism (CD) spectra were recorded with a Jasco J-815 spectropolarimeter in a 1 cm cuvette at room temperature, in the visible range for the alpha, beta bands (650–450 nm) and in the near UV range for the Soret band (475–350 nm). The Mm_CYB561D1 protein concentration for the visible range was 12 μM and for the Soret band 2.5 μM, and the spectra taken in the two regions were united after compensating for the concentration difference. ## 4.5. Redox Titration Optical redox titration was carried out in 50 mM phosphate buffer, 0.5 mM DDM, ~$1\%$ (w/v) glycerol at room temperature under humidified Ar atmosphere and continuous stirring of solution in the cuvette. The sealed cuvette was equipped with an Ag/AgCl reference mini-electrode. Redox mediators used were potassium ferricyanide (+430 mV; 10 μM), 2,3,5,6-tetramethyl-p-phenylenediamine (+275 mV; 20 μM), trimethyl-hydroquinone (+115 mV; 20 μM), duroquinone (+5 mV; 20 μM), 2-hydroxy-1,4-naphthoquinone (−145 mV; 10 μM), and anthraquinone-2,6-disulfonic acid disodium salt (−225 mV; 10 μM). Reductive titration was performed by stepwise addition of 1 or 2 μL of sodium dithionite (stock solutions were 0.1 or 1 mM). Spectra between 360 and 660 nm were recorded after successive additions of the reductant and stabilization of the detected potential [47]. ## 4.6. EPR Spectroscopy Low temperature continuous wave EPR spectra, standard first harmonic in-phase signals with 100 kHz field modulation, were recorded on an X-band Bruker (Rheinstetten, Germany) EleXsys E580 EPR spectrometer equipped with a non-cryogenic helium cooling system. The protein solutions in 50 mM phosphate buffer, 0.5 mM DDM, ~$1\%$ (w/v) glycerol were prepared in the same way as for the redox titration measurement. The solutions were loaded into quartz tubes with an inner diameter of 3 mm and kept at 193 K prior to the measurement. The quartz tubes were loaded into the pre-cooled Super High Sensitivity waveguide probehead (Bruker) for measurement at 12.5 K and 20 K. The sample space was under vacuum during measurement and was flushed with helium gas when loading or exchanging samples, and the wave guide was flushed with nitrogen in order to remove atmospheric O2. All samples were measured with the following settings: microwave power, 9.46 mW; modulation amplitude, 10.0 Gauss; scan range, 0–3000 Gauss; conversion time, 15 msec; number of scans, 25. The gain was 60 dB (as recommended by Bruker). The microwave frequency was noted for each sample and was used to calculate the reported g-values. For baseline correction, the spectrum of the empty quartz tube was subtracted from that of the corresponding sample. The wide, feature-less baseline was fitted to a polynomial which was then subtracted from the spectra in order to remove the background but also preserve the signal-to-noise ratio. The spectral subtractions and fits were done using custom software written in Igor (WaveMetrics, Lake Oswego, USA). ## 4.7. Analysis of the Ascorbate and the Redox Titration Spectra Difference absorption spectra relative to the fully oxidized sample’s absorption taken during titration were collected in a data matrix and subjected to Singular Value Decomposition (SVD) analysis [48]:D = U S VT[9] where D is the data matrix containing the spectra, U and V are the matrices of the spectral and titration eigenvectors, respectively, and S is the (diagonal) matrix of the singular values. The number of significant components (i.e., the rank of the spectral matrix) was estimated based on the singular values, on the autocorrelation of the spectral and the titration eigenvectors and on the IND, REV and F tests [49]. The titration eigenvectors, i.e., the significant columns of the V matrix obtained with ASC titration, were fitted simultaneously to the equation Vi = A1,i/(1 + K1/c) + A2,i/(1 + K2/c) + Bi.[10] where $i = 1$, 2, …, rank, c is the vector of the concentration values of the reducing agent (ascorbate), Bi is constant and K1 and K2 are the affinity constants of the two ascorbate binding sites at opposite sides of the membrane. The difference spectra, characteristic for the reduction of the protein through the successive titration steps, starting from the fully oxidized sample, can be calculated as DH = U S A1T, DL = U S A2T[11] The corresponding absolute spectra, presumably belonging to the high and low potential heme centers, are obtained by adding the absorption spectrum of the fully oxidized protein to the difference spectra DH and DL, respectively. Difference absorption spectra taken during Dth (redox) titration were similarly subjected to SVD analysis. The significant redox titration eigenvectors in the V matrix as a function of the measured reduction potential, E, in millivolts, were fitted simultaneously to the set of Nernst equations and yielded the midpoint reduction potentials of the heme-b centers. For the appropriate reaction scheme and the details of the analysis, see the Results section. ## 4.8. Structural Studies of the Mm_CYB561D1 Protein The crystal structure of the Arabidopsis thaliana CYB561B2 (4O7G.pdb, [5]) was used as template in the calculation of the putative structure of the Mm_CYB561D1 by the program Modeller [50]. Internal water molecules, the two hemes and the two docked ascorbate molecules were included in the model. The level of homology between the two proteins is 51 identical, 45 highly homologous and 21 homologous amino acids out of a total of 229 in the sequence of CYB561D1. Before homology modelling various transmembrane domain predictor programs were used to find the putative six transmembrane segments of Mm_CYB561D1 (HMMTOP=CCTOP, TMHMM, Top Pred 1.10, TMPred, Predict Protein, MemBrain, OCTOPUS, Phobius, TOPCONS, Phlius). The alignment of the two sequences, as provided by Modeller agreed well with the predicted transmembrane segments, except for the first helix, where a 10 amino acid shift had to be manually introduced in the alignment suggested by Modeller to correctly model the first transmembrane helix. Electron transfer pathway and parameter calculations were performed with the program HARLEM (https://crete.chem.cmu.edu/index.php/software/2-uncategorised/18-harlem, accessed on 27 February 2023) using the crystal structures and the homology structural model of Mm_CYB561D1. ## 5. Conclusions We have presented absorption titration, CD, and EPR spectroscopic results as well as structural homology modeling calculations to support the idea that the recombinant Mm_CYB561D1 protein belongs to the ascorbate reducible, di-heme-b containing, transmembrane electron transporter cytochrome b561 protein family. The ascorbate reducibility and the redox properties of this protein have been analyzed in details by an improved, SVD based method of spectral analysis. Our analysis also took into account, for the first time, the possibility of coupling between the reduction potentials of the two hemes. We have shown that it is not possible to unequivocally determine the reduction potentials of the two hemes in these systems without further assumptions on the (lack of) interaction between the hemes. The minor differences between the characteristic biophysical parameters (midpoint reduction potential, EPR signal) for Mm_CYB561D1 and Mm_Cyb561D2 might point to their different biological function in cancer pathogenesis. The exact mechanism of tumor suppression activity of these proteins is still unknown and remains to be elucidated. ## References 1. Tsubaki M., Takeuchi F., Nakanishi N.. 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--- title: 'Exploring the Longitudinal Stability of Food Neophilia and Dietary Quality and Their Prospective Relationship in Older Adults: A Cross-Lagged Panel Analysis' authors: - Hanna R. Wortmann - Ulrike A. Gisch - Manuela M. Bergmann - Petra Warschburger journal: Nutrients year: 2023 pmcid: PMC10005135 doi: 10.3390/nu15051248 license: CC BY 4.0 --- # Exploring the Longitudinal Stability of Food Neophilia and Dietary Quality and Their Prospective Relationship in Older Adults: A Cross-Lagged Panel Analysis ## Abstract Poor dietary quality is a major cause of morbidity, making the promotion of healthy eating a societal priority. Older adults are a critical target group for promoting healthy eating to enable healthy aging. One factor suggested to promote healthy eating is the willingness to try unfamiliar foods, referred to as food neophilia. This two-wave longitudinal study explored the stability of food neophilia and dietary quality and their prospective relationship over three years, analyzing self-reported data from $$n = 960$$ older adults (MT1 = 63.4, range = 50–84) participating in the NutriAct Family Study (NFS) in a cross-lagged panel design. Dietary quality was rated using the NutriAct diet score, based on the current evidence for chronic disease prevention. Food neophilia was measured using the Variety Seeking Tendency Scale. The analyses revealed high a longitudinal stability of both constructs and a small positive cross-sectional correlation between them. Food neophilia had no prospective effect on dietary quality, whereas a very small positive prospective effect of dietary quality on food neophilia was found. Our findings give initial insights into the positive relation of food neophilia and a health-promoting diet in aging and underscore the need for more in-depth research, e.g., on the constructs’ developmental trajectories and potential critical windows of opportunity for promoting food neophilia. ## 1. Introduction Eating a varied, balanced, and healthy diet throughout life helps protect against malnutrition in all its forms, as well as against a variety of diet-related non-communicable diseases, including type 2 diabetes mellitus, cardiovascular diseases, and cancer [1,2]. Although the advantages of a healthy diet are clear, many individuals worldwide do not adhere to dietary guidelines [3]. The fact that poor dietary quality is a leading cause of morbidity [4] makes the promotion of healthy dietary patterns even more of a priority to reduce non-communicable diseases [3]. Given the significant increase in life expectancy worldwide [5] and the increasing risk of chronic diseases and multimorbidity with age [6], older adults are a particularly important target group for promoting healthy eating. A systematic review underlines that nutrition is a key determinant of chronic disease in later life, highlighting the importance of a favorable diet for healthy aging, in terms of both physical and mental health, and thus for quality of life in older age [7]. In addition, the period of older age is characterized not only by significant normative life changes (e.g., the transition from work to retirement [8]), but often by other major life events (e.g., marital transitions [9], widowhood [10], or changing health conditions [11]), all of which can lead to a disruption of long-standing eating routines. Hence, this period may represent an important window of opportunity for dietary change [8,12], highlighting its potential for intervention strategies to promote healthy eating, and thus to enable healthy aging. To account for the complex nature of diet, the study of overall dietary patterns has emerged in recent decades as a promising alternative to conventional approaches that focus on single nutrients or foods [13]. Dietary patterns can be developed either exploratory or based on predefined patterns constructed from evidence regarding nutritional health, such as dietary guidelines. The latter a priori approaches allow for the calculation of diet quality indices that assess the diet overall, such as the well-investigated Mediterranean Diet (MD) Score [14]. Although dietary patterns are increasingly investigated in nutritional epidemiology, little is known about their stability over time, especially in older age [15]. In fact, only a few studies have examined prospective changes in dietary patterns in older adults. Whereas Samieri et al. [ 16] found no change in the adherence to the MD over 13 years in a large sample of older women, Hill et al. [ 17] found evidence that the overall dietary quality of older women declined over 14 years. Thorpe et al. [ 15] observed the stability of two exploratory dietary patterns in a sample of older adults over four years, in which the overall dietary quality increased, but only in men. Using a latent class analysis, Harrington et al. [ 18] found high dietary stability among older adults over a ten-year period. Overall, the current evidence is based on different methodological approaches and samples and appears to be inconsistent but indicates a certain stability of dietary patterns at older ages. Noticeably, all of the analyzed dietary patterns were either exploratory or based solely on dietary guidelines. Knowledge of the longitudinal stability of dietary quality based on indices that additionally include current evidence for chronic disease prevention [19] could lead to a more profound understanding of dietary intervention opportunities aimed at reducing the burden of chronic disease in later life. To develop effective intervention programs to promote health-beneficial diets in older adults, it is essential to understand not only the stability of dietary quality, but also the correlates and determinants of healthy eating. The multitude of food decisions people make daily is undoubtedly based on a complex interplay of various factors [20]. One of the many factors suggested to influence food choices is the willingness to try unfamiliar foods [21]. Described as an evolutionarily beneficial survival mechanism, individuals are inherently ambivalent towards unfamiliar foods that can provide not only a new and possibly nutritious food source, but also the risk of consuming something potentially harmful or poisonous. Faced with this conflict, known as the omnivore’s dilemma [22], individuals differ greatly in their food neophilic and neophobic tendencies [23]. While food neophilia manifests in the overt willingness to try new and unfamiliar foods [24], food neophobia describes the avoidance of and reluctance to eat novel foods [25]. Previous research has focused primarily on food neophobia [26], which was found to be negatively associated with dietary variety [27] and dietary quality, e.g., measured cross-sectionally by adherence to the MD [28] and key aspects of Nordic dietary recommendations [29] and prospectively by adherence to the Baltic Sea Diet Score over an eight-year period [30]. Moreover, cross-sectional studies have shown that food neophobia is related to a lower fruit and vegetable intake [27,31] and a reduced willingness to try healthful food alternatives [32]. In addition, food neophobia was shown to be associated with different health-related biomarkers and an increased risk of type 2 diabetes mellitus [30], as well as an increased BMI [29]. Overall, the previous evidence on food neophobia in adults underscores its role as a barrier to a health-promoting diet [27] and its potential health risks [30]. It has long been assumed that food neophilia and food neophobia were merely opposite poles of the same continuum between approaching and avoiding unfamiliar foods [33], which has resulted in little research explicitly addressing food neophilia. However, recent evidence on the distinction between food neophobia and neophilia suggested a separate consideration of food neophilia in future research, as the two constructs appear to be closely related but conceptually distinct [34,35]. Focusing on positive food-related attitudes and preferences (such as food neophilia) rather than maladaptive ones also corresponds to the idea of positive psychology [36] and has appeared to be a promising approach to promoting a healthy diet in recent years [37,38]. Studies on food neophilia in the context of nutritional research are very scarce and, to our knowledge, exclusively cross-sectional. With both studies using brief screening instruments to measure dietary quality, Lavelle et al. [ 39] found a small positive association between food neophilia and dietary quality, whereas McGowan et al. [ 40] found no association between food neophilia and a brief measure to distinguish between healthy and unhealthy food choices, but did find a small association with a lower intake of saturated fat, which is another aspect of dietary quality. Thus, while there is cross-sectional evidence that food neophilia and diet quality are positively related, no study has yet examined the longitudinal relationship between food neophilia and dietary quality. Although positive cross-sectional relations indicate that food neophilia might play a beneficial role in healthy dietary behavior, prospective evidence is needed to verify the assumption that food neophilia precedes healthy eating. Due to the cross-sectional nature of the previous studies, alternative theoretical assumptions concerning the direction of the effect between food neophilia and dietary quality are also possible. For example, it is conceivable that not only food neophilia has a positive effect on dietary quality, but also vice versa, e.g., that a higher dietary quality is associated with better cooking and food skills [39] and greater food knowledge [41], which in turn may lead to greater interest in and willingness to try unfamiliar foods. Studies addressing the longitudinal reciprocal relationship between food neophilia and dietary quality will help elucidate their interrelation over time, potentially providing important implications for intervention programs to promote a health-beneficial diet. Focusing on older adults and the possibilities for promoting healthy aging, knowledge of food neophilia in this age group and its stability across time will further expand our understanding of intervention opportunities. However, there is only limited research on food neophilia in older individuals. When comparing different age groups in non-representative samples in terms of their mean levels of food neophilia, Wortmann et al. [ 35] found no significant differences, while Van Trijp [42] found significantly higher levels of food neophilia in younger adults than in older adults. In a prospective study of young adults leaving their parental homes, Meiselman et al. [ 43] found a high stability of food neophilia during this particular period of change. To date, however, it remains unclear whether food neophilia is comparably stable during the period of older age, and whether food neophilia tends to decrease or increase in later life. To overcome the described gaps in the literature, the main purpose of the present study was to examine the stability of both food neophilia and dietary quality in older age (defined here as ages 50 and older), as well as their prospective reciprocal relationship, with a higher dietary quality referring to a higher adherence to dietary recommendations for the prevention of chronic diseases. Analyzing the longitudinal data from older adults participating in the Nutritional Intervention for Healthy Aging (NutriAct) Family Study (NFS), in a cross-lagged panel design, we addressed the following research questions: How stable are food neophilia and dietary quality in older adults over a three-year period? Does food neophilia predict dietary quality over time and vice versa? Based on the previous evidence, we hypothesized a high stability of food neophilia and dietary quality over time. Given the limited evidence on the association between food neophilia and dietary quality, their prospective reciprocal relationship was examined in an exploratory manner. ## 2.1. Study Design and Procedure Data collection for the first (T1) and second (T2) waves of the NFS took place between January 2017 and March 2019, and between September 2020 and November 2021, respectively. The NFS is a web-based, prospective, interdisciplinary study examining food choices from psychological, epidemiological, and sociological perspectives and is part of the NutriAct competence cluster funded by the German Federal Ministry of Education and Research. Study participants were recruited in groups of two or more family members (spouses and siblings). The recruitment procedures are described in detail elsewhere [44]. In brief, the recruitment of the families was based on an index person who had already participated in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study. Before inclusion in the study, all participants were asked to provide written informed consent. After enrollment in the study, the participants received login credentials for their personalized web-based questionnaires. To participate in the second wave (T2), those participants who had completed the T1 questionnaires were invited by mail. For both waves, the online questionnaires each consisted of four coherent parts [44]. They included a comprehensive dietary intake survey, as well as a set of reliable and valid instruments, to assess the potential factors influencing food choices based on the DONE framework [20]. ## 2.2. Participants For the present study, participants aged 50 years and older were included, resulting in a final sample of $$n = 960$$ participants from 409 families who completed the T1 online questionnaires. Of these participants, $$n = 829$$ from 372 families took part at T2, resulting in a dropout rate of $13.6\%$. The mean interval between T1 and T2 was 40.0 months (SD = 4.2 months). An overview of the sample characteristics at T1 and T2 is presented in Table 1. ## 2.3.1. Food Neophilia Food neophilia was measured with the German version [35] of the Variety Seeking Tendency Scale (VARSEEK) [46]. The VARSEEK consists of 8 items (e.g., “I am curious about food products I am not familiar with.”), that were rated on a 7-point Likert scale, ranging between 1 (completely disagree) and 7 (completely agree), i.e., higher mean values reflect higher food neophilia. Previous research has supported the scale’s internal consistency (Cronbach’s α =.93) and its test-retest reliability ($r = .87$) [35]. Positive correlations with ratings of willingness to try unfamiliar foods and openness as well as negative correlations with food neophobia and general neophobia support the scale’s construct validity [35]. For the present study, Cronbach’s α as well as McDonald’s ω [47] were.93 at T1 and T2. ## 2.3.2. Dietary Quality Dietary quality was calculated using a multi-step approach, resulting in the NutriAct diet score [19], a new diet score based on the current evidence for chronic disease prevention and the guidelines of the German Nutrition Society. In the first step, participants’ usual food intake was assessed following Knüppel et al. [ 48]. For this purpose, the food intake probability was calculated based on the repeated application of four 24 h food lists (24 h-FL) [49] over 12 months, enriched by frequency information from an EPIC-Potsdam Food Frequency Questionnaire-II (FFQ2) [50]. The food intake probability was then multiplied by a person-specific daily consumption amount derived from a reference population from the representative German National Nutrition Survey II (NVS II) [51], resulting in an estimate of the participants’ usual intake of a variety of different food items. In the second step, the evidence-based scoring scheme of the NutriAct diet score was applied, as described in detail by Jannasch et al. [ 19]. For this purpose, food items were first aggregated into a total of 10 food groups that were shown to be associated with the most common non-communicable chronic diseases, such as type 2 diabetes mellitus, cancer, and cardiovascular diseases. Depending on the food group, either the daily or weekly portions of the food groups were then calculated for each participant, based on the participants’ usual intake of the respective food items. To calculate the final NutriAct diet score, the intake per food group was rated up to one point, resulting in a NutriAct diet score of between 0 and 10 points. For example, given the health-promoting effect of fruit consumption, the higher the intake category (ranging between never and 2 or more portions/week), the higher the respective points (0, 0.5, 1). In summary, a higher NutriAct diet score indicates a higher dietary quality according to the current evidence for chronic disease prevention and the recommendations of the German Nutrition Society [52]. ## 2.3.3. Additional Database of Government Policies during the COVID-19 Pandemic As the T2 data were collected during the COVID-19 pandemic, we used the stringency index retrieved from the Oxford COVID-19 Government Response Tracker (OxCGRT), a global panel database capturing government policies related to containment, health, and economic policies during the COVID-19 pandemic [53], to quantify and statistically control for the stringency of government containment and closure policies in Germany at each survey date. The stringency index varies with time and can range between 0 (no measures) and 100 (total lockdown). In the present study, the mean T2 stringency index was $M = 64.65$ (SD = 11.98, range = 42.59–85.19). ## 2.4. Statistical Analyses First, descriptive analyses were performed. Pearson correlation coefficients were calculated to investigate the bivariate relationships between food neophilia and dietary quality at T1 and T2 (r >.10 small, r >.30 medium, r >.50 large effect sizes; [54]). Holm-Bonferroni corrections were performed to adjust the level of significance for multiple testing [55]. Wald tests were performed for both the study variables to compare their respective mean values at the time points T1 and T2. To estimate the effect sizes, Cohen d ($d = 0.2$ small, $d = 0.5$ medium, $d = 0.8$ large effect size) [56] was calculated [57]. As the participants were nested within families, the preliminary analyses included an examination of the intraclass correlation coefficients (ICC) as a quantitative measure of the similarity among the observations within families. The ICCs were computed by employing the type “twolevel basic” option in Mplus. Moreover, a dropout analysis was performed to determine whether the study variables were systematically associated with dropout after T1, using independent samples t-tests for the interval variables (age, weight status, food neophilia, dietary quality) and chi-square tests for the categorical variables (gender, educational status). Our dropout analysis revealed no significant differences between the participants who dropped out after T1 and those who remained in the study in terms of gender, χ2 [2] = 0.16, $$p \leq .924$$; age, t[162] = 0.72, $$p \leq 0.471$$; weight status, t[958] = −1.35, $$p \leq .179$$; educational status, χ2 [2] = 2.42, $$p \leq .299$$; food neophilia, t[958] = −0.23, $$p \leq .819$$; and dietary quality, t[958] = −0.30, $$p \leq .767.$$ Second, as food neophilia was included as a latent variable in the following cross-lagged panel analysis, confirmatory factor analyses (CFA) were conducted to test the measurement invariance (MI) of the latent construct of food neophilia across both waves, ensuring valid and meaningful across-time comparisons [58]. The MI was tested at the levels of configural, metric, scalar, and residual invariance by using a multi-step approach comparing a set of nested latent structural equation models, using all of the VARSEEK items as indicators for the latent construct. First, assuming a configural MI, we specified an unrestricted baseline model with autocorrelated errors among repeatedly measured indicators to account for the variance that an indicator shared with itself over time [59]. In the following, we compared several increasingly constrained models in which the factor loading, intercepts, and measurement error variances of the configural model were gradually set to be equal over time (thus representing metric, scalar, and residual MI, respectively). The evaluation of the model fit was based on multiple indices: the comparative fit index (CFI), the root mean square error of approximation (RMSEA), and the standardized root mean square residual (SRMR). A good model fit is indicated by a RMSEA coefficient of less than.06, a CFI above.95, and a SRMR of less than.08 [60]. As χ2 statistics are highly sensitive to large sample sizes, the model comparisons were based on absolute differences in the fit indices. Following Chen [61], a change of ≥−.010 in the CFI, supplemented by a change of ≥.015 in the RMSEA, or a change of ≥.030 in the SRMR for weak MI (and ≥.010 for strong and strict MI) indicates non-invariance. As shown in Supplementary Table S1, all of the models yielded a good fit to the data. Moreover, the model comparisons showed that restricting the models did not worsen the model fit, supporting residual invariance over time, and thus allowing for meaningful comparisons of food neophilia over time. The following analyses are based on the residual model. Third, a cross-lagged panel analysis was conducted to analyze both the temporal stability of food neophilia and dietary quality, as well as the reciprocal effects between the two constructs, using structural equation modeling (SEM). Whereas food neophilia was included as a latent variable in the model, the NutriAct diet score was included as a manifest variable to measure the dietary quality. Within the model, autoregressive and cross-lagged paths were estimated, allowing for an evaluation of the reciprocal effects between food neophilia and dietary quality over time while simultaneously controlling for the stability of the two constructs [58]. We included the participants’ gender, age, body mass index (BMI), educational status, and stringency of government containment policies at T2 as covariates in the model to statistically control for these variables. Due to the wide range of BMI values observed among the participants (see Table 1), supplementary exploratory multigroup comparisons were carried out to test for differences between the different weight status groups. For this purpose, the weight status at T1 was divided into three BMI categories (underweight: BMI < 18.5 kg/m2, average weight: BMI = 18.5–24.9 kg/m2, overweight and obesity: BMI > 25.0 kg/m2). However, due to the small sample size of participants who were underweight ($$n = 13$$), these cases were excluded from our analysis. Post-hoc analyses using Wald tests were applied to test for significant differences of the path coefficients. As the participants were nested within families, we accounted for the potential non-independence of the observations by using the robust maximum likelihood estimator (MLR) in combination with a type “complex” option in Mplus (specifying family ID as a cluster variable) for all of the analyses. This approach provides adjusted standard errors and test statistics that are robust to non-normality and non-independence of observations [62]. All of the analyses were performed using Mplus 7 [63]. All of the tests adopted a significance level of.05. ## 2.5. Missing Data All of the participants who participated in the T1 wave were included in the analyses. To account for missing data, we used a multiple imputation approach to impute the missing data at both time points, including the missing values at T1 and missing data from participants who dropped out of the study. We had the complete data for the NutriAct diet score at both T1 and T2, as answering the nutrition-related questions was a prerequisite for the further processing of the questionnaire. Missing values for the VARSEEK score were very low ($99.4\%$ and $99.2\%$ complete data sets for T1 and T2, respectively). These data were missing completely at random, as determined by Little’s [64] Missing Completely at Random (MCAR) test: χ2[41] = 35.635, $$p \leq .707$$ for T1 and χ2[28] = 16.422, $$p \leq .959$$ for T2. Following Geiser [58], 50 imputed data sets were generated, which allowed us to perform robust analyses. All of the subsequent analyses, including the descriptive analyses, measurement invariance analysis, cross-lagged panel analysis, and exploratory multigroup comparisons were based on these imputed data sets. Multiple imputation is a regression-based technique that is considered to be a state-of-the-art method for dealing with missing data [65], and outperforms traditional missing data techniques, such as listwise or pairwise deletion and single imputation techniques [66]. ## 3.1. Descriptive Analyses The bivariate correlations of the study variables food neophilia and dietary quality at T1 and T2, as well as their descriptive statistics (means and standard deviations) and ICCs, are presented in Table 2. Our analyses showed that all of the variables were significantly correlated. The food neophilia at T1 and T2 were each slightly positively associated with the dietary quality at T1 and T2. In addition, there were strong positive correlations between food neophilia at T1 and T2, as well as between dietary quality at T1 and T2. The analyses showed a significant difference between the T1 and T2 means of food neophilia, Wald[1] = 23.13, $p \leq .001$, $d = 0.31$, but no significant difference between the T1 and T2 means of dietary quality, Wald[1] = 0.39, $$p \leq .531.$$ In other words, the participants’ food neophilia slightly decreased on average from T1 to T2, whereas there was no significant change in the mean dietary quality from T1 to T2. The ICCs, as a quantitative measure of the similarity among observations within families, were low for both food neophilia and dietary quality. However, the ICCs indicate a non-independence of observations, which was statistically accounted for in all of the further analyses (see Section 2.4 for more details). ## 3.2. Temporal Stability and Reciprocal Effects of Food Neophilia and Dietary Quality The cross-lagged panel model of food neophilia and dietary quality is shown in Figure 1, with the standardized parameters reported. We additionally controlled for the participants’ gender, age, BMI, educational status, and stringency of government containment policies at T2 by including these variables as covariates in the model (they are omitted from Figure 1 for clarity). The model yielded a good fit to the data: CFI =.953, RMSEA =.054, SRMR =.039. To test for robustness, we additionally estimated the model using a full-information maximum likelihood (FIML) estimation to handle the missing data. This model fully replicated the critical pathways determined with the multiple imputation procedure (CFI =.956, RMSEA =.049, SRMR =.039). Supporting our hypotheses, the food neophilia at T1 predicted the food neophilia at T2, and the dietary quality at T1 predicted the dietary quality at T2, indicating high interindividual stability of both constructs. In addition, the analyses revealed a small positive cross-sectional correlation between food neophilia and dietary quality. Controlling for the temporal stability and the cross-sectional correlation, food neophilia did not have a significant prospective effect on dietary quality. Dietary quality showed a very small positive prospective effect on food neophilia. The model explained $67.0\%$ of variance in food neophilia and $44.5\%$ of variance in dietary quality. ## 3.3. Exploratory Multigroup Comparisons The model of multigroup comparisons showed an acceptable fit to the data (CFI =.953, RMSEA =.056, SRMR =.044). As shown in Figure 2, the results were comparable to those of the total sample. However, our results revealed a positive cross-sectional correlation between food neophilia and dietary quality only in individuals with overweight and obesity, while this association was not significant in individuals with normal weight. Additionally, a small positive prospective effect of dietary quality on food neophilia was observed only in individuals with overweight and obesity. Further post-hoc analyses using Wald tests showed that the only significant difference between the two weight status groups was in the cross-sectional correlation between food neophilia and dietary quality, Wald [1] = −0.26, $$p \leq .002.$$ ## 4. Discussion The present two-wave longitudinal study was designed to explore both the temporal stability of food neophilia and dietary quality, as well as their prospective reciprocal relationship, in a large sample of older adults over a three year period. Our analyses showed a high longitudinal stability of food neophilia and dietary quality and a positive cross-sectional relationship between the two constructs. Food neophilia had no prospective effect on dietary quality. However, we found a very small positive prospective effect of dietary quality on food neophilia. In summary, our findings allowed a deeper understanding of food neophilia and dietary quality in later life and shed light on their interplay over time. Given the paucity of studies in this field of research, our results offered initial insights into food neophilia and dietary quality in older age. The mean scores of food neophilia indicated a moderate willingness of the participants to try unfamiliar foods at both time points and were slightly lower compared to recent findings on food neophilia in a German community sample aged 18 years and older [35]. Lower scores on food neophilia could be due to the older age or other characteristics of our sample. Our analyses revealed that food neophilia slightly decreased on average over the three year study period, indicating that participants became slightly less willing to try unfamiliar foods from one time point to another. With regard to the related construct of food neophobia, the lifespan model postulated by Dovey et al. [ 67] assumes that the aversion to eat novel foods reaches a stable level in early adulthood before increasing again in later adulthood, although empirical longitudinal data to support this assumption are lacking so far. One possible explanation for our results may be that health-related concerns increase with age [68], possibly leading to a decreased overall willingness to try unfamiliar foods that may carry the risk of affecting one’s well-being (e.g., gastric distress). Another interesting aspect of the stability of food neophilia in older age can be inferred from the results of our cross-lagged panel analysis, which indicated a very high longitudinal stability, i.e., little change in the interindividual differences across time [69]. In other words, despite the slight decrease in the participants’ average food neophilia over time, each participant’s relative standing on the construct has changed very little, i.e., those with a high food neophilic tendency remained highly food neophilic, and those with a low food neophilic tendency remained less food neophilic. It is conceivable that, particularly in this age group, long-standing and established everyday routines result in little change in food neophilic tendencies and behaviors in daily life. In terms of potential intervention opportunities, one approach to promoting food neophilia in older age may be to bring change to these daily routines. This could be achieved, for example, by deliberately creating everyday situations that involve exposure to novel foods and cuisines (e.g., when grocery shopping or visiting restaurants). Other potential ways to increase food neophilia could include increasing food familiarity, nutrition knowledge, and cooking skills, as well as providing information (especially to older adults) about the potential health benefits of novel foods [70]. More research is needed to test our assumptions and deepen our understanding of potential intervention strategies. Regarding the examination of the temporal stability of dietary quality in later life, our analyses showed no change in the mean dietary quality over the three year study period, with the participants being moderately adherent to a healthy diet at both time points, as measured by several key dietary determinants of chronic disease risk [19]. In addition, the results of our cross-lagged panel analysis indicated a high longitudinal stability of dietary quality, i.e., little change in the interindividual differences across time. Our results add to the limited research on the temporal stability of dietary quality in later adulthood [15,18] and provide a useful basis for further research. Although our analyses showed no deterioration in dietary quality with age, the moderate dietary quality of the participants on average underscores that older adults are an important target group for promoting health-beneficial eating, particularly in light of the increasing risk of chronic diseases and multimorbidity with age [6]. While the analyses showed a high degree of stability of interindividual differences over time, they do not allow conclusions to be drawn in regard to the individual trajectories of change. Future prospective studies may contribute to a deeper understanding of the potential for favorable dietary change in older age [8] by focusing on a more in-depth analysis of the unique trajectories of change in individuals (or groups of individuals), as well as potential predictors using other appropriate statistical approaches, such as latent growth curve (LGC) analysis [58]. In addition to the temporal stability of food neophilia and dietary quality, a special focus of the present work was the reciprocal relationship between the two constructs. In line with the limited previous evidence on their cross-sectional association [39,40], our preliminary analyses showed a small positive correlation between food neophilia and dietary quality at both time points. Interestingly, our exploratory multigroup comparisons revealed that the positive cross-sectional association between food neophilia and dietary quality was evident only in individuals with overweight and obesity, but not in individuals with normal weight. These results suggest that while a high dietary quality appears to be associated with an increased willingness to try new and unfamiliar foods in individuals with overweight and obesity, food neophilia does not seem to play an important role in promoting healthy eating behavior in individuals with normal weight. Our findings may have implications for future research exploring the interplay between food neophilia and dietary quality, particularly with regard to how this relationship may differ based on weight status. One possible avenue for further investigation could be to explore whether interventions aimed at increasing food neophilia to promote healthy eating are more effective in individuals with overweight and obesity than in those with normal weight. In addition, including a larger sample of individuals with underweight in future studies may prove useful to provide a more comprehensive understanding of how the interplay between food neophilia and dietary quality operates across the entire weight range. Statistically controlling for the temporal stability of the constructs, as well as their cross-sectional intercorrelation, our analyses did not identify food neophilia as a significant determinant of dietary quality over time. In other words, food neophilia did not prospectively predict dietary quality in our cross-lagged panel analysis. As research on this topic is very scarce, our analyses were conducted in an exploratory manner, and we can only speculate on the reasons why we did not find a prospective effect of food neophilia on dietary quality. One possible explanation is that food neophilia per se may indeed not have a major positive long-term impact on overall dietary quality. Food neophilia describes the overt willingness to try unfamiliar foods combined with a general interest in new cuisines, recipes, and dishes [35]. However, no distinction is made as to whether these unfamiliar foods are in fact health-promoting. Given the possibility that unfamiliar foods may also be health-detrimental, it is possible that while willingness to try new foods may be associated with the consumption of a variety of different foods, dishes, and recipes, it may not necessarily contribute to a healthy diet (in the sense of following dietary recommendations to prevent chronic disease). In fact, the internationally accepted recommendation to consume a variety of foods to meet nutrient needs and reduce the risk of nutritional deficiency has increasingly become controversial, as studies on dietary variety have not only shown considerable associations with positive health outcomes [71], but also raised concerns about potential adverse outcomes of diverse diets, such as excess energy intake and obesity [72]. Thus, evidence indicates that dietary variety is not necessarily health-promoting, unless it is embedded in a health-oriented, high-quality diet that includes a variety of foods with a high nutrient density [73,74]. Hence, although we did not find a substantial positive relationship between food neophilia and the overall quality of a diet over time, it may still play an important role in promoting a healthy diet. For example, instead of promoting food neophilia in general, intervention strategies could focus on promoting food neophilia toward nutrient-rich foods specifically, such as unfamiliar fruits and vegetables, as well as healthful food alternatives (e.g., functional foods or nutritionally modified foods). In fact, consumer research indicates that the awareness and acceptance of nutritionally modified and functional products increase with age [75,76], suggesting that older adults are a promising target group for the consumption of healthful food alternatives. Future studies on the relationship between food neophilia and dietary variety within a health-oriented, high-quality diet may help to further elucidate the role of food neophilia in the context of healthy eating. Another possible explanation is that there is indeed no considerable prospective reciprocal relationship between the two constructs in later adulthood. Assuming that food neophilia does play an important role in healthy eating, it may have a greater positive impact on dietary quality in the early life stages of childhood, which are considered crucial for the development of health-promoting dietary habits and food preferences [77]. The extensive research on the related construct of food neophobia in children (for an overview see [67]) showed that exposure to novel foods results in an increased acceptance of these foods in everyday diets. Dovey et al. [ 67] suggested that this may not necessarily be the case later in life when dietary habits are already established and manifested. It seems plausible that, in later adulthood, other emerging factors, such as health cognitions and food-related physiology (for an overview see [20]), are more likely to prospectively influence long-standing dietary habits, and thus dietary quality. Nevertheless, it is possible that promoting food neophilia may contribute to the development of health-promoting dietary habits, not only in childhood, but also at later stages of life. As for the age group of older adults, research suggests that certain changes in an individual’s later life, such as the retirement transition [8,12], marital transitions [9,10], or changing health conditions [11], may provide an effective opportunity to break established (potentially long-standing) unfavorable dietary habits. In contrast to the present study that examined older participants of a relatively broad age range, future prospective studies could specifically target individuals undergoing such life transitions, e.g., those in the peri-retirement age group, to further our understanding of the potential critical windows of opportunity for the promotion of food neophilia. Furthermore, studying participants at multiple time points within a more extended study period could provide additional useful information. Such knowledge may prove useful for future studies aimed at investigating targeted intervention measures to promote healthy eating in later life, and thereby reduce the burden of chronic disease and multimorbidity. The results of our cross-lagged panel analysis suggested a very small positive prospective effect of dietary quality on food neophilia. The fact that the prior level of food neophilia (i.e., its stable portion) is controlled for, allows us to rule out the possibility that the effect was found simply due to the positive cross-sectional relationship between food neophilia and dietary quality [69], suggesting an interrelation (albeit weak) over time. However, the effect size found in this study was very small. Future studies should investigate the longitudinal association between dietary quality and food neophilia in more detail to further our understanding of the underlying developmental processes. These investigations may also include potential mediating variables (e.g., cooking and food skills [39], nutrition knowledge [41]). ## Strengths and Limitations The present study was the first to investigate the stability of food neophilia and dietary quality in older adults and their cross-lagged relationship over time. The results of our study should be interpreted in the context of its strengths and limitations. One of the key strengths of our study was its comprehensive dietary assessment strategy, which combined both short-term and long-term dietary assessment tools. Its evaluation system was not only based on the recommendations of the German Nutrition Society, but also included the current evidence for chronic disease prevention, making a distinct contribution to the few previous studies on food neophilia in the context of nutritional research, which have mainly used brief screening tools to measure dietary quality. Although the newly developed NutriAct diet score should be evaluated for its health benefits in future prospective studies, it already represents a valuable advancement in the measurement of adherence to a health-promoting diet in Germany. Another strength was the fact that the present study followed an interdisciplinary approach, combining both psychological and nutritional expertise. In addition, it included two data waves, covering a time span of over three years, as well as a large sample of almost 1000 older adults. Moreover, the number of dropouts in our study was low (<$14\%$) and no systematic dropout bias was evident. Missing values were handled by the state-of-the-art multiple imputation procedure, resulting in less biased parameter estimates compared to traditional missing data techniques. In addition, the application of a cross-lagged panel analysis enabled the bidirectional analysis of food neophilia and dietary quality while controlling for their temporal stability and cross-sectional association. Some limitations need to be acknowledged. The first limitation is the use of self-reported data, which can lead to systematic bias [78]. However, the design and concept for this study were carefully planned [44] to minimize information bias [79]. For example, short-term and long-term dietary assessment techniques were combined to assess dietary intake, as this was shown to yield less biased estimates than stand-alone instruments [80]. Moreover, food neophilia was assessed using the psychometrically validated German version of the VARSEEK [35]. In addition, we performed latent modeling of the construct of food neophilia, which allowed us to account for measurement error and to verify its temporal measurement invariance, enabling meaningful comparisons of food neophilia over time. Another limitation is that the data collection for the second wave of the NFS took place between September 2020 and November 2021, i.e., during the COVID-19 pandemic, imposing a whole new set of challenges, including the direct implications on one’s lifestyle, and thus on nutrition and eating behavior [81]. However, a recent systematic review suggests rather inconsistent results regarding the effect of the COVID-19 pandemic on dietary patterns and eating behavior in non-clinical samples [82]. Despite the lack of studies on food neophilia during the pandemic, given the high temporal stability of food neophilia, it is conceivable that the lockdowns proposed by governments during this time negatively impacted the opportunities to consume unfamiliar foods and dishes, but had less of an impact on the actual willingness to try them and its effect on healthy eating. Nevertheless, to statistically control for the potential effects of the pandemic, we added the stringency of government containment and closure policies in Germany during the second data wave as a covariate in our analyses, using the OxCGRT stringency index [53]. A further limitation concerns the representativeness of the sample, with participants showing a high level of education compared to the German population of adults over 50 years [83]. As previous studies have found a positive relationship between educational level and both food neophilia [42] and dietary quality [84], our results may thereby not be generalizable to individuals of all educational levels. Therefore, to increase the generalizability of our results, future studies should include more heterogeneous samples. ## 5. Conclusions The current work was the first to examine the stability of food neophilia and dietary quality in older, age as well as their reciprocal relationship over time, in a cross-lagged panel design. Our analyses revealed a high longitudinal stability of both food neophilia and dietary quality over a three year period. In addition, we found a small positive cross-sectional correlation between food neophilia and dietary quality, suggesting that food neophilia may play a role (albeit a small one) in the context of a health-promoting diet in older age. However, our post-hoc analyses revealed that this association was evident only in individuals with overweight and obesity, but not in individuals with normal weight. The analyses indicated that food neophilia had no prospective effect on dietary quality, whereas we found a very small positive prospective effect of dietary quality on food neophilia. In summary, the present study allowed a deeper understanding of food neophilia and dietary quality in older age and gave initial insights into their interrelation over time. As our analyses did not identify food neophilia as a significant determinant of dietary quality over time in older age, it will prove useful to understand the developmental trajectories of both constructs in more depth before investigating the potential of food neophilia for intervention strategies to promote healthy eating. In addition, future studies should expand our findings by further researching the relationship between food neophilia and dietary variety within a health-oriented, high-quality diet, as well as the potential critical windows of opportunity for the promotion of food neophilia (e.g., during critical life transitions). Additionally, it would be valuable to explore the mediating role of weight status in the interplay between food neophilia and dietary. ## References 1. 1. World Health Organization Healthy DietFact Sheet No. 394WHOGeneva, Switzerland2018. Healthy Diet (2018) 2. Mozaffarian D.. 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--- title: Cytidine Alleviates Dyslipidemia and Modulates the Gut Microbiota Composition in ob/ob Mice authors: - Kaixia Niu - Pengpeng Bai - Junyang Zhang - Xinchi Feng - Feng Qiu journal: Nutrients year: 2023 pmcid: PMC10005144 doi: 10.3390/nu15051147 license: CC BY 4.0 --- # Cytidine Alleviates Dyslipidemia and Modulates the Gut Microbiota Composition in ob/ob Mice ## Abstract Cytidine and uridine are endogenous metabolites in the pyrimidine metabolism pathway, and cytidine is a substrate that can be metabolized into uridine via cytidine deaminase. Uridine has been widely reported to be effective in regulating lipid metabolism. However, whether cytidine could ameliorate lipid metabolism disorder has not yet been investigated. In this research, ob/ob mice were used, and the effect of cytidine (0.4 mg/mL in drinking water for five weeks) on lipid metabolism disorder was evaluated in terms of an oral glucose tolerance test, serum lipid levels, liver histopathological analysis and gut microbiome analysis. Uridine was used as a positive control. Our findings reveal that cytidine could alleviate certain aspects of dyslipidemia and improve hepatic steatosis via modulating the gut microbiota composition in ob/ob mice, especially increasing the abundance of short-chain fatty acids-producing microbiota. These results suggest that cytidine supplementation could be a potential therapeutic approach for dyslipidemia. ## 1. Introduction Dyslipidemia is a lipid disorder characterized by an elevation in the level of serum total cholesterol (TC), triglyceride (TG) or low-density lipoprotein cholesterol (LDL-C) and a low level of high-density lipoprotein cholesterol (HDL-C). Various diseases, such as cardiovascular complications, fatty liver, diabetes, obesity and atherosclerosis, are clearly associated with dyslipidemia [1]. Intervention at the early stage of dyslipidemia can prevent the occurrence of associated diseases effectively [2]. Recently, the gut microbiota has been reported to play an important role in hosting lipid metabolism, which is mediated via metabolites produced by gut microbiota, such as short-chain fatty acids, bile acids and lipopolysaccharide [3,4,5]. Nucleotides, the building blocks of RNA and DNA, play important roles in various biological processes, including gastrointestinal function, energy metabolism and immune regulation [6]. Nucleotides are widely present in food, and they are also used as additives for infant formulas due to the beneficial effects provided to infants [7]. Among all the nucleotides, uridine has been extensively investigated and is reported to be able to ameliorate lipid accumulation via modulating gut microbiota composition [8,9,10,11,12]. Our previous study found that a triterpene acid derived from centella, named asiatic acid, could alleviate metabolic disorders in ob/ob mice, and the levels of cytidine in the liver were significantly increased [13]. This prompted the hypothesis that the supplementation of cytidine might alleviate metabolic disorders. Moreover, cytidine is a substrate for the salvage pathway of pyrimidine synthesis and could be metabolized into uridine via cytidine deaminase in vivo [14]. Thus, it was speculated that besides uridine, cytidine treatment might also show an alleviation effect on dyslipidemia. However, until now, the effect of cytidine on lipid metabolism has not been investigated. The aim of this study was to investigate whether cytidine treatment could alleviate metabolic disorders. Uridine was applied as a positive control, and ob/ob mice were used in this study. Moreover, the effect of cytidine on the gut composition in ob/ob mice was also studied. ## 2.1. Chemicals, Animals and Administration Cytidine ($99\%$ purity) and uridine ($99\%$ purity) were purchased from Shanghaiyuanye Bio-Technology Co., Ltd. (Shanghai, China); Ultrapure water was purchased from Watsons Group, and all other reagents used in this study were of analytical grade. Male SPF (specific pathogen-free)-grade ob/ob mice (5 weeks old) were obtained from GemPharmatech Biotechnology Co., Ltd. (Nanjing, China), and male SPF-grade C57BL/6J mice (5 weeks old) were obtained from SPF (Beijing, China) Biotechnology Co., Ltd. (Beijing, China). All animals were kept in a controlled environment with free access to food and water (temperature, 25 ± 1 °C; humidity, 50 ± $5\%$; 12 h light/12 h dark cycle) and allowed to adapt to their living conditions for one week. After acclimatization, 24 ob/ob mice aged six weeks were randomly divided into three groups as follows: model group, cytidine group, and uridine group ($$n = 8$$ per group). C57BL/6J mice were applied as a control group ($$n = 8$$). Mice in the model group and control group were fed with normal drinking water. Mice in the cytidine group and uridine group were fed with drinking water containing cytidine or uridine at a dose of 0.4 mg/mL for 5 consecutive weeks. All mice were fed a standard diet. Body weights were recorded every week, and water and food consumption were measured every day. By the end of the experiment, mice were anesthetized with pelltobarbitalum natricum. Serum samples used for the determination of biochemical indicators were collected before the mice were sacrificed via cervical dislocation. The liver and cecum were manually isolated. In order to conduct the histological examination, liver tissues were preserved in $4\%$ paraformaldehyde. The cecal contents were collected, and five samples for each group were randomly selected for microbiome analysis. All samples were stored at −80 °C. ## 2.2. Oral Glucose Tolerance Test (OGTT) After the mice were treated with cytidine or uridine for 5 weeks, oral glucose tolerance was tested. Briefly, all mice first fasted for 12 h, and then each mouse was intragastrically administered glucose solution at a dose of 2 g/kg. Blood glucose concentrations were determined using tail–tip blood sampling at 0, 15, 30, 60, 90, and 120 min after glucose was administered, and the area under the curve (AUC) was calculated. ## 2.3. Biochemical Indicator Detection and Histopathological Analysis The contents of serum TC, TG, HDL-C and LDL-C were determined using commercial assay kits supplied by Nanjing Jiancheng Bioengineering Institute (Nanjing, China). Liver tissues were fixed in $4\%$ paraformaldehyde, dehydrated, blocked with paraffin, and then sectioned and stained with hematoxylin and eosin (H&E). Histopathological changes were analyzed and recorded using a light microscope (200×) (OLYMPUS CKX43, OLYMPUS, Tokyo, Japan). The hepatic fatty vacuole area ratio in liver tissue slices was determined by quantifying the amount of stained area using Image J software (version 1.48r, National Institutes of Health, Bethesda, MD, USA). ## 2.4. Gut Microbiome Analysis Regarding gut microbiome analysis, bacterial DNA from the cecal contents was extracted, and the DNA concentration was detected using $1\%$ agarose gel electrophoresis. DNA encoding the 16S rRNA V3–V4 region was amplified using a pair of universal primers (338F and 806R). The PCR instrument was ABI GeneAmp®9700 (ABI, Foster City, CA, USA), and the following reaction conditions were used: 95 °C for 3 min, 27 cycles at 95 °C for 30 s, 55 °C for 30 s, 72 °C for 45 s and finally 72 °C for 10 min. The sequencing and data analysis were performed based on the quality control software fastp (https://github.com/OpenGene/fastp, accessed on 22 February 2023, v0.19.6) and FLASH (https://ccb.jhu.edu/software/FLASH/index.shtml, accessed on 22 February 2023, v1.2.11). Chimeras were removed, and operational taxonomic unit (OTU) clustering was obtained according to $97\%$ similarity using the UPARSE software platform (http://www.drive5.com/uparse/, accessed on 22 February 2023, v7.0.1090). The RDP classifier (https://sourceforge.net/projects/rdp-classifier/, accessed on 22 February 2023, v2.11) was applied for the acquisition of species classification information for each OTU, and the database SILVA (https://www.arb-silva.de/, accessed on 22 February 2023, v138) was utilized to classify OTUs taxonomically. Bioinformatics, including α-diversity and β-diversity analyses, were subsequently conducted. The Kruskal–Wallis rank sum test was conducted for statistical difference analysis among groups (the significance level was 0.95). The false discovery rate (FDR) approach was applied for multiple comparisons. LEfSe analysis was performed to detect features differentially represented between different groups. The different species in each group were ranked by effect size after linear discriminant analysis (LDA). ## 2.5. Statistical Analysis Statistical analysis was conducted with the GraphPad Prism 9 software (GraphPad Software, San Diego, CA, USA) followed by one-way analysis of variance (ANOVA) or the Kruskal–Wallis rank sum test. All data are expressed as the mean ± standard deviation. A p-value less than 0.05 was considered statistically significant. ## 3.1. Effect of Cytidine on Body Weight, Water and Food Intake, and Serum Lipid Level As shown in Figure 1, the body weight and food intake of ob/ob mice were significantly higher than those of C57BL/6J mice ($p \leq 0.001$). Cytidine and uridine treatment had no effect on the body weight and food intake of ob/ob mice, indicating that cytidine and uridine had no obvious toxic effects. Compared with the model group, uridine treatment significantly increased the water intake ($p \leq 0.05$). Even though no significant difference in water intake was observed between the model group and the cytidine group, an increasing trend was observed after cytidine treatment. Regarding the serum lipid levels, compared with C57BL/6J mice, ob/ob mice showed dyslipidemia symptoms with significantly higher levels of serum TC, HDL-C and LDL-C ($p \leq 0.01$, $p \leq 0.001$ and $p \leq 0.001$, respectively). After five weeks of treatment, cytidine and uridine significantly reduced the levels of TC and LDL-C ($p \leq 0.01$ and $p \leq 0.001$, respectively). Cytidine treatment significantly reduced the serum HDL-C level ($p \leq 0.05$). Regarding the level of HDL-C, a decreasing trend was observed in the uridine group when compared with the model group; however, there was no significant difference. ## 3.2. Effect of Cytidine on OGTT, Liver Index, and Hepatic Steatosis In order to investigate whether cytidine could alleviate glucose intolerance in ob/ob mice, OGTT was conducted. As shown in Figure 2, cytidine and uridine treatment could not alleviate the glucose intolerance in ob/ob mice, even though the AUC values of cytidine and uridine groups showed a slight decrease. The liver index was calculated at the end of the treatment (Figure 2C). A significant increase in the liver index was observed in the model group compared with the control group. However, cytidine and uridine treatment could not alleviate the increase in the liver index in ob/ob mice. Liver tissues were stained with H&E to visualize the histopathological changes in the liver. As shown in Figure 2D,E, obvious hepatic steatosis with greater fat deposition and bigger fat vacuoles was observed in ob/ob mice. After cytidine or uridine treatment, the hepatic steatosis was alleviated. Overall, the above results indicate that cytidine has a beneficial effect that improves hepatic steatosis in ob/ob mice. ## 3.3. Effect of Cytidine on Gut Microbiota Composition in ob/ob Mice The 16S rRNA sequencing analysis was applied to assess the effects of cytidine and uridine treatment on the composition and abundance of the gut microbiota in the ob/ob mice. Both the Chao and Shannon indexes revealed (Figure 3A,B) that the abundance and diversity of gut microbiota in ob/ob mice were significantly increased after cytidine and uridine treatment ($p \leq 0.001$). The principal coordinate analysis (PCoA) was then performed on all samples using the unweighted-unifrac distance algorithm to investigate the similarities and differences in bacterial community structures. As shown in Figure 3C, each group of samples showed a fine aggregation state, and the model group was clearly distinguished from the control group. Moreover, the control group, cytidine group and uridine group were at the same latitude. In summary, the composition and abundance of the gut microbiota in ob/ob mice were modulated after cytidine or uridine treatment. Figure 3D shows the gut microbiota composition at the phylum level, and Firmicutes and Bacteroidetes were the dominant phyla. After supplementation with dietary cytidine and uridine, the gut microbiota composition returned to normal at the phylum level. At the genus level, when compared with the control group, the abundances of several gut microbiota taxa were significantly decreased in ob/ob mice, including Lachnospiraceae_NK4A136_group ($p \leq 0.05$), unclassified_f__Lachnospiraceae ($p \leq 0.05$), Dubosiella ($p \leq 0.05$), norank_f__Lachnospiraceae ($p \leq 0.01$), Eubacterium_xylanophilum_group ($p \leq 0.01$), Roseburia ($p \leq 0.01$), norank_f__norank_o__Clostridia_UCG-014 ($p \leq 0.01$) and Lachnospiraceae_UCG-006 ($p \leq 0.01$). Compared with the model group, all of the above-mentioned gut microbiota taxa except for Dubosiella were significantly increased after cytidine or uridine treatment ($p \leq 0.05$ or $p \leq 0.01$) (Figure 3E). Regarding Dubosiella, an obvious increase in the abundance was observed after cytidine treatment; however, there was no statistical difference between the cytidine and model groups. Additionally, when compared with the control group, the abundance of norank_f__Muribaculaceae was significantly increased in the model group ($p \leq 0.05$), and after treatment with cytidine or uridine, the abundance of norank_f__Muribaculaceae was significantly decreased compared with the model group ($p \leq 0.05$) (Figure 3E). To identify the differences in gut microbiota composition among different groups, LEfSe was utilized for the screening of differential species (LDA > 4). The results show that when compared with the model group, the differential gut microbiota taxa enriched in the cytidine group included g__Lachnospiraceae_NK4A136_group, g__Dubosiella, g__unclassified_f__Lachnospiraceae, g__norank_f__Lachnospiraceae, g__Eubacterium_xylanophilum_group and g__Roseburia (Figure 3F), and the differential gut microbiota taxa enriched in the uridine group included g__norank_f__Muribaculaceae, g__Lachnospiraceae_NK4A136_group, g__unclassified_f__Lachnospiraceae, g__norank_f__Lachnospiraceae, g__Lachnospiraceae_UCG-006 and g__Eubacterium_xylanophilum_group (Figure 3G). ## 3.4. Correlation Analysis of the Gut Microbiota Taxa The correlation between serum TC, TG, HDL-C, LDL-C and gut microbiota taxa in the control, model, cytidine and uridine groups were analyzed according to the Spearman correlation algorithm. As revealed in Figure 4, norank_f__ Muribaculateae showed a significant positive correlation with all serum lipid indicators, while other gut microbiota taxa (except for Dubosiella) showed a significant negative correlation with serum lipid indicators. ## 4. Discussion Ob/ob mice possess a mutation in the leptin gene, and as a result, ob/ob mice are hyperphagic, obese, and hyperglycemic and display dyslipidemia [15]. Thus, ob/ob mice were applied in this research to evaluate the effect of cytidine on metabolic disorders, especially dyslipidemia. As we can see, compared with C57BL/6J mice, the AUC values of OGTT and the serum lipid levels in ob/ob mice were significantly increased, indicating that a severe energy metabolism imbalance occurred. Additionally, it was reported that the treatment duration of uridine is closely related to lipid accumulation. Chronic uridine feeding (16 weeks) may induce severe lipid accumulation in the liver [16]. Thus, in this study, cytidine and uridine were dissolved in drinking water (0.4 mg/mL), and the treatment duration was 5 weeks. After cytidine or uridine treatment, the AUC values of OGTT in ob/ob mice showed a certain decreasing trend, but when compared with those in the model group, there was no significant difference. The levels of serum TC, HDL-C and LDL-C were significantly improved, and the hepatic steatosis was alleviated after cytidine and uridine supplementation. In summary, our results indicate that cytidine and uridine supplementation could alleviate certain aspects of dyslipidemia in ob/ob mice. Microbiota characteristics of ob/ob mice have been extensively investigated. The ratio of Firmicutes/Bacteroidetes was closely correlated with obesity [17]. Compared with normal C57BL/J mice, the ratio of Bacteroidetes/Firmicutes in ob/ob mice is usually evidently higher, indicating that ob/ob mice suffer from metabolic disorders [18]. In our study, the ratio of Bacteroidetes/Firmicutes in ob/ob mice was 0.73, which was higher than that of C57BL/6J mice. This is consistent with the literature reported [18]. The gut microbiota has been reported to be highly associated with dyslipidemia due to its vital role in regulating host lipid metabolism [2]. Various studies were conducted, and it was revealed that the effect of the gut microbiota on dyslipidemia was mediated via microbiota-related metabolites such as short-chain fatty acids, bile acids and lipopolysaccharide [2]. In patients or experimental animals with hyperlipidemia, the abundance of short-chain fatty acids-producing microbiota and bile acids-producing microbiota was significantly decreased, while the abundance of LPS-producing microbiota was increased [19,20,21]. In this study, the abundance of Lachnospiraceae_NK4A136_group, unclassified_f__Lachnospiraceae, Dubosiella, norank_f__Lachnospiraceae, Eubacterium_xylanophilum_group, Roseburia, norank_f__norank_o__Clostridia_UCG-014 and Lachnospiraceae_UCG-006 were significantly decreased, and the abundance of norank_f__Muribaculaceae was significantly increased in ob/ob mice. Regarding Lachnospiraceae, Dubosiella, Eubacterium_xylanophilum and Roseburia, they all produce short-chain fatty acids [22,23,24]. Regarding norank_f__norank_o__Clostridia_UCG-014, no clear evidence showed that it could produce short-chain fatty acids; however, Clostridium species were reported to be closely related to diabetics [25]. norank_f__Muribaculaceae was revealed to be related to bile acid metabolism [26]. Therefore, depressing the growth of short-chain fatty acids-producing microbiota may account for dyslipidemia occurring in ob/ob mice. The abundance of all the other short-chain fatty acids-producing bacteria except for Dubosiella was significantly increased after cytidine treatment, and all these bacteria were negatively correlated with serum lipid indicators. These results suggest that cytidine could alleviate certain aspects of dyslipidemia via modulating the gut microbiota composition in ob/ob mice, especially increasing the abundance of short-chain fatty acids-producing microbiota. Nucleotides have a role in umami taste perception, and C57BL/6J mice have higher preferences for umami-tasting solutions [27]. In the present study, uridine and cytidine were dissolved in drinking water, and this might be the reason that an increase in water consumption was observed after uridine and cytidine treatment. In recent years, the effect of drinking water on gut microbiota has been extensively investigated. It has been reported that both drinking water pH and water quality could affect the gut microbiota composition [28,29,30]. Nevertheless, the study conducted by Vanhaecke’s group suggested no significant association between gut microbiota composition and drinking water consumption [28]. Therefore, even though the water consumption increased after uridine and cytidine treatment, the changes in gut microbiota composition observed in this study are credited with uridine and cytidine treatment rather than the increase in water consumption. It was reported that uridine treatment could ameliorate hepatic lipid accumulation in mice by modulating the gut microbiota composition [10]. Gut microbiome analysis revealed that uridine treatment could promote the growth of butyrate-producing microbiota in high-fat-diet mice, including Odoribacter, Ruminococcaceae, Intestinimonas, Ruminiclostridium, and Lachnospiraceae [10]. In this study, uridine was applied as a positive control, and the bacteria that changed in ob/ob mice after uridine treatment were g__norank_f__Muribaculaceae, g__Lachnospiraceae_NK4A136_group, g__unclassified_f__Lachnospiraceae, g__norank_f__Lachnospiraceae, g__Lachnospiraceae_UCG-006 and g__Eubacterium_xylanophilum_group. When comparing our results with previously reported results in both ob/ob mice and high-fat-diet mice, uridine treatment could significantly promote the growth of Lachnospiraceae. However, the changed gut microbiota taxa after uridine treatment obtained from ob/ob mice and high-fat diet mice were different. This may be caused by the fact that the gut microbiota composition and structure of ob/ob mice and high-fat-diet mice were different [31]. While this study has demonstrated that cytidine supplementation could be a potential therapeutic approach for dyslipidemia, there were limitations in this study. First, the concentrations of short-chain fatty acids were not determined, and the lipid profiles in liver tissue were not fully investigated. Second, only one dose of treatment was evaluated in this study. Third, OTUs were used in 16S rRNA amplicon data analysis instead of the more modern method of ASVs (amplicon sequence variants), which could be beneficial as a comparative assessment of the data set. Fourth, additional experiments, such as fecal microbiota transplantation, are still needed to fully elucidate the relationship between the improvement in lipid metabolism and the change in gut microbiota. In summary, the present study suggested that cytidine supplementation could be a potential therapeutic approach for dyslipidemia. However, future studies to validate the efficacy and better understand the mechanism are still needed. ## 5. Conclusions In summary, our results reveal that cytidine treatment could reduce serum lipid levels and alleviate hepatic steatosis in ob/ob mice. 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--- title: Dietary Sources of Anthocyanins and Their Association with Metabolome Biomarkers and Cardiometabolic Risk Factors in an Observational Study authors: - Hamza Mostafa - Tomás Meroño - Antonio Miñarro - Alex Sánchez-Pla - Fabián Lanuza - Raul Zamora-Ros - Agnetha Linn Rostgaard-Hansen - Núria Estanyol-Torres - Marta Cubedo-Culleré - Anne Tjønneland - Rikard Landberg - Jytte Halkjær - Cristina Andres-Lacueva journal: Nutrients year: 2023 pmcid: PMC10005166 doi: 10.3390/nu15051208 license: CC BY 4.0 --- # Dietary Sources of Anthocyanins and Their Association with Metabolome Biomarkers and Cardiometabolic Risk Factors in an Observational Study ## Abstract Anthocyanins (ACNs) are (poly)phenols associated with reduced cardiometabolic risk. Associations between dietary intake, microbial metabolism, and cardiometabolic health benefits of ACNs have not been fully characterized. Our aims were to study the association between ACN intake, considering its dietary sources, and plasma metabolites, and to relate them with cardiometabolic risk factors in an observational study. A total of 1351 samples from 624 participants ($55\%$ female, mean age: 45 ± 12 years old) enrolled in the DCH-NG MAX study were studied using a targeted metabolomic analysis. Twenty-four-hour dietary recalls were used to collect dietary data at baseline, six, and twelve months. ACN content of foods was calculated using Phenol Explorer and foods were categorized into food groups. The median intake of total ACNs was 1.6mg/day. Using mixed graphical models, ACNs from different foods showed specific associations with plasma metabolome biomarkers. Combining these results with censored regression analysis, metabolites associated with ACNs intake were: salsolinol sulfate, 4-methylcatechol sulfate, linoleoyl carnitine, 3,4-dihydroxyphenylacetic acid, and one valerolactone. Salsolinol sulfate and 4-methylcatechol sulfate, both related to the intake of ACNs mainly from berries, were inversely associated with visceral adipose tissue. In conclusion, plasma metabolome biomarkers of dietary ACNs depended on the dietary source and some of them, such as salsolinol sulfate and 4-methylcatechol sulfate may link berry intake with cardiometabolic health benefits. ## 1. Introduction Anthocyanins (ACNs) are phytochemical compounds of the subclass of flavonoids in the broader (poly)phenol class, highly present in plant foods, such as berries, grapes, eggplants, and many other colored fruits and vegetables [1,2]. Most of the dietary ACNs reach the large intestine unaffected where they may affect both gut microbial composition and microbial metabolism of ACNs [2,3]. Dietary ACNs and their microbial metabolites are suggested to play roles in the prevention and treatment of cardiometabolic diseases [4,5]. Microbial metabolites of ACNs have been shown to reach higher concentrations in the systemic circulation and may be more bioactive than the consumed ACNs per se [6]. Nonetheless, studies evaluating the association between ACN dietary intake and plasma concentrations of ACN-derived microbial metabolites in observational studies are lacking. Sources of variability in ACN metabolism by the host and gut microbiota could be related to differences in consumed types and quantity of ACNs, as well as to a food matrix effect. Ultimately, these differences might be translated into different health effects of ACNs in response to their intake. Up to the moment, besides parent ACNs, such as cyanidin, delphinidin, malvidin, and petunidin, various metabolites derived from microbial and host metabolism (i.e., protocatechuic acid, syringic acid, and 4-hydroxybenzoic acid) have been associated with the consumption of berries in dietary intervention studies [7,8,9]. However, to the best of our knowledge, associations between the intake of ACNs, annotated according to their dietary sources, microbial metabolites, and cardiometabolic risk factors have not been studied in an observational study. Our hypothesis is that specific sets of microbial metabolites would be associated with dietary ACNs from different food sources and that these will display differential associations with cardiometabolic risk factors. The aims of this study were to evaluate the association between the intake of ACNs, considering their different dietary sources, and plasma metabolites. The aim was further to explore the associations between ACN-related metabolites and cardiometabolic risk factors in a subsample of the Danish Diet, Cancer, and Health-Next Generations (DCH-NG) MAX study [10]. ## 2.1. Study Design and Subjects We studied a validation subsample within the Diet, Cancer, and Health-Next Generations (DCH-NG) cohort: the DCH-NG MAX study. The DCH-NG was an extension of the previous cohort the Diet, Cancer, and Health (DCH) [10]. A sample of 39,554 participants was included in the DCH-NG involving biological children, their spouses, and grandchildren of the DCH cohort [11]. The DCH-NG MAX study recruited 720 volunteers with residency in Copenhagen, aged 18 years old or more, between August 2017 and January 2019. The major aims of the MAX study were to validate a semi-quantitative food frequency questionnaire against the twenty-four-hour dietary recalls (24-HDR) and to examine the plasma and urine metabolome reproducibility as well as gut microbial stability on a long-term scale. Biological samples, health examinations such as anthropometric and blood pressure measurements, and two questionnaires about lifestyle and dietary habits were collected at baseline, 6, and 12 months. The DCH-NG cohort study was approved by the Danish Data Protection Agency (journal number 2013-41-$\frac{2043}{2014}$-231-0094) and by the Committee on Health Research Ethics for the Capital Region of Denmark (journal number H-15001257). The volunteers provided their written informed consent to participate in the study. All the details about clinical measurements, dietary and metabolomics data were previously detailed [11]. ## 2.2. Anthropometric Measurements Participants were asked to wear underwear and be barefoot for measuring height and weight using a wireless stadiometer and a body composition analyzer, respectively (SECA mBCA515, Hamburg, Germany). Height and weight were measured to the nearest 0.1 cm and 0.01 kg, respectively, and body mass index (BMI) was calculated. The waist circumference was measured twice at the midpoint between the lower rib margin and the iliac crest. A third measurement for the waist circumference was measured if the difference between the first two was more than 1 cm. Blood pressure and pulse rate were measured 3 times using the left arm, considering the measurement with the lower systolic blood pressure and its corresponding diastolic blood pressure value as valid. DEXA-validated bioimpedance instrument (SECA mBCA515, Germany) was used to estimate visceral adipose tissue volume. ## 2.3. Dietary Data The 24-h dietary recalls (24-HDR) were recorded at baseline, 6 and 12 months using a Danish version of the web-based tool myfood24 (www.myfood24.org/) (7 February 2023) from Leeds University [12], containing almost 1600 Danish food items. All foods consumed the day before the examinations were reported by the participants in either grams or in standard portion size. The percentage of calories using the energy equivalents for carbohydrates, proteins, and fat was used to indicate the intake of macronutrients. Complex food products were appointed as recipes or dishes. The McCance and Widdowson’s Food Composition Table [13], or recipes from the food frequency questionnaires in the DCH were used to have the standardized recipes [14]. ## Dietary Intake of Anthocyanins Estimation of the intake of polyphenols from 24-HDRs was completed by a protocol using “in-house” software developed by the University of Barcelona, the Bellvitge Biomedical Research Institute (IDIBELL), and the Centro de Investigación Biomédica en Red (CIBER) ©. A link between all 24-HDR food items or ingredients and the foods from the Phenol-Explorer database was created [15]. The intake of individual (poly)phenols in mg/day was obtained and ACN consumption from separate foods were estimated as the sum of 71 individual ACNs included in Phenol-Explorer database. The estimated intake of dietary (poly)phenols of the DCH-NG MAX study has previously been described [16]. A total of 147 food items that contain ACNs were used to estimate the total dietary ACN intake as shown in the Supplementary Table S1. Intake of berries was estimated as the sum of foods with at least $50\%$ of its composition or recipe made by berries. These include raw and frozen berries, berries marmalades or jams, and stewed berries. Dietary ACN intake related with the other foods were classified and added up according to the following food groups: dairy products with berries (including ice cream and yogurt), other fruits (i.e., plums, cherries, apples, etc.), non-alcoholic drinks (including fruit smoothies and juices), wines, vegetables, mixed dishes (meat or fish dishes with vegetables containing ACNs), and bakery (including pastry, biscuits, desserts, and waffles with berries or other ACN-containing preparations). ( Supplementary Table S2). Intakes of foods not containing ACN were disregarded. ## 2.4. Blood Sampling, Analysis of Cardiometabolic Risk Factors and Metabolomics Participants were instructed to maintain a fasting time of 1–9 h (mean fasting time: 5 h) during all the examination days. Blood samples were taken into Vacutainer tubes containing lithium heparin at baseline time 0 ($$n = 624$$), 6 months ($$n = 380$$), and 12 months ($$n = 349$$). Within 2 h of blood draw, plasma was obtained by centrifugation, and samples were stored at −80 °C. After that, plasma samples were delivered to the Danish National Biobank (DNB), where plasma was divided into aliquots and sent to University of Barcelona and kept at −80 °C until metabolomic analysis. Other blood measurements such as hemoglobin, A1c (HbA1c), serum lipids, and high sensitivity C reactive protein (hsCRP) were measured as described before [17]. ## 2.4.1. Metabolomics Analysis of Plasma Samples Repeated measures of the plasma metabolome at all three time points were used for metabolomics analysis. All the samples were prepared and analyzed using the targeted UPLC-MS/MS method described previously, with slight modifications [18,19]. Briefly, 100 µL of plasma was added into protein precipitation plates together with 500 µL cold acetonitrile containing 1.5 M formic acid and 10 mM of ammonium formate and were kept at −20 °C for 10 min to enhance protein precipitation. Then, positive pressure was applied to recover the extracts, which were taken to dryness and reconstituted with 100 µL of an 80:20 v/v water:aceto nitrile solution containing $0.1\%$ v/v formic acid and 100 ppb of a mixture of 13 internal standards. Samples were then transferred to 96-well plates and analyzed by a targeted metabolomic analysis using an Agilent 1290 Infinity UPLC system coupled to a Sciex QTRAP 6500 mass spectrometer, using the operating conditions described elsewhere [18]. The Sciex OS 2.1.6 software (Sciex, Framingham, MA, USA) was used for data processing. ## 2.4.2. Metabolomics Data Pre-Processing The POMA R/Bioconductor package (https://github.com/nutrimetabolomics/POMA) (7 February 2023) was used for the pre-processing of metabolomics data [20]. Metabolites with more than $40\%$ missing values, and those with a coefficient of variation (CV) > $30\%$ in internal quality control were removed. K-nearest neighbor (KNN) algorithm and correction of batch effects using the ComBat function (“sva” R package) were used to impute the remaining missing values [21], while auto-scaling and Euclidean distances (±1.5× Interquartile range) were used to normalize the data and remove the outliers, respectively. The final metabolomics dataset included the concentration of 408 plasma metabolites. ## 2.5. Statistical Analyses For descriptive statistics, intake of total ACNs (irrespective of their dietary source) was categorized by tertiles using 0.3–8.9 mg as thresholds. Continuous variables following a normal distribution are shown as mean ± SD, and those following a skewed distribution are shown as median (p25–p75). Sociodemographic and clinical characteristics were compared across tertiles of ACNs intake using linear mixed models in a random intercepts model adjusted for age and sex. Associations between intake of ACN dietary sources and their association with cardiometabolic risk factors were tested using linear mixed models in random intercepts models adjusted for age, sex, and BMI. First, associations between intake of total ACNs and metabolome biomarkers were analyzed using a censored regression for panel data with “censReg” and “plm” R-packages [22]. Censored regression models were applied due to the right-skewed distribution of total ACN intake and the considerable proportion of zero values ($24\%$ of non-consumers of ACNs). Covariates included in the models were age, sex, and BMI. p-values were adjusted for multiple comparisons using the Benjamini–Hochberg method, and adjusted p-values <0.05 were considered statistically significant. Second, associations between ACNs from different dietary sources and metabolites were assessed using Mixed Graphical Models (MGM) with the “mgm” R-package [23]. MGMs are undirected probabilistic graphical models able to represent associations between nodes adjusted for all the other variables in the model. MGM specifications were set to allow the maximum number of interactions in the network. Variables in the model were dietary ACN intakes by food categories (8 food groups), and the whole metabolomics set of variables. The agreement between repeated measurements for total dietary intake of ACNs and for ACN intake from different dietary sources was poor across the study evaluations (intra-class correlation coefficient < 0.15). Therefore, all observations of the study were considered independent and were included in MGM analysis ($k = 1351$). For visual clarity, only the first-order neighborhood of ACNs food sources was plotted. To evaluate the associations between metabolites and cardiometabolic risk factors linear mixed models were used in random intercepts models adjusted for age, sex, and BMI. Metabolites were selected based on the combination of both analyses, censored regression, and MGM. Standardized coefficients were plotted in a heatmap built using the “pheatmap” R-package (Kolde R [2019]. pheatmap: Pretty Heatmaps). All statistical analyses were performed using R, version 4.1.3. ( R foundation, Austria). ## 3.1. Sociodemographic, Clinical, and Dietary Characteristics At baseline, out of the 720 volunteers who agreed to participate in the study, 624 had completed clinical, dietary, and plasma metabolomics data. Of the 624 participants included, $55\%$ were female, aged (mean ± SD) 45 ± 12 years old, and had a BMI of 25 ± 4 kg/m2. At 6 months, 380 participants had completed clinical, dietary, and metabolomics data and at 12 months completed data were available for 349 participants. Only, 287 participants had completed clinical dietary and plasma metabolomics data available at all three time points. The distribution of total ACN intake was right-skewed with a median value of 1.6 (p25–p75: 0.0–26.9) mg/day and a mean value of 26.4 (SD: 60.4) mg/day. Berries were the highest contributors to total ACN intake with a mean contribution of $34\%$, followed by wines with $33\%$, and non-alcoholic drinks (which included fruit smoothies and juices) with $20\%$ of the total reported intake. Other fruits (i.e., cherries, apples, and plums) and vegetables were minor contributors with $4\%$ and $2\%$, respectively. Bakery (pastry, biscuits, and desserts), dairy products (yogurts and strawberry ice creams or ice creams with berries), and other mixed dishes (dishes including vegetables with ACNs) contributed within a similar range between 2 and $3\%$ (Supplementary Table S1). Participants were divided into tertiles based on the consumed reported intakes of total ACNs as shown in Table 1. There were no significant differences in clinical characteristics across tertiles of ACN intake. Consistently, there were no statistically significant associations between total ACN intake and cardiometabolic risk factors (data not shown). Dietary characteristics are illustrated in Supplementary Table S2. Participants in the highest compared to the ones at the lowest tertile of ACN intake showed statistically significant higher consumption of total protein, saturated fatty acids (SFA), monounsaturated fatty acids (MUFA), polyunsaturated fatty acids (PUFA), alcohol, fruits, and berries. ## 3.2. Association between Intake of ACN Dietary Sources and Cardiometabolic Risk Factors Several inverse and direct associations between self-reported intake of ACN-containing food groups and cardiometabolic risk factors were observed (Figure 1). For example, intake of berries, dairy products with berries, and ACN-containing vegetables had inverse associations with visceral adipose tissue volume, while wine had direct associations with total cholesterol, HDL-C, and systolic blood pressure. Other direct associations were found between the intake of berries and hemoglobin A1c, and between ACN-containing drinks with hsCRP. ## 3.3. Metabolome Biomarkers Associated with Total ACN Intake In censored regression analysis, 10 metabolites were positively associated with total ACN intake (Figure 2). Among them, three were exogenous metabolites, hypaphorine, salsolinol sulfate, and ethyl glucuronide, two were endogenous metabolites, including linoleoyl carnitine and glycerol, and five were gut microbial metabolites, 4-methylcatechol sulfate, 4′-hydroxy-3′-methoxyphenyl-γ-valerolactone-sulfate (MHPV-S), 5-(4-hydroxy(3,4-dihydroxyphenyl)-valeric acid sulfate (3,4-DHPHVA-S), 3,4-dihydroxyphenylacetic acid sulfate (3,4-DHPA-3S) and indolepropionic acid. On the other hand, only oleoyl carnitine, another endogenous metabolite, was inversely associated with total ACN dietary intake. ## 3.4. Metabolome Biomarkers Associated with Intake of ACNs Related to Different ACN Dietary Sources MGM analysis showed associations between self-reported ACN intake from different dietary sources and 16 metabolites (Figure 3). ACNs derived from dairy products were associated with plasma asparagine, epicatechin sulfate, urolithin C-glucuronide, and acesulfame K. ACNs from the intake of berries were associated with linoleoyl carnitine, salsolinol sulfate, glycochenodeoxycholic-3-sulfate (GCDCA-3S) and 4-methylcatechol sulfate. ACNs from wine consumption were linked with methylpyrogallol sulfate (Met-Pyr-S) and ethyl glucuronide. ACNs from vegetable intake were associated with 2-hydroxybenzoic acid and bergaptol glucuronide. ACNs from other fruits were associated with 3,4-DHPHVA-3S, 5-(3′-hydroxyphenyl)-γ-valerolactone 3’-sulfate (3-HPV-S) and 3,4-dihydroxyphenylacetic acid sulfate (3,4-DHPA-3S). Lastly, the consumption of ACNs from mixed dishes was associated with 1-methylhistidine and 2-hydroxybenzoic acid. Overall, not all the metabolites selected in the MGM analysis were related to ACNs or its microbial metabolites, but to other food components such as acesulfame K, ethyl glucuronide, etc. Therefore, metabolome biomarkers were selected considering both statistical analyses, censored regression, and MGM, to be used for the study of its association with cardiometabolic risk factors. ## 3.5. Associations between Selected ACN-Related Metabolome Biomarkers and Cardiometabolic Risk Factors Metabolites associated with ACN intake in both of the previous analyses were: salsolinol sulfate, 4-methylcatechol sulfate, linoleoyl carnitine, 3,4-DHPHVA-3S, and 3,4-DHPA-S. Figure 4 shows the associations between these metabolites and cardiometabolic risk factors. Out of the metabolites associated with berries’ ACNs, salsolinol sulfate and 4-methylcatechol sulfate were inversely associated with visceral adipose tissue volume. In addition, inverse associations were also found between salsolinol sulfate and LDL-C and diastolic blood pressure. Conversely, there was a direct association between salsolinol sulfate and triglyceride levels (Figure 4). Linoleoyl carnitine, 3,4-DHPHVA-3S, 3,4-DHPA-S did not show any statistically significant association with cardiometabolic risk factors. ## 4. Discussion The present study shows for the first time the specific associations between ACNs related to different dietary sources, and plasma metabolome biomarkers and their association with cardiometabolic risk factors in a free-living population. These results may take into account not only the quantitative and qualitative heterogeneity of ACNs presence in foods but also the internal dose of specific microbial metabolites generated from ACNs which could have been affected by the food matrix. Indeed, food matrices have been shown to influence the microbial metabolism of (poly)phenols [24]. Ultimately, we observed different associations between ACN-related metabolites and cardiometabolic risk factors in relationship with specific foods suggesting a stronger cardiometabolic benefit associated with the consumption of berries. Up to $80\%$ of the total intake of dietary ACNs came from the consumption of berries, wines, and non-alcoholic drinks in this observational study. Minor contributors were dairy foods, other fruits, and vegetables. While the MGM analysis revealed different metabolomic fingerprints associated with different dietary sources of ACNs, the resultant metabolites were not specific to ACNs. Therefore, we selected metabolites that were also significantly associated with the censored regression analysis. This was a strict criterion but in the context of such low levels of ACN intake in the overall population (median 1.6 mg/day), it is justified. After applying this selection criterion, only metabolites related to ACNs from berries and other fruits (according to MGM) were tested for their association with cardiometabolic risk factors. Metabolites specifically related to ACNs from other major food sources, such as wines, were excluded. Nonetheless, other studies showed that for example 4-methylcatechol sulfate was increased after a 15-day moderate red wine intervention trial [25]. Therefore, we cannot be fully certain that in our study the same metabolites could be related to other ACN dietary sources. Future randomized controlled trials using single foods are warranted to validate the present results. Regarding the association between metabolome biomarkers and cardiometabolic risk factors, 4-methylcatechol sulfate showed an inverse association with visceral adipose tissue volume. According to our MGM analysis, 4-methylcatechol sulfate was associated with the intake of ACNs from berries. Similarly, another metabolite associated with ACNs from berries was salsolinol sulfate. Salsolinol sulfate is an alkaloid that has been suggested as a biomarker of banana intake [26]. However, salsolinol can be produced endogenously through dopamine oxidative metabolism [27,28] and may have a role in modulating dopamine neurons activity in the striatum region of the brain [21]. In fact, patients with obesity showed impaired dopamine brain activity, underscoring a potential role for low dopamine activity in obesity (lower reward associated with food intake) [29]. Hence, we speculate that the inverse association between salsolinol sulfate and visceral adipose tissue could be mediated by brain dopamine activity. An animal study showed that a blackberry extract intervention reversed the effects of a high-fat diet increasing dopamine turnover in the brain striatum region [30]. The role of berries on brain dopamine metabolism should be further studied. On the other hand, the other selected metabolites were not associated with cardiometabolic risk factors. The median value of total ACN intake in the study was 1.6 mg/day, and such intake may not have been high enough to detect metabolome biomarkers found in randomized controlled trials (RCT) with ACN-rich foods [31,32,33]. Many short or long-term RCTs were conducted with capsulated ACNs or berries to discover biomarkers of ACNs intake. In these trials, daily intakes of ACNs typically varied from 100 to 300 mg as single dose intakes [33,34], or between 50–350 mg/day for four weeks [35,36,37]. In general, many parent ACNs and up to 70 phenolic compounds resultant of the gut microbial metabolism of ACNs have been identified [35,36]. Even though the majority of these metabolites were not identified in our study, 4-methylcatechol sulfate, 3,4-DHPHVA-3S, and 3,4-DHPA-3S had been previously associated with ACN intake. Maybe, longer half-lives of these metabolites vs. the others, or the competition of polyphenol substrates for bacteria able to metabolize them limited the production of ACNs metabolites under the low levels of ACN intake (exposure) in the study. Among the strengths of this study are its observational nature and the fact that dietary data were assessed with 24-HDRs instead of food frequency questionnaires. This last characteristic allowed us to have exact intake data both in terms of amounts and specific food items compared to food frequency questionnaires. However, this also brings the limitation of measurement errors in estimating ACN intake and the short time period surveyed (one 24-HDR at each evaluation time). Another limitation was that the median consumption of dietary ACNs within the population of the DCH-NG MAX study was 1.6 mg/day, which was considerably lower than other studies in which the median intake varied between 9.3 to 52.6 mg/day [38,39,40,41]. This fact could have limited the number of plasma metabolites associated with dietary ACNs. Furthermore, the mean fasting time of the participants at the time the blood samples were drawn was 5 h and the impact of fasting on serum metabolome is uncertain. Nonetheless, this is the first study evaluating the impact of ACNs coming from different dietary sources on plasma metabolome and therefore our results cannot be contrasted with others. While berries contain other polyphenols in addition to ACNs, further research is needed to fully understand the individual and combined effects of different polyphenols of berries on health outcomes. Our approach to isolating the effects of ACNs from berries was conducted from a bioinformatic approach and a more precise study testing the effects of isolated ACNs from berries should corroborate our results. While berries contain other polyphenols in addition to ACNs, further research is needed to fully understand the individual and combined effects of different polyphenols of berries on health outcomes. Our approach to isolating the effects of ACNs from berries was conducted from a bioinformatic approach and a more precise study testing the effects of isolated ACNs from berries should corroborate our results. Last, it is not clear if the microbial metabolites were exclusively related to the ACNs from the dietary source pointed out in the MGM analysis or could have been also produced from ACNs coming from other foods, or even from food components other than ACNs (e.g., other polyphenols apart from ACNs). Although MGM models adjust every association for all the other variables included in the analysis, these sources of confounding cannot be ruled out. In conclusion, this study shows that the metabolomic fingerprint of ACN consumption depended on its dietary sources. 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--- title: Effect of Short-Chain Fatty Acids and Polyunsaturated Fatty Acids on Metabolites in H460 Lung Cancer Cells authors: - Tianxiao Zhou - Kaige Yang - Jin Huang - Wenchang Fu - Chao Yan - Yan Wang journal: Molecules year: 2023 pmcid: PMC10005177 doi: 10.3390/molecules28052357 license: CC BY 4.0 --- # Effect of Short-Chain Fatty Acids and Polyunsaturated Fatty Acids on Metabolites in H460 Lung Cancer Cells ## Abstract Lung cancer is the most common primary malignant lung tumor. However, the etiology of lung cancer is still unclear. Fatty acids include short-chain fatty acids (SCFAs) and polyunsaturated fatty acids (PUFAs) as essential components of lipids. SCFAs can enter the nucleus of cancer cells, inhibit histone deacetylase activity, and upregulate histone acetylation and crotonylation. Meanwhile, PUFAs can inhibit lung cancer cells. Moreover, they also play an essential role in inhibiting migration and invasion. However, the mechanisms and different effects of SCFAs and PUFAs on lung cancer remain unclear. Sodium acetate, butyrate, linoleic acid, and linolenic acid were selected to treat H460 lung cancer cells. Through untargeted metabonomics, it was observed that the differential metabolites were concentrated in energy metabolites, phospholipids, and bile acids. Then, targeted metabonomics was conducted for these three target types. Three LC-MS/MS methods were established for 71 compounds, including energy metabolites, phospholipids, and bile acids. The subsequent methodology validation results were used to verify the validity of the method. The targeted metabonomics results show that, in H460 lung cancer cells incubated with linolenic acid and linoleic acid, while the content of PCs increased significantly, the content of Lyso PCs decreased significantly. This demonstrates that there are significant changes in LCAT content before and after administration. Through subsequent WB and RT-PCR experiments, the result was verified. We demonstrated a substantial metabolic disparity between the dosing and control groups, further verifying the reliability of the method. ## 1. Introduction Lung cancer is a malignant tumor from the bronchial mucosa or glands within the lungs and has the fastest-growing morbidity and mortality. It is one of the most threatening malignancies to human health and life [1,2]. More than 1.6 million people have lung cancer annually, which is a deadly form of cancer. In particular, non-small cell lung cancer (NSCLC) is quite common, accounting for $80\%$ of all lung cancer cases. Fatty acids include short-chain fatty acids (SCFAs) and polyunsaturated fatty acids (PUFAs) as essential components of lipids. Short-chain fatty acids are lipids composed of two to six carbon atoms. They include acetate, propionate, and butyrate, mainly produced by the digestion of dietary fiber by intestinal microorganisms [3,4,5]. SCFAs can be recognized by G-protein-coupled receptors (GPCRs) on the surface of cancer cells. In addition, SCFAs can also enter the nucleus of cancer cells [4], inhibit histone deacetylase activity, upregulate histone acetylation and crotonylation, and play a direct anti-cancer effect due to their small molecular weight [6,7]. SCFAs can also exert indirect anti-cancer effects by regulating immune cells [8]. Polyunsaturated fatty acids are straight-chain fatty acids with two or more double bonds and have 18–22 carbon atoms in their chain. They are generally divided into omega-3 and omega-6. Omega-3 polyunsaturated fatty acids can provide energy and prevent and treat diseases as one of the essential nutrients for the human body [9]. Recent studies have found that omega-3 PUFAs (viz., eicosapentaenoic acid (DHA), linolenic acid, and docosahexaenoic acid (EPA)) can improve the chemotherapy sensitivity of tumor cells. Moreover, they reduce the side effects of chemotherapy and protect target tissues without any adverse effects on non-target tissues. Many epidemiological survey results show that the incidence of liver, breast, prostate, and colon cancer in people with high omega-3 PUFAs is decreased [10,11]. Omega-3 PUFAs inhibit the growth of lung cancer cells and play a vital role in inhibiting their migration and invasion. The omega-6 family includes linoleic acid and arachidonic acid (ARA), precursors of pro-inflammatory oxylipins [12]. There is clinical evidence suggesting the effect of omega-6 PUFAs on the progression of lung cancer [13,14,15]. For instance, Liu et al. revealed that lung adenocarcinoma and squamous cell carcinoma patients had higher levels of free fatty acids (e.g., ARA and linoleic acid) and their hydroxyeicosapentaenoic acid (HETE) metabolites compared with the non-cancer control group, proposing these as possible markers [12]. However, for H460 cells, there are few studies on the administration of SCFAs and PUFAs, and the mechanism and different effects of SCFAs and PUFAs on lung cancer are still unclear. Metabonomics is a research method imitating the research ideas of genomics and proteomics, conducting a quantitative analysis of all metabolites in the organism and searching for the relationship between metabolites and physiological and pathological changes [16,17,18,19,20,21,22,23,24,25]. It is a component of systems biology. The objects are primarily small molecular substances with a relative molecular weight of less than 1000. Advanced analytical detection technology combined with computational analysis methods, including pattern recognition and expert system, is the basis of metabonomics research. Among these, metabolomics involves untargeted and targeted metabolomics. Untargeted metabolomics systematically and comprehensively analyzes the whole metabolome based on limited relevant research and background knowledge, obtains several metabolite data, and processes them to identify the differential metabolite [26]. Presently, untargeted metabonomic analysis is widely used in biomarker discovery, disease diagnosis, and mechanism research. Thus, it provides new ideas and directions to understand the disease mechanism. Targeted metabonomics analyzes and studies only a limited number of metabolites associated with biological events based on the principles and concepts of metabonomics [16,27]. Usually, targeted metabonomics is used for systematic confirmation after finding differential metabolites through untargeted metabonomics [28,29]. The newly developed glycomics and lipomics also belong to targeted metabonomics. Targeted metabonomics is precise in analysis and complementary to untargeted metabonomics. Metabonomics is indispensable for studying the fatty acid mechanism in H460 lung cancer cells. In this study, SCFAs involved sodium acetate and sodium butyrate, and PUFAs included linoleic acid (omega-6) and linolenic acid (omega-3) for treating H460 lung cancer cells. Untargeted metabonomics was used to find essential metabonomics enrichment pathways. Depending on the result of untargeted metabonomics, a method was established for simultaneously quantifying 71 metabolites involved in energy metabolites, bile acids, and phospholipids. It also covers glycerophospholipid metabolism, primary bile acid biosynthesis, the TCA cycle, and the ATP pathway. Then, targeted metabonomics experiments were conducted on these target metabolites. The metabolite changes are very different in H460 lung cancer cells incubated with SCFAs and PUFAs. Primarily, the content of PCs increased significantly and Lyso PCs decreased significantly when H460 lung cancer cells were incubated with linolenic acid and linoleic acid. Further analysis of the KEGG pathway indicated that the above differential metabolites could be due to the changes in LCAT expression. Thus, we verified the previous experimental metabonomics results by utilizing the WB and qPCR experiments for LCAT. We also clarified the different effects of four fatty acids on H460 lung cancer cells. ## 2.1. CCK8 Experiment The first step in studying cell metabonomics is the cell proliferation experiment of the fatty acid and control groups. To ensure that the dose does not affect cell proliferation and that further metabonomic analysis can be carried out, we conducted a CCK8 experiment on the fatty acid and control groups. The experimental results are shown in Figure S1. When the concentration of the four fatty acid groups is 500 μM, cell proliferation is unaffected. However, cell proliferation was affected to a certain extent when the linolenic acid concentration continued to increase. Therefore, 500 μM was selected as the fatty acid concentration of the four fatty acid groups. ## 2.2. Untargeted Metabolism We used hydrophilic chromatography–mass spectrometry (HILIC-MS/MS) and reverse-phase chromatography–mass spectrometry (RPLC-MS/MS) to detect more metabolites in untargeted metabonomics. Both used positive and negative ion modes simultaneously to detect polar and non-polar molecules in cell samples. Multi-dimensional statistical analysis of data collected from untargeted metabolism is depicted in Figure 1A and Figure 2A. From the figures, QC samples, the control group, and samples in the four fatty acids administration groups are in similar geometric positions. Therefore, these samples were naturally clustered, with small differences between groups, and the overall distribution was good. There was no significant singular value to be removed, and they were uniformly distributed within $95\%$ of the confidence interval (different color areas). QC sample aggregation established the stability of the instrument from the side, consistent with the previous validation results. In PCA, there is a certain degree of separation between groups. OPLS-DA was used to explore the metabolic difference between the fatty acid and the control groups. The fatty acid and the control groups were separated in the OPLS-DA score chart (Figure 1B–E and Figure 2B–E). The differences between groups were amplified after supervised target classification and discrimination, and a more noticeable trend separation was observed. It is necessary to evaluate the quality of the discriminant models. The results indicated that the interpretation rate (R2Y) of each model to the original data and the prediction rate (Q2) to the grouping were very good. The ultimate purpose of the reliable model was to screen the heterologous variables from massive metabolic data and extract information with biochemical significance. Therefore, screening differential metabolites is a crucial step in statistical processing. We selected a multi-standard evaluation method to improve the reliability and accuracy of screening. First, the variables with VIP > 1 were selected and had statistical significance based on the multi-dimensional statistical model discrimination (all variables are considered). Secondly, it was assisted by single-dimension statistical analysis (evaluation of a single variable). We adopted the Student’s t-test method without assuming data distribution in advance since the number of samples was small and the overall standard deviation was unknown. The p-value was obtained using Student’s t-test, which determined the significance of the difference between the two groups. The significance level was 0.05. The fold change ratio (FC) was obtained by dividing the average value of the variables in the administration group (before normalization, the ratio of the average values of paired data was selected) by the average value of the healthy group. This was used to make a macro performance comparison of the variables in the two groups. When FC > 1, the mean value of the fatty acid group was higher than the control group, and the variable was upregulated in the fatty acid group. When FC < 1, the mean value of the fatty acid group was lower than the control group, and the variable was downregulated in the fatty acid group. The absolute value FC > 1.5 or FC < 0.67 was selected as the screening criteria for statistics. Through the above screening criteria, and depending on the differential expression multiples and significance results, the volcano map (see Figure 2A–H) was drawn for screening the differential metabolites between the two sample groups. Based on the volcano plot results, 50 compounds were selected with the largest metabolic difference in untargeted metabolism between the heat map and correlation heat map. Based on the ratio of the average response intensity of the TOP50 differential metabolite of HILIC and RP-18 in the fatty acid and the control group samples, two heatmaps were drawn (Figure 3A for HILIC, Figure 3B for RP-18). It can be seen that phospholipids, energy metabolites, and bile acids in the administration group showed significant changes. Moreover, the upregulation of energy metabolites in the short-chain fatty acid administration group was more prominent, and the increase and decrease in phospholipids in the long-chain fatty acid group were evident. The correlation analysis results of metabolites are demonstrated in Figure 3C,D. From the results, PCs were negatively correlated with Lyso PCs, ADP in energy metabolites was positively correlated with PCs, and fumaric acid and pyruvic acid were negatively correlated with PCs. The results also revealed that there were many phospholipids and energy metabolites in untargeted metabolism TOP50, including PS (22:6), Lyso PC (20:5), PI (20:3), etc., in phospholipids, and ADP, CDP, Glyceraldehyde 3-phosphate, etc., in energy metabolites. After analyzing the correlation of these differential metabolites using untargeted metabolism, we conducted an enrichment analysis of the KEGG pathway. As shown in Figure 4, many pathways of four fatty acid changes were involved in the citrate cycle (TCA cycle), gluconeogenesis, and glycerophospholipid metabolism. The data analysis of untargeted metabonomics showed that the different metabolites before and after administration were mainly concentrated in energy metabolites, phospholipids, and bile acids. Therefore, a follow-up targeted metabonomic analysis was conducted for these three types of substances. ## 2.3.1. Pretreatment Optimization The type and polarity of the extraction system influence the extraction efficiency of target substances. Eight extraction solvent systems were designed, which were chloroform: water (1:1), methanol: water (1:1), chloroform: acetone: water (1:1:1), chloroform: ethanol: water (1:1:1), chloroform: methanol: water (1:1:1), chloroform: methanol: water (2:1:1), chloroform: methanol: water (1:1:2), chloroform: methanol: water (1:1:2), and chloroform: methanol: water (1:2:1). The above proportions were the volume ratios. As shown in Figure S2, most internal standards in chloroform: methanol: water (1:2:1) extraction recovery rates were in the 90–$110\%$ range, better than other extraction systems. The extraction system of chloroform: methanol: water (1:2:1) was subsequently utilized for pretreatment. ## 2.3.2. LC-MS Method Optimization for Targeted Metabolism The Waters BEH amide column (100 mm × 4.6 mm i.d., 3.5 μm) was selected for the chromatographic analysis of energy metabolites. Chromatographic separation was carried out, followed by the optimization of the pH of the mobile phase and the added concentration of ammonium acetate. The peak shape and response intensity were optimal when pH = 9.0 and the ammonium acetate concentration was 10 mM. Finally, we used pH, the additive concentration of mobile phase with pH = 9.0, and the ammonium acetate content of 10mM as energy metabolite for analysis. We selected Waters BEH C18 column (100 mm × 4.6 mm i.d., 3.5 μm) for the chromatographic analysis of phospholipid and bile acid. The chromatographic separation was performed, and the overall analysis time was compressed to 20 min by raising the column temperature to 50 °C. Simultaneously, 5 mM of ammonium formate was added to the aqueous phase to enhance the peak shape of some target compounds. All the optimized TIC chromatograms are represented in Figure S3. The energy metabolite chromatograms are shown in Figure S3A, and the phospholipids and bile acid chromatograms for ESI+ mode are depicted in Figure S3B. Chromatograms of phospholipids and bile acids for ESI- mode are demonstrated in Figure S3C. As shown in Table S2, parent and daughter ions for different target compounds were determined, and the DP, EP, CE, and CXP values were optimized. ## 2.3.3. Targeted Methodological Validation The method validation results are depicted in Table 1. The LODs were in the 0.001–0.766 ng/μL range, and the LOQs were in the 0.003–2.553 ng/μL range. Since the linear regression coefficients (R2) of most metabolites are above 0.99, the method possessed good linearity. The intraday and intraday RSD% precision of most metabolites was within ± $20\%$, reflecting good method precision. The recovery rate and matrix effect of cells are indicated in Table 2. The recoveries and matrix effect of internal standards in cell samples were more significant than $81.9\%$ and $87.6\%$. Therefore, this established method is suitable for quantitatively analyzing target metabolites in cell samples since the recovery rate and matrix effect of most metabolites in the cell matrix are more than $80\%$. MQC of cell samples was used for the stability study. Under short-term and long-term storage conditions, the peak area ratio was more than $83.6\%$. Therefore, the target metabolite was stable when stored at 4 °C for 24 h or −20 °C for two months (Table 3). ## 2.4. Targeted Metabolism The final variable (metabolite concentration) can directly reflect the content of a single component in a sample. Moreover, the variable strength is not on a unified scale, significantly affecting the statistical results. Therefore, the Par scaling method was used to increase the comparability of each variable across different samples. The specific operation was to divide the variable by the square of its standard deviation, and, based on not amplifying noise interference, simultaneously, the contributions of high and low-content metabolites were also considered. Figure S4 indicates the comparison differences before and after data standardization. The quantitative results were imported into the SIMCA software for multivariate analysis. PCA demonstrated that all the samples were clustered in the PCA chart (within a $95\%$ confidence interval) (see Figure 5A), and it is not necessary to exclude the extreme outliers. The method has good repeatability due to its good intra-group aggregation. We also observed that the control group was close to the sodium acetate group, and the linoleic acid group was nearby the linolenic acid group. To further compare the difference between the administration and control groups, we conducted an OPLS-DA analysis. OPLS-DA was used to explore the metabolic difference between the fatty acid and control groups. The fatty acid and control groups were separated in OPLS-DA score charts (Figure 5B–E). The OPLS-DA model parameters (R2X = 0.598, R2Y = 0.993, Q2 = 0.991; R2X = 0.717, R2Y = 0.991, Q2 = 0.993; R2X = 0.814, R2Y = 0.999, Q2 = 0.996; R2X = 0.734, R2Y = 0.999, Q2 = 0.994) established that the model has an excellent fitting and prediction ability. We also observed significant differences between the four fatty acid and control groups. We subsequently drew and analyzed the volcano plot to screen out the different metabolites between the groups. The volcano plot of targeted metabolism is depicted in Figure 5F–I. Based on the analysis, VIP > 1.0, p-value < 0.05 (Student’s t-test), and FC >1.5 were selected. The different metabolites of the four fatty acid groups compared with the control group were found by drawing the volcano plot. A follow-up analysis was also carried out. The heat map and correlation analysis of the screened differential metabolites were also determined. The heat map of targeted metabolism is shown in Figure 6A. It can be observed that these target compounds possess significant changes in different administration groups. For instance, compared to the control group, D-Fructose-1,6-Diphosphate, and α-ketoglutaric acid downregulated in the SCFA group but upregulated in the PUFA group; 16:0–18:1 PC, 16:0–18:1 PA upregulated in the SCFA group, but downregulated in the PUFA group. We noticed that in the linoleic acid and linolenic acid fatty acid group, all the PCs and Lyso PCs we targeted to detect had a specific change rule. Lyso PCs increase with the decline of PCs. The correlation analysis results of metabolites are represented in Figure 6B. Moreover, all PCs were positively associated with each other and negatively correlated with all Lyso PCs. The results of the heatmap are also verified. ## 2.5. Effects of LCAT on Metabolome Changes and Biological Validation It can be seen from the above-targeted metabolism statistical results and box plots (Figure 7) that the changing trend of PCs and Lyso PCs in the control and the administration groups had a particular rule among all the target compounds. Especially in the two long-chain unsaturated fatty acid treatment groups, viz., the linoleic acid and linolenic acid treatment group, Lyso PCs increased simultaneously with their decrease. According to the KEGG metabolic pathway map, the above changes could be due to the rise of LCAT. Therefore, we designed WB and qPCR experiments to verify the hypothesis. ## 2.5.1. mRNA Expression of LCAT We tested whether four fatty acids altered the gene expression of LCAT (Figure 8A). H460 lung cancer cells were exposed to 500 μM of sodium acetate, butyrate, linoleic acid, and linolenic acid. As shown in Figure 8A, LCAT expressions were differently affected by exposure to the four fatty acids. The expression level of LCAT was upregulated during linolenic acid treatment but downregulated when treated with sodium acetate, butyrate, and linoleic acid. These results suggested that fatty acids significantly and differently affected H460 lung cancer cells. To further study the effect of four fatty acids on LCAT expression, we conducted a WB experiment. ## 2.5.2. Protein Content of LCAT The qPCR results showed that the four fatty acids affected the gene transcription level of LCAT in varying degrees. H460 lung cancer cells were exposed to 500 μM of sodium acetate, sodium butyrate, linoleic acid, and linolenic acid to confirm the effect of four fatty acids on LCAT. Western blot results (Figure 8B–D) revealed that linolenic acid induced the strongest LCAT expression. The other three fatty acids generated no significant changes in LCAT expression. ## 3. Discussion Lung cancer has the fastest-growing incidence rate and mortality, which seriously threatens human health and safety [1]. Based on the latest data released by the World Health Organization, lung cancer incidence rate and mortality rank first among all kinds of malignant tumors globally [2]. Lipid metabolism, especially fatty acid synthesis, is an essential cellular process that can convert nutrients into metabolic intermediates for membrane biosynthesis, energy storage, and signal molecule generation [30,31]. Lipid metabolism is a vital metabolic phenotype of cancer cells. Therefore, blocking the lipid supply in cancer cells will significantly impact the bioenergetics, membrane biosynthesis, and intracellular signal transduction processes of cancer cells [32]. Among them, short-chain and polyunsaturated fatty acids have a more significant impact on lung cancer. In a recent study, sodium butyrate treatment upregulated miR-3935, thereby inhibiting the growth and migration of A549 cells [33]. In addition, sodium butyrate inhibits the growth of lung and prostate cancer by regulating the p21 expression [34]. However, the metabolic effect of sodium butyrate on lung cancer, particularly on H460 lung cancer cells, is unclear. For other SCFAs, the metabolic effects on lung cancer are also inconclusive. Polyunsaturated fatty acids (PUFAs) are straight-chain fatty acids with two or more double bonds in their structure and a carbon chain length of 18~22 carbon atoms. It is divided into omega-3 and omega-6. Omega-3 PUFAs mainly include eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), and omega-6 PUFAs primarily include linoleic acid and arachidonic acid. Omega-3 and omega-6 fatty acids are essential components of cell membranes and precursors of many biochemical reactions in vivo, including regulating blood pressure and inflammatory reaction. Linoleic acid (LA) in omega-6 and omega-3 fatty acids, viz., α- Linolenic acid (ALA), cannot be synthesized by the human body but is only generated through food intake. PUFAs can promote apoptosis and inhibit cell proliferation of many malignant tumors including breast, liver, and pancreatic tumors [9,11]. The possible mechanisms of the polyunsaturated fatty acid effect on cancer include influencing the composition and function of the biomembrane, controlling lipid peroxidation of tumor cells, inhibiting the expression and function of oncogene-encoded proteins, tumor angiogenesis, and cancer cell adhesion to endothelial cells. Although the physiological effects of fatty acids have been studied, their mechanisms and pathways of action on lung cancer, and the differences between short-chain and polyunsaturated fatty acids, have not been analyzed. This study compared the effects of short-chain and polyunsaturated fatty acids on lung cancer cells via untargeted and targeted metabolic groups. First, the effects of different concentrations of sodium acetate, sodium butyrate, linoleic acid, and linolenic acid were investigated on the survival rate of lung cancer cells. As shown in Figure S1, exposure to 500 μM of sodium acetate, sodium butyrate, linoleic acid, and linolenic acid could not affect the survival of H460 cells. However, greater than 500 μM of linolenic acid had the highest cytotoxicity in H460 cells. Therefore, we selected 500 μM of fatty acids to treat lung cancer cells and investigate their impact on metabonomics in the follow-up experiment. Untargeted metabolomics systematically identifies and analyzes the whole-life metabolite based on limited background knowledge, obtains a large amount of metabolite data, and identifies differential metabolites. Simultaneously, untargeted metabonomics is also an essential prerequisite for targeted metabonomics. We found that the metabonomic characteristics of H460 cells changed significantly after exposure to fatty acids using the untargeted analysis of the above four fatty acid and control groups. As shown in Figure 1A and Figure 2A, PCA results showed that both intra-group aggregation and inter-group separation were good. As shown in Figure 1B–E and 2B–E, OPLS-DA results showed noticeable differences between the fatty acid and control groups. We have drawn volcano plots to analyze the distribution of different metabolites and their relationship between groups (Figure 1F–I and Figure 2F–I). VIP > 1.0, p-value < 0.05 (Student’s t-test), and FC value > 1.5 times are selected to identify different metabolites between the fatty acid and the control groups. There were a lot of different metabolites in the volcano plots compared with the control group. We selected 50 representative differential metabolites to determine heatmaps and correlation analysis heatmaps (Figure 3). The heatmap results confirmed that the intra-group aggregation of our untargeted metabonomics is excellent, and some differences exist between groups. The correlation analysis heatmaps revealed that PCs were negatively correlated with Lyso PCs, ADP in energy metabolite was positively associated with PCs, and fumaric acid and pyruvate were negatively correlated with PCs, etc. The statistical analysis of untargeted metabonomics showed that the different metabolites between the fatty acid and the control groups were mainly concentrated in energy metabolites, phospholipids, and bile acids. Then, we carried out targeted metabonomics research. We first establish three LC-MS/MS methods for 71 compounds, including energy metabolites, phospholipids, and bile acids. The validity of the method was verified by the subsequent validation results. The interference of four fatty acid treatments on the cell metabolism group was substantial. There were 17 and 20 different metabolites in the sodium acetate and butyrate groups and 19 and 23 in the linoleic and linolenic acid groups, respectively. Moreover, the energy metabolite, phospholipid, and bile acid were significantly changed. For the two groups of short-chain fatty acid administration and two polyunsaturated fatty acid administration groups, we continued to perform OPLS-DA analysis. As shown in Figure 9, the results showed significant differences between the short-chain fatty acid administration group and the polyunsaturated fatty acid group. The changes in metabolites are related to several metabolic pathways, which may reveal different potential mechanisms of fatty acid administration and metabolites. According to the results of the metabolic pathway analysis, four fatty acid pathways are involved in the citrate cycle (TCA cycle), gluconeogenesis, and glycerophospholipid metabolism. The sodium butyrate group in particular had noticeable metabolic changes in the short-chain fatty acid group. Figure 10A shows that PCs and PAs are downregulated, indicating that PLD expression is upregulated. While PCs are downregulated, Lyso PCs are also downregulated, which may indicate that the expression of LCAT or PLA is upregulated. When PCs are downregulated, PSs are upregulated, indicating the upregulation of PTDSS1 expression. While Pas are downregulated, Pes are upregulated, which may indicate that PLD expression is also upregulated. While TCA and GCA are upregulated, CA is also upregulated, which may indicate that the expression of CGH is downregulated. The linolenic acid group had noticeable metabolic changes in the polyunsaturated fatty acid group. As shown in Figure 10B, Lyso PCs are upregulated, while PCs are downregulated, indicating the upregulation of LCAT or PLA expression. While PA is downregulated, PC is also downregulated, and PE remains unchanged, which may indicate that the expression of PLD is upregulated. While PA is downregulated, PG is also downregulated, which may indicate that the expression of CDS and PGS1 is upregulated. When PCs are downregulated, PSs are also downregulated, which may indicate that the expression of PTDSS1 is upregulated. While TCA and GCA are upregulated, CA is upregulated, which may indicate that the expression of CGH is downregulated. Phosphoenolpyruvate and pyruvate were downregulated, indicating the upregulation of PKLR expression or PKM activity. We quantified phospholipid concentrations and observed that linoleic acid and linolenic acid led to significant changes in PCs and Lyso PCs levels. In the two long-chain unsaturated fatty acid groups, Lyso PCs increased with the decrease in PCs. Depending on the KEGG metabolic pathway map analysis, this altered rule may be due to the change of LCAT expression after administering long-chain unsaturated fatty acids. Glycolysis is a ubiquitous pathway for glucose degradation. This process provides a certain amount of energy to the body, and the intermediate products provide raw materials for biosynthesis. The oxidative phosphorylation process is a coupling reaction to release energy from substances due to body oxidation while supplying adenosine diphosphate (ADP) and phosphorus to synthesize adenosine triphosphate (ATP) [35,36,37]. In mammalian cells, oxidative phosphorylation synthesizes the energy cells need [38,39]. The tricarboxylic acid cycle (TCA) is an essential metabolic pathway in the organism, the final metabolic pathway of the three major nutrients, and the hub of the three major nutrient metabolic links [40]. Tumor cells especially generate energy. Healthy cells release a lot of energy depending on mitochondrial oxidized carbohydrate molecules. In contrast, most tumor cells provide energy via glycolysis with relatively low productivity, called the Warburg effect. Hexokinase 2 (HK2) is the first enzyme that catalyzes hexose phosphorylation and the glycolysis pathway [41,42]. It is also the rate-limiting enzyme of the Warburg effect. HK2 enhances the proliferation of tumor cells, inhibits cell apoptosis, and promotes cell invasion and metastasis. It is crucial for the rapid growth of tumor metabolism [43,44]. Phospholipids (PLs) are the main components of cell membranes and plasma lipoproteins and are essential bioactive lipids [16,27,45]. They participate in various physiological processes, such as cell proliferation, survival, apoptosis, cytoskeleton construction, and pathophysiological processes, such as inflammation, atherosclerosis, and cancer [46,47,48,49,50,51,52,53]. Bile acid is an essential component of bile and plays a vital role in fat metabolism[54]. Bile acid can activate the Farni-like X receptor (FXR), which is crucial in regulating bile acid synthesis, secretion, and lipid and glucose metabolism in the liver [55]. Therefore, rapid and accurate quantification of metabolites is valuable and urgent for exploring the pathological mechanism of these diseases and screening potential biomarkers. Phospholipids are the main component of cell membranes and are crucial for establishing barriers protecting cells from the surrounding environment and separating and controlling many cell processes. These molecules or their derivatives act as essential signals to regulate critical cellular reactions or play special roles, including lung surfactants. The length and unsaturation of these fatty acyl chains are different since phospholipids can have different head groups and are usually composed of multiple fatty acyl chains. Therefore, phospholipids have a wide variety because each species has unique properties, which can affect the folding, structure, and function of membrane proteins [50]. The specific lipid composition of the membrane determines its physical and functional characteristics with great biological significance. Several changes in phospholipid metabolism were observed in cancer cells. It has been recognized that cancer cells require more cell membranes to proliferate. Therefore, they synthesize fatty acids used as building blocks of phospholipids. This is achieved by significant overexpression and activation of key lipogenic enzymes, such as fatty acid synthase. In addition, enzymes involved in fatty acyl chain metabolism, hydrolysis, and remodeling, such as stearyl coenzyme A desaturase and several phospholipases, are abnormally expressed in cancer tissues. In addition, some of these enzymes are affected by non-small cell lung cancer. Lecithin–cholesterol acyltransferase (LCAT) is from a family of crucial lipid metabolism enzymes with no structural characteristics. It is responsible for the reverse transport of cholesterol over the lung surface [56,57,58,59,60]. The LCAT structure indicates the molecular basis of most human diseases with known LCAT missense mutations. It paves the way for the rational development of new therapies for LCAT deficiency, atherosclerosis, and acute coronary syndrome [58,61,62,63,64,65,66,67,68,69,70]. LCAT is a potential biomarker for various cancers, including ovarian cancer [71], breast cancer [72], colorectal cancer [73], and liver cancer [74]. However, there has been limited study of LCAT in lung and lung cancer cells. Lecithin–cholesterol acyltransferase (LCAT) is a 416 amino acid glycoprotein. It transesterifies sn-2 fatty acids of phospholipid molecules into a 3- α- Hydroxyl cholesterol group into two products, viz., lysophosphatidylcholine (Lyso PC) and cholesterol ester (CE) [56,57,62]. This enzyme has a role in the lipid–water interface of lipoprotein particles. It is associated with lipoprotein particles, such as plasma HDL, LDL, newborn discoid HDL, and recombinant HDL, simulating the composition and size of newborn HDL. LCAT activity is critical to the maturation of newborn HDL granules into globular plasma HDL while maintaining the normal lipoprotein granule structure. Lecithin–cholesterol acyltransferase (LCAT) reverses cholesterol transport on the lung surface [63]. The amino acid sequence of LCAT is $50\%$ of LPLA2, which is associated with high-density and low-density lipoprotein (HDL and LDL) particles in plasma. Moreover, it catalyzes the reverse transport of cholesterol from peripheral tissues to the liver [59]. The acyltransferase activity of LCAT esterifies free cholesterol on the discoid anterior b-HDL particles, maturing into spherical a-HDL. LCAT gene mutation is responsible for somatic diseases, such as familial LCAT deficiency (FLD) and fish-eye disease (FED) 10, due to complete loss of LCAT activity, resulting from the matrix loss due to HDL particles by LCAT activity. LCAT is an enzyme that plays a crucial role in human plasma lipoprotein metabolism and is very important in maintaining cholesterol homeostasis and controlling cholesterol transport in blood circulation [60]. LCAT also performs some auxiliary reactions unrelated to cholesterol. These reactions involve the esterification of Lyso PCs to PCs [74]. Previous studies have revealed that trans-unsaturated fatty acids inhibit lecithin–cholesterol acyltransferase and alter its location specificity. Omega-3 polyunsaturated fatty acids cannot be synthesized by the human body. They are essential nutrients for the human body to synthesize various hormones and endogenous substances. The physiological functions of the body operate normally only by supplementing these oleic acids with food from the outside. Our results indicate that H460 cells regulate enzyme and metabolite levels to resist when exposed to linolenic acid. Linolenic acid induced significant changes in LCAT, while the other three fatty acids induced no changes in LCAT. Therefore, H460 cells exposed to linolenic acid experience more cholesterol reverse transcription or Lyso PC esterification to PCs than those exposed to other fatty acids. Previous studies have indicated that trans fatty acids can reduce LCAT activity. However, there is no study on cis fatty acids. This study compared the different effects of four fatty acids on LCAT. The experimental results of qPCR and WB of linolenic acid and linoleic acid groups are relatively consistent. The qPCR and WB results of the linolenic acid administration group were consistent with the metabonomics experimental results. This confirmed our conjecture concerning the effect of omega-3 fatty acids on LCAT expression. However, the results of the linoleic acid group were not consistent with the metabonomics results. Through the KEGG metabolic pathway map, in addition to LCAT, 22 enzymes, such as PLA2G4B and PLA2G4D, affect the conversion of PCs to Lyso PCs (Figure 10). Our experimental results indicate that the effect of linolenic acid while converting PCs to Lyso PCs may come from the other enzymes. ## 4.1. Chemicals and Reagents HPLC-grade methanol, acetonitrile, and chloroform were obtained from Merck (Darmstadt, Germany). HPLC-grade ammonium formate and acetate were procured from Shanghai Aladdin Reagent Co., Ltd. (Shanghai, China). The energy metabolite standards were purchased from Shanghai Anpel Laboratory Technologies (Shanghai, China) Inc. (Shanghai, China). Phospholipid standards were obtained from Avanti Polar Lipids Inc. (Alabaster, AL, USA). Bile acid standards were bought from Sigma-Aldrich (St. Louis, MO, USA). Deionized water was produced using a Direct-Q water purification system (Millipore, El Paso, TX, USA). The target compound information is demonstrated in Table S1. ## 4.2. Cultured Cells H460 lung cancer cells were obtained from Bohui Biotechnology Co., Ltd. (Guangzhou, China). Using RPMI-1640, $10\%$ FBS and $1\%$ P/S were added as a complete medium. All H460 cell culture dishes were incubated at $5\%$ CO2, 37 °C constant temperature, and saturated humidity and cultured using the completely prepared medium. Based on the cell growth rate and medium color, fresh medium was replaced in time. Before sample preparation, cells were treated with 500 μM of sodium acetate, butyrate, linoleic acid, or linolenic acid for 24 h. ## 4.3. CCK8 Experimental Procedure After counting the cells, 100 microliters of 2000 cells per well were transferred into a 96-well plate. Simultaneously, a blank group was set, and a PBS buffer was added to the most marginal hole to decrease the evaporation effect. The cells were placed in a 37 °C, $5\%$ CO2 incubator for 24 h. The adherence of the cells was observed, and the medium was aspirated from each well. Then, different concentrations of sodium acetate, sodium butyrate, linoleic acid, and linolenic acid were added to the experimental group. Following that, the fatty-acid-free medium was added to the blank group and placed in a 96-well plate at 37 °C. The medium was incubated for 24 h in a cell incubator with $5\%$ CO2 air. After the administration, 10 μL of CCK-8 solution was directly added to each well. The culture plate was placed in a 37 °C $5\%$ CO2 incubator for 1 h after adding CCK-8. The 96-well plate was taken out, a microplate reader was used to detect the OD value of each well at a wavelength of 450 nm, the processing data were analyzed, and the proliferation curve was drawn. ## 4.4. LC-MS/MS Sample Preparation The cells grown in the log phase were taken, the medium was aspirated, and the cells were washed three times using PBS. We added 0.5 mL of pre-chilled methanol at 4 °C, used a cell scraper to scrape off the adherent cells, and transferred them to a 2 mL centrifuge tube. A specific concentration of internal isotopic standard was added to each sample standard. Then, 1 mm grinding beads were added to homogenize the sample using a homogenizer, at 300 Hz, for 60 s and three cycles. After that, 250 μL of water and chloroform were added and homogenized for three cycles. Then, it was centrifuged at 14,000 rpm for 10 min at 4 °C, and the supernatant was collected, spin-dried, and reconstituted using 400 μL of $50\%$ acetonitrile/water solution. Samples were stored at −20 °C for LC-MS/MS analysis. ## 4.5. Untargeted Metabonomics For HILIC untargeted metabonomics, the chromatographic separation was performed using a Vanquish UHPLC system (Thermofisher, Massachusetts, USA. with a binary pump, a vacuum degasser, an autosampler, and a column oven) in a SeQuant ZIC-HILIC column (100 mm × 2.1 mm i.d., 3.5 µm) (Merck, Germany) at 45 °C. Mobile phase A was water containing 50 mM of ammonium formate, and mobile phase B was acetonitrile. The linear gradient was 0 min, $90\%$ B; 10 min, $50\%$ B; 12 min, $90\%$ B; 15 min, $90\%$ B. The flow rate was 0.4 mL/min, and the injection volume was 1 μL. For RP untargeted metabonomics, the chromatographic separation was performed using a Vanquish UHPLC system (ThermoFisher with a binary pump, a vacuum degasser, an autosampler, and a column oven) in an ACQUITY UPLC HSS T3 column (100 mm × 2.1 mm i.d., 1.7 µm) (Merck, Germany) at 45 °C. Mobile phase A was water containing $0.1\%$ formic acid, and mobile phase B was acetonitrile with $0.1\%$ formic acid. The linear gradient was 0 min, $1\%$ B; 12 min, $90\%$ B; 13 min, $90\%$ B; 13.1 min, $1\%$ B. The flow rate was 0.4 mL/min, and the injection volume was 1 μL. MRM analysis was performed with the Q Exactive plus mass spectrometer (ThermoFisher, Waltham, MA, USA) for untargeted metabonomics. The optimized MS parameters were HESI source in negative mode; scan mode: DDA mode, and one full scan followed by five MS/MS scans. The collision energy is NEC 15, 30, 45 to fragment the ions. Nitrogen ($99.999\%$) was utilized as a collision-induced dissociation gas. Full scan range: 70 to 1000 amu; full scan resolution: 70000, AGC: 1e6, IT: 100ms; dd-MS/MS resolution: 17500, AGC: 5e5, IT: 50ms; spray voltage: 3.2 kV (positive mode) and 3.0 kV (negative mode); capillary temperature: 320 °C; S-lens RF level: 50 V. The data acquisition and processing were undertaken using the Analyst software 1.7 (AB SCIEX) and the MultiQuant software 3.0.3 (AB SCIEX). ## 4.6.1. Calibrators and Quality Control Samples Standard stock solution: the energy metabolites and the bile acid standards were dissolved in methanol, and phospholipid standards were dissolved in chloroform to synthesize 1 mg/mL stock solutions, diluted using methanol to standard solutions. Standard mixture: Appropriate amounts of standard stock solutions and deuterated internal standards were precisely obtained to prepare a standard mixture with a concentration of 10× (10 ng/μL). The mixed solvent gradient dilution was 3×, 1×, 0.7×, 0.5×, 0.3×, 0.1×, 0.07×, 0.05×, 0.03×, 0.01×, and 0.007× as the standard linear working solution. ## 4.6.2. Method Validation The accuracy and precision were determined for three days over one week ($$n = 6$$). The stability profiles of the analytes were evaluated after storing at 4 °C and room temperature for three days and −20 °C for 20 days. The current study used the internal standard to validate the extraction recovery and matrix effect. The MQC = 0.3× ($$n = 3$$) sample was selected for verification. The extraction recovery was the percentage of the peak area extracted after adding the internal standard to the peak area of the solution. The matrix effect was the ratio of the peak area of the internal standard added after the extraction and the internal standard. ## 4.6.3. LC-MS/MS Parameters for Targeted Metabonomics For the energy metabolites, chromatographic separation was performed using a UHPLC system (Waters Corp., Milford, MA) and a Waters BEH amide column (100 mm × 4.6 mm i.d., 3.5 μm) at 25 °C. Mobile phase A was 10 mM of ammonium acetate in 10:90 acetonitrile/water, with PH = 9, and mobile phase B was 10 mM of ammonium acetate in 90:10 acetonitrile/water, with PH = 9. The linear gradient was 0 min, $90\%$ B; 2 min, $90\%$ B; 12 min, $60\%$ B; 15 min, $60\%$ B; 15.1 min, $90\%$ B; 18 min, $90\%$ B. The flow rate was 0.4 mL/min, and the injection volume was 10 μL. For phospholipids and bile acids, chromatographic separation was performed using a UHPLC system (Waters Corp., Milford, MA, USA) and a Waters BEH C18 column (100 mm × 4.6 mm i.d., 3.5 μm) at 50 °C. Mobile phase A involved 5 mM of ammonium formate in 5:95 acetonitrile/water, and mobile phase B was acetonitrile. The linear gradient used was 0 min, $27\%$ B; 0.1 min, $27\%$ B; 1.5 min, $32\%$ B; 4.5 min, $35\%$ B; 7 min, $35\%$ B; 10 min, $34\%$ B; 10.01 min, $95\%$ B; 13 min, $100\%$ B; 16 min, $100\%$ B; 16.01 min, $27\%$ B; and 20 min, $27\%$ B. The flow rate was 0.3 mL/min, and the injection volume was 10 μL. MRM analysis was performed for energy metabolites using the QTRAP 4000, a hybrid triple quadrupole/linear ion trap (AB SCIEX, Concord, ON, Canada), with the following optimized ESI- MS parameters: collision gas, medium, curtain gas, 20 psi; ion source gas 1, 50 psi; ion source gas 2, 50 psi; ion spray voltage, 5000 V and source temperature, 500 °C. Additionally, the data acquisition and processing were conducted using the Analyst software 1.7 (AB SCIEX) and the MultiQuant software 3.0.3 (AB SCIEX). MRM analysis was performed with QTRAP 4000, a hybrid triple quadrupole/linear ion trap (AB SCIEX, Concord, ON, Canada) for phospholipids and bile acids. The optimized ESI ± MS parameters were collision gas, medium, curtain gas, 15 psi; ion source gas 1, 50 psi; ion source gas 2, 50 psi; ion spray voltage, ±4500 V and source temperature, at 500 °C. The data acquisition and processing were undertaken using the Analyst software 1.7 (AB SCIEX) and the MultiQuant software 3.0.3 (AB SCIEX). Detailed mass spectrum parameters of all the target compounds are shown in Table S2. ## 4.7. PCR Experimental Procedure After RNA extraction, reverse transcription experiments were conducted to convert RNA to cDNA. Then, PCR amplification experiments were performed. The sequence of primers is depicted in Table S3. ## 4.8. WB Experimental Procedure After protein extraction, the protein concentration was determined with the BCA method. After adding the loading buffer, the protein was denatured by boiling. Subsequent SDS-PAGE electrophoresis was performed until the rainbow markers were entirely separated. The electrophoresis gel was cut to a suitable size and transferred to the membrane. After transfer, the membrane was blocked using $5\%$ BSA for 1 h. The blocked membrane was removed, the diluted primary antibody solution was added, and it was kept in a refrigerator at 4 °C overnight. After washing three times using TBST, the diluted secondary antibody solution was added and incubated for 1 h. Then, we performed development after three washes with TBST. ## 4.9. Statistical Analysis of Data The collected raw data were processed using Analyst 1.4.2 and Masslynx 4.1, and the target concentration was obtained using the standard curve. Statistical analyses were performed with Simca-P (Sartorius Company, Goettingen, Germany) and GraphPad Prism (GraphPad Software Inc., San Diego, CA, USA). The overall distribution of the unsupervised PCA samples and the supervised OPLS-DA methods are utilized to observe the differences between the groups. The response sequencing test and cyclic interactive verification methods are simultaneously used to prevent the model from overfitting and verify the stability and prediction accuracy. In multi-dimensional statistics, VIP > 1 is considered a variance variable. We process the single dimension data statistically, with Student’s t-tests and multiple changes, and finally select metabolites having ANOVA p-value < 0.05, fold change > 1.5, and maximum CV < $30\%$ as the difference variable for identification. MetaboAnalyst 4.0 heatmap, correlation analysis, and other means were used to search for potential lung cancer biomarkers with high specificity and sensitivity to understand the correlation of differential metabolites. * $p \leq 0.05$ and $p \leq 0.01$ were considered statistically significant in all the experiments. ## 5. Conclusions A metabolomics approach was used to identify the metabolic perturbations in H460 cells induced by sodium acetate, sodium butyrate, linoleic acid, and linolenic acid. The results obtained in this study showed that exposure to four fatty acids would cause significant changes in the metabolic profile of H460 cells compared with the control group. Changes in the metabolic spectrum involve multiple metabolic pathways, such as the citrate cycle (TCA cycle), gluconeogenesis, and glycerophospholipid metabolism. In addition, the results of the metabonomic analysis were also verified, indicating that the expression of LCAT changed after administration. Additionally, the administration of four fatty acids caused unique changes in H460 cells. This showed that although they had several core similarities, each compound had different effects on cells. For instance, sodium butyrate administration leads to a more potent disturbance of the cell metabolic spectrum than sodium acetate. However, sodium butyrate had a weaker effect on LCAT expression than linolenic acid. Our results demonstrate that metabonomics is a powerful tool for assessing health status and provides data for the overall health risk assessment of fatty acid intake. ## References 1. 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--- title: Phosphorus-Containing Polymers as Sensitive Biocompatible Probes for 31P Magnetic Resonance authors: - Lucie Kracíková - Ladislav Androvič - Iveta Potočková - Natalia Ziółkowska - Martin Vít - David Červený - Daniel Jirák - Richard Laga journal: Molecules year: 2023 pmcid: PMC10005191 doi: 10.3390/molecules28052334 license: CC BY 4.0 --- # Phosphorus-Containing Polymers as Sensitive Biocompatible Probes for 31P Magnetic Resonance ## Abstract The visualization of organs and tissues using 31P magnetic resonance (MR) imaging represents an immense challenge. This is largely due to the lack of sensitive biocompatible probes required to deliver a high-intensity MR signal that can be distinguished from the natural biological background. Synthetic water-soluble phosphorus-containing polymers appear to be suitable materials for this purpose due to their adjustable chain architecture, low toxicity, and favorable pharmacokinetics. In this work, we carried out a controlled synthesis, and compared the MR properties, of several probes consisting of highly hydrophilic phosphopolymers differing in composition, structure, and molecular weight. Based on our phantom experiments, all probes with a molecular weight of ~3–400 kg·mol−1, including linear polymers based on poly(2-methacryloyloxyethyl phosphorylcholine) (PMPC), poly(ethyl ethylenephosphate) (PEEP), and poly[bis(2-(2-(2-methoxyethoxy)ethoxy)ethoxy)]phosphazene (PMEEEP) as well as star-shaped copolymers composed of PMPC arms grafted onto poly(amidoamine) dendrimer (PAMAM-g-PMPC) or cyclotriphosphazene-derived cores (CTP-g-PMPC), were readily detected using a 4.7 T MR scanner. The highest signal-to-noise ratio was achieved by the linear polymers PMPC [210] and PMEEEP [62] followed by the star polymers CTP-g-PMPC [56] and PAMAM-g-PMPC [44]. The 31P T1 and T2 relaxation times for these phosphopolymers were also favorable, ranging between 1078 and 2368 and 30 and 171 ms, respectively. We contend that select phosphopolymers are suitable for use as sensitive 31P MR probes for biomedical applications. ## 1. Introduction Phosphorus magnetic resonance (31P MR) including spectroscopy (31P MRS), imaging (31P MRI) and magnetic resonance spectroscopic imaging (31P MRSI) is a unique diagnostic technique used in human and experimental medicine, enabling non-invasive in vivo assessment of fundamental physiological processes at a cellular level without the use of ionizing radiation [1,2,3]. This is possible due to the presence of vital organophosphorous compounds in living organisms containing a stable phosphorus monoisotope (31P). The nuclei of 31P have favorable magnetic properties (s = ½, γ = 17.2 MHz∙T−1) that can be easily detected by MR scanners. In medical practice, 31P MR techniques are mostly used to reveal changes in the biochemistry of high-energy nucleotide coenzymes and phospholipid building blocks. Providing valuable insights into mitochondrial metabolism, membrane composition, and intracellular pH levels [3,4], this information can be utilized to investigate a wide range of severe diseases, including diabetes, stroke, heart failure, and cancer [5,6,7,8]. However, in contrast to conventional in vivo imaging of water and fat protons (1H), 31P MR measurements are hampered by the low physiological levels of organophosphorous compounds (~1000-fold lower abundance than compounds containing 1H) and the low MR sensitivity of 31P nuclei (~2.5-fold lower magnetic momentum compared to 1H). Therefore, 31P MRexperiments often require disproportionately long acquisition times in order to achieve adequate signal-to-noise ratios (SNRs) [1]. On the other hand, the range of chemical shifts at which most of the naturally occurring phosphorus compounds resonate is relatively wide—from -15 to 15 ppm (compared to the narrow 5 ppm window for 1H spectra)—thus enabling simple signal separation [9]. Other advantages of 31P MR include that it can also measure the biodistribution of systemically administered phosphorus-containing compounds (so-called probes). The acquired data can provide, for example, information about the accumulation of the probe in a specific organ or tissue, as well as the kinetics of its excretion from the body. However, from an application point of view, it is essential that the probe be non-toxic and non-immunogenic and provides a highly intense 31P MR signal that is distinguishable from the natural biological background. In addition, it is a great advantage if the probe is of a macromolecular or colloidal nature with a long biological half-life, but is eliminated from the body after performing its function. [ 10,11] The development of probes that meet these exacting criteria is therefore a major challenge in biomedical diagnostic research. In recent decades, several different types of biocompatible phosphopolymers and phosphate-based colloids have been developed for various biomedical applications that meet the above criteria for use as potential exogenous 31P MR probes. However, systematic studies investigating the effect of their structure and composition on MR properties are lacking. Among the most exemplary materials are hydrophilic poly(organophosphazenes) (PPPs). These fully synthetic polymers, which feature a repeating phosphazene linkage (–N=P(R1R2)–) in the backbone, have been applied in many biomedical fields, including drug delivery, vaccination, and regenerative engineering [12,13]. They also offer excellent biocompatibility, biodegradability, and wide structural variability. Importantly, 31P nuclear magnetic resonance (NMR) studies have shown that PPP chemical shifts can be tuned by changing the types of R1 and R2 substituents (aliphatic vs. aromatic) and linkages (P–O vs. P–N vs. P–S bond) on the phosphorus atom in the polymer backbone, ensuring that the probe is spectroscopically distinguishable from biomolecules with phosphoester bonds [14,15]. Poly(phosphoesters) (PPEs) are also suitable synthetic phosphopolymers for 31P MR imaging. PPEs represent a broad class of biocompatible polymers characterized by the presence of repeating hydrolytically and enzymatically degradable phosphoester groups (–PO(OR1)–O–R2–O–) in the main polymer chain, thus resembling biomacromolecules such as nucleic acids or nucleotide-based coenzymes. Depending on the nature of the R1 side group, PPEs can have different chemical and physical properties, making them suitable in a wide range of biomedical applications [16,17]. Of the phosphorus-containing polymers, poly(2-methacryloyloxyethyl phosphorylcholine) (PMPC), a hydrophilic methacrylate-based polymer with a zwitterionic phosphorylcholine moiety (−OPO3––CH2CH2–N+(CH3)3) on its side chain, has recently gained considerable attention due to its remarkable properties. PMPC is non-toxic, resistant to non-specific protein adsorption and cell adhesion, and has the ability to penetrate cell membranes [18]. In addition, 2-methacryloyloxyethyl phosphorylcholine (MPC) can be copolymerized with a wide range of functionalized monomers using a variety of polymerization techniques to tune the properties of the resulting polymer. MPC is widely utilized in drug delivery, antifouling coating technology, and biosensing [18,19,20,21]. It has been further demonstrated that replacing the phosphoester group (−OPO3––) in PMPC with a thiophosphoester group (−OPSO2––) leads to a significant chemical shift in its 31P MR spectrum while allowing the material to retain its advantageous chemical, biophysical, and biological properties [22]. Another notable material for 31P MR imaging is phytate (myo-inositol hexakisphosphate), a non-toxic biodegradable biomolecule that serves as a phosphorus reservoir (energy store) and source of myo-inositol (cell wall precursor) in plant seeds [23]. It has also been shown that calcium phytate nanoparticles doped with removable paramagnetic Fe3+ ions provide relatively high 31P MR signals, and that iron ions alter MR contrast in both 1H and 31P MR [24]. The aim of our study was to perform a controlled synthesis, and compare the MR properties, of several promising 31P MR probes composed of highly hydrophilic synthetic phosphopolymers. Synthesized polymer probes based on PPP, PPE, and PMPC were characterized using a 4.7 T MRI scanner and a 1.5 T MR relaxometer. The effects of composition, polymer chain architecture, and molecular weight on 31P MRS/MRI/MRSI signal intensity and T1 and T2 31P relaxation times were evaluated. Our data demonstrate that, by tuning the physicochemical parameters of phosphopolymer chains, highly efficient probes can be prepared for future in vivo applications. ## 2.1. Synthesis and Physiochemical Characterization of Polymer Probes Our requirements for determining a 31P MR probe of suitable composition were that the material would be water soluble, biocompatible, biodegradable, have a long biological half-life, and be structurally capable of carrying a large amount of phosphorus. Hydrophilic phosphopolymers are candidates that meet these criteria. In particular, we focused on synthetic phosphorus-containing polymers composed of poly(2-methacryloxyethyl phosphorylcholines) (PMPCs), poly(organophosphazenes) (PPPs), and poly(phosphoesters) (PPEs). The exceptional biologically amenable properties of these polymers have been successfully deployed in a number of biomedical applications [12,16,18]. Our comparison of individual polymers focused on differences in composition, polymer-chain architecture, and molecular weight. We prepared linear PMPC-, PPE-, and PPP-based polymers (Mn ranging from 4 to 22 kg·mol−1) as well as star-shaped PMPC-based polymers (Mn ranging from 59 to 399 kg·mol−1) (see Table 1). While the linear polymers were prepared by the modification of previously described procedures (see below), the star-shaped polymers were synthesized de novo for the purposes of this comparative study. To ensure well-defined polymers, controlled radical or ionic polymerization techniques were used. This resulted in materials with a very narrow molecular weight distribution (Ð ≤ 1.2) as well as a large quantity of terminal functional groups for the purpose of possible post-polymerization modification, e.g., targeting units, fluorescent dyes, and therapeutics. A detailed description of the synthetic procedures and physicochemical methods used for preparation and characterization the polymer probes is provided in the Supplementary Information. A linear PMPC polymer with a propargyl (Pg) end group was synthesized by reversible addition-fragmentation chain-transfer polymerization (RAFT) of a commercially available 2-methacryloxyethyl phosphorylcholine (MPC) monomer in the presence of a functionalized chain transfer agent (CTA-Pg) and an initiator (ACVA-Pg) at a [MPC]/[CTA-Pg]/[ACVA-Pg] ratio of 34:1:0.5. The dithiobenzoate (DTB) group on the other side of the polymer chain was either replaced by a non-reactive isobutyronitrile (IBN) group by a homolytic reaction with a high molar excess of AIBN to form the polymer IBN-PMPC-Pg (2a) or by an amine-reactive thiazolidine-2-thione (TT) group using a similar reaction with a functionalized azoinitiator (ACVA-TT) to yield the polymer precursor TT-PMPC-Pg. The molecular weight and dispersity of both polymers were ~19 kg·mol−1 and 1.06, respectively. The linear polymer precursor TT-PMPC-Pg was further attached via an aminolytic reaction to a 5th generation poly(amidoamine) dendrimer (PAMAM); the molar ratio of the surface amino groups of the dendrimer to the terminal TT groups of the polymer was 2:1, which led to the binding of a sufficient amount of polymer arms and a relatively high conjugation efficiency. After removing the unreacted linear polymer on centrifugation membrane filters, a nearly monodisperse star polymer PAMAM-g-PMPC-Pg (2b) with ~20 polymer arms and a molecular weight of ~400 kg·mol−1 was obtained. A star polymer with a cyclotriphosphazene (CTP)-derived core was produced using the “grafting from” approach via the RAFT mechanism with a hexavalent chain transfer agent (CTP-(CTA-COOH)6). Polymerization of the MPC monomer in the presence of CTP-(CTA-COOH)6 led to the formation of a six-armed star polymer with terminal DTB groups, which were subsequently replaced by Pg groups by reaction with a functionalized azoinitiator (ACVA-Pg). The resulting star polymer CTP-g-PMPC-Pg (2c) was characterized by low dispersity (1.1) and a molecular weight of ~60 kg·mol−1. The chemical structures of the MPC-based linear and star polymers are shown in Figure 1, their SEC profiles are shown in Figure S5, and their characteristics are summarized in Table 1. PAMAM-g-PMPC-Pg (2b) with ~20 polymer All MPC-based phosphopolymers were prepared from a commercial monomer (MPC) by controlled solution radical polymerization (RAFT). Linear PPE and PPP were obtained by anionic or cationic polymerization of monomers specifically synthesized for this purpose. Linear PPE was produced by DBU-initiated ring-opening anionic polymerization of 2-ethoxy-2-oxo-1,3,2-dioxaphospholane in the presence of 2-propyn-1-ol. Under strictly anhydrous conditions, water-soluble biodegradable poly(ethyl ethylene phosphate) (PEEP) with a terminal Pg group PEEP-Pg (2d) was obtained. The molecular weight and dispersity of the polymer were ~4 kg·mol−1 and 1.03, respectively. The preparation of hydrophilic PPP required not only the synthesis of the monomer, but also the functionalized initiator and subsequent post-polymerization modification of the generated reactive precursor. By the action of the functionalized cationic initiator 4-(dichlorodiphenylphosphino)styrene, the trichloro(trimethylsilyl)phosphoranimine monomer was converted to a reactive poly(dichlorophosphazene) precursor with a terminal vinyl group, which was further modified with 2-(2-(2-methoxyethoxy)ethoxy)ethanol in the presence of NaH to form the final water-soluble poly[bis(2-(2-(2-methoxyethoxy)ethoxy)]phosphazene (PMEEEP-Vi) (2e). By ensuring the high purity of all reactants and maintaining anhydrous conditions throughout the polymerization process, it was possible to produce a linear polymer with a molecular weight of 22 kg·mol−1 and a dispersity of 1.2. The chemical structures of the PPE- and PPP-based linear polymers are shown in Figure 2, their SEC profiles are shown in Figure S1, and their SEC characteristics are summarized in Table 1. ## 2.2. MR Properties of Polymer Probes High MR sensitivity (signal-to-noise ratio) is essential especially in lower magnetic fields close to those used in clinical medicine. MR spectroscopic methods, including MRSI-based sequences, are much more sensitive compared to conventional MR imaging sequences (e.g., RARE), which we verified on the PMPC probe at different repetition times (see Figures S7 and S8). Therefore, we used spectroscopic sequences based on chemical shift imaging (CSI) to image the phantoms. In addition, the CSI signal can be easily monitored in different regions using a spectroscopic signal matrix and is therefore relevant for future in vivo applications where probe localization will be required. Taking into consideration all of the MR parameters measured, IBN-PMPC-Pg (2a) and PMEEEP-Vi (2e) produced the best results. The SNR values calculated for both probes were of a sufficiently high sensitivity for 31P MR detection. Furthermore, phosphorus MR relaxation times were within the range applicable for in vivo use. The shorter T1 and longer T2 relaxation times for IBN-PMPC-Pg (2a) make it the most promising of the two probes. Its shorter T1 relaxation time allows for fast scanning and its longer T2 relaxation time enables to obtain a stronger MR signal. The results for PAMAM-g-PMPC-Pg (2b) and CTP-g-PMPC-Pg (2c) were similar. Both had long T1 and T2 relaxation times and relatively low SNR values, probably because the repetition time (TR) used was much shorter (TR = 500 ms) than optimal values. Considering measurements with a longer TR would increase the measurement time and the animal/patient burden related to it, a short TR was implemented for all probes to prioritise clinical applicability. For the same reason, the lowest SNR value was for PEEP-Pg (2d), with a T1 relaxation time approximately double the times of the first two polymer probes mentioned above. If the optimal TR (approx. 5 × T1) had been chosen, measurements would have been unacceptably long. Given that the chemical composition of probes (2a–c) and the structural motifs of probes (2d and 2e) were similar to probes previously documented by our study group [20], we assumed their 1H relaxation times would also be comparable. Therefore, we did not expect an effect on MR contrast in anatomical (reference) 1H MRI. This assumption was confirmed when measuring reference 1H MR images for 31P CSI measurements, where we found no artifacts corresponding to shortened relaxation times. Although we could not find a correlation between the physicochemical parameters (composition, morphology, molecular weight) of the polymer probes and their MR properties, we can conclude that all of the prepared materials can potentially be used as exogenous probes for experimental and clinical phosphorus MR imaging, despite the fact that the presented macromolecules provide a 31P MR signal at approximately 0 ppm as for most phosphoester group-containing biomolecules. The presented polymer probes applied at higher concentrations provide a higher signal intensity than the natural biological background and therefore they should be detected even at the same chemical shift. To confirm these expectations, we performed a model experiment in which we measured MR spectra of a selected polymer probe (PMPC) and phosphocreatine (PCr), as a representative of one of the most abundant phosphorus-containing biomolecules. The molar concentration of phosphorus (cP) in the PMPC probe was set to 100 mmol·L−1, which we know is not toxic to mice [22], and the cP in PCr was 27 mmol·L−1, which corresponds to the average content of PCr in human skeletal muscle [25]. After obtaining non-localized MR spectra of both compounds, the tube containing PMPC was removed from the instrument, after which further measurement of the reference PCr was performed. The measurement results clearly show that the 31P MR signal (SNR) of the polymer probe is more than 5-fold higher than that of the reference PCr (see Figure 3). These data suggest that the prepared polymer probes should be distinguishable from the biological background under in vivo conditions. An alternative solution could be to substitute the phosphoesters in their structures with thiophosphoesters, which would lead to the production of an 31P MR signal with a different chemical shift (Δδ ~ 56 ppm) than that provided by the biological background [22]. However, this approach would require the de novo synthesis of all monomers and their subsequent conversion to high-molecular-weight products, which was not the subject of this study. ## 3.1. Chemicals 3-Amino-1-propyne, 2-chloro-2-oxo-1,3,2-dioxophospholane, 1,8-diazabicyclo [5.4.0]undec-7-ene (DBU), N,N′-dicyclohexylcarbodiimide (DCC), diethylene glycol dimethyl ether (diglyme), 4-dimethylaminopyridine (DMAP), 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDC), hexachloroethane, hexamethyldisilazane, 2-methacryloyloxyethyl phosphorylcholine (MPC), 2-(2-(2-methoxyethoxy)ethoxy)ethanol, 2-propyn-1-ol, thiazolidine-2-thione (TT), and triethylamine were purchased from TCI Europe, Zwijndrecht, Belgium. 4,4′-Azobis(4-cyanopentanoic acid) (ACVA), 2,2′-Azobis(2-methylpropionitrile) (AIBN), N-(tert-butoxycarbonyl)tyramine, butyl lithium, cesium carbonate, 4-cyano-4-(phenylcarbonothioylthio)pentanoic acid (CTA-COOH), 4-(diphenylphosphino)styrene, poly(amidoamine) dendrimer, ethylenediamine core, G5 (pAMAM), hexachlorocyclotriphosphazene (CTP-(Cl)6), phosphorus trichloride, and sulfuryl chloride were purchased from Sigma-Aldrich, Prague, Czech Republic. Toluene was pre-dried over Na (in the presence of benzophenone) and dichloromethane over P2O5 and then distilled into a flask filled with molecular sieves (4 Å). All other solvents of HPLC grade (obtained from VWR International, Stříbrná Skalice, Czech Republic) were dried over a layer of activated molecular sieves (4 Å) before use. ## 3.2. Synthesis of Monomers, Initiators and Chain Transfer Agents (1a) 2-ethoxy-2-oxo-1,3,2-dioxaphospholane monomer was synthesized by reacting 2-chloro-2-oxo-1,3,2-dioxophospholane with ethanol in acetonitrile in the presence of triethylamine [26] (see Scheme 1). (1b) Trichloro(trimethylsilyl)phosphoranimine monomer was synthesized according to a modified procedure described elsewhere [27]. Briefly, a solution of butyl lithium (2.5 m, 5.0 mmol, 2.0 mL) in hexanes was added dropwise to hexamethyldisilazane (5.0 mmol, 1.05 mL) in dry toluene (20 mL) at 0 °C. The reaction mixture was then stirred for 1 h at r.t. To this solution, PCl3 (5.0 mmol, 0.44 mL) was added dropwise; the mixture was stirred at 0 °C for 30 min and then for another 1 h at r.t. Finally, SO2Cl2 (5.1 mmol, 0.41 mL) was added dropwise and the solution stirred at 0 °C for 1 h. The crude monomer in toluene solution was used directly in the polymerization reaction. For the reaction scheme, see Scheme 2. (1c) 4-(Dichlorodiphenyphosphino)styrene cationic initiator was generated by reacting 4-(diphenylphosphino)styrene (0.1 mmol, 29.0 mg) with hexachloroethane (0.11 mmol, 26.0 mg) in dry dichloromethane (1.5 mL) according to a previously described procedure [28] (see Scheme 3). The crude initiator in DCM solution was used in the polymerization reaction without further purification. (1d) 4-Cyano-4-(1-cyano-3-ethynylcarbamoyl-1-methylpropylazo)-N-ethynyl-4-methylbutyramide (ACVA-Pg) radical initiator was synthesized by reacting ACVA with 3-amino-1-propyne in dichloromethane in the presence of EDC and DMAP [29] (see Scheme 4). (1e) 2-[1-Cyano-1-methyl-4-oxo-4-(2-thioxo-thiazolidin-3-yl)-butylazo]-2-methyl-5-oxo-5-(2-thioxothiazolidin-3-yl)-pentanenitrile (ACVA-TT) radical initiator was prepared by reacting ACVA with TT in tetrahydrofuran in the presence of DCC and DMAP [30] (see Scheme 5). (1f) 2-cyano-5-oxo-5-[(prop-2-yn-1-yl)amino]pentan-2-yl benzenecarbodithioate (CTA-Pg) RAFT agent was prepared by reacting CTA-COOH with 3-amino-1-propyne in ethyl acetate in the presence of EDC [29] (see Scheme 6). (1g) CTP-(DTB)6 hexavalent RAFT reagent was synthesized using a multistep reaction starting with CTP-(Cl)6, which was converted to hexakis{4-[2-(tert-butoxycarbonyl)aminoethyl]phenoxy}-cyclotriphosphazene by condensation reaction with N-(tert-butoxycarbonyl)tyramine in the presence of Cs2CO3. This was followed by deprotection of its amino group in methanolic HCl and subsequent conjugation with CTA-COOH in the presence of EDC and DMAP [31]. For the reaction scheme, see Scheme 7. ## 3.3. Synthesis of Polymer Probes (2a) Poly(2-methacryloyloxyethyl phosphorylcholine) (PMPC) linear polymer with a terminal propargyl (Pg) group was synthesized according to a procedure described in one of our previous papers [7]. Briefly, a mixture of CTA-Pg (41.4 mg, 105.0 µmol) and ACVA-Pg (18.6 mg, 52.5 µmol) was dissolved in 0.780 mL of dimethylacetamide (DMAc) and added to a solution of MPC (1.050 g, 3.57 mmol) in 2.775 mL of methanol. The solution was thoroughly bubbled with argon and polymerized in a sealed glass ampoule at 70 °C for 16 h. The polymerization mixture was then precipitated into 40 mL of acetone. The solid content was filtered off, dissolved in methanol, and re-precipitated into the same precipitant. After drying under vacuum, 440 mg (42 %) of PMPC-Pg polymer was obtained in the form of a pink powder. The number-average molecular weight (Mn) and dispersity (Ð) were 18.5 kg·mol−1 and 1.06, respectively. The molar content of the dithiobenzoate (DTB) end groups was 41.3 µmol·g−1, which corresponds to an average number of 1.0 DTB groups per polymer chain. The DTB end groups were subsequently converted to isobutyronitrole (IBN) or thiazolidine-2-thione (TT) groups by reacting PMPC-Pg (216.2 mg, 11.7 µmol DTB groups) with AIBN (38.4 mg, 234.0 µmol) or ACVA-TT (112.8 mg, 234.0 µmol) at 80 °C for 3 h in DMSO (0.432 mL) in a sealed pressure tube. After cooling to r.t., the reaction mixtures were purified by column chromatography using a Sephadex LH-20 cartridge in a methanol-DMSO mixture. The purified polymers were isolated by precipitation into acetone (40 mL). The solid contents were centrifuged and dried under vacuum to give 148.0 mg of IBN-PMPC-Pg and 132.0 mg of TT-PMPC-Pg as a white and a yellow powder, respectively. The Mn and Ð of IBN-PMPC-Pg were 18.8 kg mol−1 and 1.06, respectively; the Mn and Ð of TT-PMPC-Pg were 19.0 kg·mol−1 and 1.06, respectively. The molar content of the TT end groups was 42.1 µmol·g−1, which corresponds to an average number of 0.8 TT groups per polymer chain. 31P NMR (162 MHz, D2O): δ 0.16 (s) ppm. The reaction scheme for the preparation of polymer (2a) is shown in Scheme 8. (2b) Poly(amidoamine)-graft-poly(2-methacryloyloxyethyl phosphorylcholine) (PAMAM-g-PMPC) star copolymer, with terminal Pg groups and amide bonds between the PAMAM core and the PMPC arms, was prepared by reacting a two-fold molar excess of the PAMAM dendrimer (G5) with the TT-PMPC-Pg polymer as follows: TT-PMPC-Pg (83.1 mg, 3.5 µmol) was dissolved in dry methanol (0.830 mL) and mixed with 31.3 µL of stock solution (50 mg·mL−1) from PAMAM (1.6 mg, 7.0 µmol) in methanol. The solution was stirred overnight at r.t. The reaction mixture was precipitated into diethyl ether and the solid content then dissolved in PBS buffer (0.15 M, pH 7.4). The resulting star copolymer was separated from the unreacted linear polymer by membrane filtration using RC centrifugal filter units with MWCO 100 kDa in PBS (4×) and in H2O (2×). The purified star copolymer (PAMAM-g-PMPC-Pg) was isolated from the aqueous solution by lyophilization to give 50 mg of white powder. The Mn and Ð were 398.8 kg·mol−1 and 1.10, respectively. 31P NMR (162 MHz, D2O): δ 0.16 (s) ppm. The reaction scheme for the preparation of polymer (2b) is shown in Scheme 9. (2c) Hexakis [4-(2-aminoethyl)phenoxy]cyclotriphosphazene-graft-poly(2-methacryloyloxyethyl phosphorylcholine) (CTP-g-PMPC) star copolymer with terminal Pg groups was synthesized by polymerizing MPC in the presence of hexavalent RAFT agent CTP-(CTA-COOH)6 as follows: A mixture of CTA-(CPADTB)6 (12.6 mg, 5.7 µmol) and AIBN (2.5 mg, 15.7 µmol) was dissolved in DMAc (1.5 mL) and then added to a solution of MPC (200.0 mg, 0.68 mmol) in methanol (5.3 mL). The solution was thoroughly bubbled with argon and polymerized in a sealed glass ampoule at 70 °C for 16 h. The solution was precipitated into acetone (100 mL) to give 132.6 mg of crude polymer as a pink powder. The polymer was dissolved in DMAc (1.3 mL) followed by the addition of 15.7 mg of ACVA-Pg; the mixture was then allowed to react at 80 °C for 3 h in a sealed glass ampoule. The polymer was isolated by precipitation into acetone (40 mL), with the solid content dissolved in PBS buffer (0.15 M, pH 7.4). The crude product was purified by membrane filtration using RC centrifugal filter units with MWCO 100 kDa in PBS (4×) and in H2O (2×). The purified star polymer (CTP-g-PMPC-Pg) was isolated from the aqueous solution by lyophilization to give 93.0 mg of a white powder. The Mn, and Ð were 58.6 kg·mol−1 and 1.10, respectively. 31P NMR (162 MHz, D2O): δ 0.16 (s) ppm. The reaction scheme for the preparation of polymer (2c) is shown in Scheme 10. (2d) Poly(ethyl ethylenephosphate) (PEEP) linear polymer with a terminal Pg group was synthesized according to a modified procedure described elsewhere [1]. Briefly, 2-ethoxy-2-oxo-1,3,2-dioxaphospholane (214.0 mg, 1.4 mmol) was introduced via cannula into a flame-dried glass vial under an argon atmosphere. Dry toluene (160 μL) and a solution of 2-propyn-1-ol in dry toluene (0.8 mg, 120 μL) were sequentially added and the reaction mixture subsequently cooled to 0 °C. The polymerization was initiated with the rapid addition of solution of DBU in dry toluene (10.7 mg, 120 μL). After 17 h at 0 °C, the polymerization was terminated by adding excess acetic acid (15 μL). The resulting polymer was obtained by precipitating the reaction mixture into diethyl ether (10 mL). After decanting the liquid layer, the precipitate was dried under vacuum to give 180 mg of PEEP-Pg polymer as a transparent viscous liquid. The Mn and Ð of PEEP-Pg were 3.7 kg·mol−1 and 1.03, respectively. 31P NMR (162 MHz, D2O): δ -0.06 (s) ppm. The reaction scheme for the preparation of polymer (2d) is shown in Scheme 11. (2e) Poly[bis(2-(2-(2-methoxyethoxy)ethoxy)ethoxy)]phosphazene (PMEEEP) linear polymer with a terminal vinyl (vi) group was synthesized by post-polymerization modification of a poly(dichlorophosphazene) precursor with 2-(2-(2-ethoxyethoxy)ethoxy)ethanol as follows: Firstly, a solution of 4-(dichlorodiphenylphosphino)styrene (0.1 mmol) in dry DCM (1.5 mL) was added dropwise to trichloro(trimethylsilyl)phosphoranimine (5.0 mmol) in dry toluene (20 mL) at 0 °C. The mixture was heated to r.t. and stirred for another 21 h. The solution was then filtered and concentrated. The prepared poly(dichlorophosphazene) precursor was re-dissolved in dry diglyme (6.27 mL) and stored in a freezer. Secondly, 2-(2-(2-methoxyethoxy)ethoxy)ethanol (2.5 mmol, 0.4 mL) was added portion-wise to a suspension of sodium hydride (2.5 mmol, 63.0 mg) in dry THF (4 mL) and the mixture then stirred at r.t. for 1 h. To this mixture, poly(dichlorophospahzene) (100 mg·mL−1 in diglyme, 0.5 mL) was added dropwise and stirred at r.t. for 17 h. The reaction was quenched by the addition of water (2 mL), with volatiles evaporated under reduced pressure. The crude polymer was purified by column chromatography using a Sephadex G-25 cartridge in water. The purified PMEEEP-Vi polymer was obtained from the aqueous solution by lyophilization to give 97 mg of orange oil. The Mn and Ð were 22.0 kg·mol−1 and 1.20, respectively. 31P NMR (162 MHz, D2O): δ -6.09 (s) ppm. The reaction scheme for the preparation of polymer (2d) is shown in Scheme 12. ## 4.1. UV–Vis Spectrophotometry Spectrophotometric analysis of functionalized linear polymers was carried out in quartz glass cuvettes on the SPECORD PLUS UV–Vis spectrophotometer (Analytik Jena, Jena, Germany). The molar content of terminal DTB and TT groups in polymers was determined at 302 and 305 nm, respectively, in methanol using molar absorption coefficients of 12 100 and 10 300 L·mol−1·cm−1, respectively. The functionality (f) of the polymer (i.e., the average number of functional end groups per polymer chain) was calculated as the ratio of Mn of the polymer determined by GPC to Mn of the polymer determined by end group analysis using UV–Vis spectrophotometry. ## 4.2. Size-exclusion Chromatography The number- and weight-averages of molecular weights (Mn and Mw) and dispersities (Ð, Ð = Mw/Mn) for the polymer probes were determined by size-exclusion chromatography (SEC) on an HPLC system (Shimadzu, Kyoto, Japan) equipped with an internal UV–Vis diode array detector (SPD-M20A), an external differential refractometer (Optilab T-rEX, Santa Barbara, CA, USA), and a multiangle light scattering detector (DAWN HELEOS II, both Wyatt Technology, Santa Barbara, CA, USA). TSKgel SuperAW3000 and SuperAW4000 columns (Tosoh Bioscience, USA) connected in series were used to analyze samples in the mobile phase of 80 % methanol and 20 % sodium acetate buffer (0.3 m, pH 6.5) at a flow rate of 0.6 mL·min−1. The dn/dc values of 0.125, 0.104, and 0.120 mL·g−1 were used to calculate the molecular weights of the PMPC, PEEP, and PMEEEP polymers, respectively. ## 4.3. High-performance Liquid Chromatography The purity of low-molecular-weight compounds was verified on a high-performance liquid chromatography (HPLC) system (Shimadzu, Kyoto, Japan) equipped with an internal UV–Vis diode array (SPD-M20A) and ELSD (LTII) detectors using the Chromolith HighResolution RP-18e reverse-phase column (Merck, Rahway, NJ, USA), with a linear gradient (0-$100\%$) of a water–acetonitrile mixture containing $0.1\%$ TFA at a flow rate of 2.5 mL·min−1. ## 4.4. Nuclear Magnetic Resonance The structures of low-molecular-weight compounds were investigated by 1H and 31P nuclear magnetic resonance (NMR) spectroscopy in deuterated solvents on a Bruker DPX spectrometer (Bruker, Billerica, MA, USA) operating at 300.1 MHz. 1H NMR spectra were calibrated to the signal from the internal standard tetramethylsilane (δ = 0.00 MHz); 31P NMR were externaly referenced to the H3PO4 signal (δ = 0.00 MHz) shortly before the spectra were collected. ## 4.5. Magnetic Resonance Spectroscopy, Imaging and Relaxometry MR characterization of the polymer probes was performed in aqueous solutions at a normalized phosphorus concentration of 100 mmol·L−1 using a 4.7 T scanner (Bruker BioSpin, Ettlingen, Germany) and a homemade dual 1H/31P radiofrequency (RF) surface coil as previously described [22]. 31P MR spectroscopy measurements were performed using single-pulse sequences (flip angle FA = 90°, repetition time TR = 200 ms) with a varying number of averages corresponding to the acquisition time AT = 6 min to 1 h. Spectra were processed in Matlab software (Matlab v7.5.0.342, MathWorks, Natick, MA, USA) and quantified by comparing signal-to-noise ratio (SNR). 31P MR images were obtained using a chemical shift imaging (CSI) sequence (FA = 90°, TR = 500 ms, AT = 15 min to 3 h, field of view FOV = 36 mm3; resolution 2.25 × 2.25 × 5.8 mm3). 31P MRI SNR was calculated using SNR = 0.655 · S · σ−1, where S is signal intensity in the region of interest (ROI), σ is the standard deviation of background noise, and constant 0.655 reflects the Rician distribution of background noise in a magnitude MR image. The chemical shift of PTMPC relative to the reference PMPC was measured using a single-pulse sequence with the bandwidth covering the spectra of both polymers (16 181.2 Hz: 199.38 ppm). 31P relaxometry was performed using spectroscopy sequences; the 31P T1 relaxation time was measured using 10 spectroscopic single-pulse sequences with varying repetition times (TR = 200–4000 ms) and the 31P T2 relaxation time was measured using 10 spectroscopic Carr–Purcell–Meiboom–Gill (CPMG) sequences with varying echo times (TR = 5000 ms, TE = 20–1600 ms). Data were quantified by plotting amplitudes and fitting the appropriate curve (S ≈ S0 · (1 − e−t/T1) for T1; S ≈ S0 · e−t/T2 for T2), where S is signal intensity (S0 signal intensity at equilibrium) and t is time: TR for T1 and TE for T2, respectively. ## 5. Conclusions In summary, we synthesized, and compared the MR properties of five distinct water-soluble biocompatible phosphopolymers (differing in composition, structure, and molecular weight) to evaluate their use as probes in 31P magnetic resonance imaging. Specifically, we used controlled polymerization techniques to prepare highly defined materials based on linear poly(2-methacryloyloxyethyl phosphorylcholine) (PMPC), poly(ethyl ethylenephosphate) (PEEP), and poly[bis(2-(2-(2-methoxyethoxy)ethoxy)ethoxy)]phosphazene (PMEEEP) as well as star-shaped copolymers composed of PMPC arms grafted onto poly(amidoamine) dendrimer (PAMAM-g-PMPC) or cyclotriphosphazene-derived cores (CTP-g-PMPC) with a molecular weight ranging from ~4–400 kg·mol−1. In phantom experiments, all polymer probes were detected at the magnetic fields close to those used in clinical practice. These novel probes provided satisfactory SNR values based on both 31P MR spectroscopy (209.8–12.1) and imaging (10.4–1.1). Linear PMPC and PMEEEP achieved the best results followed by star PAMAM-g-PMPC, CTP-g-PMPC, and linear PEEP. The 31P T1 and T2 relaxation times of the polymer probes were within a range of 1078–2368 ms and 30–171 ms, respectively, ensuring efficient detection of MR signals. The synthesized phosphopolymers offer great promise as biocompatible, structurally tunable, and long-circulating probes for in vivo 31P MRI. In addition, due to the presence of functional groups at the ends of the polymer chains, it is possible to introduce molecules with biological and/or contrasting functions (i.e., targeting units, fluorescent labels, drugs) into their structures, reflecting their therapeutic as well as diagnostic importance. ## Figures, Schemes and Table Figure 1: Chemical structures of 31P MR probes based on linear and star-shaped MPC-based polymers: (2a) IBN-PMPC-Pg, (2b) PAMAM-g-PMPC-Pg, and (2c) CTP-g-PMPC-Pg. Figure 2: Chemical structures of 31P MR probes based on linear PPE and PPP polymers: (2d) PEEP-Pg and (2e) PMEEEP-Vi. Figure 3: 31P MR spectra of the PMPC probe and the reference PCr. The blue line represents the signal from two phantoms containing the PMPC and PCr, while the red line represents the signal from the reference PCr only. Scheme 1: Reaction scheme for the synthesis of 2-ethoxy-2-oxo-1,3,2-dioxaphospholane (1a). Scheme 2: Reaction scheme for the synthesis of trichloro(trimethylsilyl)phosphoranimine (1b). Scheme 3: Reaction scheme for the synthesis of 4-(dichlorodiphenyphosphino)styrene (1c). Scheme 4: Reaction scheme for the synthesis of 4-cyano-4-(1-cyano-3-ethynylcarbamoyl-1-methylpropylazo)-N-ethynyl-4-methylbutyramide (1d). Scheme 5: Reaction scheme for the synthesis of 2-[1-cyano-1-methyl-4-oxo-4-(2-thioxo-thiazolidin-3-yl)-butylazo]-2-methyl-5-oxo-5-(2-thioxothiazolidin-3-yl)-pentanenitrile (1e). Scheme 6: Reaction scheme for the synthesis of 2-cyano-5-oxo-5-[(prop-2-yn-1-yl)amino]pentan-2-yl benzenecarbodithioate (1f). Scheme 7: Reaction scheme for the synthesis of CTP-(DTB)6 (1g). Scheme 8: Reaction scheme for the synthesis of poly(2-methacryloyloxyethyl phosphorylcholine) linear polymer with a terminal propargyl group (2a). Scheme 9: Reaction scheme for the synthesis of poly(amidoamine)-graft-poly(2-methacryloyloxyethyl phosphorylcholine) star copolymer with terminal Pg groups (2b). Scheme 10: Reaction scheme for the synthesis of hexakis[4-(2-aminoethyl)phenoxy]cyclotriphosphazene-graft-poly(2-methacryloyloxyethyl phosphorylcholine) star copolymer with terminal Pg groups (2c). Scheme 11: Reaction scheme for the synthesis of poly(ethyl ethylenephosphate) linear polymer with a terminal Pg group (2d). Scheme 12: Reaction scheme for the synthesis of poly[bis(2-(2-(2-methoxyethoxy)ethoxy)ethoxy)]phosphazene linear polymer with a terminal vinyl group (2e). 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--- title: 'Body Mass Index Measured Repeatedly over 42 Years as a Risk Factor for Ischemic Stroke: The HUNT Study' authors: - Jens W. Horn - Tingting Feng - Bjørn Mørkedal - Dagfinn Aune - Linn Beate Strand - Julie Horn - Kenneth J. Mukamal - Imre Janszky journal: Nutrients year: 2023 pmcid: PMC10005195 doi: 10.3390/nu15051232 license: CC BY 4.0 --- # Body Mass Index Measured Repeatedly over 42 Years as a Risk Factor for Ischemic Stroke: The HUNT Study ## Abstract Background: Higher BMI in middle age is associated with ischemic stroke, but little is known about BMI over adulthood, and the risk for ischemic stroke as most studies relied on a single measurement of BMI. Methods: BMI was measured four times over a period of 42 years. We calculated average BMI values and group-based trajectory models and related these to the prospective risk of ischemic stroke after the last examination in Cox models with a follow-up time of 12 years. Results: A total of 14,139 participants, with a mean age of 65.2 years and $55.4\%$ women, had information on BMI from all four examinations, and we observed 856 ischemic strokes. People with overweight and obesity over adulthood had a higher risk for ischemic stroke with a multivariable-adjusted hazard ratio of 1.29 ($95\%$ CI 1.11−1.48) and 1.27 ($95\%$ CI 0.96−1.67), respectively, when compared to normal weight participants. Excess weight tended to have stronger effects earlier than later in life. A trajectory of developing obesity throughout life was associated with higher risk than other trajectories. Conclusions: High average BMI, especially at an early age, is a risk factor for ischemic stroke. Early weight control and long-term weight reduction for those with high BMI may decrease the later occurrence of ischemic stroke. ## 1. Introduction The age-standardized incidence and mortality rate of ischemic stroke have declined during the last several decades due to improved prevention and therapy [1,2,3]. Nonetheless, ischemic stroke is still the second most frequent cause of death [4] and the second most common cause of disability worldwide [5]. Due to the increasing age of the global population, the total number of ischemic strokes is projected to increase in the future [4]. Identification and treatment of modifiable risk factors are therefore important to improve primary prevention. While high income countries have shown impressive reductions in blood pressure and cholesterol levels, the intertwined epidemics of diabetes mellitus, overweight and obesity have worsened [2]. The prevalence of people with obesity is 14–$32\%$ in different European countries and $34\%$ in the USA [6], and it is the fastest-growing risk factor for stroke worldwide [4,7]. Previous studies have suggested that body mass index (BMI) has a dose-dependent, J-shaped association with the risk for ischemic stroke [2,8,9]. The great majority of these studies relied on a single measurement of BMI, despite dynamic changes in body weight over the lifetime [10]. Few studies have examined repeated assessments of BMI in relation to ischemic stroke risk over decades, often using self-reported body weights, and their results are conflicting [3,10,11]. Self-reported current body weight and height can be accurate, however, recalled information on body weight, especially from decades earlier, is prone to misclassification, and its accuracy depends on several characteristics, including the actual values of body weight. Thus, it may cause considerable bias [12]. This study was the first to examine the prospective associations between BMI measured repeatedly over four decades in adulthood and the risk of ischemic stroke in a large population-based study. ## 2.1. Study Population The Trøndelag Health Study (HUNT) is an ongoing longitudinal population-based study with several waves to which all inhabitants 20 years and older from North Trøndelag county were invited. The relevant waves for this work were HUNT1, conducted in 1984–1986, HUNT2, conducted in 1995–1997, and HUNT3, conducted in 2006–2008. Apart from these three phases of the HUNT study, mandatory tuberculosis screening program where height and weight were measured was conducted in the region between 1966 and 1969 that included all inhabitants older than 15 years of age [13]. In HUNT3, which was regarded as the baseline for the present study, 93,860 residents were invited to participate. Of those invited, $54\%$ [50,801] answered the questionnaires and underwent clinical examinations [14,15]. However, for the most relevant age groups, i.e., among those with high risk for stroke, this number was much higher: $71.1\%$ and $66.8\%$ for those 60–69 and 70–79 years old, respectively [15]. Non-participation studies showed that the main reasons for non-participation in HUNT were “being very busy, forgetting the invitation or not being interested” and that non-participants had somewhat lower socioeconomic status and higher mortality than participants [16]. Height and weight were measured in 48,871 HUNT3 participants ($96\%$). We excluded 1532 participants with a diagnosis of ischemic stroke (self-reported or indicated in hospital records) prior to study entry (i.e., baseline measurement at HUNT3). Of the HUNT3 participants who did not have an ischemic stroke before baseline and who had an available BMI at baseline, 15,348 participants had information on BMI from all previous measurements, i.e., the tuberculosis screening, HUNT1 and HUNT2. Out of these, we excluded 1209 participants due to a lack of baseline information on covariates (marital and smoking status, alcohol consumption, physical activity and education), and accordingly, 14,139 participants were included in the final sample for the main analysis (Figure S1). ## 2.2. Exposure Assessment Weight and height measurement, calculation of average BMI and weight change. At each of the four assessments, weight and height were measured to the nearest centimeter or nearest half kilogram while participants were wearing light clothes without shoes. BMI was calculated as body weight in kilograms divided by the square of height in meters and categorized into four groups consistent with World Health Organization cut points: <18.5 kg/m2 (underweight), 18.5–24.9 kg/m2 (normal weight), 25–29.9 kg/m2 (overweight) and ≥30 kg/m2 (obese). Body weight and height measurements for individuals who were adolescents at the time of the tuberculosis survey were converted to adult BMI categories using internationally recognized cut points [17,18]. We denoted the BMI values at the four time points here as BMI67 (obtained from the tuberculosis screening program conducted in 1966–1969), BMI85 (HUNT1 conducted in 1984–86), BMI96 (HUNT2 conducted in 1995–1997) and BMI07 (HUNT3 conducted in 2006–2008). We estimated the average BMI from BMI67 to the end of the follow-up period by taking the different follow-up times due to censoring into account. Therefore, we updated the time and the average BMI after baseline every other year (See the graphical presentation of the study setup and the formula in Figure 1). By doing so, the length of the follow-up period had less influence on the average BMI, and censored participants became more comparable to the uncensored participants. The equation for calculating average BMI from BMI67 to the end of follow up by repeated updated exposure time after baseline was:Average BMI67-EOF = ((BMI67 × timeI-II) + (BMI85 × timeII-III) + (BMI96 × timeIII-IV) + (BMI07 × timeIV-timeEOF))/timeI-EOF where: timeI-II = time from measurement I (i.e., in 1966–1969) to measurement II (i.e., in 1984–1986). timeII-III = time from measurement II to measurement III (i.e., in 1995–1997). timeIII-IV = time from measurement III to measurement IV (i.e., 2006–2008). timeIV-EOF = time from measurement IV to the end of follow-up (i.e., 2006–15 September 2019, updated every other year). timeI-EOF = time from measurement I to the end of follow-up (updated every other year). When the average BMI includes the BMI from HUNT3, we have highly correlated independent variables in our statistical models. Therefore, to address multicollinearity in the analysis of whether past or recent BMI was a more important risk factor of stroke, we also calculated average BMI only prior to baseline (HUNT3 2006–2008):Average BMI67-0 = ((BMI67 × timeI-II) + (BMI85 × timeII-III) + (BMI96 × timeIII-IV))/timeI-IV where: timeI-IV= time from measurement I (i.e., in 1966–1969) to measurement IV (i.e., in 2006–2008). We calculated the total BMI change from BMI67 to BMI07 by the following equation:Total BMI change67-07 = (((BMI85 − BMI67) × timeI-II) + ((BMI96 − BMI85) × timeII-III) + (BMI07 − BMI96) × timeIII-IV))/timeI-IV After calculating “Total BMI change” (BMI07 − BMI67), we calculated BMI change in the “Early period” (BMI85 − BMI67), the “Middle period” (BMI96 − BMI85) and in the “Late period” (BMI07 − BMI96). We classified these BMI changes in 3 categories: <−2.5 kg/m2, ≥−2.5 to <2.5 kg/m2 and ≥2.5 kg/m2. We used Stata Traj Plugin for identifying group-based trajectories. [ 19]. More detailed information about group-based trajectory modeling is provided in the Supplemental Material (File S1, Tables S5–S7). ## 2.3. Outcome Assessment Ischemic stroke diagnoses were retrieved from the electronic patient administrative systems of all hospitals providing acute medical care in Trøndelag (Levanger Hospital, Namsos Hospital and St. Olav`s Trondheim University Hospital). We used the International Classification of Disease (ICD) Ninth Revision code 433 and 434, and ICD Tenth Revision code I.63 to identify ischemic strokes from September 1987 (after the start of the electronic administrative system) to 15 September 2019 (timepoint of data extraction). The ICD codes for ischemic stroke have been found to be of high validity within the HUNT cohort [20,21]. ## 2.4. Covariates Information on covariates was retrieved from the HUNT3 questionnaires or clinical examination. Participants self-reported their marital status (unmarried, married/cohabitant, widowed/divorced/separated), smoking status (never, former, occasional, current), and alcohol consumption (abstainer, light drinker, moderate drinker and heavy drinker based on the self-reported type, amount and frequency of consumption of different alcoholic beverages). Participants reporting ≥3 h of light exercise or ≥ one hour of hard exercise per week were regarded as physically active. Based on self-reported occupation, we derived educational level according to the International Standard Classification of Occupation from Statistics Norway and categorized it into: “lower secondary school level” (10 years of schooling or less), “higher secondary school level” (10–12 years of schooling) and “tertiary education” (any fulfilled education at university or college) [22]. For 690 participants with missing information on occupation in HUNT3, we retrieved information on educational level from HUNT2 questionnaires. We also calculated the number of self-reported common chronic diseases from the following list: kidney diseases, chronic obstructive lung disease, heart failure, asthma, obstructive sleep apnea, psoriasis, cancer, epilepsy, rheumatoid arthritis, ankylosing spondylitis, sarcoidosis, osteoporosis, osteoarthritis, fibromyalgia, chronic physical pain, hyper- and hypothyroidism. All physical examinations were performed at local field centers by trained staff. Blood pressure was measured three times in a sitting position after a short rest with a Dinamap Critikon model 8100. Hypertension was defined as systolic blood pressure >130 mmHg or diastolic blood pressure >90 mmHg or as self-reported use of blood pressure medication. Non-fasting blood samples were analyzed for glucose, triglycerides and high-density lipoprotein cholesterol (HDL-C). Diabetes was defined based on self-report or on non-fasting serum glucose >11.1 mmol/L. ## 2.5. Statistical Analysis Baseline characteristics were presented for continuous variables as mean ± standard deviation (SD) and for categorical ones as percentages. For the main analysis, we used Cox proportional hazard models where age was the underlying time scale to calculate hazard ratios (HRs) with $95\%$ confidence intervals (CIs) for first-ever ischemic stroke for given categories of average BMI, with normal weight being the reference group. Participants contributed person-time from the date of HUNT3 participation until the date of first stroke diagnosis, date of death, emigration, or end of follow-up (15 September 2019), whichever occurred first. To calculate the average BMI to the end of follow-up, we updated the time since baseline every second year by the "stsplit" function in STATA. The proportional hazard assumption for each covariate was tested by -ln-ln survival curves and Schoenfeld residuals. We found no violations of the proportional assumption. Confounders were chosen by a priori knowledge about their relationships to the exposure and outcome. All models were adjusted for age and sex (model 1). In model 2, we further adjusted for smoking status, education, marital status, physical activity, alcohol consumption, and common chronic diseases. In a further model, we additionally adjusted for potential mediators such as hypertension, diabetes, HDL-C and triglycerides. To address the relative importance of the recent and past BMI in predicting ischemic stroke risk, we also adjusted for BMI at HUNT3 (baseline). In a sensitivity analysis, we stratified for sex and age at HUNT3 (<65/≥65 years) or smoking (never/former, current or occasionally smoker). We identified three groups based on the trajectories of BMI development, and these groups were also included in our Cox models with consistent normal weight as the reference group. In another analysis, we included a change in BMI over the entire follow-up period and in discrete time periods in the Cox models. These analyses were adjusted for confounders from model 2 and the regression slope of BMI from BMI67 to BMI07 to account for increasing BMI over time. Change in BMI was categorized into three groups, where the group with no essential change of BMI (≥−2.5 to <2.5 kg/m2) was the reference. We performed cubic splines for a graphical presentation of the hazard ratios for ischemic stroke in relation to BMI07 (i.e., measured at HUNT3) and average BMI from tuberculosis screening to the end of follow-up with 4 knots. For cubic splines of BMI at HUNT3, we adjusted for confounders from model 2 and, additionally, in a separate model, for the average BMI from tuberculosis screening to HUNT3. For cubic splines of the average BMI, we adjusted for covariates in model 2 and additionally for BMI at HUNT3. All statistical analysis was conducted using Stata 17 for Windows (StataCorp LP, College Station, TX, USA). The codes are available from the corresponding author upon reasonable request. The study was approved by the regional committee for ethics in medical research ($\frac{2016}{542}$REC Central), the National Directorate of Health, and the Norwegian Data Inspectorate. All HUNT study participants provided informed consent. We followed the STROBE guideline for prospective cohort studies. ## 3. Results Of 14,139 participants at the HUNT3 visit, $55.4\%$ were female, the mean age was 65.2 years (SD 9.3 years), $15.4\%$ were current smokers, and $20.1\%$ were physically inactive. Mean BMI increased progressively over time from 23.2 kg/m2 (SD 3.1) at tuberculosis screening program to 27.6 kg/m2 (SD 4.2) at HUNT3 (Table 1). The excluded population primarily lacked information on BMI from the tuberculosis screening ($86.5\%$) and was younger and consequently had a lower blood pressure, lower prevalence of diabetes and smoking, a more favorable lipid profile, and better education. During the follow-up period of 12 years and 152,843 person-years, a total of 856 participants suffered a first ischemic stroke. ## 3.1. Body Mass Index and Risk for Ischemic Stroke With both approaches to calculating average BMI, the risk of ischemic stroke increased with increasing average BMI (Table 2). The HRs were comparable, but the estimates were slightly higher when we calculated average BMI to the baseline. When we adjusted for BMI at HUNT3, the estimates for overweight and obesity were somewhat attenuated when the average BMI was calculated to the end of the follow-up period but was nearly unchanged when the average BMI was calculated to the baseline. Adjustment for metabolic consequences of obesity also attenuated the association between obesity and ischemic stroke risk (Table S1). Figure 2A shows the association of the most recent BMI, i.e., measured at HUNT 3, with risk for ischemic stroke. The risk increased steadily with BMI values higher than 27 kg/m2. When we adjusted for the average BMI until the last measurement at HUNT3, the elevated risk with higher BMI values disappeared (Figure 2C). Using average BMI from tuberculosis screening to the end of follow-up, we observed a similar association as when we used BMI from HUNT3 (Figure 2B). Adjustment for BMI from HUNT3 had little effect on the association (Figure 2D). When we examined the risk associated with BMI from each period of assessment separately, the multivariable-adjusted HRs were highest for obese and overweight participants at the tuberculosis screening and HUNT1 and attenuated gradually for both BMI groups at later HUNT examinations (Table S4). We identified three distinct BMI trajectories based on group-based trajectory modeling and referred to these groups as consistently normal weight, developing overweight and developing obesity, representing $48.8\%$ ($$n = 6900$$, events= 345), $43.4\%$ ($$n = 6149$$, events = 425) and $7.8\%$ ($$n = 1090$$, events = 86) of the participants, respectively (Figure 3). In all these categories, the BMI increased over time. The participants in the "developing obesity group” had higher blood pressure, higher prevalence of diabetes, a slightly less favorable lipid profile and reported less physical activity compared to participants in the two other groups (Table S2). The multivariable-adjusted HR for ischemic stroke was higher for the developing overweight and developing obesity group compared to the consistently normal weight group, an association that was attenuated when adjusting for established metabolic consequences of obesity. Additional adjustments for the most recent BMI had no effect on the estimates. High BMI tended to have a stronger association with stroke among men than among women. In contrast, age and smoking status did not modify the association with the risk of ischemic stroke (Table S3). ## 3.2. Change in Body Mass Index and Risk for Ischemic Stroke When we examined BMI changes over the whole period and at early, middle and late periods separately, we found $34\%$ higher risk ($95\%$ CI 3–$75\%$) of ischemic stroke for weight loss in the late period. Other weight changes were not associated with risk for ischemic stroke, although in some of the categories, our statistical power was limited (Table S4). ## 4. Discussion In this large population-based study, we observed a higher risk for ischemic stroke among participants with obesity (BMI >30 kg/m2) or overweight (BMI 25–30 kg/m2) over adulthood. The associations of excess weight with risk were particularly evident for obesity earlier rather than later in life. In addition, trajectory analysis found that developing overweight or obesity over time was associated with higher risk. Our findings regarding body weight at baseline and risk of subsequent ischemic stroke are generally in line with previous studies, although the association was somewhat weaker in the present study than in previous ones [2,8,9]. Compared to normal weight participants, obese participants at baseline were at $17\%$ higher risk for ischemic stroke in our comprehensive multivariable-adjusted model. In a meta-analysis, however, the corresponding pooled estimate was 1.64 ($95\%$ CI 1.36–1.99) in the unadjusted model, and a subgroup analysis of multivariable-adjusted studies had a pooled risk ratio of 1.50 ($95\%$ CI 1.34–1.67) [9]. There are several differences between our study and this large meta-analysis of studies, including various races and applying different statistical adjustments. Most importantly, our study population was considerably older than participants in most of the studies included in the meta-analysis. The relative risk for cardiovascular disorders can markedly decrease with increasing age [23], and paradoxically overweight can even have an inverse association at very old ages [24]. Our study uniquely included anthropometric information based on actual measurements performed decades before baseline. Previous studies that incorporated long-term information relied partly on self-reported BMI [3,10,11]. However, recalled BMI is potentially biased as its accuracy depends on actual body weight in the past [12]. In this study, a high average BMI was associated with increased ischemic stroke risk, and this association was not explained by having a high BMI at baseline (HUNT3) since adjusting for the most recent BMI from HUNT3 virtually had no effect on the estimates. In contrast, the association with BMI from HUNT3 was essentially eliminated by adjustment for earlier BMI. Our findings thus support the significance of early adulthood weight gain and the long-time exposure to excess fat tissue as a risk factor for ischemic stroke. To the best of our knowledge, no previous studies have compared the relative importance of earlier and current excess weight and risk of ischemic stroke. When analyzing BMI trajectories, all trajectory groups showed increasing weight over time, compared with earlier studies [10,25]. The "developing obesity" group had the highest risk for ischemic stroke. Earlier studies using recalled BMI generally came to the same conclusion [3,10]. Of note, trajectory analysis suggested the presence of three predominant trajectories, which effectively represented stable degrees of normal weight, overweight, or obesity. This observation tends to align with the relative lack of power observed for most categories of weight change. In line with most previous studies, we observed a higher risk for ischemic stroke among participants in middle age with recent weight loss but no association with recent weight gain [3,26,27]. Similarly to other observational studies, we could not differentiate between voluntary and involuntary weight loss. Consequently, the adverse association of weight loss with stroke risk might well reflect confounding by comorbidities leading to both weight loss and elevated stroke risk. Studies on dramatic voluntary weight loss, i.e., after bariatric surgery, documented clear clinical benefits regarding cardiovascular diseases, hypertension, hyperlipidemia, and diabetes [28]. Ischemic stroke is typically due to large and/or small vessel diseases or due to cardio-embolic events. Excess weight can affect these different etiologies via several mechanisms, including hypertension, diabetes mellitus, low HDL-C and increased triglycerides [2], which can, to different degrees, increase the risk of the different ischemic stroke subtypes [29]. Both long-lasting and short-term excess of body fat are important risk factors for diabetes mellitus [10], but only long-lasting excess body fat is associated with the development of hypertension and hypercholesterolemia. Since hypertension is the single most important risk factor for ischemic stroke, the strong association of hypertension with lifelong adiposity might explain the differential importance of current and earlier excess weight observed in the present study [30,31]. Apart from these well-known metabolic consequences, additional pathways may mediate the effect of increased fat tissue on the risk of an ischemic stroke. Fat tissue, especially visceral, is endocrinologically active and may contribute to increased systemic inflammation and reduce the excretion of adiponectin, which in turn can also facilitate insulin resistance, arteriosclerosis and endothelial dysfunction [32]. Atrial fibrillation, a major cause of cardio-embolic stroke, is also known to be influenced by long-term obesity partly via increased pericardial fat [33,34]. Obesity increases the risk of obstructive sleep apnea, which can increase the risk of ischemic stroke. The pathophysiological pathways are complex, and besides increased blood pressure and sympathetic activation, endothelial, inflammatory, metabolic and other mechanisms are also likely to be involved [35]. ## Strength and Limitations Nord-Trøndelag county has a homogenous and stable population with a migration of a mere $0.3\%$ per year. We had an exceptionally long exposure window before baseline to capture the development of body weight. We also had virtually complete follow-up utilizing validated local and nationwide registers of diagnoses of stroke [20,21,36]. Furthermore, we included a wide range of covariates in this highly-phenotyped population. Despite its strengths, our study also had some limitations. BMI does not differentiate between fat and muscle tissues. [ 37,38]. Due to a low number of cases, our precision was very low in some BMI groups, as reflected by the wide confidence intervals. We had no information about the use of antiplatelets, anticoagulants or lipid-lowering medications, although these are unlikely to confound our results considerably as we extensively controlled for demographic and lifestyle factors which are associated with the use of these medications. Moreover, as we emphasized above, we had no information on whether the weight loss of the participants was intentional. This makes our results on weight loss, especially on late weight loss, difficult to interpret, as many diseases can cause weight loss and might also increase the risk of ischemic stroke. We also had limited statistical power to investigate stroke risk among underweight participants as the number of participants and events was low in this group. Finally, our findings in this Norwegian cohort are not directly generalizable to other populations. ## 5. Conclusions High average BMI, especially at an early age, was a risk factor for ischemic stroke. 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--- title: Effects of Caprylic Acid and Eicosapentaenoic Acid on Lipids, Inflammatory Levels, and the JAK2/STAT3 Pathway in ABCA1-Deficient Mice and ABCA1 Knock-Down RAW264.7 Cells authors: - Xinsheng Zhang - Peng Zhang - Yinghua Liu - Zhao Liu - Qing Xu - Yong Zhang - Lu Liu - Xueyan Yang - Liya Li - Changyong Xue journal: Nutrients year: 2023 pmcid: PMC10005197 doi: 10.3390/nu15051296 license: CC BY 4.0 --- # Effects of Caprylic Acid and Eicosapentaenoic Acid on Lipids, Inflammatory Levels, and the JAK2/STAT3 Pathway in ABCA1-Deficient Mice and ABCA1 Knock-Down RAW264.7 Cells ## Abstract Our previous studies have found that caprylic acid (C8:0) can improve blood lipids and reduce inflammation levels and may be related to the upregulation of the p-JAK2/p-STAT3 pathway by ABCA1. This study aims to investigate the effects of C8:0 and eicosapentaenoic acid (EPA) on lipids, inflammatory levels, and the JAK2/STAT3 pathway in ABCA1-deficient mice (ABCA1−/−) and ABCA1 knock-down (ABCA1-KD) RAW 264.7 cells. Twenty 6-week ABCA1−/− mice were randomly divided into four groups and fed a high-fat diet, or a diet of $2\%$ C8:0, $2\%$ palmitic acid (C16:0) or $2\%$ EPA for 8 weeks, respectively. The RAW 264.7 cells were divided into the control or control + LPS group, and the ABCA1-KD RAW 264.7 cells were divided into ABCA1-KD with LPS (LPS group), ABCA1-KD with LPS + C8:0 (C8:0 group), and ABCA1-KD with LPS + EPA (EPA group). Serum lipid profiles and inflammatory levels were measured, and ABCA1 and JAK2/STAT3 mRNA and protein expressions were determined by RT-PCR and Western blot analyses, respectively. Our results showed that serum lipid and inflammatory levels increased in ABCA1−/− mice ($p \leq 0.05$). After the intervention of different fatty acids in ABCA1−/− mice, TG and TNF-α were significantly lower, while MCP-1 increased significantly in the C8:0 group ($p \leq 0.05$); however, LDL-C, TC, TNF-α, IL-6, and MCP-1 levels decreased significantly and IL-10 increased significantly in the EPA group ($p \leq 0.05$). In the aorta of ABCA1−/− mice, C8:0 significantly decreased p-STAT3 and p-JAK2 mRNA, while EPA significantly reduced TLR4 and NF-κBp65 mRNA. In the ABCA1-KD RAW 264.7 cells, TNF-α and MCP-1 were increased significantly and IL-10 and IL-1β were significantly decreased in the C8:0 group ($p \leq 0.05$). The protein expressions of ABCA1 and p-JAK2 were significantly higher, and the NF-κBp65 was significantly lower in the C8:0 and EPA groups ($p \leq 0.05$). Meanwhile, compared to the C8:0 group, the NF-κBp65 protein expression was significantly lower in the EPA group ($p \leq 0.05$). Our study showed that EPA had better effects than C8:0 on inhibiting inflammation and improving blood lipids in the absence of ABCA1. C8:0 may be involved mainly in inhibiting inflammation through upregulation of the ABCA1 and p-JAK2/p-STAT3 pathways, while EPA may be involved mainly in inhibiting inflammation through the TLR4/NF-κBp65 signaling pathway. The upregulation of the ABCA1 expression pathway by functional nutrients may provide research targets for the prevention and treatment of atherosclerosis. ## 1. Introduction Atherosclerotic cardiovascular disease (ASCVD) is one of the leading causes of mortality worldwide [1]. In particular, in low- and middle-income countries, ASCVD accounts for about $80\%$ of the disease burden [2]. Atherosclerosis (AS) is the pathological basis of ASCVD, and is closely related to various risk factors, including hyperlipidemia, hypertension, chronic inflammation, immune factors, etc. Exploring the target of inhibiting AS has always been a hot topic in the treatment of ASCVD. ATP-binding box transporter A1 (ABCA1) is a membrane protein that can promote intracellular cholesterol efflux and promote liver HDL production. Current studies have shown that ABCA1 not only promotes cholesterol reversal and reduces AS lipid deposition, but also participates in the inflammatory reaction process of AS. ABCA1 has been reported to inhibit the inflammatory response in the following ways [3,4,5]: first, ABCA1 directly activates Janus kinase 2 (JAK2) and the signal transducer and activator of transcription 3 (STAT3) downregulating downstream signaling molecules; the second is that ABCA1 indirectly inhibits the toll-like receptor-4 (TLR4) signaling pathway by promoting cholesterol efflux and reducing lipid raft in the cell membrane. These findings suggest that ABCA1 can play a direct role in cardioprotective effects by promoting cholesterol transport and inhibiting inflammation. Dietary fatty acids are considered important factors affecting the progress of AS. Studies have reported that saturated fatty acids (SFAs) can increase the level of low-density lipoprotein cholesterol (LDL-C), thus promoting the occurrence of AS, while unsaturated fatty acids can improve blood lipids and play a protective role in cardiovascular diseases, especially omega-3 polyunsaturated fatty acids (ω-3 PUFAs), such as eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) [6]. Fatty acids and inflammatory factors are closely related to the progress of AS [7]. Current studies suggest that PUFAs, such as linoleic acid (C18:2), omega-6 polyunsaturated fatty acids (ω-6 PUFAs), and ω-3 PUFAs from food sources, can reduce inflammation. Fish oil is rich in ω-3 PUFAs (DHA and EPA) and has anti-inflammatory effects. Its anti-inflammatory mechanism has been reported to be through inhibition of the TLR4 signaling pathway [8]. Medium-chain fatty acids (MCFAs), including caprylic acid (C8:0) and capric acid (C10:0), occur in milk fat, palm oil, and various feed materials [9]. MCFAs differ from long chain fatty acids (LCFAs) in digestion, absorption, and metabolism, and studies have found that MCFAs can reduce body weight and improve internal fat accumulation [10,11] and cholesterol metabolism [12,13]. Our previous experiments have confirmed that MCFAs can upregulate ABCA1 gene and protein expression in the apoE-deficient mouse liver, and C8:0 can promote cholesterol efflux in macrophages [14]. Further studies found that C8:0 could inhibit the levels of inflammatory cytokines in RAW 264.7 cells and in the serum of apoE-deficient mice, and confirmed that C8:0 could inhibit the inflammatory response based on the ABCA1-mediated JAK2/STAT3 pathway [15]. JAK2 is activated by ABCA1, undergoes autophosphorylation, and then phosphorylates its downstream target STAT3 [16], and then regulates the levels of nuclear factor kappa Bp65 (NF-κBp65), tumor necrosis factor α (TNF-α), interleukin-6 (IL-6), and monocyte chemoattractant protein-1 (MCP-1), which leads to the occurrence of AS [17]. This study aims to investigate the effects of C8:0 and EPA on lipid and inflammatory levels, and the JAK2/STAT3 pathway in ABCA1-deficient mice (ABCA1−/−) and ABCA1 knock-down (ABCA1-KD) RAW 264.7 cells, and to confirm that C8:0 plays a regulatory role in improving blood lipid and inflammation mainly through ABCA1. ## 2.1. Materials Fetal bovine serum (FBS), lipopolysaccharide (LPS), DMEM culture medium, and bovine serum albumin (BSA) were provided by Gibco (Grand Island, NE, USA). C8:0, palmitic acid (C16:0), and EPA were obtained from Sigma-Aldrich (St. Louis, MO, USA). Other reagents were available from Sigma-Aldrich. ## 2.2. Feed Configuration For feed content, we applied the same method as our previous research [18]. Briefly, based on high-fat feed, $2\%$ of the same amounts of different kinds of fatty acids were added. Intervention feeds included a high-fat diet (HFD group), and high-fat diets of $2\%$ C8:0 (C8:0 group), $2\%$ eicosapentaenoic acid (EPA group), and $2\%$ palmitic acid (C16:0 group). The feed was obtained from Beijing Huafukang Biotechnology Co., Ltd. (license No.: SCXK 2014-0008). The ingredient list and fatty acid compositions of the intervention diets are provided in Supplementary Materials (Tables S1 and S2). ## 2.3. Experimental Animals To obtain ABCA1 knockout mice, we used DBA/1-ABCA1 tm1jdm/J female mice (ABCA1 heterozygous mice) purchased from Jackson Laboratory (stock#003897, Bar Harbor, ME). Because ABCA1 homozygotes cannot reproduce and have a $50\%$ mortality rate, we used male C57BL/6J mice and heterozygous ABCA1 mice to reproduce more heterozygous mice and then obtained 20 homozygous female and male ABCA1 mice at 5 weeks of age. All animals were genetically identified prior to use to confirm that they were ABCA1−/− mice. Five animals per cage were housed in polycarbonate cages; temperature was maintained at 21–23 °C and humidity was maintained at 40–$60\%$, with a 12 h light/dark cycle. Both the ABCA1−/− mice and the same week-old C57BL/6J mice were kept on a HFD prior to intervention, and the HFD was replaced with intervention diets for 8 weeks after random allocation based on fasting weight. Fasting weight was measured weekly during this period (fasting did not limit drinking water at night prior to measurement). The bedding and drinking water of the mice were replaced every 2–3 days and the feed intake of the mice was recorded. All experimental procedures were approved by the Animal Care and Use Committee of the Chinese PLA General Hospital. ## 2.4. Preparation of Fatty Acids Fatty acids were prepared as in our previous research [15]. Briefly, the fatty acids were dissolved in a $95\%$ ethanol solution and then diluted with serum-free medium containing 20 mg/mL of BSA, with 100 mmol/L added to the culture hole and 50 ng/mL of LPS added to the culture wells. Before the cell experiment, the obtained solution was incubated at 37 °C for 1 h. ## 2.5. ABCA1-KD in RAW 264.7 Cells The RAW 264.7 cell line was obtained from the Peking Union Medical College, and the cells were cultured in DMEM with heat-inactivated FBS ($10\%$) and penicillin–streptomycin solution ($1\%$) in a humidified incubator with $95\%$ air and $5\%$ CO2 at 37 °C. RAW 264.7 cells at the logarithmic growth stage were inoculated into 6-well cell culture plates (2 × 105 cells per well) and cultured overnight in an incubator. The ABCA1-KD RAW 264.7 cells were constructed with three types of siRNA of ABCA1-1530, ABCA1-1701, and ABCA1-4931 (Supplementary Materials, Table S3). The most effective plasmids that inhibited ABCA1 were screened by RT-PCR, as shown in Figure 1. ABCA1-1701 had the strongest inhibitory level and was selected to construct a siRNA plasmid to obtain ABCA1-KD RAW 264.7 cells. Two hours before transfection, the culture medium was changed to serum-free DMEM. According to the plasmid transfection instructions, cultured RAW 264.7 cells (2 × 105 cells/well) were transfected with the ABCA1-1701 plasmid using Lipofectamine 2000 reagent (Life Technologies, Carlsbad, CA, USA). The cells were then tested with the optimal concentration of G418 (500 μg/mL) for about 3 weeks. A limited dilution method was applied to isolate and obtain the maximum number of stably transfected cells. DMEM medium containing $10\%$ fetal bovine serum was used during the experiment and then refilled with LPS medium (final concentration 100 ng/mL), and incubated for another 24 h after the addition of C8:0 or EPA to the culture medium. The ABCA1-KD RAW 264.7 cells were randomly divided into 5 groups ($$n = 5$$), including the control group (RAW 264.7 cells), ABCA1-KD group, ABCA1-KD + LPS group (LPS 100 ng/mL), ABCA1-KD + LPS + C8:0 (LPS 100 ng/mL, C8:0 100 μmol/L), and ABCA1-KD + LPS + EPA (LPS 100 ng/mL, EPA 100 μmol/L). Then the levels of interleukin-1β (IL-1β), IL-6, interleukin-10 (IL-10), TNF-α, and MCP-1 in the cell lysate were detected according to the instructions of the ELISA kit. Cell assay was repeated, and the protein expressions of ABCA1, JAK2/STAT3, p-JAK2/p-ATAT3, and NF-κBp65 were determined by Western blot analyses. ## 2.6. Serum Lipid Profiles Measurement Serum triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and LDL-C (Abcam, Cambridge, UK) were determined according to the commercial kit instructions, and HDL-C/LDL-C was calculated. ## 2.7. Inflammatory Level Measurement After 8 weeks, the mice were sacrificed by intramuscular injection of 10 mg/kg of xylazine hydrochloride, blood was drawn from the abdominal aorta and then centrifuged at 4 °C and 3000 r/min for 10 min, and serum was collected for detection. After the cell experiment, the cell lysate from each group was collected and centrifuged at 3000 r/min at 4 °C for 10 min, and the supernatant was collected to be measured. The IL-1β, IL-6, IL-10, TNF-α, and MCP-1 were determined following the instructions of the ELISA kit (R&D Systems, Minneapolis, MN, USA). ## 2.8. Real-Time PCR Analysis For RNA expression analysis, about 50 mg of aorta samples were taken, total RNA was isolated using TRIzol reagent (Omega Bio-Tek, Norcross, GA, USA), and then reverse transcription was performed using a reverse transcription kit (NEB, M-MLV kit). The reaction mixtures were incubated at 95 °C for 2 min for the initial denaturation, followed by 45 cycles of 25 °C/5 min, 50 °C/15 min, 85 °C/5 min, and 4 °C/10 min for cDNA, and then 50 °C/2 min, 95 °C/10 min, 95 °C/30 s, and 60 °C/30 s. Relative expression levels were calculated with the ΔCt method. Primers were designed using Primer Express Software v3.0 (Applied Biosystems, SAN Jose, California, USA) (Table 1). ## 2.9. Western Blot Analysis The 20 mg of mouse aorta tissue sample was extracted by protein lysis buffer and the cells were extracted with RIPA buffer (CST). Western blot analysis of mouse aorta tissue and cell samples referred to previous studies [18]. Immunoblotting for STAT3 (abcam, no.ab68153, 1:1000), JAK2 (abcam, no.ab108596, 1:1000), p-STAT3 (abcam, no.ab76315, 1:800), p-JAK2 (abcam, no.ab32101, 1:800), ABCA1 (abcam, no. Ab18180, 1:200), NF-κBp65 (abcam, no.ab32536, 1:1000), MYD88 (abcam, no.ab219413, 1:1000), TLR4 (Proteintech, no.19811-1-AP, 1:1000), and β-actin (Proteintech, no.66009-1-Ig, 1:5000) followed the procedures. The bands were visualized using a chemiluminescence detection system. ## 2.10. Statistical Analysis Based on our preliminary experiment [15], the sample size was estimated using GPower software v3.1.9.3 (Heinrich-Heine University, Germany). With power = $80\%$, α = 0.05, effect size = 0.85, the minimum sample size should be 5. In mouse experiments, the sample sizes for analysis of inflammatory levels, blood lipids, PCR, and Western blot were 5, 5, 5, and 4, respectively. The sample sizes for the analysis of inflammatory cytokines and Western blotting in cell experiments was 5 and 3, respectively. All data are expressed as mean ± standard deviation and for the detection of a significant difference ($p \leq 0.05$, two-tailed). The normality of the data was analyzed by Shapiro–Wilk test. The normal distribution data between the two groups were analyzed by Student’s t test, and the non-normal distribution data between the two groups were analyzed by Mann–Whitney U and Wilcoxon signed-rank tests. One-way analysis of variance was used to analyze the multigroup data, and Tukey–Kramer multiple comparison analysis was used to analyze the differences between groups. SPSS 28.0 (SPSS, Inc., Chicago, IL, USA) was used to analyze the research data. ## 3.1. Body Weight of ABCA1−/− Mice After 2 weeks of intervention, the fasting body weight of the EPA, C8:0, and C16:0 groups decreased significantly compared to that of the HFD group ($p \leq 0.05$) (Figure 2A). There were no significant differences in the average feed intake among all groups during the intervention period ($p \leq 0.05$) (Figure 2B). ## 3.2. Serum Lipid Profiles in ABCA1−/− Mice After 8 weeks of intervention, ABCA1−/−-HFD mice showed a marked reduction in TC, TG, and non-HDL-C ($p \leq 0.05$) (Figure 3A,B,F). Next, we analyzed the serum lipids of ABCA1−/− mice with different fatty acid HFD. The EPA group had a significantly lower level of TC than that of the HFD, C8:0, and C16:0 groups ($p \leq 0.05$) (Figure 3A). The C8:0 and C16:0 groups had a significantly lower TG level than the HFD and EPA groups (Figure 3B) and had a significantly higher HDL-C level than the EPA group ($p \leq 0.05$) (Figure 3C). The EPA group exhibited a significant decrease in serum LDL-C and non-HDL-C levels compared to those of the C8:0 and C16:0 groups ($p \leq 0.05$) (Figure 3D,F). ## 3.3. Serum Inflammatory Factors in ABCA1−/− Mice ABCA1 knockout can promote the release of different pro-inflammatory cytokines in mice. According to Figure 4, ABCA1−/−-HFD mice had significantly higher levels of IL-1β, IL-6, TNF-α, and MCP-1, and had a significantly lower level of IL-10 than those of WT-HFD mice ($p \leq 0.05$). Then we analyzed the effects of different fatty acid HFDs on serum inflammation in ABCA1−/− mice. The EPA group exhibited a significant decrease in serum IL-1β, IL-6, TNF-α, and MCP-1 and a significant increase in serum IL-10 compared to that of the C8:0, C16:0, and HFD groups ($p \leq 0.05$) (Figure 4). Although the C8: 0 group had a significantly lower level of TNF-α than that of the HFD and C16: 0 groups, it had a significantly higher level of MCP-1 than that of the HFD group ($p \leq 0.05$) (Figure 4A,D). ## 3.4. The mRNA Expression Levels of TLR4 and JAK2/STAT3 in the ABCA1−/− Mouse Aorta In ABCA1−/− mice, compared to the HFD group, the mRNA expression of TLR4 was significantly downregulated in the C8:0, EPA, and C16:0 groups ($p \leq 0.05$) (Figure 5D). EPA group mice had significantly lower TLR4 and NF-κBp65 mRNA expressions than those of the HFD and C16:0 groups, and had a significantly lower TLR4 mRNA expression than that of the C8:0 group ($p \leq 0.05$) (Figure 5D,F). ## 3.5. The Relative Protein Expression Levels of TLR4 and JAK2/STAT3 in the ABCA1−/− Mouse Aorta In ABCA1−/− mice, the C8:0 group had significantly lower expression levels of p-STAT3 and p-JAK2 than those of the HFD group ($p \leq 0.05$) (Figure 6B,C). The EPA group had a significantly lower expression level of NF-κBp65 than that of the HFD and C16:0 groups, and had a significantly lower expression level of TLR4 than that of the HFD, C16:0, and C8:0 groups ($p \leq 0.05$) (Figure 6D,F). ## 3.6. The Inflammatory Factors of ABCA1-KD RAW 264.7 Cells After RAW 264.7 cells induced by LPS, the levels of TNF-α, MCP-1, IL-6, and IL-1β were significantly increased, while the levels of IL-10 decreased significantly ($p \leq 0.05$) (Figure 7A–E). Similarly, the levels of TNF-α, MCP-1, IL-6, and IL-1β inflammatory cytokines in the ABCA1-KD + LPS group decreased significantly compared to the control group + LPS group, while the level of IL-10 increased significantly ($p \leq 0.05$) (Figure 7A–E). In ABCA1-KD RAW 264.7 cells with LSP, the EPA group exhibited a significant decrease in TNF-α, MCP-1, IL-6, and IL-1β and a significant increase in IL-10 compared to that of the LPS groups ($p \leq 0.05$) (Figure 7). In addition, the EPA group had a significantly lower level of TNF-α and MCP-1 than that of the C8: 0 group (Figure 7A,B); however, it had significantly higher levels of IL-1β and IL-10 than those of the C8: 0 group ($p \leq 0.05$) (Figure 7D,E). The C8:0 group had a significantly higher level of TNF-α than that of the LPS group (Figure 7A), while it had significantly lower levels of MCP-1, IL-6, IL-1β, and IL-10 than those of the LPS group ($p \leq 0.05$) (Figure 7B–E). ## 3.7. The Protein Expression of JAK2/STAT3 in ABCA1-KD RAW 264.7 Cells After LPS-induced RAW 264.7 cells, the ABCA1 expression was significantly lower, whereas the NF-κBp65, p-JAK2, and p-STAT3 expressions were significantly higher ($p \leq 0.05$) (Figure 8D,F,H). Additionally, after LPS-induced ABCA1-KD RAW 264.7 cells, the ABCA1, p-STAT3, and p-JAK2 expressions were significantly lower than those of the Control + LPS group, whereas the NF-κBp65 expression was significantly higher than that of the control + LPS group ($p \leq 0.05$) (Figure 8B,D,F,H). The C8:0 group had significantly higher expressions of ABCA1 and p-JAK2 than those of the ABCA1-KD + LPS group, while they had significantly lower expression of NF-κBp65 than that of the ABCA1-KD + LPS group ($p \leq 0.05$) (Figure 8B,D,F). Similarly, the EPA group had significantly higher expressions of ABCA1 and p-JAK2 than those in the ABCA1-KD + LPS group ($p \leq 0.05$) (Figure 8B,D,F), but had a significantly lower expression of NF-κBp65 than that of the C8:0 group ($p \leq 0.05$) (Figure 8F). ## 4. Discussion The present study showed that ABCA1 knockout resulted in dyslipidemia and increased inflammation in mice, which also resulted in significant fasting weight loss in the C8:0, C16:0, and EPA groups. Aiello et al. [ 19] reported that the survival rate and weight gain of ABCA1−/− mice after weaning were similar to those of wild-type mice, and the weight range of ABCA1−/− mice in this study was basically the same. In addition to ABCA1 defects, dietary differences have also been suggested as possible causes of weight loss. There was no significant difference in body weight between HFD-fed ABCA1−/− and wild-type mice in this study. On the contrary, Orso et al. [ 20] reported that ABCA1 knockout may cause insufficient vitamin absorption and platelet aggregation, as well as severe small intestinal lesions, resulting in decreased survival and body weight. In addition, homozygous female ABCA1-deficient mice are difficult to breed, probably due to altered hormone secretion and subsequent placental abnormalities caused by reduced estrogen and progesterone levels [21], which may also affect their metabolism and development. More recently, the important beneficial role that ABCA1 plays in modulating inflammation has been realized [22]. In ABCA1−/− mice, we found that C8:0 did not significantly improve LDL-C, TC, and HDL-C/LDL-C except for reducing TG, while EPA significantly improved LDL-C and TC, and a consistent effect was also observed on inflammation in ABCA1−/− mice and ABCA1-KD RAW 264.7 cells. Furthermore, C8:0 group mice had significantly decreased expression of p-STAT3 and p-JAK2 in the aorta, while EPA significantly decreased the expression of TLR4 and NF-κBp65 in the aorta of ABCA1−/− mice and ABCA1-KD RAW 264.7 cells. These results differ from our previous study of C57BL/6J mice [15]. These findings may help explore the different mechanisms of C8:0 and EPA in the regulation of blood lipids and inflammation. ABCA1 belongs to the ABCA subfamily. ABCA1 was found to be highly expressed in hepatocytes, intestinal cells, macrophages, and endothelial cells [23]. Studies have shown that ABCA1 plays a crucial role in cholesterol reversal [22]. Fatty acids have been reported to regulate ABCA1 expression in mouse models by activating liver cyclic AMP-dependent protein kinase A and LXR/RXR pathways [4]. For example, linoleic acid suppressed the levels of ABCA1 transcripts and protein in human macrophages [24]. On the contrary, palmitic acid, ω-6 PUFAs and linolenic acid as a precursor to EPA, had the opposite effect [24,25]. In our previous studies, we found that MCT reduced LDL-C and TC levels and improved HDL-C levels in patients with high triglycerides [26,27]. We also observed that C8:0 could reduce TC and LDL-C levels, increase the HDL-C/LDL-C ratio, and improve atherosclerosis in apoE-deficient mice [18]. In recent years, in our mouse experiments, C8:0 was found to upregulate the expression of ABCA1 in the liver [14], in the mouse aorta [15], and in RAW 264.7 cells [15]. Tangier’s disease is a high-risk ASCVD disease due to the lack of ABCA1, leading to high TG and TC and low HDL [28]. In this study, we found that ABCA1 knockout increased TG, TC, and non-HDL-C, but HDL-C did not decrease significantly, which may be related to the different intervention feeds and compensatory mechanisms. Drobnik et al. fed ABCA1+/+ and ABCA1−/− mice with a cholesterol-free diet for 14 days and found a significant decrease in both serum HDL-C and TC in ABCA1−/− mice [29]. Haghpassand et al. reported that high fat feeding increased HDL cholesterol and apoA1 levels in wild-type mice or bone marrow-transplanted ABCA−/− mice [30]. A single deficiency of ABCA1 or ABCG1 in macrophages has been reported to not increase atherosclerosis, probably because ABCA1 deficiency leads to upregulation of ABCG1 expression [31]. Similarly, ABCG1-deficient mice were shown to have decreased plasma HDL cholesterol levels when fed a high-cholesterol diet. In addition to the significant reduction in TG in C8:0, there was no significant improvement in LDL-C, TC, and non-HDL-C levels. In contrast, EPA significantly reduced LDL-C, TC, and non-HDL-C levels compared to C8:0. It is suggested that the mechanism of C8:0 and EPA in reducing lipids is different, which is worthy of further study. Research evidence has supported the role of ABCA1 in the regulation of cholesterol efflux [32] and its anti-inflammatory effects [33]. ABCA1 can regulate inflammation by participating in cellular cholesterol and phospholipid transport and the formation of lipid domains on the cell surface [3,33,34]. Dietary fatty acids not only affect blood lipids but also mediate inflammation levels, such as the way in which excessive intake of SFAs can increase the level of serum inflammatory cytokines in animals [35]. Furthermore, palmitic acid and stearic acid promoted the expression of TNF-α and IL-1β in macrophages [36]. Diets rich in fish oil can downregulate the expression of TLR4, TNF-α, IL-1, nucleotide-binding oligomerization domain protein1, and nucleotide-binding oligomerization domain protein2 in the liver of piglets [8]. Furthermore, supplementation with highly purified concentrated fish oil increased the levels of IL-10, IL-12, and IFN-γ while decreasing the levels of TNF-α and IL-6 [37]. In addition, EPA and DHA pretreatment may be beneficial for vascular inflammation in human saphenous veins undergoing a coronary bypass operation [38]. Although C8:0 belongs to SFA, both C8:0 and EPA can decrease the levels of MCP-1 and TNF-α and increase the level of IL-10 in mice and cells treated with LPS, in line with previous findings [15,18]. In mice, ABCA1 knockout increases inflammatory infiltration in vascular walls, peritoneal cavities, and blood circulation [19]. ABCA1/G1 deficiency improved LPS-induced inflammatory gene expression in mouse aortic endothelial cells [31]. In addition, THP-1 macrophage knockdown of ABCA1 inhibits downregulation of inflammatory cytokines by the apolipoprotein A-1 binding protein [39]. Patients with ABCA1 dysfunction tend to have chronic inflammation, suggesting that ABCA1 has a regulatory role in inflammation [40]. In this study, we found a significant decrease in inflammation levels by EPA after ABCA1 knockout; however, only TNF-α levels were significantly reduced by C8:0. In the ABCA1-KD RAW 264.7 cell assay, EPA was also found to significantly reduce inflammation levels compared to C8:0. The anti-inflammatory effects of C8:0 and EPA were different in ABCA1 deficiency. ABCA1 regulates both lipid metabolism and inflammation and may be the key protein in the mechanism of action of C8:0 and ω-3 PUFAs. ABCA1 suppresses inflammation through multiple mechanisms. ABCA1 regulates the inflammatory response through NF-κBp65, TLR4/MYD88, JAK2/STAT3, cAMP/PKA, and apoptosis pathways [4,41]. SFAs, especially lauric acid, palmitic acid, and stearic acid, have been found to increase the level of IL-6 expression in macrophages through the TLR4 pathway, and stearic acid can promote the release of MCP-1 by activating TLR4 [42]. The mechanism of ω-6 PUFAs inhibiting the inflammatory response includes inhibition of the TLR-4/MYD88/NF-κBp65 pathway [43] and activation of GPR120 to inhibit the TAK1/NF-κBp65/JNK pathway [44]. Our previous results suggest that C8:0 can inhibit inflammation and improve atherosclerosis through the TLR4/NF-κBp65 signaling pathway in apoE−/− mice [18]. Further studies showed that C8:0 plays an important role in both lipid metabolism and inflammation, which may be related to the signaling pathways ABCA1 and JAK2/STAT3 [15]. Compared with EPA, the transcription levels of ABCA1, JAK2, and STAT3 in the mouse aorta increased significantly in C8:0, but there was no significant difference in the expressions of JAK2 and STAT3 in LPS-stimulated RAW 264.7 cells [15]. In this study, C8:0 significantly reduced the p-STAT3 and p-JAK2 in the aorta of ABCA1−/− mice. However, EPA significantly inhibited TLR4 and NF-κBp65 expression levels. C8:0 and EPA significantly increased ABCA1 and p-JAK2, while they decreased NF-κBp65. Meanwhile, EPA had a significantly lower NF-κBp65 protein expression than that of C8:0 in LPS-stimulated ABCA1-KD cells. However, the effects of C8:0 on inflammation levels and JAK2/STAT3 pathway protein expression were somewhat inconsistent in mice and cell lines. The reason may be that ABCA1 was knocked out and knocked down in mice and cells, respectively. In addition, different tissues analyzed in animal and cell experiments may have different results. Our study showed that C8:0 plays a regulatory role in improving blood lipids and inflammation primarily through ABCA1, while EPA mainly inhibits inflammation through the TLR4/NF-κBp65 pathway. The specific mechanism is worth further exploration. However, there are some limitations to this study. [ 1] In the mouse experiment, the sample size was small because the ABCA1 homozygote mice could not reproduce. [ 2] The effects of ABCA1 knockout on different inflammatory pathways are unclear, and there are compensatory mechanisms, which will affect the results of the study. [ 3] In the future, we should observe the effects of C8:0 on inflammation and atherosclerosis in ABCA1- and apoE-gene-deficient mice. [ 4] The binding protein of C8:0 is still unknown and may be the key protein for its function; therefore, further study is necessary. [ 5] Dyslipidemia and inflammatory responses play a key role in the progression of atherosclerosis. In our study, no atherosclerotic lesions were found in ABCA1−/− mice fed a high-fat or palmitate diet for 8 weeks, which is consistent with previous findings [19,45]. Obviously, the mechanism of ABCA1 knockout in AS needs to be further studied, as well as the existing compensatory mechanism and the changing mechanism of the effects of C8:0 and EPA. ## 5. Conclusions ABCA1 plays an important role in the regulation of lipid metabolism and inflammatory pathways. Our data showed that ABCA1 deficiency resulted in dyslipidemia and increased inflammation in mice, and that ABCA1 knockdown promoted increased inflammatory levels in RAW 264.7 cells. We found that EPA significantly improved cholesterol metabolism, while C8:0 showed only a significant decrease in TG. In addition, EPA inhibited inflammation levels significantly better than C8:0 in both ABCA1−/− mice and ABCA1-KD cells. These results differ from our previous studies of C57BL/6J mice and RAW 264.7 cells. 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--- title: 'Citrus × paradisi L. Fruit Waste: The Impact of Eco-Friendly Extraction Techniques on the Phytochemical and Antioxidant Potential' authors: - Jolita Stabrauskiene - Mindaugas Marksa - Liudas Ivanauskas - Pranas Viskelis - Jonas Viskelis - Jurga Bernatoniene journal: Nutrients year: 2023 pmcid: PMC10005199 doi: 10.3390/nu15051276 license: CC BY 4.0 --- # Citrus × paradisi L. Fruit Waste: The Impact of Eco-Friendly Extraction Techniques on the Phytochemical and Antioxidant Potential ## Abstract Citrus fruits have been the subject of extensive research over the years due to their impressive antioxidant properties, the health benefits of flavanones, and their potential use in the prevention and treatment of chronic diseases. Grapefruit have been shown in studies to improve overall health, with numerous potential benefits, including improved heart health, reduced risk of certain cancers, improved digestive health, and improved immune system function. The development of cyclodextrin complexes is an exciting approach to increasing the content of flavanones such as naringin and naringenin in the extraction medium while improving the profile of beneficial phenolic compounds and the antioxidant profile. This research aims to optimize the extraction conditions of the flavanones naringin and naringenin with additional compounds to increase their yield from different parts of grapefruit (Citrus × paradisi L.) fruits, such as albedo and segmental membranes. In addition, the total content of phenolic compounds, flavonoids, and the antioxidant activity of ethanolic extracts produced conventionally and with -cyclodextrin was examined and compared. In addition, antioxidant activity was measured using the radical scavenging activity assay (ABTS), radical scavenging activity assay (DPPH), and ferric reducing antioxidant power (FRAP) methods. The yield of naringin increased from 10.53 ± 0.52 mg/g to 45.56 ± 5.06 mg/g to 51.11 ± 7.63 mg/g of the segmental membrane when cyclodextrins (α, β-CD) were used; naringenin increased from 65.85 ± 10.96 μg/g to 91.19 ± 15.19 μg/g of the segmental membrane when cyclodextrins (α, β-CD) were used. Furthermore, the results showed that cyclodextrin-assisted extraction had a significant impact in significantly increasing the yield of flavanones from grapefruit. In addition, the process was more efficient and less expensive, resulting in higher yields of flavanones with a lower concentration of ethanol and effort. This shows that cyclodextrin-assisted extraction is an excellent method for extracting valuable compounds from grapefruit. ## 1. Introduction Natural bioactive compounds are in demand as humans become more health conscious, especially regarding a balanced diet. Epidemiological studies have shown that consumers of polyphenolic compounds are less susceptible to chronic diseases [1,2]. From this perspective, the fruits of Citrus × paradisi L. are rich in physiologically active components such as phenolic compounds, vitamins, carotenoid pigments, and fiber [3]. Grapefruit (Citrus × paradisi L.) is one of the world’s most popular fruits. In Eastern medicine, it is used as an appetite stimulant, antidiarrheal, emetic, and expectorant to treat flatulence, scurvy, acne, and eczema [4]. In addition, recent studies have shown that extracts of citrus peels and juices, as well as the biologically active components isolated from them, have a wide range of beneficial effects on living organisms, including antioxidant, antimicrobial, cardiovascular, anticancer, and antidiabetic activity (Figure 1) [5,6,7,8,9]. Due to the great pharmacological potential of citrus fruits, flavonoids and phenolic chemicals are the most studied biologically active molecules in the pharmaceutical field. Two of the most important flavanones in grapefruit fruits are naringin and narirutin [10]. Naringenin (5,7,4′-trihydroxy flavanone), a polyphenolic flavonoid, is an aglycone derivative of hydrogenated flavone [11]. The bacteria of the gut microbiome convert naringin into active naringenin [12]. This flavonoid molecule is an essential part of the human diet and responsible for our foods’ color and bitter–sour taste. The anticancer, antiproliferative, and antitumor effects of naringenin are based on its DNA repair ability [13,14]. It has inhibited breast, liver, prostate, melanoma, and spinal cord glioblastoma cells. Naringenin also influences the intrinsic (mitochondrial) and extrinsic (receptor) apoptotic pathways. The effect of naringenin on apoptosis inhibits proliferation and angiogenesis [15]. Naringenin reduces leukocyte accumulation by inhibiting macrophages’ chemotaxis molecules, which draw leukocytes to inflammation [14,16]. In addition, it activates NF-E2–related factor 2 (Nrf2), an anti-inflammatory factor, in macrophages which is another way it influences. It can also reduce pro-inflammatory cytokines such as IL-33, TNF-, IL-1, and IL-6, suppressing nuclear factor-κB (NF-ĸB) activation [17], boosting antioxidant ability, and reducing superoxide anions and other reactive oxygen species (ROS) [18]. Antidiabetic activity: Studies conducted in vitro and in vivo show that naringenin is essential in preventing and treating insulin resistance and type 2 diabetes [6]. This bioflavonoid can reduce the amount of glucose absorbed by the intestinal brush and the amount of sugar stored in the kidneys [19]. In addition, naringenin improves glucose uptake and utilization and contributes to glucose reabsorption by muscle and adipose tissue. According to the research, naringenin stimulates the growth of pancreatic cells, which has a beneficial effect. These cells have enhanced glucose-sensing abilities because of their training. It has been hypothesized that naringenin causes pancreatic cells to be more sensitive to the effects of glucose and has a pro-apoptotic effect on these cells [12]. Naringenin reduces inflammation caused by phenyl-β-benzoquinone, acetic acid, formalin, capsaicin, carrageenan, and superoxide anions [20]. This bioflavonoid also possesses antinociceptive and analgesic properties in vivo. Naringenin also regulates transient receptor potential (TRP) channels in nociceptors. Hence, it helps in analgesia [21]. Antibiotic resistance is a global problem. Bacteria are resistant to all groups of antibiotics [22]. The use of antibiotics in the food, veterinary, and medical industries has raised this concern. Overprescribing antibiotics to asymptomatic patients, the COVID-19 pandemic, and broad-spectrum antibiotics have worsened this situation. Acinetobacter Baumann, vancomycin-resistant Enterococcus faecalis, methicillin-resistant Staphylococcus aureus, and beta-lactam-resistant Klebsiella pneumonia result from overuse. Naringenin kills Gram-positive bacteria, including S. aureus and MRSA [23,24]. However, only a few clinical studies are using this bioflavonoid as an antibiotic. Unfortunately, neither the US nor the EU databases have registered any clinical trials, so these results are unavailable. Pharmacological safety is shown at 900 mg [25]. Flavanones are also liver protective. Naringenin reduces hyperglycemia, hyperlipidemia, and gluconeogenesis [26], reduces triglyceride formation, and significantly reduces low-density lipoproteins (LDL) and triglycerides in diabetic mice. Furthermore, based on articles, naringenin increased high-density lipoproteins (HDL) levels in Wistar albino rats [19,27]. Antioxidant activity in the traditional sense is identified by the hydroxy substituents (OH) on their molecules showing a high reactivity towards reactive oxygen species (ROS) and reactive nitrogen species (RNS) [5,22,28]. Because of this, the ability of a given molecule to function as an antioxidant increases when that molecule contains OH radicals; in the case of naringenin, there are three of these residues. After this, OH donates its hydrogen to free radicals (R), eventually stabilizing naringenin by resonance. Ring B is an essential part of the typical structure of flavonoids. This is because when hydroxyl groups are present in the ring, flavonoids can stabilize hydroxyl (OH), peroxyl (ROO), and peroxynitrite (ONOO) radicals, thereby forming relatively stable flavonoid radicals (Figure 2) [29,30]. UV spectrophotometry is the most common method for measuring phenolic compounds, flavonoids, and phenolic acids [31]. They measure the absorption of the reaction mixture in the visible spectrum. These methods are fast, simple, and reliable but lack chromatographic selectivity. Colorimetric techniques are used to quantify phenolic compounds based on their abundance and the complexity of the plant matrix [32]. One of these methods is that of Folin–Ciocalteu, which uses a specific reagent composed of several chemicals (sodium molybdate, sodium tungstate, etc.) [ 33]. The method is based on electron transfer reactions. However, the method requires a reference substance (in this case, gallic acid) to measure the total phenolic content of the extract. Next, the method with aluminum chloride (AlCl3) is used to test the total amount of flavonoids. These forms chelate complexes of aluminum and flavonoids [34]. DPPH (2,2-diphenyl1-picrylhydrazyl), ABTS (2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid), and FRAP (Ferric Reducing Antioxidant Power) are spectrophotometric techniques for the preliminary testing of the antioxidant activity of plant extracts [35,36]. ABTS uses ABTS•+, a stable blue-green radical with a maximum absorbance of 734 nm. Before use, allow the ABTS solution and potassium persulfate (K2S2O8) to react in the dark at room temperature for 12–16 h to generate the ABTS radical cation. Next, this ion reacts with phenolic chemicals to change the blue color to greenish or colorless. Data are Trolox equivalents [34]. The FRAP method is comparable to the ABTS and DPPH methods; however, the reaction is carried out in an acidic medium. The effectiveness of the antioxidant in reducing Fe (III) in an acidic solution is the focus of this approach [35]. Pharmacologically active chemicals usually contain excipients. Excipients help in the formulation of drugs, and they increase drug stability, dosage uniformity, and bioavailability [36]. Cyclodextrins are composed of a ring of glucose molecules linked in a specific arrangement. The structure creates a cavity within the molecule, which can be used to bind to other molecules. This property makes cyclodextrins useful for drug delivery, as they can bind to drugs and deliver them to the desired location in the body. Additionally, cyclodextrins have a high degree of solubility in water, which makes them useful for water purification applications. Another property of cyclodextrins is their ability to form complexes with other molecules. This property makes them useful for food processing applications, as they can be used to bind to food additives and other compounds to improve their stability and shelf life. Additionally, cyclodextrins can be used to bind to flavors and aromas, which can be used to enhance the flavor and aroma of food products. Cyclodextrins (CDs) form inclusion complexes in aqueous solutions, making them suitable for assisted extraction. Lipophilic guest molecules or fragments reside within these inclusion complexes [37], so complexation can increase flavanone solubility and antioxidant properties [38]. In addition, CDs decrease the taste of naringin generations and decrease their interaction with intestinal CYP3A4 metabolized drugs [39]. The most common types of cyclodextrins are α-CD, β-CD, and γ-CD, which contain six, seven, and eight glucopyranose units, respectively (Figure 3). The exterior of these molecules is hydrophilic, which helps CDs interact favorably with water. Furthermore, the hydrophobic cavity of these molecules can accommodate a variety of guest molecules, including polar compounds (alcohols, acids, amines, and small inorganic anions) and nonpolar compounds (aliphatic and aromatic hydrocarbons) [40,41]. Figure 4 shows a graphical representation of the structure of CDs. Recently, CDs have been used to separate polyphenols, including phenolic acid and flavonoids, from various natural sources. For example, Li Cui et al. [ 2012] found that β-CD inclusion complexation increased the solubility of naringin in water, which accelerated enzyme hydrolysis to form naringenin [42,43]. In addition, the inclusion complex foundation protects CDs from oxidation and decay [44]. Based on these assessments, it becomes clear why there is so much interest in conducting additional in-depth studies on treated grapefruit peel as a natural, economical, and accessible antioxidant source. The purpose of this research is twofold: (I) to describe and optimize the extraction conditions of the flavanones naringin and naringenin with additional compounds to increase their yield from different parts of the grapefruit fruit, such as albedo and segmental membranes; and (II) to describe and quantify the flavonoid profiles and antioxidant activity of processed grapefruit peel and juice. Both objectives are addressed in this study. ## 2.1. Material The grapefruit’s segmental membranes and skins were gathered for secondary raw materials when the juice was extracted from grapefruit (Citrus × paradisi L., variety Star Ruby, Italy, unknown place). After being diced up in a food processor, these components were frozen at −18 ± 0.9 °C until extraction. Figure 5 depicts the fruit slices that were utilized in this investigation. Standards of naringin, naringenin, and narirutin were obtained from Sigma Aldrich in Steinheim, Germany. Hydrochloric acid, sodium hydroxide, acetic acid, methanol, acetonitrile, Trolox, α-, β-, and γ-CDs were obtained from Sigma Aldrich in HH, DE. Ethanol ($96\%$) was obtained from Vilniaus Degtine in Vilniaus, LT. GFL2004 was used to manufacture filtered water (Burgwedelis, DE). The following reagents were also utilized: aluminum chloride, hexamethylenetetramine, acetic acid, 2,20-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), 2,4,6-Tris(2-pyridyl)-s-triazine (TPTZ), potassium persulfate, ferrous sulfate heptahydrate, saline phosphate buffer, and hydrogen peroxide from Sigma Aldrich (Schnelldorf, Germany); disodium hydrogen phosphate obtained from Merck (Darmstadt, Germany); 2,2-diphenyl1-picrylhydrazyl radical (DPPH). ## 2.2.1. Extracts’ Preparation Control sample: a control batch was performed based on previous studies using a combined extraction method with 50 and $70\%$ ethanol (v/v). The extraction process used in this study is shown in Figure 6 [45]. Test sample: The extracts were made under the same conditions. The $50\%$ or $70\%$ ethanol (v/v) was employed as the solvent (10 mL), and the additional substances (0.1 ± 0.105 g of α-, β-, γ-CDs) were added with plant material (1 ± 0.05 g). After centrifuging the materials for 10 min at 1789× g, the supernatant was discarded by decantation. The extracts were then filtered using PVDF syringe filters with a pore size of 0.22 m, and an HPLC analysis was used to determine the total quantity of flavanones. A list of samples prepared in the UAE with thermal hydrolysis and excipients is given in Table 1. ## 2.2.2. HPLC–PDA Conditions Waters 2695 liquid chromatography with a photodiode array detector was used (Waters 996, 200–400 nm wavelength range). We used an ACE C18 chromatography column (250 mm × 4.6 mm) with a sorbent particle size of 5 μm to separate physiologically active substances. The process details of the HPLC method were as follows. Gradient elution was used to separate the tested substances. Each extract was injected in a volume of 10 L and measured at 280 nm. Eluent A: acetonitrile at a rate of 1 mL/min; Eluent B: water. 0.0 min, $10\%$ A; 5 min, $20\%$ A; 25 min, $40\%$ A; 30 min, $100\%$ A; 35 min, $100\%$ A; 36 min, $10\%$ A. The temperature of the column was set at 25 °C. Peaks were found by comparing the UV-Vis spectra of each peak to valid reference standards and measuring their retention times. The samples were subjected to two different analyses. The chromatograms of the standards for naringenin, naringin, and narirutin are shown in Figure 7. The Methodological Review of Natural Products by Wolfender [2009] [29,46] was used for quantification and validation. Standard stock solutions with primary concentrations of 100 µg/mL of naringin, narirutin, and naringenin were prepared in $70\%$ methanol, and calibration curves were constructed using six standard solution concentrations. Three injections per concentration were performed to determine linearity. To construct calibration equations, naringin, naringenin, and narirutin were plotted against known concentrations of the respective standard solutions. The least squares approach was used to calculate a linear regression equation. The regression coefficients of all calibration curves were R2 > 0.999, confirming the linearity of the concentration ranges. The sensitivity of the approach was determined by determining the limit of detection (LOD) and the quantification (LOQ). The concentrations that produced a signal-to-noise ratio of 3 and 10, respectively, were used to determine LOD and LOQ. A standard mixture of naringin, naringenin, and narirutin was used during the intraday and inter-day precision testing. Five repeated non-consecutive injections of the regular combination on the same day on four different days proved the method’s accuracy. Relative standard deviation (RSD) is used to describe the results. The retention times and spectra of standards (naringin, naringenin, and narirutin) were compared to those prepared in extracts in this work. Linearity was determined by calculating the correlation coefficient R2 of the calibration curve (naringin R2 = 0.99992, naringenin R2 = 0.99992, narirutin R2 = 0.99999) and the peak areas were used for quantification. The linearity range of naringin was 1.166 to 33.34 μg/mL, 0.472 to 15.12 μg/mL for naringenin, and 1.2757 to 80.5 μg/mL for narirutin. The concentrations of naringenin, naringin, and narirutin were expressed as µg/g, mg/g, and mg/g dry weight (DW), respectively (Table 2). ## 2.3. Statistical Data Analysis SSPS version 20.0 (IBM Corporation, Armonk, NY, USA) was used to analyze the data. Data are presented as mean and standard deviation (SD). All quantitative data were performed in triplicate. The Friedman and Wilcoxon tests were used to make the comparisons that should be made between the three different metrics. In addition, Spearman’s test was used to determine correlation and regression coefficients. At the end, a comparison was made between the two groups using the Mann–Whitney U test. Results were considered statistically significant ($p \leq 0.05$). ## 2.4. Preparation of Extracts for Total Phenolic, Flavonoid, and Antioxidant Activity We used a modification of this ultrasound-assisted extraction method to adapt it for our application to determine the total phenolic, flavonoid amount, and antioxidant activity. An ultrasound-assisted extraction method (UP-250; frequency range: 19–25 kHz, 250 W, probe amplitude: 35 μm) was used to prepare the extraction from grapefruit waste product and fresh juice. Control sample: the total content of phenols, flavonoids, DPPH, ABTS, and FRAP was determined using the flavedo, albedo, segmental membrane. A total of 10 g of plant material was poured with a solvent ($50\%$ ethanol v/v) and treated with an ultrasonic bath at 50 ± 5 °C for 30 min. The tested sample was produced under the same conditions but with additional excipients (0.1 g ± 0.01 g β-CDs) (Figure 8). A fresh juice tested sample was created; 10 mL of fresh juice and 0.1 g ± 0.01 g of β-cyclodextrins were added and treated for 30 min at 50 ± 5 °C using an ultrasound bath. ## 2.4.1. Analysis of Total Phenolic Content A spectrophotometric analysis method determined the total amount of phenolic compounds in Citrus × paradisi L. The extract was mixed with the Folin-Ciocalteu phenol reagent, 2 mL of $7\%$ sodium carbonate (Na2CO₃) was added, and the mixture was kept in a dark place for 60 min. A gallic acid calibration curve $y = 12.069$x; R2 = 0.9978 was used to create the calibration curve. The data were given in milligrams of gallic acid equivalent per gram of dry weight (mg GAE/g DW) [47]. ## 2.4.2. Total Flavonoid Content Evaluation The colorimetric aluminum chloride technique was modified to determine the total flavonoid concentration of the extracts [48]. First, 0.2 mL of the extract was combined with 2 mL of $96\%$ (v/v) ethanol, 0.1 mL of $30\%$ acetic acid, 0.3 mL of $10\%$ aluminum chloride (AlCl₃), and 0.4 mL of $5\%$ hexamethylenetetramine solutions. After 30 min of incubation, the absorbance of the reaction mixture was measured at 475 nm using a spectrophotometer (Shimadzu UV-1800; Kyoto, Japan). Then, the total flavonoid content was determined using a calibration curve ($y = 5.0867$x; R2 = 0.9985). The result was computed using the following procedure and expressed as mg of rutin equivalent per gram of dry weight (RE/g DW):TFC = C × Ve × F/m, where TFC–total flavonoid content; mg RE/g DW;C–concentration of standards used mg/L;Ve–the volume of solvent used;F–dilution coefficient of the sample; m–a mass of the sample, g. ## 2.5.1. Radical-Scavenging Assay (ABTS) ABTS (2,2′-Azino-bis-(3-ethylbenzthiazoline-6-sulonic acid) was oxidized using potassium persulfate to generate the ABTS•⁺ radical. It showed absorption maxima at wavelengths 645, 734, and 815 nm. A 7 mM (ABTS) aqueous solution was created and stored in the dark for 12 to 16 h to generate a dark solution containing the radical cation. Before usage, the ABTS radical cation was diluted with water, and its initial absorbance at 734 nm was measured using a spectrophotometer to be about 0.70 ± 0.01. To assess radical scavenging activity, 2.0 mL of the ABTS working standard was combined with 200 µL in a test cuvette. Using Trolox, the calibration curve was created ($y = 0.0001728$x; R2 = 0.9832). Results were reported regarding the Trolox equivalent per gram of dry weight (TE per g DW) [49]. ## 2.5.2. Radical Scavenging Assay (DPPH) The free radical scavenging activity of the extract was carried out with slight modifications. For the first step, 10 μL of each ethanolic solution was mixed with 3 mL of 2,2-diphenyl-1-picrylhydrazyl (DPPH) solution. Then, the reaction mixture was incubated in the dark at room temperature for 1 h, and the absorbance at 517 nm was measured using a spectrophotometer (Shimadzu UV-1800; Kyoto, Japan). The calibration curve was obtained with Trolox ($y = 0.00623$x; R2 = 0.9923). The results were expressed as Trolox equivalent per gram dry weight (TE/g DW) [50]. ## 2.5.3. Ferric Reducing Antioxidant Power (FRAP) The FRAP test was performed by combining 0.3 M of acetate buffer with a pH of 3.6, 10 mM of a solution containing 2,4,6-tripiridil-s-triazino, 40 mM of hydrochloric acid, and 20 mM of a solution containing ferric (III) chloride (10:1:1). After that, 10 µL of the sample was combined with 3 mL of the FRAP reagent, and the resulting mixture was well combined. After 30 min incubation, using a spectrophotometer, the level of absorption was determined to be 593 nm (Shimadzu UV-1800; Kyoto, Japan). Ferrous sulfate was used to obtain the calibration curve, which had the following equation: $y = 0.00010$x + 0.0646; R2 = 0.9915 [51]. ## 3. Results and Discussion The flavanones most prevalent in grapefruit fruit are glycosides, specifically naringin and narirutin. On the other hand, naringenin (in the form of aglycone) is not very soluble in water; as a result, the majority of the extracts that were tested either had very low amounts of aglycone or only traces of it [42]. Consequently, one of the essential tasks is to increase the amount of naringenin in the extracts and to do so in the simplest, most viable way. We selected the most optimal extraction conditions based on the previous research methods and used the conjugated extraction method. Using only ultrasound extraction method (UAE) without thermal hydrolysis showed poor results compared with thermal hydrolysis. Thermal hydrolysis is necessary to obtain a higher yield of naringenin (Table 3). First, the control extraction with the α, β, and γ-CDs was performed using the conjugated extraction method described in Section 2.2.1 and compared to the test extraction (prepared under the same conditions). Table 3 illustrates the findings that were obtained from the control extraction. ## 3.1. The Quantity of Flavanones Using the Additional Substances α-, β-, γ-Cyclodextrins The structure of cyclodextrins (CD) can explain the increased yield of flavanones in the extracts from plant material. Additionally, the CD encapsulation can change the physicochemical features, such as water solubility and the substance’s stability [36]. CDs can protect chemically unstable drug molecules from environmental factors, reducing or preventing drug hydrolysis, oxidation, and enzymatic degradation. One common application is reducing bitterness in citrus fruit juice by forming a naringin-β-CD complex [38]. The research of Pereira et al. in 2021 revealed that by using inclusion complexes, an impressive conversion rate from naringin to naringenin could be achieved, reaching up to $98.7\%$ and $56.2\%$. On top of this remarkable success, β-CD has been noted as having a positive effect on water solubility; therefore, enzymatic hydrolysis of naringin will likely improve significantly [44]. Based on previous studies, we picked the albedo and segmental membrane parts for our investigation. The most significant quantities of naringenin were detected using combination extraction methods (ultrasonic extraction method with thermal hydrolysis). Due to its polarity for flavonoids, research shows that methanol, among other organic solvents, is exceptionally capable of extraction. Because of this, efforts are being undertaken to find non-toxic and entirely biodegradable alternatives, such as ethanol, that can produce the same or similar outcomes without harming the environment [52,53]. The primary goal was to improve the flavanone yield in the extract; an organic solvent was chosen as a co-solvent with CDs because aglycones are more hydrophobic than hydrophilic chemicals and have limited solubility in water. As a result, once the combination with CD degrades, flavanone deposits might develop in water. This procedure may be avoided by using an organic solvent. In control, the maximum level of naringenin was detected in the part of the segmental membrane using $70\%$ ethanol (67.59 ± 3.37 µg/g). By using cyclodextrins, this amount could be increased by more than $34\%$. Comparing α, β, and γ-cyclodextrins, a statistically significant increase was observed in the AA1 sample (α-CDS, $50\%$ ethanol v/v) 43.44 ± 2.17 µg/g compared to AT1 (23.58 ± 1.17 µg/g), AB1 (16.21 ± 0.81 µg/g), AG1 (0.84 ± 0.042 µg/g), and SA1 (91.19 ± 4.55 µg/g) compared to ST1 (65.84 ± 3.29 µg/g) and SB1 (0.45 ± 0.02 µg/g). The results of our samples concluded that the combination of naringenin and α-CD with the smallest cavity size provided a higher yield of the aglycone compared to other cyclodextrins. Statistical analysis indicated that this significant increase was remarkable ($p \leq 0.05$). According to the results of our research, $50\%$ ethanol generated a higher concentration of naringenin than $70\%$ (v/v) ethanol using α-CD: (AA1) 43.44 ± 2.17 µg/g–(AA2) 28.77 ± 1.44 µg/g from albedo, and (SA1) 91.19 ± 4.55 µg/g–(SA2) 86.69 ± 4.33 µg/g from segmental membrane parts, ($50\%$ and $70\%$, respectively). In addition, control samples AT1, AT2, and ST1 and ST2 were compared to test samples using α, β, and γ-CDs yielding statistically significant findings for naringenin using α-CD. The quantitative yield of naringenin using excipients ($1\%$) is shown in Figure 9. Both naringin and narirutin are examples of hydrophobic polyphenols with a low water solubility of 38 and 500 µg/mL, respectively, at room temperature [54]. In addition, these polyphenols are inherently polar due to at least one sugar moiety in their molecular structures [55]. They are unable to form a stable complex with α-CD because they have the lowest affinity for it. In contrast to the findings obtained with control samples, extracts obtained with α-CD in this investigation showed statistically significant improvement (Figure 10) ($p \leq 0.05$). The results of flavanones showed in Table 4. Based on the literature and previous research, our study chose 50 or $70\%$ ethanol v/v. The extraction with water did not show any meaningful results, even after adding additional substances. Meanwhile, using $50\%$ ethanol and α-CD significantly increased the amount of aglycones from the albedo and segmental membrane parts by an average of 1.6 and 1.05. Naringin from the albedo part (18.87 ± 0.94 mg/g–11.58 ± 0.58 mg/g, 50–$70\%$, respectively) and narirutin in the sample from the albedo part 3.33 ± 0.16 mg/g–2.25 ± 0.11 mg/g, 50–$70\%$, respectively. The results of this study demonstrate that the quantity of flavanone in the extracts examined was significantly elevated when α and β cyclodextrins were used as compared to extracts used as controls. This study also showed that CDs increased flavanone yield while requiring a lower solvent concentration. Figure 11 displays the quantitative yield of naringin and naringenin when using excipients at a concentration of $1\%$. The amount of naringin increased statistically significantly in the test sample with β-CDs, AA1, SA1, SA2, and SB2 compared to the control group AT1, ST1 AT2, and ST2. In addition, when comparing the test samples, higher amounts of naringin were found in the segmental membrane with β-CD using solvent $70\%$ ethanol v/v (SB2) 58.08 ± 2.90 mg/g versus SB1 ($50\%$ ethanol v/v) 51.11 ± 2.55 mg/g. The amount of narirutin was increased by α-CD in the albedo part, compared to the control sample (3.33 ± 0.16 mg/g vs. 2.24 ± 0.12 mg/g) and slightly increased by γ-CD in the segmental membrane 2.15 ± 0.11 mg/g). For naringin, the best results were obtained with β-CD from segmental membrane, using solvent $70\%$ ethanol v/v. ## 3.2. Total Phenolic and Flavonoid Content Determination It was evaluated how many total phenolic and flavonoids were determined using the ultrasound assistant extraction method (control sample) versus conjunction with $1\%$ β-cyclodextrins (CDs) (tested sample), as described in Section 2.4. Many medicinal plant species exhibit interspecific chemical diversity, which is important to study and evaluate. Chemical diversity studies provide information on the active ingredient’s composition across species, varieties, and plant parts. Because of this, using UV–visible light spectrophotometry, pilot tests were carried out to measure the total content of phenolic compounds and flavonoids from various parts of Citrus × paradisi L. fruits. According to the findings of the tests, the total phenolic content varied from 2.48 ± 0.124 mg GAE/g DW (flavedo), 3.58 ± 0.17 mg GAE/g DW (albedo), 2.56 ± 0.12 mg GAE/g DW (segmental membrane), and 2.89 ± 0.14 mg GAE/g DW (juice). Most samples prepared with excipients contained more total phenolic compounds than control samples ($p \leq 0.05$). This finding is significant because it shows that excipients can enhance the number of phenolic compounds found in a sample and shows promise for future pharmacology developments. Furthermore, the fact that these results have been statistically significant at p ≤ 0.05 reinforces their credibility. Using β-CDs, the highest amount was found in the albedo part and increased from 3.58 ± 0.17 to 18.42 ± 0.92 mg GAE/g DW; the segmental membrane increased from 2.56 ± 0.128 to 17.8 ± 0.89 mg GAE/g DW. The total phenolic TPC content in grapefruit ethanol extract is shown in Figure 12. According to Gorinsten et al., total phenolic content (TPC) content was significantly higher in fresh grapefruit peel (155 ± 10.3 mg/100 g) than in peeled grapefruits (135 ± 10.1 mg/100 g) [56]. The difference in total flavonoid content across studies might be attributed to differences in variety, location, or analytical methodologies [34]. Xi et al. observed that the total phenolic content varied by variety and fruit portion, varying from 3.17 to 4.63 mg/g GAE FW in the peel, 2.43 to 3.46 mg/g FW in the pulp, 0.29 to 0.52 mg/g FW in the juice, and 2.12 to 3.36 mg/g FW in the seeds [57]. According to the findings of the studies, the concentration of phenolic compounds present in albedo is higher than that found in any of the other portions of the fruit. This disparity may be attributable to the distinct parts of the fruit having a unique phenolic compound profile and a unique quantity of phenolic compounds. In addition, the solvent used to extract the substance, the method used to remove it, and the quality of the raw material might all contribute to the variations. The concentration of flavonoids varies depending on the plant’s development stage since they are the most abundant group of chemicals found in plants and substantially influence the plant when growing. The majority of flavonoids, also known as flavanone glycosides, are only found in citrus trees. Other types of plants have very few of these compounds. Grapefruit contains several flavonoid glycosides, the most important of which are naringin, hesperidin, and narirutin [34,52]. Flavonoids are abundant in various parts of the fruit, resulting in significant differences between fruit types. Another study found that fruit parts and cultures had different total flavonoid levels. For example, the peel has values between 5.12 mg and 8.30 mg per gram fresh weight; the pulp can range from 3.86 to 5.38 mg/g, from 0.26 to 0.44 mg/g in juice, from 3.16 to 9.27 mg/g in whole fruits and from 18, 61 to 25.33 mg/g in seeds. As a result, seeds contained more flavonoids than juices (5496 times as much as the juice) [57]. Nurcholis et al. investigated the effects of extraction procedures and durations on total flavonoid and phenolic contents (TFCs and TPCs) in a solvent such as methanol. According to the researchers, different extraction procedures and durations significantly influenced the TFCs, TPCs, and antioxidant activities of Java cardamom fruit methanol extracts [58]. Using the aluminum chloride technique, the flavonoid content of grapefruit fruit was analyzed, and the findings are presented in Figure 13. The TFC ranged from 1.26 ± 0.08 to 4.91 ± 0.24 mg RE/g DW. Flavedo extracts had the highest flavonoid levels (2.52 ± 0.13 mg RE/g DW). The total flavonoid content ranged from 1.78 ± 0.09 to 4.66 ± 0.23 mg RE/g DW using β-CDs using $50\%$ ethanol (v/v) as a solvent. When comparing grapefruit extracts made under the same conditions but with an additional excipient, the total flavonoid content increases by 2.516 ± 0.1278 mg RE/g DW to 4.66 ± 1.26 mg RE/g DW in the flavedo parts. Meanwhile, using AlCl3 to determine the total flavonoids reduces the number of flavonoids from 4.91 ± 0.24 mg RE/g DW to 1.887 ± 0.094 mg RE/g DW in fresh juice. ## 3.3. Antioxidant Activity After researching the total phenolic and flavonoid content of Citrus × paradisi L., the next step is to investigate and evaluate the antioxidant activity of the various parts of the fruit. The findings will be useful in evaluating and standardizing the quality of raw materials and their products. Furthermore, they will make it possible to anticipate the antioxidant activity of grapefruit extracts derived from various parts of the fruit when tested in vivo [59]. The antioxidant capacity of plant extracts can be affected by a wide variety of factors, including the extraction process, the solvent used, the kind of fruit, and the stage of ripeness at which the fruit was harvested [60]. Consequently, we decided to assess the number of antioxidants present in grapefruit using different methods (radical scavenging, antioxidant activity, and reducing power) [61]. Cyclodextrins, for example, can assist overcome the disadvantages of antioxidants in functional foods. In addition, cyclodextrins are also used as anti-browning agents to prevent the enzymatic browning of food. Finally, research shows that cyclodextrins act as secondary antioxidants and help typical antioxidants resist enzymatic browning. Based on the literature review, the antioxidant capacity in the flavedo, albedo, segmental membrane, and fresh juice was determined. Extracts were extracted using ultrasound (as described in Section 2.4) (control test) and compared to extracts with the addition of β-cyclodextrins (test sample). To this end, the variation in antioxidant capacity was determined by assessing the effect of cyclodextrin on antioxidant capacity. Figure 14 depicts the results of calculations made with DPPH to determine the radical-scavenging activity of grapefruit fruit in its various areas. After determining the antioxidant activity by the DPPH method, it was observed that the fresh juice and flavedo parts of the studied fruit neutralized the DPPH radical the most. The order of action was juice > flavedo > segmental membrane > albedo (1429.25 ± 71.01 μmol/g > 517.14 ± 25.86 μmol/g > 500.27 ± 22.54 μmol/g > 368.50 ± 15.42 μmol/g). β-CD was only slightly increased by DPPH radical inhibition in flavedo extracts from 517.14 ± 25.86 μmol TE/g DW to 630.76 ± 31.54 μmol TE/g DW. However, the CDs reduced the antioxidant activity in segmental membranes and juice. The ABTS radical cation decolorization test is a method used to measure the antioxidant activity level of a substance. A solution of ABTS (2,2′-azino-bis (3-ethylbenzthiazoline-6-sulfonic acid)) and potassium persulfate is used. When these two compounds are mixed, they form a stable radical cation that can be used to measure the antioxidant activity of a substance. The radical cation ABTS is blue and can be decolorized by antioxidants. The extent of discoloration is proportional to the antioxidant activity of the substance to be tested [62]. The free radical scavenging activity of Citrus × paradisi L. extract varied considerably: from 4.36 ± 0.218 µg TE/g to 18.61 ± 0.93 µg TE/g. The highest ABTS radical-cation binding activity was observed in fresh grapefruit juice at 18.61 ± 0.93 µg TE/g. In this study, all test samples developed using CDs had significantly increased antioxidant activity than the control samples. In the sample with flavedo (from 8.97 ± 0.448 µg TE/g to 18.61 ± 0.93 µg TE/g) and fresh juice (from 18.61 ± 0.93 µg TE/g to 20.56 ± 1.028 µg TE/g), the highest binding of the ABTS radical showed (Figure 15). Conversely, the segmental membrane had the lowest antioxidant activity (4.36 ± 0.218 µg TE/g). A compound’s ability to reduce other substances might be a valuable signal of its prospective antioxidant action. Combinations with reducing power imply that they are electron donors and can decrease the oxidized intermediates produced during the process of lipid peroxidation, functioning as both primary and secondary antioxidants [63]. It was discovered by Gupta et al., who investigated two distinct harvests of pomelo fruit, that the flavedo of the fruit had the highest antioxidant capacity and FRAP activity. On the other hand, the albedo of the fruit was found to have the highest accumulation of naringin. These findings were based on the findings that the flavedo of the fruit had the highest antioxidant capacity and FRAP activity. On the other hand, pomelo juice showed the most increased DPPH free-radical scavenging activity and the highest tannin concentration [59]. The results of a FRAP experiment that was conducted on various grapefruit components may be found in Figure 16. Compared to their capacity to scavenge free radicals, the ability of some samples to reduce iron ions (Fe3⁺) to iron ions (Fe2⁺) was significantly more impressive. Applying β-CD resulted in a one- to two-fold increase in the ethanol samples’ reduction power. In this investigation, the outcomes of the test samples (which had β-CDs) were noticeably superior to those of the control samples (devoid of β-CD). The spectrophotometric FRAP method determined that fresh grapefruit juice and extracts from the flavedo showed the highest reducing activity among the tested extracts, and the lowest reducing activity was found in the segmental membrane. The use of β-cyclodextrins showed statistically significant results in all tested extracts. In some samples, the reductive activity doubled; for example, the sample from flavedo parts increased reduction from 10.44 ± 0.522 µg TE/g to 20.44 ± 1.02 µg TE/g. Figure 16 depicts the FRAP assay of ethanolic grapefruit extracts with and without excipients. According to the findings of our research, the segmental membrane extract was the sample that possessed the least amount of antioxidant potential, as indicated by its TE values of 500.27 ± 25.89 μmol/g, 4359.57 ± 24.89 μmol/g, and 7136.00 ± 304.87 μmol/g for DPPH, ABTS, and FRAP, respectively. These numbers are expressed as standard errors. On the other hand, the fresh juice exhibited the highest level of antioxidant activity; the results for DPPH, ABTS, and FRAP were as follows: 1429.25 ± 90.86 μmol/g, 18152.01 ± 698.72 μmol/g, and 12336 ± 616.8 μmol/g, respectively. In addition, the extract from the flavedo part of the grapefruit showed significant results for both DPPH and ABTS (TE 517.14 ± 25.15 and 8969.91 ± 448.50 μmol/g, respectively), and the extracts from the albedo part showed strong FRAP reducing activity (TE 10169.33 ± 508.46 μmol/g). ## 4. Conclusions In summary, waste derived from grapefruit can be recycled in the manufacture of future pharmaceutical and medical consumables. Therefore, the extraction of natural products and the research of natural goods and their possible uses are attracting increasing attention. However, to achieve the main goal, such as achieving higher yields of bioactive chemicals from natural sources in less time, new and safe, economically feasible, environmentally friendly extraction processes must first be created. Cyclodextrins are a type of carbohydrate molecule that exhibit various unique properties that make them useful for various applications. These properties include their ability to form complexes with other molecules, their high solubility in water, and their ability to bind to drugs and other compounds. As a result, cyclodextrins have a variety of uses in the medical, food processing, and water purification industries and are essential tools in improving the safety and effectiveness of these industries. The results of this study demonstrate that the quantity of flavanone in the extracts examined was significantly elevated when α and β cyclodextrins were used as compared to extracts used as controls. This study also showed that CDs increased flavanone yield while requiring a lower solvent concentration. The highest yield of naringenin was found in SA1, 91.19 ± 2.93 µg/g, prepared with α-CD and $50\%$ ethanol v/v, and for naringin 58.68 ± 2.93 mg/g, prepared with β-CD and $70\%$ ethanol v/v. The highest narirutin amount was observed in AA1 3.33 ± 0.166 mg/g using α-CD in $50\%$ ethanol v/v. In contrast, γ-CD did not provide a statistically significant outcome in this investigation. In the culinary, nutraceutical, and medicinal industries, increasing the naturally occurring flavanone aglycones in fruit materials may generate new potential for utilizing these beneficial chemicals in various applications. Grapefruit contains many nutrients and biologically active compounds. The results show that grapefruit’s albedo parts and segmental membranes have the highest total content of phenols, naringin, and naringenin. Grapefruit (Citrus × paradisi L.) extracts from the fresh juice and flavedo parts have greater levels of antiradical activity. These findings will be beneficial for processing grapefruit and creating products because there is a lack of literature on the phytochemical and taste qualities. According to the findings of this study, discarded fruit parts possess varied qualities and can also be employed to produce high-quality nutritional supplements and medicines. ## References 1. 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--- title: Exposure to Obesogenic Environments during Perinatal Development Modulates Offspring Energy Balance Pathways in Adipose Tissue and Liver of Rodent Models authors: - Diana Sousa - Mariana Rocha - Andreia Amaro - Marcos Divino Ferreira-Junior - Keilah Valéria Naves Cavalcante - Tamaeh Monteiro-Alfredo - Cátia Barra - Daniela Rosendo-Silva - Lucas Paulo Jacinto Saavedra - José Magalhães - Armando Caseiro - Paulo Cezar de Freitas Mathias - Susana P. Pereira - Paulo J. Oliveira - Rodrigo Mello Gomes - Paulo Matafome journal: Nutrients year: 2023 pmcid: PMC10005203 doi: 10.3390/nu15051281 license: CC BY 4.0 --- # Exposure to Obesogenic Environments during Perinatal Development Modulates Offspring Energy Balance Pathways in Adipose Tissue and Liver of Rodent Models ## Abstract Obesogenic environments such as Westernized diets, overnutrition, and exposure to glycation during gestation and lactation can alter peripheral neuroendocrine factors in offspring, predisposing for metabolic diseases in adulthood. Thus, we hypothesized that exposure to obesogenic environments during the perinatal period reprograms offspring energy balance mechanisms. Four rat obesogenic models were studied: maternal diet-induced obesity (DIO); early-life obesity induced by postnatal overfeeding; maternal glycation; and postnatal overfeeding combined with maternal glycation. Metabolic parameters, energy expenditure, and storage pathways in visceral adipose tissue (VAT) and the liver were analyzed. Maternal DIO increased VAT lipogenic [NPY receptor-1 (NPY1R), NPY receptor-2 (NPY2R), and ghrelin receptor], but also lipolytic/catabolic mechanisms [dopamine-1 receptor (D1R) and p-AMP-activated protein kinase (AMPK)] in male offspring, while reducing NPY1R in females. Postnatally overfed male animals only exhibited higher NPY2R levels in VAT, while females also presented NPY1R and NPY2R downregulation. Maternal glycation reduces VAT expandability by decreasing NPY2R in overfed animals. Regarding the liver, D1R was decreased in all obesogenic models, while overfeeding induced fat accumulation in both sexes and glycation the inflammatory infiltration. The VAT response to maternal DIO and overfeeding showed a sexual dysmorphism, and exposure to glycotoxins led to a thin-outside-fat-inside phenotype in overfeeding conditions and impaired energy balance, increasing the metabolic risk in adulthood. ## 1. Introduction Since the 1980s, the incidence and prevalence of obesity and type 2 diabetes (T2D) have been escalating worldwide, being associated with Westernized diet intake and a sedentary lifestyle [1,2,3]. Abnormal body fat accumulation in obesity can promote the development of other diseases such as T2D, characterized by pancreatic β-cells dysfunction and insulin resistance in target organs [4,5]. Insulin resistance in peripheral organs stimulates insulin’s continuous release, leading to hyperinsulinemia and the exhaustion of pancreatic β-cells [4,6,7]. Furthermore, increased levels of free fatty acids (FFAs) in obesity contribute to lipotoxicity, one of the main factors for insulin resistance [8]. Overfeeding and energy balance dysregulation are among the main factors contributing to the development of obesity [9]. Under obesogenic conditions, energy balance regulators such as ghrelin, neuropeptide Y (NPY), leptin, glucagon-like peptide-1 (GLP-1), and dopamine levels are altered, disrupting the mechanisms involved with insulin secretion and energy storage and expenditure [10,11,12,13,14]. Furthermore, westernized diets are rich in saturated and monounsaturated fats, simple carbohydrates, and poor in fibers, making them a major source of advanced glycation end-products (AGEs) and their precursors—glycotoxins (reviewed by [15,16,17,18,19]). Previous reports from our laboratory showed that adipose tissue glycation in high-fat diet-fed rats impairs its expandability—which may be related to energy balance mechanisms dysregulation—leading to insulin resistance [20,21]. Moreover, glycation reduction through pharmacological strategies prevents such harmful effects [21,22]. Neuroendocrine pathways such as NPY and dopamine act in peripheral tissues to regulate lipid and glucose metabolism [23,24,25]. However, the effects mediated by dopamine and NPY depend on their receptor subtype. Different dopamine receptors trigger opposite effects: while dopamine receptor 1 (D1R) induces lipolysis and catabolic activity through AMP-activated protein kinase (AMPK) activation [26,27], dopamine receptor 2 (D2R) inhibits lipolysis by decreasing AMPK activity, hormone-sensitive lipase (HSL), and ATP citrate lyase (ACL), and it also induces lipogenesis by increasing Acetyl-CoA carboxylase (ACC) activity [25,26]. Regarding NPY receptors in white adipose tissue (WAT), NPY receptor 1 (NPY1R) induces lipogenic effects, while NPY receptor 2 (NPY2R) is associated with adipogenic and angiogenic processes [23,28,29,30,31]. Known as the stomach-derived hunger hormone, ghrelin binding to growth hormone secretagogue receptor 1α (GHS-R1α) in the hypothalamus regulates energy balance by NPY/Agouti-related protein (AgRP) neurons activation [32,33]. In WAT, the direct activation of GHS-R1α on adipocytes decreases insulin sensitivity and stimulates adiposity [12,34,35]. Acyl-ghrelin in retroperitoneal adipose tissue (AT) increases sterol regulatory element-binding transcription factor 1 (SREBP1C), a master regulator of lipogenesis, while decreasing fatty acid (FA) transport, which contributes to fat accumulation [35,36]. Ghrelin acts as an anti-inflammatory agent in the liver and promotes hepatic lipogenesis by activating the mTOR-PPARy signaling pathway, although its role on glucose and lipid metabolism remains unknown [34,37]. Overall, both NPY and acyl ghrelin levels are increased in patients with obesity and T2D, contributing to adiposity and reduced insulin sensitivity [11,12,38,39], whereas dopamine action is dependent on subtype receptors. Maternal nutrition impacts offspring gene expression and epigenome, metabolism, and cellular function, affecting organ development and later newborns’ lives [40]. Gestation and lactation play a major role in programming windows. Maternal nutrition and metabolic status induce alterations in the intrauterine environment and breastmilk composition that determine offspring adiposity [41,42]. Worldwide, studies demonstrated a higher risk of obesity development when the organism was exposed to maternal overnutrition and obesity [43,44,45]. At the central level, it has already been demonstrated that both maternal obesity and postnatal overfeeding, besides leading to overweight, induce NPY hypothalamic changes in offspring [40,46]. Moreover, maternal obesogenic diets, such as westernized diets, can induce obesity and contribute to metabolic complications during lactation [47,48], showing the importance of maternal diet/lifestyle during lactation. Thus, it is necessary to understand if insulin-sensitive tissues such as the liver and AT undergo alterations in the pathways that regulate energy expenditure after exposure to an unhealthy maternal lifestyle and postnatal overfeeding. In the present study, we compared the role of different obesogenic environments, namely maternal hypercaloric diets (gestation and lactation) and postnatal overfeeding, in energy balance mechanisms, particularly ghrelin, NPY, and dopamine signaling in insulin-sensitive tissues of young animals. Additionality, in order to disclose the role of maternal diet in the metabolic state, we addressed the impact of glycotoxins, common in Western diets, in postnatal overfed rats. We hypothesize that animals with postnatal overweight induced by both maternal obesity and overfeeding show an adaptation of the mechanisms of energy storage and expenditure to unhealthy motherhood and the larger amount of food available. However, exposure to maternal glycation in postnatal overfed rats may hamper these compensatory mechanisms, aggravating the risk for obesity, T2D, and other metabolic diseases that are major healthcare concerns worldwide. ## 2.1.1. Maternal Diet-Induced Obesity (DIO) during Gestation and Lactation Female Sprague-Dawley rats were fed an HFHS diet (containing $42\%$ metabolizable energy from fat, $27\%$ from proteins, and $31\%$ from carbohydrates) before pregnancy until lactation. At PND 21, newborns were weaned and fed with chow. At PND 42, male and female offspring were euthanized, and peripheral tissues were collected for molecular and cellular analysis. In this study, liver and visceral AT (VAT) samples were used from a previously published study, where the experimental design (Figure 1A) and all the biochemical profiles of the rats with 42 days were already described (control litters = 6; HFHS litters = 6; number of males from control dams = 4; number of males from dams submitted to HFHS diet = 5; number of females from control dams = 4; number of females from dams submitted to HFHS diet = 5). More information about this animal model is available in Stevanović-Silva et al. [ 49,50]. The results of this animal model are depicted in Figure 1 and Figure 2. ## 2.1.2. Postnatal Overfeeding and Glycation Models The procedures were approved by the Animal Welfare Committee (ORBEA) of the Coimbra Institute for Clinical and Biomedical Research (iCBR), Faculty of Medicine, University of Coimbra. Animal experimentation was performed following the European Community directive guidelines for the use of laboratory animals ($\frac{2010}{63}$/EU), transposed into Portuguese law in 2013 (Decreto-Lei $\frac{113}{2013}$). Wistar rats were housed under standard conditions (ventilation; 22 °C temperature; $55\%$ humidity; 12 h/12 h light/dark cycle) with ad libitum access to food and water. This work used three animal models of young Wistar rats (postnatal overfeeding model, a maternal glycation model, and postnatal overfed rats exposed to Maternal Glycotoxins). After delivery, all litter sizes were reduced to 8 pups for standardization. After birth, body weight was monitored at postnatal day (PND) 0, PND 4, PND 7, PND 14, PND 21, PND 35, and PND 45. On PND 21, newborns were weaned and separated from their mothers until PND 45 and fed a standard diet. During this period, food consumption was weekly monitored. At PND 45, triglyceride levels were measured in the tail vein (Accutrend, Roche, Mannheim, Germany), and an insulin tolerance test was performed. After blood collection, animals were anesthetized with an IP injection of ketamine/chlorpromazine and euthanized by cervical displacement, and the VAT and liver were collected for molecular analysis. The dams were anesthetized with an IP injection of ketamine/chlorpromazine, milk samples were collected, and the females were euthanized by cervical displacement at 21 PND for liver collection and tissue morphology analyses. ## Postnatal Overfeeding Model On the third day after the birth, a small litter (SL) protocol was implemented by reducing the litter size to 3 pups per dam to induce postnatal overfeeding and overweight (Figure 3A) (control litters = 7; SL litters = 5; number of males from normal litters (NL) = 35; number of males from SL = 9; number of females from NL = 12; number of females from SL = 6). The results of this animal model are depicted in Figure 3 and Figure 4. ## Maternal Glycation Model Wistar dams were injected via intraperitoneal (IP) with S-P-Bromobenzylgutathione cyclopentyl diester—BBGC (5 mg/kg)—a selective inhibitor of Glyoxalase 1 (GLO1), during the first six days post-partum, whereas vehicle dams were injected with the vehicle dimethyl sulfoxide—DMSO (60 µL) (Figure 5A) (control litters = 7; vehicle litters = 5; BBGC group = 5; number of male pups from control dams = 35; number of male pups from dams treated with vehicle = 22; number of male pups from dams treated with BBGC = 29; number of female pups from control dams = 12; number of female pups from dams treated with vehicle = 9; number of female pups from dams treated with BBGC = 9). The results of this animal model are depicted in Figure 5, Figure 6 and Figure 7. ## Postnatal Overfed Rats Exposed to Maternal Glycotoxins Model Male offspring from dams treated with BBGC as described in the maternal glycation model were submitted to an SL reduction to 3 pups for litter at PND 3 (Figure 8A) (SL litters = 5; BBGC + SL litters = 3; number of SL = 9; number of BBGC + SL = 9). The results of this animal model are depicted in Figure 8. ## 2.2. Milk Sample Collection and Determination of Total Antioxidant Capacity and Triglycerides The Wistar female dams were anesthetized and injected with oxytocin (Facilpart) at a concentration of 10 UI/mL after 6 h of fasting, on day 21 postpartum. Milk samples were collected, milk triglycerides were determined using the Accutrend, Roche, Germany, and milk total antioxidant capacity was assessed with an assay kit (ab65329) according to the manufacturer’s instructions. ## 2.3. Plasma Determinations Wistar rats’ blood samples were collected by cardiac puncture under anesthesia and immediately before sacrifice in Vacuette K3EDTA tubes (Greiner Bio-one, Kremsmunster, Austria) at PND 45. Blood samples were immediately centrifuged (2200× g, 4 °C, 15′) and the plasma fraction was stored at −80 °C until performing the Rat Insulin ELISA Kit (Mercodia, Uppsala, Sweden), according to the manufacturer’s instructions. Total and HDL cholesterol were determined using the Prestige 24i Tokyo Boeky system with reagents from Cormay, Poland. ## 2.4. Histology—Haematoxylin-Eosin Livers and VAT from dams and offspring of exclusive metabolic programming during lactation models were fixed in formalin solution ($10\%$), dehydrated in an increasing series of alcohol concentrations ($70\%$ to $100\%$), cleared in xylene, and then embedded in histological paraffin. The livers were sectioned in a microtome, on a non-serial section of 4 µm thickness ($$n = 3$$/group) and subsequently dried overnight at room temperature (RT). The paraffin-embedded liver sections were submitted to paraffin-removing protocols, using xylol, progressive hydration (EtOH $100\%$/$70\%$/$30\%$ 3′/each and Milli-Q water during 3′ at RT), and stained with hematoxylin and eosin (H&E). Then, the liver sections were washed again, and coverslips were mounted using a mounting medium (DAKO, Kyoto, JAPAN). Lastly, images (100×) were captured in a Zeiss microscope with an incorporated camera (Zeiss, Jena, Germany). All full-size representative images in the manuscript are presented in the Supplementary Data. ## 2.5. Western Blot Hepatic and VAT samples were collected and washed with PBS and disrupted in lysis buffer (0.25 M Tris-HCl, 125 mM NaCl, $1\%$ TritonX-100, $0.5\%$ SDS, 1 mM EGTA, 1 mM EDTA, 20 mM NaF, 2 mM Na3VO4, 10 mM βglycerophosphate, 2.5 mM sodium pyrophosphate, 10 mM PMSF, 40 µL of protease inhibitor) using the TissueLyser system (Quiagen, Hilden, Germany). The bicinchoninic acid (BCA) Protein Assay Kit was carried out on the supernatant (14,000 rpm for 20 min at 4 °C, followed by the addition of Laemmeli buffer (62.5 mM Tris-HCl, $10\%$ glycerol, $2\%$ SDS, $5\%$ β-mercaptoethanol, and $0.01\%$ bromophenol blue). Tissue samples (20 µg) were loaded onto SDS-PAGE and electroblotted into polyvinylidene difluoride (PVDF) membrane (Advansta, San Jose, CA, USA). Tris-buffered saline-tween (TBS-T) $0.01\%$ and bovine serum albumin (BSA) $5\%$ were used to block the membranes, which were then incubated with primary (overnight, 4 °C) and secondary antibodies (2 h RT), following the dilutions listed in the Supplementary Table S1. The proteins of interest were detected using enhanced chemiluminescence (ECL) substrate with the LAS 500 system (GE Healthcare, Chicago, IL, USA). The bands of interest were quantified with Image Quant 5.0 software (Molecular Dynamics). The results were expressed as a percentage of control and normalized for the loading control (calnexin, 83 kDa). ## 2.6. Statistical Analyses The results are presented as the mean ± standard error of the mean (SEM). Statistical analysis was performed with GraphPad Prism 8 (GraphPad Software, Inc., San Diego, CA, USA). The normality of the data was assessed with the Shapiro–Wilk normality test. Accordingly, data with two conditions were analyzed with a nonpaired t-test or Mann–Whitney test, and data with more than two conditions were analyzed with the Kruskal–Wallis test or with a one-way ANOVA followed by Tukey’s post hoc test. Differences were considered for $p \leq 0.05.$ ## 3.1.1. Maternal DIO Modulates Energy Balance Mechanisms in the VAT and Liver of Male Offspring Exposure to maternal obesity increased male offspring body weight until weaning day, as previously published by Stevanović-Silva et al. [ 50] (summary presented in Figure 1B). Maternal obesity did not cause alterations in hepatic levels of IR (Figure 1C), AMPK (Figure 1D), phosphorylated AMPK (Figure 1E), and PPARα in male offspring (Figure 1F). Exposure to the maternal high-fat high-sugar (HFHS) diet induced a drastic reduction in D1R levels in the male offspring liver ($p \leq 0.001$ vs. control) (Figure 1H) without affecting the levels of NPY1R (Figure 1G). Regarding VAT, maternal obesity induced by the HFHS diet decreased total IR levels ($p \leq 0.05$ vs. control) (Figure 1I) in male offspring without changing AMPK (Figure 1J) or PPARγ levels (Figure 1L). NPY1R (Figure 1M), NPY2R (Figure 1N), and GHS-R1α (Figure 1O) were significantly increased in male offspring VAT ($p \leq 0.001$ vs. control, $p \leq 0.05$ vs. control, and $p \leq 0.05$ vs. control, respectively), suggesting adipogenesis and lipogenesis upregulation. Moreover, the maternal HFHS diet increased male offspring D1R levels ($p \leq 0.05$ vs. control) (Figure 1P) and AMPK phosphorylation ($p \leq 0.05$ vs. control) (Figure 1K) in VAT compared to offspring from dams fed a standard diet. ## 3.1.2. Maternal Obesity Alters Levels of Energy Balance-Regulating Receptors in the VAT of Female Offspring Maternal DIO female offspring were also studied to assess a potential sexual dimorphism in the liver and VAT adaptation to the maternal metabolic state. As occurs in males, maternal obesity increased female offspring body weight until weaning day (in summary in Figure 2A) (previously published [49]). Similar to male livers, there were no alterations in total IR levels (Figure 2B), and a significant reduction in D1R (Figure 2G) in female offspring exposed to the maternal HFHS diet during the perinatal period was observed ($p \leq 0.001$ vs. control). Nevertheless, in female offspring livers, NPY1R, PPARα, and p-AMPK levels also decreased ($p \leq 0.05$ vs. control, $p \leq 0.05$ vs. control, and $p \leq 0.05$ vs. control, respectively) (Figure 2D–F, respectively), while total levels of AMPK increased ($p \leq 0.05$ vs. control) (Figure 2C), showcasing the disparity between sexes. Regarding VAT, besides the increased p-AMPK in female offspring ($p \leq 0.05$ vs. control) (Figure 2J), the total levels of AMPK also augmented ($p \leq 0.05$ vs. control) (Figure 2I), suggesting that the higher activation of AMPK is a consequence of additional AMPK being available. The changes in the NPY2R, D1R, and total IR levels observed in males were not noticed in the VAT of female offspring (Figure 2H,M,O). Surprisingly, the NPY1R levels were significantly decreased in the female offspring VAT ($p \leq 0.05$ vs. control) (Figure 2L), demonstrating the sexual dysmorphism of such mechanisms. In the case of the ghrelin receptor, as well as in males, an increase in VAT was observed in female offspring ($p \leq 0.05$ vs. control) (Figure 2N). ## 3.1.3. Postnatal Overfeeding Modulates Energy Balance Mechanisms in the VAT and Liver of Male Offspring In this study, we used males from SL as a model of postnatal overfeeding without modification in the maternal diet, shown to induce obesity early in life [46,51]. Indeed, animals from SL had significantly higher body weight than the control group at the weaning PND 21 ($p \leq 0.001$ vs. control) (Figure 3B). The higher weight gain was maintained over time until the day of euthanasia—PND 45 ($p \leq 0.01$ vs. control) (Figure 3C), despite no changes in food intake being observed (Figure 3D). The decay of the glucose rate during the insulin tolerance test per minute (kITT) revealed that insulin sensitivity was not affected in SL rats (Figure 3E), although plasma insulin levels were decreased ($p \leq 0.001$ vs. control) (Figure 3F). Triglyceride ($p \leq 0.05$ vs. control) (Figure 3G) and HDL cholesterol levels were increased in male overfed rats ($p \leq 0.05$ vs. control) (Figure 4I) without affecting cholesterol total level (Figure 3H). Similar to the maternal HFHS diet model (Figure 1), total liver levels of IR (Figure 3J), PPARα (Figure 3M), NPY1R (Figure 3N), AMPK (Figure 3K), and phosphor-AMPK (Figure 3L) were not affected, and D1R levels were decreased ($p \leq 0.05$ vs. control) (Figure 3O) in the SL animals with higher consumption of breastmilk during the perinatal period. Moreover, histology showed dispersed fat accumulation zones in the liver of overfed male animals (Figure 3P), while no changes were observed in the liver weight (supplementary data—Figure S1A). The alterations observed in VAT in postnatal overweight offspring induced by overfeeding were different from rats exposed to maternal DIO. Interestingly, the total IR levels in the VAT were increased ($p \leq 0.01$ vs. control) (Figure 3Q) suggesting an adaptation for the reduced levels of insulin in the plasma. AMPK activation was lower in male overfed rats ($p \leq 0.001$ vs. control) (Figure 3S), while the total AMPK (Figure 3R) and PPARγ (Figure 3T) levels were maintained. Regarding neuroendocrine mechanisms controlling adipogenic and lipogenic processes, NPY1R, GHS-R1α, and D1R levels in WAT were maintained (Figure 3U,W,X, respectively), while an increase in the adipogenic-related NPY2R levels ($p \leq 0.05$ vs. control) was also observed (Figure 3V). This was consistent with an increase in fat mass in overfed animals ($p \leq 0.01$) (Figure 3Y), without adipocyte hypertrophy (Figure 3Z,α). ## 3.1.4. Female Postnatal Overfed Rats Exhibit Similar Metabolic Adaptations to Male Offspring except the Downregulation of VAT Adipogenic Mechanisms Food intake (Figure 4C) and liver weight (supplementary data—Figure S1B) were not altered in SL female animals, and contrary to SL males, postnatal overfeeding did not induce overweight in female rats over the 45 days (Figure 4A,B), nor did it increase triglyceride levels (Figure 4F). Regarding insulin levels, the outcome of postnatal overfeeding was the same in both sexes: insulin plasma levels were reduced ($p \leq 0.001$ vs. control) (Figure 4E), while insulin sensitivity did not alter according to kITT (Figure 4D). Cholesterol levels in the plasma of overfed females increased ($p \leq 0.05$ vs. control) (Figure 4G), accompanied by an increase in HDL levels ($p \leq 0.05$ vs. control) (Figure 4H). No changes in the studied mechanisms were observed in the postnatally overfed females’ livers: total IR, total AMPK, p-AMPK, PPARα, NPY1R, and D1R levels (Figure 3I–N, respectively). Histology demonstrated few areas of lipid droplet accumulation in the liver (Figure 3O), as occurs in male overfed animals. Regarding AT, total IR levels are maintained in overfed females (Figure 4P), contrary to overfed male rats, as well as total AMPK and p-AMPK levels (Figure 4Q,R, respectively). Regarding energy storage mechanisms, both NPY1R and NPY2R were reduced in the VAT of postnatal overfed females ($p \leq 0.01$ vs. control and $p \leq 0.05$ vs. control, respectively) (Figure 4T,U, respectively), which was consistent with decreased PPARγ levels ($p \leq 0.01$ vs. control) (Figure 4S), unaffected fat mass (Figure 4X) and adipocyte size (Figure 4Y,Z), indicating that lipogenesis and adipogenesis may be reduced in postnatally overfed females. Ghrelin receptor (Figure 4V) and D1R levels were not altered in the VAT of SL females (Figure 4W). ## 3.2.1. Glycation Changes Breastmilk Composition The glycation induced by BBGC (5 mg/kg) through IP did not alter the body weight or glycemic profile of dams (Figure 5B,C, respectively). Furthermore, BBGC did not cause toxicity in the liver (Figure 5D). However, glycation changed breastmilk quality, decreasing triglyceride levels ($p \leq 0.01$ vs. control) and total antioxidant capacity ($p \leq 0.05$ vs. control) (Figure 5E,F, respectively). A known methylgyoxal-derived AGE is N-(5-hydro-5-methyl-4-imidazolon-2-yl)-ornithine (MG-H1) [52]. This compound was increased in VAT from male offspring exposed to maternal glycotoxins ($p \leq 0.05$ vs. control; $p \leq 0.05$ vs. vehicle) (Figure 5G), showing that inhibition of glyoxalse I in dams leads to AGEs accumulation in VAT offspring. ## 3.2.2. Exposure to Glycotoxins during Lactation Decreases Offspring Food Intake and Impairs Insulin-Dependent Glucose Uptake without Affecting the Insulin Levels of Male Offspring Obesogenic environments contribute to body weight alteration in early life, which is not often observed in lean or adult experimental models of glycation [53]. Here, we observed that maternal glycation did not affect the body weight of male offspring either during the breastfeeding period or after weaning (Figure 6A,B, respectively), although lower food consumption was observed ($p \leq 0.01$ vs. control) (Figure 6C). Despite no alterations In insulin plasma levels (Figure 6E), the kITT was reduced in male offspring exposed to maternal glycation ($p \leq 0.05$ vs. control) (Figure 6D), suggesting lower insulin sensitivity. Exposure to glycotoxins reduced triglyceride levels in breastmilk without affecting the plasma triglyceride, total, and HDL cholesterol levels in male offspring (Figure 6F–H). ## 3.2.3. Exposure to Maternal Glycation Reduces Both NPY and Dopamine Signalling in the Liver from Male Offspring without Affecting the VAT Although insulin sensitivity was reduced in male offspring exposed to glycotoxins during lactation, total IR levels in the liver and WAT were not altered (Figure 6I,P, respectively). Total and activated AMPK also remained similar between groups in the hepatic tissue (Figure 6J,K, respectively). In the liver, maternal glycation induced a reduction in NPY1R ($p \leq 0.01$ vs. control; $p \leq 0.05$ vs. vehicle), D1R ($p \leq 0.01$ vs. control), and PPARα levels ($p \leq 0.001$ vs. control; $p \leq 0.01$ vs. vehicle) (Figure 6M,N,L, respectively). Furthermore, maternal glycation appeared to induce portal inflammatory infiltration at the hepatic level (Figure 6O), while no changes were observed in the liver weight (Supplementary Data—Figure S1C). Adult models exposed to glycated products showed that glycation does not cause significant changes in VAT function in the lean phenotype [53]. Here, we demonstrate that male offspring from dams treated with glyoxalase 1 inhibitor did not present changes in energy expenditure or storage mechanisms in VAT. The receptors of NPY, ghrelin, and dopamine evaluated (NPY1R, NPY2R, GHS-R1α, and D1R, respectively) display preserved levels when compared with the vehicle and with the control (Figure 6T–W, respectively) as well as the downstream proteins AMPK and PPARγ (Figure 6Q–S). Furthermore, neither fat mass (Figure 6X) nor adipocyte size was altered in male offspring exposed to maternal glycation at PND 45 (Figure 6Y,Z). ## 3.2.4. BBGC-Induced Maternal Glycation Does Not Affect Food intake, Metabolic Profile and VAT Mechanisms of Lipid Storage and Energy Expenditure in Female Offspring As well as in the male offspring, during the lactation period and after weaning, no alteration in body weight of females exposed to maternal glycation (Figure 7A,B) or liver weight (supplementary data—Figure S1D) was observed. Contrary to what was observed in male offspring, food intake was not affected in BBGC female offspring (Figure 7C). Furthermore, no changes in kITT were observed in female offspring (Figure 7D), suggesting that male offspring are more susceptible to alteration in insulin sensitivity and feeding regulation when exposed to the same maternal condition. Although plasma insulin levels in BBGC female offspring were decreased ($p \leq 0.01$ vs. control), this was also observed in the vehicle group ($p \leq 0.01$ vs. control) (Figure 7E). Triglyceride (Figure 7F) and HDL (Figure 7H) levels were also unaltered between groups, while cholesterol levels were affected by vehicle ($p \leq 0.05$ vs. control; $p \leq 0.05$ vs. BBGC) (Figure 7G). Total IR levels were also maintained in both the liver and WAT of female offspring (Figure 7I,P, respectively). The total levels of AMPK were increased in the liver of female offspring exposed to glycotoxins ($p \leq 0.001$ vs. control; $p \leq 0.001$ vs. vehicle) (Figure 7J) while its activity remained similar to the other groups (Figure 7K). PPARα levels also remained similar between all conditions (Figure 7L), and no inflammatory infiltration was observed in the livers of female rats exposed to maternal glycation (Figure 7O). NPY1R levels were decreased in females from dams treated with DMSO ($p \leq 0.01$ vs. control) and from dams subjected to glyoxalase 1 inhibition ($p \leq 0.01$ vs. control) (Figure 7M). However, the effect on D1R observed in the vehicle group ($p \leq 0.01$ vs. control) seems to be partially independent of the reduction induced by BBGC ($p \leq 0.001$ vs. control; $p \leq 0.05$ vs. vehicle) (Figure 7N), which is in accordance with the results observed in male offspring. As occurs in male offspring exposed to maternal glycation during lactation, in BBGC female offspring, the WAT mechanisms associated with energy expenditure and storage were not affected by maternal glycation alone (Figure 7P–W) as were the fat mass and the adipocyte size (Figure 7X–Z). These results demonstrated once again that glycation alone may not be sufficient to alter the energy balance in the VAT of lean animals at PND 45. ## 3.2.5. Maternal Glycation Inhibits SL-Induced Weight Gain in Male and Impairs VAT Mechanisms of Adaptation to Overfeeding BBGC also reduced milk triglyceride levels ($p \leq 0.05$ vs. SL) and total antioxidant capacity ($p \leq 0.05$ vs. control) in SL dams (Figure 8B,C, respectively). Exposure to maternal glycation during lactation prevented male offspring weight gain induced by postnatal overfeeding from PND14 to PND35 ($p \leq 0.05$ vs. SL) (Figure 8D,E). No alterations were observed in food intake or the kITT (Figure 8F,G, respectively) or in the liver weight (supplementary data—Figure S1E) in the BBGC + SL group as occurs in SL animals with healthy motherhood. The alterations in plasma insulin ($p \leq 0.05$ vs. control) (Figure 8H) and triglyceride ($p \leq 0.05$ vs. control) (Figure 8I) levels observed in SL males were maintained in SL + BBGC offspring. Cholesterol levels were reduced in SL rats exposed to maternal glycation ($p \leq 0.05$ vs. SL) (Figure 8J), while the increase in HDL-cholesterol levels observed in male SL was not noticed when the animals were exposed to glycotoxins (Figure 8K). Total IR levels were maintained in the liver and VAT of male SL + BBGC offspring compared to SL (Figure 8L,S, respectively), and its upregulation in the liver of SL males was maintained in relation to control rats. AMPK levels and activity in the liver did not alter in BBGC + SL rats (Figure 8M,N) as occurs in animals exposed to both conditions independently. Surprisingly, the PPARα levels were not altered upon exposure to maternal glycation in animals overfed and induced by SL (Figure 8O) as occurred in lean offspring. As well as observed in the liver from lean male offspring exposed to maternal glycation, in obese conditions, the decrease in NPY1R levels was also verified ($p \leq 0.01$ vs. SL) (Figure 8P), while the downregulation of D1R levels observed with both conditions per se was maintained ($p \leq 0.001$ vs. control) (Figure 8Q). These changes were associated with liver inflammatory infiltration, which occurs in lean male offspring from dams treated with BBGC (Figure 8R). Importantly, this male offspring did not show visible liver fat accumulation, contrary to the overfed ones. The decrease in p-AMPK levels induced by postnatal overfeeding in VAT was lost (Figure 8U), while the total levels of AMPK were maintained (Figure 8T) in male offspring exposed to glycotoxins. As already mentioned, postnatal overfeeding causes adaptations in VAT, namely NPY2R overexpression. Exposure to glycated products during lactation induced VAT energy balance dysregulation in postnatal overfed offspring. Despite no changes in PPARγ, NPY1R, or GHS-R1α levels in the VAT of overfed offspring from dams treated with BBGC (Figure 8V,W,Y, respectively), NPY2R levels significantly decreased ($p \leq 0.05$ vs. SL) (Figure 8X) and D1R further decreased compared to the control ($p \leq 0.05$ vs. control) (Figure 8Z). Interestingly, although the visceral fat mass did not change in these animals (Figure 8α), their adipocyte size was significantly reduced ($p \leq 0.05$ vs. SL), following reduced NPY2R levels, and several multivesicular adipocytes were observed, a marker of adipocyte dysfunction (Figure 8β,γ). ## 4. Discussion Exposure to unhealthy motherhood during the perinatal period has been suggested as a risk factor for the rise of metabolic diseases worldwide. Herein, we describe: [1] the distinct metabolic and neuroendocrine effects in the liver and VAT caused by two obesogenic conditions during developmental programming phases, exposure to perinatal maternal HFHS diet and postnatal overfeeding; [2] the sexual dysmorphism of such responses; and [3] the impact of maternal glycation in disrupting mechanisms of adaptation to postnatal overfeeding (Figure 9). Maternal gestational obesity induced by the HFHS diet in the same model causes postnatal overweight and hepatic lipid accumulation in male offspring [49,50]. Here we show that this maternal diet decreases liver D1R levels in both sexes. In adult diabetic rats, lower D1R levels were also observed. Its upregulation by bromocriptine was associated with lipid mobilization from the liver and hepatic steatosis improvement [54]. However, Stevanović-Silva et al. showed no lipid accumulation in female offspring [49,50]. Interestingly, the liver of female offspring presented lower levels of NPY1R, which suggests less inhibition of CTP-1 and lipid accumulation [55], regardless of PPARα level reduction. In male offspring VAT, maternal DIO decreased IR levels, which may impact glucose uptake and triglyceride formation [56]. However, p-AMPK was upregulated, which is known to promote HSL activity and lipid oxidation. This upregulation was associated with increased D1R levels, in accordance with previous reports showing its role in lipid oxidation [27]. Moreover, the receptors associated with adipogenic and lipogenic processes (NPY1R, NPY2R, and GHS-R1α) levels were also increased, suggesting an adiposity effect of maternal DIO in the offspring. These results suggest that maternal obesity may trigger compensatory mechanisms to enhance lipid storage in male offspring VAT while also counteracting this by simultaneously improving energy expenditure in VAT. Such VAT alterations were sex specific since NPY1R levels were decreased in female offspring, suggesting a reduction in lipogenic/adipogenic processes, contrary to what occurs in males. Indeed, fat distribution depends on sex, with females more prone to accumulate subcutaneous AT (SAT), while males store lipids predominantly in VAT [56,57]. SAT has a lower lipolysis rate but is more efficient on the FFAs uptake than VAT [56,57]. On the other hand, VAT has a lower capacity to store lipids and a more inflammatory environment [56,57]. One limitation of our study is the lack of SAT data to compare the same mechanisms in male and female SAT. Thus, the discrepancy between sex may be associated with the fatty liver presented by the male offspring. Males have a larger VAT area than females, exposing the liver to a higher amount of FFA via the portal vein, which induces fat ectopic deposition in the liver [58]. Postnatal overweight was also induced by neonatal overfeeding, although body weight gain did not occur in females and no increased food consumption was observed after weaning. Male overfed rats have lower plasma insulin levels, suggesting insulin release impairment. Accordingly, Robert A. et al. [ 2002] demonstrated that the SL animal model at two different ages (PND 26 and PND 110) presents an impairment in the release of insulin from β-cells [59]. Interestingly, IR total levels were increased in male VAT, which may be a compensatory response to hypoinsulinemia. In male rats, overfeeding also promoted an impairment of D1R hepatic levels, as occurs in maternal HFHS-induced early-life obesity. These findings suggest that this may be an important mechanism of lipid retention in the liver, being associated with the presence of lipid droplets in this tissue (Figure 3N). In VAT, neonatal overfeeding in male rats also upregulates NPY2R signaling, potentiating AT expansion and preventing hypertrophy, while no other mechanisms were changed. Thus, the impact of postnatal overweight on VAT energy balance mechanisms apparently depends on the nature of the obesogenic environment. In fact, contrarily to rats exposed to maternal DIO, p-AMPK levels were reduced in VAT from overfed male animals. This suggests that, besides higher caloric intake from milk, nutritional cues may have a direct impact on VAT mechanisms of energy expenditure. It is possible that maternal consumption of fats and sugars stimulates not only energy storage but also energy dissipation pathways in the male offspring. Again, sex was shown to be a crucial factor in energy balance mechanisms. Neonatal overfeeding in females reduces lipogenic and adipogenic processes by decreasing NPY/PPARγ signaling, which may be the reason for the absence of weight gain and may be related to the distinct stimulation of different fat pads, as already discussed. Given that maternal diets are often rich in sugars, we also intended to study the effects of exposure to maternal glycotoxins on the mechanisms of adaptation to obesogenic environments. Previous studies have used direct administration of methylglyoxal (MG)—an AGEs precursor—in dams [48], and we aimed to use a more physiological model using BGGC injection, inhibiting the enzyme responsible for MG detoxification—GLO1. In the study by Francisco et al. [ 2018], breastmilk triglyceride levels were increased upon maternal MG ingestion. However, we verified the opposite and a lower antioxidant capacity, suggesting that maternal glycation alters breastmilk composition. As described before, AGEs formation debilitates antioxidant defenses (reviewed by [53]), and we have here shown that maternal glycation has also an impact on breastmilk antioxidant capacity and possibly on the detoxification mechanisms of the offspring. As occurs in adults exposed to glycotoxins, BBGC did not affect maternal or male and female offspring body weight [20]. Nevertheless, Francisco et al. [ 2018] showed that offspring from MG-treated dams present excessive weight gain only after PND 77, which may be a consequence of metabolic dysregulation secondary to the dose used [48]. As observed in adult models, animals exposed to maternal glycation do not develop insulin resistance, glucose dysmetabolism, or changes in insulinemia [20], although a lower kITT indicates an insufficiency in insulin action, suggesting a predisposition to insulin resistance. Regarding female offspring, plasma insulin levels were decreased, although female offspring of DMSO-treated dams also present this deficiency. Thus, there is no evidence of insulin resistance, consistent with several studies that have reported that females are less prone to develop insulin resistance due to hormonal sex differences [57]. Other studies indicate that glycation plays a more prevailing role in obesity than in normal conditions [57]. Here we observed that maternal glycation does not alter the molecular mechanisms associated with energy balance in the WAT of lean female and male offspring. However, we detected that impairment of D1R levels in the livers of both sexes, as observed in the overfeeding models. These results suggest that changes in milk composition induced by glycation also impact the hepatic level, decreasing D1R and PPARα, a transcription factor known to be activated by FA, and thus possibly reducing lipid oxidation [60]. In addition, NPY1R levels are also diminished in the liver of male offspring exposed to maternal glycotoxins. Moreover, maternal glycation induced inflammatory infiltration in the liver of male offspring. Interestingly, NPY1R has been linked with an anti-inflammatory action associated with a pro-inflammatory M1-like phenotype in other regions, namely the kidney and cardiac system [61,62]. Regarding female livers, no major changes were observed besides those associated with the vehicle, DMSO, and the stress of the daily IP injection. Diverse studies have shown that females are more prone to develop obesity when exposed to maternal stress than males [63]. Moreover, the effects of maternal prenatal stress on gut microbiota colonization are sex-dependent [64]. As a regulator of gut hormone release [65], alterations induced by maternal alterations in the microbiota may change the release of gut-derived hormones. Previous reports from our group showed that weight gain induced by the HF diet in adult Wistar rats is lost when MG is administered, suggesting that glycation impairs the necessary tissue plasticity to respond to higher nutritional fluxes [20,53]. Indeed, the weight gain provoked by postnatal overfeeding is lost when the young animals are exposed to maternal glycotoxins during lactation. However, the metabolic effects of overfeeding in male offspring regarding plasma triglycerides and insulin levels were maintained when exposed to maternal glycation. Such inhibition of weight gain was associated with the loss of compensatory NPY2R upregulation in overfeeding-induced overweight rats, suggesting a decrease in adipogenesis rates promoting a lower energy storage capacity. Indeed, despite the fact that maternal glycation did not change fat mass, the adipocytes were smaller when the animals experienced maternal glycation. Moreover, several regions show multivesicular adipocytes, a marker of impaired lipid storage and adipocyte dysfunction. Thus, maternal glycation may modify the AT environment, especially in conditions of overfeeding/WAT expansion. So, exposure to glycotoxins during lactation in obesogenic conditions apparently inhibits the development of storage mechanisms to compensate for higher food consumption. These alterations predispose to the development of metabolic syndrome due to their contribution to adipocyte hypertrophy, lipotoxicity, hypoxia in the AT, and insulin resistance, hallmarks of an unhealthy WAT phenotype. In the liver, maternal glycation impairs NPY1R in lean and overfed animals. Moreover, the hepatic inflammatory infiltration provoked by glycotoxins exposure during lactation was also noticed. These results suggest that alterations in NPY1R signaling and inflammatory infiltration are an exclusive consequence of maternal glycation exposure, as in the livers of overfed animals, NPY1R levels were preserved. ## 5. Conclusions Dysregulation of energy balance mechanisms is one of the major factors contributing to the development of metabolic diseases. Insults such as obesogenic environments during the perinatal period program adaptations of nutrient-sensing mechanisms, readjusting anabolic/catabolic processes. Here, we characterized, for the first time to our knowledge, the changes in VAT and liver energy balance pathways caused by maternal obesity during pregnancy and postnatal overfeeding, although different rat strains were used. Besides the weight gain, young male animals exposed to the maternal HFHS diet developed compensatory mechanisms regarding energy storage and expenditure, allowing a higher AT plasticity. Postnatal overfeeding apparently protects from AT dysfunction by enhancing energy storage. So, the different consequences in both models suggest that maternal nutritional cues during lactation have a crucial role in their modulation. Moreover, females showed distinct mechanisms in both models, suggesting that lipid partitioning may be different in both sexes since the early stages of development and the energy balance mechanisms in SAT should be of future interest. Nevertheless, despite developing these compensatory peripheral mechanisms, modulation of such pathways can become unhealthy since these compensatory actions may be lost later in life. This may disturb energy balance and therefore contribute to predisposing to the development of metabolic complications. To characterize the role of Westernized diet consumption during gestation and lactation, we studied the role of maternal glycation under normal and obese conditions on VAT and liver energy balance mechanisms. Although glycated products do not have major effects on a lean phenotype, they are related to a compromise of AT plasticity, which may lead to AT dysfunction and lipotoxicity when combined with an obesogenic environment (overfeeding). We followed a protocol based on GLO-1 inhibition in order to avoid possible supraphysiological doses after MG administration. However, it is possible that exposure to MG in high-sugar diets may be even higher than in our protocol. In the future, both protocols may be compared for such effects. Our work points out the relevance of better characterizing these mechanisms in later stages of development and how they predispose to insulin resistance and metabolic diseases at a more advanced age. The role played by pregnancy and lactation should also be disclosed using cross-fostering. 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--- title: A Turn-On Lipid Droplet-Targeted Near-Infrared Fluorescent Probe with a Large Stokes Shift for Detection of Intracellular Carboxylesterases and Cell Viability Imaging authors: - Chenglin Li - Sifan Li - Xinsheng Li - Tao Yuan - Jialei Xu - Xixin Gu - Jianli Hua journal: Molecules year: 2023 pmcid: PMC10005208 doi: 10.3390/molecules28052317 license: CC BY 4.0 --- # A Turn-On Lipid Droplet-Targeted Near-Infrared Fluorescent Probe with a Large Stokes Shift for Detection of Intracellular Carboxylesterases and Cell Viability Imaging ## Abstract Carboxylesterases (CEs) play important physiological roles in the human body and are involved in numerous cellular processes. Monitoring CEs activity has great potential for the rapid diagnosis of malignant tumors and multiple diseases. Herein, we developed a new phenazine-based “turn-on” fluorescent probe DBPpys by introducing 4-bromomethyl-phenyl acetate to DBPpy, which can selectively detect CEs with a low detection limit (9.38 × 10−5 U/mL) and a large Stokes shift (more than 250 nm) in vitro. In addition, DBPpys can also be converted into DBPpy by carboxylesterase in HeLa cells and localized in lipid droplets (LDs), emitting bright near-infrared fluorescence under the irradiation of white light. Moreover, we achieved the detection of cell health status by measuring the intensity of NIR fluorescence after co-incubation of DBPpys with H2O2-pretreated HeLa cells, indicating that DBPpys has great potential applications for assessing CEs activity and cellular health. ## 1. Introduction As significant members of the serine hydrolase superfamily [1], carboxylesterases (CEs) catalyze the cleavage of esters of substrates into the corresponding alcohols and carboxylic acids in living organisms and are intimately involved in various metabolic and transport processes in the human body (e.g., protein metabolism, ester metabolism, detoxification, gene expression and biological signaling) [2,3,4]. Studies have shown that an abnormal expression of CEs is closely associated with many diseases such as malignancy, hyperlipidemia, atherosclerosis, Wolman disease and diabetes mellitus [5,6]. Other than being involved in biological metabolism, the targeting, delivery and release of some prodrugs are also associated with intracellular CEs activity [7]. More importantly, as one of the important biomarkers of cell metabolism, CEs play an important role in cell viability and cytotoxicity assays [8]. Therefore, the development of a reliable assay method to detect CE levels and thus reflect cellular status is much needed. Lipid droplets (LDs), as one of the vital hubs in various intracellular physiological processes, are widely present in all types of mammalian cells [9,10]. Once thought to be merely inert fat particles, they are now considered to be highly dynamic, mobile organelles that interact and cooperate closely with a variety of cellular organelles [11]. Recent studies have shown that LDs are elevated in the hypoxic environment of tumors, giving LDs the potential for application in cancer diagnosis and treatment [12]. As one of the latest hot spots in the field of bioimaging, fluorescent probes targeting lipid droplets are also emerging. Researchers have developed a variety of fluorophore-based fluorescent probes for intracellular lipid droplet imaging [13,14,15]. However, there are still relatively few fluorescent probes that combine endogenous material detection and lipid droplet imaging. Fluorescent probes are widely used because of their non-invasive nature, high sensitivity, low cost and real-time imaging [16,17]. Fluorescent probes are more suitable for the detection of biological endogenous molecules due to their better interference immunity, lower detection limits and more pronounced changes than colorimetric probes that vary in absorption. After reaction with the target molecule, fluorescent probes show changes in the fluorescence signal, such as wavelength, emission intensity and lifetime [18,19,20,21]. By tracking these changes, a number of fluorescent probes have been reported for the detection of CEs activity [13,22,23,24]. For example, Tang and Liu et al. [ 25] reported a fluorescent light-up probe for the specific detection of lysosomal esterase, but its emission wavelength was less than 540 nm. Our group previously reported a new diketopyrrolopyrrole-based ratiometric fluorescent probe for intracellular esterase detection, but its fluorescence emission could not reach the near-infrared region and the Stokes shift was small [26]. Compared to conventional fluorescent probes, near-infrared (NIR, 650–900 nm) fluorescent probes exhibit extraordinary advantages, such as deeper penetration in tissues, elimination of interference from background autofluorescence and great facilitation of the imaging of molecular processes in vivo [27,28,29,30,31]. Meanwhile, the large Stokes shift facilitates bioimaging by avoiding significant overlap between excitation and fluorescence spectra, as well as self-quenching caused by backscattering of biological samples [32,33]. Proverbially, the construction of donor–acceptor (D-A) structures has been considered an effective method for broadening the absorption/emission wavelengths of fluorescent probes [34,35,36]. Reduced phenazine as a typical donor (D) has the advantages of strong electron-donating ability, multiple modification sites, excellent stability and low biotoxicity [37,38,39]. In this paper, by introducing p-cyanopyridine to phenazine as an acceptor (A), we designed a new D-A fluorescent fluorophore, DBPpy, with NIR emission and large Stokes shifts of more than 250 nm. Benefiting from its strong hydrophobicity, DBPpy could locate in subcellular organelle lipid droplets (LDs). Based on previous works [26,40,41], 4-bromomethyl phenyl ester has been commonly used for the recognition of CEs. Herein, we attached the moiety to the DBPpy to form a “turn-on” fluorescent probe DBPpys (Scheme 1). In the presence of various interfering substances, DBPpys still presented a specific reaction to CEs with a low LOD of 9.38 × 10−5 U/mL. Furthermore, the probe DBPpys was able to detect endogenous CEs activity in HeLa cells. After pretreating with H2O2, the viability of HeLa cells could be detected by changes in fluorescence, indicating that DBPpys has great potential applications for assessing CEs activity and cellular health. ## 2.1. Synthesis and Photophysical Properties The synthetic route for DBPpys is shown in Scheme S1. First, the compound DBP-CHO was synthesized according to our previous literatures [42,43]. In the next step, p-cyanopyridine was introduced to DBP-CHO via the Knoevenagel reaction. Finally, the NIR fluorescent probe DBPpys was obtained via the electrophilic substitution of DBPpy and 4-bromomethyl phenyl ester. The detailed synthesis steps and reaction conditions were presented in the Supporting Information, and the important intermediates and target compounds were well characterized by 1H nuclear magnetic resonance (NMR) and 13C NMR spectroscopies and high-resolution mass spectrometry (HRMS) (Figures S8–S17). After obtaining the target compounds, we tested their photophysical properties. As shown in Figure 1A, the maximum absorption peak of DBPpy was at 465 nm, together with a shoulder peak at 550 nm in mixed solvent (DMF:HEPES, v:$v = 4$:6). Additionally, an NIR emission at 717 nm was observed, and the fluorescence quantum yield of DBPpy was $4.8\%$. The Stokes shift of DBPpy was more than 250 nm in this mixed solvent, which is favorable for confocal imaging. We speculate that the large Stokes shift is facilitated by the donor–acceptor (D-A) structure’s intermolecular charge transfer (ICT) process from the phenazine donor (D) to the cyanopyridine electron acceptor (A). It is known that the ICT process is more likely to be influenced by the polarity of the solvent [44,45]. As shown in Figure S1B, the absorption spectra of DBPpy were similar in different solvents, which indicated that no conformational transition occurred at the ground state. However, the fluorescence emission spectra of DBPpy varied considerably with the different solvents. In nonpolar solvents such as toluene, the emission peak of DBPpy was located at 635 nm, while it was redshifted to 710 nm in DMF. With increasing polarity, the fluorescence redshifted and the emission intensity gradually decreased (as shown in Figure 1B and Figure S1A), which implied that ICT may have occurred. ## 2.2. Spectroscopic Response of DBPpys to CEs Primarily, the response time of DBPpys towards CEs was measured in DMF:HEPES (v:$v = 4$: 6, pH = 7.4) at 37.4 °C. The fluorescence response of 10 μM DBPpys to 0.08 U/mL CEs during 0–90 min is shown in Figure S2. The fluorescence intensity increased significantly at 720 nm with increasing reaction time and leveled off after 60 min, indicating that the reaction could be completed within 60 min. Subsequently, the response of 10 μM DBPpys to different concentrations of CEs was tested after 60 min. After the reaction of DBPpys with CEs in this solvent mixture, the probe showed a clear color change from green to brown, and the intensity of the absorption peak at 400 and 690 nm decreased while that of the peak at 465 and 550 nm increased (as shown by the arrow in Figure 1C). Meanwhile, the fluorescence intensity at 720 nm gradually increased with the addition of CE equivalents in DBPpys solution (Figure 1D,F), which was consistent with the emission peak of DBPpy, indicating that the pyridine cation of DBPpys might turn into pyridine after the reaction with CEs. Interestingly, the fluorescence intensity at 720 nm in the range from 0.005 U/mL to 0.02 U/mL also showed a good linear relationship with the concentration of CEs with an equation of R1 = 117,260 × C (U/mL) + 164.66245 (R2 = 0.99684) (Figure 1G), suggesting that DBPpys can be used for endogenous imaging of CEs. The detection limit was calculated as 9.38 × 10−5 U/mL based on 3σ/k, where σ is the standard deviation of the blank measurement and k is the slope of the linear equation. ## 2.3. Exploration of Reaction Mechanism Since the fluorescence emission spectrum of the probe DBPpys after the addition of CEs was essentially the same as that of DBPpy, we hypothesized that the reaction mechanism was the specific excision of the acetate group by CEs, which led to the departure of the benzene ring adjacent to phenazine in the quinone form, thus returning to the structure of DBPpy, and the NIR fluorescence was eventually restored (Figure 2A). To verify this idea, we performed mass spectroscopy and high-performance liquid chromatography (HPLC) analysis. After extraction with dichloromethane, the high-resolution mass spectra of DBPpys with CEs were measured (Figure 2B). The mass fragments observed at 834.0416 and 606.0630 belonged to DBPpys ([M]cal+ = 755.1227) and DBPpy ([M + H]cal+ = 607.0708). Fortunately, we found the ion peak of the putative reaction intermediate DBPpy−O ([M + H]cal+ = 713.1121) in the mass spectrum, clearly validating the proposed mechanism. As shown in Figure 2C, the retention times of DBPpys and DBPpy were 8.1 and 14.4 min, respectively. When CEs was added to DBPpys solution, the 8.1 min peak belonging to DBPpys decreased, and a new peak at 14.4 min close to that of DBPpy appeared. ## 2.4. Temperature and pH Effect on the Probe and Selectivity In order to test whether DBPpys can be used for the detection of CEs in a biological environment, the performance of DBPpys in reaction with CEs under different temperatures and pH conditions was investigated. The probe DBPpys showed almost no fluorescence in the absence of CEs, and the fluorescence intensity of DBPpys increased with the addition of CEs under normal human body temperature and pH conditions (as shown in Figure 3A,B). More importantly, the NIR fluorescence emission was significantly enhanced with the addition of CEs at 37.4 °C and pH = 7.4, close to the biological environment. This result indicates that the probe can be effectively detected in a physiological environment. The chemical composition in the physiological environment is more complex, and probe DBPpys requires good selectivity for its application in living organisms. We measured the changes in emission intensity before and after the reaction of DBPpys with CEs after the addition of different interfering substances, including inorganic salts (Na2CO3, Na2SO4 and KCl), reactive oxygen species (ROS) (H2O2 and ClO−), three common amino acids (Hcy, Cys and reduced glutathione (GSH)), adenosine 5′-diphosphate (ADP), adenosine 5′-triphosphate (ATP) and proteins (bovine serum albumin (BSA), leucine aminopeptidase (LAP), human serum albumin (HSA), α-fetoprotein (AFP) and aminopeptidase N (APN)). DBPpys exhibited excellent specificity for CEs despite the presence of different interfering substances (Figure 3C). This indicates that DBPpys can be well applied in biological environments and is expected to be used for endogenous imaging of CEs in cells. ## 2.5. Intracellular Endogenous CE Detection As discussed above, DBPpys could assess CEs activity in vitro. The applications of DBPpys in living cells were further examined. First, the cytotoxicity of DBPpys was evaluated by CCK-8. DBPpys showed low cytotoxicity to HeLa cells, with cell survival exceeding $90\%$ after 24 h of incubation at a concentration of 40 μM, indicating that DBPpys had little effect on cells at the working concentration (10 μM) and is suitable for application in biological systems (Figure S3). To investigate the optimal co-incubation time of DBPpys with HeLa cells, as shown in Figure 4, we obtained fluorescence imaging maps after different reaction times by confocal laser scanning microscopy (CLSM). As can be seen from Figure 4A–C, HeLa cells co-incubated with DBPpys for 10 min already started to show fluorescence in the red channel. The fluorescence intensity increased with longer incubation times and reached its maximum when the incubation time was 60 min (Figure 4 and Figure S5A). Therefore, DBPpys has a good ability to respond to endogenous CEs in live cells; consequently, we chose 60 min as the best incubation time for DBPpys with HeLa cells. Subsequently, to verify the effect of CEs activity on the imaging effect of DBPpys, a CEs inhibitor, 4-(2-aminoethyl)benzenesulfonyl fluoride hydrochloride (AEBSF), was selected for further study [46,47]. The toxicity of AEBSF was evaluated using CCK-8, and it was confirmed that AEBSF had little effect on cells at working concentrations (Figure S3C). As shown in Figure S5, NIR fluorescence intensity of the cellular red channel was high after co-incubation with DBPpys for 60 min when no or a small amount of AEBSF was added. In contrast, as shown in Figure S4B, as the AEBSF content continued to increase, NIR fluorescence intensity decreased sharply, and at 2 mM, the fluorescence was almost invisible to the naked eye. The signal changes confirmed the specific response of DBPpys to endogenous CEs in HeLa cells and indicated that it can distinguish the CEs activity in cells based on the magnitude of fluorescence intensity. ## 2.6. Detection of Healthy Status of Cells Pretreated with H2O2 Hydrogen peroxide (H2O2) is an intermediate product of cellular oxygen metabolism commonly found in aerobic organisms. Many studies have shown that H2O2, as a common biomarker of stable ROS and oxidative stress in organisms, has significant damaging effects on cells and tissues, being capable of affecting cell viability and even causing cellular damage and death. CEs viability will be significantly stronger in healthy HeLa cells than in damaged ones. It also offers the possibility of detecting the healthy status of cells by analyzing the activity of CEs. Consequently, we pretreated HeLa cells with different concentrations of H2O2 before incubation with DBPpys (10 μM) to test whether the probe DBPpys can accurately reveal the health status of the cells. As shown in Figure 5A–C, HeLa cells not pretreated with H2O2 showed a clear NIR emission signal in the red channel, indicating that CEs were highly active in the cells, thus proving that the state of the cells was hardly impaired. As the concentration of H2O2 pretreatment increased, NIR fluorescence gradually became weaker (as shown in Figure S5C), proving that CEs activity was restricted and the activity of the reaction with DBPpys was reduced, which was attributed to the gradual decrease in cellular activity after H2O2 stimulation. When the H2O2 pretreatment concentration reached 8 mM, NIR fluorescence almost disappeared and was no longer observable through the naked eye, and the bright-field images showed unhealthy cell status. These results strongly proved that DBPpys can be used to assess the health status of cells. ## 2.7. Intracellular Lipid Droplet Colocalization Lipid droplets (LDs), a subcellular organelle capable of regulating cellular energy homeostasis, have received a lot of attention from researchers in recent years [48,49,50]. Since the fluorescence of DBPpys was severely quenched, we only needed to explore whether DBPpy could be used as a lipid droplet-targeted probe. The oil–water partitioning experiment (Figure S6) showed that DBPpy could be easily transferred from the lower aqueous phase to the upper oleic acid layer by simple oscillation, demonstrating its strong hydrophobicity. The ClogP value of DBPpy was 6.859, as calculated using Chemdraw Professional 16.0, whereas previous studies have shown that compounds usually have excellent LD-targeting ability when their ClogP values are higher than 5.0. Next, we further validated the LD-targeting ability of DBPpy by co-staining HeLa cells using the commercial LD-imaging agent BODIPY $\frac{505}{515.}$ As depicted in Figure 6A, the NIR fluorescence signal in the red channel is from DBPpy, and the fluorescence signal in the green channel is from the commercial dye BODIPY ($\frac{505}{515}$). It is evident that the two signals can overlap well, with a high Pearson correlation factor (R) of 0.90 and excellent consistency in changes in intensity profiles of regions of interest (ROI) (Figure 6(A8,A12)). To demonstrate the specific targeting ability of DBPpy on LDs, we also performed confocal experiments on other organelles, such as mitochondria and lysosomes. As shown in Figure 6B, the commercial lysosomal probe Lyso tracker Green DND-26 and the commercial mitochondrial probe Mito tracker Green FM were used to co-incubate with DBPpy in HeLa cells, but the Pearson coefficients were only 0.39 and 0.35, respectively. The results showed that DBPpy could target well in LDs, revealing that DBPpy was an excellent candidate for LD localization. ## 3.1. Materials All reagents were bought from commercial sources (Leyan (Beijing, China), Energy Chemical (Anqing, China), Sigma-Aldrich (Shanghai, China), Adamas-beta (Shanghai, China)) and used without further processing. All solvents were purified and dried before use by standard methods. The solvents used in spectrum analysis were HPLC-grade. The solutions for analytical studies were prepared with deionized water treated using a Milli-Q System (Billerica, MA, USA). Human cervical carcinoma cells (HeLa cells) were purchased from Shanghai Xinyu Biotechnology Co., Ltd. (Shanghai, China). Green fluorescence dye BODIPY$\frac{493}{503}$ lipid droplets were purchased from GlpBio Co. (Montclair, CA, USA). LysoTracker Green DND 26 and MitoTracker Green FM were purchased from Life Technologies Co (Carlsbad, CA, USA). DMEM High Glucose w/L-Glutamine w/Sodium Pyruvate (DMEM), Fetal Bovine Serum (FBS) South America (FBS), Penicillin-Streptomycin Solution 100× and Trypsin $0.25\%$–EDTA $0.02\%$ in HBSS were from Yuli Biotechnology Co., Ltd (Shanghai, China). Cell-Counting-Kit-8 (CCK-8) and 4-(2-Aminoethyl)-benzenesulfonyl fluoride hydrochloride (AEBSF) were from Aladdin (Shanghai, China). ## 3.2. Instruments 1H NMR and 13C NMR spectra were obtained with Bruker AM 400 MHz spectrometer and Ascend 600 MHz spectrometer using CDCl3 or DMSO-d6 as solvent, and tetramethylsilane (TMS) was used as an internal standard. Electrospray ionization and electron spray ionization were determined using Waters Micromass LCT mass spectrometer and Xevo G2 TOF MS. Absorption spectra were recorded on a Varian Cary 500 UV-vis spectrophotometer. Fluorescent spectra were recorded on a RF-6000 Fluorescence Spectrophotometer (SHIMADZU). Cell fluorescence images were captured using Leica Microsystems’ TCS SP5 II confocal fluorescence microscope. ## 3.3. Titration and Calculation of LOD In a 1.0 mL cuvette containing DMF-HEPES solution (4:6, v/v), various equivalents of CEs were added to DBPpys (10 μM) at pH = 7.4. The mixtures were shaken at 37.4 °C for 30 min, and then fluorescence spectra were acquired with an excitation wavelength of 720 nm. A linear relationship in the range from 0.005 to 0.02 U was illustrated by the square of correlation coefficient equaling 0.99684. Based on the linear fitting in Figure 1F, the LOD was estimated with the following formula: Limit of detection (LOD) = 3σ/k where σ is the standard deviation of blank measurements ($$n = 11$$), and k is the slope of the linear fitting curve between the emission intensity at 720 nm and the concentration of CEs from 0.005 to 0.02 U, respectively. ## 3.4. Anti-Interference Na2CO3, Na2SO4, KCl, H2O2, ClO−, Hcy, Cys, GSH, ADP, ATP, BSA, LAP, HSA, AFP and APN were obtained from commercial sources (Le Yan) and used without additional purification. DBPpys (10 μM) reacted with interfering substances (200 μM) or CEs (0.1 U) for 30 min in a shaker at 37.4 °C. ## 3.5. Cell Cultures The HeLa cells were propagated in T-25 flasks and cultured at 37 °C under a humidified $5\%$ CO2 atmosphere in Dulbecco’s Modified Eagle Medium (DMEM) containing $10\%$ heat-inactivated fetal bovine serum (Invitrogen, Calsbad, CA, USA) and $1\%$ penicillin-streptomycin (10,000 U/mL penicillin and 10 mg/mL streptomycin). ## 3.6. Cytotoxicity Assay by CCK-8 The HeLa cells were seeded in 96-well plates and cultured in standard 0.2 mL DMEM medium containing $10\%$ FBS (Invitrogen, Calsbad, CA, USA) and $1\%$ antibiotics (penicillin, 10,000 U mL−1, and streptomycin, 10 mg mL−1) for 24 h (37 °C, $5\%$ CO2). The DBPpys stock solution was diluted to required concentrations (0 μM, 2.5 μM, 5 μM, 10 μM, 20 μM and 40 μM) with culture medium and then the previous medium was replaced by the diluted solutions. After incubation for 24 h, absorbance was measured at 450 nm using a multifunctional microplate reader (Synergy H1, BioTek Instruments, Vermont, USA). The relative cell viability (%) was calculated by the following formula: cell viability = ODtreated/ODcontrol × $100\%$ ## 3.7. Intracellular CE Detection The HeLa cells were seeded in confocal dishes and incubated for 24 h. The cells were co-incubated with DBPpys at a final concentration of 10 μM (containing $1\%$ DMSO) and incubated for 5–360 min at 37 °C in an atmosphere of $5\%$ CO2 and $95\%$ air. In the inhibition experiments, HeLa cells were first incubated with different concentrations of AEBSF (0.5 mM, 1 mM, 2 mM) for 30 min and then incubated with DBPpys (10 μM) for 60 min. Fluorescence imaging was then performed using a confocal laser scanning microscope (CLSM, Leica Microsystems, TCS SP5 II, Wetzlar, Germany). The fluorescence signals of cells incubated with probes were collected at 650–750 nm, using a laser at 550 nm as excitation resource. ## 3.8. Cell Co-Localization Imaging The HeLa cells were seeded in confocal dishes and incubated for 24 h. The cells were incubated with Mito Tracker Green FM (200 nM) or Lyso Tracker Green DND-26 (100 nM) for 30 min at 37 °C. The culture medium was removed, and cells were then washed twice with PBS and co-incubated with DBPpy (10 μM) for an additional 60 min. ## 4. Conclusions In this work, we designed a NIR fluorophore DBPpy with a large Stokes shift (over 250 nm) by constructing a D-A structure, which could specifically localize in LDs. By introducing 4-bromomethyl phenyl ester to DBPpy, the fluorescent probe DBPpys for CEs was developed, which showed a “turn on” NIR fluorescence signal and a distinct color change from green to brown with a low detection limit of 9.38 × 10−5 U/mL in vitro. Additionally, in the presence of other analytes, DBPpys showed specific recognition of CEs. Moreover, endogenous CEs activity is closely related to cell viability. Therefore, the probe DBPpys was successfully used to analyze the survival status of cells pretreated with H2O2 by detecting the differences in fluorescent intensity. These results provide a strategy for the development of additional NIR fluorescent probes for CEs activity monitoring and a potential tool for in vivo fluorescence imaging. ## Figures and Scheme Scheme 1: Schematic representation of the possible response mechanisms of fluorescent probe DBPpys activated by CEs. Figure 1: (A) Normalized absorption spectra (black solid line) and fluorescence spectra (red dotted line) of DBPpy in DMF:HEPES (v:v = 4:6, pH = 7.4). (B) Normalized fluorescence spectra (dotted lines) of DBPpy in different solvents and the absorption spectrum (solid line) of DBPpy in DMF. (C) Absorption spectra of DBPpys (10 μM) toward different concentrations of CEs in DMF:HEPES (v:v = 4:6, pH = 7.4). (D) Fluorescence spectra of DBPpys (10 μM) toward the concentrations of CEs from 0 U/mL to 0.5 U/mL in DMF:HEPES (v:v = 4:6, pH = 7.4). (E) Fluorescence intensity of DBPpys (10 μM) at 720 nm after reaction with the concentrations of CEs from 0 U/mL to 0.5 U/mL in DMF:HEPES (v:v = 4:6, pH = 7.4). (F) Linear fitting curve of the fluorescence intensity of DBPpys at 720 nm (10 μM) toward the concentrations of CEs from 0.005 U/mL to 0.02 U/mL. Figure 2: (A) Possible mechanism of DBPpys response to CEs. (B) HRMS of DBPpys before and after reaction with CEs. (C) HPLC analysis of DBPpys after reaction with CEs at different times. Figure 3: (A) Fluorescence intensity at 720 nm of DBPpys (10 μM) with (red point) and without (black point) CEs in DMF:HEPES (v:v = 4:6) at different temperatures. (B) Fluorescence intensity of DBPpys (10 μM) at 720 nm with (red point) and without (black point) CEs in DMF:HEPES (v:v = 4:6) with different pH values. (C) Fluorescence intensity of DBPpys (10 μM) at 720 nm before (orange columns) and after (green columns) reaction with CEs in the presence of 200 μM of various interfering substances in DMF:HEPES (v:v = 4:6, pH = 7.4) at 37.4 °C. a: blank; b: Na2CO3; c: Na2SO4; d: KCl; e: H2O2; f: ClO−; g: Hcy; h: Cys; i: GSH; j: ADP; k: ATP; l: BSA; m: LAP; n: HSA; o: AFP; p: APN. Figure 4: Fluorescent imaging of HeLa cells stained with 10 μM DBPpys at different incubation times. Incubation times: (A–C) 5 min; (D–F) 15 min; (G–I) 30 min; (J–L) 45 min; (M–O) 60 min; (P–R) 90 min; (S–U) 120 min. (A,D,G,J,M,P,S) bright field; (B,E,H,K,N,Q,T) red channel (650–750 nm); (C,F,I,L,O,R,U) merge image. λex = 550 nm. Scale bar = 50 μm. Figure 5: Fluorescent images of HeLa cells incubated with 10 μM DBPpys for 60 min after pretreating with different amounts of H2O2 for 2 h. H2O2 concentrations: (A–C) 0 mM; (D–F) 1 mM; (G–I) 2 mM; (J–L) 4 mM; (M–O) 8 mM. 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--- title: 'Association between Arachidonic Acid and the Risk of Schizophrenia: A Cross-National Study and Mendelian Randomization Analysis' authors: - Yan Gao - Xiaowen Hu - Dandan Wang - Jie Jiang - Minghui Li - Ying Qing - Xuhan Yang - Juan Zhang - Yue Zhang - Chunling Wan journal: Nutrients year: 2023 pmcid: PMC10005211 doi: 10.3390/nu15051195 license: CC BY 4.0 --- # Association between Arachidonic Acid and the Risk of Schizophrenia: A Cross-National Study and Mendelian Randomization Analysis ## Abstract Polyunsaturated fatty acids (PUFAs), especially long-chain PUFAs (LCPUFAs), are crucial for both the structural and functional integrity of cells. PUFAs have been reported to be insufficient in schizophrenia, and the resulting cell membrane impairments have been hypothesized as an etiological mechanism. However, the impact of PUFA deficiencies on the onset of schizophrenia remain uncertain. We investigated the associations between PUFAs consumption and schizophrenia incidence rates through correlational analyses and conducted Mendelian randomization analyses to reveal the causal effects. Using dietary PUFA consumption and national schizophrenia incidence rates in 24 countries, we found that incidence rates of schizophrenia were inversely correlated with arachidonic acid (AA) and ω-6 LCPUFA consumption (rAA = −0.577, $p \leq 0.01$; rω-6 LCPUFA = −0.626, $p \leq 0.001$). Moreover, Mendelian randomization analyses revealed that genetically predicted AA and gamma-linolenic acid (GLA) were protective factors against schizophrenia (ORAA = 0.986, ORGLA = 0.148). In addition, no significant relationships were observed between schizophrenia and docosahexaenoic acid (DHA) or other ω-3 PUFAs. These findings show that the deficiencies of ω-6 LCPUFAs, especially AA, are associated with schizophrenia risk, which sheds novel insight into the etiology of schizophrenia and a promising diet supplementation for the prevention and treatment of schizophrenia. ## 1. Introduction Schizophrenia is a complex psychotic disorder characterized by a bundle of neuropsychiatric manifestations, including cognitive impairment, apathy and social withdrawal [1]. The onset of schizophrenia typically occurs in young adulthood and can result in life-long impairment [1]. The median annual incidence rate of schizophrenia is 15.2 per 100,000 with substantial variations across countries and cultural groups [2]. Both genetic and environmental factors that impair neurodevelopment are considered risk factors for schizophrenia, including genetic predisposition provided by mutant neurodevelopmental genes and nutritional factors in prenatal life [1,3]. The indication of maternal malnutrition as a causative factor in schizophrenia was evidenced by higher incidence rates of schizophrenia among offspring during famines that occurred in the Netherlands during 1944–1945 [4] and in China during 1959–1961 [5]. An especially important nutrient group for the course of brain development is polyunsaturated fatty acids (PUFAs). PUFAs are essential fatty acids that are mainly obtained from the diet. The dietary intake of PUFAs varies widely due to geographic and cultural differences across countries [6]. Linoleic acid (LA, C18:2 ω-6) and alpha-linolenic acid (ALA, C18:3 ω-3) are the most abundant dietary PUFAs [6], and can be endogenously converted to long-chain PUFAs (LCPUFAs) to a limited degree. Arachidonic acids (AAs, C20:4 ω-6) and docosahexaenoic acid (DHA, C22:6 ω-3) are the major LCPUFAs in diets in most counties. LCPUFAs are an important nutrient group for brain development. In infancy and early childhood, both ω-6 and ω-3 LCPUFAs, especially AA and DHA, are critical for supporting brain growth and maturation [7]. Several studies on children have revealed improved performance in cognition and motor skills after LCPUFA supplementation [8,9,10], and LCPUFA deficiency can lead to psychotic symptoms in those with neurodevelopmental disorders [11]. Moreover, meta-analyses have shown that PUFA concentrations are insufficient in schizophrenia [12,13], and a similar tendency was found in individuals at an ultrahigh risk of developing psychosis [14]. Our study aims to investigate the associations between PUFAs and schizophrenia risk. Correlations between PUFA dietary consumption and national incidence rates of schizophrenia are evaluated from an epidemiological perspective, and Mendelian randomization (MR) analysis is conducted to address the causative relationships and evade residual confounding. We specifically hypothesize that lower LCPUFAs would be associated with an increased schizophrenia risk. ## 2. Materials and Methods Our study consists of cross-national correlation analysis and MR analysis. A schematic diagram of our study design is shown as below (Figure 1). ## 2.1.1. Data Acquisition on the Incidence Rates of Schizophrenia The incidence rates of schizophrenia were obtained from published epidemiological studies. We included studies published between January 1975 and December 2021, which were identified through the search terms (incidence (Title/Abstract) AND schizophrenia (Title/Abstract)) applied in MEDLINE, PsychINFO and EMBASE, as well as the literature referred to in systematic reviews [2,15]. As the study design had a large impact on estimating the incidence rate of schizophrenia [16], the criteria for including studies in this analysis are listed as follows: Studies were included if they reported primary data on the incidence of schizophrenia according to the diagnostic criteria of the International Classification of Diseases (ICD) for schizophrenia. Studies within special populations only (e.g., twins, young adults) or within birth cohorts were excluded. When multiple studies on schizophrenia incidence in the same country were available, the study that reported the investigated population at risk as the total population or the one that sampled from the most recent period was preferred. Incidence was recorded as a rate per 100,000 population. Ethical approval was obtained in all original studies. ## 2.1.2. Data Acquisition on the Dietary Consumption of PUFAs Dietary consumption data of PUFAs were obtained from the Food and Agriculture Organization of the United Nations and the Australian Food Composition Database (previously called NUTTAB). The method of calculations followed the methodological process of establishing a global database of nutrients [17]. Briefly, food consumption data were obtained from the Food and Agriculture Organization, which annually compiles Supply and Utilization Accounts and provides internally consistent information about up to 394 food and agricultural commodities across nations. We extracted the available information about food supply quantity per capita (g/day) for countries with available incidence data (Table S1). To provide a more accurate estimate of the consumption of individual PUFAs, we introduced a wastage index to adjust for retail and consumption loss across countries [18] as well as the refuse factor to adjust for inedible parts [17]. Then, we matched these individual food items with the food items in the Australian Food Composition Database, a database that provides complete information on known dietary sources for PUFA intake. Finally, we added up the contributions of individual food items to each PUFA. Therefore, individual consumption of each PUFA within each country per year was calculated in aggregate. PUFA groups (total ω-3 PUFA, total ω-6 PUFA, total ω-3 LCPUFA, and total ω-6 LCPUFA) consumption was indirectly measured by summing up. ## 2.1.3. Correlation Adjustment A twenty-year lag interval was introduced to evaluate the causative relationships between PUFA intake in early life and schizophrenia risks in consideration of an adequate time span for the onset of psychiatric symptoms. Since PUFA consumption is correlated with economic development, we used the GDP per capita to account for the socioeconomic differences, paralleling previous studies [19,20,21,22]. ## 2.1.4. Statistical Analysis In the cross-national association study, seventeen individual PUFAs and four summary PUFA groups were included for analyses. The analysis involved simple Pearson’s product moment correlation and partial correlation analyses adjusting for economic factors. These statistical procedures were conducted in SPSS 20. ## 2.2. Mendelian Randomization Analysis for PUFAs and Schizophrenia To investigate the potential causal associations between PUFAs and schizophrenia, we applied a two-sample MR approach by using summary data from genome-wide association studies (GWASs). Ethical approval was obtained in all original studies. ## 2.2.1. PUFA Exposure Data Acquisition We obtained published association results for genetic instruments of ω-6 [23] and ω-3 PUFAs [24] from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, which are the most common datasets of ω-6 and ω-3 PUFA levels used for MR analysis [25,26,27,28]. The summary statistics were based on a meta-analysis of GWAS of individuals of European ancestry, which are 8631 European individuals for ω-6 PUFAs and 8866 European individuals for ω-3 PUFAs (Table 1). Participants for the meta-analysis were drawn from 5 cohort studies (Table S2), including the Atherosclerosis Risk in Communities Study (ARIC, $$n = 3269$$), the Cardiovascular Health Study (CHS, $$n = 2404$$), the Coronary Artery Risk Development in Young Adults Study (CARDIA, $$n = 1507$$), the Invecchiare in Chianti Study (InCHIANTI, $$n = 1075$$), and the Multi-Ethnic Study of Atherosclerosis (MESA, $$n = 707$$). In these studies, fatty acids were measured using gas chromatography technique. Levels of all individual fatty acids, including LA (average range, 19.96~$24.78\%$), gamma-linolenic acid ((GLA) average range, 0.11~$0.12\%$), dihomo-γ-LA ((DGLA) average range, 3.13~$3.33\%$), AA (average range, 8.00~$12.10\%$), ALA (average range, 0.14~$0.44\%$), DHA (average range, 2.29~$3.66\%$), eicosapentaenoic acid ((EPA) average range, 0.56~$0.88\%$) and docosapentaenoic acid ((DPA) average range, 0.83~$0.95\%$), were expressed as a percentage of total fatty acid (Table 1 and Table S2). ## 2.2.2. Schizophrenia Outcome Data Acquisition We obtained the summary statistics data on genetic associations for schizophrenia from Psychiatry Genomics Consortium (PGC) [29]. To mitigate population stratification bias, we extracted genetic data from the schizophrenia GWAS meta-analysis with the largest proportion of European population that included 36,989 cases and 113,075 controls, of which 34,241 sporadic cases and 45,604 ancestry-matched controls were used in the subsequent MR analysis (Table 1). To avoid bias due to overlapping sample sets in MR analyses, we checked the 49 individual participating studies of schizophrenia GWAS and found no overlap with the fatty acid GWASs. All genetic statistical data were downloaded from the PGC database (http://pgc.unc.edu, accessed on 28 January 2021). ## 2.2.3. Univariable MR Analysis In this MR analysis, four ω-6 PUFAs (LA, GLA, DGLA, and AA) and four ω-3 PUFAs (ALA, EPA, DHA, and DPA) were included. All included SNPs were significantly associated with PUFA levels at the genome-wide significance level ($p \leq 5$ × 10−8). Bias in effect estimates of MR analysis can be induced by correlation between SNPs. In order to minimize this bias, our genetic instruments used in each PUFA were limited to independent SNPs without linkage disequilibrium (R2 < 0.1). We calculated F statistics to assess the strength of each instrument. If F > 10, there is sufficient strength to avoid weak instrument bias in the MR analysis. The F statistics were computed by the formula F = (N-k-1) × R2/(1 − R2), in which R2 = 2 × MAF × (1 − MAF) × β2. N and k refer to the sample size and numbers of the instrument separately. All SNPs used in univariable MR analysis satisfied the MR criteria (F-statistic > 10) [30]. In addition, the instrumental SNPs are biologically relevant to PUFAs. The instruments of single PUFAs with adjustments for their precursors, including GLA, DGLA, and AA, were also obtained in ω-6 PUFAs GWAS from the CHARGE consortium [23]. We harmonized the summary-level data to ensure that the allele for each SNP corresponded between each PUFA and schizophrenia. In the main analysis, we used the fixed-effects inverse-variance-weighted (IVW) method to assess the causal effects between exposure and outcome. The odds ratio (OR) of schizophrenia was calculated per 1 SD increment in genetically predicted plasma fatty acid levels. We applied $p \leq 0.05$ as statistical significance criteria to classify SNPs as potentially influential. Two-sample MR analysis was implemented by using the “MendelianRandomization” package in R 4.0. ## 2.2.4. Multivariable MR Analysis We also conducted two-sample multivariable MR analysis to simultaneously estimate the direct effect of ω-3 and ω-6 PUFAs on schizophrenia in consideration of their nexus in metabolism. Considering the tight relationships between ω-3 and ω-6 PUFAs of sharing the same metabolic enzymes, multivariable MR analysis focused on estimating LA (18:2 ω-6) and ALA (18:3 ω-3) effects in comparison to each other, as well as AA (C20:4 ω-6) and EPA (C20:5 ω-3) effects in comparison to each other. Links regarding other PUFAs in equivalent positions in the synthesis pathway were not evaluated due to the lack of GWAS summary data from the same GAWS study. A total of eight SNPs related to ω-3 and ω-6 PUFAs were extracted from the CHARGE consortium. The final instrument set contained 5 SNPs due to linkage disequilibrium clumping. The schizophrenia outcome data were previously described. Multivariable MR was conducted using the R package “Mendelian Randomization”. The F-statistic was calculated to assess instrument strength, which was performed using the “MVMR” package. ## 2.2.5. Pleiotropic Associations We also considered potential biological and socioeconomic confounders that may affect the associations between PUFAs and schizophrenia risk. We explored the effect of some potential confounders such as C-reactive protein (CRP), educational attainment, and vitamin D levels [31,32,33]. A previous study found that instrumental variables of PUFAs were not causally related to education attainment and vitamin D [34], which illustrate that it was unlikely that PUFA instruments would affect the schizophrenia risk through these traits. Additionally, to address potential confounder CRP, we conducted linkage disequilibrium (LD) analyses [35] to investigate whether the inflammatory cytokine would affect the association between AA and schizophrenia risk. Genetic associations of CRP were obtained from a recent GWAS based on a large sample of >200,000 European individuals, which identified 58 genome-wide significant loci [36]. Four significant SNPs (rs10832027 and rs1582763 in chromosome 11, rs10521222 and rs1558902 in chromosome 16) in CRP GWAS are in the linkage distance to our instruments rs174547 and rs16966952 separately, which were assessed in the subsequent LD analyses. Furthermore, we searched PhenoScanner [37] for other potential pleiotropic associations of the instrumental variables with risk factors for schizophrenia. ## 3.1. Cross-National Correlation between Dietary PUFA Intake and Schizophrenia Incidence Rates Initially, 41 potentially related papers were collected based on the strict inclusion criteria described above to obtain national incidence rates of schizophrenia [38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78] (Table S3). Eight countries had more than one study reporting the incidence rate of schizophrenia. With further exclusions on studies decades ago or case findings based on the whole population rather than who was at an age known to be at risk, a unique incidence rate of each country was included for subsequent analysis (Table 2). The incidence rates of schizophrenia varied from 4.1 per 100,000 population in the United Kingdom to 58.5 per 100,000 population in India. Eating habits vary across different countries and cultures, leading to a global difference in the major dietary PUFA categories and a wide range of PUFA intake. The NUTTAB included 17 dietary PUFAs, and the scales of the dietary PUFAs differed by two orders of magnitude; on average, LA accounted for $88.3\%$ of the total PUFA intake, whereas adrenic acid (AdrA, C22:4 ω-6) accounted for only $0.08\%$. AA and DHA were the major individual LCPUFAs among most countries (Figure 2). PUFA compositions of daily intake across countries and a potential confounder, GDP per capita, are displayed in Table 2. Correlation analyses showed that a higher consumption of AA was correlated with a lower schizophrenia incidence rate (rAA = −0.577, $p \leq 0.01$) (Figure 3A). Moreover, its precursor (eicosadienoic acid, C20:2 ω-6) and product (AdrA) were related to schizophrenia incidence rates in similar relationships. Dietary consumption of other individual PUFAs, including DHA, did not show statistically significant correlations (Figure 3B). Given the potential socioeconomic effects on the consumption of food, we introduced the GDP per capita across different countries to adjust economic levels by partial correlation analysis. As a result, AA consumption remained inversely correlated with the incidence rates of schizophrenia. We further investigated schizophrenia incidence rates against different PUFA families. Similarly, the results showed that higher ω-6 LCPUFA consumption was correlated with a lower incidence rate of schizophrenia (rω-6 LCPUFA = −0.626, $p \leq 0.001$) (Figure 3C), whereas no significant relationship was observed between ω-3 LCPUFA consumption and schizophrenia incidence rates (Figure 3D). In addition to LCPUFAs, an association analysis between total ω-6 PUFA consumption and schizophrenia was conducted and found no significant relationship. Total ω-3 PUFA consumption showed a weak negative association with schizophrenia incidence rates, which was no longer significant after adjustments for economic factors. In addition, there were no correlational relationships between the incidence rates of bipolar disorder or depressive disorder and dietary PUFAs within the limited available dataset [79,80,81,82] (Table S4). Taken together, these results suggest that the high intake of ω-6 LCPUFAs, especially AA, would decrease the risk of schizophrenia onset. ## 3.2. Mendelian Randomization Analysis of PUFA and Schizophrenia To further address the causal relationship between PUFAs and schizophrenia, we performed MR analyses. The variants used as genetic instrumental variables of the eight different PUFAs are listed in Table 3. These SNPs regarding each PUFA were independent without linkage disequilibrium and were not directly associated with schizophrenia at the genome-wide significance threshold. For all instruments related to each PUFA, F-statistics > 10, suggesting that the analyses were unlikely to be affected by weak instrument bias (Table 3). The results of the univariable MR analyses were based on the inverse-variance-weighted method. For ω-6 PUFAs, genetic predisposition to higher levels of AA or GLA was significantly associated with lower schizophrenia risk (ORAA = 0.986, $$p \leq 0.030$$; ORGLA = 0.148, $$p \leq 0.004$$), whereas ω-3 LCPUFAs showed only protective tendencies without statistical significance (Figure 4A). In contrast, the precursors of LCPUFAs, LA and ALA, showed the opposite effects on schizophrenia (ORLA = 1.008, $$p \leq 0.264$$; ORALA = 3.601, $$p \leq 0.072$$) (Figure 4A). Single-SNP analyses between AA and schizophrenia showed that the direction of the causal relationship driven by rs174547 was the same as the direction driven by rs16966952. The results of single-SNP analyses between GLA and schizophrenia were similar (Figure 4B,C). These associations were driven by the SNP rs174547 in FADS1 and rs16966952 in NTAN1/PDXDC1. These genes are strongly connected with fatty acid metabolism. FADS1 is involved in the desaturation of LA and ALA, contributing to the biosynthesis of LCPUFAs such as AA and DHA. PDXDC1 expression level appeared to be changed in mice fed with a high-fat diet. The pleiotropy assessment found that rs174547 was most strongly associated with AA and other multiple metabolic traits that are closely related to PUFAs, such as lipid metabolism phenotypes. The SNP rs16966952 was predominantly associated with DGLA and PUFA-related metabolic traits (Table S5). Given the instrumental variables related, most of the traits are downstream of the main exposure of interest. These metabolic relationships are considered as vertical pleiotropy and have no effects on the consequence of MR analysis. Other traits include pulse rate, peripheral blood cell count, height, asthma, hair, or balding pattern, none of which are considered as potential risk factors for schizophrenia. Moreover, neither of these SNPs was directly related to the other risk factors for schizophrenia at the genome-wide significance threshold (Table S5). As for some potential confounders such as CRP and educational attainment and vitamin D levels, there are some evidences to manifest AA-related SNPs would not affect the schizophrenia risk through these traits. Our LD analysis results show that all of the significant SNPs of CRP were independent with our instruments rs174547 and rs16966952 (R2 < 0.1), which implied that CRP is not genetically related to AA (Figure S1). A previous study reported that AA is not causally related to educational attainment and vitamin D [34]. These results suggest it is unlikely that the genetic instruments of AA and GLA affect schizophrenia via these confounders. Considering the close relationships between PUFAs, we applied further MR analyses. Given the sharing of the elongating and desaturating enzymes by ω-6 and ω-3 PUFAs, multivariable MR analyses were applied to estimate the direct effects of one fatty acid in consideration of another. In the associations between schizophrenia and two PUFAs, AA and EPA, which were assessed together, there is evidence for a causal effect of AA as a protective factor of schizophrenia (ORAA = 0.981, $$p \leq 0.009$$), whereas no statistically significant evidence was found with EPA (Table 4). In the associations between schizophrenia and LA and ALA, which were assessed together, effect estimates for LA and ALA aligned well with the univariable MR estimates (ORLA = 1.005, $$p \leq 0.805$$; ORALA = 3.747, $$p \leq 0.512$$) (Table 4). Given the metabolic pathway in each PUFA group, further univariable MR analyses were applied to estimate the effect of a single PUFA within consideration of its preceding fatty acid. In this MR analysis, controlling for precursor PUFA, low schizophrenia risk was consistently observed with higher levels of AA and GLA (ORAA = 0.986, $$p \leq 0.021$$; ORGLA =0.240, $$p \leq 0.002$$) (Table S6). Overall, the MR analyses indicate that AA and GLA are protective factors against schizophrenia. Consistent with our cross-national correlation results, insufficient AA may be a potential causal factor leading to the onset of schizophrenia. ## 4. Discussion In this study, we aimed to investigate the relationships between PUFAs and schizophrenia risk using epidemiological methodology and Mendelian randomization analysis. Our results showed that ω-6 LCPUFAs, especially AA, are potential protective factors against schizophrenia. A higher intake of AA and ω-6 LCPUFAs, according to measures of food consumption, was significantly correlated with a lower incidence rate of schizophrenia across countries. Consistently, a genetically predicted low level of AA was revealed to increase schizophrenia risk by the MR analysis. In contrast, ω-3 LCPUFAs, including DHA and EPA, showed only a slight tendency to be protective against schizophrenia risk, which was an effect without statistical significance. Both ω-3 and ω-6 LCPUFAs are rich in seafood and animal food. Previous studies have focused on the correlational relationships between the prevalence of affective disorders and seafood consumption [83,84] and found that lower seafood consumption was robustly associated with higher prevalence rates. Our study focused on the relationships between PUFAs and schizophrenia, and indicates that a high intake of ω-6 LCPUFAs, especially AA, was associated with a low risk of schizophrenia. Careful consideration was given to potential confounding factors. The relationship between each PUFA and schizophrenia was adjusted for based on country socioeconomic levels assessed by GDP per capita. Correspondingly, the results of our two-sample MR study, which is less prone to confounding than cross-national correlation studies, showed that AA was a protective factor against schizophrenia risk. Our MR results were based on large GWASs and the same race, which minimized the possibility of population stratification bias. The genes involved in the MR analysis, the FADS1 gene in LCPUFA synthesis, was reported to influence IQ performance, illustrating the crucial effects of PUFAs on early brain development [85]. NTAN1 is an N-terminal asparagine amidase that is related to social behavior and memory [86]. The results conducted by the MR analyses reveal that these variants may not directly lead to disease but instead affect schizophrenia risk by altering LCPUFAs, especially AA, and their subsequent traits. Therefore, these results both indicate that AA may play an important protective role against schizophrenia onset. Although both ω-6 and ω-3 LCPUFAs are critical in brain function and development, our results show that ω-6 LCPUFAs, but not ω-3 LCPUFAs, function as protective factors against developing schizophrenia. Our findings provide supportive evidence for the hypothesis that the onset of schizophrenia might be determined by insufficient AA, a major ω-6 LCPUFA in the brain; this is released for the production of its eicosanoid metabolites to support adequate signal transduction [87], and when present at abnormal levels, mental activities due to the structural enrichment of AA and its vital functions in the brain will be disrupted. AA deficiency impairs normal brain function [88] and contributes to schizophrenia-like phenotypes [89], and supplementation with AA can alleviate psychotic manifestations to some extent [9,10,90,91,92,93]. Lee et al. found that Lpiat1−/− mice, a model of AA deficiency in phosphatidylinositol synthesis, died within a month after birth and showed atrophy of the cerebral cortex and hippocampus [88]. M. Maekawa et al. found that gestational and early postnatal dietary deprivation of AA in mouse offspring elicited schizophrenia-like phenotypes in adulthood [89]. Meanwhile, M. Maekawa et al. [ 90] found that additional AA in breastmilk increases the total number of neurons in not only normal infant rats but also schizophrenia-like infant rats with information-processing problems, whereas few effects of DHA administration were observed. Other findings showed that high levels of AA helped improve cognitive development in children [9,10] and alleviate cognitive dysfunction in elderly individuals [91,92,93]. A recent study further confirmed this finding by observing that higher ω-6 LCPUFA levels in childhood, but not ω-3 LCPUFA, reduced the risk of psychotic experiences or psychotic disorder in adulthood [11]. These findings suggested that AA plays an essential role in neurodevelopment, which manifests as both a disruption in cognitive performance and psychotic symptoms during the onset of schizophrenia. Our findings provide supportive evidence from an epidemiological perspective and an MR-analysis-based causal inference. In contrast to AA and ω-6 LCPUFAs, we found that ω-3 LCPUFAs were not significantly related to schizophrenia risk. Several cross-national studies reported strong relationships between fish consumption and mood disorder [83,84,94], which indicates that ω-3 LCPUFAs may have an impact on affective disorders. In contrast, there were no associations between seafood consumption and schizophrenia prevalence [84]. Furthermore, M. Maekawa et al. found that mice supplemented with DHA showed better behaviors in sustaining motivation or the propensity to respond, while mice supplemented with AA showed better performance in a cognitive processing task [89]. Meta-analyses have indicated that ω-3 LCPUFA supplementation may have beneficial effects on affective symptoms in major depressive disorder [95] and bipolar depression [96], whereas it failed to reveal therapeutic benefits on psychotic symptoms in schizophrenia [97]. Taken together, these findings also indicated that ω-3 LCPUFA showed limited influence on the onset of schizophrenia. ## 5. Conclusions In conclusion, our findings, combining the results of diet-dependent epidemiological analyses and gene-dependent MR analyses, show that AA and ω-6 LCPUFAs are protective factors against schizophrenia risk. These results imply diet enrichment might help prevent the onset of schizophrenia. Studies on AA supplementation for individuals with ultra-high risk for psychosis would help further characterize the nature of the AA and schizophrenia associations. ## References 1. Mueser K.T., McGurk S.R.. Schizophrenia. Lancet (2004) 363 2063-2072. DOI: 10.1016/S0140-6736(04)16458-1 2. McGrath J., Saha S., Welham J., El Saadi O., MacCauley C., Chant D.. A systematic review of the incidence of schizophrenia: The distribution of rates and the influence of sex, urbanicity, migrant status and methodology. BMC Med. (2004) 2. DOI: 10.1186/1741-7015-2-13 3. Lewis L.. 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--- title: 'Identifying Factors Which Influence Eating Disorder Risk during Behavioral Weight Management: A Consensus Study' authors: - Hiba Jebeile - Caitlin M. McMaster - Brittany J. Johnson - Sarah P. Garnett - Susan J. Paxton - Anna L. Seidler - Rebecca A. Jones - Andrew J. Hill - Sarah Maguire - Caroline Braet - Genevieve Dammery - Denise E. Wilfley - Louise A. Baur - Natalie B. Lister journal: Nutrients year: 2023 pmcid: PMC10005214 doi: 10.3390/nu15051085 license: CC BY 4.0 --- # Identifying Factors Which Influence Eating Disorder Risk during Behavioral Weight Management: A Consensus Study ## Abstract This study aimed to understand clinician, researcher and consumer views regarding factors which influence eating disorder (ED) risk during behavioral weight management, including individual risk factors, intervention strategies and delivery features. Eighty-seven participants were recruited internationally through professional and consumer organizations and social media and completed an online survey. Individual characteristics, intervention strategies (5-point scale) and delivery features (important/unimportant/unsure) were rated. Participants were mostly women ($$n = 81$$), aged 35–49 y, from Australia or United States, were clinicians and/or reported lived experience of overweight/obesity and/or ED. There was agreement ($64\%$ to $99\%$) that individual characteristics were relevant to ED risk, with history of ED, weight-based teasing/stigma and weight bias internalization having the highest agreement. Intervention strategies most frequently rated as likely to increase ED risk included those with a focus on weight, prescription (structured diets, exercise plans) and monitoring strategies, e.g., calorie counting. Strategies most frequently rated as likely to decrease ED risk included having a health focus, flexibility and inclusion of psychosocial support. Delivery features considered most important were who delivered the intervention (profession, qualifications) and support (frequency, duration). Findings will inform future research to quantitatively assess which of these factors predict eating disorder risk, to inform screening and monitoring protocols. ## 1. Introduction The need to consider eating disorder risk as part of weight management is increasingly being recognized [1,2,3,4]. In community samples, the development of eating disorders is influenced by multiple biological, psychological, developmental and sociocultural factors together with disordered eating behaviors [5,6,7,8]. A systematic review of 25 studies found body dissatisfaction to be the most consistent predictor of eating disorder risk in adolescents, followed by depression and low self-esteem [9]. Female sex and high body mass index (BMI) were also identified as important factors, both of which show strong associations with body dissatisfaction [9]. Adverse childhood experiences and interpersonal functioning are associated with eating disorder risk, with childhood sexual abuse and appearance-related teasing and victimization identified as having the most convincing evidence for being risk factors in an umbrella review of meta-analyses [10]. Similarly, experience of weight stigma in people with overweight or obesity is associated with disordered eating [11]. However, current literature on eating disorder risk has several limitations. Many studies are of a cross-sectional design, include females alone [9] and have predominantly been conducted in populations with an adult (or equivalent) BMI < 25 kg/m2. Importantly, it is unclear which factors increase risk of eating disorders in the context of weight management. Extrapolation or generalizing evidence of eating disorder development to the context of weight management should be approached with caution. Some known eating disorder risk factors may be less applicable, or additional risk factors specific to weight management may not yet be identified. If there are additional factors specific to weight management not captured by current literature, these should be identified so that preventative measures can be put in place. Behavioral weight management interventions are often first-line treatment for overweight or obesity [12,13,14,15]. Although interventions tend to include a combination of dietary modification, physical activity, sleep and behavior change strategies, a large degree of heterogeneity exists between interventions. Evidence from systematic reviews demonstrate that behavioral weight management can support weight loss for up to two and five years, in adolescents and adults, respectively [16,17]. However, some common components of weight management may be risk factors for eating disorders. For example, caloric restriction and reduced intake of energy-dense foods are often recommended as part of weight management interventions [18], although dietary restraint is an established risk factor for binge eating [5]. Similarly, self-monitoring of weight or caloric intake, while beneficial for weight loss outcomes in weight management settings [19], is associated with increased disordered eating in community samples [20]. Other strategies, such as promoting regular meal-time routines, goal setting and family-based treatment are used both in weight management and to address disordered eating [21]. Thus, to improve our understanding of the intersection between behavioral weight management and eating disorder risk, it is important to be able to differentiate between intervention strategies likely to modify eating disorder risk in this context. The Eating Disorders In weight-related Therapy (EDIT) Collaboration aims to explore the complex risk factor interactions that may precede changes in eating disorder risk during behavioral weight management interventions (www.editcollaboration.com; accessed on 17 November 2022) [22]. Specifically, the EDIT Collaboration seeks to identify early individual predictors of eating disorder risk and understand which components of weight management interventions may contribute to change in eating disorder risk. EDIT is the first program of research to examine eating disorder risk at the individual level during weight management interventions. There may be individual risk factors or intervention strategies relevant to this context not previously examined in the literature. Indeed, expert and lived experience opinion can provide important insights in setting research agendas to address such research gaps. To capture these potentially missing contributors, we aimed to understand clinician, researcher and consumer views regarding which individual characteristics may increase risk of eating disorders in the context of weight management, and which intervention strategies may increase or decrease risk of eating disorders. The study aim was to synthesize the views of these groups and better understand how individual characteristics and intervention strategies may influence eating disorder risk during weight management interventions. ## 2.1. Study Design and Participants This study had a cross-sectional design, with an online survey administered on Qualtrics software (Qualtrics, Provo, UT, USA). Target participants were adults aged ≥18 years with clinical, research and/or lived experience of an eating disorder and/or overweight and obesity. The survey was first sent by email to members of the EDIT Collaboration, which includes an international membership of clinicians, researchers and stakeholders working across the fields of obesity and eating disorders. Additional participants were recruited internationally via advertisement through professional societies and advocacy associations representing consumers with lived experience, including Australia New Zealand Obesity Society, Australia New Zealand Academy for Eating Disorders, Dietitians Australia, National Eating Disorder Collaboration, Academy for Eating Disorders, European Association for the Study of Obesity, British Dietetic Association, Obesity Action Coalition, InsideOut Institute for Eating Disorders, Weight Issues Network and The Obesity Collective. Organizations advertised the survey to members either via a newsletter, through a discussion forum, website listing, interest group, or on social media. The survey was also advertised on social media by study investigators. Snowball sampling was used by asking participants to distribute the survey with colleagues. Data collection occurred between 7 February and 6 March 2022. The study was approved by the Human Research Ethics Committee of The University of Sydney [$\frac{2021}{822}$]. We aimed for a sample size of 60 to 100 participants, with at least 20 participants from each target population (clinicians, researchers and lived experience), to allow for a broad range of views to be captured across the target populations. Participants provided informed consent online when initiating the survey and by returning a partial or completed survey response. All responses were anonymous. Fraud detection functions available as part of Qualtrics software were used to detect possible duplicate responses, and those from bots (multiple responses, often from a software program) were detected using reCAPTCHA technology. ## 2.2. Survey Development and Data Collection A list of individual characteristics potentially relevant to eating disorder risk and common components of weight management interventions were drafted based on the literature and the study team’s experience with weight management trials. Nine clusters of individual characteristics were drafted, including participant demographics and weight status, general medical history, weight-related medical history, eating-disorder-related medical history, mental-health-related medical history, psychosocial health, eating behaviors and history of dieting. Intervention components were categorized as delivery features and intervention strategies. Delivery features were defined as “a broad number of intervention characteristics that relate to how an intervention is delivered” [23] and were adapted from the Template for Intervention Description and Replication checklist [24]. Delivery features included the target population, who delivered the intervention, mode of delivery, intervention setting and the number and range of outcome assessments used within an intervention. Intervention strategies describe the behavior change content of weight management interventions. Intervention strategies were first grouped into five broad categories (intervention intent, framing and outcomes; dietary strategies; eating behaviors and disordered-eating-related strategies; movement and sleep-related strategies; and psychosocial-health-related strategies) and then as clusters of unique, related strategies within each category. Each category included several clusters. The individual characteristics and intervention components were refined through an iterative consultation process with the EDIT Collaboration Scientific and Stakeholder Advisory Panels via four online workshops (June 2021). At each workshop, the items within each category were discussed, new items were added, and similar items were combined or grouped. Feedback from each workshop was included in subsequent workshops. Finally, the list of individual characteristics and intervention components were further refined to remove repetition, ensure consistent language and address clustering. The survey was available in English and was estimated to take 30 to 40 min to complete. Participants’ demographics (including age, gender and ethnicity) and clinical, research and lived experience with eating disorders and/or overweight or obesity were captured. The survey had three parts:Individual characteristics—Participants were asked to rate the relevance of individual participant characteristics to the risk of developing an eating disorder in the context of weight management interventions. Items were rated on a five-point Likert scale from 1 = not relevant at all to 5 = very relevant. Participants were prompted to add individual characteristics not already included as free text. Intervention strategies—Participants were asked to rate various strategies used during weight management interventions as to whether they would likely increase, decrease or have no impact on eating disorder risk. Items were rated on a five-point Likert scale from 1 = very likely to reduce eating disorder risk to 5 = very likely to increase eating disorder risk. Participants were prompted to add intervention strategies not already included as free text. Delivery features—Participants were asked to rate the importance (important, not important, unsure) of key delivery features in relation to eating disorder risk during weight management interventions. Participants were prompted to add delivery features not already included as free text. At the end of the survey, participants were given the opportunity to provide additional comments on eating disorder risk during weight management interventions. ## Content Validity and Pilot Testing Content validity was assessed by expert review of the survey instrument by a clinician, researcher and an individual with lived experience of eating disorders from within the EDIT Collaboration. Reviewers were asked to comment on the survey’s content and wording of questions and scales in relation to the aims of the survey. The survey was subsequently updated based on this feedback. The online version of the survey was then pilot tested by five stakeholders not involved in survey development (from within and external to the EDIT Collaboration), including four clinicians and/or researchers working in obesity, eating disorders, or both, and a person with lived experience of obesity. The pilot sample was asked to identify any errors or barriers in form and presentation of the survey and use of the online system. ## 2.3. Analysis Descriptive statistics were used to summarize demographic data and frequency of responses for Likert scale questions using SPSS version 27 (IBM SPSS Statistics for Windows, Version 27.0. Armonk, NY, USA: IBM Corp). We considered possible variations in response based on professional group (clinician, researcher) and/or lived experience; however, this was not possible, as most respondents reported experience across groups. Free-text responses suggesting additional items to be considered and general comments were collated in Microsoft Excel. Responses were independently coded by two authors and grouped into broad themes, with consensus achieved through discussion (HJ and CMM). ## 3. Results There were 121 participants who provided consent and initiated the survey. Of these, one record was identified as a duplicate by Qualtrics and removed, and 33 records were excluded, as no questions other than in the demographics section were completed. The remaining 87 responses were included in analyses. There were no statistically significant differences in age, gender, professional background or discipline of those who completed the survey and those who completed the demographic section only. ## 3.1. Participant Demographics Characteristics of respondents are summarized in Table 1. Most respondents were women ($$n = 81$$, $93\%$) aged 35 to 49 years ($$n = 35$$, $40\%$). Most ($$n = 71$$, $82\%$) reported lived experience of high weight, eating disorders and/or were carers of a person with higher weight or an eating disorder ($$n = 22$$, $25\%$ higher weight alone; $$n = 12$$, $14\%$, any eating disorder alone). Twenty-five participants ($29\%$) reported lived experience of both higher weight and eating disorders (Table 1). ## 3.2. Individual Characteristics Across all nine clusters of individual characteristics, there was agreement ($64\%$ to $99\%$) that these were somewhat likely, likely or very likely to be relevant to eating disorder risk in the context of weight management (Figure 1, Table S1). Age at menopause (unlikely $35\%$, likely $47\%$, not sure $18\%$) and diagnosis of oppositional defiant disorder ($23\%$, $44.5\%$, $32.5\%$) had a greater proportion of responses as unlikely to be relevant to eating disorder risk or not sure. Additional individual characteristics which may be relevant to eating disorder risk in the context of weight management, identified by stakeholders, are summarized in Table 2. Additional items suggested included consideration of genetic risk factors, mental health comorbidity (e.g., history of addiction, self-harm, poor executive function), diagnosis of obstructive sleep apnea, emotional response to dieting attempts and weight loss, use of medications that increase or decrease appetite regulation, social media use and the influence of the family and environmental context on the individual. It was also suggested that risk factors may vary by type of eating disorder and that disorder-specific risk should be considered. A common view among participants was that “weight-related attitudes of family and professionals involved in care” and experience of weight stigma and “fat phobia” from health professionals are important to consider. These include, for example, “exposure to self-disparaging comments about weight, especially from parents,” “levels and experiences of weight stigmatizing micro aggressions resulting in shame/guilt” and “how often has medical care been withheld until the individual loses enough weight?” This may appear as “micro aggressions” from staff, e.g., “You want to be healthy, right.” It was also suggested that the “power imbalance between the health professional giving the weight management advice” [and the patient] is particularly important “where clients have an identity that relates to historically significant minority groups.” Participants suggested that eating disorder risk factors should be considered within an individual’s personal context, for example, “all of the above need to say ‘it depends’ none of these can be answered for groups, only individuals,” and “all of these need to be considered in relation to the personal context in which a person lives e.g., they may have these risks but live in a protective family, community or wider society or they may be in an unsafe environment e.g., thin ideal expectations.” This was also referred to as the “psychological resilience of the family.” ## 3.3.1. Intervention Framing and Outcomes Intervention strategies relating to the framing of the weight management intervention (Table S2) were rated as being more likely to increase eating disorder risk if they focused on weight-related outcomes, e.g., ‘aims for weight loss’ ($83\%$ rated as more likely to increase risk) or ‘use of weight-focused language during the intervention’ ($75\%$). Strategies relating to broader aspects of health were rated as being more likely to decrease eating disorder risk, e.g., ‘measures mental health outcomes’ ($62\%$ rated as more likely to decrease risk) and provides ‘education that health outcomes are not dependent on weight’ ($73\%$). It was suggested that the rate of weight loss is an important consideration, with faster weight loss suggested to increase risk. Communication and education approaches used during interventions were suggested as being important to consider in relation to eating disorder risk (Table 2). For example, it was suggested that the “name of the program and language/images used in program material, comparison to others, expectations, ‘success’ stories” and having a “decreased emphasis on personal responsibility—somewhat likely to decrease risk.” Similarly, “explaining the science of weight/appetite regulation and weight stigma” was suggested to decrease eating disorder risks, as does focusing on self-esteem, body acceptance and acting to “frame weight loss as a path to health and longevity.” It was suggested to “frame weight change as a potential/possible side effect of behavior change, rather than as a focus of behavior change. Focus on improved ability to do or engage in the things that are important to their quality of life” and to provide patient choice in the selection of a weight management approach, i.e., “ask the person, support the person to find their truth.” One respondent suggested that informed consent should be a routine component of weight management, asking, “do you include informed consent? With information about the low likelihood of success at maintaining long term weight loss and the metabolic harms of weight-cycling?” In contrast, several participants suggested that the “the concept of weight management itself is problematic” and “any focus on weight or other numbers poses a significant risk” to “developing disordered eating behaviors and body image concerns.” Participants suggested that “weight management is not less harmful when delivered by medical professionals than diets that are self-directed” and that interventions should either “focus on weight or focus on health, you can’t do both at the same time.” It was suggested that clinicians provide “constant encouragement” without measurement of weight. As an alternative to weight management, “comprehensive knowledge and understanding of the Health at Every Size (HAES)® paradigm” was suggested to reduce eating disorder risk. ## 3.3.2. Dietary Strategies Dietary prescription, categorization and monitoring were rated as being more likely to increase eating disorder risk, e.g., ‘very low energy diet’ ($86\%$ rated as more likely to increase risk) and ‘dietary monitoring—energy based’ ($91\%$). Strategies focused on flexibility and using individualized or family-based approaches were rated as being more likely to decrease eating disorder risk, e.g., ‘flexible meal plan’ ($61\%$ rated as more likely to decrease risk) and ‘family-oriented approach to dietary change’ ($64\%$). Use of behavior change techniques such as problem solving, goal setting and shopping support (practical support) had mixed responses, with some rating these as likely to increase risk, decrease risk or have no impact on eating disorder risk (Table S3). Communication approaches used when talking about food were suggested to be relevant to eating disorder risk, including using dichotomizing language, e.g., “framing of foods as healthy/unhealthy” as well as the role of family dynamics, e.g., “have to be really careful about involving family/partner. If they’re overly controlling, critical or micromanaging the food purchase/intake then that would be highly likely to lead to guilt, shame, food secrecy, etc. Whole family needs to be educated about ED risk and flexible eating.” This is particularly the case if “food and selection choices different to rest of household e.g., more restrictive.” Dietary prescription and restriction, including weighing and measuring foods, were suggested to increase eating disorder risk: “all counting, categorizing, and limiting leads straight back to disordered eating,” but “considering how different foods feel in the body, what foods provide enhanced energy levels, sit well in the stomach, keep you satiated for a long time…that’s great” (Table 2). Additionally, “household food security needs to be considered to reduce risk.” ## 3.3.3. Eating Behaviors and Disordered-Eating Related Strategies All eight strategies related to addressing disordered eating and promoting healthy eating behaviors, e.g., mealtime routines and mindful eating, were rated as being more likely to decrease eating disorder risk ($71\%$ to $86\%$ rated as likely to decrease risk; Table S4). Participants suggested education on the link between eating behaviors and energy restriction and consideration of cultural context of eating behaviors as additional important strategies (Table 2). Participants suggested screening for disordered eating during weight management; with appropriate referral and support, “will it (screening) increase the number of eating disorders you pick up in a service—yes. Will it exacerbate eating disorder symptoms in a client—no. It might help them receive appropriate and supportive treatment, but it’s unlikely to make the problem worse. Ignoring will make the eating disorder more likely.” Additionally, it was suggested that people seeking weight management may be “highly desperate for thinness and to escape stigma which makes their tolerance for risky and unsafe measures exceptionally high. They are likely to abide by the rules and feel exceptional defeat and failure when they fail to lose or begin to regain,” and that “anyone with a history of ED or currently has an ED should not be offered dieting aka weight management.” It was suggested that without appropriate identification, disordered eating may be exacerbated, e.g., “I entered the weight management program with an active eating disorder and was taught how to refine my eating disorder behaviors.” Several participants suggested that clinicians need greater awareness of and training on eating disorders and resources to be able address this as part of weight management. ## 3.3.4. Movement and Sleep Related Strategies Strategies based on prescriptive exercise plans or programs and self-monitoring of physical activity were rated as being more likely to increase eating disorder risk, e.g., ‘encourages strict/formal activity plan’ ($80\%$ rated as more likely to increase risk). Strategies promoting flexibility, enjoyable movement, cultural adaptations to exercise and focusing on improving sleep hygiene were rated as being more likely to decrease eating disorder risk, e.g., ‘promotes joyful movement and activity’ ($82\%$ rated as more likely to decrease risk) and ‘provides flexible exercise plan’ ($69\%$). Strategies including use of group exercise class, personal training, education on increasing physical activity, addressing sedentary behaviors and behavior change strategies had mixed responses (Table S5). Participants suggested that addressing sleep quality (circadian alignment and total sleep time), the attitudes of personal trainers, motivations for exercise and individual traits were important considerations (Table 2), e.g., “risk associated with the personal trainer would highly depend on the attitudes and methods of the trainer….focused on developing healthy habits and increasing goals and life satisfaction, this is unlikely to increase risk…..focused on appearance management and weight loss, this would likely increase risk…..whether pedometers/monitoring tools increase risk would be very dependent on the individual and how inflexible/addictive their traits are.” It was also suggested that “encouraging people to expand their definition of what movement can entail...mental and physical health motivations for exercise vs. shape/weight/appearance” would be beneficial. ## 3.3.5. Psychosocial Health Related Strategies Interventions based on any of the identified psychological frameworks ($55\%$ to $75\%$ rated as more likely to decrease risk), and those that address and monitor mental ($64\%$ to $78\%$) and psychosocial health ($66\%$ to $76\%$), weight stigma ($67\%$ to $73\%$) or body image ($76\%$ to $78\%$), and the use of behavior change strategies ($67\%$ to $80\%$) were rated as being more likely to decrease eating disorder risk (Table S6). Participants suggested providing education on “internalized and externally received weight biases,” “that undereating is related to anxiety, depression and difficulty concentrating,” the difficulty with long-term weight loss maintenance as the “sense of failure leads to debilitating shame” and on Health At Every Size (HAES)® principles. In contrast, others suggested that it would not be appropriate to address weight stigma alongside weight management (Table 2), e.g., “Increasing the ability to recognize and managing stress/trauma related to weight stigma will only help if the person is not also consistently being told and encouraged to lose weight in a weight management setting.” Some participants commented that although a range of individual strategies within this category were rated as being more likely to decrease eating disorder risk as part of this survey, e.g., use of cognitive behavioral therapy or family-based treatment, these would not outweigh the potential increase in risk assigned to other strategies, e.g., “I truly hope that the responses here are understood to be a non-endorsement of any weight management strategies despite certain endorsements of features discussed (e.g., intuitive eating, body appreciation, self-compassion, joyful movement, etc.).” ## 3.4. Delivery Features Overall, all delivery features were rated as being important to consider in relation to eating disorder risk, with support provided during the intervention, e.g., frequency and duration of contact ($82\%$ rated important) and the training and qualifications ($81\%$) of the person delivering the intervention being most important (Table S7). Participants suggested providing “step up/step down and clear pathways; continuity of care; multidisciplinary care.” The use of telehealth was raised as a potential risk factor as “there is data to show that self-focused attention during video conference calls can increase appearance concerns and drive body dissatisfaction. Telehealth delivery via videoconferencing may not be helpful for this population.” Ensuring a safe and supportive environment “without intersectional biases” with “shared understanding of group rules and behaviors,” having “chairs that correctly and safely support (a person)” and “messaging in physical settings (i.e., signage about the ‘obesity epidemic’)” were suggested as being important. Similarly, having appropriately trained health professionals was also suggested to be important: “I do believe in body autonomy so if a client chooses to lose weight, then practitioners need to have a thorough understanding of eating disorders and body image issues,” and “for each item related to feedback on a behavior, I wasn’t sure what to choose because risk would depend on type/content of feedback.” Participants suggested that there is high heterogeneity with how weight management intervention strategies are delivered and received by individuals. For most interventions, eating disorder risk “depends on the person,” i.e., some people will have a positive experience and others will have a stigmatizing and harmful experience with the same intervention or the same provider, e.g., “I have seen acceptance therapies promote disordered behavior, I have seen body positivity increase peoples negative self-evaluation, and I have seen them work for others. But these things that are used so often are not the answer for everyone.” ## 4. Discussion This study aimed to understand clinician, researcher and lived experience views on the individual characteristics and intervention strategies that may contribute to eating disorder risk in the context of behavioral weight management interventions. There was broad agreement that individual risk factors, based on the existing literature, were relevant to eating disorder risk in this context. Similarly, most intervention strategies were able to be categorized as being more likely to increase or decrease risk, with few having mixed findings. There was less consensus on the perceived direction of effect for specific behavior change strategies, such as providing feedback on behavior change. Importantly, aspects of eating disorder risk unique to people with overweight or obesity and in the context of weight management were identified, including having a genetic predisposition to obesity, experiencing stigma from health professionals and having a history of bariatric surgery. Similarly, communication approaches, attitudes and beliefs of health professionals and the environmental context were identified as important components of weight management interventions to consider. This consultation process has identified new insights into the intersection between eating disorder risk and weight management interventions and will contribute to improved accuracy of assessment of eating disorder risk during clinical trials and clinical practice and to the future design of interventions. As part of this consultation process, more than 50 individual characteristics were identified as being relevant to eating disorder risk during weight management. Many of these are consistent with the current literature on risk factors for eating disorders in the community [5,9,10]; however, additional factors specific to people with higher weight and/or the context of weight management were identified. This highlights the importance of consumer consultation when considering safety of interventions. It is important for us to understand the prevalence of these factors and the likelihood that they quantitatively predict eating disorder risk. In practice, assessing such a broad range of risk factors is resource-intensive and relies on having access to a multidisciplinary team. Additionally, many of these are not included in existing eating disorder assessments, [25] e.g., stigma from health professionals, history of bariatric surgery, weight-related teasing, childhood trauma. Thus, to facilitate routine screening and monitoring in research and clinical practice, we also need to understand which factors are most predictive of eating disorder risk in individuals undergoing weight management interventions [26]. The EDIT Collaboration is combining individual participant data from clinical trials of weight management interventions to address these research questions [27]. Our consultation process resulted in the identification of more than 100 individual components of weight management interventions (delivery features and intervention strategies). This is important because traditional evidence synthesis broadly categorizes complex interventions into sub-groups based on overarching features of interventions. For example, in a 2021 systematic review examining the effect of components of behavioral weight management on change in weight for adults, nine characteristics of interventions were considered [16]. Similarly, our 2019 systematic review examined changes in eating disorder risk during pediatric weight management and categorized the dietary strategies used in the interventions into two groups (nutrition education only or having a prescribed energy target) [28]. Yet, the present study identified 29 specific dietary strategies related to eating disorder risk. These intervention strategies varied in their perceived direction of effect, with some perceived to increase eating disorder risk and others perceived to decrease risk, highlighting the need to examine and deconstruct complex interventions in much more detail. Similarly, detailed examination of delivery features for differing effects is an important consideration. For example, the use of telehealth was suggested by survey respondents to differ from the broad strategy of online intervention delivery. This is due to emerging evidence finding an association between the use of telehealth that involves viewing oneself on a video screen and appearance concerns [29]. Thus, in understanding the effects of weight management interventions, it is important to deconstruct these into their smallest measurable components. The findings from this study have informed the development of a detailed coding framework to be used as part of the EDIT Collaboration (manuscript under review) and can be used to examine other safety or effectiveness outcomes (e.g., weight regain, health-related QOL, depression etc.). The broad consultation approach of this study allowed additional complexities relating to the intersection of obesity and eating disorders to be identified. In particular, the role of weight stigma from health professionals, the types of communication and language used by health professionals and the individual variation in how such messages are experienced by different people were strong themes. The association between weight stigma and disordered eating is well established [30]; however, to our knowledge, there is no clear method that can be used to identify and measure a person’s experience of stigma as part of weight management interventions. This highlights the importance of considering individual experience as part of our understanding of treatment response and including stakeholders in setting the research agenda. Further research is needed to understand how to assess weight stigma during weight management interventions and how to consider individual variation in response. This was the first international consultation process aiming to improve our understanding of eating disorder risk during weight management interventions. We used a rigorous development process, including expert consultation and review, allowing a comprehensive range of factors to be investigated. The inclusion of open text boxes at each stage of the survey increased the likelihood of greater coverage, and thus, more informative results. The survey had broad reach, and clinicians, researchers and people with lived experience across disciplines of overweight/obesity and eating disorders responded to the survey. Importantly, most participants reported lived experience of overweight, obesity and/or an eating disorder. Therefore, we were able to capture a diversity of views on the intersection between weight management interventions and eating disorders, i.e., some participants appear to support the notion that eating disorder risk can be considered as part of weight management, while others suggested that weight management should not occur. To our knowledge, this is the first research reporting such diversity of views and experiences. This allowed additional complexities relating to the intersection of obesity and eating disorders to be identified. There were also several limitations. Although the survey was publicized though international associations, the survey was only available in English, limiting participation to those who are fluent in the English language. Most participants were from Australia or the United States and identified as being white and women, limiting the geographic, cultural and gender diversity of participants. The sample was composed of people working across and/or with lived experience of overweight/obesity and eating disorders and are likely those with an interest in this intersecting area. We were unable to analyze differences in responses between professional groups or people with and without lived experience due to the degree of overlap in respondent background. ## 5. Conclusions This study provides insight into the views of clinicians, researchers and people with lived experience regarding eating disorder risk during weight management interventions. Findings highlight the importance of stakeholder consultation and will inform future assessment of eating disorder risk during weight management interventions. The interaction between individual characteristics and intervention strategies identified as relevant for eating disorder risk should be examined in future research and considered in clinical practice. 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--- title: 'Food Categories for Breakfast and Mental Health among Children in Japan: Results from the A-CHILD Study' authors: - Yukako Tani - Aya Isumi - Satomi Doi - Takeo Fujiwara journal: Nutrients year: 2023 pmcid: PMC10005218 doi: 10.3390/nu15051091 license: CC BY 4.0 --- # Food Categories for Breakfast and Mental Health among Children in Japan: Results from the A-CHILD Study ## Abstract There is limited evidence that what children eat for breakfast contributes to their mental health. This study aimed to examine the associations between food categories for breakfast and mental health among children in Japan. A proportion of participants aged 9–10 years in the Adachi Child Health Impact of Living Difficulty (A-CHILD) study in Japan who consumed breakfast daily were included ($$n = 281$$). Foods eaten for breakfast were reported by the children each morning for 7 consecutive days, and defined according to the food categories in the Japanese Food Guide Spinning Top. Child mental health was assessed by caregivers using the Strength and Difficulties Questionnaire. The mean intake frequencies per week were six times for grain dishes, two times for milk products, and one time for fruits. Linear regression analysis revealed an inverse association between the frequent intake of grain dishes, whether rice or bread, and problem behaviors after adjustment for potential confounders. However, confectionaries, which mainly consisted of sweet breads or pastries, were not associated with problem behaviors. The intake of non-sweet grain dishes at breakfast may be effective for preventing behavioral problems in children. ## 1. Introduction Mental disorders in children are a major cause of health-related burdens, with approximately one-fifth of children suffering from mental disorders [1,2]. In a 2015 meta-analysis, the estimated worldwide pooled prevalence of individual mental disorders in children, such as any anxiety, depressive disorder, attention-deficit/hyperactivity disorder, or conduct disorder, ranged from $2\%$ to $7\%$ [3]. A recent systematic review evaluating the effect of COVID-19 lockdowns on children’s mental health reported a substantial increase in mental disorders after the lockdowns compared with before the lockdowns [4]. Mental health problems in children can lead to long-term negative effects during the life course, including poor economic outcomes, injury risk, and premature death [5,6,7]. Moreover, a significant proportion of mental health problems in adults begin during early life [8,9]. Therefore, it is important to identify preventive factors that can be targeted by interventions from childhood. Breakfast is one of the potential preventive factors for children’s mental health. Breakfast is generally defined as the first meal of the day and eaten within 2 h of waking up, typically no later than 10:00 a.m. [10]. Breakfast interrupts the overnight fast and provides fuel to the brain and body [11]. Compared with adults, children may be more susceptible to the adverse effects of brief fasting because of the greater metabolic demands of the brain relative to the liver and muscle glycogen stores and gluconeogenic capacity [12]. The most investigated benefit of breakfast for children is its association with cognitive performance [13,14,15]. A systematic review showed that several tasks requiring attention, executive function, and memory were more reliably facilitated after breakfast consumption relative to fasting [13]. While there is some evidence for an association between breakfast skipping and mental disorders in children [16,17,18], the association may also depend on the quality of the breakfast [19,20]. High-quality breakfasts, such as those including bread, dairy products, and fruits but not confectionaries, were associated with better mental health, while low-quality breakfasts were associated with poorer mental health than breakfast skipping [19,20]. These findings suggest that the types of food eaten for breakfast are more important than whether or not breakfast is eaten. Meals vary by culture and should be individually considered. Generally, breakfast has less variety and the same foods are eaten, while lunch and dinner have greater variety [21]. Western breakfasts are relatively simple, with three recommended food groups: cereals, dairy products, and fruits [22]. Thus, in a study conducted in Western culture settings, a high-quality breakfast is defined as the consumption of bread/cereals and/or dairy products and no commercially baked goods (e.g., biscuits/pastries) [20]. Another study defined a high-quality breakfast as the consumption of three or more of the following five food groups: bread/cereals, vegetables, fruits, dairy products, and meat/meat alternatives [19]. In Japan, there is no clear definition of a high-quality breakfast. Japanese breakfasts are relatively complex and have more variety than western breakfasts [23]. The Japanese diet underwent dramatic changes and became more Westernized between 1950 and 1975, characterized by the increased intake of milk, meat, poultry, eggs, and fat, and decreased intake of barley, potatoes, and rice [24,25]. Interestingly, the foods consumed in Japan have varying degrees of Westernization by meal types. For example, bread and dairy products are mainly eaten at breakfast rather than at lunch and dinner [26]. The ideal Japanese breakfast is considered to be derived from traditional Japanese cuisine (e.g., rice and miso soup) with nutritionally recommended items such as salads, but there is a gap between the ideal meal and reality [27]. Two main staple options for breakfast exist in Japan, rice-based and bread-based, with those preferring each option described as “gohan-ha” and “pan-ha”, respectively [23,26]. A study analyzing breakfast meal patterns among Japanese adults reported four types of patterns, including two staple meal patterns, as follows: rice-based (rice/vegetable/pulse/seasoning), bread-based (bread/dairy/fruit/sugar), meat/egg/fat, and tea/coffee [23]. However, the food types eaten by children for breakfast and their associations with mental health among children are not well understood. Therefore, the aim of the present study was to investigate the types of food eaten for breakfast by children in Japan and to determine their relationships with mental health. There is consensus on the importance of eating breakfast; however, there is limited evidence that what children eat for breakfast contributes to their mental health. The goal of this study was to contribute to the improvement of children’s mental health by providing evidence about what to eat for breakfast to prevent problem behaviors in children. ## 2.1. Study Design and Subjects This study used data on dietary habits and health from the Adachi Child Health Impact of Living Difficulty (A-CHILD) project, which was initiated in 2015 to evaluate the determinants of health among children in Adachi, Tokyo, Japan [28]. Specifically, the study used adjunct data obtained for all fourth-grade students aged 9–10 years enrolled in nine public elementary schools in Adachi City in 2018. Children aged 9–10 years were selected because it has been suggested that by the age of 8–10 years, children can reliably report their food intake [29]. Nine schools were selected by the local government based on their representativeness for social and geographic environments [30]. The survey consisted of two phases: the first was a breakfast survey for children, and the second was a health survey for children and their caregivers. For the breakfast survey, the questionnaires were distributed at school and children reported the foods eaten for breakfast for 7 consecutive days between September and November 2018. For the health survey, the questionnaires were distributed at school and taken home for their caregivers to complete and return in October 2018. Among the 455 child–caregiver pairs, 328 pairs provided informed consent and completed both the breakfast and health questionnaires. A total of 281 pairs were included in the present analysis, after excluding those with missing data for breakfast ($$n = 34$$) or mental health status ($$n = 13$$). We included children whose breakfast data included all 7 days because our objective was to examine what food types children should be eating for breakfast among those who ate breakfast daily. The sample comprised 131 male and 150 female children. The A-CHILD protocol and use of the data for the present study were approved by the Ethics Committee at Tokyo Medical and Dental University (Approval No. M2016-284). ## 2.2. Breakfast Breakfast intake was assessed using a self-reported questionnaire in which the children were asked to fill in what food types they ate for breakfast over a 7-day period in a specified week. A registered dietitian coded each day’s breakfast menus as 0 or 1 for the inclusion or exclusion of six food categories according to the Japanese Food Guide Spinning Top, developed by the Ministry of Health, Labour and Welfare and the Ministry of Agriculture, Forestry and Fisheries of Japan to help people implement the Dietary Guidelines for Japanese [31]. The six food categories were: grain dishes; vegetable dishes; meat, fish, egg, and soybean dishes (main dish); milk and milk products; fruits; and confectionaries. Breads were classified according to whether they contained sweet fillings, with sweet breads containing cream, chocolate, or bean paste, or sugared breads being classified as confectionaries in accordance with the Standard Tables of Food Composition in Japan [32]. For example, if the breakfast menu was “toast, broccoli, and milk”, the categories of grain dishes, vegetable dishes, and milk and milk products were coded as 1, and the others were coded as 0. Similarly, if the breakfast menu was “chocolate bread and banana”, the categories of fruits and confectionaries were coded as 1, and the others were coded as 0. We did not account for serving sizes because the breakfast menus did not provide information on volumes. For the analysis, the average weekly frequency for each food category (total daily intakes of each category divided by seven) was used. For a detailed analysis of grain dishes, we calculated the mean frequencies for the intake of rice, bread, noodles (udon, soba, ramen, pasta), and cereals, which are typical staple foods in Japan. In addition, the children were categorized into three groups according to the frequency of rice or bread intake, as the two major staple foods in Japan. High rice eaters were defined as those who ate rice four or more times per week, high bread eaters were defined as those who ate bread four or more times per week, and all others were defined as low rice and bread eaters. ## 2.3. Mental Health Status We evaluated the children’s difficult behaviors and prosocial behaviors as children’s mental health. The assessments of difficult behaviors and prosocial behaviors were conducted using the Japanese version of the Strength and Difficulties Questionnaire (SDQ) [33]. The SDQ is composed of 25 items and has five subscales: emotional symptoms, conduct problems, hyperactivity/inattention, peer relationship problems, and prosocial behaviors. Caregivers rated the child behaviors using a scale of 0 (not true)–2 (certainly true). The total score of four subscales (emotional symptoms, conduct problems, hyperactivity/inattention, and peer relationship problems) was calculated as the total difficult behavior score. These scores were converted into a scale of 0–100 to aid the interpretation of coefficients from the statistical analyses, as described previously [34,35]. A higher total difficult behavior score meant a higher level of difficult behaviors. A higher prosocial behavior score meant a higher level of prosocial behaviors. In our study sample, the Cronbach’s alpha values for the total difficult behavior score and prosocial behavior score were 0.82 and 0.73, respectively. ## 2.4. Covariates Children’s statuses were assessed by a self-reported questionnaire. We considered that physical activity, sleep habits, and household socioeconomic status can be associated with both the quality of breakfast and mental health through their epidemiological relevance. Physical activity was assessed by the frequency of undertaking physical activity outside of school for ≥30 min and categorized into three groups (never/rarely, 1–4, or ≤5 times/week). Bedtime was categorized into three groups based on the children’s weekday habits (before 9:00 p.m., 9:00–9.59 p.m., and after 10:00 p.m.). Household and caregiver statuses were assessed by caregiver-reported questionnaires [36]. Household status included the household income and cohabitation status. The caregiver’s status included the mother’s age, mother’s educational attainment, mother’s employment status, and respondent’s mental health. The respondent’s mental health was assessed by the Kessler 6 scale (Japanese version) [37]. The cut-off point was a score of 5 and higher scores indicated frequent problems of psychological distress [38]. The mother’s educational attainment was categorized into three groups (low: junior high school, high school, or dropped out from high school; middle: professional school, college, or dropped out from college; or high: college or higher). The mother’s employment status was categorized into five groups (full-time, part-time, self-employed, side work, or unemployed). ## 2.5. Statistical Analysis First, we performed a multivariate linear regression model to examine the associations between food categories for breakfast and mental health assessed as difficult behaviors and prosocial behaviors. Two models were constructed: Model 1 included individual food categories and was adjusted for potential confounders (child’s sex, physical activity, and bedtime; household income and cohabitation status; caregiver’s K6; and mother’s age, education, and employment); and Model 2 included further adjustment for all types of food categories (grain dishes, vegetable dishes, fish and meat dishes, milk and milk products, fruits, and confectionaries) to examine the relationships independently of other food categories. Second, to conduct a detailed analysis of the grain dishes, a multivariate linear regression model was used to examine the associations between the types of grain dishes and mental health. For sensitivity analyses, the frequencies of rice and bread consumption were calculated separately and their associations with mental health were analyzed. The two models described above were used. A word cloud for the children’s breakfast menus was created on the User Local website [39]. All analyses were conducted using Stata version 15 (StataCorp, College Station, TX, USA). ## 3. Results The characteristics of the participants are presented in Table 1. Half of the participants were girls, a quarter exercised at least five times a week, more than one-third went to bed after 10:00 p.m., and approximately $90\%$ lived with both their father and mother. The mothers were most often approximately 40 years of age, and nearly $80\%$ were employed. The most frequently consumed food type at breakfast was grain dishes, at six times a week (Table 2). The second most consumed food types were vegetable dishes and meat, fish, egg, and soybean dishes, with consumption once every two days. Milk products were consumed twice a week, while fruits and confectionaries were consumed once a week. In the analysis by types of grain dishes, rice and bread were consumed similarly, at three times per week. The consumption rates of noodles and cereals were low, at approximately one-tenth the rates of rice and bread. When the children were grouped by types of grain dishes, $36\%$ ate rice at least four times per week (high rice eaters) and $39\%$ ate bread at least four times per week (high bread eaters). The word cloud for the breakfast menus reported by the children is shown in Figure S1. The breakfast menus were a mix of Japanese and Western dishes, with rice, bread, yogurt, salad, miso soup, milk, sausage, and eggs being commonly consumed items. The main grain dishes eaten for breakfast were rice and bread. Vegetable dishes often eaten for breakfast were tomatoes, cucumbers, broccoli, and lettuce. Among the foods categorized as meat, fish, egg, and soybean dishes, the consumption of processed foods such as sausages and ham, eggs, and natto (fermented soybeans) tended to be high. Yogurt and milk were the most frequently consumed milk products. The main fruits eaten at breakfast were bananas, oranges, and apples. Most of the foods classified as confectionaries were sweet breads or wheat-based pastries such as chocolate bread, red bean buns, donuts, and pancakes. The associations between food categories for breakfast and mental health are shown in Table 3. The multiple linear regression analysis revealed that the more frequent consumption of grain dishes or fruits was associated with fewer problem behaviors (grain dishes: coefficient = −8.74, $95\%$ CI: −17.0 to −0.51; fruits: coefficient = −7.63, $95\%$ CI: −13.7 to −1.56) after the adjustment for potential confounders (Model 1). After the adjustment for all other food categories, the association for fruits was weaker (coefficient = −6.39, $95\%$ CI: −13.1 to 0.28), but the association for grain dishes remained significant (coefficient = −12.2, $95\%$ CI: −22.8 to −1.58) (Model 2). None of the food categories showed significant associations with prosocial behaviors. The results of the regression analyses for mental health according to the types of grain dishes are shown in Table 4. Children who consumed rice or bread more frequently had fewer problem behaviors than those who consumed these items less frequently (high rice eaters: coefficient = −6.51, $95\%$ CI: −10.7 to −2.27; high bread eaters: coefficient = −5.20, $95\%$ CI: −9.25 to −1.14) (Model 2). Significant associations remained after the additional adjustment for all food categories except grain dishes. There were no significant differences between rice and bread dishes. Similar results were obtained when examining the associations between the frequencies of rice and bread intake and mental health (Table S1). ## 4. Discussion To the best of our knowledge, this is the first study to examine the associations between food categories eaten for breakfast and mental health in Japanese children. Grain dishes were the main breakfast category for the children and these were consumed almost daily, with most of the children consuming rice or bread as cereals. Among the food categories at breakfast, grain dishes and fruits were associated with fewer problem behaviors. Rice and bread were both associated with a lower risk of problem behaviors. We found that rice and bread were the two major grain dishes eaten at breakfast by the children. This pattern is consistent with a study on the breakfast patterns in Japanese adults, which found four types of patterns, including two staple meal patterns, as follows: rice-based (rice/vegetable/pulse/seasoning), bread-based (bread/dairy/fruit/sugar), meat/egg/fat, and tea/coffee [23]. Another study on Japanese university students found that the mean frequency of rice or bread intake at breakfast was three times a week, similar to the present findings [40]. The consumption of grain dishes other than bread and rice, i.e., noodles and cereals, was low, at approximately one-tenth the rates of rice and bread. Instead, children often consumed sweet breads categorized as confectionaries. Therefore, breakfast patterns other than bread or rice may be sweet bread patterns for children in Japan. The present finding that the more frequent consumption of grain dishes, whether rice or bread, at breakfast was associated with fewer problem behaviors in children is plausible, because grain dishes are rich in carbohydrates that can be converted into glucose, which is primary fuel for the brain. The brain is sensitive to fluctuations in glucose supply, and the maintenance of adequate blood glucose concentrations between meals is thought to be beneficial for optimal cognition [11]. A study of children aged 9–11 years found improvements in memory and task performances after the consumption of glucose-containing drinks [41]. Therefore, the consumption of grain dishes at breakfast may have been effective for the supply of glucose after the overnight fast. Meanwhile, biological findings have indicated that carbohydrate intake increased the brain uptake of tryptophan from the plasma, leading to the synthesis of serotonin in the brain [42]. Serotonin, a neurotransmitter, plays an important role in mood alleviation [43]. Therefore, eating carbohydrate-rich grain dishes for breakfast may increase tryptophan bioavailability in the central nervous system, leading to the alleviation of mood. In terms of glucose, many foods classified as confectionaries, such as chocolate bread, cream buns, donuts, and sugar toast, were also glucose-rich dishes. However, the consumption of confectionaries was not associated with problem behaviors in this study. One explanation may be the difference in glycemic load (GI). Compared with grain dishes high in polysaccharides, confectionaries are high in monosaccharides and have a higher GI. Lower GI values can minimize blood glucose fluctuations, and lower GI breakfasts were reported to be associated with better attention and less frustration in children compared with high-GI breakfasts [44]. A recent review found that sugar intake induced multiple physiological responses, including systemic inflammation, dopamine signaling disorders, oxidative stress, and insulin resistance, all of which are associated with depression [45]. Given these findings, it is possible that mental health is better served by the intake of non-sweet carbohydrates over sweet carbohydrates at breakfast. The frequent consumption of fruits at breakfast was also associated with fewer problem behaviors in children. This finding is in line with the results of several observational studies on children, although breakfast was not evaluated [45,46]. A study in Australia showed that children with problem behaviors consumed fewer servings of fruits than children without problem behaviors [45]. Another study conducted on adolescents found that significantly fewer problem behaviors were observed with the increased intake of fresh fruits [46]. In the indicated studies, vegetable dishes and fruits were both associated with better mental health [45,46], but these associations were not observed in the present study. In our study, the association between fruits and mental health was no longer significant after the adjustment for other food categories including grain dishes. These findings suggest that the fructose in fruits may have contributed to problem behaviors or that other foods consumed with fruits may have had an effect (confounding). Our study has some limitations. First, the validity and reliability of the breakfast measurement method in children aged 9–10 years have not been confirmed. Instead of estimating detailed foods and nutrients from the foods described by the children, we devised a way to achieve the measurements with high accuracy by only assessing the presence or absence of food category intakes. Second, we were unable to assess the quantities of each food category eaten for breakfast. Meanwhile, for foods associated with multiple food categories, we were often unable to identify the ingredients present in the foods. For example, some children did not report the details of the ingredients in their soups and stews. Therefore, the intake of vegetable dishes and meat, fish, egg, and soybean dishes, which are often included as ingredients, were often not counted as intake, possibly leading to underestimations in the results. In the future, it is necessary to devise methods that encourage children to describe the foods in detail and to obtain evaluations from caregivers to examine the validity of the method. Third, we were unable to evaluate dietary data other than breakfast intakes. However, the children were offered the same nutritious lunch at school during the day. Finally, the generalizability of the present results is limited because meals vary from culture to culture. The children in the present study ate rice or bread dishes as their main breakfast foods, had vegetable dishes and meat/fish/egg/soybean dishes every other day, and consumed fruits less frequently. Studies in other areas are needed. Despite these limitations, the fact that we were able to obtain breakfast data for children over a 7-day period provided very valuable evidence. ## 5. Conclusions Elementary school children in Japan consumed more rice and bread dishes for breakfast compared with other food categories. Vegetable dishes and meat/fish/egg/soybean dishes were consumed every other day, and fruits and confectionaries were consumed about once a week. Children who consumed grain dishes or fruits had fewer problem behaviors than children who did not. Grain dishes, whether rice or bread, were associated with reduced problem behaviors, while confectionaries, which mainly consisted of sweet breads or pastries, were not associated with problem behaviors. These results should aid in the development of dietary recommendations for breakfast among Japanese children. Although further examinations are warranted in longitudinal and intervention studies, the intake of non-sweet grain dishes or fruits at breakfast may be effective for preventing behavioral problems in children. This study could add evidence that it is not only whether children eat breakfast but also the type of food they eat for breakfast matters. The public health impact of this study is to make caregivers aware of the benefits of having their children eat grain dishes or fruits for breakfast. Neither grain foods nor fruits require complex preparation and are therefore easy to prepare at home. Few schools offer breakfast in Japan, and the food contents of breakfasts served in the “school breakfast program” in Japan were shown to vary from school to school [47]. In the future, serving grain dishes and fruits may contribute to improvements in children’s mental health. ## References 1. Kieling C., Baker-Henningham H., Belfer M., Conti G., Ertem I., Omigbodun O., Rohde L.A., Srinath S., Ulkuer N., Rahman A.. Child and adolescent mental health worldwide: Evidence for action. Lancet (2011.0) 378 1515-1525. DOI: 10.1016/S0140-6736(11)60827-1 2. Belfer M.L.. Child and adolescent mental disorders: The magnitude of the problem across the globe. J. Child. Psychol. Psychiatry (2008.0) 49 226-236. DOI: 10.1111/j.1469-7610.2007.01855.x 3. Polanczyk G.V., Salum G.A., Sugaya L.S., Caye A., Rohde L.A.. 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--- title: 'Factors Associated with Diet Quality among Adolescents in a Post-Disaster Area: A Cross-Sectional Study in Indonesia' authors: - Nikmah Utami Dewi - Ali Khomsan - Cesilia Meti Dwiriani - Hadi Riyadi - Ikeu Ekayanti - Diah Ayu Hartini - Rasyika Nurul Fadjriyah journal: Nutrients year: 2023 pmcid: PMC10005219 doi: 10.3390/nu15051101 license: CC BY 4.0 --- # Factors Associated with Diet Quality among Adolescents in a Post-Disaster Area: A Cross-Sectional Study in Indonesia ## Abstract The diet quality of adolescents in low-middle-income countries is low. Especially in post-disaster areas, adolescents are not a priority target for handling nutritional cases compared with other vulnerable groups. The aim of this study was to examine the factors associated with diet quality among adolescents in post-disaster areas in Indonesia. A cross-sectional study was performed with 375 adolescents aged 15–17 years, representing adolescents living close to the areas most affected by a significant disaster in 2018. The variables obtained include adolescent and household characteristics, nutritional literacy, healthy eating behavior constructs, food intake, nutritional status, physical activity, food security, and diet quality. The diet quality score was low, with only $23\%$ of the total maximum score. Vegetables, fruits, and dairy scored the lowest, whereas animal protein sources scored the highest. Higher eating habits of animal protein sources; being healthy; normal nutritional status of adolescents; higher vegetable and sweet beverage norms of mothers; and lower eating habits of sweet snacks; animal protein sources; and carbohydrate norms of mothers are associated with higher diet quality scores in adolescents ($p \leq 0.05$). Improving the quality of adolescent diets in post-disaster areas needs to target adolescent eating behavior and changes in mothers’ eating behavior. ## 1. Introduction Adolescents are a critical group in the manifestation of non-communicable diseases in adulthood; they provide an important contribution to nutritional improvement between generations [1]. Fulfillment of nutrition at this stage will have future impacts [2]. Appropriate diet quality is necessary for growth and the prevention of nutritional status-related macro- and micronutrient deficiencies or excess intakes [3]. Low diet quality is a major contributor to nutritional problems in low-middle-income countries (LMICs), where malnutrition remains a serious public health problem [1,4,5,6]. In Indonesia, the prevalence of underweight, stunting, and overweight adolescents aged 16–18 years reached $8.1\%$, $26.9\%$, and $13.5\%$, respectively, in 2018 [7]. The percentage of adolescent obesity increased by almost half from the previous year ($7.3\%$), whereas the prevalence of underweight and stunting decreased to $19.4\%$ and $31.2\%$, respectively, in 2013 [8]. Achieving good diet quality is difficult in LMICs, where starchy staple foods dominate diets, whereas the sources of animal foods, fruits, and vegetables are unavailable or difficult to obtain [9,10]. Other factors, including attitude, nutrition literacy, family support, friends or influential people for adolescents, and the ability to eat a balanced diet are also obstacles that hinder the achievement of good diet quality in adolescents [11,12,13]. In post-disaster areas, the diet quality of adolescents can be worse because this group is not a priority target for addressing nutritional cases, which typically focus on other vulnerable groups, such as toddlers and pregnant women [14]. Additionally, the food security of the family and the socioeconomic structure of the community have changed, thereby affecting the quality of the family’s diet, including that of adolescents [15]. In post-disaster areas, diet quality and its influencing factors have yet to be studied in detail. In contrast, interventions that focus on improving the quality of diets in the adolescent group in the post-disaster period need to be performed, particularly during rehabilitation and post-construction when individuals start living in normal conditions and determine the fulfillment of food in their respective households. This study aimed to determine the factors that influence diet quality in adolescents in post-disaster areas in Indonesia. The research results can be useful for designing nutrition and health programs for adolescents in post-disaster areas. ## 2.1. Study Population From October 2021 to January 2022, a cross-sectional study was conducted on adolescents aged 15–17 years attending high school in the Indonesian city of Palu, which is located close to the area most affected by a major natural disaster in September 2018. The inclusion criteria were students in class X or XI, who lived with their mother, were willing to participate in the study, and signed an informed assent themselves and informed consent from their mother. Sample determination was calculated on the basis of the formula [16], using $95\%$ and $5\%$ confidence and precision levels, respectively; the proportion used was $40.71\%$, which is the proportion of adolescents with vulnerable households. This proportion is used because the study sample is the subject of an initial De-Nulit study. The De-Nulit study is a study of nutritional literacy and diet quality in adolescents in food-insecure households in Indonesia. A total of 405 adolescents were randomly taken, and only 395 were successfully interviewed and had complete data. ## 2.2. Eating Habit and Construction of a Diet Quality Score Adolescent food consumption includes eating habits of carbohydrates; vegetables; fruits; animal (including dairy) and plant protein sources; salty, sweet, and fatty foods; and sweet beverages, as assessed using a food frequency questionnaire. Answer scores were >1 time per day (score 5), 1 time per day (score 4), 3–6 times per week (score 3), 1–2 times per week (score 2), and <3 times per month (score 1) [7]. Diet quality in adolescents was assessed using the IGS3-60, which is the Healthy Eating Index developed for adolescents in Indonesia [17] and incorporates the iron component. The types of food consumed by the participants were grouped into carbohydrate foods, animal-based protein sources, plant-based protein sources, fruits, vegetables, dairy, and iron. All components in the diet quality assessment were food groups, except for iron. The inclusion of iron in the diet quality index is based on the fact that special attention needs to be addressed to the prevalence of anemia in adolescents in Indonesia, which is a moderate-level public health problem [7]. The average number of food portions was based on a 2-day non-consecutive 24-h food recall. Information on the type and amount of food intake was collected in household measurement and subsequently converted into grams using a food picture [18]. Modified IGS3-60 validation was performed by comparing the IGS value with the mean adequacy ratio. The correlation value was 0.82 ($p \leq 0.01$). ## 2.3. Other Covariates The data collected included adolescent characteristics, such as age, and gender, nutritional knowledge, nutritional literacy, attitudes, subjective norms, behavioral control, intention to have a healthy diet, influence of friends, and parents, food consumption, diet quality, nutritional status, physical activity, and health conditions. The data obtained from mothers in the form of the socioeconomic conditions of adolescents and their families included household expenditures, mother’s educational level, household size, family type, knowledge of nutrition, maternal nutritional literacy, and maternal food norms, as well as food allocation in the household and food security. Household expenditures were assessed as a proxy indicator of household income. Moreover, mother’s educational level, maternal nutritional literacy, household size, family type, food norms, and maternal food consumption habits were examined. Expenditures were categorized into quartiles. The mother’s educational level was divided into no school, basic education, secondary education, and higher education [19]. The maternal nutritional literacy was determined on the basis of the mean score of functional literacy, interactive literacy, and critical literacy components. The household size was divided into small, medium, and large [20]. The family type was divided into electron (the family consists of a father or a mother and unmarried children), nuclear (if a father, a mother, and unmarried children were in the family), atom (a father, a mother, unmarried children, and other unmarried family members), molecular (two married couples in different generations with or without family who are married or unmarried), and joint (two or more married couples in one generation or three or more couples in multi-generation) [21]. The mother’s eating norm was determined on the basis of the mean value of the Healthy Eating Norm [22]. Information on the Healthy Eating Norm was obtained from the question, “How often do you eat the following foods and drinks so that you can live a healthy life until you are old?” followed by a list of food groups classified on the basis of the balanced nutrition guidelines [22,23]. The Healthy Eating Norm response scale consisted of never, <3 times per month, 1–2 times per week, 3–6 times per week, 1 time per day, and >1 time per day [7]. Food allocation in households was assessed using a Likert scale question. Mothers were asked to rank each household member based on food allocation in the order from “more diverse” to “least diverse;” subsequently, it will be determined whether the adolescent is a priority or not a priority in family food allocation [24]. Food allocation consisted of carbohydrate and protein sources, vegetables, and fruits. Mothers’ eating habits were determined on the basis of the mean score for eating vegetables; fruits; animal (including diary) and plant protein sources; salty, sweet, and fatty foods; and sweet beverages measured using a food frequency questionnaire with a response scale of <3 times per month, 1–2 times per week, 3–6 times per week, 1 time per day, and >1 time per day [7]. Household food security was measured using the Household Food Insecurity Access Scale questionnaire consisting of nine questions [25] that were validated for adolescent households in Indonesia [26]. This variable was categorized into secure (0–1), slightly food insecure (2–7), moderate food insecure (8–14), and severe food insecure (15–27). The parents’ and peers’ influence was determined on the basis of the Social Support Scales scores [27]. The Social Support Scales consisted of 14 questions to assess the influence of parents and 11 questions to determine the influence of peers. The questionnaire was translated to Bahasa Indonesia and validated using Cronbach’s alpha >0.80. Nutrition literacy was assessed using a validated questionnaire (Cronbach’s alpha ≥ 0.70) that was modified from the Nutrition Literacy Inventory (NLI-28) [28]. The scoring was based on a Likert scale consisting of five choices, including “strongly agree,” “agree,” “undecided,” “disagree,” and “strongly disagree.” Each statement was scored from 1 point as the lowest to 5 points as the highest. The mean score was used in the statistical test. The construction of eating behavior consisted of attitudes, subjective norms, behavioral control, and intentions to have a healthy diet. These Theory of Planned Behavior constructs on a healthy diet were assessed using a validated and reliability-tested questionnaire [29]. The scoring was based on five answer choices for each statement, such as “strongly agree,” “agree,” “undecided,” “disagree,” and “strongly disagree.” Responses to each positive statement were scored from 5 to 1 (strongly agree to disagree strongly), and negative statements were scored from 1 to 5 (strongly agree to disagree strongly). Attitudes, subjective norms, behavioral control, and intentions were determined on the basis of the mean score in the statistical analysis. Body image was determined using the Contour Drawing Rating Scale (CDRS) method [30]. The CDRS has been validated in Malaysian adolescents who are very close to Indonesian culture and body structure [30]. Participants were asked to choose one of the nine images that most closely resembled the current state of their body and their most desirable body image. Body image is a range of values for the desired and actual body shape. Values ranged from −8 (wants to be skinny) to 8 (wants to be fat). To measure the body mass index (BMI) according to age, the nutritional status of adolescents assessed included weight and height. BMI is calculated by comparing weight (kilograms) with the square of height (meters). The BMI according to the age of adolescents was classified on the basis of the World Health Organization classification, which includes severe malnutrition (<−3 SD), thinness (−3 to <−2 SD), good nutrition (normal, −2 to +1 SD, over nutrition (overweight, +1 to +2 SD), and obesity (obese, >+2 SD) [31]. Physical activity was assessed using the adolescent’s physical activity level (PAL). Information on the participant’s physical activity was collected through a 24-h physical activity recall for two non-consecutive days. The average duration of the participant’s physical activity (hours) for 24 h multiplied by the physical activity ratio score for each activity refers to the FAO [32]. PALs of 1.40–1.69, 1.70–1.99, and 2.00–2.40 were categorized as light (light), moderate (moderate), and heavy (vigorous) activities, respectively. Health status was assessed by the number of days the participant was absent from school in a month. Participants were categorized as healthy if they had never been sick and had never been unable to attend school in the past month and were categorized as sick if they did not attend school at least one day because of illness. ## 2.4. Statistical Analysis Data normality was identified using the Kolmogorov–Smirnov test and was found to be not normally distributed. However, each variable’s mean, standard deviation, and presentation are presented descriptively to provide comparable information with previous studies. The chi-square test and Kruskal–Wallis test were applied to assess the difference between gender, differences between adolescents eating habits, and mothers’ eating habits, and norms. The Spearman correlation test was used to inspect the correlation between the construction of eating behavior and diet quality and between adolescents’ eating habits, mothers’ eating habits, and mothers’ eating norms. To examine factors related to adolescent diet quality, a logistic regression analysis was performed. The diet quality score as the dependent variable was divided into two categories based on the mean score. To examine the diet quality score based on gender and nutritional status after removing participants with a ratio of energy intake and basal metabolic rate below 0.9, a sensitivity analysis was performed [33]. In the process of performing logistic regression analysis, re-coding was performed on several variables because it has a high error standard after analysis with initial coding. The variables included adolescents’ eating habits, mothers’ eating habits, and mothers’ eating norms. The frequencies of eating <3 times a month, 1–2 times a week, and 3–6 times a week were combined into one category, whereas the other frequencies remained. Analysis was performed using SAS, and the p-value of statistical significance was <0.05. ## 3. Results A total of 395 adolescents were included in this study, with $66.3\%$ and $33.77\%$ female and male participants, respectively. Most adolescents were living in small households ($80.3\%$), with nuclear families ($51.4\%$) being the major family type. The average expenditure in an adolescent family was 2.4 million rupiahs, with the educational levels of mothers dominated by elementary education graduates ($43.8\%$). Thirty-nine percent of adolescents were living in food-secure households. The rest were adolescents who were living in households with mild-to-severe food insecurity. Adolescents were a family priority in the allocation of food (>$78\%$). Furthermore, most adolescents had a normal nutritional status ($77.5\%$), with a mild activity level ($52.7\%$) and a body image of feeling fat or wanting to be skinny ($57.5\%$). No difference was observed between gender characteristics except the physical activity level. Female participants were more sedentary ($95.5\%$) than male participants ($78.2\%$) (Table 1). Adolescents’ eating habits differ from their mothers’, except for carbohydrate and plant-based protein sources. More than $90\%$ of mothers and children consumed carbohydrate sources more than once a day, whereas plant-based protein sources were most frequently consumed only 3–6 times a week (>$35\%$). Adolescents more frequently consumed animal protein sources as well as sweet snacks, sweet beverages, salty snacks, and fatty foods than their mothers ($p \leq 0.05$). In contrast, mothers more frequently consumed vegetables and fruits than adolescents ($p \leq 0.05$) (Figure 1, Table 2). A significant positive correlation between adolescents’ and mothers’ eating habits was noted ($p \leq 0.05$), except for the habit of eating sweet snacks, which was observed to have no correlation between adolescents’ and mothers’ eating habits (Table 2). Compared with eating norms, a significant difference between the mother’s eating habits and her eating norms, as well as the mother’s eating norms and the adolescent’s eating habits was noted (Table 2). Maternal norms were higher, particularly in the eating habits of vegetables, fruits, and animal, and vegetable protein sources, than adolescent eating habits. No difference was observed between the norms of drinking sweets, salty snacks, and fatty foods between the adolescents’ eating habits and the mothers’ eating norms. However, a positive correlation was noted between mothers’ eating norms and mothers’ and adolescents’ eating habits for all food components ($p \leq 0.05$). Only the eating habits of carbohydrate sources showed no correlation between mothers’ eating norms and mothers’ and adolescents’ eating habits ($p \leq 0.05$). From the behavior-forming constructs, healthy eating behavior was positively correlated with attitudes and subjective eating norms. However, unhealthy eating behavior had a negative correlation with intention. Healthy and unhealthy eating behaviors were correlated ($r = 0.46$) and positively related to the dietary quality (Table 3). The adolescents’ food intakes were less than the recommended daily portions. Only protein-based animal dishes had an intermediate portion close to the recommended daily portion (Table 4). Moreover, the mean total score of the diet quality was low, with only 16 of the maximum score of 70. Vegetables, fruits, and dairy scored lower, with average scores of 0.0, 0.5, and 0.7, respectively. The highest score was on a protein-based animal dish, with a score of 5.8 of the maximum score of 10. After removing more than $50\%$ of adolescents with underreporting energy, the diet quality score was higher by five points. Males had significantly higher scores than females ($p \leq 0.05$) (Table 4). The change was mainly seen in the iron score, which was much higher for males than females. Iron intake in males meets the Estimated Average Requirements (EAR) but not in females. Additionally, the diet quality score was significantly higher in the obese group than that in the normal group when presenting on the basis of nutritional status ($p \leq 0.05$) (Table 5). However, the difference between the obese and normal groups was only observed in female participants. Considering the underreporting group, it was observed that the diet quality score was not different between the nutritional status group in female and male participants. Binary logistic regression analysis included variable participant characteristics and behavior components, revealing that diet quality was associated with adolescent functional nutrition literacy, health status, nutritional status, and eating habits of animal-based protein sources ($p \leq 0.05$). Mothers’ eating habits and norms, including sweet beverages, sweet snacks, and animal-based protein sources, as well as mothers’ eating norms of carbohydrates and vegetables were related to the adolescents’ diet quality ($p \leq 0.05$). Adolescents with higher functional nutrition literacy, healthy, and eating animal-based protein sources more frequently—with mothers consuming sweet beverages and high norms of vegetables—were associated with higher diet quality ($p \leq 0.05$). Conversely, obese adolescents with mothers who preferred to eat animal protein and sweet snacks less frequently and had a low norm of eating carbohydrates were associated with lower diet quality ($p \leq 0.05$) (Table 6). ## 4. Discussion The aim of this study was to identify factors related to the quality of adolescent diets in post-disaster areas. The quality score of adolescents in this study was low, with only $23\%$ of the total maximum score. Some food group scores have scores below one, including vegetables, fruits, and dairy. Furthermore, certain conditions that were more vulnerable to food shortages, including conflict areas, show similar results [34,35]. However, in this study, we observed that the scores of animal-based protein sources were higher than those of vegetable, fruit, or carbohydrate sources. The results of our study are in contrast with those of other studies that reported that fruit and vegetable intake was higher than that of animal protein sources in developing countries; however, their vegetable and fruit intake also did not fulfill the recommended value [36,37]. Low animal food intake in vulnerable conditions is associated with low availability of animal food sources [38]. However, in this study, the adolescents live close to the sea; therefore, the geography of the place makes animal-based protein sources derived from the sea, including fish, easy to obtain and favored by the adolescents [39,40]. In this study, the diet quality score was lower than that of most studies, except for the study in Brazil [41]. Compared with our study, the mean adolescent diet quality score in the urban areas of the Indonesian capital was $33\%$ or above 10 points [42]. In contrast, in urban Malaysia, the mean diet quality score was much higher, at $56\%$ [43], which is similar to the quality of diets in some developed countries [44,45]. Analysis involving adolescents without underreporting also showed that the diet quality score in this post-disaster area was low ($31\%$), close to the diet quality score of adolescents in urban Indonesia [42]. Females had lower scores than males, which also agrees with the results of other studies [41,42]. Adolescent food habits also have a significant role in the quantity of adolescent food intake. However, we observed that the high consumption of adolescents does not necessarily indicate a high score on the dietary quality score of carbohydrate-source foods consumed more often than animal-source foods. The high frequency of food consumption is only occasionally positively correlated with dietary quality [46]. Adolescents can often consume certain food groups. However, portions cannot meet the recommended values; therefore, quantitatively, the amount of food intake needs to be adequate [47]. In this study, a positive correlation was noted between adolescents’ and mothers’ eating habits, as well as adolescents’ eating habits and mothers’ eating norms. Adolescents’ eating habits are related to the mothers’ eating habits and inherent eating norms [48,49]. The largest correlation was observed in fatty eating habits and salty snacks, with adolescents eating more frequently than their mothers. Moreover, several previous studies have stated a correlation between adolescent eating habits and maternal eating norms, particularly in the low eating habits of vegetables and fruits and the high consumption of sweet, salty, and fatty foods [50,51,52]. The trend of fatty and salty foods is rapidly increasing in developing countries [53]. With the development of food technology that produces packaged foods, the variety of processed snack foods has mushroomed to remote areas, causing individuals on the edge of the city to acquire high access to snack foods [53]. Since rehabilitation, the community’s condition has gradually improved in the post-disaster area; therefore, economic growth has returned to normal. Trade, including ultra-processing food and street food, is expanding again. Furthermore, adolescents’ eating habits are influenced by factors that shape eating behavior, including attitudes, subjective norms, and behavioral control. We observed that subjective norms were positively correlated with positive and negative eating habits. The influence of other individuals is related to positive and negative eating habits in adolescents [54]. The support of others is indispensable to increasing self-confidence and self-efficacy [55]. In this study, the intention was negatively correlated with negative eating habits. In contrast, a positive although insignificant correlation was noted between adolescents’ positive intentions and eating habits. Something similar was noted in studies of food-insecure adolescents [56]. Adolescents’ intentions predict behavior in performing something, particularly if it is followed by adolescent environmental support, such as good food availability and access [57]. In this study, eating habits, and behavior constructs, such as attitudes, and subjective norms, were positively correlated with adolescents’ dietary quality. However, the association between the construction of behavior changes and diet quality diminished after being controlled for other variables in the regression test. Simultaneously, the eating habits of animal-based protein sources became significantly positively correlated. Protein is a significant component of the daily diet and is necessary for normal growth and development in adolescents [58]. Compared with other food sources, animal-based food sources have the highest total dietary quality scores. This relationship suggests that animal-based food sources contribute to the high-quality value of the diet. Similar to previous studies, animal protein sources’ contribution to dietary quality is $60\%$ [59]. However, the value of the animal protein source score still needs to reach the maximum recommended score. Additionally, the intake of other food groups remains less than that of animal protein sources; therefore, it only slightly contributes to the quality score of the adolescent diet. Other factors that were observed to have an association with adolescent diet quality after adjusting for other variables include eating sweet snacks and mothers’ norms of eating carbohydrates and vegetables. Eating sweet snacks and mothers’ norms of eating carbohydrate sources were observed to be negatively related to adolescents’ diet quality. In contrast, mothers’ norms of eating vegetables were positively associated with adolescents’ diet quality. A mother’s eating habits can arise from her norms and subsequently be followed by the adolescent; therefore, it becomes their habit [60]. Mothers’ eating habits set an example for adolescents to emulate [49]. Conversely, mothers may not be used to eating certain foods, such as sweet snacks, or vegetables. However, high food norms influence mothers to provide greater access for adolescents to obtain these foods for consumption [61]. Furthermore, the regression test revealed that mothers’ sweet drinking habits were positively related to adolescent diet quality scores, and mothers’ animal-based protein source food eating norms were significantly negatively related. We observed an interaction effect between the food norms of mothers in animal protein sources and household food security status. Likewise, mothers’ sweet drinking habits were observed to interact with the habit of eating sweet snacks. Advanced analysis by performing a separate analysis based on the effect of the interactions noted could not be performed because of the small sample size. Other diet quality-associated variables were functional nutritional literacy, adolescent health status, and nutritional status. Functional nutrition literacy is basic literacy that is the foundation for higher-level literacy, such as interactive nutrition literacy and critical nutrition literacy [62]. A person’s ability to understand nutritional messages and information and an understanding of balanced nutrition helps adolescents choose the foods that must be ingested to improve the quality of their diet [63,64]. In adolescents who are not sick, the diet quality is known to be better than that of sick adolescents. In sick conditions, there is a tendency to choose bland foods owing to changes in appetite due to physiological influences [65,66]. The nutritional status being negatively related to dietary quality is also because of adolescents’ tendency for monotonous food selection [67]. In obese adolescents, eating is dominated by high-energy foods, including fatty foods [68]. Our study shows that obese adolescents have higher but insignificant diet quality scores in fruits and lower diet quality scores in all other food components. However, this result should be cautiously interpreted since we also noted that obese adolescents underreport their intake more than other nutritional status intakes in this study. We performed a sensitivity analysis. However, the number of obese adolescents decreased by more than half; therefore, we could not determine the total mean habitual intake of obese adolescents. This study provides an overview of the diet quality of adolescents in vulnerable post-disaster areas who need more attention to efforts to improve their nutrition and health. We have included various factors that could affect diet quality in this study. However, variables still need to be fully covered, including the availability of food in the household and the preferences of the mother in food preparation. This study has yet to reach out to adolescents who are not in school and may have different eating habits and other factors related to the diet quality of adolescents who are in school. ## 5. Conclusions Eating habits, health status, and nutritional status are factors that are related to the diet quality of adolescents. Moreover, mothers’ eating habits and norms are related to the diet quality of adolescents in post-disaster areas. 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--- title: 'Caffeine and the Risk of Diabetic Retinopathy in Type 2 Diabetes Mellitus: Findings from Clinical and Experimental Studies' authors: - Nuria Alcubierre - Minerva Granado-Casas - Patricia Bogdanov - Cristina Hernández - Hugo Ramos - Esmeralda Castelblanco - Jordi Real - Esther Rubinat-Arnaldo - Alicia Traveset - Marta Hernández - Carmen Jurjo - Jesús Vioque - Eva Maria Navarrete-Muñoz - Rafael Simó - Didac Mauricio journal: Nutrients year: 2023 pmcid: PMC10005220 doi: 10.3390/nu15051169 license: CC BY 4.0 --- # Caffeine and the Risk of Diabetic Retinopathy in Type 2 Diabetes Mellitus: Findings from Clinical and Experimental Studies ## Abstract The aim of this study was to assess the potential benefits of caffeine intake in protecting against the development of diabetic retinopathy (DR) in subjects with type 2 diabetes (T2D). Furthermore, we tested the effect of topical administration of caffeine on the early stages of DR in an experimental model of DR. In the cross-sectional study, a total of 144 subjects with DR and 147 individuals without DR were assessed. DR was assessed by an experienced ophthalmologist. A validated food frequency questionnaire (FFQ) was administered. In the experimental model, a total of 20 mice were included. One drop (5 μL) of caffeine (5 mg/mL) ($$n = 10$$) or vehicle (5 μL PBS, pH 7.4) ($$n = 10$$) was randomly administered directly onto the superior corneal surface twice daily for two weeks in each eye. Glial activation and retinal vascular permeability were assessed using standard methods. In the cross-sectional study in humans, the adjusted-multivariable model showed that a moderate and high (Q2 and Q4) caffeine intake had a protective effect of DR (odds ratio ($95\%$ confidence interval) = 0.35 (0.16–0.78); $$p \leq 0.011$$ and 0.35 (0.16–0.77); $$p \leq 0.010$$, respectively). In the experimental model, the administration of caffeine did not improve either reactive gliosis or retinal vascular permeability. Our results suggest a dose-dependent protective effect of caffeine in the development of DR, while the potential benefits of antioxidants in coffee and tea should also be considered. Further research is needed to establish the benefits and mechanisms of caffeinated beverages in the development of DR. ## 1. Introduction Diabetic retinopathy (DR), an ophthalmological complication of diabetes, is the main cause of vision loss and blindness in subjects with diabetes [1]. In Catalonia (Spain), DR is still a relatively frequent microangiopathic complication [2]; furthermore, the incidence of DR is expected to increase due to the increasing incidence of diabetes, obesity, and an ageing population [3]. Nutritional therapy is an integral part of diabetes management and may contribute to the prevention of late diabetes complications [4]. Caffeine (1,3,7-trimethylxanthine) is an active food component with important health implications. The main dietary sources of caffeine intake are coffee, tea, cola or energy drinks, and chocolate, as well as some food products such as gum or alcoholic beverages [5]. Moreover, caffeine is the most consumed behaviorally active compound in the world. The richest food source of caffeine is coffee, used daily by most of the general population worldwide [6]. A meta-analysis performed with prospective studies demonstrated that a higher consumption of coffee is associated with a lower risk of developing type 2 diabetes (T2D) [7]. In contrast, some interventional studies performed with a small sample of subjects with diabetes found that caffeine intake increased postprandial glycaemia (8.9 ± 0.7 mmol/L) compared with baseline (6.7 ± 0.4 mmol/L) after carbohydrate intake [5,8]. A recent prospective study that evaluated the potential association between the consumption of different subtypes of coffee and the incidence of cardiovascular diseases observed a $12\%$ reduction in cardiovascular disease-risk with a consumption of 2–3 cups/day compared with non-drinkers [9]. On the other hand, two reviews performed to assess the beneficial effects of caffeine on different pathologies concluded that the effects in the retina is still uncertain [6,10]. Furthermore, a recent systematic review performed with interventional and observational studies indicated that the association between caffeine and DR is still unclear [11]. A cross-sectional study observed that coffee consumption of over 2 cups/day was inversely correlated with the prevalence of DR in subjects with T2D [12]. However, another cross-sectional study found that daily consumption of caffeine was unfavorable for the retinal microvasculature in adults with increased cardiovascular risk [13], while Neelam et al. [ 14] did not find any association between coffee consumption and the risk of DR in individuals with diabetes after adjusting for potential confounders. Finally, an in vitro study showed a protective effect of caffeine on the blood retinal barrier in a cellular model of diabetic macular edema, showing an $18\%$ reduction in the apoptosis after caffeine treatment [15]. In terms of the association between tea consumption and the risk of DR, some in vitro-in vivo studies have found a neuroprotective effect of green tea on the retina of diabetic rats [16,17]. Nevertheless, there is a lack of scientific evidence published on this issue. Overall, inconsistent results about the effect of caffeine intake and DR have been published. To our knowledge, this is the first study to assess the relationship between caffeine intake and DR-risk in subjects with T2D, as well as testing the effect of caffeine in an experimental diabetic model. We hypothesized that caffeine intake could have a protective effect on the development of DR in this population. Therefore, the aim of the study was to assess the potential relationship between caffeine intake (including the consumption of food sources) and DR in subjects with T2D without other late diabetic complications. Furthermore, we tested the effect of topical administration of caffeine on the early stages of diabetic retinopathy in an experimental diabetic model (db/db mouse). ## 2.1. Human Study Design and Subjects In this cross-sectional study, we included a total of 144 subjects with T2D with DR and 147 individuals with T2D without DR. This is a sub-analysis from a previous published study designed to assess the quality of life and treatment satisfaction of subjects with T2D [17]. All the participants were aged from 40 to 75 years. Participants were recruited from the DR screening and treatment program at the Department of Ophthalmology from March 2010 to January 2013, as reported previously [17]. An experienced ophthalmologist assessed and classified retinopathy according to the international clinical classification system [18]. The exclusion criteria were the presence of other advanced diabetes complications (i.e., macroalbuminuria or renal failure, and the presence of previous cardiovascular disease), and any condition that could affect clinical and nutritional variables (i.e., inflammatory intestinal illness, pancreatitis, chronic hepatic or pulmonary diseases, and pregnancy). The study was conducted according to the guidelines and principles of the Declaration of Helsinki. The local Ethics Committee approved this study, and all the study subjects signed a written informed consent form. ## 2.2. Clinical and Socio-Demographic Characteristics A detailed description of the assessment of the various clinical variables was recently published [17]. Briefly, the following socio-demographic and clinical characteristics were collected from patient medical records: age, sex, self-reported ethnic group, smoking habit, physical activity, educational level, blood pressure, lipid profile, antihypertensive and lipid-lowering drugs, and glycated hemoglobin (HbA1c). Data on diabetes duration and antidiabetic treatments were also recorded. Blood and urine samples were collected after fasting for 12 h according to standard laboratory methods. Hypertension and dyslipidemia were defined if the subject was being treated with any antihypertensive or lipid-lowering drug, respectively. Physical activity was classified as sedentary if an individual spent less than $10\%$ of daily energy expenditure performing any activity that requires 4 METs (The Metabolic Equivalent) at a minimum (a physical activity with an expenditure similar as walking for 30 min per day), according to the method by Bernstein et al. [ 19]. ## 2.3. Caffeine Intake The usual intake of foods and nutrients was assessed using a validated 101-item semiquantitative food frequency questionnaire (FFQ) [20]. Briefly, one of the researchers (N.A.) administered the questionnaire to each participant individually. Participants were asked to report how often they had usually consumed each food item over the past year based on nine options ranging from never or less than once a month to 6 or more times per day. Serving sizes were specified for each food item in the FFQ. Nutrient data for each food in the questionnaire were mainly obtained from the US Department of Agriculture’s food composition tables and complemented with Spanish tables of food composition [21,22]. Moreover, nutrient intake and food consumption were adjusted by energy intake using the residual method, where each nutrient is regressed on total calories and the population mean is then added back to the calculated residuals [23]. The caffeine intake was calculated by including the sources of this nutrient as follows: coffee ($89.5\%$ of the reported caffeine intake of the study participants), light soft drinks ($6.72\%$ of the reported caffeine intake), soft drinks ($1.56\%$ of the reported caffeine intake), tea and infusions ($0.87\%$), decaffeinated coffee ($0.56\%$), chocolate ($0.59\%$), chocolate cookies ($0.09\%$), cakes ($0.05\%$), ice cream ($0.03\%$), liquors ($0.02\%$), dairy products ($0.01\%$) and cereals ($0.01\%$). ## 2.4. Experimental Study Twenty male db/db (BKS.Cg-Dock7m +/+ Leprdb/J) mice and 10 non-diabetic control male mice (db/+) matched by age were purchased from Charles River Laboratories (Calco, Italy). The mice were kept under strict environmental conditions of humidity ($60\%$), cycles of 12h/12 h light/darkness and temperature (20 °C), with unlimited access to filtered water and “ad libitum” food (ENVIGO Global Diet Complete Feed for Rodents, Mucedola, Milan, Italy). All procedures were performed following the Animal Care and Use Committee of Vall d’Hebron Research Institutem. Furthermore, the study was performed according to the recommendations of the Association for Research in Vision and Ophthalmology Statement for the Use of Animals in Ophthalmic and Vision Research. Caffeine eye-drops ($$n = 10$$) or vehicle eye drops ($$n = 10$$) were randomly administered to 10-week old diabetic mice. Using a micropipette (Eppendorf Research® plus pipette 0.5–10 µL Ref. 3123000098; Hamburg, Germany) one drop (5 μL) of caffeine (5 mg/mL), or vehicle (5 μL phosphate-buffered saline [PBS], pH 7.4) was administered directly onto the superior corneal surface twice per day in each eye for two weeks. Moreover, one drop of both compounds (i.e., caffeine or vehicle) was administered to the eyes 1 h before the mice were euthanized. The investigators who evaluated the results were not aware of the treatment received by the mice. ## 2.5. Assessment of Neurovascular Damage Glial activation was evaluated in db/db mice treated with caffeine, vehicle and non-diabetic mice (db/+) by analyzing the expression of GFAP (Glial fibrillary acidic protein) using a Laser Scanning Confocal microscope according to the following procedures, as previously reported [24,25]. Paraffin sections were deparaffinized in xylene (VWR, Barcelona, Spain), rehydrated in ethanol (Sigma, St Louis, MO, USA) and fixed in acid methanol (−20 °C) for 1 min. After washing with 0.01 M PBS 4, pH 7.4 (Biowest, Labclinics, Barcelona, Spain), the sections were incubated in blocking solution ($3\%$ BSA, Tween $0.05\%$ PBS; Sigma Aldrich, St Louis, MO, USA) for 1 h at room temperature. The sections were incubated with an anti-GFAP rabbit monoclonal (1:500; ab7260; Abcam, Cambridge, UK) overnight at 4 °C, and the following day, were incubated with a fluorescent ALEXA 488 secondary antibody (anti-rabbit or anti-mouse) (Life Technologies S.A, Madrid, Spain) in blocking solution (Protein Block Serum-Free Ready-To-Use DAKO Agilent X0909, Agilent Technologies, Inc., Santa Clara, CA, USA) for 1 h, after washing. Nuclei were counterstained with the blue fluorescent DNA stain Hoechst 33,342 (2′-[4-ethoxyphenyl]-5-[4-methyl-1-piperazinyl]-2,5′-bi-1H-benzimidazole trihydrochloride trihydrate) (Thermo Fisher Scientific, OR, USA) after washing. Last, the sections were mounted in Prolong Gold antifade mounting medium (Invitrogen, Thermo Fisher Scientific, Bend, OR, USA) using a coverslip. Images were captured with a confocal laser scanning microscope (FV1000; Olympus, Hamburg, Germany) at a resolution of 1024 × 1024 pixels. Five fields (three from the central retina and two from the peripheral retina) from each section were analyzed using ImageJ software (National Institutes of Health, Bethesda, MD, USA). The degree of glial activation as determined by the GFAP staining was scored as follows: Müller cell end-feet region/ganglion cell layer (GCL) only (score 1); Müller cell end-feet region/GCL plus a few proximal processes (score 2); Müller cell end-feet plus many processes, but not extending to the inner nuclear layer (INL) (score 3); Müller cell end-feet plus processes throughout with some in the outer nuclear layer (ONL) (score 4); Müller cell end-feet plus many dark processes from the GCL to the outer margin of the ONL (score 5) [26]. The permeability of retinal vascular was assessed by measuring the leakage of albumin from the blood vessels into the retina using the well-established Evans Blue albumin method (ex vivo). Briefly, Evans Blue (E2129 SIGMA, Sant Louis, MO, USA) was injected intraperitoneally (17 mg/kg body weight, at a concentration of 5 mg/mL dissolved in PBS pH 7.4), following which the mice turned visibly blue, demonstrating the correct uptake and distribution of the dye. The mice were euthanized after 120 min, the eyes were enucleated and flat-mounted slides were prepared and cover slipped with a drop of Prolong Gold antifade mounting medium (Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA). A confocal laser scanning microscope (FV1000; Olympus, Hamburg, Germany) was used to generate digital images from a number of random fields from all the retinas at 60× using a 561-nm laser line, with each image being recorded with an identical beam intensity at a resolution of 1024 pixels × 1024 pixels. Z-stack retinal images (step size 1.16 μm) of different regions of the vascular tree were acquired and the number of extravasations per field (at 60×) was counted to analyze the albumin-bound Evans Blue. ## 2.6. Statistical Analysis Mean values (with standard deviations [SD]) or absolute and relative frequencies (in percentages) were derived for quantitative or qualitative variables, respectively. Mann-Whitney tests or Chi-squared tests were used to compare whether there were differences between the groups. Bonferroni-corrected p-values were also provided to adjust for multiple pairwise comparisons. Bivariable analysis between caffeine intake, coffee and tea consumption and the outcome, i.e., the presence of DR, was performed to obtain crude odds ratio (OR) and their $95\%$ confidence interval ($95\%$ CI). Multiple logistic regression was used to explore the association between independent variables (i.e., caffeine intake, coffee, and tea consumption) and the outcome variable (DR). We estimated OR and their $95\%$ CI, adjusting for potential confounding factors such as age, sex, educational level, physical activity, hypertension, dyslipidemia, diabetes duration, glycated hemoglobin (HbA1c), body mass index (BMI) and tobacco exposure. In the animal experiments, statistical comparisons were performed using a one-way ANOVA performing the Bonferroni test. For statistical purposes, the GFAP score (extent of glial activation) was categorized as “normal” (scores 1 and 2) and “pathological” (scores 3, 4 and 5), and any differences between groups was analyzed using the Fisher’s test. For all comparisons, the significance level was set at 0.05. The data was analyzed using R 3.0.1 software [27]. ## 3.1. Clinical Study The clinical characteristics of the study participants are shown in Table 1. In comparison with individuals with no DR, those with this condition were older ($$p \leq 0.024$$) and showed a larger waist circumference ($$p \leq 0.034$$), higher systolic blood pressure (SBP) ($p \leq 0.001$), a higher frequency of hypertension ($$p \leq 0.005$$), longer diabetes duration ($p \leq 0.001$), higher glycated hemoglobin (HbA1c) levels ($p \leq 0.001$), and higher high density lipoprotein cholesterol (HDL-c) levels ($$p \leq 0.031$$). In addition, these subjects had a lower educational level ($$p \leq 0.003$$). In terms of diabetes treatment, a higher frequency of subjects with DR were treated with both oral antidiabetic drugs (OAD) and insulin ($p \leq 0.001$). ## Caffeine Intake and Diabetic Retinopathy We observed a higher frequency of subjects with DR in the lowest quartile of caffeine intake compared with individuals without DR ($30.6\%$ vs. $19.7\%$, $$p \leq 0.045$$) (Table 2). However, we did not observe differences between groups in coffee and tea consumption. On the other hand, the unadjusted bivariable analysis showed a protective association between the Q2 and Q4 quartiles of caffeine intake and the prevalence of DR (odds ratio [OR] ($95\%$ confidence interval [CI]) = 0.46 (0.24–0.90); $$p \leq 0.022$$ and 0.47 (0.24–0.92); $$p \leq 0.027$$, respectively) (Table 2). However, no significant association was observed between coffee and tea consumption, and the prevalence of DR. The multivariable logistic models performed with caffeine intake and coffee and tea consumption as continuous variables did not show any association between caffeine intake or coffee and tea consumption and the presence of DR (Table 3). However, hypertension and diabetes duration were associated with a high risk of DR in both models (OR ($95\%$ CI) = 1.91 (1.04–3.52); $$p \leq 0.037$$ and 1.87 (1.02–3.46); $$p \leq 0.043$$ for hypertension) and (OR ($95\%$ CI) = 1.10 (1.06–1.15); $p \leq 0.001$ and 1.10 (1.06–1.16); $p \leq 0.001$ for diabetes duration), respectively. On the other hand, the multivariable model performed with quartiles of caffeine intake showed that a moderate and high (Q2 and Q4) daily intake of caffeine had a protective effect on DR (OR ($95\%$ CI) = 0.35 (0.16–0.78); $$p \leq 0.011$$ and 0.35 (0.16–0.77); $$p \leq 0.010$$, respectively) (Table 4). However, we did not observe any association between quartiles of coffee and tea consumption and the prevalence of DR. As expected, hypertension (OR ($95\%$ CI) = 2.16 (1.17–4.05), $$p \leq 0.014$$ and 1.87 (1.02–3.47), $$p \leq 0.043$$), diabetes duration (OR ($95\%$ CI) = 1.11 (1.06–1.16), $p \leq 0.001$ and 1.10 (1.06–1.16), $p \leq 0.001$) and HbA1c (OR ($95\%$ CI) = 1.60 (1.28–2.03), $p \leq 0.001$ and 1.55 (1.24–1.97), $p \leq 0.001$) were identified as risk factors of DR in both models. ## 3.2. Experimental Study We did not find any differences in blood glucose concentrations and body weight during the study between db/db mice treated with caffeine and db/db mice treated with vehicle. As expected, in non-diabetic control mice (db/+) GFAP expression was mainly confined to the retinal ganglion cell layer (GCL) (Figure 1A). The diabetic mice (db/db) treated with vehicle presented with significantly higher GFAP expression than non-diabetic mice matched by age (Fisher’s exact test: $p \leq 0.01$). The administration of caffeine was not able to decrease reactive gliosis. The number of vascular extravasations was significantly reduced in non-diabetic mice compared with diabetic mice treated with vehicle. A lower rate of vascular extravasations was observed in diabetic mice treated with caffeine eye drops in comparison with those treated with vehicle, but the difference was not statistically significant (Figure 1B). ## 4. Discussion Our results suggest a protective effect of moderate and high (Q2 and Q4) daily caffeine intake and the presence of DR. However, no association was found between coffee and tea consumption, the main food sources of caffeine, and the presence of DR in these individuals. In addition, when we performed the experimental study with db/db mice, we did not observe any effect of caffeine on the retina. In our study, a protective association between moderate and high (Q2 and Q4) caffeine intake and the risk of DR was found; however, this relationship was not observed when the analysis was performed with caffeine intake as a continuous variable, and there was no relationship between DR and daily coffee and tea consumption, suggesting a non-linear association between caffeine and DR. In scientific literature, conflicting results have been published. A cross-sectional study performed in Korea with a large sample of subjects with T2D found an inverse correlation between the consumption of 2 or more cups of coffee per day and the prevalence of any type of DR after adjusting for potential confounders, which is in line with our results [12]. On the other hand, another cross-sectional study performed with a small sample of subjects with the presence of one or more cardiovascular risk factors, including diabetes, found that caffeine intake was positively associated with retinal venular caliber suggesting that caffeine might have an unfavorable effect on the retinal microvasculature, which is in contrast to our results [13]. Nevertheless, the authors did not observe any association between coffee and tea consumption and retinal vessel calibers. Furthermore, two interventional studies performed with small samples of healthy subjects found that caffeine intake (100 mg in one study and 200 mg in the other study) had an acute constricting effect in the retina [28,29]; this is also in contrast with our results. Nevertheless, a cross-sectional study performed with a large sample of subjects with diabetes did not find any association between coffee consumption and the risk of macular degeneration and DR after adjusting for potential confounders [14]. As described in a review, epidemiological studies have shown a preventive effect of green tea in the development of DR [30], showing a risk-reduction in $50\%$ compared with non-drinkers [31]. However, no interventional studies have been performed to assess the effects of tea consumption on the prevention of DR [30]. A recent systematic review of interventional and observational studies concluded that the association between the consumption of coffee and DR is still unclear [11]; therefore, further studies are needed to establish the effect of caffeine intake on DR. We did not observe any effect of caffeine in the retina in our in vitro study in db/db mice. This is in contrast with an in vitro study performed with human cells that found a protective effect of caffeine that was shown to inhibit apoptotic cell death induced by hyperglycemic/hypoxic insult [15]. Furthermore, a recent narrative review described decreased ischemic injury after caffeine exposure in animal models of glaucoma [32]. Moreover, a possible protective effect of green tea reducing the production of reactive oxygen species in the retinal nerves of diabetic rats was previously described [16,30]. The possible mechanisms behind the neuroprotective effects of caffeine have been related to the antagonism of adenosine receptors in retinal degenerative diseases [6]. Neural damage mainly occurs because of energy deficit and ionic imbalance, thus leading to oxidative stress and inflammation in the retina. Moreover, an increased adenosine concentration after an ischemic event should be considered due to the protective benefits of adenosine receptors [32]. Coffee and tea are major contributors of total antioxidant intake, as chlorogenic acid and flavonoids, respectively, which could have protective effects on the development of certain forms of cancer, arthritis and cardiovascular diseases [6,16]. An in vitro study showed that coffee extract and chlorogenic acid reduced the apoptosis of retinal cells induced by hypoxia and nitric oxide, suggesting a prevention of coffee consumption in retinal degeneration [33]. In addition, an in vivo–in vitro study showed a reduction in vascular retinal damage with chlorogenic acid [34]. Moreover, a recent review described the potential protective effect of chlorogenic acid for the prevention of diabetic complications [35]; however, there are still a lack of robust clinical trials assessing the application of chlorogenic acid. In our experimental study, caffeine had no effect on the retina. Moreover, in the clinical study, no association was found between the consumption of tea and coffee drinks and DR. It is, therefore, possible that other compounds contained in caffeinated beverages such as antioxidants may be responsible for the beneficial effects. The limitations of this study are the cross-sectional design of the clinical study whereby a causal relationship between caffeine intake and the development of DR cannot be established. Moreover, this is a sub-analysis of a previous study, mainly designed to assess the quality of life and treatment satisfaction of subjects with T2D [17]. Furthermore, the small sample size might have reduced the probability of finding statistical significance. In addition, the presence of other compounds in caffeinated beverages, i.e., antioxidants, that were not analyzed could influence the results of the study. On the other hand, this study was performed with a large and well-defined sample of individuals with DR and non-DR without other late diabetic complications. The FFQ can be used to estimate caffeine intake and food consumption over the previous five-year period showing good reproducibility [20,36]. Moreover, this is the first study that assessed the relationship between caffeine intake, including coffee and tea consumption, and the development of DR, encompassing both clinical and experimental aspects. ## 5. Conclusions A moderate and high caffeine intake was associated with a $65\%$ reduced risk of DR in subjects with T2D without other late diabetic complications. However, the experimental model did not support the findings in humans. This could be due to the presence of other potential compounds, i.e., antioxidants, in coffee and tea which could have protective effects in the retina. 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--- title: Comparison of In Vitro Estrogenic Activity of Polygoni multiflori Radix and Cynanchi wilfordii Radix via the Enhancement of ERα/β Expression in MCF7 Cells authors: - Reshmi Akter - Dong Uk Yang - Jong Chan Ahn - Muhammad Awais - Jinnatun Nahar - Zelika Mega Ramadhania - Jong Yun Kim - Gyong Jai Lee - Gi-Young Kwak - Dong Wook Lee - Byoung Man Kong - Deok Chun Yang - Seok-Kyu Jung journal: Molecules year: 2023 pmcid: PMC10005224 doi: 10.3390/molecules28052199 license: CC BY 4.0 --- # Comparison of In Vitro Estrogenic Activity of Polygoni multiflori Radix and Cynanchi wilfordii Radix via the Enhancement of ERα/β Expression in MCF7 Cells ## Abstract Postmenopausal women experience several symptoms, including inflammation and a sharp rise in oxidative stress caused by estrogen deprivation. Although estrogen replacement therapy (ERT) is generally regarded as an effective treatment for menopause, it has been used less frequently due to some adverse effects and high costs. Therefore, there is an immediate need to develop an effective herbal-based treatment that is affordable for low-income populations. Acordingly, this study explored the estrogen-like properties of methanol extracts from Cynanchum wilfordii (CW) and Poligonum multiflorum (PM), two important medicinal plants in Republic of Korea, Japan, and China. Due to the similar names and morphologies of these two radixes, they are frequently confused in the marketplace. Our previous colleagues discriminated between these two plants. In this study, we investigated the estrogenic activity of PM and CW using several in vitro assays with their possible mechanism of action. First, their phytochemical contents, such as gallic acid, 2,3,5,4′-tetrahydroxystilbene-2-O-glucoside (TSG) and emodin, were quantified using high-performance liquid chromatography (HPLC). Secondly, estrogen-like activity was assessed utilizing the well-known E-screen test and gene expression analysis in estrogen receptor (ER)-positive MCF7 cells. ROS inhibition and anti-inflammatory effects were analyzed using HaCaT and Raw 264.7 cells, respectively. Our findings demonstrate that PM extracts significantly increased the expression of the estrogen-dependent genes (ERα, ERβ, pS2) and boosted MCF7 cell proliferation in comparison to CW extracts. Additionally, PM extract demonstrated a significant reduction in reactive oxygen species (ROS) production as well as an enhanced antioxidant profile compared to the CW extract. Further, the PM extract treatment significantly reduced the generation of nitric oxide (NO) in RAW 264.7 cells, a murine macrophage cell line, demonstrating the anti-inflammatory properties of the extract. Finally, this research offers an experimental foundation for the use of PM as a phytoestrogen to minimize menopausal symptoms. ## 1. Introduction The herbal plants of Poligonum multiflorum (PM) from the family Polygonaceae and Cynanchum wilfordii (CW) from the family Apocynaceae are widely available in Republic of Korea, Japan, and China and are used as oriental medicine. CW is known as Baekshuoh in Republic of Korea and Beishuwu in China, and PM is called Hashuoh in Republic of Korea and Heshuwu in China [1]. Stilbenes and anthraquinones are the primary components in PM, with 2,3,5,4′-tetrahydroxystilbene-2-O-glucoside (TSG), emodin-8-O-D-glucoside (EMG), and physcion-8-O-D-glucoside (PG) being dominant in PM; the bioactivities of PM are thought to be caused by these molecules [2]. More than 300 substances have been identified from Cynanchum species, with steroids, alkaloids, terpenes, flavonoids, polysaccharides, and steroidal glycosides being the main components [3]. Studies have shown that PM possesses anti-bacterial, anti-inflammation, anti-oxidant, liver protection, bone protection, anti-HIV, anti-diabetic, anti-atherosclerotic, anti-tumor, and anti-cancer activities [2]. CW root has been used in traditional Korean medicine to treat hypertension and geriatric and musculoskeletal diseases, including gray hair, muscle impotence, bone weakness, hypercholesterolemia, and tumors [4]. However, because of their similar morphologies and names, CW and PM are frequently utilized indiscriminately in the Korean herbal medicine market [5]. PM radix is made up of dried root tubers of P. multiflorum. In contrast, CW radix is truly an appellative word for the root tubers of C. wilfordii, according to the Korean, Japanese, and Chinese pharmacopeias [6]. Traditional methods of authenticating medicinal plants have relied on their morphological characteristics; however, identification can be difficult depending on the growth phases and current environmental circumstances [7]. Several studies have attempted to discriminate between these plant species to standardize their usage as medicine [1,8]. In a previous study by our colleagues, C. wilfordii, C. auriculatum, and P. multiflorum were discriminated through chloroplast genes via multiplex PCR [5]. Menopause is a normal biological stage of a woman’s life, characterized by the cessation of menstruation as a result of estrogen deprivation, which typically happens between the ages of 40 and 58 [9]. Estrogen is a regulatory hormone that plays a crucial role in women’s sexual and reproductive development [10] and is mainly a class of steroids produced by the ovary or placenta. After women enter menopause, estrogen levels decline significantly due to aromatase inhibitors [11]. Menopause triggers several menopause-related conditions, including insomnia [12], osteoporosis [13], metabolic disorders [14], and cardiovascular diseases [15]. Systemic inflammation is fueled by the loss of estrogen during the menopause transition [16]. Menopause is marked by a rise in pro-inflammatory serum indicators (IL1, IL6, TNF-α), an increase in cell sensitivity to these cytokines, a decrease in CD4 T and B lymphocytes, and inflammation development [17]. Estrogen replacement therapy (ERT) has been the gold standard for treating menopause symptoms [18]. However, ERT has multiple side effects, including an increased risk of breast cancer [19]. Continued hormone therapy may increase the risk of ovarian cancer, endometrial cancer, blood clots, strokes, and gallbladder diseases [20]. Due to these adverse effects, research into alternative treatments is critical. Many researchers have found natural herbs attractive to treat menopause and related symptoms. Natural products are medicinal compounds used to treat innumerable disorders since prehistoric times. The identification of the nutritional values, biological activities, and potential health benefits of natural products and their chemical compounds has increased their acceptance by individuals for treating uncountable diseases in recent years [21]. Moreover, the adverse side effects of synthetic drugs have brought more attention to natural product research. The present study focuses on a comparative analysis of PM and CW and their effects against menopause and postmenopausal symptoms, such as inflammation; we further investigated the chemical compounds responsible for the estrogen-like activity. To our best knowledge, this is the first study examining the effect of these plants on menopause and related symptoms with underlying mechanisms. ## 2.1. Phytochemical Analysis Using HPLC Phenolic phytochemicals, the most prevalent family of bioactive molecules, can be found in various plant sources, including fruits, vegetables, and drinks [22]. Plant phenolics include tannins, flavonoids, phenolic acids, lignins, and stilbenes. Gallic acid, also referred to as 3,4,5-trihydroxybenzoic acid, is a phenolic substance found both in a free state and as gallotannin (a component of tannins) [23]. Compared with well-known antioxidant vitamins, phenolic acids have substantially stronger in vitro antioxidant activity [24]. These phytochemicals have gained popularity due to their numerous dietary health benefits and capabilities, including their anti-cancer, anti-allergenic, anti-atherogenic, anti-thrombotic, anti-microbial, anti-inflammatory, cardioprotective, and immunoregulatory characteristics [25]. P. multiflorum and C. wilfordii contain several phytochemicals [26,27]. However, our results showed that the dry weights of 0.17 ± 0.016 mg/g gallic acid and a trace amount of ellagic acid were found in PM. Neither gallic acid nor ellagic acid was found in CW. TSG (2,3,5,4,-tetrahydroxystilbene-2-O-β-d-glucoside), one of the key active ingredients of PM, possesses antioxidant, anti-inflammatory, anti-tumor, anti-HIV, and liver-protective properties [28]. Emodin ((3-methyl-1,6,8-trihydroxyanthraquinone), another principal constituent of PM, exhibits anti-cancer, anti-inflammatory, anti-viral, anti-bacterial, anti-osteoporotic, anti-diabetic, hepatoprotective, and immunosuppressive activity [29]. Our results showed that 39.01 ± 0.280 mg/g DW and 1.18 ± 0.155 mg/g DW TSG were present in PM and CW, respectively, while 0.84 ± 0.003 mg/g DW of emodin was present in PM and it was not present in CW (Table 1). Additionally, compared to CW, PM contains more gallic acid, TSG, and EG, according to our findings (Figure 1). ## 2.2. Total Phenolic and Total Flavonoid Contents Natural sources of various phytochemicals, including phenols, flavonoids, alkaloids, glycosides, lignins, and tannins, include plants and plant products. The most prevalent phytochemicals involved in antioxidant activities are phenols and flavonoids found in various fruits, vegetables, and medicinal plants [30]. Flavonoids and phenolics are secondary plant metabolites shielding plant cells from oxidative stress and environmental toxins. They are well-known as antioxidants and have been the subject of interest due to their advantages for human health, including their ability to treat and prevent numerous diseases [31]. Their redox characteristics, crucial in adsorbing and neutralizing free radicals, quenching singlet, triplet oxygen, or degrading peroxides, are thought to be the primary cause of this action. They possess potential effects against inflammation, ulcer, depression, tumor, and cancer [32]. The amounts of phenolic compounds in aqueous extracts of PM and CW are listed in Table 2. The TPC values ranged from 14.03 ± 0.03 to 2.08 ± 0.01 mg/g, represented as gallic acid equivalents (GAE), whereas the TFC contents ranged from 4.81 ± 0.01 to 5.84 ± 0.03 mg/g, expressed as rutin equivalents (RE). ## 2.3. Antioxidant Activity: DPPH and Reducing Power Assays An antioxidant is a chemical that prevents or delays the oxidative damage that can occur to organisms’ cells by scavenging free radicals, such as peroxide or hydroperoxide, which lowers the risk of developing degenerative diseases [33]. Moreover, numerous severe human diseases, including cancer, Alzheimer’s disease, heart, renal, and liver conditions; fibrosis; atherosclerosis; arthritis; neurological disorders; and aging, may be brought on by abnormal free radical generation [34,35]. Antioxidants are a class of substances that shield cells from free radicals and can slow the onset of illnesses, such as cancer and aging, and boost the immune system [36]. To preserve food and stop the oxidation process, synthetic antioxidants with a neutral flavor have been employed as chemicals for decades but may have carcinogenic effects [37]. Plant antioxidants are crucial to human health because they aid in the body’s ability to combat free radicals and lessen the impact of oxidative stress [38]. To evaluate the antioxidant activity of our extracts, diphenyl-picryl-hydrazine, a stable free radical, was used. The potential of the DPPH free radical to change from violet to yellow depends on its acceptance of a proton donation from the extracts. In the DPPH results, the scavenging capacity of PM and CW was 0.95 ± 0.01 and 0.81 ± 0.01 µg GAE/mg extract, respectively. This result shows that PM has slightly higher antioxidant efficacy than CW. A reducing power test can be used to determine the ability of Fe3+ to transfer to Fe2+, which subsequently combines with FeCl3 to generate the blue (Fe3+)4[Fe2+(CN)6]3 complex, which has an absorption peak at 700 nm. The ability of the sample to transport electrons was associated with reducing power. The enhanced absorbance suggested an increase in the reducing power of the plant extract [39]. The antioxidant capacity of PM and CW was 3.37 ± 0.01 and 1.80 ± 0.10 µg GAE/mg extract, respectively. These findings suggested that PM extracts showed notable antioxidant properties because of the number of phytochemicals (Table 3). ## 2.4. The Proliferation of Human MCF-7 Cells Estrogens are well-known for promoting cellular growth. Phytoestrogens influence and enhance estrogen action reciprocally. Phytoestrogens have an estrogen-like impact when estrogen levels are low; when levels are high, they display antiestrogenic activity by competitively binding to estrogen receptors [40]. MCF-7 cells, an estrogen-sensitive cell line, proliferate when exposed to estrogen-like compounds [41,42]. This characteristic may be used to identify whether a chemical is an estrogen because the proliferative effect of natural estrogen is thought to be the hallmark of estrogen action [43]. The MCF-7 cell proliferation assay measures how the cell reacts to an estrogenic or an antiestrogenic substance through the ER-mediated pathway [44]. PM and CW extracts were investigated for the ability to increase the cell proliferation of estrogen-dependent MCF-7 cells. The potentiality of PM and CW extracts to boost the proliferation of estrogen-dependent MCF-7 cells was examined by an E-screen assay. The results revealed a considerable increase in cell proliferation caused by PM extracts (31.25–250 µg/mL) (Figure 2). However, there was no proliferative effect from the CW sample. E2 was used as a positive control since it considerably boosted the proliferation of ER-positive MCF-7 cells. Stilbenes such as 2,3,5,4′-tetrahydroxystilbene-2-O-β-D-glucoside (TSG) is a potent phytoestrogen group. A previous study showed that TSG positively affected MCF7 cell proliferation [45]. In our study, we investigated the proliferative effect of TSG on McF7 cells, as PM contains a higher amount of TSG. Additionally, a prior study showed that emodin and emodin 8-O-b-d-glucopyranoside boosted MCF-7 proliferation from 1 to 10 mM [46]. ## 2.5. Effects of Plant Extracts on Cell Viability The MTT test was used to measure the vitality of RAW 264.7 and the human keratinocyte cell line (HaCaT) to detect the cytotoxic effect of the PM and CW extracts. As shown in the results (Figure 3a,b), there was no discernible change in the viability of the cells between the control group and the cells treated with 31.25–250 µg/mL PM and CW extract in RAW 264.7 cells; however, 250 µg/mL CW showed slight cytotoxicity in both cells. Based on this result, we selected 200 µg/mL for further experiments. ## 2.6. Effect on Lipopolysaccharide-Induced Nitric Oxide (NO) Production In response to inflammation or damage, inducible nitric oxide synthase (iNOS) produces more significant quantities of NO, a signaling molecule crucial to the inflammatory response. In the initial phases of the inflammatory response, macrophages play a pivotal role [47]. Lipopolysaccharide (LPS) activates macrophages, and the production of proinflammatory mediators, such as NO, rises [48]. In treating inflammatory illnesses, using NO inhibitors is an effective therapeutic strategy [49]. In addition, estrogen exerts an anti-inflammatory effect, and deprivation of estrogen levels may increase the risk of inflammation [50]. We investigated the inhibitory effects of PM and CW on nitric oxide generation produced 1 h before and 24 h after applying LPS. Because NO is highly unstable in biological environments and quickly oxidizes to nitrite, the nitrite level in the culture medium was chosen as a measure of NO generation. L-NMMA, a typical nitric oxide inhibitor [51], served as the positive control. Figure 4 demonstrates that NO generation is significantly increased in LPS-treated cells compared with PM- and CW-treated LPS-induced cells. Additionally, antioxidants play significant roles in redox pathways by shielding the cell from inflammatory and apoptotic processes. Moreover, prior research has demonstrated that flavonoids and phenolics can reduce inflammation by reducing intracellular cytokines and NO production [52,53]. Moreover, previous studies suggested that both TSG and emodin exhibited potential anti-inflammatory effects via inhibiting NO output [54,55]. As PM demonstrated notable flavonoids, phenolics, TSG, and emodin, it also demonstrated more excellent NO generation defense than CW. We also investigated the effect of estradiol against NO production and found that E2 significantly inhibited NO levels. ## 2.7. Suppression of Elevated Levels of Reactive Oxygen Species (ROS) The production of ROS and its eradication by the cellular antioxidant system are balanced in cells under normal circumstances [56]. In postmenopausal women, oxidative stress is increased due to decreased estrogen availability [57]. The overproduction of ROS can harm a cell’s oxidative health by destroying the structural integrity of the cell [58]. H2O2, a precursor to many radicals, can increase cell ROS levels by piercing the cell membrane [59]. Among the various types of human cells, epidermal keratinocytes reside in the skin’s outermost layer and are constantly exposed to external stimuli, such as UV radiation and H2O2. As a result, these cells have self-protective functions against environmental threats such as oxidative stress. Although there have been few comparative studies with cells from other organs, epidermal keratinocytes can be considered a type of cell that can compete with ROS. The endogenous redox regulation system in keratinocytes is highly organized regarding the redox state that occurs in response to external stimuli [60]. Pro-inflammatory cytokines were activated through the Mitogen-activated protein kinases (MAPKs) signaling pathway. Thus, the ROS generation using HaCat cells can demonstrate the antioxidant model in general, related to the activation of several signaling pathways, including inflammatory and estrogens [61]. Understanding ROS regulation in metabolic inflammation and estrogen signaling pathways may provide the basis for developing therapeutic strategies for managing metabolic dysfunctions [62]. To investigate in vitro antioxidant potential, 500 µM H2O2 was first used to stimulate ROS formation in the HaCaT cells before they were treated with PM and CW extracts. Using the fluorescent probe DCFH-DA, the influence of intracellular ROS levels in the HaCaT cells was examined (Figure 5). After H2O2 stimulation, HaCaT cell ROS levels and fluorescence intensity considerably increased, while both PM and CW lowered the fluorescence intensity. Because estrogens bind to estrogen receptors and use intracellular signaling pathways to up-regulate the production of antioxidant enzymes, they have antioxidant characteristics [63]. We also determined the effect of estrogen on ROS generation, and the result showed that E2 exhibited significant ROS inhibition. Up to 200 µg/mL, PM showed a significant ROS inhibitory effect when compared with CW. Polyphenols and flavonoids stop the production of intracellular ROS and shield cells from oxidative damage [64]. PM contains abundant polyphenols and flavonoids and might help prevent HaCaT cells from oxidative damage caused by H2O2. ## 2.8. Estrogen Receptor mRNA Expression and Estrogenic Activity in Human MCF-7 Cells The physiological effects of estrogenic substances are significantly modulated by the estrogen receptor subtypes (ERα and ERβ) [65]. The natural estrogen 17β-estradiol (E2) has a high affinity for binding to both ER-α and ER-β. Similar ligand-binding specificities are shared by dietary phytoestrogen as they share structural similarities with synthetic estrogen [66]. ER is primarily expressed in the uterus, ovary, breast, kidney, bone, and liver. ER is also found in the ovary, colon, central nervous system, heart, lung, and prostate [67]. Isoflavones, stilbene, coumestan, and lignan are four phenolic chemicals categorized as phytoestrogens [68]. TSG boosted ER expression in MCF-7 cells. Furthermore, TSG reduced estrogen deficiency-induced osteopenia in animal models of osteoporosis caused by ovariectomy [69]. To investigate the effect of PM and CW extracts on the proliferation-promoting effects along with ERα and ERβ activation, we used RT-PCR. We focused on ERα, ERβ, and the estrogen-regulated gene pS2 found in the breast cancer cell line MCF7. Numerous studies have used the ER-subtype-mediated route to investigate the phytoestrogenic effects of target substances in vitro and animals [70,71]. Our results revealed that PM extract notably increased the expression of ERα and ERβ in MCF7 cells. Both genes were significantly expressed compared with estradiol (E2) expression. We also checked the effect of TSG on all genes (ERα, ERβ, and pS2 genes). TSG demonstrated a nearly identical action as estrogen. PM greatly impacted the upregulation of ERα, ERβ, and pS2 gene expression, whereas CW had very little influence (Figure 6). Additionally, PM contains a sufficient amount of the overall phenolic and flavonoid content and a notable amount of gallic acid and has displayed a more preferable estrogenic effect than CW. Therefore, the estrogen-like effect of PM extract was mainly mediated via the ER-mediated pathway. ## 3.1. Collection and Preparation of Plant Material Samples Polygoni multiflori Radix and Cynanchi wilfordii Radix were purchased from Donguiherb Co., Ltd. (Seoul, Republic of Korea). As described by Nguyen et al., 2021 [72], plant materials were extracted with minor modifications. We added $80\%$ methanol to 1 g dried powder of P. multiflorum and C. wilfordii roots and extracted the samples for 15 min in an ultrasonic bath three times. The extracted solution from each extraction was combined and evaporated at 45 °C with a rotary evaporator (Eyela, Japan). The extract was then diluted in 5 mL of HPLC-grade MeOH and filtered with a 0.45 µm syringe filter before analysis by HPLC. ## 3.2. Preparation of Standard Solutions Gallic acid, 2,3,5,4′-tetrahydroxystilbene-2-O-β-D-glucoside (TSG), and emodin were purchased from Sigma-Aldrich (Darmstadt, Germany), Ensolbio Sciences (Daejeon, Republic of Korea), and Extrasynthese (Genay, France), respectively. The individual standard stock solutions of gallic acid, TSG, and emodin were prepared at a concentration of 1000 mg/L. The various concentrations of standard solutions were plotted against the peak area to create a standard curve to quantify the ingredients in the plant materials (Table 4). ## 3.3. High-Performance Liquid Chromatography (HPLC) High-performance liquid chromatography (HPLC) was performed as previously described [73]; the HPLC conditions for analyzing gallic acid, TSG, and emodin are shown in Table 2. The HPLC system consisted of an Agilent 1260 infinity system equipped with an Agilent 1260 Infinity Quaternary Pump (G1311B), Agilent 1260 Infinity Standard Autosampler (G1329B), Agilent 1260 Infinity Column Thermostat Compartment (G1316A), and Agilent 1260 Infinity Variable Wavelength Detector (G1314F). The ZORBAX Eclipse Plus C18 column (250 mm × 4.6 mm, 5 μm particle size) (Milford, MA, USA) was chosen as a stationary phase. For Gallic acid and TSG analysis, the eluent composition was as follows: (0–8 min, 90–$80\%$ B; 8–30 min, 80–$55\%$ B; 30–60 min, 55–$30\%$ B). Isocratic elution of $0.1\%$ phosphoric acid in water and methanol was chosen to determine emodin in the plant materials. The HPLC analysis conditions are shown in Table 5. ## 3.4. Determination of Total Phenolic and Total Flavonoid Contents The Folin–Ciocalteu technique was used to determine each sample’s total phenolics and flavonoids following the previous method [74], with a few minor adjustments. After extracting 0.5 g of dried powdered material three times in 20 mL of $80\%$ methanol for 1 h, the filtrate was mixed for evaporation. The crude extract was redissolved in distilled water for further compound analysis. Next, 0.3 mL of each extract was mixed with 1.5 mL of Folin–Ciocalteu reagent in wells of a 96-well microplate to measure total phenolics. The mixture was then incubated for 5 min after being thoroughly shaken. Next, 1 mL of $7.5\%$ Na2CO3 solution was added, and the sample was left in the dark for 30 min. The absorbance at 715 nm was finally measured. Gallic acid was used as a standard to create a standard curve for evaluating the total phenolic content. Gallic acid equivalent (GAE) was used, and the results were expressed in mole per milligram of extract (µg GAE/mg extract). The combination reaction of 0.3 mL of each extract, 0.3 mL $5\%$ NaNO2, and 0.3 mL $10\%$ AlCl3 was used to determine the total flavonoid content. After the mixture was incubated for 6 min, 0.5 mL of 1 N NaOH was added. The absorbance was immediately determined at 510 nm after thoroughly mixing the solution. Rutin was used as a calibration curve to determine the total flavonoid content, and the results are represented as mol rutin equivalent mole per milligram of extract (µg RE/mg extract). ## 3.5. DPPH Scavenging Assay Using a slightly modified version of the previously published procedure [75], the DPPH method was used to assess the free radical scavenging activity. A 96-well plate was filled with 20 µL of PM and CW extracts and 180 µL of DPPH solution; the plates were then vigorously shaken and incubated for 30 min in the dark at 25 °C. The absorbance was then determined at 517 nm. The following formula was used to calculate the percentage of inhibition of the samples:(1 − Absorbance of sample/Absorbance of control) × 100. The reducing power activity of the samples was assessed by mixing 100 µL of the samples with 250 µL of pH 6.6 phosphate buffer and 250 µL of potassium ferricyanide ($1\%$). The mixture was then incubated for 20 min at 40 °C in a water bath. After cooling the mixture, 250 µL of $10\%$ trichloroacetic acid was added. After centrifuging the mixture at 8000 rpm for 10 min, the supernatant was combined with 20 µL of freshly made $0.1\%$ ferric chloride solution and 100 µL of distilled water. The absorbance was calculated at 700 nm. The blank was run without the addition of any extracts. Gallic acid was used as standard, and the results are represented in milligrams of gallic acid equivalents per gram (mg GAE/g DW) of the sample. ## 3.6. Chemical and Reagents for Cell Culture The human breast cancer cell line (MCF-7) and murine macrophage RAW 264.7 cell line were obtained from American Type Culture Collection (ATCC). Dulbecco’s modified Eagle’s medium (DMEM) was purchased from Daegu (Republic of Korea). Fetal bovine serum (FBS) and P/S were provided by GenDEPOT. Charcoal-dextran and 17β-estradiol were obtained from Sigma (St. Louis, MO, USA). ## 3.7. Cell Culture MCF7 cells, which proliferate in response to estrogen treatment, were cultured in DMEM (containing 4500 mg/L D-glucose, L-glutamine, sodium pyruvate, and sodium bicarbonate) with $10\%$ fetal bovine serum (FBS) stripped in charcoal-dextran and $1\%$penicillin–streptomycin (P/S). RAW 264.7 cells were cultured in DMEM containing $10\%$ FBS and $1\%$ P/S. The cells adhered overnight in a humidifier set at 37 °C with a $95\%$ air/$5\%$ CO2 environment. ## 3.8. E-screen Assay The E-screen assay was developed based on MCF7 cell proliferative action in response to estrogens [76]. The slightly modified E-screen MCF-7 cell proliferation assay was carried out according to Resende et al. [ 77]. Briefly, confluent MCF-7 cells were washed with phosphate-buffered saline (PBS) and trypsinized for 1 min. The detached cells were resuspended in DMEM and seeded into 96-well plates at 2 × 104 cells/well. The cells were then incubated in an incubator (37 °C with $5\%$ CO2) for 24 h and allowed to adhere. To obtain estrogen-deprived conditions, the medium was aspirated, and an estrogen-free medium was introduced; the medium contained $5\%$ charcoal–dextran-stripped human serum and phenol-red-free DMEM (Invitrogen). The MCF-7 cells were treated with different PM and CW concentrations and cultured for 24 h. In addition, 17β-estradiol and cells without any treatment were used as the positive control. ## 3.9. Cell Proliferation Assay Cell proliferation was quantified by using 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide (MTT) (Sigma-Aldrich, Gillingham, UK) assays. The cells were cultured for 24h. Next, 20 μL of MTT solution (5 mg mL−1 stock in PBS pH 7.1, diluted 1:2.5 (v/v) in assay media) was added to the cells and cells were incubated for 3 h; the medium was replaced with 1 mL dimethyl sulfoxide (DMSO). The absorbance was determined at a wavelength of 570 nm using a microplate reader (Molecular Devices Inc., Sunnyvale, CA, USA). Cell proliferation was represented as a percentage compared to the negative control, which was taken to mean $100\%$ cell proliferation. ## 3.10. Cell Viability Assay The MTT cell viability assay was used to check for any potential cytotoxicity in the PM, and CW extracts on RAW 264.7 cells. Through mitochondrial succinate dehydrogenase, viable cells transform soluble yellow MTT into an insoluble purple formazan. Cells were seeded in 96-well at 2 × 104 cells/well and incubated for 24 h. The cells were then treated with different concentrations of PM and CW. The supernatant was discarded, and 20 µL MTT solution was added to each well; the cells were incubated for 3 h. Following the steps outlined by [78], the cells were stained with 100 µL of DMSO to turn the insoluble formazan crystals into a colored solution; the cell survival rate was measured at 570 nm using an ELISA reader (Bio-Tek Instruments, Inc., Winooski, VT, USA). ## 3.11. Measurement of Cellular ROS in HaCaT Cells The degree of reactive oxygen species (ROS) formation can be assessed using a common cell-permeable fluorogenic probe, 2′,7′-dichlorofluorescein diacetate (H2DCFDA). In 96-well cell culture plates, HaCaT cells were seeded at 1 × 104 cells per well and incubated overnight to achieve $100\%$ growth confluency. After 24 h of culture in the mixed medium of the PM and CM extract (200 µg/mL), the HaCaT cells were stimulated with 100 μM H2O2 for 2 h. DCFH-DA (10 M) solution was added to each well to stain the cells, which were then left to sit in the dark for 30 min. The cells were then washed twice with ice-cold PBS. Finally, using a Spectra Fluor multiwell fluorescence reader (Tecan, Maninder, Austria), the fluorescence emission intensity was measured between 485 and 495 nm, respectively, following a previous procedure [79] with some slight modifications. ## 3.12. Measurement of Cellular NO Production in RAW 264.7 Cells The NO inhibition by the samples was determined in LPS-stimulated RAW 264.7 cells following the previously reported method [80]. Briefly, the cells were pretreated with PM and CW before being stimulated with 1 g/mL LPS. The cells were then incubated for 24 h in an incubator. Nitrite levels in the media were measured using the Griess reagent; 100 µL of the supernatant was combined with 100 µL of the Griess reagent. The absorbance was determined at 540 nm using a microplate reader (Bio-Tek Instruments, Inc.). ## 3.13. Gene Expression Analysis MCF-7 cells were plated in 12-well plates at 5 ×105 cells/well. The medium was aspirated, and phenol red- and serum-free DMEM with or without PM and CW (100 µg/mL) were added. After 24 h, the cells were washed, and the total RNA was extracted using TriZol LS reagents (Invitrogen, Carlsbad, CA, USA) before reverse transcription polymerase chain reaction (RT-PCR), followed by the cDNA synthesis using a commercial cDNA synthesis kit (Onebio, Lithuania) was used. The cDNA synthesis process was performed at 42 °C for 1 h, followed by 5 min at 72 °C. The targeted gene was then amplified using the generated cDNA. The list of RT-PCR primers is shown in Table 6. The following parameters were employed for the PCR amplifications: 94 °C for 5 min for one cycle, followed by 94 °C for 1 min, 56 °C for 30 s, and 72 °C for 1 min for 30 cycles. ImageJ1.30v software was used for data analysis [81]. GAPDH expression was used to standardize the relative gene expression levels. Table 6 \| Genes \| Forward Primers \| Reverse Primers \| Reference \| \| --- \| --- \| --- \| --- \| \| ERα \| CCGCTCATGATCAAACGCTCTAAG \| GCCCTCTACACATTTTCCCTGGTT \| [82] \| \| ERβ \| TTCCCAGCAATGTCACTAACTT \| TTGAGGTTCCGCATACAGA \| [82] \| \| pS2 \| AATGGGCAGCCGTTAGGAAA \| GCGCCCAATACGACCAAA \| [82] \| ## 3.14. Statistical Analysis All of the data were expressed as the mean SE of at least three independent experiments. GraphPad Prism was used to conduct statistical analysis (GraphPad Software, La Jolla, CA, USA). Student’s t-test and two-way analysis of variance were used to determine the total variations between treated groups and untreated (control) groups (ANOVA). The difference was considered significant at * $p \leq 0.05$, $p \leq 0.01$, * $p \leq 0.001.$ ## 4. Conclusions The current work used HPLC analysis to identify the phytochemicals in PM and CW extracts. The phytochemical of TSG, a significant phytoestrogen, is in substantial amounts in PM extract. Additionally, the PM extracts contained higher levels of total flavonoids and phenolics than CW extracts. PM possessed more antioxidant qualities compared with CW. In contrast to CW, PM dramatically boosted ER receptor expression in both samples. Estrogen deficiency is a significant contributor to inflammation. PM exhibited an anti-inflammatory effect in the RAW 264.7 cell line. In conclusion, P. multiflorum showed better estrogenic, ROS inhibition, and anti-inflammatory activities, and C. wilfordii showed a weaker effect. These results indicate that although the plants share similar morphology, their pharmacological actions differ. The PM extracts can be a better alternative to reduce postmenopausal symptoms, but should be further evaluated using inflammation in menopause in in vivo. In addition, careful authentication of these plants should be carried out to avoid improperly selecting these medicinal plants in the dried form. ## References 1. 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--- title: Phenolic Compounds from Wild Plant and In Vitro Cultures of Ageratina pichichensis and Evaluation of Their Antioxidant Activity authors: - Elizabeth Alejandra Motolinia-Alcántara - Adrián Marcelo Franco-Vásquez - Antonio Nieto-Camacho - Roberto Arreguín-Espinosa - Mario Rodríguez-Monroy - Francisco Cruz-Sosa - Angelica Román-Guerrero journal: Plants year: 2023 pmcid: PMC10005229 doi: 10.3390/plants12051107 license: CC BY 4.0 --- # Phenolic Compounds from Wild Plant and In Vitro Cultures of Ageratina pichichensis and Evaluation of Their Antioxidant Activity ## Abstract Ageratina pichichensis, is commonly used in traditional Mexican medicine. In vitro cultures were established from wild plant (WP) seeds, obtaining in vitro plant (IP), callus culture (CC), and cell suspension culture (CSC) with the objective to determine total phenol content (TPC) and flavonoids (TFC), as well as their antioxidant activity by DPPH, ABTS and TBARS assays, added to the compound’s identification and quantification by HPLC, from methanol extracts obtained by sonication. CC showed significantly higher TPC and TFC than WP and IP, while CSC produced 2.0–2.7 times more TFC than WP, and IP produced only $14.16\%$ TPC and $38.8\%$ TFC compared with WP. There were identified compounds such as epicatechin (EPI), caffeic acid (CfA), and p-coumaric acid (pCA) in in vitro cultures that were not found in WP. The quantitative analysis shows gallic acid (GA) as the least abundant compound in samples, whereas CSC produced significantly more EPI and CfA than CC. Despite these results, in vitro cultures show lower antioxidant activity than WP, for DPPH and TBARS WP > CSC > CC > IP and ABTS WP > CSC = CC > IP. Overall, A. pichichensis WP and in vitro cultures produce phenolic compounds with antioxidant activity, especially CC and CSC, which are shown to be a biotechnological alternative for obtaining bioactive compounds. ## 1. Introduction Ageratina pichichensis, commonly known as “axihuitl”, is a herb belonging to the Asteraceae family, endemic to the Americas and native to Mexico. This plant has been used especially in Mexican folk medicine for treating skin infections, wounds, indigestion and gastritis, and is supported by several scientific studies where the extracts from its aerial parts possess antimicrobial, antifungal, anti-inflammatory, anti-ulcer and healing properties [1,2,3,4,5]. Due to these pharmacological activities, clinical trials have been conducted and demonstrated its efficiency in the treatment of onychomycosis, chronic leg ulcers, diabetic foot ulcers, and vulvovaginal candidiasis [6,7,8,9,10,11]. Because the pharmacological potential of any medicinal plants depends on the secondary metabolites they produce, the extracts from the aerial parts of A. pichichensis have stated the presence of a wide variety of bioactive compounds including benzofurans, benzochromenes, terpenes and phenolic compounds [12]. One of the most abundant phytochemical groups in plants are the phenolic compounds, produced trough the secondary metabolism in plants with remarkable physiological and morphological importance. Phenolic compounds range from simple molecules, such as phenolic acids, to large molecules such as tannins, and include different classes such as flavonoids, lignans, xanthones, anthraquinones and hydroxycinnamic acids [13,14]. Among the functional activities displayed by the phenolic compounds, the antioxidant properties are the most sought after and highlighted, due to their ability to stabilize and deactivate free radicals, before they attack targets in biological cells, preventing the action of these radical molecules, involved in the development of multiple acute and chronic disorders such as diabetes, atherosclerosis, aging, immunosuppression, and neurodegeneration [15,16]. Obtaining bioactive compounds from wild plants (WP) generally represents less than $1\%$ wt., which leads to an overexploitation, threat or extinction of plant species [17,18]. In vitro plant cell culture is an environmentally friendly alternative to increase the production of bioactive compounds as systems that ensure the continuous production of bioactive compounds [19,20]. In vitro plant cell cultures include callus culture (CC) and cell suspension culture (CSC), the latter being one of the most widely used, as it has excellent scaling attributes for the production of bioactive compounds [21,22]. In vitro cultures of A. pichichensis have been obtained which produce compounds with anti-inflammatory activity [23]; however, there are no reports of the production of phenolic compounds and their possible antioxidant activity. Therefore, the aim of this work is to establish in vitro cultures of A. pichichensis, such as germinated plant in vitro (IP), CC and CSC, initiated and derived from WP, to enhance the production of bioactive compounds, identifying the major compounds extracted from each culture, and evaluating the antioxidant activity by 2,2-diphenylpicrylhydrazyl (DPPH), 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), and thiobarbituric acid reactive substances (TBARS) assays. ## 2.1. Establishment of the In Vitro Cultures of A. pichichensis The in vitro cultures of A. pichichensis were obtained from the germination of seeds excised from WP (Figure 1a). The resultant IP (Figure 1b) were 2 months old and used to obtain the callus cultures (CC) from explants of nodal segments (Figure 1c). In turn, they were cultured for two months and those with a friable appearance were disaggregated and incorporated into a liquid culture medium for obtaining the A. pichichensis cell suspension culture (CSC; Figure 1d). For callus induction, the effect of the concentration of plant growth regulators (PGR): auxin (NAA; α-naphthaleneacetic acid) and cytokinin (KIN; kinetin) in the explants from nodal segments on the morphogenetic response is shown in Table 1. The morphogenetic response was evident at day 20 of culture. Of all the treatments tested, only eight displayed a positive response regarding the callus formation, where T3, T4, and T7 showed percentages higher than $50\%$. From these data, as preliminary information for establishing the better PGR conditions for callus induction, the higher percentage of callus formation was obtained when NAA and KIN were 4.52–6.79 μM and 0.46 μM, respectively, in treatments T3 and T4. In both cases, the callus formed developed friable appearance and light-yellow to beige color (Figure 2). Treatment T7 also displayed the simultaneous production of callus and roots while T3 and T4 showed the production of callus and plants. Several studies have reported that the combination of auxin and cytokinin favors the production of callus [24,25]. Similar results were obtained by Sánchez-Ramos et al. [ 26] for the establishment CC from leaf explants of A. pichichensis. The treatments T3 and T4 were transferred to Murashige and Skoog (MS) liquid medium using the same concentration of PGR as in the CC (Figure 2A). The establishment of CSC was determined based on the performance of better callus disaggregation. Treatment T4 displayed greater disaggregation and dispersion of the cells that T3 at day seven of culture. Cell disaggregation when CSC processes are looked for is a parameter of great relevance for ensuring the mass transfer of nutrients to each individual cell, ensuring the cell viability, and improving the production of bioactive compounds. In this work, due to the best disaggregation of callus in the liquid medium, treatment T4 was selected for the establishment of CSC, growth kinetics were maintained for 21 days (Figure 2B), reaching a maximum biomass of 8.32 ± 0.22 g/L at 16 days, a growth rate (µ) of 0.1576 days−1 and a doubling time (dt) of 4.39 days−1. The following was performed, and the phytochemical characterization was performed at 12 and 16 days of culture. Similar results were obtained for Sánchez-Ramos et. al. [ 23] on CSC of A. pichichensis obtained from leaf explants at concentrations of, differing in this study, in which CSCs were obtained from nodal segment explants. ## 2.2. Quantification of Phenolic Compounds Phenolics and flavonoids are a type of natural compounds present in plants considered of great relevance due to their important medicinal attributes and benefits for humans [27]. Quantification of phenolic compounds (phenols and flavonoids) was conducted on methanolic extracts from different plant tissues and cultures of A. pichichensis: aerial parts from WP, IP (2-month-old), CC (20-days-old), and CSC cultures (12-days-old). Regarding the total phenolic content (TPC), the different extracts of A. pichichensis yielded TPC values from 14.24 to 122.12 mg gallic acid equivalent/g dried biomass weight (mgGAE/gDW), where the CC from treatments T3 and T4 showed significantly higher TPC ($p \leq 0.05$), followed by WP and CSC at 12 days of culture, and the CSC at 16 days of culture, being IP the extract with the lowest content, as shown in Table 2. Differences between the treatments indicate that probably the culture of CC for 20 days may induce higher stress to the culture due to the consumption of substrate compounds, eliciting the major synthesis of secondary metabolites, approaching to that found in the extracts from the wild plant. For the CSC, the time of culture influenced the yielding of TPC, suffering a reduced content when culture went from 12 to 16 days. In the case of IP cultured for 2 months, the extract exhibited the lowest TPC yield; this could be associated to the requirement of the plant to reach its adult state (around 41–100 days for some plants of the Asteraceae family) [28,29], and being grown in adequate and aseptic conditions; the plant is growing in a process directed towards its primary metabolism for the preferential production of biomass, over the production of secondary metabolites. Total flavonoid content (TFC) ranged between 29.72 to 209.93 mg quercetin equivalent/gDW (mgQCT/gDW), as described in Table 2, and following the same behavior found for TPC. The CC from T3 and CSC at 12 days of culture showed significantly higher TFC yields ($p \leq 0.05$), followed by CC obtained from T4 > CSC at 16 days of culture and WP > IP. Once again, IP showed the lowest yield for TFC, agreeing to the lowest content of TPC. Even though WP and PI are closely related to the plant of origin, the differences observed in the production of phenolic compounds and flavonoids are associated with the differences in the biotic and abiotic stress conditions to which plants are exposed. These differences in the environment promote the activation of secondary metabolism as an adaptive response to ensure plant survival under stress conditions [30,31]. Similar results have been obtained on the production of phenolic compounds in plants growing under natural conditions versus in vitro plants of Baccharis antioquensis (Asteraceae) [32], Tanacetum parthenium (Asteraceae) [30], Thalictrum foliolosum (Ranunculaceae) [33], Argylia radiata (Bignoniaceae) [31], and Passiflora alata Curtis (Passifloraceae) [34]. On the other hand, in vitro plant cell cultures have been employed as a strategy to increase the production of bioactive compounds, ensuring their continuous production [19,20]. In particular, CSC is considered a simple and cost-effective method that offers the possibility for obtaining bioactive compounds on a large scale [22,35]. Moreover, in vitro CC extract exhibited significantly higher TPC and TFC ($p \leq 0.05$) than WP and IP treatments, agreeing with the reported by Castro et al. [ 19] for in Byrsonima verbascifolia (Malpighiaceae), Coimbra et al. [ 36] for Pyrostegia venustaa (Bignoniaceae), and Arciniega-Carreón et al. [ 37] for Ibervillea sonorae (Cucurbitaceae), who achieved higher production of phenolic compounds in the in vitro CC. Based on these results, CSC produced 2.0–2.7 times more TFC than WP, whereas IP (2-months-old) produced only $38.8\%$ TFC of that obtained in WP. Increased production of phenolic compounds in CSC has also been reported by Arciniega-Carreón et al. [ 37] in cultivation of Ibervillea sonorae (Cucurbitaceae), where TPC production was 10–$20\%$ higher than in extracts obtained from plant growing under natural conditions. Dary Mendoza et al. [ 20] reported that TPC and TFC in suspension cell cultures of Thevetia peruviana (Apocynaceae) were higher compared to explants obtained from the plant growing under natural conditions and in vitro callus culture. The high content of phenolic compounds in in vitro cultures may be due to the presence of growth regulators, especially the combination of auxins and cytokinin, which favor the production of bioactive compounds [38,39]. These results indicate that in vitro techniques promote and increase the production of phenolic compounds in A. pichichensis. ## 2.3. HPLC Analysis The extracts of A. pichichensis and its cultures were analyzed by HPLC. Previous reports about the phytochemical analysis of species belonging to the Asteraceae family inform the presence of phenolic acids, flavonoids, and terpenoids compounds. Therefore, the elucidation of major bioactive compounds in A. pichichensis and its cultures was based on the use of different standard compounds where only six were identify: gallic acid (GA), caffeic acid (CfA), p-coumaric acid (pCA), catechin (CAT), rutin (RUT), and epicatechin (EPI) as shown in Figure 3. Among the identified compounds, the phenolic acid GA (retention time, rt = 4.908 min), and flavonoids CAT (rt = 12.375 min) and RUT (rt = 17.33 min) were found in WP extracts, whereas only the flavonoid EPI (rt = 14.255 min) was identified for IP extracts. On the other hand, extracts obtained from CC-T3 and CC-T4 were similar in the elution profiles, differing on the height of some signals, being higher for CC-T4. In these extracts, three phenolic acids were identified: GA, CfA (rt = 13.858 min), and pCA (rt = 17.741 min), and the flavonoid EPI. The use of CSC as source of bioactive compounds when culture during 12 o 16 days show chromatographic patterns such as those exhibited by CC extracts, differing in the absence of CfA in CSC-T4 12d. These differences could be related to the different metabolic routes and growth stages of the in vitro culture, that lead to enhancing or limiting the production of specific metabolites in response to different stress factors. The production of phenolic acids and flavonoids in vitro cultures is frequently reported [36,40,41,42,43,44,45]. In this study, GA was found in all extracts, except IP, while the flavonoids EPI and pCA were produced in all the in vitro cultures of A. pichichensis. According to the quantitative analysis of the phenolic compounds identified in the extracts (Table 3), there are significant differences between the concentration of each compound in the different treatments, excepting EPI in CC-T3 and CC-T4. The compound with the lowest abundance is GA (CC-T4 > CC-T3 > WP > CSC-T4 16d > CSC-12d) and EPI the most abundant (CSC-T4 16d > CSC-12d > CC-T4 = CC-T3 > IP). According to these results, in vitro cultures of A. pichichensis were not only able to produce compounds that the WP did not, but also CSCs produced more of them (GA and CAT). Finally, it is highlighted that CSCs produce more compounds such as: CAT, CfA, EPI and pCA in contrast to CC. Similar results were obtained by Ali et al. [ 46] in CC and CSC of Artemisia absinthium L. (Asteraceae) and Modarres et al. [ 42] in Salvia leriifolia Benth (Lamiaceae) cultures. The identified compounds in this study have been investigated for their medicinal and biological properties as antioxidants [47,48], anticancer [49], antimicrobial [50] and anti-inflammatory agents [51]. Despite other compounds were found in the chromatographic characterization, further characterization should be done for elucidating the complete outlook of the bioactive compounds synthesized in the cell cultures treatments, identifying the major compounds and their contribution to the functional activities. ## 2.4. Antioxidant Activity The advantage of using in vitro cell culture is mainly justified by the improvement of bioactive molecules production in shorter times, where the antioxidant activity is one of the most important biological functions, the extracts of A. pichichensis were characterized by the capacity of their extracted compounds to scavenge or inhibit free radical molecules through ABTS, DPPH, and TBARS assays. Figure 4 shows the antioxidant capacity of the different extracts of A. pichichensis against different free radicals when exposed to concentrations between 10 to 1000 μg dried extract (DE)/mL. Trolox was used as control. For ABTS inhibition, the extract from WP required lower concentration for inhibiting this radical reaching a plateau around 178 µgDE/mL, but higher than Trolox. For the rest of treatments, when the antioxidant concentration was below 100 µgDE/mL, the better antioxidant capacity was performed by IP, followed by CC-T3, and the rest of treatments, but above this concentration, the ihibition of ABTS was modified substantially by the CC and CSC treatments, with a significant reduction of antioxidant capacity in the IP extracts. Regarding the scavenging of the DPPH radical, Trolox and WP extract showed the same trend observed for ABTS, but for this radical CSC-T4 12d, CSC-T4 16d, and CC-T3 were grouped with better antioxidant capacity than CC-T4 and IP extracts. In the case of TBARS, Trolox and WP extract required lower concentrations for the inhibition of lipid peroxidation, followed by CSC-T4 12d, CC-T3, CSC-T4 16d, CC-T4, and IP extracts as those with the lesser antioxidant capacity. For all the treatments, the percentage of scavenging effect on the DPPH and ABTS radicals was increased with increasing the concentration of A. pichichensis extracts, where the highest percentage of DPPH radical inhibition was obtained in a concentration of 1000 μgDE/mL, with percentages of 94.32 ± 0.66, 90.14 ± 1.58, 93.14 ± 0.39 and 94.16 ± $0.19\%$ for WP, IP, CC and CSC extracts, respectively. In the case of Trolox, the highest percentage inhibition was achieved at a concentration of 56.25 μgDE/mL. Table 2 shows the values for the IC50 from each extract required for inhibiting the $50\%$ of the initial concentration of free radical. Because free radicals are known to play a definite role in a wide variety of pathological manifestations. Antioxidants fight against free radicals and protect us from various diseases. They perform their action either by scavenging the reactive oxygen species or protecting the antioxidant defense mechanisms [15]. The different extracts from A. pichichensis showed statistical differences ($p \leq 0.05$) in antioxidant activity, their DPPH radical scavenging potential was as follows: WP > CSC > CC > IP, corresponding to IC50 values in Table 2. Similar results were obtained in the ABTS assay, the highest percent inhibition was obtained at a concentration of 1000 μgDE/mL with 99.16 ± 1.19, 96.29 ± 9.27, 98.47 ± 0.56 and 94.47 ± $0.56\%$ for WP, IP, CC and CSC extracts, respectively. The ABTS radical scavenging potential was as follows: WP > CS = CC > IP, agreeing to IC50 (Table 2). Similar results were reported by Esmaeili et al. [ 52], when evaluating the antioxidant activity of plant extract growing in natural conditions and in vitro cultures of Trifolium pratense L. (plant and callus), the antioxidant potential reported was as follows: WP > CC > PI. Differences observed between the two assays (DPPH and ABTS) could be related to the chemical nature of the bioactive substances contained in each extract, and to the nature of the radical used to measure this property. In the case of the ABTS radical cation (ABTS+), it is reactive against most antioxidants, and is soluble in both aqueous and organic solvents, allowing it to be applied in a wide range of pH and/or ionic strength [53], whereas the DPPH assay is based mainly on the electron transfer reaction, and the interactions between antioxidants-DPPH· radicals are determined by the structural conformation of the antioxidants. Thus, some compounds react very rapidly with DPPH·, reducing the number of DPPH· molecules in correspondence to the number of available hydroxyl groups in the antioxidant compound. Nevertheless, this mechanism seems to be more complex and the observed reactions are slower in most antioxidants [54]. Hence, the extracts methanolic of A. pichichensis contain diverse types of phenolic compounds with different polarities and solvent affinities, affecting the scavenging capacity of the crude extracts. The antioxidant activity of A. pichichensis extracts, evaluated by TBARS assay (Figure 4C), also showed that the highest percentage inhibition was obtained at a concentration of 1000 μgDE/mL, reaching 94.48 ± 2.17, 62.25 ± 3.08, 90.65 ± 4.17 and 94.04 ± $2.45\%$ for WP, IP, CC, and CSC extracts, respectively. According to the IC50 (Table 2), the activity of the extracts was as follows WP >CSC>CC>CC>IP. In general, the best results of antioxidant activity were in WP extracts followed by in vitro cultures of A. pichichensis (CC and CSC), in the three assays evaluated (DPPH, ABTS and TBARS), especially in the inhibition of lipid peroxidation, evaluated by the production of TBARS. It is noteworthy that even when CC-T3 extract displayed better antioxidant activity, its capacity to disaggregate for leading to CSC cultures was significantly lower than CC-T4, therefore, this culture can be considered as an alternative for good antioxidant compounds production when no CSC establishment is required. Regarding the CSC when cultured during 12 or 16 days, no significant differences were observed for DPPH and ABTS, being significantly better for CSC-T4 12d for TBARS assay. Similar results were reported by Costa et al. [ 55] on water/ethanol extracts of Lavandula viridis L’Hér growing under natural conditions and callus culture, which were efficient in inhibiting lipid peroxidation. The results obtained in this study suggest that A. pichichensis extracts contain bioactive compounds that are capable of donating hydrogen to a free radical to eliminate potential damage. ## 2.5. Correlation between Phenolic Content and Antioxidant Activity To elucidate the relationship between phenol and flavonoid content and antioxidant activity, Pearson’s correlation analysis was applied considering the TPC, TFC and IC50 obtained in the DPPH, ABTS and TBARS assays (Figure 5). Based on the results obtained, TPC showed a positive correlation ($p \leq 0.05$) with TFC, which demonstrates a direct correlation, i.e., the higher the phenol content, the higher the flavonoid content. In the case of TPC, the correlation was negative with DPPH ($p \leq 0.05$), ABTS ($p \leq 0.01$) and TBARS ($p \leq 0.01$). As for TFC, the correlation was also negative, however, with lower significance, for DPPH ($$p \leq 0.05$$), ABTS ($$p \leq 0.05$$) and TBARS ($p \leq 0.05$). Therefore, it could be concluded that the relationship of phenol and flavonoid content is inversely proportional to the IC50, i.e., the higher the content of bioactive compounds, the higher the antioxidant activity, and the lower IC50 value. ## 3.1. Plant Material Wild plant (WP) and seeds of Ageratina pichinchensis were collected in Tepoztlán Morelos, Mexico, identified by Biol. Gabriel Flores Franco and deposited at the HUMO Herbarium of the Universidad Autónoma del Estado de Morelos (UAEM), with the voucher number 33913. ## 3.2.1. Plant The seeds of A. pichichinses were surface disinfected in a solution of commercial detergent for 10 min; passed through a sodium hypochlorite solution at $10\%$ (v/v) for 15 min and through an ethanol solution at $10\%$ (v/v) for 30 s; the material was washed three times with sterile water for 5 min and finally inoculated in Murashige and Skoog medium (MS) [56], supplemented with 30 g/L of sucrose, 2 g/L of phytagel. Plants were kept in a growth chamber at 25 ± 2 °C and in photoperiods of 16 h light/8 h darkness; at the end of 2 months of incubation, a proportion was used for extraction (Section 3.3) and the other for callus induction. The resultant plants were coded as IP. ## 3.2.2. Callus Induction Calluses (CC) were obtained following the methodology described by Sánchez-Ramos et al. [ 26], with some modifications in the concentrations of growth regulators and the type of explant used. The nodal segments of IP were cut into pieces and four explants were transferred into jars containing 25 mL of Murashige and Skoog medium (MS) [56], and plant growth regulators (PGRs): auxin (NAA; 2,4-dichlorophenoxyacetic acid) and cytokinin (kinetin, KIN). Different concentrations of NAA (0.0, 0.45, 2.26, 4.52 or 6.79 μM) were combined with KIN (0.0, 0.46, 1.39, or 2.32 μM) and were kept in a growth chamber at 25 ± 2 °C and in photoperiods of 16 h light/8 h darkness. Callus subcultures were carried out every 20 days and the percentage of callus induction was determined. ## 3.2.3. Cell Suspension Culture The 20-day-old calluses were used to establish the cell suspension cultures (CSC). Fresh calluses (5.71 ± 0.71 g) were transferred to 250 mL Erlenmeyer flasks containing 50 mL of MS liquid culture medium and PGRs as in the callus cultures. The CSC were kept under stirring (110 rpm) and incubated at 25 ± 2 °C and in photoperiods of 16 h light/8 h darkness. CSC were sub-cultured every 12 days using 200 µm nylon filters (Whatman No. 1) to obtain a homogeneous cell culture, an inoculum of 40 g/L were transferred to 500 mL Erlenmeyer flasks with 100 mL of liquid culture medium [23]. ## 3.3. Preparations of Extracts Fresh biomass of the aerial parts of WP and IP of A. pichichensis (Figure 1) was dried in a drying oven at 40 °C. Samples (1 g) were used to obtain the extracts by sonication (BRANSONIC, CPX1800H, Danbury, CT, USA) at 40 kHz frequency with 10 mL of methanol at room temperature (25 °C) during 15 min. Then, the extracts were centrifuged at 6000× g for 15 min. The supernatant phase was recovered, and the pellet was used for a second and third extraction under the same conditions. Finally, the supernatants were mixed, and the solvent was evaporated under reduced pressure, the resultant dried extracts were stored at −70 °C in amber vials until further analysis. ## 3.4.1. Total Phenolic Content (TPC) The total phenolic content (TPC) was determined according to the colorimetric method described by Giordano et al. [ 31] and Singleton et al. [ 57], with some modifications. Briefly, an aliquot of 200 µL of each extract was mixed with 1 mL of Folin–Ciocalteu reagent 1:10 (v/v), after 1 min, 0.8 mL of Na2CO3 $7.5\%$ (w/v) were mixed for 30 s and kept for 60 min in the dark at room temperature, after the reaction time the absorbance was measured at 765 nm in a spectrophotometer (Genesys 2, Spectronics, Waltham, MA, USA), gallic acid was used to construct a standard curve (0–100 µg/mL), the results were expressed in mg of gallic acid equivalents (GAE)/g of dried biomass weight (DW) from each treatment of A. pichichensis evaluated. ## 3.4.2. Total Flavonoid Content The total flavonoid content (TFC) was determined by the colorimetric method described by Barreira et al. [ 58], an aliquot of 250 µL of each extract was mixed with 1250 µL of distilled water and 75 µL of NaNO2 $5\%$ (w/v), after 5 min 150 µL of AlCl3-6H2O $5\%$ (w/v) was added, incubated for 6 min at room temperature, then, 500 μL of NaOH (1 M) and 275 μL of distilled water were added, the solution was mixed and the absorbance was measured at 510 nm. Quercetin was used to construct a standard curve (0–400 µg/mL), the results were expressed as mg quercetin equivalents (QCT)/gDW from each treatment of A. pichichensis evaluated. ## 3.5. High Performance Liquid Chromatography (HPLC) The identification of compounds was carried out on solutions prepared from plant extracts and in vitro cultures at 1000 ppm and standards compounds at 20 ppm. HPLC analysis was performed following the methodology proposed by Ramirez-Lopez et al. [ 59] with the following modifications, an HPLC equipment (Shimadzu SPD-10A, SpectraLab Scientific Inc., Markham, ON, Canada) with a Zorbax Eclipse XDB C 18 column (4.6 mm × 250 mm, 5μm) was used. The mobile phases A and B were employed as follows: mobile phase A contained $0.1\%$ formic acid in MilliQ water and phase B contained $0.1\%$ formic acid in acetonitrile (HPLC grade). Data acquisition was applied for 45 min with a total run of 60 min. Gradient elution was as follows: $92\%$ A/$8\%$ B, t 0 min; $85\%$ A/$15\%$ B at 5 min; $40\%$ A/$60\%$ B at 45 min; $40\%$ A/$60\%$ B at 55 min; and back to initial conditions $92\%$ A/$8\%$ B at 60 min a flow rate of 1 mL/min. The extracts were monitored at a wavelength of 280 and 370 nm. Gallic acid (GA), caffeic acid (CfA), p-coumaric acid (pCA), catechin (CAT), rutin (RUT), and epicatechin (EPI), were used as standard compounds. ## 3.6.1. DPPH Radical Scavenging Activity The radical scavenging capacity by DPPH was determined according to Dominguez et al. [ 60]. Fifty µL of extract in methanol at different concentrations were added to an ethanolic solution of DPPH (100 µM, 150 µL) in 96 wells microplates. Mixtures were incubated for 30 min at 37 °C in the dark and their absorbances were measured at 515 nm in a microplate reader Synergy HT™ (BioTek Instruments, Winooski, VT, USA); Trolox was used as standard The DPPH radical scavenging activity (%) was calculated as follows:[1]DPPHScavenging activity (%)=A0−A1A0×100 where A0 is the absorbance of the blank sample (without antioxidant) and A1 is the absorbance of the sample containing the extract. Median inhibition concentration (IC50) values were calculated from plotted graph of percentage scavenging activity against the concentration of the extracts and denote the concentration of antioxidant required to inhibit $50\%$ of radical and expressed as μg dried extract (DE)/mL. ## 3.6.2. ABTS Radical Scavenging Activity The radical scavenging capacity by ABTS was determined according to Re et al. [ 61]. The ABTS stock solution was prepared by adding 90.3 mg of ABTS salt and 16.1 mg of K2S2O8 in 25 mL of distilled water. Stock solution was stored in the dark for 16 h at room temperature before use. The ABTS+ radical solution was diluted with distilled water until reach an absorbance value of 0.70 ± 0.05 at 734 nm. Afterwards, 1000 μL of diluted ABTS+ radical solution was mixed with 10 μL of different concentrations of extracts. The mixture was allowed to react for 10 min, at 30 °C in the dark and their absorbances were measured at 734 nm in a microplate reader Synergy HT™ (BioTek Instruments, Winooski, USA); Trolox was used as standard. The ABTS+ radical scavenging activity (%) was calculated as follows:[2]ABTSScavenging activity+ (%)=A0−A1A0×100 where A0 is the absorbance of the blank sample (without antioxidant) and A1 is the absorbance of the sample containing the extract. Median inhibition (IC50) values were calculated from the plotted graph of percentage scavenging activity against the concentration of the extracts and the concentration denoted of the antioxidant required to inhibit $50\%$ of radical and expressed as μgDE/mL. ## Animals Adult male Wistar rat (200–250 g) was provide by Instituto de Fisiología Celular, Universidad Nacional Autónoma de México (UNAM). Procedures and care of animals were conducted in conformity with Mexican Official Norm for Animal Care and Handling (NOM-062-ZOO-1999). They were maintained at 23 ± 2 °C on a $\frac{12}{12}$ h light–dark cycle with free access to food and water. ## Rat Brain Homogenate Preparation Animal euthanasia was carried out avoiding unnecessary pain with CO2. The cerebral tissue (whole brain) was rapidly dissected and homogenized in phosphate-buffered saline (PBS) solution (0.2 g of KCl, 0.2 g of KH2PO4, 8 g of NaCl, and 2.16 g of NaHPO4 ·7 H2O/l, pH adjusted to 7.4) as reported elsewhere [60,62] to produce a $\frac{1}{10}$ (w/v) homogenate. Homogenate was centrifuged for 10 min at 800× g. The supernatant protein content was measured using the Folin–Ciocalteu’s phenol reagent Lowry. [ 63] and adjusted with PBS at 2.66 mg of protein/mL. ## Induction of Lipid Peroxidation and Thiobarbituric Acid Reactive Substances (TBARS) Quantification As an index of lipid peroxidation, TBARS levels were measured using rat brain homogenates according to the method described by Ng et al. [ 64], with some modifications. Supernatant (375 µL) was added with 50 µL of 20 µM EDTA and 25 µL of each extract concentration solved in methanol (25 µL of methanol for control group) and incubated at 37 °C for 30 min. Lipid peroxidation was started adding 50 µL of freshly solution FeSO4 100 µM and incubated at 37 °C for 1 h. The TBARS content was determined as described by Ohkawa et al. [ 65], with 500 µL of TBA reagent ($0.5\%$ 2-thiobarbituric acid in 0.05 N NaOH and $30\%$ trichloroacetic acid, in 1:1 proportion) added at each tube and cooled on ice for 10 min, centrifugated at 13,400× g for 5 min and heated at 80 °C in a water bath for 30 min. After cooling at room temperature, the absorbance of 200 µL of supernatant was measured at 540 nm in a Bio-Tek Microplate Reader Synergy HT. Trolox was used as standard. Concentration of TBARS was calculated by interpolation in a standard curve of tetra-methoxypropane (TMP) as a precursor of malondialdehyde (MDA) [66]. Results were expressed as nmol of TBARS per mg of protein. The percentage of inhibition of lipid peroxidation (IR%) was calculated as follows:[3]IR (%)=A0−A1A0×100 where A0 is the absorbance of the blank sample (without antioxidant) and A1 is the absorbance of the sample containing the extract. Median inhibition (IC50) values were calculated from a plotted graph of percentage scavenging activity against the concentration of the extracts and denoted the concentration of antioxidant required to inhibit $50\%$ of radical and expressed as μgDE/mL. ## 3.7. Statistical Analysis All the experiments were carried out in triplicate and the results were expressed as means ± SD. TPC, TFC, and antioxidant activity (DPPH, ABTS and TBARS) were compared using the Tukey and ANOVA test. A Pearson correlation test was performed to establish significant effects among the variables: TPC, TFC, antioxidant activity by DPPH, ABTS and TBARS assays. Significant levels were defined at $p \leq 0.05$ and $p \leq 0.01.$ Statistical analysis was tested with SigmaPlot 12.5 software (Systat Software Inc, 2011, Palo Alto, CA, USA). A Pearson correlation test was performed in R Core Team [2022]: R: a language and environment for statistical computing.–R Foundation for Statistical Computing, Vienn. ## 4. Conclusions In vitro cultures (CC and CSC) were obtained from the IP nodal segment of A. pichichensis in the presence of a combination of NAA and KIN in MS medium. The disaggregation of the CC was a decisive factor for the establishment of CSC, because only one treatment exhibited disaggregation in liquid medium. On the other hand, in vitro CC and CSC cultures were able to produce compounds that WP did not, and CSC produced more phenolic compounds than CC. Based on these results, in vitro CC and CSC cultures are an alternative for obtaining phenolic compounds continuously and in a shorter time than plants growing under natural conditions of A. pichichensis. However, although in vitro cultures were able to produce a higher number of phenolic compounds, the antioxidant activity obtained in the different assays evaluated were significantly lower than in WP. These differences could be related to the affinity of the extracted compounds for the different radicals evaluated, so, a more robust characterization of A. pichichensis extracts is necessary for the identification and quantification of the majority compounds and their contribution in the antioxidant activity. Finally, TPC and TFC showed a correlation inversely proportional to the IC50 found in the DPPH, ABTS and TBARS assays. 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--- title: Excessive Consumption of Alcoholic Beverages and Extremely High Levels of High-Density Lipoprotein Cholesterol (HALP) in the ELSA-Brasil Cohort Baseline authors: - Oscar Geovanny Enriquez-Martinez - Taísa Sabrina Silva Pereira - Jose Geraldo Mill - Maria de Jesus Mendes da Fonseca - Maria del Carmen Bisi Molina - Rosane Harter Griep journal: Nutrients year: 2023 pmcid: PMC10005235 doi: 10.3390/nu15051221 license: CC BY 4.0 --- # Excessive Consumption of Alcoholic Beverages and Extremely High Levels of High-Density Lipoprotein Cholesterol (HALP) in the ELSA-Brasil Cohort Baseline ## Abstract Background: It has already been established that the consumption of alcoholic beverages increases high-density lipoprotein cholesterol (HDL-C) levels in dose–response. Methods and Results:A cross-sectional analysis was carried out with 6132 participants of both sexes aged between 35 and 74 years, who were active and retired workers from six Brazilian states. Heavy drinkers were categorized by sex: men > 210 g/week and women > 140 g/week; moderate drinkers: men ≤ 209 g/week and women ≤ 139 g/week. The HDL-C level was dichotomized into normal (40 mg/dL–82.9 mg/dL) and extremely high (≥83 mg/dL). We used binary logistic regression to assess associations between baseline alcohol intake and HDL-C, which were adjusted for sex, age, income, physical activity, kilocalories and body mass index (BMI), and we found an positive association between extremely high HDL-C and the excessive consumption of alcoholic beverages. These participants were mostly women with a high income, lower waist circumference, kilocalorie consumption and also a higher consumption in all categories of alcoholic beverages. Conclusion: Excessive alcohol consumption was associated with a higher probability of extremely high HDL-C. ## 1. Introduction The consumption of alcoholic beverages increased from 6.4 to 6.6 for the years 2016 to 2020 in per capita consumption in L, with estimates reaching 7.0 L for 2025 [1,2]; other estimates show that by 2030, the world per capita consumption will have reached 7.6 L, and the proportion of drinkers will increase by $0.22\%$ annually, defined as a public health problem [3]. High-density lipoproteins (HDLs) have been considered as the ’good cholesterol’ that brings benefits to the body, mainly for cardiovascular health [4], through activity in the cholesterol efflux and anti-inflammatory, anti-oxidative, anti-thrombotic and anti-apoptotic characteristics [5,6,7]. Serum levels of HDL-C have been extensively investigated in relation to cardiovascular health. Low HDL-C levels have been associated with the incidence of cardiovascular diseases (CVDs) [8,9], leading to recommendations to increase this lipoprotein [10]. Currently, however, a U-shaped relationship is reported between HDL-C levels and cardiovascular events, showing that both low and very high levels of HDL-C presenta higher risk ofdeveloping cardiovascular events [11] and leading to mortality for all causes in both sexes [12]. Little evidence evaluating extremely high levels of HDL-C (HALP) in association with health, mainly cardiovascular disease, exists [13]. The main evidence describes genetic causes that generate HALP in Asian countries [14] and in the Dutch population [15]. In Latin America, there was one studyon the Brazilian population associated with increased carotid intima–media thickness [16], but studies on the association between HALP and CVD have not been concluded yet [17]. Contrary to what has already been reported regardingthe benefits of moderate consumption, the effects of excessive consumption areless clear and point to greater harmful effects on cardiovascular health, especially when evaluated amongbinge drinkers (episodic excessive drinkers) or with a specific type of alcoholic drink [18,19]. Our hypothesis is that the excessive consumption of alcoholic beverages increases HDL-C levels, reaching extreme levels of this lipoprotein. It has already been described that the excessive consumption of alcoholic beverages increases the risk of cardiovascular disease [20], and this behavior is identified as one of the biggest contributors to the burden of disease in the world increasing inflammatory and oxidative parameters that predispose one to a higher burden of CVD [1,21]. Some scientific evidence describes these relationships, e.g., the “Lipoprotein phenotyping study” reported an increase in the rate of cardiovascular events with HDL-C values >75 mg/dL [22]; in another study,“Incremental Decrease in End Point Through Aggressive Lipid Lowering” (IDEAL), these findings were reinforced with HDL-C >80 mg/dL [23]. We did not find any previous studies that directly relate excessive alcohol consumption and extremely high levels of HDL-C;it has only been described in a cross-sectional study of 3700 Russian subjects ($75\%$ men and $47\%$ women)who hadexcessive consumption levels of alcohol [24]. Additionally, it is known that Russians have higher average levels of HDL-C cholesterol compared to populations in other countries; in addition to these levels, Russia stands out for having higher rates of cardiovascular disease when compared to other countries [25], suggesting a relationship in excessive alcohol consumption and HALP. It is perceptible that the consumption of alcoholic beverages changes the health parameters of populations [26], and it certainly causesa modification in the lipid profile, which increases dose–response HDL-C levels [27]. The types of alcoholic beverages also play a role in modifying these parameters: positive associations have been found between wine consumption and increased HDL-C [28] and decreased HDL-C for beer [29]. This highlightsthe gaps in our knowledge of this association, especially with extremely high levels of HDL-C. Therefore, this study aimed to evaluate the consumption of excessive alcoholic beverages and itsrelationship with extremely high levels of HDL-C at the ELSA-Brasil baseline. ## 2.1. Study Design and Population The current study is an observational, cross-sectional study developed from the baseline of the Longitudinal Study of Adult Health (ELSA-Brasil) [22]. The baseline of the ELSA-Brasil was established between 2008 to 2010 and consisted of data collection through interviews, examinations and laboratory analyses. ELSA-Brasil is a cohort of 15,105 adults: men and women who are active and retired workers from six higher education institutions (Federal University of Espírito Santo, Federal University of Minas Gerais, Federal University of Bahia, University of São Paulo, Federal University of Rio Grande do Sul and the Oswaldo Cruz-FIOCRUZ Foundation). The ELSA-Brasil sample is made up of men and women aged 35–74 years who are eligible for the study; in terms of race/color,$52\%$ are white, $28\%$ are brown or mixed color, $16\%$ are black, $3\%$ are Asian (mainly Japanese) and $1\%$ are indigenous. Exclusion criteria for ELSA-Brasilarecurrent or recent (<4 months prior to the first interview) pregnancy, the intention to quit working at the institution in the near future, severe cognitive or communication impairment and, if retired, residence outside of a study center’s corresponding metropolitan area. ELSA-Brasil already has data from 3 waves: wave 1 (2008–2010), wave 2 (2012–2014) and wave 3 (2017–2018);ELSA-COVID [2020] and wave 4 (2022–2022) are currently being collected. On a previously scheduled day, the participants appeared at 7:00 am in each research center for clinical, biochemical and questionnaire exams. In this way, a previously validated, comprehensive set of questionnaires, clinical measurements and laboratory tests wascarried out. As this wasa multicenter study, data collection was standardized. Data werecollected in 2 phases. The first, lasting approximately1 h, included obtaining informed consent and conducting the initial interview at the participant’s job site. The second, comprising additional interviews and examinations, lasted for approximately 6 h and wasconducted at a study clinic. In this study, wave 1 was analyzed, formed by 6132 participants after we applied the following exclusion criteria: bariatric surgery, cardiovascular disease, implausible kcal consumption, implausible alcohol consumption, abstemious (0 mL/week), low HDL-C and missing data. ( Figure 1). ## 2.2. Ethical Aspects This study followed the international ethical standards found in the Declaration of Helsinki [2000]. All procedures involving human subjects were approved by the Research and Ethics Committee of each country as follows:$\frac{669}{06}$ (USP), $\frac{343}{06}$ (FIOCRUZ), $\frac{041}{06}$ (UFES), $\frac{186}{06}$ (UFMG), $\frac{194}{06}$ (UFRGS) and $\frac{027}{06}$ (UFBA). All participants signed a written informed consent form in both stages, with the anonymity of the information obtained being assured. ## 2.3. Study Variables All covariates included in the analysis were obtained through face-to-face interviews by clinical or laboratory procedures, and the variables measured in this study were socio-demographic, lifestyle, anthropometric, diet, consumption of alcohol and biochemical (serum lipoproteins). The following socio-demographic variables were evaluated by closed questionnaires withvariables such asage (years), sex (male or female), income (in tertile), education level (incomplete primary, primary, high school or university), marital status (married, separated/divorced, single, widower or other—with previous union), ethnicity (not white or white). Lifestyle variables were evaluated using closed questionnaires or specific measures of lipid-lowering drugs (yes no no);body mass index (BMI) was used to classify nutritional status (thin <18.5 kg/m2; normal ≥18.5 kg/m2 and <24.9 kg/m2, overweight, obese ≥25 kg/m2) [25], and physical activity in leisure (low, moderate or high), smoking (never smoked, ex-smoker or smoker), consumption of alcoholic beverages (moderate or excessive), anthropometric measurements (weight (kg), height (mts) and waist circumference (cm)) were collected in a standardized way [30].Diet variables, (kilocalories, lipids, carbohydrates and proteins) were collected using the FFQ validated for the Brazilian population [31,32], as well as alcohol consumption (mL/week), beer (mL/week), spirits (ml/week), total alcohol (ml/week) and total alcohol (g/week)using the closed question questionnaire, and serum lipoproteins TC (mg/dL), triglycerides(mg/dL), HDL-C(mg/dL) and LDL-C (mg/dL)). ## 2.4. Alcohol Consumption Alcohol consumption was reported through structured questionnaires with closed questions, which were used in each ELSA-Brasil research center to determine the types of alcoholic beverages (beer, wine and spirits), and the frequency and amount of consumption (daily, weekly or monthly) [33]. For this study, we worked with the total consumption of alcoholic beverages derived from the sum of millimeters/week of each participant and transformed it to g/week, classifying alcoholic beverage consumers as moderate and excessive drinkers according to the following equation:Volume milliliter week alcohol × alcohol content/100 = Volume Volume × 0.8 = grams/week Heavy drinkers were categorized by sex: men >210 g/week and women >140 g/week; and moderate drinkers: men ≤209 g/week and women ≤139 g/week [34]. ## 2.5. Blood Analysis Blood samples were obtained by venipuncture using scalp and vacuum collection tubes. Fasting samples were collected, and at the time of collection, the participant was informed about the procedure, and we verified the fulfillment of the given guidelines through a questionnaire. The temperature of the collection room was maintained between 20 and 24 °C, and the samples were properly stored and transported to the project’s Central Laboratory, located at the University Hospital of São Paulo [35]. In this study, the biochemical parameters were obtained in two stages: after fasting for 8 to 12 h and 2 h after ingesting an overload of glucose. The samples were quickly processed to obtain serum, which was stored locally at −80 °C until it was sent to the ELSA-Brasil Central Laboratory (São Paulo) for the monthly determination of the analytes. The blood collection, processing, and biological transport protocol were standardized as described by Fedeli et al. [ 35]; the variables analyzed in this study were total cholesterol(enzymatic, colorimetric cholesterol oxidase method—ADVIA 1200 Siemens®), LDL-C (homogeneous colorimetric enzymatic method without precipitation), HDL-C (homogeneous colorimetric method without precipitation) and TGs (glycerolphosphate peroxidase method according to Trinder (enzymatic and colorimetric)). The tubes used for analysis, storage and transport were BD Vacutainer tubes with 9 mL volumes and BD Vacutainer scalps measuring 21 G and 23 G. For storage, a 2 mL Greiner Cryogenic Tube was used. The extremely high HDL-C cut-off point was definedby means of the 90th percentile, being, for this population, defined asHDL-C(≥83 mg/dL).Then, it was dichotomized into normal (40 mg/dL–82.9 mg/dL) and extremely high (≥83 mg/dL). ## 2.6. Statistical Analysis The categorical (sociodemographic, lifestyle, anthropometric, consumption of alcohol and biochemical) variables dependent on HDL-C were analyzed using the chi-square test. The continuous (anthropometric, diet and biochemical) variables were expressed as mean and standard deviation and evaluated by the t-test. Likewise, they were dichotomized by HDL-C levels (normal and extremely high—HALP) and beverage consumption (moderate and excessive), performing the same statistical tests, depending on the nature of the variables. Crude and adjusted binary logistic regression models were createdto estimate odds ratios (OR) and $95\%$ confidence intervals ($95\%$ CI) to assess the association between HDL-C levels (dependent variable) and alcohol consumption (independent variable). The models used the dichotomized variables of HDL-C (normal and extremely high) and alcohol consumption (moderate and excessive). Model 1 was adjusted for sex, age and income, and model 2 was adjusted for model 1+ physical activity, kilocalories and BMI. All analyses were performed using Stata Statistical Software (release 13, StataCorp LP, College Station, TX, USA), and the level of significance was $5\%$. ## 3. Results The study sample had 6132 participants, who were predominantly male ($55.6\%$) and hada $6.8\%$ prevalence of HALP. We analyzed socio-demographic, lifestyle, anthropometric, consumption and biochemical variables using the HDL-C levels. It was evident that most participants with HALP were female ($p \leq 0.001$), in the third income tertile ($p \leq 0.001$), completed higher education ($p \leq 0.001$), were married ($p \leq 0.001$), did not consume lipid-lowering drugs ($p \leq 0.001$), had a normal nutritional status ($p \leq 0.001$), had low leisure-time physical activity ($p \leq 0.001$) and had a moderate consumption of alcoholic beverages ($p \leq 0.001$). Table 1 The highest means for HALP were for age ($p \leq 0.001$), total cholesterol ($p \leq 0.001$) and HDL-C ($p \leq 0.001$) Table 2. Table 3 shows the socio-demographic, lifestyle, anthropometric, consumption and biochemical variables dependent on HDL-C levels and alcohol consumption. When analyzing the consumption of alcoholic beverages, we found participants categorized with HALP in the two categories of consumption (moderate and excessive) were mostlywomen ($p \leq 0.001$), had a university degree ($$p \leq 0.005$$) and had a normal body mass index ($$p \leq 0.050$$). Higher proportions were observed in moderate consumption for those who had never smoked and in excessive consumption for ex-smokers ($p \leq 0.001$). When analyzing the anthropometric, consumption, and biochemical variables depending on HDL-C levels and alcohol consumption, we found higher means in excessive consumption for weight ($$p \leq 0.023$$), height ($$p \leq 0.006$$) waist circumference ($$p \leq 0.011$$), kilocalorie consumption ($$p \leq 0.040$$) and all the categories of alcoholic beverages ($p \leq 0.001$) and for the consumption of wine ($$p \leq 0.007$$) (Table 4). Table 5 presents the binary logistic regression with progressive adjustments (sex, age, income, physical activity, kilocalories and BMI). We found a positive association with extremely high levels of HDL-C (HALP) and excessive alcohol consumption (OR = 1.92; $95\%$ CI (1.4–2.5)). ## 4. Discussion The objective was to evaluate the excessive consumption of alcoholic beverages and the association with extremely high levels of HDL-C (HALP) in a Brazilian population; a positive relationship was found. Participants with excessive drinking are more likely to have extremely high levels of HDL-C (HALP). These participants are mostly women with a high income, lower waist circumference, lower kilocalorie consumption and higher consumption in all categories of alcoholic beverages. We found a prevalence of $11.8\%$ of excessive alcohol consumption, which agrees with a population-based longitudinal study [29] and with the Brazilian population [36]. In our study, a prevalence of $6.8\%$ of HALP was also estimated, which is lower in North Americans ($3.8\%$) [37] and Asians ($1.9\%$) [11], because the cut-off points for defining HALP in these populations were higher than in our study. HDL-C levels differ when compared by sex, which is evident in our study, as the largest proportion of participants with HALP were women. The behavior of this lipoprotein and the corresponding changes depend on the physiological and hormonal statuses of women, showing an inverse association between the serum levels of HDL-C and estrogens [38]. Menopause is, therefore, a key factor in this relationship, as postmenopausal women have higher HDL-C values when compared to premenopausal women [39]. The physiological stage present in our population, determined by the average age of our sample, is consistent with the onset of these hormonal and physiological changes. There is a direct relationship between education and cognition, relating to behaviors in favor of health care [40]. Consistent evidence shows this association in women [41,42,43], and the highest proportions of HALP were found in participants with high incomes and education levels, which is a result also evident in Chilean women [44]. It is important to recognize the nature of our sample, as they are employed or retired from federal universities, characteristics that impact sociodemographic variables in relation to the general population, especially those with higher education levels and incomes. Our findings show a lower value of triglycerides in participants with HALP, agreeing with a study that evaluated sixNorth American cohorts, explaining this physiological behavior by abnormalities in lipid metabolic pathways, especially in the exchange of HDL-C to VLDL particles [45]. Another important factor is nutritional status: an imbalance in this variable is consideredas a risk factor for the global burden of disease [46]. In turn, obesity increases the risk of cardiovascular diseases, generating atheroma and changes in lipoproteins (dyslipidemias) [47]. The inverse association between nutritional status and serum HDL-C levels is well known. Increased BMI and WC present greater chances of low HLD-C, explaining this relationship in $57.1\%$ and $36\%$of cases, respectively [48]. The guidelines which aim to reduce weight as a strategy to increase HDL-C [49,50] were found to be relevant in our study, as participants with HALP have lower levels of anthropometric variables. It is well known that the effect of weight loss by dietary modification, physical exercise or bariatric surgery influence changes in lipoproteins [51]. Different health outcomes are associated with the consumption of alcoholic beverages, depending on the quantity and type of these beverages, mainly related to cardiovascular effects [52], diabetes [53] and cancer [54], leading to mortality from all causes [55]. The findings relating the consumption of alcoholic beverages and HDL-C are emphatic in the relationship between these two variables, in dose response [27] or also in the form of “u”or “J”-shaped responses, attributing the protective role to HDL-C. These responses may result from activity on the efflux of cholesterol, and anti-inflammatory, antioxidant, antithrombotic and antiapoptotic characteristics [5,6,7], leading to a false hypothesis of cardiovascular protection with the association of alcoholic beverages and HDL-C levels [12]. However, this increase in HDL-C levels, dependent on the consumption of alcoholic beverages, must be carefully evaluated. Currently, there are some counterpoints to be analyzed when establishing these relationships. In a large population-based study in Japan, a genetic mutation was found in the cholesterol ester transport protein enzyme (CETP), responsible for the metabolism and levels of HDL-C. These findings report an association ofthe incidence of ischemic changes, being statistically significant and U-shaped, with very low and extremely high levels (HALP) of HDL-C [11]. Increased HDL-C levelshavebeen taken as evidence that therapeutic recommendations need to be made for the benefit of cardiovascular health;it is known that CETPis a mediator in the exchange of cholesterol ester and reverse cholesterol. For this purpose, specialized drugs have been developedfor the inhibition of CETP, demonstrating that when this protein is inhibited, HDL-C levels increase considerably, reaching extremely high levels (HALP) with a significant increase in mortality and cardiovascular events [56,57]; it has been failedto hypothesize that just increasing HDL-C at very high levels could be beneficial to cardiovascular health. This level of HDL-C may favor atherogenesis, because the molecules that predominate are HDL-2, rich in cholesterol esters and less able to carry out reverse cholesterol transport [58]. The antioxidant potential attributed to HDL-C in plasma has also been analyzed. There are other components with greater antioxidant potential than isolated HDL-C, such as fibrinogen, immunoglobulin G, uric acid albumin and ascorbate, with albumin being the dominant antioxidant and HDL-C with lower power 1–$2\%$ total antioxidant contribution in plasma [59], leading researchersto reflect on the antioxidant role of this lipoprotein. The findings suggest that the relationship between HDL-C levels and cardiovascular health is still complex, going beyond just plasma concentrations [60]. HDL-C is a heterogeneous lipoprotein with different components, so the association with CVD should be further analyzed. HDL-C has been found to contain alpha-2 macroglobulin, CoC3 (complement C3), HP (haptoglobin) or PLMG (plasminogen), leading to the idea that not only will the increase in HDL-C contribute to cardiovascular protection, but also, the balance between the intrinsic components of these lipoproteins [61] and molecules that interact in HDL-C metabolism [23] should bothbeevaluated. The effects of alcohol on health are still controversial, mainly due to itscharacteristics, referring to type, consumption pattern and quantity. It has already been described that excessive consumption has a negative impact on the health of the population and has implications for public health [1,62]. The possible mechanism by which the consumption of alcoholic beverages brings benefits to cardiovascular health is attributed to the increase in HDL-c levels, with a false belief that extremely high levels could bring even greater benefits [29]. In a recent meta-analysis, it wasshown that interest in studying the effects of alcohol consumption has increased considerably in recent years, in addition to reports of an increase in HDL-C levels (3.66 mg/dL 2.22–5.13) per 30 g of alcohol/day [63], associated with an increase in the rate of lipoprotein transport [64]. However, the results are still controversial, as the mechanisms by which alcohol influences HDL-C are not fully understood. Our results should be considered in terms of clinical implications, as the increase in the dose–response of alcohol consumption and HDL-C levels should be analyzed carefully, as it would lead to the idea that excessive consumption would further increase HDL-C levels. C. It is known that extremely high levels of HDL-C are associated with dysfunctions in lipid metabolism or genetic load, reflected in non-functional HDL-C and correlated with greater risks in cardiovascular health. Therefore, when an extremely high level of HDL-C is observed, it must be classified as a risk indicator, and health treatment must be given. As we currently think that HDL-C has no risk point, the recommendations only suggest increasing it without a cutoff point; this has clinical implications because the determination of HDL-C levels is a routine and easy test that brings important information that is sometimes undervalued. The strengths and weaknesses of this study werethat ELSA-Brasil is not a representative study of the Brazilian population, but it represents an important portion of the population, providing important data for South American countries. The alcoholic beverage instrument has memory bias, but it was applied by trained personnel and had data quality control. Participants with implausible consumption were excluded. Thisis one of the first findings in a South American population, presentingimportant information about this population that has specific characteristics. 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--- title: Diosmin and Bromelain Stimulate Glutathione and Total Thiols Production in Red Blood Cells authors: - Lukasz Gwozdzinski - Joanna Bernasinska-Slomczewska - Anna Wiktorowska-Owczarek - Edward Kowalczyk - Anna Pieniazek journal: Molecules year: 2023 pmcid: PMC10005239 doi: 10.3390/molecules28052291 license: CC BY 4.0 --- # Diosmin and Bromelain Stimulate Glutathione and Total Thiols Production in Red Blood Cells ## Abstract Diosmin and bromelain are bioactive compounds of plant origin with proven beneficial effects on the human cardiovascular system. We found that diosmin and bromelain slightly reduced total carbonyls levels and had no effect on TBARS levels, as well as slightly increased the total non-enzymatic antioxidant capacity in the RBCs at concentrations of 30 and 60 µg/mL. Diosmin and bromelain induced a significant increase in total thiols and glutathione in the RBCs. Examining the rheological properties of RBCs, we found that both compounds slightly reduce the internal viscosity of the RBCs. Using the MSL (maleimide spin label), we revealed that higher concentrations of bromelain led to a significant decrease in the mobility of this spin label attached to cytosolic thiols in the RBCs, as well as attached to hemoglobin at a higher concentration of diosmin, and for both concentrations of bromelain. Both compounds tended to decrease the cell membrane fluidity in the subsurface area, but not in the deeper regions. An increase in the glutathione concentration and the total level of thiol compounds promotes the protection of the RBCs against oxidative stress, suggesting that both compounds have a stabilizing effect on the cell membrane and improve the rheological properties of the RBCs. ## 1. Introduction Due to their healing properties, variety of compounds of plant origin are used in the treatment of cardiovascular diseases. This wide group includes diosmin, a flavone glycoside found abundantly in citrus fruits. Recently, numerous in vitro and in vivo studies have demonstrated the wide range of biological activity of diosmin, which includes antioxidant, antihyperglycemic, anti-inflammatory, anti-mutagenic, and anti-ulcer properties, as well as anti-cancer and antibacterial properties. This flavonoid is employed in the treatment of cardiovascular diseases, particularly chronic venous insufficiency, such as varicose veins, as well as to improve microcirculation. Diosmin is also used to improve liver cells protection and for neuroprotection [1,2]. However, this flavonoid’s molecular mechanism of action has not yet been elucidated, although some potential molecular targets for diosmin have been reported, e.g., P-glycoprotein (P-gp), IKKβ, acetylcholinesterase (AChE), and aldose reductase (AR) [3]. Nevertheless, further research expounding diosmin’s mechanism of action in cellular processes is still needed. Many studies have demonstrated the antioxidant properties of diosmin, including effective modulation of the activity of a number of enzymes and biomarkers, which were related to the disturbance of the oxidative balance in various diseases. Oxidative imbalance, which results in oxidative stress, is associated with the development of numerous diseases, including cardiovascular diseases, e.g., myocardial ischemia, varicose vein, thrombosis, stroke (ischemia-reperfusion damage), but also diabetes, neurodegenerative diseases, cancer, and many others [4,5,6,7]. As a drug, diosmin is often used with other flavonoids. It has been shown that a combination of diosmin with hesperidin was very effective in the treatment of chronic venous insufficiency [8]. Diosmin showed antioxidant properties by reducing the oxidative stress in the rat heart following in vivo ischemia/reperfusion [9]. The ischemia-reperfusion injury was characterized by a decrease in the activity of enzymatic antioxidants (SOD, CAT, and GPx) and a decrease in the level of GSH [10]. The administration of diosmin led to an increase in enzymatic antioxidants and the level of GSH. A decrease in lipid peroxidation products was also found [9]. Another types of diosmin activity in oxygen metabolism can be observed in intracellular processes of the selected cell lines. It has been shown that in DU145 cells, diosmin led to radical oxidative stress by increasing the overall production of reactive oxygen species (ROS), which caused changes in the potential of the mitochondrial membrane and, as a consequence, apoptosis and cell death [11]. Diosmin also initiated genotoxicity by inducing double-strand breaks in DNA and creating micronuclei [12]. In MCF-7 and MDA-MB-231 breast cancer cells, diosmin initiated an increase in the p53 protein levels. A similar increase in the level of p27 protein was observed in MDA-MB-231 and SK-BR-3 cells. All three cell lines have also been noted to increase the p2 protein levels. Moreover, in MCF-7 cells, this flavonoid led to the externalization of phosphatidylserine, an increase in the activity of multiple caspases, and the depolarization of the mitochondrial membrane potential. Diosmin also induced the oxidative stress in the studied cell lines. The highest increases in ROS, total superoxide, and mitochondrial superoxide production were observed in the MCF-7 cells. Moreover, in these cells, diosmin initiated single- and double-strand DNA breaks. The flavonoid also initiated autophagy in all cell lines tested. This process was enhanced in MDA-MB-231 and SK-BR-3, but not in MCF-7 cells [13]. Pineapple fruit and the other pineapple plant parts, such as leaves and stem, were formerly successfully used in folk medicine to treat open wounds and inflammation. The studies of the pineapple extract revealed a number of interesting biological properties, such as bactericidal, anti-swelling, and anti-inflammatory effects [14]. Bromelain, a mixture of protein digesting enzymes, such as thiol endopeptidases and phosphatase, glucosidase, peroxidase, cellulase, escharase, and several protease inhibitors, is the main ingredient of pineapple extract. Bromelain has cardioprotective, immunomodulating, and antioxidant properties. This enzyme is used in the treatment of angina, bronchitis, and sinusitis, but also in thrombophlebitis. In addition, bromelain also has some anti-cancer effects and promotes cancer cells apoptosis [15,16]. In vivo and in vitro studies have shown that bromelain can reduce the symptoms of cardiovascular diseases [17,18]. Moreover, it has anticoagulant and fibrinolytic properties which have been used in the treatment of thromboembolism, as well as in the treatment of thrombophlebitis. It has been shown that bromelain initiates thrombus destruction, as well as reduces platelet clumping and lowers blood viscosity [19,20]. It was also found in vivo that bromelain’s fibrinolytic properties were effective in dissolving atherosclerotic plaque, reducing the risk of developing atherosclerotic disease. Due to its anti-inflammatory properties, bromelain reduces postoperative symptoms in patients undergoing alveolar ridge preservation after tooth extraction [21]. Oxidative stress can lead to changes in the activity of antioxidant systems in erythrocytes, damaging in their cell membrane, and changing their rheological properties. Consequently, such actions will cause obvious changes in the function of red blood cells. In the light of recent findings, erythrocytes seem to be an important regulator of cardiovascular function in pathophysiological conditions. The RBCs are critically involved in cardiovascular homeostasis as a regulator of cardiovascular function by exporting adenosine triphosphate (ATP) and nitric oxide (NO) bioactivity and in redox balance via their effective antioxidant systems [22,23]. The alteration in the function of the RBC may lead to oxidative stress, alterations in protein content and enzyme activities, and increased adhesion to the vascular wall. Such disorders, defined as ‘erythropathy,’ ultimately result in the ability to trigger impairment of vascular and cardiac function [24]. The aim of this research was to study the effect of diosmin and bromelain on human red blood cells. The influence of both compounds on the induction of the oxidative stress and the total antioxidant capacity of the RBCs was determined. Moreover, the level of total thiols and glutathione, as well as the level of amino groups in the hemolysate, were specified. The internal viscosity of the RBCs and the conformational state of hemoglobin were examined. The fluidity of the RBCs plasma membranes was estimated after treatment with diosmin and bromelain. ## 2. Results In this work, the influence of diosmin and bromelain on the initiation of the oxidative stress in red blood cells was investigated. The red blood cells from healthy individuals were treated with two concentrations of diosmin and bromelain at a final concentration of 30 µg/mL and 60 µg/mL. Our previous studies on endothelial cells showed that, at a concentration of 50 µg/mL, these compounds cause a decrease in cell survival by about 7–$15\%$ (unpublished data). Hence, the above choice of concentrations of the tested compounds. Figure 1 shows the level of TBARS and carbonyl compounds after RBC treatment with diosmin and bromelain. We found that both compounds lowered the total level of carbonyl compounds, but the results were not statistically significant. A significant decrease in the level of carbonyl groups was observed only in RBCs treated with bromelain at a concentration of 60 µg/mL. However, the TBARS level remained unchanged. The total non-enzymatic antioxidant capacity (NEAC) was also determined. Diosmin and bromelain led to an increase in the total non-enzymatic antioxidant capacity, but these results were not statistically significant (Figure 2). In the hemolysates of the RBCs treated with diosmin and bromelain, the total level of thiols, glutathione, and amino groups was determined. In our study diosmin and bromelain led to a significant increase in total thiols and glutathione in red blood cells. However, no differences were found in the level of amino groups (Figure 3). The influence of both compounds on the internal viscosity of the red blood cells was investigated using tempamine. The condition of the internal proteins was determined using MSL. The conducted research showed that diosmin and bromelain led to a statistically significant decrease in the internal viscosity of the cells (Figure 4). One of the objectives of the study was to investigate the effect of diosmin and bromelain on the internal components of red blood cells, mainly hemoglobin, which accounts for over $95\%$ of all proteins present inside the RBCs. MSL spin label rotation allows for the evaluation of the changes in the RBCs internal environment. From the EPR spectra of the whole RBCs labeled with MSL, the relative rotational correlation time (τc) was calculated. Figure 4 shows the effects of diosmin and bromelain on the mobility of proteins and peptides in the whole red blood cells. A slight increase in τc upon diosmin and bromelain RBCs treatment was observed. Nevertheless, a higher bromelain concentration (60 µg/mL) caused a significant increase in this parameter in the RBCs. For the estimation of the changes in the conformational state of hemoglobin in the hemolysate, two spin labels, MSL and ISL, were used. After the RBCs treatment with a higher concentration of diosmin (60 µg/mL) and both concentrations of bromelain (30 µg/mL and 60 µg/mL), compared to the control, a statistically significant increase in the rotational correlation time of the MSL spin label attached to hemolysate proteins was observed (Figure 5). In the case of the ISL spin label bound to hemolysate proteins, a statistically significant increase in rotational correlation time compared to the control was observed only after the incubation of the RBCs with a higher concentration of diosmin (60 µg/mL) (Figure 5). The fluidity of the red blood cell membrane was assessed using three spin-labeled fatty acids 5-DS, 12-DS, and 16-DS. These acids have a paramagnetic nitroxide group located at different distances from the carboxyl group of the fatty acid chain, so they enable the determination of the fluidity of the lipids at different depths of the lipid monolayer of the cell membrane. Comparing the values of the h+1/h0 (the ratio of the height of the low-field line amplitude to the height of the midfield line amplitude) parameter, determined with the EPR spectra of the spin label incorporated into the membranes, we showed differences in the fluidity of RBCs cell membranes treated with diosmin and bromelain (Figure 6). The decrease in the h+1/h0 parameter of the 5-DS after the RBCs treatment with diosmin or bromelain reflects the slight decrease in lipid fluidity in the subsurface region of the membrane. A statistically significant decrease in membrane fluidity near the surface of the membrane lipid monolayer was observed only after the incubation of RBCs with 60 µg/mL of bromelain. On the other hand, we found no changes in the lipid fluidity in the deeper regions of the monolayer after the RBCs treatment with diosmin and bromelain using labeled 12-DS and 16-DS fatty acids. ## 3. Discussion Diosmin (diosmetin 7-O-rutinoside) is a disaccharide derivative which consists of aglycone diosmetin. After the administration of diosmin into the digestive system, enzymes of the intestinal microflora hydrolyze diosmin to its aglycone, diosmetin [2]. Moreover, bromelain is a mixture of protein digesting enzymes. Interestingly, bromelain is largely absorbed in the body, without losing proteolytic activity, and it does not cause any major side effects [15]. Many studies have shown that diosmin and bromelain can initiate ROS production and oxidative stress in cells [8,12,25,26]. Furthermore, it has been shown that bromelain can initiate ROS-induced ferroptosis in Kras mutant CRC cells via ACSL-4. ACSL-4 performs a key role in regulating ferroptosis and directs cells to this type of cell death [27]. Bromelain and diosmin reduced the viability of tumor cells by inducing apoptosis via the mitochondrial pathway [28,29]. The influence of both compounds on the initiation of oxidative stress was investigated. We found that both compounds lowered the total level of carbonyl compounds. The TBARS level remained unchanged; however, the tendency to increase non-enzymatic antioxidant capacity was observed. In our previous paper, we showed a decrease in NEAC in plasma, as well as a decrease in the level of thiols in plasma and hemolysate obtained from varicose vein RBCs. On the other hand, we observed an increase in parameters related to oxidative stress, such as the level of protein carbonyl compounds and the level of thiobarbituric acid reactive substances (TBARS) in RBCs from varicose veins [30]. Erythrocytes transport oxygen to cells and tissues, but are also exposed to oxygen reactive forms, both in the circulation and within the cell by the self-oxidation of hemoglobin [31,32]. Therefore, the RBCs have the specialized antioxidant enzyme systems, such as superoxide dismutase, catalase, and thioredoxin peroxidase, and low molecular weight antioxidants, such as glutathione, ascorbic acid, tocopherol, and others [33]. Glutathione is the most important low molecular weight antioxidant present in RBCs, and its concentration (0.6–3.6 mM) is higher compared to other thiols and other antioxidants, such as ascorbic acid or tocopherol [34,35]. The measurement of glutathione (GSH) and thiol content in RBCs can be used as a marker of oxidative stress. Thiol-containing compounds perform an important role in protecting the biological systems from oxidative damage. Because thiol compounds are very sensitive to oxidation, they can be oxidized by mild oxidants, such as superoxide and hydrogen peroxide. Therefore, they are crucial in protecting cells from ROS [36]. The total level of thiols, glutathione, and amino groups was determined. Diosmin and bromelain induced a statistically significant increase in total thiols and glutathione at both concentrations used. The RBCs have a specialized enzyme system for the synthesis of glutathione, consisting of glutathione synthase, glutamate-cysteine ligase-catalytic subunit, and glutathione reductase. In turn, oxidized glutathione and glutathione conjugates formed under various conditions are expelled from the red blood cells. Sustainable exports of reduced glutathione and other thiols, such as cysteine and homocysteine, from the RBCs to plasma have also been demonstrated [37]. These results indicate that red blood cells can significantly affect the extracellular pool of glutathione, participating in cooperation with the liver and other tissues in the synthesis and metabolism of GSH. In addition, it has been shown that the ability of the RBCs to synthesize glutathione is 150 times higher than the glutathione turnover rate. These results indicate a significant reserve of efficiency of the RBCs for the synthesis of glutathione [38]. We have determined the red blood cells parameters that affect the rheological properties and the deformability of the RBCs as they pass through the capillaries in microcirculation. The deformability of the RBCs is influenced by factors such as interior viscosity, the fluidity of plasma membranes, and the condition of the membrane cytoskeleton [39]. We specified the effect of diosmin and bromelain on the internal viscosity of red blood cells using EPR spectroscopy with tempamine. Both compounds led to a significant decrease in the viscosity of the intracellular fluid of the RBCs. Changes in the viscosity of the internal fluid prompted us to study the effect of diosmin and bromelain on the internal components of red blood cells, mainly hemoglobin, which constitutes over $95\%$ of all proteins present in the RBCs. The use of the spin labeling method in EPR spectroscopy made it possible to determine the changes in the mobility of internal components (cytosolic thiols), including hemoglobin. The mobility of cytoplasmic peptides and proteins in the whole red blood cells was investigated using the MSL. The label crosses the RBCs membrane and reacts with the thiol groups of peptides and proteins. Our earlier studies showed that over $90\%$ of the spin label binds to components present in the cytosol, mainly to glutathione, and to a lesser extent, to hemoglobin and membrane proteins [40]. The treatment of red blood cells with diosmin and bromelain led to an increase in the relative rotational correlation time of the bound to the cytosolic thiols. However, a statistically significant increase in τc was only observed with a higher concentration of bromelain. One of the objectives of the study was to investigate the effect of diosmin and bromelain on the internal components of red blood cells, mainly hemoglobin, which accounts for over $95\%$ of all proteins present inside the RBCs. Potential changes in the conformational state of hemoglobin were determined using two labels, MSL and ISL, reacting with the thiol groups. Both spin labels have been shown to bind to the –SH groups of the cysteine-93 of the β-globin chains in hemoglobin [41,42]. The RBCs treatment with diosmin and bromelain resulted in an increase in the rotational correlation time of the bound spin label to hemoglobin. This increase of τc of MSL was statistically significant for the higher concentration of diosmin and both concentrations of bromelain used. An increase in τc for both compounds was also observed for the ISL, howeverthese results were statistically significant for higher concentration of diosmin. The fluidity, microviscosity, or stiffness (rigidity) of the plasma membrane is a frequently used parameter in determining its physical state. Membrane fluidity depends on a number of factors, such as the chemical structure of phospholipids, the degree of saturation with fatty acids, the ratio of protein to lipids, and the presence of cholesterol. Plasma membranes are characterized by a fluidity gradient from the water boundary to the inside of the bilayer [43,44]. Membrane fluidity determines the passive and active transport of electrolytes and non-electrolytes across membranes into and out of the cell [45,46]. In addition, the fluidity of the membrane affects the deformability of the red blood cells when passing through capillaries with a diameter smaller than the diameter of the RBCs. Treatment of red blood cells with diosmin and bromelain led to a decrease in lipid fluidity in the subsurface membrane compared to the control, as revealed by the 5-DS probe. However, these results were statistically significant for higher concentration of bromelain. We did not observe changes in lipid fluidity in the deeper regions of the monolayer (hydrophobic core) using 12-DS and 16-DS labeled fatty acids and treating the RBCs with diosmin and bromelain. Using three fluorescent probes, 6-AS, 12-AS, and 16-AP, derivatives of stearic and palmitic acids, respectively, located at different depths of the monolayer vesicles, it was shown that naringenin, rutin, genistein, genistin, biochanin A, equol, dihydrodaidzein, and dihydrogenistein led to a decrease in membrane fluidity. The results of the study suggest that both glycosides and aglycones acted similarly to cholesterol and α-tocopherol, which are located in the hydrophobic core of the membrane, and led to a strong decrease in lipid fluidity in this region of the membrane [47]. In addition, flavonoids have the ability to stabilize membranes by reducing their fluidity [47]. In turn, orally administered diosmin led to an increase in the RBCs stiffness and a decrease in cholesterol in red blood cell membranes in rats. However, there were no changes in the osmotic fragility of RBCs, but a dose-dependent decrease in the ratio of cholesterol to phospholipids was found [48]. ## 4.1. Chemicals The following chemicals were purchased from Sigma Chemical Co. (St. Louis, MO, USA): 4-Amino-TEMPO (tempamine), 4-Maleimido-TEMPO (MSL), 4-(2-Iodoacetamido)-TEMPO (ISL), 5-doxyl-stearic acid (5-DS), 12-doxyl-stearic acid (12-DS), 16-doxylstearic acid (16-DS), o-phthalaldehyde (OPA), 4,4-dithiodipyridine, 2,4,6-trinitrobenzene sulfonic acid (TNBS), 2,4-dinitrophenylhydrazine (DNPH), and 2,4,6-tripyridyl-S-triazine (TPTZ). All other reagents of analytical purity were obtained from POCH S.A. (Gliwice, Poland). The investigated compounds, diosmin (3′,5,7-Trihydroxy-4′-methoxyflavone 7-rutinoside) and bromelain from the pineapple stem, were purchased from Sigma-Aldrich (St. Louis, MO, USA). Both compounds were dissolved according to the manufacturers’ suggestions: diosmin was dissolved in DMSO, and bromelain was dissolved in PBS. ## 4.2. Red Blood Cells Isolation All experiments were performed on human erythrocytes isolated from the buffy coat obtained from the Regional Center for Blood Donation and Hemotherapy in Lodz. For the RBC separation, the blood buffy coat was washed three times with PBS (10 mM phosphate-buffered saline, pH 7.4). Erythrocytes were suspended in Ringer’s solution to a hematocrit of $50\%$ and separately incubated for 24 h at 37 °C with diosmin at a final concentration of 30 µg/mL and 60 µg/mL or bromelain at a final concentration of 30 µg/mL and 60 µg/mL. After incubation, the cells were washed with PBS and used for future experiments. EPR measurements of the RBCs’ internal viscosity, membrane fluidity, internal peptides, and proteins changes were conducted on the whole RBCs. The remaining experiments were carried out on hemolysate. Every single experiment was performed on cells or hemolysate from one donor, and n-numbers represent cells from different individuals. ## 4.3. Hemolysate Preparation The hemolysate was obtained from washed RBCs by adding cold water at a ratio of 1:1 and vortexed for 10 min, according to the method described by Drabkin [49]. The hemolysate was centrifuged at 4000× g for 10 min to separate erythrocyte ghosts. The total hemoglobin (Hb) concentration in the hemolysate was estimated as cyanmethemoglobin using Drabkin’s reagent, and absorbance was measured at 546 nm [49]. The molar absorption coefficient of hemoglobin was used to calculate the protein concentration in the samples (ε = 44 mmol−1·L·cm−1). ## 4.4. Determination of Carbonyl Groups The protein carbonyl content in hemolysate was determined with 2,4- dinitrophenylhydrazine (DNPH) [50]. The reaction between the protein carbonyl groups and DNPH led to the formation of protein-conjugated dinitrophenylhydrazones (DNP), of which the absorbance can be measured at 370 nm. The content of the carbonyl compounds was calculated using the millimolar absorption coefficient (ε = 22 mmol−1·L·cm−1) and expressed as nanomoles per milligram of hemoglobin (nmol/mg Hb). The data were expressed as the mean ± standard deviation ($$n = 16$$). ## 4.5. Determination of Thiobarbituric Reactive Substances The lipid peroxidation in the hemolysate was measured by determining the interaction of thiobarbituric acid (TBA) with the breakdown product of lipid peroxidation under acid pH conditions using TBARS assay [51], with the modifications of Rice-Evans et al. [ 52]. The end product of the reaction was determined at 535 nm, and the TBARS concentration was calculated using the millimolar absorption coefficient (ε = 156 mmol−1 L·cm−1) and expressed as nanomoles per milligram of hemoglobin (nmol/mg Hb). The data were expressed as the mean ± standard deviation ($$n = 16$$). ## 4.6. Determination of Thiol Groups The concentration of the thiol groups in the hemolysate was determined using the Egwim and Gruber method with 4, 4′-dithiodipyridine [53]. The absorbance of 2-thiopyridon, a product of the reaction between thiols and 4, 4′-dithiodipyridine, was measured at 324 nm. The concentration of the thiol group was estimated based on the standard curve prepared from various concentrations of reduced glutathione and expressed as nanomoles per milligram of hemoglobin (nmol/mg Hb). The data were expressed as the mean ± standard deviation ($$n = 16$$). ## 4.7. Determination of Glutathione Content The concentration of reduced glutathione (GSH) in the hemolysate was estimated according to the fluorescence assay using o-phthalaldehyde (OPA) [54]. The reaction of GSH with o-phthalaldehyde led to a fluorescent product with high fluorescence, allowing GSH to be precisely quantified. The OPA-derived fluorescence was measured at an excitation wavelength of 365 nm and emission at 430 nm. The glutathione concentration was calculated using the calibration curve prepared from various concentrations of reduced glutathione and expressed as nanomoles per milligram of hemoglobin (nmol/mg Hb). The data were expressed as the mean ± standard deviation ($$n = 16$$). ## 4.8. Determination of Amino Groups The concentration of the free amino group in hemolysate was estimated using the method described by Crowell et al. [ 55]. The absorbance of the product of the reaction between amines and 2,4,6-trinitrobenzene sulfonic acid (TNBS), was measured at 335 nm. The concentration of the amino groups was estimated based on the standard curve prepared for different homocysteine concentrations, and was calculated as nmol/mg per milligram of hemoglobin (nmol/mg Hb). The data were expressed as the mean ± standard deviation ($$n = 16$$). ## 4.9. Total Non-Enzymatic Antioxidant Capacity The non-enzymatic antioxidant capacity (NEAC) of the hemolysate was determined by the reaction involving the reduction of Fe3+-TPTZ (iron[III]-2,4,6-tripyridyl-S-triazine) to Fe2+ TPTZ in presence of antioxidants. The absorbance of the reaction product, measured at 593 nm, is proportional to the level of antioxidants [56]. A calibration curve was prepared using different concentrations of Trolox. The results were calculated as nmol of Trolox equivalents per milligram of hemoglobin. ( nmol/mg Hb). The data were expressed as the mean ± standard deviation ($$n = 16$$). ## 4.10. Electron Paramagnetic Resonance (EPR) The EPR spectra were estimated on the Bruker ESP 300 E spectrometer (Rheinstetten, Germany) at room temperature, operating at a microwave frequency of 9.73 GHz. The instrumental settings were as follows: the microwave power was 10 mW, the center field was set at 3480 G, with a 100 kHz modulation frequency and a range of 80 G, a modulation amplitude of 1.01 G, and a time scan of 256 s. ## 4.11. Internal Viscosity of RBCs The internal viscosity of the RBCs was estimated using tempamine, according to the method described by Morse [57]. The RBCs (hematocrit: $50\%$) were labeled with tempamine dissolved in ethanol solution (the final concentration of tempamine was 1 mM) for 0.5 h at room temperature. For the elimination of the extracellular signal of the label, before the measurement, the RBCs were washed with 80 mM potassium ferricyanide in 5 mM phosphate buffer pH 7.4. From the EPR spectra, the relative rotational correlation time (τc) was calculated from an equation formulated by Keith et al. [ 58]; [1]τc=kw0h0h−1−1 where τ𝑐 is the time when the spin label undergoes a full rotation, 𝑘 is constantly equal to 6.5 × 10−10 [s], w0 is the width of the midline of the spectrum, h0 is the height of the midline of the spectrum, and h−1 is the height of the high-field line the of the spectrum. The erythrocyte internal viscosity was calculated according to the following formula:[2]η(RBC)=τc (RBC)τc (H2O)η(H2O) where; τc(RBC) is the rotational correlation time for tempamine inside the erythrocyte, τc(H2O) is the rotational correlation time for tempamine in water, and ηH2O is the water viscosity equal to 1 cP. The data were expressed as the mean ± standard deviation ($$n = 12$$). ## 4.12. The RBCs Internal Peptides and Proteins Changes For the analysis of the changes of internal cytoplasmic peptides and proteins of erythrocyte EPR, 4-Maleimido-TEMPO (MSL) was used as the spin label [40]. The RBCs (hematocrit: $50\%$) were labeled with MSL in an ethanol solution (the final concentration of MSL was 1 mM) for 1 h at room temperature. For the elimination of the spin label excess, the RBCs were washed several times with cold phosphate buffer saline (PBS), until the disappearance of the EPR signal in the supernatant. From the EPR spectra, the relative rotational correlation time (τc) was calculated from an equation formulated by Keith et al. [ 58]. The data were expressed as the mean ± standard deviation ($$n = 12$$). ## 4.13. The Conformational State of Hemolysate Proteins For the investigation of the conformational changes of hemolysate proteins (mainly hemoglobin), two spin labels, MSL and ISL, were used. Under physiological conditions, both spin labels reacted with the thiol groups of proteins [59] The hemolysate was labeled using ethanol solutions of MSL or ISL and incubated for 1 h at 4 °C (the final concentration of the spin label was 0.1 mM). The unbound spin label was removed by 24 h of dialysis against 10 mM phosphate buffer (pH = 7.4). From the obtained EPR spectra, the mobility of the spin label attached to the proteins was estimated by calculating the rotational correlation time (τc) [58]. The data were expressed as the mean ± standard deviation ($$n = 11$$). ## 4.14. RBCs Membrane Fluidity The lipid membrane fluidity of the RBCs was evaluated with the EPR technique using spin-labeled fatty acids (5-DS, 12-DS, and 16-DS). An ethanol solution of the spin label fatty acid was added into the RBCs (hematocrit: $50\%$) to the final concentration of 0.1 μM and incubated for 30 min at room temperature. From the EPR spectra, the h+1/h0 parameter was calculated, where h+1 is the height of the low-field line of the spectrum, and h0 is the height of the mid-line of the spectrum. The data for 5-DS, 12-DS, and 16-DS were expressed as the median and interquartile range (IQR: from lower quartile Q1 to upper quartile Q3) ($$n = 11$$). ## 4.15. Statistical Analysis The normality of data was tested using the Shapiro–Wilk test, and variance homogeneity was verified with the Levene’s test. For the variables showing a departure from normality, the data were presented as the median and interquartile range (IQR: from lower quartile Q1 to upper quartile Q3). For variables data, with no departure from normality, data were presented as mean ± standard deviation (SD). The significance of the differences between the groups was estimated by a one-way ANOVA using Tukey’s post hoc multiple comparisons test for the data with no departure from normality. For the data with a departure from normality, the non-parametric Kruskal–Wallis test was used. Statistical significance was accepted at $p \leq 0.05.$ The statistical analysis was performed using Statistica v. 13.3 (StatSoft Polska, Krakow, Poland). ## 5. Conclusions Diosmin and bromelain have different antioxidant effects on human red blood cells. We found an increase in the concentration of glutathione in the cell and in the total level of thiol compounds, which contributes to the antioxidant protection of the RBCs and plasma against oxidative stress. 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--- title: Lutein Prevents Liver Injury and Intestinal Barrier Dysfunction in Rats Subjected to Chronic Alcohol Intake authors: - Suli Zhao - Yebing Zhang - Haoyue Ding - Shouna Hu - Xiaoqing Wu - Aiguo Ma - Yan Ma journal: Nutrients year: 2023 pmcid: PMC10005241 doi: 10.3390/nu15051229 license: CC BY 4.0 --- # Lutein Prevents Liver Injury and Intestinal Barrier Dysfunction in Rats Subjected to Chronic Alcohol Intake ## Abstract Chronic alcohol intake can affect both liver and intestinal barrier function. The goal of this investigation was to evaluate the function and mechanism of lutein administration on the chronic ethanol-induced liver and intestinal barrier damage in rats. During the 14-week experimental cycle, seventy rats were randomly divided into seven groups, with 10 rats in each group: a normal control group (Co), a control group of lutein interventions (24 mg/kg/day), an ethanol model group (Et, 8–12 mL/kg/day of $56\%$ (v/v) ethanol), three intervention groups with lutein (12, 24 and 48 mg/kg/day) and a positive control group (DG). The results showed that liver index, ALT, AST and TG levels were increased, and SOD and GSH-Px levels were reduced in the Et group. Furthermore, alcohol intake over a long time increased the level of pro-inflammatory cytokines TNF-α and IL-1β, disrupted the intestinal barrier, and stimulated the release of LPS, causing further liver injury. In contrast, lutein interventions prevented alcohol-induced alterations in liver tissue, oxidative stress and inflammation. In addition, the protein expression of Claudin-1 and Occludin in ileal tissues was upregulated by lutein intervention. In conclusion, lutein can improve chronic alcoholic liver injury and intestinal barrier dysfunction in rats. ## 1. Introduction Alcoholic liver disease (ALD) refers to a group of liver pathological changes that include hepatic steatosis, alcoholic hepatitis and alcoholic liver fibrosis [1]. One of the primary causes of the condition is the chronic excessive intake of alcohol [2]. Specifically, chronic alcohol exposure can induce the enhanced activity of cytochrome P450 2E1 (CYP2E1) in the liver, contributing to the buildup of excessive reactive oxygen species (ROS) in the body, which can cause hepatic oxidative stress, dysregulation of lipid metabolism and inflammation [3,4]. Furthermore, chronic alcohol intake can also lead to oxidative damage in the gut [5], resulting in high intestinal permeability and ecological dysbiosis of the microbiota, causing endotoxemia in the organism and leading to further liver damage [6]. In addition to abstinence from alcohol and hepatoprotective drug therapy, some studies have found that supplementation with antioxidant and/or anti-inflammatory dietary supplements, such as carotenoids, vitamins, curcumin and probiotics, can be beneficial remedies for alcoholic liver disease [7]. Lutein is a carotenoid with antioxidant and anti-inflammatory properties [8] which is abundant in egg yolk and dark green leafy vegetables [9,10] and is generally regarded as a safe (GRAS) molecule [11]. By supplementing with lutein, aberrant lipid metabolism in the liver may be effectively resolved [12], and arsenic-induced oxidative stress damage in the liver can be alleviated [13]. Furthermore, lutein decreases LPS-induced mortality in mice via modifying NF-κB-mediated inflammatory pathways [14]. Early studies have found that supplementation with lutein might improve the levels of antioxidant enzymes in rats and thus play a protective role against long-term ethanol intake-induced liver damage, but the specific mechanism is still unclear [15]. In vitro studies have shown that lutein reduces intestinal tight junction opening and barrier impairment [16], and population studies have revealed that blood lutein levels in children are linked with intestinal barrier dysfunction even after controlling for seasonal variables [17]. It has been found that alcohol intake might disrupt the intestinal barrier and cause liver damage in the body [2], but it is uncertain what role lutein plays in chronic alcohol intake-induced intestinal barrier disruption and liver damage. The goal of this study was to assess the role and mechanism of lutein in liver injury and intestinal barrier dysfunction caused by chronic alcohol consumption, as well as the effect of lutein on intestinal microbiota in rats. ## 2.1. Materials The lutein (C40H56O2, HPLC $80\%$) was provided by Shanghai Yuanye Biotechnology Co. (Shanghai, China). The alcohol ($56\%$ (v/v) ethanol) was provided by Beijing Red Star Co. (Beijing, China). Diammonium glycyrrhizinate, which possesses excellent anti-inflammatory, immunomodulatory and liver function enhancement properties, was supplied by Lianyungang Zhengda Tianqing Pharmaceutical Group Co. (Lianyungang, China) [18]. The corn oil was supplied by Yihai Kerry Arawana Holdings Co., Ltd. (Shanghai, China). All remaining reagents utilized in this investigation were of analytical or HPLC purity. ## 2.2. Experimental Models and Treatments Seventy male Wistar rats (weight 140–160 g, 6 weeks old) were acquired from SPF (Beijing, China) Biotechnology Co. Rats were acclimatized for two weeks in a 12 h light/dark cycle at the proper temperature (22–25 °C) and humidity (50–$60\%$). Food and water were available at all times. The International Guide for the Care of Laboratory Animals was followed, and all animal experiments in this work were authorized by the Qingdao University Experimental Animal Welfare Ethics Committee (No. 20210315Wistar7720210706104). At the end of the acclimatization feeding, rats were randomly allocated to a normal control group (Co, $$n = 10$$), a control group of medium-dose lutein interventions (CoLU, 24 mg per kg BW, $$n = 10$$), an ethanol model group (Et, $$n = 10$$), an intervention group with low-dose lutein (LLU, 12 mg per kg BW, $$n = 10$$), an intervention group with medium-dose lutein (MLU, 24 mg per kg BW, $$n = 10$$), an intervention group with high-dose lutein (HLU, 48 mg per kg BW, $$n = 10$$), and a positive control group (DG, diammonium glycyrrhizinate, 200 mg per kg BW, $$n = 10$$). The gavage dose of lutein was chosen with reference to previous studies [12,19]. Lutein dissolved in corn oil was given to each intervention group every day, while the other groups were given equal amounts of corn oil for 14 weeks. From the third week, the DG group was given diammonium glycyrrhizinate, and all groups except the two control groups were given alcohol (8 mL per kg BW of $56\%$ (v/v) ethanol (3.54 g/kg BW ethanol)) orally for two weeks to acclimatize them to alcohol consumption, followed by 12 mL per kg BW of $56\%$ (v/v) ethanol (5.30 g/kg BW ethanol)) orally for the next 10 weeks by gavage 6 h after administration of lutein or diammonium glycyrrhizinate. After the last gavage operation, fasting was performed for 12 h. Rats’ abdominal aortas were sampled for blood for subsequent index testing. Samples of rat liver tissue, small intestinal tissue, and cecum contents were also quickly removed for other experiments. ## 2.3. Histopathological Analysis of Liver and Small Intestine Fresh liver tissue was dissected quickly, and the liver index was determined as follows: liver index (%) = liver weight (g)/body weight (g) × $100\%$. The small intestine was dissected, and the length of the duodenum, jejunum and ileum was measured. Following that, small amounts of liver tissue and ileum tissue were put in paraformaldehyde ($4\%$) solution and embedded in paraffin, and the paraffin blocks were cut into 5 μm thick sections using the frozen section technique for the preparation of hematoxylin and eosin (H&E) staining. A histopathological examination of the liver and the ileum was performed using a light microscope (BX60, Olympus, Tokyo, Japan). Liver pathology was scored by pathologists in a blinded manner according to previously published scoring criteria [20], as detailed in Table 1. In addition, small intestinal villus length and intestinal crypt depth in the field of view of ileal sections were measured under a microscope for each group. ## 2.4. Biochemical Index Measurement To collect serum for further testing, blood was centrifuged at 3000 rpm for 10 min at low temperatures followed by the determination of alanine aminotransferase (ALT), aspartate aminotransferase (AST), triglyceride (TG) and total cholesterol (TC) levels in serum using appropriate kits (Nanjing Jiancheng Institute of Biological Engineering, Nanjing, China). The level of Gamma-glutamyl transferase (GGT) in serum was determined using a fully automated biochemical analyzer (AU5400, Beckman, Los Angeles, CA, USA). Under low-temperature settings, liver tissue homogenates were produced by centrifugation with pre-cooled saline at 12,000 rpm for 10 min. The levels of glutathione peroxidase (GSH-Px), catalase (CAT), malondialdehyde (MDA) in the liver, and superoxide dismutase (SOD) and total antioxidant capacity (T-AOC) in serum were measured using appropriate kits (Nanjing Jiancheng Institute of Biological Engineering, Nanjing, China). The enzyme-linked immunosorbent test kits (Wuhan BOSTER Bioengineering Co., Ltd., Beijing, China) were employed to assess the amounts of interleukin 1β (IL-1β) and tumor necrosis factor α (TNF-α) in the serum. In addition, serum levels of D-lactate (D-LA), intestinal fatty acid binding protein (FABP2), as well as lipopolysaccharide-binding protein (LBP), were assessed using enzyme-linked immunosorbent test kits (Nanjing Jiancheng Institute of Biological Engineering, Nanjing, China). The level of lipopolysaccharide (LPS) in serum was also determined using enzyme-linked immunosorbent assay kits (Xiamen Huijia Biotechnology Co., Ltd., Xiamen, China). ## 2.5. Western Blot Cytoplasmic or nuclear proteins were extracted from liver and ileum tissues according to the kit instructions (Jiangsu KeyGEN BioTECH Co., Ltd., Nanjing, China); the specific steps are described in the Supplementary Materials. The concentrations of protein extracts were measured using appropriate kits (Beyotime, Zhenjiang, China). Subsequently, the protein extracts were separated using $10\%$ SDS-PAGE gels before being transferred to polyvinylidene difluoride (PVDF) membranes, which were subsequently bathed in $10\%$ skim milk, then incubated with primary antibodies CYP2E1 (1:1000), NF-κB (1:10,000) (Abcam, Cambridge, UK), Nrf2 (1:3000) (Affinity, Nanjing, China), HO-1 (1:3000) (Abmart, Shanghai, China), MyD88 (1:1000) (Boster, Wuhan, China), TLR4 (1:1000), IκB-α (1:1000) (CST, Boston, MA, USA), Occludin (1:1000), Claudin-1 (1:1000), ZO-1 (1:1000) (Proteintech, Wuhan, China), β-actin (1:10,000) (Abways, Shanghai, China), or Lamin B1 (1:10,000) (Bioworld, Minneapolis, MN, USA) overnight at 4 °C. The membranes were then rinsed three times for ten minutes each with 1 × TBST. The washed primary antibody response membranes were incubated for one hour with a matching secondary antibody (Bioeasy, Beijing, China) at room temperature, protected from light, then washed and exposed to appropriate developing reagents. β-actin and Lamin B1 were utilized as cytoplasmic and nuclear protein reference proteins, respectively. ## 2.6. Real-Time Quantitative Polymerase Chain Reaction Total RNA was extracted from rat liver and ileum using TRIZOL per the instructions (Tiangen Biochemical Technology (Beijing) Co., Beijing, China). The isolated RNA was reverse transcribed to cDNA and subsequently analyzed by real-time fluorescent quantitative polymerase chain reaction (PCR) with gene-specific primers, namely, ADH1, ALDH2, Claudin-1, Occludin, ZO-1 and β-actin. Amplification was performed as described in [21]. The sequences of the gene primers are shown in Table 2. ## 2.7. Determination of Microbiota in Cecal Contents Since cecum bacteria content was abundant and sufficient, fecal samples could be obtained. Six cecum content samples were randomly selected from each group and 16S rDNA gene sequencing was performed by Beijing Biomarker Technologies Co (Biomarker Technologies Co., Ltd., Beijing, China). The flora in the cecum content was extracted according to the method in [22]. The V3–V4 region of the 16S rRNA gene was amplified using PCR (universal primers, forward (5′–3′): ACTCCTACGGGAGGCAGCA and reverse (5′–3′): GGACTACHVGGGTWTCTAAT) to obtain the amplified product, which was subsequently sequenced using the Illumina NovaSeq 6000 platform. The valid data (non-chimeric reads) obtained were subjected to α-diversity and β-diversity analysis using QIIME2 2020.6 software. The α-diversity and β-diversity of each group were analyzed by the Wilcoxon rank sum test. Line Discriminant Analysis (LDA) Effect Size (LEfSe) was used to find biomarkers with statistical differences among groups from the gate level to the species level. Subsequently, the Kruskal–Wallis test was used to further search for differential bacteria at the genus level, and the results were corrected using the false discovery rate based on the Benjamini–Hochberg method (BH-FDR). The raw data have been uploaded to the NCBI database with the accession number PRJNA879916. ## 2.8. Statistical Analysis Except for the intestinal microbiota, all data are shown as mean ± SEM, and statistical differences among groups were examined using one-way analysis of variance (ANOVA), with multiple comparisons using the least significant difference (LSD). The Kruskal–Wallis test in the nonparametric test was used for the comparison of data that did not meet the requirements of the parametric test. The results of pairwise comparisons between multiple groups were corrected for significance level using the Bonferroni method. Statistical analysis and graph plotting were performed using the SPSS 26.0 software (SPSS, Chicago, IL, USA) and GraphPad Prism 8.0 (GraphPad, San Diego, CA, USA). The data of the intestinal microbiota were expressed as M (QR) or mean ± SD, and the data analysis were performed using BMK Cloud (http://www.biocloud.net, accessed on 1 October 2022). HemI 1.0 (http://hemi.biocuckoo.org/down.php, accessed on 1 November 2022) software was used to create the heatmap [23]. The p-value < 0.05 was judged as showing a significant difference. ## 3.1. Effects of Lutein on Food Intake, Body Weight and Liver Tissue At the beginning of the trial, there was no significant difference in the amount of food intake and body weight for each group. However, at week 14, the Et group’s food intake decreased more than in the Co group ($p \leq 0.05$, Figure 1a). In addition, the Et group had the slowest weight gain during the experiment; at week 14, the Et group had a lower weight than the Co group ($p \leq 0.05$). In the DG group, the rats showed an increase in body weight compared to the Et group ($p \leq 0.05$, Figure 1b). While the liver index showed an increase in the Et group compared with the Co group, a significant decrease in the elevated liver index caused by alcohol was observed in the DG group compared with the Et group (each $p \leq 0.05$, Figure 1c). The liver pathology observations showed significant pathological changes in the Et group, including disturbed hepatic cord arrangement, inflammatory cell infiltration and hepatocyte fat vacuolation. In contrast, lutein and diammonium glycyrrhizinate treatment attenuated such changes (Figure 1d). Meanwhile, the Et group showed a significant increase in liver pathology scores compared with the Co group, while MLU, HLU and DG groups reduced pathology scores compared to the Et group (each $p \leq 0.05$, Figure 1e). ## 3.2. Effect of Lutein on Serum Biochemical Indices As shown in Table 3, compared with the Co group, long-term ethanol treatment increased blood levels of ALT and AST by $38.89\%$ and $35.00\%$, respectively (each $p \leq 0.05$). In comparison with the Et group, the MLU, HLU and DG groups considerably decreased serum levels of ALT ($20.90\%$, $22.59\%$ and $39.21\%$, respectively) and AST ($28.03\%$, $23.16\%$ and $33.06\%$, respectively) (each $p \leq 0.05$). In comparison with the Et group, the MLU, HLU, and DG groups decreased serum levels of GGT, although the differences were not significant. Compared with the Co group, TG and TC levels in the Et group were significantly increased by $41.18\%$ and $50.00\%$, respectively (each $p \leq 0.05$). Compared with the Et group, serum TG levels in HLU and DG groups were significantly decreased by $27.78\%$ and $37.50\%$, respectively (each $p \leq 0.05$). ## 3.3. Effect of Lutein on Alcohol Metabolism in the Liver In this experiment, the gene expression levels of ADH1 and ALDH2 in liver was verified by real-time fluorescence quantitative PCR. The mRNA expression levels of ADH1 and ALDH2 did not differ significantly between the Co and Et groups ($p \leq 0.05$). However, the mRNA expression levels of ADH1 and ALDH2 showed a significantly greater increase in the HLU group than in the Et group (3.54-fold and 2.65-fold, respectively, each $p \leq 0.05$). At the same time, the mRNA expression levels of ADH1 were increased by 2.30-fold and 3.67-fold in the CoLU and DG groups, respectively, compared with the Et group (each $p \leq 0.05$, Figure 2a,b). ## 3.4. Effect of Lutein on Oxidative Stress in the Liver Immunoblotting was used to determine the amount of cytochrome P450 (CYP2E1) protein expression in liver tissue. Compared with the Co group, the Et group dramatically enhanced CYP2E1 protein expression by $88.80\%$. In comparison with the Et group, the MLU, HLU and DG groups reduced CYP2E1 expression by $44.16\%$, $53.15\%$ and $39.33\%$, respectively (each $p \leq 0.05$, Figure 3a,b). The Et group upregulated the protein expression level of Nrf2 by 1.01-fold compared with the Co group ($p \leq 0.05$), while the expression of Nrf2 in the remaining groups, in comparison with the Et group, was not noticeably different (Figure 3c). Furthermore, compared with the Et group, the HLU group significantly increased the expression level of HO-1 by $52.49\%$ ($p \leq 0.05$, Figure 3d). The levels of antioxidant factors in the organism were then examined and showed that the Et group significantly decreased the levels of GSH-Px, SOD and T-AOC in contrast to the Co group (each $p \leq 0.05$). Furthermore, compared to the Et group, the levels of GSH-Px significantly increased in the HLU and DG groups, and the level of SOD and T-AOC significantly increased in the MLU, HLU and DG groups (each $p \leq 0.05$, Figure 3e–g). In addition, lutein intervention showed a tendency to reduce MDA levels compared with the Et group, although there was no significant difference (Figure 3h). CAT levels in HLU and DG groups were significantly higher than in the Et group, as shown in Figure 3i (each $p \leq 0.05$). ## 3.5. Effects of Lutein on Levels of Inflammatory Cytokines The level of inflammatory cytokines in each group was evaluated, as shown in Table 4. The Et group dramatically increased the levels of pro-inflammatory cytokines TNF-α as well as IL-1β in comparison with the Co group ($26.58\%$ and $68.26\%$, respectively; each $p \leq 0.05$). The level of TNF-α in the MLU, HLU and DG groups markedly decreased ($18.99\%$, $15.14\%$ and $26.42\%$, respectively), in comparison to the Et group, while the levels of IL-1β in the HLU and DG groups significantly decreased ($23.59\%$ and $26.24\%$, respectively; each $p \leq 0.05$). In addition, there was no significant difference in LBP levels among the groups. Serum LPS levels in the Et group were higher than in the Co group by $21.43\%$, while LPS levels in the HLU group were significantly lower than in the Et group by $10.64\%$ (each $p \leq 0.05$). ## 3.6. Effects of Lutein on Expression of Proteins Associated with Inflammatory Pathways Immunoblotting revealed that the Et group drastically increased the protein expression levels of NF-κB, TLR4 as well as MyD88 in liver tissues by 1.37-fold, 1.14-fold and 1.25-fold, respectively, compared with the Co group, while decreasing the protein expression level of IκB-α by $52.39\%$ (each $p \leq 0.05$). The HLU and DG groups decreased the protein expression levels of NF-κB ($33.10\%$ and $34.82\%$, respectively), TLR4 ($41.59\%$ and $48.36\%$, respectively), and MyD88 ($47.81\%$ and $46.19\%$, respectively) and upregulated the protein expression level of IκB-α (1.22-fold and 1.61-fold, respectively) compared with the Et group (each $p \leq 0.05$). Furthermore, the MLU group decreased the protein expression levels of NF-κB and MyD88 by $34.60\%$ and $44.97\%$, respectively, and elevated the protein expression of IKBα by 1.10-fold, compared with the Et group, and the LLU group also reduced the protein expression level of MyD88 by $32.95\%$ (each $p \leq 0.05$, Figure 4a–e). ## 3.7. Effect of Lutein on the Small Intestine Tissue Pathological changes in small intestine tissues were then analyzed. The small intestine tissue was divided into three segments: duodenum, jejunum and ileum, and the length of each segment was subsequently measured. The results showed that there was no significant difference in duodenum and jejunum lengths among all groups (Figure 5a,b), but there was a significant difference in ileum length among all groups. The ileum length of the LLU, MLU, HLU and DG groups was significantly longer than the Et group (each $p \leq 0.05$, Figure 5c). In addition, pathological sections of ileal tissue showed changes in ileal structure in the Et group, such as severe atrophy, rupture, and breakage of villi. After the intervention of lutein, especially high-dose administration of lutein, the pathological changes of ileal tissue were improved, and the phenomenon of intestinal villi breakage and atrophy was reduced (Figure 5d). Compared with the Co group, the villus length was significantly shortened in the Et group ($p \leq 0.05$, Figure 5e). The villus length was longer in the MLU, HLU and DG groups than in the Et group (each $p \leq 0.05$). The crypt depth was shallower in the Et group than in the Co group ($p \leq 0.05$, Figure 5f). The villus length to crypt depth ratio was greater in the MLU and HLU groups than in the Co group (each $p \leq 0.05$, Figure 5g). ## 3.8. Effect of Lutein on the Ileal Barrier This experiment verified the expression of ileal barrier proteins and genes using immunoblotting and real-time fluorescence quantitative PCR. Compared with the Co group, the protein expression levels of the intestinal barrier proteins Claudin-1, Occludin, and ZO-1 were downregulated by $39.35\%$, $54.70\%$ and $47.78\%$, respectively, in the Et group (each $p \leq 0.05$). The MLU group significantly upregulated the protein expression levels of Claudin-1 as well as ZO-1 ($44.24\%$ and $64.11\%$, respectively), in comparison with the Et group, while the HLU group significantly upregulated the protein expression levels of Claudin-1, Occludin and ZO-1 ($50.12\%$, $78.66\%$ and $95.55\%$, respectively) (each $p \leq 0.05$). The protein expression levels of Claudin-1, as well as Occludin, were considerably greater in the DG group than in the Et group (0.82-fold and 1.04-fold, respectively) (each $p \leq 0.05$, Figure 6a–d). The mRNA expression of Occludin and Claudin-1 was significantly decreased in the Et group compared with the Co group ($50.17\%$ and $81.61\%$, respectively). The mRNA expression of Occludin in the MLU group increased by $98.58\%$, and the mRNA expression levels of Claudin-1 and Occludin in the HLU group increased by 3.36-fold and 1.24-fold, respectively, compared with the Et group (each $p \leq 0.05$, Figure 6e,f). However, the mRNA expression levels of ZO-1 did not differ significantly among the groups (Figure 6g). Furthermore, the level of D-LA in the Et group was significantly higher than that in the Co group ($p \leq 0.05$), while low-, medium- and high-dose lutein administration did not significantly reverse the elevation of the indicator (Figure 6h). Compared with the Co group, the level of FABP2 in the Et group was significantly increased, while the level of FABP2 in the HLU and DG groups was significantly decreased compared with the Et group ($p \leq 0.05$, Figure 6i). ## 3.9. Effect of Lutein on the Microbiota of Cecum Contents The Simpson and Shannon indexes, the measure of species diversity, and the Chao1 and ACE indexes, the measure of species abundance, were used to evaluate α-diversity among the groups. The results indicated that the Simpson index was not markedly distinguished among the groups. However, the Shannon index of MLU and HLU groups was larger than that of the DG group ($p \leq 0.05$, Figure 7a,b). Compared with the Co group, the Chao1 and ACE indexes in the Et group were increased (each $p \leq 0.05$). In contrast, the Chao1 and ACE indexes in the DG group were decreased compared with the Et group (each $p \leq 0.05$, Figure 7c,d). The results of PCoA analysis based on unweighted unifrac distance indicated that the microbial communities could be well separated among the groups ($R = 0.543$, $p \leq 0.01$). In addition, there was a considerable variation in β-biodiversity between the Co and Et groups ($R = 0.635$, $p \leq 0.01$), while there was also a considerable variation in β-biodiversity between the Et and each intervention group (Et vs. LLU, $R = 0.287$, $p \leq 0.05$; Et vs. MLU, $R = 0.428$, $p \leq 0.05$; Et vs. HLU, $R = 0.813$, $p \leq 0.01$; Et vs. DG, $R = 0.809$, $p \leq 0.01$; Figure 7e). Subsequently, the characteristic bacteria with significant differences in each group were analyzed using the LefSe method (LDA > 2.5, Figure 7f). In the Co group, Monoglobus was enriched at the genus level, and the unclassified Lchnospiraceae NK4A136 group was abundant at the species level (each $p \leq 0.05$). In the CoLU group, Akkermansia, Phascolarctobacterium, as well as Incertae sedis, were enriched at the genus level (each $p \leq 0.05$). In the Et group, the Enterorhabdus and uncultured rumen bacterium were abundant at the genus level, and Eggerthellaceae was enriched at the family level (each $p \leq 0.05$). In the LLU group, the Eubacterium xylanophilum group, Faecalitales, and Bifidobacterium were enriched at the genus level, and Bifidobacterium animalis were abundant at the species level (each $p \leq 0.05$). In the MLU group, Allobaculum, Dubosiella, as well as Colidextribacter were abundant at the genus level, while in the HLU group, Alloprevotella, Faecalibacterium, and Quinella were abundant at the genus level and Bifidobacterium longum was abundant at the species level (each $p \leq 0.05$). In the DG group, Blautia, Coriobacteriaceae UCG 002, Bacteroides, and Negativibacillus were enriched at the genus level (each $p \leq 0.05$). Subsequently, Kruskal–Wallis tests were performed at the genus level to look for differential bacteria in each group (Figure 7g). The relative abundance of the Eubacterium ruminantium group was increased in the three groups following low-, medium-, and high-dose lutein intervention, with the relative abundance of this bacterium in the LLU and MLU groups being considerably higher than in the DG group (each $p \leq 0.05$). In the HLU group, the relative abundance of Subdoligranulum was considerably greater than in the Co, CoLU, Et, and LLU groups (each $p \leq 0.05$), the relative abundance of Quinella was substantially more than in the Co and CoLU groups (each $p \leq 0.05$), the relative abundance of Faecalibacterium was significantly higher than in the Co, CoLU, Et and LLU groups (each $p \leq 0.05$), and the relative abundance of Coprobacter was higher than in the Co, CoLU, Et and LLU groups (each $p \leq 0.05$). In the DG group, the relative abundance of Prevotella 9 was considerably more than in the Et and LLU groups (each $p \leq 0.05$), the relative abundance of Megasphaera was substantially more than in the Co, Et, and LLU groups (each $p \leq 0.05$), the relative abundance of Lachnospiraceae ND3007 group was substantially higher than in the Et and LLU groups (each $p \leq 0.05$), and the relative abundance of Dialister was substantially higher than in the Co, Et and LLU groups (each $p \leq 0.05$). The relative abundance of Phascolarctobacterium was substantially lower in the LLU group than in the Co and CoLU groups (each $p \leq 0.05$). The species distribution of cecum contents at the order level for each group is shown in Figure 7h. ## 3.10. Correlation of Differential Bacterial Genera with Biochemical Indicators As shown in Figure 8, Enterorhabdus was positively correlated with ALT ($r = 0.35$, $p \leq 0.05$), TNF-α ($r = 0.37$, $p \leq 0.05$), TG ($r = 0.36$, $p \leq 0.05$), and LPS ($r = 0.32$, $p \leq 0.05$). Faecalibacterium was negatively correlated with ALT (r = −0.43, $p \leq 0.01$), AST (r = −0.37, $p \leq 0.05$), TNF-α (r = −0.35, $p \leq 0.05$), GGT (r = −0.32, $p \leq 0.01$), and MDA (r = −0.52, $p \leq 0.05$) and positively correlated with SOD ($r = 0.35$, $p \leq 0.05$). Subdoligranulum was negatively correlated with ALT ($r = 0.41$, $p \leq 0.01$), AST (r = −0.33, $p \leq 0.05$), TG (r = −0.35, $p \leq 0.05$), TNF-α (r = −0.38, $p \leq 0.05$), LPS (r = −0.36, $p \leq 0.05$), and MDA (r = −0.52, $p \leq 0.01$) and positively correlated with SOD ($r = 0.40$, $p \leq 0.01$). Phascolarctobacterium was negatively correlated with TNF-α (r = −0.40, $p \leq 0.01$), TG (r = −0.31, $p \leq 0.05$), LPS (r = −0.36, $p \leq 0.05$), LBP (r = −0.36, $p \leq 0.05$), and D-LA (r = −0.41, $p \leq 0.01$). ## 4. Discussion The current study investigated the role and mechanism of lutein in the liver and intestinal barrier damage in rats induced by persistent alcohol. The findings revealed that lutein might reduce liver and intestinal barrier damage in rats via antioxidant and anti-inflammatory actions and enrich beneficial flora in rats. Chronic alcohol consumption may lead to disturbances in the nutritional metabolism of the organism [24], causing slow weight gain in experimental rats [25]. Consistent with previous studies, our alcohol-fed rats showed a phenomenon of reduced food intake and weight, possibly because alcohol intake causes anorexia in rats [26], which increases the risk of malnutrition, thus causing low body weight. Meanwhile, the levels of transaminases (ALT, AST) in serum were raised as a result of hepatocyte damage which was induced by ethanol [27,28], whereas high-dose lutein intervention attenuated alcohol-induced transaminase elevation, demonstrating the protective effect of lutein on hepatocytes [15,29]. In addition, long-term alcohol intake can promote TG and TC synthesis and hepatocyte steatosis in the organism [30,31], leading to increased serum TG and TC levels and a corresponding increase in the liver index [32]. Lutein can prevent excessive lipid accumulation [33] and also effectively ameliorate liver damage in rats with nonalcoholic fatty liver [12]. Consistent with this, supplementation with high-dose lutein significantly reduced TG levels in chronic-alcohol-consuming rats in the current work. Liver is the main site of ethanol metabolism, and alcohol dehydrogenase (ADH), aldehyde dehydrogenase 2 (ALDH2), and cytochrome P450 2E1 (CYP2E1) in liver cells are closely related to alcohol metabolism [34]. Long-term alcohol intake can reduce the activities of ADH1 and ALDH2 and increase CYP2E1 protein expression in the liver, and this generates excessive reactive oxygen species (ROS) that induce liver damage [4,35,36]. In particular, the massive accumulation of ROS disrupts lipid metabolism in the liver and elevates MDA, the end product of lipid peroxidation, as well as TG levels [37,38]. After high-dose lutein intervention, the mRNA expression of ALDH2 related to alcohol metabolism in the liver was promoted, which may be related to the fact that antioxidants can promote alcohol metabolism and resist alcohol-induced liver injury by regulating the feedback mechanism between Nrf2 and ALDH2 [39]. Despite this, there was no significant difference in Nrf2 expression between the Et group and the HLU group in this study. However, high-dose lutein intervention promoted the expression of HO-1, an antioxidative factor downstream of Nrf2, so lutein may promote alcohol metabolism by playing an antioxidative role, but further studies may be needed to explore the relationship between lutein and alcohol metabolism. Chronic alcohol feeding may elevate the protein levels of Nrf2 in the liver cell nuclei of experimental rats, which may be a compensatory response [40,41]. However, long-term alcohol consumption did not increase the expression of the antioxidative factor HO-1, thereby also reducing the generation of the enzymatic antioxidative factors GSH-Px and SOD, which resist the excessive accumulation of ROS in the organism [42,43,44]. Meanwhile, chronic alcohol intake depletes the body of enzymatic antioxidative factors [45], thus creating a vicious circle and promoting disease progression. However, the lutein intervention reduced the alcohol-induced elevated protein expression level of CYP2E1, while further alleviating the levels of TG and MDA, suggesting that lutein may have ameliorated liver injury through antioxidative effects. At the same time, administration of high-dose lutein further promoted the expression of the antioxidative factor HO-1, thus resisting the alcohol-induced decrease in the levels of antioxidative factors SOD and GSH-Px, and also has the potential to raise T-AOC, which can reflect the organism’s non-enzymatic antioxidative capacity [46]. In conclusion, lutein may play an antioxidative role in antagonizing alcohol-induced liver injury by regulating the Nrf2/HO-1 signaling pathway. Chronic alcohol intake causes inflammatory damage and oxidative stress in the liver. Ethanol and its metabolites can induce dissociation of the IKK complex (composed of NF-κB and IκB-α) and degrade IκB-α, thereby dissociating the complex to release NF-κB as well as promoting the release of inflammatory factors, such as TNF-α and IL-1β, further aggravating liver damage [47]. Furthermore, chronic alcohol consumption leads to elevated blood levels of the endotoxin LPS, which is one of the central mediators of alcoholic hepatitis [48]. The lipopolysaccharide-binding protein (LBP) in the blood binds to intestinal-derived LPS, which can transport it to the liver and induce inflammatory damage in the liver [49]. Specifically, upon entering the liver, LPS first binds to the recognition receptor Toll-like receptor 4 (TLR4) [50], which, when the TLR4-MyD88 complex is formed, can also subsequently release NF-κB as well as increase the levels of its downstream inflammatory factors [51,52], further aggravating the liver injury. In the present study, chronic ethanol intake activated the inflammatory pathway in the liver and thus promoted the release of the pro-inflammatory factors TNF-α as well as IL-1β, leading to inflammatory damage in the liver. This liver damage may be caused by the combined action of alcohol through its metabolites and the LPS pathway. This study also found that supplementation with lutein reversed alcohol-induced changes in inflammatory protein expression, reduced the level of inflammatory factors, and ameliorated liver injury to some extent. Oxidative stress and inflammatory responses are closely related pathological alterations, and there is mutual crosstalk between Nrf2 and NF-κB [53], so lutein may be acting in joint resistance to alcohol-induced liver damage by exerting antioxidative and anti-inflammatory capacities. Meanwhile, chronic alcohol intake can induce oxidative stress in the intestine [54], causing increased intestinal permeability and disrupting the intestinal tight junction [5], which consists of transmembrane proteins such as Occludins and Claudins as well as intracellular molecules such as ZO-1 [55]. At the same time, intestinal tight junction disruption leads to intestinal barrier breakdown, which promotes the release of LPS and further causes liver damage [48]. Previous studies have found that intestinal leakage after chronic alcohol exposure occurs in the ileum, but not in the duodenum or jejunum [56]. Therefore, we examined the tight junctions of the ileum. We found that chronic alcohol intake disrupts the tight junctions of the ileum, leading to decreased protein levels of Claudin-1, Occludin and ZO-1, and considerably elevating the level of I-FABP, which is positively correlated with intestinal permeability [57], resulting in an impaired intestinal barrier. Previous studies had found that lutein can reduce small intestinal villus injury and intestinal cell shedding, inhibit the increase of lipid peroxide, and improve intestinal oxidative injury caused by ischemia-reperfusion (I/R) [58,59]. In addition, lutein could improve intestinal oxidative stress damage by exerting antioxidative effects [60], and it can also regulate intestinal tight junction opening and improve the impaired gut barrier [16]. In this research, supplementation with high-dose lutein up-regulated the protein and gene expression of Claudin-1 and Occludin and alleviated the high FABP2 status in the organism, which improved the impaired intestinal barrier. By supplementing with lutein, the LPS level was reduced, and the liver inflammation caused by alcohol was further alleviated. Furthermore, alcohol intake can lead to bacterial overgrowth in the cecum [61]. The current study found that the Chao1 and ACE indexes, which measure the number of bacteria, were increased after alcohol intake, indicating that alcohol caused bacterial overgrowth in the cecum. The Et group was significantly enriched for Eggerthellaceae, a pathogenic bacterium [62], and Enterorhabdus, a Gram-negative bacterium, in which LPS is a part of the cell wall of Gram-negative bacteria. Since alcohol causes a leaky gut, it further promotes the increase of LPS in the blood and aggravates liver damage. In addition, Enterorhabdus were positively correlated with factors that could promote the release of pro-inflammatory cytokines and damage the epithelial barrier [63]. In contrast, after high-dose lutein intervention, the intestinal flora composition changed dramatically (Et vs. HLU, $R = 0.813$, $p \leq 0.01$), although the Chao1 and ACE index did not significantly alter. In contrast to the Et group, the HLU group was enriched for Bifidobacterium longum, which may create short-chain fatty acids [64]. In addition, Bifidobacterium longum has been found to improve alcohol-induced liver and intestinal barrier damage [65]; this also implies that, by enriching Bifidobacterium longum, lutein intervention might reduce alcohol-induced damage in the body. Previous research has discovered that prolonged excessive alcohol use reduces the relative abundance of Subdoligranulum and Faecalibacterium [66,67], particularly when intestinal permeability is high [68]. In the current research, however, the administration of high-dose lutein intervention improved alcohol-induced intestinal barrier damage and reduced intestinal permeability, while the relative abundance of Subdoligranulum, as well as Faecalibacterium, was increased, which also further indicated the regulatory effect of lutein on alcohol-induced intestinal barrier injury and flora disorders. A previous study has found that lutein can improve liver injury induced by binge drinking in rats by inducing antioxidative and anti-inflammatory effects [69]. The present study investigated the protective effect of lutein on liver injury induced by chronic alcohol intake. Our study focused on the dose-response relationship and the combined effects of the liver–gut axis, and also explored the relationship between lutein and gut microbiota, which were not examined in the previous study. The mechanisms of liver injury induced by acute and chronic alcohol feeding were similar but different. Changes in gene expression after long-term ethanol feeding might sensitize the liver to alcohol-induced damage that is not seen after acute alcoholism [70]. At the same time, long-term alcohol feeding can lead to increased protein expression of CYP2E1, and intestinal permeability will also increase with the long-term consumption of ethanol, at which time TLR4-mediated liver inflammatory injury is particularly important [71,72,73]. The previous study has found that short-term high alcohol intake induces oxidative stress and inflammatory responses in the liver. Lutein intervention enhanced the protein expression of Nrf2 in the liver of rats, up-regulated the level of antioxidants in the body, down-regulated the protein expression of NF-κB and its downstream target factors COX-2 and iNOS, and reduced the level of inflammation-related factors [69]. However, in the present study, lutein administration did not significantly increase the expression of Nrf2 in the liver, which may be related to the continuous oxidative stress in the body caused by long-term alcohol exposure. However, lutein administration could promote the protein expression of the antioxidative factor HO-1 downstream of Nrf2, regulate the NF-κB/TLR4 inflammatory pathway, and alleviate the damage to the intestinal barrier, thereby reducing the oxidative and inflammatory damage to the liver and ileum caused by long-term alcohol intake. Recent studies have found that autophagy plays an important role in the pathology of alcoholic liver disease, as acute alcohol intake enhances autophagy in the liver and, conversely, chronic alcohol feeding inhibits autophagy, although many conflicting studies have emerged [74,75,76]. However, existing studies have found that lutein can induce protective autophagy in cells under stress, while at the same time having a resistance effect on harmful excessive autophagy in cells [77,78]. Although the relationship between lutein and autophagy in the liver under continuous alcohol intake was not investigated in this study, it has provided ideas for future research. ## 5. Conclusions In conclusion, in the current trial, liver injury and intestinal barrier dysfunction induced by chronic alcohol use in rats were attenuated by lutein. Moreover, the protective effect of lutein is connected to its regulation of the Nrf2/HO-1 pathway and the TLR4/NF-κB pathway. Furthermore, high-dose lutein supplementation has the potential to enrich beneficial flora (e.g., Bifidobacterium longum, Faecalibacterium, and Subdoligranulum). This animal study implies that lutein might be utilized as a dietary supplement to prevent and alleviate chronic alcoholic liver damage and intestinal barrier dysfunction; however, further human studies are needed to validate this. ## References 1. 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--- title: 'An 8-Week Peer Health Coaching Intervention among College Students: A Pilot Randomized Study' authors: - Zi Yan - Jessica Peacock - Juliana F. W. Cohen - Laura Kurdziel - Sarah Benes - Seungbin Oh - April Bowling journal: Nutrients year: 2023 pmcid: PMC10005245 doi: 10.3390/nu15051284 license: CC BY 4.0 --- # An 8-Week Peer Health Coaching Intervention among College Students: A Pilot Randomized Study ## Abstract This study explored the effects of an 8-week peer coaching program on physical activity (PA), diet, sleep, social isolation, and mental health among college students in the United States. A total of 52 college students were recruited and randomized to the coaching ($$n = 28$$) or the control group ($$n = 24$$). The coaching group met with a trained peer health coach once a week for 8 weeks focusing on self-selected wellness domains. Coaching techniques included reflective listening, motivational interviews, and goal setting. The control group received a wellness handbook. PA, self-efficacy for eating healthy foods, quality of sleep, social isolation, positive affect and well-being, anxiety, and cognitive function were measured. No interaction effects between time and group were significant for the overall intervention group (all $p \leq 0.05$), while the main effects of group difference on moderate PA and total PA were significant ($p \leq 0.05$). Goal-specific analysis showed that, compared to the control group, those who had a PA goal significantly increased vigorous PA Metabolic Equivalent of Task (METs) ($p \leq 0.05$). The vigorous METs for the PA goal group increased from 1013.33 (SD = 1055.12) to 1578.67 (SD = 1354.09); the control group decreased from 1012.94 (SD = 1322.943) to 682.11 (SD = 754.89); having a stress goal significantly predicted a higher post-coaching positive affect and well-being, controlling the pre-score and other demographic factors: $B = 0.37$ and $p \leq 0.05.$ Peer coaching showed a promising effect on improving PA and positive affect and well-being among college students. ## 1. Introduction According to the Centers for Disease Control and Prevention [1], approximately one in two people in the United States have at least one chronic health condition, such as heart disease, cancer, hypertension, diabetes, or obesity. One in four adults have two or more chronic health conditions. It has been estimated that a major portion of chronic conditions could be prevented by behavior-related lifestyle interventions [1]. College is a critical lifetime transition period for young adults to develop and independently practice healthy behaviors [2]. Poor health behavior practices and associated mental health challenges have emerged as critical risks among undergraduate students in the United States [3]. College students are at risk of gaining weight due to a lack of physical activity (PA), poor nutrition [4], and lack of sleep [5]. Approximately $25\%$ of college students suffer from mental health challenges, such as anxiety, depression, and alcohol use disorder [6]. The previous literature has documented the evidence of lifestyle interventions as a public health strategy to prevent and manage chronic illness [7], as well as improve well-being [8]. Among various lifestyle interventions, health coaching holds promise to promote healthy behavior practices due to its ability to address multiple behaviors, health risks, and the self-management of illness in a cost-effective manner [9]. Health coaches foster individuals’ autonomy and intrinsic motivations [10,11], as well as provide support for the intrapersonal process that “energizes and directs behaviors towards healthier and more successful human functioning” [12]. Although there is a lack of agreement on which theory of health coaching is most prominently grounded, it is suggested that the strategies used in health coaching, including listening, asking powerful questions, and motivational interviewing, are consistent with constructs of the self-determination theory (SDT) [13]. According to the SDT, when health coaches provide an environment and guidance to facilitate individuals’ competence, autonomy, and relatedness, they will be intrinsically motivated to set health-related goals and work towards their best health status and human function [12,14]. The use of health coaching has become widespread in recent years and its effectiveness in promoting health behavior practices has been well supported by the available evidence [15,16]. Health coaching studies have shown significant improvements in various health outcomes, including weight loss and management, improved mental health, and enhanced performance of activities of daily living for various populations, including adults with fibromyalgia, eating disorders, and heart diseases [17,18]. Colleges and universities are ideal settings to improve well-being among young adults. Behavioral interventions in college settings have the potential to reach a larger group of young adults. Colleges and universities also provide a unique opportunity for students with health-related majors to be trained and serve as the health coaches for their peers. Peer support is reported to be particularly important in health promotion programs among youth and young adults [19]. Living in a similar physical and social environment allows the student coaches to better understand the experience and challenges of the individuals they coach. In addition, being of similar ages and sociocultural backgrounds may facilitate rapport in the coaching relationship. Previous peer education studies also showed that student coaches improved cultural competence and health behavior practices after participating in peer health promotion programs [20,21]. Previous health coaching studies have primarily focused on physiological outcomes (i.e., blood pressure, HbA1c, blood glucose, cholesterol, BMI, or body weight), while behavioral outcomes were often disregarded [22]. Those health coaching studies that evaluated behavior outcomes often had pre-decided wellness topics such as PA or nutrition [23,24] on which coaching would be focused, regardless of what health topics may be of importance to those being coached. While this approach can more easily quantify the dosage of the intervention on the specific health or wellness topic, it restricts participants’ autonomy over wellness topics. Restricting specific wellness topics also limits the ability to detect the potential interaction among different health behaviors. For example, research has shown that increased PA is also associated with better sleep quality, as well as improved mental health [25,26,27]. Treating a higher-order construct, such as motivation, may link treatment to lower-order constructs, such as specific behaviors. Thus, it is possible that the clients who worked on improving one aspect of wellness through coaching may have other behavioral benefits [28]. In addition, prior health coaching studies have primarily targeted participants with certain types of disease or chronic conditions, such as cancers, diabetes, obesity, cardiovascular disease, and pulmonary diseases [29,30,31], with limited studies on healthy populations such as college students. For the handful of health coaching programs that targeted healthy college students, none of them have used randomized designs (RCTs) [32,33,34]. In a recent review study, An and colleagues noted that very few RCTs have evaluated the effects of health coaching and called for more RCTs of the health coaching studies [22]. In response to the nationwide call for more community- and evidence-based programs to improve population health, the research team designed this pilot program to gather scientific evidence to evaluate the effectiveness of the peer health coaching program in the college campus setting. Therefore, the current study aimed to assess the effectiveness of a randomized, 8-week peer health coaching program on PA, nutrition, sleep, social isolation, and mental health, among college students. ## 2.1. Study Design The study used a randomized, 8-week interventional design to evaluate the efficacy of a peer health coaching intervention delivered in a college setting. The study was conducted at a midsize private college (i.e., student population ~4000 to 5000 students) located in New England, USA. The baseline and post-intervention assessment were conducted in January 2022 and May 2022, respectively. The Institutional Review Board (IRB) at the college approved all study procedures. ## 2.2. Participants Student participants. Freshmen and sophomore students 18 years or older were eligible to participate. Participants were recruited through campus flyers, recruitment tables in front of the student center building, and classroom visits to a course that all freshmen are required to take. We also purposely recruited from programs serving historically underrepresented populations, including first-generation students and students from minoritized races/ethnicities, since such students were disproportionately affected by the pandemic and generally experienced greater barriers to participation in health programming. A total of 52 participants were recruited. After completing the pre-assessment, they were randomly assigned into the coaching group ($$n = 28$$) or the control group ($$n = 24$$). ## 2.3.1. Health Coaches Student coaches were health science major undergraduate students who were enrolled in a series of two health coaching courses that prepared them for basic coaching skills and improving skills and self-efficacy through practice. The courses were taught by a group of faculty. Two of those faculty have received WellCoaches® health coaching certification; other faculty had related expertise (e.g., cultural studies, counseling, mental health). In the first coaching course, students had received the basic training on coaching theories and techniques. They all passed a mock health interview exam by the end of the first coaching course and before they started the second coaching courses, in which they needed to complete 50 coaching sessions. This study was conducted as part of their 50-session training. During this study and throughout the course, student coaches met with the course instructor and other coaches weekly to discuss their coaching progress and challenges. In addition, each coach met with the course instructor individually every other week to receive additional feedback and support. ## 2.3.2. Procedures After completing the baseline assessment, participants were randomized to either the intervention (i.e., coaching group) or the control group. Covariate adaptive randomization was adopted to balance the participants between the intervention and control groups on gender, first-year students, and students of color. Participants in the coaching group met with their assigned 1:1 peer health coach once a week for 8 weeks. Coaching meetings were scheduled for 30–40 min. Coaching meetings were in-person but zoom coaching meetings were allowed if students were sick or had safety concerns related to COVID-19. See Appendix Figure A1 for the flow chart of the intervention. ## 2.3.3. Intervention The health coaching program was designed to facilitate and emphasize several aspects. The first coaching session focused on self-goal identification. At the first coaching session, the health coaches assisted the students to identify 2–3 areas within the topics of PA, nutrition, sleep, and social support that they would like to improve on. During each coaching meeting, student coaches evaluated their previous weekly goals and discussed their gains and challenges and areas that they would like to work on in the coming weeks. Second was “Peer support”. In each session, coaches facilitated the discussion using coaching techniques, including reflective listening, affirmation, motivational interviewing, etc. Lastly, the health coaching program facilitated and emphasized “Goal setting.” By the end of each session, coaches assisted student participants to come up with two SMART goals (i.e., specific, measurable, action-based, realistic, time-limited) that they felt ready to work on. Notably, participants were encouraged to set behavioral goals that they would like to work on. That said, no specific behavior goal was pre-set for participants. For instance, one participant may set a weekly physical activity goal of walking more, whereas another participant may work on doing more moderate-vigorous exercise. In the following week, students then conducted a self-evaluation on the completion rate from 0 percent to 100 percent on each of the wellness goals. Students in the control group received a wellness handbook that was created by the research team. This handbook provided information related to how to improve physical activity, nutrition, sleep, stress, and social support in the college setting. Students in the control group were asked to use this handbook as an information source if they would like to improve their health behavior practices. ## 2.4.1. Demographics Gender, age, race, and whether they were first-generation students or student athletes were collected. In addition, socioeconomic status was measured by the MacArthur Scale of Subjective Social Status [35]. Students indicated their perception of their social status by rating a 10-point Likert-type scale from 1 (lowest standing in the community) to 10 (highest standing in their community). Previous studies indicated good evidence of reliability and validity on scores of the measure [36,37]. ## 2.4.2. Physical Activity (PA) The International Physical Activity Questionnaire (IPAQ)—short form was used as the subjective measure of physical activity. A total of 7 items were structured to provide separate METs (min/week) on walking; moderate-intensity activity; vigorous-intensity activity, total physical activity, and time spent on sitting (walking MET—minutes/week = 3.3 × walking minutes × walking days; moderate MET—minutes/week = 4.0 × moderate-intensity activity minutes × moderate days; vigorous MET—minutes/week = 8.0 × vigorous-intensity activity minutes × vigorous-intensity days). This questionnaire has demonstrated acceptable reliability and validity on the total score in previous studies [38]. Exercise self-efficacy was also assessed using a five-item measure to assess the confidence to perform physical activity in five different situations (i.e., vacation, feeling tired, bad mood, not having enough time, and bad weather [39]. This measure has been shown to predict physical activity in previous studies [40]. ## 2.4.3. Diet Healthy eating self-efficacy was assessed as a summary of nine five-point Likert-scaled questions about self-confidence for eating healthy foods at the mall, after school, with friends, under stress, feeling down, bored, at a fast-food restaurant, alone, and at family dinner (not confident at all = 1 to very confident = 6; (Cronbach $a = 0.83$)) [41]. ## 2.4.4. Sleep The Pittsburgh Sleep Quality Index (PSQI) [42] was used as a subjective measure of sleep quality. It differentiates “poor” from “good” sleep quality by measuring seven areas (components): subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medications, and daytime dysfunction over the last month. A Global PSQI score was calculated by adding scores from the 7 areas together. A total score of 5 or higher indicates a poor sleep quality. ## 2.4.5. Social Isolation NIH Item bank V2.0 Social Isolation (Short Form) from PROMIS was used to measure social isolation. Participants responded to a four-item scale. An example question was: “In the last 7 days, how often do you feel (e.g., I felt left out).” Responses ranged from Never [1] to Always [5]. The sum score ranged from 4 to 20, with a higher score indicating higher social isolation. ## 2.4.6. Mental Health For the purposes of this study, we utilized validated measures developed as part of the NIH-funded Patient-Reported Outcomes Measurement Information System (PROMIS). Positive affect and well-being were measured using the Neuro-QOL Item Bank v1.0—Positive Affect and Well-Being (Short Form). An example question was “Lately, my life was satisfying…” Responses range from Never [1] to Always [5]. The instrument is 9 items with a score range of 9 to 45, where higher scores indicated a higher overall positive affect and well-being. Anxiety was measured by the Neuro-QOL Item Bank v1.0—Anxiety (Short Form) from the PROMIS. An example question was “In the past 7 days, I felt nervous.” Responses ranged from Never [1] to Always [5]. The instrument features 8 items with a score range of 8 to 40. A higher score indicates a higher anxiety level. Cognitive function was measured by NIH Neuro-QOL Item Bank v2.0—Cognition Function (Short Form) from the PROMIS. Four questions started with “In the past 7 days… (e.g., I had to read something several times to understand it).” Answering options ranged from “Never [5]” to “Very often (several times a day) [1].” Another four questions started with “How much DIFFICULTY do you currently have… (e.g., learning new tasks or instructions?)”, and answering options range from “None [5]” to “Cannot do [1].” The total score ranged from 8 to 40, with higher scores indicating better cognitive function. ## 2.5. Data Analyses Intention-to-treat analysis was performed. In addition to descriptive statistics, repeated MANOVA was used to explore the overall impact of the coaching intervention on physical activity, sleep, nutrition, mental health, and social isolation between participants in the coaching group and the control group. In addition, participants in the coaching group were further divided into subgroups based on the wellness goals they identified. Repeated ANOVA and MANOVA analyses were used to explore the coaching effect on the goal-specific domain. For instance, participants who identified physical activity as one of their coaching goals were compared to the control group on the physical activity variables. Regression analysis was performed to examine the effects of the demographic factors on the post-assessment scores, controlling the pre-assessment scores. All data were analyzed using SPSS 21 (IBM, Armonk, New York, NY, USA). ## 3. Results Demographics: Two participants ($7\%$) in the coaching group ($$n = 28$$) dropped the study during the intervention. Among the rest of the 26 students who completed the 8-week intervention, 23 ($82\%$) completed the post-assessment. For the remaining 24 participants in the control group, 20 ($83\%$) of them completed the post-assessment. Table 1 also showed that there was no significant difference in the demographic characteristics between the coaching and control groups. Intervention Effect Analysis. Means and standard deviations for all variables are shown in Table 2. No variables were significantly different at baseline between intervention and control groups. All scales demonstrated moderate to good reliability, with Cronbach’s alpha values ranging from 0.80 to 0.95. Repeated measures MANOVA tests showed that all the interaction effects between time and group were not significant, all p values > 0.05; the main effects of group difference on Moderate PA MET and total PA MET were significant: F[1,39] = 4.76, $$p \leq 0.035$$, F[1,39] = 4.99, and $$p \leq 0.031$$, respectively. The vigorous PA MET was marginally significant: F[1,39] = 4.06 and $$p \leq 0.051.$$ The main effects of time and group were not significant for all other variables, all p values > 0.05. ## Goal-Specific Analysis Physical Activity. A total of 15 participants in the coaching group ($65\%$) identified PA as their coaching goals and discussed PA-related goals for at least one coaching meeting. The average goal meeting rate was $73\%$. Repeated MANOVA results showed that the interaction effects between time and group on Vigorous MET was significant F[1,32] = 6.42, $$p \leq 0.017.$$ For those who identified PA as one of their coaching goals, vigorous MET increased from 1013.33 (1055.12) to 1578.67 (1354.09), while the control group decreased from 1012.94 (1322.94) to 682.11 (754.89). All other interaction and main effects were not significant, all p values> 0.05. We also explored whether having PA as a coaching goal, as well as the demographic factors, would predict more PA changes. The regression analyses showed that, controlling for pre-PA level and the demographic factors, coaches who had PA as a coaching goal had a marginal significant higher post-total PA MET ($B = 0.35$, $$p \leq 0.053$$) and a significant higher post-vigorous PA MET ($B = 0.44$, $p \leq 0.01$) than those who did not have PA as a coaching goal and those who were in the control group. See Table 3 and Table 4. Diet. Fifteen students ($65\%$) in the coaching group identified improving diet as one of their coaching goals. The overall self-evaluation goal completion rate was $87\%$. The interaction between group and intervention as well as the main effect of group and time were not significant: F[2,39] = 0.01, F[1,39] = 0.41, and F[1,39] = 0.76, respectively, all p values > 0.05. The Healthy eating efficacy for the goal-specific group changed from 27.4 (8.51) to 28.2 (7.03), compared to from 25.68 (5.73) to 26.47 (5.32) for the control group. Having diet as a coaching goal, as well as other demographic factors, did not predict the healthy eating efficacy score. See Table 5 for details. Sleep. A total of 13 participants in the coaching group ($57\%$) identified sleep as one of their coaching goals, with the goal completion rate of $79.96\%$. The interaction between group and intervention was not significant: F[1,28] = 3.60 and $p \leq 0.05.$ The group differences were significant: F[1,28] = 5.18 and $p \leq 0.05.$ Among the 13 participants who identified sleep as one of their coaching goals, the average Global PSQI score decreased from 6.18 (2.35) in the pre-test to 4.36 (2.20) in the post-test; as for the control group, the score changed from 7.74 (2.76) to 7.26 (3.58). Regression analysis showed no significant intervention effect on predicting post-PSQI scores. See Table 5 for the details. Social Isolation. Seven students ($30\%$) identified improving social-related support as one of their coaching goals, with the self-evaluated goal completion rate of $75\%$. There were no significant intervention or group effects, all p values > 0.05. The social isolation score of the intervention group changed from 9.33 (5.84) to 10.33 (6.12), while the control group changed from 9.74 (3.79) to 9.15 (3.67) from the pre- and post-assessment. Compared to those in the control group, having the social goal as a coaching goal did not predict the post-social isolation score; however, females had significantly less social isolation than males, controlling all other variables: B = −0.42 and $p \leq 0.01.$ Mental Health. Although no student directly identified “mental health” as one of the coaching goals, seven participants in the coaching group identified stress management as one of their coaching goals ($30\%$) and discussed stress-related goals during at least one coaching session. The overall self-evaluated goal completion rate was $83.09\%$. The interactions between group and intervention on anxiety, positive affect and well-being, and cognitive function were not significant, all p values > 0.05. The main effect of the group was significant on anxiety (F[1,24] = 5.18, $p \leq 0.05$). The anxiety score for the goal-specific group changed from 14.14 (6.04) to 14.71 (7.18), while for the control group it changed from 22.05 (7.82) to 20.52 (7.29). All other main effects were not statistically significant. Compared to those who were in the control group, those who had a stress goal had a significantly higher post-positive affect and well-being score, controlling pre-positive affect score and other demographic factors, $B = 0.37$, $p \leq 0.05.$ No relationships were identified between a stress goal and post-anxiety and cognitive function. ## 4. Discussion Although the concept of a health coach and peer education model has been well developed, there has been very little empirical research on the effectiveness of peer health coaching programs. Even less is known regarding the effectiveness of health coaching among non-clinical young adult populations in the higher education setting. To the authors’ knowledge, this is the first published study in which multiple health behavior outcomes were evaluated via a health coaching RCT among college students. The most important findings were that the students in the coaching group who worked on PA showed significant intervention effects on vigorous PA and marginally significant improvement on total PA over the 8-week period. This is consistent from previous studies. For instance, a meta-analysis with 27 randomized trials shows a small, significant effect size (SMD = 0.27) in PA improvement achieved by health coaching among people aged 60 years or older [43]. In another meta-analysis study [22] that examined health coaching intervention among adults with cardiovascular disease, the effect size of health coaching intervention is small (effect size < 0.20) for physical activity and diet. The current study provided preliminary evidence of the effectiveness of health coaching on physical activity among healthy young adults. Although there was no significant intervention effect on walking and moderate PA, the improvement of vigorous PA and marginally significant improvement on total PA among the participants indicated that they engaged in more planned exercise behaviors after the intervention. Interestingly, while vigorous and total PA increased among the participants, there was a decrease in vigorous and total PA among the control group. This may be due to the fact that post-assessment was one week before the final examinations, and the increased stress and study time may have made students in the control group less likely to engage in PA [44]. This also demonstrates that peer health coaching not only buffered the negative impact of increased stress and lack of time, but also provided additional support for students to engage in PA during a challenging time. We also found that individuals who worked on stress-management-related goals had an improved positive affect and well-being, compared to those in the control group. This is supported by the previous research indicating that stress evokes a negative affect [45] and successful stress management could improve an individual’s positive affect [46]. In addition to discussing stress-management-related issues, previous research also suggested that the peer support provided by the health coaches may play a role in improving participants’ positive affects [47]. Future path analysis and qualitative studies may provide additional evidence for this relationship. Another strength of the study was that it provided the participants with the antonym to self-select the wellness topics and behavioral goals, and the health coaches were in the supporting role in the coaching relationship. This is different from other health coaching intervention studies that involved adults with health conditions (e.g., diabetes, cardiovascular diseases) in which the topics and behavioral goals were often pre-determined, and health outcomes (vs. behavioral outcomes) were often assessed [11]. Considering that our participants were healthy young adults, they usually did not have health conditions that required them to achieve specific behavioral outcomes. In this case, providing autonomy and peer support would invoke their internal motivation [14,48]. This approach may make the assessment of implementation fidelity more difficult, but it has gained empirical value by supporting participants with on-going, real-world problems. For example, while final exams were approaching towards the end of the study, more student participants chose to work on stress-management-related topics with their coaches. Although there is no direct evidence from the current study, it is possible that the improved stress management skills may benefit other health behavioral practices for the participants. In addition, more flexibility in coaching topics provides practical value for colleges and universities who may consider adopting peer coaching programs in future. Although not statistically significant, there was a trend towards the improvement of sleep quality among participants who worked on sleep-related goals, compared to the control group. Type II error due to the small sample size may play a role here. No intervention effect was detected among participants who worked on diet-related goals. This may be because, compared to other behavioral goals, diet-related goals were very diverse, from eating breakfast to drinking more water. This makes the intervention effect very challenging to measure without significant noise. We suggest future studies consider personalized diet measures based on the specific diet goals. Given that first-year students, students of color, students from low-income families, and first-generation students are often disproportionately affected by the pandemic and have a more difficult time transitioning to the academic and social demands of college [49], we purposely recruited students from those populations with success; over $23\%$ of the students were first-generation and $37\%$ were non-white, as compared to $20\%$ and $16\%$ in the college student body as a whole. There were no significant differences in intervention uptake or outcomes by demographic group; however, additional adaptations could be made to the health coaching approach to improve engagement among specific groups and potentially decrease health disparities disproportionately experienced by historically marginalized and under-represented student groups. We suggest future studies continue to explore the potential moderating or mediating effect of demographic factors on health coaching engagement and effectiveness. There were several limitations of this study. First, the study began in January and ended in May 2022, during which seasonality became a challenge for the participants. That is, the end-of-semester stress may cause college students to have increased stress, worse nutrition, less PA, and poor sleep quality [44,49]. Second, although having student participants choose the health topics that they were interested in provided them with autonomy, working on several topics may have lowered the intervention dosage they received for each topic. For example, participants who chose to work on PA, sleep, and stress had less exposure to each of those three topics than those who only worked on PA or sleep throughout the intervention period. This type of study design also imposes challenges on statistical analyses as it is impossible to know the exact topics that participants would work on. Furthermore, when clients chose the coaching topic of their interest, it may have posed selection bias as they were more motivated to work on this topic than participants in the control groups. Future studies may consider screening participants’ well-being interest and then place them into different coaching groups. In addition, we did not perform a priori power analysis due to limited research on this topic and the pragmatic constraint of the limited trained coaches we had with whom to conduct the study. However, our power, calculated based on the number of participants enrolled, was $50\%$ to detect a 0.5 effect size. Although this is much lower than the ideal power of $80\%$, we still generated some significant results, indicating a trend towards effectiveness that could be fully ascertained with a full-effectiveness trial. Finally, the current study did not follow up on the participants’ post-intervention. The fidelity of health coaching intervention should be further explored in future studies. The current study was conducted in a college campus in which intervention was delivered by students majoring in health science who went through training via two health coaching courses. Although they had limited coaching experiences compared to the professional health coaches, they provided unique peer support that other professional coaches would not be able to provide. This study also set an example of an innovative health promotion program in the higher education setting. Additionally, from the education experiential learning perspective, the peer health coaching programs also served as an education and practice opportunity for students with related majors. The experience and skills they developed through this intervention will further prepare them for their future career, especially those who will work in client-facing settings. ## 5. Conclusions The post-COVID-19 era poses new challenges for colleges to think creatively to assist young adults with their overall well-being [50,51]. The current pilot study was set in a real-world setting where college students chose their health coaching topics and received support from their peers. 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--- title: Sweetener System Intervention Shifted Neutrophils from Homeostasis to Priming authors: - Thomas Skurk - Tamara Krämer - Patrick Marcinek - Agne Malki - Roman Lang - Andreas Dunkel - Tiffany Krautwurst - Thomas F. Hofmann - Dietmar Krautwurst journal: Nutrients year: 2023 pmcid: PMC10005247 doi: 10.3390/nu15051260 license: CC BY 4.0 --- # Sweetener System Intervention Shifted Neutrophils from Homeostasis to Priming ## Abstract Background: Non-nutritive sweeteners (NNS) are part of personalized nutrition strategies supporting healthy glycemic control. In contrast, the consumption of non-nutritive sweeteners has been related to person-specific and microbiome-dependent glycemic impairments. Reports on the effects of NNS on our highly individual cellular immune system are sparse. The recent identification of taste receptor expression in a variety of immune cells, however, suggested their immune-modulatory relevance. Methods: We studied the influence of a beverage-typical NNS system on the transcriptional profiling of sweetener-cognate taste receptors, selected cytokines and their receptors, and on Ca2+ signaling in isolated blood neutrophils. We determined plasma concentrations of saccharin, acesulfame-K, and cyclamate by HPLC-MS/MS, upon ingestion of a soft drink-typical sweetener surrogate. In an open-labeled, randomized intervention study, we determined pre- versus post-intervention transcript levels by RT-qPCR of sweetener-cognate taste receptors and immune factors. Results: Here we show that the consumption of a food-typical sweetener system modulated the gene expression of cognate taste receptors and induced the transcriptional regulation signatures of early homeostasis- and late receptor/signaling- and inflammation-related genes in blood neutrophils, shifting their transcriptional profile from homeostasis to priming. Notably, sweeteners at postprandial plasma concentrations facilitated fMLF (N-formyl-Met-Leu-Phe)-induced Ca2+ signaling. Conclusions: Our results support the notion of sweeteners priming neutrophils to higher alertness towards their adequate stimuli. ## 1. Introduction Due to the high processing of food and its omnipresence, today’s nutrition contains an increasing number of compounds. As an example, the obesity pandemic might, in part, be caused by a continuously increasing use of added sugars and sweeteners in daily nutrition [1,2]. In recent decades, the use of non-nutritive sweeteners (NNS) [3] has largely increased [4,5] as one strategy to prevent a chronic positive energy balance and excessive weight gain [6,7,8]. Such compounds, which determine the chemosensory properties of foods, are nowadays recognized as bioactives in a more general sense [9,10,11,12,13,14,15,16,17]. In particular, the bioavailability and physiological effects of non-metabolizable NNS have long been under debate [18,19,20,21,22,23,24,25,26]. Beyond their function as adequate, attractive stimuli of our canonical chemical sense of taste, sugars and sweeteners are sensed via their cognate receptors at many locations elsewhere in the body [22,27,28], for example, in the gastrointestinal system [29]. Indeed, a recent study demonstrated that the preference for sugar over sweeteners depended on their differential sensing by duodenal neuropod cells [30]. Moreover, another recent study reported a significantly elevated glycemic response in healthy individuals during the short-term consumption of saccharin and sucralose through interaction with the gut microbiome and its metabolic response [31]. These results suggested highly personalized responses of the human microbiome to the consumption of certain NNS. Non-nutritive sweeteners via their cognate taste receptors were recently shown also to modulate our cellular immune system [13,14], which is highly variable among individuals [32]. The experimental evidence of such modulation of immune cell functions is commonly obtained from testing the effects of substrates, e.g., sweeteners, on cytokine expression or secretion in isolated primary blood leukocytes. For example, in whole blood cultures, artificial sweeteners were reported to elicit a suppressive effect on IL6 secretion [15]. In particular, saccharin derivatives have recently been demonstrated to inhibit the synthesis of TNF-α and IL8 in THP-1 monocytes, suggesting an anti-inflammatory activity [33]. While in a previous study, food-typical concentrations of saccharin did not induce oxidative or inflammatory stress in circulating mononuclear cells [34], its consumption, however, has been associated recently with impaired glucose tolerance and compromised GLP-1 secretion in rodent models and humans [35,36,37]. Despite uncertainty on the health effects of artificial sweeteners, the development of inflammation-modulating, saccharin-based antagonists of interferon signaling is remarkable [11]. An early review from 1980 concluded that aspartame had no impact on inflammation, at least in animal models [38]. A recent review, however, pointed out that the consumption of aspartame may, indeed, ultimately lead to systemic inflammation [39,40]. Cyclamate is banned in North America [41,42,43], but is widely used in other Western countries, typically in combination with other NNS, such as saccharin, for example in diet soft drinks [44]. Non-nutritive sweeteners may elicit a bitter off-taste as a function of concentration, by the activation of selective bitter taste receptors [45,46,47,48]. The mechanism by which NNS blends become perceptually superior to single compounds has been suggested by a recent study, at least for saccharin and cyclamate, as the mutual suppression of their respective bitter receptors [49]. Beyond the chemical sense of taste, however, there is a need for more information on the bioavailability and common plasma concentrations of food-borne flavor compounds [50], such as sweeteners in particular, and on their molecular effects on potential targets such as peripheral blood immune cells. Recently, we have identified mRNA expression for some eighty chemosensory G protein-coupled receptors (GPCRs) as genuine biomarkers for subpopulations of circulating leukocytes, including the sweet taste receptor dimer and all bitter taste receptors [9,14,51]. Moreover, we demonstrated the functional expression of saccharin-specific sweet and bitter taste receptors in neutrophils [14]. Here, we describe an open-labeled, placebo-controlled, randomized intervention study determining the plasma concentration and biokinetics of the beverage-typical sweeteners saccharin, cyclamate, and acesulfame-K, dissolved in water and analyzed by HPLC-MS/MS. By means of RT-qPCR, we interrogated sweeteners’ effects on the transcriptional regulation of sweetener-specific chemosensory GPCRs and of certain cytokines and their receptors in isolated neutrophils from buffy coat samples in vitro, and from the intervention study-derived blood samples ex vivo. We further investigated (i) by Ca2+-fluorimetric experiments, whether a diet lemonade-typical NNS mixture can induce intracellular Ca2+ signaling, and (ii) by laser-guided, fluorescence-activated cell analysis, whether the same NNS mixture can modulate fMLF-induced Ca2+ signaling in isolated human neutrophils. ## 2.1. Reagents Calcium buffer was composed of 140 mM NaCl (Carl Roth, Karlsruhe, Germany), 20 mM HEPES (VWR, Radnor, PA, USA), 5 mM KCl (Sigma-Aldrich, St. Louis, MO, USA), 1.8 mM CaCl2 (Sigma-Aldrich, St. Louis, MO, USA), and 0.5 mM D-glucose (VWR, Radnor, PA, USA), adjusted pH 7.4. The EGTA buffer was prepared according to the calcium buffer, but contained 0.5 mM EGTA (Sigma-Aldrich, St. Louis, MO, USA) instead of CaCl2. Probenecid was purchased from Sigma-Aldrich (St. Louis, MO, USA). Sodium hydroxide (NaOH) and dimethyl sulfoxide (DMSO) were both purchased from VWR (Radnor, PA, USA). Pluronic® F-127 was acquired from AAT Bioquest Inc. (Sunnyvale, CA, USA) and dissolved in H2O ($10\%$ solution). Fura-8 AM was obtained from PromoCell GmbH (Heidelberg, Germany). Thapsigargin was acquired from Cell Signaling Technology (Cambridge, UK). To prepare an aqueous solution of physiological sweetener mix, sodium cyclamate (Thermo Fisher Scientific, Waltham, MA, USA), saccharin (VWR, Radnor, PA, USA), and acesulfame-K (Cayman Chemical, Ann Arbor, MI, USA) were purchased. Fluo-4 AM was acquired from Bio-Techne (Minneapolis, MN, USA). N-Formyl-Methionyl-Leucyl-Phenylalanine (fMLF) was obtained from Tokyo Chemical Industry (Tokyo, Japan). Lactisole was purchased from Cayman Chemical (Ann Arbor, MI, USA). DPBS (Genaxxon bioscience GmbH, Ulm, Germany) was used for cell purification. Unless otherwise stated, reagents were initially dissolved in DMSO. ## 2.2. Trial Design and Subjects The study was designed as a pilot in a cross-over design to address whether the ingestion of typical amounts of sweeteners may modulate the transcription of chemosensory GPCRs or cytokines and their receptors in isolated neutrophils. The primary and secondary endpoints of our intervention study were the quantification of plasma levels of sweeteners, the quantification of transcript levels of chemosensory GPCRs or cytokines and their receptors in isolated neutrophils, and the statistical analysis of the intervention effects as compared to the water intervention control. These and other analyses not prespecified are considered exploratory. The primary and secondary endpoints of our intervention study did not change during the research or post hoc analyses. The human intervention study was conducted in accordance with the Declaration of Helsinki, and approved by the ethical commission of the faculty of medicine at the Technical University of Munich, Germany (#$\frac{5798}{13}$, approved on 27 June 2013), and was registered in the WHO partner register DRKS (#DRKS00005083). The study was performed at the ZIEL Institute for Food and Health, Human Study Center of the Else Kröner-Fresenius-Center of Nutritional Medicine (Technical University of Munich, Germany) during July and August 2013. Five female (age range 27–32 yrs, bw 60.3 ± 6.5 kg) and five male (age range 30–47 yrs, bw 80.6 ± 3.5 kg) healthy, non-smoking volunteers were asked to participate in a study consuming either mineral water alone or a combination of saccharin (75.6 mg/L, sodium salt, 0.37 mM), acesulfame-K (52.7 mg/L, potassium salt, 0.27 mM), and cyclamate (227.7 mg/L, sodium salt, 1.13 mM), frequently used in commercially available lemonades, dissolved in mineral water on 2 different occasions. The study was performed after a 7 day run-in period, strictly avoiding non-nutritional sweetener-containing foods (chewing gum, sugar-sweetened beverages, etc.) ( Figure 1). To reduce bias, volunteers were randomly allocated by SNOSE (sequentially numbered, opaque single envelopes) to receive either mineral water or the surrogate. Envelopes were prepared by a co-worker from the study center not directly involved in the study conductance. Due to the difference in the sweetness of the test drinks, the study could not be blinded. After overnight fasting, volunteers were invited to the study center, a venous catheter was inserted, and a baseline blood sample (0 h) was taken; thereafter, either mineral water (10.7 mL/kg bw) as a control, or the mixture of chemically defined sweeteners in mineral water (10.7 mL/kg bw) was given. The volume of the test beverage was calculated to standardize the intake to yield a saccharin concentration of 0.8 mg/kg bw, which is equivalent to commercially available products. Test beverages had to be ingested within 15 min. On test days, blood samples for the isolation of PMNs (see below) or analytics were taken after 0, 4, 8, and 24 h (Figure 1). For the isolation of PMNs, we used EDTA (1 mg/mL) tubes (9 mL), which were immediately transferred to the wet lab and subjected to Ficoll density gradient centrifugation (see below). Those samples for analytics were immediately centrifuged at 3000× g for 10 min at 4 °C, were subsequently aliquoted (500 μL portions), and stored at −80 °C. The tested drinks caused no side effects; all ten participants completed the intervention study and were used for analysis. ## 2.3. Human Blood Polymorphonuclear Neutrophil (PMN) Purification Human blood polymorphonuclear neutrophils were isolated from buffy coat samples (Bavarian Red Cross Blood Bank, or Sonnen-Gesundheitszentrum, Dr. Gerd Becker, Munich, Germany), or immediately isolated from the samples coming out of the intervention study (9 mL EDTA tubes), and purified using Ficoll density gradient centrifugation to at least $90\%$ purity as confirmed by flow cytometry (MACSQuant Analyzer 10, Miltenyi Biotec, Bergisch Gladbach, Germany), as described previously [9]. For in vitro stimulation experiments with sweeteners, cells were resuspended to 1 × 106/mL in RPMI 1640, and then were used for RNA isolation. For Ca2+ fluorimetric experiments, 30 mL of buffy coat samples were mixed with 5 mL of pre-warmed DPBS. Human blood PMNs were isolated using MACSxpress® Whole Blood Neutrophil Isolation Kit, followed by MACSxpress™ Erythrocyte Depletion Kit (Miltenyi Biotec, Bergisch Gladbach, Germany), according to the manufacturers’ recommendations. ## 2.4. RNA Isolation and cDNA Synthesis Total RNA from blood neutrophils was isolated and purified using the RNeasy Mini Kit with an on-column DNAse-I digest (Qiagen, Hilden, Germany), to avoid genomic DNA contamination. The quality of RNA was analyzed with an Experion™ automated electrophoresis system (Bio-Rad, Hercules, CA, USA), using standard sense RNA analysis LabChips, according to manufacturer’s instructions. The quality of tested RNA samples was determined by the RNA quality index (RQI), which ranged between 7.5 and 10 on a scale from 0–10. cDNA was synthesized from 300 ng total RNA using iScript cDNA Synthesis Kit (Bio-Rad, Hercules, CA, USA), following the manufacturer’s recommendations. ## 2.5. RT-qPCR The RNA expression levels of investigated genes in PMNS were quantitatively compared by Reverse Transcription-quantitative Polymerase Chain Reaction (RT-qPCR) as relative expression, normalized to an average of 3 selected and stably expressed reference genes (GAPDH, RPL13A, and ACTB), according to the MIQE guidelines [52]. Further normalization was performed to the respective reference gene-controlled lowest RNA expression level, or untreated control, which was set as 1. All RT-qPCR reactions on human PMNS cDNA were performed using GoTaq qPCR master mix (Promega, Madison, WI, USA) in an Mx3000P cycler (Stratagene, Santa Clara, CA, USA): 95 °C (3 min), 40× (95 °C (15 s), 58 °C (30 s), 72 °C (30 s)). The cycle of quantification (Cq) data was evaluated using MxPro software (Stratagene, Santa Clara, CA, USA), and Cq values ≥ 38 were considered as negative. Relative quantification (RQ) of mRNA expression levels of 84 neutrophil-related cytokines and their receptors, as well as other immune cell activation and transcription factor regulation markers (Table S1), were investigated by RT-qPCR using a customized RT2 Profiler PCR array (Sabiosciences, Qiagen, Valencia, CA, USA), as described previously [9]. All 48 cognate taste receptor genes, as well as immune-related and neutrophil function-associated genes investigated during sweetener mix intervention, are listed in Table S2. ## 2.6. Quantification of Artificial Sweeteners in Human Plasma Standard solutions: Stock solutions of the analytes (roughly 10 mM) were individually prepared by dissolving the exactly weighed solids of acesulfame-K, sodium saccharin and sodium cyclamate in methanol. The internal standard p-toluolsulfonic acid hydrate was prepared in acetonitrile at a concentration of ~10 mM, and the exact concentration was determined by qNMR versus a caffeine standard [53]. The internal standard was diluted with acetonitrile (1 + 9999; v/v) to obtain an internal standard working solution of 1 µM concentration. Matrix-matched calibration: For matrix-matched calibration, we prepared spiked plasma samples, which were processed and analyzed in triplicates to establish calibration curves, as described in detail in the Supplemental Material (Figure S1). Quantitative analysis: An aliquot of the plasma sample (100 µL) was mixed with the internal standard working solution (1 mL) in an Eppendorf cup. The mixture was vortexed and centrifuged (12,500 rpm, 4 °C, and 15 min). The clear supernatant was decanted into a new reaction tube and was evaporated in a stream of nitrogen. The residue was taken up in a mixture of water and acetonitrile (95 + 5, v/v, 100 µL). Aliquots (1 µL) were injected into the HPLC-MS/MS system. Chromatographic separation: The samples were injected onto a Luna phenylhexyl column (150 × 2 mm, 3 µ, Phenomenex Darmstadt, Germany) protected by a guard column of the same material (2 × 2 mm, 3 µ, Phenomenex, Darmstadt, Germany). Eluents were $1\%$ formic acid in acetonitrile (eluent A) and $1\%$ formic acid in water (eluent B). The solvent was delivered at a flow of 300 µL/min. After 2 min of isocratic elution (A/B $\frac{5}{95}$), the composition was changed to A/B $\frac{95}{5}$ within 3 min following a non-linear gradient. After 1 min of isocratic elution, the starting conditions were re-established within 0.2 min and kept constant (3.8 min) for equilibration. Instrumentation: MS/MS data were acquired on a 3200 triple quadrupole mass spectrometer (ABSciex, Darmstadt, Germany). Potentials applied for the introduction of the compounds into the ion source (Q1 mass, declustering potential) and the fragmentation-related parameters (cell entrance potential, collision energy, cell exit potential) were optimized by using the auto-tune function of the software (Analyst 1.5.1, ABSciex, Darmstadt, Germany). The Q1/Q3 pairs are given in Table S3. Details are given in the Supplemental Material. In-house validation was performed by spiking analyte-free human plasma with known amounts of the analytes and subsequent analysis (Table S4). Accuracy ranged between 91.5 and $116.8\%$ for all analytes, and precision from 0.5 to $9.5\%$ RSD. The determined post-prandial concentrations of sweeteners in human plasma at all intervention times are given in Table 1. ## 2.7. Gene Ontology (GO) Term-Based Network and Cluster Analysis Sets of sweetener intervention-regulated VIP-gene transcripts at the respective post-intervention times were analyzed with respect to functional networks and their underlying GO terms, using the network analyzing software apps Bingo (v3.0.3) and ClueGo (v2.5.5) [54] in Cytoscape (v3.7.1) [55]. The BiNGO analysis was performed using the hypergeometric test and the Benjamini and Hochberg False Discovery Rate (FDR) correction with a chosen significance level of 0.00001. The main ontology subcategory for which the gene sets were tested was “GO_Biological_Process”. The node size reflects the number of genes annotated to a node, multiplied by the normalized sum of the VIP-values of the genes annotated, with the lowest sum set to 1. The node coloring represents the corrected p-value. The white nodes, although not overrepresented, are the parental nodes of overrepresented categories down the path, whereas the yellow to orange nodes display the growing overrepresented nodes [56]. Additionally, for each VIP-gene list at the respective post-intervention time, a ClueGO cluster analysis was performed using the settings “GO_BiologicalProcess” and “GO_ImmuneSystemProcess”, referring to the database UniProt [57]. ## 2.8. Spectrofluorimetric Ca2+ Influx Kinetics in Isolated PMNs In Vitro Four Mio PMNs/mL were centrifuged at 300× g for 10 min. The cell pellet was resuspended in 1 mL calcium buffer, supplemented with final concentrations of 1 mM probenecid and $0.04\%$ Pluronic® F-127. Probenecid was used to block organic anion transport, thereby reducing the leakage of Ca2+ dyes Fura-8 AM or Fluo-4 AM from neutrophils [58,59], while Pluronic® F-127 was used to facilitate the solubilization of water-insoluble dyes and to help disperse acetoxymethyl (AM) esters. A total of 4 µM of Fura-8 AM was added, and the cell suspension was incubated for 45 min at 37 °C and $5\%$ CO2. The cell suspension was washed twice with calcium buffer or EGTA buffer, respectively. After a final centrifugation step at 300× g for 10 min, the cell pellet was resuspended in 2 mL calcium buffer or EGTA buffer, both containing 1 mM probenecid. Fluorescence was measured by a SAFAS Flx-Xenius Spectrofluorometer (SAFAS-Société Anonyme de Fabrication d’Appareillages Scientifiques, Monaco), using 340 nm and 410 nm as dual excitation wavelength and 520 nm as single emission wavelength. Settings were adjusted as follows: integration time, 1 s; bandwidth at excitation and emission, 10 nm; complete filtering; cadence, 50 s. The PMT voltage was set manually to achieve around $50\%$ emissivity for both excitation wavelengths. For all fluorescence measurements, samples contained less than $0.1\%$ DMSO to ensure cell viability. After 2 min and 10 min, different reagents were added, including 1 µM thapsigargin and 30× of physiological sweetener mix (30 µM sodium cyclamate, 21 µM saccharin, 9 µM acesulfame-K). The ratio of F520340/F520410 was calculated and normalized to the initial ratio at 0 s by using Excel (Microsoft Office Professional 2016, Microsoft Corporation, Redmond, WA, USA). Plots were generated and statistics were performed by using SigmaPlot 14.0 (Systat Software GmbH distributed from Inpixon GmbH, Düsseldorf, Germany). Significances were calculated by paired, two-tailed Student’s t-test. Data are shown as mean ± SD ($$n = 3$$). ## 2.9. Ca2+ Signaling Measured by Laser-Guided Flow Cytometry A total of 0.5 Mio PMNs/mL were centrifuged at 300× g for 10 min. The cell pellet was resuspended in 250 µL calcium buffer, supplemented with final concentrations of 1 mM probenecid, $0.04\%$ Pluronic® F-127, and 4 µM of Fluo-4 AM. The cell suspension was incubated for 45 min at 37 °C and $5\%$ CO2. The cell suspension was washed twice with calcium buffer. After a final centrifugation step at 300× g for 10 min, the cell pellet was resuspended in 500 µL MACSQuant® Running Buffer (Miltenyi Biotec, Bergisch Gladbach, Germany). Cells were pre-stimulated with 1× of physiological sweetener mix (1 µM sodium cyclamate, 0.7 µM saccharin, 0.3 µM acesulfame-K) for 10 min, followed by the addition of fMLF with concentrations ranging from 0.1 nM to 20 nM, with sweeteners being present throughout. For testing an allosteric inhibition of TAS1R2/TAS1R3, lactisole (500 µM) was added to the sweetener mix during pre-stimulation of the cells. Fluorescence was measured until 10,000 events were obtained by laser-guided flow cytometry using MACSQuant® Analyzer 16 (Miltenyi Biotec, Bergisch Gladbach, Germany). Wavelengths were 494 nm for excitation and 506 nm for emission. For flow cytometry, reagents were diluted in MACSQuant® Running Buffer, containing less than $0.1\%$ DMSO to ensure cell viability. Data were analyzed by using Excel (Microsoft Office Professional 2016, Microsoft Corporation, Redmond, WA, USA) and SigmaPlot 14.0 (Systat Software GmbH distributed by Inpixon GmbH, Düsseldorf, Germany). EC50 values and curves were derived from fitting the function f(x)=((min−max)(1+(xEC50)Hillslope))+max to the data by nonlinear regression (SigmaPlot 14.0, Systat Software). A total of $0.1\%$ DMSO was used as negative control and subtracted from each measurement, using a $5\%$ gate as false positive signal. Significances were calculated by a two-tailed Student’s t-test. ## 2.10. Statistical Analysis Normality Testing (Shapiro–Wilk), paired Student’s t-test, or Wilcoxon Signed Rank Test for the in vitro data was performed with SigmaPlot 14.0 (Systat Software GmbH distributed from Inpixon GmbH, Düsseldorf, Germany). RT2 Profiler PCR array (Sabiosciences, Qiagen, Valencia, CA, USA) data were analyzed according to the manufacturer’s specifications [60]. Statistical comparisons for the primary outcome measures of the human intervention study were between the sweetener-supplemented test drink and water alone. Data from the intervention study were analyzed with R (version 3.5.2) using packages tidyverse [61], ropls [62], and cowplot [63]. Statistical evaluation of group differences was achieved employing multivariate analysis by orthogonal partial least square discrimination analysis (OPLS-DA) [64]. ## 3.1. Saccharin Upregulated Sweet Taste Receptor Gene Expression in Isolated Neutrophils In Vitro In peripheral blood monocytes (PBMCs), the regulation of GPCR gene expression is well described [65]. In these cells, for instance, transcript levels of TAS1R3 have been associated with children’s sugary and fatty food consumption [66]. Moreover, a study in human neutrophils of healthy individuals identified a TAS1R locus with significant, inter-individual, epigenetic variations [67,68]. Taste receptors show broad substrate specificity, with saccharin being a typical agonist of the heterodimer of the sweet taste receptor heterodimers TAS1R2 and TAS1R3 in micromolar concentrations [69,70,71,72], as well as for the bitter-taste receptors TAS2R31 and TAS2R43 [46]. Moreover, saccharin activated migration of primary neutrophils in a concentration-dependent manner in chemotactic transmigration assays with an EC50 of ca. 10 µM [14]. We, therefore, first tested in vitro whether sweet taste receptor subunits’ transcript levels are affected in isolated human neutrophils when challenging them with saccharin in vitro. Incubation for 24 h with saccharin at a concentration of 100 µM resulted in a 2-3-fold, significant upregulation of mRNA for both sweet taste receptor subunits TAS1R2 and TAS1R3 (Figure 2A). In contrast, RNA expression of a non-chemosensory, immune-relevant GPCR, FPR1, which responds to nanomolar concentrations of the chemotactic peptide N-Formyl-Methionyl-Leucyl-Phenylalanine (fMLF) [73], was not affected by saccharin (Figure 2A). ## 3.2. Saccharin Upregulated Gene Expression of Neutrophil Chemokines and Their Receptors in Isolated Neutrophils In Vitro As a proof of principle, we then investigated whether challenging neutrophils with 100 µM saccharin for 24 h altered the transcription levels of 84 genes (Table S1) coding for neutrophil-related cytokines and their receptors, as well as for other markers involved in immune cell activation and transcription factor regulation (Figure 2B,C). We observed significant upregulation of 14 transcripts (Figure 2B). Among those, three chemokines, CCL26 (342.5 ± 56.9), CCL2 (298.1 ± 74.6), CXCL1 (88.0 ± 33.6), which are chemoattractants for a variety of leukocytes, including neutrophils, as well as receptor CXCR1 (168.5 ± 44.8), showed the highest fold-change in transcript levels (Figure 2C). Notably, the gene transcription of two neutrophil chemokine receptor/ligand pairs, CXCR1/IL8 (CXCL8) and CCR4/CCL2, was significantly upregulated (Figure 2C). ## 3.3. Bio-Appearance of Sweeteners in Healthy Volunteers Determining bio-appearance and typical plasma levels of bioactive food compounds is essential for assessing their bio-activity. We, therefore, tested a surrogate beverage, which consisted of diet lemonade-typical concentrations of acesulfame-K (0.27 mM), Na cyclamate (1.13 mM), and saccharin (0.37 mM) in water (Supplemental Material), in a cohort of 10 healthy, adult volunteers, and measured plasma levels of sweeteners at 0 h, 4 h, 8 h, and 24 h (Figure 1 and Figure 3, Table 1). While many non-metabolizable NNS reach peak plasma concentrations already after 0.5 h [23], we chose longer time intervals, since we were interested in a transcriptional regulation, which typically occurs delayed after dietary intervention. In the present study, we observed the highest levels of all compounds 4 h after ingestion, which declined at 8 h until 24 h, where they nearly reached base-line levels again (Figure 3, Table 1 and Table S5). ## 3.4. Plasma-Typical Sweetener Concentrations Increased Transcript Levels of Their Cognate Taste Receptors in Isolated Neutrophils In Vitro Beyond activating the sweet taste receptor heterodimer TAS1R2/TAS1R3, sweeteners are known to also trigger certain bitter taste receptors [46,47,71,74]. Saccharin, cyclamate, and acesulfame-K are sweeteners most frequently used in blends with a reduced bitter off-taste, presumably due to the mutual suppression of their respective bitter taste receptor activation [49]. We, therefore, investigated in vitro whether plasma-typical single sweetener concentrations (see Table 1, 4 h post-intervention) may regulate specifically those mRNA levels of their cognate sweet and bitter taste receptors in isolated neutrophils in vitro. After a 24 h incubation of isolated neutrophils with sweeteners, these cells showed significantly increased transcript levels of about 3- to 8-fold of at least one of the sweet taste receptor subunits (Figure 4, Table S6). Notably, all sweeteners upregulated transcript levels exclusively of their respective bitter taste receptors but not of non-cognate receptors. For example, saccharin and cyclamate significantly upregulated transcript levels 4- to 8-fold of TAS2R31, TAS2R43 and TAS2R1, TAS2R38, respectively, but not vice versa (Figure 4). ## 3.5. Beverage-Typical Sweetener Mix Intervention Regulated Transcript Levels of Cognate Taste Receptors in Isolated Neutrophils Ex Vivo The post-intervention determined transcript levels of sweetener-cognate taste receptors in neutrophils ex vivo (Figure 5) basically followed the post-intervention plasma level kinetics (Figure 3). However, a significant regulation could only be determined for transcripts of the sweet taste receptor subunits, TAS1R2 and TAS1R3, at 8 h post-intervention (Figure 5). Notably, this increase in transcript levels was fully reversible 24 h post-intervention. ## 3.6. OPLS-DA Score Plots Reveal Reversible Differences between Water and Sweetener Intervention Groups over Time An objection of RT-qPCR-derived transcript levels to a multivariate analysis by OPLS-DA showed a clear separation of the intervention effects already 4 h after sweetener ingestion. However, this group difference became most prominent after 8 h (Figure 6 and Figure S2). Strikingly, although weaker, the intervention effect with the sweetener mixture was still detectable after 24 h (Figure 6, Figures S2 and S3), despite standardized nutrition throughout the study course. ## 3.7. VIP-Plots of Transcripts Indicate Post-Intervention-Specific and Functionally Diverse Gene Sets as Most Relevant Discriminants between Intervention Times The VIP (variable importance in projection)-plots of transcripts for taste receptors and immune factors (Figure 6B) illustrate the individual contribution of each analyte to the separation in the OPLS-DA analysis (Figure 6A) at the respective times after sweetener intake, and unambiguously demonstrate their varying significance for the OPLS-DA score plot. Overall, it appeared that early on (4 h), transcript levels of mainly chemokines have the highest discriminating power, whereas at 24 h post-intervention the transcript levels of mainly taste and chemokine receptors, as well as some pro-inflammatory and neutrophil priming-related chemokines and receptors, such as TNF-α, CCL22, and TLR4 [75,76,77], have the strongest influence on the separation of intervention times (Figure 6B and Figure S3). GPR17, a receptor evolutionarily related to chemokine receptors [78], also displayed the highest discriminating power at 8 h and 24 h post-intervention. One exception was transcript levels for taste receptor subunit TAS1R3, which showed the highest discriminating power at 4 h, but this influence was lost at 24 h (Figure 6B and Figure S3). At this time, however, most of the other taste receptor transcript levels still contributed substantially to the differences observed between the intervention arms (Figure 6B). Another exception was CCR1, which was an important variable for the differentiation between all sweetener intervention time points and 0 h control. CCL11 and CCL26 are chemokines with chemotactic activity for the recruitment of a variety of leukocytes [79,80,81]. In the present intervention study, both chemokines, as well as some of their receptors, CCR1, CCR2, and CX3CR1, appeared to be early determinants (4 h, 8 h) for the separation of the intervention times vs. 0 h, with CCR1 being an important variable even at 24 h post-intervention (Figure 6B and Figure S3). CCR7 and one of its ligands, CCL21, have diverse migratory functions in adaptive immunity [65]. CCL21 appeared to be an important variable for the separation of water control and sweetener intervention at all post-intervention times investigated. Interestingly, its receptor CCR7 became an important variable for the differentiation of 24 h post-treatment and 0 h control (Figure 6B and Figure S3). SPP1, a cytokine that stimulates cytokine production and leukocyte recruitment to inflammatory sites [82], as well as TLR4, which mediates the production of inflammatory cytokines via NF-κB signaling in the innate immune system [83], both were important variables for the differentiation of 8 h or 24 h post-intervention times and 0 h control (Figure 6B and Figure S3). Also, TNF-α, which is a key cytokine in the inflammatory responses of leukocytes [75], became important for the differentiation only between 24 h post-treatment and 0 h control (Figure 6B and Figure S3). ## 3.8. Network and Cluster Analyses Reveal That the Most Relevant Discriminants between Intervention Times Associate with Specific Functional Gene Ontology Networks To investigate whether the intervention time-specific VIP-gene lists (see Figure 6, Table S7) and their associated and overrepresented GO terms underly different functional clusters, we performed a functional network and cluster analysis. The functional network analysis (BiNGO), indeed, revealed striking differences across post-intervention times (Figure 7A–C, left panels). Exclusively at 4 h post-intervention, a homeostasis-related network of significantly overrepresented GO terms became apparent (Figure 7A and Figure S4, left panel), which is absent at later post-intervention times (Figure 7B,C, left panels). Moreover, a cluster analysis (ClueGO) at 4 h post-intervention revealed chemokine activity as the category comprising the majority of significantly overrepresented GO terms (Figure 7A, right panel). At 8 h post-intervention, the homeostasis network is replaced by the category signaling (Figure 7B, left panel). In line with this, the cluster analysis revealed that about $80\%$ of significantly overrepresented GO terms associate with the chemokine-mediated signaling pathway (Figure 7B, right panel). At 24 h post-intervention, the GO term categories response to chemical/stimulus and inflammatory response became more apparent, and chemotaxis- and locomotory behavior-related GO term categories showed lower significance levels as compared to previous intervention times (Figure 7C, left panel). Indeed, the number of genes underlying the GO term category inflammatory response increased with post-intervention time (Table S8), as reflected by the VIP value-corrected node size (Figure 7C, left panel, Table S9). Cluster analysis at 24 h post-intervention revealed that the percentage of, for instance, significantly overrepresented GO terms under the chemokine-mediated signaling pathway decreased from $79\%$ at 8 h to $67\%$, whereas $25\%$ of GO terms newly associated with sensory perception of taste and $8\%$ newly associated with regulation of inflammatory response to antigenic stimulus (Figure 7C, right panel, Table S10). A GO term functional network analysis of all 48 genes investigated comprised all VIP gene-related sub-networks predominant at the different sweetener intervention times, albeit at different significance levels (Figure S5). For example, the GO term category inflammatory response was more apparent in the functional network analysis of all 48 genes investigated (Table S8) at orders of magnitude higher significance levels (Figure S5, color-coded scale), as compared to each sweetener intervention time (Figure 7, left panels). The percentage of significantly overrepresented GO terms of the VIP gene sets at each respective intervention time (Figure 7, right panels), however, was largely increased, compared to a GO term cluster analysis with all 48 genes analyzed (Figure S6, Table S11). ## 3.9. Sweetener Mix-Induced Ca2+ Influx Increased Neutrophils’ Sensitivity for fMLF Intracellular Ca2+ signaling is involved in most neutrophils’ cellular immune responses [84,85] and in neutrophil priming [86,87]. Intracellular Ca2+ in neutrophils typically is modulated via GPCRs and Ca2+-store- or ligand-operated Ca2+-channels, resulting in Ca2+-release from intracellular stores and/or Ca2+-influx over the plasma membrane [84,85,88,89,90,91,92]. It has long been known that many priming agents induce transient Ca2+ signals and enhance an activation-induced Ca2+-influx into neutrophils [86,87]. Since our intervention study suggested that NNS may shift the transcriptional profile of neutrophils from ‘homeostasis’ to ‘priming’, we thus set out to investigate whether NNS interfere with the Ca2+ homeostasis in neutrophils isolated from commercially available buffy coat samples. The 30× sweetener mix, including saccharin, acesulfame-K, and cyclamate, neither induced a detectable, transient Ca2+ release from intracellular stores, nor a Ca2+ influx signal in spectrofluorimetric experiments with Fura-8-loaded neutrophils (Figure 8A). However, under ‘activation’ conditions where Ca2+ stores had been emptied (Thapsigargin), the 30× sweetener mix induced a long-lasting, small, albeit significant Ca2+ influx. This Ca2+ influx signal was abolished under conditions omitting extracellular Ca2+ (EGTA) (Figure 8A,B). We next investigated whether NNS at post-prandial plasma concentrations are capable of modulating Ca2+ signaling in neutrophils, which has been activated by the application of the damage- or pathogen-associated formylpeptide fMLF (N-formyl-Met-Leu-Phe), a prototypical high-affinity ligand for the formyl peptide receptor FPR1 [93,94,95]. The leukocyte attractant fMLF regulates innate immunity and host defense via several FPR-mediated signaling pathways, including Ca2+ signaling [88,89,96,97]. Thus, we tested whether a diet lemonade-typical NNS mix, comprising saccharin, acesulfame-K, and cyclamate at concentrations identified post-prandially in human plasma (1× sweetener mix, see Figure 3, Table 1), can modulate fMLF-induced Ca2+ signaling in isolated human neutrophils. Surprisingly, a 10 min pretreatment of isolated neutrophils with the 1× sweetener mix shifted the fMLF concentration–response curve to a significantly lower EC50. This effect was only partially reverted by lactisole, an inhibitor of the sweet taste receptor [98] (Figure 8C,D), suggesting a potential involvement of additional receptors in neutrophils as targets of NNS, e.g., bitter taste receptors [14,46,49], or the TRPV1 receptor [99,100]. ## 4. Discussion Artificial high-intensity sweeteners are widely consumed due to their reduced energy content and low glycemic effect, which is relevant especially for bw control and under hyperglycemic conditions. While their use in food has been controversially discussed for decades concerning their potentially negative impact on metabolism [18,20,21,24,31,36], they are however considered safe for the general population under certain conditions [39,41,42,43]. Beyond our canonical chemical sense of taste, the expression of the sweet-taste receptor on, e.g., intestinal cells [101,102] or blood immune cells [14] suggested their modulation by the typical post-prandial plasma concentration of artificial sweeteners. Moreover, sweeteners have been demonstrated to selectively target their cognate bitter taste receptors [46,47,49,74] with extra-oral functions in, e.g., the respiratory tract [13,103], gastrointestinal system [104], or the immune system [14,51]. Previously, we have shown in a siRNA-controlled study that saccharin was capable of triggering sweet taste receptor-dependent chemotactic migration in isolated neutrophils in vitro [14]. Beyond sweet and bitter taste receptors, sweeteners may activate further biological targets. For instance, it has been suggested that saccharin activates chemesthesis-related TRPV1 channels [100], albeit at rather high (mM) concentrations. Neutrophils do express TRP channels, including the TRPV1 channel [99,105,106,107,108]; however, in our intervention study, we did not investigate TRP transcript levels. In the present study, we show that plasma-typical concentrations of sweeteners upregulated transcript levels of their cognate taste receptors in vitro and ex vivo. Whereas mainly cytokines emerged as significant early discriminants in the present sweetener intervention study, at 24 h post-intervention, mainly taste and immune-related receptors appeared to have the highest discriminating power between pre-intervention and sweetener intervention. These observations were corroborated by our cluster analysis. The significant contributors that we identified to distinguish fasting state from sweetener intake ex vivo across all post-prandial time points overlapped with $60\%$ of the transcripts that we found to be significantly regulated by saccharin 24 h in vitro. Comparing just the 24 h sweetener mixture intervention in vivo with the 24 h saccharin challenge in vitro, three significantly regulated transcripts emerged under both conditions, among them GPR17 and CCR1. Chemokine GPCR-related receptor GPR17 has been suggested to regulate inflammatory immune responses [109]. Indeed, inflammation/stress-related significantly overrepresented GO terms became more prominent 24 h post-intervention, according to our BiNGO analysis. During the in vivo challenge with the sweetener mixture, CCR1 emerged as an important variable for the differentiation between 0 h control and sweetener intervention at all post-prandial time points, as did CXCR4, at least at 4 h and 24 h post-intervention. The activation status of neutrophils shows high flexibility from resting state over primed to fully activated [87]. In light of the context that (i) CCR1 and CXCR4 transcripts are regulated in neutrophils when exposed to inflammatory environments or pro-inflammatory, neutrophil priming-related cytokines [110,111,112], and that (ii) ubiquitin is an endogenous ligand of CXCR4 and an anti-inflammatory immune modulator [113], we might speculate that one late post-prandial effect of saccharin is to shift neutrophils to a primed state with inflammatory alertness. This is in line with our observation that at 24 h post-intervention, inflammation-related immune modulators, such as TLR4 and TNF-α, were major discriminants. TNF-α, which is frequently used as a model priming agent, is a key cytokine in the inflammatory responses of leukocytes [114]. A hallmark of primed neutrophils is delayed apoptosis, which could be at least partially mediated via TLR4, a principal regulator of neutrophil survival [115]. Increased inflammatory alertness is corroborated by our network analysis, showing that the significantly overrepresented GO term ‘inflammatory response’ became most prominent at 24 h post-intervention. Chemokines and their GPCRs, such as CCL21 and CCR7, are not only important mediators of innate and adaptive immune responses in acute inflammation or infection, but also are continuously fine-tuning an immunological homeostasis, balancing immunity and tolerance [65,77,116,117,118]. In the present study, CCL21 was an important discriminant at all post-intervention time points, its receptor CCR7 at least at 24 h post-intervention. SPP1, which in the present study was a very important variable for the differentiation between 0 h control and 8 h or 24 h post-intervention, has been suggested to have homeostatic immune functions [119], but also has been shown to be involved in inflammation [82]. Since in our study significantly overrepresented, homeostasis-associated GO terms were absent at 8 h and 24 h post-intervention, but inflammation/stress-related GO terms gained prominence at 8 h and 24 h post-intervention, a function of SPP1 might therefore be associated with an inflammation-related neutrophil function. Care must be taken, though, to not interpret the results from our sweetener intervention study solely towards inflammation-related processes. Indeed, the GO term inflammatory response was overrepresented in the functional network analysis of all 48 genes investigated, and at much higher significance levels as compared to the network analysis of the VIP gene sets, which were relevant for separating experimental groups at any sweetener post-intervention time from 0 h control. This, however, may reflect the lack of eminent inflammation- and neutrophil activation-related genes, such as IL-8 (CXCL8) and its receptors, CXCR1 and CXCR2, in all VIP gene sets, suggesting that a full-blown inflammatory immune response would hardly be triggered by the consumption of food-typical sweetener concentrations. Notably, a recent study reported that an 8-week intervention with diet soda sweetened with sucralose and acesulfame K altered the inflammatory transcriptome of adipose tissue from overweight females, but, however, did not alter circulating inflammatory markers [120]. Importantly, though, inflammation-related cytokines, such as TNFα, may have modulatory functions on, e.g., taste receptor responses [121], suggesting an intimate relationship between a chemosensory and/or chemoreceptive internal detection of food ingredients and an immune function. Knowing that restricting our study to a selection of immune mediators might confer a potential risk of bias, however, our study clearly demonstrates a biological principle that specific food ingredients do interfere with the transcriptional profile in neutrophils. Interestingly, in our hands the 30× sweetener mix induced a detectable Ca2+ influx only into activation-simulated neutrophils with thapsigargin-depleted intracellular Ca2+ stores displaying elevated cytosolic Ca2+ levels. Vice versa, a fMLF-induced Ca2+ signaling was facilitated in the presence of 1× sweetener mix, with the fMLF concentration–response curve shifted to lower concentrations. This suggests crosstalk between NNS- and fMLF-activated receptors and their signaling pathways in neutrophils, with a synergistic effect, at least at the level of Ca2+ signaling. G protein-coupled receptor crosstalk, heterologous sensitization, and the facilitation of fMLF/FPR1-induced Ca2+ signaling are well-known principles in neutrophils [99,122,123]. Since an elevated Ca2+ level in neutrophils is a prerequisite for most of neutrophils’ cellular functions, thus, the simultaneous presence of NNS and pathogen- or damage-associated molecular patterns (PAMPs or DAMPs) may lead to the more sensitive and efficient cellular immune responses of neutrophils. A recent study corroborates our results: Mol et al. [ 2021] demonstrated that a full activation of neutrophils required the simultaneous presence of adequate stimuli for at least two different receptor systems [124]. The minor, non-significant effect of lactisole in the present study may point to the involvement of, e.g., lactisole-insensitive but sweetener-specific TAS2R bitter taste receptors [14,46,49], which may have participated additionally to the sweet taste receptor in mediating the significant shift of the fMLF concentration-Ca2+ response relation to lower concentrations after priming with sweetener mix. ## 5. Conclusions In summary, we show that post-prandial, plasma-typical concentrations of artificial sweeteners altered a resting state transcriptional profile of their cognate taste receptors, as well as factors typically orchestrating innate immunity in peripheral blood leukocytes towards a more priming-related status, involving factors of pro-inflammatory signaling. Their differential transcriptional kinetics suggest cytokines or taste and immune receptors as early or late discriminants, respectively, between the pre-intervention control and sweetener intervention groups. 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--- title: 25 Hydroxyvitamin D Serum Concentration and COVID-19 Severity and Outcome—A Retrospective Survey in a Romanian Hospital authors: - Adriana Topan - Mihaela Lupse - Mihai Calin - Cristian Jianu - Daniel-Corneliu Leucuta - Violeta Briciu journal: Nutrients year: 2023 pmcid: PMC10005256 doi: 10.3390/nu15051227 license: CC BY 4.0 --- # 25 Hydroxyvitamin D Serum Concentration and COVID-19 Severity and Outcome—A Retrospective Survey in a Romanian Hospital ## Abstract Interest in the immunomodulatory function of vitamin D has grown since the COVID-19 pandemic started. Our study investigated the possible association between vitamin D deficiency and COVID-19 severity, intensive care needs, and mortality in patients hospitalized with COVID-19. A prospective cohort study was performed on 2342 COVID-19 hospitalized patients between April 2020 and May 2022 in a Romanian tertiary hospital for infectious diseases. A multivariate generalized linear model for binary data was fit with dependent variables: severe/critical form of COVID-19, intensive care need, and fatal outcome as a function of vitamin D deficiency, controlling for age, comorbidities, and vaccination status. More than half of the patients ($50.9\%$) were classified with vitamin D deficiency based on a serum concentration of less than 20 ng/mL. There was a negative association between vitamin D and age. Vitamin D-deficient patients presented with more cardiovascular, neurological, and pulmonary diseases, as well as diabetes, and cancer. In multivariate logistic regression models, vitamin D-deficient patients had higher odds of severe/critical forms of COVID-19 [OR = 1.23 ($95\%$ CI 1.03–1.47), $$p \leq 0.023$$] and higher odds of death [OR = 1.49 ($95\%$ CI 1.06–2.08), $$p \leq 0.02$$]. Vitamin D deficiency was associated with disease severity and death outcome in hospitalized COVID-19 patients. ## 1. Introduction COVID-19 therapeutics is challenging due to the lack of ideal treatments and new SARS-CoV-2 variants [1,2]. The most recent WHO guidelines on COVID-19 therapeutics, based on a total of 5398 trials, and registered at the beginning of January 2023, show that there are still some uncertainties regarding the impact of treatment on patient outcomes [3]. Vaccinations significantly reduce hospitalizations and mortality in COVID-19 but their effectiveness may vary in time, depending on booster doses [4,5]. Associated risk factors for severe outcomes have been documented, such as old age and comorbidities (e.g., cardiovascular diseases, cancer, diabetes, obesity, and chronic kidney disease) [6,7,8]. Despite vaccination prophylaxis and treatment recommendations, global COVID-19 mortality was still $1.02\%$ at the beginning of February 2023 [9]. In view of these data, COVID-19 still requires more potent therapy. Since the beginning of the COVID-19 pandemic, there has been increased research on vitamin D’s potential to lower the risk of COVID-19 severity. Vitamin D is essential for bone health and has significant non-skeletal benefits [10]. Vitamin D is a steroid prohormone whose synthesis begins at the skin’s surface, where the 7 dehydrocholesterol precursor is converted into cholecalciferol by the sunlight’s ultraviolet B rays. The diet only provides a small amount of vitamin D. After the first hydrolyzation step in the liver, 25 hydroxyvitamin D (25(OH) D) or calcifediol is produced, which is the major circulating form of vitamin D utilized in clinical settings to determine the body’s vitamin D status. The second hydrolyzation process of vitamin D occurs at the kidney level, producing 1,25 dihydroxy vitamin D (1,25(OH)2 vitamin D) or calcitriol, the active metabolite, with its main functions in intestinal calcium, phosphorus absorption, bone mineralization, as well as in immune and tumoral cell differentiation [11,12]. In response to the European Commission’s request, the European Food Safety Authority’s dietary reference vitamin D value is 20 ng/mL. This represents an appropriate target value obtained from cutaneous synthesis and dietary intake and can be used as a marker of vitamin D status in both adult and pediatric populations [13]. Calcitriol is a steroid hormone that interacts with specific vitamin D receptors (VDRs). VDRs are found in a variety of extraskeletal tissues [14,15], including immune system cells, such as activating CD4, CD8 T cells, neutrophils, and antigen-presenting cells. Moreover, 1,25(OH)2 vitamin D induces an innate antiviral mechanism in response to viral infections, such as rhinovirus, respiratory syncytial, and influenza virus [16]. Experimental studies have shown that it induces upregulations in cathelicidin and beta-defensins, both of which have important antimicrobial roles [17,18]. Vitamin D intervenes in adaptative immunity by suppressing Th1 cytokines and, subsequently, the production of cytotoxic T cells, decreasing Th17, reducing inflammation, and promoting regulatory T cells. The main result is a decrease in the cytokine storm that leads to serious viral infections, such as COVID-19 [18], as uncontrolled inflammation is the main cause of COVID-19 severity [19,20]. In view of these data, the main objective of our study was to establish a possible association between vitamin D deficiency in patients hospitalized with COVID-19 and COVID-19 severity, intensive care needs, and in-hospital mortality. Our secondary objective was to characterize vitamin D-deficient patients regarding age and associated diseases. ## 2.1. Study Design and Setting We conducted a retrospective study at the Clinical Hospital of Infectious Diseases Cluj-Napoca, a tertiary infectious disease hospital in Romania. Beginning in March 2020, the hospital was transformed into a first-line hospital for COVID-19 patients. ## 2.2. Participants Inclusion criteria: diagnosis of COVID-19 (based on a positive SARS-CoV-2 rapid antigen test or a SARS-CoV-2 molecular test), hospitalization between 1 March 2020 and 31 May 2022, age ≥ 18 years old, with a serum concentration of 25(OH)D (assessed on admission day). Exclusion criteria: 25(OH)D not performed on admission day. ## 2.3. Vitamin D Analysis Measurement of serum 25(OH)D was performed by chemiluminescence immunoassay UNICEL Dxl 800, using a fully automatic analyzer Beckman Coulter. Interpretations of 25(OH)D concentrations (according to the reagent kit protocol) were as follows: deficient if <20 ng/mL, insufficient if between 20 and 30 ng/mL, and sufficient if ≥ with 30 ng/mL Similar levels are recommended by the American Endocrine Society [12]. ## 2.4. Variables The data registered were age, sex, and comorbidities (cardiovascular, pulmonary, rheumatological, neurological, renal, hepatic, diabetes mellitus, obesity, and cancer). Vaccination status was recorded as unvaccinated, incomplete primary vaccination (meaning one out of two doses if the primary series had two doses), complete primary vaccination, and the booster dose. The value of the serum concentration of 25(OH)D (upon admission) was registered. COVID-19 severity classification was as follows: asymptomatic, mild (without pulmonary involvement), medium (pulmonary involvement but with oxygen saturation > $93\%$ on room air), severe (severe: more than 30 breaths/min or oxygen saturation < $93\%$ at rest or PaO2/FIO2 < 300 mmHg, and critical: respiratory failure requiring mechanical ventilation, shock, and/or other organ failures that need intensive care), according to the WHO classifications [21]. The severity of COVID-19 was evaluated at discharge. Based on the diagnosis found in the electronic health records of patients and using the 10th revision of the International Statistical Classification of Diseases [22], infectious disease specialists classified the comorbidities into the previously mentioned groups. Intensive care unit (ICU) stays and in-hospital mortality were recorded. The hospital’s ethics committee approved the study. Informed consent was obtained from each patient upon admittance. ## 2.5. Statistical Analyses Quantitative data that were not normally distributed are shown as median and interquartile ranges. The Chi-squared or Fisher’s exact tests were used to compare categorical data between two independent groups (in cases of low expected frequencies). We compared non-normally distributed quantitative data between two separate groups using the Wilcoxon rank sum test; for multiple independent groups, we used the Kruskal–Wallis test. Using the cut-off of 20 ng/mL for 25(OH)D, patients were categorized into two groups: <20 ng/mL and >20 ng/mL. A second analysis was conducted on a three-group classification: <20 ng/mL (meaning deficiency), between 20–30 ng/mL (meaning insufficiency), and ≥30 ng/mL (meaning sufficiency). The multivariate logistic regression analysis (adjusted for age ≥ 65, diabetes, obesity, cancer, and cardiac, pulmonary, hepatic, rheumatologic, neurologic diseases, COVID-19 vaccination, and the number of doses) was conducted to evaluate the association between concentrations of 25(OH)D groups, COVID-19 severity, ICU need, and mortality. Furthermore, we verified the association between the 25(OH)D concentration as a continuous variable and the dependent variables adjusted for the same confounders within a multiple logistic regression model, with a smoothing spline for vitamin D concentration, fitted within a general additive model. p-values of less than 0.05 were regarded as statistically significant for all statistical analyses. The statistical studies were performed using R version 4.1.2 [23]. ## 3. Results The study consisted of 2342 patients who fulfilled all inclusion criteria and were hospitalized between 27 April 2020 and 31 May 2022. A total of 1194 ($50.9\%$) patients were classified with vitamin D deficiency based on having less than 20 ng/mL of 25(OH)D. ## 3.1. Patients Characteristics The demographics and clinical data (according to the 25(OH)D serum concentrations) of patients are presented in Table 1 and Table S1. More than half of the COVID-19 hospitalized patients were older than 65 years. A high percentage of patients had associated cardiovascular diseases. Obesity was present in almost one-third of the total included patients. A high percentage of our study group presented with severe/critical forms of COVID-19 and $7.64\%$ died during hospitalization. Complete vaccination was recorded in $14.1\%$ of our study group; $4.4\%$ of patients received a booster dose (Table 1). Vitamin D deficiency was more prevalent in older patients, with more cardiovascular, neurological, and pulmonary diseases, as well as diabetes and cancer. Severe/critical forms of COVID-19, need for intensive care, and death were more prevalent in patients with vitamin D deficiency ($p \leq 0.05$). There were no significant differences regarding associated obesity, hepatic, and rheumatological diseases. ## 3.2. 25(OH)D and Age We analyzed the distribution of 25(OH)D serum concentration according to age intervals; the results are presented in Figure 1. There was an inversely proportional relationship between age and 25(OH)D, i.e., the patient’s age increases as the concentration of vitamin D decreases ($p \leq 0.001$). ## 3.3. 25(OH)D According to COVID-19 Severity and Death Outcome We analyzed the differences in 25(OH)D concentrations according to COVID-19 severity and poor outcomes (death); the results are presented in Figure 2 and Figure S2. The 25(OH)D concentrations were associated with severity ($p \leq 0.001$), as higher COVID-19 severity forms are associated with lower vitamin D levels. Patients who died had vitamin D concentrations of [median 15.63 (IQR 9.96–24.73)], 4.42 ng/mL lower ($95\%$ CI 2.3–5.36) than survivors (p ≤ 0.001). ## 3.4. Multivariate Analyses Predicting Severe/Critical COVID-19, ICU Needs, and Death Results of the multivariate binary regression models with dependent-variable–severe/critical COVID-19 as a function of 25(OH)D deficiency, controlled for age, associated diseases, and vaccination status are presented in Table 2. The 25(OH)D < 20 vs. ≥20 ng/mL was significantly associated with a severe/critical form of COVID-19 [OR = 1.23 ($95\%$ CI 1.03–1.47), $$p \leq 0.023$$]. When considering the three groups of 25(OH)D in the regression model, there was a statistically significant difference between <20 vs. ≥30, which increased the odds of severe/critical forms of COVID-19 (Supplementary Table S2). Furthermore, there was a statistically significant association between the 25(OH)D concentrations as continuous variables and the odds of a severe/critical form of COVID-19 ($p \leq 0.001$) in a multivariate logistic regression model with the same adjustments. The relationship between 25(OH)D concentrations and the odds of severe/critical forms of COVID-19 was non-linear (Supplementary Figure S1). The results of the multivariate logistic regression models that predict death as a function of 25(OH)D deficiency, and adjusted for age, comorbidities, and vaccination, are presented in Table 3. The 25(OH)D < 20 vs. ≥20 ng/mL was significantly associated with evolution to death [OR = 1.49 ($95\%$ CI 1.06–2.08), $$p \leq 0.02$$]. When considering the three groups of 25(OH)D concentrations in regression, there was a statistically significant difference between <20 and 20–30, which increased the odds of severe/critical forms of COVID-19 (Supplementary Table S3). Furthermore, there was a statistically significant association between the 25(OH)D concentration as a continuous variable and the odds of severe/critical forms of COVID-19 ($$p \leq 0.039$$) in a multivariate logistic regression model with the same adjustments. The relationship between 25(OH)D concentrations and the odds of severe/critical forms of COVID-19 was non-linear (Supplementary Figure S1). The results of the multivariate logistic regression models that predict intensive care need as a function of 25(OH)D deficiency, adjusted for age, comorbidities, and vaccination, are presented in Table 4. For the models predicting intensive therapy needs, vitamin D status was not statistically significantly associated with the outcome, considering the two (as well as the three) groups of 25(OH)D concentrations (Supplementary Table S4). ## 4. Discussion The SARS-CoV-2 infection outcomes in individuals are dependent on multiple variables, such as age or different comorbidities, and infection consequences can include asymptomatic hospital admissions, respiratory support requirements, and death. Since the beginning of the COVID-19 pandemic, significant measures to fight this disease were taken, including increasing the supply of personal protective equipment, highlighting the value of social distancing, and authorizing the emergency use of vaccinations and antivirals for therapy. Although there has been progress in preventing and treating COVID-19, interest in the use of nutraceuticals, particularly vitamin D (as a way to stimulate the immune system and decrease inflammation), has emerged. Numerous observational studies and meta-analyses, investigating the link between low serum 25(OH)D concentration with the prevalence and severity of COVID-19, have been reported [24,25,26,27,28]. According to a meta-analysis (that included 536,105 patients) vitamin D deficiency was not significantly associated with susceptibility to COVID-19 infection or mortality and vitamin D supplements did not improve patients’ prognoses [29]. Another large meta-analysis that involved nearly 2 million adults suggested that vitamin D deficiency/insufficiency increases susceptibility to COVID-19 (and evolving to a severe form), although the risk of bias was high [30]. On the other hand, a recent systematic review and meta-analysis showed that vitamin D supplementation had no effect on the probability of COVID-19 infection but may reduce mortality and prevent ICU admission in COVID-19 patients [31]. A meta-analysis and trial sequential analysis on four randomized clinical trials published in January 2023 suggested an association between vitamin D supplementation and ICU needs for COVID-19 patients [32], although there was a high risk of bias in three out of five trials, while the trial with the lowest risk of bias did not show an association with a shortened length of in-hospital stay. In this study, we assessed the serum concentrations of 25(OH)Din patients infected with SARS-CoV-2. In our cohort of 2342 COVID-19 patients, we found a high prevalence of vitamin D deficiency ($50.9\%$). Vitamin D deficiency is extremely common worldwide and is considered a global pandemic [12]. In Europe, $40\%$ of people are vitamin D deficiency, according to reports [33]. In the United States and Canada, $24\%$ and $37\%$, respectively, are vitamin D-deficient [34]. Over one-third of Australia’s population suffers from vitamin D deficiency [35]. According to one study, Romania has a high prevalence of vitamin D deficiency ($59\%$), especially in the elderly and women and during cold seasons [36]. Niculescu et al. ( in a study that included 8024 Romanian subjects) reported a higher prevalence of vitamin D deficiency and seasonal variation in older adults [37]. It is important to consider regional variations in 25(OH)D concentrations that are influenced by latitude, genetics, lifestyle, and dietary sources [33,38,39]. More than half of the COVID-19 hospitalized patients were older than 65 years. Patients with vitamin D deficiency were older (median difference 6 years (IQR 4–7)) than non-deficient patients. Low vitamin D blood concentration is linked to osteoporosis, osteomalacia, and sarcopenia; thus, vitamin D deficiency is common in the elderly, especially in those with multiple associated diseases and comedication [40]. A high percentage of patients in our study group had associated cardiovascular comorbidities. We found a significant difference in vitamin D deficiency for the subgroup of patients with cardiovascular diseases. Several studies linked vitamin D deficiency to cardiovascular diseases. The implied mechanisms might be activation of the renin–angiotensin–aldosterone system, abnormal nitric oxide regulation, oxidative stress, or altered inflammatory pathways [41]. A large cross-sectional study from the United States showed that, after correcting for age, sex, ethnicity, and physical activity, there was an inverse relationship between 25(OH)D concentration and blood pressure [42]. According to Wang et al., patients with low concentrations of circulating 25(OH)D were more likely than vitamin D-sufficient controls to experience cardiovascular problems, such as hypertension [43]. In the Framingham heart study, low serum 25(OH)D was linked to a $60\%$ increase in death due to cardiovascular events [44]. On the other hand, in the VINDICATE trial, no benefits on systolic or diastolic blood pressure were observed after high doses of vitamin D supplementation in chronic heart failure patients [45]. Through a variety of direct and indirect processes, vitamin D deficiency may affect cerebrovascular homeostasis and increase the risk and severity of stroke and cognitive dysfunction [46]. In our group of patients, we found a significant difference in vitamin D deficiency for the subgroup of patients with neurological diseases. Almost one-quarter of our study group patients had associated diabetes. We found a significant difference in vitamin D deficiency for the subgroup of patients with diabetes. The development of insulin resistance and type 2 diabetes may be caused by vitamin D deficiency, as a normal concentration of vitamin D may reduce low-grade inflammation, which is associated with insulin resistance. Cancer was found in a low percentage of our patients but a significant vitamin D deficiency was found in this subgroup. Vitamin D was suggested to prevent cancer cell proliferation, apoptosis, cell differentiation, angiogenesis, and metastasis [47]. Data show that higher 25(OH)D concentrations inhibit colorectal carcinogenesis, breast cancer, and prostate cancer [48]; however, further research is needed on the benefits of vitamin D supplementation for cancer patients [49]. Previous diagnosed pulmonary diseases were recorded in $12.25\%$ of patients in our study group. A significant difference in vitamin D deficiency was found in the subgroup of patients with associated pulmonary diseases. Numerous studies have found links between low vitamin D levels, affected lung functioning, and increased risk of inflammation, as almost all pulmonary diseases (e.g., acute respiratory distress syndrome, asthma, chronic obstructive pulmonary disease, pneumonia, and tuberculosis, cystic fibrosis) have inflammatory pathogenesis [50]. A meta-analysis of randomized clinical trials on vitamin D regarding acute respiratory infection (performed before the COVID-19 pandemic) showed that vitamin D supplementation reduced the risk of acute respiratory infections by $12\%$ [51]. Obesity was present in almost one-third of the total included patients. A significant difference in vitamin D deficiency was not found in the subgroup of patients with obesity, though obese patients had a median close to the cutoff value of 20 ng/mL [19.85 (IQR 13.82–27.09)]. A systematic review with a meta-analysis of studies that evaluated the association between 25(OH)D concentrations and obesity showed that vitamin D deficiency was associated with obesity, irrespective of age or geographical location [52]. Due to the insufficiently explored effects on vitamin D receptors from adipose tissue, vitamin D deficiency could not be ruled out as a contributing factor to obesity [53]. We did not find significant differences in vitamin D deficiency for the subgroup of patients with rheumatological diseases; this might be explained by the small size of the subgroup (77 patients, $3.29\%$) and, possibly, by a previous investigation of vitamin D deficiency in this subgroup of patients and supplementary intake for correction. In rheumatology, to prevent glucocorticoid-induced osteoporosis and to lower the risk of fractures, vitamin D supplementation is indicated [54]. We found significant differences in vitamin D sufficiency for the subgroup of patients with endocrine diseases. Low 25(OH)D concentrations have been linked to autoimmune thyroid diseases, such as Hashimoto’s thyroiditis and Graves’ disease [55]. Anti-TPO antibody titers and thyroid volume appear to be associated with vitamin D insufficiency in Hashimoto’s disease, and supplementation was linked to a decrease in antibody titers and TSH levels [56]. A lower percentage of patients with endocrine diseases in our study group presented with vitamin D deficiency, which might also be explained by a previous investigation of vitamin D deficiency in this subgroup of patients and supplementary intake for correction. The 25(OH)D concentrations were associated with severity ($p \leq 0.001$), as higher COVID-19 severity forms are associated with lower vitamin D levels. A decrease in the median value of vitamin D was found in our study as the severity form increased, while patients who died had concentrations of vitamin D [median 15.63 (IQR 9.96–24.73)] that were 4.42 ng/mL lower ($95\%$ CI 2.3–5.36) than survivors (p ≤ 0.001). Bennouar et al. also found lower mean 25(OH)D concentrations in non-survivors (14.1 ± 9.8 ng/mL) compared to survivors (23.9 ± 14.7 ng/mL) [57]. Comorbidities represent an increased risk for COVID-19 severe outcomes in clinical practice. In the statistical analysis, we performed multivariate analyses in order to adjust regression models to the patient’s age, sex, associated diseases, and vaccination status. In the multivariate logistic regression model, we found a statistically significant association between vitamin D deficiency concentration and a severe form of COVID-19 [OR = 1.23 ($95\%$ CI 1.03–1.47), $$p \leq 0.023$$]. The association remained statistically significant when considering three groups of 25(OH)D concentrations, as well as when using the 25(OH)D as a continuous variable. An interesting finding was the nonlinear relationship between the 25(OH)D concentration and the log odds of a severe/critical form of COVID-19. The odds of a severe form of COVID-19 decreased with higher concentrations and then changed its direction (but remained in the protective zone); however, in the increasing region, the confidence intervals are too wide to be considered strong evidence. Levels of 25(OH)D above 30 ng/mL were found to be protective against severe/critical disease forms in a different trial consisting of 611 patients with COVID-19 and 25(OH)D concentrations assessed at admittance [58]. The multivariate analysis revealed a statistically significant relationship between vitamin D insufficiency concentration levels and death [OR = 1.49 ($95\%$ CI 1.06–2.08), $$p \leq 0.02$$]. The association remained statistically significant when considering three groups of 25(OH)D concentrations, as well as when using 25(OH)D as a continuous variable. An interesting finding was the nonlinear relationship between the 25(OH)D concentration and the log odds of death, similar to the relationship with the severe form. A high mortality rate was documented in our study group ($7.64\%$). At the end of October 2021, when the Delta variant dominated, Romania held the first position, globally, regarding daily COVID-19 deaths per million persons, while it cumulatively confirmed COVID-19 deaths per million people on 31 May 2022; the end of our study interval was 3307.09 compared to the world level, where it was 774.65 deaths per million people [59]. Similar results were shown by other studies in which 25(OH)D concentrations were measured on the admission day [60,61,62]. While significantly lower 25(OH)D concentrations in moderate and severe COVID-19 diseases were found compared to mild diseases, no correlation was found between the 25(OH)D concentration measured at admittance and inflammatory biomarkers in a Romanian study involving 203 COVID-19 hospitalized patients [63]. Regarding the multivariate logistic regression, predicting intensive therapy needs, 25(OH)D concentrations were not associated with intensive care needs, although recent meta-analyses showed that vitamin D administration resulted in decreased ICU admission in patients with COVID-19 [33,34]. Radujkovic et al. found higher risks of mechanical ventilation needs/deaths in patients with 25(OH)D concentrations lower than 12 ng/mL at admittance [62]. As ICU was frequently overwhelmed, our results could be explained by the inclusion of the multivariate regression model of patients who were transferred to the ICU, but often patients with ICU needs were treated in infectious disease wards by intensivists. ## Limitations and Strength Our study’s strengths included the large size of the study group and adjustments made for a large number of confounders. Any cause–effect relationship was precluded by the retrospective observational design of our study, implying the possibility of residual confounding. All group differences cannot be accounted for by the multivariate models. The 25(OH)D concentration was assessed on admission day (but irrespective of the day from the onset of the disease). We did not assess pre-analytical factors, such as the fasting versus non-fasting state, or the time of day of the blood sample collection. Vitamin D supplementation prior to admission was not assessed as a part of the study. As COVID-19 standard treatments frequently changed over the two-year pandemic interval, an important limitation of our study is represented by the absence of adjustment in the multivariate analysis to specific COVID-19 medication used during the hospitalization of patients. Another limitation is represented by the absence of adjustment in the multivariate analyses to the wave of the pandemic, as differences in disease severity are associated with different variants of concern of the virus [64]. ## 5. Conclusions More than half of the patients in the study group were classified with vitamin D deficiency based on a serum concentration of less than 20 ng/mL. There was an inversely proportional relationship between age and vitamin D; patients with vitamin D deficiency presented with more cardiovascular, neurological, and pulmonary diseases, as well as diabetes and cancer. 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--- title: Metabolite profiling and bioactivity of Cicerbita alpina (L.) Wallr. (Asteraceae, Cichorieae) authors: - Dimitrina Zheleva-Dimitrova - Alexandra Petrova - Gokhan Zengin - Kouadio Ibrahime Sinan - Vessela Balabanova - Olivier Joubert - Christian Zidorn - Yulian Voynikov - Rumyana Simeonova - Reneta Gevrenova journal: Plants year: 2023 pmcid: PMC10005263 doi: 10.3390/plants12051009 license: CC BY 4.0 --- # Metabolite profiling and bioactivity of Cicerbita alpina (L.) Wallr. (Asteraceae, Cichorieae) ## Abstract Cicerbita alpina (L.) Wallr. is a perennial herbaceous plant in the tribe Cichorieae (Lactuceae), Asteraceae family, distributed in the mountainous regions in Europe. In this study, we focused on the metabolite profiling and the bioactivity of C. alpina leaves and flowering heads methanol-aqueous extracts. The antioxidant activity of extracts, as well as inhibitory potential towards selected enzymes, involving in several human diseases, including metabolic syndrome (α-glucosidase, α-amylase, and lipase), Alzheimer’s disease, (cholinesterases: AChE, BchE), hyperpigmentation (tyrosinase), and cytotoxicity were assessed. The workflow comprised ultra-high-performance liquid chromatography—high-resolution mass spectrometry (UHPLC-HRMS). UHPLC-HRMS analysis revealed more than 100 secondary metabolites, including acylquinic, acyltartaric acids, flavonoids, bitter sesquiterpene lactones (STLs), such as lactucin, dihydrolactucin, their derivatives, and coumarins. Leaves showed a stronger antioxidant activity compared to flowering heads, as well as lipase (4.75 ± 0.21 mg OE/g), AchE (1.98 ± 0.02 mg GALAE/g), BchE (0.74 ± 0.06 mg GALAE/g), and tyrosinase (49.87 ± 3.19 mg KAE/g) inhibitory potential. Flowering heads showed the highest activity against α-glucosidase (1.05 ± 0.17 mmol ACAE/g) and α-amylase (0.47 ± 0.03). The obtained results highlighted C. alpina as a rich source of acylquinic, acyltartaric acids, flavonoids, and STLs with significant bioactivity, and therefore the taxon could be considered as a potential candidate for the development of health-promoting applications. ## 1. Introduction Cicerbita alpina (L.) Wallr. ( Lactuca alpina (L.) A.Gray) (alpine chicory, blue sow thistle) is a perennial herbaceous plant in the tribe Cichorieae (Lactuceae), Asteraceae family, and is distributed in the mountainous regions in Europe [1]. Commonly, its edible shoots are used as a vegetable or for salads [2]. As a vegetable, the species has a commercial value, and recently, some trials for cultivation have been performed [3,4]. Based on the literature survey of the secondary metabolites, the species contains sesquiterpene lactones and sesquiterpene lactone glucosides, which are also used as chemosystematic/chemophenetic markers for the tribe [5]. Roots/subaerial parts contain the guaianolides 11β,13-dihydrolactucin, 8-acetyl-lactucin, 8-acetyl-11β,13-dihydrolactucin, and lactucin, 8-acetyl-15β-D-glucopyranosyllactucin, and the non-guaiane type sesquiterpene sonchuside A [6,7,8]. Additionally, furanocoumarins (imperatorin, isoimperatorin, oxypeucedanin, and ostruthol) were detected in the roots [9]. The shoots of alpine chicory are characterized by phenolic acids such as chlorogenic acid, 3,5-dicaffeoylquinic acid, caftaric acid, and mostly cichoric acid [2]. The usage of traditionally consumed wild edible plants of the genus Lactuca, which expands dietary variety, is declining, and their potential health-promoting effect is undervalued [10]. In comparison to lettuce (Lactuca sativa L.) and its cultivars, wild lettuces are still poorly studied chemically. As the wild relatives constitute a gene pool for the popular vegetable, their chemical composition could be of importance for breeders. One of the most characteristic features of the Lactuca species examined so far is their ability to synthesize bitter sesquiterpene lactones of different structural types, including guaianolides, germacranolides, and eudesmanolides [5,11]. The presence of sesquiterpene lactones, e.g., the guaianolide lactucin and its derivatives, are responsible for the anti-inflammatory, analgesic, and sedative activities [12] as well as for the bitter taste and the antifeedant activity of the species [13,14]. The presence of phenolics in plant species, e.g., caffeic acid derivatives, reveals the radical scavenging potential and UV radiation protection [15,16]. Moreover, the occurrence of cichoric acid is related to a variety of potential health benefits such as antioxidant, anti-inflammatory, obesity prevention, and neuroprotection effects [17]. Previous investigation revealed a large amount of antioxidant caffeic acid derivatives in the edible shoots of C. alpina. Assessed in the DPPH assay, the dicaffeoyl derivatives cichoric acid and cynarin (1,5-dicaffeoylquinic acid) had the higher free radical scavenging potential of related monocaffeoyl derivatives, caffeoyltartaric acid (caftaric acid) and chlorogenic acid, respectively [18]. Despite the studies on the chemical composition of C. alpina so far, there is no in-depth investigation of both secondary metabolites and the biological potential of the species. Detailed information on the metabolite profile in leaves and flowering heads would provide valuable information on the biofunctional potential of these compounds. Herein, the majority of annotated acylquinic acids, acyltartaric acids, and flavonoids were reported for the first time in C. alpina. Moreover, this is the initial report on the chemical composition of alpine chicory flowering heads. Further investigation aims at the cultivation of C. alpina as a potential candidate for attenuating metabolic-related disorders with nutraceutical and pharmaceutical applications. ## 2.1. UHPLC-HRMS Profiling of Specialized Metabolites in C. alpina Extracts Based on the retention times, MS and MS/MS accurate masses, fragmentation patterns in MS/MS spectra, relative ion abundance, and comparison with reference standards and literature data, 110 specialized natural products were identified or tentatively annotated in C. alpina extracts (Table 1). The total ion chromatograms (TIC) of the studied extracts in negative ion mode are depicted in Figure S1. ## 2.1.1. Carboxylic, Hydroxybenzoic and Hydroxycinnamic Acids Hydroxybenzoic acids [1, 5, 6, 9, 11, 13], their glycosides (2–4, 7–8), quinic [10], shikimic acid [12], hydroxycinnamic acids [16, 18, 19, 20], and their glycosides (14–15, 17) were identified based on comparison of retention times, exact masses and fragment spectra with reference standards and literature data (Table 1) [19]. Compounds 21 [M−H]− at m/z 295.046 and 22 [M−H]− at m/z 309.060 gave base peaks at m/z 133.012 [C4H5O5]− and 147.028 [C5H7O5]−, respectively, corresponding to the loss of a caffeoyl residue (−162.033 Da). The subsequent loss of H2O (−18 Da) and CO2 (−44 Da) in the fragmentation pathways of the aforementioned compounds indicated the presence of hydroxyl and carboxyl groups. MS/MS spectrum of 22 was similar to that of 21, except for the appearance of one methylene group. Thus, 21 and 22 were identified as caffeoyl-malic acid and caffeoyl-methylmalic acid, respectively (Table 1). ## 2.1.2. Acylquinic Acids A variety of acylquinic acids (AQA), including six mono-, eleven di-, and three triacylquinic acids, were identified/annotated in C. alpina extracts (Table 1, Figure S2). Fragmentation patterns and diagnostic ions in the MS/MS spectra of AQA were reported elsewhere [19,20,21]. Three isobars shared the same deprotonated molecule [M−H]− at m/z 353.086; the main compound 25 was assigned to chlorogenic acid (5-caffeylquinic acid) by the base peak at m/z 191.055 [quinic acid-H]−. The positional isomer neochlorogenic acid (3-caffeylquinic acid) [23] was discernable by the higher relative abundances of the fragment ions at m/z 179.033 ($58.6\%$) and 135.043 ($51.0\%$) than those of 25. Compounds 23 and 25 were also unambiguously identified by comparison with reference standards. In the MS/MS spectrum of 26, a base peak at m/z 173.044 [quinic acid-H-H2O]−.was observed, indicating a caffeoyl residue at position 4 of the quinic acid. Thus, 26 was annotated as 4-caffeylquinic acid [20]. The compound 3-p-coumaroylquinic acid (3-p-CoQA) [24] was deduced from the base peak at m/z 163.039 [p-CoA-H]− (Table 1). Compounds 27 and 29 showed a base peak at m/z 191.055 and fragment ions at m/z 163.038 [p-CoA-H]- and 193.050 [FA-H]-, respectively, and were assigned to 5-p-coumaroylquinic and 5-feruloylquinic (FQA) acids [19,20,22,23] (Table 1). Four compounds (34–37) shared the same deprotonated molecule at m/z 515.119 together with the distinctive fragment ions for dicaffeylquinic acids (diCQA) at m/z 353.087 [M−H-caffeoyl]−, 335.076 [M−H-caffeoyl-H2O]−, 191.055 [QA-H]−, 179.0339 [CA-H]−, and 173.044 [QA-H-H2O]−. Compounds were identified based on the different intensities of above mention ions and comparison with Clifford’s hierarchical key [19,20,22,23] (Table 1). The presence of an abundant ion at m/z 173.045 in the MS/MS spectra indicated vicinal diCQA (34 and 37). Compound 34 gave diagnostic fragment ion at m/z 335.077 [CQA-H-H2O]− along with m/z 135.044 as observed for 3,4-diCQA [19]. On the other hand, the low abundant “dehydrated” ion at m/z 335 indicated 4,5-diCQA [37]. Regarding compound 35, the abundant ion at m/z 191.055 ($99.3\%$) and the absence of the peak at m/z 335.077 indicated 3,5-diCQA, while 36 was ascribed as 1,5-diCQA. Compounds 38, 41, and 42 shared the same deprotonated molecules at m/z 499.125 (C25H23O11), together with fragment ions characteristic for p-coumaric acid at m/z 337.093 [p-CoQA-H]−, m/z 163.039 [p-CoA-H], and m/z 119.049 [p-CoA-H-CO2]−. The compounds were annotated as p-coumaroyl-caffeoylquinic acids (Table 1). Similarly to the MS/MS fragmentation patterns of diCQA and F-CQA, compounds 28, 30, and 31 were assigned to hydroxydihydrocaffeoyl-caffeoylquinic acids (HC-CQA) [19,24]. Compounds 32 and 33 yielded a precursor ion at m/z 677.173 (consistent with C31H33O17) accompanied by the relevant fragment ions at m/z 515.121 [M−H-Hex]−, 353.088 [M−H-Hex-caffeoyl]− and 191.055 [M−H-Hex-2caffeoyl]− indicating subsequent losses of a hexose unit and two caffeoyl residues. The 3,4-diCQA core [32] was deduced from the ions at m/z 173.044 ($100\%$) and 179.034 ($74.7\%$), and 135.044 ($61.8\%$). Accordingly, 32 was ascribed as 3,4-dicaffeoylquinic acid-hexoside. In the same manner, 1,3-dicaffeoylquinic acid-hexoside [33] was discernable by the ions at m/z 135.044 ($100\%$), 179.034 ($97.2\%$), and 341.088 ($53.7\%$). Compound 43 showed a deprotonated molecule at m/z 677.152, together with relevant fragment ions at m/z 515.120 [M−H-caffeoyl]−, 353.088 [M−H-2caffeoyl]−, and 191.055 [M−H-3caffeoyl]−, indicative for triCQA. The 3,4,5-tricaffeoylquinic acid is evidenced by the abundant fragment ions at m/z 173.044 ($94\%$), 179.034 ($72.9\%$), and 135.044 ($77.0\%$) [21]. ## 2.1.3. Acyltartaric Acids Similarly to acylquinic acids, a variety of acyltartaric acids (ATA) was annotated, including two mono-ATA, seven di-ATA, and three triacyltartaric acids (Table 1). MonoATA were deduced from the prominent ions at m/z 149.008 [TA-H]− (tartaric acid, TA) supported by m/z 112.984 [TA-H-2H2O]− and 103.002 [TA-H-H2O-CO]−. Within this group, caffeoyltartaric [44] and p-coumaroyltartaric acid [49] were found. Compounds 47 and 48 were consistent with dicaffeoyltartaric acids affording prominent fragment ions at m/z 311.041 ($83.6\%$) and 149.008 ($100\%$) (Figure 1). The assignment of two isobars of feruloyl-caffeoyltartaric acids 52 and 55 (at m/z 487.088) was confirmed by the fragments at m/z 325.057 [M−H-caffeoyl]− and 293.031 [M−H-ferulic acid]−. Ferulic acid was also deduced from the fragment ions at m/z 193.050 [ferulic acid-H]− and 134.036 [ferulic acid-H-CO2-CH3]−, while caffeic acid was evidenced by m/z 179.038 [caffeic acid-H]−, 161.023 [caffeic acid-H-H2O]−, and 135.044 [caffeic acid-H-CO2]−. In the same way, three p-coumaroyl-caffeoyltartaric acid isomers 50, 53, and 54 were annotated at m/z 457.077 [M−H]− supported by the prominent ions at m/z 295.046 [M−H-caffeoyl]−, 163.039 [p-coumaric acid-H]−, and 119.049 [p-coumaric acid-H-CO2]−. MS/MS spectra of three tricaffeoyltartaric acids (45, 46, and 51) were acquired. They afforded a precursor ion at m/z 635.106 together with the transitions at m/z 635.106 → 473.073 → 293.031 → 149.009 resulting from the losses of three caffeoyl residues. This class of secondary metabolites shows a significant degree of stereoisomerism [25]. ## Flavones, Flavonols, and Flavanones A key step in the dereplication/annotation of flavonoid glycosides was the neutral loss of 162.05, 146.05, 132.04, 176.03, and 204.06 Da, corresponding to hexose, deoxyhexose, pentose, hexuronic acid, and acetylhexose [26]. A series of ions in (−) ESI/MS/MS from neutral losses of CH2O (−30 Da), C2H2O (−42 Da), CO (−28 Da), CO2 (−44 Da), H2O (−18 Da), (CH2O + CO) (−58 Da), (H2O + CO) (−46 Da), (2CO) (−56 Da), and (CO2 + CO) (−72 Da) were used for the dereplication of the flavonoid aglycones [27]. Fragment ions resulting from the retro-Diels–Alder (RDA) reaction of the flavonoid skeleton are informative. Ions formed after C-ring cleavage of the aglycon are presented as i,jA–/i,jB– (in negative mode) [27]. ## Flavones In the MS/MS spectra of compounds 73, 77, 79, 81, 87, and 88, the aglycone was identified as the flavone apigenin [93] based on fragment ions at m/z 239.036 [Api-H-CH2O]−, 211.040 [Api-H-CH2O-CO]−, together with RDA ions 1,3B− at m/z 117.033, 1,3A− at m/z 151.003, and 0,4A− at m/z 107.012. Luteolin [91] and ten of its glycosides (63–65, 68, 71–72, 80, 82–83, and 89) were also identified. The identification of the aglycone luteolin was determined based on a series of fragment ions at m/z 285.041 [Lu-H]−, 255.030 [Lu-H-CH2O]−, 257.042 [Lu-H-CO]−, 241.051 [Lu-H-CO2]−, 227.034 [Lu-H-CH2O-CO]−, 211.039 [Lu-H-H2O-2CO]−, together with RDA ions 1,3B− at m/z 133.029, 1,3A− at m/z 151.003, and 0,4A− at m/z 107.012 (Table 1). In the MS/MS spectra of 83 and 88, a consecutive loss of dihydroxybutyryl (C4H6O3) (−102.031 Da), acetyl (C2H2O) (−42.011 Da), and hexosyl (C6H10O5) (−162.053) radicals were observed. Thus, the compounds were annotated as 7-hydroxybutyryl-O-acetylhexosides of luteolin [83] and apigenin [88], respectively (Table 1, Figure S3). Table 1 \| № \| Identified/Tentatively Annotated Compound \| Molecular Formula \| Exact Mass[M − H]− \| Fragmentation Pattern in (−) ESI-MS/MS \| tR(min) \| Δ ppm \| Distribution \| Level of Identification [28] \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Carboxylic, hydroxybenzoic acids, and their glycosides \| Carboxylic, hydroxybenzoic acids, and their glycosides \| Carboxylic, hydroxybenzoic acids, and their glycosides \| Carboxylic, hydroxybenzoic acids, and their glycosides \| Carboxylic, hydroxybenzoic acids, and their glycosides \| Carboxylic, hydroxybenzoic acids, and their glycosides \| Carboxylic, hydroxybenzoic acids, and their glycosides \| Carboxylic, hydroxybenzoic acids, and their glycosides \| Carboxylic, hydroxybenzoic acids, and their glycosides \| \| 1 \| gallic acid a \| C7H6O5 \| 169.0131 \| 169.0131 (35.1), 125.0228 (100), 97.0278 (4.2), 79.0173 (1.0) \| 1.15 \| 6.843 \| 12 \| 1 \| \| 2 \| gallic acid-O-hexoside \| C13H16O10 \| 331.0671 \| 331.0671 (100), 169.0128 (2.9), 168.0052 (34.6), 149.9945 (15.0), 125.0229 (29.5), \| 1.25 \| 0.212 \| 12 \| 2 \| \| 3 \| syringic acid-O-hexoside \| C15H20O10 \| 359.0985 \| 359.0985 (44.7), 197.0446 (100), 179.0340 (23.4), 135.0437 (32.9), 123.0436 (21.5), 107.0484 (0.9) \| 1.52 \| 0.473 \| 12 \| 2 \| \| 4 \| protocatechuic acid-O-hexoside \| C13H16O9 \| 315.0722 \| 315.0721 (100), 153.0180 (0.3), 152.0101 (58.4), 109.0286 (9.0), 108.0200 (85.0), 81.0329 (0.8) \| 1.75 \| 0.048 \| 12 \| 2 \| \| 5 \| vanillic acid a \| C8H8O4 \| 167.0350 \| 167.0338 (25.5), 152.0103 (7.4), 149.0230 (37.9), 123.0071 (100), 108.0201 (31.4) \| 2.01 \| −1.232 \| 12 \| 1 \| \| 6 \| protocatechuic acid a \| C7H6O4 \| 153.0182 \| 153.0180 (16.9), 109.0279 (100), 91.0172 (0.5), 81.0328 (1.4) \| 2.06 \| 8.966 \| 12 \| 1 \| \| 7 \| protocatechuic acid-O-hexoside isomer \| C13H16O9 \| 315.0722 \| 315.0730 (35.6), 153.0180 (100), 135.0438 (0.4),123.0435 (56.4), 109.0278 (27.4) \| 2.17 \| 2.681 \| 12 \| 2 \| \| 8 \| 4-hydroxybenzoic acid-O-hexoside \| C13H16O8 \| 299.0772 \| 299.0772 (14.5), 137.0229 (100), 109.0281 (0.6) \| 2.49 \| 0.136 \| 12 \| 2 \| \| 9 \| 4-hydroxybenzoic acid a \| C7H6O3 \| 137.0244 \| 137.0228 (100), 108.0200 (7.0), 93.0328 (2.9) \| 2.86 \| 11.804 \| 12 \| 1 \| \| 10 \| quinic acid \| C7H12O6 \| 191.0561 \| 191.0550 (100), 173.0444 (1.8), 155.0340 (0.4), 127.0386 (3.3), 111.0435 (1.6), 93.0329 (6.0), 85.0278 (19.7) \| 3.21 \| 5.712 \| 12 \| 2 \| \| 11 \| gentisic acid a \| C7H6O4 \| 153.0182 \| 153.0179 (76.4), 135.0073 (29.2), 109.0279 (100), 91.0172 (3.7), 81.0328 (1.1) \| 3.86 \| 9.293 \| 12 \| 1 \| \| 12 \| shikimic acid \| C7H10O5 \| 173.0455 \| 173.0451 (100), 144.0443 (16.6), 93.0329 (68.6) \| 6.24 \| 5.991 \| 12 \| 2 \| \| 13 \| salicylic acid a \| C7H6O3 \| 137.0244 \| 137.0230 (34.6), 109.0278 (3.2), 108.0200 (3.5), 93.0330 (100) \| 6.29 \| 10.417 \| 12 \| 1 \| \| Hydroxycinnamic acids and derivatives \| Hydroxycinnamic acids and derivatives \| Hydroxycinnamic acids and derivatives \| Hydroxycinnamic acids and derivatives \| Hydroxycinnamic acids and derivatives \| Hydroxycinnamic acids and derivatives \| Hydroxycinnamic acids and derivatives \| Hydroxycinnamic acids and derivatives \| Hydroxycinnamic acids and derivatives \| \| 14 \| caffeic acid-O-hexoside \| C15H18O9 \| 341.0867 \| 341.0879 (4.8), 179.0338 (100), 135.0437 (58.7), 123.0436 (0.5), 107.0488 (0.7) \| 2.44 \| 0.160 \| 12 \| 2 \| \| 15 \| caffeoylgluconic acid \| C15H18O10 \| 357.0827 \| 357.0826 (24.7), 339.0728 (11.3), 195.0501 (100), 179.0339 (13.6), 177.0392 (18.8), 161.0233 (4.5), 135.0437 (22.6), 129.0178 (11.5), 87.0070 (10.8) \| 2.77 \| 0.392 \| 12 \| 2 \| \| 16 \| rosmarinic acid a \| C18H16O8 \| 359.0767 \| 359.0769 (80.7), 315.0886 (9.8), 161.0595 (100), (65.1), 135.0286 (2.6) \| 3.15 \| 0.865 \| 1 \| 1 \| \| 17 \| caffeic acid-O-hexoside isomer \| C15H18O9 \| 341.0867 \| 341.0878 (10.9), 179.0338 (100), 135.0436 (71.3), 107.0486 (1.0) \| 3.29 \| 0.104 \| 12 \| 2 \| \| 18 \| p-coumaric acid a \| C9H8O3 \| 163.0389 \| 163.0388 (8.0), 135.0437 (0.7), 119.0485 (100) \| 3.36 \| 7.590 \| 12 \| 1 \| \| 19 \| caffeic acid a \| C9H8O4 \| 179.0338 \| 179.0338 (20.5), 135.0436 (100), 117.0333 (0.6), 107.0486 (1.3) \| 3.55 \| 6.602 \| 12 \| 1 \| \| 20 \| ferulic aci a \| C10H10O4 \| 193.0493 \| 193.0493 (1.4), 178.0259 (25.5), 149.0598 (2.0), 134.0358 (100) \| 3.56 \| 1.332 \| 12 \| 1 \| \| 21 \| caffeoylmalic acid \| C13H12O8 \| 295.0464 \| 295.0464 (0.3), 179.0339 (13.0), 161.0229 (0.3), 135.0437 (9.0), 133.0124 (100), 115.0021 (27.2), 89.0227 (1.7), 72.9914 (2.6), 71.0122 (10.3) \| 4.15 \| 1.422 \| 12 \| 2 \| \| 22 \| caffeoylcitramalic acid \| C14H14O8 \| 309.0607 \| 309.0607 (0.5), 179.0304 (4.7), 161.0231 (6.4), 147.0284 (100), 135.0437 (3.7), 129.0178 (39.8), 101.0228 (7.2), 87.0071 (1.0), 85.0279 (9.2) \| 4.77 \| 2.979 \| 12 \| 2 \| \| Acylquinic acids \| Acylquinic acids \| Acylquinic acids \| Acylquinic acids \| Acylquinic acids \| Acylquinic acids \| Acylquinic acids \| Acylquinic acids \| Acylquinic acids \| \| 23 \| neochlorogenic (3-caffeoylquinic) acid a \| C16H18O9 \| 353.0867 \| 353.0878 (40.7), 191.0551 (100), 179.0338 (58.6), 173.0442 (3.9), 161.0229 (1.8), 135.0436 (51.0), 127.0389 (1.5), 111.0432 (1.7), 93.0327 (4.1), 85.0277(8.1) \| 2.35 \| 0.015 \| 12 \| 1 \| \| 24 \| 3-p-coumaroylquinic acid \| C16H18O8 \| 337.0929 \| 337.0912 (8.8), 173.0447 (1.7), 163.0388 (100), 135.0436 (0.8), 119.0487 (29.8), 93.0329 (2.8), 85.0277 (0.6) \| 3.04 \| 5.042 \| 1 \| 2 \| \| 25 \| chlorogenic (5-caffeoylquinic) acid a \| C16H18O9 \| 353.0867 \| 353.0878 (4.8), 191.0550 (100), 179.0340 (1.0), 173.0445 (0.7), 161.0233 (1.5), 135.0435 (0.9), 127.0386 (1.5), 111.0434 (1.0), 93.0330 (2.8), 85.0279 (7.9) \| 3.21 \| 0.070 \| 12 \| 1 \| \| 26 \| 4-caffeoylquinic acid \| C16H18O9 \| 353.0867 \| 353.0880 (28.1), 191.0551 (54.0), 179.0339 (65.7), 173.0443 (100), 161.0233 (3.2), 135.0437 (53.9), 127.0385 (2.2), 111.0436 (3.8), 93.0329 (23.0), 85.0278 (10.2) \| 3.38 \| 0.495 \| 12 \| 2 \| \| 27 \| 5-p-coumaroylquinic acid \| C16H18O8 \| 337.0929 \| 337.0931 (10.5), 191.0550 (100), 173.0444 (7.2), 163.0388 (5.3), 135.0433 (0.2), 127.0386 (1.0), 119.0486 (5.0), 111.0436 (2.4), 93.0329 (16.8), 85.0279 (4.8) \| 3.95 \| 0.651 \| 12 \| 2 \| \| 28 \| 3-caffeoyl-5-hydroxydihydrocaffeoylquinic acid \| C25H26O13 \| 533.1303 \| 533.1303 (81.2), 371.0981 (10.4), 353.877 (19.4), 335.0773 (4.9), 191.0551 (100), 179.0339 (49.5), 173.0443 (15.7), 161.0231 (7.0), 135.0437 (60.5), 111.0436 (2.2), 93.0330 (9.0), 85.0279 (7.2) \| 4.02 \| 0.462 \| 12 \| 2 \| \| 29 \| 5-feruloylquinic acid \| C17H20O9 \| 367.1035 \| 367.1035 (16.7), 191.0550 (100), 173.0444 (14.1), 135.0391 (0.2), 134.0358 (11.3), 127.0384 (1.2), 111.0436 (4.0), 93.0329 (26.2), 85.0279 (5.5) \| 4.41 \| 0.015 \| 12 \| 2 \| \| 30 \| 1/3/5-caffeoyl-4-hydroxydihydrocaffeoylquinic acid \| C25H26O13 \| 533.1306 \| 533.1306 (100), 371.1010 (9.3), 353.0878 (5.9), 335.0779 (10.3), 191.0550 (22.5), 179.0339 (22.4), 173.0444 (75.7), 161.0230 (15.9), 135.0436 (71.7), 111.0433 (4.4), 93.0330 (20.8), 85.0276 (1.8) \| 4.45 \| 0.912 \| 12 \| 2 \| \| 31 \| 1-caffeoyl-3-hydroxydihydrocaffeoylquinic acid \| C25H26O13 \| 533.1305 \| 533.1305 (32.1), 371.0985 (54.9), 353.0892 (4.0), 335.0781 (3.2), 197.0449 (4.3), 191.0552 (15.5), 179.0340 (11.6), 173.0444 (19.1), 161.0235 (4.2), 135.0437 (100), 123.0437 (2.4), 93.0330 (5.4), 85.0278 (1.5) \| 4.55 \| 0.912 \| 1 \| 2 \| \| 32 \| 1,3,4-tricaffeoylquinic acid \| C31H34O17 \| 677.1533 \| 677.1533 (79.4), 515.1213 (38.0), 353.0876 (22.3), 335.0804 (0.5), 191.0549 (11.8), 179.0338 (74.7), 173.0444 (100), 135.0437 (61.8), 93.0329 (30.0), 85.0276 (1.9) \| 5.11 \| 3.170 \| 1 \| 2 \| \| 33 \| 1,3,5- tricaffeoylquinic acid \| C31H34O17 \| 677.1528 \| 677.1528 (93.1), 515.1365 (41.5), 353.0878 (11.8), 341.0877 (53.7), 335.0748 (1.0), 191.0550 (15.5), 179.0339 (97.2), 173.0444 (55.1), 161.0233 (6.4), 135.0437 (100), 93.0329 (25.3), 85.0275 (3.7) \| 5.68 \| 2.358 \| 12 \| 1 \| \| 34 \| 3,4-dicaffeoylquinic acid a \| C25H24O12 \| 515.1195 \| 515.1200 (100), 353.0878 (15.4), 335.0768 (7.2), 191.0551 (32.2), 179.0339 (55.8), 173.0444 (63.7), 161.0230 (14.7), 135.0437 (45.3), 111.0436 (4.1), 93.0329 (16.1), 85.0278 (2.0) \| 5.69 \| 0.972 \| 12 \| 1 \| \| 35 \| 3,5-dicaffeoylquinic acid a \| C25H24O12 \| 515.1195 \| 515.1193 (17.6), 353.0879 (100), 191.0551 (99.3), 179.0338 (47.6), 173.0445(4.4), 161.0232 (4.5), 135.0437 (63.3), 111.0434 (2.9), 93.0329 (4.1), 85.0279 (11.1) \| 5.89 \| 0.465 \| 12 \| 1 \| \| 36 \| 1,5-dicaffeoylquinic acid a \| C25H24O12 \| 515.1195 \| 515.1200 (36.9), 353.0879 (80.6), 335.0774 (1.5), 191.0551 (100), 179.0339 (50.5), 173.0448 (9.5), 135.0438 (51.0), 127.0388 (0.9), 111.0435 (3.2), 93.0328 (5.1), 85.0279 (7.9) \| 6.03 \| −0.465 \| 12 \| 1 \| \| 37 \| 4,5-dicaffeoylquinic acid a \| C25H24O12 \| 515.1195 \| 515.1201 (80.6), 353.0880 (52.4), 335.0794 (0.6), 191.0551 (32.7), 179.0339 (65.4), 173.0443 (100), 135.0436 (59.7), 127.0383 (1.4), 111.0435 (3.9), 93.0329 (24.5), 85.0277 (4.5) \| 6.24 \| 0.465 \| 12 \| 1 \| \| 38 \| 3-p-coumaroyl-5-caffeoylquinic acid \| C25H24O11 \| 499.1246 \| 499.1256 (17.7), 353.0882 (5.4), 337.0932 (65.5), 191.0552 (8.2), 173.0443 (7.7), 163.0388 (100.0), 161.0231 (2.5), 135.0436 (2.8), 119.0487 (36.8), 111.0436 (1.5), 93.0331 (2.8), 85.0277 (0.7) \| 6.52 \| 1.994 \| 12 \| 2 \| \| 39 \| 3-feruloyl-5-caffeoylquinic acid \| C26H26O12 \| 529.1351 \| 529.1362 (12.5), 367.1035 (84.8), 335.0779 (0.7), 193.0496 (100), 191.0552 (3.7), 179.0337 (0.5), 173.0443 (8.0), 161.0232 (7.4), 135.0386 (3.2), 134.0358 (61.1), 111.0437 (1.5), 93.0331 (2.5), 85.0278 (0.5) \| 6.81 \| 1.929 \| 12 \| 2 \| \| 40 \| 1,3-dicaffeoyl-5-hydroxydihydrocaffeoylquinic acid \| C34H32O16 \| 695.1616 \| 695.1616 (73.0), 533.1310 (7.4), 515.1198 (7.1), 353.0775 (9.6), 191.0551 (100), 179.0339 (36.7), 173.0443 (15.8), 135.0437 (58.3), 111.0432 (1.3), 93.0327 (7.5), 85.0278 (6.6) \| 6.84 \| 0.227 \| 1 \| 2 \| \| 41 \| 4-p-coumaroyl-5-caffeoylquinic acid \| C25H24O11 \| 499.1246 \| 499.1251 (28.9), 353.0870 (0.5), 337.0931 (62.3), 191.0552 (3.2), 173.0443 (100), 163.0388 (15.6), 135.0436 (1.2), 119.0486 (0.4), 111.0437 (3.7), 93.0329 (22.5), 85.0277 (0.5) \| 6.93 \| 0.952 \| 12 \| 2 \| \| 42 \| 4-caffeoyl-5-p-coumaroylquinic acid \| C25H24O11 \| 499.1246 \| 499.1249 (100), 353.0875 (70.4), 337.0931 (8.9), 191.0552 (69.6), 179.0327 (70.6), 173.0444 (91.5), 161.0237 (73.8), 135.0437 (73.8), 119.4812 (2.4), 93.0331 (23.3), 85.0279 (4.8) \| 7.63 \| 0.952 \| 12 \| 2 \| \| 43 \| 3,4,5-tricaffeoylquinic acid \| C34H30O15 \| 677.1512 \| 677.1521 (3.40). 515.1195 (31.2), 353.0875 (45.6), 335.0771 (17.8), 191.0549 (43.9), 179.0339 (72.9), 173.0443 (94.0), 161.0230 (28.1), 135.0437 (77.0), 111.0438 (5.1), 93.0329 (21.2) \| 7.78 \| 1.339 \| 12 \| 2 \| \| Acyltartaric acids \| Acyltartaric acids \| Acyltartaric acids \| Acyltartaric acids \| Acyltartaric acids \| Acyltartaric acids \| Acyltartaric acids \| Acyltartaric acids \| Acyltartaric acids \| \| 44 \| caftaric acid \| C13H12O9 \| 311.0411 \| 311.0411 (0.4), 179.0338 (71.1), 149.0077 (100), 135.0436 (49.3), 112.9864 (1.3), 103.0020 (1.6), 87.0071 (13.0) \| 2.39 \| 0.819 \| 12 \| 2 \| \| 45 \| tricaffeoyltartatric acid \| C31H24O15 \| 635.1064 \| 635.1064 (19.6), 473.0940 (41.5), 455.0835 (7.9), 341.0881 (11.4), 293.0304 (11.8), 219.0292 (7.0), 179.0338 (100), 161.0228 (4.0), 149.0076 (0.8), 135.0436 (75.4), 112.9865 (8.2), 103.0006 (0.4), 87.0073 (3.2) \| 3.87 \| 3.396 \| 12 \| 2 \| \| 46 \| tricaffeoyltartatric acid \| C31H24O15 \| 635.1065 \| 635.1065 (23.0), 473.0940 (65.6), 455.0833 (10.2), 341.0877 (51.3), 293.0304 (36.1), 219.0293 (16.7), 179.0338 (98.8), 161.0231 (12.8), 149.0078 (14.0), 135.0436 (100), 112.9865 (23.9), 87.0071 (8.1) \| 4.33 \| 3.491 \| 12 \| 2 \| \| 47 \| dicaffeoyltartaric acid (cichoric acid) \| C22H18O12 \| 473.07 \| 473.0726 (6.6), 311.0410 (83.6), 293.0304 (20.7), 179.0338 (80.3), 149.0077 (100), 135.0436 (71.6), 161.0230 (3.8), 112.9865 (8.2), 103.0021 (2.6), 87.0071 (14.8) \| 4.95 \| 0.171 \| 12 \| 2 \| \| 48 \| dicaffeoyltartaric acid (cichoric acid) \| C22H18O12 \| 473.07 \| 473.0740 (6.2), 311.0410 (92.9), 293.0305 (18.1), 179.0338 (53.1), 161.0233 (4.4), 149.0078 (100), 135.0437 (44.8), 112.9865 (11.5), 103.0021 (2.4), 87.0072 (16.7) \| 5.25 \| 0.210 \| 12 \| 2 \| \| 49 \| p-coumaryltartaric acid \| C13H12O8 \| 295.0447 \| 295.0447 (1.8), 163.0387 (100), 133.0491 (5.3), 119.0486 (29.4), 149.0076 (0.8), 112.9864 (7.7), 103.0020 (2.4), 87.0070 (6.5) \| 5.71 \| 4.272 \| 12 \| 2 \| \| 50 \| caffeoyl-p-coumaroyltartaric caid \| C22H18O11 \| 457.0777 \| 457.0777 (3.1), 295.0459 (86.4), 277.0352 (37.9), 231.0298 (3.6), 219.0294 (24.9), 179.0339 (47.6), 203.0341 (20.1), 163.0388 (100), 149.0078 (10.2), 135.0437 (55.0), 119.0486 (52.5), 112.9864 (38.0), 103.0022 (2.8), 87.0071 (14.7) \| 5.73 \| 0.143 \| 12 \| 2 \| \| 51 \| tricaffeoyltartatric acid \| C31H24O15 \| 635.1056 \| 635.1056 (29.6), 473.0729 (93.8), 341.0668 (77.5), 323.0569 (3.0), 293.0305 (37.9), 219.0293 (13.5), 179.0339 (17.9), 161.0232 (14.6), 149.0083 (2.5), 145.0282 (20.9), 135.0437 (28.4), 112.9865 (100), 87.0072 (8.2) \| 6.01 \| 2.587 \| 1 \| 2 \| \| 52 \| caffeoyl-feruloyltartaric acid \| C23H20O12 \| 487.0881 \| 487.0881 (5.1), 325.0566 (100), 307.0463 (35.2), 293.0305 (65.1), 233.0450 (17.3), 219.0293 (32.3), 193.0497 (87.2), 179.0338 (72.7), 161.0230 (20.1), 135.0437 (76.7), 134.0358 (60.8), 112.9864 (52.5), 103.0020 (4.3), 87.0071 (16.5) \| 6.09 \| 0.142 \| 12 \| 2 \| \| 53 \| caffeoyl-p-coumaroyltartaric acid isomer \| C22H18O11 \| 457.0778 \| 295.0459 (100), 277.0354 (17.2), 219.0292 (17.2), 203.0345 (7.3), 179.0338 (1.9), 163.0388 (85.0), 149.0076 (35.3), 135.0436 (39.0), 119.0488 (39.8), 112.9864 (31.7), 103.0022 (2.9), 87.0071 (14.2) \| 6.11 \| 0.275 \| 12 \| 2 \| \| 54 \| caffeoyl-p-coumaroyltartaric acid isomer \| C22H18O11 \| 457.0771 \| 457.0771 (5.8), 295.0456 (97.0), 277.0349 (13.2), 219.0290 (15.6), 203.0348 (10.3), 179.0338 (35.1), 163.0388 (95.9), 161.0235 (9.8), 149.0076 (100), 135.0436 (48.9), 119.0486 (51.2), 112.9864 (40.3), 103.0021 (6.3), 87.0070 (25.7) \| 6.67 \| 1.060 \| 1 \| 2 \| \| 55 \| caffeoyl-feruloyltartaric acid isomer \| C23H20O12 \| 487.0885 \| 487.0885 (47.4), 325.0566 (77.9), 293.0303 (2.7), 179.0339 (9.3), 163.0235 (100), 161.0231 (52.3), 135.0437 (23.0), 134.0362 (1.1), 112.9865 (4.2), 103.0020 (19.0), 87.0070 (3.5) \| 7.87 \| 0.556 \| 12 \| 2 \| \| Flavones, flavonols, and flavanones \| Flavones, flavonols, and flavanones \| Flavones, flavonols, and flavanones \| Flavones, flavonols, and flavanones \| Flavones, flavonols, and flavanones \| Flavones, flavonols, and flavanones \| Flavones, flavonols, and flavanones \| Flavones, flavonols, and flavanones \| Flavones, flavonols, and flavanones \| \| 56 \| quercetin 3-O-hexosyl-O-hexuronide \| C27H28O18 \| 639.1212 \| 639.1212 (100), 463.0887 (79.8), 343.0458 (1.0), 301.0349 (23.2), 300.0276 (44.3), 271.0247 (46.5), 178.9987 (3.5), 151.0025 (5.6), 107.0125 (0.8) \| 3.26 \| 1.460 \| 2 \| 2 \| \| 57 \| quercetin 3,7-O-dihexoside \| C27H30O17 \| 625.1418 \| 625.1418 (100), 463.0876 (21.7), 462.0809 (35.1), 301.0353 (36.0), 300.0256 (7.8), 271.0247 (45.3), 151.0023 (5.7), 121.0282 (0.6), 107.0122 (1.7) \| 3.40 \| 1.212 \| 2 \| 2 \| \| 58 \| isoetin O-hexosyl-O-hexoside \| C27H30O17 \| 625.1414 \| 625.1414 (100), 463.0892 (0.7), 301.0353 (66.7), 300.0274 (17.9), 271.0243 (0.5), 243.0294 (0.8), 151.0024 (3.4), 149.0231 (3.9), 107.0122 (2.2) \| 4.01 \| 1.708 \| 2 \| 3 \| \| 59 \| apigenin 7-O-hexosyl-O-hexuronide \| C27H28O16 \| 607.1315 \| 607.1315 (100), 445.0759 (1.8), 431.0985 (21.1), 345.0608 (0.4), 311.0579 (2.8), 269.0454 (64.5), 268.0377 (41.4), 151.0027 (2.0), 117.0177 (1.9), 107.0124 (0.9) \| 4.32 \| 1.766 \| 2 \| 2 \| \| 60 \| isoetin-7-O-hexoside \| C21H20O12 \| 463.0887 \| 463.0887 (100), 301.0351 (36.2), 300.0276 (34.7), 271.0242 (0.7), 227.0348 (0.7), 243.0291 (6.6), 151.0022 (7.6), 149.0442 (6.3), 149.0236 (0.9), 107.0120 (3.5) \| 4.55 \| 0.455 \| 2 \| 3 \| \| 61 \| isorhamnetin 3,7-O-dihexoside \| C28H32O17 \| 639.1577 \| 639.1577 (100), 578.2167 (1.2), 519.1140 (0.7), 477.1035 (6.3), 476.0967 (15.3), 315.0513 (9.8), 314.0408 (9.4), 313.0356 (56.9), 300.256 (0.7), 285.0405 (9.0), 151.0019 (1.5), 107.0120 (0.3) \| 4.65 \| 1.529 \| 2 \| 2 \| \| 62 \| eriodictyol 7-O-dihexoside \| C27H32O16 \| 611.1624 \| 611.16.24 (71.2), 287.0562 (75.1), 267.1056 (0.8), 151.0023 (100), 135.0436 (59.2), 125.0228 (5.7), 107.0121 (23.2) \| 4.58 \| 0.969 \| 2 \| 1 \| \| 63 \| luteolin 7-O-dihexoside \| C27H30O16 \| 609.1468 \| 609.1468 (74.9), 447.0940 (0.3), 285.0402 (100), 284.0326 (7.64), 267.0293 (0.4), 256.0377 (0.9), 241.0497 (0.8), 217.0498 (1.3), 199.0393 (2.1), 151.0024 (3.7), 133.0280 (5.1), 107.0122 (2.3) \| 4.72 \| 1.120 \| 12 \| 1 \| \| 64 \| luteolin 7-O-hexosyl-O-hexuronide \| C27H28O17 \| 623.1260 \| 623.1260 (64.1), 461.0724 (6.4), 285.0403 (100), 284.0320 (2.42), 256.0376 (0.8), 241.0497 (0.8), 229.0495 (0.2), 217.0498 (1.2), 199.0392 (2.0), 151.0023 (1.9), 133.0278 (5.5), 107.0123 (1.9) \| 4.84 \| 1.071 \| 1 \| 2 \| \| 65 \| luteolin 7-O-pentosyl-O-hexoside \| C26H28O15 \| 579.1302 \| 579.1302 (74.6), 447.0934 (0.3), 285.0403 (100), 256.0364 (1.7), 227.0339 (0.9), 217.0500 (1.3), 151.0023 (4.0), 133.0279 (4.7), 107.0124 (2.0) \| 5.08 \| 1.082 \| 12 \| 2 \| \| 66 \| rutin a \| C27H30O16 \| 609.1461 \| 609.1469 (100), 301.0347 (37.2), 300.0276 (62.2), 271.0248 (35.6), 255.0296 (16.8), 243.0291 (7.1), 227.0353 (1.9), 211.0404 (0.7), 199.0384 (0.5), 178.9982 (3.1), 163.0024 (1.0), 151.0025 (5.2), 121.0276 (0.5), 107.0122 (1.6) \| 5.09 \| 1.317 \| 12 \| 1 \| \| 67 \| isoquercitrin a \| C21H20O12 \| 463.0884 \| 463.0884 (100), 301.0343 (36.7), 300.0273 (81.5), 271.0249 (31.4), 227.0342 (0.89), 178.9974 (2.63), 243.0291 (6.57), 151.0022 (7.56), 107.0120 (3.47) \| 5.18 \| 0.455 \| 1 \| 1 \| \| 68 \| luteolin 7-O-rutinoside a \| C27H30O15 \| 593.1520 \| 593.1520 (84.1), 285.0405 (100), 284.0331 (11.61), 256.0371 (1.2), 241.0522 (0.6), 217.0493 (0.6), 199.0389 (1.1), 151.0021 (3.4), 133.0279 (4.9), 107.0123 (2.0) \| 5.22 \| 1.428 \| 12 \| 1 \| \| 69 \| eriodictyol-7-O-hexoside \| C21H22O11 \| 449.1086 \| 449.1086 (15.3), 287.0560 (100), 151.0023 (66.2), 135.0437 (52.9), 125.0229 (3.8), 107.0122 (13.7) \| 5.26 \| 0.856 \| 2 \| 2 \| \| 70 \| eriodictyol-7-O-hexuronide \| C28H16O6 \| 463.0830 \| 463.0830 (72.0), 287.0471 (40.2), 286.0437 (100), 151.0023 (33.0), 135.0436 (20.4), 125.0224 (1.7) \| 5.33 \| 1.447 \| 1 \| 2 \| \| 71 \| luteolin 7-O-hexuronide \| C21H18O12 \| 461.0731 \| 461.0731 (100), 285.0403 (100), 267.0289 (0.3), 241.0501 (0.8), 229.0480 (0.2), 217.0503 (1.1), 151.0024 (4.7), 133.0280 (9.5), 107.0122 (2.5) \| 5.35 \| 1.173 \| 1 \| 2 \| \| 72 \| luteolin 7-O-glucoside a \| C21H20O11 \| 447.0933 \| 447.0933 (100), 285.0401 (86.0), 284.0325 (35.24), 256.0374 (3.9), 239.0343 (0.6), 227.0343 (2.1), 211.0391 (1.1), 151.0023 (5.0), 133.0279 (4.1), 107.0123 (2.9) \| 5.39 \| 0.012 \| 12 \| 1 \| \| 73 \| apigenin 4′-O-hexoside \| C21H20O10 \| 431.0987 \| 431.0987 (83.9), 269.0455 (100), 225.0548 (4.4), 151.0024 (3.5), 117.0330 (9.3), 107.0122 (4.9) \| 5.44 \| 0.673 \| 2 \| 2 \| \| 74 \| chrysoeriol 4′-O-dihexoside \| C28H32O16 \| 623.1628 \| 623.1628 (32.7), 299.0559 (100), 298.0479 (0.2), 284.0326 (43.0), 227.0339 (0.6), 151.0022 (1.3), 133.0279 (0.2), 107.0119 (0.6) \| 5.47 \| 1.640 \| 2 \| 2 \| \| 75 \| quercetin 3-O-acetylhexoside \| C23H22O13 \| 505.0992 \| 505.0992 (5.9), 463.0870 (1.1), 301.0344 (33.9), 300.0274 (87.0), 271.0247 (42.5), 255.0294 (18.5), 243.0298 (7.7), 227.0334 (1.4), 178.9976 (28.3), 163.0028 (2.4), 151.0019 (5.4), 107.0123 (1.2) \| 5.61 \| 0.765 \| 12 \| 2 \| \| 76 \| isoetin 4′-O-hexoside \| C21H20O12 \| 463.0886 \| 463.0886 (96.52), 301.0353 (100), 300.0271 (7.99), 151.0024 (16.76), 149.0231 (30.03), 107.0122 (10.60) \| 5.63 \| 0.455 \| 1 \| 3 \| \| 77 \| apigenin 7-O-rutinoside \| C27H30O14 \| 577.1572 \| 577.1572 (33.6), 269.0454 (100), 151.0021 (1.0), 149.0231 (0.9), 117.0330 (3.7), 107.0125 (1.7) \| 5.81 \| 1.648 \| 2 \| 1 \| \| 78 \| isorhamnetin 3-O-glucoside a \| C22H22O12 \| 477.1042 \| 477.1040 (100), 315.0504 (11.2), 314.0434 (59.5), 299.0210 (3.5), 271.0248 (16.6), 257.0447 (4.8), 243.0293 (25.7), 227.0346 (3.9), 215.0343 (3.6), 199.0390 (1.8), 151.0021 (1.7), 135.0435 (5.5) \| 5.92 \| 0.253 \| 12 \| 1 \| \| 79 \| apigenin 7-O-glucoside a \| C21H20O10 \| 431.0982 \| 431.0981 (100), 268.0375 (65.3), 269.0447 (28.6), 211.0394 (1.9), 151.0023 (4.1), 117.0329 (1.8), 107.0123 (2.4) \| 6.04 \| 0.831 \| 12 \| 1 \| \| 80 \| luteolin 7-O-glucoside a \| C21H20O11 \| 447.0933 \| 447.0933 (23.7), 285.0403 (100), 284.0334 (1.1), 211.1346 (0.2), 151.0024 (5.2), 133.0280 (10.0), 107.0123 (2.4) \| 6.05 \| 0.012 \| 12 \| 1 \| \| 81 \| apigenin 7-O-hexuronide \| C21H18O11 \| 445.0778 \| 445.0778 (27.5), 269.0454 (100), 225.0546 (1.6), 175.0236 (14.9), 151.0022 (1.8), 117.0331 (7.2), 107.0122 (2.7) \| 6.10 \| 0.372 \| 12 \| 2 \| \| 82 \| luteolin 7-O-acetylhexoside \| C23H22O12 \| 489.1043 \| 489.1043 (100), 447.1004 (0.3), 327.0506 (1.1), 285.0403 (70.2), 284.0326 (36.2), 227.0343 (1.9), 151.0024 (4.0), 133.0280 (3.1), 107.0125 (2.8) \| 6.23 \| 1.003 \| 12 \| 2 \| \| 83 \| luteolin 7-O-dihydroxybutyryl-O-acetylhexoside \| C27H28O15 \| 591.1364 \| 591.1364 (100), 529.1375 (12.9), 489.1044 (37.9), 447.0934 (28.0), 327.0497 (1.0), 285.0404 (91.1), 284.0326 (30.3), 227.0340 (1.0), 151.0025 (4.5), 133.0282 (6.4), 107.0121 (1.2) \| 6.26 \| 1.365 \| 12 \| 2 \| \| 84 \| isorhamnetin 3-O-acetylhexoside \| C24H24O13 \| 519.1149 \| 519.1149 (100), 387.1098 (0.8), 357.0608 (0.7), 315.0509 (47.5), 314.0434 (58.8), 271.1098 (0.8), 357.0608 (0.7), 315.0509 (47.5), 314.0434 (58.8), 271.0248 (25.7), 257.0454 (2.3), 243.0295 (21.2), 227.0347 (4.9), 151.0025 (4.0), 133.0281 (2.9), 135.0433 (0.2), 107.0123 (1.1) \| 6.46 \| 0.994 \| 12 \| 1 \| \| 85 \| isoetin \| C15H10O7 \| 301.0352 \| 301.0352 (100), 271.0245 (0.3), 255.0290 (0.9), 151.0022 (4.3), 149.0231 (21.3), 137.0230 (0.2), 133.0279 (0.3), 121.0278 (0.9), 107.0214 (3.3) \| 6.48 \| −0.418 \| 2 \| 3 \| \| 86 \| chrysoeriol-4′-O-hexuronide \| C23H22O12 \| 475.0882 \| 475.0888 (100), 299.0558 (27.6), 284.0325 (45.1), 256.0376 (5.2), 227.0347 (1.6), 199.0385 (0.9), 151.0024 (4.9), 133.0278 (4.7), 107.0120 (3.4), \| 6.75 \| 1.265 \| 12 \| 2 \| \| 87 \| apigenin 7-O-acetylhexoside \| C23H22O11 \| 473.1093 \| 473.1093 (100), 413.0882 (3.4), 311.0438 (3.1), 297.0413 (1.3), 269.0448 (20.8), 268.0377 (52.6), 211.0392 (1.4), 151.0023 (3.9), 117.0330 (1.9), 107.0123 (2.7) \| 6.76 \| 0.730 \| 2 \| 2 \| \| 88 \| apigenin 7-O-dihydroxybutyryl-O-acetylhexoside \| C27H28O14 \| 575.1411 \| 575.1411 (45.6), 513.1402 (12.8), 473.1091 (40.5), 431.0986 (40.8), 413.0847 (0.4), 311.0564 (0.8), 269.0453 (100), 268.0376 (32.7), 151.0021 (2.4), 117.0031 (4.8), 107.0121 (3.2) \| 6.93 \| 0.837 \| 2 \| 2 \| \| 89 \| luteolin 7-O-acetylhexoside isomer \| C23H22O12 \| 489.1041 \| 489.1041 (100), 447.0960 (0.8), 327.0508 (1.3), 285.0401 (73.7), 284.0325 (38.1), 227.0344 (2.1), 151.0023 (4.9), 133.0280 (3.7), 107.0122 (3.2) \| 7.05 \| 0.431 \| 12 \| 2 \| \| 90 \| eriodyctiol a \| C15H12O6 \| 287.0561 \| 287.0563 (18.5), 151.0023 (100), 135.0437 (89.3), 125.0228 (5.1), 109.0277 (1.8), 107.0123 (12.5) \| 7.42 \| −0.074 \| 12 \| 1 \| \| 91 \| luteolin a \| C15H10O6 \| 285.0405 \| 285.0402 (100), 257.0457 (0.3), 241.0984 (0.9), 229.0490 (0.2), 217.0498 (1.1), 199.0391 (1.7), 151.0023 (4.9), 133.0280 (23.3), 107.0123 (4.0) \| 7.57 \| −1.057 \| 12 \| 1 \| \| 92 \| quercetin a \| C15H10O7 \| 301.0354 \| 301.0354 (100), 257.0477 (22.1), 178.9975 (17.6), 151.0023 (36.7), 121.279 (9.4), 107.0122 (9.5) \| 7.63 \| 0.080 \| 2 \| 1 \| \| 93 \| apigenin a \| C15H10O5 \| 269.0457 \| 269.0453 (100), 225.0549 (0.5), 201.0545 (0.8), 151.0023 (6.4), 117.0330 (20.4), 107.0122 (5.3) \| 8.62 \| −0.917 \| 12 \| 1 \| \| 94 \| cirsiliol \| C17H14O7 \| 329.0667 \| 329.0667 (100), 314.0434 (60.3), 299.0197 (32.3), 300.0237 (2.8), 271.0247 (25.5), 243.296 (4.8), 199.0399 (1.9), 161.0231 (11.5), 151.0024 (2.0), 133.0279 (0.3), 107.0120 (0.2) \| 8.87 \| 0.012 \| 2 \| 1 \| \| 95 \| chrysoeriol \| C16H12O6 \| 299.0551 \| 299.0551 (100), 285.0347 (7.3), 284.0325 (81.1), 256.0379 (18.0), 151.0023 (4.6) \| 8.91 \| −3.449 \| 12 \| 2 \| \| Sesquiterpene lactones and derivatives \| Sesquiterpene lactones and derivatives \| Sesquiterpene lactones and derivatives \| Sesquiterpene lactones and derivatives \| Sesquiterpene lactones and derivatives \| Sesquiterpene lactones and derivatives \| Sesquiterpene lactones and derivatives \| Sesquiterpene lactones and derivatives \| Sesquiterpene lactones and derivatives \| \| № \| Tentatively Annotated Compound \| Molecular Formula \| Exact Mass [M + H]+ \| Fragmentation Pattern in (+) ESI-MS/MS \| tR (min) \| Δ ppm \| Distribution \| Level of Identification [28] \| \| 96 \| 11β,13-dihydro-15-glucopyranosyllactucin \| C21H28O10 \| 441.1745 \| 441.1745 (28.0), 279.1222 (100), 261.1116 (22.0), 243.1011 (12.2), 233.1168 (5.6), 215.1064 (30.6), 197.0959 (5.9), 187.1115 (13.9), 169.1018 (8.7), 159.0804 (19.7), 131.0856 (12.6), 105.0703 (4.7), 91.0548 (4.6), 81.0341 (7.7), 79.0549 (1.2) \| 3.12 \| −2.388 \| 12 \| 2 \| \| 97 \| 11β,13-dihydrolactucin \| C15H18O5 \| 279.1219 \| 279.1219 (54.9), 261.1115 (20.2), 243.1011 (36.5), 233.1168 (23.8), 215.1064 (100), 197.0958 (27.0), 187.1114 (50.3), 169.1010 (29.9), 159.0803 (82.4), 131.0855 (45.9), 105.0702 (19.8), 91.0547 (20.7), 81.0705 (3.3), 79.0548 (5.8) \| 3.93 \| −2.795 \| 12 \| 2 \| \| 98 \| lactucin \| C15H16O5 \| 277.1069 \| 277.1069 (57.5), 259.0959 (25.7), 241.0855 (45.1), 231.1010 (22.1), 213.0907 (100), 195.0804 (35.8), 185.0960 (80.1), 167.0854 (38.6), 157.1012 (16.9), 142.0778 (32.5), 129.0701 (30.8), 109.0286 (3.7), 91.0547 (29.3), 81.0342 (3.4), 79.0549 (9.1) \| 4.86 \| −0.650 \| 12 \| 2 \| \| 99 \| 15-hydroxytaraxacin \| C15H14O4 \| 259.0959 \| 259.0959 (100), 241.0855 (43.6), 231.1012 (30.5), 213.0907 (91.2), 195.0802 (32.9), 185.0959 (69.1), 167.0853 (28.5), 157.1011 (9.1), 142.0777 (36.8), 129.0701 (21.6), 109.0287 (10.9), 79.0549 (5.4) \| 5.74 \| −2.298 \| 12 \| 2 \| \| 100 \| 8-acetyl-15β-D glucopyranosyllactucin a \| C23H28O11 \| 481.1699 \| 482.2729 (2.7),319.1171 (100), 259.0963 (79.327), 241.0854 (16.5), 213.0909 (35.5), 203.1062 (2.7), 185.0960 (17.8), 167.0852 (10.7), 157.1012 (2.8), 129.0701 (7.7), 97.5018 (13.0), 85.0288 (23.9) \| 5.94 \| −1.077 \| 12 \| 1 \| \| 101 \| 8-deoxylactucin \| C15H16O4 \| 261.1115 \| 261.1115 (51.4), 243.1011 (32.8), 225.0905 (20.7), 215.1064 (100), 197.0959 (43.9), 187.0753 (80.1), 169.1010 (46.9), 159.0804 (66.0), 131.0856 (45.1), 121.0285 (20.5), 91.0547 (17.7), 81.0706 (1.1) \| 6.09 \| −2.625 \| 12 \| 2 \| \| 102 \| 8-acetyl-11β,13-dihydro-15-glucopyranosyllactucin \| C23H30O11 \| 483.1853 \| 483.2204 (9.7), 321.1326 (100), 261.1114 (72.2), 243.1009 (37.2), 215.1064 (32.9), 205.0860 (5.8), 187.0755 (15.9), 169.1006 (11.8), 159.0806 (17.8), 131.0858 (12.5), 105.0699 (2.6), 91.0547 (4.9), 81.0343 (5.5) \| 6.12 \| −1.631 \| 12 \| 2 \| \| 103 \| 8-acetyl-11β,13-dihydrolactucin \| C17H21O6 \| 321.1329 \| 321.1358 (3.4), 261.1116 (100), 243.1011 (11.7), 233.1170 (3.4), 215.1064 (39.2), 205.0860 (12.1), 187.1114 (16.4), 169.1011 (16.9), 159.0803 (21.9), 131.0856 (17.6), 105.0702 (6.1), 91.0548 (6.1), 81.0708 (1.0), 79.0547 (1.1) \| 6.25 \| −1.229 \| 12 \| 2 \| \| 104 \| 8-acetyllactucin \| C17H18O6 \| 319.1170 \| 319.1170 (2.6), 259.0961 (100), 241.0855 (23.7), 231.1013 (11.0), 213.0907 (56.1), 195.0802 (14.6), 185.0960 (39.6), 167.0854 (13.7), 157.1012 (14.1), 142.0777 (14.0), 129.0700 (11.9), 109.0287 (21.0), 79.0550 (4.4), 81.0341 (7.3), 91.0548 (6.1) \| 6.99 \| −2.020 \| 12 \| 2 \| \| 105 \| sonchuside A a \| C21H32O8 \| 411.2026[M − H]− \| 457.2084 (+HCHO) (100), 411.2026 (30.9), 249.1495 (28.8), 205.1596 (1.1), 145.0602 (3.7), 127.0496 (5.2) \| 7.85 \| 0.484 \| 1.2 \| 1 \| \| Coumarins \| Coumarins \| Coumarins \| Coumarins \| Coumarins \| Coumarins \| Coumarins \| Coumarins \| Coumarins \| \| 106 \| aesculin \| C15H16O9 \| 341.0863 \| 341.0863 (7.6), 179.0336 (100), 151.0390 (2.6), 135.0437 (1.9), 133.0284 (7.9), 123.0441 (9.3), 85.0289 (2.2), 69.0341 (0.4) \| 2.71 \| −0.222 \| 12 \| 2 \| \| 107 \| 7-hydoxycoumarin (umbelliferone) \| C9H6O3 \| 163.0388 \| 163.0388 (100), 145.0283 (40.7), 135.0440 (64.7), 121.0650 (1.7), 117.0337 (40.6), 107.0495 (8.9), 89.0391 (48.2), 79.0549 (6.8) \| 3.16 \| −1.230 \| 12 \| 2 \| \| 108 \| aesculetin \| C9H6O4 \| 179.0337 \| 179.0337 (100), 151.0389 (5.9), 133.0284 (15.2), 123.0442 (24.6), 105.0701 (2.8), 91.0548 (1.5) \| 3.47 \| −0.979 \| 12 \| 2 \| \| 109 \| coumarin \| C9H6O2 \| 147.0440 \| 147.0440 (49.4), 119.0493 (100), 91.0547 (85.2), 65.0394 (12.1), 53.0394 (0.4) \| 3.94 \| −0.517 \| 12 \| 2 \| \| 110 \| ostruthol \| C21H22O7 \| 387.1431 \| 387.1431 (66.9), 369.1321 (57.5), 351.1218 (16.5), 299.0544 (11.6), 233.0806 (6.7), 203.0697 (6.8), 191.0701 (8.1), 165.0545 (100), 166.0577 (2.7), 151.0386 (7.6), 137.0397 (48.1), 114.0916 (12.6), 91.0547 (2.7), 79.0549 (7.2) \| 6.52 \| −1.962 \| 12 \| 2 \| Compounds 58, 60, and 76 were identified as glycosides of isoetin [85]. Isoetin is a flavone, an isobaric compound of quercetin with [M−H]− at m/z 301.035, with an additional hydroxyl group in the B ring. The structure could be evidenced by the presence of more intense RDA fragment ion 1,3B− at m/z 149.023 ($21.32\%$) compared to 1,3A− at m/z 151.003 ($4.27\%$) (Figure 2). The 1,3B− ion is typical for flavones, while 1,2A− could be found in the MS/MS spectrum of the flavanols. The flavone isoetin could be distinguished from the flavonol quercetin by the presence of fragment 1,3B− at m/z 149.023, while 1,2A− at m/z 178.997 is characteristic of quercetin and its glycosides [27]. Compounds 74 and 86 were identified as glycosides of chrysoeriol [95] (m/z at 299.055) based on the MS/MS spectra with diagnostic fragment ions resulting from the successive loss of methyl radical •CH3 (at m/z 284.032), CO (at m/z 256.037) and CHO (at m/z 227.034), as well as RDA ions 1,3A− at m/z 151.003, 0,4A− at m/z 107.012, and 1,3B− at m/z 133.028. ( Table 1). Compound 94 ([M−H]− at m/z 329.066) showed fragment ions at m/z 314.043 [M−H-•CH3]-, 299.019 [M−H-2•CH3]−, 271.025 [M−H-2•CH3-CO]−, 227.035 [M−H-2•CH3-CO-CO2]-, as well as RDA fragment ions at m/z 161.023 (1,3A−-CH4-H2O), 133.027 (1,3B−) 151.002 (1,3A−-CH4-CO) (Table 1, Figure S4). Compound 94 was identified as cirsiliol [19]. ## Flavonols Compounds 56–57, 66–67, and 75 revealed losses of hexosyl-hexuronic acid, dihexose, rutinose, hexose, and acetylhexose, respectively; the aglycone was recorded at m/z 301.034 and corresponded to quercetin [92]. As a result of RDA cleavage of C-ring, fragment ions 1,3A− at m/z 151.002, 0,4A− at m/z 107.012, 1,2A− at m/z 179.998, and 1,2B− at m/z 121.028 were formed (Table 1, Figure S2). Similarly, 61, 78, and 84 were identified as glycosides of isorhamnetin [19]. ## Flavanones In the MS/MS spectra of 62, 69, and 70, loss of dihexose, hexose, and hexuronic acid, respectively, was observed; the aglycone was recorded at m/z 287.056 corresponding to eriodictyol [27]. Key fragments in the identification of these compounds were the RDA ions 1,3B− at m/z 135.043 and 1,4A− at m/z 125.022 (Table 1, Figure S5). Depending on the intensity and the ratio of the fragment ions [Y0]− and [Y0-H]−, the sites for binding the sugar parts to the aglycones were also determined [29]. The identification of compounds 66–68, 72, 78–80, and 90–93 was confirmed by comparison with reference standards. ## Sesquiterpene Lactones (STLs) STL dereplication is based on fragmentation patterns and diagnostic ions in the positive ionization mode as more informative for this class of specialized metabolites [19,30]. Based on accurate mass MS spectra, MS/MS fragmentation, relative intensities of precursor and fragment ions, and elemental composition, nine guanolide STLs (96–104), derivatives of lactucin and dihydrolactucin, were tentatively identified in C. alpina extracts. Among them, three are glycosylated (96, 100, and 102), and three are acetylated (102–104). MS/MS fragmentation of terpenes, including the presence of characteristic ions corresponding to the loss of H2O (−18 Da), 2H2O (−36 Da), CO (−28 Da), CO2 (−44 Da), CH3COOH (−60 Da), as well as accompanying loss of H2O + CO (−46 Da), 2H2O+CO (−64 Da), H2O+CO2 (−62 Da) (Figure S6). In addition, in negative ion a germacranolide [105], sonchuside A was identified in C. alpina leaves extract (Table 1). The identification of compounds 100 and 105 were confirmed by comparison with reference standards [8]. ## Coumarins Compound 109 ([M+H]+ at m/z 147.044) gave a base peak at m/z 119.049 and an intense ion at m/z 91.054 ($85.17\%$), resulting from the sequential loss of two CO groups. Thus, the coumarin structure was proposed for 109 [31]. A similar MS/MS spectrum was obtained for 107, but here an initial loss of H2O was observed, resulting from the loss of the OH group, and the compound was identified as 7-hydroxycoumarin (umbelliferon) (Table 1). By analogy, but with two hydroxyl groups, the fragmentation patterns of aesculetin [108] and aesculin [106] are explained (Table 1, Figure S7) [31]. ### Antioxidant and Enzyme Inhibitory Activity Regarding the content of total phenolic compounds, the leaves showed a higher content (75.13 ± 0.51 mg GAE/g), while a higher content of total flavonoids was observed in the flowering heads of C. alpina (Table 2). Various tests were performed to determine the antioxidant profile of the plant extracts. Tests based on different mechanisms have been used in the current work. The results are presented in Table 2. C. alpina leaves showed higher antioxidant activity in all of the used methods. The DPPH radical scavenging activity of the leaves extract was 132.80 ± 3.77 mg TE/g, and for ABTS, the value was found to be 139.54 ± 0.57 mg TE/g (Table 2). The reducing capacity of the extracts was evaluated by CUPRAC and FRAP experiments (Table 2). The CUPRAC method evaluated the conversion of Cu (II) to Cu (I), and FRAP indicates the reducing potential of the antioxidant, which reacts with the colorless TPTZ/Fe3+ complex to form the blue-colored TPTZ/Fe2+. The leaf extract has a high reducing potential (CUPRAC: 212.93 ± 11.59 mg TE/g and FRAP 141.12 ± 6.64 mg TE/g). One of the most important mechanisms of action of antioxidants is the chelation of pro-oxidant metals. Iron is the most active metal that causes oxidative changes in cells, mainly proteins and lipids. Table 2 presented the total antioxidant activity of the extracts, assessed by the phospho-molybdenum method and the metal-chelating ability. Again, the leaves exhibited the highest total antioxidant activity (1.55 ± 0.04 mmol TE/g) and metal-chelating ability (36.97 ± 0.51 mg EDTAE/g). The enzyme inhibitory activity of the studied extracts was determined against acetyl- and butyrylcholinesterase, α-amylase, α-glucosidase, and tyrosinase (Table 3). C. alpina leaves extract showed higher acetylcholinesterase (1.98 ± 0.02 mg GALAE/g) and butyrylcholinesterase inhibitory activity (0.74 ± 0.06 mg GALAE/g) (Table 3). Flowering heads showed no butyrylcholinesterase activity. Both enzymes are considered therapeutic targets in the treatment of Alzheimer’s disease. The C. alpina leaves extract also showed high activity against the enzyme tyrosinase (49.87 ± 3.19 mg KAE/g) (Table 3). This enzyme plays a key role in the biosynthesis of melanin, being responsible for skin pigmentation. Increased melanin formation leads to skin diseases such as hyperpigmentation, skin spots, etc. Tyrosinase inhibitors are becoming increasingly important hypopigmenting agents in cosmetic and medicinal products. Regarding α-amylase and α-glucosidase inhibitory effects, the C. alpina flowering heads extract (amylase: 0.47 mmol ACAE/g and glucosidase: 1.05 mmol ACAE/g) was more active on both enzymes than leaves extract (amylase: 0.28 mmol ACAE/g and glucosidase: 0.60 mmol ACAE/g). Inhibition of these enzymes is known to be an important therapeutic strategy to control blood glucose levels in diabetic patients after a carbohydrate-rich diet. In this sense, the tested C. alpina parts could be considered as a multifunctional bioactive agent from antioxidants to enzyme inhibitors, and thus, the presented study could be valuable to provide an effective raw material in the pharmaceutical, nutraceutical, and cosmeceutical industries. ## 2.3. Multivariate Analysis After the univariate analysis, eleven specialized metabolites were used to generate the PLS-DA model. PLS-DA plot demonstrated significant discrimination of both leaves and flowering heads of C. alpina (Figure 3A). A point to note is that there is not any overlap between both extracts, and the model has performed a $100\%$ separation (Figure 3B). The best performance of the model was achieved for 1 component. Afterward, the importance of each bioactivity for the generating of the first component was investigated. As suggested by [32], VIPs above 1 are important and have a significant role in this model. Thus, referring to Figure 3C, all bioactivities, except acetylcholinesterase and tyrosinase, have an important role in the discrimination of the leaves and flowering heads of C. alpina. Therefore, C. alpine leaves appear to be more perspective as a result of their prominent bioactivity (Figure 3D). The bioactivities varied significantly within the studied C. alpina plant parts due to the presence of different metabolites in each organ responsible for the specific biological function and role in plant development, reproduction, and growth [33]. Besides, to visualize the molecules’ contrast between both plant extracts, a line plot was plotted using the peak area database. Before the graphic representation, the peak area was log2 transformed. There is a great variation in the molecule levels for all the subclasses (Figure S8). Regarding the first subclass (carboxylic, hydroxybenzoic, and hydroxycinnamic acids), salicylic acid [13] and syringic acid-O-hexoside [3] were relatively abundant in the leaves extract, while the level of quinic acid [11] was relatively higher in the flowering heads extract. Concerning the second subclass (hydroxycinnamic acids and derivatives), rosmarinic acid [16] was found only in the leaves. In addition, the leaves were rich in caffeoylcitramalic acid [22], while the flowering heads exhibited a relatively high concentration of caffeic acid-O-hexoside isomer [17] and caffeic acid [19]. As regards the acylquinic acids subclass, four compounds, including 3-p-coumaroylquinic acid [24], 1-caffeoyl-3-hydroxydihydrocaffeoylquinic acid [31], 1,3,4-tricaffeoylquinic acid [32], and 1,3-dicaffeoyl-5-hydroxydihydrocaffeoylquinic acid [40] were not presented in the flowering heads extract. However, this extract possessed a relatively high amount of several compounds i.e., neochlorogenic (3-caffeoylquinic) acid [23], 5-feruloylquinic acid [29], 3,4,5-tricaffeoylquinic acid [43], to mention only a few. In contrast, the leaf extract was rich in $\frac{1}{3}$/5-caffeoyl-4-hydroxydihydrocaffeoylquinic acid [30]. Overall, the leaf extract was relatively rich in secondary metabolites belonging to the hydroxycinnamoyltartaric acids subclass. Relating to flavones, flavonols, and flavanones, five compounds are present only in the leaves, while flowering heads have 16. However, among the metabolites presented at once in both plant organs, the flowering heads are richer in several compounds than the leaves, including luteolin 7-O-rutinoside [68], quercetin 3-O-acetylhexoside [75], isorhamnetin 3-O-glucoside [78], apigenin 7-O-glucoside [79], luteolin 7-O-glucoside [80], apigenin 7-O-hexuronide [81], luteolin 7-O-acetylhexoside [82], isorhamnetin 3-O-acetylhexoside [84], eriodyctiol [90], luteolin [91], apigenin [93] and chrysoeriol [95]. Concerning the last two subclasses, both parts present practically the same compounds, notwithstanding some differences observed in 8-acetyl-15β-D -glucopyranosyllactucin [100], 15-hydroxytaraxacin [99], aesculin [106]. Thus, the flowering heads extract is richer in flavonoids compared to the leaves but contains fewer polyphenols, e.g., acylquinic acids and displays lower bioactivity. Moreover, an antagonistic effect between some of the secondary metabolites might exist. Plant polyphenols, e.g., flavonoids and phenolic acids, are multifunctional and can act as reducing agents, hydrogen-donating antioxidants, and singlet oxygen quenchers [15]. Key points in the structure of flavonoids responsible for the antioxidant activity are as follows: the o-dihydroxy structure in the B ring, the 2,3 double bond in conjugation with a 4-oxo function in the C ring, and the 3- and 5-OH groups with 4-oxo function in A and C rings, requiring for maximum radical scavenging potential. Thus, quercetin is satisfied all the above-mentioned determinants and is a more effective antioxidant than the flavanols [15] Regarding the phenolicacids, it was found that the diphenolics, chlorogenic and caffeic acids, demonstrated higher radical scavenging ability than monophenolics (p-coumaric acid), consistent with the chemical criteria applied to diphenolics. Methoxylation of the hydroxyl group in the ortho position of the diphenolic acids, as in ferulic acid, results in a decrease in the scavenging reaction, hydroxylation as in caffeic acid in place of methoxylation is substantially more effective. Ferulic acid is, indeed, expected to be more effective than p-coumaric acid due to the electron-donating methoxy group allowing increased stabilization of the resulting aryloxyl radical through electron delocalization after hydrogen donation by the hydroxyl group [15]. Previous investigation revealed that dicaffeoyl derivatives cichoric acid and 1,5-dicaffeoylquinic acid demonstrated higher DPPH activity compared to monocaffeoyl derivatives, caffeoyltartaric acid (caftaric acid) and chlorogenic acid, respectively [18]. EC50 values for monocaffeoyl derivatives were found to be in the order of 20 μM, while those for dicaffeoyl derivatives had values of about 10 μM [18]. AChE activity of sesquiterpene lactones (lactucin and lactucopicrin) and different chicory extracts was previously determinated using isothermal titration calorimetry (ITC) and docking simulation. The results showed strong interactions of STLs as well as extracts from chicory with AChE. In a test of enzyme activity inhibition after introducing acetylcholine into the model system with STL, a stronger ability to inhibit the hydrolysis of the neurotransmitter was observed for lactucopicrin, which is one of the dominant STL in chicory. The inhibition of enzyme activity was more efficient in the case of extracts [34]. α-Amylase and α-glucosidase inhibitory activities of caffeic and chlorogenic acids in a dose-dependent manner (2–8 μg/mL) were previously evaluated. However, caffeic acid had a significantly higher inhibitory effect on α-amylase with IC50 3.68 μg/mL and α-glucosidase (IC50 = 4.98 μg/mL) than chlorogenic acid (α-amylase IC50 = 9.10 μg/mL and α-glucosidase IC50 = 9.24 μg/mL). Furthermore, both phenolic acids exhibited high antioxidant properties, and caffeic acid demonstrated a higher DPPH effect [35]. The presented study revealed 110 secondary metabolites, including 13 carboxylic, hydroxybenzoic acids, and their glycosides, 9 hydroxycinnamic acids, and derivatives, 21 acylquinic acids, 12 acyltartaric acids, 40 flavones, flavonols, and flavanones, 5 coumarins and 10 sesquiterpene lactones. Ninety-five of all annotated compounds are reported for the first time in C. alpina. Our results for the total flavonoid content could be compared to those obtained for the wild collection of alpine chicory by Alexandru et al. [ 4], while the total phenolic content is significantly higher than their result. Oppositely, the data for the edible shoots of cultivated C. alpina are prominently higher compared to our data. ## 2.4. Cytotoxicity Assay To evaluate the C. alpina cytotoxicity, a human monocytic cell line (THP-1 cells) mimics the behavior of the extracts toward the immune system was used (Figure 4). After 24 h of incubation of macrophage cell line THP-1 with flowering heads and leaves extracts, a slight rise of metabolic activity is measured between 2 and 200 µg.mL−1. We reach toxicity of $70\%$ for 2000 and 3000 µg.mL−1 for flowerings head extracts, and of almost $90\%$ for leaves, at the same concentration. The latter concentrations are very high and could be considered meaningless. ## 3.1. Plant Material C. alpina leaves and flowering heads were collected at Vitosha Mt., Bulgaria at 1720 m a.s.l. ( 42.60° N 23.25° E), during the full flowering stage in August 2022. The plant was identified by one of us (V. B.) according to Stojanov et al. [ 36]. A voucher specimen was deposited at the Herbarium Academiae Scientiarum Bulgariae (SOM 177 802). Twenty plant samples were separated into leaves and flowering heads and dried at room temperature. ## 3.2. Chemicals Acetonitrile (hypergrade for LC–MS), formic acid (for LC-MS), and methanol (analytical grade) were purchased from Chromasolv (Bulgaria). The authentic standards gallic, vanillic, protocatechuic, gentisic, salicylic, p-coumaric, rutin, isoquercitrin, luteolin 7-O-rutinoside, luteolin 7-O-glucoside, isorhamnetin 3-O-glucoside, apigenin 7-O-glucoside, luteolin, quercetin, apigenin, and chrysoeriol were obtained from Extrasynthese (Genay, France). Rosmarinic, caffeic, ferulic, chlorogenic, neochlorogenic, 3,4-dicaffeoylquinic, 3,5-dicaffeoylquinic, 1,5-dicaffeoylquinic, and 4,5-dicaffeoylquinic acid were supplied from Phytolab (Vestenbergsgreuth, Germany). Sonchuside A and 8-acetyl-15β-D -glucopyranosyllactucin were previously isolated and identified by Zidorn et al., 2005 [8]. ## 3.3. Sample Extraction Air-dried powdered leaves (50 g) and flowering heads (10 g) were extracted with $80\%$ MeOH (1:20 w/v) by sonication (100 kHz, ultra-sound bath Biobase UC-20C) for 15 min (×2) at room temperature. The methanol was evaporated in vacuo, and water residues were lyophilized (lyophilizer Biobase BK-FD10P) to yield crude extracts as follows: leaves—10.67 g and flowering heads—0.90 g. Then, the lyophilized extracts were dissolved in $80\%$ methanol (0.1 mg/mL), filtered through a 0.45 μm syringe filter (Polypure II, Alltech, Lokeren, Belgium), and an aliquot (2 mL) of each solution was subjected to UHPLC–HRMS analyses. The same extracts were used for pharmacological tests. ## 3.4. Ultra-High-Performance Liquid Chromatography–High Resolution Mass Spectrometry (UHPLC-HRMS) Mass spectrometry analyses were carried out on a Q Exactive Plus mass spectrometer (ThermoFisher Scientific, Inc., Waltham, MA, USA) equipped with heated electrospray ionization (HESI-II) probe (ThermoScientific). The mass spectrometer was operated in negative and positive ESI modes within the m/z range from 100 to 1000. The other parameters were as follows: spray voltage 3.5 kV (+) and 2.5 kV (−); sheath gas flow rate 38; auxiliary gas flow rate 12; spare gas flow rate 0; capillary temperature 320 °C; probe heater temperature 320 °C; S-lens RF level 50; scan mode: full MS (resolution 70,000) and MS/MS [17,500]. The chromatographic separation was performed on a reversed-phase column Kromasil EternityXT C18 (1.8 µm, 2.1 × 100 mm) at 40 °C. The chromatographic analyses were run using $0.1\%$ formic acid in water (A) and $0.1\%$ formic acid in acetonitrile (B) as a mobile phase. The flow rate was 0.3 mL/min. The run time was 33 min. The following gradient elution program was used: 0–1 min, 0–$5\%$ B; 1–20 min, 5–$30\%$ B; 20–25 min, 30–$50\%$ B; 25–30 min, 50–$70\%$ B; 30–33 min, 70–$95\%$; 33–34 min 95–$5\%$B. Equilibration time was 4 min [19]. Data were processed by Xcalibur 4.2 (Thermo Scientific) instrument control/data handling software. Metabolite profiling using MZmine 2 software was applied to the UHPLC–HRMS raw files of the studied C. alpina extracts. ## 3.5. Total Phenolic and Flavonoid Content Total phenols and flavonoids were evaluated as gallic acid (GAE) and rutin (RE) equivalents, respectively, using spectrophotometric methods. The experiments were performed as previously reported [37,38]. ## 3.6. Determination of Antioxidant and Enzyme Inhibitory Activities Extracts antioxidant and enzyme inhibitory effects (0.2–1 mg/mL) were evaluated using spectrophotometric assays. Detailed protocols were reported elsewhere [19,39]. ## 3.7. Cell Line and Culture The human monocytic THP-1 (TIB-202) cell line was obtained from the American Type Culture Collection (ATCC, Manassas, VA, USA). The cells grown in RPMI 1640 were supplemented with $10\%$ fetal bovine serum (FBS), $1\%$ glutamine, $1\%$ penicillin/streptomycin, and $0.5\%$ Amphotericin B. Cells were cultured in a humidified atmosphere at 37 °C, under a $5\%$ CO2 atmosphere [21]. ## 3.8. Cytotoxicity Assay THP-1 cells (at 1.105 cells/mL) cells in RPMI medium (Thermo-Fisher) supplemented with $10\%$ FBS were seeded in each well of a 48-well plate ($$n = 4$$). Cells were permitted to adhere for 24 h, and then treated with leaves and flowering heads C. alpina extracts in the medium for 24 h. Then 40 µL of WST-1 testing solutions (Sigma-Aldrich, St. Louis, MO, USA) was added to each well and the plate incubated at 37 °C for 2 h. The contents of each well were laid down in 3 wells of a 96-well plate. The absorbances were measured at 350 and 630 nm with an Omega StarLab spectrophotometer (Omega, Ortenberg, Germany). ## 3.9. Statistical Analysis In the antioxidant and enzyme inhibitory assays, the values are expressed as mean ± SD of three parallel experiments. In terms of antioxidant and enzyme inhibitory abilities, the student t-test (α = 0.05) was performed to determine differences between the tested extracts. The statistical analysis was performed using XlStat 16.0 software. Clustered image maps (CIM) were used to visualize metabolite variation among the extracts. Prior to CIM analyses, data were normalized and centered. Afterward, supervised partial least-square discriminant analysis (PLS-DA) was performed to discriminate the different parts regarding their biological activities. Then CIM was applied to PLS-DA outcomes to characterize each extract. Lastly, Pearson’s correlation coefficients were calculated to evaluate the relationship between secondary metabolites and biological activities, respectively. ## 4. Conclusions More than 100 secondary metabolites, including carboxylic, hydroxybenzoic, hydroxycinnamic, acylquinic, and acyltartaric acids, flavones, flavonols, flavanones, sesquiterpene lactones, coumarins, and their derivatives were annotated/dereplicated in the C. alpina leaves and flowering heads. Ninety-five, including acylquinic acids, acyltartaric acids, and flavonoids, were reported for the first time in C. alpina. Cichoric, caftaric, and chlorogenic acids dominated in the leaves, while apigenin, 3,5-di, and 3,4-dicaffeoylquinic acids dominated in the flowering heads profiling. The connection between the different plant parts and biological activity was performed using multivariate statistical analyses. The pronounced antioxidant activity (DDPH, FRAP, CUPRAC, ABTS, Chelating, and Phosphomolibdenum capacity) and enzyme inhibitory potential against AChE, BChE, tyrosinase, and lipase of the leaves extract could be related to the higher content of total polyphenols and the presence of acyltartaric and monoacylquinic acids compare to flowering heads. The prominent α-glucosidase and α-amylase inhibitory activity of the flowering heads correspond to the higher level of total flavonoids, luteolin, apigenin, and their glycosides. The studied extracts expressed low cytotoxicity towards THP-1 viability. In addition to inducing an antioxidant response, C. alpina extracts displayed enzyme inhibitory effects in vitro, which generates interest in the plant as a potential candidate for attenuating metabolic-related disorders. 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--- title: 'Intergenerational Inheritance of Hepatic Steatosis in a Mouse Model of Childhood Obesity: Potential Involvement of Germ-Line microRNAs' authors: - Francesc Ribas-Aulinas - Sílvia Ribo - Eduard Casas - Marta Mourin-Fernandez - Marta Ramon-Krauel - Ruben Diaz - Carles Lerin - Susana G. Kalko - Tanya Vavouri - Josep C. Jimenez-Chillaron journal: Nutrients year: 2023 pmcid: PMC10005268 doi: 10.3390/nu15051241 license: CC BY 4.0 --- # Intergenerational Inheritance of Hepatic Steatosis in a Mouse Model of Childhood Obesity: Potential Involvement of Germ-Line microRNAs ## Abstract Childhood obesity increases the risk of developing metabolic syndrome later in life. Moreover, metabolic dysfunction may be inherited into the following generation through non-genomic mechanisms, with epigenetics as a plausible candidate. The pathways involved in the development of metabolic dysfunction across generations in the context of childhood obesity remain largely unexplored. We have developed a mouse model of early adiposity by reducing litter size at birth (small litter group, SL: 4 pups/dam; control group, C: 8 pups/dam). Mice raised in small litters (SL) developed obesity, insulin resistance and hepatic steatosis with aging. Strikingly, the offspring of SL males (SL-F1) also developed hepatic steatosis. Paternal transmission of an environmentally induced phenotype strongly suggests epigenetic inheritance. We analyzed the hepatic transcriptome in C-F1 and SL-F1 mice to identify pathways involved in the development of hepatic steatosis. We found that the circadian rhythm and lipid metabolic process were the ontologies with highest significance in the liver of SL-F1 mice. We explored whether DNA methylation and small non-coding RNAs might be involved in mediating intergenerational effects. Sperm DNA methylation was largely altered in SL mice. However, these changes did not correlate with the hepatic transcriptome. Next, we analyzed small non-coding RNA content in the testes of mice from the parental generation. Two miRNAs (miR-457 and miR-201) appeared differentially expressed in the testes of SL-F0 mice. They are known to be expressed in mature spermatozoa, but not in oocytes nor early embryos, and they may regulate the transcription of lipogenic genes, but not clock genes, in hepatocytes. Hence, they are strong candidates to mediate the inheritance of adult hepatic steatosis in our murine model. In conclusion, litter size reduction leads to intergenerational effects through non-genomic mechanisms. In our model, DNA methylation does not seem to play a role on the circadian rhythm nor lipid genes. However, at least two paternal miRNAs might influence the expression of a few lipid-related genes in the first-generation offspring, F1. ## 1. Introduction Childhood obesity is a major risk factor for chronic adult diseases, including type-2 diabetes, cardiovascular disease, non-alcoholic fatty liver, or some types of cancer [1,2,3], which collectively shorten lifespan [4]. Furthermore, epidemiological and experimental evidence show that early life nutritional imbalances and childhood obesity may also influence the following generation offspring (reviewed in [5,6,7]). For example, a series of retrospective epidemiological studies (the Överkalix cohort) have shown that grand-paternal excessive nutrient availability during the pre-pubertal growth increases the prevalence of diabetes and diabetes-associated mortality in their grandsons (but not their granddaughters) [6,8]. Conversely, limited food supply during the same developmental period increased longevity in the grandchildren, again in a sex-dependent manner. Sex-specific parental inheritance suggests a mechanism of inheritance involving epigenetic modifications in the sexual chromosomes [9,10]. However, the exact molecular mechanisms are far from being fully elucidated, since no biological material is available in this retrospective historical cohort. The Överkalix cohort is now considered part of a broader scenario that recognizes that paternal health may exert have strong effects in the offspring through various non-genetic mechanisms. It has been named as Paternal Origins of Health and Disease (POHaD) [11,12,13]. Furthermore, it can be argued that interventions aimed to improve paternal health might be potential strategies for improving metabolic offspring health. We have previously developed a mouse model of early adiposity (i.e., childhood obesity) through litter size reduction [14]. Mice bred in small litters (SL) exhibited rapid neonatal growth and developed obesity as early as by age 15 days [15]. As adults, SL mice displayed several metabolic disturbances, including glucose intolerance, insulin resistance, adult obesity, and hepatic steatosis [15,16]. The earliest significant defect was hepatic insulin resistance that was noticeable by postnatal day 15 (PD15). At the molecular level, hepatic steatosis was due primarily to misalignment of the circadian rhythm, which in turn mediated hepatic steatosis [16,17]. Here, we confirmed that hepatic steatosis was transmitted to the offspring (F1) via the paternal lineage [18]. Thus, here we count on an experimental model in which it is feasible to explore the potential contribution of epigenetic mechanisms in mediating metabolic imbalances across generations. Epigenetic inheritance through the paternal lineage implies that the nutritionally induced epigenetic marks are transmitted through the gametes (sperm) [19]. It has been widely reported that in humans, obesity, aging or dietary exposures may modify the sperm epigenome [20,21,22,23,24,25]. It is proposed that these modifications might be maintained during the processes of fecundation and embryogenesis and impact on offspring metabolic health [12,26]. However, due to obvious ethical issues, it is really difficult to demonstrate that sperm-harboring epigenetic marks may play a causal role on offspring physiology in humans. This is easier to address in experimental models (mice and rats), because the males are typically removed from the cage when the female is pregnant. Hence, the male can only contribute to his offspring through the information contained in the spermatozoa [5]. In these experimental procedures, male physiology or behavior does not play a role on offspring phenotypic outcomes. Many laboratories, including ours, have provided compelling data supporting that several environmental challenges, including endocrine-disrupting chemicals, nutrition, pharmacological compounds, and behavioral factors may influence the germline epigenome [27,28,29,30]. Examples of germline epigenetic variation due to nutritional challenges include DNA methylation and small non-coding RNAs, which have been reported in many species, including humans [21,26,31,32,33,34,35,36,37]. Here, we aimed to shed light on the mechanism/pathways that may link paternal early overnutrition-overweight (in SL-F0 mice) with offspring metabolic phenotypes, primarily hepatic steatosis. Specifically, here, we conducted an unbiased transcriptomic analysis in the livers of the C-F1 and SL-F1 mice and found that the circadian rhythm ranked among the most significantly deregulated Gene Ontology term. In turn, the circadian rhythm might underlie altered lipid metabolism in SL-F1 mice. Next, we explored whether the germline epigenome (DNA methylation and small non-coding RNAs) might be involved in the inheritance of hepatic clock genes. ## 2.1. Animal Care and Experimental Design Protocols were approved by the Universitat de Barcelona Animal Care and Use Committee, as previously described [18]. Eight-week-old virgin females were mated with not sibling males (ICR-CD1, Envigo Laboratories, Sant Feliu de Codines, Spain). Upon pregnancy, females were housed individually with ad libitum access to standard chow (2014 Tekland Global, Envigo Laboratories, Spain). Mice were maintained under constant temperature (21–23 °C), humidity (55 ± $10\%$) and dark–light cycles (12 h/12 h). After delivery, litter size was adjusted to 8 pups (control group, C) or 4 pups per dam (small litter group, SL). Both C and SL offspring are designated as the parental generation, F0 (Figure 1A). F0 pups were nursed freely and weaned at 3 weeks onto standard chow. C-F0 and SL-F0 males were mated at age 3 months with non-sibling external control females to generate the first generation-offspring, F1 (Figure 1A). At birth, all litters were adjusted to 8 pups per dam to normalize early neonatal nutrition and growth. Mice were euthanized, after 12 h fasting, via CO2 inhalation. The liver was weighted, rapidly frozen in liquid nitrogen, and stored at −80 °C for further analyses. In this study, we only included males because, as previously reported, SL females were protected against hepatic steatosis and hepatic insulin resistance [15]. ## 2.2. TAG and Cholesterol Determination Hepatic lipid and cholesterol content were determined from frozen tissue as previously described [16]. ## 2.3. Tissue Culture and Incubations with AZA The murine hepatocyte Hepa-1c cell line was cultured as previously described [34]. Briefly, Hepa1c cells were maintained under standard growth conditions (DMEM, $10\%$ fetal bovine serum). The 60–$70\%$ confluent cells were treated with increasing concentrations 5-AZA (Merck, Madrid, Spain) or vehicle for 48 h. ## 2.4. Sperm Isolation Sperm was isolated from 4-month-old mice as previously described [34]. Briefly, the reproductive tract was retrieved from the mouse. The epididymal conduct of both sides was punctured with a needle, and sperm was isolated by gently shaking the epididymis. The sperm was collected in a culture dish containing warmed PBS solution. Purity of the sperm was confirmed by microscopy. ## 2.5. DNA and RNA Extraction Genomic DNA from tissues was extracted using the Wizard® Genomic DNA Purification Systems Kit (Promega Biotech Ibérica S.L., Madrid, Spain). Sperm DNA was isolated by using the DNeasy Blood & Tissue Kit (Izasa-Qiagen, Barcelona, Spain). Total RNA was isolated by using TriReagent (Sigma-Aldrich, Madrid, Spain) according to the manufacturers’ protocol. ## 2.6. Affymetrix Microarrays Microarray hybridization and analysis have been performed as previously described, using GeneChip® Affymetrix Mouse 430 2.0 whole genome arrays (Thermo Fisher Scientific, Sant Cugat del Vallès, Spain) [34]. Briefly, 3 microarrays were hybridized for each group (C-F1, SL-F1). Each array contained the pooled RNA from three independent mice. Expression values were summarized after background correction and normalization steps using the RMA methodology [38]. Differential expression analysis was performed by the non-parametric approach Rank Prod [39]. Oligonucleotides presenting changes between groups with q-values lower than 0.1 were considered significant. The tool David [40] was used for the calculation of the functional clustering enrichment statistical analysis of the Gene Ontology Terms and Kegg Pathways databases considering the list of significant genes. The data have been deposited at the GEO, accession number GSE55304. ## 2.7. Agilent DNA Methylation Microarrays For CpG island microarray, genomic DNA from C and SL mice was enriched for the unmethylated fraction. Briefly, 500 ng genomic DNA was divided onto two fractions. One of them (250 ng) was subjected to immunoprecipitation. The DNA libraries of immuno-precipitated and non-precipitated samples were labeled (Cy3 and Cy5, respectively) and hybridized onto Agilent 105K Mouse CpG Island microarrays (ID 015279). Before microarray data analysis, outliers and low signal intensity within 2.6 standard deviations of background were removed (Feature Extraction software v.10.7, Agilent Technologies, Santa Clara, CA, USA). Likewise, background was normalized by using the normexp method setting the offset at 10. After normalization, sample DNA methylation and detection were performed by using the Agilent Genomic Workbench, which provides the algorithms for methylation detection. Here, for measuring the degree of enrichment (or de-enrichment) by the methylation-enrichment step, we used the LogOdds score algorithm [41]. Briefly, we compared the overall distribution of enrichment for probes to calculate the probability that the CpG island was methylated or unmethylated (adjusted p-value < 0.05). This is expressed as the log of the odds ratio (probability of methylated/probability of unmethylated; LogOdds). Under this condition, a LogOdds score of 0 is equally likely to be methylated/unmethylated. In contrast, large absolute values are increasingly likely to be methylated or unmethylated (positive values or negative values, respectively). Microarray hybridization and bioinformatics analysis were performed at Bioarray S.L. (Elche, Alicante, Spain). ## 2.8. Small RNA Sequencing and Analysis Testis RNA samples from 17 different male mice were used for small RNA sequencing. Samples were prepared in two different batches. One of the two batches contained mice from different litters to reduce the confounding effect of genetic relatedness on transcript profiles. All samples had an RNA integrity number of at least 8.0. Libraries were prepared using the TruSeq small RNA preparation protocol with size selection using Pippin prep and sequenced on Illumina Hiseq2500. The sequencing adaptor (TGGAATTCTCGGGTGCCAAGGAACTCCAGTCAC) was removed using cutadapt version 2.10 [42] requiring a minimum adaptor match of 9 nt (–O 9) and keeping only reads of a minimum read length of 19 nt (-m 19) and a maximum read length of 36 nt (-M 36). Reads were then filtered with the FASTX toolkit 0.0.14 (http://hannonlab.cshl.edu/fastx_toolkit, accessed on 1 January 2022, VBNVNBV) with a minimum quality score of 30 over at least $90\%$ of the read length. Small RNA reads were mapped against the mouse genome (GRCm38) using bowtie version 1.2.3 [43], reporting all best scoring alignments (options–a–best–strata), allowing up to one mismatch (–v 1). We used feature Counts [44] to count reads mapping to tRNAs, microRNAs and known PIWI-interacting RNA loci, requiring a minimum 18 nt overlap between the small RNA and the genomic annotation feature. Multi-mapping reads were counted as a fraction of the number of times they map to the genome. For tRNAs and miRNAs, we counted only reads mapping sense to the genome annotation feature. For piRNAs, we counted reads mapping on both strands. The coordinates of miRNAs were retrieved from miRBase release 22.1. The coordinates of tRNA genes, predicted by tRNAscan-SE [45], were retrieved from the UCSC Genome Browser. The coordinates of loci-producing mouse piRNAs were retrieved from [46]. Differential expression was analyzed using DESeq2 version 1.34 [47] and R version 4.1.2. Differential expression was analyzed by controlling for batch when data sequenced in different batches were used. ## 2.9. Real-Time Quantitative PCR (qPCR) Total RNA was isolated from frozen tissue (Trizol®, Merck, Madrid, Spain) and used for cDNA synthesis (Promega, Barcelona, Spain). Transcript levels were quantified by qPCR using the SYBR Green PCR Master Mix (Promega, Spain). Results were normalized to b-Actin and subsequently median-normalized to arbitrary units (A.U.) 1 in the control group. The list of primers is detailed in Table S1. ## 2.10. Statistical Analysis Results are expressed as mean ± SEM. Statistical analyses were performed using a two-tailed t test or a one-way ANOVA as indicated (IBM SPSS Statistics 19, Madrid, Spain). A * p-value < 0.05 and *** p-value < 0.001 was considered significant. ## 2.11. Data and Resource Availability The Affymetrix microarray datasets have been deposited at the GEO with accession number GSE55304, and the small RNA sequencing data have been deposited at the GEO with accession number GSE215030 (Accession numbers GSM6620144, GSM6620145, GSM6620150, GSM6620151, GSM6620155, GSM6620159, GSM6620162, GSM6620165, GSM6620166, GSM6620169, GSM6620176). ## 3.1. Paternal Overnutrition Induced Hepatic Steatosis in the First-Generation Offspring We have developed a mouse model of early adiposity (i.e., childhood obesity) and long-term metabolic dysfunction through litter size reduction at birth (Figure 1A) [14,15]. Mice reared in small litters (SL-F0) developed hepatic steatosis (increased hepatic triglyceride content) as adults (Figure 1B). The offspring of SL mice (SL-F1, from now onward) also developed glucose intolerance and insulin resistance [18]. Furthermore, here, we confirmed that SL-F1 mice also accumulated greater levels of hepatic triglycerides and cholesterol when compared to the controls (Figure 1C). We next explored potential pathways involved in the development of hepatic steatosis in SL-F1 mice. We analyzed the global gene expression profiling (Affymetrix) and found that 394 genes were significantly deregulated in the liver of SL-F1 mice (Figure 1D, Table S2). Two ontologies appeared nearly significantly deregulated: The Circadian Rhythm (GO: 0007623) and Lipid Metabolic Process (GO:0006629) (Figure 1E). ## 3.2. Litter Size Reduction Altered the Expression of Genes Involved in the Circadian Rhythm and Lipid Metabolism Strikingly, the previous ontologies appeared already altered in the liver of the progenitors (SL-F0 mice) [16]. In addition, we provided evidence that in SL-F0 mice, the clock genes played a causal role in mediating hepatic steatosis through regulating lipid metabolism. Therefore, here, we explored whether hepatic steatosis in SL-F1 mice might be also attributed, in part, to a similar process. First, we found that some important core clock genes (Per3, Cry1, Npas2) and downstream clock-controlled genes (Dbp1, Nfil3) appeared in this ontology (Table S3). Next, we confirmed (qPCR) that Period 1 and Period 2, which are involved in regulating lipid metabolism, were deregulated in the liver of adult SL-F1 mice (Figure 2A). Together, these data support that ancestral nutrition in SL-F0 males might program the expression of a few hepatic clock genes in the next-generation offspring, F1. As reported in the F0, it might be possible that the clock genes influence, in part, the expression of lipid-related genes. It is known that $20\%$ of the liver transcriptome exhibits rhythmic behavior (Figure 2B) [48]. In our dataset (Table S4), $57\%$ of the genes included in the ontology (GO: 0006629) displayed rhythmic behavior (Figure 2C,D). This over-representation supports the idea that to some extent, the circadian rhythm might influence hepatic lipid metabolism in SL-F1 mice. We next validated some of them in the liver of 4–5-month-old mice (Figure 2E). Furthermore, we also confirmed that other lipogenic genes, which do not display cyclic behavior, were also deregulated in the liver of SL-F1 mice (Mogat1, Dgat2, Acly) (Figure 2F). In contrast, target genes involved in lipid oxidation, such as Cpt1a, Cpt2 were unaltered in SL-F1 mice (Figure 2F). Together, these results support that lipid accumulation in SL-F1 mice might be the result of lipid synthesis, which might be marginally dependent of the circadian clock. ## 3.3. DNA Methylation Did Not Correlate with the Expression of Clock and Lipid-Related Genes The inheritance of nutritionally acquired phenotypes is likely attributable to non-genetic mechanisms, with DNA methylation and/or non-coding RNAs as plausible candidates [27,49]. Therefore, here, we first analyzed the DNA methylation profile in the sperm of male mice. We found that 763 CpG sites appeared differentially methylated in the sperm of SL-F0 male mice (Figure 3A; Table S5). These data agrees with previous reports suggesting that obesity and diet may modify germ-line DNA methylation. We next analyzed DNA methylation in the liver of the offspring (C-F1 and SL-F1). We found that 1747 CpG sites were differentially methylated in the liver of SL-F1 when compared to the controls (Figure 3A; Table S6). However, none of the differentially methylated CpG sites was present in both tissues (Figure 3A). These data suggest that nutritionally induced changes in methylation are not transmitted to the offspring. Furthermore, we tested any associations between the hepatic transcriptome and hepatic methylome (Figure 3B, Table S7). Twenty genes out of 394 appeared to be potentially deregulated in association with changes in DNA methylation. These data suggest that only $6\%$ of our differentially expressed transcriptome is potentially under the control of DNA methylation. In summary, our results indicate that early nutrition may change patterns of sperm DNA methylation. We have no evidence that they are transmitted onto the offspring (at least into the liver) and, therefore, influence the hepatic transcriptome. In support, the ontologies associated to the sperm methyl marks included primarily GOs associated to regulation of gene expression and development (Figure 3C), which did not match the ontologies associated to the transcriptome (Figure 1D). Together, DNA methylation does not seem to play a major role in regulating the hepatic transcriptome in our model. ## 3.4. Two miRNAs Were Differentially Expressed in the Testes of SL-F0 Mice and Might Be Linked to the Hepatic Lipid Metabolism in the Offspring Some intergenerationally transmissible phenotypes have been linked to an altered abundance of sperm-borne small non-coding RNAs [50,51]. We therefore tested the association between small RNAs expressed in the male germline with metabolic dysfunction in the offspring. We sequenced small RNAs from whole testes and searched for differences in the abundance of microRNAs (miRNA), tRNA fragments (TRFs) and piRNAs (specifically mapping to piRNA-producing loci) between C-F0 and SL-F0 mice. First, and as previously published, we confirmed that piRNAs were the most abundant species of sncRNAs in the testes of either group (Figure 4A) [52]. The abundance of all classes of small RNAs was very similar between groups (Figure 4B, Table S8). Only two miRNAs appeared differentially expressed between groups (mmu-miR-547, mmu-miR-201; adj p-values 0.01 and 0.08, respectively). Both miRNAs map together within the same chromosome and regulate one each other [53]. In addition, it is described that both are expressed in mature spermatozoa but not in oocytes [54]. Finally, both miRNAs are still detectable in the morula stage but not in the blastocyst. These data support the possibility that they might be paternally inherited. The question again was whether these miRNAs might regulate either clock genes or lipid-related genes. We took advantage that the hepatic miRNA targetome has been recently reported [55]. First, we found that the miR-547 may interact with 339 transcripts, whereas miR-201 could regulate the expression of 816 mRNAs in murine hepatocytes (Figure 4C, Table S9). Interestingly, the miR-547-associated most significant ontology included the regulation of lipid biosynthetic process (GO: 0051055, GO: 0045834, GO: 0010888) (Figure 4D). Likewise, most significant ontologies associated to miR-201 included flavonoid (GO:0052696) and the lipid metabolic process (GO: 0006629) (Figure 4E). Prominently, Acox1, Apob or Scd1, which appeared differentially expressed in the transcriptome assay, are potential targets for both miRNAs (Figure 4C). In contrast, neither miR-201 nor miR-457 targeted any clock gene. Together, these data support the possibility that at least two paternally derived miRNAs (miR201, miR547) might be transmitted to the following generation and target genes involved in hepatic lipid homeostasis (Figure 5). ## 4. Discussion We have previously developed a mouse model of early adiposity through litter size reduction [14]. The model closely portrays the human pathophysiology associated to childhood obesity. First, litter size reduction led to transient neonatal hyperphagia and obesity onset as early as by postnatal day 7 (PD7) [15]. Next, SL-F0 mice developed progressive insulin resistance, glucose intolerance, hyperglycemia, and hepatic steatosis with ageing [15]. Hepatic steatosis in SL-F0 mice was attributed, in part, to the misexpression of clock genes, which, in turn, influenced the transcription of genes involved in lipid homeostasis [16,17] (Figure 5). Interestingly, some SL-F0 metabolic phenotypes, including glucose intolerance and insulin resistance, were paternally transmitted to the following generation offspring, which is likely through epigenetic mechanisms (Figure 5) [18]. Here, we confirmed that the offspring of SL-F0 mice (SL-F1 mice) also developed hepatic steatosis (i.e., elevated triglyceride content), albeit at a lower degree than in the parental generation. We speculate that progressive weakening of the phenotype is compatible with a process associated to epigenetic inheritance. Conversely, if hepatic steatosis was due to genetic mutations, the phenotype should remain similar in both generations. At the molecular level, unsupervised transcription profiling uncovered that the ontologies with a lower False Discovery Rate were the circadian rhythm (GO: 0007623; FDR = 0.11) and lipid metabolic process (GO: 0006629; FDR = 0.08). Noteworthy, these ontologies also appeared significantly altered in the liver of their progenitors, SL-F0 mice, with higher significance. Again, these data support that the intergenerational transmission of hepatic steatosis might be likely attributed to germ-line-mediated epigenetic modifications. In SL-F0 mice, the circadian rhythm played a causal role in the development of hepatic steatosis through modulating the expression of lipogenic genes [13]. Specifically, the Period and Cryptochrome genes, which can regulate lipid metabolism [16,56,57], were highly dysregulated in SL-F0 mice. Therefore, here, we explored whether in SL-F1 mice lipid metabolism might also be under the control of the clock genes (Figure 5). We confirmed, via qPCR, that Per1 and Per2, but not Per3, were misexpressed in the liver of SL-F1 mice. Neither Cry1 nor Cry2 were altered in the liver of SL-F1 male mice. Therefore, although we could identify some changes in key period genes, the minimal number of changes does not provide compelling evidence to support that, as in SL-F0 mice, the circadian rhythm is causally influencing the expression of lipid-related genes. Hence, here, we propose that in the liver of SL-F1 mice, the dysregulation of lipid metabolism and hepatic steatosis is not likely due to dysfunction of the circadian rhythm. To fully elucidate whether the circadian rhythm might be involved in our model would require recording gene expression and/or TAG hepatic dynamics throughout a 24 h cycle. This issue needs to be evaluated in the future. Furthermore, we postulate that paternal transmission of the environmentally induced hepatic steatosis is likely attributed to germline epigenetic modifications (Figure 5) [27,28,58]. Among epigenetic marks, DNA methylation and non-coding RNAs have been recognized as plausible carriers of epigenetic information across generations in response to nutritional challenges [31,33,34,59]. Therefore, here, we set to study whether germline DNA methylation and/or small non-coding RNAs (sncRNAs) might be involved in regulating lipid metabolism in the liver of SL-F1 males. To note, here, we focused on the paternal inheritance only for two reasons: Firstly, we had shown that SL-F0 females did not develop hepatic steatosis and insulin resistance [15]. Therefore, we focused on the robust inheritance of hepatic steatosis that occurred through the male lineage. Secondly, intergenerational effects through the male lineage should be mediated, primarily, by epigenetic mechanisms. In contrast, maternally mediated transgenerational effects will be due to a complex interplay between metabolic, behavioral, mitochondrial, and epigenetic modifications [19,27]. Therefore, from a mechanistic perspective, paternal inheritance is simpler than maternal effects and, hence, dissecting the potential molecular mechanisms involved in these processes is far easier in males than females. Here, we found that 763 CpG sites appeared differentially methylated in the sperm of SL-F0 mice when compared to the controls (Figure 5). These data agree with previous articles demonstrating that nutritional challenges, obesity, or diabetes may influence the sperm methylome [59,60,61,62,63,64,65]. The question is whether these germline nutritionally induced methyl marks are (i) truly inherited and (ii) influence gene expression profile in target organs of the next-generation offspring, F1. In order to address the first question (are sperm methyl-marks inherited?), we determined the DNA methylation profile in the liver of C-F1 and SL-F1 mice. In total, 1747 CpG sites were differentially methylated in the liver of SL-F1 mice. Yet, no CpG sites were differentially methylated in common: sperm-SL-F0 and liver-SL-F1. These data strongly suggest that environmentally-induced sperm methyl marks are not inherited by the following generation. In agreement, several authors questioned whether sperm DNA methylation might be the actual carrier of epigenetic information across generations at least in the context of metabolic diseases [66,67]. In this regard, it is generally acknowledged that nutritional or metabolic challenges induce small changes in sperm DNA methylation, typically between 5 and $10\%$ [67]. Furthermore, in some examples, sperm-worn methyl marks do not re-appear in the tissues of the offspring [31,66,68,69]. Despite these concerns, the truth is that the potential role of germline DNA methylation as a carrier of epigenetic inheritance is a matter of ongoing debate, with some examples in support [34,58] and a few others against [31,66,67]. We argue that part of these discrepancies should be attributed to the combination between the type of environmental challenge (diet, disrupting chemical, open diabetes, etc.), the window of exposure to the challenge (in utero, early lactation period, adulthood), or the species (mice, rat, human). This combinatorial heterogeneity might underlie the differences reported in the literature. Clearly, a systematic review is warranted to obtain a clearer picture regarding the conditions in which DNA methylation might carry information across generations. Addressing the second question, it is interesting to remark that when we compared the hepatic transcriptome and methylome, only 25 genes appeared in this list. Therefore, only $6\%$ of the misexpressed genes SL-F1 livers can be potentially attributed to changes in DNA methylation. Hence, most of the hepatic transcriptome is likely regulated by other mechanisms. In summary, the previous data suggest that, in our model, germline changes in DNA methylation (SL-F0) are not inherited and do not likely underlie the misexpression of clock and lipid metabolic genes in the liver of SL-F1 mice. Alternative to DNA methylation, some intergenerationally transmissible phenotypes have been linked to an altered abundance of sperm-borne small non-coding RNAs [50,51,64,70,71]. A few studies have provided proof for a causal role of miRNAs [37,51] and tRNA fragments [35,36] in mediating the inheritance of metabolic dysfunction. We therefore explored the content of sncRNAs in the testes of C-F0 and SL-F0 males. We did not find relevant differences in the three most abundant small RNA species: piRNAs, miRNAs, and TRFs. Yet, two miRNAs showed different statistical abundance between groups: mmu-miR-547 and mmu-miR-201. Sperm miRNAs are one subspecies of sncRNAs that are sensitive to environmental changes, including dietary manipulations and obesity [37,51]. It is intriguing that only two miRNAs, out of several thousands of sncRNAs, appeared differentially expressed in our model. This could be due to several factors. Firstly, miRNAs might be more sensitive to nutritional challenges than the other RNA species. In agreement, other authors have found that obesity–nutrition induces more alterations in the miRNA fraction than in the others [31,37,72,73]. Secondly, the period of exposure (fetal, neonatal, adult life) and intensity may also influence other sncRNAs. For example, high-fat feeding or low-protein feeding for several weeks in adults results in a higher number of changes not only in miRNAs but also on TRFs [31,35,36,37]. Together, the combination between the window of exposure and obesogenic challenge might determine which sncRNAs species are more susceptible of being dysregulated [12]. Additional studies are warranted to fully elucidate these complex interactions. It is noteworthy that both miRNAs are expressed from the same chromosomic region [53] and coordinately regulate one each other, which might explain their similar statistical significance in our dataset. These two miRNAs are especially attractive in mediating the paternal inheritance of hepatic lipid dysregulation to SL-F1 mice for two reasons:-First, both miRNAs are expressed in Sertoli cells during postnatal development [53]. Together, they are involved in the maturation of spermatocytes and are prominently expressed in mature spermatozoa but not in oocytes [54]. Hence, they are strong candidates for being carriers of paternal information onto the offspring.-Second, the hepatic miRNA-targetome has been recently reported [55]. It provides evidence that miR-201 and mir-547 may regulate the expression of lipid-related target genes, including Acox1, Cpt2, Apob, or Scd1. Considering that these genes were identified in our hepatic transcriptome, here, we make the case that both microRNAs might be inherited–transmitted from SL-F0 male mice and influence the hepatic lipid transcriptome. These data support that both miRNA-201 and miR-457 are paternally transmitted and could therefore be potential carriers of information across generations. In support, it has unequivocally shown that miRNAs may influence offspring phenotype. For example, paternal miRNAs from obese male mice have been experimentally transferred into oocyte cytoplasm during fertilization. These miRNAs can modulate gene expression during embryo development [51]. Next, the miRNAs may regulate a vast array of mRNAs. Therefore, a small set of miRNAs might impact on many different cellular functions playing a crucial role in the pathogenesis of metabolic and endocrine dysfunctions, cardiovascular or cancer diseases and infectious illness [50,51]. At this point, we recognize that despite previous evidence supporting the role of miRNAs in mediating the intergenerational inheritance of hepatic steatosis, there are several caveats that need to be considered. Firstly, we recognize that exploring the sncRNA content in testes might be a potential limitation, since the composition of sncRNAs in the sperm, which is the actual carrier of genetic and epigenetic information, and the testes might differ. However, differentiating male germ cells comprise the majority of cells in testes. Therefore, the relative abundance of the different types of small RNAs in testis predominantly reflects their relative abundance in the male germline. In this regard, as previously noted, both miR-201 and miR-547 are abundant in isolated mature mouse sperm cells but not oocytes [54]. In addition, both miRNAs remain abundant in the morula stage but become completely undetectable in blastocysts [54]. These data support that both miRNA-201 and miR-457 are paternally transmitted and could therefore be potential carriers of information across generations. Secondly, it is unclear how could the miRNAs influence the adult phenotypes, in the F1, if they disappear during development, after the blastocyst stage? It is proposed that paternally inherited RNAs might influence the offspring phenotype through modulating early developmental stages [50]. Upon fertilization, paternally derived RNAs will stay in the cytoplasm of the newly formed egg. As cell division progresses, the set of paternal RNAs will be progressively diluted and replaced by zygotic RNAs. It is postulated that paternally inherited sncRNAs are required for ensuring appropriate early embryo development. Thirdly, it is well established that miRNAs may regulate a huge number of transcripts (at the translational level) because they can bind mRNAs with some in specificities. In agreement, miR201 and miR547 can potentially modulate the expression of several hundred genes in hepatocytes (i.e., hepatic targetome). However, only a few targets appeared differentially expressed in our transcriptomic dataset. If both miRNAs are intergenerationally inherited and target lipid-related genes, we should expect a greater percentage of genes in the dataset. Here, we speculate that it is likely that part of the effects of these two miRNAs occurs during early embryogenesis rather than in the adult liver and that the hepatic lipid deregulation we observed in SL-F1 mice is secondary to early alterations. Clearly, this hypothesis deserves being investigated in the future. For example, it would be necessary to measure the expression of these miRNAs in the embryos and the adult liver of SL-F1 and C-F1 mice. In addition, in case we detect their presence in the previous tissues, functional assays would be also warranted. To finish, we recognize that we centered our search on the potential role of germinal DNA methylation and sncRNAs on offspring clock liver transcription profile. Yet, other epigenetic marks, such as histones, could be involved. For example, histone acetylation is essential during chromatin compaction and linked to the epigenetic inheritance during early gametogenesis [74], and circadian rhythm modulation [74,75,76]. Consequently, additional and deeper analyses should be considered in the future to shed light on transgenerational paternal inheritance mechanisms in our model. To conclude, our multi-omics approach supports that neither DNA methylation nor sncRNAs directly modify or influence the circadian rhythm in the first-generation offspring, F1. Furthermore, we found no evidence that DNA methylation might impact on genes involved in lipid metabolism. We did find, however, that at least two miRNAs might be paternally inherited (miR-201 and miR-547) and influence hepatic lipid metabolism through modifying early embryogenesis and/or through modulating transcription in adult hepatocytes. The elucidation of these two potential mechanisms deserves further investigation. ## 5. Conclusions We have previously shown that paternal overfeeding during early development (lactation) triggered the inheritance of metabolic disturbances to the following generation offspring, including glucose intolerance, insulin resistance, and hepatic steatosis. The inheritance of environmentally induced metabolic dysfunction may be attributed to non-genetic mechanisms, namely epigenetic mechanisms. Hepatic steatosis, in both the parental and first-generation offspring, may be attributed to two ontologies: circadian rhythm and lipid-related genes. In this study, we find no evidence to support that germline DNA methylation contributes to modulating the hepatic lipid metabolism in SL-F1 male mice. We found that two germ-line derived micro RNAs might influence hepatic gene expression through either (a) modulating early steps on embryo development or (b) through directly targeting lipid-related genes in adult hepatocytes. ## References 1. 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--- title: Differential Lipid Accumulation on HepG2 Cells Triggered by Palmitic and Linoleic Fatty Acids Exposure authors: - Francisca S. Teixeira - Lígia L. Pimentel - Susana S. M. P. Vidigal - João Azevedo-Silva - Manuela E. Pintado - Luís M. Rodríguez-Alcalá journal: Molecules year: 2023 pmcid: PMC10005272 doi: 10.3390/molecules28052367 license: CC BY 4.0 --- # Differential Lipid Accumulation on HepG2 Cells Triggered by Palmitic and Linoleic Fatty Acids Exposure ## Abstract Lipid metabolism pathways such as β-oxidation, lipolysis and, lipogenesis, are mainly associated with normal liver function. However, steatosis is a growing pathology caused by the accumulation of lipids in hepatic cells due to increased lipogenesis, dysregulated lipid metabolism, and/or reduced lipolysis. Accordingly, this investigation hypothesizes a selective in vitro accumulation of palmitic and linoleic fatty acids on hepatocytes. After assessing the metabolic inhibition, apoptotic effect, and reactive oxygen species (ROS) generation by linoleic (LA) and palmitic (PA) fatty acids, HepG2 cells were exposed to different ratios of LA and PA to study the lipid accumulation using the lipophilic dye Oil Red O. Lipidomic studies were also carried out after lipid isolation. Results revealed that LA was highly accumulated and induced ROS production when compared to PA. Lipid profile modifications were observed after LA:PA 1:1 (v/v) exposure, which led to a four-fold increase in triglycerides (TGs) (mainly in linoleic acid-containing species), as well as a increase in cholesterol and polyunsaturated fatty acids (PUFA) content when compared to the control cells. The present work highlights the importance of balancing both PA and LA fatty acids concentrations in HepG2 cells to maintain normal levels of free fatty acids (FFAs), cholesterol, and TGs and to minimize some of the observed in vitro effects (i.e., apoptosis, ROS generation and lipid accumulation) caused by these fatty acids. ## 1. Introduction In 2019, the worldwide incidence of non-alcoholic fatty liver disease (NAFLD) was one hundred and seventy million, corresponding to twice the number of cases registered in 1990 [1]. One in three adults and one in ten children in the United States have hepatic complications regarding NAFLD, which currently has no approved pharmacological treatment. Consequently, liver transplantation will continue to represent the most efficient procedure to treat this disorder [2,3]. The aforementioned condition (i.e., NAFLD) is associated with intrahepatic lipid accumulation ($5\%$ compared to healthy individuals). The accumulation of intracellular triglycerides via lipid droplet (LD) formation is correlated to increased susceptibility to oxidative and cytokine stress, as well as inflammatory and hepatic injury characterized by NAFLD complications commonly referred as non-alcoholic steatohepatitis (NASH) [4,5]. Usually, the pathology evolves from fatty liver to NASH, and the main consequences are hepatic fibrosis and cirrhosis [3,6]. The main risk factors of NASH are being overweight, obesity, alcohol consumption, type II diabetes, treatments involving glucocorticoids, and hepatitis C viral infection [6]. Higher free fatty acid (FFA) uptake, increased hepatic lipid accumulation, de novo lipogenesis, and endoplasmic reticulum (ER) stress are hallmarks of NAFLD development and progression [7,8,9]. Once released from adipocytes, FFA can result in hepatocyte dysfunction and dysregulated lipid metabolism [10]. Individually, FFAs exert different effects on physiological processes, including cellular membranes properties, lipolysis, inflammation, and endocrine signaling regulation. Consequently, modifications in such processes due to altered unsaturated/saturated fatty acids (FA) plasma levels can lead to the development of hepatocyte dysfunction [11,12,13]. Lipid droplets are cellular organelles that originate from the ER and consist of a core of neutral lipids (cholesterol esters and triglycerides) enveloped by phospholipids and proteins [3]. These storage vesicles have great influence on lipid metabolism and on the progression of NAFLD. The growth of LDs is dependent on triglyceride and enzymatic phospholipid synthesis, and its catabolism is related to TG hydrolysis (releasing fatty acids) via β-oxidation. Interestingly, lipase activity and additional TG hydrolysis is regulated by proteins complexed to LD, such as perilipins, which are correlated to peroxisome proliferator-activated receptor-α (PPAR-α) activity [14,15]. These receptors are ligand-activated nuclear transcription factors that bind and respond to different FFAs. Their binding to both saturated and unsaturated FFAs regulate, to a different extent, their uptake, oxidation, and inhibit of de novo fatty acid synthesis mainly in tissues that retain high fatty acid catabolism (e.g., liver, kidney, heart, and skeletal muscles) [11]. The increased oxidation of FAs triggers reactive oxygen species (ROS) formation, lipid peroxidation, DNA damage, mitochondrial dysfunction, and release of pro-inflammatory cytokines [3,6]. As the accumulation of lipids in hepatocytes is considered a pathologic hallmark, in vitro models of steatosis have been used to study the hepatocellular consequences of lipid accumulation in human hepatic cells. These models include hepatocyte cell lines and primary hepatocytes treated in culture with monounsaturated and saturated fatty acids in order to mimic and explore novel features of NAFLD [16]. Considering that Western diets are characterized by high intake of animal fats containing palmitic acid (PA) and seed oils with high linoleic acid (LA), this study aims to determine the effect and accumulation of PA and LA on hepatocytes [17,18,19]. Thus, LA and PA accumulation impact hepatocyte metabolic inhibition, apoptosis, ROS-generated species, and lipid profile, which were parameters assayed in this research work. ## 2.1. Metabolic Inhibition of LA and PA on HepG2 Cells To understand the interference of each fatty acid, saturated (palmitic acid—PA) and polyunsaturated (linoleic acid—LA), in the hepatocyte metabolic inhibition, the PrestoBlue assay was performed. Figure 1 represents the results obtained from the metabolic inhibition assay after the exposure of HepG2 cells to the mixture containing LA and PA at the concentration of 1 mM, varying the proportions (v/v) of these FFA. As shown in Figure 1, a higher metabolic inhibition was observed for LA:PA 1:0 (v/v) ratio (approx. $100\%$ vs. $20\%$ in Control Cells). On the other hand, lower concentrations or the absence of LA in the 1 mM FFA mixture [LA:PA 1:1, 1:2 and 0:1 (v/v) ratios] were associated with lower metabolic inhibition values, namely, $1.54\%$, −$50.01\%$, and −$15.41\%$, respectively. These results indicate that LA induces higher metabolic inhibition than PA. However, such behavior may also be related to the proportion of dead, apoptotic, and live cells. ## 2.2. Flow Cytometric Analysis of LA and PA Effect on Apoptotic Stages To evaluate if the metabolic inhibition results were in accordance with cell death, the apoptosis stages detection kit was used. The obtained percentage of live, early apoptotic, necrotic, and dead cells after the exposure to 1 mM LA:PA at different ratios (1:0; 1:1 and 0:1 v/v) are shown in Figure 2. Regarding the percentage of live cells, results suggested that no significant differences between exposure to FFA and the control group were found. Thus, the obtained values were 82.32 ± $6.33\%$ for Control, 63.19 ± $7.39\%$ for LA:PA at 1:0 (v/v), 68.46 ± $0.62\%$ for 1:1 (v/v), and 64.98 ± $6.12\%$ for 0:1 (v/v). For early apoptosis, the percentages of live cells were: 1.40 ± $0.68\%$ for Control, 0.73 ± $0.27\%$ for LA:PA at 1:0 (v/v), 0.88 ± $0.19\%$ for 1:1 (v/v), and 2.59 ± $0.30\%$ for 0:1 (v/v) ($p \leq 0.05$). Moreover, LA (1 mM) exposure resulted in a higher level of necrotic cells (26.89 ± $3.92\%$) compared to the other assayed conditions ($p \leq 0.05$). Interestingly, the percentage of dead cells was higher when PA was in the mixture. The 0:1 (v/v) ratio, containing 1 mM of PA, significantly led to the highest percentage of cell death (22.85 ± $3.63\%$) when compared to control. Linoleic and palmitic acids exert different effects on HepG2 cells, observed by both metabolic inhibition and apoptotic stages determination assays. Thus, from these two experiments, the observations suggest that LA may impair cell metabolism (Figure 1), while LA and PA might result in different cell death stages (Figure 2). As these apoptotic events can be triggered by oxidative stress [10], and the assessment of intracellular reactive oxygen species (ROS) was evaluated for 1 mM of LA:PA at 1:0, 1:1, and 0:1 v/v ratios. ## 2.3. Assessment of Intracellular Reactive Oxygen Species (ROS) The assessment of intracellular reactive oxygen species (ROS) was evaluated to confirm if LA accumulation result in higher oxidative stress than PA in HepG2 cells. The results of relative fluorescence units (RFU) after exposure of hepatocytes to 2′,7′-dichlorofluorescein diacetate (DCFDA) for 5 h are depicted in Figure 3. The generated ROS were monitored after a 24 h exposure of hepatocytes to the different FFA ratios. Depending on the nature of the FFA, different levels of ROS were observed. Cells exposed only to LA (LA:PA 1:0 v/v) showed an increase ($p \leq 0.05$) in ROS production after 2 h of DCFDA probe incubation when compared to the other ratios. Moreover, after 3 to 5 h of probe incubation, the presence of PA (LA:PA 1:1 and LA:PA 0:1 v/v) showed a higher ($p \leq 0.05$) RFU value when compared to the control. Hence, the mixture of 1 mM LA:PA 1:1 (v/v) resulted in a similar RFU value as the presence of LA:PA at 0:1 (v/v) ($p \leq 0.05$). After 5 h, superior ROS production and, therefore, higher RFU values, were achieved ($p \leq 0.05$) with the presence of LA (16589 RFU value), which was approximately the double of the RFU value obtained for LA:PA 1:1 and LA:PA 0:1 (v/v), 8653 and 7180 RFU respectively. The reported results are in accordance with the hypothesis that, due to the presence of double bonds, LA is more prone to oxidation than PA on HepG2 cells. ## 2.4. Lipophilic Dye Oil Red O (ORO) The parameters previously evaluated (e.g., metabolic inhibition, apoptotic stage determination, and ROS assessment) were of great relevance to discriminate some cellular effects of LA, PA, as well as the mixture of both FFA at equal concentrations. Besides these findings, and because PA is mostly associated with higher risk of cardiovascular diseases and as LA is an essential fatty acid [20,21], their accumulation on HepG2 cells was also studied. Changes in cellular metabolism and lipid accumulation are increased by NAFLD and in vitro studies using ORO lipophilic dyes are usually used to predict lipid presence [5]. Hence, the visualization of HepG2 cells after FFA exposure and ORO staining can be seen in Figure 4I. In Figure 4II are expressed the ORO semi-quantification values after 1 and 2 mM FFA exposure using different ratios to confirm a possible dose-dependent effect. Results revealed some differences in lipid accumulation since, compared to control, higher LA concentrations led to higher lipid staining [Figure 4I(B) and Figure 4I(C)]. In Figure 4II, it is possible to observe that the normalized absorbance values of the control and LA:PA 0:1 (v/v) at 1 and 2 mM are not statistically different ($p \leq 0.05$), which indicates a low accumulation of PA independently of the assayed concentration (1 and 2 mM). Based on the previously discussed results, the LA:PA 1:1 (v/v) ratio was selected to study the lipid profile of HepG2 cells, as this ratio increased lipid accumulation, as well as reactive oxygen species without metabolic inhibition impairment. ## 2.5. Total Fatty Acids Profile The fatty acids (FA) content (mg FA/g cell pellet) obtained by GC with flame-ionization detector (GC-FID) is shown in Table 1. The analysis of the total FA (free and esterified) profile, observed in the cell pellet after exposure to LA:PA 1:1 (v/v) indicates, in general, a significant accumulation in terms of total FAs (106.78 ± 0.11 vs. 256.66 ± 0.54 mg FA/g cell pellet on unexposed and exposed, respectively). Moreover, the FA profile showed that the LA was at lower concentrations ($p \leq 0.01$) in the control when compared to the exposed cells (1.12 ± 0.01 and 116.75 ± 0.26 mg FA/g cell pellet, respectively). Overall, PA and LA were the main fatty acids present but, after exposure, the PA concentration (60.77 ± 0.12 mg FA/g cell pellet) was approximately half of the LA concentration value (116.75 ± 0.26 mg FA/g cell pellet; $p \leq 0.01$). Additionally, stearic (C18), cis-11,14-eicosadienoic (C20:2c11c14), cis-8,11,14-eicosatrienoic (C20:3c8c11c14), and arachidonic acid (C20:4c5c8c11c14) were other detected FAs with significantly increased concentrations when cells were cultured in the presence of the FFA mixture. The results confirm that LA and PA are not equally accumulated and correlate with the previous observations of this research work concerning ORO staining results (Figure 4). ## 2.6. Free Fatty Acids, Cholesterol and Triglycerides Analysis by Gas Chromatography—Mass Spectrometry (GC-MS) The performed FA analysis (Section 2.5.) allowed to quantify total FA independently if they were in the free form (i.e., FFA) or esterified (i.e., TG). Although LA and PA were exposed to the same concentration [LA:PA 1:1 (v/v)], data suggest that LA was highly accumulated. Thus, the following GC-MS analyses were performed to understand if this accumulation was as FFA or TG. The fold-change variation [LA:PA 1:1 (v/v)/control cells] of the free PA, LA, oleic and stearic acids, as well as cholesterol and triglyceride content, can be seen in Figure 5. In general, an alteration of the lipid profile after FFA exposure was observed, but no variation in free PA or oleic and stearic acid content was detected (fold-change value of 1×, Figure 5). Free LA presence was 24× fold-over after exposure. Therefore, such variation is not as marked as that observed in the total FA analysis (Section 2.5.), which may indicate that, after uptake, the FFA is transformed into another lipid (e.g., triglycerides). Indeed, a four-fold increase in triglycerides (TGs) was observed, suggesting that the FFAs are being esterified into TGs. These results also revealed a two-fold increase in free cholesterol, whose toxicity in hepatocytes can impair inflammation and posterior fibrosis, leading to liver damage [3]. Overall, the exposure to FFA mixture alters the lipid profile of HepG2 cells, and the observed TG increment is consistent with the LA transformation into a more stable form (i.e., TG). ## 2.7. Triglycerides Analysis by High Performance Liquid Chromatography—Evaporative Light Scattering Detector (HPLC-ELSD) Different triglyceride groups were detected by HPLC-ELSD analysis (Figure 6I) of the treated cells, and the fold-change values of the triglyceride content determined after FFA exposure [Control Cells vs. LA:PA 1:1 (v/v)] (Figure 6II) were calculated. The results were in accordance with those obtained by GC-MS, which indicated a four-fold increase in the content of TGs in cells treated with FFA (Figure 5). ## 2.8. Triglyceride Analysis by Liquid Chromatography Coupled with Electrospray Ionization—Quadrupole—Time of Flight (LC-ESI-qTOF) Mass Spectrometry Previous results revealed that 1 mM LA:PA at 1:1 (v/v) exposure increased intracellular TG accumulation. The analysis of the TG species was performed by LC-ESI-qTOF and the isotopic pattern of the molecular ion, as well as its MS2 fragmentation, which allowed TG identification by comparison with the reference spectra (used database: MS-Finder v3.52 and Lipid Maps®). As can be seen in Figure 7, three species with significant variation ($p \leq 0.05$) were detected, and data suggested that there was a predominance of LA in the structure of the identified TGs. In general, PA was also present, but only in two of the identified species and in one of the sn positions (i.e., TG C16:0, C18:2, C18:2 and TG C16:0, C18:1, C18:2). Thus, results revealed that the presence of TG C18:2, C18:2, C18:2; TG C16:0, C18:2, C18:2 and TG C16:0, C18:1, C18:2 was an effect from the LA and PA exposure and further accumulation. ## 3. Discussion Fatty acids such as linoleic acid (LA) and palmitic acid (PA) have different physical and biological properties. Thus, LA is an essential fatty acid, and PA is the precursor of long chain fatty acids, such as oleic acid [16,20]. The present work revealed their different accumulation in hepatocytes, being preferential for LA. Such evidence was established by the intracellular detection of LA and long-chain polyunsaturated fatty acids (PUFA), such as arachidonic acid (20:4 n-6). Furthermore, the neutral lipids staining was higher for higher LA concentrations, indicating intracellular accumulation of lipid species. This accumulation also resulted in a four-fold triglyceride (TG) increase, suggesting that the free fatty acids are being esterified into TGs. Besides, previous research works by other authors evidenced impact on cellular energy metabolism caused by lipid accumulation using HepG2 cells under high-glucose requirements [22]. Additionally, a two-fold increase in cholesterol was observed. This increase can contribute to cell damage since it is established that free cholesterol is hepatotoxic, inducing inflammation and fibrosis [3,18]. The hepatocyte protection mechanism linked to lipo-toxicity of FFA [23] is generally balanced by FA esterification with glycerol to produce less-toxic TGs [10]. Consequently, the accumulation of intracellular triglyceride and cholesteryl esters is characteristic of hepatic steatosis [16,22]. Other studies showed that, after unsaturated and saturated FA exposure, lipid accumulation in hepatocytes was higher when using unsaturated FA [8,12,16]. Akazawa et al. [ 2018] described that the predominance of saturated FFAs in hepatocytes can decrease cellular membrane fluidity and induce ER stress, causing cell death [10]. On the other hand, other studies reported that oxidized linoleic acid (unsaturated FFA) metabolites induce liver mitochondrial dysfunction and apoptosis [17]. Nevertheless, current results revealed that LA and PA contribute to cellular apoptosis, but higher percentage of cell death was observed after PA exposure. Moreover, LA and PA induced ROS production, but at different levels, with LA being the fatty acid with major contribution to this effect. Several research works have described that unsaturated FFA can easily trigger ROS production and consequently epoxy- and hydroxy- fatty acid production [4,10,17]. For example, high levels of n-6 PUFA as LA can led towards inflammatory and oxidative stages of NAFLD [18]. Overall, intracellular FA accumulation can result in uncomplete conversion to TGs or β-oxidation, which generate toxic lipids and cell damage [11]. ## 4.1. Materials and Chemicals For culture assays, Dulbecco’s Modified Eagle Medium (DMEM), fetal bovine serum (FBS), and penicillin–streptomycin antibiotic were purchased from Thermofischer, Waltham, MA, USA. HepG2 (HB-8065™) cells were purchased from American Type Culture Collection (ATCC). PrestoBlue (Thermofischer, Waltham, MA, USA). Fatty Acid Free BSA was purchased from Merck (Darmstadt, Germany). Dimethyl sulfoxide was purchased (DMSO) (Molecular Biology Grade, Merck (Darmstadt, Germany)). For total cellular protein quantification, Pierce™ BCA Protein Assay Kit was purchased from Thermofischer (Waltham, MA, USA). Palmitic Acid/Hexadecanoic Acid (>$99\%$) was purchased from Larodan (Stockholm, Sweden), and linoleic acid (>$99\%$) was purchased from Merck (Darmstadt, Germany). For GC-MS analysis, all the samples were derivatized with N, O-Bis(trimethylsilyl) trifluoroacetamide with $1\%$ trimethylchlorosilane (BSTFA), purchased from Merck (Darmstadt, Germany). Regarding LC-ESI-qTOF analysis, all reagents were LC-MS grade, and isopropanol (IPA), acetonitrile (ACN), formic acid, and ammonium formate were purchased from VWR (Radnor, PA, USA). For HPLC-ELSD analysis, chloroform (HPLC grade, ≥$99.8\%$) was purchased from Thermofischer (Waltham, MA, USA), tetrahydrofuran (THF) (HPLC grade, ≥$99.9\%$) was purchased from Merck (Darmstadt, Germany), ultra-pure water was obtained through a Milli-Q system (Merck Millipore, Burlington, MA, USA), and acetic acid (HPLC grade) was obtained from Carlo Erba Reagents (Val de Reuil, France). The reagents used for lipid extraction were obtained as follows: dichloromethane (DCM) (HPLC grade, ≥$99.9\%$) and n-Hexane (≥$97\%$) from VWR Chemicals (Radnor, PA, USA), Methanol (HPLC grade, ≥$99.9\%$) from Honeywell (Charlotte, NC, USA), sodium methoxide (5.4 M, 30 wt$.\%$ solution in Methanol) from Thermofischer (Waltham, MA, USA), sulfuric acid (95.0–$97.0\%$) from Honeywell (Charlotte, NC, USA), and dimethylformamide (DMF) (HPLC grade, ≥$99.5\%$) from Thermofischer (Waltham, MA, USA). ## 4.2. Linoleic Acid (LA) and Palmitic Acid (PA) Preparation Stock solutions of each fatty acid were prepared at 20 mM in DMEM with $1\%$ of fatty acid free BSA. For PA, the stock solution was heated at 75 °C for 10 min and a further 15 min on a Bandelin Sonorex Super RK 106 Ultrasonic Bath (Berlin, Germany) to improve dissolution. Fatty acids mixture was diluted to 1 mM final concentration using different ratios of LA:PA (1:0, 1:1 and 0:1 v/v) and then filtered using 0.20 µm-pore size membrane. ## 4.3. Cell Culture Hepatocellular carcinoma HepG2 cells were kept in culture in Dulbecco’s Modified Eagle Medium (DMEM) supplemented with $10\%$ of FBS and $1\%$ of Penicillin-streptomycin Antibiotic at 37 °C, with $5\%$ of CO2 in a humidified atmosphere. HepG2 cells were used at $75\%$ confluency. ## 4.3.1. Presto-Blue Metabolic inhibition of lipidic extracts on HepG2 were evaluated using PrestoBlue assay according to manufacturer’s instructions. Cells were seeded at 1 × 104 cells/well in 96-well plates and then exposed to FFA diluted in DMEM with $0.1\%$ fatty acid free BSA for 24 h, in quadruplicates. Wells with media supplemented with LA and PA (without cells) were used to subtract a possible influence of the samples in the PrestoBlue fluorescence signal. Cells treated with $10\%$ DMSO were used as negative control. After incubation, PrestoBlue reagent was added to the media and incubated for 2 h. The fluorescence signal was read in a Synergy H1 microplate reader (BioTek, Instruments, Inc., Winooski, Vermont, USA). Results were expressed in percentage of metabolic inhibition in comparison to cells without treatment. At least two independent experiments were performed. ## 4.3.2. Flow Cytometric Analysis of LA and PA Effect on Apoptotic Stages Cells were seeded at 3 × 105 cells/well in 24-well plates and then exposed to FFA for 24 h, in duplicates. After 24 h, cells were washed twice using phosphate-buffered saline (PBS), trypsinized, and washed again with PBS twice by centrifugation (1500 rpm, 5 min, 22 °C). To perform the staining procedure, fluorochrome-labeled Annexin V was used to precisely target apoptotic cells. The apoptotic and necrotic events of cells treated with the different fatty acids (LA and PA) were determined using the Biolegend FITC Annexin V Apoptosis Detection Kit with 7-AAD Cat. 640922 (San Diego, CA, USA), according to the manufacturer’s instructions and analyzed by flow cytometry using a BD Accuri™ C6 Plus Flow Cytometer (New Jersey, USA). Results are expressed in percentage of live (Av− 7AAD−), early apoptotic (Av+ 7AAD−), necrotic (Av− 7AAD+), and dead (Av+ 7AAD+) cells. ## 4.3.3. Assessment of Intracellular Reactive Oxygen Species (ROS) The intracellular generation of ROS in HepG2 cells was determined using the 2′,7′-dichlorofluorescein diacetate (DCFDA) (D6883, Thermofischer) probe. Before the FFA exposure, cells were seeded in black, Thermofisher 96 clear-bottom well plate (Waltham, MA, USA) at 1 × 105 cells/mL and added to each well 100 μL of the appropriate sample (FFA mixture) solutions and incubated during 24 h. Afterwards, the DCFDA probe was prepared and added to the final concentration of 25 μM to each well. The fluorescence of the dye was measured immediately (0 h) using Synergy H1 microplate reader (BioTek, Instruments, Inc.) and for 1, 2, 3, 4 and 5 h of probe incubation at $\frac{495}{529}$ nm (Ex./Em.) ( Adélia Mendes, personal communication, 2022). ## 4.4. Determination of Total Lipid Accumulation by Oil Red O Staining (ORO) The neutral lipid content accumulation was evaluated according to the method described by Forbes-Hernández et al. [ 2017] [24], with some modifications. Cells were seeded at a density of 3 × 105 cells/mL (1 mL) in 24-well plates for 24 h and then treated with the FFAs mixtures (500 µL of mixture per well). After 24 h of exposition, cells were washed twice using PBS (500 µL per well) and fixed in $4\%$ Paraformaldehyde (PFA) diluted in PBS for 30 min to 1 h. Oil Red O (ORO) stock solution was prepared in 2-propanol at 3.5 mg/mL and filtered through a 0.45 μm pore size hydrophobic membrane. Then, a working solution was set up using three parts of the ORO stock solution and two parts of water. Fatty acid treated and control cells were stained with Oil Red O working solution for 15 min at room temperature (250 µL per well), and PBS was later used to remove the non-stained ORO by washing wells three consecutively times. Afterwards, cell images were taken using ZEISS (Jena, Germany) Optical Microscope Axiocam 208 Color Camera. For semi-quantitative analysis of the stained lipids, 250 µL per well of 2-propanol was added to the 24-well plate containing cells, and then the solution was transferred to a 96-well microplate, and the absorbance was measured at 510 nm in Synergy H1 microplate reader (BioTek, Instruments, Inc., Winooski, Vermont, USA). The absorbance was normalized by cellular protein quantification at 562 nm using Pierce™ BCA Protein Assay Kit. ## 4.5. Lipid Extraction of the Cellular Content After 24 h of fatty-acid exposure, cells were observed, and then the supplemented fatty acid medium was aspired, and cells were once washed with PBS. Cells were trypsinized using TrypLE for 10 min and transferred to a falcon tube to be centrifuged (1500 rpm, 5 min, 22 °C). The obtained pellet was collected, and PBS was added and centrifuged (1500 rpm, 5 min, 22 °C), and the supernatant discarded afterwards. The total lipid content of each pellet was acquired using methyl-tert-butyl ether (MTBE) and methanol according to Matyash et al. [ 2008] extraction procedure [25]. ## 4.6. LC-ESI-qTOF Matyash cell extracts were prepared at a stock solution of 1 mg/mL and dissolved in IPA:ACN [9:1] at 0.20 mg/mL to be further analysed. Additionally, for quality requirements after data acquisition, a quality control (QC) sample was prepared using a pool of the samples and injected in positive mode on an UHPLC instrument (Elute; Bruker, Billerica, MA, USA) equipped with an Acquity UPLC BEH C18 (17 µm) pre-column (Waters, Milford, MA, USA), an Intensity Solo 2 C18 (100 × 2.1 mm) column (Bruker, Billerica, MA, USA), and coupled with an UHR–QTOF detector (Impact II; Bruker). Injection method was based on conditions reported by Sarafian et al. [ 2014] [26] and Calderón et al. [ 2019] [27], with some modifications. The mobile phases composition was the following: ACN:upH2O [6:4, v/v] (Phase A) and IPA:ACN [9:1, v/v] (Phase B), both containing $0.1\%$ (v/v) of formic acid and 10 mM ammonium formate modifiers. Gradient of B phase flow was as follows: 0.0 min—$40\%$; 2.0 min—$43\%$; 2.1 min—$50\%$; 12.0 min—$54\%$; 12.1 min—$70\%$; 18.0 min—$99\%$; 20.0 min—$99\%$; 20.1 min—$40\%$; 22 min—$40\%$. The flow rate was set at 0.4 mL/min, and column temperature was set at 55 °C. Injection volume was 3 µL in positive and 5 µL in negative ionization mode. For MS analysis, the following parameters were applied: end plate offset voltage 500 V, capillary voltage 4500 V (positive ionization) or 3000 V (negative ionization), nebulizing gas pressure of 35 psi, drying gas flow 8 L/min, drying gas temperature 325 °C, quadrupole ion energy 3 eV (positive ionization) or 5 eV (negative ionization), collision energy 10 eV (positive ionization) or 5 eV (negative ionization). Acquisition was performed in an Auto MS/MS scan mode over a mass range of m/z 50–1500. For both ionization modes, an external mass calibration was performed with a solution of IPA:upH2O [1:1, v/v] added with $0.2\%$ (v/v) formic acid and $0.6\%$ (v/v) NaOH 1 M, continuously injected at 180 µL/h. The acquired data were treated using MS-DIAL (v 4.90) and compound identification by MS-FINDER (v 3.52). ## 4.7. GC-MS The Matyash extracts were analysed after derivatization into their trimethylsilyl derivatives. For that, to 1 mg of sample 70 µL of DCM and 60 µL of BSTFA were added. After 30 min incubation at 70 °C, DCM was added to a final volume of 400 µL. Afterwards, the samples were analysed on a GC-MS (triple quadrupole) model EVOQ (Bruker, Karlsruhe, Germany) mass spectrometer, coupled with a Rxi-5Sil MS column (30 m × 250 µm × 0.25 µm) at constant flow of 1 mL/min. Helium was used as carrier gas, as described by Teixeira et al., [ 2022] [28]. In summary, the injector was set at 330 °C, and the oven temperature at 60 °C. After a 5 min hold, the temperature was increased at 3 °C/min until 330 °C and maintained for more 20 min. The mass spectrometer detector was operated in electron ionization mode (EI) at −70 eV, the source temperature of 280 °C, the transfer line at 300 °C, and a quadrupole in a scan range of 33 to 1000 amu per second. The compound identification was based on the comparison of the obtained mass spectra with the information on the NIST Library (v. 2.3), as well as by comparison with reference compounds. ## 4.8. GC-FID To determine the fatty acids profile, the performed method was based on Fontes at al. [ 29]. The obtained washed cell pellets were prepared at 1 mg/mL final concentration in hexane. Glyceryl tritridecanoate (≥$99\%$, Merck) was used as internal standard, followed by 2.26 mL of methanol, and 240 µL of sodium methoxide (5 M). The samples were vortexed and incubated at 80 °C for 10 min. After cooling in ice, 1.25 mL of DMF were added prior to 1.25 mL of sulphuric acid (3 M). Samples were vortexed and incubated at 60 °C for 30 min. Finally, after cooling, samples were vortexed and centrifuged (1250× g; 18 °C; 5 min). The organic phase containing fatty acid methyl esters (FAME) was collected to a tared vial and then the hexane (approx. 1 mL) was evaporated and re-suspended in 300 uL of hexane for further analysis in a GC-FID apparatus equipped with a BPX70 column (60 m x 0.25 mm x 0.25 µm, SGE Trajan). The analysis conditions were set as follows: injector temperature 250 °C, split 25:1, injection volume 1 μL; detector (FID) temperature 275 °C; hydrogen was carrier gas at 20.5 psi; oven temperature program: started at 60 °C (held 5 min), then raised at 15 °C/min to 165 °C (held 1 min) and finally at 2 °C/min to 225 °C (held 2 min). The identification of fatty acids was carried out by comparison with a Supelco 37 FAME mixture sample (CRM47885). ## 4.9. HPLC-ELSD For the triglyceride analysis, the Plante et al. [ 2011] [30] methodology was used with some modifications. Approximately 3 mg/mL of the cellular Matyash extract were dissolved in methanol: chloroform mixture (1:1, v/v) was injected in a high-performance liquid chromatograph (HPLC) (1260 Infinity II; Agilent, Santa Clara, CA, USA) equipped with an evaporative light scattering detector (ELSD) and a Zorbax Eclipse Plus C8 column (2.1 × 100 mm; Agilent). The detector operation conditions were set with the evaporator at 30 °C, the nebulizer at 40 °C, and nitrogen as carrier gas with a flow rate set at 1.5 SLM. The mobile phases were prepared and filtered by a 0.20 µm-pore size hydrophobic membrane: A (methanol, water and acetic acid, 750:250:4 (v/v)) and B (acetonitrile, methanol, tetrahydrofuran, and acetic acid, 500:375:125:4 (v/v)) in a gradient mode were set between 0–45 min ($100\%$ Phase A, 46–59 min—$30\%$ Phase A and $70\%$ Phase B, 60–65 min—$10\%$ Phase A and $90\%$ Phase B, and 65.10–72 min—$100\%$ Phase A). The determination was carried out by injecting 10 µL of sample with a flow rate of 0.5 mL/min with the oven set at 40 °C. ## 5. Conclusions The reported results showed that PA led to lower metabolic inhibition and, interestingly, when compared to LA, PA was poorly accumulated. Thus, LA was associated with higher accumulation in both FFA and TG forms. Triglyceride variation was analyzed and potential species identified, such as TG C18:2, C18:2, C18:2; TG C16:0, C18:2, C18:2 and TG C16:0, C18:1, C18:2, probably as a protective mechanism to limit FFA impact on cell damage. In Western diets, LA and PA sources are highly abundant [17,18,19]. The present work showed the importance to balance both PA and LA fatty acids concentrations in HepG2 cells to maintain normal levels of FFAs, cholesterol and TGs. Consequently, therapies that induce fatty acids lipolysis, when it is not possible to control their intake, are currently being studied to induce liver homeostasis and avoid irreversible stages of fibrosis [23]. Although, in this situation, the use of drugs capable of increasing β-oxidation of fatty acids would be useful in controlling lipid accumulation, it still represents a pharmacological intervention that does not solve an underlying problem resulting from an unhealthy lifestyle. Consequently, these treatments can be very helpful in extreme situations (i.e., obesity), but it should not be forgotten that maintaining a healthy diet (i.e., inclusion of vegetables and fruits followed by moderate consumption of animal fats and seed oils), when combined with frequent physical exercise and consistent sleeping routine, would help to minimize some of the observed in vitro effects of fatty acids in this research work. 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--- title: Phytochemicals, Antioxidant and Antimicrobial Potentials and LC-MS Analysis of Centaurea parviflora Desf. Extracts authors: - Fatima Zohra Hechaichi - Hamdi Bendif - Chawki Bensouici - Sulaiman A. Alsalamah - Boutheina Zaidi - Mustapha Mounir Bouhenna - Nabila Souilah - Mohammed I. Alghonaim - Abderrahim Benslama - Samir Medjekal - Ashraf A. Qurtam - Mohamed Djamel Miara - Fehmi Boufahja journal: Molecules year: 2023 pmcid: PMC10005273 doi: 10.3390/molecules28052263 license: CC BY 4.0 --- # Phytochemicals, Antioxidant and Antimicrobial Potentials and LC-MS Analysis of Centaurea parviflora Desf. Extracts ## Abstract Centaurea parviflora (C. parviflora), belonging to the family Asteraceae, is an Algerian medicinal plant used in folk medicine to treat different diseases related to hyperglycemic and inflammatory disorders, as well as in food. The present study aimed to assess the total phenolic content, in vitro antioxidant and antimicrobial activity and phytochemical profile of the extracts of C. parviflora. The extraction of phenolic compounds from aerial parts was conducted using solvents of increasing polarity starting from methanol, resulting in crude extract (CE), to chloroform extract (CHE), ethyl acetate extract (EAE) and butanol extract (BUE). The total phenolic, flavonoid and flavonol contents of the extracts were determined using the Folin–Ciocalteu and AlCl3 methods, respectively. The antioxidant activity was measured with seven methods: 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay, galvinoxyl free-radical-scavenging test, 2,2′-Azino-Bis(3-Ethylbenzothiazoline-6-Sulfonic Acid) (ABTS) assay, cupric reducing antioxidant capacity (CUPRAC), reducing power, Fe+2-phenanthroline reduction assay and superoxide-scavenging test. The disc-diffusion method aimed at testing the sensitivity of bacterial strains toward our extracts. A qualitative analysis with thin-layer chromatography of the methanolic extract was performed. Moreover, HPLC-DAD-MS was used to establish the phytochemical profile of the BUE. The BUE was found to contain high amounts of total phenolics (175.27 ± 2.79 µg GAE/mg E), flavonoids (59.89 ± 0.91 µg QE/mg E) and flavonols (47.30 ± 0.51 µg RE/mg E). Using TLC, different components such as flavonoids and polyphenols were noted. The highest radical-scavenging ability was recorded for the BUE against DPPH (IC50 = 59.38 ± 0.72 µg/mL), galvinoxyl (IC50 = 36.25 ± 0.42 µg/mL), ABTS (IC50 = 49.52 ± 1.54 µg/mL) and superoxide (IC50 = 13.61 ± 0.38 µg/mL). The BUE had the best reducing power according to the CUPRAC (A0.5 = 71.80 ± 1.22 μg/mL), phenanthroline test (A0.5 = 20.29 ± 1.16 μg/mL) and FRAP (A0.5 = 119.17 ± 0.29 μg/mL). The LC-MS analysis of BUE allowed us to identify eight compounds including six phenolic acids and two flavonoids: quinic acid, five chlorogenic acid derivatives, rutin and quercetin 3-o-glucoside. This preliminary investigation revealed that the extracts of C. parviflora have a good biopharmaceutical activity. The BUE possesses an interesting potential for pharmaceutical/nutraceutical applications. ## 1. Introduction Algeria, because of its particular geographical location and climatic diversity, presents a rich and very large vegetation. There are more than 3000 plant species, $15\%$ of which are endemic, belonging to several botanical families [1]. Several plants of the Asteraceae family are grown for their nutritional value (sunflower, artichoke, lettuce, chicory, chamomile, etc.) or as decorative plants (dahlias, asters, etc.) [ 2]. Indeed, it has been reported that the flowers and leaves of this family possess antibacterial, antifungal, antiviral, anti-inflammatory, antiproliferative and anti-leishmaniasis activity [3,4]. The secondary metabolite diversity of the Asteraceae explains their multiple pharmacological activities, and as a result, many species of this family are used in traditional medicine [5]. The genus Centaurea comprises more than 500 species, 45 of which grow naturally in Algeria, seven of which occur in the Sahara Desert [6]. Centaurea species are latex-free-resin or -essence plants that grow in tufts or in seedlings, usually in spring, and occur in different habitat types [7]. The main secondary metabolites of Centaurea species are represented by triterpenes, flavonoids and lignans, and they are also known to produce sesquiterpene lactones [8,9,10,11]. These secondary metabolites can be isolated from the leaves, aerial parts and sometimes roots of Centaurea [12]. Centaurea parviflora Desf. is a suffrutescent plant at its base, with a height of 40–60 cm, growing in dense, intricate bushes. The appendix of the bracts lacks a whitish scarious part or has a faintly marked scarious part with 8–12 lateral laciniures. The highest leaves are not decurrent on the stem. The plant is characterized among the other knapweeds by its small (5 mm wide, 15 mm long), solitary flower head. The appendices are characterized by a strongly covered median spine. The flowers are purple with black, bellied, pubescent achenes with four marked streaks. C. parviflora is endemic to Algeria and Tunisia [13]. In Algeria, the plant is quite rare, but can be found in different biogeographical sectors from the west to the east of the country and from the coast to the highlands. Many species of the genus Centaurea have been used in traditional medicine to treat various ailments and diseases. These species of the genus are used as anticancer, anti-inflammatory, antinociceptive, antipyretic, anti-arteropic, antineoplastic, anti-ulcerogenic and antimicrobial treatments. They are also used for rheumatic pain, cardiovascular problems, headaches, gastrointestinal symptoms and parasites and as a fever reliever, stimulant remedy and wound healer [6,14,15,16,17,18]. They are highly active in living systems and therefore have a strong pharmacological interest, which explains the long-term use of these plants in traditional medicine [19]. Due to their interesting health properties, polyphenols have received a growing interest and popularity, even though their amounts in most natural sources are often not sufficient for an optimal dietary intake. Phenolic compounds are commonly found in plants, and many of these effective components have several biological activities including antioxidative, anti-diabetic, anti-carcinogenic, antimicrobial, anti-allergic, anti-mutagenic and anti-inflammatory properties [20]. The present study aimed to carry out phytochemical profiling through a qualitative analysis with thin-layer chromatography and HPLC-DAD-MS of the butanolic extract and the TPCs. It also evaluated the in vitro antioxidant and antimicrobial activities of C. parviflora. ## 2.1. Extraction Yield Extraction is the main step for recovering and isolating phytochemicals from plant materials. The extraction yield can be affected by the chemical nature of the phytochemicals, the method used, the solvent used, as well as the presence of interfering substances [21,22,23]. ## 2.2.1. Thin-Layer Chromatography Analysis (TLC) After the development of the chromatogram, and when the solvent reached the upper line, the plate was removed, dried and examined under the UV lamp in order to identify the constituents present [24]. TLC profiling of the plant extracts in different solvent systems confirms the presence of a diverse group of phytochemicals. The qualitative analysis made it possible to highlight numerous spots, especially those colored in blue, mauve, yellow and pink. These three stains confirm the presence of phenolic compounds [25]. According to the results (Table 1 and Figure 1), the following remarks can be noted: The number of spots in system 1 and system 3 is higher than that in the other three systems (2, 4 and 5); this means that the plant C. parviflora is rich in phenolic compounds. Finally, we can conclude that the TLC results confirm the presence of phenolic compounds and flavonoids in the extracts of the C. parviflora plant and also reinforce what we obtained for the biological activity. The number of spots indicates that the quantity of phenolic compounds in C. parviflora is higher. This confirms that this plant is rich in phenolic compounds, and these results are confirmed by our finding above (Table 1), where the plant appeared to be rich in TPC (Tukey’s HSD test: p-values < 0.01). Moreover, the LC-MS analysis of the butanolic fraction of C. parviflora mentioned six phenolic acids and 2 flavonoids, quinic acid, five derivatives of chlorogenic acid, rutin and quercetin 3-o-glucoside, which confirm the richness of this plant in phenolic compounds. ## 2.2.2. LC-MS Analyses A solution of 1000 ppm was prepared in methanol and injected into the LC-ESI-MS instrument. Both positive and negative ionization chromatograms show a rich concentration of the majority compounds between 0.5 and 11 min (Figure 2 and Figure 3). Compound 1: The peak that appears at a retention time of 0.68 mn with m/$z = 191.00$ and a crude formula of C7H12O6 was identified as quinic acid (Figure 4 and Figure 5). Compounds 2, 3, 4 and 5: The peak that appears at the retention times of 0.72, 1.00, 1.22, 1.62 mn with m/$z = 353.00$ and a crude formula of C16H18O9 was identified as chlorogenic acid (Figure 6 and Figure 7). Compound 6: The peak that appears at a retention time of 3.58 mn with m/$z = 609.00$ and a crude formula of C27H30O16 was identified as rutin (Figure 8 and Figure 9). Compound 7: The peak that appears at a retention time of 4.26 mn with m/$z = 463.00$ and a crude formula of C21H20O12 has been identified as quercetin 3-O-glucoside (Figure 10 and Figure 11). Compound 8: The peak that appears at a retention time of 5.46 mn with m/$z = 463.00$ and a crude formula of C30H10O14 was identified as chlorogenic acid derivative (Figure 12 and Figure 13). Analysis of the results from the Agilent Mass Hunter Workstation Qualitative Analysis Software B.06.00 for negative ionization in comparison with the data in the literature allowed us to identify eight compounds (Table 2). An attempt to identify the phenolic compounds contained in the BUE was based on accurate mass comparisons [M-H] of pseudo-molecular ions with those found in accordance with the literature. The LC-MS analysis of the butanolic extract of C. parviflora identified eight compounds including six phenolic acids and two flavonoids: quinic acid, five derivatives of chlorogenic acid, rutin and quercetin 3-o-glucoside. ## 2.3. Antioxidant Activity The total phenolic content (TPC), total flavonoid content (TFC) and total flavonol (TFOL) content and the antioxidant activity of the different extracts and fractions of C. parviflora were determined using different methods, and the results are shown in Table 1 and Table 3. The difference in the polyphenol and flavonoid contents of the crude extracts and their fractions results from the difference in polarity of the organic solvents, the extraction time and temperature, the solid–liquid extraction ratio as well as the chemical and physical characteristics of the samples [30]. By comparing the results obtained, the TPC, TFC and TFOL of the different extracts were analyzed and presented in Table 1. The results of the determination of the total phenolic compounds were obtained by extrapolating the absorbance of the extracts on the gallic acid calibration curve. The results show that the BUE is the extract richest in polyphenols with a content of 175.27 ± 2.79 µg GAE/mg of extract followed by the EAE (136.94 ± 2.94 µg GAE/mg of extract, Tukey’s HSD test: p-value < 0.01). In fact, the CE and CHE have the lowest phenolic contents, with 113.51 ± 2.95 and 105.47 ± 1.35 GAE/mg extract, respectively. According to the results in Table 1, the BUE is the richest in flavonoids with 59.89 ± 0.91, followed by the CE with a content of 24.49 ± 0.49 µg QE/mg extract. The flavonol content of the extracts was determined using the aluminum trichloride colorimetric method (AlCl3) at 440 nm. As shown in Table 1, the results indicate that the BUE exhibited higher flavonols (47.30 ± 0.51), followed by EAE (27.86 ± 1.45 µg QE/mg of extract). The TPC of plant extracts depend on the type of extract, i.e., the polarity of the solvent used in extraction. The high solubility of phenols in polar solvents provides a high concentration of these compounds in the extracts obtained using polar solvents. The concentration of flavonoids in plant extracts depends on the polarity of the solvents used in the extract preparation [31]. Therefore, many studies have demonstrated that polar solvents give higher yields than non-polar solvents, since polar solvents have the ability to spread within the plant powder, reaching the vegetable matrix and therefore recovering the possible metabolites. In contrast, non-polar solvents, which are immiscible with water, do not have the ability to extract the maximum number of bioactive molecules because of the water contained in the plant tissue. The anti-radical activity of the extracts toward the radical DPPH• was evaluated with spectrophotometry at 517 nm following the reduction of this radical, which is accompanied by a change from violet to yellow color. In this test the results were compared to the reference standards (BHA and BHT). The BUE showed the best anti-radical activity compared with the other extracts (IC50 = 59.38 ± 0.72 µg/mL); this activity was twice as low as BHT (IC50 = 22.32 ± 1.19 µg/mL) and eleven times as low as BHA (IC50 = 5.73 ± 0.41 µg/mL). The scavenging activity towards the galvinoxyl radical was evaluated with spectrophotometry at 428 nm following the anti-radical reaction, which changes the solution’s color from dark yellow to light yellow. In this test the results were compared to the reference standards (BHA and BHT). From the values of IC50 (μg/mL) calculated from the inhibition percentage curves, it is noted that the BUE showed the highest scavenging activity with a value of (IC50 = 36.25 ± 0.72 μg/mL) followed by the EAE (IC50 = 97.72 ± 3.07μg/mL), CE (IC50 = 88.87 ± 1.86 μg/mL) and the CHE (IC50 > 200 μg/mL). The BUE’s activity was ten times lower than against BHT (IC50 = 3.32 ± 0.18 µg/mL) and six times lower than against BHA (IC50 = 5.38 ± 0.06 µg/mL). This test confirms the results of the previous test. The antioxidant activity assessed using CUPRAC is based on the measurement of the absorbance at 450 nm, which indicates the reduction in the presence of an antioxidant of the stable complex neocuproine-copper (II) (blue color) to the stable complex neocuproine-copper (I) (orange color). Based on the results in Table 3, the BUE has the highest inhibitory activity of the samples studied, with a value of A0.5 = 71.80 ± 1.22 µg/mL, followed by the EAE, CE and CHE with values of A0.5 equal to 92.00 ± 4.85, 128.44 ± 4.14 and 188.33 ± 4.62 µg/mL, respectively (Tukey’s HSD test: p-value < 0.01). The BUE’s activity is seven times lower than that of BHT (A0.5 = 9.62 ± 0.87µg/mL) and twenty times lower than that of BHA (A0.5 = 3.64 ± 0.19 µg/mL) (Tukey’s HSD test: p-value < 0.01). The results of this test confirm the results of the first two tests. Potassium ferricyanide is reduced in the presence of an antioxidant to form potassium ferrocyanide, which then reacts with ferric chloride to form a blue-green ferrous iron complex, which has a maximum absorbance at 700 nm. The results are shown in Table 3. The results of the reducing potency test confirm the results of the first three tests: the BUE, with a value of A0.5 = 119.17 ± 0.29 µg/mL, showed the best activity compared to other extracts from the same plant (Tukey’s HSD test: p-value < 0.01). This activity is much lower than that of the two standards, ascorbic acid (A0.5 = 6.77 ± 1.15 µg/mL) and tannic acid (A0.5 = 5.39 ± 0.91 µg/mL), but is three times lower than the standard α-tocopherol (A0.5 = 34.93 ± 2.38 µg/mL). This result may be due to the presence of electron donor compounds, since the reducing-power method follows the mode of action of electron transfer (ET). In the presence of an antioxidant, ferric iron (Fe+3) is reduced to ferrous iron (Fe+2), and the latter forms a stable complex with orange-red phenanthroline, which has a maximum absorbance at 510 nm. Compared to the other extracts, the BUE showed the best iron-reduction activity, with A0.5 = 20.29 ± 1.16 µg/mL, twenty times lower than BHA (A0.5 = 0.93 ± 0.07 µg/mL) and nine times lower than BHT (A0.5 = 2.24 ± 0.17 µg/mL). The results of this activity are consistent with the previous five methods. ABTS (2,2′-azino-bis (3-ethylbenzothiazoline-6-sulphonic acid)) is a stable organic compound used in the evaluation of anti-radical activity, with a maximum absorbance at 734 nm. In the presence of an antioxidant donor of hydrogen, the cation ABTS•+ undergoes a reduction from blue-green to a neutral colorless state. The results of the ABTS test revealed that the BUE has the best anti-radical activity (IC50 = 49.52 ± 1.54 µg/mL) compared to other extracts from the same plant. Its activity is moderate compared to those of the two standards, BHT and BHA (IC50 = 1.29 ± 0.30, 1.81 ± 0.10 µg/mL, respectively). The results of the sixth test confirm the results of the previous tests. The superoxide-radical-scavenging activity of the extracts was measured. As shown in Table 3, all the extracts have a scavenging activity against the superoxide radical and the highest activity was recorded for the BUE (IC50 = 13.61 ± 0.384 µg/mL), followed by the EAE (IC50 = 14.36 ± 0.90 µg/mL), the CE (IC50 = 21.91 ± 0.70 µg/mL) and finally the CHE (IC50 = 64.77± 2.37µg/mL) (Tukey’s HSD test: p-value < 0.01). Taking into account the high inhibition values of the extracts at the different concentrations, these results are almost within the range of the standards used, tannic acid and α-tocopherol (IC50 = 3.125 µg/mL). Different plant extracts are potential sources of the natural chemical components responsible for antioxidant activities. It appears that the butanolic extract of C. parviflora shows a perfect antioxidant activity against DPPH, galvinoxyl, ABTS and superoxide free radicals as well as a reducing power in the CUPRAC, Fe+2-phenanthroline, and ferric-reducing tests. It should be noted that the order of efficacy of the studied extracts was similar in all the methods used, along with their polyphenol content, confirming that the effect of the extracts is completely consistent with the richness of these extracts in TPC. Several studies showed that chronic diseases such as cancer, ageing and cardiovascular, inflammatory and neurodegenerative pathologies are associated with oxidative stress, a metabolic condition that causes cell degeneration. Antioxidant compounds present in fruits and vegetables appear to play a major role in the protection against oxidative stress. Besides fruits and vegetables, plant beverages such as coffee contribute to the dietary intake of antioxidants [32]. Epidemiological studies have shown relationships between the consumption of polyphenol-rich foods and the prevention of diseases such as cancer, coronary heart disease and osteoporosis, and the results of those studies have promoted an interest in polyphenols. Dietary polyphenols are thought to be beneficial for human health by exerting various biological effects such as free-radical scavenging, metal chelation, modulation of enzymatic activity, and alteration of signal transduction pathways [33]. The role of phenolic compounds has been widely shown in the protection against certain diseases due to their possible interaction with many enzymes and their antioxidant properties. Specifically, flavonoids are attributed various properties: antitumor, anti-radical, anti-inflammatory, analgesic, anti-allergic, antispasmodic, antibacterial, hepatoprotective, estrogenic and/or anti-estrogenic [34]. Polyphenols are associated with many physiological processes in the quality of food that occur when the plant is subjected to mechanical injuries. The ability of a plant species to resist attack by insects and microorganisms is often correlated with the content of phenolic compounds [35]. Due to the conjugated ring structure and the presence of hydroxyl groups, many phenolic compounds have the potential to act as antioxidants by hydrogenation or complexation with oxidizing species to scavenge or stabilize free radicals involved in the oxidation process [36]. Chlorogenic acid (CGA) is an important polyphenolic compound that occurs naturally in various agricultural products such as coffee, beans, potatoes, and apples [37]. It has been suggested that this compound exhibits an antioxidant property, and it has been reported that CGA inhibits vitamin A oxidation and protects against the oxidation of epinephrine in vitro. Recently, results from in vivo studies have suggested that CGA provides beneficial effects during ischemia-reperfusion injury of rat liver and paraquat-induced oxidative stress in rats [38]. Chlorogenic acids are phenolic compounds formed by the esterification of cinnamic acids. They exhibit various pharmacological properties. Its antioxidant, anti-obesity, anti-viral, anti-hypertension, antipyretic and anti-inflammatory properties are being studied by various researchers [39]. Chlorogenic acid is a biologically active polyphenol that is soluble in ethanol and acetone. Chlorogenic acids are present in abundance in green coffee beans. They are also found in potatoes, prunes and bamboo. Its wide availability makes it economical and aids its easy application. In addition, it has been found that chlorogenic acids have the capability to modulate lipid metabolism and glucose in both genetic and lifestyle-related metabolic disorders [40]. Chlorogenic acid and caffeic acid have vicinal hydroxyl groups on an aromatic residue, and they exhibit antimutagenic, anti-carcinogenic and antioxidant activities in vitro, which is attributed to the scavenging of reactive oxygen species (ROS) [41]. Chlorogenic acid is a principle phenolic compound in nectarine fruit pulp and has strong antioxidant activity, which is positively correlated with ROS-scavenging ability in peach and nectarine fruit. However, little is known about the effects of polyphenols on fruit proteins. In previous studies, researchers have demonstrated that exogenous CHA can significantly delay the senescence of nectarine fruit [42]. In total, ten flavonoids representing four major categories were screened to evaluate their antioxidant activity. Rutin showed the highest level of free-radical-scavenging capacity, followed by kaempferol, luteolin, quercetin, apigenin, hesperidin, sinensetin, naringenin, naringin and 3,5,6,7,8,3’,4’-heptamethoxyflavone [43]. However, quercetin and quercetin 3-O-glucoside displayed antioxidant activities that were consistent with the reported activities of these flavonoids in the literature [44]. The results obtained by Razavi [45] showed that quercetin 3-O-glucoside exhibits a high antioxidant effect with an RC50 of 22 µg/mL, has low cytotoxicity and has no antibacterial effects. Quercetin 3-O-glucoside also exhibits a high phytotoxic effect, with an IC50 value of 282.7 µg/mL. ## Antimicrobial Activity The antimicrobial activities of the C. parviflora extract were evaluated using the well-diffusion technique, and the results are shown in Table 4 and Figure 14. The antimicrobial activities of the methanolic extract against bacterial reference and pathogenic fungi were assessed (Table 4). According to the obtained data, the disc method made it possible to determine the action of the plant extracts dissolved in DMSO on the different strains. These tests revealed a good antimicrobial effect of the methanolic extract against most of the tested microbes expect Escherichia coli and Candida albicans. Broadly speaking, we found values for the inhibition zone diameter (IZD, mm) ranging from 7.00 to 12.12 mm and 8.03 to 15.23 mm, respectively, for the 20 and 30 mg/mL extract concentrations. The extracts at a concentration of 30 mg/mL are active and exhibit antimicrobial activity by inhibiting the in vitro growth of microbial germs: S. aureus ATCC6538 (IZD, 8.64 and 7.00 mm), S. aureus ATCC25923 (IZD, 13.22 and 12.00 mm), Salmonella (IZD, 8.03 and 7.00 mm), A. niger (IZD, 8.22 and 7.00 mm) and Pseudomonas sp. ( IZD, 15.23 and 12.12 mm). The marked observation to emerge from the data comparison was the potential inhibition effect of C. parviflora extract against Staphylococcus aureus, a leading cause of skin and soft tissue infections, and Pseudomonas sp., which particularly causes infections in the blood, lungs (pneumonia) or other parts of the body after surgery. This is in good agreement with the findings of Naeim et al. [ 46], who reported a high antibacterial activity of the methanol extract from C. pumilio roots and aerial parts against Staphylococcus aureus and Acinetobacter baumannii strains (MIC, 62.50 μg/mL and 250 μg/mL, respectively). Furthermore, Karamenderes et al. [ 47] demonstrated high antiprotozoal (Plasmodium falciparum and Leishmania donovani) and antimicrobial (Candida albicans, Candida glabrata, Candida krusei, Cryptococcus neoformans, Mycobacterium intracellulare, Aspergillus fumigatus, and methicillin-resistant Staphylococcus aureus) activities of ten Centaurea L. species growing in Turkey. The data obtained are broadly consistent with the major trends reported in the literature [48]. To cure diseases in humans and animals brought on by microorganisms that are medication-resistant, new antimicrobial medicines are required. Additionally, there is a persistent desire from consumers for “natural” and/or “preservative-free” foods and cosmetics that are microbiologically safe [49]. Plants are a significant source of bioactive constituents including phenols, aromatic components, terpenoids, sterols, essential oils, alkaloids, tannins and anthocyanins that play a significant role in the treatment of many diseases [50]. Methanolic extracts can potentially be used as natural antimicrobial and antioxidant agents against infectious diseases in humans and for the preservation of food products. The development of such natural agents will also help to solve environmental problems caused by synthetic products and synthetic drugs such as pollution and resistance of certain microorganisms. Phenolic acids have recently gained substantial attention due to their various practical, biological and pharmacological effects. The extended list of chlorogenic acids contains approximately 400 compounds. Chlorogenic acid is an important and biologically active dietary polyphenol, playing several important and therapeutic roles such as antioxidant activity, antibacterial, hepatoprotective, antiviral and antimicrobial [51]. ## 3.1. Plant Materials The aerial parts of C. parviflora Desf. were harvested in Djebel Djedoug (forest of Dréat, Msila, northern Algeria in June 2021 (altitude 1179 m, latitude 35.8838° or 35°53′2″ North, longitude: 4.3284° or 4°19′42″ East). The taxonomic identification of plant materials was confirmed by Dr. M.D. Miara at the Department of Biology, University of Tiaret, using Flora of Algeria [13], and an herbarium specimen was archived in the Herbarium of Tiaret University, Tiaret, Algeria. ## Chemicals Chloroform, ethyl acetate, butanol, Folin–Ciocalteu reagent, sodium bicarbonate, aluminum chloride, tannic acid, ascorbic acid, 2,2-diphenyl-1-picrylhydrazyl (DPPH), 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt (ABTS), potassium persulfate, hydrogen peroxide, disodium hydrogen phosphate, sodium phosphate monobasic dihydrate, ferrous chloride, trichloroacetic acid (TCA), nitroblue tetrazolium (NBT), ferric chloride, butylated hydroxyanisol (BHA) and butylated hydroxytoluene (BHT) were supplied by Sigma-Aldrich (St. Louis, MO, USA). The extraction of the plant powder was conducted according to Zaak et al. [ 52] and Mammeri et al. [ 53]. The first step consisted of a solid–liquid extraction, where an amount of 50 g of aerial-art powder was macerated at room temperature three times with 300 mL methanol–water ($80\%$). The combined filtrates were evaporated under low pressure with a rotary evaporator (40 °C) to yield the crude methanolic extract (CE). Then, the crude extract was subjected to liquid–liquid extraction (fractionation) using organic solvents of increasing polarity: chloroform giving chloroform extract (CHE), ethyl acetate giving ethyl acetate extract (EAE) and n-butanol giving butanol extract (BUE). All solvents were removed using a rotary evaporator. ## 3.3.1. Qualitative Analysis with Thin-Layer Chromatography (TLC) Thin-layer chromatography is a rapid analytical technique and method for separating compounds. It applies to pure molecules and extracts. It is a rapid physicochemical method based on the phenomena of adsorption, interaction and polarity. Compounds can be characterized according to their Rf (retention factor). It allows us to obtain a general idea of the metabolites present and allows an easy and fast control of the purity of a compound when the operating conditions are well-determined. The methanolic extract was checked with TLC on analytical plates over silica gel. Aluminum plates coated with Merck 60F254 silica gel were used. Solvent systems of different polarities were prepared (Table 5). The methanolic extract was applied to pre-coated TLC plates using capillary tubes and developed in a TLC chamber using mobile phases. The developed TLC plates were air-dried and observed under visible light and ultra violet light (UV, 254 and 366 nm). The movement of the analyte was expressed as its retention factor (Rf):Rf = Distance travel by solute/Distance travel by solvent (Rf—Retention factor) ## 3.3.2. HPLC Analysis The phenolic constituents in the butanolic fraction of C. parviflora were investigated using LC-MS analysis and tentatively identified by comparison with the literature. The HPLC analysis was performed using the Agilent 6420 series triple-quadrupole double-MS instrument with the latest generation of high-performance liquid chromatography, the HPLC 1260 infinity LC, equipped with a vacuum degreaser, Infinity 1260 automatic injector, double-piston pump in series and ultra-sensitive diode (DAD) baratte UV detector. The column used for chromatographic separation was a Zorbax Eclipse Plus C18 (1.8 μm, 150 mm × 4.6 mm) (Agilent Technologies, Palo Alto, CA, USA). In separating the compounds from the butanolic fraction of C. parviflora, the rate of flux used was 0.80 mL/min and the analysis was performed at room temperature. A gradient elution was run, utilizing as eluent A water with $0.1\%$ formic acid and as eluent B acetonitrile. The following multistage linear gradient was applied: 0 min, $15\%$ B; 35 min, $95\%$ B; 40 min, $95\%$ B; 55 min, $15\%$ B and, finally, a conditioning cycle of 5 min, with the same conditions for the following analysis. The injection volume was 10 µL. The flow rate of the mobile phase was 0.4 mL/min, and the temperature of the column was maintained at 40 °C. The mass spectrometer was operated in the negative ion mode with a capillary voltage of 4000 V and the drying gas stream was the nitrogen. ## 3.4.1. Total Phenolic Content (TPC) The TPC of extracts was estimated with the method of Müller [54]. A mixture of 200 µL sample and 1 mL of $10\%$ Folin–Ciocalteu reagent was incubated for 4 min, and then 800 µL of sodium carbonate ($7.5\%$) was added. All reactants were finally incubated for 2 h and the absorbance was taken at 765 nm against the corresponding blank. Gallic acid was used as a standard and a calibration curve was prepared in the same conditions ($y = 0.0034$ x + 0.1044; R2 = 0.9972). The results were expressed as µg GAE/mg dry extract. ## 3.4.2. Total Flavonoid Content (TFC) The total flavonoid content was evaluated following the aluminum trichloride method of Topçu [55] with some modifications. An amount of 300 µL of extract was mixed with the same volume of aluminum trichloride ($2\%$) and incubated for 15 min. The absorbance was measured at 430 nm. For this assay, quercetin was used to prepare a calibration curve in the same conditions ($Y = 0.0071$x + 0.0274; R2 = 0.9985). The results were expressed as QE/mg. ## 3.4.3. Total Flavonol (TFOL) Content To evaluate the flavonol content of our extracts, 1 mL of sample and 1 mL of aluminum trichloride ($2\%$) were mixed with 1.5 mL of aqueous sodium acetate ($5\%$) and incubated at 25 °C for 2.5 h. The absorbance was then read at 440 nm and the results were expressed as RE/mg dry extract using a calibration curve prepared with rutin ($y = 0.0122$x + 0.0179; R2 = 0.9991) [56]. ## 3.4.4. DPPH Free-Radical Scavenging The DPPH free-radical-scavenging activity was assessed using the Blois method [57]. Briefly, in a 96-well microplate (PerkinElmer Multimode Plate Reader EnSpire, Waltham, MA, USA), 40 µL of the sample at different concentrations was added to 160 µL of freshly prepared DPPH (0.1 mM). The absorbance of the reaction mixture was measured using spectrophotometry at a wavelength of 517 nm after an incubation of 30 min at room temperature in darkness. The percentage of inhibition was calculated using the following equation:% Inhibition = (Ac − At/Ac) × 100 where Ac is the absorbance of the control and At is the absorbance of the test. The inhibitions obtained were plotted against the sample concentrations and the resulting plots were used to calculate the IC50 (the concentration of the sample that reduced $50\%$ of the DPPH). ## 3.4.5. Galvinoxyl Free-Radical Scavenging The galvinoxyl free-radical-scavenging activity was determined using Shi’s [58] method. In a 96-well microplate, a volume of 160 µL of galvinoxyl (0.1 mM) was added to 40 µL of sample prepared at different concentrations. After 120 min, the absorbance was measured at a wavelength of 428 nm. The inhibition percentage was calculated according to the following equation:% Inhibition = (Ac − At/Ac) × 100 where Ac is the absorbance of the control and At is the absorbance of the test. The inhibitions obtained were plotted against the sample concentrations and the resulting plots were used to calculate the IC50 (the concentration of the sample that reduced $50\%$ of the calvinoxyl). ## 3.4.6. ABTS-Scavenging Assay The radical-scavenging activity was evaluated using the stable cation radical ABTS, as described by Re et al. [ 59]. The ABTS radical was generated by mixing the ABTS solution (7 mM) with 13.24 mg of potassium persulfate for 16 h. The resulting solution was refrigerated, then diluted to reach an absorbance of 0.7 ± 0.02 at 734 nm. A 100 µL volume of the sample was mixed with 1.9 mL of ABTS•+ solution and incubated. After 7 min, the absorbance was measured at 734 nm. BHA and BHT were used as standard compounds. The ABTS inhibition was calculated using the following formula:% Inhibition = (Ac − At/Ac) × 100 where Ac is the absorbance of the control and At is the absorbance of the test. The inhibitions obtained were plotted against the sample concentrations and the resulting plots were used to calculate the IC50 (the concentration of the sample that reduced $50\%$ of the ABTS). ## 3.4.7. Superoxide-Radical-Scavenging Activity (O2•−) The activity was determined according to the method of Elizabeth and Rao [60]. In a 96-well plate with a volume of 200 μL for each well, 40 μL of the sample solution at different concentrations (3.125, 6.25, 12.5, 25, 50, 100 and 200 μg/mL), 130 μL of alkaline DMSO (20 mg of NaOH in 100 mL DMSO), and 30 μL of NBT (10 mgNBT in 10 mL of distilled water). Absorbances are measured instantaneously at room temperature at 560 nm (results were expressed as the average of three separate measurements ± standard deviation). A blank was prepared for each concentration. Tannic acid and ascorbic acid were used as reference compounds. Percent inhibition of superoxide was calculated according to the following equation:% Inhibition = (Ac − At/Ac) × 100 where Ac is the absorbance of the control and At is the absorbance of the test. The inhibitions obtained were plotted against the sample concentrations and the resulting plots were used to calculate the IC50 (the concentration of the sample that reduced $50\%$ of the superoxide). ## 3.4.8. CUPric Reducing Antioxidant Capacity (CUPRAC) This activity was determined with the method of Apak et al. [ 61]. In a 96-well plate, 40 μL of the sample was added to 60 μL of ammonium acetate buffer (1 M, PH = 7), 50 μL of neocuproine (7.5 mM) and 50 μL of CuCl2 (10 mM). After one hour, the absorbance was recorded at 450 nm with a microplate reader. The antioxidant activity results were calculated as A0.5 (µg/mL). ## 3.4.9. Ferric-Reducing Power The reducing power of the extracts was determined according to the methods of Bouratoua [62]. A volume of 10 μL of the extract at different concentrations was mixed with 40 μL of phosphate buffer solution (0.2 M, pH = 6.6) and 50 μL of a potassium ferricyanide [K3Fe (CN)6] solution ($1\%$). The mixture was incubated at 50 °C for 20 min. Then, 50 µL of trichloroacetic acid ($10\%$) was added to stop the reaction and the whole was centrifuged at 3000 r/min for 10 min. Finally, 50 µL of the supernatant solution was mixed with 50 µL of distilled water and 10 µL of FeCl3 ($0.1\%$) and the absorbance was recorded at 700 nm after incubation for 10 min. An increase in the absorbance corresponds to an increase in the reducing power of the test extracts. ## 3.4.10. Phenanthroline Test The reduction activity in terms of the formation of the Fe+2-phenanthroline complex of the extracts was measured using the method described by Szydlowska-Czerniaka [63] and Aissani et al [64]. A volume of 10 μL of various concentrations of extract or standards was added to the reaction mixture containing deoxyribose, then 50 μL FeCl3 ($0.2\%$), 30 µL phenanthroline ($0.5\%$) and 110 µL of the methanol were added. The mixture was vigorously agitated and incubated for 20 min in the oven at 30 °C. The absorbance was determined at 510 nm. The results were calculated as A0.5 (μg/mL), corresponding to the concentration indicating 0.50 absorbance. ## 3.4.11. Microorganisms A total of 8 microbial cultures belonging to bacteria, yeast and fungi species were used in this study. The antimicrobial activity of the methanolic extract of C. parviflora was evaluated In vitro with the growth of several bacterial, fungal and yeast strains, namely P. aeruginosa, S. aureus, B. subtilis, E. coli, Salmonella, Aspergillus niger and Candida albicans. The microorganisms were provided by the laboratory of the Department of Microbiology, M’sila University, M’sila, Algeria. The crude methanolic extract was diluted to a final concentration of 30 mg/mL, then sterilized by filtration (0.45 m) with Millipore filters. The antimicrobial tests were carried out using the disc-diffusion method, using 100 µL of suspension containing 108 CFU/mL of bacteria or 106 CFU/mL of yeast spread on nutrient agar (NA) and Sabouraud dextrose agar (SDA), respectively. The discs (6 mm in diameter from Whatman N° 1 filter paper) were impregnated with 10 µL of the methanolic extracts placed on the surface of the agar. Negative controls were prepared using the same solvents. The Petri plates were placed at a low temperature (+4 °C) for 15 to 30 min to allow the extracts to diffuse into the agar before the bacteria began to multiply. After 24 h of incubation at 37 °C for bacterial strains and 48 h for yeast and fungi isolates, the diameter of inhibition (mm) surrounding the disc was measured using a graduated ruler. The diameter is proportional to the sensitivity of the germ studied. Each assay in this experiment was repeated twice. ## 3.5. Statistical Analyses The results of the tests carried out are expressed as an average ± SD of analyses in three tests. The values of IC50 ($50\%$ inhibition concentration) and A0.5 (the concentration indicating 0.50 absorbance) are calculated using the linear regression method from the two curves: [% inhibition = f (concentration)] for IC50 and [Absorbance = f (concentration)] for A0.5. The data were tested first for normality (Kolmogorov–Smirnov test) and equality of variance (Bartlett test) to fulfill the requirements of parametric analyses, and log-transformations were applied when these assumptions were not met. One-way analysis of variance (ANOVA) and Tukey’s honestly significant difference (HSD) test were performed with the software STATISTICA v.8 in order to test for the significant global and pairwise comparisons, respectively. ## 4. Conclusions The extraction of the aerial parts of C. parviflora using various solvents led to four extracts who’s phenolic, flavonoid and flavonol contents differ. The methanolic extract had a good antimicrobial effect, making it a product with potential interest in the pharmaceutical industries. Moreover, the antioxidant activity of the different extracts was studied in vitro using seven techniques. The best antioxidant capacity was observed with the BUE and EAE. The results showed that there is a divergent correlation between the TPC of the extracts and its activity as an antioxidant and antimicrobial. This relationship became clearer after the phytochemical analysis, which showed the existence of compounds with specialized effects. 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--- title: Stimulation of GHRH Neuron Axon Growth by Leptin and Impact of Nutrition during Suckling in Mice authors: - Lyvianne Decourtye-Espiard - Maud Clemessy - Patricia Leneuve - Erik Mire - Tatiana Ledent - Yves Le Bouc - Laurent Kappeler journal: Nutrients year: 2023 pmcid: PMC10005278 doi: 10.3390/nu15051077 license: CC BY 4.0 --- # Stimulation of GHRH Neuron Axon Growth by Leptin and Impact of Nutrition during Suckling in Mice ## Abstract Nutrition during the early postnatal period can program the growth trajectory and adult size. Nutritionally regulated hormones are strongly suspected to be involved in this physiological regulation. Linear growth during the postnatal period is regulated by the neuroendocrine somatotropic axis, whose development is first controlled by GHRH neurons of the hypothalamus. Leptin that is secreted by adipocytes in proportion to fat mass is one of the most widely studied nutritional factors, with a programming effect in the hypothalamus. However, it remains unclear whether leptin stimulates the development of GHRH neurons directly. Using a Ghrh-eGFP mouse model, we show here that leptin can directly stimulate the axonal growth of GHRH neurons in vitro in arcuate explant cultures. Moreover, GHRH neurons in arcuate explants harvested from underfed pups were insensitive to the induction of axonal growth by leptin, whereas AgRP neurons in these explants were responsive to leptin treatment. This insensitivity was associated with altered activating capacities of the three JAK2, AKT and ERK signaling pathways. These results suggest that leptin may be a direct effector of linear growth programming by nutrition, and that the GHRH neuronal subpopulation may display a specific response to leptin in cases of underfeeding. ## 1. Introduction Numerous studies have highlighted the importance of nutrition during the early postnatal period for healthy development, in accordance with the developmental origin of health and adult diseases (DOHaD) hypothesis [1,2,3,4,5]. The development of the brain, and of the hypothalamus in particular, is particularly sensitive to changes in nutritional status, notably in the early postnatal period [6,7,8,9,10]. Physiological functions controlled by the hypothalamus can be altered following undernutrition during the suckling period, with effects on reproduction [11,12,13,14], food intake and metabolism [15,16,17,18], and linear growth [4,19]. Postnatal linear growth is regulated by the neuroendocrine somatotropic axis in mammals [4,20]. Within this axis, growth hormone (GH) is secreted by the somatotrophs of the pituitary gland. This secretion is tightly controlled by the growth hormone-releasing hormone (GHRH) and somatostatin (SRIH) neuropeptides, which are synthesized by the neurons of the arcuate (Arc) and periventricular (PeV) nuclei of the hypothalamus, respectively. SRIH inhibits GH secretion, whereas GHRH stimulates the secretion and synthesis of GH. Importantly, GHRH also stimulates somatotroph proliferation and differentiation during the early postnatal period, thereby programming the linear growth trajectory [21]. Indeed, undernutrition during the sucking period induces a permanent growth delay associated with a lowering of somatotropic axis activity [4]. In underfed pups, the innervation of the median eminence by GHRH neurons is delayed, leading to permanent somatotroph hypoplasia [4,19,22]. Developing GHRH neurons may, therefore, constitute a specific target of nutritional factors in the regulation of linear growth and establishment of adult size potential. Consistent with this hypothesis, the GH- and nutrition-dependent factor IGF-I has been shown to stimulate the axonal growth of GHRH neurons in a specific manner [19]. Leptin is secreted by adipocytes in proportion to the mass of white adipose tissues. It is the most widely studied of the nutritional factors potentially involved in hypothalamus development. Leptin has been reported to act on different neuronal populations, including the NPY neurons of the arcuate nucleus to control metabolism, and the ventral premammillary nucleus neurons to generate a permissive signal for reproduction [14,23,24]. However, the impact of leptin on the programming of linear growth remains unclear. Leptin signaling alterations have been associated with smaller adult size in humans [25,26]. Moreover, $45\%$ of GHRH neurons express the leptin receptor, constituting a large population of Stat3+ neurons after leptin injection in rodent models [27]. In addition, mice with a knockout of the leptin receptor in cells expressing the GH receptor (GH-R)—including GHRH neurons as a first target for the negative feedback regulation of GH secretion—present a postnatal growth delay [28]. This suggests a possible direct role for leptin in the development of GHRH neurons, although no such role has been demonstrated to date. We therefore investigated whether leptin could directly regulate the axonal growth of GHRH neurons in the first few days of life. ## 2.1. Animal Experiments All animal procedures were performed in accordance with institutional directives and EU directive $\frac{2010}{63}$/EU for animal experiments and the care of laboratory animals. All procedures were approved by the French national ethics committee 05 Charles Darwin (Project protocol agreement number Ce$\frac{5}{2012}$/006 and APAFIS#8367-2016123008077830 vI). The mice in this study were housed under standard SOPF conditions, in individual ventilated cages at 22 °C under a 12 h light/12 h dark cycle, with free access to water and a standard chow diet for reproduction (LASQCdiet Rod18-R, sterilized; LASVendi, Soest, Germany) (Supplementary Table S1). All cages were enriched with commercial cotton nesting material. As previously described [19,29], C57Bl/6J Ghrh-eGFP male mice [30] were mated with wild-type C57Bl/6J females (Charles River Laboratories, L’Arbresle, France), and pregnant mice were housed individually to prevent any bias in pup nutrition during the suckling period, which extends from birth until the age of 16 days. At birth, pups were redistributed and cross-fostered such that there were five to six newborns per dam for normal milk feeding (normally fed), and nine newborns per dam in the group subjected to dietary restriction (underfed) [4,19,29,31]. All litters were submitted to the cross-fostering process. Pups were harvested from their original cage, individually rolled in the moistened dirty litter of the receiving dam, and then dried with a small piece of cotton from the nest of the receiving dam. Pups from the receiving dam were adopted by another one and eventual supernumerary newborns euthanized. Litters had globally equal numbers of male and female pups. Mice were studied at 7 and 10 days of age, depending on the experiment. Brains for explant cultures were harvested from seven-day-old pups after decapitation. Tissues and blood samples were harvested from 10-day-old mice under deep isoflurane anesthesia. The terminal blood sampling was performed by cardiac puncture with a 24G needle and a syringe coated with EDTA. Blood samples were centrifuged for 20 min at 2000 rpm at 4 °C. The plasma was isolated, frozen on dry ice and kept at −20 °C until use for individual biochemical determinations (see below). The number of mice or litters used is indicated in the results and figure legends. For all experiments and statistical analyses, the pups studied originated from at least three different litters, to prevent maternal bias. ## 2.2. Arcuate Explant Culture Experiments Arcuate nuclei explants were prepared for culture in vitro, as previously described [19,29,32]. Briefly, brains were harvested from seven-day-old normally fed or underfed pups after rapid decapitation. For each experiment, brains from both male and female pups of the entire litter were pooled and processed together, to ensure that enough explant material was obtained for high-quality culture [19]. Thus, even though females are known to be less sensitive to nutritional restriction than males [4], which would decrease the magnitude of any effect observed, we harvested arcuate nucleus explants from pups of both sexes. We assessed eGFP expression in the median eminence directly, by inverted fluorescence microscopy (Evos Cell Imaging, Evos; Thermo Fisher, Wilmington, DE, USA). The brains were cut into 300 µm slices and incubated on membranes (Whatmann, Cytiva Europe GmbH, Velizy-Villacoublay, France) in MEM medium (Thermo Fisher Scientific) supplemented with $10\%$ FCS, $1\%$ glucose, and penicillin/streptomycin. Arcuate nuclei were then micro-dissected and cultured in neurobasal medium (Thermo Fisher Scientific) supplemented with methylcellulose, B-27 supplement (B-27) with insulin (#17504-044, Thermo Fisher Scientific), glucose, L-glutamine, and penicillin/streptomycin (Thermo Fisher Scientific). Cultures were performed in four-well plates, with each well filled with eight to nine explants. Arcuate explants from one litter of normally fed pups were plated on a single plate, whereas explants from one litter of underfed pups were plated in three plates. Explant experiments with normally fed and underfed pups were conducted sequentially, according to mouse production. The explants were cultured for 24 h, and were then left untreated (control condition) or were treated with 100 ng/mL leptin (6.25 nM, PeproTech, Neuilly-Sur-Seine, France) alone or in combination with 100 ng/mL IGF-1 (13.2 nM, R&D Systems, Abingdon, UK), 6.6 µM LY_294002 (LY, a PI3K inhibitor) (#1130, TOCRIS, Noyal Châtillon sur Seiche, France), 0.33 nM PD_0325901 (PD, a MAPK inhibitor) (#4192, TOCRIS), and/or 60 nM of NSC_33994 (NSC, a JAK2 inhibitor) (#4338, TOCRIS) for another 24 h. The culture was rinsed with PBS and fixed by incubation in $4\%$ PFA for 30 min. The explants were then subjected to immunohistochemistry with primary antibodies directed against the leptin receptor, neurofilament (NF), eGFP/GHRH and/or AgRP, followed by the corresponding secondary antibodies (see Table 1 for antibody references and dilutions). All incubations were performed in 1x phosphate-buffered saline (PBS)/$1\%$ normal serum/$0.05\%$ tween 20. Neurofilament axons, eGFP/GHRH and AgRP axons were visualized under a 4X or 10X objective on a BX612 Olympus fluorescence microscope equipped with a DP71 using a charge-coupled device (CCD) camera or a BX43 Olympus equipped with a DP73 CCD camera, as indicated in figure legends. Leptin receptor labeling on Ghrh-eGFP+ axons was visualized under a 40x objective. Axon length was analyzed with the NeuronJ plugin of ImageJ software, as previously described [19,29,33]. Briefly, we measured the lengths of up to 40 individual axons/explants and then calculated the mean length for each explant. The total axon length for each well was calculated from the mean for each explant. We accounted for variability of the explants cultures by normalizing axon growth in treated conditions against control conditions (untreated) on each plate, and presenting the results as a fold-difference. For statistical analyses, each plate was considered as a single experiment ($$n = 1$$). The number of plates studied in each experiment is indicated in the figure legends. ## 2.3. Biochemical Analysis Plasma leptin concentrations in 10-day-old male mice were determined individually with the Mouse/Rat Leptin (R&D systems) ELISA kit, according to the manufacturer’s instructions. Absorbance was measured with a spectrophotometer (TECAN, GENios Pro). For western-blot analysis, micro-dissected arcuate nuclei were harvested from one entire seven-day-old litter and processed in a separate set of experiments. The micro-dissected nuclei were incubated for 2 h in B-27-supplemented neurobasal medium and split into 2 pools, one of which was left untreated while the other was stimulated by incubation with leptin (100 ng/mL) for 15 min. Explants were rinsed with ice-cold PBS and total protein was extracted with NEB buffers in the presence of a cocktail of protease and phosphatase inhibitors (Roche), as previously described [19]. The explants from each litter were considered as a single sample ($$n = 1$$). For each sample, we separated 10 µg of cytoplasmic protein by electrophoresis in a NuPAGE Bis-Tris (4–$12\%$) gel (Life Technologies), and the resulting bands were transferred to PVDF membranes. The membranes were then incubated with primary antibodies directed against total and serine-473-phosphorylated AKT, total and threonine-202/tyrosine-204-phosphorylated p$\frac{44}{42}$ MAPK (ERK$\frac{1}{2}$), total and serine-298-phosphorylated MAP2K1 (MEK1), total and tyrosine-$\frac{1007}{1008}$-phosphorylated JAK2, total and tyrosine-705-phosphorylated STAT3, and then with the corresponding secondary antibodies (see Table 2 for antibody references and dilutions). Antibody binding was detected with the Radiance Plus Femto Chemiluminescent Substrate (#AC2103, Azure Biosystems) or SuperSignal West Pico Chemiluminescent Substrate (#37070, Thermo Fisher Scientific), with a CCD camera on a ChemiDoc Touch Imaging System (Biorad). The membranes were then washed and incubated with an antibody against actin, which was detected with the SuperSignal West Pico Chemiluminescent Substrate, to allow for a normalization of loading between samples. The number of samples is indicated in the figure legends. ## 2.4. Statistical Analysis All data are presented as the mean ± SEM. For explant cultures, the results of dual and triple immunohistochemistry (IHC) experiments were analyzed by two-way ANOVA with Bonferroni correction for multiple comparisons, and the results of single IHC were analyzed by one-way ANOVA with a Newman–Keuls multiple comparison test. Body weight, plasma leptin concentrations and Western blot results were analyzed in Mann–Whitney tests. Statistically significant results are marked with asterisks, * $p \leq 0.05$; $p \leq 0.01$; * $p \leq 0.001.$ ## 3. Results The increase in litter size, from six to nine pups per dam, altered pup nutrition during the suckling period [4,19,29,31]. This resulted in delayed growth, which became evident in comparisons with age-matched normally fed pups at the age of seven days (Figure 1A) and was persistent throughout life, as previously reported [4,19]. Consistent with this finding, plasma leptin concentration was lower in 10-day-old underfed pups than in age-matched normally fed pups (Figure 1B). We investigated the possibility that leptin might stimulate the development of GHRH neurons directly, by determining whether growing GHRH axons expressed the leptin receptor (Ob-R) in the Ghrh-eGFP mouse model [19,30], in which the identification of axons from GHRH neurons is simple. We confirmed, in our arcuate nuclei explants micro-dissected from seven-day-old normally fed Ghrh-eGFP pups and cultured in vitro, that growing axons of GHRH neurons expressed the leptin receptor (Figure 1C). Moreover, the distal part of growing GHRH+ axons and the growth cone of these axons appeared to be enriched in the leptin receptor, consistent with a potential role of leptin signaling in the axon growth of GHRH neurons. ## 3.1. Leptin Stimulates Axon Growth in GHRH Neurons in Arcuate Nucleus Explants from Normally Fed Pups We then investigated whether leptin could regulate axon growth in arcuate neurons, using arcuate nucleus explants harvested from seven-day-old normally fed pups. Leptin was initially reported to stimulate axon growth in AgRP/NPY+ orexigenic neurons [15,34]. We therefore first confirmed that we were able to measure this stimulation in the 24 h treatment conditions applied in this study. The AgRP+ labeled axons treated with leptin for 24 h were 1.16 ± 0.06 times longer than the control (untreated) axons, with lengths of 541 ± 88 µm in control conditions and 627 ± 98 µm following leptin stimulation ($$n = 4$$ experiments, $p \leq 0.05$) (Figure 2A,C). All the neurons of the arcuate nuclei were labeled with an antibody directed against the ubiquitously expressed neurofilament protein (NF), and a stimulatory effect of leptin was visible in the total neuron populations of arcuate nuclei (Figure 2B,D). Treatment with leptin for 24 h resulted in arcuate neuron axons that were 1.31 ± 0.07 times longer than those observed in control conditions: 484 ± 45 µm for control conditions and 628 ± 49 µm following leptin treatment ($$n = 7$$ experiments, $p \leq 0.01$). We then looked at the specific effects of leptin on axon growth in the subpopulation of GHRH neurons. In the explants used for the NF analysis described above, we found that Ghrh-eGFP+ axons treated with leptin were 1.33 ± 0.13 times longer than control axons: 385 ± 27 µm in control conditions and 510 ± 47 µm after leptin treatment ($$n = 5$$ experiments, $p \leq 0.01$) (Figure 2B,D). These results suggest that leptin levels may directly regulate the development of GHRH neurons. We previously showed that IGF-1 selectively stimulates axon growth in GHRH neurons [19]. We therefore investigated the possible synergy between leptin and IGF-1. The treatment of arcuate nucleus explants with both leptin and IGF-I for 24 h did not lead to any additional increase in axon length relative to leptin treatment alone, either in the total neuron population of the arcuate nucleus or in GHRH neurons (Figure 2C,D). Similar results were obtained for AgRP neurons. The absence of a synergic stimulatory effect of IGF-I and leptin on axon growth in GHRH neurons suggests that these two molecules may use the same signaling pathways. Indeed, IGF-I is known to act through its IGF-type 1 receptor (IGF-1R) and the AKT and MAPK signaling pathways. By contrast, leptin binds to its specific receptor (Ob-R), which is a class 1 cytokine receptor, and acts by stimulating the JAK-STAT signaling pathway. Connections between the JAK-STAT signaling pathway and the AKT and the MAPK signaling pathways have been reported [10,15,35]. We investigated whether a specific leptin-stimulated signaling pathway was involved in axonal growth by treating arcuate nucleus explant cultures with both leptin and a specific inhibitor of JAK-STAT (NSC), PI3K-AKT (LY) or MAPK (PD) signaling. The treatment of explants with leptin plus one of these inhibitors (NSC/LY/or PD) significantly decreased ($p \leq 0.001$) the leptin-induced axon growth of arcuate neurons relative to stimulation with leptin alone (Figure 3). For the total (NF+) arcuate neuron population, axon growth decreased to 0.85 ± 0.04 (leptin/NSC), 0.79 ± 0.03 (leptin/LY) and 0.86 ± 0.05 (leptin/PD)-times shorter than that in control conditions (Figure 3A,B). Similarly, for GHRH neurons, the axon growth decreased to levels of 0.84 ± 0.03-, 0.85 ± 0.06- and 0.84 ± 0.03-fold those in control conditions, respectively (Figure 3A,C). Similar results were obtained with the AgRP+ neuron population: a decrease in axon growth to 0.80 ± 0.04-, 0.83 ± 0.06- and 0.79 ± 0.04-fold control levels, respectively (Figure 3A,D). Overall, these results suggest that the JAK/STAT, AKT and MAPK signaling pathways are all important for the leptin-induced growth of axons in arcuate nucleus neurons, including GHRH neurons. ## 3.2. GHRH Neurons in Arcuate Nucleus Explants from Underfed Pups Do Not Respond to Leptin Stimulation We previously reported that underfeeding during suckling was associated with a resistance of GHRH neurons to IGF-I-induced axon growth [19]. Given the role of IGF-I signaling pathways downstream from leptin (i.e., interactions between the three signaling pathways), we investigated whether underfeeding also affected leptin-induced axon growth. We therefore performed experiments similar to those described above on arcuate nucleus explants harvested from age-matched (seven-day-old) underfed pups, with evaluations of the capacity of leptin to stimulate axon growth. We found that the growth of axons from AgRP neurons from underfed pups was significantly ($p \leq 0.05$) stimulated by 24 h of treatment with leptin, although this effect seemed to be weaker than that in normally fed pups (see Figure 3). Leptin increased the growth of AgRP+ axons by 1.11 ± 0.02-fold relative to control conditions (Figure 4A,C): 500 ± 39 µm in control conditions and 555 ± 47 µm following leptin treatment ($$n = 5$$ experiments). Total arcuate nucleus neurons were then analyzed by labeling the ubiquitous NF. Axon length was found to be 1.12 ± 0.04 times greater in leptin-treated neurons: 544 ± 42 µm in control conditions and 611 ± 48 µm following leptin treatment ($$n = 6$$ experiments). However, this difference was not statistically significant (Figure 4B,D), possibly due to the greater variability in total arcuate nucleus populations or the higher basal rates of axon growth observed (544 ± 42 µm in underfed controls vs. 484 45 µm in normally fed controls). In GHRH neurons, leptin treatment modulated axon length by a factor of 1.09 ± 0.05, from 394 ± 40 µm in control conditions to 428 ± 46 µm following leptin treatment ($$n = 6$$ experiments), although this difference was not statistically significant (Figure 4B,D). As previously observed in arcuate nucleus explants from normally fed pups, no additive or synergic stimulation of axonal growth was observed when explants from underfed pups were treated with both leptin and IGF-I (Figure 4C,D). We investigated the absence of response to leptin stimulation in GHRH neurons from underfed pups by studying the JAK-STAT, PI3K-AKT and MAPK signaling pathways. Arcuate nucleus explants from both underfed and normally fed pups were subjected to brief stimulation with leptin, and the fold-induction of phosphorylated forms (e.g., the ratio of phospho-JAK2/total-JAK2/actin) in leptin-treated explants relative to untreated explants was determined by Western blotting. The fold-induction of phosphorylated JAK2 was significantly lower in arcuate nucleus explants harvested from underfed pups than in those harvested from normally fed pups (Figure 5A). Similar results were obtained for the induction of phosphorylated STAT3 (Figure 5B) and phosphorylated AKT in leptin-treated arcuate nucleus explants from underfed relative to normally fed pups (Figure 5C). The MAPK signaling pathway also displayed lower levels of induction for phosphorylated MEK1 (Figure 5D), phosphorylated ERK1 (Figure 5E) and phosphorylated ERK2 (Figure 5F) following leptin treatment in underfed pups relative to normally fed pups. No differences in the total levels of the proteins analyzed (i.e., total-AKT, total-Jak2, etc.) in the arcuate nucleus explants were observed between underfed and normally fed pups (data not shown). These data suggest that the JAK/STAT, PI3K/AKT and MAPK signaling pathways are all altered in arcuate nucleus explants from underfed pups. ## 4. Discussion We show here that leptin stimulates the development of hypothalamic GHRH neurons during the first few days of life, via the JAK/STAT, PI3K/AKT and MAPK signaling pathways. In underfed pups, GHRH neurons were unable to respond to leptin stimulation despite the culture of explants in a controlled environment. This absence of response was associated with changes in the activation of the JAK/STAT, PI3K/AKT and MAPK signaling pathways. These findings support the hypothesis that linear growth may be a direct target of leptin signaling during the early postnatal period, and suggest that leptin may be a potential effector of linear growth programming by nutrition with IGF-1, despite the lack of synergy observed. Our findings also suggest that the subpopulation of GHRH neurons may present a specific response to leptin. The involvement of leptin as a nutritional factor in hypothalamus development has been extensively studied, and it has been suggested that leptin affects the wiring of neurons, particularly those involved in the regulation of food intake, metabolism and reproduction [15,36]. Indeed, the NPY+ and POMC+ neurons of the arcuate nucleus that innervate the PVN and regulate food intake and metabolism were the first neurons shown to be sensitive to leptin signaling in the early postnatal period [15]. Leptin also has a role in reproductive function, as it has been shown to be a permissive signal for puberty, with stimulatory effects in the ventral premammillary nucleus and the arcuate NPY neurons crucial for the activation of Kiss1 neurons and puberty onset [36,37]. Leptin levels have also been associated with linear growth and differences in adult size in humans [25,26]. Consistent with this role, $45\%$ of GHRH neurons have been reported to express the leptin receptor and to be Stat3+ following leptin stimulation [27]. However, the absence of an effect on adult size of leptin receptor knockout in mouse GHRH neurons raised questions about the potential indirect role of leptin [27]. Nevertheless, the results presented here suggest that leptin has a direct effect on axon growth in GHRH neurons, the development of which during the first week of life is crucial for the establishment of pituitary GH synthesis capacities and to program the growth trajectory. Our findings are consistent with the permanent linear growth delay associated with changes to the somatotropic axis in the Ob-R KO mouse model, in which the Ob-R is knocked out in cells expressing the GH receptor, including the GHRH neurons, the first target for the negative feedback regulation of GH secretion [28]. These findings do not rule out additional indirect growth regulation by leptin, because Ob-R deletion throughout the entire brain results in a much stronger, permanent growth delay [28]. IGF-I is one of the nutritional factors shown to modulate linear growth and to stimulate axon growth in GHRH neurons during the first week of life [19,22]. IGF-I acts through its dedicated receptor, IGF-1R, stimulating the downstream AKT/PI3K and MAPK/ERK signaling pathways. This stimulatory action appears to be specific, because insulin, which is known to improve neuronal development and to act via the same signaling pathway, has no effect on axon growth in GHRH neurons [29]. In a breast cancer model, leptin and IGF-1 have been shown to increase cell proliferation and migration, and to act in synergy in terms of the activation of their receptors, indicating interactions between these two signaling pathways [38]. Moreover, it has been suggested that leptin and IGF-1 may each cross-activate the receptor of the other (i.e., the IGF-1R and leptin-R, respectively) [38]. However, leptin and IGF-I do not seem to have synergic effects or to interact in the stimulation of axon growth in GHRH neurons, even though leptin also stimulates the PI3K/AKT and MAPK signaling pathways [23]. This may reflect a feature specific to this neuronal subpopulation. It is possible that the co-stimulation of GHRH neurons with IGF-I and leptin leads to partial desensitization and/or a negative feedback loop limiting axon growth. Indeed, IGF-1 could stimulate a negative feedback loop, notably through Stat5 and SOCS6, similar to that previously described in neural stem cells [39]. The neuronal subtype also seems to have an important effect on the action of leptin in the hypothalamus. Indeed, our findings indicate that GHRH neurons were insensitive to leptin in arcuate nucleus explants harvested from underfed pups. This finding conflicts with previous reports that the arcuate NPY and POMC neurons innervating the PVN can respond to leptin stimulation in vitro regardless of the presence or absence of leptin signaling during their development (i.e., Ob/Ob mice) [15]. However, the role of leptin in the developing hypothalamus appears to be dependent on the neuronal subpopulations and their projections. Indeed, leptin has been shown to affect the NPY glutamatergic neurons rather than the POMC neurons projecting into the autonomic area [34,40]. The specificity of neuronal subpopulations in terms of the response to leptin stimulation may depend on excitatory class [18,41] and signaling pathways. Indeed, the leptin receptor is known to activate the JAK/STAT, MAPK and PI3K/AKT signaling pathways. In particular, the MAPK/ERK signaling pathway has been shown to be crucial for the development of both NPY and POMC neurons, whereas STAT3 appears to be required for the development of POMC neurons and to have a limited effect on NPY neurons [23,34]. We found that the leptin-stimulated growth of axons in the GHRH neuron subpopulation was associated with the stimulation of the PI3K/AKT, MAPK and JAK/STAT signaling pathways. The involvement of each of these three signaling pathways was confirmed by pharmacological inhibition experiments. Moreover, these three signaling pathways also appeared to play an important role in the responsiveness of GHRH neurons to leptin linked to nutritional status, because underfeeding was associated with an abolition of leptin-induced activation of the PI3K/AKT, MAPK and JAK/STAT signaling pathways. This abolition was not associated with a change in leptin receptor gene expression in the hypothalamus of underfed pups relative to normally fed pups (data not shown). The GHRH neuron subpopulation therefore seems to display a specific response to nutritional status and nutrition-related hormones, regulating its development. A more detailed characterization of this specific subpopulation will be required to decipher the underlying mechanisms. ## 5. Conclusions In conclusion, our results suggest a direct effect of leptin on axon growth in GHRH neurons and support the hypothesis that leptin may regulate linear growth in response to the availability of nutrients during the suckling period. They are also consistent with previous reports suggesting that leptin has a global effect on brain development, particularly in the hypothalamus, which controls all the important physiological functions of the organism [11,15,18,34]. ## References 1. 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--- title: 'High Protein Intake at Lunch Is Negatively Associated with Blood Pressure in Community-Dwelling Older Adults: A Cross-Sectional Study' authors: - Hélio José Coelho-Júnior - Samuel da Silva Aguiar - Ivan de Oliveira Gonçalves - Riccardo Calvani - Matteo Tosato - Francesco Landi - Anna Picca - Emanuele Marzetti journal: Nutrients year: 2023 pmcid: PMC10005279 doi: 10.3390/nu15051251 license: CC BY 4.0 --- # High Protein Intake at Lunch Is Negatively Associated with Blood Pressure in Community-Dwelling Older Adults: A Cross-Sectional Study ## Abstract Background: The present study was conducted to explore the association between protein intake across the main meals and hypertension (HTN)-related parameters in community-dwelling Brazilian older adults. Methods: Brazilian community-dwelling older adults were recruited in a senior center. Dietary habits were assessed through 24 h recall. Protein intake was classified as high and low according to median and recommended dietary allowance values. Absolute and body weight (BW)–adjusted protein consumption levels were quantified and analyzed according to ingestion across the main meals. Systolic (SBP) and diastolic blood pressure (DBP) were measured using an oscilometric monitor. Participants were categorized as hypertensive according to physician diagnosis or the detection of high SBP and/or DBP values. Results: One hundred ninety-seven older adults were enrolled in the present study. Protein intake at lunch was independently and negatively associated with SBP. Furthermore, a lower prevalence of HTN (diagnosed by a physician) was observed in participants with higher intakes of protein. These results remained significant after adjustment for many covariates. However, significance was lost when kilocalories and micronutrients were included in the model. Conclusions: Findings of the present study indicate that protein intake at lunch was independently and negatively associated with systolic BP in community-dwelling older adults. ## 1. Introduction Hypertension (HTN) is a chronic condition characterized by sustained elevations in blood pressure (BP) levels [1]. The highest prevalence of HTN is found in older adults, with more than $70\%$ of those aged 65+ years being affected worldwide [2]. These data deserve concern, given that HTN is a leading cause of negative outcomes, including cardiometabolic, cerebrovascular, and renal events, in addition to premature death [2]. Changes in lifestyle habits are a cornerstone in the prevention and treatment of HTN [3]. Significant reductions in BP are achieved after adoption of healthy nutritional habits, including specific diet patterns (e.g., Dietary Approaches to Stop Hypertension [DASH] diet) [4], low sodium [5], and high potassium [6]. An increasing number of studies have investigated the association between protein intake levels and HTN-related parameters. Large cohort studies and meta-analyses have provided encouraging results, showing that people with high protein intake display low BP values [7,8,9,10]. However, other studies have reported higher BP levels in those with greater protein ingestion [11] or no associations between protein intake and BP values [12,13,14]. Most investigations examined mixed samples of adults from different age groups, while studies exclusively based on old populations are scarce. The effects of protein intake on cardiovascular health might be dependent on protein sources [12] and distribution across meals [15]. For instance, Berryman et al. [ 15] examined a large cohort of American adults and found that greater protein intake early in the day was inversely associated with hemodynamic parameters. Based on these premises, the present study was conducted to explore the association between protein intake and HTN-related parameters in community-dwelling Brazilian older adults. We also examined the possible influence of protein distribution across main meals on BP parameters. ## 2.1. Study Design This was a cross-sectional study that investigated the association between protein intake and HTN-related parameters in community-dwelling older adults. The study protocol was approved by the Research Ethics Committee of the University of Campinas (Campinas, Brazil). All study procedures were conducted in compliance with the Declaration of Helsinki and Resolution $\frac{196}{96}$ of the National Health Council. Participants were thoroughly informed about the study procedures and objectives before they provided written consent. The manuscript was prepared in accordance with the STROBE statement [16]. ## 2.2. Participants Participants were recruited by convenience between January 2016 and December 2018 in a community senior center located in Brazil. The community senior center offers daily sessions for flexibility, aquatic and multicomponent physical exercises, dance classes, adapted sports, nursing and medical care, and cognitive stimulation therapy. Candidate participants were considered eligible if they were 60 years or older, lived in the community, and possessed sufficient physical and cognitive abilities to perform all assessments required by the protocol. Candidates were excluded if they were on hormone replacement therapy and/or psychotropic drugs. ## 2.3. Hemodynamic Parameters The procedures for BP measurement were adapted from the VII Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High BP (JNC7) [1]. BP was measured in the morning for approximately 80 s. Participants were not fasting when BP was measured. Measurements were obtained three times with 1 min rest intervals, on three different days. Mean values were used for the analysis. BP was measured after participants remained seated for 15 min in a quiet room with feet parallel at one shoulder width, both forearms and hands on the table, supinated hands, back against the chair, without moving or talking. An automatic, noninvasive, and valid [17] arterial BP monitor (Microlife-BP 3BT0A, Microlife, Widnau, Switzerland) was used to measure systolic BP (SBP), diastolic BP (DBP), and heart rate (HR). An appropriate cuff was selected after measuring the arm circumference of each participant (Sanny, São Paulo, Brazil) and was placed at approximately the midpoint of the upper left arm (heart level). ## 2.4. Dietary Assessment Food intake was assessed using a 24 h recall report. This method uses an open-ended questionnaire to provide quantitative and subjective estimations of actual food consumption [18]. Trained investigators asked the participants to describe in detail all foods they consumed on a meal-by-meal basis, including snacks, during the previous 24 h period. Interviews occurred on Tuesdays, Wednesdays, Thursdays, and Fridays to avoid bias associated with weekends. Participants were asked to describe in detail the cooking methods (e.g., fried, grilled, roasted), amounts in portions, product brands, sauces, spices, and condiments consumed, and the use of dietary supplements. The amounts of beverages consumed were also recorded, and participants were asked to describe if and how beverages were sweetened. Two-dimensional aids (e.g., photographs), household utensils (e.g., standard measuring cups and spoons), and food models were used as memory aids to assess portion sizes. Diet composition was estimated using NutWin software, version 1.5 (Federal University of São Paulo, Brazil). ## 2.5. Anthropometric Measurements and Hypertension Prevalence A weight scale with a stadiometer was used to measure body weight (BW) and height. The body mass index (BMI) was subsequently calculated as follows: (a) body weight (kg)/ height (m²). Information pertaining to the prevalence of HTN was collected by two researchers through self-report and careful review of medical charts kept by the community senior center. Medical charts were updated every six months by a local physician. The presence of HTN was determined according to (a) physician diagnosis, or (b) the detection of high SBP and/or DBP values according to JNC7 [1]. ## 2.6. Statistical Analysis The normal distribution of variables was ascertained via the Shapiro–Wilk test. Continuous variables are expressed as the mean ± standard deviation (SD) or absolute numbers (percentage). Pearson’s correlation analysis was used to explore the association between protein intake, BP, and HR. Associations with a p-value lower than 0.05 were included in the regression analyses. The final model was adjusted for age, sex, BMI, kilocalories, and micronutrients. To test associations between categorical data, a chi-squared test was conducted. Variables were dichotomized into “high” and “low” levels based on the following median values: SPB = 135 mmHg, DBP = 77 mmHg, HR = 77 bpm, protein intake = 96.6 g/day, BW-adjusted protein intake = 1.5 g/kg of BW/day, calcium = 832 mg/day, magnesium = 392.7 mg/day, potassium= 3606 mg/day, and sodium = 1540 mg/day. BW-adjusted protein intake was also categorized as “high” versus “low” according to the recommended dietary allowance (RDA) for protein (0.8 g/kg of BW/day) and median values. Significance was set at $5\%$ (p-value < 0.05) for all tests. Regression analyses were significant if the $95\%$ confidence interval (CI $95\%$) did not include the value of 1. All analyses were performed using SPSS software (version 23.0, SPSS Inc., Chicago, IL, USA). ## 3.1. Characterstics of Participants One hundred ninety-seven older adults were enrolled in the present study. The main characteristics of participants are shown in Table 1. Participants were young older adults (mean age: 68.3 ± 6.8 years) and mostly female ($83\%$). Mean BMI values (28.7 ± 5.0 kg/m2) indicated that participants were frequently overweight. HTN, based on a physician’s diagnosis, was highly prevalent in the study population ($58.5\%$). A higher prevalence ($64.6\%$) was observed according to high BP values. Mean SBP (134.3 ± 17.9 mmHg) was slightly above the cutoff values for HTN, whereas mean DBP (76.0 ± 10.8 mmHg) was marginally below [1]. Mean protein intake was noticeably higher than RDA values (1.5 ± 0.6 g per kg of BW) [19]. ## 3.2. Associations between Dietary Habits and Hypertension-Related Parameters Using Continuous Data Pearson’s correlations are shown in Table 2, Table S1, and Table S2. No significant associations were observed between SPB, DBP, and HR with absolute or BW-adjusted protein intake, or BW-adjusted protein consumption at breakfast and dinner. However, a significant association was observed between BW-adjusted protein consumption at lunch and SBP (r = −0.20, p-value = 0.007). Results did not change after adjustment for age, BMI, kilocalories, and micronutrient intake (r = −0.20, $$p \leq 0.009$$). Results of linear regression are shown in Table 3. Unadjusted analyses indicated that absolute and BW-adjusted protein intake were significantly associated with SBP. A significant association was also observed between BW-adjusted protein consumption at lunch and SBP. Statistical significance of the association of absolute and BW-adjusted protein intake with SBP was lost when the model was adjusted for age, BMI, sex, kilocalories, and micronutrient intake. The association between BW-adjusted protein consumption at lunch and SBP remained significant. ## 3.3. Associations between Dietary Habits and Hypertension-Related Parameters Using Binary Data Results of chi-squared statistics are shown in Table 4. A significant association was observed between HTN diagnosed by a physician and absolute and BW-adjusted protein intake. No significant associations were observed between protein consumption and HTN estimated based on BP values. Binary regression was conducted using HTN diagnosed by a physician as a dependent variable (Table 5). In the unadjusted analysis, absolute and BW-adjusted protein intake were inversely associated with HTN. Results remained significant for absolute, but not BW-adjusted protein, when the model was adjusted for age, BMI, and sex. However, no significance was found when kilocalories and micronutrients were included as covariates in the analysis. ## 4. Discussion The main findings of the present study indicate that protein intake at lunch was independently and negatively associated with SBP in a sample of community-dwelling older adults. Furthermore, a lower prevalence of HTN (diagnosed by a physician) was observed in participants with higher intakes of protein. These results remained significant after adjustment for age, BMI, and sex. Significance was lost when kilocalories and micronutrients were included in the model. Numerous studies have examined the association between daily consumption of proteins, BP measures, and HTN. Investigations have produced conflicting results, with studies reporting positive, negative, and null relationships. In line with our findings, Der Kuil et al. [ 12] observed no significant relationships between total protein intake and changes in SBP and DBP in Dutch adults. The authors also noted that protein intake had no influence on the incidence of HTN [20]. Similar findings were reported by Tielemans et al. [ 13] in older Dutch men. Liu et al. [ 14] found no significant associations between protein intake and the prevalence of HTN in poorly nourished rural Chinese people. Increased protein intake also failed to ameliorate hemodynamic parameters in randomized clinical trials. Indeed, Hodgson et al. [ 21] did not report differences in BP values in Australian older adults who consumed protein supplements for two years. Results of the INTERSALT study, which included more than 10,000 adults, indicated that dietary protein markers, urinary nitrogen, and urea excretion were negatively associated with SBP and DBP [7]. These findings were supported by secondary analyses of the Framingham cohort conducted in young and middle-aged adults [8,9]. In contrast, Umesawa et al. [ 22] observed that total protein intake was inversely associated with DBP in Japanese people. Regarding positive associations, Hajjar et al. [ 11] reported significant relationships between protein intake and SBP in North American adults. These conflicting results might be explained by differences in sample characteristics (e.g., age, sex) [7], protein intake [23] and quality [13,22], HTN status [12], and the covariates included in the analyses [9,12,13,23]. Our sample was composed of overweight community-dwelling young older adults with a relatively high intake of proteins and controlled BP levels. In contrast, inverse associations seem to be stronger in older women [7] with untreated HTN [12]. An interesting scenario was recently offered by the study of He et al. [ 23], in which the relationship between protein consumption and HTN was U-shaped, suggesting that a moderate intake of protein had no influence on HTN. However, this view was not supported by Mehrabani [24], who noted a dose–response relationship between protein intake and BP values. We observed that protein intake at lunch was independently and negatively associated with SBP. Another study indicated that consuming more protein early in the day was associated with a better cardiometabolic profile, including low BP and LDL cholesterol levels, whereas people who consumed more protein at dinner were more likely to display insulin resistance [15]. A possible explanation for these results is based on the role of protein consumption on satiety, and its consequences on calorie ingestion and circadian rhythm. Protein consumption induces satiety [25]. In fact, people who consume protein-rich meals in the morning report more satiety over the day, whereas those who consume low-protein commonly experience hunger before and after the main meals [26]. Hunger is a major regulator of eating patterns [27,28]. Appetite involves subsequent energy intake in the forms of large amounts of food during discrete intervals, small amounts of food continuously over long periods, or snacks [27,28]. Such a variation in meal patterns changes hormonal synthesis [29,30] and is associated with weight gain [31]. In contrast, high protein intake in the morning is accompanied by a high expression of genes associated with lipid metabolism [32], as well as reduced glucose levels, better insulin sensitivity, and lower SBP values [26]. Taken together, these observations suggest that the relationship between high protein intake at lunch and low SBP observed in the present study might occur as a result of the effects of protein on satiety. The impact of protein on BP is also dependent on protein composition and amino acid (AA) availability [9,12,13,23,33]. For instance, L-arginine is an essential AA that serves as a substratum for nitric oxide production, which acts as a major endothelium vasodilator [34]. Randomized clinical trials have observed that chronic supplementation with L-arginine might reduce BP levels, regardless of HTN status [35,36]. Tryptophan is an aromatic AA precursor for the synthesis of serotonin [37]. When activated, serotonin receptors induce direct arterial vasoconstriction in different vascular beds [38]. Serotonin receptors are also expected to be hyperactivated in HTN [38]. Tyrosine is a precursor for norepinephrine synthesis, consequently affecting sympathetic activity [37]. Peripheral and central administration of tyrosine reduces BP in normotensive and hypertensive rats in a dose-dependent fashion [39,40]. In humans, the intake of tyrosine was negatively associated with BP in people from the Rotterdam study cohort [41]. However, no longitudinal associations were observed between AA intake and HTN incidence [41]. Other authors have argued that studying the effects of many AAs simultaneously might provide a more realistic scenario than investigating their individual effects [42]. Using a principal component analysis approach, Teymoori et al. [ 42] noted that the consumption of branched, alcoholic, and aromatic A As was associated with an increased risk of HTN, whereas sulfuric and small AAs showed a trend to be associated with lower HTN incidence. Hence, although there are many AA candidates, studies are required to provide a more detailed picture. A complementary explanation for our results involves the association between muscle mass and hemodynamic parameters. Protein intake is a major regulator of muscle mass by providing essential AAs, mainly branched-chain AAs, to stimulate muscle protein synthesis [43,44]. The failure to properly promote muscle anabolism predisposes one to the gradual loss of muscle mass [45], a scenario called muscle atrophy, and raises the risk of developing numerous health-related conditions, including frailty and sarcopenia [46,47]. More recently, an increasing number of studies have found that community-dwelling adults with low muscle mass markers display elevated BP and a high prevalence of HTN [48,49]. The exact mechanisms underlying this scenario still need to be elucidated but might include arterial stiffness, oxidative stress, and inflammation [50,51]. Our study is not free of limitations. First, some investigations observed significant associations between protein sources—animal and vegetal—and BP-related parameters. Specifically, He et al. [ 23] found a U-shaped relationship between plant-based protein and the incidence of HTN, whereas the association with animal protein, mainly white and red meat, was J-shaped. In addition, protein sources might influence cardiometabolic risk factors [8]. Although findings on the subject are conflicting [8,9,12,13,14,22,33], this topic deserves deeper exploration. Second, variables that might influence protein intake and/or BP, including physical activity and exercise, oral health, medication use, and the prevalence of chronic conditions, were not controlled for. Third, although BP was measured using valid oscilometric BP monitors, ambulatorial BP monitoring seems to be a better predictor of cardiovascular events [52]. Fourth, our findings were obtained in community-dwelling Brazilian older adults, and extrapolations to other countries or in other settings should be made with caution. Fifth, other instruments than the 24 h dietary recall might be necessary to capture dietary patterns over long periods (e.g., diet diary). Sixth, participants of the present study had a high mean BW-adjusted protein intake (1.5 ± 0.6 g/kg of BW). 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--- title: The Study on Sea Buckthorn (Genus Hippophae L.) Fruit Reveals Cell Division and Cell Expansion to Promote Morphogenesis authors: - Jing Zhao - Zhihua Zhang - Hongdan Zhou - Zengfu Bai - Kun Sun journal: Plants year: 2023 pmcid: PMC10005282 doi: 10.3390/plants12051005 license: CC BY 4.0 --- # The Study on Sea Buckthorn (Genus Hippophae L.) Fruit Reveals Cell Division and Cell Expansion to Promote Morphogenesis ## Abstract Due to its unique flavor and high antioxidant content, the sea buckthorn (genus Hippophae L.) fruit is increasingly favored by consumers. Developing from the perianth tube, the sea buckthorn fruit varies greatly among species in both size and shape. However, the cellular regulation mechanism of sea buckthorn fruit morphogenesis remains unclear. This study presents the growth and development patterns, morphological changes, and cytological observations of the fruits of three Hippophae species (H. rhamnoides ssp. sinensis, H. neurocarpa, and H. goniocarpa). The fruits were monitored every 10–30 days after anthesis (DAA) for six periods in their natural population on the eastern margin of the Qinghai-Tibet Plateau in China. The results showed that the fruits of H. rhamnoides ssp. sinensis and H. goniocarpa grew in a sigmoid mode, while H. neurocarpa grew in an exponential mode under the complex regulation of cell division and cell expansion. In addition, cellular observations showed that the mesocarp cells of H. rhamnoides ssp. sinensis and H. goniocarpa were larger in the area with prolonged cell expansion activity, while H. neurocarpa had a higher cell division rate. Elongation and proliferation of the mesocarp cells were found to be essential factors affecting the formation of fruit morphology. Finally, we established a preliminary cellular scenario for fruit morphogenesis in the three species of sea buckthorn. Fruit development could be divided into a cell division phase and a cell expansion phase with an overlap between 10 and 30 DAA. In particular, the two phases in H. neurocarpa showed an additional overlap between 40 and 80 DAA. The description of the sea buckthorn fruit’s transformation and its temporal order may provide a theoretical basis to explore the growth mechanism of fruits and regulate their size through certain cultivation techniques. ## 1. Introduction Sea buckthorn (genus Hippophae L. of family Elaeagnaceae), an ancient plant with modern value [1], is a perennial shrub or dungarunga with spiny branches. It exists naturally in the frigid regions of temperate and subtropical zones [2,3,4]. Sea buckthorn is dioecious, diploid (2n = 24), pollinated by wind, and has facultative parthenogenesis (FP). The distribution of sea buckthorn, aided by climate, soil, altitude, and other ecological factors, formed an abundant germplasm resource [5,6]. Of them, H. Rhamnoides ssp. sinensis is the most important and widely distributed species in China [7]. However, the agronomic potential of many Hippophae species remains underused or undisclosed. H. neurocarpa is a late-differentiated and most evolutive taxon of the group, distributed only in the high altitudes of the Qinghai-Tibet Plateau, and H. goniocarpa is a new taxon of the genus Hippophae, found only in a few areas of the Qilian County, Qinghai Province and the Songpan County, Sichuan Province [7,8]. They are also endemic species in China. The flowers of sea buckthorn are tiny, without a corolla, and appear before the leaves; male flowers have 2 sepals and 4 stamens, while female flowers have 2 sepals and 1 stigma, an inferior ovary, and yellow or orange pulpy fruits derived from the perianth tube. Each fruit contains one seed that is ovoid, shiny, and brownish-black in color [5,6]. Due to their unique taste and high medicinal value, the fruits of sea buckthorn are processed into drinks, jams, and dietary supplements and consumed worldwide [8,9]. Over the past few decades, researchers have focused on the various bioactive compounds present in fruits, including organic acids, phenols, flavonoids, and vitamins [9,10,11,12]. They have potential health-promoting benefits in humans due to their antioxidant and anti-inflammatory properties [13,14,15]. However, despite the growing interest in sea buckthorn, little is known about its fruit development and growth patterns. Potential fruit size is genetically controlled and is a qualitative trait that determines consumer preferences [16,17]. Fruit size is influenced by many factors, including water and nutrient availability and other environmental factors, such as climate, soil, and light [18]. Furthermore, it is also affected by anatomical features, including cell size, shape, and arrangement, cell wall thickness, cell-to-cell contact and volume of air space, and epidermal thickness [19,20,21,22]. In addition, different types of fruits show different developmental patterns. For example, most fruits, such as peaches [23], tomatoes [24], apples [25], and loquats [26], continue to engage in cell proliferation during early fruit development, with long-term cell expansion at later stages. However, the avocado pericarp continues to divide until before ripening [27]. Available information on sea buckthorn fruit focuses on either the fruit anatomy or the physicochemical properties of the mature drupes. Studies on the course of events leading to fruit growth and development are scarce. Therefore, this study aims to describe the variations in growth patterns and anatomical cytoarchitecture of three sea buckthorn species, namely H. rhamnoides ssp. sinensis, H. neurocarpa, and H. goniocarpa, and identify the key cellular program shift points to illustrate the coordination of cell division and cell expansion in controlling fruit morphogenesis. This study offers important information to understand the development and productivity of sea buckthorn fruits. ## 2.1. General Observation The dea buckthorn fruit was set 10 days after pollination, and the perianth tube became part of the developing fruit. There was no clear differentiation in the newly formed fruits at 10 days after anthesis (10 DAA) of the three Hippophae species (Figure 1a,g,m). The fruits were green in color and covered with glossy peltate trichomes that were gradually shed during the fruit’s development. Eventually, only a few of them persisted over the mature fruits (Figure 2). As the fruits matured (90–120 DAA), the peel color of the H. rhamnoides ssp. sinensis fruit changed from green to yellow. It was also single-seeded. From a tactile perspective, the fruits were near-spherical in shape with a soft peel that could be crushed easily. The fruits of H. goniocarpa were elliptical in shape and orange in color. The fruits of H. neurocarpa were long, cylindrical, and bent, having five grooves on the brown color rind (Figure 1f,l,r). ## 2.2. Fruit Growth Pattern The fruit growth of H. rhamnoides ssp. sinensis, H. goniocarpa, and H. neurocarpa was defined by morphological changes, including transverse diameter, longitudinal diameter, fresh fruit weight, and volume (Figure 3). These morphological traits fitted well to the logistic model, and each quadratic coefficient was greater than a 0.9 regression coefficient (Table 1). Although these Hippophae fruits exhibited diverse fruit size and shape (Figure 1), the growths of their transverse and longitudinal diameter showed similar sigmoid growth curves, exponentially increasing at 0 to 50 DAA and continuously growing with a lower rate from 50 to 120 DAA (Figure 3a,b). Moreover, the fresh fruit weight and volume of the three Hippophae fruits also increased with an increase in the longitudinal and transverse diameters (Figure 3c,d). Fruit weight of H. rhamnoides ssp. sinensis and H. goniocarpa showed a sigmoidal growth trend, while H. neurocarpa fruit showed exponential growth. Additionally, in both H. rhamnoides ssp. sinensis and H. goniocarpa, the linear portion of the curves corresponded to a phase of intense development from 70 to 90 DAA, after which both fruit weight and volume remained steady while the maturation events occurred in fully expanded fruits. During early development (10 DAA), there was no significant difference in the fruit shape index among the three species (Figure 4). When fruits reached 30 DAA, the fruit shape index of H. rhamnoides ssp. sinensis and H. goniocarpa decreased gradually until the fruits reached 50 DAA. H. rhamnoides ssp. sinensis approached 1, while H. goniocarpa was close to 1.5. However, the fruit shape index of H. neurocarpa increased along with fruit development and approached 2.5 when the fruit reached 120 DAA (Figure 4). ## 2.3. Cellular Structure Changes Cellular changes in the fruits of the three Hippophae species were represented by tissues taken from 10 to 120 DAA. The pericarp of sea buckthorn that developed from the perianth tube was specialized into three parenchyma cell layers, namely the exocarp, mesocarp, and endocarp (Figure 5). The 1-2-layered exocarp was covered by peltate trichomes, with a few stomatal apparatuses. The mesocarp was the fleshy part of the hypanthium, with 5 to 8 layers of cell thickness. The 1-2-layered endocarp was located in the innermost of the pericarp cells. In addition, the size of the parenchyma cells in the mesocarp was larger compared with those in the exocarp and the endocarp, as observed in the transverse section of the fruit (Figure 5). Parenchyma cells in the mesocarp of S1 (stage of 10 to 30 DAA) were small, irregular, and tightly packed. Some of the parenchyma cells were specialized to form six vascular bundles, arranged on the medial side of the mesocarp in a circular pattern (Figure 6a,b, Figure 7a,b, and Figure 8a,b). After fertilization, the number of mesocarp cells in H. rhamnoides ssp. sinensis and H. goniocarpa continued to increase until 30 DAA (Figure 9a). The relative cell proliferation rate also showed that most cell numbers of the three species were produced during early development (Figure 9b). After 30 DAA, the mesocarp became 8-10-cell layers thick, accumulating phenolics, oils, polysaccharides, and proteins. However, their specific contents still needed to be measured further. As the fruits matured (from 90 to 120 DAA), parenchyma cells were found to be more irregular and vacuolated, along with enlarged intercellular spaces. The visibility of vascular bundle tissues was reduced (Figure 6e,f, Figure 7e,f and Figure 8e,f). The cells of the three Hippophae species fruits began to enlarge approximately 10 DAA until 70 DAA, after which the cell size of H. rhamnoides ssp. sinensis and H. goniocarpa entered a fast cell expansion period, with a quick cell area increase from 70 to 120 DAA and growing to a final size at 120 DAA (Figure 9c). During the same period, the mesocarp cell area of H. neurocarpa decreased due to water loss in the fruit, whereas the number of cells increased until 120 DAA (Figure 9a,c). An analysis of the relative cell expansion and proliferation rates of the three Hippophae species fruits in Figure 9b,d revealed that, compared with cell division, H. rhamnoides ssp. sinensis, H. neurocarpa, and H. goniocarpa fruits consumed more time for cell expansion during the whole fruit development. The longer time required for cell enlargement led to the finding that the mesocarp cells of mature fruits were almost ten to one hundred times larger compared with the cells of the perianth tube during anthesis. ## 3. Discussion Fruit size is an attractive phenotypic trait associated with commercial value. The remarkable diversity of fruit size makes sea buckthorn a good biological system to study the genetic basis and regulating mechanisms in fruit development. Cell division and cell expansion usually directly influence the formation and development of final fruit sizes. The contribution of these two mechanisms to fruit growth can differ between species or cultivars. In melon and pumpkin, differences in the duration and the degree of cell expansion were observed [28,29]. Similarly, differences in the duration of cell divisions post-bloom were observed in different varieties of blueberry [30]. In this study, we investigated the dynamic fruit size changes in H. rhamnoides ssp. sinensis, H. goniocarpa, and H. neurocarpa from the morphology and cellular level aspects, and aimed to identify the key cellular program shift points to illustrate the coordination of cell division and cell expansion in controlling fruit morphogenesis. ## 3.1. Fruit Growth and Development in Hippophae L. In Hippophae spp., the perianth tube contributes to the formation of the fleshy layer. Furthermore, the ovary wall develops into a thin papery pericarp called the seed capsule, either separated from or attached to the seed coat [6,7,31]. The fruit type in Hippophae is not a true berry, as its description does not fully fit into the botanical classification of any fruit [32,33]. In a detailed investigation of H. rhamnoides cv., Harrison and Beveridge [34] suggested that the fruit of sea buckthorn should be described as achene, because the presence of a single seed in the fruit and indehiscent attachment of seed from a single point and development from a unilocular ovary are consistent with an “achene”. However, the achene by definition does not have a well-differentiated seed coat [33], while in sea buckthorn, the seed coat possesses a distinguishable testa. Additionally, another typical feature of achene is the dry nature of the fruit, which contrasts with the fleshy fruits in sea buckthorn. The fruit of sea buckthorn is similar to Elaeagnus angustifolia L., and therefore better described as “acrosarcum” (perianth tube forming fleshy parts and seed embedded in fleshy pulp) or “pseudo drupe” (the pericarp lacks a stony endocarp) [6,35]. Mangla et al. [ 6] also believed that the fruit of sea buckthorn might be appropriately described as a pseudo-drupe. The fruits of sea buckthorn are used in a variety of medicinal and nutritional products. Fruits are collected from the female plants in the wild. It is known that the species fruits profusely and also propagates by forming root suckers, in a case very similar to Paspalum grasses [36] and Urochloa [37]. The occurrence of diverse reproductive pathways assures the possibility of generation of novel genotypes through sexuality, while apomictic reproduction maintains adaptive genotypes and ensures reproduction in the absence of pollination [6]. At present, there is a question as to whether Hippophae fits better into a single or double S model. In this study, other than the pattern followed by H. neurocarpa, H. rhamnoides ssp. sinensis and H. goniocarpa followed a single S model similar to that of other fruits such as apples [18,25] and loquats [26,38]. H. neurocarpa fruit displayed a single sigmoid curve where length, diameter, fresh weight, and volume increased exponentially as the fruit developed from 10 to 120 DAA. Similar growth patterns were also found in Eugenia stipitate [39], Rastali banana [40], and Carissa congesta [41]. The result also revealed that the fruit shape index of Hippophae varied with time. At the beginning of fruit set, the fruit shape index was high, giving H. rhamnoides ssp. sinensis and H. goniocarpa fruit an elongated shape. As the fruit grew, the elongation gradually slowed down while transverse diameter increased rapidly. When the fruit reached 90 DAA, the fruit of H. rhamnoides ssp. sinensis appeared almost roundish in shape, and the shape of the H. goniocarpa fruit was ellipsoidal. The longitudinal diameter growth of H. neurocarpa was higher than the transverse diameter growth, so the fruits of H. neurocarpa were cylindrical at 90 DAA. During development, fruit becomes the sink organ to accumulate photosynthate products from photosynthesis, such as sugar and water [42]. Thus, this is the major contributor to the increase in length, diameter, weight, and volume in Hippophae fruit. ## 3.2. Effects of Cell Division and Cell Extension on the Fruit Size of Hippophae L. Cell division and cell expansion during fruit development are the key parameters affecting the final fruit size [43,44]. Cell observations showed that cell division increased rapidly shortly after flowering and fertilization. Compared with the early stage of development, there was no significant cell number increase in the mature fruits of the three Hippophae species. Cell number also increased after anthesis in loquats [26,45] and apples [18,25], and the number of cortex cells in a mature apple increased to five or more times that of receptacle cells during anthesis [25]. A large amount of variation in the cell number of the cortex might be an important reason for the larger size of the apple, especially the fleshy part [26]. In general, a combination of a greater cell division capacity and an enhanced degree of cell enlargement are involved in the increase in the fruit size [27]. Cell division continues in the skin of an avocado until shortly before ripening [46], whereas other fruits, such as sweet cherries [47], tomatoes [24], and apples [18,25], engage in cell proliferation early in fruit development, with long expansion until mature. In banana, it was demonstrated that “the maximum fruit filling rate is the product of pulp cell number and maximum cell filling rate” [48]. The investigations above show that cell division and cell enlargement might function individually or may cooperate with one another to determine the fruit size. In our study, compared with cell division, more time was spent on cell expansion in the sea buckthorn fruit during growth, which made the size of the pulp cells in the middle to late fruit development stages about ten to one hundred times bigger when compared with the size of the cells in the early fruit-setting stage or flower development stage. However, in H. neurocarpa, cell division was still active in the middle and late stages of fruit development, which could be due to the large number of cells required to make up for the small cell size at the maturity stage. ## 3.3. A Model of Cell Regulation in Fruit Development of Hippophae L. Based on the observations of the main morphological indexes of fruit growth and development, we established a preliminary model of cell regulation in fruit development in three species of Hippophae, as shown in Figure 10. The whole fruit growth process can be divided into a cell division phase and a cell expansion phase, with an overlap between 10 and 30 DAA. In particular, the two phases in H. neurocarpa showed an additional overlap between 40 and 80 DAA. Based on the degree of cell division and the intensity of cell expansion, fruit formation was divided into four stages, including cell proliferation, slow growth stage (or fruit hardcore stage), rapid growth stage, and fruit ripening. ## 4.1. Plant Materials The fruiting trees of three Hippophae species (H. rhamnoides ssp. sinensis, H. neurocarpa and H. goniocarpa) was monitored, from May to September 2021, in adult individuals of a natural hybrid zone of sea buckthorn in the eastern margin of the Qinghai-Tibet Plateau of Qilian County, Qinghai Province, China (38°15′ N, 100°16′ E). The average annual precipitation is 415.5 mm, and the average annual temperature is −1 °C. Ten plants from the native population of each Hippophae species were selected based on their overall homogeneity with respect to canopy size and matching phenological stages of the plant and inflorescence. During the flowering season, inflorescence development was closely monitored. Samples of fruits were collected starting 10 days after anthesis (10 DAA) until 120 days after anthesis (120 DAA), when the fruits were commercially ripened. Part of the fruits were used for growth kinematics inspection, while the others were used for sampling. ## 4.2.1. Fruit Characteristics of Sea Buckthorn during Development Within 24 h of harvest, fruit longitudinal diameter was measured from the fruit stem end to the proximal end of each fruit by using a digital vernier caliper (LR44 AG13, Hengliang, China). Furthermore, the transverse diameter was measured at two opposite sides of mid region. The mean values of the fruit diameter were then calculated. The fresh weight was determined by using an electronic balance. The volume of fruits was estimated by immersing the fruit in a water-filled measuring cylinder (25 mL) and measuring the amount of water displaced by the complete immersion. The fruit shape index was calculated according to the following equation: fruit shape index = longitudinal diameter/transverse diameter. Moreover, at least fifty fruits were measured per repetition at each time point. ## 4.2.2. The Microstructure of Fruits at Different Development Stages For each sample point, three different fruits were picked and used for paraffin section analysis with the following procedure. First, the fruits were immediately fixed in FAA ($70\%$ ethanol:formaldehyde:acetic acid with a volume ratio of 90:5:5) for 24 h, dehydrated through a series concentration of ethanol (70, 85, 95, and $100\%$, each for 1 h, respectively), transferred to xylene for 2 h (replace with new xylene after 1 h), and embedded in paraffin. Furthermore, longitudinal and cross sections with 10 μm thickness were cut using a rotary microtome (Leica RT2235, Barcelona, Spain). The sections were stained with $0.1\%$ safranin O and Fast Green solutions and mounted using Canada balsam. Lastly, the well-stained sections were sealed with resin and coverslips and photographed (Leica DM6 B, Leica Microsistemas S.L.U., Barcelona, Spain). For the SEM study, the samples were vaccumed and post-fixed in FAA for 24 h. Samples were then subjected to dehydration process in an increasing gradient of ethanol series, 30 min in each concentration. The samples were then dried in a SCIENTZ-10N vacuum freeze dryer (SCIENTZ, Ningbo, China), mounted on metal stubs, and sputter coated(Vision Precision Instruments, Beijing, China) in gold. Prepared samples were observed under high vacuum with thermal field emission scanning electron microscopy (Carl Zeiss AG, Oberkochen, Germany). The sea buckthorn fruit is a pseudo-drupe, and for the convenience of description, the fruit pericarp cells were roughly divided into exocarp, mesocarp, and endocarp cells, from the exterior to the core cells, in this study. The anatomical parameters of H. rhamnoides ssp. sinensis, H. neurocarpa, and H. goniocarpa fruits at different stages of development were measured using Image J software (https://imagej.net/ij/index.html/, accessed on 7 February 2022) [49]: the cell area and cell number of mesocarp cells were measured. The relative cell proliferation rate and relative cell expansion rate were determined from the cell number and cell area data as follows. Relative growth (%) = (parameter value of a period/parameter value of fruit ripening period − parameter value of previous period/parameter value of fruit ripening period) × 100 [50]. The period from 10 to 30 DAA was defined as S1 (stage 1), and in the same manner, the periods from 30 to 50 DAA, 50 to 70 DAA, 70 to 90 DAA, and 90 to 120 DAA were set as S2, S3, S4, and S5, respectively. ## 4.3. Statistical Analysis All parameters were subjected to the analysis of variance (ANOVA) using SPSS 20.0, with means being analyzed by regression analyses at $p \leq 0.05$ using the statistical software Origin 2020. Data in the graphs are mean ± SD. ## 5. Conclusions The growth characteristics and cellular developmental properties of H. rhamnoides ssp. sinensis, H. goniocarpa, and H. neurocarpa were observed throughout their developmental stages. The results showed that the fruits of H. rhamnoides ssp. sinensis and H. goniocarpa grew in a single sigmoid mode, while H. neurocarpa grew in an exponential mode under the complex regulation of cell division and cell expansion. The results of cellular observations showed that the mesocarp cells of H. rhamnoides ssp. sinensis and H. goniocarpa were larger in cell area, with prolonged cell expansion activity, whereas H. neurocarpa had a higher cell division rate. Elongation and proliferation of the mesocarp cells were essential factors affecting fruit morphology. Finally, a preliminary cellular scenario for three species of sea buckthorn was established for fruit morphogenesis. Fruit development was divided into a cell division phase and a cell expansion phase, with an overlap between 10 and 30 DAA. These two phases in H. neurocarpa overlapped once again between 40 and 80 DAA. 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--- title: Vitamin D Deficiency Prevalence in Hospitalized Patients with COVID-19 Significantly Decreased during the Pandemic in Slovakia from 2020 to 2022 Which Was Associated with Decreasing Mortality authors: - Juraj Smaha - Peter Jackuliak - Martin Kužma - Filip Max - Neil Binkley - Juraj Payer journal: Nutrients year: 2023 pmcid: PMC10005285 doi: 10.3390/nu15051132 license: CC BY 4.0 --- # Vitamin D Deficiency Prevalence in Hospitalized Patients with COVID-19 Significantly Decreased during the Pandemic in Slovakia from 2020 to 2022 Which Was Associated with Decreasing Mortality ## Abstract The coronavirus disease 2019 (COVID-19) pandemic has led to changes in lifestyle, which could influence vitamin D status on a population level. The purpose of our study was to compare 25-hydroxyvitamin D (25[OH]D) levels in patients hospitalized because of severe COVID-19 during two waves of the pandemic ($\frac{2020}{21}$ vs. $\frac{2021}{22}$). A total of 101 patients from the $\frac{2021}{22}$ wave were compared with 101 sex- and age-matched subjects from the $\frac{2020}{21}$ wave. Patients from both groups were hospitalized during the winter season from 1 December to 28 February. Men and women were analyzed together and separately. The mean 25(OH)D concentration increased from 17.8 ± 9.7 ng/mL to 25.2 ± 12.6 ng/mL between waves. The prevalence of vitamin D deficiency (<20 ng/mL) decreased from $82\%$ to $54\%$. The prevalence of adequate serum 25(OH)D concentration (>30 ng/mL) increased from $10\%$ to $34\%$ ($p \leq 0.0001$). The proportion of patients with a history of vitamin D supplementation increased from $18\%$ to $44\%$ ($p \leq 0.0001$). Low serum 25(OH)D concentration was independently associated with mortality after adjusting for age and sex for the whole cohort of patients ($p \leq 0.0001$). The prevalence of inadequate vitamin D status in hospitalized patients with COVID-19 in Slovakia decreased significantly, probably due to a higher rate of vitamin D supplementation during the COVID-19 pandemic. ## 1. Introduction In December 2019, several cases of viral pneumonia of unknown etiology emerged in the city of Wuhan, Hubei province, China [1]. In the same month, a new viral pathogen—severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)—was discovered, and a new disease, coronavirus disease 2019 (COVID-19), was identified. The virus spread quickly worldwide, and on 11 March 2020, the World Health Organization declared COVID-19 a pandemic [2]. During the first half of 2020, almost all countries started implementing safety measures, such as social distancing, stay-at-home orders, the closing of non-essential facilities, and a ban on traveling to halt spreading of the virus. While necessary for constraining the virus, possible adverse effects of these measures on lifestyle, eating habits, physical activity, mood, and social life have been discussed. For example, Ammar et al. showed that home confinement during the COVID-19 pandemic negatively affected all levels of physical activity and led to more time spent sitting. Additionally, an unhealthy pattern of food consumption was exhibited [3]. A small study showed that serum markers such as glucose, total cholesterol, and LDL increased post-lockdown even in previously healthy young adults [4]. Vitamin D was widely discussed during the pandemic by medical professionals, as well as by the lay public. Several studies pointed toward the possible adverse effects of vitamin D deficiency on mortality and disease severity in COVID-19 [5]. Sources of vitamin D include cutaneous synthesis from cholecalciferol upon ultraviolet B radiation exposure, diet (e.g., cod, liver, salmon, egg yolk, or beef liver), and supplementation. However, sunlight exposure is the predominant source of vitamin D [6]. Many speculated that changes in eating habits and, more importantly, less time spent in sunlight during lockdown could negatively influence vitamin D status on a population level [7]. Several studies, predominantly in younger patients and children, compared serum levels of 25(OH)D in the first months of the pandemic with pre-pandemic levels, with inconclusive results. Yu et al. And, similarly, Rustecka et al. showed that the COVID-19 pandemic restrictions led to decreased serum 25(OH)D levels among pediatric populations [8,9]. On the other hand, Meoli et al. did not show a higher prevalence of vitamin D insufficiency among late adolescents during the pandemic [10]. Contrary to the negative results of lifestyle changes and lower sunlight exposure to vitamin D status, the rising awareness of the potential harmful effects of vitamin D deficiency could lead to higher use of vitamin D supplements among the general population [11]. In our previous work dealing with changes of 25(OH)D in hospitalized patients with COVID-19 between the end of December 2021 and the beginning of January 2022 [12], we noticed that the average 25(OH)D concentration in patients admitted to the hospital was significantly higher than we observed in the previous period. We speculated that during the pandemic, after the end of stay-at-home orders, the lack of sunlight did not have such a profound effect on the concentration of vitamin D status. Alternatively, increasing awareness of the potential beneficial effects of vitamin D on immune functions during the pandemic could have led to higher use of supplementation in the general public, with a positive net effect on vitamin D status. The aim of the present study was to compare the serum concentrations of 25(OH)D between the second ($\frac{2020}{2021}$) and the third wave ($\frac{2021}{2022}$) of the pandemic in hospitalized patients with COVID-19 in Slovakia. ## 2. Materials and Methods We analyzed patients hospitalized in the internal medicine department of University Hospital Bratislava, Ruzinov, during the second (Group 1) and the third (Group 2) wave of the COVID-19 pandemic. Patients from both waves were hospitalized during the winter season: the second wave was considered from 1 December 2020 to 28 February 2021, and the third wave was considered from 1 December 2021 to 28 February 2022. During the second wave, a total of 2696 COVID-19 patients were hospitalized at the University Hospital Bratislava, Ruzinov. During the third wave, a total of 860 COVID-19 patients were hospitalized at our facility. A total of 101 (61 males/40 females; $12\%$ from all hospitalized COVID-19 patients at our facility) patients from the third wave of the pandemic fulfilled our inclusion criteria and were compared to 101 (61 males/40 females; $4\%$ from all hospitalized COVID-19 patients at our facility) sex- and age-matched subjects from the second wave of the COVID-19 pandemic. Patients were first matched for sex, then for age ±1 year. If several options were available for the match, patients with the closest value of BMI were chosen. A total of 202 patients (102 males/100 females) fulfilling inclusion criteria were included in this study. The inclusion criteria were as follows:COVID-19 pneumonia was the primary diagnosis upon admission;A severe COVID-19 infection was present;The presence of SARS-CoV-2 was detected by a reverse transcriptase–polymerase chain reaction (RT–PCR) using a nasopharyngeal swab;Serum 25(OH)D levels were obtained precisely at admission. Severe COVID-19 was defined as clinical signs of pneumonia and one of the following: respiratory rate > 30 breaths per minute, severe respiratory distress; or oxygen saturation < $90\%$ on room air [13]. Our facility’s laboratory did not routinely perform epidemiological surveillance using whole-genome sequencing (WGS). The Public Health Authority of the Slovak Republic launched a systematic national epidemiological surveillance using WGS in selected laboratories from 1 March 2021. From March 2021 until the end of June 2021, the most prevalent variant of concern detected was Alpha (B.1.1.7). Delta variants (B.1.617.2) were present in the Slovak population until the end of 2021. The Omicron variant appeared rapidly at the beginning of 2022 and continued to be prevalent until March 2022, with dominant lineages BA.2 and BA.2.9 [14]. Demographic characteristics, comorbidities, hematological and biochemical laboratory results on admission, and information regarding the intensity of oxygen therapy were collected from electronic medical records and discharge summaries by two physicians using a standardized approach. All patients included in the study received six milligrams of intravenous dexamethasone daily, according to the standard of care. All patients with oxygen therapy via a high-flow nasal cannula or invasive mechanical ventilation received six milligrams of dexamethasone plus one of the following: anakinra subcutaneously (100 mg twice daily for three days, followed by 100 mg daily for seven days), tocilizumab intravenously (8 mg/kg actual body weight administered as a single i.v. dose), or baricitinib orally (4 mg once daily up to 14 days, dose adjusted according to the actual eGFR) in the second wave, and baricitinib (4 mg once daily up to 14 days, dose adjusted according to the actual eGFR) orally during the third wave. All patients admitted up to January 2021 were supplemented with vitamin D during hospitalization according to the following scheme per local protocol in University Hospital Bratislava: loading dose 30,000 IU of cholecalciferol daily for the first three days, followed by 7500 IU cholecalciferol per day. After January 2021, the local treatment protocol was updated, and vitamin D supplementation during hospitalization was no more part of the standard of care in our institution. Serum 25(OH)D concentrations (in ng/mL) were obtained on admission using an automated electrochemiluminescence system (Eclesys Vitamin D Total II, 2019, Roche Diagnostics GmBH, Mannheim, Germany) with repeatability < 20 ng/mL SD ≤ 1.1 ng/mL; >20 ng/mL coefficient of variation ≤ $5.5\%$ and intermediate precision < 20 ng/mL SD ≤ 1.4 ng/mL; and >20 ng/mL coefficient of variation ≤ $7.0\%$. The serum 25(OH)D detection limit was 3 ng/mL [15]. All patients in the study were assessed with the same vitamin D detection method, and there was no change in the 25(OH)D measurement method between the waves. Analyses were performed in a laboratory, which was part of an external quality assessment system from accredited groups SEKK Czech Republic; NEQAS, GenQA Great Britain; and INSTAND, RfB Germany [16]. A serum 25(OH)D concentration > 30 ng/mL was considered vitamin D sufficiency; a concentration between 20 and 30 ng/mL was considered vitamin D insufficiency, and vitamin D deficiency was defined as a serum 25(OH)D concentration < 20 ng/mL in accordance with existing guidelines [17]. Vitamin D status seems to be sex-related [18]. Therefore, both sexes were analyzed together, as well as separately. Analyses were also performed according to the age of the participants. Patients were divided into two groups according to age. Younger age was defined as a chronological age < 65 years, and older age was defined as a chronological age of 65 and more [19]. Both sexes were analyzed in both age groups, together and separately. For statistical analysis of continuous variables, an unpaired t-test of mean values was used, and for analysis of categorical variables, a chi-square test of independence was used. For analysis of vitamin D serum level categories (sufficiency, insufficiency, deficiency) in the whole cohort and age and sex groups, a chi-square test with contingency tables was used. The mean serum 25(OH)D levels were compared in the whole cohort and in sex and age categories using an unpaired t-test of mean values. The relationship between serum 25(OH)D concentration and mortality adjusted for sex and age was assessed in the whole cohort. Logistic binary regression analysis with death as a dependent variable was used. Statistical analyses were performed using the SPSS program (ver. 21.0; IBM Corp., Armonk, NY, USA). The p-value < 0.05 was considered to be statistically significant. ## 3. Results A total of 101 sex- and age-matched patients in each wave (61 males, 40 females, mean age 69 years) were analyzed. Baseline clinical and laboratory characteristics between the waves (second wave—Group 1; third wave—Group 2) are displayed in Table 1. The mean concentration of 25(OH)D on admission during the second wave of the pandemic (Group 1) was 17.8 ng/mL, which increased to 25.2 ng/mL during the third wave (Group 2) ($p \leq 0.0001$). On admission, $82\%$ of patients from Group 1 were 25(OH)D deficient, and $10\%$ were 25(OH)D sufficient. In Group 2, $54\%$ of patients were 25(OH)D deficient, and $34\%$ of patients were 25(OH)D sufficient ($p \leq 0.0001$). The proportions of patients regarding vitamin D cutoff values in both groups are displayed in Figure 1A. There was no difference in the prevalence of major comorbidities except for chronic kidney disease, which was frequently observed in Group 1 ($p \leq 0.001$), and for dementia, which was more frequent in Group 2 ($$p \leq 0.02$$). The major comorbidities associated with a cardiopulmonary reserve—chronic heart failure, chronic pulmonary disease, anemia and concomitant pulmonary embolism—did not differ significantly between groups. The proportion of patients with a history of vitamin D supplementation increased from $18\%$ to $44\%$ ($p \leq 0.0001$). Changes in vitamin D concentrations in both sexes were also analyzed separately (Table 2). In the population of males, the proportion of vitamin-D-deficient patients decreased from $84\%$ to $48\%$, and the proportion of vitamin-D-sufficient patients increased from $8\%$ to $37\%$ ($p \leq 0.0001$) (Figure 1C). The mean 25(OH)D concentration in males increased by 9.1 ng/mL, from 17.2 ng/mL to 26.3 ng/mL ($p \leq 0.0001$). In females, the proportion of vitamin-D-deficient patients decreased from $80\%$ to $62\%$, and the proportion of vitamin-D-sufficient patients increased from $13\%$ to $28\%$ ($$p \leq 0.29$$) (Figure 1B). The mean 25(OH)D concentration in females increased by 4.9 ng/mL, from 18.7 ng/mL to 23.6 ng/mL ($$p \leq 0.07$$). The prevalence of vitamin D deficiency decreased in younger as well as in older patients (Table 3). In patients < 65 years old, the proportion of vitamin-D-deficient patients decreased from $81\%$ to $44\%$, and the proportion of vitamin-D-sufficient patients increased from $3\%$ to $34\%$. In older patients (>65 years), the prevalence of vitamin D deficiency decreased by $24\%$, and the prevalence of vitamin D sufficiency increased by $20\%$ (Figure 2). In men, a statistically significant decrease of vitamin-D-deficient patients was observed in both age groups; in younger (< 65 years) males by $44\%$, and in older males (> 65 years) by $29\%$ ($p \leq 0.001$ and $p \leq 0.002$, respectively). In women, the prevalence of vitamin-D-deficient patients decreased in both age groups, although the difference was not statistically significant. In older females (> 65 years,), the prevalence of vitamin D deficiency decreased from $79\%$ to $60\%$, and vitamin D sufficiency increased from $15\%$ to $31\%$, which was borderline statistically significant ($$p \leq 0.056$$) (Table 3). The most significant absolute change of 25(OH)D concentration between waves was observed in younger males (10.7 ng/mL, $p \leq 0.002$), and the smallest absolute change of 25(OH)D concentration was observed in younger females (2.5 ng/mL, $$p \leq 0.68$$) (Figure 3). Except for younger females, in all other groups, a statistically significant increase of mean 25(OH)D concentration was observed between waves (see Table 3 and Figure 3). Regarding markers of inflammation, the highest numbers of monocytes and lowest numbers of lymphocytes were observed in Group 2. There was no difference between the numbers of neutrophils and C-reactive protein between both groups (Table 1). In Group 2, a slight reduction of mortality of $6\%$ was observed, which was not statistically significant ($$p \leq 0.58$$). Binary logistic regression analysis performed on the whole cohort (all patients admitted during two COVID-19 waves) showed that there was a significant association of 25(OH)D concentration with mortality, even after adjusting for age and sex (Table 4). An increase in serum 25(OH)D concentration of one ng/mL leads to approximately a $7\%$ increase in the chance of survival (Figure 4). ## 4. Discussion In this study, we found a significant reduction in the prevalence of vitamin D deficiency among hospitalized patients with COVID-19 between the second and third waves of the pandemic. The prevalence of vitamin-D-deficient patients decreased by $28\%$, and the prevalence of vitamin D sufficiency increased by $24\%$. This change in vitamin D status coincided with a more than doubling of the proportion of patients taking vitamin D supplementation. Moreover, an increase in mean 25(OH)D concentration was observed for both men and women regardless of age group, except for females younger than 65. These findings are surprising given the high prevalence of vitamin D deficiency in Europe, and likely even higher in Eastern Europe [20]. The increase of serum 25(OH)D levels by one ng/mL was associated with a ~$7\%$ reduction in mortality for the whole cohort of patients (both groups combined). To date, several studies have investigated the changes in vitamin D status during the COVID-19 pandemic; the majority focused on people under the age of 18 years [21,22]. A meta-analysis of five studies comprising 4141 people under the age of 18 showed significantly lower serum 25(OH)D levels during the COVID-19 pandemic compared with pre-pandemic years [23]. These lower serum 25(OH)D levels were not observed among infants (under one year), where either no change [9] or even an increase [8] of 25(OH)D concentration was observed. This could have been the result of regular vitamin D supplementation in this age group and more attentive caretaking of the youngest children during the COVID-19 pandemic [9]. Concerns about a decrease in the serum concentration of vitamin D due to pandemic measures have not yet been confirmed in an adult population. Two studies from Northern Italy did not find a clinically relevant impact on vitamin D status from confinement during the first year of the COVID-19 pandemic (January–December 2020) compared with pre-pandemic years [24,25]. Lippi et al. observed an increase in serum 25(OH)D, accompanied by a reduced prevalence of 25(OH)D deficiency during the COVID-19 lockdown (March to May 2020), followed by a slight reduction of median serum 25(OH)D levels in the post-lockdown period (May to December 2020) [24]. In the study from South Korea on adults aged 19 years and older, which also included measurements of vitamin D from the second year of the pandemic (measurements up to November 2021), a significant increase in serum 25(OH)D concentration was observed in females, as well as in males. Contrary to our results, this increase in 25(OH)D serum levels was more significant in females, especially in elderly females [26]. Articles emphasizing the potential beneficial role of vitamin D supplementation in preventing and ameliorating COVID-19 infection were widely discussed and cited during the pandemic [27]. Indeed, several meta-analyses showed that vitamin D deficiency was associated with a worse prognosis and mortality of COVID-19 pneumonia, although with a high risk of bias and heterogeneity in multiple observational studies [28,29]. Some authors advocated supplementation with higher than commonly recommended doses of vitamin D during the pandemic to achieve and sustain serum 25(OH)D concentrations above 50 ng/mL [30]. Even some governmental agencies endorsed vitamin D supplementation, addressing the concerns about the potential worsening of musculoskeletal health on a populational level during the pandemic [31]. All this could have led to a higher awareness of the possible extraskeletal effects of vitamin D in the field of research and the general public. Somagutta et al. analyzed people’s micronutrient searches using an online platform. Vitamin D searches rose eight-fold in 2020–2021 from 2004, while nearly doubling throughout 2019–2021. This was probably due to curiosity about the effectiveness of vitamin D during the COVID-19 pandemic and could be translated into vitamin D supplement usage [11]. In a trend analysis of laboratory-based 25(OH)D samples comparing the yearly average of 25(OH)D in the 12 months before the onset of the COVID-19 pandemic with the first 12 months of the pandemic in Ireland, a yearly mean 25(OH)D concentration increase of 1.1 ng/mL/year was observed. If the 25(OH)D duplicate was selected as the last in sequence for the trend analysis in that study, then the average 25(OH)D increase was even higher at 2 ng/mL/year [32]. At the same time, the dose of new-to-market vitamin D supplements increased significantly during the pandemic, with an increase in the frequency of supplements exceeding the upper intake level and the maximum safe level. The prevalence of patients with serum 25(OH)D levels above 50 ng/mL increased substantially, which concerned the study’s authors [32]. No case of vitamin D hypervitaminosis was seen in our cohort of COVID-19 patients, and only three patients exceeded the 50 ng/mL cutoff level. Vitamin D supplementation for the prevention of acute respiratory tract infections repeatedly demonstrates a significant overall protective effect of this intervention compared with placebo control. Martineau et al. showed that patients who were very vitamin D deficient and those not receiving bolus doses experienced the most benefit [33]. Jolliffe et al. showed that patients receiving regular doses of vitamin D (400–1000 IU) for up to 12 months and those with younger ages benefited most [34]. Regarding COVID-19, a recent systematic review and meta-analysis showed that dietary supplementation with vitamin D was associated with a significantly lower risk of COVID-19 severity and mortality [35]. Several meta-analyses of randomized controlled trials indicated a beneficial role of vitamin D supplementation on ICU admission [36] and mortality [37]. At the same time, some did not prove any effect of supplementation in COVID-19 patients [38]. In the present study, the main aim was to compare changes in vitamin D status between COVID-19 waves in Slovakia. However, the outcome regarding vitamin D status on admission was also evaluated. A slight insignificant decrease in mortality in Group 2 was observed. At the same time, for the whole cohort of patients (both Group 1 and 2 combined), an independent inverse relationship between serum 25(OH)D levels at the time of admission and mortality was detected. The major limitation regarding mortality was that the treatment protocol changed considerably during the COVID-19 pandemic at our institution, and patients from different COVID waves were treated differently. For example, only the patients from Group 1 were supplemented with vitamin D during hospitalization. Vitamin D is a threshold nutrient, so patients with a severe deficiency will most likely benefit from supplementation [39]. Specifically, regarding COVID-19 disease, Gibbons et al. showed that patients with the lowest levels of 25(OH)D (0–19 ng/mL) exhibited the most significant decrease in COVID-19 infection following supplementation [40]. We could speculate that some severely 25(OH)D deficient patients in Group 1 could have improved their nutritional status upon supplementation and thus could have exhibited a milder course of the disease and lower mortality. Similarly, there was a difference in the treatment strategy with anti-inflammatory agents between waves. While in Group 1, patients on HFNV received predominantly anakinra and tocilizumab, these treatments were largely unavailable during the third wave of the pandemic in Slovakia. Patients with HFNV in Group 2 were treated predominantly with baricitinib instead. Thus, changes of the treatment strategy could have had a major impact on disease outcomes. Some authors found a significant inverse relationship between low 25(OH)D and inflammatory markers in COVID-19 [41], while others did not [42]. In the present study, values of CRP and neutrophils did not differ significantly between groups despite significantly increased mean 25(OH)D levels in Group 2. It can be argued that the effect of vitamin D on the course of COVID-19 is mediated by antimicrobial peptides like cathelicidin, which cannot be assessed by standard serum inflammatory biomarkers like CRP or IL-6. Interestingly, in Group 2, a significantly higher number of monocytes in peripheral blood was present. An active form of vitamin D can induce the proliferation of monocytes. It can improve macrophage function, such as phagocytosis, chemotaxis, and production of cathelicidins, thus ultimately modulating the innate and adaptive immune response [43,44]. The bioavailability of 25(OH)D to macrophages is a crucial determinant of the physiological control of its immune response [44]. Monocytes and macrophages are linked to the heterogeneity of the SARS-CoV-2 infection course and, depending on the signals from the microenvironment (e.g., vitamin-D-receptor-related signaling), could be either friends or foes in COVID-19 [45,46]. Serum 25(OH)D levels could drop rapidly with the onset of acute inflammatory illness, suggesting that inflammation can affect 25(OH)D metabolism in various ways [47,48,49]. Our previous work from a real clinical practice showed that serum 25(OH)D levels decreased significantly in patients with COVID-19 pneumonia during the first 48 h after hospital admission. The absolute 25(OH)D change between hospital admission and day 4 was 4.8 ng/mL [12]. Smolders et al. showed experimentally that serum 25(OH)D levels decreased within hours of initiating a systemic inflammatory response. Thus, patients who were ill for a longer period before hospitalization could have had lower 25(OH)D levels upon admission [50]. Whether low 25(OH)D in COVID-19 reflects functional vitamin D deficiency linked to the worse prognosis or represents only a laboratory phenomenon remains to be found in adequately designed randomized trials of vitamin D supplementation. High levels of misinformation exposure were observed during the pandemic, with $73\%$ of people reporting some exposure to misinformation about COVID-19 vaccination. Exposure to misinformation was directly correlated with vaccine hesitancy [51]. Similarly, it must be noted that the potential immunomodulatory effects of vitamin D have often been overestimated, and the results of studies were misinterpreted during the COVID-19 pandemic. Of great concern is that misleading sources also suggested or directly stated there was no evidence to support COVID-19 public health prevention measures and, at the same time, stated that vitamin D had preventative or curative abilities against COVID-19 [52]. During the second wave of the pandemic, vaccination was not widely available in Slovakia. The vaccination program started on 26 December, and at the end of February 2021 only $6.46\%$ of the population had received the first dose of the vaccine, most of whom were healthcare workers and other first responders. This changed considerably throughout 2022, and by the end of February 2022 approximately $46\%$ of people had received at least one dose [53]. However, more than $70\%$ of hospitalized patients during the third wave of the COVID-19 pandemic were unvaccinated despite Slovakia’s widely available COVID-19 vaccination at that time. We can hypothesize that the observed significantly higher vitamin D levels resulted partly from alternative “immune boosting” strategies not endorsed by major medical entities [54,55]. Our study has several limitations. We evaluated a relatively small group of patients. It is a single-center study within a specific geographic area; thus, results cannot be widely generalizable to the populations of other geographical regions. The exact dose and duration of vitamin D supplementation before hospitalization were unknown. Serum 25(OH)D concentrations in winter are generally about 50–$70\%$ of summertime values, and there is evidence that the 25(OH)D accumulates in skeletal muscle cells, which provide a functional store during the winter months [56]. However, we only knew patients’ values of serum 25(OH)D at the beginning of the hospitalization, i.e., during winter months. Interestingly, a significantly higher prevalence of patients with chronic kidney disease was observed in Group 1. This was probably caused by our institution’s triage policy during the second wave when COVID-19 patients were admitted to the hospital according to the major comorbidities they had at the time of infection (e.g., a patient with COVID-19 with severe kidney disease was sent to the internal medicine department). This practice changed during the third wave when the general COVID-19 ward was established. Nevertheless, the kidneys play an important role in vitamin D metabolism and regulation of its circulating levels. The progression of chronic kidney disease is associated with lowering 25(OH)D serum levels [57]. Serum 25(OH)D is bound to vitamin D binding globulin, which is filtered in the glomerulus and then reabsorbed in the proximal tubules by binding to megalin and cubilin receptors [58]. With CKD progression, associated proteinuria and decreased megalin activity could lead to renal wasting of a considerable amount of vitamin D and VDBP, resulting in more profound vitamin D deficiency [59]. This renal wasting of vitamin D could be exacerbated during acute inflammation [49] and is of particular interest in the population of patients with COVID-19. Our study also has several strengths. To the best of our knowledge, this is the first study comparing changes in serum 25(OH)D concentration in hospitalized patients with COVID-19 between selected waves during the pandemic. Potential confounders of vitamin D deficiency did not significantly affect our results because patients were sex- and age-matched. There was also no significant difference in BMI between groups, and venous samples were taken during the same season of the year. ## 5. Conclusions In conclusion, our study showed that the prevalence of vitamin D deficiency in patients hospitalized because of COVID-19 decreased significantly during the 12 months between the second and the third wave of the pandemic in Slovakia. The prevalence of vitamin D sufficiency increased both in males and females, although only in males was this change statistically significant. The mean 25(OH)D concentration increased by 7.45 ng/mL/year. The most significant absolute change was observed in younger males and the smallest in the cohort of young females. The inverse relationship between vitamin D serum levels and mortality from COVID-19 was detected. Further research in trend analysis of yearly changes of 25(OH)D serum concentration before, during, and after the COVID-19 pandemic is indicated on a broader population level. ## References 1. Zhou P., Yang X.L., Wang X.G., Hu B., Zhang L., Zhang W., Si H.R., Zhu Y., Li B., Huang C.L.. 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--- title: 'Processed Food–Sweets Patterns and Related Behaviors with Attention Deficit Hyperactivity Disorder among Children: A Case–Control Study' authors: - Wu Yan - Shuang Lin - Dandan Wu - Yanan Shi - Lihua Dou - Xiaonan Li journal: Nutrients year: 2023 pmcid: PMC10005288 doi: 10.3390/nu15051254 license: CC BY 4.0 --- # Processed Food–Sweets Patterns and Related Behaviors with Attention Deficit Hyperactivity Disorder among Children: A Case–Control Study ## Abstract Background: Previous studies have focused on the associations between core symptoms and dietary intake in children with attention deficit hyperactivity disorder (ADHD). However, few studies have explored how dietary patterns and behaviors relate to the risk of ADHD. The aim of our study is to explore the associations between dietary patterns and behaviors and the risk of ADHD, which could provide evidence for follow-up and treatments for children with ADHD. Methods: We conducted a case–control study, including 102 children diagnosed with ADHD and 102 healthy children. The food frequency questionnaire (FFQ) and the children’s eating behavior questionnaire (CEBQ) were used to investigate food consumption and eating behaviors. We applied exploratory factor analysis for constructing dietary patterns, and the factor scores were adopted for log-binomial regression to assess the associations between how dietary patterns and eating behaviors affect the risk of ADHD. Results: We extracted five dietary patterns with a cumulative contribution rate of $54.63\%$. Processed food–sweets scores revealed positive associations with an increased risk of ADHD (OR = 1.451, $95\%$ CI: 1.041–2.085). Moreover, processed food–sweets tertile 3 was associated with an increased risk of ADHD (OR = 2.646, $95\%$ CI: 1.213–5.933). In terms of eating behaviors, the group with a higher score relating to a desire to drink was also positively correlated with the risk of ADHD (OR = 2.075, $95\%$ CI: 1.137–3.830). Conclusions: In the treatment and follow-up of children with ADHD, dietary intake and eating behaviors should be considered. ## 1. Introduction Attention deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders in childhood, and it is characterized by age-inappropriate inattention, hyperactivity, and impulsivity [1]. It has been reported that the prevalence of ADHD in children around the world is about 5.9–$7.1\%$, and the trend is increasing year by year [2]. In China, almost 23 million children and adolescents suffer from ADHD, with a prevalence rate of $6.26\%$ [3]. Children with ADHD may affect their family relationships, school performance, and social interactions [4]. In addition, the core symptoms may persist into adolescence or even adulthood. Studies have shown that 30–$70\%$ of patients may still have significant symptoms into adulthood [5,6], seriously impacting and heavily burdening patients, families, and society. Given that, ADHD has attracted extensive attention in the field of medicine and public health worldwide. It is worth noting that children with ADHD are more likely to suffer from dietary intake problems, such as unreasonable nutrient intake, disordered dietary structures, and poor eating behaviors [7,8]. Dietary patterns and nutrient intake may be associated with the increased or decreased risk of ADHD in children. A meta-analysis based on 14 studies suggested that a diet high in refined sugar and saturated fat increases the risk of disease, whereas diets characterized by high consumption of fruits and vegetables reduce the risk of ADHD [8]. Among them, as a combination of diets and nutrients with different characteristics, dietary patterns represent a wider range of nutrient consumption. A study on Chinese children found that fish–white meat diets and algae–mineral protein nutrition patterns may effectively control ADHD in children [9]. A case–control study suggested that a Western diet or low adherence to a Mediterranean diet was positively associated with ADHD symptoms [10]. A study conducted in South Korea showed that the high consumption of traditional healthy dietary patterns and the low consumption of snack patterns of fast food and beverages were negatively correlated with ADHD in school-age children [11]. Therefore, providing suitable dietary patterns is beneficial in the treatment and follow-up of ADHD. As a significant approach for children to receive nutrition, food intake can be considered largely affected by eating behaviors. Available evidence shows that children with ADHD are accompanied by more unhealthy eating behaviors, and emotional or overeating may be triggered by hyperactivity or impulsivity. For example, food addiction/overeating was significantly associated with ADHD diagnoses in both children and adults and may be an important variable in the relationship between ADHD and obesity [12]. Addiction-like eating behaviors are associated with an excessive intake of high-fat foods and/or refined carbohydrates, further contributing to overweight and obesity in children [13]. In addition, children with ADHD and poor eating behaviors also have increased rates of comorbid disorders in adulthood [14]. A cross-sectional study conducted in the United States showed that inattention and hyperactivity/impulsivity in preschool children were positively associated with food responsiveness, emotional overeating, and slow eating [15]. These studies emphasize the relationship between children’s ADHD symptoms and eating behavior, even physical development, and also illustrate the importance of healthy eating behaviors. It is well known that eating behaviors during childhood are the basis of dietary consumption and nutrition acquisition. Poor eating behaviors may lead to unbalanced dietary nutrient intake and even affect growth and development. However, dietary intake and dietary-behavior-related issues are usually ignored in the treatment and follow-up of children with ADHD. In this research, we designed a case–control study and applied factor analysis to investigate the associations between dietary patterns and eating behaviors with the risk of ADHD. We hypothesized that certain dietary patterns or behaviors might be associated with the increased risk of ADHD. Our study not only provides evidence for the follow-up and treatments of ADHD, but also supports references for correcting growth deviations in children with ADHD. ## 2.1. Study Population This is a case–control study with a matching design that includes children who visited the Department of Children Health Care from June 2020 to December 2020. The case group included 102 children who were diagnosed with ADHD by the attending physicians or above in the psychological–behavioral clinic. The inclusion criteria for the case group are as follows: [1] age 6–13 years. [ 2] First visit and meeting the diagnostic criteria for the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) [1]. [ 3] No history of anti-ADHD medication. The exclusion criteria are as follows: ADHD symptoms caused by other neurodevelopmental disorders, psychotic disorders, mood disorders, medications, or organic diseases. The control group was matched with healthy children who underwent physical examination during the same period. They also completed the DSM-V assessment and ruled out ADHD. The protocol was approved by the Medical Ethics Committee of the Children’s Hospital of Nanjing Medical University (Ethics number: NMUB2018074). The children and their parents participated in the assessment voluntarily, and written informed consent was obtained from their parents. ## 2.2. Dietary and Behavioral Assessment In this study, food frequency questionnaires (FFQs) were used to investigate the frequency and total intake of various food types. The questionnaires were designed by a panel of experts, including scientists in the fields of epidemiology and nutrition [9]. In the published children’s studies, the FFQ has been recognized as a reliable tool for collecting data with good validity and reliability [16,17]. It is widely used in epidemiological studies concerning the association between dietary intake and health outcomes. A semi-quantitative food frequency questionnaire was used to obtain children’s food consumption in the last month [18], including 18 food categories such as rice, coarse grains, vegetables and fruits, meat, fish and shrimp, snacks, sugary drinks, and so on. According to the frequency of food consumption, food consumption was classified as follows: 2 or more times a day, once a day, 4–6 times a week, 2–3 times a week, once a week, 2–3 times a month, once a month, less than once a month. Photographs of food portion sizes were presented to assist in estimating food consumption. The product of the participants’ single intake and daily consumption frequency was the total daily intake. The total daily intake was presented in grams (g) or milliliters (mL) by household measurement. Furthermore, the daily dietary energy intake and nutrient intake of children were analyzed using a 24 h dietary review method and dietary nutrition analysis software, Nutrition Star (Child care V 5.3.0) (ShangHai ZhenDing Computer Science Technology Co., Ltd., Shanghai, China). In addition, the Children’s Eating Behavior Questionnaire (CEBQ), developed by Jane Wardle et al., was used to evaluate children’s eating behaviors [19]. It is widely used to evaluate eating behaviors aged 2–13 years old, and has achieved good internal consistency, reliability, and validity in various studies [20,21]. There are 35 questions in the questionnaire, which are divided into 8 types of eating behaviors, including the food avoidance dimension (satiety responsiveness, slowness in eating, food fussiness, emotional undereating) and food tendency dimension (food responsiveness, enjoyment of food, desire to drink, emotional overeating). The questionnaire is based on a 5-point Likert scoring method (never = 1; rarely = 2; sometimes = 3; often = 4; always = 5). In general, a higher score indicates more serious eating behavior problems. ## 2.3. Covariates Physical measurement technicians measured the height and weight of the children and the parents, with an accuracy of 0.1 cm for height and 0.01 kg for weight, and the body mass index (BMI) was calculated. In addition, the participants completed the self-designed questionnaires under the guidance of the investigators, which included demographic characteristics, parents’ education level, annual family income, and the parent–child relationship. These covariates were taken into account in the analysis. ## 2.4. Statistical Analysis Epidata3.1 was used to input the questionnaire and scale the data. Continuous data that followed a normal distribution are expressed as mean ± standard deviation, and an independent sample t-test was used to compare the differences between the subjects with ADHD and the controls. The non-normal continuous variables are expressed as median (P25, P75), and the Mann–Whitney U test was used to compare the difference between the groups. The qualitative data are represented by frequency (%), and s Chi-square test was used to compare the groups. Exploratory factor analysis was used to construct the dietary patterns of 18 food consumption categories obtained by the FFQ. It is mainly based on the principle of dimensionality reduction, exploring the correlation coefficient matrix between different food types and grouping by the correlation size of various foods so that the correlation between the foods in the same dietary pattern is high, while the correlation between the foods in different dietary patterns is low. Firstly, the Kaiser–Meyer–Olkin (KMO) test and Bartlett sphericity test were performed to evaluate whether factor analysis was appropriate (KMO > 0.6, $p \leq 0.05$). Secondly, common factors were extracted based on principal component analysis, and five common factors were determined based on the dietary pattern, with a factor load coefficient > 0.35. The extracted common factors were further rotated by the maximum variance method to achieve a simpler structure and better interpretation, where a positive loading indicates a positive association between a given food and the pattern, while a negative loading indicates a negative association with the pattern. The factor loading represents the correlation coefficient between each food and the dietary pattern, reflecting the importance of each food in the dietary pattern; that is, foods with a high absolute load are considered to be the main factor of the dietary pattern. Next, the variance contribution rate of each factor was calculated, which is an index to measure the importance of the common factors. In addition, factor scores were calculated to evaluate the participant’s score on each common factor, which was the outcome of the dimensionality reduction of the original variable and could be used for regression analysis. Log-binomial regression was used to examine the association between the different dietary pattern factor scores (continuous, after calculating the tertile) and the odds of ADHD. The tertiles divide a sequence of numbers into three equal parts, including low-value (Tertile 1), medium-value (Tertile 2), and high-value (Tertile 3) groups. A p-value of <0.05 based on 2-tailed test results was considered statistically significant. All of the analyses were performed with R version 3.2.2. ## 3.1. Characteristics of Participants Table 1 shows the characteristics of the children included in the study. There were 102 children in the ADHD group and an equal number in the control group, which included 67 males and 35 females. There were no significant differences in age, gender, BMI, and daily screen time between the two groups. In addition, there were also no significant differences in family characteristics such as parents’ BMI, parents’ education, and annual family income, but there were significant differences found in the parent–child relationship between the two groups (Table 1). ## 3.2. Dietary Patterns Extraction The KMO statistic was 0.707, and the Bartlett sphericity test was $p \leq 0.05$, indicating that there was a significant correlation between the analyzed variables; therefore, it is suitable for factor analysis. A total of five dietary patterns were extracted, namely coarse grains–poultry–vegetables (tubers, bean products, coarse grains, poultry and meat, and vegetables), processed food–sweets (processed meat, fried food, puffed food, sugared beverages, and candies), dairy–seafood (milk and dairy products, fish, and prawns), fruits–nuts (mushrooms and seaweed, nut, and fruits), and staple foods (eggs, rice products, and flour products). The variance contribution rate of each factor was calculated by dividing the square sum of each factor load by the number of variables, which was used to measure the importance of common factors. Overall, the five dietary patterns cumulative variance accounted for $54.63\%$ (Table 2). ## 3.3. Associations between Dietary Patterns and ADHD Log-binomial regression was used to investigate the association between dietary patterns and the risk of ADHD diagnosis. Model 1 was an unadjusted model, and model 2 was further adjusted for covariates. The results showed that there were significant positive associations between the processed food–sweets scores and the diagnosis of ADHD (OR = 1.451, $95\%$ CI: 1.041–2.085). In addition, when the scores were converted to tertiles, the risk of ADHD was increased in the tertile 3 group (OR = 2.646, $95\%$ CI: 1.213–5.933). We also found that the risk of ADHD was significantly increased in the tertile 3 group with staple food patterns (OR = 2.246, $95\%$ CI: 1.013–5.085) (Table 3) (Figure 1). ## 3.4. Comparison of Eating Behaviors Since the eating behavior scores did not conform to a normal distribution, the Mann–Whitney U test was applied to analyze the differences between the ADHD and control groups. No differences were found between the two groups of children in terms of food-avoidant behaviors, while regarding the food approach dimension, ADHD children showed significantly higher “desire to drink” scores compared to the controls (Table 4). ## 3.5. Associations between Eating Behaviors and ADHD In addition, the association between eating behavior characteristics and ADHD risk was examined, and no significant association was found between the eating behavior scores and ADHD risk. The children were divided into two groups based on the median score; the risk of ADHD was associated with the upper median group on the dimension of “desire to drink”, and there were no differences in the other dimensions (Table 5) (Figure 2). ## 3.6. Comparison of Daily Nutrient Intake The daily intakes of nutrients obtained from the 24 h dietary review were compared between the two groups. It was found that the daily intakes of energy, fat, carbohydrate, iodine, and nicotinic acid in the ADHD group were significantly higher than those in the control group (Table S1). ## 4. Discussion In our study, five mainstream dietary patterns were identified to investigate eating behaviors in children. A processed food–sweets pattern rich in processed meat, fried food, puffed food, sugared beverages, and candies was positively associated with ADHD. The staple food pattern of flour products, rice products, and eggs was also associated with an increased risk of ADHD. In addition, in terms of eating behavior, the desire to drink in the food approach dimension was also positively associated with ADHD. Therefore, the findings validated the consistency of processed food–sweets dietary pattern and desire to drink behavior. To our knowledge, we are the first group to combine dietary patterns and eating behaviors to explore their relationship with ADHD after identifying risk factors separately and then mutually validating each factor. Clinicians and researchers are expected to explore comprehensive interventions for dietary structure and behaviors in order to achieve greater benefits. Our study found that processed food–sweets scores were associated with the risk of ADHD diagnosis. Similarly, in a study of Iranian children, adherence to fast food and sweet dietary patterns was associated with a higher prevalence of ADHD [22], and similar results were found in Spain [10]. Sugar consumption is considered to be closely related to ADHD. After sugar enters the blood, it causes rapid changes in glucose levels and produces more adrenaline, which not only provides more short-term energy for physical activities but also shows more excitement or impulsiveness. Even healthy children who ingest high doses of sugar on an empty stomach produce high levels of adrenaline, which in turn can cause tremors, anxiety, excitement, and poor concentration [23]. In addition, sugar-rich foods could trigger the reward system [24,25], and neuroimaging studies suggest that the neurobiological mechanisms of ADHD involve reward system dysfunction [26,27]. The reward system is known as the pleasure center of the brain, the most important of which is the dopamine system [28]. Normally, the dopamine released by the nerve impulse is quickly reabsorbed in equal amounts, making the body produce pleasure and the brain receives a “reward”. The intake of sugar would activate the central reward system, induce the release of dopamine and produce a rewarding effect, as well as produce memories related to the sugar reward. In addition, it induces the body to ingest sugar again, even producing addictive behaviors, and long-term addictive behaviors overstimulating the brain and leading to self-protection, reducing the sensitivity of the dopamine receptors [29]. We also found that excessive staple food intake was associated with the risk of ADHD. Children with ADHD are often troubled with characteristics such as impulsivity and emotional instability, which may lead to poor eating behaviors, such as being picky, a desire to drink, etc., and ultimately more high-fat and/or refined carbohydrate foods [13]. These foods balance mood disorders as a form of self-treatment to modulate disturbances of dopamine metabolism and reward–punishment effects [30]. The daily intakes of energy, carbohydrate, and fat in the ADHD group were significantly higher than those in the control group, which was consistent with the “processed food-sweet” dietary pattern. Carbohydrates include all kinds of sugars, and the intake of sweets may account for a large proportion in this population. In addition, processed food is often high in energy and fat, which may be associated with increased daily energy and fat intake in children with ADHD [31]. In addition, iodine is the main raw material for synthesizing thyroid hormones, which is closely related to the neurobehavioral development of children [32]. A Norwegian cohort study found that insufficient iodine intake by mothers during pregnancy was associated with increased ADHD symptom scores in children [33]. In our study, the increased daily iodine intake of ADHD children may be related to seafood consumption. Finally, nicotinic acid is an essential nutrient for humans and animals. Liver, kidney, yeast powder, and wheat germ are rich in nicotinic acid [34]. Nicotinic acid has been shown to be effective in alleviating anxiety and depression [35], but its effect remains unclear in children with ADHD. A review that critically appraised the effects of different dietary therapies on ADHD suggested that restricted elimination diets may be beneficial, artificial food coloring elimination was a potentially valuable treatment method, and that additional dietary supplements may also achieve some positive results, but a larger sample size and an evaluation of long-term results are still needed to determine the potential value [36]. A narrative review suggested that vitamin D and magnesium supplementation appeared to improve behavioral function and mental health in children with ADHD [37]. Additionally, blood zinc levels were negatively associated with ADHD [9], and zinc supplementation could reduce ADHD symptoms in children with zinc deficiency [38]. In contrast, biologics, particularly Lactobacillus rhamnosus GG, do not have sufficient evidence to be recommended as probiotic supplements for the treatment of ADHD [37]. The study provides new ideas for the relationships between ADHD and nutrient intake, and more research is needed to verify these findings in the future. The FFQ was used to obtain the frequency and quantity of various foods consumed by ADHD children before the study period. It has the advantage of understanding dietary patterns and habits compared with short-term diet reviews. However, the results may not be accurate due to the fact that the report is recalled. The 24 h dietary review was a dietary log filled by the ADHD children and parents. The weight of various foods was calculated by the nutritionist, and the dietary nutrition analysis software calculated the energy intake and nutrient content of children’s daily diets, which may be more accurate. Therefore, the two evaluation methods were combined in our study. Among the evidence between eating behaviors and ADHD diagnosis, a longitudinal study suggested that food responsiveness was an early marker of ADHD symptoms at 6 years of age [39]. In contrast, children with ADHD also tend toward more dietary, behavioral problems [8], similar to the “desire to drink” problem we found; therefore, physical growth deserves attention. In our study, the BMI of ADHD children showed an increasing trend, even if there was no statistical difference. A meta-analysis based on 42 studies found that the proportion of obesity in children with ADHD increased by $40\%$ compared with the control group [40]. A cohort study also found that children with ADHD at the age of 6 years had a significant increase in body fat content after 3 years of follow-up compared with the control children [41]. In addition, some studies have shown that the proportion of height and weight loss in ADHD children is significantly higher than that of the control group [42], which means that ADHD children face more problems in terms of growth deviation. Therefore, the clinical treatment and follow-up of ADHD should not only focus on improving the patient’s symptoms but also regularly evaluate dietary intake and behavior and monitor physical growth. The major strengths of this study included the fact that the ADHD cases were first-time visits without any medication or other intervention, as the use of certain medications would affect children’s appetite, which would also lead to changes in dietary intake. Secondly, all of the assessments were conducted on outpatients by trained psychologists and dietitians, with high compliance and producing good-quality information. Thirdly, the FFQ combined with factor analysis summarized dietary patterns, which represented a broader picture of food and nutrient consumption, so it was more valuable than single food or nutrition in predicting disease risk, which also showed that the results were reliable and stable. Finally, it was found that the “processed food-sweet” dietary patterns and “desire to drink” eating behaviors were both associated with the risk of ADHD in children. There were some limitations in our study. Due to the cross-sectional design, the causal relationships between dietary intake, behavior, and a diagnosis of ADHD could not be obtained, and patients with eating disorders could not yet be identified, as these need to be confirmed in a longitudinal study. Other possible confounders associated with ADHD and dietary intake, such as labor complications, breastfeeding, and household composition, cannot be ignored [9] and require further confirmation in subsequent studies. In addition, the population included in the study were school-age children, which some of meals were provided at school. Parents may be less aware of the number of school meals their child receives, and their reported dietary intake may be biased, so self-reported information from the children was also collected. A retrospective recollection of dietary information over the past month may be challenging, and it is essential to incorporate daily dietary reviews. The sample size of this study may limit the power of our evidence to some extent. Finally, the factor analysis cumulative variance was $54.63\%$, indicating other dietary patterns that have not been extracted; however, these values should be interpreted with caution as it depends on the number of variables included in the factor analysis. ## 5. Conclusions This case–control study found that processed food–sweets and staple foods were significantly associated with an increased the risk of ADHD, as was the desire to drink behavior. Therefore, improving health education related to eating behaviors and dietary patterns might be an effective and practical method for ADHD prevention and control among Chinese children. It is worth further investigating causality and determining whether dietary manipulation helps improve the core symptoms. 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--- title: Polygonati Rhizoma Polysaccharide Prolongs Lifespan and Healthspan in Caenorhabditis elegans authors: - Yage Luan - Yu Jiang - Rong Huang - Xuan Wang - Xiujuan He - Yonggang Liu - Peng Tan journal: Molecules year: 2023 pmcid: PMC10005289 doi: 10.3390/molecules28052235 license: CC BY 4.0 --- # Polygonati Rhizoma Polysaccharide Prolongs Lifespan and Healthspan in Caenorhabditis elegans ## Abstract Polygonati Rhizoma is the dried rhizome of Polygonatum kingianum coll.et hemsl., Polygonatum sibiricum Red. or Polygonatum cyrtonema Hua, and has a long history of medication. Raw Polygonati Rhizoma (RPR) numbs the tongue and stings the throat, while prepared Polygonati Rhizoma (PPR) can remove the numbness of the tongue, and at the same time enhance its functions of invigorating the spleen, moistening the lungs and tonifying the kidneys. There are many active ingredients in Polygonati Rhizoma (PR), among which polysaccharide is one of the most important active ingredients. Therefore, we studied the effect of Polygonati Rhizoma polysaccharide (PRP) on the lifespan of Caenorhabditis elegans (C. elegans) and found that polysaccharide in PPR (PPRP) was more effective than Polysaccharide in RPR (RPRP) in prolonging the lifespan of C. elegans, reducing the accumulation of lipofuscin, and increasing the frequency of pharyngeal pumping and movement. The further mechanism study found that PRP can improve the anti-oxidative stress ability of C. elegans, reduce the accumulation of reactive oxygen species (ROS) in C. elegans, and improve the activity of antioxidant enzymes. The results of quantitative real-time PCR(q-PCR) experiments suggested that PRP may prolong the lifespan of C. elegans by down-regulating daf-2 and activating daf-16 and sod-3, and the transgenic nematode experiments were consistent with its results, so it was hypothesized that the mechanism of age delaying effect of PRP was related to daf-2, daf-16 and sod-3 of the insulin signaling pathway. In short, our research results provide a new idea for the application and development of PRP. ## 1. Introduction With the changes in diet, environment, and lifestyle in modern society, population aging has become an important social issue, and aging and age-related diseases have attracted increasing attention [1]. Aging is an inevitable physiological process with multifactorial interactions and is usually accompanied by a variety of diseases, such as metabolic diseases [2], cardiovascular diseases [3], and neurological diseases [4]. Therefore, how to extend life span, find anti-aging strategies, and improve health has become a research hot-spot. Polygonati Rhizoma is the dried rhizome of Polygonatum kingianum coll.et hemsl., Polygonatum sibiricum Red. or Polygonatum cyrtonema Hua, and has a long history of medication. RPR numbs the tongue and stings the throat, while prepared PPR can remove the numbness of the tongue, and at the same time enhance its functions of invigorating the spleen, moistening the lungs, and tonifying the kidneys. There are many active ingredients in PR, including polysaccharides [5], saponins [6], flavonoids [7], and alkaloids [8], among which polysaccharide is one of the most important active ingredients. Modern pharmacological studies have shown the wide range of pharmacological effects of PR, mainly in kidney protection, cardio protection, antioxidant, anti-Alzheimer’s disease, and anti-cancer [9]. For example, Shen et al. reported that PCP significantly increased superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) activity and decreased malondialdehyde (MDA) content in 3-, 6-, and 9-month-old mice, indicating that PCP can increase antioxidant enzyme activity to prevent lipid peroxidation and oxidative stress induced by forceful exercise [10]. Li et al. found that for cervical cancer He La cells, PCHPs could up-regulate the expression of apoptotic genes Bak, Cytc, Puma, and caspases-3 and related proteins, while down-regulating the expression of anti-apoptotic genes Bcl-2, Bcl-x L, and Bcl-2 proteins, thus promoting cancer cell apoptosis to exert anti-tumor effects [11]. Studies have shown that there are about 60~$80\%$ of human homologous genes in C. elegans genome, and it has the advantages of a short life cycle and easy operation, so C. elegans is an ideal biological model for anti-aging studies [12]. However, there have been no reports on the difference of anti-aging effect and the mechanism of the anti-aging effect of PRP before and after processing in C. elegans, so in this paper, we mainly studied the activity and mechanism of PRP in delaying senescence of C. elegans. ## 2.1. Analysis of the Monosaccharide Composition of RPRP and PPRP The ratio of mannose, rhamnose, glucose, galactose, and arabinose in RPRP and PPRP was determined by HPLC derivatization, the molar ratio of mannose: rhamnose: glucose: galactose: arabinose in RPRP was 0.18: 0.06: 1: 0.11: 0.07; the molar ratio of mannose: rhamnose: glucose: galactose: arabinose in PPRP was 0.97: 0.42: 1: 3.02: 0.67. The results showed that the content of galactose in polysaccharide increased after the concoction of PRP. ## 2.2. Effect of RPRP and PPRP on the Longevity of N2 The experimental survival rate curve of the N2 lifespan is shown in Figure 1. Compared with the blank group, the survival rate of nematodes in the PPRP group was increased and the average lifespan was prolonged by $10.44\%$, the difference was statistically significant; compared with the blank group, although the RPRP group could prolong the maximum lifespan of nematodes, the average lifespan was prolonged by $6.20\%$, the difference was not significant (Table 1). It indicates that PPRP has a better effect of prolonging the lifespan of C. elegans than RPRP at this experimental concentration. ## 2.3. Effect of RPRP and PPRP on Lipofuscin Accumulation in N2 Lipofuscin is not eliminated by cytosolic action in C. elegans and accumulates in cells in an age-dependent manner, thus reflecting the aging status of nematodes, therefore the intestinal lipofuscin level of nematodes is an important marker of aging [13]. As shown in Figure 2a, the blue fluorescence of lipofuscin accumulated in nematodes under the fluorescence microscope. In Figure 2b, both RPRP and PPRP significantly reduced the accumulation of lipofuscin in nematodes compared with the blank group, but there was no significant difference in the lipofuscin fluorescence of nematodes between RPRP and PPRP. ## 2.4. Effect of RPRP and PPRP on Pharyngeal Pump Frequency and Locomotion Frequency of N2 In addition to lifespan, the healthspan has become an increasingly important parameter for evaluating resistance to aging [14]. Thus, we also examined the frequency of pharyngeal pumping and locomotion in nematodes. The experimental results of nematode pharyngeal pumping rate on day 4 and day 8 of adult worms are shown in Figure 3a. Compared with the blank group, on day 4 of adult nematodes, both RPRP and PPRP could improve the nematode pharyngeal pumping frequency, and the difference was statistically significant; on day 8 of adult nematodes, although RPRP and PPRP could also improve the nematode pharyngeal pumping frequency, the difference was not significant. It indicates that both RPRP and PPRP can reduce the senescence level of nematodes. The nematode motility frequency was measured on day 4 and day 8 of adult nematodes. As shown in Figure 3b, the nematode locomotion frequency could be improved by PPRP on the 8th day of adult, and the difference was statistically significant; on the 4th day of adult, both RPRP and PPRP could improve the locomotion frequency of nematodes, but the difference was not significant. It indicates that the C. elegans’ health cycle can be prolonged further by PPRP. ## 2.5. Effect of RPRP and PPRP on the Growth and Reproduction of N2 As shown in Figure 4a, there was no significant difference in nematode body length in both RPRP and PPRP compared to the blank group. It is suggested that at this experimental concentration, RPRP and PPRP had no adverse effect on nematode growth. The results of the nematode spawning experiment are shown in Figure 4b. Compared with the blank group, both RPRP and PPRP had no significant effect on the egg production of nematodes. It is suggested that this experimental concentration does not affect the normal reproduction of nematodes. ## 2.6. Effect of RPRP and PPRP on the Resistance to Oxidative Stress in N2 Juglone, as a pro-oxidant, causes oxidative stress and is considered a natural toxin [15]. Therefore, high concentrations of juglone are important for causing the rapid death of C. elegans, while the presence of antioxidants inhibits this effect [16]. The results of the antioxidant experiment are shown in Figure 5. Compared with the blank group, PPRP significantly improved the survival rate of nematodes in the oxidized environment of juglone, and the maximum lifespan of nematodes was extended by 3 h. The maximum lifespan of nematodes in RPRP was extended by 2 h, but the difference was not statistically significant. This indicates that the nematode resistance to oxidative stress is improved more by PPRP than RPRP. ## 2.7. Effect of RPRP and PPRP on Antioxidant Enzymes of N2 Antioxidant enzymes play an important role in reducing oxidative damage. SOD and CAT are two major antioxidant enzymes in nematodes that scavenge superoxide radicals that cause oxidative damage to biomolecules [17]. In order to study the effect of the polysaccharide of C. elegans on the activity of antioxidant enzymes in nematodes, the SOD and CAT activities were measured. As shown in Table 2, it was found that the PPRP could significantly increase the activity of SOD and CAT in C. elegans. ## 2.8. Effect of RPRP and PPRP on Reactive Oxygen Species (ROS) in N2 As nematodes age, their resistance to external stimuli decreases and reactive ROS increases, further accelerating the aging process and forming a vicious cycle [18]. In this study, we determined the effect of RPRP and PPRP on ROS in nematodes using the H2DCF-DA fluorescent probe method. The green fluorescence produced by ROS in nematodes after H2DCF-DA staining under fluorescence irradiation is shown in Figure 6a, and the experimental results are shown in Figure 6b. RPRP and PPRP could reduce ROS in nematodes compared to the blank group, and the difference was statistically significant. It indicates that the delay of nematode senescence by PRP may be related to the reduction of ROS levels in nematodes. ## 2.9. Effect of RPRP and PPRP on the Longevity of daf-16 Mutant To explore whether the lifespan-prolonging effect of PRP was dependent on the longevity gene daf-16 [19], the daf-16 mutant strains CF1038 and DR26 were selected for lifespan experiments. The results are shown in Table 3 and Table 4. Compared with the blank group, RPRP and PPRP did not have any significant lifespan extension effect on CF1038 and DR26 transgenic nematodes, indicating that the daf-16 is required for PRP to exert its anti-aging effect. ## 2.10. Effect of RPRP and PPRP on sod-3 Expression Typically, activation of daf-16 subsequently leads to activation of other stress-responsive genes, such as sod-3, a key enzyme gene that protects nematodes from ROS [20]. Therefore, CF1553 (sod-3::GFP) transgenic nematodes were selected for the experiment, which showed green fluorescence around the head, tail, and around vulva under fluorescence microscopy, as shown in Figure 7a, and the experimental results were shown in Figure 7b. Compared with the blank group, both RPRP and PPRP increased the expression of sod-3::GFP in CF1553 transgenic nematodes, and it is hypothesized that the sod-3 plays an important role in the delayed aging of C. elegans. ## 2.11. Effect of RPRP and PPRP on mRNA in N2 RT-PCR was performed to detect the effects of RPRP and PPRP on the expression levels of daf-2, age-1, daf-16, sod-3, ctl-1, and skn-1 mRNA. As shown in Figure 8, both RPRP and PPRP decreased the expression of daf-2 and increased the expression of daf-16 and sod-3 in nematodes compared with the blank group, which verified that the RPRP and PPRP could increase the activity of SOD in the previous content and that RPRP and PPRP depend on daf-16 to exert anti-aging effects. ## 2.12. Effect of RPRP and PPRP on the Longevity of daf-2 Mutant In order to further verify whether the anti-aging effect exerted by PRP is related to daf-2, transgenic nematodes CB1370 were selected for the lifespan experiment, and the experimental results are shown in Table 5. Compared with the blank group, the average lifespan of nematodes in RPRP was extended by $3.21\%$, and the average lifespan of nematodes in PPRP was extended by $1.48\%$, which were not significantly different, further proving that PRP exerts anti-aging effects related to daf-2. ## 3. Discussion PR is considered a “longevity and longevity medicine”, and the tonic effect is enhanced by wine processing, but there are few related studies. In this paper, we found that compared with the blank group, RPRP showed certain anti-aging potential, and PPRP showed a significant anti-aging effect, and the effect of PPRP was better than that of RPRP, which is consistent with the concoction principle of PR concoction for potency enhancement, and clarifies the scientific connotation of concoction from the perspective of aging. C. elegans has the advantages of small size, ease of handling, short life cycle, detailed genetics and signaling pathways, high genetic conservation with human genes, and cost-effectiveness of high-throughput screening [21], making it an excellent model organism for studying aging in current studies. Therefore, in this paper, we chose various indicators to evaluate the anti-aging activity of PRP and found that PPRP significantly prolonged the lifespan of nematodes, reduced the accumulation of lipofuscin in nematodes, and delayed the aging of nematodes. With the prevalence of aging-related diseases, the healthspan has also become an essential parameter for assessing the anti-aging potential of drugs. Therefore, by measuring the pharyngeal pump and motility of adult nematodes, it was found that for the same period of nematodes, PPRP could significantly improve the health level of nematodes. In addition, the anti-aging effect of the drug should be accompanied by minimizing damage to the organism. Therefore, in this paper, we measured the effect of the PRP on the egg production and body length of nematodes, and the results showed that there was no adverse effect on the growth and development of C. elegans at this experimental concentration. Currently, various theories propose different mechanisms of aging, including the free radical damage theory, the caloric restriction theory, and the telomere aging theory. Free radicals are continuously produced in the body along with metabolism and have a robust oxidative reaction capacity. The body has its own free radical scavenging systems, such as catalase, which can scavenge excess free radicals in the body and maintain the dynamic balance of free radicals. According to the theory of free radical damage, as aging occurs, the body cannot maintain the dynamic balance of free radical production and scavenging. Excessive free radicals can trigger lipid peroxidation in cell membranes and can also cause nucleic acid degeneration and dysfunction in cells, causing the body to develop towards aging [22]. In this paper, we simulated the peroxidation phenomenon generated by the increase of free radicals through oxidative stress experiments, and measured the effects of PRP on reactive oxygen species and antioxidant enzymes in nematodes. The results showed that PPRP could significantly improve the antioxidant property of nematodes, reduce the level of ROS in nematodes, and increase the activity of SOD and CAT, which also reflected the effect of PPRP that played a role in delaying the senescence of C. elegans by reducing oxidative damage. C. elegans, one of the ideal models for studying the mechanism of drug-delayed aging, has been reported in more studies on its lifespan-related signaling pathways and is highly conserved in humans [23,24,25]. Among these signaling pathways, insulin/IGF-1 signaling (IIS) was the first one established for aging and was identified in Cryptobacterium hidradi through mutations in the age-1 encoding phosphatidylinositol 3-kinase (PI3K) and mutations in the daf-2 encoding the IGF-1 receptor [26], IIS is one of the key pathways known to regulate lifespan [27]. Ctl-1 enables catalase activity and is predicted to be involved in the hydrogen peroxide catabolic process and response to hydrogen peroxide. Sod-3 enables superoxide dismutase activity and is involved in the removal of superoxide radicals. Skn-1 functions in the p38 MAPK pathway to regulate the oxidative stress response and in parallel to DAF-16/FOXO in the daf-2 mediated insulin/IGF-1-like signaling pathway to regulate lifespan. It has been shown that the IIS pathway is involved in the lifespan-prolonging effect of saponins from bitter melon (BMS) under oxidative stress [28]. It has also been reported that Sonneradon A(SDA) prolongs nematode lifespan by affecting the upstream and downstream factors associated with daf-16 in the IIS pathway [29]. In a lifespan experiment on a daf-16 mutant strain, an important gene in the IIS, it was found that PRP requires the involvement of the daf-16 to exert its anti-aging effect. Furthermore, sod-3, as the downstream gene of daf-16, is usually also readily activated, so the fluorescence of the sod-3 mutant was measured, and the results showed that PRP could significantly increase the expression of sod-3. Next, RT-PCR was performed on IIS pathway-related genes, PRP was found to significantly decrease the expression of daf-2 and increase the expression of daf-16 and sod-3, which was consistent with the results of the daf-16 transgenic nematode assay and the results of the sod-3 fluorescence expression assay. To further verify the role of daf-2, a lifespan experiment of daf-2 mutant strains was conducted, and the results showed that PRP delayed senescence in C. elegans in association with daf-2. In addition, we found that RPRP significantly reduced the accumulation of lipofuscin in nematodes, suggesting that RPRP has anti-aging activity, and combined with the results of mechanistic studies, we hypothesized that the age delaying effect of RPRP is related to daf-2, daf-16 and sod-3. PPRP has significant effects on several indicators of anti-aging activity such as lifespan, lipofuscin, and body bending, and mechanistic studies found that the anti-aging effect of PPRP was also associated with daf-2, daf-16, and sod-3. Therefore, we suggest that PPRP has a better anti-aging effect, and further speculate that the anti-aging effect of PRP is associated with daf-2, daf-16, and sod-3 in the IIS pathway. ## 4.1. Chemicals and Reagents NKA-9 macroporous adsorption resin was purchased from Solarbio Co. (Beijing, China); agar powder, peptone, and tryptone were purchased from Beijing Auboxing Biotechnology Co., Ltd. (Beijing, China); reactive oxygen species (ROS) kit, SOD kit (batch no. 20220307), CAT visible light kit (batch no. 20220330), and total protein quantitative test kit (batch no. 20220416) were purchased from Nanjing JianCheng Bioengineering Institute (Nanjing, China). ## 4.2. C. elegans The strains used in this study were wild-type N2; CB1370, daf-2(e1370) Ⅲ; DR26, daf-16(m26); CF1038, daf-16(mu86); CF1553, sod-3::GFP; TJ1052, age-1(hx546)Ⅱ(obtained from the Chinese Academy of Sciences). ## 4.3. Preparation of RPRP and PPRP Using the wine stewing method to concoct PPR, we took the appropriate amount of RPR and added $20\%$ of yellow wine, smothered for 6 h, placed in a stew pot, stewed for 10 h with water heating and then stewed for 8 h, cut thick slices and dried at 60 °C for 48 h. Extraction of crude polysaccharide by water extraction and alcohol precipitation method, followed by Savage method to remove protein, NKA-9 macroporous resin to remove pigment, 3500Da dialysis bag to remove small molecule impurities, concentrated under reduced pressure and then lyophilized. ## 4.4. Analysis of The Monosaccharide Composition of RPRP and PPRP The samples were subjected to acid hydrolysis and derivatization and then determined by HPLC. The chromatographic column was an Agilent HPLC column (4.6 × 250 mm, 5 μm); mobile phase: (A) acetonitrile-(B) 0.05 mol/L phosphate buffer solution; gradient elution: 0–10 min, 15–$17\%$ (A); 10–18.5 min, 17–$22.5\%$ (A); 18.5–20 min, 22.5–$23.5\%$ (A); 20–32 min, 23.5–$30\%$ (A); volume. $23.5\%$ (A); 20–32 min, 23.5–$30\%$ (A); volume flow rate 0.8 mL/min; column temperature 30 °C; detection wavelength 250 nm; injection volume 10 μL. ## 4.5. Exposure Experiments The NGM solid medium was prepared according to the literature method [30]. The blank group added OP50 E. coli bacterial suspension dropwise on the surface of the medium as food for nematodes, and the experimental group coated E. coli bacterial suspension containing 2 mg/mL of RPRP and PPRP on the surface of the medium as food, respectively. ## 4.6. Longevity Experiments The number of dead nematodes was recorded every 24 h. The surviving nematodes were transferred to a new medium every 48 h until all nematodes were dead. The N2, CF1038, and DR26 nematodes were incubated at 20 °C, and the CB1370 nematodes were incubated at 16 °C. ## 4.7. Lipofuscin Experiment On day 8 of N2 adulthood, nematodes were transferred to slides containing $2\%$ agarose pads for filming, observed, and photographed under a fluorescent microscope using excitation wavelengths of 340~380 nm and emission wavelengths of 430 nm. Fluorescence intensity was measured using Image J software. ## 4.8. Pharyngeal Pump Assay and Locomotion Assay The number of pharyngeal pumps and the number of nematodes doing sinusoidal movements in 20 s per nematode were measured on days 4 and 8 of N2 adulthood. ## 4.9. Body Length Experiment Adults were transferred to $2\%$ agarose pads, anesthetized and photographed with a body microscope to measure their body length. ## 4.10. Reproduction Experiments One nematode in each medium was transferred every 24 h until the nematodes no longer laid eggs. The egg-laying medium was incubated at 20 °C for 2 d before counting the number of offspring. ## 4.11. Anti-oxidative Stress Assay The adults were transferred to NGM medium containing 400 μM juglone and the number of surviving nematodes was counted every 1 h until all nematodes were dead. ## 4.12. Antioxidant Enzyme Assay Approximately 1000 nematodes were collected from each medium and the experiments were carried out according to the kit instructions to determine SOD and CAT enzyme activities. ## 4.13. Determination of ROS The nematodes were transferred to a slide containing $2\%$ agarose pads, observed under a fluorescent microscope at 485 nm excitation wavelength and 530 nm emission wavelength, photographed and the fluorescence intensity was measured using Image J software. ## 4.14. sod-3 Fluorescence Expression Assay Adult CF1553 was transferred to slides containing $2\%$ agarose pads and photographed under a fluorescence microscope using excitation wavelength 485 nm and emission wavelength 530 nm, and then the fluorescence intensity was measured using Image J software. ## 4.15. RT-PCR Experiment Approximately 1000 nematodes were collected into centrifuge tubes and rinsed 2–3 times. Total nematode RNA was extracted using an ultra-pure RNA extraction kit, and RNA concentration and purity were detected by UV absorption, RNA integrity was detected by denaturing agarose gel electrophoresis. The cDNA was synthesized by reverse transcription using the kit and then tested by RealTime PCR samples. Primer sequences for the genes of interest were as follows: ## 4.16. Statistical Methods Data were analyzed by SPSS 20.0 software, and the measurement data were expressed as mean ± standard deviation (x¯ ± s), and one-way ANOVA was used, and GraphPad Prism 8 software was applied to make graphs. $p \leq 0.05$ was considered statistically significant. ## 5. Conclusions Our studies show that RPRP significantly reduces the accumulation of lipofuscin in nematodes, PPRP significantly extends the lifespan of nematodes, reduces lipofuscin accumulation, and increases pharyngeal pump frequency and body bending frequency. We, therefore, consider that PPRP has better anti-aging activity than RPRP. Mechanistic studies have shown that PRP can improve the resistance to oxidative stress, reduce the accumulation of ROS in C. elegans and increase the activity of antioxidant enzymes. 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--- title: Severity of Liver Fibrosis Is Associated with the Japanese Diet Pattern and Skeletal Muscle Mass in Patients with Nonalcoholic Fatty Liver Disease authors: - Yoshinari Matsumoto - Hideki Fujii - Mika Harima - Haruna Okamura - Yoshimi Yukawa-Muto - Naoshi Odagiri - Hiroyuki Motoyama - Kohei Kotani - Ritsuzo Kozuka - Etsushi Kawamura - Atsushi Hagihara - Sawako Uchida-Kobayashi - Masaru Enomoto - Yoko Yasui - Daiki Habu - Norifumi Kawada journal: Nutrients year: 2023 pmcid: PMC10005291 doi: 10.3390/nu15051175 license: CC BY 4.0 --- # Severity of Liver Fibrosis Is Associated with the Japanese Diet Pattern and Skeletal Muscle Mass in Patients with Nonalcoholic Fatty Liver Disease ## Abstract It is not fully clear as to which dietary patterns are associated with the pathogenesis of nonalcoholic fatty liver disease (NAFLD) in Asia. We conducted a cross-sectional study of 136 consecutively recruited patients with NAFLD ($49\%$ female, median age 60 years). Severity of liver fibrosis was assessed using the Agile 3+ score, a recently proposed system based on vibration-controlled transient elastography. Dietary status was assessed using the 12-component modified Japanese diet pattern index (mJDI12). Skeletal muscle mass was assessed by bioelectrical impedance. Factors associated with intermediate–high-risk Agile 3+ scores and skeletal muscle mass (75th percentile or higher) were analyzed by multivariable logistic regression. After adjustment for confounders, such as age and sex, the mJDI12 (OR: 0.77; $95\%$ CI: 0.61, 0.99) and skeletal muscle mass (75th percentile or higher) (OR: 0.23; $95\%$ CI: 0.07, 0.77) were significantly associated with intermediate–high-risk Agile 3+ scores. Soybeans and soybean foods were significantly associated with skeletal muscle mass (75th percentile or higher) (OR: 1.02; $95\%$ CI: 1.00, 1.04). In conclusion, the Japanese diet pattern was associated with the severity of liver fibrosis in Japanese patients with NAFLD. Skeletal muscle mass was also associated with the severity of liver fibrosis, and intake of soybeans and soybean foods. ## 1. Introduction Nonalcoholic fatty liver disease (NAFLD) is a type of liver disease that includes simple fatty liver and steatohepatitis, not caused by excessive alcohol consumption [1]. The pathogenesis of nonalcoholic steatohepatitis (NASH) with inflammation of the liver tissue is important in NAFLD, because progression of NASH leads to cirrhosis, liver failure, and the risk of hepatocellular carcinoma [2,3,4,5,6]. NAFLD is also associated with an increased risk of cardiovascular-disease-related mortality [7,8] and total mortality [2,5]. Some expert reviews have shown that body weight loss is the only remedy that prevents the progression of NAFLD [9,10] and that body weight loss by lifestyle modification, including diet and exercise, is desirable [11,12]. Other factors, such as diet quality and composition, have been associated with the progression of NAFLD [13]. The Mediterranean diet pattern—which consists mainly of plant foods and olive oil, and relatively few animal products—has been shown to reduce hepatic fat accumulation and insulin resistance in an intervention study of patients with NAFLD [14], and observational studies have also reported that an increase in Mediterranean diet score—which indicates a dietary pattern similar to the Mediterranean diet—is associated with an improvement in measures of liver fat mass in patients with NAFLD [13]. However, from the perspective of food culture, it may be difficult to promote the Mediterranean diet pattern to the Asian region, including Japan. It is also not fully clear as to which dietary patterns are associated with the pathogenesis of NAFLD in Asia. The Japanese diet pattern (JD) is a diet pattern unique to Japan, that consists of soybeans and soybean foods, fish and shellfish, vegetables, mushrooms, seaweed, green tea, and fermented foods, such as pickles and miso [15]. In Japan, dietary intervention with the Japanese diet pattern showed positive effects on weight loss and associated improvements in serum lipid metabolism, and it is expected to improve health maintenance [16]. However, to our knowledge, there are no reports on the relationship between the Japanese diet pattern and NAFLD. Muscle is an important tissue involved in glucose metabolism, and a decreased skeletal muscle mass is associated with impaired glucose tolerance [17]. As impaired glucose tolerance is a risk factor of NAFLD [18], maintenance of skeletal muscle mass may be important in the management of the condition in patients with NAFLD. In addition, sarcopenia—a loss of skeletal muscle mass and muscle function—is a risk factor for NAFLD independent of insulin resistance [19], suggesting a possible muscle–liver organ interaction even outside of glucose metabolism. However, the association between the severity of liver fibrosis in NAFLD and skeletal muscle mass has not been fully validated. In addition, dietary factors affect skeletal muscle mass, but the relationship between skeletal muscle mass and dietary factors in patients with NAFLD is unclear. Therefore, this study aimed to examine the relationships between the Japanese diet pattern and the severity of liver fibrosis in patients with NAFLD. We also investigated the relationships between skeletal muscle mass, dietary factor, and the severity of liver fibrosis in patients with NAFLD. ## 2.1. Study Subjects We recruited 200 patients with suspected fatty liver on ultrasonography from September 2021 to November 2022 for a prospective cohort study of patients aged 20 years or older who attended the Department of Hepatology, Osaka Metropolitan University Graduate School of Medicine. We performed a cross-sectional analysis using baseline data. Of the 200 patients, we excluded 9 patients with excessive alcohol intake (>30 g/day in male; >20 g/day in female) [20], 32 patients for whom a dietary survey could not be performed, 1 patient whose dietary survey results were underreported, 20 patients for whom body composition assessment could not be performed, and 22 patients whose ultrasound scan results could not be validated. Because 20 excluded patients were categorized in two of the above conditions, 64 patients were excluded and 136 patients were included in the final analysis (Figure 1). In accordance with the Declaration of Helsinki, written informed consent to participate in the study was obtained from all patients prior to the start of the study. The study was approved by the Ethics Committee of Osaka City University (now known as Osaka Metropolitan University) School of Medicine (approval number: 2021-088; approval date: 17 June 2021). ## 2.2. Patient Characteristics Data were extracted regarding body mass index (BMI), daily alcohol intake, past medical history, and current drug history. ## 2.3. Alcohol Intake Screening Daily alcohol consumption was calculated in grams, using our modified template [21]. Briefly, we assessed drinking frequency (daily, weekly, monthly, or yearly) and the volume of alcohol intake by beverage type (e.g., beer, shochu, Japanese sake, whisky, and wine). The volume of alcohol intake was converted to grams of ethanol, and values for each beverage type were added. The specific density of alcohol was defined as 0.79 g/mL. Alcohol intake greater than 0 g ethanol was defined as “having drinking habits”. ## 2.4. Assessment of Liver Fibrosis Risk To assess the risk of liver fibrosis, vibration-controlled transient elastography of the liver was carried out with the FibroScan® Mini 430 (Echosens, Paris, France). The FibroScan Mini 430 simultaneously measures the liver stiffness measurement and the controlled attenuation parameter. The controlled attenuation parameter has been designed to measure liver ultrasonic attenuation (go-and-return path) at 3.5 MHz, on both M and XL probes [22]. The final controlled attenuation parameter and liver stiffness measurement results were expressed in dB/m and kPa, respectively. Only examinations with at least 10 valid measurements per patient were accepted for analysis. The risk of liver fibrosis was assessed using the Agile 3+ score, a recently proposed scoring system based on vibration-controlled transient elastography. To calculate the Agile 3+ score, the aspartate aminotransferase/alanine aminotransferase ratio, diabetes status, sex, age, and liver stiffness measurements were used [23]. The Agile 3+ score has been reported to be as predictive of liver-related events in patients with NAFLD as the existing liver fibrosis assessment score, Fibrosis-4 [23,24]. An Agile 3+ score of 0.451 was used as the cut-off value that achieved a sensitivity of greater than or equal to $85\%$ [23], and was also used to divide the patients into two groups: Agile 3+ scores of less than 0.451 (low-risk Agile 3+ score) and 0.451 or higher (intermediate–high-risk Agile 3+ score). ## 2.5. Food and Nutrient Intake Survey and Calculation of mJDI12 Food and nutrient intake status was assessed using a brief-type self-administered diet history questionnaire (BDHQ) [25,26]. The BDHQ is used to assess the approximate intake of food and nutrients for the month prior to the time of the survey. In accordance with previous reports, 1 patient with less than 600 kcal energy intake was excluded as an underestimate [27]. The 12-component modified Japanese diet pattern index (mJDI12) was calculated to evaluate dietary patterns [28]. The mJDI12 is based on the intake of 12 foods or food groups, and consists of the Japanese diet pattern index (JDI) [29] based on the intake of 9 foods or food groups, plus 3 food groups that were considered to contribute to the composition of the Japanese diet pattern by a qualitative systematic review [15,28]. The mJDI12 was calculated based on the intake per 1000 kcal of soybeans and soybean foods, green and yellow vegetables, fruit, fish and shellfish, pickles, mushrooms, seaweeds, green tea, rice, miso soup, beef and pork, and coffee. We scored our patients as either at, above or below the median intake for each food or food group for each sex [28]. For beef and pork and coffee, 1 point was scored for an intake below the median; for all other foods or food groups, 1 point was scored for an intake above the median. The higher the mJDI12 score, the closer the patient’s diet is to a Japanese diet pattern. ## 2.6. Assessment of Body Weight and Body Composition Measures of body weight and body composition were determined by a vertical direct segmental multi-frequency bioelectrical impedance analysis analyzer (InBody© 270, InBody USA, Cerritos, CA, USA). The InBody© 270 records a user’s weight, skeletal muscle mass, percent of body fat, and BMI, to the nearest 0.1 kg (without shoes and in light clothing with pockets emptied). The method of measuring body composition via bioelectrical impedance analysis has been previously validated and used in similar clinical studies [30]. ## 2.7. Statistical Analysis Results are presented as the median (25th percentile, 75th percentile) for continuous variables and as number (%) for categorical data. The normality of each continuous variable was tested using the Shapiro–Wilk test. As normality was not found for almost all variables, statistical tests for continuous variables between two groups were performed using the Mann–Whitney U test. Categorical data were tested with a Fisher’s exact test. Effect size is given as r for continuous variables and as Cramer’s V for categorical data. In this study, statistical power calculations for the cross-sectional analysis were performed post hoc, as prior sample-size calculations for the cross-sectional analysis had not been performed. We used multivariable logistic regression analysis to test the association of intermediate–high-risk Agile 3+ scores with mJDI12 and skeletal muscle mass, and to test the association of mJDI12 with skeletal muscle mass (75th percentile or higher, stratified by sex). In multivariable logistic regression analysis, a low value of events per variable may reduce the reliability of the results [31]. Hence, for covariates that needed to be adjusted, a logistic regression analysis for the outcome was performed to calculate a propensity score, and the effect of the covariates on the outcomes was adjusted by imputing one propensity score as a covariate [32]. The following factors that may affect dietary habits and the Agile 3+ score were used to calculate the propensity score for intermediate–high-risk Agile 3+ scores: sex, age, BMI, diabetes mellitus, hypertension, dyslipidemia, alcohol intake, and medication status (ursodeoxycholic acid, calcium channel blocker, angiotensin receptor blocker, diuretic agent, hydroxymethylglutaryl-coenzyme A reductase inhibitor, tocopherol acetate, dipeptidyl peptidase-4 inhibitor, sodium-glucose cotransporter 2 inhibitor, biguanide, and hypouricemic agent). To calculate propensity scores at or above the 75th percentile for skeletal muscle mass, we used the factors of sex, age, BMI, diabetes mellitus, hypertension, dyslipidemia, and alcohol intake, which are thought to influence dietary habits and skeletal muscle mass. The association between mJDI12 and nutrient intake was examined by multiple regression analysis, stratified by sex. Age and BMI were adjusted as covariates. Because of the exploratory nature of this study, we did not consider a multiplicity of tests. All statistical tests were two-tailed, and $p \leq 0.05$ was considered statistically significant. Statistical analyses were performed using SPSS ver. 29 (IBM Japan, Tokyo, Japan). ## 3.1. Patient Characteristics and Laboratory Data Related to Liver Status The basic characteristics of the patients and laboratory data related to their liver status are presented in Table 1. The number of male and female participants in the study population was about the same. There were 46 patients ($34\%$) with intermediate–high-risk Agile 3+ scores. ## 3.2. Patient Characteristics Grouped by the Agile 3+ Score Risk Basic patient characteristics were compared between the low-risk and the intermediate–high-risk Agile 3+ score groups (Table 2). The intermediate–high-risk Agile 3+ score group had a significantly higher median age of 15 years and significantly more patients with diabetes and hypertension than the low-risk group. In addition, there was a higher percentage of users of diabetes and hypertension medications in the intermediate–high-risk Agile 3+ score group than in the low-risk group. ## 3.3. Comparison of mJDI12 and its Component Intake in Patients, Grouped by the Agile 3+ Score Risk There was no significant difference in mJDI12 between the low-risk and the intermediate–high-risk Agile 3+ score groups (Table 3). The intake per 1000 kcal of foods or food groups comprising the mJDI12 was significantly higher for pickles and significantly lower for seaweed in the intermediate–high-risk Agile 3+ score group than in the low-risk Agile 3+ score group. ## 3.4. Relationship between mJDI12 and Nutrient Intake The mJDI12 showed significant positive associations with protein, potassium, magnesium, iron, folate, vitamin C, β-carotene, α-tocopherol, and dietary fiber intake in patients of both male and female sex. The mJDI12 also showed significant positive associations with calcium, cholesterol, and salt in female, but not male, group (Supplementary Material Table S1). ## 3.5. Association between Intermediate–High-Risk Agile 3+ Scores, and mJDI12 and mJDI12 Components The association between intermediate–high-risk Agile 3+ scores and mJDI12 and its components was tested by multivariable logistic regression analysis (Table 4). When mJDI12 was used as a continuous variable, mJDI12 had significantly lower odds of intermediate–high-risk Agile 3+ scores ($$p \leq 0.037$$). Having an mJDI12 at the 75th percentile or higher (stratified by sex) was associated with significantly lower odds of an intermediate–high-risk Agile 3+ score ($$p \leq 0.038$$). The association between the intake of foods or food groups comprising the mJDI12 and the intermediate–high-risk Agile 3+ score was also examined; the intake of soybeans and soybean foods, fish and shellfish, and seaweeds at or above the 75th percentile, was associated with significantly lower odds of intermediate–high-risk Agile 3+ scores. Seaweeds showed similar results with continuous variables (Supplementary Material Table S2). ## 3.6. Relationship between Intermediate–High-Risk Agile 3+ scores and Skeletal Muscle Mass There was no significant association with intermediate–high-risk Agile 3+ scores when skeletal muscle mass was entered as a continuous variable. When skeletal muscle mass was stratified by sex, skeletal muscle mass at the 75th percentile or higher had significantly lower odds of intermediate–high-risk Agile 3+ scores ($$p \leq 0.022$$) (Table 5). ## 3.7. Relationship between Skeletal Muscle Mass, and mJDI12 and its Components As skeletal muscle mass at the 75th percentile or higher was a factor significantly associated with intermediate–high-risk Agile 3+ scores, the association of mJDI12 for skeletal muscle mass at the 75th percentile or higher was tested by logistic regression analysis (Table 6). The mJDI12 was not a factor significantly associated with skeletal muscle mass for continuous variables ($$p \leq 0.18$$), or for the 75th percentile or higher ($$p \leq 0.12$$). For the mJDI12 components, the intake of soybeans and soybean foods was a significant factor associated with skeletal muscle mass at the 75th percentile or higher ($$p \leq 0.049$$) (Supplementary Material Table S3). ## 4. Discussion In this study, we reveal that a daily diet following the Japanese diet pattern was significantly associated with a lower risk of advanced fibrosis in patients with NAFLD. Among the Japanese diet components, a higher intake of soybeans and soybean foods, fish and shellfish, and seaweeds was associated with a lower risk of advanced fibrosis. To the best of our knowledge, this is the first report to reveal that the Japanese diet pattern is associated with the severity of liver fibrosis in patients with NAFLD. The Mediterranean diet pattern may be effective as a diet for patients with NASH [14], and the Japanese diet pattern might also be associated with a lower risk of liver fibrosis. The Japanese diet pattern is a food pattern that consists mainly of fish and shellfish, and is rich in high-fiber foods, such as seaweeds and mushrooms, and fermented foods, such as pickles. Intervention with a Japanese diet pattern improved lipid metabolism in young adults [16]. In addition, a Japanese diet pattern score has been associated with lower obesity rates and ischemic heart disease incidence in a global comparative study [33]. Interestingly, we found that mJDI12 was also significantly associated with the intake of nutrients with antioxidant properties, such as vitamin C, β-carotene, and α-tocopherol. Oxidative stress is associated with the pathogenesis of NASH [34], and intervention with antioxidant nutrients, such as vitamin E, may have a hepatoprotective effect [35]. It may be possible that results were consistent with our results in Supplementary Material Table S1 and Table 4. Although the effect of dietary fiber intake on progression of NAFLD has not been fully studied in humans [36], the Japanese diet pattern may be associated with a lower risk of the progression of NAFLD, because of the reported cholesterol-lowering effects and improvement of insulin resistance from high dietary fiber intake [37]. In addition, the association between the Japanese diet pattern and the risk of liver fibrosis may be influenced by the intestinal microbiota, because dietary fiber affects the intestinal microbiota and the intestinal environment [37], and because there may be an association between the intestinal microbiota and the intestinal environment, and the pathogenesis of NASH [38]. In this study, there was a significant association between a low risk of advanced liver fibrosis and high skeletal muscle mass after adjustment for confounders such as age, sex, and BMI. Patients with cirrhosis are prone to skeletal muscle loss caused by various pathological effects [39]. An association between liver fibrosis or steatohepatitis and sarcopenia has been reported in patients with NAFLD [40], and the results obtained in the present study on the association between the risk of liver fibrosis and skeletal muscle mass imply that skeletal muscle mass is low as a result of advanced liver fibrosis. In addition, skeletal muscle plays an important role in glucose metabolism, and it has been reported that impaired glucose tolerance is higher in patients with low skeletal muscle mass [17]. Furthermore, because it has been reported that low skeletal muscle mass is associated with the development and pathological progression of NAFLD in a longitudinal study [41], the relationship between skeletal muscle mass and liver fibrosis may be reciprocal. In our survey of the association between skeletal muscle mass and mJDI12, we found that mJDI12 was not significantly associated with skeletal muscle mass. However, soybeans and soybean food intake was a factor significantly associated with having a skeletal muscle mass at the 75th percentile or higher. As soy protein has been reported to increase skeletal muscle mass gain more than casein—a milk protein—in subjects with low physical activity [42], it would be interesting to further analyze data from patients that eat a diet high in soybeans and soybean foods in conjunction with an analysis of physical activity levels. In dietary interventions for patients with NAFLD, it has been reported that reduced protein intake, when reducing energy intake, is associated with decreased skeletal muscle mass [43]. Maintaining an intake of soybeans and soybean foods as a source of protein may be important for the maintenance of skeletal muscle mass. We showed that soybeans and soybean food intake was associated with a lower risk of liver fibrosis and greater skeletal muscle mass. Although genistein, an isoflavone abundant in soybeans, did not decrease aspartate aminotransferase and alanine aminotransferase in patients with NAFLD after 8 weeks of intake, it did improve body fat loss, decrease the waist–hip ratio, and reduce blood triglyceride levels [44], suggesting that genistein inhibits the development of NAFLD. In addition, the soy protein, β-conglycinin, may reduce the risk of NASH, as it inhibited NASH progression in a mouse model of NASH [45]. Thus, soy-specific components may have a combined hepatoprotective effect on lipid metabolism in the liver and on skeletal muscle mass. There are limitations to this study that should be considered. The first limitation is that our study was cross-sectional. Dietary patterns may change as a result of disease states; therefore, our results need to be validated in prospective and intervention studies. Because our study is a prospective cohort study, we plan to examine changes in the Japanese diet pattern and the risk of advanced liver fibrosis in our patients. The second limitation is that the risk of advanced liver fibrosis was evaluated using the Agile 3+ score. A more accurate assessment of advanced liver fibrosis requires evaluation by liver biopsy. However, liver biopsy evaluations vary by pathologist and have very poor inter-reader variability and modest intra-reader variability [46]. The third limitation is that our patients were recruited from a single institution. Patient characteristics and treatment conditions may differ among hospitals; therefore, a multicenter study is needed to generalize the results of this study. The fourth limitation is the method used to calculate the mJDI12. The mJDI12 was calculated from the median intake of 12 foods or food groups in our study population, so the median may vary depending on the characteristics of the subjects. It would be desirable to calculate the median intake in subjects who are non-obese and not at risk of fatty liver, and to calculate the mJDI12 in patients with NAFLD based on that median value. However, associations between mJDI12 and the intake of several foods or food groups were able to be observed when mJDI12 was calculated using data from our study population of patients with NAFLD. ## 5. Conclusions The Japanese diet pattern and skeletal muscle mass may be associated with the severity of liver fibrosis in patients with NAFLD, and soybeans and soybean food intake may affect skeletal muscle mass. ## References 1. Powell E.E., Wong V.W.S., Rinella M.. Non-Alcoholic Fatty Liver Disease. Lancet (2021) 397 2212-2224. DOI: 10.1016/S0140-6736(20)32511-3 2. Dulai P.S., Singh S., Patel J., Soni M., Prokop L.J., Younossi Z., Sebastiani G., Ekstedt M., Hagstrom H., Nasr P.. 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--- title: 'Antioxidant and Anti-Inflammatory Effects of Oral Supplementation with a Highly-Concentrated Docosahexaenoic Acid (DHA) Triglyceride in Patients with Keratoconus: A Randomized Controlled Preliminary Study' authors: - Cristina Peris-Martínez - José Vicente Piá-Ludeña - María José Rog-Revert - Ester Fernández-López - Joan Carles Domingo journal: Nutrients year: 2023 pmcid: PMC10005296 doi: 10.3390/nu15051300 license: CC BY 4.0 --- # Antioxidant and Anti-Inflammatory Effects of Oral Supplementation with a Highly-Concentrated Docosahexaenoic Acid (DHA) Triglyceride in Patients with Keratoconus: A Randomized Controlled Preliminary Study ## Abstract A prospective, randomized, single-center preliminary study was performed in patients with keratoconus stages I–III (Amsler–Krumeich), who received a high rich docosahexaenoic acid (DHA) (1000 mg/day) supplement for 3 months versus untreated patients. One eye per patient was evaluated. Thirty-four patients were recruited ($75\%$ men, mean age 31 years), with 15 randomized to the control group and 19 to the DHA-treated group. Corneal topography variables and plasma biomarkers of oxidative stress and inflammatory status were evaluated. A panel of fatty acids in blood samples was also assessed. There were significant between-group differences in the astigmatism axis, asphericity coefficient, and intraocular pressure in favor of the DHA group. Additionally, between-group significant differences in total antioxidant capacity (TAC), malondialdehyde (MDA), free glutathione (GSH) and GSH/GSSG ratio, as well as reduced values of inflammatory markers, including interleukin (IL)-4, IL-6, and vascular endothelial growth factor (VEGF-A) were found. These preliminary findings support the usefulness of the antioxidant and anti-inflammatory effects of DHA supplementation for targeting underlying pathophysiological mechanisms of keratoconus. Prolonged duration of DHA supplementation may be needed to detect more noticeable clinical changes in corneal topography. ## 1. Introduction Keratoconus is a multifactorial ectatic corneal disorder, characterized by a progressive process of corneal thinning and steeping leading to irregular astigmatism with decreased visual acuity. Keratoconus is a complex condition, and a wide variety of both genetic and environmental factors have been identified in the etiology of the disease [1,2]; however, the specific pathophysiological mechanisms remain ambiguous [3]. Traditionally, the condition has been described as a non-inflammatory disease since keratoconic corneas are strikingly lacking histological and clinical features of inflammation, such as cellular infiltration and neovascularization [4,5]. Recent studies, however, have shown an alteration in the expression of molecules involved in inflammatory processes [5,6], oxidative stress [7,8], extracellular matrix proteolysis, degradation of the corneal collagen, disturbed regulation of the corneal microenvironment [9], and cellular apoptosis [10,11], evidencing the participation of all these biological mechanisms in the pathogenesis of keratoconus. In addition, lipid mediators along with fatty acids (such as stearic, oleic, and palmitic acids) are one of the main components of human cornea and are involved in complex processes associated with inflammatory reactions in corneal injury and repair [12]. The profiling of the metabolome of keratoconus has also revealed a metabolomics signature that discriminates keratoconus from the normal cornea [13]. Regardless of different treatment modalities of corneal surgery, particularly for advanced corneal ectasias [14], the involvement of inflammatory mediators (interleukins (IL) and tumor necrosis factor alpha (TNF-α)), matrix metalloproteinases (MMP-9), oxidative stress-related products, and nutritional and/or metabolic imbalance, affecting a variety of metabolites, hormones, micronutrients, vitamins, minerals, and fatty acids [3] has been the rationale of including diet changes and nutritional supplementation in traditional conservative management of keratoconus [15]. A systematic review and meta-analysis showed that patients with keratoconus, as compared with controls, had significantly lower levels of vitamin D, zinc, and selenium levels [16]. In a prospective observational pilot study of 20 patients with keratoconus and vitamin D deficiency, vitamin D supplementation increased cell availability of copper and stabilized the disease in nearly two-thirds of the eyes [17]. In another study, decreased vitamin D levels significantly increased non-progressive keratoconus probability by 1.23 times and progressive keratoconus probability by 1.29 times more than the control group [18]. On the other hand, reduced levels of vitamin D, copper, zinc, and selenium have been shown in a comparative study of patients with keratoconus and age-matched healthy subjects [19]. Arginine supplementation in a model of human corneal fibroblasts improved extracellular matrix secretion and deposition by keratoconus cells [20]. Keratoconus dietary supplements based on antioxidant properties of vitamins and minerals are available in the market as over the counter popular corneal protection formulas. Among omega-3 polyunsaturated fatty acids (PUFAs), docosahexaenoic acid (DHA, C22:6 n-3), a critical component of cell membrane phospholipids, exerts pleiotropic effects at both central and peripheral levels with health benefits in many aspects of neuronal, immune, cognitive, and cardiovascular functions [21,22,23]. Clinical studies of dietary supplementation with a highly concentrated DHA triglyceride have shown consistent anti-inflammatory, antioxidant, antiangiogenic, and antiproliferative effects targeting pathophysiological pathways involved in different eye diseases [24], including diabetic retinopathy and macular edema [22,23,24,25,26,27,28], ocular surface disorders [29,30], meibomian gland dysfunction [31,32], and pseudoexfoliative glaucoma [33]. Based on this experience, it was considered of interest to explore the antioxidant and anti-inflammatory potential of a highly concentrated DHA triglyceride product in patients with keratoconus. For this purpose, a prospective preliminary study was designed to assess the effects of 3-month DHA nutritional supplementation on clinical variables, and inflammatory and oxidative stress biomarkers of patients with early and moderate keratoconus. ## 2.1. Design and Participants This was a single-center, prospective, randomized, and controlled preliminary study carried out between February 2019 and January 2022 at the Unit of Corneal and Anterior Eye Diseases of FISABIO Medical Ophthalmology Center (FOM) in Valencia, Spain. The primary objective of the study was to determine the effect of daily supplementation with a nutraceutical formulation of a highly concentrated DHA triglyceride plus minerals on ophthalmological parameters and biomarkers of oxidative stress and inflammation in blood samples. Secondary objectives were to assess changes in lipidomic biomarkers and correlations between ophthalmological variables and biomarkers of oxidative stress and inflammation in patients treated with the nutraceutical product. Eligible patients were men or women aged 18 years or older, diagnosed with keratoconus stages I to III according to the Amsler–Krumeich classification [34], non-contact lens wearers, without history of previous corneal surgery, capacity to volunteer, and willing and able to follow the study protocol. The diagnosis of keratoconus was made by one experienced clinician (C.P.-M.) based on typical ophthalmological features on corneal topography and at least one keratoconus sign on slit-lamp examination [35]. Exclusion criteria were as follows: advanced keratoconus (stage IV of the Amsler–Krumeich classification); presence of other ectasias (including iatrogenic ectasia secondary to ocular surface surgery with excimer laser, radial keratotomy, traumatic corneal ectasia, etc.); eyelid alterations; previous ocular surgery; any ocular or systemic condition that may affect the interpretation of results; glaucoma or ocular hypertension; history of ocular trauma, infection, or inflammation; current treatment with topical or systemic anti-inflammatory drugs; use of nutritional supplements including omega-3 fatty acids, vitamins, and minerals (unless a washout period of 1 month has been established); hypersensitivity to fish proteins; pregnant women; refusal to sign the written informed consent; and patients deemed ineligible by the ophthalmologist. The study was conducted in accordance with the Declaration of Helsinki and approved by the Clinical Research Ethics Committee (CEIC) of FISABIO Medical Ophthalmology Center and the Foundation for the Promotion of Health and Biomedical Research in the Community of Valencia (protocol code PI_77, approval date 26 July 2018) (Valencia, Spain). All participants signed the written informed consent form. ## 2.2. Intervention Patients who met the inclusion criteria were assigned a consecutive number according to the order of arrival, and then randomized to treatment with the nutraceutical DHA supplement (DHA group; even number) or no supplementation (control group; odd number). Patients randomized to the DHA group received a high dose DHA formulation (Tridocosahexanoin-AOX® $70\%$) (Brudyitis®, Brudy Lab, S.L., Barcelona, Spain). This is a highly concentrated DHA triglyceride having a high antioxidant activity patented to prevent cellular oxidative damage [36,37]. The composition of the product includes 602 mg of omega-3 PUFAs, 500 mg of which are DHA triglyceride, 61 mg eicosapentaenoic acid (EPA), and 42 mg docosapentaenoic acid (DPA), a mixture of essential trace elements (zinc 5 mg, selenium 27.5 µg, copper 0.5 mg, and manganese 1 mg), and glutathione 5 mg. This supplement is registered as a food supplement in the Spanish Agency for Food Safety and Nutrition (AESAN). Patients were advised to take two capsules of the supplement once daily preferably at the time of breakfast. The duration of treatment was 3 months. ## 2.3. Study Procedures The study included a selection visit 14 days before entering the study to check the inclusion criteria, provide full information of the characteristics of the study, and perform a standard medical history. The selection visit was followed by a baseline visit (visit 0), a visit on day 14 (±3 days) (visit 1), and a final visit on day 90 (±7 days) (visit 2). At baseline, the following ophthalmological examinations were performed: corrected and uncorrected visual acuity using an ETDRS optotype at 2 m distance from the observer; slit lamp biomicroscopy; corneal topography (Pentacam® HR, Oculus Inc., Arlington, WA, USA); and measurement of intraocular pressure (IOP) using a pneumotonometer (Goldmann applanation tonometer) and rebound tonometer (iCare®). In addition, a blood sample from a peripheral vein in fasting conditions was drawn for laboratory tests. In patients assigned to the DHA group, three boxes of the nutraceutical product were provided for 45 days of treatment. At visit 1, baseline ophthalmological examinations were repeated, and patients assigned to the DHA group received the corresponding supplement for treatment during the remaining 45 days until the end of the study. At visit 2 (end of study), the same ophthalmological examination was performed, and blood samples were collected. Capsules returned at visit 1 and at the final visit were counted to determine adherence to the study treatment. Adherence to treatment was considered when at least $80\%$ of capsules had been consumed. Tolerance and product safety were assessed at the end of the study. Ophthalmological variables were flat keratometry (K1, measured in diopters); steep keratometry (K2, diopters); maximum keratometry (Kmax, diopters); mean keratometry (Km, diopters); astigmatism axis (in degrees); degree of astigmatism (diopters); asphericity coefficient (Q) in the horizontal and vertical hemi-axes; corneal apex thickness (CAT, µm); central corneal thickness (CCT, µm); minimum corneal thickness (MCT, µm); chromatic aberration (CA, mm); IOP Goldmann applanation tonometer (GAT, mmHg); and IOP rebound (RBT, mmHg). Biochemical variables included lipidomic biomarkers, antioxidant biomarkers, and inflammatory biomarkers. The panel of fatty acids included the components of the main families of saturated fatty acids (SFAs) (myristic acid, palmitic acid, stearic acid, arachidic acid, behenic acid, and lignoceric acid); monounsaturated fatty acids (MUFAs) (palmitoleic acid, oleic acid, cis-vaccenic acid, gondoic acid, erucic acid, and nervonic acid); n-6 PUFAs (linoleic acid, γ-linoleic acid, eicosadienoic acid, dihomo-γ-linolenic acid, arachidonic acid (ARA), adrenic acid, and osbond acid or docosapentaenoic acid); and n-3 PUFAs (α-linoleic acid, eicosapentaenoic acid (EPA), docosapentaenoic acid, and DHA). In addition, other fatty acids ratios were calculated as n-6 PUFA/n-3 PUFA and omega-3 index (EPA + DHA). Antioxidant activity biomarkers were total antioxidant capacity (TAC), malondialdehyde (MDA), total glutathione (GSH), free GSH, and GSH/glutathione disulfide (GSSG). Biomarkers of inflammation included IL-1β, IL-4, IL-6, IL-10, TNF-α, and vascular endothelial growth factor-A (VEGF-A). ## 2.4. Laboratory Analyses Gas chromatography (GC) was used for the analysis of fatty acids. Technical details included a gas chromatograph mass spectrometer (GCMS-QP2010Plus) and auto injector and autosampler (all from Shimadzu, Tokyo, Japan); a high polarity capillary column (internal diameter 15 m × 0.10 mm, film thickness 0.10 µm) (Suprawax-280, Teknokroma Analítica, S.A., Barcelona, Spain); and GCMS solution software for data acquisition. In order to optimize the whole spectrum of fatty acid analysis, functioning conditions of MS operating parameters were optimized (10,000 amu/s for scan rate, 40–400 m/z for mass range, 1.0 kV for capillary voltage). Temperatures were set at 255 and 200 °C for the interface and ion source, respectively. The peaks of fatty acid methyl esters (FAMEs) were identified through electron ionization mass spectra using NIST11 library and through GC retention times, compared with a reference FAME mixture (GLC-744, Nu-Che Prep. Inc., Elysian, MN, USA). The results were expressed in relative amounts (percentage molar of total fatty acids) of duplicate sampling. Total antioxidant capacity (TAC) expressed as µM copper-reducing equivalents (Cat. No. STA-360) and lipid peroxidation (thiobarbituric acid reactive substances (TBARS)) assessed as malondialdehyde (MDA) levels (Cat. No. STA-330) were measured in plasma samples using the OxiSelect™ assay kit (Cell Biolabs, San Diego, CA, USA) following the manufacturer’s instructions. The OxiSelect™ assay (Cell Biolabs, San Diego, CA, USA) was used to measure total antioxidant capacity (TAC) and malondialdehyde (MDA) levels in plasma samples. MDA levels were indicative of lipid peroxidation (thiobarbituric acid reactive substances (TBARS)). The manufacturer’s instructions were followed. TAC levels were expressed as µM copper-reducing equivalents and MDA as µM. A fluorescently labeled microsphere-based multiplex immunoassay was used for simultaneous analysis of IL-1β, IL-4, IL-6, IL-10, IL-18, TNF-α, and VEGF-A in plasma samples. Fluorescence was read on the Luminex-100 ISv2 system (Cat. No. HCYTOMAG-60K-05, Milliplex Map Human Cytokine/Chemokine; Linco Research/Millipore, Saint-Charles, MO, USA). The intra- and inter-assay coefficient of variation for each cytokine was: IL-1β: 7 and $12\%$; IL-4: 3 and $11\%$; IL-6: 2 and $10\%$; IL-10: 2 and $11\%$; IL-18: 2 and $11\%$; TNF-α: 3 and $19\%$; and VEGF-A: 3 and $15\%$, respectively. To determine glutathione levels, the DetectX® Glutathione kit (Cat. No. K006) (Arbor Assays, MI, USA) validated for red blood cells or erythrocytes, was used, following the manufacturer’s instructions. The kit is designed to measure quantitatively free or reduced glutathione (GSH) and oxidized glutathione (GSSG). Total glutathione is the sum of GSH and GSSG. The measurement of glutathione is carried out by reading the fluorescence after the reaction of the reagents present in the kit with the different samples, at an emission wavelength of 510 nm and an excitation wavelength of 390 nm. Results are calculated using the means of the readings for each sample, control, and standard. The calibration curve is generated by data reduction with fit to a 4-parameter logistic curve (4PLC). The concentration values obtained are multiplied by the dilution factor used and finally normalized by dividing them by protein values obtained in the BCA protein assay. ## 2.5. Statistical Analysis In the analysis, one eye per patient was included. In patients with bilateral keratoconus, the eye with the most advanced stage was selected. Data of patients who completed the 3-month study period were analyzed. Categorical variables are expressed as frequencies and percentages, and quantitative variables as mean and standard deviation (SD) or standard error of the mean (SEM). For the comparison of categorical variables, the chi-square test or the Fisher’s exact test was used, and for the comparison of continuous variables, the Student’s t test was applied. In both study groups (DHA and controls), within-group mean differences between variables at baseline and at the end of the study were compared with the Student’s t test for paired samples. The Student’s t test for independent samples (Welch’s t test) was used for the analysis of between-group differences at baseline and at the final visit (end of study). Statistical significance was set at $p \leq 0.05.$ Correlations between ophthalmological variables and oxidative stress and inflammatory biomarkers in DHA-treated patients were analyzed with the Spearman’s correlation coefficient. The R (R Core Team, 2022) statistical package was used for the analysis of ophthalmological variables, and the Statistical Package for the Social Sciences (SPSS) version 25.0 (IBM Corp., Armonk, NY, USA) was used for the analysis of biochemical variables. ## 3.1. Baseline Data of Patients During the study period, 34 patients were diagnosed of keratoconus, met the inclusion criteria, and attended all study visits. There were 25 men and 9 women, with a mean (SD) age of 31 [10] years. The mean time elapsed since the diagnosis of keratoconus was 3.7 (2.8) months. At the beginning of the study, 19 patients were allocated to the DHA group and 19 to the control group. However, four patients with unilateral keratoconus did not complete visits 1 and/or 2 and were excluded from the analysis. The final study population included 19 patients in the DHA group and 15 in the control group (Table 1). The distribution of baseline variables was similar in patients assigned to the DHA group and in those assigned to the control group. ## 3.2. Changes of Ophthalmological Variables Results of ophthalmological variables at baseline and at the end of the study are shown in Table 2. In the control group, statistically significant differences in the within-group comparisons were not found, except for a significant increase in CCT ($$p \leq 0.045$$). In the DHA group, within-group differences were not found in any ophthalmological measures, although IOP (GAT) showed a decrease, which was marginally significant ($$p \leq 0.052$$). In the analysis of between-group differences, values of the astigmatism axis were significantly lower in the DHA group both at baseline ($$p \leq 0.006$$) and at the end of the study ($$p \leq 0.021$$) as compared with the control group. Additionally, D values of the asphericity coefficient both in the horizontal and vertical hemi-axes were significantly lower in the DHA group (Table 2). ## 3.3.1. Lipidomic Variables In the analysis of the panel of fatty acids, values at baseline and at the end of the study showed negligible changes either in the DHA group or in the control group for individual fatty acids of the SFA and MUFA families (data not shown). Among n-6 PUFAs and n-3 PUFAs, noticeable changes were only observed in ARA and DHA values. As shown in Table 3, the supplementation with DHA was associated with statistically significant within-group and between-group differences as compared with the control group. In controls, however, ARA levels showed a significant decrease at the end of the study as compared with baseline. ## 3.3.2. Antioxidant Variables Results of antioxidant variables are shown in Table 4. Plasma TAC levels showed a statistically significant increase in the DHA group with within-group and between-group differences, whereas MDA levels decreased significantly, with within-group and between-group differences. The GSH/GSSG ratio decreased in both study groups with statistically significant differences in the within-group comparisons. Values of GSH/GSSG ratio at the end of the study were significantly higher in the DHA group as compared with the control group. Changes in plasma TAC levels and GSH/GSSG ratio in the two study groups are shown in Figure 1. ## 3.3.3. Inflammation-Related Variables Results of variables related to the inflammatory status are shown in Table 5. In the group of patients treated with the DHA supplement, there were statistically significant decreases in IL-6, TNF-α, and VEGF-A at the final visit as compared with baseline, whereas in the control group, there were significant increases in IL-1β, IL-4, and IL-10 at the end of the study as compared with baseline. Statistically significant differences in the between-group comparisons were found for IL-4, IL-6, and VEGF-A with lower values in the DHA group (Figure 2). ## 3.4. Correlations between Ophthalmological Variables and Biomarkers of Oxidative Stress and Inflammation Table 6 shows significant direct and inverse correlations between ophthalmological variables and biomarkers of oxidative stress and inflammation in patients assigned to the DHA supplementation group. The strongest positive correlations were found between K2 and IL-4 levels, and between IOP (GAT) and GSH and GSH/GSSG ratio. Negative correlation included K1 with GSH/GSSG ratio, astigmatism axis with TNF-α, and CCT with IL-6. Finally, adherence to the active study product was greater than $80\%$, and adverse events were not registered in any of the patients independently of the group to which they were assigned. ## 4. Discussion This prospective randomized study conducted in patients with keratoconus stages I-III of the Amsler–Krumeich classification was designed to assess whether the antioxidant and anti-inflammatory effects of the omega-3 fatty acid, DHA, may result in an amelioration of some ophthalmological parameters recorded by corneal topography as compared to patients who did not receive the nutraceutical supplementation. The duration of the study was 3 months. The effect of DHA on concentrations of biomarkers of oxidative stress and inflammation in blood samples was also evaluated. Changes in keratometry parameters were not observed in any of the study groups when readings at the end of the study were compared with baseline. The astigmatism axis did not show within-group differences, but there were between-group differences at baseline and at the end of the study, with higher values in the control group. Similar findings in relation to the asphericity coefficient were found. In subjects assigned to the control group, there was a significant increase in CCT at the end of the study, whereas an increase in CCT was not observed among DHA-treated patients. The IOP measured by Goldmann applanation tonometry showed a decrease in the DHA group, which almost reached statistical significance ($$p \leq 0.052$$), but between-group differences were not observed. In line with the antioxidative and anti-inflammatory effects of DHA, we found significant differences in the comparison between the study groups regarding plasma levels of TAC, MDA, free GSH, and GSH/GSSG ratio, as well as reduced values of inflammatory markers, including IL-4, IL-6, and VEGF-A. These observations are consistent with data obtained in previous studies carried out in different eye diseases (such as non-proliferative diabetic retinopathy, diabetic macular edema, dry eye, meibomian gland dysfunction, or exfoliative glaucoma) with the use of this highly concentrated DHA triglyceride as a nutraceutical supplement [24,25,26,27,28,29,30,31,32,33]. Overexpression of tear inflammatory cytokines in patients with keratoconus has been reported in different studies [38,39,40,41]. In a systematic review and meta-analysis of case-control and cross-sectional studies with 374 patients (374 eyes) with keratoconus showed tear levels of IL-1β, IL-6, and TNF-α significantly increased in keratoconus compared with normal controls, with standardized mean differences of 1.93 ($95\%$ CI 0.22 to 3.65, $$p \leq 0.03$$) for IL-1β, 1.22 ($95\%$ CI 0.59 to 1.84, $p \leq 0.001$) for IL-6, and 1.75 ($95\%$ CI 0.66 to 2.83, $$p \leq 0.002$$) for TNF-α [42]. Moreover, overexpression of IL-6 and TNF-α in tears of subclinical keratoconus [43] indicate that chronic inflammatory events are involved in the pathogenesis of keratoconus. It should be noted that in the present study, analysis of inflammatory-related markers in tear samples was planned. However, problems related to adequate amount of tear fluid sampling prevented a complete analysis of inflammatory biomarkers both in control and patients supplemented with DHA. Failure to collect adequate tear samples was partly due to logistic reasons, particularly lockdown restrictions and limited visiting access to the hospital during the COVID-19 pandemic. In addition to the impossibility of assessing concentrations of inflammatory markers in tear samples, limitations of the study include the single-center design, the small study population, and the short duration of supplementation (only 3 months). Recruitment of eligible patients was also difficult due to the impact of the COVID-19 pandemic on healthcare. Novelty of the study, however, relies on the fact that supplementation with a highly concentrated DHA product in patients with keratoconus has not been previously evaluated. Topical omega-3 PUFA proved to be beneficial in association with a faster regeneration of corneal nerve fibers in patients with keratoconus after epithelium-off corneal collagen cross-linking [44]. In a recent study, topical omega-3 increased tear film stability more prominently than sodium hyaluronate following cross-linking [45]. Although a significant effect of DHA supplementation in the overall amelioration of corneal topography parameters was not observed, it may be argued that the antioxidant and especially the anti-inflammatory effects of DHA may not be sufficiently selective for clinical detection when targeting specific underlying mechanisms involved in early-moderate stages of keratoconus. On the other hand, it is also possible that DHA supplementation for only 3 months may be a short time to elicit clinically apparent changes. Although the mean age of the patients was 31 years, it is important to note that the patients with keratoconus aged about 20 are in a quick progress process and should be instructed about the risk of progression associated with age. Based on the present preliminary results, a multicenter study extended for more than 1 year would be helpful to assess the long-term effect of DHA supplementation in patients with keratoconus. An international follow-up study would be particularly desirable as keratoconus parameters differ in different ethnicities [46] and by certain demographics (e.g., Down’s syndrome) [47]. ## 5. Conclusions In patients with mild to moderate keratoconus, daily supplementation with a highly-concentrated DHA triglyceride (1000 mg/day) nutritional supplement for 3 months was associated with significant improvements in antioxidant (TAC, MDA, GSH/GSSG) and inflammatory status (IL-4, IL-6, TNF-α, VEGF-A) biomarkers as compared with untreated controls. Prolonged duration of DHA supplementation may be needed to detect more noticeable clinical changes of corneal topography-related measures. ## References 1. Nowak D.M., Gajecka M.. The genetics of keratoconus. Middle East Afr. J. Ophthalmol. (2011) 18 2-6. 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--- title: 'Mediterranean Diet and Lung Function in Adults Current Smokers: A Cross-Sectional Analysis in the MEDISTAR Project' authors: - Roxana-Elena Catalin - Francisco Martin-Lujan - Patricia Salamanca-Gonzalez - Meritxell Palleja-Millan - Felipe Villalobos - Antoni Santigosa-Ayala - Anna Pedret - Rosa M. Valls-Zamora - Rosa Sola journal: Nutrients year: 2023 pmcid: PMC10005310 doi: 10.3390/nu15051272 license: CC BY 4.0 --- # Mediterranean Diet and Lung Function in Adults Current Smokers: A Cross-Sectional Analysis in the MEDISTAR Project ## Abstract Background: Previous studies have shown that adherence to the Mediterranean Diet (MeDi) has a positive impact on lung function in subjects with lung disease. In subjects free of respiratory diseases, but at risk, this association is not yet well established. Methods: Based on the reference data from the MEDISTAR clinical trial (Mediterranean Diet and Smoking in Tarragona and Reus; ISRCTN 03.362.372), an observational study was conducted with 403 middle-aged smokers without lung disease, treated at 20 centres of primary care in Tarragona (Catalonia, Spain). The degree of MeDi adherence was evaluated according to a 14-item questionnaire, and adherence was defined in three groups (low, medium, and high). Lung function were assessed by forced spirometry. Logistic regression and linear regression models were used to analyse the association between adherence to the MeDi and the presence of ventilatory defects. Results: Globally, the pulmonary alteration prevalence (impaired FEV1 and/or FVC) was $28.8\%$, although it was lower in participants with medium and high adherence to the MeDi, compared to those with a low score ($24.2\%$ and $27.4\%$ vs. $38.5\%$, $$p \leq 0.004$$). Logistic regression models showed a significant and independent association between medium and high adherence to the MeDi and the presence of altered lung patterns (OR 0.467 [$95\%$CI 0.266, 0.820] and 0.552 [$95\%$CI 0.313, 0.973], respectively). Conclusions: MeDi adherence is inversely associated with the risk impaired lung function. These results indicate that healthy diet behaviours can be modifiable risk factors to protect lung function and reinforce the possibility of a nutritional intervention to increase adherence to MeDi, in addition to promoting smoking cessation. ## 1. Introduction The etiopathogenesis of chronic respiratory diseases, such as asthma and chronic obstructive pulmonary disease (COPD), is multifactorial [1,2]. It includes some individual conditions that cannot be modified, such as age, gender or genetic predisposition and some environmental factors, whereas lifestyle factors, including smoking and environmental exposure, physical activity and diet are modifiable and can be changed [3]. Although cigarette smoking is the predominant risk factor, there is consistent evidence from epidemiologic studies that other environmental factors are also involved in chronic airflow limitation, including outdoor and indoor air pollution or exposure to biomass fuel, and second-hand smoke during pregnancy or early childhood [4,5]. Lower socioeconomic status has been consistently associated with airflow obstruction, but it is unclear whether this pattern reflects environmental exposures and infections, poor nutrition and unhealthy dietary habits, physical inactivity, or other related factors [6]. Dietary intake may be a major risk factor for impaired lung function, and healthy dietary habits may protect respiratory health [3]. Cross-sectional and prospective evidence has shown that certain natural antioxidants and fatty acids provided from foods could neutralise the harmful effect of tobacco on lung function. Thus, for example, the consumption of fruits and vegetables, with a high content of antioxidant vitamins, phenolic compounds, minerals and dietary fibre, as well as to omega 3 fatty acids, found in oily fish and seafood, show benefits on the pathophysiology of the respiratory disease [7]. In contrast, a high consumption of processed meat has been associated with worse lung function and a higher risk of diseases of the respiratory system, which could be explained by its high content of nitrites, nitrates and advanced glycation products that cause inflammation and oxidative damage [8]. In addition, excessive alcohol intake has also been found to have detrimental effects on lung function [9]. Although an individual analysis of dietary components has been evaluated, an approach focused on investigating individual nutrients or foods could overlook the complexity of their interactive or synergistic effects within the diet, as neither foods nor nutrients are consumed in isolation [10,11]. Thus, the study of dietary patterns, characterised by relatively high consumption of some foods with relatively low consumption of others in a single exposure, is a more attractive approach to study the association of diet and respiratory diseases [12]. Previous research has shown that the adherence to a “healthy” (or prudent) dietary pattern (high in fruits, vegetables, whole grains, lean meat, fish, antioxidants, and fibre and low in fat and dairy products) may reduce the risk of lung impairment [3]; conversely, a “western” type pattern (high in processed and refined foods, high in sugars and fats, and low in antioxidant and fibre levels) can increase the risk of COPD and asthma attacks in children and adults [13,14]. To date, the most researched and highly promising dietary pattern for exerting a protective effect on respiratory function is the Mediterranean Diet (MeDi) [3]. The traditional MeDi is well-balanced diet, characterised by high consumption of vegetables, legumes, fruits, fish, nuts, wholegrains including non-refined cereals, and olive oil, foods rich in antioxidants, phenolic compounds and mono- and polyunsaturated fatty acids, with beneficial effects on inflammation and oxidation [15]. This dietary pattern has been the subject of research for years and the evidence for its beneficial health effects is overwhelming [16], particularly regarding cardiovascular prevention [17]. In this context, the interest of extending the MeDi recommendation to tobacco-related respiratory disease is evident. However, there is still limited evidence regarding the MeDi pattern and lung function in healthy populations but with sufficient risk factors for disease. Therefore, the main objective of the present study was to study the association between MeDi adherence and the risk of impaired lung function in a cohort of smokers without known respiratory disease. ## 2.1. Study Design Observational study based on data obtained in the recruitment phase of the MEDISTAR study, a parallel, multicentre, cluster-randomised, controlled clinical trial to assess the effect of MeDi on lung function in smokers without previous respiratory disease (identifier Clinicaltrials.org: NCT03362372, accessed on 1 December 2017). Details of the study design have been reported elsewhere [18]. ## 2.2. Selection of Participants and Obtaining the Sample The study population was obtained from patients attended in 20 primary care centres that provide medical care to a population of about 280,000 inhabitants, managed by the Catalan Institute of Health in Tarragona, Catalonia, Spain. The inclusion criteria in the MEDISTAR clinical trial were: age 25 to 75 years and active smoker together with cumulative consumption ≥ 10 pack-years. Subjects with a history of respiratory disease, chronic or terminal disorder, or any reason that might alter follow-up or testing during the study were excluded. All participants signed an informed consent before being included. Figure 1 illustrates the CONSORT flowchart of participants from MEDISTAR study to those included in the present analysis. A total of 403 subjects were enrolled between 1 July 2017 and 30 June 2018 and were randomly assigned into two groups: an intervention group to increase MeDi adherence through nutritional re-education, and a control group that follows their usual eating style. For the present analysis, the participants of both groups were stratified by their degree of adherence to the MeDi according to the 14-item MEDAS questionnaire that was developed in the PREDIMED trial [17]. Responses to this questionnaire have previously been shown to be valid for assessing adherence to the MeDi [19]. Low adherence to the MD was considered when the score obtained in the test was 0–6 points, medium adherence when it was 7–8 points, and high adherence when it was 9–14 points. ## 2.3. Study Variables The main study variable was the presence of pulmonary alteration. Pulmonary function examinations were performed by trained and certified technicians in agreement with the American Thoracic Society and European Respiratory recommendations [20]. Forced spirometry was done using a spirometer model DATOSPIR-600© (SIBELMED, Barcelona, Spain) with a disposable Lilly type transducer, and the accuracy of the device was verified daily. The spirometric parameters were measured as a percentage of the predicted values, focusing on the lung function parameters FEV1 (forced expiratory volume in the first second), FVC (forced vital capacity) and the relationship between them (FEV1/FVC ratio). Spirometry was done before and after inhalation of a bronchodilator. For the present analysis, we only considered pre-bronchodilator parameters. Abnormal lung function was defined as an FVC and/or FEV1 value < $80\%$ of the predicted value or FEV1/FVC ratio < 0.7. In addition, three spirometric patterns were considered: “normal spirometry” (FVC and FEV1 ≥ $80\%$ of the predicted value and FEV1/FVC ratio ≥ 0.7), “obstructive ventilatory defect” (FEV1/FVC ratio < 0.7), and “non-obstructive ventilatory defect” (FVC < $80\%$ of the predicted value and FEV1/FVC ratio > 0.7) [20]. As secondary variables were considered the following data: [1] Socio-demographic: age, sex, marital status (classified as single/widowed, married and divorced/separated), educational level (does not know to write or read, primary studies, secondary studies, and university degree) and employment status (working, working at home, unemployed/disability/retired, and student). [2] Clinical morbidities: medical history of cardiovascular disease, circulatory disease, oncologic disease and endocrinology disease. [3] Smoking: current consumption (number cigarettes per day) and cumulative consumption (pack-years smoked, calculated by multiplying the number of packs of cigarettes smoked per day by the number of years the person has smoked). [4] Alcohol consumption: grams of alcohol/week. To unify criteria when calculating alcohol consumption, the World Health Organization stipulated its measurement through the Standard Drink Unit (SDU). In Spain, one SDU equals 10 g of alcohol, and the consumption limit is <20 g/day (2 SDUs) for men and <10 g/day (1 SDU) for women, and the risk consumption > 28 SDUs/week for men and >14–17 SDUs/week for women, assuming that any consumption (however minimal) implies risk [21]. [5] Physical activity: according to the short Catalan version of the International Physical Activity Questionnaire (IPAQ-SF), the participants were classified as engaged in high (vigorous physical activity of at least 1500 Mets, 3 or more days per week, or a combination of walking and/or moderate to vigorous physical activity, 7 days per week), moderate (moderate physical activity of at least 600 Mets and/or walk for at least 30 min, 5 or more days per week) and low physical activity (some physical activity but it is insufficient), and inactive (no physical activity) [22]. [6] Physical examination: blood pressure (systolic [SBP] and diastolic [DBP], measured twice in sitting position on the right arm, calculated as the mean value of the two measurements, in mmHg), height (cm, using a conventional stadiometer), weight (kg, with the participant in light clothing and without shoes), BMI (in kg/m2) and its categorisation according to the classification criteria of the World Health Organization (normal weight, <25.0 kg/m2; overweight, 25–29.9 kg/m2 and obesity, ≥30 kg/m2), and waist circumference (WC; measured midway between the lower rib and the iliac crest with a standard anthropometric tape). [7] Laboratory data: levels of glucose (mg/dL), total cholesterol and fractions, triglycerides (mg/dL), and basic blood count parameters (haemoglobin [g/dL], haematocrit [%], erythrocytes [106/mm3]). ## 2.4. Statistical Analysis Data were extracted from a centralised database created ad hoc for the MEDISTAR study. For this analysis, participants were classified into three groups according to the degree of adherence to the MeDi: low (MeDi-low), medium (MeDi-medium) or high (MeDi-high). At first we made a descriptive analysis differentiated by the 3 adherence groups. The categorical variables were described by their frequency distribution and continuous variables were described by the mean and standard deviation (SD) or median, first quartile and third quartile, depending on whether or not they had a normal distribution, respectively. The Shapiro-Wilk test was used to decide the normality of the variables with a significance of 0.01. To detect differences between three groups, χ2 test was used for categorical variables and ANOVA-test or Kruskall-Wallis test was employed according to the normal distribution of each variable. For post-hoc comparisons Tukey or Benjamini and Hochberg tests were used. In order to analyse the relationships between MeDi adherence and lung function outcomes, multiple logistic and linear regression models were applied. Multivariate logistic regression models were performed for three outcomes, impaired FEV1, impaired FVC and impaired FEV1 and/or FVC. For each response variable, three types of models are shown according to the adjustment variables: an unadjusted model with the 3 groups of adherence to the MeDi as the only explanatory variable; an adjusted model adding sex and age, and finally an adjusted model adding multiple variables selected according to a stepwise algorithm performed in both directions. The model shown in the results was chosen according to the minimal value of Akaike Information Criterion (AIC) and clinical relevance. The results are presented as odds ratio (OR) with $95\%$ confidence intervals (CI). Multiple linear regression models were applied to explain the MeDi adherence group and impaired FEV$1\%$ predicted value and impaired FVC% predicted value. For each of the two response variables, three models are shown according to the adjustment variables: an unadjusted model with the 3 groups of adherence to the MeDi as the only explanatory variable; an adjusted model adding sex and age, and finally an adjusted model adding multiple variables selected according to a stepwise algorithm performed in both directions, the model shown in the results was chosen according to the minimal value of AIC and clinical relevance. Results are presented using the beta coefficients with $95\%$CI. All statistical analysis tests will be conducted with R Statistics package (R foundation for statistical computing, Vienna, Austria; version 4.1.2) and will be considered significant when $p \leq 0.05.$ ## 2.5. Ethical Approval The protocol was approved by the Ethics Committee of the University Institute for Primary Care Research—IDIAPJGol (registration code P$\frac{17}{089}$). The study was conducted according to the principles established by the Helsinki Declaration, in the Good Clinical Practice guidelines of the International Conference on Harmonisation (ICH GCP), and Spanish legislation regarding the protection of personal information was also followed. The subjects received information about the objectives of the study and the activities related to their participation, and signed an informed consent before their inclusion. ClinicalTrials.gov Identifier: NCT03362372. ## 3. Results A total of 403 participants were included with a mean age of 51.1 (SD 10.1) years, $66\%$ women. Regarding lung function parameters, the study population had a mean %FEV1-predicted value of 92.6 (SD 17.4), a mean %FVC-predicted value of 90.3 (±16.0), and a mean FEV1/FVC ratio of 0.78 (SD 0.74). Regarding the degree of adherence to the MeDi the prevalence of the low adherence was $23.8\%$ ($95\%$ CI 19.9 to 28.2), the medium adherence was $40.0\%$ ($95\%$ CI 35.3 to 44.8) and the high adherence was $36.2\%$ ($95\%$ CI 31.7 to 41.0). Table 1 summarises the main general characteristics of participants grouped according to levels of adherence to the MeDi. As can be seen, there were no significant differences between the groups in terms of distribution by sex, age, level of physical activity, alcohol consumption and smoking. We can see that in the high adherence to MeDi group there is a tendency towards a higher alcohol consumption which can be explained by the fact that wine is considered part of the MeDi and the MEDAS questionnaire does not discriminate between wine and other beverages. It should be noted that the wine is included in the MeDi. In addition, no statistically significant differences were observed between groups regarding the physical examination and the analytical variables studied, except for a more favourable lipid profile in those with high adherence (significant value for LDL cholesterol, and close for total cholesterol and triglycerides). The Supplementary Material shows the positive answers given by the participants and the influence of each question of the MEDAS questionnaire on the total score, according to the degree of adherence to the MeDi (Table S1). Table 2 shows the results of the main parameters for evaluating lung function and the prevalence of impairment according to the degree of adherence to the MeDi. The prevalence of altered lung function was $28.8\%$ overall (impaired FEV1 and/or FVC), $26.1\%$ for FVC and $21.1\%$ for FEV1, but it differed according to the degree of adherence to the MeDi. The prevalence of pulmonary alteration was significantly higher in the low-adherence group compared to the medium and high-adherence group. ( $38.5\%$ vs. $24.2\%$ and $27.4\%$, respectively; $$p \leq 0.044$$). Regarding the type of spirometric pattern, the differences did not reach statistical significance. The logistic regression models for the independent variables related to the impaired pulmonary function are shown in Table 3. We carried out an unadjusted model (only included the MEDI adherence), a model adjusted by sex and age, and a third one adding other variables that were selected according to the minimal value of AIC criterion and their clinical relevance (BMI and smoking current consumption). Logistic regression analysis showed a significant relationship between the presence of impaired lung function and the degree of adherence to the MeDi: regarding the subjects with low adherence, those with medium and high adherence presented a lower probability of functional alteration (OR 0.510 [$95\%$CI from 0.416 to 0.946; $$p \leq 0.016$$] and 0.602 [$95\%$CI from 0.348 to 1.042; $$p \leq 0.070$$], respectively). The degree of adherence to the MeDi remained an independent risk factor after adjusting for sociodemographic, clinical and lifestyle variables (OR 0.467 [$95\%$CI 0.266 to 0.820; $$p \leq 0.008$$] and 0.552 [$95\%$CI 0.313 to 0.973; $$p \leq 0.040$$], respectively). The multivariate linear regression models of the independent variables related to the pulmonary function data (FEV1 and FVC) are shown in Table 4. We analysed an unadjusted model (only included the MEDI adherence), a model adjusted by sex and age, and a third one including other variables that were selected according to the minimal value of AIC criterion and its clinical relevance (smoking current consumption, BMI and SDU/week). Considering FEV1 as the main variable, we can observe that the female sex increases the FEV$1\%$ predicted value (Beta regression coefficient 5.87; $95\%$CI 2.242 to 9.497). Regarding tobacco consumption, a higher consumption is inversely related (Beta regression coefficient −0.143; $95\%$CI from −0.272 to −0.038), while higher alcohol consumption increases the FEV$1\%$ predicted value (Beta regression coefficient: 0.374; $95\%$CI 0.074 to 0.675). Considering the FVC and applying the same models, only tobacco and sex were related with statistical significance to modify the FVC% predicted value (Beta regression coefficient: −0.143 [$95\%$CI −0.239 to −0.048] and 6.029 [%95 CI 2.789 to 9.268], respectively). Regarding the main variable, adherence to the Mediterranean Diet in linear regression was not significantly related to the variation of FEV$1\%$ and FVC% predicted value. ## 4. Discussion This study analyses and reports on the relationship between adherence to the MeDi pattern and respiratory function in a population of adult current smokers free of lung disease, treated at primary care centres in the health area of Tarragona in Catalonia, Spain. Their results show that greater adherence to the MeDi is inversely associated with a lower pulmonary function alteration prevalence ($24.2\%$ and $27.4\%$ in medium and high adherence vs. $38.5\%$ in low adherence; $$p \leq 0.004$$), and lower risk of presenting impaired lung function (OR 0.467 [$95\%$CI 0.266, 0.820] and 0.552 [$95\%$CI 0.313, 0.973] for medium and high adherence, respectively), after adjusting for potential relevant confounding factors such as smoking, physical activity, and anthropometry. These findings add to the overall evidence for a protective effect of “healthy” dietary pattern on respiratory health and highlight the importance of studying diet as a whole [3]. In general, 2 dietary patterns can be distinguished in nutritional epidemiology: a “healthy” (or prudent) dietary pattern, characterised by high consumption of fruits, vegetables, whole grains, fibre, lean meat and fish, and low in fat and dairy products, and another less healthy “western” type with high intake of refined grains, red and processed meats, chips, fizzy drinks, high in sugars and fats, and low in antioxidant and fibre levels [23]. In a previous cross-sectional study published by our group, we identified three dietary patterns associated with lung function: a Mediterranean-style pattern, a western-style pattern, and an alcohol-consumption pattern [24]. In the adjusted multivariable model, impaired pulmonary function was positively associated with the western-style and alcohol-consumption patterns, but no association was found with the Mediterranean-style diet, especially in women (OR 5.62 [$95\%$CI95 from 1.17 to 27.02], OR 11.4 [$95\%$CI from 2.25 to 58.47], and OR 0.71 [$95\%$CI from 0.28 to 1.79], respectively). Previously, two large prospective studies of US men and women showed that the risk of newly diagnosed COPD decreased as the prudent dietary pattern score increased [25,26]. Similarly, a healthy dietary pattern has been previously described, characterised by high consumption of fruit, vegetables, oily fish and wholemeal cereals, but low consumption of white bread, added sugar, full-fat dairy products, chips and processed meat, which was associated with better lung function and reduced prevalence of COPD among older people in the UK [27]. In contrast, a cross-sectional analysis from the Netherlands found that the “traditional” pattern, was associated with lower lung function and higher COPD prevalence [28]. More recently, a study in a Korean cohort has reported that a dietary pattern low in vegetable intake was negatively associated with lung function (particularly the FEV1/FVC ratio) and a higher prevalence of COPD [29]. The importance of dietary pattern in asthma was also highlighted in two systematic reviews and meta-analyses, concluding that adherence to MeDi may be effective in preventing asthma or wheezing in children; but these associations are controversial in the case of adults [30,31]. Recently, researchers from the ARIC (Atherosclerosis Risk in Communities) study compared the effect of a westernised diet versus a prudent diet on asthma, COPD, respiratory symptoms, and lung function [11]. They found that asthma prevalence was not related to dietary intake pattern, although people who ate a western diet had a higher prevalence of wheeze, cough, and phlegm and lower measures of lung function. In contrast, prudent dietary intake was protective for COPD and cough, as well as lung function deficits. Diet and nutrition have been recognised as modifiable risk factors for the development and progression of multiple chronic diseases, including lung diseases [32]. Although the fundamental public health message regarding lung diseases continues to be smoking cessation, the multifactorial nature of many chronic lung diseases opens the possibility of intervening in other modifiable risk factors, such as nutrition [33]. Globally, the available evidence shows that a healthy diet lowered the risk of developing COPD, whereas a westernised diet increased the risk [13,34]. The MeDi is a healthy dietary pattern characterised by a high consumption of extra virgin olive oil, fruit, vegetables, fresh produce, nuts and legumes, a low intake of sweetened beverages, red meat and ready-made meals, and a moderate consumption of fish and seafood, poultry, fermented dairy products and red wine (with meals) [15]. Important epidemiological studies have reported the role of the MeDi in the prevention of chronic diseases such as cardiovascular diseases, diabetes or cancer [17,35], but the evidence on its relationship with lung function and the risk of pathology chronic respiratory disease is more limited. A published study using population-based prospective data from the Västerbotten Intervention Program cohort from Sweden, showed that an intermediate and high MeDi score was inversely associated with the development of COPD (after adjustment for smoking intensity, OR 0.73 [$95\%$CI from 0.53 to 0.99] and OR 0.59 [$95\%$CI from 0.35 to 0.97], respectively) [36]. An observational and cross-sectional study conducted among community-dwelling older adults also examined the association between adherence to a MeDi and lung function (evaluated through peak expiratory flow rate; PEF, l/min) [37]. The results of a logistic regression showed a significant association between high adherence to MeDi with reduced risk of having PEF rate < $80\%$ of its peak predictive value (OR 0.65 [$95\%$CI from 0.48 to0.89]). Our study extends these results to a population of current smokers without lung disease, since we observed that those participants with intermediate and high adherence to the MeDi, compared with those with a low score, presented a lower prevalence of lung function alterations during the manoeuvres of spirometry tests ($24.2\%$ and $27.4\%$ vs. $38.5\%$, respectively; $$p \leq 0.04$$) and less probability of lung function alteration (OR 0.467 [$95\%$CI from 0.266 to 0.820] and OR 0.552 [$95\%$IC from 0.313 to 0.973], respectively). Overall, the results indicate that adherence to MeDi is an independent predictor of lung function, and dietary interventions could be a possible preventive measure in adults with a high risk of developing impaired lung function. However, caution should be exercised as no intervention studies have been reported so far and, to our knowledge, no direct evidence has been published on the effects of MeDi modification on lung function [38]. The pathophysiological mechanisms to explain the pulmonary benefits associated with MeDi are not yet fully understood [3]. Probably, the most appropriate explanation could be related to anti-inflammatory and antioxidant properties associated with the MeDi pattern [15]. Antioxidants are thought to play a protective role in the pathogenesis of lung impairment by scavenging free radicals and other oxygen species that cause cellular damage and inflammation [39]. Since MeDi protects against cellular oxidation and inflammation in several systems, it is reasonable to consider that these effects would also apply to lung tissues [40]. Various components of this dietary pattern, such as fruit and vegetables, vitamins C and E, flavonoids, ß-carotene, fatty acids, and various minerals have been shown to exert a protective effect on oxidative and inflammatory processes and could have a protective effect on lung function [34,37]. The ECRHS survey, a population-based study, reported that total fruits and vegetables intake is associated with a slower decline in FEV1 and FVC, which might be partly explained by the flavonoid contents in this food group [10]. In the same line, the Health ABC study, a population-based survey in older adults, also showed that a higher intake of antioxidant nutrients was associated with a slower lung function decline [41]. The favourable fatty acid profile of the MeDi, with a high monounsaturated fatty acids and polyunsaturated fatty acids n-3, is associated with an anti-inflammatory effect through inhibition of eicosanoids derived from arachidonic acid [42]. MeDi is also accompanied by a high intake of omega-3 fatty acids, through the weekly consumption of fatty fish and shellfish, which provides anti-inflammatory action. Some evidence shows that the consumption of omega-3 fatty acids, mainly eicosapentaenoic acid (C20: 5) and docosahexaenoic acid (C22: 6), interferes with the inflammatory response and can prevent some of the mechanisms involved in the pathophysiology of various diseases [43,44]. Unhealthy lifestyle choices have significant detrimental impacts. Well-established evidence shows that cancer, cardiovascular disease, diabetes, and chronic respiratory disease share modifiable risk factors such as smoking, alcohol consumption, physical inactivity, and nutritional status, as well as unhealthy diet [45]. For this reason, from a public health perspective, interventions to improve lifestyle habits are considered a priority [46]. Tobacco smoke is the most important factor in the etiopathogenesis of respiratory pathology, although other factors may also be involved [1]. In the present study, a sample of current smokers with cumulative consumption ≥ 10 pack-years, aged between 25 and 75 years (both inclusive), was selected. These subjects are the most likely to present impaired lung function associated with smoking [5]. Indeed, both FEV$1\%$ predicted value and FVC% predicted value were inversely associated with smoking cumulative consumption (pack-year) in linear regression analysis (β regression coefficient −0.155 [$95\%$CI from −0.272 to −0.038; $$p \leq 0.010$$] and −0.143 [$95\%$CI from −0.239 to −0.048; $$p \leq 0.003$$], respectively). These new data are in line with previous evidence [4]. Regarding alcohol consumption, in the present study we did not find any independent effect on the alteration of lung function but we did find it with respect to the impaired FEV$1\%$ and FVC% predicted value (β regression coefficient 0.374 [$95\%$CI from 0.074 to 0.675; $$p \leq 0.015$$] and 0.244 [$95\%$CI from 0.028 to 0.517; $$p \leq 0.015$$], respectively). Also in a previous cross-sectional study of our group, impaired pulmonary function was positively associated with alcohol-consumption pattern, especially in women [24]. However, a recent systematic review concludes that the evidence on the influence of alcohol consumption on the rate of decline in lung function and the risk of COPD is still inconsistent [23]. While excessive alcohol consumption is associated with decreased lung function, lower consumption might have protective effects in the general population. Therefore, adequate longitudinal cohort studies are required to clarify the influence of alcohol consumption on the progression of pulmonary function decline. The finding in our study of no interaction between adherence to the MeDi and physical activity with lung function also deserves comment, since a synergistic effect has been suggested among current smokers. Also, in the ILERVAS cohort study, no synergistic effects were observed between MeDi and physical activity with respect to better lung function [47]. In contrast, a previous study conducted in the Copenhagen City Heart Study suggested the association between physical activity and lung function, but did not consider the influence of diet, which is well known to be closely related to physical activity and nutritional state [48]. In addition, it must be considered that this relationship could be bidirectional, and that lung function could be affecting the physical activity level, since exercise limitation is a well-known consequence of chronic respiratory conditions [49]. However, people with normal lung function (as is the case for the majority of our sample) have physical activity levels in a wide range, and their behaviour is affected by many other factors besides lung function [50]. Additionally, it is important to recognise that people who smoke fewer cigarettes may lead somewhat healthier lifestyles, including diet and regular exercise. Therefore, it is possible that the interaction between smoking, diet, physical activity and lung function can only be adequately studied in clinical studies with samples covering wide ranges of both parameters [51]. Obesity has an essential effect on lung function. Most studies believe that obesity-related indicators are negatively correlated with lung function changes [52]. Obesity interferes with respiratory function due to mechanical compression and chronic inflammation of the airways [53]. This impact on lung function is independent of smoking, although its interaction is likely to potentiate mechanisms of inflammation and lung remodelling [54]. BMI is commonly used clinical measures of central obesity, and their association with lung function has been widely demonstrated [55]. In a previous study, our group demonstrated that worse anthropometry is associated with a greater probability of impaired lung function in smokers without known respiratory disease [56]. Since there is a strong interrelationship between diet and BMI, we adjusted for BMI in the regression models. However, our current study found that greater adherence to the MeDi pattern was associated with better lung function, and that the beneficial effect of diet was not affected by BMI. In this sense, a recent study has reported a higher impact on pulmonary function when metabolic alterations are present with or without obesity [57]. According to their data, the metabolically healthy obese group had better lung function compared to metabolically unhealthy groups. In addition, subjects with a “metabolically healthy” pattern have a lower proportion of cardio-metabolic diseases, such as dyslipidaemia, diabetes or hypertension, which have been associated with decreased lung function [58]. It has also been reported that the relationship between lung function and some adiposity indices present an inverted U-shaped curve, in such a way that the worst values of FEV1 and FVC occur at the extreme values of anthropometry [59]. In the present study, the average BMI was in the overweight range (25~30 kg/m2) and the laboratory data could be considered close to the “metabolically healthy” pattern in all three MeDi adherence groups [60]. All of this could explain why we did not observe an independent effect of BMI on lung function. In any case, the extent to which BMI is a confounding factor and/or a mediator of the associations between dietary habits and lung function would require a specific longitudinal study [61]. In this study, we also included adjustment for various sociodemographic factors, since lung function has been correlated with differences in ethnicity, age, and sex, in addition to smoking [62]. Consistent with the findings of other studies, our results also showed gender behaviour with respect to the impact on lung function, but not for age. We observed that women compared to men had a lower risk of impaired lung function (OR 0.51 [$95\%$CI from 0.360 to 0.904; $$p \leq 0.017$$]), and FEV$1\%$ or FVC% predicted value (β regression coefficient 5.870 [$95\%$CI from 2.242 to 9.497; $$p \leq 0.002$$] and 6.220 [$95\%$CI from 2.893 to 9.546; $p \leq 0.001$], respectively). Sex-associated differences in the effects of increasing adiposity on lung function have been previously reported and values of decreased lung function are expected to be greater in men than women [63]. Furthermore, dietary macronutrients could have different effects on lung function in men and women [64]. However, it is not clear whether the effect of dietary modification may differ between men and women, although sex differences have been described in other studies [65]. In any case, our current data corroborate a different impact on lung function according to sex, reinforcing the relevance of this variable when evaluating lung function associated with the MeDi. An inverse association between socioeconomic status (educational level, marital status, income, occupation, etc.) and lung function has been described in the epidemiological literature for decades [66]. For this reason we considered the adjustment for socioeconomic factors (family situation, educational level and employment status), even though they may not be relevant from the statistical point of view (according to the AIC index) to be included in the best model of the regression analysis. Most likely, a multitude of confounding factors (specific to each setting) play a role in the complex relationship between socioeconomic factors and lung function. In addition, changes in social factors throughout life could also influence the dietary pattern and the effect of environmental exposures [67]. ## Limitations and Strengths We also recognise several strengths of the present study. Although it is an observational study, it constitutes the first analysis of the association of the MeDi with lung function in the smoking population without previous respiratory disease and, therefore, provides new information that could complement the available evidence. Our group also previously reported on the feasibility of conducting a randomised controlled clinical trial to assess the efficacy of a Social Networks 2.0-supported dietary intervention in primary health care settings [38]. To the best of our knowledge, no other study has been designed to incorporate the dimension of adherence to diet in the smoking patient as a comprehensive form of patient-centred health care [18]. To quantify the main variable, lung function, we use spirometry measurements, which are the gold standard markers for the diagnosis of the most prevalent respiratory diseases [20]. Lung function data were obtained in the context of stringent spirometry protocols with well-trained field workers. In our study, lung damage and respiratory abnormalities were mild in magnitude and even subclinical, but they could have a detrimental impact on long-term health [38]. However, it should be noted that our definition of impaired lung function represents a simplified case definition for epidemiological purposes and not a definitive clinical diagnosis. Additionally, we focus on the MeDi pattern rather than individual nutrients in foods and believe our pattern analysis can provide practical guidance for public health [68]. Besides these strengths, our study also has a number of limitations that are worth noting and which require us to interpret some data with caution. We highlight the cross-sectional design, which prevents us from drawing clear conclusions with respect to causality. Perhaps subjects with higher adherence MeDi may be more health conscious and engage more in healthy behaviours, which could potentially confound associations between diet and lung function. In any case, “reverse causation” does not seem a likely explanation for the main findings, since it is not understood why individuals developing worse lung function would choose to eat a less healthy diet [65]. Although several studies have shown that the dietary pattern in adults remains reasonably stable over time, with the data from this cross-sectional study, we can only assume an association and not a causal relationship between adherence to the MeDi and impaired lung function [69]. In any case, the longitudinal follow-up provided for in this study will help to elucidate the temporal relationships between lifestyle factors, including adherence to the MeDi, and the presence of impaired lung function. Another limitation of the study is the sample size. We estimated an initial sample size in the MEDISTAR study of 750 volunteers, but we only recruited 403 eligible to participate. The sample size achieved, although comparable to other studies in this field, possibly this limited the statistical power to detect differences between lung function and adherence to the MeDi, and has caused some doubts when interpreting and comparing the results obtained [3]. Despite this, we consider that the database is large enough to allow us to adjust for potential confounding effects of main factors, including sex and age, sociodemographic factors, and factors related to lifestyle such as smoking, consumption of alcohol, physical activity and some nutrition indices, such as the BMI. In this sense, as has been argued, our results are consistent with other previous studies. However, as in any observational study of diet, unaddressed confounding is a concern in interpreting results. Although many known confounding factors were taken into account, the possibility of residual confounding due to other factors that have not been evaluated in our study (such as the other environmental sources of oxidants/antioxidants, air pollution or occupational exposures) cannot be ruled out. In addition, our study population included only middle-aged and older adult smokers, so we also recognise that our study population could represent a group of people who differ from the general population in terms of health awareness and smoking behaviour. ## 5. Conclusions In conclusion, the results of the present study show that adherence to the MeDi is an independent predictor of impaired lung function in adult smokers without known lung disease. After taking into account other factors related to their sociodemographic characteristics and lifestyle, a medium-high adherence to MeDi diet was associated with a lower risk of impaired lung function. In addition to the preventive benefits of the MeDi for cardiovascular disease, diabetes, and cancer, increased adherence to MeDi pattern could also play a protective role in the pathogenesis of chronic respiratory diseases. Thus, dietary interventions could be a useful preventive measure in adults at high risk, although smoking cessation remains the main target to reduce the burden of these diseases. ## References 1. Singh D., Agusti A., Anzueto A., Barnes P.J., Bourbeau J., Celli B.R., Criner G.J., Frith P., Halpin D.M.G., Han M.. Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Lung Disease: The GOLD science committee report 2019. Eur. Respir. J. (2019) 53 1900164. DOI: 10.1183/13993003.00164-2019 2. 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--- title: The Emerging Prevalence of Obesity within Families in Europe and its Associations with Family Socio-Demographic Characteristics and Lifestyle Factors; A Cross-Sectional Analysis of Baseline Data from the Feel4Diabetes Study authors: - George Siopis - George Moschonis - Kyriakos Reppas - Violeta Iotova - Yuliya Bazdarska - Nevena Chakurova - Imre Rurik - Anette Si Radó - Greet Cardon - Marieke De Craemer - Katja Wikström - Päivi Valve - Luis A. Moreno - Pilar De Miguel-Etayo - Konstantinos Makrilakis - Stavros Liatis - Yannis Manios journal: Nutrients year: 2023 pmcid: PMC10005317 doi: 10.3390/nu15051283 license: CC BY 4.0 --- # The Emerging Prevalence of Obesity within Families in Europe and its Associations with Family Socio-Demographic Characteristics and Lifestyle Factors; A Cross-Sectional Analysis of Baseline Data from the Feel4Diabetes Study ## Abstract The Feel4Diabetes study is a type 2 diabetes prevention program that recruited 12,193 children [age: 8.20 (±1.01) years] and their parents from six European countries. The current work used pre-intervention data collected from 9576 children–parents pairs, to develop a novel family obesity variable and to examine its associations with family sociodemographic and lifestyle characteristics. Family obesity, defined as the presence of obesity in at least two family members, had a prevalence of $6.6\%$. Countries under austerity measures (Greece and Spain) displayed higher prevalence ($7.6\%$), compared to low-income (Bulgaria and Hungary: $7\%$) and high-income countries (Belgium and Finland: $4.5\%$). Family obesity odds were significantly lower when mothers (OR: 0.42 [$95\%$ CI: 0.32, 0.55]) or fathers (0.72 [$95\%$ CI: 0.57, 0.92]) had higher education, mothers were fully (0.67 [$95\%$ CI: 0.56, 0.81]) or partially employed (0.60 [$95\%$ CI: 0.45, 0.81]), families consumed breakfast more often (0.94 [$95\%$ CI: 0.91 0.96]), more portions of vegetables (0.90 [$95\%$ CI: 0.86, 0.95]), fruits (0.96 [$95\%$ CI: 0.92, 0.99]) and wholegrain cereals (0.72 [$95\%$ CI: 0.62, 0.83]), and for more physically active families (0.96 [$95\%$ CI: 0.93, 0.98]). Family obesity odds increased when mothers were older (1.50 [$95\%$ CI: 1.18, 1.91]), with the consumption of savoury snacks (1.11 [$95\%$ CI: 1.05, 1.17]), and increased screen time (1.05 [$95\%$ CI: 1.01, 1.09]). Clinicians should familiarise themselves with the risk factors for family obesity and choose interventions that target the whole family. Future research should explore the causal basis of the reported associations to facilitate devising tailored family-based interventions for obesity prevention. ## 1. Introduction Overweight and obesity is the largest non-communicable disease pandemic affecting more than 1.9 billion adults and nearly four hundred million children and adolescents [1]. Overweight and obesity are risk factors for other non-communicable diseases, such as type 2 diabetes (T2D), cardiovascular disease, and certain forms of cancer, that collectively account for more than 40 million deaths per annum [2]. Moreover, overweight and obesity are also a risk factor for coronavirus disease-19 (COVID-19), with the Centers for Control and Disease Prevention (CDC) in the US reporting that 4 in 5 hospitalised people in the US with COVID-19 have overweight or obesity [3], and the World Obesity Federation emphasising a “dramatic correlation” between COVID-19 mortality and obesity [4]. The prevalence of obesity worldwide is on the rise, having nearly tripled in thirty years between 1975 and 2016 [1]. Childhood overweight and obesity rates are increasing at an alarming rate, with the prevalence of overweight and obesity among children and adolescents aged 5–19 years having risen from $4\%$ in 1975 to more than $18\%$ in 2016 [1]. Obesity exhibits a steeper trend than that of overweight in children and adolescents, with $8\%$ of boys and $6\%$ of girls having obesity in 2016, compared to less than $1\%$ in 1975 [1]. An association between the obesity status of children and that of parents and certain behaviours of parents has been previously reported [5]. Preliminary evidence shows that children are more likely to have obesity if their parents have obesity or if their parents follow unhealthy lifestyle practices, such as an unhealthy diet and not being physically active enough [6,7]. Despite the important role of the social and physical environment within the family in shaping behaviours that affect energy balance and consequently the weight status of family members, research has primarily focused on the identifications of obesity risk factors only at an individual level. A previous study from Finland reported the association between parental body mass index (BMI), family structure, socioeconomic factors, and childhood overweight [8]. However, to the best of our knowledge, no previous study has assessed these associations in more than one country. Investigating these associations in diverse demographic and socioeconomic settings is important to understand the risk factors for childhood overweight and obesity and to allow for an informed approach to devising effective and feasible obesity prevention strategies. Previous studies have explored obesity within families, and how overweight and obesity of the parents can affect the weight of the children, but either the sample sizes were small or they used single country data [9,10,11]. This study used the pre-intervention demographic and anthropometric data collected from children and their parents participating in the Feel4Diabetes study [12], to report the prevalence of family obesity and examine the potential associations of family obesity with the demographic characteristics and lifestyle factors of residents from the six European countries that participated in the Feel4Diabetes study. ## 2. Materials and Methods Detailed information on the materials and methods used to recruit the study sample and collect data in the Feel4Diabetes study, as well as on the validity and/or reliability of the tools and/or procedures followed is presented elsewhere [13,14]. ## 2.1. Study Design and Sampling Procedures The Feel4Diabetes study (http://feel4diabetes-study.eu/, NCT02393872) was a large-scale community-based, family-involved study that aimed to promote a healthy lifestyle, including healthy eating and increased physical activity, in families from six European countries, namely Belgium, Bulgaria, Finland, Greece, Hungary, and Spain. The Feel4Diabetes intervention was implemented during two school years (2016–2018). The study was conducted within selected socioeconomic areas in the participating European countries and the recruitment was based on a standardised, multi-stage sampling procedure, [12]. Specifically, in low to middle income countries, i.e., Bulgaria and Hungary, all the municipalities within the participating regions were eligible for recruitment, while in high-income countries, i.e., Belgium, Finland, Greece, and Spain, families within low SES municipalities were recruited [15]. In high-income countries, low SES municipalities were defined as those with the lowest educational level and/or the highest unemployment rates, as retrieved from official resources and local authorities within each country. To be considered for inclusion, children and their parents had to be living in low socioeconomic areas or belong to vulnerable subgroups of the population, or both. Children also had to be attending one of the first three grades of compulsory education. There were no exclusion criteria other than those directly opposite to the inclusion criteria. The details of the study design and sampling procedures have already been published [12]. In brief, primary schools in the selected municipalities served as entry-points to communities. Parents of children in the first three grades of these schools were invited to participate in the study. Regarding sample size, a sample of 600 families per treatment arm was required to achieve a statistical power greater than $80\%$ (at a two-sided $5\%$ significance level) for reducing screen time by 0·2 h/d in children within 8 months. However, to account for an estimated dropout rate of about $20\%$, there was an aim to recruit a total number of about 9000 families in the six participating countries. ## 2.2. Ethics Approvals and Consent Forms The Feel4Diabetes study adhered to the Declaration of Helsinki and the conventions of the Council of Europe on human rights and biomedicine [16]. Prior to initiating the study, researchers in participating countries obtained ethics approval from local authorities. Participants were presented with a detailed description of the study and asked to fill in and sign consent forms for their participation, and were given the chance to withdraw from the study at any point. ## 2.3. Data Collection Data were collected at baseline [2016], and during the first [2017] and the second year [2018] of the program by rigorously trained researchers, who were trained as part of a central training which aimed to standardize researchers and minimize intra- and inter-observer variability. [ 14]. In brief, the central training of the Feel4Diabetes intervention ensured that the anthropometric and blood pressure data collected from study participants were valid and comparable [14]. Furthermore, the questions used to assess children’s and parents’ dietary and physical-activity-related behaviours had an acceptable test–retest reliability, with several questions showing excellent reliability (Intraclass Correlation Coefficients > 0.81) [13]. ## 2.3.1. Socio-Demographic Characteristics Information on the socio-demographic characteristics (e.g., age, gender, race, education, marital and employment status) of parents and their children within the examined families was collected via self-reported questionnaires. Continuous variables were dichotomised as follows: age: <45 years vs. ≥45 years, education: <9 years, 9–14 years, and >14 years. Forty-five years is generally the age that middle age starts [17,18], although the exact age is disputed [19]. Nine years is the duration of compulsory education in most European education systems [20]. The categorical variable “occupation” was trichotomised as “employed full-time”, “employed part-time”, and “unemployed/other”. ## 2.3.2. Anthropometry Standing height was measured without shoes and was recorded to the nearest tenth of a centimetre (i.e., 0.1 cm) using telescopic stadiometers: SECA 213, SECA 214, SECA 217, and SECA 225. Body weight was measured with light clothing and without shoes and recorded to the nearest 0.1 kg. The equipment for measuring body weight included electronic weight scales: SECA 813 and SECA 877. Body mass index (BMI) was calculated according to the WHO formula and its reporting followed the WHO classification for adults [1] and for children [21,22]. Family obesity was defined as the presence of obesity in at least two out of the three family members participating in the Feel4Diabetes study (i.e., both parents or the child and any of the two parents). ## 2.3.3. Lifestyle Factors Parents filled in questionnaires about their and their children’s energy balance related behaviours, which were relevant to dietary intake, physical activity, and screen time. The reliability of the questionnaires regarding these lifestyle behaviours was evaluated in a pilot study and was found to be acceptable [13]. ## Dietary Intake Consumption of water, fruit and vegetables, dairy (unsweetened or sweetened), cereals (low fibre or wholegrain), soft drinks (with or without sugar), sweets, savoury snacks, fast food, and breakfast were assessed with the use of a Food Frequency Questionnaire (FFQ). The general question in the FFQ was: “Indicate how often you (parent) and your child consume: water, soft drinks with or without added sugar, fruit/berries (fresh or frozen), fruit and berries (canned or dried), vegetables, dairy (sweetened or unsweetened), sweets, cereals (low fibre or wholegrain), salty snacks/fast-food, and breakfast”. Depending on the food items, answer options were less than 1 time or day/week, 1 or 2 times or days/week, 3 or 4 times or days/week, 5 or 6 times or days /week, as well as 1 or 2 portions or cups/day, 3 or 4 portions or cups/day, 5 or 6 portions or cups/day, >6 portions or cups/day. These categorical variables were then recoded into numerical ones and were used to calculate the sum of daily consumption of all aforementioned food items by all family members. Outliers for the total daily food consumption (defined as values above three standard deviations from the mean) were capped and reassigned the value of the mean plus three standard deviations. The daily breakfast consumption was measured by the following questions. “ How many days do you/does your child usually eat breakfast?” separately for weekdays and weekend days. The number of days consuming breakfast on weekdays and weekend days were summed for all family members. ## Physical Activity and Screen Time Moderate-to-vigorous physical activity (MVPA) was subjectively measured by the following questions: “How many days during the last week did you (parent) spend in MVPA for a total of at least 30 min per day?” and “How many days during the last week did your child spend in MVPA for a total of at least 1 h per day?”. Parents’ and children’s screen-time behaviour during the week was assessed by the following question: “About how many hours per day do you (parent) and your child usually devote to screen-activities (excluding school/work)?”. The questions that were used to collect this data are two separate ones (one for the parent and one for the child). Answer options (categorical values) were expressed in hours per day. Answers options: None, <30 min/day, 30 min to <1 h/day, 1 to <2 h/day, 2 to <3 h/day, …, ≥7 h/day. Afterwards, these categorical values were recoded into continuous variables, which were expressed as number of days per week when each family member meets physical activity recommendations, or as hours per day of screen time. Each one of these continuous variables was summed for all family members. ## 2.4. Statistical Analysis All statistical analyses were performed using the Statistical Package for Social Sciences (SPSS Inc., Chicago, IL, USA), version 25.0. The normality of the distribution of continuous variables was tested by the Kolmogorov–Smirnov test. Normally distributed continuous variables are presented as means and standard deviations (SD), while non-normally distributed ones are presented as medians and interquartile ranges (IQR). Categorical variables are presented as percentages (%). Between-group differences of continuous variables were tested using either one-way analysis of variance (ANOVA) or the non-parametric Kruskal–Wallis test for normally and non-normally distributed variables, respectively. The significance of the association between categorical variables was examined using the chi-squared (χ²) test. Stratified analyses were carried out using an economic classification of countries at the time of submission of the study protocol (2014–2015), i.e., “low-income” (Bulgaria and Hungary), “under austerity measures” (Greece and Spain), and “high-income” (Belgium and Finland). Regarding the characterization of Greece and Spain as counties under austerity measures, this is a definition that was based on historical financial data indicating that both Greece and Spain faced a sovereign debt crisis following the world financial crisis of 2007–2008 [23]. This resulted in a series of reforms and austerity measures that led to recession, loss of income, and a negative impact on both countries’ healthcare systems in the following years, which coincided with the time period when the Feel4Diabetes study was conducted. Univariate logistic regression analyses were initially carried out to examine the crude associations between family obesity (dependent variable) and family socio-demographic characteristics and lifestyle factors (independent variables) for the total sample and by economic classification of countries. Those variables that were found to be significantly associated with family obesity at a univariate level, were then all entered to relevant multivariate logistic regression models, which allowed the examination of potentially direct (i.e., independent from other potential confounders) significant associations with family obesity. The existence of multicollinearity was tested via the examination of the correlation matrix and the variance inflation factor (VIF) for all independent variables. The correlation coefficients and the VIF value did not relevel any multicollinearity that could bias the results of the multivariate regression models. The Bonferroni correction was applied to control for multiple comparisons. All reported p-values were two-tailed, and the level of statistical significance was set at $p \leq 0.05.$ ## 3. Results The results presented in this section are derived from the statistical analyses conducted on children–parents pairs with full data on the examined variables ($$n = 9576$$). Figure 1 shows the participant flow. ## 3.1. Prevalence of Family Obesity in the Total Sample, by Country’s Economic Classification, and by Country Figure 2 presents the data on the prevalence of family obesity in the total sample by country’s economic classification and by country. Overall, nearly seven percent ($$n = 628$$ out of 9576; $6.6\%$) of participating families in the total sample had obesity. Countries under austerity measures displayed the highest family obesity rate ($$n = 244$$ out of 3192; $7.6\%$) followed by low-income countries ($$n = 270$$ out of 3850; $7\%$), with high-income countries displaying the lowest prevalence of family obesity ($$n = 114$$ out of 2534; $4.5\%$) ($p \leq 0.001$ as per the data resented in Table 1). Further stratification by country revealed that the highest family obesity prevalence was seen in the south–east part of Europe, with Greece displaying the highest prevalence at $9.2\%$ ($$n = 168$$ out of 1826), followed by Hungary at $8.6\%$ ($$n = 123$$ out of 1437), and Bulgaria at $6.1\%$ ($$n = 147$$ out of 2413). The north–west part of Europe displayed lowest family obesity rates, with Finland at $6\%$ ($$n = 62$$ out of 1029), Spain at $5.6\%$ ($$n = 76$$ out of 1366) and Belgium at $3.5\%$ ($$n = 52$$ out of 1505). Table 1 also presents the prevalence of family obesity and all combinations of the presence of obesity in different family members that were used in the definition of family obesity (i.e., two obese parents/non-obese child; One obese parent/ obese child; Two obese parents/obese child) by country’s economic classification. According to this data the prevalence of obesity in families with one obese parent and an obese child was found to be significantly higher in high-income countries under austerity measures ($$n = 109$$ out of 3192; $3.4\%$) and low-income countries ($$n = 131$$ out of 3850; $3.4\%$) compared to high-income countries ($$n = 33$$ out of 2534; $1.3\%$) ($p \leq 0.001$ as per the data resented in Table 1). No other statistically significant differences in the prevalence of the different definition categories of family obesity were observed among countries with economic classification. ## 3.2. Socio-Demographic Characteristics of Families in the Total Sample and by Economic Classification of Countries Table 1 presents the socio-demographic characteristics of participating families. Approximately, half of the children in the study were boys and half girls. The vast majority of parents were younger than 45 years old ($90.4\%$ of mothers and $77.7\%$ of fathers). High-income countries displayed overall the highest education percentages of parents ($63.3\%$ of mothers and $48.3\%$ of fathers having completed more than 14 years of education) along with the highest full-time employment rates for them, with the exception of mothers that displayed a marginally higher full-time employment rate in low-income countries compared to high-income ones ($62\%$ vs. $60.9\%$, respectively) and fathers displaying a slightly higher percentage for education in countries “under austerity measures” ($50.8\%$). Conversely, low-income countries displayed the lowest education status for mothers and fathers (equally low with countries under austerity measures—both at $11\%$), along with the highest unemployment rates for fathers ($19.2\%$) and second highest for mothers ($32.1\%$, the highest was seen in countries under austerity measures at $35.5\%$). ## 3.3. Dietary Intake, Physical Activity Levels, and Screen Time of Families in the Total Sample and by Economic Classification of Countries Table 2 presents the data on participants’ dietary intake, physical activity levels, and screen time. Participating families from countries under austerity measures displayed the least healthy behaviours (e.g., lower vegetable and fruit intake, higher sweetened dairy intake, higher low-fibre cereal and lower wholegrain cereal intake, less physical activity) compared to low-income and high-income countries ($p \leq 0.001$). However, the average screen time in high-income countries under austerity measures was lower compared to the rest of the countries falling under the other two economic classification categories ($p \leq 0.001$). On the contrary, participating families from high-income countries reported consuming more vegetables and wholegrain cereal, along with meeting the recommended physical activity levels more frequently, compared to participants from low-income countries ($p \leq 0.001$) and the ones in high-income countries under austerity measures ($p \leq 0.001$). ## 3.4. Associations between Sociodemographic Characteristics and Family Obesity Table 3 presents the univariate logistic regression analyses examining the crude associations between each one of the family sociodemographic characteristics and family obesity for the total sample and by country’s economic classification. Compared to families with parents younger than 45 years of age, there was a $50\%$ higher likelihood of family obesity when the mother’s age was equal to or older than 45 years in the total sample. The likelihood of family obesity was higher in low-income ($55\%$ higher) and high-income countries ($173\%$ higher), but not in high-income countries under austerity measures when the mother’s age was equal to or older than 45 years, and when the father’s age was equal to or older than 45 years only in high-income countries ($123\%$ higher). Regarding education, there was a lower likelihood of family obesity in families with parents that had completed more years of education, in those with parents having completed more than 14 years of education showing the lowest likelihood of family obesity ($76\%$ less for fathers and $66\%$ less for mothers in high-income countries), followed by families with parents having completed 9–14 years ($53\%$ less for fathers only, in high-income countries) compared to having completed less than nine years of education. This trend was seen in all countries regardless of their economic classification, but its values were more significant for fathers than for mothers. On the other hand, more significant associations with family obesity were observed for the occupation of the mothers, with a significantly lower likelihood of family obesity seen when the mother was full-time ($33\%$ less likelihood) or part-time employed ($40\%$ less likelihood), compared to unemployed in the total sample, and a $64\%$ lower likelihood of family obesity when the mother was full-time and $50\%$ lower likelihood when she was part-time employed in high-income countries, and a $46\%$ lower likelihood when the mother was part-time employed in high-income countries under austerity measures. Significantly lower odds of family obesity prevalence were also seen when the father was full-time employed in high-income countries ($48\%$ lower likelihood) and in high-income countries under austerity measures ($38\%$ lower likelihood). ## 3.5. Associations between Lifestyle Factors and Family Obesity Table 4 presents the univariate logistic regression analyses examining the crude associations between lifestyle factors and family obesity for the total sample and by country’s economic classification. An increased likelihood of family obesity was observed in low-income countries with higher soft drink and savoury snack consumption, with the odds increasing by $10\%$ for every portion of “diet” soft drink and by $8\%$ for every portion of savoury snacks consumed per day, respectively. Furthermore, in high-income countries, the likelihood of family obesity increased by $20\%$ for every portion of “diet” soft drink consumed per day and by $48\%$ for every time per day that sweetened dairy was consumed. Reduced odds for family obesity were seen in high-income countries, by $8\%$ for every day of the week that breakfast was consumed, and interestingly, by $18\%$ for every portion of sweets per day consumed. In high-income countries under austerity measures, the chances of family obesity increased by $15\%$ for every cup of water consumed per day and by $10\%$ for every hour spent in front of a screen per day. On the other hand, the chances for family obesity decreased by $21\%$ for every time per day sweetened dairy were consumed, by $8\%$ for every day of the week that breakfast was consumed, and by $9\%$ for every day of the week that the family was meeting physical activity recommendations. ## 3.6. Associations between Sociodemographic Characteristics and Lifestyle Factors with Family Obesity Table 5 presents the multivariate logistic regression analyses examining the associations between those sociodemographic characteristics and lifestyle factors that were found to have statistically significant odds ratios at a univariate level, and family obesity for the total sample and by country’s economic classification. In the total sample as well as in low-income countries and high-income countries under austerity measures, the likelihood of family obesity was lower for fathers who had completed more years of education. Furthermore, in high-income countries, the status of the education of the father did not produce statistically significant results but the employment status of the mother did, with the odds for family obesity being reduced by $45\%$ when the mother was full-time employed. Regarding lifestyle factors, an increase of $11\%$ in the chances of family obesity was seen in the total sample ($24\%$ in high-income countries under austerity measures and $6\%$ in low-income countries) for every cup of water consumed per day. Higher odds for family obesity were also seen in the total sample, increasing by $14\%$ for every portion of savoury snacks consumed per day, and in high-income countries, increasing by $29\%$ for every portion of “diet” soft drinks consumed per day. On the other hand, a reduction in the likelihood of family obesity by $5\%$ in the total sample ($9\%$ in high-income under austerity measures countries and $5\%$ in low-income countries) was observed for every day of the week that the family was meeting the physical activity recommendations. Interestingly, a $13\%$ reduction in the total sample ($29\%$ in low-income countries) for the chances of family obesity was observed for every portion of sweets consumed per day. ## 4. Discussion This study utilised a rich dataset on the anthropometric characteristics of parents and children living in six European countries, in order to report the prevalence of family obesity and also to examine its potential associations with the participants’ self-reported socio-demographic and lifestyle characteristics. Overall, one in every fifteen participating families had obesity. The prevalence of family obesity, defined as the presence of obesity in at least two out of the three family members participating in the Feel4Diabetes study (i.e., both parents or the child and any of the two parents), was higher in high-income countries under austerity measures, with one in thirteen families, and lower in high-income countries, with one in twenty-two families displaying obesity. Our findings are in agreement with previous research that has shown a disproportional distribution of the prevalence of non-communicable disease, with lower socioeconomic areas displaying higher prevalence rates [24,25,26]. Lower socioeconomic status is often associated with a lower overall education status, lower health literacy, and lower employment rates [27]. Indeed, our study demonstrated strong associations between the education status of the parents and family obesity, with the latter exhibiting its highest values when either parent had attained fewer years of education. Patients have previously reported that health literacy facilitates the necessary dietary changes towards achieving their health objectives [28]. In terms of employment, family obesity was lower when either parent was employed compared to being unemployed. Our finding agrees with previous research that has highlighted lower rates of engagement with health services for unemployed people with type 2 diabetes [29,30]. When examining each country on its own, the highest family obesity prevalence was observed in Greece, where one in eleven families displayed obesity. This finding is in agreement with previous research which has shown that the social and physical environment that shapes energy-balance-related behaviours in the family is less supportive in Greece and low-income European countries, compared to high-income European countries [31]. Children’s social environment, which primarily consists of family (i.e., parents and siblings), peers, and teachers, determines their health behaviour, mainly via modelling, encouragement, support, rule setting, and rewarding. In this social context, children can adopt eating and sedentary behaviour from their parents and peers, can be encouraged or rewarded for eating fruits and vegetables by their parents or teachers, but also teased about these food choices by their peers. Physical environment, such as home, school, and neighbourhood, is of equal importance to the social one since it can also play a pivotal role in the adoption of certain health behaviours by children. In this context, it is known that availability and accessibility to certain foods, sports equipment and sports facilities are important factors in determining children’s obesity-related behaviour [32]. Family obesity prevalence was lower when the parents’ age was less than 45 years, compared to when it was equal to or older than 45 years. Younger parents tend to be more physically active and previous research has demonstrated lower childhood obesity rates in families with younger parents [33,34]. Parents are role models for young children and therefore younger parents that are more active may be positively influencing the health behaviour within the family, including more favourable physical activity and eating habits [6,7,35,36,37]. Our study showed increased family obesity odds with increased screen time. This can partially be explained via the association between increased screen time with reduced time spent being physically active [38,39], although this association has not always been found [40]. Another explanation relies on the finding that the longer the screen time, the higher the odds for binge-eating and overconsumption of food, even in the absence of hunger cues [41,42,43,44,45]. In terms of its associations with dietary intake, the odds for family obesity were reduced when breakfast was consumed more frequently. This is agreement with studies that have shown that skipping breakfast is correlated with obesity [46,47,48]. It remains to be determined if this association has a causative component, e.g., an amplified morning thermogenesis [49], or if it is due to, e.g., children having breakfast also being likely to adopt other healthy behaviours such as a healthy diet in general and physical activity. Interestingly, reduced odds for family obesity were observed in participants from high-income countries with an increased daily consumption of sweets. A possible explanation for this paradox may be that the overall energy balance in this population was favourable for a normal body mass index. Indeed, participants from high-income countries displayed the highest levels of physical activity, meaning that their increased energy expenditure may counteract the extra calories derived from sweets. A potential underreporting of sweets consumption by parents for their children (because, e.g., this is a “socially acceptable” answer) in low-income and in high-income countries under austerity measures cannot be excluded. Finally, different perceptions of what counts as sweets in the different countries (e.g., in some European countries, the consumption of cookies with milk might be considered as a nutritious snack, instead of eating a sweet with milk) could provide another explanation. Regarding the associations between family obesity and other energy-balance-related behaviours, increased odds for family obesity were observed with an increased consumption of processed foods such as soft drinks, savoury snacks, and sweetened dairy. These findings are in agreement with the literature [50,51,52,53]. Several characteristics of processed foods have been described as obesogenic, including their increased energy content, their nutrient profile, their effect on distorting digestive hormone balance and inducing high-glycaemic responses, and their inclusion of cosmetic additives with pro-inflammatory and obesogenic properties such as carboxymethylcellulose and polysorbate-80 [51,53,54]. Such additives promote adipogenesis via interfering with the expression of genes that affect fatty acid oxidation and fat deposition within the adipocyte [55]. Moreover, their xenobiotic nature triggers reactive oxygen species (ROS) generation and the formation of lipid hydroperoxides that promote the accumulation of lipids in the tissues, leading to obesity [54,56,57]. The strengths of this study lie in the sample size and the robust data collection methods of the Feel4Diabetes study, including the standardised way that anthropometry measurements were conducted in the different study centres, which involved centrally trained research team members in order to minimise any inter-observed variability, and the validated questionnaires used to collect dietary, physical activity, and screen-time information [12,13]. Additionally, we performed a series of tests for a potential confounding effect, by including numerous socio-demographic and lifestyle variables in the regression models; however, as we have not exhausted all such variables, the possibility of confounding cannot be excluded. A limitation of the study design is that causal associations cannot be explored. Furthermore, missing data were not accounted for in the analyses. Moreover, the self-reporting of part of the collected data is prone to recall bias and social desirability. Finally, as Feel4Diabetes used school as an entry point to recruit participants, the results might not be applicable to single adults or families with no children at all or no primary school children. However, within the context of this study’s target population, the results can be generalised to the whole population of adults/families with children attending primary school, considering that the participation rate of the families was quite high [12]. ## 5. Conclusions and Implications for Practice and Future Research In conclusion, family obesity prevalence was higher in high-income countries under austerity measures and low-income countries compared to high-income countries. Reduced odds for family obesity were observed when the parents were younger, had completed more years of education, were employed, when breakfast was consumed more frequently, and when physical activity recommendations were met more frequently. The odds increased with the consumption of processed foods and with increased screen time. Clinicians should familiarise themselves with the risk factors for family obesity and choose obesity interventions that target the whole family rather than individuals. Future research should explore the causal basis of the reported associations to facilitate an insight into the most important risk factors for family obesity within different regions in Europe that will appropriately inform European and country-specific public health policy. 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--- title: Sex-Specific Response of the Brain Free Oxylipin Profile to Soluble Epoxide Hydrolase Inhibition authors: - Jennifer E. Norman - Saivageethi Nuthikattu - Dragan Milenkovic - John C. Rutledge - Amparo C. Villablanca journal: Nutrients year: 2023 pmcid: PMC10005333 doi: 10.3390/nu15051214 license: CC BY 4.0 --- # Sex-Specific Response of the Brain Free Oxylipin Profile to Soluble Epoxide Hydrolase Inhibition ## Abstract Oxylipins are the oxidation products of polyunsaturated fatty acids and have been implicated in neurodegenerative disorders, including dementia. Soluble epoxide hydrolase (sEH) converts epoxy-fatty acids to their corresponding diols, is found in the brain, and its inhibition is a treatment target for dementia. In this study, male and female C57Bl/6J mice were treated with an sEH inhibitor (sEHI), trans-4-[4-(3-adamantan-1-yl-ureido)-cyclohexyloxy]-benzoic acid (t-AUCB), for 12 weeks to comprehensively study the effect of sEH inhibition on the brain oxylipin profile, and modulation by sex. Ultra-high-performance liquid chromatography–tandem mass spectrometry was used to measure the profile of 53 free oxylipins in the brain. More oxylipins were modified by the inhibitor in males than in females (19 versus 3, respectively) and favored a more neuroprotective profile. Most were downstream of lipoxygenase and cytochrome p450 in males, and cyclooxygenase and lipoxygenase in females. The inhibitor-associated oxylipin changes were unrelated to serum insulin, glucose, cholesterol, or female estrous cycle. The inhibitor affected behavior and cognitive function as measured by open field and Y-maze tests in males, but not females. These findings are novel and important to our understanding of sexual dimorphism in the brain’s response to sEHI and may help inform sex-specific treatment targets. ## 1. Introduction Oxylipins are products of polyunsaturated fatty acids (PUFAs), produced through oxidation via cytochrome p450 (CYP), lipoxygenase (LOX), and cyclooxygenase (COX) or non-enzymatic oxidation pathways [1]. Studies have found that oxylipins have a role in many biological processes and diseases, including inflammation, metabolic diseases, and cardiovascular diseases [2,3,4,5,6,7]. Further, evidence supports a role for oxylipins in modulation of neuroinflammation and neurodegenerative diseases, including dementias [8,9,10,11,12]. Soluble epoxide hydrolase (sEH), is involved in the metabolism of certain oxylipins. Specifically, sEH converts epoxy-fatty acids produced by CYP enzymes into their corresponding diols, those produced from arachidonic acid are epoxyeicosatrienoic acids (EpETrEs) and their corresponding diols, dihydroxyeicosatrienoic acids (DiHETrEs) [13]. sEH is found in many tissues, including the brain [14,15]. Within the brain, sEH is found in many regions and within various cell types, including glial cells, neurons, and vascular cells [15]. Research on sEH has implicated it as a treatment target for many disease types, including metabolic, inflammatory, and cardiovascular diseases [13,16,17,18,19,20]. Further, sEH has been associated with, and the inhibition of its activity has been proposed as a treatment target for, diseases of the brain, including dementias [9,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40]. sEH inhibition has been demonstrated to positively impact modulation of neuronal activity, glial cell activity, cell survival, neuroinflammation, blood flow, and blood–brain barrier permeability; further, it has been suggested that many of these benefits are due to the subsequent increase in epoxy-fatty acid levels, in particular, the levels of EpETrEs [41,42,43,44,45,46,47]. Studies have shown sex differences in sEH expression and activity in multiple tissues, including the brain [48,49,50]. In rodents, females have been shown to have lower expression of sEH in the whole brain and cerebral vessels compared to males, as well as smaller infarct volumes after middle cerebral artery occlusion (MCAO) [49,50]. In line with this, others have found that transgenic mice, expressing human sEH in endothelial cells, exhibited impaired endothelial-dependent vasodilation, and that the effect of induced expression of sEH was more pronounced in females [47]. These findings have been supported by in vitro work as well. In endothelial and neuronal cultures, cells derived from males exhibited higher levels of sEH and were more susceptible to ischemic damage than cells derived from females [51,52]. Evidence indicates that these sex differences in sEH and their downstream consequences are due, at least in part, to sex hormones. It has been demonstrated that 17β-estradiol suppresses cerebral sEH expression and that ovariectomy abolishes the sex differences in infarct size after MCAO [50,53]. Given the sex differences in levels of the sEH enzyme, it would be likely that the response to deficiency or inhibition of sEH would be sex-dependent, and this is supported by the literature. Genetic deletion of sEH reduces infarct volumes in males after MCAO, but has no effect in females [49,50]. Further, we have previously demonstrated a sex-dependent response of the microvascular transcriptome to sEH inhibition [54]. Thus, studies investigating the therapeutic potential of targeting sEH need to address males and females separately and not generalize results from one sex to the other or not attend to biologic sex. Although the substrates and products of sEH are often examined, to our knowledge, the effect of inhibiting sEH on the overall brain free oxylipin composition has not been investigated. In this study, we therefore sought to characterize the effect of inhibiting sEH on the free oxylipin profile of the brain. We chose to examine the brain free oxylipin pool, as these are generally seen as the biologically active form [1]. Further, given the substantial evidence for sex differences in sEH expression and response to its modulation, we examined the impact of sEH on the brain free oxylipin profile in each sex separately. We hypothesized that providing mice with an sEH inhibitor (sEHI) would alter the brain oxylipin profile towards a neuroprotective, anti-inflammatory profile, which would be more pronounced in males. ## 2. Materials and Methods An overview of the experimental groups and the experimental timeline can be found in Figure 1. The experimental details follow in the sections below. ## 2.1. Animals Research was conducted in conformity with the Public Health Service Policy on Humane Care and Use of Laboratory Animals and ARRIVE 2.0 guidelines [55] and was approved by the Institutional Animal Care and Use Committee of the University of California, Davis (protocol number 20943, approval date 18 April 2019). Mice were housed in a temperature- and humidity-controlled environment with a 12 h light/dark cycle at the University of California, Davis Mouse Biology Program. Female mice were housed with up to three mice per cage; male mice were housed singly. Activity, water, and food intake were monitored by vivarium staff to ensure the well-being of the mice. Male and female C57Bl/6J mice were purchased from Jackson Laboratories (stock 000664). From 20 weeks of age, all mice were fed a commercially available purified diet (catalog number TD.08485 from Envigo Teklad Diets, Madison, WI, USA), provided ad libitum. The macronutrient content of the diet was as follows: $13\%$ kcal from fat, $19.1\%$ kcal from protein, and $67.9\%$ kcal from carbohydrates. ## 2.2. Soluble Epoxide Hydrolase Inhibitor (sEHI) One group of male mice ($$n = 7$$) and one group of female mice ($$n = 7$$) were treated with the soluble epoxide hydrolase inhibitor (sEHI), trans-4-[4-(3-adamantan-1-yl-ureido)-cyclohexyloxy]-benzoic acid (t-AUCB, Cayman Chemical, Ann Arbor, MI, USA). The t-AUCB was provided in the drinking water using polyethylene glycol 400 (PEG400) (Millipore, Burlington, MA, USA) as a vehicle from 20 weeks of age until sacrifice at 32 weeks of age (Figure 1B). We chose this timeline of treatment and sacrifice to align with our previous publications regarding sEHI, which found this length of treatment sufficient to induce changes in the brain microvascular transcriptome [54,56,57]. Consistent with prior protocols, the final contents of the drinking water were $1\%$ (by volume) PEG400 and 10 mg/L t-AUCB [58,59]. In agreement with others, mice consumed approximately 7 to 7.5 mL of water each day [60]. This resulted in mice consuming approximately 2.5 to 3 mg of t-AUCB per kg per day. For control groups, one group of male mice ($$n = 7$$) and one group of female mice ($$n = 7$$) were not treated with t-AUCB. The PEG400 vehicle was not added to drinking water of these control mice; however, $1\%$ PEG400 is a low amount and in prior studies has been shown to not have a biological effect [61]. Overall, there were four experimental groups of mice: [1] male control (those not receiving sEHI), [2] male + sEHI (treated with sEHI), [3] female control (those not receiving sEHI), and [4] female + sEHI (treated with sEHI) (Figure 1A). ## 2.3. Assessment of Estrus Cycle Phase For all female mice, vaginal lavage with phosphate-buffered saline (PBS) was performed at the end of the study after administration of anesthesia and prior to sacrifice. The PBS with collected vaginal cells was then applied to a glass slide and allowed to dry. Slides were stored at room temperature until staining with $0.1\%$ crystal violet. Phase of estrus cycle was assessed by examining the stained cells using light microscopy. Samples were categorized as proestrus, estrus, metestrus, or diestrus based on the cell types observed as previously described [62]. ## 2.4. Tissue Collection At the end of the study period, mice were fasted for 8 h before being anesthetized with a combination of Ketamine and Xylazine. Blood was collected by ventricular puncture under anesthesia, then mice were euthanized. The brain was immediately harvested and snap-frozen in liquid nitrogen to preserve the lipid profiles and integrity. We estimate the total time for this process was less than 5 min. Samples remained in frozen storage (at −80 °C) until extraction of the oxylipins. ## 2.5. Serum Analyses Serum was separated from whole blood by centrifugation and samples were stored at −80 °C until analysis. Glucose and total cholesterol were measured using enzymatic assays from Fisher Diagnostics (Middleton, VA, USA). Insulin was determined by electrochemiluminescence from Meso Scale Discovery (Rockville, MD, USA). All assays were performed in triplicate by the University of California Davis Mouse Metabolic Phenotyping Center (UCD MMPC). ## 2.6. Mouse Behavioral and Cognition Testing We assessed behavior and cognitive function utilizing the open field test and the Y-maze test. The open field test assesses locomotor activity and anxiety-like behavior in mice, with increased time in the center of the field being indicative of lower levels of anxiety [63]. The Y-maze is widely used to assess spatial and learning memory and assesses the mouse’s active retrograde working memory, by observing how often mice explore the three arms of the maze in succession [64]. ## 2.6.1. Open Field Test Mice were adapted to the testing room for 30 min, then placed in the center of a Columbus Instruments Opto-Varimex 4 for the extent of a 20-min trial. Movement was measured as x, y, and z bream breaks. The perimeter was defined as the outer 8.4 cm region of the 43.5 cm box, or ~$60\%$ of total surface area. The open field tests were performed by the UCD MMPC. ## 2.6.2. Y-Maze Mice were adapted to testing room for 30 min, then placed in the center of the Y-maze and were tracked with an overhead camera for the extent of an 8-min trial. An elevated white plastic Y-maze with three 40 cm arms at 120-degree angles. An alternation score was computed as the number of times the three arms were sequentially entered. The % alternation score is the number of alternations divided by maximum alternation triplets. The Y-maze tests were performed by the UCD MMPC. ## 2.7. Analysis of the Brain Free Oxylipin Profile We extracted the free oxylipins from the right hemisphere of brain tissue from each mouse for analysis and quantification by ultra-high-performance liquid chromatography–tandem mass spectrometry (UHPLC-MS/MS). The UHPLC-MS/MS measurements were performed once. All measured oxylipins and internal standards with their abbreviations can be found in Table S1. ## 2.7.1. Free Oxylipin Extraction from Brain Oxylipins were extracted from the right hemisphere of brain tissue as previously described [65]. Briefly, the tissue was homogenized using a bead homogenizer and zirconia beads in methanol with butylated hydroxytoluene and acetic acid, spiked with a mixture of deuterated surrogate standards. Homogenized samples were centrifuged. The supernatant was loaded onto solid phase extraction columns, the columns were rinsed, and the oxylipins were eluted with methanol and ethyl acetate. After drying under nitrogen, the oxylipins were reconstituted in 100 µL methanol. ## 2.7.2. UHPLC-MS/MS Analysis of Free Oxylipins Ten µL of the reconstituted free oxylipin extract was analyzed by UHPLC-MS/MS using an Agilent 1290 Infinity UHPLC system coupled to an Agilent 6460 Triple Quadrupole mass-spectrometer (Agilent Technologies, Santa Clara, CA, USA), equipped with an Agilent ZORBAX Eclipse Plus C18 column (2.1 × 150 mm, 1.8 μm particle size; Agilent Technologies, Santa Clara, CA, USA; Cat #959759-902). The specifics of the UHPLC-MS/MS analyses were described previously [65]. Optimization parameters and parent and product ion monitoring pairs are described in Table S2. We have provided the raw mass spectra for the standard, blank, and a representative sample for 15-HETE in Figure S1. ## 2.7.3. Data Analysis Oxylipins with no discernible peaks, or where $50\%$ of all groups had a result of not detected, were removed from analyses. The oxylipins removed from analyses for these reasons were: Resolvin E1, 17-HDoHE, 17[18]-EpETE, 14[15]-EpETE, 11[12]-EpETE, 8[9]-EpETE, 14,15-DiHETE, 11,12-DiHETE, 8,9-DiHETE, 15-HEPE, 8-HEPE, LTB3, PGE3, 8,9-DiHETrE, 20-HETE, LXA4, LTC4, LTE4, 20-OH-LTB4, 20-COOH-LTB4, PGB2, and 13-oxo-ODE. For oxylipins kept in analyses, any non-detects were replaced by $\frac{1}{5}$ of the minimum positive value of that variable to estimate the limit of detection. The peaks for PGD1 and PGE1 were indistinguishable; therefore, they were combined and are referred to as PGD/E1. For many samples, PGD2 signals were above the standard curve; the values were included in the analyses. Some samples were lost due to inadvertent breakage of tubes during homogenization of the tissue, which resulted in analysis of brain free oxylipins of $$n = 6$$ samples for the male control and male sEHI groups. ## 2.8. Statistical Analysis Statistical analyses of EpETrE/DiHETrE ratios, body weight, serum parameters, and cognitive/behavioral function were performed using Prism (GraphPad Software, San Diego, CA, USA). Any outliers were determined by ROUT with $Q = 1$%. Pairwise analyses between control and sEHI groups were performed using t-test (with Welch’s correction if variances were not equal) or Mann–Whitney test (if not normally distributed). Significance was determined at $p \leq 0.05.$ Statistical analyses of oxylipins were performed using MetaboAnalyst 5.0 [66,67]. All pairwise comparisons of oxylipins between control and sEHI groups were performed on data without transformation using the non-parametric Wilcoxon rank-sum test, since data were not normally distributed for all oxylipins. We used sparse partial least squares discriminate analysis (sPLS-DA), which utilizes the selection of the most discriminative features to classify samples [68], and a hierarchical clustering heatmap to examine all four groups of mice. sPLS-DA and the hierarchical clustering heatmap were performed using log-transformed data. The heatmap was clustered using Euclidean distance and Ward clustering methods. Boxplots were generated using Prism (GraphPad Software, San Diego, CA, USA). Significance was determined at $p \leq 0.05$ based on the raw p-value, when the FDR p-value was below 0.05 this was also indicated. Spearman correlation analyses of significant oxylipins with the cognitive/behavioral function outcomes were conducted using Prism (GraphPad Software, San Diego, CA, USA). Significance was determined at $p \leq 0.05$, ## 3. Results To confirm that the sEHI dose was sufficient to inhibit sEH, we compared the sEH substrate to product ratio of total EpETrE/DiHETrE of sEHI-treated mice to controls without sEHI for each sex. In both males and females, mice receiving the sEHI had higher EpETrE/DiHETrE ratios than controls (Figure 2). Body weight did not differ between mice in the sEHI-treated and control groups in both males and females (Table 1). Fasting levels of serum insulin, glucose, and cholesterol did not differ between sEHI-treated and control mice in either sex (Table 1). There were no differences between the sEHI-treated and control females in estrus cycle phase (data not shown). ## 3.1. Effects of sEHI on the Brain Oxylipin Content in Males We analyzed the brain free oxylipins of males treated with sEHI as compared to controls (not receiving the inhibitor), using pairwise comparison. There were 19 oxylipins that differed between these two groups in males. Eleven of these oxylipins were products of arachidonic acid metabolism (Figure 3), most of which were downstream of LOX enzymes, including 5-HETE, 5,15-DiHETE, LTB4, 6-trans-LTB4, LTD4, 11-HETE, 15-HETE, and 8,15-DiHETE. Two arachidonic acid products were downstream of CYP enzymes and direct products of sEH (5,6-DiHETrE and 14,15-DiHETrE), and one (9-HETE) was a product of non-enzymatic oxidation. Seven oxylipins were products of linoleic acid metabolism (Figure 4A), most of these were downstream of LOX enzymes, including 9-HODE, 9-oxo-ODE, 13-HODE, 9,10,13-TriHOME, and 9,12,13-TriHOME. Two linoleic acid products were downstream of CYP enzymes and direct products of sEH (9,10-DiHOME and 12,13-DiHOME). One oxylipin was a LOX product of α-linolenic acid metabolism (9-HOTrE) (Figure 4B). Of the 19 oxylipins significantly changed ($p \leq 0.05$) in sEHI-treated males, the majority were decreased compared to controls, except three (9-HETE, 11-HETE, and 15-HETE). ## 3.2. Effects of sEHI on the Brain Oxylipin Content in Females We analyzed the free oxylipin content of the brain in females with and without sEHI treatment in the same manner as in males. There were three oxylipins that were statistically different ($p \leq 0.05$) between sEHI-treated female mice and controls. All these oxylipins were products of arachidonic acid metabolism (Figure 5). 12-oxo-ETE, a product of LOX metabolism, was higher in the sEHI-treated mice than in controls. The two other oxylipins were both products of COX metabolism, PGF2a and TXB2, and were lower in the sEHI-treated mice than in controls. ## 3.3. Comparison of the Effect of sEHI on the Brain Oxylipin Profile in Males and Females With the aim to compare the impact of sEHI on the brain free oxylipin profile between males and females, we performed sPLS-DA to assess the separation between groups. The scores plot of the sPLS-DA demonstrated that both groups of females, with and without sEHI, clustered with the males receiving the sEHI, while control males without sEHI separated from the other three groups (Figure 6A). Loading plots, which show the variables selected by the sPLS-DA for a given component, can be seen in Figure 6B,C. The top three variables selected for component one matched the three oxylipins that were significantly altered by the sEHI in females, while all ten of the variables selected for component two were significantly altered by sEHI in males. We chose to further analyze the relationship between response to sEHI and sex using a hierarchical clustering heatmap. In the heatmap, the male control mice cluster separately from all other groups, while the male sEHI-treated, as well as female control and sEHI-treated, mice were not distinctly separated by the clustering analysis (Figure 7). It can also be seen that three oxylipins (9-HETE, 11-HETE, and 15-HETE) defined the male control group cluster and were lower in concentration as compared to the other groups. These three oxylipins were also found to be significantly increased by sEHI in males by pairwise comparisons. A summary of the concentrations of each oxylipin analyzed in males and females, with and without the inhibitor, can be found in Table S3. ## 3.4. sEHI Effects on Behavior and Cognition in Male and Female Mice We assessed behavior and cognitive function by the open field test and Y-maze test and only observed an effect of sEHI in male mice. Male mice treated with sEHI spent a greater percentage of time in the center of the open field test when than control male mice (Figure 8A). There was no difference between sEHI-treated and control female mice in the percent of time spent in the center of the open field test (Figure 8B). We saw no statistically significant differences between sEHI-treated mice and control mice in performance on the Y-maze test. There was a trend toward an increase in percent alternating triplets in sEHI-treated male mice compared to male controls, but it did not reach statistical significance ($$p \leq 0.0827$$) (Figure 8E). There was no difference between groups in females (Figure 8F). There was no difference in total distance traveled in both the open field test and the Y-maze test between the sEHI-treated and control groups in males or females (Figure 8C,D,G,H). To further explore the relationship between the oxylipins and measures of behavior and cognitive function, we performed correlation analyses. As we only observed changes to behavior and cognitive function in males, we focused on the data from males, and oxylipins that were significantly altered by sEHI in males. We found a significant negative correlation between the percent time spent in the center on the open field test and 9-HOTrE (r = −0.6163, $$p \leq 0.0376$$), LTB4 (r = −0.599, $$p \leq 0.0433$$), 5,6-DiHETrE (r = −0.6532, $$p \leq 0.0252$$), and 14,15-DiHETrE (r = −0.6014, $$p \leq 0.0428$$) (Figure S2). We found that the percent alternating triplets in the Y-maze test negatively correlated with 13-HODE (r = −0.8182, $$p \leq 0.0019$$), 9,10-DiHOME (r = −0.6532, $$p \leq 0.0252$$), and 9,12,13-TriHOME (r = −0.6573, $$p \leq 0.0238$$) and positively correlated with 9-HETE ($r = 0.5944$, $$p \leq 0.0457$$) and 11-HETE ($r = 0.5944$, $$p \leq 0.0457$$) (Figure S3). ## 4. Discussion This study is the first comprehensive assessment of the brain oxylipin profile, and how it is impacted by inhibition of sEH in both sexes. Oxylipins are bioactive products of PUFA oxidation and have been demonstrated to play a role in neurological function, neuroinflammation, and neurodegenerative diseases, including dementias [8,11,12]. In addition, inhibition of sEH has been investigated as a potential therapeutic target for a wide range of neurological disorders, including dementias [13,21,22,26,30,31,34,35,36,37]. Therefore, the impact of sEHI on the comprehensive profile of oxylipins is an important area of study to further understand how this treatment might be beneficial in neurodegenerative diseases such as dementia, a major global killer of men and women. Importantly, we investigated sex differences, which are all too often overlooked and could provide insight into sex-specific treatments. By measuring the free oxylipin content of the brain with and without sEHI in males and females, we report an important novel finding; sEHI impacts the brain oxylipins of males differently, with a distinct pattern, and to a greater extent than in females. We discuss our findings in the context of prior work in the field, and implications for mechanisms, cognition, and therapeutics. ## 4.1. Implications of Oxylipins Altered by sEHI in Males We observed lower levels of the sEH products 5,6-DiHETrE, 14,15-DiHETrE, 9,10-DiHOME, and 12,13-DiHOME in sEHI-treated male mice compared to controls. These differences were to be expected and provide evidence that our inhibitor treatment was sufficient to inhibit sEH activity. Interestingly, 5,6-DiHETrE and 14,15-DiHETrE levels were negatively correlated with behavior indicative of reduced anxiety in the open field test, and 9,10-DiHOME levels were negatively correlated with memory function as measured by the Y-maze test. The DiHETrEs are the less biologically active metabolites of EpETrEs, and have been shown to have multiple protective effects in the brain, including anti-inflammatory effects, modulation of neuronal activity, regulation of blood flow, and improvement of cell survival for neurons and glial cells [41]. Further, serum levels of 12,13-DiHOME were previously shown to associate with white matter hyperintensities, an indicator of subcortical ischemic vascular damage [40]. Therefore, reductions in DiHETrE and DiHOME levels are a neuroprotective shift in the oxylipin profile. We also observed differences in oxylipins not directly produced by sEH in male mice treated with the inhibitor. Most of these changes also appeared to be neuroprotective. 15-HETE is one of the oxylipins increased in the sEHI-treated group. 15-HETE is important for angiogenesis and recovery after MCAO and ischemia and has been shown to be present in lower levels in the brains of mice in an Alzheimer’s disease (AD) model [69,70,71,72]. Several oxylipins were lower in the sEHI-treated male brains compared to controls and have been previously found to be associated with brain injury or neurodegeneration: 5-HETE, LTB4, LTD4, and 9-HODE. 5-HETE was previously shown to be higher in the cerebrospinal fluid (CSF) of traumatic brain injury patients as compared to controls [73]. Brain levels of LTB4 have been associated with neuroinflammation and cognitive decline and were found to be increased in the CSF of AD patients [74,75]. In the current study, we also demonstrated that LTB4 levels were negatively correlated with behavior indicative of reduced anxiety in the open field test. LTD4 has been demonstrated to increase microglial activation, as well as facilitate amyloid β accumulation and cognitive impairment [76]. 9-HODE has previously been associated with white matter hyperintensities and reduced grey matter volume [10]. Thus, many of the differences seen in the brain oxylipin profiles of sEHI-treated and control male mice indicate a more neuroprotective oxylipin profile in the sEHI-treated mice. ## 4.2. Implications of Oxylipins Altered by sEHI in Females In females, only three oxylipins were altered by sEHI treatment. None of these are a direct product of sEH; although, we did see a significant increase in the total EpETrE/DiHETrE ratio. The lack of significant changes in any of the individual products of sEH may be due to already low levels of sEH in females as reported previously [49,50]. The oxylipins found to be different between sEHI-treated and control mice were PGF2a, TXB2, and 12-oxo-ETE. PGF2a and TXB2 were decreased in mice receiving the inhibitor, while 12-oxo-ETE was increased. The existing literature suggests that reduced PGF2a levels may contribute to the neuropathology of dementias. PGF2a levels are reduced in the plasma and CSF of patients with AD and positively correlate with Mini-Mental State Examination scores [9,75]. Additionally, brain tissue from AD patients has a reduced capacity for the synthesis of PGF2a as compared to controls [77]. On the other hand, a reduction in TXB2 levels appears to be neuroprotective. TXB2 levels have been demonstrated to be higher in the brains of individuals with AD-like dementia, than in controls [78]. Additionally, patients with elevated levels of circulating TXB2 were found to have worse prognoses after stroke [79]. We were unable to find literature on the role of 12-oxo-ETE in the brain. Therefore, there were fewer changes in the free oxylipin composition of female sEHI-treated mice as compared to males, and the potential functional and mechanistic consequences are less clear than in males, partly due to the small number of oxylipins changed. ## 4.3. Sex Differences in the Response of the Brain Oxylipin Profile to sEHI The sPLS-DA scores plot of the oxylipin content showed that male control and sEHI-treated groups were distinct, while the control and sEHI-treated female groups overlapped. Further, the hierarchical clustering heatmap demonstrated that the male sEHI-treated and control groups clustered separately, while sEHI-treated and control females did not. This would indicate that sEHI has a larger effect on the brain free oxylipin content of males as compared to females. Interestingly, none of the oxylipins altered by sEHI were common between females and males. The oxylipins that were altered in males were primarily downstream of LOX and CYP enzymatic activity, while the oxylipins altered in females were downstream of the LOX and COX. Others have demonstrated that sEH expression is higher in males, and that genetic deletion of sEH had a greater impact on reducing the consequences of cerebral ischemia in males than in females [49,50]. Thus, the sex differences in our study are supported by previous findings of the consequences of blocking sEH in the brain. In addition, the greater impact of inhibiting sEH in males could be explained by higher levels of the enzyme present at baseline in males. Our data suggest that treatment with sEHI shifts the brain oxylipin profile of males to be similar to that of females. In the sPLS-DA plot, the male control mice were distinct from the female mice, while the sEHI-treated male mice overlapped with both control and sEHI-treated females. Further, in the hierarchical clustering heatmap, only control males were distinctly clustered together. Within the heatmap, three of the oxylipins (9-HETE, 11-HETE, and 15-HETE) were visibly lower in the male control mice compared to all other groups. These three oxylipins were also found to be significantly increased by the sEHI in pairwise comparison. We previously demonstrated that these three oxylipins were higher in the brains of female mice than in male mice and were not affected by dietary sucrose content [65]. Although little is known about the functions of 9-HETE and 11-HETE, we saw a positive correlation between levels of 9-HETE and 11-HETE with memory as measured by the Y-maze test in males. Further, 15-HETE has been demonstrated to be neuroprotective [69,70,71,72]. Therefore, sEHI has a greater impact on the oxylipin profile of the brain in male mice, in part by shifting it to be more similar to that of female mice, thereby favoring less neurodegeneration. ## 4.4. Sex Differences in the Cognitive and Behavioral Outcomes with sEHI The more robust response of males to the sEHI was also reflected in the results of the cognitive and behavioral tests of our study. We saw that, in males, sEHI-treated mice spent a greater amount of time in the center of the open field test, indicating a reduction in anxiety behavior [63], while no effect of sEHI was seen in females. In agreement with our findings, sEHI has previously been reported to increase the time spent in the center of the open field test in males [80]. We also saw a trend towards an increase in percent alternating triplets on the Y-maze test, suggesting improved working memory [64], in males; however, these changes were not observed in females. In agreement with our findings, an improvement in Y-maze test performance in a mouse model of AD when sEH was genetically deleted has been reported previously [39]. The sex of the mice studied was not specified; therefore, it is not possible to compare our sex differences findings to these results. Others have demonstrated that various sEHI compounds improve memory as measured by other cognitive function tests in the context of multiple disease models; however, these studies either only studied males, or combined males and females into one group [35,36,37,80,81,82,83,84,85]. Therefore, to our knowledge, our study is the first to describe sex differences in the effects of sEHI on murine behavior and memory. ## 4.5. Potential Mechanisms of sEHI Effects on Brain Oxylipins Although four of the oxylipins altered by sEHI in males are direct products of sEH (5,6-DiHETrE, 14,15-DiHETrE, 9,10-DiHOME, and 12,13-DiHOME), the majority of the oxylipins that we found to differ between sEHI-treated mice and controls in males and females were not. This brings into question the mechanism behind the effect of sEHI on these other oxylipins. One possibility is that there are indirect effects of sEHI exerted through levels of its substrates and products. For example, EpETrEs have been shown to block the nuclear translocation of NFκB, which reduces expression of 5-LOX and COX-2 [13]. Many of the oxylipins that we found to be lower in the sEHI-treated male mice are downstream of 5-LOX. Additionally, PGF2a and TXB2, which were decreased in sEHI-treated females, are downstream of COX-2. Furthermore, knockout of sEH has been shown to increase the levels of 9-HETE, 11-HETE, and 15-HETE in plasma [86]. This supports our results and provides evidence that our findings are unlikely to be due to off-target effects; although, further research is needed. Therefore, the mechanism by which sEHI alters the level of oxylipins that are not direct substrates or products of sEH remains an important area for further study. ## 5. Conclusions This study addressed a previously unexplored area of research—the response of the brain oxylipin profile to sEHI and the sex specificity in this response. We demonstrate that the oxylipin profile of the brain in males is impacted by sEHI to a greater extent than that of females. The changes induced by sEHI shift the brain oxylipin profile of males to be more similar to that of females and towards neuroprotection. Further, we show that this shift is associated with an improvement in behavior and memory in males. The dramatically different number of oxylipins impacted by sEHI between the sexes highlights the importance of research in both sexes. Further research addressing sex as a biologic variable may help to identify sex-specific treatment targets and strategies for neurodegenerative diseases such as dementia. ## References 1. Gabbs M., Leng S., Devassy J.G., Monirujjaman M., Aukema H.M.. Advances in Our Understanding of Oxylipins Derived from Dietary PUFAs. Adv. Nutr. 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--- title: 'Acute Insulin Secretory Effects of a Classic Ketogenic Meal in Healthy Subjects: A Randomized Cross-Over Study' authors: - Alberto Battezzati - Andrea Foppiani - Alessandro Leone - Ramona De Amicis - Angela Spadafranca - Andrea Mari - Simona Bertoli journal: Nutrients year: 2023 pmcid: PMC10005334 doi: 10.3390/nu15051119 license: CC BY 4.0 --- # Acute Insulin Secretory Effects of a Classic Ketogenic Meal in Healthy Subjects: A Randomized Cross-Over Study ## Abstract The classic ketogenic diet (KD) is a high-fat, low-carbohydrate diet that mimics a starvation state with sufficient caloric intake to sustain growth and development. KD is an established treatment for several diseases, and it is currently evaluated in the management of insulin-resistant states, although insulin secretion after a classic ketogenic meal has never been investigated. We measured the insulin secretion to a ketogenic meal in 12 healthy subjects ($50\%$ females, age range 19–31 years, BMI range 19.7–24.7 kg/m2) after cross-over administrations of a Mediterranean meal and a ketogenic meal both satisfying ~$40\%$ of an individual’s total energy requirement, in random order and separated by a 7-day washout period. Venous blood was sampled at baseline and at 10, 20, 30, 45, 60, 90, 120, and 180 min to measure glucose, insulin, and C-peptide concentrations. Insulin secretion was calculated from C-peptide deconvolution and normalized to the estimated body surface area. Glucose, insulin concentrations, and insulin secretory rate were markedly reduced after the ketogenic meal with respect to the Mediterranean meal: glucose AUC in the first OGTT hour −643 mg × dL−1 × min−1, $95\%$ CI −1134, −152, $$p \leq 0.015$$; total insulin concentration −44,943 pmol/L, $95\%$ CI −59,181, −3706, $p \leq 0.001$; peak rate of insulin secretion −535 pmol × min−1 × m−2, $95\%$ CI −763, −308, $p \leq 0.001.$ We have shown that a ketogenic meal is disposed of with only a minimal insulin secretory response compared to a Mediterranean meal. This finding may be of interest to patients with insulin resistance and or insulin secretory defects. ## 1. Introduction The ketogenic diet is a dietary regimen providing very low carbohydrate, high fat, and modest protein, that has an established role in the treatment of drug-resistant epilepsy [1], glucose transporter type 1 deficiency syndrome [2,3,4,5], and other neurologic diseases [6], well tolerated and safe [3,7] also over ten years of continuous application [8,9]. Low carbohydrate and ketogenic diets have become increasingly popular in the treatment of metabolic syndrome, obesity, and type 2 diabetes and various meta-analyses have shown their usefulness [10,11,12], although not conclusively [13]. International consensus establishes carbohydrates as the base of the food pyramid for healthy nutrition. Nevertheless, several studies have shown that an abundant intake of starchy foods and sugars, the main source of energy in Western diets including the Mediterranean Diet, may promote excessive insulin responses [14] with negative health consequences. An excessive ß-cell secretory activity may independently cause weight gain and insulin resistance [15,16,17,18,19,20]. Therefore, it has been hypothesized that diet-induced hyperinsulinemia could be the cause of insulin resistance [21,22], inflammation [23] vasoconstriction [24], and atherogenesis [25] that increase the cardiometabolic risk. In contrast, ketosis-inducing diets seem to reduce insulin resistance improving glucose and insulin levels, by requiring less insulin to be disposed of, suggesting being particularly useful in patients with insulin resistance triggered or maintained by hyperinsulinemia as well as in those with insulin secretory defects that prevent a normal glucose tolerance. Moreover, ketogenic diets seem to promote a non-atherogenic lipid profile and reduce blood pressure, particularly when associated with weight loss. Interestingly, ketogenic dietary interventions with ad libitum caloric intake or only moderate caloric restriction (trials reviewed in [26]), produce strong reductions in fasting insulinemia, HOMA-index, and postprandial insulin responses [27] that are disproportionate to the modest differences in weight loss compared to control diets. Therefore, the reduction in insulin concentrations and the improvement in insulin resistance appear to be a direct consequence of this dietary regimen and not mediated by weight loss. The above-mentioned trials [26] showed that the ketogenic diet reduces both parameters in a few weeks but did not ascertain the temporal primacy of one of them. Therefore, it is still unclear whether a ketogenic diet primarily affects insulin secretion and then insulin sensitivity or the reverse. A reasonable hypothesis is that each of the ketogenic meals in this dietary plan essentially produces an almost flat insulin-secretory response thanks to the very limited amount of carbohydrates provided, despite the presence of proteins and lipids that are known to stimulate insulin secretion, even prior to that ketosis is established and independently from weight loss. Support for this hypothesis would require the exact knowledge of the amount of insulin that is secreted after a ketogenic meal in comparison to an isocaloric Mediterranean meal, but this is not available. The quantification of insulin required to metabolize a certain meal type cannot be directly obtained by the observation of circulating peripheral insulin concentration profiles, because insulin extraction dynamically changes during the meal as insulin receptors are saturated [28]. However, the quantification of insulin secretion and insulin extraction can be derived by modeling the profile of C-peptide, co-secreted with insulin but unaffected by first-pass hepatic extraction [29]. The aim of this study was therefore to measure the insulin secretory response to a typical ketogenic meal providing ~$40\%$ of individual energy needs and to compare it to the response to an isocaloric Mediterranean meal in healthy subjects, by modeling the circulating glucose, insulin, and C-peptide profiles. ## 2.1. Subjects This study was conducted at the International Center for the Assessment of Nutritional Status (ICANS), University of Milan (Italy). Twelve healthy subjects ($50\%$ females), adults with an age range of 19–31 years, and with a normal weight (BMI range 19.7–24.7 kg/m2), were recruited on a voluntary basis among students at the University of Milan. Exclusion criteria were the following: overweight or obese; the presence of diseases causing significant impairment of nutritional status (i.e., Crohn’s disease, neoplasia, end-stage renal failure, cirrhosis, congestive heart failure, and chronic infection); endocrine diseases (i.e., hyper-hypothyroidism and diabetes mellitus); consumption of medications affecting endocrine function within the previous two months; recent (<1 month) occurrence of acute illness or injury; elite athleticism. The characteristics of the study subjects are reported in Table 1. This study was conducted according to the guidelines laid down in the Declaration of Helsinki. Approval was obtained by the Institutional Ethical Committee (n. $\frac{32}{17}$, 19 September 2017) and informed consent was signed by all subjects. ## 2.2. Experimental Protocol In a randomized, cross-over study, the subjects received mixed standardized meals of different compositions on two different days spaced apart by a washout period of 7 days. The meals were consumed at 9 o’clock in the morning after having fasted for at least 12 h. Each meal satisfied ~$40\%$ of an individual’s total energy requirement, estimated by multiplying predicted resting energy expenditure [30] by the corresponding physical activity level [31]. Each subject consumed two meals of identical energy content but differing in macronutrient composition: Mediterranean meal: consisted of a sandwich of white bread, ham, oil, and tomato. Of the energy provided (39.1 ± $1.3\%$ of TEE), 55.4 ± $1.1\%$ was derived from carbohydrates (136.4 ± 16.3 g), 27.0 ± $0.8\%$ from lipids, and 17.6 ± $1.0\%$ from protein; the content of dietary fiber was 7.1 ± 1.0 g (7.4 ± 0.3 g/1000 kcal). Ketogenic meal: consisted of mascarpone cheese, hazelnuts, and cocoa powder with a ketogenic ratio (the ratio of the fat amount in grams to the combined protein and carbohydrate amount in grams) of 4:1 (mean 3.95, range 3.78–4.08). As previously described [3,32], of the energy provided (39.1 ± $1.0\%$ of TEE), 3.4 ± $0.2\%$ was derived from carbohydrates (7.0 ± 1.1 g), 89.5 ± $0.2\%$ from lipids, and 7.1 ± $0.3\%$ from protein; the content of dietary fiber was 2.8 ± 0.4 g (2.9 ± 0.1 g/1000 kcal). The meal had a ketogenic ratio of 4:1 (4 g of lipids for every gram of carbohydrate and protein). The evening before each experiment, the subjects were asked to consume a standardized dinner consisting of rice or pasta dressed with oil and/or Parmesan cheese and/or tomato sauce, meat or fish, vegetables seasoned with olive oil, bread, and fresh fruit. Water was the only beverage allowed. On the day of the test, the subjects arrived at the laboratory at 8 o’clock in the morning: they were seated in a comfortable room and intravenous catheters were placed into an antecubital vein. Venous blood samples were obtained at baseline and at 10, 20, 30, 45, 60, 90, 120, and 180 min after test meal consumption in order to assess plasma glucose, insulin, and C-peptide. Further samples were collected at 240, 300, and 360 min after the ketogenic meal to account for the slower kinetics of gastric emptying, fat digestion, absorption, and metabolism. Blood samples were immediately centrifuged, and plasma was stored at −80 °C until laboratory analyses. Finally, we asked all subjects to fill in a Checklist of Medication Side Effects within 24 h after the experiments, to detect eventual adverse effects of the meals. ## 2.3. Laboratory Analyses We measured circulating levels of glucose, insulin, and C-peptide at baseline and every 10 min in the first half hour, and every 30 min thereafter. All parameters were assayed by a commercial kit (Roche Diagnostics) with Cobas Integra 400 Plus and Cobas 411 (Roche Diagnostic). ## 2.4. Analysis and Modelling of Meal Insulin secretion (pmol × min−1 × m−2): pancreatic insulin secretion, calculated from C-peptide deconvolution and normalized to estimated body surface area. Basal insulin secretion (pmol × min−1 × m−2): insulin secretion before the start of the meal. Total insulin secretion (nmol × m−2): integral of insulin secretion during the whole meal period. The parameters of the model were estimated from glucose and C-peptide concentrations by regularized least squares, as previously described [29]. Regularization involves the choice of smoothing factors that were selected to obtain glucose and C-peptide model residuals with SDs close to the expected measurement error (~$1\%$ for glucose and ~$4\%$ for C-peptide). Insulin secretion rates were calculated from the model every 5 min. The integral of insulin secretion during the meal was denoted as total insulin output. Incremental insulin secretion: was calculated as total insulin secretion − basal insulin secretion × duration of the study. Insulin clearance: was calculated in the fasting state as the ratio between fasting insulin secretion and fasting insulin concentration and during the meal as the ratio between the integral of insulin secretion and that of insulin concentration. Positive areas under the meal curve (AUC) were calculated by trapezoidal integration over the entire meal, only considering values above the baseline. ## 2.5. Statistical Analysis Statistical analysis was carried out using R version 4.2.1 [33]. Subjects’ characteristics are presented as a median and interquartile range, while results are presented as mean and standard error. To examine the influence of OGTT time and meal type on glucose, insulin, C-peptide, and insulin secretion rate, we used the two-way analysis of variance (ANOVA). Fisher’s least significant difference (LSD) procedure was used for post-hoc multiple comparisons of means. A p-value < 0.05 was considered statistically significant. ## 3. Results Means of OGTT parameters and modeled parameters by OGTT time, and results from two-way ANOVA are reported in Table 2. ## 3.1. Glucose, Insulin, and C-Peptide The time courses of glucose, insulin, and C-peptide concentrations after the Mediterranean and Ketogenic meals are shown in Figure 1. Fasting state. Fasting plasma glucose, insulin, and C-peptide were not clinically different between the test meal studies (Fisher LSD p-values 0.64, 0.94, and 0.90, respectively). Glucose. After the Mediterranean meal, glucose concentration increased and reached an incremental peak of 24 ± 3 mg/dL at 20 min, then decreased and returned to baseline at 60 min (Fisher LSD $$p \leq 0.17$$). After both meals, glucose concentration decreased significantly at 60 min (Fisher LSD $$p \leq 0.04$$) with a decremental nadir of −8 mg/dL, $95\%$ CI −15, −1 and then returned to baseline at 90 min (Fisher LSD $$p \leq 0.14$$). Compared to the Mediterranean meal, glucose concentrations after the ketogenic meal were significantly lower between 10 and 30 min (Fisher LSD p-values: 10 min $p \leq 0.001$, 20 min $p \leq 0.001$, 30 min $$p \leq 0.04$$). The mean glucose concentration during the whole study was not different (Mediterranean − ketogenic: 62 ($95\%$ CI 43, 80), $p \leq 0.001$), but the mean glucose concentration was higher in the first 60 min after the Mediterranean meal (Mediterranean − ketogenic: 11 ($95\%$ CI 2.5, 19), $$p \leq 0.015$$). Insulin. After the Mediterranean meal, insulin concentration increased and reached an incremental peak of 457 ± 99 pmol/L at 20 min, which was 12.4 ± 1.9 fold the basal concentration, then it decreased but remained elevated until the end of the study. After the ketogenic meal, insulin concentration increased significantly to reach an incremental peak of 44 ± 10 pmol/L at 30 min, which was 2.2 ± 0.4 fold the basal concentration, then it slowly decreased and returned to the basal concentration at 180 min (Fisher LSD $$p \leq 0.06$$). Compared to the Mediterranean meal, insulin concentrations after the ketogenic meal was significantly lower at all time points after baseline (all Fisher LSD p-values < 0.001, except at 180 min $$p \leq 0.01$$). The mean insulin concentration during the study was also lower (Mediterranean − ketogenic: 250 ($95\%$ CI 171, 329), $p \leq 0.001$). C-peptide. After the Mediterranean meal, C-peptide concentration increased and reached an incremental peak of 5.41 ± 0.65 ng/mL at 30 min, which was 4.24 ± 0.28 fold the basal concentration, then it decreased but remained elevated for the whole of the study. After the ketogenic meal, C-peptide concentration increased significantly to reach an incremental peak of 0.83 ± 0.14 ng/mL at 30 min, which was 1.56 ± 0.11 fold the basal concentration, then it slowly decreased but never returned to the basal concentration during the duration of the study. Compared to the Mediterranean meal, incremental C-peptide concentrations after the ketogenic meal were significantly lower at all time points after baseline (all Fisher LSD p-values < 0.001). The mean C-peptide concentration during the study was also lower (Mediterranean − ketogenic: 4.0 ($95\%$ CI 3.2, 4.8), $p \leq 0.001$). ## 3.2. Modeled Insulin Secretion and Insulin Clearance The time course of the insulin secretory rate is shown in Figure 2. Fasting state. Fasting insulin secretion and clearance were consistent with previous reports in healthy subjects [34,35] and were not clinically different between the test meal studies (Mediterranean − ketogenic: insulin secretion −14 ($95\%$ CI −32, 2.7), $$p \leq 0.091$$; insulin clearance −0.36 ($95\%$ CI −0.95, 0.22), $$p \leq 0.2$$). Insulin secretion. After the Mediterranean meal, insulin secretion increased and reached an incremental peak of 554 ± 97 pmol × min−1 × m−2 at 10 min, which was 8.1 ± 1.3-fold the basal concentration, then it decreased but remained elevated for the whole study. After the ketogenic meal, insulin secretion increased significantly to reach an incremental peak of 45 ± 8 pmol × min−1 × m−2 at 10 min, which was 1.5 ± 0.1 fold the basal concentration, then it slowly decreased and returned to the basal concentration at 60 min (mean compared to 0: $$p \leq 0.3$$). Compared to the Mediterranean meal, incremental insulin concentrations after the ketogenic meal were significantly lower at all time points after baseline (all Fisher LSD p-values < 0.001). The mean insulin secretion during the study was also lower (Mediterranean − ketogenic: 238 ($95\%$ CI 201, 276), $p \leq 0.001$). Table 3 reports total insulin secretion and the mean insulin clearance in the 3 h after the meals. Table 3 shows that the basal rate of insulin secretion increased to a peak rate that was 8.9 ± 1.2 folds the basal after the Mediterranean meal, whereas the peak rate after the ketogenic meal was only 1.8 ± 0.1 folds the basal. During the 3-h study, the incrementally secreted insulin was 17 ± 2 times larger after MED than the KETO meal, which would correspond to approximately 11.9 ± 0.8 IU of insulin vs 0.8 ± 0.1 IU of insulin. Finally, insulin clearance was reduced during the test when compared to basal values (0.54 ± 0.04 times during the Mediterranean meal, 0.70 ± 0.12 times during the ketogenic meal), but the effect was more pronounced after the Mediterranean meal (total/basal insulin clearance was 0.16 ± 0.12 times lower in the Mediterranean meal). ## 4. Discussion The quantification of insulin required to metabolize a certain meal type is basic information that may help to predict or to explain the prandial response of subjects affected by insulin secretory defects or by insulin resistance, and to rationally allocate patients to personalized dietary treatments. We found that a Mediterranean meal accounting for $40\%$ of daily dietary intake, requires, for its metabolism, the production of 7.8 ± 0.8 times the amount of insulin compared to fasting values, temporarily spiking the insulin secretory rate to 8.9 ± 1.2-fold the basal values. This marked insulin response is further amplified in the peripheral tissues by a reduced insulin clearance after the meal (OGTT insulin clearance was 0.54 ± 0.04 times the basal values after the Mediterranean meal), which is largely due to first-pass liver uptake. In sharp contrast, a ketogenic meal accounting for the same caloric amount requires a much lesser amount of insulin, increased by a small fraction of that produced post-absorptively, and smaller changes in insulin clearance. Several points need to be underscored to place our results in the proper context. First, we describe here the insulin secretory responses of healthy subjects. We do not anticipate that the response to the ketogenic meal could be amplified or further reduced in subjects with obesity, metabolic syndrome, or type 2 diabetes, but after a Mediterranean meal, we can imagine an increased insulin secretion in insulin-resistant subjects and the opposite in subjects with insulin secretory defects, both situations potentially leading to hyperglycemia. Both situations could benefit from a ketogenic meal, as reduced prandial insulin secretion and glucose concentration would translate into better insulin sensitivity, a hypothesis that deserves further investigation. Second, we tested an isocaloric meal, that is not intended to be placed in the context of a hypocaloric diet. This experiment allowed us to quantify the physiologic effect of a ketogenic meal independent of caloric restriction. The information derived can be translated into the context of a maintenance diet, but, obviously, insulin secretion is expected to be reduced if smaller size meals are provided to pursue weight loss. Third, we tested the effect of a single meal on subjects that were following a western diet. From our data, we cannot predict the insulin secretory responses of subjects in chronic ketosis on a ketogenic diet. Nonetheless, the insulin concentrations profiles reported by [27] were flattened after ketogenic meals in comparison to control meals in a way very similar to the experiments described here. Taken together, our data suggest that the disposal of a ketogenic meal requires an amount of insulin secretion that is smaller compared to a Mediterranean meal, independently from caloric reduction, weight reduction, and prevailing ketosis. The role of a ketogenic diet in the management of obesity, metabolic syndrome, type 2 diabetes, and other insulin-resistant conditions has been mainly exploited in the context of weight loss programs in which the generation of ketone bodies may reduce appetite, promote satiety and provide the brain with a fuel alternative to glucose [36]. Our study underscores another aspect that can be valuable in the treatment of the same diseases, i.e., that ketogenic meals (even single meals) can satisfy individual energy requirements without significant amounts of insulin secretion. This is an essential feature of ketogenic meals that would help to reduce hyperinsulinemia-driven insulin resistance [22] and would help patients with limited insulin secretory capability to metabolize their food intake without developing significant hyperglycemia. In practical terms, this is reflected in the number of international units of insulin that were required in our study to manage the number of carbohydrates contained in the Mediterranean and ketogenic meal, which are slightly less than the amounts that are suggested as a starting point for carbohydrate counting in type I diabetes [37], probably due to the difference in efficiency between exogenous and endogenous insulin. Notice that the hyperinsulinemic action of the Mediterranean meal is not limited to first-phase insulin secretion, but is also presented in the second phase, where we see an elevation of insulin concentrations despite normalization of glucose levels approximately at the end of the first hour of OGTT. This may be due to the overall meal size and mediated by incretin action. [ 35] highlighted how two meals of different sizes (in their study 260 kcal and 520 kcal) can produce quite different insulin profiles while maintaining similar glucose excursion after the meal: in the larger meal, they show an equally fast insulin response in the first phase, coupled with a prolonged and heightened insulin secretion in the second phase. They show how this difference is mediated by differences in Glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) secretion that cause insulin elevation despite normalization of blood glucose. In our study, the more dramatic effect may be due to the size of the even larger meals (951 [840, 1037] kcal for the Mediterranean meal and 950 [848, 1019] kcal for the ketogenic meal), and may also explain the insulin response to the ketogenic meal in absence of a significant carbohydrate intake [38]. Insulin clearance by the liver is a long-recognized phenomenon that is now being revaluated as a possible factor in diabetes risk. Insulin is secreted in a pulsatile fashion by pancreatic β cells into the portal vein and reaches the liver, where up to $80\%$ of secreted insulin is degraded after receptor-mediated uptake [39]. The evolutionary reasons behind this first passage to the liver are not well understood, but our data reinforce the notion that insulin clearance is a dynamic variable and controllable parameter in the overall regulation of systemic insulin levels [40]: when insulin requirements are higher, such as after eating a high-carbohydrate meal (here the Mediterranean meal), insulin clearance does appear to be suppressed more than after a low-carbohydrate meal (here the ketogenic meal), in order to achieve higher systemic insulin concentrations at equal insulin secretion rates. This study has several strengths: the cross-over design limited the influence of inter-personal variability on the outcomes; the caloric content of the meals was tailored to individual energy requirements, to more closely mimic the free-living weight-maintenance diet; modeling of pancreatic parameters has highlighted the physiological bases of the differences displayed in insulin response of the two meals. Nonetheless, this study has some limitations: the sample size was small, although reasonably sized to the expected effect size; basal energy requirements were predicted rather than measured. In conclusion, we have shown that a ketogenic meal is disposed of with only a minimal insulin secretory response compared to a Mediterranean meal. This finding may be of interest to patients with insulin resistance and or insulin secretory defects. ## References 1. Desli E., Spilioti M., Evangeliou A., Styllas F., Magkos F., Dalamaga M.. 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--- title: 'RETRACTED: A Mendelian Randomization Analysis Investigates Causal Associations between Inflammatory Bowel Diseases and Variable Risk Factors' authors: - Mohamed J. Saadh - Rashmi Saxena Pal - José Luis Arias-Gonzáles - Juan Carlos Orosco Gavilán - Darshan JC - Mohamed Mohany - Salim S. Al-Rejaie - Abolfazl Bahrami - Mustafa Jawad Kadham - Ali H. Amin - Hrosti Georgia journal: Nutrients year: 2023 pmcid: PMC10005338 doi: 10.3390/nu15051202 license: CC BY 4.0 --- # RETRACTED: A Mendelian Randomization Analysis Investigates Causal Associations between Inflammatory Bowel Diseases and Variable Risk Factors ## Abstract The question of whether variable risk factors and various nutrients are causally related to inflammatory bowel diseases (IBDs) has remained unanswered so far. Thus, this study investigated whether genetically predicted risk factors and nutrients play a function in the occurrence of inflammatory bowel diseases, including ulcerative colitis (UC), non-infective colitis (NIC), and Crohn’s disease (CD), using Mendelian randomization (MR) analysis. Utilizing the data of genome-wide association studies (GWASs) with 37 exposure factors, we ran Mendelian randomization analyses based on up to 458,109 participants. Univariable and multivariable MR analyses were conducted to determine causal risk factors for IBD diseases. Genetic predisposition to smoking and appendectomy as well as vegetable and fruit intake, breastfeeding, n-3 PUFAs, n-6 PUFAs, vitamin D, total cholesterol, whole-body fat mass, and physical activity were related to the risk of UC ($p \leq 0.05$). The effect of lifestyle behaviors on UC was attenuated after correcting for appendectomy. Genetically driven smoking, alcohol consumption, appendectomy, tonsillectomy, blood calcium, tea intake, autoimmune diseases, type 2 diabetes, cesarean delivery, vitamin D deficiency, and antibiotic exposure increased the risk of CD ($p \leq 0.05$), while vegetable and fruit intake, breastfeeding, physical activity, blood zinc, and n-3 PUFAs decreased the risk of CD ($p \leq 0.05$). Appendectomy, antibiotics, physical activity, blood zinc, n-3 PUFAs, and vegetable fruit intake remained significant predictors in multivariable MR ($p \leq 0.05$). Besides smoking, breastfeeding, alcoholic drinks, vegetable and fruit intake, vitamin D, appendectomy, and n-3 PUFAs were associated with NIC ($p \leq 0.05$). Smoking, alcoholic drinks, vegetable and fruit intake, vitamin D, appendectomy, and n-3 PUFAs remained significant predictors in multivariable MR ($p \leq 0.05$). Our results provide new and comprehensive evidence demonstrating that there are approving causal effects of various risk factors on IBDs. These findings also supply some suggestions for the treatment and prevention of these diseases. ## 1. Introduction Chronic, progressive inflammatory bowel disease (IBD) can cause bowel injury, the condition for hospitalization, disability, and a reduction in life quality. Although North America and Europe have the highest rates, developing nations such as Asia have seen rising incidence rates as a result of these regions’ increased development [1]. Although the exact source of these phenomena is unknown, it can be connected with the intricate interaction between the environment and genetics [2]. Due to the statement that the incidence speeds of IBD are higher in developed countries, the latter has been linked to disease development [3]. In addition, the urbanization of societies is associated with changes in diet, antibiotic use, hygiene status, microbial exposure, and pollution, which have been implicated as potential environmental risk factors for IBD. Environmental risk factors for individual, familial, community-based, country-based and regionally based origin could all contribute to the pathogenesis of IBD [4,5,6]. Lending further support to the critical importance of environmental influences is the recognition of the central role of the gut microbiota in the development and propagation of inflammation in IBD [7]. Although host genetics might partly determine gut microbial structure, external environmental exposure from the time of birth to adulthood continue to alter the composition, structure, and function of the gut microbiome, thereby dynamically altering the risk and natural history of disease throughout life [8,9]. Discovering how environmental factors influence the onset of IBD and contribute to its pathogenesis could ultimately help to determine how individuals can reduce their risk of disease or have a milder clinical course. The search for pathogenic environmental factors is also important, as many unmet therapeutic needs and suboptimal outcomes in IBD remain. Mechanistic insights obtained from robustly defining environmental influences could also lead to the identification of new therapeutic targets and treatment strategies. The best treatment should be started early in disease progression to avoid problems, because IBD can be a progressive and chronic condition. To conduct clinical remission and mucosal healing, which can improve life quality, the current treatment paradigm for ulcerative colitis (UC), Crohn’s disease (CD), and non-infective colitis (NIC) uses biological therapy [10]. However, measuring the causal impact of these variable factors on IBD can be challenging due to possible reverse causality and potential residual confounders that can induce spurious associations and mask the effects of real risk factors. Therefore, it is essential to clarify whether these possible risk factors recreate causal functions in the evolution of IBD or serve as transferred risk factor consequences. In addition, genetic and environmental variables work together to cause the disease, but the exact mechanisms are still poorly understood, particularly concerning the non-genetic hazards. Hundreds of variations have already been correlated to IBD by genome-wide association studies (GWASs) [11,12], but our understanding of the different risk variables and genetic relations that influence IBD is still restricted. Numerous factors that may affect the IBD risk have been specified by prior studies, but these investigations have not always come to the same results, in part because of the difficulties in establishing satisfactory statistical authority, avoiding bias, and correcting for confounding aspects [13,14]. Additionally, only a small number of research studies have examined the genetic–environmental interactions linked to IBD, including single-nucleotide polymorphisms (SNPs) [15,16]. Therefore, both environmental and genetic elements play a function in the complex etiology of IBD. Numerous modifiable factors have been investigated [12], but none are reliably detrimental nor protective. There is currently a lack of solid research to conduct IBD prevention. In addition, there has not been an organized action to compile and evaluate this evidence, despite the vast number of research studies that have looked at environmental factors and IBD, including Mendelian randomization (MR) analyses. MR is an instrumental variable analysis method for examining causal relationships between disease outcomes and risk factors [17]. It employs genetic variants that are closely connected with a risk factor as instrumental variables (IVs) and mimics a randomized controlled background in which all other factors excluding exposure are almost distributed over subgroups. Thus, MR analysis can avoid reversing causation and confounding biases that are common in observational studies. In this study, two-sample Mendelian randomization analysis was performed to investigate the causal effects of 37 genetically predicted potential risk factors and nutrients, including related diseases, drugs, lifestyle, surgeries, lipid and glucose metabolism, blood parameters, and obesity traits, on IBD (including UC, CD, and NIC) in the European population. This study aimed to provide a comprehensive overview of putative variable risk factors for IBD and offer novel insights into the etiology of IBD or colitis disorders. ## 2.1. MR Design MR was utilized to investigate the relationships between various risk factors and IBD or different types of colitis. A total of 37 primary risk factors were selected and classified into eight categories: exposure to drugs, lifestyle behaviors, surgeries, related diseases, blood parameters, lipid metabolism, glucose metabolism, and obesity traits. SNPs associated with these risk factors were utilized as the instrumental variables. The following three assumptions served as the foundation for the MR study: [1] the SNPs are closely connected with the risk factors; [2] the SNPs are irrelevant to various confounders; [3] the SNPs only influence the outcomes via the risk factors. This study was performed according to the STROBE-MR procedures (Figure 1). ## 2.2. Selection of Genetic Instruments GWASs of participants of European ancestry were selected as data sources for genetic agencies associated with the 37 risk factors. Genetic instruments of cigarettes per day, smoking, and alcoholic drinks were extracted from the GSCAN (GWAS and Sequencing Consortium of Alcohol and Nicotine use) consortium [11]. GWAS summary statistics for coffee, tea, fruit, and vegetable intake; vitamin D consumption; and breastfeeding were obtained from the MRC-IEU (MRC Integrative Epidemiology Unit) consortium. IVs for education level and physical activity were chosen from SSGAC (Social Science Genetic Association Consortium) [18]. GWAS summary statistics for appendectomy, tonsillectomy, and autoimmune diseases were received from FinnGen [19]. The UK Biobank study was utilized as the data source for GWAS summary statistics for lipid metabolism traits, including n-3 PUFA, n-6 PUFA, triglycerides, apolipoprotein A-I, and total cholesterol [20]. Genetic instruments for whole-body fat mass and body mass index (BMI) were selected from Neale Lab (http://www.nealelab.is (accessed on 24 May 2022)). GWAS summary statistics for waist circumference, waist-to-hip ratio, and hip circumference were obtained from GIANT (Genetic Investigation of Anthropometric Traits) [21]. For CKD [22], cesarean delivery [23], celiac disease [24], SLE [25], blood calcium [26], blood lactose [27], zinc intake [28], T2D [29], fasting glucose, HbA1c, fasting insulin [30], antibiotics, and Isotretinoin [31,32] were selected from associated GWAS studies. Significance SNP levels ($p \leq 5$ × 10−8) were obtained, and those with a window (≥10,000 kb) and smaller linkage disequilibrium probability (R2 > 0.001) were included. ## 2.3. GWAS Summary Statistics for IBD Cohorts GWAS summarization for UC, CD, and NIC was received from the FinnGen consortium. The R5 release of the FinnGen data was utilized [19]; this data set contains 1213 cases and 164,254 controls for CD, 2155 cases and 186,103 controls for UC, 411 cases, and 103,973 controls for NIC. All selected GWASs from the Biobank obtained ethical approval from FinnGen Steering Committee, and individuals provided informed consent. ## 2.4. Statistical Analysis The F-statistic was utilized to evaluate genetic instrument strength. F-statistics (F = beta2/se2) were calculated for each SNP, and a general F-statistic was calculated for all SNPs for the corresponding exposure. F > 10 was considered to be sufficient strength. All F-statistics were over 10. The random-effect inverse-variance-weighted (IVW) technique was utilized as the main analysis method to estimate the association between genetic liability to modifiable risk factors and the risk of pancreatitis. Given that the analysis is sensitive to outliers and horizontal pleiotropy, three sensitivity analyses, including the weighted median, MR-Egger, and MR-PRESSO methods, were used to examine the consistency of the results. The weighted median model was used; this can produce unbiased estimates under the precondition that at least $50\%$ of the selected IVs are valid [33]. MR-Egger regression was used to obtain cogent causal estimates under the influence of pleiotropy [34]. The MR-PRESSO method was conducted to specify outliers due to the existing pleiotropy; causal effect estimates were obtained with the IVW approach after removing these outliers [35]. In addition, a leave-one-out sensitivity test was applied to examine if the SNPs possessing effective horizontal pleiotropic effects could affect the causal estimates [36]. MR-PRESSO and Cochran’s Q statistics were used to evaluate pleiotropy and heterogeneity, respectively. The multivariable MR analysis of the genetic associations between the instruments and UC was adjusted for appendectomy, while the multivariable MR analysis of the associations between the instruments and CD was adjusted for alcohol consumption and smoking. All statistical analyses were performed using R 4.2.1, (R Foundation for Statistical Computing, Vienna, Austria), with the R packages “TwoSampleMR” (https://github.com/MRCIEU/TwoSampleMR) and “MRPRESSO”. ( https://github.com/rondolab/MR-PRESSO (accessed on 24 July 2022)). The results are reported as odds ratios (ORs) with corresponding $95\%$ confidence intervals (CIs). A Bonferroni-corrected significance level of $p \leq 1.67$ × 10−4 ($\frac{0.05}{30}$) was used and p-values ranging from 1.67 × 10−4 to 0.05 were classified as suggestive of causal associations. ## 3.1. Baseline Characteristics of the 36 Candidate Risk Factors Thirty-seven potential risk factors were included in the analyses. The risk factors were classified into eight categories: lifestyle behaviors, surgeries, exposure to drugs, related diseases, blood parameters, lipid metabolism, glucose metabolism, and obesity traits (Table 1). The lifestyle behaviors included smoking, alcohol consumption, coffee consumption, tea consumption, fruit intake, vegetable intake, breastfeeding, cesarean delivery, vitamin D intake, and physical activity. The surgeries included appendectomy and tonsillectomy, and the related diseases include T2D, CKD, and autoimmune diseases (including celiac disease and SLE). The blood parameters included calcium, lactose, CRP, and zinc. Exposure to drugs included antibiotics and isotretinoin. Additionally, three traits related to glucose metabolism, five traits related to lipid metabolism, and five obesity traits were analyzed. SNP numbers ranged from 11 to 523. Across the 37 variable potential risk factors that were explored, the F-statistics of their separate genetic instruments were all greater than the empirical threshold of 10, indicating no possible inadequate instrument bias. ## 3.2. Causal Effects of Various Factors on UC The univariable MR analyses revealed that genetically predicted appendectomy (OR = 1.368, $p \leq 0.001$) and vegetable intake (OR = 0.731, $$p \leq 0.001$$), fruit intake (OR = 0.726, $$p \leq 0.001$$), breastfeeding (OR = 0.791, $$p \leq 0.002$$), level of n-6 PUFAs (OR = 0.854, $$p \leq 0.038$$), and level of n-3 PUFAs (OR = 0.200, $$p \leq 0.001$$) were connected with increased risk of UC (Figure 2). Genetic predisposition to smoking, tea intake, fruit intake, higher triglycerides, and whole-body fat mass, as well as increased waist circumference, were suggestively associated with UC. The ORs were 1.342 ($$p \leq 0.021$$) for smoking initiation, 1.301 ($$p \leq 0.015$$) for tea intake, 0.475 ($$p \leq 0.021$$) for physical activity, 0.859 ($$p \leq 0.017$$) for total cholesterol, 1.363 ($$p \leq 0.003$$) for whole-body fat mass, and 1.392 ($$p \leq 0.005$$) for BMI. Possible pleiotropy and heterogeneity were observed for n-3 PUFAs (ppleiotropy = 0.035; pheterogeneity = 0.025) and n-6 PUFAs (ppleiotropy = 0.002; pheterogeneity = 0.005). Thus, MRPRESSO analysis was performed after eliminating the outliers. The association remained unchanging in the MRPRESSO-corrected outcomes (Supplementary Table S1). ## 3.3. Causal Effects of Various Factors on CD Genetically predicted appendectomy, tonsillectomy, cesarean delivery, fruit intake, and vitamin D intake were significantly related to raised risk of CD, while genetically predicted antibiotic exposure, smoking, alcohol consumption, vegetable intake, physical activity, autoimmune diseases, and T2D, as well as blood calcium, blood lactose, blood zinc, n-3 PUFA levels, and waist-to-hip ratio (WHR), were significantly associated with CD (Figure 3). The odds of CD increased with the increase in smoking (OR = 1.595, $$p \leq 0.005$$), alcoholic drinks per week (OR = 1.728, $$p \leq 0.020$$), blood lactose (OR = 1.024, $$p \leq 0.032$$), cesarean delivery (OR = 1.301, $$p \leq 0.002$$), and WHR (OR = 1.281, $$p \leq 0.023$$) and the decrease in vegetable intake (OR = 0.660, $$p \leq 0.011$$), blood calcium (OR = 1.729, $$p \leq 0.018$$), blood zinc (OR = 0.538, $$p \leq 0.017$$), and n-3 PUFAs (OR = 0.222, $$p \leq 0.021$$). Genetically predicted autoimmune diseases and T2D were suggestively related to raised risk of CD (autoimmune: OR = 1.123, $$p \leq 0.008$$; T2D: OR = 1.121, $$p \leq 0.029$$). There was possible heterogeneity in alcoholic drinks per week (pheterogeneity = 0.05) and appendectomy (pheterogeneity = 0.039). Physical activity (OR = 0.536, $$p \leq 0.006$$) and breastfeeding (OR = 0.710, $$p \leq 0.002$$) were protective of CD; however, possible pleiotropy for physical activity was observed (ppleiotropy = 0.041) (Supplementary Table S2). ## 3.4. Causal Effects of Various Factors on NIC Next, the causal relationships between risk factors and NIC were examined (Figure 4). Notably, genetic liability to alcohol consumption (OR = 1.545, $$p \leq 0.001$$) and fruit intake (OR = 0.497, $$p \leq 0.001$$) was strongly associated with increased odds of NIC. Genetic liability to smoking and appendectomy, as well as decreased vegetable intake, n-3 PUFA intake, and vitamin D deficiency, was suggestively related to raised risk of NIC. The odds of NIC increased with the increase in smoking (OR = 1.884, $$p \leq 0.018$$), appendectomy (OR = 1.876, $$p \leq 0.002$$), breastfeeding (OR = 0.710, $$p \leq 0.014$$), vegetable intake (OR = 0.266, $$p \leq 0.003$$), n-3 PUFA intake (OR = 0.476, $$p \leq 0.042$$), and vitamin D deficiency (OR = 0.435, $$p \leq 0.017$$). There was possible heterogeneity in appendectomy (pheterogeneity = 0.036) and n-3 PUFAs (pheterogeneity = 0.002) (Supplementary Table S3). ## 3.5. Multivariable MR Analysis of IBD In the multivariable MR model, smoking (OR = 1.415, $$p \leq 0.022$$), physical activity (OR = 0.519, $$p \leq 0.002$$), breastfeeding (OR = 0.466, $$p \leq 0.001$$), n-3 PUFAs (OR = 0.290, $$p \leq 0.017$$), n-6 PUFAs (OR = 0.785, $$p \leq 0.017$$), fruit intake (OR = 0.164, $$p \leq 0.009$$), vegetable intake (OR = 0.661, $$p \leq 0.017$$), and vitamin D (OR = 0.312, $$p \leq 0.047$$) had similar significant causal effects on UC after adjusting for genetically predicted appendectomy, whereas apolipoprotein A-I, total cholesterol, BMI, whole-body fat mass, hip circumference, and waist circumference did not reach statistical significance (Figure 5A). This suggests that these latter associations could be affected by appendectomy. Adjusting for the genetic risk of alcohol consumption and smoking did not change the associations between CD and antibiotic exposure (OR = 1.368, $$p \leq 0.003$$), physical activity (OR = 0.694, $$p \leq 0.013$$), appendectomy (OR = 1.281, $$p \leq 0.003$$), blood zinc (OR = 0.637, $$p \leq 0.019$$), fruit intake (OR = 0.164, $$p \leq 0.001$$), vegetable intake (OR = 0.630, $$p \leq 0.002$$), n-3 PUFAs (OR = 0.316, $$p \leq 0.023$$), and vitamin D (OR = 0.304, $$p \leq 0.021$$), while no significant associations remained between CD and autoimmune diseases, and T2D (Figure 5B). Finally, multivariable MR models of NIC were examined (Figure 5C). Smoking (OR = 1.884, $$p \leq 0.008$$), alcoholic drinks (OR = 1.186, $$p \leq 0.002$$), breastfeeding (OR = 0.344, $$p \leq 0.001$$), n-3 PUFAs (OR = 0.639, $$p \leq 0.002$$), fruit intake (OR = 0.598, $$p \leq 0.001$$), vegetable intake (OR = 0.367, $$p \leq 0.002$$), and vitamin D (OR = 0.636, $$p \leq 0.007$$) had similar significant causal effects on NIC after adjusting for genetically predicted appendectomy. Genetically predicted exposure to drugs, related diseases, blood parameters, glucose metabolism, and obesity traits were no longer significant risk factors for NIC in the multivariable MR model. ## 4. Discussion Genetic susceptibility factors play crucial functions in IBD development [37]. The development and progression of IBD are multidimensional, with interactions between environmental and genetic elements [38]. Therefore, a critical evaluation of the environmental and genetic factors related to IBD is offered in this study, which encompasses MR analyses of observational research. A total of 37 variables, such as lifestyle behavior, dietary intake, blood parameters, obesity traits, related diseases, drug exposure, and glucose and lipid metabolism were examined. Among them, we found 10 risk variables and 10 defensive factors with epidemiological evidence of moderate-to-high strength. The methodological standards among the MR analyses greatly differed. Several variables were connected with CD, UC, or NIC. The found specificity might represent various pathogeneses and traits of these diseases [39]. Subgroup analyses for smoking [40,41,42,43,44,45], breastfeeding [46], cesarean delivery [23,47], and high vitamin D levels have found significant differential relationships among groups [48]. These variations could be explained by unique genetic predispositions and environmental exposures happening in particular geographical regions. The investigation by publication year revealed more conventional calculations for fiber intake and breastfeeding in analyses disseminated after the year 2000 [12,49,50]. Cigarette smoking and alcohol use are two well-recognized lifestyle risk factors for colitis. Smoking promotes the progression from US to CD and accelerates the development of IBD [43,51]. MR analysis confirmed that smoking was related to a higher risk of IBD. The impacts of smoking on NIC were partially attenuated after adjusting for alcohol consumption, suggesting that this association is not robust enough in NIC. Alcohol consumption contributes to the progression or initiation of IBD and amplifies the relationship between the risk factors of genetics and CD in a dose-dependent manner [52]. Our results verified the causal associations between CD or NIC and alcohol consumption. Notably, the risk of CD due to genetically predicted alcohol consumption was higher than that of NIC. However, there was no proof of a positive connection between UC and alcohol consumption. Furthermore, alcohol accounts for 40–$70\%$ of CD etiologies and only $20\%$ of NIC etiologies; thus, this causal relationship may be statistically attenuated by other NIC risk factors. The associations between coffee or tea consumption, and IBD are controversial. Some studies have reported that tea and coffee reduce the risk of colitis, while another prospective cohort analysis discovered no relationship between tea consumption and the risk of colitis [53,54]. Our MR study found no associations between genetically predicted coffee consumption and colitis risk. Moreover, appendectomy, autoimmune diseases, and CKD were associated with increased risk of IBD events [55,56,57,58,59,60]. Autoimmune diseases, including celiac disease, and SLE have been reported to be associated with IBD in previous studies. The current MR analysis supports a suggestive association between autoimmune diseases and CD; however, this association did not persist when corrected for smoking and alcohol consumption [61,62,63,64]. No effect was seen for causal connections between IBD and celiac disease, SLE, nor CKD in this study. Additionally, appendectomy was associated with IBD. However, the risk was reduced if more than five years had expired between colitis diagnosis and appendectomy, proposing that the connection may be biased by unneeded appendectomies operated in individuals with developing colitis [56,57]. Tonsillectomy was linked with CD but not NIC or UC. However, patients undergoing tonsillectomy have often been exposed to previous antibiotics [65]. Furthermore, the current results indicate that physiologically normally higher blood calcium levels raised CD risk, while there were no connections between IBD and genetic predisposition to higher lactose and CRP. Retrospective case-control studies were used to determine the pre-illness diet in order to infer the significance of nutrition in CD and UC development. According to pediatric case-control research study by Amre et al. [ 66], there is a negative correlation between vegetable or fruit intake and the development of CD. The impacts of dietary micronutrients and macronutrients on the risk of disease have also been more robustly estimated by previous research in North America and Europe. Women in the most elevated percentile of dietary fiber consumption had significantly decreased risk of incident CD than those in the lowest quintile in a considerable cohort of 170,776 females followed for 28 years [67]. In contrast, dietary fiber from bran, cereal, and whole grains was not linked to modified risk of disease. The highest percentage of dietary fiber intake from fruits was related to decreased CD risk [67]. Consuming fruit lowered the risk of developing CD and UC [68,69,70,71]. In addition, eating vegetables reduced the risk of UC. These results have some biological plausibility. Fibers can impact the function of the intestine, which is compromised in inflammatory bowel disease (IBD), and plant ingredients can influence the microorganism translocation across the intestine mucosa [72]. CD was also linked to lactose level but did not impact UC, which is consistent with a previous study [73]. Dietary n-3 PUFA consumption has been also shown, in two prospective cohort studies, to be inversely related to UC risk, but dietary n-6 PUFA consumption is positively connected with incident UC risk [74,75]. n-3 PUFA consumption reduced the clinical colitis severity in an investigation of generated colitis in mice [76]. These results are consistent with other studies that showed the preventive effect of n-3 PUFAs on the risk of IBD [77,78,79]. In addition, the current results suggested that high vitamin D levels were related to diminished risk of UC, CD, and NIC, and these associations persisted in the CD cohort after adjusting for alcohol consumption [80,81]. Furthermore, high zinc intake was found to be negatively correlated with women’s chance of improving CD in some cohort investigations [77,82]. With 16 mg daily zinc consumption, or double the suggested everyday dose, the risk was detected to be reduced. Low blood zinc was linked to raised risk of surgeries, hospitalizations, and illness-related complications in colitis patients [83]. Additionally, improvement in results was linked to the normalization of zinc levels. Zinc supplementation was linked to a decline in intestinal permeability, as specified by the mannitol:lactulose ratio, in small interventional research [84]. In addition, apolipoprotein A-I was previously reported to be related to the severity of UC [85]. However, these results provided no effects of any associations between IBD risk and genetically predicted apolipoprotein A-I. Notably, genetically predicted total cholesterol was suggestively connected with a lower odds ratio of UC, whereas these associations were not significant after adjustment for appendectomy. There were no relationships between total cholesterol and IBD, in agreement with a previous investigation [86]. A previous meta-analysis documented a positive link between CD risk and T2D [87,88]. However, there were no associations between UC nor NIC and T2D in the findings of our study. Despite this, we observed a suggestive relationship between CD risk and T2D; however, this relationship was not significant after adjusting for alcohol consumption. Causal associations between IBD and fasting glucose, fasting insulin, or HbA1c were not observed in this study. Furthermore, suggestive associations between obesity traits and UC were observed, which is consistent with previous research [89,90]. However, these associations did not persist after adjusting for appendectomy, suggesting that elevated risk of appendectomy due to higher BMI or whole-body fat mass may explain this relationship. It is worth mentioning that some protective factors for colitis were also identified in the present MR study. Relationships between colitis, and both physical activity and breastfeeding have not been reported in previous studies. Genetically predicted breastfeeding was also associated with lower risk of UC, CD, and NIC. Lack of breastfeeding has been linked to immune-mediated illnesses and clostridium difficile colonization [91]. The protective effect might be mediated by increased innate mucosal immunity development as a result of microbiome interaction [92,93]. Physical activity may modulate colitis risk by affecting multiple pathways, including individuals’ health behaviors, living environments, and lifestyles [94]. Our results also verified the causal associations between CD and cesarean delivery. Notably, the risk of CD due to genetically predicted cesarean delivery was higher than that of UC and NIC, which is consistent with previous research [23,47]. However, there was no proof of a positive connection between UC or NIC and cesarean delivery. Furthermore, antibiotics raised the likelihood of eventual CD but not UC or NIC. In initial experiments, a dose–response association was found; therefore, IBD risk was elevated by all antibiotic classes [33]. Antibiotics can change the taxonomic richness and diversity of the human gut microbiota while impairing its metabolic condition, altering the composition of the microbiome [26,95]. Antibiotics have been observed to exacerbate the dysbiosis prevalent in CD patients, and a more changed microbiota has been detected in CD than in NIC or UC [34,96]. Isotretinoin was not linked to UC, CD, or NIC, according to the results. There are several strengths of the attending investigation related to the data source and research design. First, the MR design promoted the computation of the causal links between heritable complex traits, which avoids the biases inherent in conventional observational epidemiological studies. We applied multiple sensitivity computations to verify the plausibility of the instrumental variable assumptions and interpreted the outcomes after viewing horizontal pleiotropy and outliers. Second, this study systematically analyzed the most extensive number of variable causal factors for IBD to date. In this regard, no MR studies have analyzed the causal consequences of genetic liability on potential risk factors for IBD or colitis. Third, the GWAS data utilized in this analysis were primarily taken from participants of European ancestry, which can decrease the bias of population stratification. Aside from autoimmune diseases, this study avoided sample overlap between most exposure types and outcomes, thereby controlling the type 1 error to be as low as possible. Nonetheless, there are some limitations of the current study that also need to be considered. First, as in all MR studies, it is difficult to confirm a lack of bias for horizontal pleiotropy. Thus, the MR-PRESSO global analysis and MR-Egger regression were operated to detect widespread horizontal pleiotropy [29,30]. Importantly, the results of this study remained robust after the discarding of outlier variants identified using the MR-PRESSO outlier test. Second, the sample size for NIC was rather small, which could limit the power of statistics to detect true causal relationships. ## 5. Conclusions In this study, an MR investigation comprehensively reveals the causal relationship between IBD and a variety of lifestyle factors, associated disorders, drug exposure, surgeries, blood markers, lipid metabolism, glucose metabolism, nutrients, and obesity. Additionally, this study lists the precise genera of variable risk factors implicated in UC, CD, NIC, or IBD pathogenesis. Our discovery may potentially provide fresh perspectives on the design of targeted IBD, UC, NIC, and CD prevention and therapy strategies. ## References 1. Shivashankar R., Lewis J.D.. 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--- title: 'A Pilot Study: The Reduction in Fecal Acetate in Obese Patients after Probiotic Administration and Percutaneous Electrical Neurostimulation' authors: - Octavian Parascinet - Sebastián Mas - Tianyu Hang - Carolina Llavero - Óscar Lorenzo - Jaime Ruiz-Tovar journal: Nutrients year: 2023 pmcid: PMC10005340 doi: 10.3390/nu15051067 license: CC BY 4.0 --- # A Pilot Study: The Reduction in Fecal Acetate in Obese Patients after Probiotic Administration and Percutaneous Electrical Neurostimulation ## Abstract Previous data suggested that anti-obesity interventions, such as percutaneous electric neurostimulation and probiotics, could reduce body weight and cardiovascular (CV) risk factors by attenuation of microbiota alterations. However, potential mechanisms of action have not been unveiled, and the production of short-chain fatty acids (SCFAs) might be involved in these responses. This pilot study included two groups of class-I obese patients ($$n = 10$$, each) who underwent anti-obesity therapy by percutaneous electric neurostimulations (PENS) and a hypocaloric diet (Diet), with/without the administration of the multi-strain probiotic (Lactobacillus plantarum LP115, Lactobacillus acidophilus LA14, and Bifidobacterium breve B3), for ten weeks. Fecal samples were used for SCFA quantification (by HPLC-MS) in relation to microbiota and anthropometric and clinical variables. In these patients, we previously described a further reduction in obesity and CV risk factors (hyperglycemia, dyslipemia) after PENS-Diet+Prob compared to PENS-Diet alone. Herein, we observed that the administration of probiotics decreased fecal acetate concentrations, and this effect may be linked to the enrichment of Prevotella, Bifidobacterium spp., and Akkermansia muciniphila. Additionally, fecal acetate, propionate, and butyrate are associated with each other, suggesting an additional benefit in colonic absorption. In conclusion, probiotics could help anti-obesity interventions by promoting weight loss and reducing CV risk factors. Likely, modification of microbiota and related SCFA, such as acetate, could improve environmental conditions and permeability in the gut. ## 1. Introduction In fifty years, obesity has increased from $4.8\%$ to $12.9\%$ of the adult population, and from $0.8\%$ to $6.7\%$ in children and adolescents [1,2,3]. Obesity is a complex metabolic pathology caused by several genetic and nongenetic agents, such as environmental factors. It manifests as changes in body appearance but also correlates with glycemic and lipidemic alterations, oxidative stress, chronic inflammation, and increased risk of lethal diseases [3]. In fact, obesity is a major risk factor for type-2 diabetes mellitus (T2DM) and cardiovascular diseases (CVD) [4,5], and the interrelationship between these pathologies may suggest the participation of common mechanisms. Several studies that involved animals and humans have recently demonstrated a striking connection between the development of CVD and an imbalance in the gut microbiota composition (dysbiosis) along with the presence of their derived metabolites [6,7]. Up to 100 trillion symbiotic microbes live in the gut. Healthy microbiota in humans is highly diverse and mainly composed of Firmicutes, Bacteroides, Proteus, Actinomycetes, Fusobacteria, and Verrucomicrobia [8]. Under obesity, bacterial microbiota may suffer alterations in taxonomic diversity and composition, as well as in gut distribution. Notably, a metagenomic study using 16S rRNA gene sequencing revealed microbiome alterations between obese and lean mice [9,10]. Microbiota modifications were linked with two dominant bacterial phyla, Firmicutes and Bacteroidetes. The ratio of Firmicutes/Bacteroidetes has been proposed as a marker for obesity. However, this ratio has been found variable along animal and human studies [11]. Interestingly, these bacteria produce different substrates and metabolites to promote or inhibit the growth of different microorganisms. Additionally, these products can be assimilated into the bloodstream along the intestine leading to different effects on the organism [12,13]. Active elements include short-chain fatty acids (SCFA), vitamins, amino-acids and antioxidant, anti-inflammatory, and analgesic products, as well as potentially harmful agents such as carcinogens and immunotoxins [14,15]. Short-chain fatty acids (SCFAs) are residual metabolites excreted by the gut microbiota after the degradation of dietary fiber and indigestible carbohydrates. Commensal bacteria such as Bifidobacterium, Bacteroides, Enterobacter, Faecalibacterium, and Roseburia species may be able to ferment these fibers and carbohydrates into SCFA. SCFAs are fatty acids composed of two to six carbons: hexanoic, pentanoic, and more abundantly, acetic, propionic, and butyric acid. They are not only required for the nutritional demands of microorganisms, but also impact host immunity and metabolism as well as regulating local atmosphere conditions and growth of other bacteria [16]. SCFA have been related to beneficial cardiometabolic outcomes in adiposity, glycemia, insulin sensitivity, inflammation, and dyslipemia [16,17,18]. However, after obesity, the levels of fecal and plasma SCFA in clinical studies have been controversially described [19,20,21]. Nevertheless, SCFA-producing microbiota might account as a promising target to control metabolic alterations under obesity. Some multi-strain probiotic made of Lactobacillus and/or Bifidobacterium have enhanced obesity and associated CV risk factors in clinical trials and animal models [22,23,24,25,26,27]. Other approaches like the percutaneous electro-neurostimulation of the T6 dermatome (PENS) led to weight loss by production of a somato-autonomic reflex that slow stomach emptying and induce early satiety [28]. This intervention can increase patient adherence to diet by regulation of growth hormones, ghrelin, and IGF-1 [28,29]. In fact, in a previous report [30], we described that addition of probiotics to PENS under hypocaloric diet further improved weight loss and the glycemic and lipid profile in class-I obese patients, in parallel to an enrichment of specific bacteria. However, potential mechanisms of these anti-obesity interventions have not been elucidated. Herein, our aim was to seek for a metabolic link between those microbiota alterations and the beneficial outcomes produced by the probiotic administration. ## 2.1. The Pilot Study As described in Lorenzo et al. [ 26], this pilot study (NCT03872245) was performed in the Obesity Unit of the Garcilaso Clinic in Madrid (Spain), including two groups of class-I obese patients ($$n = 10$$, each) with a female/male ratio of 2.33 in both cases, who underwent anti-obesity therapy by percutaneous electric neurostimulations (PENS) and hypocaloric diet (Diet), with/without administration of the multi-strain probiotic (Adomelle®; Bromatech, Milan, Italy). Exclusion criteria were (a) untreated endocrine diseases causing obesity, (b) previous treatment with hormones, prebiotics, probiotics, or with nutritional supplements, (c) diagnosis of previous CVD or cancer, or (d) portable electrical devices. ## 2.2. PENS, Hypocaloric Diet, and Probiotic Administration Patients who previously were unsuccessfully treated only with the hypocaloric diet were randomly assigned to the PENS-Diet or PENS-Diet+Prob for ten consecutive weeks. The PENS of dermatome T6 was performed by using the Urgent PC 200 Neuromodulation System® (Uroplasty, Minnetonka, MN, USA), as previously described [30]. Patients were placed in a supine position and PENS was delivered by a needle electrode inserted in the left upper quadrant along the medio-clavicular line at two centimeters below the ribcage and at 0.5–1 cm of depth. The PENS was undertaken at a frequency of 20 Hz at the highest amplify (0–20 mA) without causing any pain. The participants underwent one 30-min session every week for ten consecutive weeks. In addition, a 1200 Kcal/day diet was uni-formly prescribed during PENS interventions in both groups of patients, as previously described [30]. The diet followed a Mediterranean style (carbohydrates $51\%$, proteins $23\%$ and fat $26\%$) with a high intake of fruit and vegetables, a moderate intake of meats, and olive oil as the main source of fat. A record of food intake was applied along the study. Also, all patients followed an exercise activity of 1h/day brisk walking. The multi-strain probiotic consisted of a mixture of Lactobacillus plantarum LP115 (<1 × 109 colony forming units, CFU), Lactobacillus acidophilus LA14 (1 × 109 CFU), and Bifidobacterium breve B3 (<1 × 109 CFU). It was given (2 tablets/day) with water after meals, without altering the amount of food intake. Additionally, all patients followed an exercise activity of 1 h/day brisk walking. The work was carried out in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki). The Ethical Committee of Clinical Research (Medicine, Esthetic, and Longevity Foundation) approved this investigation (ref.: Garcilas-19-3; Feb 2019). ## 2.3. Clinical and Microbiota Variables Clinical variables such as BMI (kg/m2), weight loss (WL), the percentage of total weight lost (%TWL), the percentage of excess BMI lost (%EBMIL), systolic (SBP) and diastolic (DBP) blood pressure, fasting glucose, glycated hemoglobin (HbA1c), and the lipid profile [triglycerides (TG), total cholesterol, LDL-cholesterol (LDLc), HDL-cholesterol (HDLc)] were measured at the Clinical Analytical Department of the Hospital Fundación Jiménez Díaz. Additionally, fecal samples were isolated and frozen (−80 °C) before and after the PENS-Diet or PENS-Diet+Prob treatments to analyze the intestinal microbiota [30] and the composition of short-chain fatty acids (SCFAs). ## 2.4. Fecal SCFA Quantification Fecal samples were thawed and derivatized with 3-nitrophenylhydrazine (3-NPH), as described by Han et al. [ 31]. The derivatization process started by mixing 50 μL of fecal matter with 50 μL of AcN ($50\%$) in deionized water. The mixture was then centrifuged for 10 min at 5000 g under 4 °C. Forty μL of the supernatant was mixed with 20 μL of 200 mM 3-NPH, 20 μL of 120 mM 1-Ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC), and $6\%$ pyridine (Sigma-Aldrich, Burlington, USA). They were incubated for 30 min at 40 °C. Finally, 920 μL of AcN ($10\%$) in deionized water was added into each tube and samples were frozen at −30 °C until HPLC-MS analysis. The standard samples of SCFA (acetic, propionic, and butyric acid) were prepared in a $50\%$ AcN:H2O solution and derivatization was carried out in the same manner as fecal samples. The analysis for acetate, propionate, and butyrate was performed by LC-MS/MS at the mass spectrometry facility of Complutense University (Madrid). The quantitative analysis by MRM used an LC-ESI-QQQ 8030 Shimadzu mass spectrometer and a Phenomenex Gemini 5 μm C18 110 A 150 × 2 mm column (Agilent, Santa Clara, USA). A phase gradient was applied to a 20 μL injection volume: phase A (H2O + $0.01\%$ formic acid) and phase B (AcN + $0.01\%$ formic acid). The flow rate of the mobile phase was stabilized at 0.6 mL/min, and the total elution time of the compounds was set at 11 min. Firstly, phase B was applied at $20\%$ for 2 min, after which phase B was set up to $40\%$ for 5 additional minutes. Then, phase B was raised from $40\%$ to $100\%$ (from minutes 8 to 11) and later, it returned to the initial conditions (Supplementary Table S1A). Mass spectra of the parental and fragmented ions were used for quality and quantity determination of SCFAs (Supplementary Table S1B). ## 2.5. Statistical Analysis The statistical analyses were performed by R 4.1.1 software. The Shapiro–Wilk test was used to analyze the normality of variables. Then, non-parametric tests were used for all variables. Within each group (PENS-Diet or PENS-Diet+Prob), the Wilcoxon signed-rank test was used to compare median values of metabolite concentrations before and after treatment. The Mann–Whitney test was performed to compare the differential values of each metabolite between the PENS-Diet and PENS-Diet+Prob treatments. Spearman’s correlation was used to analyze the relationship between SCFA concentration and clinical and microbiota variables. Finally, a quantile regression analysis was performed for variables showing greater association in Spearman’s correlation. ## 3.1. Probiotics Administration Further Reduced Obesity and CV Risk Factors The characterization of this pilot study was previously published by Lorenzo et al. [ 30]. Briefly, at baseline, there were no significant differences in BMI, age, and sex between the PENS-Diet and PENS-Diet+Prob groups. After treatments, PENS-Diet induced a significant reduction in body weight, systolic and diastolic blood pressure, fasting glucose, plasma triglycerides, and total cholesterol (Table 1a). However, PENS-Diet+Prob triggered a further improvement in weight loss, %TWL, and %EBMIL, and a reduction in plasma HbA1c and triglycerides, in addition to elevated HDLc levels (Table 1a). Additionally, there was a significant association between probiotic administration and the differences between these factors (not shown). Thus, the addition of probiotics to the PENS-Diet promoted a higher enhancement against obesity and cardiovascular risk factors [30]. However, the probiotics-derived mechanisms of action are not fully known, and modification of microbiota and their metabolites could be involved. ## 3.2. Probiotics Induced Microbiota Alterations and Decreased Fecal Acetate As described [30], the PENS-Diet+Prob intervention significantly reduced the Firmicutes/Bacteroidetes ratio and enriched Prevotella spp., A. muciniphila, and Bifidobacterium spp. compared to PENS-Diet (Table 1b). Interestingly, Bifidobacterium spp. and A. muciniphila have been associated with improvements in gut dysbiosis, cardiometabolic markers, and insulin resistance by the regulation of fecal and plasma SCFA [20,32,33]. Thus, we next quantified the levels of the most abundant fecal SCFA in our patients. In PENS-Diet, we detected non-significant variations of SCFA between before and after treatment, whereas in PENS-Diet+Prob subjects, acetate was significantly lessened (−$54\%$, $$p \leq 0.023$$) and butyrate and propionate exhibited a reduction trend (Figure 1). Additionally, both the PENS-Diet and PENS-Diet+Prob groups exhibited significant positive correlations between changes in SCFAs. In PENS-Diet, differential acetate significantly correlated with propionate (Rho = 0.94, $p \leq 0.01$) (Figure 2a), while in PENS-Diet+Prob, differential acetate was also significantly linked to butyrate (Rho = 0.89, $p \leq 0.01$) and propionate (Rho = 0.89, $p \leq 0.01$) (Figure 2b). Thus, fecal SCFAs, particularly acetate and butyrate, may be decreased by gut microbiota after probiotics administration. In this regard, by univariate quantile regression, we found that in PENS-Diet, differential acetate and butyrate were significantly associated with each other (β = 8.39, $p \leq 0.01$, and β = 0.119, $p \leq 0.01$) (Figure 3a,b), and acetate was associated with propionate (β = 5.31, $$p \leq 0.059$$) (Figure 3c). In PENS-Diet+Prob, differential acetate was associated with butyrate (β = 2.27, $$p \leq 0.078$$) (Figure 3a,b) and was propionate in both ways (β = 2.63, $p \leq 0.01$ and β = 0.28, $p \leq 0.01$) (Figure 3c,d). ## 3.3. Association between SCFAs, Bacterial Microbiota, and Cardiovascular Risk Factors The reduction in fecal SCFAs might be associated with the enrichment of specific bacteria and with the improvement of clinical outcomes in obese patients. In PENS-Diet subjects, a negative correlation between differential acetate and Enterococcus (Rho = −0.67, $$p \leq 0.035$$) was noted. Additionally, propionate was inversely correlated with Lactobacillus (Rho = −0.93, $$p \leq 0.008$$) and butyrate with Bacteroidetes (Rho = −0.86, $$p \leq 0.014$$) (Figure 4a). Interestingly, butyrate and propionate concentrations directly correlated with total cholesterol (Rho = 0.76, $$p \leq 0.049$$ and Rho = 1, $p \leq 0.01$, respectively) (Figure 2a). On the other hand, in PENS-Diet+Prob, reductions in acetate could be associated with Actinobacteria decrease (Rho = 0.71, $$p \leq 0.047$$), while butyrate levels might be positively linked to Bifidobacterium spp. ( Rho = 0.71, $$p \leq 0.047$$) (Figure 4b). However, fecal butyrate might be inversely correlated with fasting glucose (Rho = −0.71, $$p \leq 0.047$$) and directly with HDLc (Rho = 0.78, $$p \leq 0.023$$) (Figure 2b). ## 4. Discussion In this pilot study, an anti-obesity intervention by PENS and a hypocaloric diet for ten weeks induced weight loss and improvement of blood pressure, glycemia, and hyperlipidemia in class-I obese patients. Importantly, the concomitant administration of probiotics (L. plantarum, L. acidophilus, and B. breve B3) led to further amelioration of these parameters. These probiotics enhanced the growth of Prevotella spp., Bifidobacterium spp., and A. muciniphila, and reduced the Firmicutes/Bacteroidetes ratio. As a potential consequence, the SCFA acetate decreased in fecal samples and this effect could be linked with clinical outcomes. SCFA can induce anorexigenic and insulinotropic peptides (i.e., leptin, PYY, GLP-1) and stimulate anti-inflammatory responses [16,17]. In obese mice, exogenous administration of butyrate reduced hepatic steatosis and inflammation, improving the gut barrier integrity [34]. Both propionate and butyrate increased plasma incretins and insulin sensitivity [17], and acetate enhanced cardiac hypertrophy, insulin sensitivity, and oxidative stress, and elevated plasma HDLc levels [35]. Also in these mice, amelioration of obesity was associated to the probiotics stimulated fecal production of SCFA [25,26]. However, after obesity, the levels of fecal SCFA (acetate, propionate, and butyrate) have been controversially described. A reduction of SCFA has been mostly observed in obese rodents [25,26], but in human obesity, variable concentrations of SCFA have been unveiled [19,20,21]. The concentration of fecal SCFA is inherently derived from their production and absorption rates. Most of SCFA absorption is in proximal colon and thus, caecal SCFA levels are directly correlated with their concentrations [36]. In contrast, an inverse link between fecal SCFA (i.e., acetate) and their absorption rate was previously reported [37]. Likely, gut barrier can be disturbed in obesity by alterations in microbiota, mucus, immune system, and environmental conditions (pH, water, ions) [38], and thus, SCFA permissibility and their potential benefits could be diminished. In obese subjects, the higher presence of stool SCFA were associated with reductions in A. muciniphila and Bacteroides, and increased blood pressure, proinflammatory markers, and the lipid/glycemic profiles [21]. A. muciniphila has been described as a mucin-degrading bacteria with protective roles on intestinal gut barrier [39,40]. In this regard, reconstitution of unbalanced microbiota may be achieved by enrich-ment with specific bacteria from probiotics. Multi-strain formula of probiotics has elicited favorable activities against metabolic and cardiovascular diseases. They improved body weight, insulin resistance, GLP-1 release, and hyperlipidemia [8]. Previous reports have tested the combination of both Lactobacillus and Bifidobacterium probiotics in diet-induced obese mice [41]. Remarkedly, this combination led to higher weight loss and hypoli-pidemic effects than probiotics alone. Thus, multi-strain probiotics may induce faster growing and stabilization of their bacteria and trigger synergetic actions on host intestine by metabolites production, which could lead to attenuation of metabolic and cardiovas-cular risk factors. In this sense, a multi-strain probiotic made of Lactobacillus and Entero-coccus produced higher concentrations of SCFA (i.e., acetate and butyrate) than each bacte-rium alone [42]. Probiotics might also enhance other SCFA-producing bacteria and in-crease SCFA permeability at the intestine [43,44]. SCFA could promote gut barrier repair by triggering other bacteria and enterocytes and colonocytes growing [16,45]. The close correlation between acetate and butyrate levels also suggests their positive action on intes-tinal permeability. In this line, Bifidobacterium spp. and A. muciniphila can generate acetate and promote butyrate synthesis by other bacteria [46,47]. In our study, PENS-Diet+Prob, but not PENS-Diet alone, enriched Prevotella spp., Bifidobacterium spp., and A. muciniphila, which could encourage gut barrier integrity by balancing microbiota and releasing acetate. Then, acetate and other SCFA might be better assimilated to promote anorexigenic, insulinotropic, and anti-inflammatory responses, helping on the reduction in body weight, glycemia, and hyperlipidemia [34]. Also, acetate-consuming bacteria with favorable actions would have obtained nutrients to grow and regain gut eubiosis. Interestingly, we found only significant reductions for fecal acetate and tendencies to decrease for propionate and butyrate, after probiotics. Likely, longer treatments of these probiotics (or others) might have influenced also on more SCFA. In this sense, administration of Lactobacillus rhamnosus for 20 weeks in obese women provoked a decrease in both fecal acetate and butyrate [48]. Moreover, acetate may be more sensitive to obesity, diet modifications or probiotics than other SCFA. De la cuesta-Zuluaga et al described a greater increase of acetate than that of propionate and butyrate in overweight/class-I obese individuals, compared to their lean counterparts [21], and higher degrees of obesity have been associated with elevation of several fecal SCFA (acetate, propionate, and butyrate) [19]. Altogether, this multi-strain probiotics may reduce obesity and CVD risk factors at least in part by increasing Bifidobacterium spp. and A. muciniphila and derivate SCFA like acetate. ## Limitations of the Study Although this is a pilot study, an obvious limitation is the reduced sampling size which can influence statistical power. Additionally, a group of subjects who follow only a diet regime, PENS intervention, or probiotic intake could offer comparative data about microbiota distribution and metabolite release. Since multiple factors (presence of comorbidities, habits, etc.) could influence probiotics and SCFA actions, our data should be taken with care. Finally, a direct comparison of SCFA levels in plasma and fecal samples and the analysis of gut tissue could quantify alterations in SCFA absorption under obesity and after treatments. All these variables will be considered in a future study. ## 5. Conclusions Administration of probiotics could be useful at least for coadjutant therapy for ameliorating body weight and CVD risk factors under obesity. Probiotics may enrich specific bacteria and change microbiota composition and distribution along the intestine. A mix of probiotics Lactobacillus plantarum, Lactobacillus acidophilus, and Bifidobacterium breve B3 induced the growth of Prevotella spp., Bifidobacterium spp., and A. muciniphila. Interestingly, some of these bacteria can produce metabolites, such as acetate, with potential cardioprotective actions (hypolipidemic, insulinotropic, anti-inflammatory). In turn, acetate might enhance the gut environment and permeability to selective nutrients and metabolites, and thus, it could favor their assimilation to the systemic circulation. More clinical assays are required to investigate the gut absorption rates and potential cardioprotective actions of SCFA under obesity, with and without probiotic administration. ## References 1. Chooi Y.C., Ding C., Magkos F.. The epidemiology of obesity. Metabolism (2019) 92 6-10. DOI: 10.1016/j.metabol.2018.09.005 2. Blüher M.. Obesity: Global epidemiology and pathogenesis. Nat. Rev. Endocrinol. (2019) 15 288-298. DOI: 10.1038/s41574-019-0176-8 3. 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--- title: Time in Bed, Sleeping Difficulties, and Nutrition in Pregnant New Zealand Women authors: - Barry William McDonald - Patricia Ellyett Watson journal: Nutrients year: 2023 pmcid: PMC10005346 doi: 10.3390/nu15051130 license: CC BY 4.0 --- # Time in Bed, Sleeping Difficulties, and Nutrition in Pregnant New Zealand Women ## Abstract We consider the relationship between time in bed (TIB) and sleeping difficulties with demographic variables and nutrient intakes in the second (T2) and third (T3) trimester of pregnancy. Data were acquired from a volunteer sample of New Zealand pregnant women. In T2 and T3, questionnaires were administered, diets were obtained from one 24 h recall and three weighed food records, and physical activity was measured with the use of three 24 h diaries. In total, 370 women had complete information in T2 and 310 in T3. In both trimesters, TIB was associated with welfare or disability status, marital status and age. In T2, TIB was associated with work, childcare, education and pre-pregnancy alcohol consumption. There were fewer significant lifestyle covariates in T3. In both trimesters, TIB declined with increasing dietary intake, especially water, protein, biotin, potassium, magnesium, calcium, phosphorus and manganese. Adjusted for weight of dietary intake and welfare/disability, TIB declined with increasing nutrient density of B vitamins, saturated fats, potassium, fructose and lactose; and TIB increased with carbohydrate, sucrose and vitamin E. Subjective sleeping difficulties increased with the week of gestation, morning sickness severity, anxiety, dairy and saturated fat intake, and they decreased with fruit, vegetable and monounsaturated fat intake. The study highlights the changing influence of covariates throughout the pregnancy and corroborates several published findings on the relationship of diet and sleep. ## 1. Introduction There has been much recent interest in the relationship between diet and sleep, with several broad-based reviews [1,2,3,4,5,6,7]. The evidence shows that those with short sleep duration tend to consume more energy from fats [8,9], while high-carbohydrate (CHO) diets tend to be associated with longer sleep. Evidence regarding protein is mixed, and a recent review has concluded it has little effect on sleep [10]. The relationship between diet and sleep duration is thought to be bidirectional [1,4,6] and possibly non-linear, with optimal sleep duration (neither too short nor too long) being associated with healthier diets [11,12]. The timing of nutrient intake (chrononutrition) is also important [13,14]. The focus of sleep studies varies. Some observational studies (usually large samples) concerning sleep and diet focus on sleep duration—for example, Refs. [ 12,15,16]. Other studies consider a number of different aspects of sleep quality, of which duration is only a part: characteristics such as overall time in bed (TIB), sleep latency (time between going to bed and falling asleep), sleep efficiency (proportion of bed time actually asleep), frequency of awakening, or sense of not feeling refreshed after a night’s sleep [17,18,19]. The methods of measuring sleep also vary. Laboratory-based studies using polysomnography are considered the gold standard for comprehensive measurement of sleep quality [2], but they are usually limited to small samples, such as those in intervention studies—for example, Ref. [ 20]. Wrist-mounted actigraphs are now a popular alternative for moderate-sized samples, again allowing accurate and objective measurement of a number of sleep characteristics [16,17,21]. An activity diary is another objective night-by-night measure of TIB or sleep, although inevitably with more error than electronic measurement. Activity diaries have the virtue of serving large samples with minimal cost. For example, Ref. [ 22] considers activity in 2454 children and adolescents, reported by a parent or caregiver, based on two 24 h activity diaries for one randomly chosen weekday and one randomly chosen weekend day (Ref. [ 17] recommends that the averages be based on more night-by-night measures). Other studies measure sleep based on a subjective estimate of average sleep over a week or month. Larger studies tend to measure sleep duration through a question, such as “How much sleep do you usually get at night on weekdays or workdays” (NHANES question) [23], or similar [12,14,15]. The responses may then be categorized in hours. Sleep quality is frequently assessed using the Pittsburgh Sleep Quality Index (PSQI) [24], which considers seven aspects, with a total score that is usually dichotomized to distinguish good and poor quality sleep. Poor quality sleep tends to be associated with very short or very long sleep duration and chronic health problems [4,24,25]. There has been limited research on the relationship between diet and sleep specifically in pregnant women. In the GUSTO study of pregnant Singaporean women [26], data from a 24 h recall were used in a healthy eating index and an analysis of dietary pattern as defined by principal components analysis. The researchers found that a healthier diet was associated with better quality sleep, as measured by PSQI, but did not find a relationship between diet and sleep duration. A study of pregnant African American women [27] found shorter TIB being associated with higher intake of fruit and vegetables, and diet variables were related to sleep timing (midpoint of time asleep). A study of pregnant overweight or obese women [17] found that those with a more pro-inflammatory diet (high in sugar, fats, ultra-processed and fast foods) differed significantly in two aspects of sleep quality from those with anti-inflammatory diet (high in fruits, vegetables, fish and grains); namely, they had significantly longer sleep latency and some evidence of a longer period of lying in bed after waking. A study of pregnant Australian women [28] found those with diets with a high percentage of energy from CHO and monounsaturated fatty acids (MUFA) had poorer sleep quality, but there was no difference found for duration. Although not focusing on diet, a study [29] found women who were overweight or obese prior to pregnancy and who then had excess gestational weight gain (indicative of higher energy intake) tended to have shorter sleep duration and more sleep disruption. The study of diet and sleep in pregnancy is complicated by changes that occur over the course of pregnancy. It is well recognized that sleep quality (though not necessarily duration) tends to deteriorate in the third trimester [30,31]. A study of Saudi women [32] found that PSQI rose (i.e., quality deteriorated) in the second and third trimester compared to the first trimester, and sleep duration was longer in late pregnancy. That study found that women with low income, low serum vitamin D, high energy intake and long periods of sitting down were particularly likely to find their sleep quality deteriorating over the course of the pregnancy. On the other hand, there is mixed evidence of a change in diet over pregnancy. The GESTAFIT study of Spanish women [33] found that third-trimester women had higher intakes of fruits, vegetables and whole dairy products but no other significant changes in intake or in adherence to a Mediterranean food pattern; their overall conclusion was that food behavior did not change over pregnancy. A study of overweight and obese American women [34] found no significant difference in fat intake or fruit and vegetable intake across the three trimesters of pregnancy. By contrast, the GUSTO study of Singaporean women [35] found increasing consumption of milk, fruit and vegetables and decreasing intake of tea, coffee, soft drinks and seafood as the pregnancy progressed. There have been other studies in young-to-midlife women, where pregnancy was not a factor. A study of 80 young Japanese women [21] examined dietary intake and quality and their association with sleep efficiency, as measured by actigraphy. They found that energy intake, protein intake and intakes of vitamin K and B2 and several minerals were lower in a group with low sleep efficiency. A much larger sample of midlife Mexican women [36] identified dietary patterns based on a food frequency questionnaire, with sleep quality measured by PSQI. They found that those following a healthier diet tended to have better sleep quality. A study of 92 non-pregnant female Saudi students found high polyphenol intake to be a protective factor against poor sleep [37]. A study of Iranian women and infants post-partum [38] found that better quality diet—as measured by the dietary approaches to stop hypertension (DASH) criterion—was associated with better quality sleep according to the PSQI. The present study considered the relationship between nutrient intake and TIB in New Zealand (NZ) pregnant women in the second and third trimesters and the effect of associated covariates. TIB was used as the response because the data were gleaned from 24 h activity diaries, and not all women distinguished sleep from bed rest. We also examined whether there was any association with subjective difficulty sleeping. The purpose was to find evidence that confirms the literature—or not—on the relationship of nutrient intake with sleep or TIB and to suggest new associations to be explored in future research. ## 2. Materials and Methods This article considers data from the “Nutrition in Pregnancy” study collated by the authors at Massey University, NZ [39]. The ethical approval for the study was obtained from the Massey University Human Ethics Committee and the Auckland Ethics Committee North Health. The funding was provided for 500 subjects, with selection biased toward a greater proportion of NZ Māori or Pacific Island Polynesian women and women of lower socioeconomic status than in the general population. Subjects lived in rural and urban centers in the upper North Island of NZ and were volunteers recruited mostly through advertising in free child-health clinics or religious or community facilities throughout the study area, with some recruited by media advertising and word-of-mouth. As the aim was to provide a representative snapshot of nutrition in the pregnant population, there was no preliminary dietary information given to the volunteers, but they were supplied with personalized information after the data were collected. In total, 504 women around the 14th week of pregnancy were recruited. Subjects were matched to an interviewer of their own ethnicity. Interviewers had some qualification in nutrition or community health and were trained to administer questionnaires in an unbiased way to elicit 24 h dietary recall data, obtain physical measurements (e.g., skinfolds) and to train the subjects to fill out 24 h dietary records and activity diaries. The interviewers visited each subject at a place of her choosing near the start of month 4 (second trimester—T2) and month 7 (third trimester—T3) of her pregnancy and also post-partum. Questionnaires were administered in the subject’s preferred language to determine demographic, medical, health and lifestyle details. The questionnaires were similar in content and format to those used to determine this information in NZ national nutrition surveys [40], for which statistical reliability and validity had been established. The study questions and protocols were assessed by independent nutritional experts, ethics committees and approved by the NZ Ministry of Health who funded the study. Maternal height, weight and triceps, biceps and costal skinfolds were measured using calibrated standard equipment according to the procedures set out by Gibson [41]. Gestational age was calculated from the last date of menstruation. The severity of morning sickness was ascertained by a questionnaire, coded as: None [0], Nausea occasionally [1], Nausea few hours/day [2], Nausea and vomit few hours/day [3], Nausea all day [4], Nausea all day, vomit occasionally [5], Vomit all day [6], Hospitalized [7]. Dietary intake was assessed in both T2 and T3. The interviewer administered a 24-hour dietary recall, followed by a 3-day food record kept by the subject. In the recall, the interviewer used numerous aids to assess the weight or volume of each food and beverage portion consumed in the previous 24 h. After the recall interview and training, subjects recorded in their preferred language all food and beverage portions consumed over 3 days using the measuring cups and spoons provided to assess the volume ingested. Days were not necessarily consecutive; every four days of diet assessment included one weekend day. Foodworks, utilizing the NZ Food Composition database (NZ Institute for Plant and Food Research), was employed to calculate the nutrient intake for each woman each day. Subsequent analysis of variance of nutrient intake found no significant difference overall between the 24-hour recalls and 3-day food records in T2 and T3 diets ($$p \leq 0.099$$, Pillai’s test), nor were there significant differences in nutrient density between the methods (MANOVA $$p \leq 0.158$$). Therefore, the 24-hour recall data were combined with the 3-day diet record data to generate a mean intake of each nutrient for each woman in T2 and T3. In addition to beverages, tap water intake was also estimated in the questionnaires in T2 and T3 by recall without aids to estimate the volume, and hence, the values are less accurate than for dietary water [42]. Around the same time in T2 and T3, subjects completed three 24 h physical activity diaries. The diaries had squares for every 10 min, from 9 a.m. one day to 9 a.m. the following day, although subjects could start at any time of day. The diaries did not have to be consecutive days. Subjects recorded in each square what they were doing for most of the time in that 10 min period. Subjects were prompted that “if you were doing anything for a long period of time, e.g., sleeping, just write ‘sleep’ in the first square and an arrow to when you finished sleeping”. However, some wrote “bed”, for example, so our data were unable to distinguish actual sleep from lying down at rest. As there may be a difference in activity between T2 and T3, this article considers the relationship between minutes of overall TIB and nutrient intake in each trimester separately. The T2 and T3 figures represent two different snapshots of (in most cases) the same individuals but using separate nutrient and TIB data each month ($76\%$ of T2 respondents and $91\%$ of T3 respondents feature in both analyses). Data were checked using standard statistical techniques (e.g., crosstabs, scatterplots). Minitab 19 was used for statistical analysis. TIB was found to vary widely according to work and health status, etc. Therefore, to reduce the effect of outliers on the analysis, the most extreme $1.5\%$ of short durations and $2.5\%$ of long down durations were winsorized (replaced by 420 and 780 min, respectively). The latter cutpoint (13 h) is abnormally long for sleep alone and may indicate health problems, but the range of times still gives quantitative insight into individuals who need more or less sleep or bed rest. The winsorized TIB durations were used as the dependent variable in bivariate and multiple linear regressions. Nutrient intakes were analyzed on a log-to-base-2 scale, so that regression slopes can be interpreted as the effect of doubling the intake. Nutrient density (intake per MJ of energy consumed) was also considered. The variables considered as possible covariates included maternal anthropometric measures, ethnicity, socioeconomic measures, work hours, family, lifestyle and childcare details, morning sickness, alcohol consumption prior to pregnancy and gestational age at the time of interviews We could not adjust for all possible confounders because of our relatively small sample; instead, we first identified significant non-nutritional covariates in simple regression and then tried them in a multiple regression model for TIB. The maximal multiple regression model with all significant variables (p ≤ 0.05) was chosen, but to ensure no effect was missed, all reasonable alternative variables were re-examined for any significant predictors. The finalized regression models were based on cases with complete data for the included predictors. The nutrients discussed below are those that were significant in the presence of significant background covariates and remained significant when other nutrients were included. In addition, the T2 interview included some questions on typical weekly and daily consumption of broad food groups. One question was “How many times a week do you eat…?” with the following food groups: Breads/Cereals; Fruit; Vegetables; Milk/Dairy; Meat/Alternatives; Takeaways. The response options were: Daily (coded 4); 3–4 times a week [3]; 1–2 times a week [2]; Rarely [1]; Never [0]. A follow-up question asked “How many times a day do you eat…” with the same food groups as above and with the subject’s open-ended responses converted into the number of servings. The coded values were considered as covariate values in a regression model. The questionnaire also had a question relevant to subjective sleep quality. In both T2 and T3, subjects were asked “Do you have any of the following:”, which listed a variety of symptoms or medical conditions, including “Difficulty sleeping”. Subjects could choose from the following options: Never (coded 0), Rarely (coded 1), Sometimes [2] and Often [3]. The answers to this question were analyzed as the dependent variable in an ordinal logistic regression model. Other symptoms, including nausea, constipation, frequent urination and others, were considered as potential covariates in a regression model. The questionnaire further asked “How have you been feeling lately?” with the following headings: Full of energy; Tired; Calm and Peaceful; Worn Out; Happy; Anxious; Fit and Well; Depressed. The response options in each case were: All the time (coded 4); Most of the time [3]; Some of the time [2]; A little of the time [1]; and None of the time [0]. The coded responses were considered as covariate values in a regression model. ## 3. Results Actual gestation at the T2 questionnaire averaged 21.2 weeks (standard deviation SD 5.2) and at the T3 questionnaire, 30.5 weeks (SD 2.9). Dietary information was collected for $96\%$ of women at T2 and $88\%$ at T3. The T2 physical activity diaries were completed by $73\%$ of subjects, and T3 diaries by $62\%$. Compliance at T2 was higher ($82\%$) among those in the top seventy percent of household incomes than those in the lower income group ($56\%$) and higher among Europeans and Asians ($85\%$) than among Polynesians ($46\%$). Median TIB was 585 min (9.75 h) for women in T2 ($$n = 371$$) and 597 min (9.96 h) at T3 ($$n = 310$$). This difference was not large (12 min) but significant ($$p \leq 0.043$$, paired t-test). The minimum was 305 min (5.1 h) and maximum 1115 min (18.6 h). To reduce the effect of such outliers on regressions, the bottom $1.5\%$ of times and top $2.5\%$ were winsorized to 420 and 780 min. This still enables an investigation of significant trends that separate long and short sleepers. There was no association between TIB and the proportion of out-of-bed time spent in high-energy-expenditure activities, such as sport/exercise or vigorous housework, or between TIB and the proportion of time in sedentary activities. ## 3.1. Demographic and Non-Dietary Variables As shown in Table 1, there was a significant decrease in TIB in T2 with increasing age (2.2 min less per year, $$p \leq 0.007$$), years of high school education (13.4 min less per year, $$p \leq 0.004$$) and household income level ($$p \leq 0.028$$). In T2, women in paid work averaged 29 min less TIB than those not in paid work ($$p \leq 0.001$$), or 0.7 min less per 1 h of paid work ($$p \leq 0.002$$), but by T3, there was no difference ($$p \leq 0.771$$). Those dependent on government welfare payments averaged 48 min more TIB than the others ($p \leq 0.001$). Rural women averaged 24 min more TIB than urban women in T2 ($$p \leq 0.015$$), but the difference did not persist through to T3. Three women were disabled, which was too few for significance, even though they averaged 108 min more TIB than non-disabled women. Married women had significantly less TIB than unmarried women (37 min in T3, $$p \leq 0.001$$), and there was a similar difference for presence of any live-in partner (40 min at T3), but the latter difference was not significant ($$p \leq 0.087$$), possibly due to small numbers of un-partnered women. There was no significant difference in TIB between nulliparous women and those with a child/children, but there was a marginal decrease in TIB with the number of preschoolers (children under 5 years); this became significant in multiple regression. TIB decreased marginally with increasing actual number of gestational weeks at T2 measurements (this became significant in multiple regression) but not at T3. There was no relationship between TIB and current morning sickness, but there was a marginal increase in TIB with the frequency of nausea ($$p \leq 0.075$$) and constipation ($$p \leq 0.071$$) (a subsequent table shows that difficulty sleeping was significantly related to the severity of morning sickness). On the other hand, women who felt full of energy in T3 spent significantly less TIB ($$p \leq 0.003$$). No association was found between TIB and being a smoker, BMI or frequency of feeling anxious or depressed, but there was an increase in TIB with the thickness of biceps skinfold ($$p \leq 0.045$$). Finally, in T2, there was an association with usual (pre-pregnancy) alcohol consumption; those who usually drank beer averaged 28.5 min longer TIB than those who did not ($$p \leq 0.004$$), while those who usually drank wine averaged 21.8 min less TIB than those who did not ($$p \leq 0.010$$). A similar association was found with the quantity of beer and wine consumed (square root of usual g of beer or wine). No bivariate association was found for the usual consumption of spirits (hard liquor), but in a multivariate model, the usual consumption of spirits pre-pregnancy was associated with less TIB at T2. Few women admitted drinking alcohol during pregnancy, and so, consumption during pregnancy was not a significant covariate of TIB. These results suggest that any study of sleep or TIB in pregnancy needs to take account of the gestational age of the fetus at the time of study, as both the sleep duration (or TIB) and the relevant covariates may vary throughout the course of pregnancy. Specifically, covariates related to pre-pregnant lifestyle may still affect sleep or TIB in early pregnancy but have diminished importance in later pregnancy. ## 3.2. Relationship of Individual Nutrient Intakes with TIB Table 2 shows that significant relationships exist between nutrient intakes and the winsorized TIB in both T2 and T3. Nutrients are analyzed on the log-to-the-base2 scale, which means that the slopes are interpretable as the change in minutes of TIB associated with a doubling of nutrient intake; for example, a woman who consumed twice the total water (in food, beverages and plain water) as another woman in T2 is estimated to spend 31 min less TIB than the other woman. The p-values tend to be smaller in T2 due to more data. The most significant predictors of decreasing TIB in both trimesters were increasing water consumption ($p \leq 0.001$) and increasing dietary intake (weight of food and beverages) ($p \leq 0.001$). The dry weight of intake and overall energy intake were not significant, although several individual nutrients (protein, thiamine, riboflavin, biotin, potassium, magnesium, phosphorus, calcium, manganese and copper) were significantly ($p \leq 0.05$) negatively related to TIB in both trimesters. In addition, T2 showed a significant decline in TIB with increasing glucose, fructose, pantothenate, niacin, folate and zinc, while T3 showed a significant decline with increasing lactose. The analysis shows that there are a large number of nutrient intakes potentially related to TIB. Some of this will be due to nutrient intakes being correlated. In the next subsection, we therefore consider nutrient density after adjusting for log2 (dietary weight). ## 3.3. Significant Relationships between TIB and Individual Nutrient Densities Table 3 only shows the significant relationships between nutrient densities and TIB. Nutrient density is defined here as log2 (nutrient intake/total energy in MJ), and it is a measure of diet quality. To adjust for diet quantity, log2 (dietary intake) is included in the regression. High dietary intake is related to shorter TIB. Table 3 also adjusts for whether or not the woman was dependent on government welfare payments (such as unemployment or sickness benefit) or was disabled; as mentioned in Section 3.1, women in this category averaged significantly higher TIB. Table 3 shows the relationships for significant individual variables. TIB was significantly longer in T2 if the diet was rich in carbohydrate, and sucrose in particular, and significantly shorter if the diet was rich in thiamin, riboflavin or niacin. In T3, the duration of TIB was shorter for diets rich in saturated fats. For example, doubling the sucrose density corresponded to an additional 16.6 min TIB in T2, while doubling the SFA density corresponded to 28 min less TIB in T3. ## 3.4. Multivariate Models for TIB Table 4 shows multiple regression models for TIB in each trimester. In T2, around month 4, there are several confounders, which help explain the variation in TIB: longer times with welfare/disability status ($$p \leq 0.002$$); shorter times with hours of paid work ($$p \leq 0.005$$), actual week of gestation ($$p \leq 0.022$$), number of preschool children ($$p \leq 0.001$$) and whether the woman usually drank spirits prior to pregnancy ($$p \leq 0.011$$). The last confounder may be related to societal differences, as well as to the alcohol itself. Longer TIB was associated with higher nutrient density of total sugars ($$p \leq 0.012$$) and vitamin E ($$p \leq 0.008$$) but negatively related to dietary intake ($p \leq 0.001$) and nutrient density of fructose ($$p \leq 0.008$$) as well as potassium ($$p \leq 0.002$$). By T3, most confounder variables no longer significantly explain the variation in TIB, with the exception of welfare/disability status ($p \leq 0.001$). Shorter TIB was associated with higher dietary intake ($p \leq 0.001$) and diets rich in saturated fatty acids ($p \leq 0.039$). The proportion of variation explained by confounders and diet is much lower in late pregnancy. ## 3.5. Dietary Pattern Analysis for Nutrient Density A factor analysis was carried out on the nutrient density variables to identify the possible associations between dietary pattern and TIB. Using the same confounders as in Table 4, and adjusted for log2 (dietary intake), only one marginally significant dietary pattern (factor) was discovered ($$p \leq 0.071$$) based on nutrient densities. In T2, this factor loaded positively on CHO and total sugars, especially sucrose, glucose and fructose, and vitamin C, and negatively on total fats, SFA, MUFA, PUFA, starch and sodium and chlorine. Women at two standard deviations above the mean for this factor (high sugar diet) were in bed an estimated 29.5 min longer than women at two standard deviations below the mean (high fat, salt and starch diet). Similarly, in T3, only one marginally significant factor was discovered ($$p \leq 0.070$$). This factor was almost the same as in T2, except that the negative end also loaded heavily on cholesterol, retinol, vitamin A and B12 but not on starch. Women at two standard deviations above the mean for this factor (high sugar diets) were in bed for an estimated 34 min longer than women at two standard deviations below the mean (diets high in fats, salt and other animal products). ## 3.6. Association with Subjective Sleeping Difficulties In both trimesters, subjects were asked “Do you have any of the following”, with Difficulty Sleeping as one of the symptoms. Subjects could respond as follows: Never [0], Rarely [1], Sometimes [2], Often [3]. Table 5 summarizes the responses. Sleeping difficulties increased as the pregnancy proceeded; the median category changed from Rarely [1] to Sometimes [2]. A paired t-test showed the mean change in response code was 0.58 (SE 0.05, $95\%$CI 0.48 to 0.68, $p \leq 0.001$). This indicates decreasing sleep quality over the course of pregnancy. We consider an ordinal logistic regression for Difficulty Sleeping. Note that negative coefficients correspond to increasing difficulty sleeping. Table 6 shows that, in T2, difficulty sleeping increased significantly with the week of gestation ($$p \leq 0.005$$), severity of morning sickness ($$p \leq 0.018$$), frequency of anxiety ($$p \leq 0.033$$) and depression ($p \leq 0.001$), intake of niacin ($$p \leq 0.008$$) and a high intake of SFA (0.008) and low intake of MUFA ($$p \leq 0.002$$) (these last could be expressed as a ratio of SFA/MUFA, $$p \leq 0.004$$). If food group frequencies are used in place of dietary intake, T2 sleeping difficulties increased with typical daily servings of dairy ($$p \leq 0.042$$) but decreased with daily servings of vegetables ($$p \leq 0.029$$). Table 7 shows that, in T3, the severity of morning sickness earlier in the pregnancy continued to predict sleeping difficulties ($p \leq 0.001$), as did (independently) whether symptoms of morning sickness were still continuing at T3 ($$p \leq 0.044$$). Sleeping difficulties continued to be related to anxiety ($$p \leq 0.002$$), but depression was not significant. Sleeping difficulties increased with higher ratio of vitamin B6 to dietary weight of intake ($$p \leq 0.001$$) but decreased with intake of β-carotene ($$p \leq 0.016$$). In terms of food group frequencies (these were only collected in T2), sleeping difficulties in T3 still increased with typical weekly dairy intake ($$p \leq 0.020$$) and decreased with weekly fruit intake ($$p \leq 0.008$$). ## 4.1. TIB vs. Time Sleeping This paper differs from most in using TIB as the response variable instead of time asleep. This is because it was a secondary analysis of activity diary data. A different sample [43] of 197 NZ pregnant women showed those with less TIB averaged larger babies and less maternal weight gain post-partum, while those with greater daily activity levels tended to have higher wellbeing, longer gestation and less probability of the infant needing admission to a neonatal intensive care unit. TIB values, however, are larger than time sleeping, which can make the interpretation difficult. For self-reported sleep, the commonly used categories are <5 h for very short, 5–<7 h for short, 7–<9 h for normal and ≥9 h for long sleep (Ref. [ 25]). An actigraphy study [29] of sleep duration in overweight or obese US women in late pregnancy gave a mean (SD) of 419 [88] min (around 7 h) per night and 88 [55] min of sleep during the day. A study of urban African American pregnant women [27] reported a mean (SD) TIB of 8.6 (2.1) h. By contrast, our median TIB was 585 min (9.75 h) for women in T2 and 597 min in T3 (as a comparison, Ref. [ 43], based on a sample with a larger proportion of rural women, reports medians of 605 and 622 for “sleep/lie down”, i.e., TIB, in T2 and T3). Part of the difference between TIB and sleep duration may be due to reading or watching television in bed, or non-sleeping bed rest due to morning sickness or ill health. Indeed, some daytime TIB was probably included—c.f. the mean 88 min daytime sleep reported for pregnant women in Ref. [ 29]. Thus, the intrinsic difference between TIB and sleep duration makes direct comparison of times difficult. On the other hand, the trends may be similar; variables that are significantly correlated with sleep duration may also be significantly correlated with TIB and vice versa. We look for this similarity of trends. ## 4.2. Association of TIB with Demographic Variables In our study, the bivariate analysis found that TIB was negatively associated with age, household income, education and presence of a partner, and positively associated with frequency of anxiety and depression. However, none of these associations remained significant in multiple regressions. Shorter TIB in the second trimester was found, in multiple regression, to be significantly associated with work and family responsibilities (especially with preschool children), while rural women, those on very low income (welfare) or those with disabilities tended to have longer TIB. Our data did not show any association between TIB and BMI or ethnicity. There was a trend of shorter TIB with actual week of gestation in T2, but the weekly trend was no longer significant by T3. There is limited literature relating demographic variables to sleep duration or TIB in pregnant women [32]. The small sample of urban African American pregnant women [27] reported non-significant trends of less TIB among older women, those married and those with a college degree. Another US study of pregnant women [44] found lower sleep duration if they were older and had graduated high school (but not college graduates), but, contrary to our findings, less so if they were unmarried, unemployed and on low income (the contradiction may be partly explained by different levels of welfare support in NZ compared to the USA). Ref. [ 45] reports unemployment, low income and low education to be predictive of poor sleep quality (in terms of PSQI). A study of Saudi women [32] found that high income was somewhat preventive against the deterioration in sleep quality and duration over the course of pregnancy. ## 4.3. Association of TIB with Nutrients We found no association of TIB with total energy intake but a negative association with total dietary intake, and in particular, total water and dietary water (moisture). Others [25,46] have found a similar association with water intake. The type of beverage is important; a study of NHANES data [47] with self-reported sleep durations found those in the short sleep group (≤5 h) consumed more sugar-sweetened beverages than a reference group (7–8 h), while longer sleepers (≥9 h) consumed significantly less coffee and less plain water than the reference group. A study [48] of sweetened beverages in children similarly found shorter sleep duration in those consuming soft drinks at least daily. Along related lines, we found TIB decreased significantly with increasing glucose and fructose in T2 and with lactose in T3. TIB decreased significantly with increasing protein (considered on its own) but not total fat or total CHO. However, after adjusting for total weight of dietary intake, the effect of protein became no longer significant. After adjusting for dietary intake, TIB was found to increase significantly with diets relatively dense in CHO and sucrose in T2 (variables considered individually). In a multivariate model, this relationship was clarified as increasing TIB with increasing density of total sugars but decreasing density of fructose. Thus, it appears that diets rich in fructose tend to result in shorter TIB, while other types of CHO tend toward longer TIB. In T3, it was found that, after adjusting for dietary intake, TIB decreased significantly with the increasing density of SFA. Ref. [ 9] found short sleep duration was associated with fat intake generally. It was not possible to produce a regression model that was simultaneously significant for densities of sugars and densities of fats, since these effects are negatively correlated. Dietary pattern analysis came close to resolving the issue, indicating that, based on principal component (PC) analysis, women with relatively high sugar diets (on one end of the PC scale) tended toward longer TIB, while women with diets relatively high in fats and animal product diets (on the other end of the PC scale) tended to have shorter TIB. However, the significance of the PC was only marginal. A study of adolescents [16] similarly found shorter sleep duration being associated with more calories from fats and less from CHO. A study of Turkish adults [19] also identified high SFA as being associated with short sleep duration. There were a large number of micronutrients individually negatively correlated with TIB, notably biotin and other B vitamins, potassium, magnesium, calcium, phosphorus, manganese. After adjusting for dietary intake, the nutrient densities of niacin, riboflavin and thiamin were individually negatively associated with TIB in T2. The multivariate model in Table 4, also adjusted for density of sugars and fructose, found TIB was positively related to nutrient density of vitamin E and negatively related to density of potassium. The last result accords with a randomized controlled trial [49], which found shorter sleep duration among men receiving potassium supplements. Therefore, there is something there, but the correlations among nutrients make this a confusing picture, and it will take a larger sample to sort the effects out. In particular, some researchers (e.g., Refs. [ 11,19]) have found that many nutrients had a U-shaped relationship with sleep duration. Our results are restricted to overall linear associations, not U-shaped relationships. In those terms, our study indicates that shorter TIB is broadly associated with better nutrition. In the words of an anonymous reviewer: “Although causality cannot be inferred, it seems that women who are getting adequate nutrients may need less resting time, and thus are spending less time lying in bed (while potentially still getting adequate amounts of sleep)”. ## 4.4. Sleeping Difficulties No association was found between TIB and the severity of morning sickness, but those women with more severe morning sickness had a higher frequency of sleeping difficulties in both T2 and (interestingly) T3, when most women no longer had symptoms. Our data showed the frequency of women experiencing sleeping difficulties was lower with increased intake of vegetables (T2) and fruit and beta-carotene (T3), but sleeping difficulties were more frequent with increased intake of dairy products in both trimesters. Given that fruit and vegetable intakes are correlated, this concurs reasonably well with the GESTAFIT study [33], which found sleep quality (measured by PSQI) was better with increasing fruit intake in the second trimester and increasing olive oil intake and adherence to a Mediterranean food pattern in the second and third trimester. Similarly, our study showed the frequency of sleeping difficulties increased with intake of SFA in T2 and vitamin B6 in T3, which compares with the GESTAFIT finding of worse sleep quality with increasing red meat and subproducts (significant) and poultry (not significant) intakes. The small study of African American pregnant women [27] reported that shorter TIB was associated with higher intake of fruits and vegetables but (somewhat contradictorily) also of pastries. The study of Turkish adults [19] identified high CHO, beta-carotene, vitamin E, thiamin, vitamin B6, vitamin C, calcium, magnesium, potassium as being associated with good sleep quality. Our finding of less sleeping difficulty with increasing beta-carotene confirmed that of Ref. [ 19], but our multivariate model did not allow us to confirm the other findings. An experimental study [50] found B6 supplementation to have little effect on sleep, but subjects given a high-dose B complex supplement had lower self-rated sleep quality and were significantly more tired on waking. Several researchers (e.g., Ref. [ 32]) have considered the relationship of vitamin D and sleep quality, but our data did not show any relationship. Ref. [ 34] found sleep disturbances being associated with dietary fat intake but short (better) sleep latency being associated with higher fruit and vegetable intake. Our data also showed MUFA intake being associated with fewer sleeping difficulties, which confirms a finding [28] that higher MUFA intake is associated with improved sleep quality in pregnancy. ## 4.5. Alcohol Alcohol consumption has been shown to be associated with poorer sleep quality in observational [51,52] and clinical settings [53]. A large community study [51] found a dose–response relationship between alcohol consumption and worse sleep quality six years later and that after adjusting for confounders, “consumption of hard liquor [spirits] but not beer or wine, was significantly associated with poor sleep quality”. By contrast, Ref. [ 52] found increasing alcohol consumption was associated with shorter sleep duration in young males but not in a small sample of females. Our study found a positive association between the amount of pre-pregnancy beer and TIB, a negative association for wine and, in multiple regression, that a history of pre-pregnancy consumption of spirits was associated with shorter TIB. However, we did not find an association between alcohol consumption and subjective frequency of sleeping difficulties. These findings may be confounded by sociodemographic factors associated with differences in the choice of beverage. The findings suggest that the association of alcohol with TIB, sleep duration and sleep quality may be different for beer, wine and spirits, and therefore, the alcohol type must be considered in a nuanced way. ## 4.6. Limitations A limitation of this study is that the focus of data collection was on the activity level rather than sleep alone; therefore, the durations of sleep and bed rest were conflated into TIB, which may also include daytime rest. The use of a wrist-mounted actigraph would give more accurate data. Another limitation is that our study did not separate weekend and weekday sleep, which can give rise to different results, e.g., “social jetlag” [54]. Future studies should routinely collect this information. Additionally, TIB was obtained from only three days of activity diary each trimester, and unlike in Ref. [ 22], the days were not pre-assigned. Collection over more nights spanning a week would be an advantage [17]. It may not be too much of a burden on research participants if they were asked to diarize the week’s day-to-day sleep measurements. If complete 24 h activity diaries were required, this would have a high compliance burden or require actigraphs. Actigraphs themselves require cost and time for dispersal and retrieval. The difficulty of comparing TIB with average sleep estimates is discussed in Section 4.1. This study did not separately consider the factors related to long TIB. Long TIB probably reflects poor sleep quality, i.e., longer sleep latency and longer time in bed after waking. Some researchers (e.g., Ref. [ 25]) look for variables related to both short and long sleep categories compared to a baseline (central) category. We did not analyze the factors associated with longer TIB because we felt such times would be excessively confounded by health issues (such as lying in because of morning sickness) or behavioral matters (such as reading or watching television in bed), for which we had no data. Not analyzing long TIB separately does make the interpretation of relationships more difficult, as the comments in this paper are, perforce, restricted to significant overall linear trends and not U-shaped relationships between TIB and nutrient intakes. Another limitation is that sleeping difficulties are measured by a single four-category variable, which may be less reliable than asking several questions. The validated PSQI questionnaire [24] would have given a more faceted approach to sleep quality than our simple question of the frequency of “difficulty sleeping”. We would recommend PSQI for future research. Both our question and PSQI rely on subjective choice of response by the subject. The high degree of correlation among the nutrients makes the interpretation of the nutritional effects difficult. Some controlling for correlation was performed by adjusting for weight of dietary intake and using nutrient density as a covariate. However, in a survey of free-living adults, it is not possible to eliminate all associations between the nutrients. This is where experimental studies are very valuable, as diets can be adjusted to increase or decrease specific nutrients and measure whether this specific adjustment has a biological consequence. Despite these limitations, our study was sufficiently sensitive to: confirm several trends of the relationship between diet and sleep applicable to TIB; explore the differences in TIB and the confounders based on the trimester of pregnancy; and suggest some associations, which can be investigated in future research on sleep. ## 4.7. 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--- title: Red Meat Intake, Indole-3-Acetate, and Dorea longicatena Together Affect Insulin Resistance after Gastric Bypass authors: - Ana Paula Aguiar Prudêncio - Danielle Cristina Fonseca - Natasha Mendonça Machado - Juliana Tepedino Martins Alves - Priscila Sala - Gabriel R. Fernandes - Raquel Susana Torrinhas - Dan Linetzky Waitzberg journal: Nutrients year: 2023 pmcid: PMC10005351 doi: 10.3390/nu15051185 license: CC BY 4.0 --- # Red Meat Intake, Indole-3-Acetate, and Dorea longicatena Together Affect Insulin Resistance after Gastric Bypass ## Abstract Roux-en-Y Gastric bypass (RYGB) promotes improvement in type 2 diabetes (T2D) shortly after surgery, with metabolic mechanisms yet to be elucidated. This study aimed to investigate the relationship between food intake, tryptophan metabolism, and gut microbiota on the glycemic control of obese T2D women after RYGB surgery. Twenty T2D women who underwent RYGB were evaluated before and three months after surgery. Food intake data were obtained by a seven-day food record and a food frequency questionnaire. Tryptophan metabolites were determined by untargeted metabolomic analysis, and the gut microbiota was determined by 16S rRNA sequencing. The glycemic outcomes were fasting blood glucose, HbA1C, HOMA-IR, and HOMA-beta. Linear regression models were applied to assess the associations between the changes in food intake, tryptophan metabolism, and gut microbiota on glycemic control after RYGB. All variables changed after RYGB ($p \leq 0.05$), except for tryptophan intake. Jointly, the variation in red meat intake, plasma indole-3-acetate, and Dorea longicatena was associated with postoperative HOMA-IR {R2 0.80, R2 adj 0.74; $p \leq 0.01$}. Red meat intake decreased three months after bariatric surgery while indole-3-acetate and Dorea longicatena increased in the same period. These combined variables were associated with better insulin resistance in T2D women after RYGB. ## 1. Introduction Metabolic surgery is a successful treatment for morbid obesity and type 2 diabetes (T2D) [1]. In addition, improvement of T2D after Roux-en-Y Gastric bypass (RYGB) has been noted shortly after surgery, and it is not entirely explained only by weight loss. Many factors have been proposed to elucidate the glycemic improvement of T2D patients following RYGB, including age [2], T2D diagnosis time [2,3], preserved beta pancreatic cell function [4], and preoperative C-peptide levels [5]. Recently, the expansion of metabolomic investigations has raised new evidence linking changes in several metabolites with glycemic enhancement after RYGB, particularly phospholipids, long-chain fatty acids, bile acids, and amino acids [6]. In the field of amino acid research, tryptophan has gained attention, and two studies have demonstrated a relationship between tryptophan metabolites and glucose homeostasis after RYGB [7,8]. Tryptophan, an essential neutral amino acid, is not endogenously synthesized by humans. Dietary intake is necessary, and the main tryptophan food sources include milk and dairy products, eggs, meat, cocoa, and peanuts [9]. In addition to being a serotonin precursor, tryptophan participates in many metabolic pathways and physiological responses. The kynurenine pathway is responsible for approximately $95\%$ of the circulating tryptophan degradation. Metabolites of this pathway are involved in inflammation, and immune response [9], and have been found to be related to diabetes [10]. Kynurenine metabolites can be synthesized by both the host metabolism and gut microbiota. Some bacteria participate in the conversion of tryptophan into kynurenines and derivatives, such as Clostridium clostrioforme and the genus Staphylococcus ssp. [ 11]. Yet another tryptophan metabolic pathway is the indole pathway, triggered in the host, but mainly by gut microbiota bacteria [12] such as Bifidobacterium longum, Bacteroides fragilis, and Eubacterium halli [13]. Metabolites from the indole pathway have a physiological effect by stimulating enteroendocrine cells to secrete glucagon-like secretory peptide-1 (GLP-1) [13]. GLP-1 has a hypoglycemic action due to its insulinotropic properties and the ability to delay apoptosis of pancreatic beta cells [14]. However, to the best of our knowledge, the relationship between indole derivatives and gut microbiota on glycemic improvement after RYGB has not yet been studied in humans. The intake of fiber and certain food groups seem to have an impact on the circulating metabolites of tryptophan. Qi et al. [ 2022], when studying 3938 participants from the HCHS/SOL Cohort, observed positive associations between red meat and refined cereal intake with metabolites of the kynurenine pathway, as well as positive associations between the consumption of dietary fiber and indole derivatives [12]. The impact of food intake on tryptophan and glycemic metabolism is already known, as are the changes in gut microbiota after RYGB [15,16,17,18]. Moreover, tryptophan metabolites produced both by host and gut bacteria appear to be related to the physiopathology of obesity and T2D as well as to the T2D improvement after RYGB [7,8]. However, the effect of changes in food intake, gut microbiota, and tryptophan metabolites on glycemic homeostasis is not yet thoroughly understood. Thus, we aimed to investigate the relationship between food intake, tryptophan metabolism, and gut microbiota on glycemic control in obese T2D women after RYGB surgery. The impact of our findings includes new insight into the knowledge of T2D relief after RYGB and raises possible future therapeutic targets. ## 2.1. Study Design and Subjects This is a single-institution study, approved by the local ethics committee (CaPPesq 4.019.801), which was a part of the SURmetaGIT trial [19] registered with www.ClinicalTrials.gov (NCT01251016; 8 December 2015). Written informed consent was acquired from all participants before the beginning of the study. All protocol interventions were performed following the Declaration of Helsinki guidelines. Women with obesity-related T2D and candidates for RYGB were recruited from the Surgical Gastroenterology Department of the Hospital das Clínicas of the University of São Paulo, School of Medicine. Data collection was performed between February 2011 and December 2014. Inclusion criteria were as follows: women (18–60 years) with body mass index (BMI) ≥ 35 kg/m2, associated with T2D diagnosis (fasting blood glucose [FBG] ≥ 126 mg/dL and glycated hemoglobin [HbA1C] ≥ $6.5\%$) and/or use of oral hypoglycemic agent [20]. Patients with recent participation in other interventional study protocols, or with Helicobacter pylori infection, thyroid, or hepatic diseases, under insulin therapy or antibiotic, probiotic, and prebiotic use in the month preceding fecal sample collection were excluded. The RYGB procedure was previously described in the SURMETAGIT protocol [19]. Briefly, open RYGB without silicon rings with a standardized length of biliary-pancreatic limb (50–60 cm) and alimentary limb (100–120 cm) were performed. Food intake surveys, plasma, and fecal samples were collected before and three months after RYGB. Plasma samples were obtained by centrifugation (2800 rpm at 4 °C for 10 min) of blood samples collected after a 12-h fast in ethylenediaminetetraacetic acid (EDTA)-containing tubes (Complete™ mini, EDTA free, Lifescience, Roche Diagnostics Corporation, Indianapolis, IN, USA). These samples were maintained at −80 °C until biochemical and metabolomic analysis. The fecal samples were self-collected by the patients at home, by using a specific specimen collection system (Commode Specimen; Fisher Scientific, Ottawa, ON, Canada). After collection, fecal samples were immediately frozen at −20 °C and transported under controlled temperature to our laboratory, where they were immediately aliquoted (100 mg) into cryogenic vials (without thawing) and stored at −80 °C until gut microbiota evaluations. ## 2.2. Food Intake Food intake data were obtained by a seven-day food record (7dFR) and a food frequency questionnaire (FFQ), applied one week before both stool sample collections, as previously described by our group [19]. Briefly, food reported in 7dFR was registered in cooking units (such as tablespoons), guided by illustrations from a manual offered to all patients [21]. The research team converted these units to grams or milliliters after standardization [22]. Energy intake, macronutrients, and total fiber were determined by Virtual Nutri Plus® software, which includes the Brazilian Table of Food Composition (TACO) [23] and the Table of Food Composition: Support to Nutritional Decision [24]. In the present study, we estimated the tryptophan, usual energy, and nutrient intake, as well as the food groups of interest. Tryptophan intake was determined from food sources available in the Brazilian Food Composition Table [23] and the Food Composition Table of the United States Department of Agriculture (USDA) [25]. The Multiple Source Method (MSM) was applied to estimate the usual energy and nutrient intakes through the online platform [26]. All nutrients were adjusted for total energy intake by the residue method [27]. From the FFQ, we determined the daily intake of two food groups: red meat (beef and viscera, i.e., liver, heart, and kidney) and refined cereals (rice, pasta, bread, cakes, salty crackers, and cookies). ## 2.3. Tryptophan Metabolites Tryptophan Metabolites were identified in plasma samples by untargeted metabolomic analysis, performed previously by our group at the NIH West Coast Metabolomics Center (WCMC), located at the Genome Center at the University of California, Davis (United States of America) [28]. Seven tryptophan metabolites were captured by mass spectrophotometry using a multiplatform approach combining three analytical platforms: 6530 Accurate-Mass Q-TOF LC/MS e Agilent 1290 Infinity II LC System (Agilent Technologies Ò), a high-performance liquid chromatography (HPLC)—TOF tandem mass spectrometer (MS/MS) method with hydrophilic interaction column (HILIC)—for polar compounds—and the charged hybrid surface column (CSH) for non-polar compounds; and TOF-coupled gas chromatography on the Agilent 6890 GC Pegasus III TOF MS instrument. The analytical variation and the reproducibility of the profiles were verified during the analyses with standard technic to guarantee the consistency of the results and ensure the instrument itself did not cause large random or systematic deviations from the data obtained during sample acquisition. This was accomplished using a mixture of reference molecules that covered all chemical classes of the metabolites identified in typical analyses (quality control samples). The raw data obtained were converted using the Analysis Base File Converter software (Reifycs Inc., Tokyo, Japan). Data from metabolites identified by LC-MS were processed by the free MS-DIAL software developed at WCMC (http://prime.psc.riken.jp/Metabolomics_Software/; 13 November 2017). Primary metabolites analyzed by GC-MS spectra were processed based on BinBase data. The results were filtered based on multiple parameters to exclude inconsistent peaks. All BinBase entries were compared to mass spectra from the FiehnLib library with 1200 authentic spectra using retention index information and mass spectra or from library 11 from the National Institute of Standards Technology (NIST). Data obtained from the plasma samples were reported as the height of quantitative peaks, normalized by the sum of the intensity of all identified metabolites (mTIC), and used for further statistical analyses. The fold change (FC) was applied to determine the relative changes and to describe its effect size and the direction of metabolite changes. The calculation consisted of the ratio between the postoperative/preoperative mean, and values < 1 were converted and added with a negative sign (−). ## 2.4. Gut Microbiota (GM) The GM evaluations were previously developed at MetaGenoPoliS at Jouy-en-Josas, France (http://www.mgps.eu; 5 October 2017) by obtaining fecal DNA and amplifying the V4 region of the 16S rRNA gene, as detailed in the International Human Microbiome Standards (IHMS) SOP06 (http://www.microbiome-standards.org; 11 October 2017) and documented by our group [29]. In the present study, the bioinformatic analysis of the 16S rRNA data was conducted by amplicon sequence variants (ASV) analysis to achieve better resolution for bacteria identification. ASV analysis was carried out at the Bioinformatics Platform in Rene Rachou Institute, Fiocruz Minas (Belo Horizonte, MG, Brazil. Briefly, raw sequence reads of the 16S rRNA gene analysis and ASV calling were performed using the DADA2 [30]. The primers used in the amplification were removed, and sequences with more than two expected errors were discarded. The remaining sequences were used to train an error identification and correction model. The forward and reverse readings, already corrected, were concatenated to form ASVs, remove chimeric sequences, and quantify ASVs. Each ASV had its taxonomic classification assigned by the TAG.ME package [31], using the specific model for the amplicon that corresponds to the V4 region, according to the Silva database [32]. The alpha diversity indexes of the microbial communities (Simpson, Shannon, observed species, Fisher, Ace, and Chao1) were calculated using the Phyloseq package (1.40.0) [33]. ## 2.5. Outcomes Biomarkers of glycemic control were used as outcomes in statistical regression models. Systemic concentrations of fasting blood glucose (FBG), glycated hemoglobin (HbA1c), and insulin were measured by an enzymatic method (glucose), liquid chromatography (HbA1c), and electrochemiluminescence (insulin), at the Central Laboratory Division of HC-FMUSP, as previously described by our group [19]. Additionally, the Homeostasis Assessment Model (HOMA) was applied to determine the degree of insulin resistance (IR) and the functional capacity of the pancreatic beta cells (Beta) [34]. ## 2.6. Statistical Analysis Continuous variables are presented as the mean and standard deviation or median and interquartile range, while categorical variables are presented as absolute and relative frequencies. The normality of continuous variables was assessed using the Shapiro–Wilk test. Differences in the relative abundance of gut microbiota bacteria between the periods studied (preoperative and three months postoperatively) were determined using the Phyloseq package (1.40.0) [33]. Comparisons between each period of the variables of food consumption and metabolites were performed by paired t-test or Wilcoxon test. A significance level of $5\%$ ($p \leq 0.05$) was adopted for these analyses. Associations between the independent variables—food consumption, metabolites, and intestinal microbiota—individually or in groups—and the variables related to glycemic control (outcomes) were assessed by simple and multiple linear regression, respectively. For GI variables, we included only bacteria that vary between periods ($p \leq 0.05$) in the regression models. We adopted a significance level of $5\%$ ($p \leq 0.05$) to evaluate the glycemic outcomes affected by the independent variables—alone or within its groups (food consumption, metabolites, and intestinal microbiota). To investigate the effect of combined variables from the different groups (food consumption, metabolites, and intestinal microbiota) on glycemic outcomes we performed multiple regression models. Initially, we performed linear regression models to pre-select independent variables that were associated with glycemic outcomes individually or in groups (p ≤ 0.1) [35]. After this previous selection of independent variables, the olsrr package [36] was used to find the best combination of two or more variables from different groups that could explain the dependent variable in question (FBG, HbA1c, HOMA-IR, and HOMA-Beta). The best subset of predictors was estimated for each dependent variable. The selection of these subsets was based on the F statistic values, the significance of the estimates, adjusted R2, mean square error, Masllow’s Cp, and Akaike’s information criterion. After finding the best models ($p \leq 0.05$), they were tested for normality, heteroscedasticity, multicollinearity, and autocorrelation. Finally, based on the best model found to explain the dependent variables in question (FBG, HbA1c, HOMA-IR, and HOMA-Beta), we assumed a significance of $p \leq 0.05$ to determine significant effects with the intervention. The comparison tests were performed with the Statistical Package for Social Science (SPSS) program, version 12.0. For the regression models, specific packages of the R software (version 4.2) were used. ## 3.1. Patient’s Descriptive Data Twenty women were included in the study. At baseline, participants were 47 ± 6.5 years old, with a BMI of 46.5 ± 5.9 kg/m2, had all glycemic biomarkers compatible with T2D, and used at least one oral hypoglycemic agent. As shown in Table 1, all anthropometric and biochemical data changed after RYGB, except for High-Density Lipoprotein Cholesterol (HDL-c) levels. Biomarkers of glycemic control indicate that RYGB promoted an improvement of T2D. Only two participants maintained the use of oral hypoglycemic agents at three months after surgery. ## 3.2. Food Intake Three months after RYGB, all participants presented a reduction in their intake of energy, macronutrients, red meat, and refined cereals. However, probably due to the changes in food choices after surgery when protein food groups such as milk and eggs were preferred over meat, tryptophan intake did not differ between the two recorded periods (Table 2). In addition, $35\%$ of patients reported albumin supplement intake. These food groups and albumin supplements are sources of tryptophan. ## 3.3. Tryptophan Metabolites As shown in Table 3, RYGB promoted changes in plasma tryptophan metabolites; N-acetyl-serotonin and indole-3-acetate increased after surgery. Conversely, only anthranilic acid decreased in the same period. These changes indicate a metabolic effect of RYGB on the three major tryptophan pathways (Figure 1). ## 3.4. Gut Microbiota (GM) RYGB did not change the GM alpha diversity (Supplementary Figure S1) but affected the GM composition. We observed changes in 27 ASV bacteria taxa between pre- and postoperative time points, in which 3 were reduced and 24 increased. These represent 5 differences in bacteria phyla, and 22 differences in species, as described in Table 4. As shown in Figure 2, among the most prevalent gut bacteria phyla, only Verrucomicrobia and Fusobacteria abundance increased three months after RYGB (vs. preoperative). ## 3.5. Regression Models Univariate and multivariate regression models showed associations between the variation (∆ postoperative—preoperative) in food intake, tryptophan metabolites, and gut microbiota with surrogate markers of glycemic control after bariatric surgery (Supplementary Table S1). The variation in tryptophan metabolites, individually or together, did not affect any postoperative glycemic biomarkers ($p \leq 0.05$). The variation in Fusobacterium nucleatum (sq381) was directly associated with postoperative glycemia {0.09 (0.02, 0.16); $$p \leq 0.05$$}. For food intake, a variation in red meat intake was positively associated with postoperative glycemia {0.10 (0.03,0.17); $$p \leq 0.03$$} and HOMA-IR {0.01 (0.005, 0.01); $$p \leq 0.002$$}. Therefore, the greater reduction in red meat intake after RYGB was directly associated with postoperative glycemia and HOMA-IR. In addition, multiple linear regression revealed that the variation in protein {−0.05 (−0.09, −0.02); $$p \leq 0.03$$} and red meat {0.01 (0.01, 0.01); $$p \leq 0.0001$$} intake, individually and together, affected HOMA-IR (R2 0.68, adjusted R2 0.63; $p \leq 0.01$). Individually, a variation in protein intake was inversely associated with HOMA-IR, thus, the smaller reduction in protein intake after RYGB, the better improvement of postoperative insulin resistance. The only significant model that assembled variables from each group (food intake, metabolites, and gut microbiota) on dependent glycemic variables was the HOMA-IR model (Table 5). Together, the reduction in red meat intake {0.01 (0.005, 0.01); $$p \leq 0.0003$$}, an increase in plasma indole-3-acetate {−0.001 (−0.001, −0.0001); $$p \leq 0.06$$}, and Dorea longicatena (sq1408) {0.03 (0.01, 0.06); $$p \leq 0.06$$} were able to explain the improvement of postoperative insulin resistance (HOMA-IR) {R2 0.80, R2 adj 0.74; $p \leq 0.01$}. ## 4. Discussion Our study showed that RYGB promoted the glycemic improvement of all biomarkers evaluated. At three post-operative months, all patients achieved targets for fasting blood glucose, %HbA1c, and HOMA-IR. We also demonstrated that food intake, gut microbiota, and tryptophan metabolite changes affected glucose homeostasis after RYGB. Regarding food intake, we observed changes in protein source choices during the postoperative period, marked by a decrease in red meat intake. Reduction in red meat consumption is very common after RYGB as it is reported to be a less tolerated food after the anatomical changes induced by the surgery [37,38,39]. This intolerance may be a consequence of changes in protein digestion caused by the reduction in pepsin synthesis in the gastric pouch, as well as inadequate chewing and increased satiety due to changes in the gut hormones involved in gastric motility and gastric acid secretion [37,38]. Variation in red meat intake was positively associated with postoperative glycemia and HOMA-IR, while protein intake variation was inversely related to postoperative HOMA-IR. Since red meat intake was reduced after RYGB, postoperative glycemia and insulin resistance could be partially explained by a significant decrease in red meat intake. Literature data regarding the association between red meat intake and T2D risk are conflicting. While observational studies have suggested that a higher red meat intake increases the risk of T2D incidence [40,41,42], randomized controlled trials do not confirm associations of red meat intake patterns with the glycemic biomarkers of T2D patients [43]. These divergent results could be attributed to confounding factors that affect glycemic homeostasis and are usually associated with red meat intake, such as alcohol consumption, sedentary lifestyle, and low fiber intake [43]. Nevertheless, red meat compounds seem to affect both beta pancreatic cell function and hepatic insulin extraction by increasing reactive oxygen species and hepatic glucose synthesis, respectively [44]. Decreased protein intake after RYGB was associated with improved postoperative insulin resistance. The anatomic gastrointestinal changes promoted by RYGB increase the availability of partially-digested nutrients to intestinal microbiota, including proteins [45]. When they reach the large intestine, partially digested proteins induce proteolytic bacteria growth, increasing the potential to synthesize pro-inflammatory metabolites, such as hydrogen sulfide (H2S) and trimethylamine-N-oxide (TMAO) [46]. However, a dietary source of protein may influence the intestinal microbiota composition [47]. In this context, reduced red meat intake and a preference for milk, eggs, and albumin supplements seem to decrease some fecal pro-inflammatory bacterial species [48], shifting the gut microbiota composition to more beneficial bacteria [49], which may interfere with glucose homeostasis [50]. Fusobacterium (F.) nucleatum was increased after surgery and its changes affected postoperative glycemia improvement. F. nucleatum is an anaerobic bacterium that engages in diverse interactions with other microorganisms and humans and can be both beneficial and detrimental in nature [51]. To the best of our knowledge, this is the first study to report the association between F. nucleatum and glycemia levels after RYGB. Thus, the mechanisms involved in this finding remain unclear. In our study, increased Akkermansia Muciniphila and reduced Faecalibacterium prausnitzii did not affect T2D improvement after RYGB. This is notable as both bacteria have been associated with better metabolic biomarkers in healthy and T2D individuals [52,53,54,55]. Changes in tryptophan metabolites induced by RYGB included decreased anthranilic acid and an increase in both N-acetyl-serotonin and indole-3-acetate; these alterations might indicate a downstream change in the kynurenine pathway, with a shift towards the serotonin and indole pathways, respectively. This redirection of tryptophan pathways may be due to a low-grade inflammation reduction [56] and gut microbiota changes after RYGB [57]. Tryptophan is converted to kynurenine by pro-inflammatory and stress hormones and activation of the IDO and TDO enzymes, respectively [58]. The new scenario with fewer pro-inflammatory signals may reduce the conversion of tryptophan to kynurenine so more tryptophan is available for the indole and serotonin pathways [59]. Furthermore, indole producer bacteria have been shown to increase after surgery, such as Dorea longicatena, and Akkermansia muciniphila [11]. Moreover, changes in tryptophan metabolites after bariatric surgery have been described. Christensen et al. 2018 [60] showed a reduction in plasma tryptophan, kynurenine, and all kynurenine metabolites, except anthranilic acid, three months after bariatric surgery (sleeve gastrectomy and biliopancreatic diversion with duodenal switch). Favennec et al. 2016 [7] also observed a reduction in plasma tryptophan, kynurenine, and all kynurenine metabolites, and an increase in serotonin one year after sleeve gastrectomy and RYGB. Kwon et al. 2021 [61] reported an increase in indoxyl sulfate but no changes in indole-3-acetate and indole-3-pyruvate three months after sleeve gastrectomy. Yeung et al. 2022 found reduced levels of tryptophan, kynurenic acid, and xanthurenic acid three months after RYGB [8]. In disagreement with these four studies, we did not find an association in the variation of tryptophan metabolites individually on any glycemic biomarkers after bariatric surgery. However, the variation of indole-3-acetate together with red meat intake and Dorea longicatena was able to potentially explain the improvement of postoperative insulin resistance. Dorea longicatena is a producer of indole-3-acetate [11], and both were increased after RYGB. Indole-3-acetate activates aryl hydrocarbon receptors, reducing inflammation and insulin resistance [62]. In addition, indole derivates stimulate insulin secretion through the GLP-1 release [58]. Furthermore, higher plasmatic levels of indole-3-acetic acid have been associated with lower insulin resistance after sleeve gastrectomy [61]. Despite that, to the best of our knowledge, this is the first study to investigate the interaction of food intake, tryptophan metabolism, and gut microbiota variables on glycemic homeostasis, in addition to reporting that alterations of red meat intake, indole-3-acetate, and Dorea longicatena together affects insulin resistance after RYGB. As a limitation of this present study, the findings do not eliminate the potentiality of other tryptophan metabolites to also affect insulin resistance and glycemic biomarkers after RYGB. In addition, we could only include a small number of participants. Furthermore, some researchers have shown that the experimental absence of gut microbiota could change the concentration of tryptophan in plasma, leading to a reduction in the kynurenine-to-tryptophan ratio [63]. Even if tryptophan metabolism by gut microbiota seems relatively simple at the molecular level, and its metabolic transformation of tryptophan into other metabolites seems undeniable, it is challenging to determine which metabolite each bacterium can produce due to the high diversity and complexity of the microbiome [64]. Thus, we encourage further investigations with a greater panel of indole and kynurenine derivates and a larger number of patients to validate our findings. ## 5. Conclusions Early after RYGB, in obese T2D women, there are changes in tryptophan metabolism, food intake, and gut microbiota. Some of these changes can be related to glycemic homeostasis. Together, alterations of red meat intake, indole-3-acetate, and Dorea longicatena seem to improve the surrogate markers associated with insulin resistance at the three-month post-operatory period. ## References 1. Nguyen N.T., Varela J.E.. Bariatric Surgery for Obesity and Metabolic Disorders: State of the Art. Nat. Rev. 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--- title: Developmental Programming-Aging Interactions Have Sex-Specific and Developmental Stage of Exposure Outcomes on Life Course Circulating Corticosterone and Dehydroepiandrosterone (DHEA) Concentrations in Rats Exposed to Maternal Protein-Restricted Diets authors: - Elena Zambrano - Luis A. Reyes-Castro - Guadalupe L. Rodríguez-González - Roberto Chavira - Consuelo Lomas-Soria - Kenneth G. Gerow - Peter W. Nathanielsz journal: Nutrients year: 2023 pmcid: PMC10005360 doi: 10.3390/nu15051239 license: CC BY 4.0 --- # Developmental Programming-Aging Interactions Have Sex-Specific and Developmental Stage of Exposure Outcomes on Life Course Circulating Corticosterone and Dehydroepiandrosterone (DHEA) Concentrations in Rats Exposed to Maternal Protein-Restricted Diets ## Abstract The steroids corticosterone and dehydroepiandrosterone (DHEA) perform multiple life course functions. Rodent life-course circulating corticosterone and DHEA trajectories are unknown. We studied life course basal corticosterone and DHEA in offspring of rats fed protein-restricted ($10\%$ protein, R) or control ($20\%$ protein, C), pregnancy diet first letter, and/or lactation second letter, producing four offspring groups—CC, RR, CR, and RC. We hypothesize that 1. maternal diet programs are sexually dimorphic, offspring life course steroid concentrations, and 2. an aging-related steroid will fall. Both changes differ with the plastic developmental period offspring experienced R, fetal life or postnatally, pre-weaning. Corticosterone was measured by radioimmunoassay and DHEA by ELISA. Steroid trajectories were evaluated by quadratic analysis. Female corticosterone was higher than male in all groups. Male and female corticosterone were highest in RR, peaked at 450 days, and fell thereafter. DHEA declined with aging in all-male groups. DHEA: corticosterone fell in three male groups but increased in all-female groups with age. In conclusion, life course and sexually dimorphic steroid developmental programming-aging interactions may explain differences in steroid studies at different life stages and between colonies experiencing different early-life programming. These data support our hypotheses of sex and programming influences and aging-related fall in rat life course serum steroids. Life course studies should address developmental programming-aging interactions. ## 1. Introduction The rodent serum steroids corticosterone and dehydroepiandrosterone (DHEA) regulate multiple key age-related cellular mechanisms across the life-course, such as metabolic function [1] and oxidative stress (OS) [2]. It is, therefore, important to establish data on the trajectory of serum concentrations of both steroids across as much of the life-course as possible. In primates, cortisol is produced in the adrenal zona fasciculata and DHEA in the zona reticularis [3] and, to a lesser extent, gonads [4]. In rodents, corticosterone is secreted by the adrenal cortex while DHEA is locally produced in different tissues (gonads and the nervous system) [5]. Very few normative life course serum steroid concentration studies covering nearly all the life course are available for glucocorticoids and DHEA [6,7,8,9]. An early life course, sexually dimorphic increase and subsequent aging-related serum corticosterone and DHEA fall occur in rat offspring of normally fed and high-fat, high-energy fed mothers [6]. Simultaneous studies of both sexes are limited. Due to the well-recognized sexual dimorphism of the hypothalamic-pituitary-adrenal axis (HPAA) function, it is necessary to compare life course data in both sexes. A major deficiency in most published studies is the lack of longitudinal data from multiple life course time points that permit regression analysis across the whole lifespan rather than categorical analyses from limited time points. Both high circulating glucocorticoid concentrations in Cushing’s syndrome [10] and low circulating concentrations in Addison’s disease [11] result in premature aging [1]. There is some evidence that DHEA increases longevity and improves important age-related functions such as cognition [12,13]. DHEA is a potential mediator of reactive oxygen species scavenger synthesis and has also been reported to augment insulin sensitivity and peroxisome proliferator activation [14] actions which potentially would reduce OS and lengthen lifespan. In the present study, rat mothers selected randomly were fed a restricted protein diet ($10\%$ protein diet; R) while controls ate $20\%$ protein (C). Control and R mothers were fed from conception through pregnancy and/or lactation, producing four offspring (F1) groups, the first letter maternal diet in pregnancy and the second letter maternal diet in lactation—CC, RR, CR, and RC. All F1 were fed normal laboratory chow diet after weaning. Therefore, differences in outcomes were due to the feeding regimens during these two plastic stages of development. We studied life course circulating corticosterone and DHEA in F1 at six stages of the life course from postnatal day (PND) 21–850 (normal lifespan ~1000 days). Several investigators have studied F1 developmental programming outcomes and functional aging trajectories resulting from similar maternal protein-restricted diets [15,16,17,18]. Despite the fact that programming by a low maternal protein diet has been extensively studied in multiple systems [17,19,20,21,22,23,24,25,26,27], there are no detailed data on life course programming of F1 corticosterone and DHEA by this extensively studied maternal dietary programming challenge in pregnancy and/or lactation. Although maternal nutritional programming research currently focuses more on maternal obesity and high-calorie diets, there are still many areas of the world where the maternal nutritional challenge is a low intake of nutrients, including protein. We aimed to determine life course F1 serum corticosterone, DHEA and DHEA:corticosterone ratio (DHEA:CORT) changes. This study had two critical and previously little-addressed goals. We sought corticosterone and DHEA life course serum values at six ages spread across approximately $85\%$ of the life course in F1 of these four maternal F1 groups. Maternal low-protein diets have been shown to program F1 metabolism, cardiovascular function and reproductive outcomes [28,29,30,31]. We posed two hypotheses: first, that F1 exposed during development to a maternal low protein diet show sexually dimorphic age-related programming of life course circulating steroid concentrations: second, we hypothesized this F1 programming is dependent on the precise plastic developmental period F1 are exposed to R—fetal life, during lactation or both periods. ## 2.1. Animal Care and Maintenance Diet and breeding details are published [32]. Briefly, Wistar rats ate Purina Laboratory Chow 5001. Lighting was on from 07:00 to 19:00 h. All procedures were approved by the Instituto Nacional de Ciencias Medicas y Nutricion, Salvador Zubiran, Mexico City Animal Experimentation Ethics Committee (INCMNSZ, BRE-105). We studied a rat model of offspring programming by maternal protein-restricted diet (R—$10\%$ protein). Female rats were bred around postnatal day (PND) 120. Prior to pregnancy, all breeding females were fed a control diet (C—$20\%$ protein). C and R diets were fed in either pregnancy and/or lactation to produce offspring of four maternal groups—first letter pregnancy diet, second letter lactation diet—CC, RR, CR and RC. Food and water were available ad libitum. Delivery day was considered PND 0. To ensure offspring homogeneity, litters with less than 10 or more than 14 pups were excluded from the study. Litters of 12–14 pups were adjusted to 12, and litters of 11 pups were retained as 11 while maintaining as close to a 1:1 sex ratio as possible. After weaning (PND 21), all offspring ate a C diet. Blood was obtained from one offspring in the litter on PND 21, 110, 220, 450, 650, and 850 (Figure 1). One rat was bled separately at each age. The subjects were mostly siblings. In our colony, the normal life span is ~1000 days in the offspring of control-fed mothers [32]. ## 2.2. Steroid Measurements After 6 h of fasting, between 12:00 p.m. and 2.00 p.m., rats were anesthetized with isoflurane and decapitated [33]. For each group, trunk blood was collected, centrifuged at 2880 RCF for 15 min at 4 °C to remove red blood cells, and serum was frozen until assayed. Serum corticosterone concentrations were determined using a commercial rat DPC Coat-a-count kit (TKRC1) (Diagnostic Products, Los Angeles, CA, USA) [33] and DHEA serum concentrations were determined by enzyme-linked immunosorbent assay (ELISA) using a commercial kit DRG Instruments GmbH (Marburg, Germany, cat #: EIA-3415) [34]. Other publications [35,36] have used ELISA kits to measure DHEA and found concentrations similar to ours [33]. ## 2.3. Statistical Analysis We conducted a quadratic analysis of life course changes in all endocrine variables. PND 21 values were excluded from analysis in all data sets due to proximity to weaning with its attendant marked influence on the offspring of lactation and approach of puberty, which is accompanied by many rapid changes occurring over just a few days. Corticosterone values at PND 110 and 220 were similar and also similar at PND 650 and 850. With this in mind, we simplified the construction of the life course corticosterone trajectory by restricting the corticosterone data analysis to PND 220, 450, and 650. From these trajectories, we calculated the timing of the peak corticosterone concentration and its value. PND 21 DHEA was excluded for similar reasons to corticosterone values. PND 850 DHEA values were included in the quadratic analysis since there was a continued DHEA fall in RR in both sexes and RC in males. We analyzed the two sexes separately. CC female corticosterone was higher than male at PND 220 and 450 ($p \leq 0.01$ by non-paired t-test), CC female DHEA was higher than male at all ages except PND 220 ($p \leq 0.05$), and DHEA: CORT ratio lower at PND 220 and 450 ($p \leq 0.01$). Therefore, the sexes were analyzed separately throughout. ## 3.1. Effect of Timing of Maternal Dietary Challenge on Aging Trajectory of Offspring Serum Corticosterone Individual and group male and female corticosterone serum concentrations across the life course are shown in Figure 2A and Figure 2B, respectively. Significantly different groups are indicated above lines at each age. Male (Figure 2C) and female (Figure 2D) best fit quadratic curves show the timing of peak values with a vertical line. Day of peak values was similar in all groups (Figure 2E). At the estimated peak, female corticosterone was greater than male in all groups (Figure 2F). RR corticosterone was greater than all other groups in both sexes. RC females were greater than CC and CR females (Figure 2F). ## 3.2. Effect of Timing of Maternal Dietary Challenge on Aging Trajectory of Offspring Serum DHEA Individual and group male and female DHEA serum concentrations are shown in Figure 3A and Figure 3B, respectively, with significantly different groups indicated above lines at each age. Best-fit quadratic curves are presented in males (Figure 3C) and females (Figure 3D). Figure 3C, D show that serum DHEA concentrations are similar in both sexes until about PND 400. Thereafter, male serum DHEA concentration falls rapidly in RR, RC, and CR, and slightly slower in CC. A quadratic is a superior fit in all cases. In females (Figure 3D), there is a little trend for the three groups and a modest (quadratic) decline in RR. ## 3.3. Effect of Timing of Maternal Dietary Challenge on Aging Trajectory of Offspring Serum DHEA:CORT Since DHEA has been considered by many investigators as extending lifespan, we evaluated the individual and group male and female DHEA:CORT ratio values (Figure 4A,B, respectively). Significantly different groups are indicated on the lines above the ages. Life course serum DHEA:CORT ratio trajectories show marked sexual dimorphism. In males (Figure 4C), the trajectories are linear, rising slightly in CR but falling in the other three male groups. Female ratios (Figure 4D) are similar to males but show concave-up curves that increase in all-female groups in the second half of life. ## 3.4. Effect of Timing of Maternal Dietary Challenge on Daily Mean Corticosterone, DHEA and DHEA:CORT Ratio Female daily mean corticosterone was higher than male in all groups ($p \leq 0.05$). RR corticosterone serum levels were higher than the other groups in males and females, and RC was higher than CC and RC in females (Figure 5A). There were no daily mean DHEA differences in male groups, but RR was higher than CC in females (Figure 5B). RR and CR females had higher and lower daily mean DHEA than males, respectively ($p \leq 0.05$) (Figure 5B). Female daily serum DHEA:CORT ratio was lower than males in all groups ($p \leq 0.05$) (Figure 5C). CR and RC were lower and higher than CC and RR, respectively ($p \leq 0.05$) (Figure 5C). ## 4.1. Need for Studies That Address Life-Course Programming of Circulating Glucocorticoids Since glucocorticoids play multiple key metabolic roles at different stages of the life course, the first step in understanding the role of glucocorticoids in aging is to establish the precise timing of life course changes in basal circulating glucocorticoids in relation to normal aging. We identified one of the major obstacles to filling this need is the nature of the available published data. In most age-related glucocorticoid studies, data are only presented at a few ages—generally two or, at the most, three [6,16]. In addition, well-accepted markers of aging and frailty, such as grip strength, are changing as early as one-third of the way through the lifespan [37]. Therefore, to understand the antecedents of aging, steroid values are needed well before any of the known hallmarks of aging emerge [38]. In addition, it is essential that robust life course data contains values from as many ages across the lifespan as practically possible. Studies in both sexes are required as we have shown marked life course and sex-specific changes in serum corticosterone in the rat [6]. A recent life-course study on the trajectory of mortality index with age shows marked differences between the two sexes. Importantly in relation to male and female differences, the relationship between males and females is constantly changing [39]. ## 4.2. Criteria for Studies That Address Life-Course Programming of Circulating Glucocorticoids As mentioned above, to characterize life course aging changes in circulating glucocorticoids, blood sampling should begin as early in life as possible in order to address the potential early emergence of developmental programming outcomes. The plastic periods in which developmental programming can occur and lead to pathologies such as diabetes and vascular and endocrine comorbidities that influence the study subject’s lifespan are even seen in fetal life [40,41,42,43]. In order to standardize aging confounds, we started data collection at weaning in our well-established, in-house colony of rats that have homogeneous developmental histories and known maternal and paternal phenotypes [32]. It is important to note that this vital developmental information is usually unavailable in commercially sourced animals. Studies need to ensure homogeneity and lack of cofounds, such as siblings within a study group. Siblings within a group biases data due to the excessive influence of programming by a single or small group of mothers. There is much debate about the life course of glucocorticoids and DHEA changes. We have now studied life course changes in circulating corticosterone and DHEA in twelve independent groups of rats, the eight groups presented here and four groups in a study of offspring of obese mothers [6]. All groups showed a similarly timed age-related fall in serum corticosterone, though with different absolute group blood concentrations. Since we conducted these studies with homogeneous animals of uniform backgrounds, we hope these findings provide firm information for this important debate, and it is clear that corticosterone concentrations fall in the second half of the rat life course. Clearly, there are differences in the developing HPAA response to the same maternal nutritional programming challenge presented in different developmental windows. For example, since the steroid response to RR is greater than RC in both sexes, it would appear that programming influences are present in both pregnancy and lactation. The similarities in the timing of the corticosterone peaks in all groups would suggest HPAA neural regulation. It is of interest that two independent studies in a nonhuman primate, the baboon, show a fall in cortisol across the life course with remarkable similarity in the rates of fall coefficient [7,9]. In baboons, DHEA falls similarly across the life course in males and females [8]. ## 4.3. Chronobiology of the HPAA System and Importance of Blood Sampling Time The HPAA is subject to multiple internal and external environmental influences that change markedly across the life course, such as stress and nutrition. All published studies addressing basal HPAA physiology, including our own, inevitably include the effects of confounds produced by the study design. In light of the well-known circadian rhythms that affect the HPAA, sampling frequency and timing will affect findings. Our samples were obtained 5–7 h into the light period, the resting time of rodent sleep-wakefulness when metabolism is basal and stable. Another study in 60-day-old male rats showed similar early light-phase corticosterone low baseline stability [44]. We sampled in the final two hours of this period of greatest corticosterone stability. At other times, serum corticosterone may change by $50\%$ within a 2-hr period [44]. Indwelling catheters and tethers have been used to allow frequent, relatively unrestrained sampling of rhythms and rapid variations. However, these approaches also introduce the effects of surgical instrumentation and mobility restriction. The characteristics of HPAA rhythmicity have been extensively reported in several nonhuman primate studies [45,46,47]. DHEAS is observed to decline with age. A 24-h rhythm study of steroid values at 10 and 26 years showed that maximum, mean and minimum cortisol values were increased in the older group, especially in the light, the active phase of the day [46]. Multiple functions of DHEA have been described [48]. For example, it has been implicated in reproductive functions since it can be bioconverted into estrogens and testosterone [49]. It is also involved in stress regulation due to its anti-glucocorticoid activity [50]. DHEA counteracts a variety of negative effects of excessive glucocorticoid action, e.g., on visceral obesity and decreased insulin sensitivity in elderly individuals [51]. During acute stress, DHEA concentrations are increased, but in chronic stress, the DHEA response is attenuated. Also, DHEA promotes neuroplasticity, neurogenesis and neuroprotection [52]. Since many of the functions of DHEA counteract or act opposite to those of cortisol, it is important to study their actions together. One of the alternatives is not only to express the concentration of each of these hormones but also to indicate the ratio [53]. Since DHEA is protective against aging, the maintenance of the female ratio in later life in comparison with the male fall in the DHEA:CORT ratio may represent one of the mechanisms responsible for the so-called “female aging advantage” [39]. It is clear that there is marked sexual dimorphism in these later life changes. ## 4.4. Potential Mechanisms Responsible for the Fall in Corticosterone in the Second Half of Life The absolute level of circulating corticosterone is mainly determined by the setting of the activity level of the HPAA. In vitro studies of steroid production by dispersed adrenal cells from 2, 5, 12, and 18-month-old rats show decreased corticosterone production and adrenocorticotropic hormone (ACTH) responsiveness as rats age [54]. With age, fresh male rat adrenal homogenates become less able to synthesize cholesterol for steroidogenesis [54]. 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) activity is lowest at PND 365 and 550. Interestingly, this decrease is not associated with decreased activity of steroidogenic enzymes [54]. Male rat corticosterone production by isolated adrenal cells in response to ACTH and cyclic adenosine monophosphate (cAMP) has been studied from 6–24 months. Aged adrenocortical cells lose most of their corticosterone production in response to both ACTH and cAMP. The authors state, “Analysis of the data suggests that from 6 to 12 months, an intracellular steroidogenic lesion develops; in addition, there may be a loss in ACTH receptors on the plasma membrane. After 12 months, these defects increase and are accompanied by a decrease in receptor sensitivity to ACTH”. These data support our findings of a fall in circulating corticosterone that begins around mid-life [55]. Changes in cellular miRNAs are another potential mechanism responsible for age-related decreases in glucocorticoid production. ACTH and dexamethasone control several miRNAs in adrenal steroidogenic cells [56]. These findings further increase the number of mechanisms that merit investigation in order to determine the mechanisms responsible for the fall in glucocorticoids across the life course demonstrated in this study. Since steroids are products of the mitochondria, studies are needed on the changes in relevant mitochondrial function in zona fasciculata with age. ## 4.5. Mechanism Responsible for Different Corticosterone Concentrations in the Different Programming Outcomes Maternal rat corticosterone is increased as a result of protein restriction at 19 days gestation. This increase results in altered offspring HPAA function postnatally when corticosterone is lower in both male and female RR pups at PND 2 than control, presumably because of increased negative feedback by the high maternal concentrations a few days previously [15,57]. The setting of the HPAA in rats has been shown to be modified by developmental programming. Mothers of different strains of Long Evans rats show different amounts of licking and grooming of their pups during lactation. The amount of maternal licking and grooming over the first week of the pup’s newborn life strongly influences the pup’s later-life response to stress. Offspring of mothers who perform low amounts of licking and grooming have fewer glucocorticoid receptors in their hypothalamus, resulting in lower negative glucocorticoid feedback on the HPAA. In contrast, high levels of maternal care, licking and grooming programs more hypothalamic glucocorticoid receptors. As a result, when stressed, offspring exposed to a greater amount of maternal care secrete less ACTH and glucocorticoids than offspring of low-licking and grooming mothers [58]. Sexual dimorphism has been shown in the programming of HPAA feedback. In one study, female, but not male, offspring of mothers stressed by environmental changes during pregnancy showed increased HPAA [59]. Alteration of hypothalamic glucocorticoid receptors following programming by different maternal nutritional challenges might result in decreased negative glucocorticoid HPAA feedback and resultant increased corticosterone secretion. In keeping with the view that the level of HPAA negative feedback is altered by maternal protein diet programming, male rat corticosterone concentrations were higher in RR than CC offspring before, during and after 20 min immobilization [60]. Similar changes were observed in females, although the response to the stress was lower [61]. The cellular mediators of these effects of maternal separation remain to be established. There are many examples of the effects of challenges during development on the later life function of the HPAA. In one rat study, maternal separation at PND 10 increased pup adrenal expression of steroidogenic acute regulatory protein and steroidogenic enzymes [62]. ## 4.6. Differences in Corticosterone and DHEA Life-Course Trajectories and Metabolic Consequences for the Health Span In primates, cortisol is produced in the adrenal zona fasciculata and DHEA in the zona reticularis [3] and, to a lesser extent, gonads [4]. DHEA is a precursor in the production of testosterone and estrogen, but its androgenic effect is weak [4]. Serum DHEA concentrations are relatively high in the fetus and neonate, low during childhood and increase during puberty [63]. In rodents, corticosterone is secreted by the adrenal cortex while DHEA is locally produced in different tissues (gonads and the nervous system). Gonads contribute to circulating DHEA, and its synthesis may be regulated by factors independent from those involved in the stress response, which should be carefully considered when analyzing DHEA to study HPAA activation, as gonadal or placental DHEA production may bias the putative adrenal response. In rats, the highest levels of DHEA were observed in the spinal cord compared to plasma. The pathway of DHEA synthesis in the rodent brain is controversial, as the 17α-hydroxylase (CYP17A) enzyme expression is low in adult rats, and alternative CYP17A-independent pathways were suggested. In several rat, bovine, and human brain model systems, DHEA biosynthesis is mediated by oxidative stress/Fe2+, independent of the CYP17A enzyme. This mechanism is not fully understood [5]. Corticosterone and DHEA functions and metabolism, although somewhat interactive, are different. Therefore, it is not surprising that the trajectories of the two steroids are different. One key aging review describes nine hallmarks of aging: genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, de-regulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, and altered intercellular communication [38]. All of these hallmarks need investigation in relation to the life course of the steroids corticosterone and DHEA, whose changes we describe here. To the best of our knowledge, no specific studies have been designed to specifically address the effects of glucocorticoids on the nine markers of aging. In the fetal primate liver, proteasome and oxidative phosphorylation genes are down and miRNA changes in fetuses of obese mothers [64]. DHEA decreases with age in men and women [65]. DHEA is a weak estrogenic precursor and may play a role in the female aging advantage [39]. The nature of the role DHEA plays in aging is controversial but has been suggested to be biphasic, with protection from aging at high concentrations and increased susceptibility at low concentrations [66]. Glucocorticoids and glucocorticoid receptors are found in mitochondria, and treatment with glucocorticoids alters mitochondrial DNA transcription. Thus, decreased glucocorticoid and glucocorticoid receptors could play a role in decreased mitochondrial steroid production [67]. The caloric restriction, which extends the life span and improves age-related disease, is also associated with a decreased fall in DHEA [68]. Glucocorticoids have both pro and apoptotic effects [69], and whether a pro-apoptotic or anti-apoptotic effect is induced is often tissue- and/or cell type-specific [69]. There have only been a few studies of the effects of glucocorticoids on autophagy, another age-related cellular process. Glucocorticoids stimulated the transcription of autophagy genes such as ATG5, LC3, Beclin 1, and SQSMT1/p62, as well as activated AMPK to promote muscle atrophy [70]. Elevation of corticosterone in rodents results in the development of hepatic steatosis [14,71]. We have shown elevated early stages of non-alcoholic fatty liver disease (NAFLD) in the offspring of obese rats fed high-fat diets. It would be valuable to observe changes in the offspring of protein-restricted mothers with similar life-course corticosterone changes. One of the most valuable studies that should be conducted in the future on the effects of these life course corticosterone changes would be to administer corticosterone to mimic the RR profile in CC rats and observe effects on the nine markers of aging mentioned above. Limitations of this study. We restricted our studies to rodents and acknowledge that considerable data in nonhuman primates and humans indicate an increase in circulating steroids as well as a decrease (our own data and from others in the baboon). However, as remarked earlier, additional stimuli to the study subjects will undoubtedly affect the corticosterone values. It is clear that species differences, especially in developmental programming and basal metabolic rate, need further investigation. One major limitation is that these data do not test the dynamic aspects, such as steroid secretion rates in response to specific stressors. Strengths. The strengths of the study are the large portion of the life course ~$85\%$ over which corticosterone and DHEA values are obtained, the representation of both sexes to enable the evaluation of sexual dimorphisms and the comparison of the four different programming backgrounds with relatively high n values in both sexes. ## 5. Conclusions and Implications This comprehensive study addresses three major features of life-course changes in circulating corticosterone and DHEA in rats. [ 1] First, we studied four different maternal nutritional groups covering pregnancy and lactation together and separately -CC, RR, CR and RC. The data show different effects of the period offspring experienced maternal nutrient restriction, fetal life or postnatally, during lactation pre-weaning. Many studies only report two dietary groups, CC and RC, because they are directed only at the issue of catching up. Male and female corticosterone concentrations were highest in RR, peaking around 450 days and falling thereafter. DHEA concentrations declined with aging in all the male groups but only in the RR female group. [ 2] By reporting data from both sexes, we determine the extent of sexual dimorphism. Many studies still only report values in one sex, even though sexual dimorphism is a major principle of both programming and aging. Female corticosterone concentrations were higher than males in all groups. DHEA has anti-aging actions, and thus the fall in the aging DHEA:CORT ratio in three male groups compared with an increase in all female groups may relate to the well-documented longer female than male life span. Potential mechanisms for the difference in life course steroid trajectories include the reported differences in the programming of HPAA glucocorticoid receptors and hence negative feedback. [ 3] Finally, we once again strongly state the fundamental need for data across the whole life course to clarify the current conflicting opinions on the life-course trajectory of circulating hormones such as corticosterone and DHEA. By obtaining concentrations at six ages across the life course, we showed clearly that the trajectory is convex upwards with a peak around halfway through life. When data are limited to a few categorical life course points, often one young and one old, the possibility exists of false conclusions as to whether values rise, are unchanged or fall with age. This possibility is illustrated in Figure 6, where the precise choice of the two categorical time points can lead to the conclusion that the hormone rises with aging, is unchanged across the life course or falls with aging. With multiple data points at different ages, it is possible to determine the situation at either any one point or overall life course trajectory. Regarding relevance to aging, the data in this manuscript show a clear age-related steroid fall in all eight groups. We [7] and others [9] have shown a fall in cortisol in the second half of nonhuman primate life that shows some similarity to the fall in rat corticosterone reported here, providing a translational link between rodent and human steroid changes with age. ## References 1. Clegg A., Hassan-Smith Z.. 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--- title: Associations between Plasma Essential Metals Levels and the Risks of All-Cause Mortality and Cardiovascular Disease Mortality among Individuals with Type 2 Diabetes authors: - Zhaoyang Li - Ruixin Wang - Tengfei Long - Yali Xu - Huan Guo - Xiaomin Zhang - Meian He journal: Nutrients year: 2023 pmcid: PMC10005369 doi: 10.3390/nu15051198 license: CC BY 4.0 --- # Associations between Plasma Essential Metals Levels and the Risks of All-Cause Mortality and Cardiovascular Disease Mortality among Individuals with Type 2 Diabetes ## Abstract Epidemiological evidence regarding the possible link between multiple essential metals levels and all-cause mortality and cardiovascular disease (CVD) mortality among type 2 diabetes (T2D) patients is sparse. Here, we aimed to evaluate the longitudinal associations between 11 essential metals levels in plasma and all-cause mortality and CVD mortality among T2D patients. Our study included 5278 T2D patients from the Dongfeng–Tongji cohort. LASSO penalized regression analysis was used to select the all-cause and CVD mortality-associated metals from 11 essential metals (iron, copper, zinc, selenium, manganese, molybdenum, vanadium, cobalt, chromium, nickel, and tin) measured in plasma. Cox proportional hazard models were used to estimate hazard ratios (HRs) and $95\%$ confidence intervals (CIs). Results: With a median follow-up of 9.8 years, 890 deaths were documented, including 312 deaths of CVD. LASSO regression models and the multiple-metals model revealed that plasma iron and selenium were negatively associated with all-cause mortality (HR: 0.83; $95\%$CI: 0.70, 0.98; HR: 0.60; $95\%$CI: 0.46, 0.77), whereas copper was positively associated with all-cause mortality (HR: 1.60; $95\%$CI: 1.30, 1.97). Only plasma iron has been significantly associated with decreased risk of CVD mortality (HR: 0.61; $95\%$CI: 0.49, 0.78). The dose-response curves for the association between copper levels and all-cause mortality followed a J shape (Pfor nonlinear = 0.01). Our study highlights the close relationships between essential metals elements (iron, selenium, and copper) and all-cause and CVD mortality among diabetic patients. ## 1. Introduction Diabetes mellitus (DM), as a major public health worldwide, has shown a steady and rapid growth trend of prevalence during the past few decades. In China, the latest research based on a national data showed that the estimated prevalence of diabetes has significantly increased from $10.9\%$ in 2013 to $12.4\%$ in 2018 [1]. Sustained high levels of blood glucose can cause a series of complications, and type 2 diabetes (T2D) patients have higher mortality risk than the general population [2]. As one of the top ten causes of death globally [3], diabetes directly caused 1.5 million deaths and $48\%$ of all deaths due to diabetes occurred before the age of 70 years in 2019 [4]. Therefore, it is urgent to explore the factors related to the mortality in diabetic patients from a comprehensive perspective. Essential trace elements, such as iron, zinc, and copper, are required by living organisms, including human beings [5]. However, only appropriate levels of these elements can maintain the best functional state of health [6]. More and more research is linking essential trace elements and all-cause mortality and CVD mortality in general population. For example, Shi et al. [ 7] found significantly positive associations between copper, molybdenum, and vanadium and all-cause mortality and CVD mortality and negative associations between selenium and all-cause mortality and CVD mortality among the general Chinese population. In a 5-year follow up study, Long et al. [ 8] found inverse associations between zinc and selenium and incident CVD risk in patients with T2D. However, evidence for the association between essential trace elements levels and mortality among individuals with T2D are limited. As far as we are aware, only one recent study using 2003–2004 and 2011–2014 data from the National Health and Nutrition Examination Survey (NHANES) examined the association of serum selenium concentrations with all-cause mortality and heart disease mortality among individuals with T2D and reported a negative association between them [9]. However, humans are exposed to a variety of essential metal elements simultaneously in real life. It is indicated that the levels of essential metals, such as zinc and copper, are different between those with diabetes and healthy individuals [10]. Accordingly, more investigations are needed to further illustrate potential associations between multiple essential trace elements and all-cause mortality and CVD mortality among T2D patients. Therefore, in the current study, we aimed to conduct a cohort study to assess the potential associations between 11 essential metals and the incidence of all-cause mortality and CVD mortality among T2D patients derived from the Dongfeng–Tongji cohort. ## 2.1. Study Subjects All study participants were from the Dongfeng–Tongji (DFTJ) cohort, which has been described previously [11]. The DFTJ cohort is a prospective cohort study initiated between September 2008 and June 2010 with the enrollment of 27,009 retired workers of the Dongfeng Motor Corporation. Information on lifestyle, medical history, and health conditions was updated every 5 years by means of questionnaire and physical examination. For the current analysis, subjects diagnosed with type 2 diabetes at baseline ($$n = 5173$$) and during the first follow-up period ($$n = 1509$$) were enrolled initially. Participants were excluded if they had insufficient blood samples ($$n = 404$$), CHD and stroke at baseline ($$n = 401$$), cancer at baseline ($$n = 190$$), missing information on BMI, smoking status, drinking status, education information ($$n = 129$$), fasting blood glucose (FBG) level ($$n = 243$$), duration of diabetes ($$n = 22$$), estimated glomerular filtration rate (eGFR) level ($$n = 14$$), and were lost to follow up ($$n = 1$$). Finally, 5278 subjects were included in the present study (Figure S1). The present study was approved by the Medical Ethics Committee at the School of Public Health, Tongji Medical College, Huazhong University of Science and Technology in 2008 (approval no: 2008-03). All participants gave their written informed consent. ## 2.2. Measurement of Metals Exposure Plasma levels of the 11 essential metals, including zinc, selenium, manganese, molybdenum, vanadium, cobalt, chromium, nickel, and tin, were measured by the quadrupole inductively coupled plasma-mass spectrometry (ICP-MS, Agilent 7700 × series; Agilent Technologies). Details of the procedure and the limits of detection (LOD) have been described by Long et al. [ 8], and the percentages of samples below detection limits of study participants at baseline in the current study are displayed in Table S1. In the samples with metal levels below the detection limit, we imputed the metal levels using half of the detection limit. ## 2.3. Assessment of Covariates Data on age, gender, lifestyle (smoking status, alcohol drinking status, and physical activity), presence of hypertension, hyperlipidemia, CVD, cancer, medical history (such as antihypertensive drugs and lipid-lowering medication), and family history of disease were obtained by a face-to-face interview based on demographic questionnaire. Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters (kg/m2), which were measured during the process of physical examination. More detailed information has been described previously [11]. The level of FBG, serum lipids (including triglyceride (TG), total cholesterol (TC), high-density lipoprotein (HDL), low-density lipoprotein (LDL), and the blood pressure value (including systolic blood pressure (SBP) and diastolic blood pressure (DBP)) were measured at baseline. In addition, eGFR were calculated according to the Modification of Diet in Renal Disease equation [12]. ## 2.4. Ascertainment of Type 2 Diabetes T2D was ascertained according to the American Diabetes Association (ADA) criteria [13] if the participants meeting any of the following criteria: [1] self-reported physician’s diagnosis of diabetes in community hospitals or higher-level hospitals; [2] FBG level ≥ 7.0 mmol/L; [3] HbA1c level ≥ $6.5\%$; and [4] usage of diabetes medication (insulin or oral hypoglycemic agent). ## 2.5. Ascertainment of All-Cause and CVD Mortality The confirmation method of mortality status has been described previously [7]. Briefly, all-cause deaths and CVD deaths were obtained by integrating the information recorded in the medical insurance system of Dongfeng Motor Company with the questionnaire and survival physical examination information during the follow-up. Meanwhile, the specific causes of death were ascertained and classified following the International Classification of Diseases Tenth Revision (ICD-10) by the trained staff who were blinded to the participant questionnaire data. ## 2.6. Statistical Analyses Differences in the basic characteristics between deaths and survivors were analyzed by using the Student’s t-test or Mann–Whitney U test for continuous variables, and Chi-squared test for categorical variables. The correlations between essential metals were assessed by using the Spearman’s rank correlation analyses. Concentrations of the plasma essential metals were natural logarithm (ln), due to their skewed distributions. Considering the correlations among the plasma metals, the least absolute shrinkage and selection operator (LASSO) penalized regression analyses were first used to select the most significant metals associated with all-cause mortality or CVD mortality. Then, the significant metals derived from LASSO model were included in the Cox proportional hazards models to estimate hazard ratios (HRs) and $95\%$ confidence intervals (CIs) for the associations of metals with all-cause mortality or CVD mortality. We adjusted for the following variables based on a priori knowledge of their potential associations with exposure and outcome: age (years), gender (male/female), BMI, education (primary or below/junior high school/high school/college or above), smoking status (current smoker/ex-smoker/never smoker), drinking status (current drinker, ex-drinker and never drinker), physical activity status, presence of hypertension, presence of hyperlipidemia, baseline FBG, duration of diabetes, and use of antidiabetics and eGFR at baseline. The family history of CVD was additionally adjusted on the basis of the above covariates when the outcome was CVD mortality. Restricted cubic spline regression (RCS) linked to Cox regression models were conducted to explore the potential non-linear relations between continuous plasma metals concentrations and all-cause mortality or CVD mortality. Knots were set at the 20th, 40th, 60th and 80th percentiles in the models, and the reference value was set to the 10th percentile. Subjects were further stratified by age (<65 or ≥65 years), gender (women or men), BMI (<24.0 or ≥24.0 kg/m2), smoking status (current smoker, ex-smoker and never smoker), drinking status (current drinker, ex-drinker and never drinker), eGFR levels (<90 mL/min/1.73 m2 or ≥90 mL/min/1.73 m2), diabetes duration (<5 or ≥5 years), presence of hypertension, and presence of hyperlipidemia. The p values for the product terms between continuous metals levels, and the stratification variables were used to estimate the significance of interactions. In addition, we estimated the combined associations between metals that were significantly associated with incident all-cause mortality in the multiple-metals model, and the metal level was dichotomized as low (Quartile1 + Quartile2) and high (Quartile3 + Quartile4), and a four-category variable was created for two metals (i.e., low/low, high/low, low/high, and high/high), with the lowest risk group as the reference. Sensitivity analyses were conducted to test the robustness of our results. First, we excluded subjects who died within the first 2 years of follow-up to reduce the potentially reverse causation. Second, we excluded subjects with eGFR levels lower than 60 mL/min/1.73 m2 at baseline. Third, we excluded the accidental death during the follow-up. All data were analyzed using SPSS software (version 22.0, SPSS Inc., Chicago, IL, USA) and R software (version 3.6.1, R Foundation for Statistical Computing, Vienna, Austria). LASSO penalized regression analyses were performed using the “glmnet” and “survival” package in R. A two-sided p-value < 0.05 was considered statistically significant. ## 3.1. Characteristics of Study Participants During 44,017.87 person-years of follow-up, a total of 890 deaths (including 312 CVD deaths) were identified. Compared with survivors, those who died during the follow-up period were more likely to be older, male, with a higher percentage of hypertension and hyperlipemia and longer duration of diabetes. Meanwhile, those who died during the follow-up period were more likely to be current smokers, current drinkers, and less physically active; in addition, their levels of FBG were higher, but eGFR were lower, in contrast to those survivors (Table 1). Participants who died during the follow-up tended to have lower levels of zinc and selenium, but higher levels of manganese, molybdenum, vanadium, cobalt, nickel, and tin (all p values < 0.05). The correlations between the 11 metals were indicated in Figure S2. Specifically, most of the metals were significantly correlated with each other, except for selenium and nickel, selenium and vanadium, selenium and manganese, iron and molybdenum, and zinc and copper (all p values > 0.05). The absolute value range of the correlation coefficients between metals is 0.1–0.68. ## 3.2. Association between Plasma Metals Levels and All-Cause Mortality According to the results from LASSO models (Table S2), five metals, including iron, copper, zinc, selenium, and molybdenum, were the most significant metals associated with all-cause mortality. Among them, the highest coefficient is allocated to selenium (coefficient = −0.399), followed by copper (coefficient = 0.358), iron (coefficient = −0.123), zinc (coefficient = −0.020), and molybdenum (coefficient = 0.023), respectively. We further conducted multiple-metals models by including the above five metals simultaneously in the models (Table 2). Ln-transformed metals were included as continuous variables in the model, and we examined the associations between one unit increase of Ln-transformed metals levels and all-cause mortality. The corresponding adjusted HRs ($95\%$CIs) were 0.83 (0.70,0.98), 1.60 (1.30,1.97), and 0.60 (0.46,0.77) for iron, copper, and selenium, respectively. Meanwhile, compared with the lowest quartile of plasma metals, the multivariable-adjusted HRs ($95\%$CIs) of all-cause mortality in the highest quartile were 0.79 (0.64,0.96), 1.50 (1.23,1.82), and 0.72 (0.58,0.88) for iron, copper, and selenium, respectively. However, no significant association between zinc and molybdenum and all-cause mortality were observed. Further cubic splines analysis revealed a significant nonlinear dose–response relation (J shaped) between copper and all-cause mortality (Pfor nonlinearity = 0.01, Figure 1B), and a surge was observed when the levels of copper are around 1006.88 μg/L. Significant linear associations between iron (Pfor overall = 0.001 with Pfor nonlinearity = 0.612) and selenium (Pfor overall = 0.0004 with Pfor nonlinearity = 0.215; Figure 1A,C) and all-cause mortality were observed. Subgroup analyses (Figures S3–S5) indicated that the positive association between copper levels and all-cause mortality was more pronounced among subjects with lower levels of BMI and eGFR and with shorter duration of diabetes (Pfor interaction = 0.015, 0.002 and 0.017, respectively; Figure S4), and the negative association between selenium levels and all-cause mortality was more pronounced among subjects who were younger and with lower levels of eGFR (Pfor interaction = 0.003 and 0.024, respectively; Figure S5). No significant interactions were found for iron and the stratified factors. In addition, sensitivity analyses indicated that association between plasma selenium, copper and all-cause mortality remained largely unchanged when we excluded participants who died within first 2 years of follow-up or participants with eGFR levels lower than 60 mL/min/1.73 m2 or who died accidentally during follow-up, although the inverse associations between iron and all-cause mortality did not reach statistical significance because of decreased power caused by the reduction of the sample size (Table S3). Metals interaction analyses did not observe any significant interaction between plasma copper, selenium, and iron (all Pfor interaction > 0.05). However, in the joint association analysis of multiple metals, we did observe that individuals with high levels of iron and selenium had a significantly lower risk of all-cause mortality than those with low levels of iron and selenium (HR = 0.66, $95\%$CI: 0.55, 0.79; p value < 0.001; Table 3). ## 3.3. Association between Plasma Metals and CVD Mortality Iron was the only significant metal associated with CVD mortality from the LASSO models; the coefficient of iron was −0.306 (Table S2). The Cox regression models revealed that each one unit increase in ln-transformed plasma iron (HR: 0.61; $95\%$CI: 0.49,0.78) were significantly associated with decreased CVD mortality (Table 3). The multivariate-adjusted HRs ($95\%$CIs) across quartiles of plasma iron concentrations were 1.00 (reference), 0.70 (0.51,0.95), 0.69 (0.51,0.93), and 0.52 (0.38,0.72) for CVD mortality (Ptrend < 0.001). No significant non-linear associations for iron were observed (Pfor overall = 0.0005 with Pfor nonlinearity = 0.914) from the restrict cubic spline analysis (Figure S7). Subgroup analyses (Figure S6) indicated that the negative association between iron levels and CVD mortality were more pronounced among subjects with higher levels of BMI and subjects without hypertension (Pfor interaction = 0.01 and 0.003, respectively). The sensitive analyses showed that the above negative associations were essentially unchanged when we excluded participants who died within the first 2 years of follow-up or participants with eGFR levels lower than 60 mL/min/1.73 m2 or who died accidentally during follow-up. ## 4. Discussion In this study of 5278 T2D individuals with a median follow up of 9.8 years, we found plasma copper was associated with increased risk of all-cause mortality, while plasma selenium was associated with decreased risk of all-cause mortality. Meanwhile, we found that plasma iron was significantly and negatively correlated with both all-cause and CVD mortality. In addition, the dose–response curves for the association between copper levels and all-cause mortality followed a J shape. Up to our knowledge, this was the first investigation to evaluate the prospective associations between plasma multiple essential metals and all-cause and CVD mortality among individuals with T2D. ## 4.1. Copper Copper, as the third most abundant essential metal in the human body after zinc and iron, is involved in many physiological pathways and plays important roles in physiological processes [14]. Nonetheless, it is also crucial to remark that both copper deficiency and overload play key roles in the occurrence and development of many diseases. For example, copper deficiency is closely related to diseases such as Menkes disease and non-alcoholic fatty liver disease [15], while copper overload can also be closely related to cardiovascular diseases and cancer [16]. Accumulating evidence has supported the positive association between copper levels and all-cause mortality in the general population. For example, the most recent cohort study based on the general population in China reported positive associations between plasma copper and all-cause mortality [7]. Similar conclusions were also derived from early studies: Bates et al. [ 17] and Mursu et al. [ 18] reported positive associations between dietary copper intake and all-cause mortality. In addition, two prospective studies also reported significant association between higher levels of serum copper concentrations and all-cause mortality [19,20]. All the above conclusions are similar to those of diabetes patients in this study. The positive correlation between copper and all-cause mortality is reasonable. High levels of free copper are related to excessive oxidative stress [21]. The main copper binding protein in plasma is ceruloplasmin. An in vitro study has shown that chronic hyperglycemia can damage the copper-binding properties of ceruloplasmin [22], which may increase the level of plasma-free copper, resulting in excessive oxidative stress and a series of subsequent adverse health effects. In vivo and in vitro experiments also revealed that copper exposure can promote intimal thickening caused by vascular injury, promote LDL uptake in macrophages and finally promote the occurrence of atherosclerosis [23]. Moreover, metformin, a very common drug used by diabetes patients, had a strong affinity for copper and could play a role in inhibiting blood glucose production through interaction with mitochondrial copper [24]. Notably, we found a significant non-linear relationship of a J shape between copper levels and all-cause mortality among diabetes patients. Specifically, when copper level exceeded about 1006.88 μg/L, the HR significantly increased. This level is within the serum concentration range of the general population (635–1589 μg/L) [25], and the concentration is also slightly lower than the serum copper concentration level (1230 μg/L) in one previous study [20], which also reported a significant positive association between serum copper levels and all-cause death risk in the general population from Germany. This may reflect the increased susceptibility of diabetes patients to environmental exposure compared with the general population to a certain extent. In addition, the study found the positive association between higher copper and increased all-cause mortality risk was more pronounced among subjects with lower levels of BMI and eGFR and with shorter duration of diabetes. Considering the positive association between BMI and diabetes duration and mortality [26,27], the higher levels of BMI and the longer duration may mask the effects of copper on all-cause mortality. Lower levels of eGFR are independently associated with an increased risk of all-cause mortality [28], and elevated circulating copper levels have been associated with decline in kidney function [29]. Therefore, the synergistic interaction between lower eGFR levels and higher plasma copper levels is reasonable. ## 4.2. Iron Iron, the first most abundant essential metal in the human body, is involved in a wide variety of important cellular processes. However, excessive or lack of iron can be harmful to the body [30]. To our knowledge, although several studies have focused on the iron status and mortality in general population, scarce data are available about iron levels and mortality among diabetic patients [31,32]. One small cohort study among 287 patients with both T2D and coronary artery disease reported a U-shaped relationship between serum ferritin levels and all-cause mortality, but a negative linear association between transferrin saturation (serum iron level/total iron-binding capacity (TIBC) × $100\%$) and all-cause mortality [33]. One subsequent prospective study among 8003 US adults reported that prediabetes individuals with elevated transferrin saturation had substantially increased mortality risk [34]. In the current study, we found significant inverse associations between plasma iron level and all-cause mortality and CVD mortality. Possible reasons for the inconsistency of the above results may be attributed to differences in exposure range, sample size, and differences in population characteristics, as well as variation in biomarkers selected in different studies. Among these possible factors, it is particularly noteworthy that the selected markers are different. Although iron in plasma only accounts for $0.1\%$ of the total iron in human body, it is of great significance to meet the daily needs of erythropoiesis [35]. Erythropoiesis is crucial to maintain hemoglobin levels. One prospective study based on the general older population indicated that the decline of hemoglobin levels was an independent risk factor for death risk [36]. Thus, the significant association between higher plasma iron and decreased all-cause mortality and CVD mortality found in the present study is reasonable. However, both low iron intake and high iron intake were associated with an increased risk of mortality in Chinese women, derived from one prospective cohort study with a mean follow-up of 9.9 years [37], while dietary intake of total iron was positively associated with mortality from stroke and total CVD in Japanese men, derived from one cohort study ($$n = 58$$,615) with a median follow-up of 14.7 years [38]. To summarize, more studies using indices, such as serum ferritin, transferrin, and transferrin saturation, which can reflect iron status comprehensively, are needed. Subgroup analyses indicated that the negative association between iron levels and CVD mortality were more pronounced among subjects with higher levels of BMI and without hypertension. However, epidemiological studies indicated that the serum iron level of adults with higher BMI is lower [39]. Moreover, one recent study showed that compared with diabetes patients with lower BMI (<30 kg/m2), the transferrin saturation of subjects with higher BMI (>30 kg/m2) was significantly lower, which may suggest the possibility of lower serum iron levels among T2D patients with obesity [40]. We could not explain the potential synergy between higher levels of BMI and plasma iron shown by the interaction analysis. One possible explanation is the negative association between lower BMI and the risk of CVD mortality masked the role of iron in reducing the risk of CVD mortality, and more studies are needed to further illustrate this unexpected finding. Hypertension was reported to be a widely accepted risk factor for all-cause mortality worldwide and is associated with an increased risk of CVD [41], and thus may play an antagonistic role in the process of iron reducing the risk of CVD mortality. ## 4.3. Selenium Selenium, as another important trace element, was found to play pivotal roles in many physiological processes, including maintenance of normal function of endogenous antioxidant system, thyroid hormone metabolism, and immunological and anti-inflammatory process. Accordingly, the association between selenium status and healthy effects have been widely characterized. Currently, much research links selenium levels to all-cause mortality or cardiovascular mortality, based on observational studies and post hoc analyses of randomized controlled trials among the general population. One recent meta-analysis [42] based on 12 previous observational studies revealed that low selenium level was associated with an increased risk of all-cause mortality risk. Another meta-analysis [43] of 43 randomized controlled trials emphasized that antioxidant mixtures can reduce the risk of all-cause mortality only when selenium was part of the mix. As far as we know, only one study [9] investigated the associations between serum selenium concentrations and the risk of all-cause mortality and heart disease mortality among US adults with T2D. The authors indicated that higher serum selenium concentrations were associated with lower all-cause mortality and heart disease mortality. Our study confirmed the inverse associations between plasma selenium level and all-cause mortality. Meanwhile, a similar linear dose-response relationship was also observed in a relatively narrow concentration range (concentration rang: 50.4–148.6 μg/L in the present study vs. 89–182 μg/L in the above study). Although a considerable variability about the curve was observed when the concentration range of selenium is higher than 148.6 μg/L, this is largely due to the small size of subjects with plasma concentrations greater than 148.6 μg/L ($$n = 39$$). The potential mechanisms underlying the inverse association between selenium and all-cause mortality have not yet been elucidated in detail. As an antioxidant in the form of selenoprotein, selenium can reduce the production of ROS and inflammation. In vivo studies have also found that supplementation of selenium can not only enhance the activities of superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), but also reduce the levels of pro-inflammatory cytokines, such as IL-1, IL-6, TNF-a, and INF-γ [44]. Additionally, a greater decline in all-cause mortality associated with plasma selenium among younger subjects and individuals with higher level of eGFR were observed. Older people are prone to high risk of insufficient intake of selenium due to loss of appetite and reduced feeding and digestive capacity [45]. Low serum selenium levels were observed in patients with advanced kidney disease in early studies [46], which might explain the negative association between selenium levels and CVD mortality being more pronounced among younger subjects and subjects with higher levels of eGFR. Although no significant interactions were found, we did observe that subjects with high iron and selenium had a lower risk of all-cause mortality among individuals with T2D. In addition to the close relationship between selenium and iron and the occurrence and development of diabetes [47], these two elements also play an important role in thyroid function [48], bone integrity [49], and many other aspects. Moreover, insulin-transferrin-selenium (ITS), as a basal medium supplement, is sometimes supplemented during routine serum-free bovine cell and embryo culture [50,51], suggesting the significance of iron and selenium in maintaining cell survival. However, more experimental efforts are needed to explore the combined exposure dose and potential mechanisms underlying the potential combined effect between iron and selenium in the future. ## 4.4. Other Essential Elements In addition to the above three metals, no significant association was found between the other eight metals (manganese, molybdenum, vanadium, cobalt, chromium, nickel, and tin) and all-cause mortality or CVD mortality among individuals with T2D. Although zinc and molybdenum were selected using the LASSO regression, no significant associations between zinc or molybdenum and all-cause and CVD mortality were observed in further multiple-metals model analysis. There is very limited population-based evidence on the relationship between zinc and diabetes death. One previous study found that lower levels of zinc were significantly and positively correlated with the risk of CHD risk in diabetes patients [52]. In vitro experiments showed that zinc supplements could inhibit the vascular calcification of vascular smooth muscle cells exposed to high levels of glucose [53]. The prevalence of vascular calcification is far greater in DM patients than those without DM, and vascular calcification is a well-established independent predictor of CVD [54]. Considering CVD is the number one cause of death in diabetes cases and that, in the present study, the baseline plasma zinc level of the survivors was significantly higher than that of the subjects who died during the follow-up, zinc may have a protective effect on reducing the mortality risk of diabetes patients. In addition, although plasma zinc is a reliable biomarker for reflecting zinc status in healthy people [55], inflammatory status and medication can affect plasma zinc level [56], suggesting that plasma zinc may not be the best biomarker for reflecting zinc status in disease status, including in diabetes. Thus, more prospective studies using appropriate biomarkers are warranted to further explore and confirm. As for molybdenum, no studies on the relationship between molybdenum level and long-term health effects of diabetes have been published to date. Only one recent population study found that high plasma molybdenum levels were significantly and positively correlated with all-cause mortality and CVD deaths in the general population [7]. However, one early study based on the Chinese population found that molybdenum supplementation was negatively correlated with cerebrovascular disease death [57]. In addition, early animal studies also found that molybdate can improve the immune capacity of diabetes rats and restore the ability of antioxidant enzymes in rats [58]. These differences may be attributed to variation in study design and methodology, as well as inherent differences in population characteristics (e.g., exposure levels, disease state and nutritional factors). Thus, further research is warranted to clarify the exact associations between molybdenum levels and all-cause mortality and CVD mortality among diabetes patients. The current study has several strengths. First, to our knowledge, this is the first prospective study to evaluate associations between 11 essential elements and all-cause mortality and CVD mortality in a middle-aged and older Chinese population with T2D. Second, LASSO models were used to select metals highly associated with all-cause mortality and CVD mortality, which is better than the traditional regression method for the situation of multiple metals with high correlation [59]. In addition, the outcomes were obtained according to strict medical and death records. However, some limitations should also be addressed. First, plasma selenium levels reflect the short-term status rather than the long-term status measured in the whole blood or erythrocyte [60]. However, one previous study [61] derived from the DFTJ cohort reported a significant positive correlation between plasma and whole blood levels of selenium ($r = 0.52$, $p \leq 0.001$). Iron, ferritin, transferrin, and transferrin saturation are more suitable to assess the body’s stores than plasma iron level and have been widely used in many studies. Unfortunately, these indices were not measured in the present cohort. Thus, more prospective studies using appropriate biomarkers are warranted to further explore and confirm. Additionally, we only measured the concentration of each metal once at baseline. Nevertheless, most metals including selenium levels showed a good repeatability at baseline (in 2008) and first follow-up (in 2013) in our previous study [61]. Second, we were unable to directly assess the glycemic control and severity of diabetes. However, we have adjusted for the duration of diabetes, baseline blood glucose level, and use of drugs for the treatment of diabetes in the analysis, and the results remained significant. Third, the plasma levels of metals can be influenced by dietary intake. Unfortunately, detailed information about the diet patterns is unavailable in our study. ## 5. Conclusions Findings from the present perspective cohort study suggest that higher plasma copper levels are associated with a higher risk of all-cause mortality, while higher plasma selenium and iron levels are associated with a lower risk of all-cause mortality among participants with diabetes. In addition, higher plasma levels of iron are also associated with decreased risk of CVD mortality. These data suggest the importance of monitoring the level of copper and supplementation of iron and selenium in the prevention of mortality among individuals with diabetes. 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--- title: 'Clinical and Peripheral Biomarkers in Female Patients Affected by Anorexia: Does the Neutrophil/Lymphocyte Ratio (NLR) Affect Severity?' authors: - Alice Caldiroli - Davide La Tegola - Letizia Maria Affaticati - Francesca Manzo - Francesca Cella - Alberto Scalia - Enrico Capuzzi - Monica Nicastro - Fabrizia Colmegna - Massimiliano Buoli - Massimo Clerici - Antonios Dakanalis journal: Nutrients year: 2023 pmcid: PMC10005379 doi: 10.3390/nu15051133 license: CC BY 4.0 --- # Clinical and Peripheral Biomarkers in Female Patients Affected by Anorexia: Does the Neutrophil/Lymphocyte Ratio (NLR) Affect Severity? ## Abstract Anorexia Nervosa (AN) is a disabling disorder characterized by extreme weight loss and frequent chronicization, especially in its most severe forms. This condition is associated with a pro-inflammatory state; however, the role of immunity in symptom severity remains unclear. Total cholesterol, white blood cells, neutrophils, lymphocytes, platelets, iron, folate, vitamin D and B12 were dosed in 84 female AN outpatients. Mildly severe (Body Mass Index—BMI ≥ 17) versus severe (BMI < 17) patients were compared using one-way ANOVAs or χ2 tests. A binary logistic regression model was run to investigate the potential association between demographic/clinical variables or biochemical markers and the severity of AN. Patients with severe anorexia (compared to mild forms) were older ($F = 5.33$; $$p \leq 0.02$$), engaged in more frequent substance misuse (χ2 = 3.75; OR = 3.86; $$p \leq 0.05$$) and had a lower NLR ($F = 4.12$; $$p \leq 0.05$$). Only a lower NLR was predictive of severe manifestations of AN (OR = 0.007; $$p \leq 0.031$$). Overall, our study suggests that immune alterations may be predictive of AN severity. In more severe forms of AN, the response of the adaptive immunity is preserved, while the activation of the innate immunity may be reduced. Further studies with larger samples and a wider panel of biochemical markers are needed to confirm the present results. ## 1. Introduction Eating disorders are behavioral conditions characterized by severe and persistent abnormal eating behaviors and associated distressing thoughts and emotions [1,2]. Anorexia nervosa (AN) is the eating disorder with the highest mortality rate of all psychiatric disorders due to medical complications associated with the illness, as well as suicide [3]. It is defined by an intense fear of weight gain and/or disturbed body image, determining severe dietary restriction and other weight-loss behaviors [4,5,6,7,8]. This debilitating and often chronic and relapsing condition has a prevalence of 0.8–$6.3\%$ in females and 0.1–$0.3\%$ in males [9]. AN is often associated with pronounced psychiatric comorbidity, emotional distress and functional impairment, as well as high rates of medical complications, especially in its most severe manifestations [10,11]. Medical sequelae of AN may involve all systems, including immunity. The gastrointestinal tract seems to be the most commonly affected system [12]. To date, it is widely recognized that nutritional deficiencies, malnutrition and starvation impair the human immune system, affecting cell-mediated immunity [13,14], as reflected by the depletion of leukocyte, lymphocyte and T-cell counts in restricting-type AN subjects without modifications of B cells [15,16]. Even though preliminary evidence demonstrated that AN was associated with immune system deficit, data regarding inflammatory alterations in patients affected by AN are still contrasting [17]. Notably, some authors pointed out that patients with AN, although severely malnourished, are relatively free from presenting an increased risk of common viral infections [15,18], suggesting that the adaptation process occurring in this disorder is multifaceted and worth studying. Similar to other psychiatric disorders, AN appears to be characterized by immune system dysregulations. As concerns innate immunity, abnormalities in neutrophil chemotaxis, adherence and microbicidal activity have been suggested [19,20], as well as decreased levels of complement components [21,22] and Natural Killer (NK) cells [23,24]. Notably, these abnormalities are similar to those observed in primarily malnourished patients [25], unlike alterations found in the adaptive immune system. In particular, the CD4/CD8 ratio is seemingly increased in AN compared to primary malnutrition, likely due to a greater reduction in CD8 rather than CD4 cells counts [26,27]. On the other hand, fewer studies have focused on humoral immunity in AN, with generally inconclusive results [25]. Together with the complete blood count, a number of studies supported the clinical utility of measuring biochemical parameters that can be affected by AN-related malnutrition [28,29], such as vitamins (particularly vitamin D, vitamin B12 and folate), electrolytes, albumin and trace elements (e.g., copper, manganese, selenium and zinc) [30]. Among biochemical alterations, hypercholesterolemia was repeatedly observed [31] and a recent meta-analysis reported increased levels of total cholesterol, low-density lipoprotein (LDL), high-density lipoprotein (HDL) and triglycerides in acutely ill patients, with alterations in total cholesterol and LDL persisting after weight restoration [32]. Notably, a strong interplay exists between nutritional status and the immune–endocrine system [14]: chronic, low-grade inflammatory state has been already demonstrated in patients affected by AN [33]. For example, in the cytokine profile of anorexic patients, higher serum levels of Interleukin (IL)-1β, IL-6, Tumor Necrosis Factor (TNF)-α and IL-15 have been noted compared to healthy controls [34,35,36], as also reported by two meta-analyses [37,38]. These findings suggested the presence of a low-grade inflammatory state in AN, which is particularly evident in adult patients [25,38]. However, more recently, contradicting results have emerged regarding cytokine alterations, which seem to question the hypothesis of a uniform pro-inflammatory state across anorexic patients. Two recent papers [39,40] did not confirm the findings of increased TNF-α, IL-1β or IL-6 in AN. In addition, the study by Keeler and colleagues [41], demonstrated significantly lower TNF-α and IL-6 levels in affected individuals. Therefore, the amount and direction of cytokine changes in AN remain to be clarified. It is also worth mentioning that alterations in gut microbiota have been described in AN, which appear not to normalize with weight gain [42,43], thus potentially representing more than an epiphenomenon. In addition, both clinical [44] and pre-clinical [45] research observed increased levels of oxidative stress in patients suffering from this disorder, contributing to the persistence of an over-inflammatory state in these individuals [46]. Finally, the neutrophil to lymphocyte ratio (NLR) and platelet to lymphocyte ratio (PLR), two well-established markers of low-grade inflammation, have been recently correlated with certain features of AN, such as a history of childhood maltreatment [47] and altered bone mineral density [48]. Even though it seems plausible that biochemical parameters may play a role in the onset and progression of AN [49], it is still unknown whether they represent state or trait markers of illness. Moreover, the correlation between peripheral biomarkers with illness severity has been poorly investigated until now, despite evidence of an association between AN severity and concentration of some inflammatory parameters [37]. In light of these considerations, the objective of the present study was to explore the relationship between a number of peripheral biomarkers with the severity of AN, as measured by body mass index (BMI). ## 2.1. Sample and Study Design We conducted a cross-sectional study, recruiting outpatients consecutively admitted in a 20-month period (from January 2021 to August 2022) to the San Gerardo Hospital Outpatient Clinic for Eating Disorders. Eligible subjects suffered from AN according to the Diagnostic and Statistical Manual of Mental Disorders (DSM) 5th edition (DSM-5) [4], were able and willing to provide informed consent. The study included participants aged between 17.5 and 45 years. Exclusion criteria were the following: [1] intellectual disability; [2] malnourishment due to severe organic disease; [3] pregnancy or breastfeeding; [4] taking vitamin B12 supplements; [5] patients in menopause. All participants provided written informed consent. The study was performed in compliance with the Helsinki Declaration of 1975, as revised in 2008, and the study protocol was approved by the ethics review board of San Gerardo Hospital. Diagnosis was determined via administration of the diagnostic items of the Italian version of the Eating Disorder Examination (EDE) Interview-17.0D [50]. We collected information about the following clinical variables: age, education, marital status, age at onset, duration of illness, duration of untreated illness (DUI), presence of substance use disorders, type of substance misuse, presence of psychiatric comorbidities, type of psychiatric comorbidities, presence of medical comorbidities, type of medical comorbidities, Body Mass index (BMI), presence of amenorrhea, current psychopharmacological treatment, pharmacological prescription at first visit to our psychiatric services and type of main pharmacological treatment. DUI was considered as the time elapsed between the onset of the disorder and the first treatment with evidence of efficacy according to guidelines [51,52]. Blood samples were collected in fasting conditions in the morning during the first visit at our department to measure: total number of white blood cells, lymphocytes, neutrophils, platelets per μL, and plasma levels of vitamin D (ng/mL), vitamin B12 (pg/mL), total cholesterol (mg/dL), iron (mcg/dL) and folic acid (ng/mL). The NLR was then calculated dividing the total neutrophils count by the total lymphocytes count. ## 2.2. Statistical Analyses Descriptive analyses of included variables were performed for the whole sample, and frequency with percentages and mean with standard deviation were calculated for qualitative and quantitative variables, respectively. The total sample was divided into two groups according to severity of AN: mild, when BMI ≥ 17 [4]; severe, when BMI < 17, the latter including moderately severe AN (16 < BMI < 16.99), severe AN (15 < BMI < 15.99) and extremely severe AN (BMI < 14.99) according to the DSM-5 severity criteria [4]. It was not possible to compare the 4 groups identified by DSM criteria in light of the small sample size. The two groups were compared for quantitative variables (including biochemical values) and for qualitative ones using, respectively, univariate analyses of variance (one-way ANOVAs) and χ2 tests. Then, a logistic regression model was performed, including the significant variables from univariate analyses as independent variables and presence of moderate–severe (versus mild) AN as a dependent one. The goodness of the model was assessed by Omnibus and Hosmer–Lemeshow tests. The level of statistical significance was set at p ≤ 0.05. The aforementioned statistical analyses were performed through The Statistical Package for Social Sciences (SPSS) for Windows (version 28.0, Milan, Italy). ## 3. Results The whole sample consisted of 84 female patients. Forty-seven ($56.0\%$) patients were classified as suffering from a mild form of AN, 37 ($44.0\%$) with a moderate–severe manifestation of the illness. The mean age was 23.31 (±7.38) years. Table 1 summarizes the clinical and biochemical data, and differences between the two groups. Patients affected by severe AN, compared to their counterparts, were older ($$p \leq 0.024$$) and had a lower NLR ($$p \leq 0.048$$). Moreover, subjects with severe AN (compared to the others): showed a trend in having a longer duration of illness ($$p \leq 0.088$$) and DUI ($$p \leq 0.085$$), higher vitamin B12 plasma levels ($$p \leq 0.078$$) and more frequent substance use disorders ($$p \leq 0.053$$). The goodness-of-fit test (Hosmer and Lemeshow Test: χ2 = 5.328; df = 8; $$p \leq 0.722$$) showed that the model including age, duration of illness, DUI, substance misuse, NLR and vitamin B12 plasma levels as possible predictors of AN severity was reliable, allowing for a correct classification of $81.3\%$ of the cases. In addition, the model was significant overall (Omnibus test: χ2 = 18.126; df = 6; $$p \leq 0.006$$). Higher NLR plasma levels were associated with mild rather than severe AN ($$p \leq 0.031$$) (Table 2). ## 4. Discussion The results of our study suggest that the severity of AN might be influenced by alterations in different biological systems. In particular, the main finding of our study was that a greater severity of AN is associated with a lower NLR. NLR is an easily obtainable parameter and a documented marker of physiologic stress and low-grade systemic inflammation [53]. NLR rises during stressful situation when a shift from adaptive to innate immunity is usually observed [54]. The normal range of NLR in adult, non-geriatric, healthy populations varies between 0.78 and 3.53 (mean 1.65 ± 1.96 SD [55] or 1.70 ± 0.70 [56]. In our sample, the mild-severity group of anorexic patients had a mean value of 1.74 ± 0.94 (range 0.40–5.42), in line with reports in the general population. On the other hand, severe anorexic patients presented with a mean NLR plasma level of 1.23 ± 0.66 (range 0.35–3.21), and hence with a slight shift to lower values. Although neither group showed a substantially different NLR compared to that reported in the general population, our results did suggest significant variations as a function of AN severity. The reason for a significantly lower NLR in more severe AN patients is yet to be elucidated. One hypothesis is based on the generally accepted evidence that starvation and malnutrition, as well as single nutrient deficiencies, may alter the production of leukocytes from the bone marrow. As reported by several authors, anemia, leukopenia and thrombocytopenia are frequent complications of anorexia nervosa [14,16] and may be a consequence of the degeneration of the bone marrow. Degenerative processes include serous fat atrophy and gelatinous transformation, which appear to be related to the lack of carbohydrates in the diet of AN patients [57]. The particular kind of malnutrition of patients with AN differs from the protein-energy malnutrition (PEM), which represents the most common cause of human immunodeficiency [58]; in fact, differently from PEM, in AN, the risk of common viral infections is not increased [15]; CD4+ T-cell counts are usually preserved with reduced percentages of CD8+ T-cells [26,27,33]. The observation that nutrition rehabilitation ameliorates leukocyte alterations supports the hypothesis that hematological alterations in AN are driven by malnourishment [23]. It is, however, worth mentioning that NLR seems to increase, rather than decrease, in other forms of malnutrition. Indeed, NLR was found to be significantly higher in geriatric outpatients who were malnourished or at risk of malnourishment [59]. Similarly, an NLR ≥ 5.0 was found to be predictive of nutritional risk in cancer inpatients [60], whilst an NLR ≥ 2.62 identified protein-energy wasting in individuals with a diagnosis of chronic kidney disease [61]. Our results appear to be consistent with the available literature, although the data are still poor and controversial. Lambert and collaborators [62] found a correlation between body mass fat and leukocyte count, albeit this finding was not confirmed by other authors [63]. In addition, a greater severity of weight loss was reported to correlate with the degree of bone marrow failure [64] and with a higher CD4/CD8 ratio [65], suggesting that, with the progression of weight loss, lymphocyte production is prioritized over other immune cells in order to preserve the adaptive immune system functioning. More recently, Saito and collaborators [66] demonstrated that, while the CD8 T-cell count did not vary according to the severity of nutritional status, the absolute lymphocyte count and the CD4 proportion presented a positive and a negative correlation with BMI, respectively. Considering the interplay between cytokines, neuropeptides and neurotransmitters, a second possible explanation of a lower NLR in severe AN patients compared to their counterparts is based on the assumption that inflammation plays a specific role in the etiology of AN. This hypothesis has been already suggested [25,33,67], although, to date, the role of inflammatory markers in AN is still unclear, as is the state or trait nature of this alteration. Several studies demonstrated that, despite the low BMI, blood levels of different pro-inflammatory cytokines such as TNF-α, IL-1α and IL-6 were significantly increased in AN compared to HCs [37,38,68]. Nevertheless, few data have been published until now regarding the role of illness severity in the pro-inflammatory state of patients affected by AN [69]. Consistently with our findings, Nilsson and colleagues [40] found a positive association between BMI and IL-6 plasma levels in AN, while other authors reported that chronic AN results were associated with a non-inflammatory status [70]. Globally, these preliminary findings support the hypothesis of a prominent pro-inflammatory status at the beginning of the disorder (mild AN) and of the contribution of immune dysregulation in the persistence of symptoms of AN, similar to what happens in other psychiatric conditions [71]. It is plausible that the progression of AN favors the shift from an over-activation of innate immunity to a predominance of adaptive immunity concomitantly with a change in the cell composition of bone marrow [72]. A trend of substance use disorders seems to characterize severe versus mild forms of AN. Our finding is consistent with previous literature reporting that the risk of substance use disorders in AN is influenced by subtype (i.e., it is more prevalent in the binge-eating/purge type than in restrictive type) [73] and by severity of symptoms, particularly in adolescents and for alcohol misuse [74]. The comorbidity of substance use disorders in subjects affected by AN is associated with a worse prognosis and an increased risk of somatic diseases and mortality [75,76]. Finally, vitamin B12 plasma levels resulted as higher in patients with a severe versus a mild form of AN, with a trend to statistical significance. Different authors reported that vitamin B12 plasma levels were normal or even elevated in subjects affected by AN [30,77]. Similar to our findings, Corbetta and colleagues [78] demonstrated that the levels of vitamin B12 and folate depended on the severity of illness, leading to the hypothesis that these vitamins may represent early markers of liver dysfunction. Another recent study supported this statement, demonstrating changes in vitamin B12 plasma levels concomitantly with liver dysfunction in acute AN patients [79]. These results should be interpreted in light of several study limitations. First, the sample size was relatively small. Second, the recruited subjects were all outpatients and this could represent a recruitment bias, as extremely severe AN patients were largely excluded. Third, peripheral markers were chosen a priori, being adherent to the routinely assessed parameters, but at the same time excluding, for example, other vitamins or cytokine plasma levels. In this regard, we are planning to expand the present study measuring circulating levels of TNF-α, IL-6 and CRP, as well as other inflammatory cytokines and antioxidant factors. In fact, it has been demonstrated that there is an antioxidant deficiency in AN [44,80]. Although modifications of the lipoprotein profile, the structure of phospholipids and constituents of myelin seemed to be related to specific clinical features of AN, such as body image distortion [81,82], the neurobiological underpinnings of AN are still uncertain and need to be further explored. Fourth, we measured AN severity using BMI as single parameter, according to the DSM-5; however, severity of AN is a more complex concept which includes, for example, rapidity of weight loss and body composition. Finally, some supplements as well as substance misuse might have influenced the values of some biochemical markers; nevertheless, we excluded patients taking supplements of vitamin B12. ## 5. Conclusions Our study demonstrated that women with AN presented different immune and biochemical alterations according to the degree of illness severity. In particular, NLR was lower in severe AN patients than in the mildly severe group. This finding supports the hypothesis of a dysregulated immune system in AN, although it remains difficult to determine whether these immunological changes are directly involved in the development and maintenance of the disorder or secondary to malnutrition. 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--- title: 'Pregnancy after Bariatric Surgery: Nutrition Recommendations and Glucose Homeostasis: A Point of View on Unresolved Questions' authors: - Silvia Burlina - Maria Grazia Dalfrà - Annunziata Lapolla journal: Nutrients year: 2023 pmcid: PMC10005384 doi: 10.3390/nu15051244 license: CC BY 4.0 --- # Pregnancy after Bariatric Surgery: Nutrition Recommendations and Glucose Homeostasis: A Point of View on Unresolved Questions ## Abstract Obesity is increasing in all age groups and, consequently, its incidence has also risen in women of childbearing age. In Europe, the prevalence of maternal obesity varies from 7 to $25\%$. Maternal obesity is associated with short- and long-term adverse outcomes for both mother and child, and it is necessary to reduce weight before gestation to improve maternal and fetal outcomes. Bariatric surgery is an important treatment option for people with severe obesity. The number of surgeries performed is increasing worldwide, even in women of reproductive age, because improving fertility is a motivating factor. Nutritional intake after bariatric surgery is dependent on type of surgery, presence of symptoms, such as pain and nausea, and complications. There is also a risk of malnutrition after bariatric surgery. In particular, during pregnancy following bariatric surgery, there is a risk of protein and calorie malnutrition and micronutrient deficiencies due to increased maternal and fetal demand and possibly due to reduction of food intake (nausea, vomiting). As such, it is necessary to monitor and manage nutrition in pregnancy following bariatric surgery with a multidisciplinary team to avoid any deficiencies in each trimester and to ensure the well-being of the mother and fetus. ## 1. Introduction Obesity is increasing in all age groups and, consequently, its incidence has also risen in women of childbearing age. In Europe, the prevalence of maternal obesity varies from 7 to $25\%$ [1]. Obesity is associated with short- and long-term complications for both the mother and the child [2,3]. As for short-term complications for the mother, obese women are more likely to have early pregnancy loss, gestational diabetes mellitus (GDM), pregnancy-induced hypertension, cesarean section, thrombosis, post-partum hemorrhages, and infections. On the other hand, long-term health consequences are associated with weight retention after delivery, which increases future cardiometabolic risk [2,3,4]. As regards short-term complications for the child, there is an increased risk of congenital malformations, being large for gestational age (LGA), macrosomia, shoulder dystocia, premature birth, and stillbirth [5,6]. In addition, babies of obese women have a higher risk of developing obesity in childhood, and this increases the chance of type 2 diabetes and cardiovascular disease into adulthood [5,6,7]. As such, it is highly recommended that obese women lose weight before conception to improve maternal and fetal outcomes [8,9,10]. Lifestyle and pharmacological interventions are the main cornerstones for weight loss; however, in the case of patients with class III obesity (BMI ≥ 40 kg/m2) and patients with class II obesity (35–39 kg/m2) with associated comorbidities, bariatric surgery (BS) has proven to be effective [10]. Obese women who undergo BS need to be informed that the probability to become pregnant after BS is increased. In particular, pregnancies are not recommended shortly after BS and need to be planned after the phase of maximum weight loss due to the possible occurrence of micronutrient deficiencies. The consequences of micronutrient deficiencies on offspring well-being are not well-studied. International guidelines recommend conception at least 12 to 18 months after BS or until stabilization of weight after BS occurs [11,12]. After BS, it is necessary to manage pregnant women with a multidisciplinary team, especially to prevent micronutrient deficiencies for offspring. In this paper, we aim to summarize the available evidence regarding the micro- and macronutrient needs of pregnant women after BS. In addition, we aim to consider unresolved questions in the management of women after BS, including maternal weight gain recommendations during pregnancy and glucose homeostasis. ## 2. Micronutrient Needs and Their Monitoring (Table 1) All guidelines recommend multivitamin and mineral supplementation prior to conception and throughout pregnancy. In particular, biochemical assessment to determine the specific micronutrient needs for women after BS, both before and during pregnancy, is strongly advised [13]. Table 1 \| Unnamed: 0 \| DRI/RDA/AI \| BS \| AGB \| Note \| \| --- \| --- \| --- \| --- \| --- \| \| Folic acid \| 0.6 mg \| 0.4 mg–1 mg \| \| 4–5 mg in obese and diabetic women \| \| Calcium \| 1000 mg \| 1200–1500 mg \| \| \| \| Copper \| 1 mg \| 2 mg \| >1 mg \| \| \| Iron \| 27 mg \| 45–60 mg \| >18 mg \| \| \| Selenium \| 60 µg \| 50–100 µg \| \| \| \| Zinc \| 11 mg \| 8–22 mg \| \| \| \| Vitamin A \| 10,000 IU \| 5000 IU–10,000 IU \| \| Beta-carotene form in pregnancy \| \| Vitamin B1 \| 1.4 mg \| <12 mg \| \| \| \| Vitamin D \| 600 IU \| >1000 IU (40 mcg) \| \| \| \| Vitamin E \| 15 mg \| 15 mg \| \| \| \| Vitamin K \| 90 µg \| 90–120 µg \| \| \| This supplement should contain the following at a minimum: copper (2 mg), zinc (15 mg), selenium (50 μg), folic acid (0.4–1 mg), iron (45–60 mg or >18 mg after adjustable gastric band), thiamine (>12 mg), vitamin E (15 mg), and beta-carotene (vitamin A, 5000 IU) [13]. The retinol form of vitamin A should be avoided during pregnancy due to a teratogenicity risk [14,15] As for folic acid supplementation, current guidelines suggest that folic acid at a dose of 4 or 5 mg daily should be given to patients who remain obese or diabetic after BS during the periconception period and throughout the first trimester [16]. As for vitamin B12, it is suggested to provide in the preconception period a dose of 1 mg every 3 months via intramuscular depot injection and monthly during pregnancy [17,18]. Alternatively, oral supplementation (1 mg/day) can be used to increase the compliance of the patient. All guidelines for women in pregnancy after BS recommend an iron supplement at a minimum dose of 45 mg elemental iron daily (>18 mg for adjustable gastric band); this should be increased as needed to maintain ferritin within normal range. Clinical recommendations also suggest iron infusion if oral iron supplementation is not sufficient [19]. Vitamin D should be supplemented to maintain a concentration of 50 nmol/L or greater with serum parathyroid hormone within normal limits, and if necessary, calcium should be added to maintain parathyroid hormone within normal limits [20]. It is recommended to check the following indices at least once per trimester and to use pregnancy-specific ranges for: serum folate; serum vitamin B12; serum ferritin (iron studies including transferrin saturation and full blood count); serum vitamin D with calcium, phosphate, magnesium, and parathyroid hormone; serum vitamin A; prothrombin time, INR, and serum vitamin K1 concentration; serum protein and albumin; and renal function and liver function tests [18]. ## 3. Macronutrient Needs As regards macronutrients, there are no clear guidelines. There are no specific recommendations as regards energy intake, carbohydrates (CHO), fat, or fluid; it is recommended to follow the Institute of Medicine (IOM) guidelines for pregnancy, even if they are not specific for pregnancy after BS [21]. As for macronutrient composition, it should be evaluated and monitored by a dietician throughout pregnancy to meet the metabolic demands of the pregnancy and to achieve fetal growth targets [17]. Energy requirements should be individualized on the basis of pre-pregnancy BMI, gestational weight gain (GWG), and physical activity level, with limitations on energy-dense foods if excessive GWG is identified. Only for protein intake is there a particular recommendation: a minimal target for protein intake after BS of 60 g/day and up to 1.5 g/kg ideal body weight per day [11], but higher amounts of protein intake (up to 2.1 g/kg ideal body weight per day) may be required in individual cases [19]. These recommended supplementation guidelines after BS are based on the results of a prospective one-year observational study showing an inverse relationship between protein intake and lean tissue loss [22] It is important to underline that in normal pregnancy, daily protein intake of 0.9 g protein/kg body weight in the second trimester and 1.0 g/kg in the third trimester is recommended [21]. In Table 2, recommendations from guidelines for general patients after BS, general pregnant women, and pregnant women after BS are summarized. ## 4. Glucose Homeostasis in Pregnancy after Bariatric Surgery Previous bariatric surgery has benefits in pregnancy as well as harms for mother and child. Regarding benefits, pregnancies after BS are associated with lower risk of GDM development, of LGA or macrosomia, and of hypertensive disorders in pregnancy. As for possible risks, a higher risk of small for gestational age (SGA) infants is reported in the literature [23,24,25]. In a normal pregnancy, there is increased insulin sensitivity in the first trimester, while the second and third trimester are characterized by progression of insulin resistance to guarantee a higher glucose load and a normal growth. After BS, there is an improvement in insulin resistance that, on one hand, can explain the decreased risk of GDM development, but on the other hand, could explain the increased risk of SGA, because insulin resistance could be insufficient in the third trimester to provide enough glucose flux to the fetus. It is important to underline that glucose homeostasis after BS depends on the type of surgical technique used. BS procedures can be classified as restrictive techniques (sleeve gastrectomy (SG)), malabsorptive techniques (intestinal bypass), or mixed (restrictive and malabsorptive: Roux-en-Y gastric bypass (RYGB)). In particular, glucose homeostasis takes longer to improve in restrictive procedures (such as SG) [26]; on the other hand, a full type 2 remission is observed within days or weeks in the case of mixed procedures (such as RYGB) [27,28]. ## 5. Diagnosis of Diabetes in Pregnancy after BS Women undergoing BS often remain overweight or obese; as such, they are at high risk of developing type 2 diabetes and GDM. As such, it is necessary to diagnose preexisting diabetes or GDM to avoid maternal and fetal complications related to hyperglycemia in pregnancy. There are no specific guidelines or cut-off values for the diagnosis of preexisting diabetes or GDM in pregnant women after BS, so it is recommended to follow international guidelines [29]. The oral glucose tolerance test (OGTT) with 75 g glucose is the gold standard for the diagnosis of GDM [29], but in women after BS, there are concerns with regards to its tolerability and accuracy. A Consensus Recommendation has proposed the use of seven-point capillary blood glucose monitoring or continuous glucose monitoring (CGM) for one week between 24 and 28 weeks of pregnancy [18]. In women in remission of type 2 diabetes after BS, it is recommended to screen for diabetes with fasting plasma glucose or glycated hemoglobin at booking and in the second trimester [18]. There are no specific glycemic targets for pregnant women after BS complicated by GDM or type 2 diabetes, so it is recommended to use international or local targets [18], generally 95 mg/dL for fasting glucose and 140 mg/dL for glucose level one hour after a meal. ## 6. Postprandial Hyperinsulinemic Hypoglycemia It is known that the gastrointestinal tract produces and secrets many polypeptides that play an important role in glucose homeostasis [30]. The more important hormones released by the gastrointestinal tract are incretins that stimulate postprandial insulin secretion in response to food intake. The two main incretins are the gastric inhibitory polypeptide (GIP) and the glucagon-like peptide-1 (GLP-1) [31]. Data available in the literature suggest that postprandial GLP-1 significantly increases, mainly in malabsorptive BS, but why this happens remains incompletely elucidated [32]. On the other hand, postprandial GIP levels are reduced after RYGB but are not changed after restrictive surgical procedures [33]. The increased secretion of GLP-1 is one candidate mediator of an important complication of BS, particularly RYGB: postprandial hyperinsulinemic hypoglycemia (PHH). PHH occurs in late dumping syndrome, typically within 1 to 3 h of ingestion of CHO-rich meals [34]. Rapid transition through the gastrointestinal tract stimulates L-cells and their secretion of GLP-1, followed by the increased secretion of insulin by the pancreatic beta-cells [35]. Recently, Larraufie et al. explored the importance of GLP-1 in post-BS secretion. They analyzed transcriptomics and peptidomics of enteroendocrine cells and demonstrated that elevated GLP-1 levels correlated with increased nutrient delivery to the gastrointestinal tract. They concluded that increased GLP-1 secretion after BS is the result of increased nutrient transit to the distal gut. They also demonstrated that GLP-1 is the major driver of insulin secretion [36]. In particular, GLP-1 level evaluation could be useful to prevent complications after BS, especially during pregnancy. PHH can have few symptoms, which causes delayed or missed diagnosis. It is important to underline that at the moment there is no consensus on diagnostic criteria of PHH. Recently, confirmation of Whipple’s triad with symptoms (anxiety, sweating, altered sensorium, hunger, and syncope) at a glucose level below 54 mg/dL and remission of symptoms after restoration of glucose level has been proposed [37]. During pregnancy, hypoglycemia can be harmful for the mother and even for the fetus in terms of fetal growth restriction. Recent data suggest a correlation between hypoglycemia events and reduced birthweight in pregnancy after BS [38,39]. Recognition of hypoglycemia episodes in pregnant women after BS is necessary to prevent fetal complications, such as fetal growth restriction. Available provocation tests are the OGTT and the mixed meal tolerance test; however, at the moment, there is not an accepted standard meal test. Nutritional management of PPH is necessary and includes recommended ingestion of low-glycemic-index CHO, small CHO portions, frequent intake of food with at least six small meals a day, and ingestion of CHO mixed with protein [37] (Table 2). Another common effect of BS is early dumping syndrome that occurs within 1 h of ingestion of food, especially rapidly absorbed CHO, and is characterized by the presence of symptoms such as dizziness, flushing, palpitations, and gastrointestinal symptoms (abdominal pain, diarrhea, and nausea). If suspicious of early dumping, rapidly absorbed CHO should be avoided, ingestion of liquid should be avoided 30 min before and after eating, and patients should be advised to eat slowly and to pay attention to portion size and meal frequency during the day [18] (Table 2). ## 7. Glucose Homeostasis Evaluation after BS Data on glucose homeostasis in pregnancy after BS are few and heterogeneous. The majority of studies have utilized OGTT with 75 g or 100 g to evaluate glucose homeostasis. A high prevalence of hypoglycemia during OGTT is reported from $5.26\%$ to $90\%$ of all women evaluated [40]. In particular, it seems that pregnant women after RYGB have a glycemic raise at 60 min followed by hypoglycemia at 120 min in $54.8\%$ of cases. If OGTT is extended to 180 min, $90\%$ of women develop hypoglycemia. When the relation between alteration of OGTT values and risk of SGA was evaluated, a positive association was found between fetal growth and maternal glucose nadir during OGTT [38]. In addition, an association between maternal hypoglycemic events during OGTT and SGA infants was found. OGTT can be helpful to analyze glucose homeostasis after BS, but CGM could provide a more detailed analysis of glucose homeostasis, especially after BS. In fact, with CGM it is possible to find all glycemic excursions, all hypoglycemic events, and time spent in range, above range, or below range, and these data could help clinicians to better individualize macronutrient needs. A recent meta-analysis has evaluated the rate and the timing of post-bariatric hypoglycemia (PBH) with CGM in non-pregnant patients [41]. The weighted mean prevalence of PBH was $54.3\%$, with a comparable rate of PBH in patients treated with RYGB and SG. The weighted mean prevalence of nocturnal PBH was $16.4\%$, with a comparable rate of PBH in patients treated with RYGB and SG. It was also found that the rate of PBH increased with increasing time from surgery. Bonis et al. studied the CGM profiles of 35 pregnant women after RYGB. They found a CGM profile similar to that described in non-pregnant RYGB patients. In particular, CGM profile was characterized by high mean maximum glucose level (200 mg/dL) and low mean minimum glucose level (<50 mg/dL). The time to reach post-prandial glycemic peak was short (<60 min) [42]. Interestingly, Gohier et al. studied the CGM profiles of 122 pregnant women who had undergone RYGB surgery. They found that $73\%$ of women had CGM profile abnormalities; in particular, $55\%$ of the women spent a higher time over 140 mg/dL, and $68\%$ of the women spent a higher time below 60 mg/dL. In addition, they demonstrated that CGM profile abnormalities are associated with fetal complications. In particular, being LGA was associated with a higher time spent over 140 mg/dL and with an excessive maternal weight gain during pregnancy; prematurity and being small for gestational age were associated with a higher time spent below 60 mg/dL [43]. It is important to underline that it is necessary to have more data to define better optimal glycemic targets in pregnant women after BS. Further studies on CGM in pregnant women after BS are necessary to find the target range of time to prevent maternal and fetal complications. CGM could be a valid option in pregnancy after BS to better analyze glucose profile and, in particular, to find all hypoglycemic events, even without specific symptoms. These important data could help all clinicians and, in particular, dieticians to modify macronutrient intake to avoid hypoglycemic events that could be harmful to the fetus. ## 8. Conclusions In conclusion, there are specific guidelines that clearly define micronutrient needs and their monitoring in pregnancies after BS. On the other hand, there are many unresolved questions in the management of nutrition in pregnancy after BS. In particular, it is necessary to better define the target of maternal weight gain during pregnancy. At the moment, IOM guidelines are used, but they are not specific to these patients because they do not consider weight loss after BS and the gap time between surgery and pregnancy. In addition, it is necessary to better define macronutrient needs in these women, taking into account the type of surgery (restrictive techniques, malabsorptive techniques, or mixed), prepregnancy BMI, recommended GWG, physical activity level, and the presence of PPH or early dumping syndrome. In this complex scenario, it seems that evaluation of hormones secreted by the gastrointestinal tract (particularly GLP-1 secretion) and analysis of glucose profiles of these women (particularly with CGM) could provide important information to answer these unresolved questions and to better individualize specific needs in order to prevent maternal and fetal complications associated with pregnancy after BS. ## References 1. Devlieger R., Benhalima K., Damm P., van Assche A., Mathieu C., Mahmood T., Dunne F., Bogaerts A.. Maternal obesity in Europe: Where do we stand and how to move forward?: A scientific paper commissioned by the European Board and College of Obstetrics and Gynaecology (EBCOG). 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--- title: Effect of Immunomodulating Extract and Some Isolates from Etlingera rubroloba A.D. Poulsen Fruits on Diabetic Patients with Tuberculosis authors: - Muhammad Ilyas Y. - Idin Sahidin - Asriullah Jabbar - Agung W. M. Yodha - Ajeng Diantini - Ivan Surya Pradipta - Riezki Amalia - Raden Maya Febrianti - Yuni Elsa Hadisaputri - Mohammad Ghozali - Euis Julaeha journal: Molecules year: 2023 pmcid: PMC10005397 doi: 10.3390/molecules28052401 license: CC BY 4.0 --- # Effect of Immunomodulating Extract and Some Isolates from Etlingera rubroloba A.D. Poulsen Fruits on Diabetic Patients with Tuberculosis ## Abstract Diabetes mellitus (DM) is a disease easily complicated by tuberculosis (TB) due to impaired function of the innate immune response. The successes of the discovery of immunomodulatory compounds needs to be continued to introduce new insights into the innate immune response. In previous studies, plant compounds of Etlingera rubroloba A.D. Poulsen (E.rubroloba) were demonstrated to have potential as an immunomodulators. This study aims to isolate and identify the structure of the compounds of E.rubroloba fruit that could effectively improve the function of the innate immune response in individuals with DM infected with TB. The isolation and purification of the compounds of the E.rubroloba extract were carried out by radial chromatography (RC) and thin-layer chromatography (TLC). Identification of the isolated compound structures was determined by measuring the proton (1H) and carbon (13C) nuclear magnetic resonance (NMR). In vitro testing was performed on the immunomodulating activity of the extracts and isolated compounds on DM model macrophages infected with TB antigens. This study succeeded at isolating and identifying the structures of two isolate compounds, namely Sinaphyl alcohol diacetat (BER-1), and Ergosterol peroxide (BER-6). The two isolates were more effective as immunomodulators than the positive controls were, which differed significantly (* $p \leq 0.05$) at the reducing interleukin-12 (IL-12) levels and Toll-like receptor-2 (TLR-2) protein expression and increasing the human leucocyte antigen-DR (HLA-DR) protein expression in DM infected with TB. The isolated compound was discovered in E. rubroloba fruits, which has been reported to have the potential to be developed as an immunomodulatory agent. Follow-up testing to determine the mechanism and effectiveness of these compounds as immunomodulators for DM patients is required so that they are not susceptible to TB infection. ## 1. Introduction Diabetes mellitus (DM) is a major health problem that has received great attention around the world, as it causes $70\%$ of the total deaths [1]. The global DM epidemic causes increasing economic losses, especially in developing countries, such as countries in Asia and Africa. The 2017 International Diabetes Federation (IDF) report shows that global DM treatment costs total more than USD 727 billion per year or around 12 percent of global health financing [2], and there has been a significant increase in the DM handling costs. A complication of DM that often arises from severe infectious diseases is tuberculosis (TB), which contributes to increasing morbidity and mortality in DM cases [3]. The global prevalence of TB in DM cases has increased 20-fold compared to that in non-DM cases, where $45\%$ of DM sufferers reported experiencing TB complications [4,5]. The increased susceptibility of DM sufferers to TB disease is partly due to the high global prevalence of TB disease, especially in Indonesia, which is ranked third in the world [6]. DM susceptibility to TB infection is highly dependent on the ability of the DM individual’s immune response to eradicate TB, where the conditions of hyperglycemia and cellular insulinopenia contribute directly to impaired innate immune cell function, such as the decreased phagocytosis of macrophages, the impaired secretion of interleukin-12 (IL-12), the function of Toll-like receptor-2 (TLR-2), and the decreased expression of the molecule major histocompatibility complex class-II (MHC-II) [7], which causes DM sufferers to become easily infected with TB [8]. Previous studies have reported several problems with the innate immune response in DM, particularly macrophage cells. These cells act as the first line of defense against TB infection, such as the recognition process by TLR-2 and IL-12 cytokine secretion as an effector of calcic pathway macrophage activation, as well as MHC-II antigen presentation as a response. In terms of adaptive immunity, it is important to strengthen the function of macrophages in people with DM by administering immunomodulating compounds. Administering immunomodulating compounds to people with DM is important to strengthen the innate immune response and ensure that they are not prone to complications with TB infection. It is also conducted to achieve TB eradication through the innate immune response mechanism by the macrophages, which is the first line of defense. The current lack of effective immunomodulating compounds is an obstacle to overcoming the problem of decreased immune system functioning in people with DM, so it is necessary to seek and develop immunomodulating compounds. An alternative to the development of immunomodulatory compounds derived from natural materials, whose mechanism of action is known to modulate the innate immune system in DM and TB-infected DM people, is important. Several studies have shown that secondary metabolites isolated from plants, such as delphinidin, malvidin, resveratrol, and quercetin, have immunomodulatory activity [9,10]. The plant Etlingera rubroloba A.D. Poulsen was reported to contain secondary metabolites tannins, flavonoids, saponins, alkaloids, polyphenols, terpenoids, triterpenoids, and phenolics [11], as well as the isolated secondary metabolites from the stem extract of E. rubroloba, namely Sinaphyl alcohol diacetate [12,13]. Pharmacological activities of this plant have been reported, such as E.rubroloba stems, which act as antioxidants, xanthine oxidase inhibitors, and anti-inflammatories [12,13,14]. Extracts and fruit fractions of E. rubroloba are known as immunomodulators in DM mice infected with TB, increasing the phagocytic activity of macrophage cells and helper T lymphocytes (CD4) in vivo [11,15,16]. E. rubroloba fruit juice has been used by the Wawonii and Tolaki ethnic communities in southeast Sulawesi to increase immunity (immunomodulator) [17]. E. rubroloba is a newly discovered species and is endemic in southeast Sulawesi, so there are a few studies that report the content of secondary metabolites, isolated compounds, and their pharmacological activity, especially the immunomodulating effect on DM [18]. Therefore, in this research, we obtained natural compounds that are safe and effective immunomodulators and can be used for the development of new drugs, thereby providing a solution for DM sufferers to prevent complications with TB disease. ## 2.1. Phagocytosis Activity and Levels of Interleukin-12 Extracts and Fractions In Vivo Phagocytosis activity is determined by counting the number of macrophage cells that phagocytize antigens in 100 macrophage cells. The value of the phagocytosis of macrophages is expressed as a percentage, and IL-12 level examination is carried out using the sandwich ELISA method [16]. The measurement results are shown in Table 1. Table 1 shows that the phagocytic activity level of macrophages was highest in the treatment group fraction C, followed by fractions A, D, B, E, and F, and it was the lowest in the negative control group (DM+TB). The statistical results of Tukey’s post hoc test showed no significant difference between the A, B, D, E, and F group fractions with the positive control ($p \leq 0.05$) in terms of macrophage phagocytosis activity and IL-12 levels. Meanwhile, the C fraction, ethyl acetate fraction, and extract showed no significant difference in macrophage phagocytosis activity and IL-12 levels with those of the positive control ($p \leq 0.05$), where the increased phagocytosis activity and decreased levels of IL-12 fraction C, ethyl acetate fraction, and the extract are better than those of the positive control. ## 2.2. Isolation and Purification of Fraction C Fraction C from E. rubroloba fruit was the most active immunomodulator. Separation and purification of the secondary metabolites were carried out using TLC, VLC, and radial chromatography. Fraction C was separated for the first time by the VLC method using the eluent system optimized at TLC, namely n-hexane and ethyl acetate at ratios of (10:0); (9:1); (8:2); (7:3); (6:4); (5:5); (4:6); (3:7); (2:8); (1:9); (0:10) (v/v), showing a good separation pattern. There are two isolates that were obtained from the isolation of the secondary metabolites. The purity of the isolates was tested using three eluent systems: n-hexane: ethyl acetate (7:3), n-hexane: acetone (7:3), and n-hexane:chloroform: acetone (7:2:1), which showed that the chromatogram obtain by TLC showed a single spot. Thus, it was concluded that the isolated compound was pure, with the BER-1 weight being 37.7 mg and BER-6 weight being 32 mg. The results of the test can be seen in Figure 1. ## 2.3. Identification of Isolated Compound Structures The two isolate compounds were successfully isolated from the secondary metabolites of the E. rubroloba fruit extract, then structural determination and identification were carried out based on 1D (1H, 13C) NMR spectroscopy data analysis. The results of the proton (1H) NMR spectra to determine the total number of protons include: the chemical shift (δH (ppm), multiplicity ((singlet (s), doublet (d), triplet (t), quartet (q), multiplet (m)), and the interpretation of the carbon (13C) NMR to determine the total amount of carbon in a compound. The interpretation of the results of the proton and carbon NMR of the isolated compounds can be seen in Table 2. The interpretation of the 1H-NMR and 13C-NMR spectra of the isolate compounds in Table 2 and a comparison with the literature show that the BER-1 isolate is Sinaphyl alcohol diacetate, while BER-6 is the isolate, Ergosterol peroxide. The structural formulas of the compounds are shown in Figure 2. ## 2.4. Immunomodulating Effects of E. rubroloba Fruit In Vitro The immunomodulation effect is based on the parameters of the IL-12 level, TLR-2 protein expression, and HLA-DR. ## Effect of Extracts and Isolate Compounds on IL-12 Levels The results of the measurement of IL-12 levels at various concentrations showed that the extracts and compounds isolated from E. rubroloba fruit exerted different immunomodulating effects based on the reduced levels of IL-12 (Figure 3). The average IL-12 levels for each treatment in Figure 3 and Table 3 show that the highest IL-12 levels of all the groups were in the control group of DM and the DM+TB macrophage cells. The results of the Tukey LSD post hoc test can be seen in Table 3. The results of Tukey’s post hoc test (Table 3) between the negative control group (DM+TB) with isolates and extracts were significantly different (* $p \leq 0.05$), indicating that there are immunomodulating potential of isolates and extracts, while the positive control with isolates and extracts showed significant differences in terms of the effect of reducing IL-12 levels (* $p \leq 0.05$). ## 2.5. Effects of Extracts and Isolate Compounds on TLR-2 Protein Expression The results of the measurements of TLR-2 protein expression with various concentrations, in Figure 3, show that the compounds and extracts from the fruit of E. rubroloba show different immunomodulation activities. The results of the measurements of the average TLR-2 protein expression in each treatment can be seen in Figure 4. The graph of the average TLR-2 protein expression for each treatment in Figure 4 shows that of all the groups, the highest negative control value was for the DM+TB macrophage cell group. It also shows decreased TLR-2 expression in the isolate and extract groups. The post hoc test results (Table 4) of the negative control (DM+TB) and the extract and isolate groups show that there was a significant difference between them (* $p \leq 0.05$). This shows that the compound isolate and fruit extract of E. rubroloba have the potential to be immunomodulators by reducing the expression of the TLR-2 protein. The results of the post hoc Tukey test were positive for the BER-1, BER-6, and extract groups. There were no significant differences (* $p \leq 0.05$) in the effect of reducing the TLR-2 expression; this explains that extracts and isolates from E. rubroloba fruit extract have the effect of reducing TLR-2 expression. ## 2.6. Effects of Extracts and Isolate Compounds on HLA-DR Protein Expression The results of the HLA-DR expression measurements of various concentrations in Figure 5 show that the compound isolates and extracts from E. rubroloba fruit show different immunomodulation activities. The average HLA-DR protein expression for each treatment in Figure 5 and Table 5 show that the lowest HLA-DR protein expression values were in the negative control group (DM+TB) and DM control. The results of the measurement of the average HLA-DR expression in each treatment can be seen in Figure 5. The highest mean HLA-DR protein expression was found in the extracts group, followed by BER-1, BER-6, and the positive control as compared to that of the negative control. The results of Tukey’s post hoc statistical test in Table 5 show that the isolates and extracts have a lot of potential as immunomodulators as compared to that of the negative control (* $p \leq 0.05$). The results of Tukey’s post hoc test for each treatment group can be seen in Table 5. BER-1 isolates had the same effect of increasing the HLA-DR expression as the positive control did (* $p \leq 0.05$), while isolate BER-6 and the extract had the same effect of increasing the HLA-DR expression as the positive control did (* $p \leq 0.05$). The increase in HLA-DR expressions of the isolate and extract was larger than the positive control one was, so it can be concluded that the extract and isolates are effective as immunomodulators. ## 3. Discussion Considering the increased phagocytic activity and the effect of decreasing IL-12 levels in DM model animals infected with TB, it is likely that there is a chemical compound contained in the extract and fruit fraction of E. rubroloba, which can improve the immune system function and anti-inflammatory effects in DM conditions. These results are supported by the research of Ilyas et al. [ 15], who reported that the ethanol extract and fraction of E. rubroloba fruit contains the compounds chavicol-β-D-glucoside, erigeside II, and 2-methoxyanofinic acid. These compounds are secondary metabolites of penylpropanoid-glycoside, terpenoid group, alkaloids, triterpenoids, and flavonoids, which have potential as immunomodulators and anti-inflammatories. These compounds have an immunostimulatory effect with mitogenic properties that stimulate the proliferation of T lymphocyte cells and B lymphocyte cells through the production of IL-12, IL-4, and IL-1 cytokines [15,19]. The proliferation of T lymphocyte cells will increase the activity of phagocytic cells such as macrophages [16]. We tested the immunomodulating effect of isolate compounds and extracts using in vitro methods on DM model macrophages infected with TB antigen using these parameters: IL-12 levels, TLR-2, and HLA-DR/MHC-II protein expression. The results showed that type 2 DM conditions showed an increase in IL-12, as an inflammatory condition that commonly occurs at the start of type 2 DM disease with long-term effects that can lead to complications such as tissue damage and a severe infection [20,21,22]. This study reaffirms previous research, which found that there was a larger increase in the IL-12 levels in the DM and DM-infected DM macrophage model groups (DM+TB) compared to those in the extract and isolate treatment groups. This is due to macrophage cells secreting more IL-12 at the start of TB infection to reduce the number of bacteria and control the development of TB disease [23]. Cytokine regulation in type 2 DM is very important, especially in the impact of complications of infectious diseases. It has been shown that the pro-inflammatory cytokine IL-12 has increased and is associated with acute inflammation at the start of type 2 DM [21]. Long-term increase in IL-12 has a negative impact on type 2 DM because it can cause organ or cell damage by inducing an oxidative stress-inflammation-dependent mechanism, which triggers oxidative stress and blood vessel damage by decreasing the expression of vascular endothelial growth factor receptor-2 [22]. The isolates and extracts in this study were shown to be able to regulate pro-inflammatory cytokines by reducing the IL-12 levels, which are needed to control excessive inflammatory responses in DM conditions and during TB infection and prevent more severe complications of TB infection [21]. During TB infection, a sufficient amount of IL-12 is needed to regulate the macrophages controlling TB pathogenicity in type 2 DM by secreting anti-bacterial cytokines, such as tumor necrosis factor (TNF), interferon-gamma (IFN-γ), and interleukin-6 (IL-6), as the effectors for cellular innate immune cells at overcoming an infection [24]. It can also increase the interaction of macrophage immunocompetent cells, NK cells, and T cells while fighting TB infection [25]. Inflammation has been shown to be a component of type 2 DM triggered through Toll-like receptor (TLR-2) signaling, in conjunction with free radical production, which is correlated with insulin resistance. Furthermore, TLR-2 and related signaling molecules in immune cells trigger increased inflammation in the adipose tissue of type 2 DM patients and contribute to the worsening of type 2 DM and susceptibility to complications with TB infection [26]. TLR-2 protein expression in this study showed larger increases in the DM and DM+TB model macrophage cell groups compared to those in the extract and compound isolate groups. TLR-2 expression in DM increased after the infection with TB antigens as a protective immune response by the host against TB infection. Furthermore, it initiated cellular innate immune responses to control the development of TB infection, which has been shown to have a negative impact such as complications in type 2 DM [27], as well as contribute to inflammatory events by signaling through DAMPs ligands [28,29]. Therefore, the regulation of TLR-2 in type 2 DM is important for controlling the inflammatory reaction that occurs. An increase in the amount of TLR-2 is directly related to the occurrence of an inflammatory response by increasing the pro-inflammatory cytokine IL-12, but on the other hand, the presence of a sufficient amount of TLR-2 is required by the host to protect against TB infection [26,30]. The research revealed that the expression of TLR-2 after the administration of isolates and extracts to DM model macrophage cells infected with TB antigens decreased compared to those of the DM and DM+TB groups. This study is in line with several previous studies that explained that a decrease in TLR-2 expression is needed to control excessive inflammatory responses in DM conditions or during TB infection and prevent more severe complications with TB infection [21,23]. A sufficient expression of TLR-2 protects the host during TB infection by regulating the pro-inflammatory and anti-inflammatory cytokines [31]. It plays a role in increasing the activity of phagocytic cells, which eliminate the TB infection, and modulate type 2 DM to prevent TB infection [32,33]. A sufficient amount of MHC/HLA-DR in type 2 DM regulates the immune response through the presentation of peptide epitopes from TB antigens, which are processed by the adaptive immune system to activate helper T lymphocyte cells to regulate the immune response to infection [34]. HLA-DR plays a role in NK cell activation through the expression of NKp30 and NKp46 receptors and causes the activation of adaptive immunity during TB infection [35]. The limited expression of HLA-DR affects the susceptibility of type 2 DM to complications related to the immune system, especially TB infection [36]. Type 2 DM shows a decrease in the expression of MHC-II/HLA-DR due to damage to the pancreas organ, causing a susceptibility to complications in type 2 DM [37] and contributing to the prognosis and the susceptibility to infectious complications [38]. This study reinforces the results of previous studies, where there was a larger increase in HLA-DR protein expression in DM+TB after were given isolates and extracts compared to those of the DM and DM+TB controls. The increased expression of HLA-DR in DM is very important to anticipate the risk of a susceptibility of type 2 DM to complications related to the immune system, such as TB infection [36]. It also plays a role in the activation of phagocytic cells, such as NK cells and macrophages, and causes the activation of adaptive immunity during TB infection [35]. Previous studies also reported that increasing the amount of HLA-DR had a protective effect against TB infection by increasing the activation of cytotoxic T lymphocytes (CD8) and helper T lymphocytes (CD4) [39,40]. The activity of the isolated compounds is thought to be influenced by the presence of functional groups in the molecular structure (Figure 2). Ketone groups, hydroxyl groups, and alkene groups in the aliphatic chain are found in the Sinaphyl alcohol diacetat (BER-1), which are derivatives of Sinaphyl alcohol from the phenylpropanoid group. The presence of ketone and hydroxyl groups in the aliphatic chain may have the effect of increasing the HLA-DR expression. The ketone and hydroxyl groups are not owned by the compound Ergosterol peroxide (BER-6), a steroid group compound that is thought to affect the increase in HLA-DR expression to a lesser extent than the BER-1 isolates do. Based on the structure and activity relationship of the isolate compounds BER-1 and BER-6, the structural parts responsible for their pharmacological activity are the aromatic (diaryl) group, ketone, hydroxyl groups, and double bonds in the cyclic chain (heptane) [41]. It is this functional group that allows the pharmacological effect of the isolated compound to decrease IL-12 and TLR-2 protein expression and increase HLA-DR protein expression. ## 4. Materials and Methods The research procedure involved several steps: sample collection and preparation, extraction and fractionation, screening tests for phagocytosis activity and levels of interleukin-12 (IL-12) extracts and fractions in vivo, the isolation and purification of isolate compounds, the identification of isolate compound structures, and tests of immunomodulation of extracts and isolates on in vitro DM macrophage models infected with TB antigens. ## 4.1. Sampling and Preparation of Samples The samples used in this study were E. rubroloba fruit taken from Punggaluku Village, Laeya District, South Konawe Regency, southeast Sulawesi Province. The fruit sample (12.25 kg) was sorted while it was wet to separate out the foreign matter under running water. The sample was then dried to reduce the water content and prevent microbial growth and damage to the sample. Furthermore, simplicia chopping was carried out to increase the surface area of the sample, so that the extraction process of secondary metabolites in the sample during extraction process was maximized. ## 4.2. Extraction and Fractionation A total 3.1 kg of E. rubroloba fruit simplicia powder was macerated with $96\%$ ethanol for 3 × 24 h, with a solvent replacement every 24 h. The macerate was concentrated using a rotary vacuum evaporator at 50 °C and thickened in a water bath at 50 °C to obtain a viscous ethanol extract of 334.2 g, with a yield of $10.78\%$. Fractionation of E. rubroloba fruit extract was carried out using vacuum liquid chromatography (VLC) method [42,43]. One hundred grams of extract was fractionated with VLC 4 times using n-hexane:ethyl acetate eluent obtained from eluent optimization. The results of the fractions from VLC were then tested by the thin-layer chromatography (TLC) method to determine the combination of fractions with the same Rf value and stain spots on the chromatogram. The resulting VLC fractions were then combined. Six main fractions were obtained and tested for TLC with n-hexane eluents: ethyl acetate (9:1), (7:3), and (6:4). Fractions A (1.18 g), B (3.18 g), C (4.01 g), D (3.86 g), E (23.94 g), and F (26.59 g) were then weighted. The obtained fractions were screened for immunomodulation activity in vivo with the parameters of macrophage cell phagocytosis activity and IL-12 levels to determine which fractions were effective as immunomodulators. Finally, isolation and purification of the most effective fraction were carried out. ## 4.3. Screening Test of Phagocytosis Activity and Levels of Interleukin-12 (IL-12) Extracts and Fractions In Vivo Acclimatization and grouping of test animals: BALB/c male mice were adapted to the environment for 7 days in cages filled with husks. The number of mice was 5 per group, which was calculated based on the Federer formula [11]. Preparation of the test preparation: The extract and the fraction were suspended at a dose of 0.1 mg/kg bw in Na. $0.5\%$ CMC, which was adjusted to the test animals’ body weight [15,16]. Preparation of Positive Control Preparations: The comparison of preparation used was 0.0005 g of commercial meniran (Phyllanthus niruri Linn.) extract in 30 mL of Na. CMC $0.5\%$, according to the dose given to mice. It was converted based on dose conversion calculations [44]. BALB/c Mice Models of Diabetes Mellitus: This research was approved by the Health Research Ethics Commission, Institute for Research and Community Service, Halu Oleo University (project number 706/UN29.20/PPM/2020). A total of 30 BALB/c male mice were modeled of DM by induced diabetogenic streptozotocin (Stz) dose of 150 mg/g bw intraperitoneally in Na citrate buffer, pH 6.0 [45]. The DM model of the test animals in this study was obtained 2 × 24 h after Stz induction with an average fasting glucose level of >200 mg/dL [11,46]. Treatment of BALB/c mice: A total of 40 DM and normal BALB/c mice were randomly divided into 10 groups. The test animals orally administered 0.5 mL/mice once day for 14 days [15,16] with the following treatment:FA group: BALB/c mice were given fraction A at a dose of 0.1 mg/g bw;FB group: BALB/c mice were given fraction B at a dose of 0.1 mg/g bw;FC group: BALB/c mice were given fraction C at a dose of 0.1 mg/g bw;FD group: BALB/c mice were given fraction D at a dose of 0.1 mg/g bw;FE group: BALB/c mice were given fraction E at a dose of 0.1 mg/g bw;FF group: BALB/c mice were given fraction F at a dose of 0.1 mg/g bw;FEA group: BALB/c mice were given fraction Ethyl acetate at a dose of 0.1 mg/g bw;BER-X group: BALB/c mice were given E. rubroloba fruit extract at a dose of 0.1 mg/g bw;K+ group: BALB/c mice were given a commercial meniran (Phyllanthus niruri Linn.) extract suspension at a dose of 0.101 mg/g bw;Group K-: BALB/c mice were given $0.5\%$ Na-CMC suspension;KN group: the control group of normal test animals was only given standard feed. Macrophage Cell Phagocytosis Test: Each mouse was infected with 0.1 mL of BCG antigen intraperitoneally on the eighth day. After 3 h, the test animals were euthanized with 0.2 mL of ketamine HCL. Peritoneal fluid was taken, stained on a glass object, and fixed with methanol, then $10\%$ giemsa stain was added. The preparations were observed under a microscope at a magnification of 10×–1000× [16]. Calculating Phagocytosis Activity of Macrophage Cells: The phagocytic activity of mouse peritoneal macrophage cells was calculated based on the value of phagocytosis activity (SPA), which is the percentage of macrophage cells that actively carry out the phagocytosis process in 100 macrophage cells [11,42]. [ 1]Phagocytic Activity=Amount of Active Macrophage CellsTotal Macrophage Cell Amount × 100 % ## 4.4. Isolation and Purification of E. rubroloba Fruit Isolates Separation and purification of secondary metabolites from E. rubroloba fruit were carried out in several stages. Thin-Layer Chromatography (TLC): The concentrated extract was spotted on a TLC plate, then eluted with a mixture of n-hexane:ethyl acetate (9:1). The Rf value of each spot formed was calculated, and the separation was observed. The eluents with a well degree of separate of stains, used for the process of separate compounds [43,47]. Radial Chromatography (RC): The radial chromatography plate was inserted into the RC apparatus and moistened with eluent before use. The fruit fraction of E. rubroloba was injected into the plate using a syringe. The separation process of the compounds was observed using a 254–366 nm UV lamp. Each separate component was collected in a vial, and its purity was determined using the TLC method in various eluent systems. Components with only a single stain after being tested by the TLC method were considered to be pure isolates [48]. ## 4.5. Identification of Isolate Compound Structures The pure isolate compound obtained from E. rubroloba fruit was determined by a spectroscopic technique. The spectrum was measured using an NMR spectrometer by measuring the 1H NMR and 13C NMR spectra determined using a JEOL JNM-ECZ500R/S1 FT NMR spectrometer (Japan) operating at 500.159 MHz (1H) and 125.765 MHz (13C) [43,49]. ## 4.6. Immunomodulation Test of Extracts and Isolates in an In Vitro DM Model Stimulated with TB Antigen Preparation of Extract and Test Isolates: The extracts and isolates were dissolved in $0.0025\%$ w/v DMSO in sterile distilled water, then the extracts, isolates, and positive control compounds were prepared in a series of concentrations at 50, 100 and, 200 ppm [14,50]. One mL of each extract and compound isolate was prepared for testing the immunomodulation effect. Preparation of RAW Macrophage Cell Line Culture of DM model: The RAW macrophage cell line culture resulting from 24 h incubation, 250 µL, was taken and transferred to a 75 cm2 container at 8 × 104 cells/cm2 containing complete RPMI growth media. Then, anhydrous glucose (Gibco) concentration of 15 mMol was added to RPMI media containing $80\%$ confluent growth macrophage RAW cells. Insulin levels in RAW macrophage cell cultures were measured to ensure that the DM model had been formed using an ELISA microplate reader. The DM model was formed when glucose levels were >120 mg/dL (7 mMol) or higher and the insulin levels decreased by $50\%$ from the normal levels (normal insulin levels <12 mIU/L). DM model macrophage cells were then resuspended at a concentration of 250,000 cells/mL for further testing [51]. Expression Testing of TLR-2, HLA-DR with Flow Cytometry: Examination of TLR-2 and HLA-DR expression was carried out using the flow cytometry method with the TLR-2 recombinant rabbit monoclonal antibody anti-mouse kit (Invitrogen catalog no. MA5-32787) and the CD74 polyclonal antibody kit and fluorescein isothiocyanate (FITC) conjugate (BIOSS, catalog no. OS-2518R-FITC). The incubation media resulting from the RAW macrophage cell culture in the wells were added to each 400 µL of the test material in complete RPMI 1640 media, namely ethanol extract samples, isolate compounds, and positive controls at concentrations of 50, 100, and 200 (µg/mL), and a solvent control (DMSO $0.0025\%$ w/v), in triplicate, then incubated in $5\%$ CO2 incubator at 37 °C for 4 h. They were infected with ESAT-6 TB antigen at a concentration of 0.5 µg/mL (105 CFU) [52], as much as 100 µL per well, then incubated in a $5\%$ CO2 incubator at 37 °C for 60 min. By following the protocol in the kit, readings were taken using a FACS CaliburTM flow cytometer with a reading duration of 50,000 events. FACS data were read and analyzed based on the strategy of determining the boundaries and areas of macrophage cells with the FlowJoTM application. Furthermore, using a laser combination that captured the intensity of TLR-2, HLA-DR antibodies, macrophage populations with TLR-2, HLA-DR-positive characteristics were selected [53,54]. Testing IL-12 Levels with ELISA: The macrophage culture medium stimulated by Mtb antigen for all the treatment groups was transferred into an Effendorf tube, centrifuged at 1500 rpm for 5 min, and the supernatant was removed using a pipette for ELISA examination. IL-12 levels were examined using the ELISA method with the mouse IL-12 Elisa kit 96T (BT LAB catalog no. E2658Mo). The IL-12 examination procedure followed the protocol in the ELISA kit. Measurement of 96-well plate by ELISA reader at 450 nm wavelength [11]. ## 4.7. Data Analysis The data analysis method used in this study is one-way analysis of variance (ANOVA) method, with a $95\%$ confidence level and a significance level ($5\%$ error rate (α = 0.05). Data analysis was performed with Tukey’s LSD post hoc analysis ANOVA and Tukey data analysis using the GraphPad Prism version 5. ## 5. Conclusions This study found two active isolates of immunomodulating compounds in DM infected with TB, which significantly reduced the IL-12 levels and TLR-2 protein expression, where the average decrease in IL-12 levels and TLR-2 expression was lower than that of the positive controls. The HLA-DR protein expression showed a significant increase, which was higher than that of the positive control. 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--- title: “What Made My Eating Disorder Worse?” The Impact of the COVID-19 Pandemic from the Perspective of Adolescents with Anorexia Nervosa authors: - Susanne Gilsbach - Beate Herpertz-Dahlmann journal: Nutrients year: 2023 pmcid: PMC10005403 doi: 10.3390/nu15051242 license: CC BY 4.0 --- # “What Made My Eating Disorder Worse?” The Impact of the COVID-19 Pandemic from the Perspective of Adolescents with Anorexia Nervosa ## Abstract [1] Background: the COVID-19 pandemic and subsequent confinements have led to a dramatic increase in anorexia nervosa (AN) in adolescent patients, whereas the effect on symptom severity and the influencing factors are not yet clear, especially not from the adolescents’ perspective. [ 2] Methods: from February to October 2021, 38 adolescent patients with AN completed an adjusted version of the COVID Isolation Eating Scale (CIES), a self-report questionnaire asking for ED symptomatology before and during the COVID-19 pandemic and for their experiences with remote treatment. [ 3] Results: patients reported a significant negative impact of confinement on ED symptoms, depression, anxiety, and emotional regulation. During the pandemic, engagement with weight and body image was related to social media, and mirror checking increased. The patients were more preoccupied with cooking recipes and had more eating-related conflicts with their parents. However, the differences in the amount of engagement with social media actively glorifying AN before and during the pandemic did not remain significant after correction for multiple comparisons. The minority of patients who received remote treatment found it to be only limitedly helpful. [ 4] Conclusions: from the patients’ perspective, the COVID-19 pandemic-associated confinement had a detrimental effect on the symptoms of adolescent patients with AN. ## 1. Introduction The COVID-19 pandemic had a detrimental effect on the mental health of children and adolescents (see for example [1,2]). In addition to an increase in more general mental health problems, such as anxiety and depression [2], the prevalence of eating disorders (ED), especially anorexia nervosa (AN), has increased all over the Western world and across all age groups during the COVID-19 pandemic (see [3] for an overview). Since the peak onset of AN occurs in adolescence [4], the pandemic-associated impact on the number and symptom severity of adolescent patients with AN is of particular interest. Gilsbach et al. [ 5] found a pandemic-associated increase in admissions of adolescents with AN to specialized treatment centers across Europe. Furthermore, the specialists for AN perceived their patients as being affected negatively by the COVID-19 pandemic on a variety of symptoms, among others concern about weight, diet and body image, loneliness, agitation, nervousness, and frequency of physical activities. Similarly, Herpertz-Dahlmann et al. [ 6] described a $40\%$ increase in hospitalizations because of childhood AN and a more than $30\%$ increase because of adolescent AN in a nationwide sample in Germany. The high admission rates were not, however, caused by an increase in readmission rates, but were more likely due to COVID-19 induced new onsets of AN or more severe courses of the disorder with more patients needing hospital admissions. Interestingly, there was also an increase in childhood male AN patients in this sample. Otto et al. [ 7] reported twice as many medical admissions to a pediatric hospital in the US due to EDs during the COVID-19 pandemic compared to the corresponding pre-pandemic time frame. Accordingly, Agostino and Burstein [8] found stable pre-pandemic numbers of AN cases in a Canadian pediatric tertiary care hospital and a steep upward trend with more diagnosed cases and more hospitalizations for newly diagnosed patients during the first wave of the pandemic. However, when examining AN symptom severity and influencing factors associated with the COVID-19 pandemic, research results have been contradictory (see [9] for an overview). Most researchers have found a pandemic-related worsening of adolescent AN symptoms using medical parameters such as the body mass index (BMI) and heart rate at admission [8,10], as well as by assessing psychopathology (see, for example [11]). Others have found an increase in AN cases but no changes in symptom severity with regard to medical parameters such as incidences of bradycardia, postural hypotension, requirements for electrolyte supplementation, nasogastral feeding, BMI, and amenorrhea [12,13]. Describing psychopathology, Akgül et al. [ 14] found that $42.1\%$ of their adolescent sample (mostly patients with AN, some with bulimia nervosa) even reported a positive effect of the lockdown on their ED symptoms, and only $21.1\%$ reported worsening. Accordingly, in an adult sample consisting of all types of EDs, only ¼ of the patients reported a worsening of symptoms during the lockdown [15]. Furthermore, research on the exact mechanisms of the COVID-19 pandemic on ED symptoms has been scarce and mostly qualitative. In a survey with 159 former patients with AN (mean age 22.4 years old, age range 14–62), approximately $70\%$ reported that eating, shape, and weight concerns, a drive for physical activity as well as loneliness, sadness, and inner restlessness all increased during the pandemic. Access to in-person psychotherapy and visits to general practitioners (including weight checks) decreased by $37\%$ and $46\%$, respectively [16]. Branley-Bell and Talbot [17] revealed in a qualitative study of patients with all types of EDs and across all ages several key themes affecting ED symptoms negatively, such as the “disruption to living situation, increased social isolation and reduced access to usual support networks, changes to physical activity rates, reduced access to healthcare services, disruption to routine and perceived control, changes to relationship with food, increased exposure to triggering messages”, while positively mentioned was an increase in digitally available social support systems. Correspondingly, in interviews with adolescent patients suffering from AN discussing experiences with COVID-19 confinement, the emerging themes consisted of “restrictions of personal freedom, interruption of the treatment routine, changes in ED und other psychopathology”. “ Less stress, more family time, autonomy and self-organization skills” emerged as opportunities arising from the pandemic [18]. In sum, the results regarding pandemic-related effects on AN symptomatology have been contradictory, and quantitative data regarding mediating factors have been scarce. In the current study, we aimed to assess changes in ED symptom severity due to the COVID-19 pandemic and to determine the contributing factors in an adolescent sample. We chose the only currently existing validated self-report questionnaire to assess the impact of confinement on EDs, the COVID Isolation Eating Scale (CIES) [19], and we adjusted it for adolescent patients. We hypothesized that self-reported symptom severity would be worse overall during the confinement than before the pandemic. More precisely, we hypothesized that there would be a pandemic-related negative impact on different features contributing to the severity of AN, such as eating behaviors, emotional regulation, and an increase in depressive and anxious symptomatology. We also anticipated an increase in body-related media consumption, as well as eating-related conflicts with parents. Finally, with a view toward future treatment recommendations, we assessed how many of our patients had received remote treatment and how effective they had found it to be. ## 2.1. Participants From February to October 2021, all patients ($$n = 40$$) who were treated in the Department for Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy of the RWTH-Aachen, Germany for an eating disorder (ED) (anorexia nervosa (AN) or bulimia nervosa (BN) according to DSM-5) completed an adjusted version of the CIES [19] as part of the diagnostic routine. The investigation period began during the second lockdown, which started with minor restrictions in November 2020 and included major restrictions including school closures from January 2021 until May 2021. The preceding first lockdown including school closures dated from March 2020 to May 2020. ## 2.2. Original Questionnaire and Adjustments The CIES is a self-report questionnaire to assess the impact of confinement on the psychopathology of patients with an ED during the COVID-19 pandemic. It is the only validated questionnaire of this type. The CIES asks for sociodemographic information, as well as current height and weight and weight before the onset of the COVID-19 pandemic, and it is then subdivided into four sections. The first section consists of items about the “circumstances during confinement” with questions about the living conditions, work, the financial situation and whether the patient was ill with COVID-19 or knew someone who was (8 items). The second section contains questions regarding the current diagnosis, comorbidities, and items assessing the “effects of confinement on eating disorder symptoms” (10 items; concerns about weight, attempts to reduce the quantity of eating and the number of meals, bingeing/purging, use of laxatives/diuretics, and exercise or other activities to control weight). The third section assesses “reactions to confinement” (34 items, e.g., emotional eating, anxiety, depression, dysfunctional thoughts, and addictive behaviors). The 10 items of section two and all items of section three are answered on a 5-point Likert scale (never–always) and should be answered twice, respectively, “before confinement” and “currently”. The fourth section contains an evaluation of experiences with remote therapeutic interventions, asking about feasibility, acceptance, and satisfaction on a five-point Likert scale (totally disagree–totally agree) (10 items) and open questions about challenges, strengths, and weaknesses of remote treatment (3 items). According to [19], the CIES measures five underlying factors: factor 1 (F1)—eating disorder-related symptoms, represented by the 10 items of section two; factor 2 (F2)—effects of confinement on the eating-related style (10 items from section three, such as eating for comfort, craving for food, feeling ashamed for eating); factor 3 (F3)—anxiety and depressive symptoms (11 items from section three, such as sleep problems, upsetting thoughts, loneliness, limited social contact, health concerns related to COVID-19, or sexual problems); factor 4 (F4)—emotional regulation (5 items from section three, such as irritability, aggression against self or others, and feeling of loss of control); and factor (F5), which is defined by the 10-item evaluation of remote treatment. Please refer to [19] for further information about the CIES, including its psychometric properties, which were good to excellent (Cronbach’s alpha between 0.81 and 0.92) for the Spanish version. The CIES was translated into 19 languages. Since the original CIES was developed for adults with a variety of eating disorders, we made slight adjustments to the questionnaire for our purposes. We omitted questions concerning obesity and its consequences, such as diabetes mellitus, since these aspects were not relevant for our patient group. Furthermore, we adjusted the demographic questions according to the age and life situations of our patients, e.g., we asked about school and parents, not about work and partners. Finally, we added questions regarding social media use and conflicts with parents about eating behaviors. Our adjustments, however, did not prevent the calculation of the main factors since all relevant items remained in the original version and we analyzed the added questions separately. ## 2.3. Statistical Analyses For all statistical analyses, we used IBM SPSS Statistics software, version 27.0 for Windows (Released 2020; IBM Corp., Armonk, NY, USA). For comparisons of pre-confinement with post-confinement values, we used the paired t-test. Furthermore, we computed estimations of effect sizes using Cohen’s d coefficient (│d│ < 0.2 no, │d│ > 0,2 low, │d│ > 0.5 medium, │d│ > 0.8 high effect). F5 was not computed due to the lack of a comparison group, but the mean scores for the items belonging to F5 were depicted separately. ## 3.1. Sample Characteristics Thirty-eight patients suffered from AN, and two suffered from BN. The two patients with BN were excluded from the analysis due to the small number. Please see Table 1 for sociodemographic and clinical information. All patients lived with their families at the time of confinement. Fourteen ($36.8\%$) underwent homeschooling, twenty-two ($57.9\%$) received a combination of homeschooling and in-person schooling, one ($2.6\%$) went to school in person, and one answer was missing ($2.6\%$). At the time of completion of the questionnaire, none of the patients had suffered from COVID-19, and four ($10.5\%$) had family members or friends who had experienced COVID-19. One ($2.6\%$) patient reported financial problems due to the COVID-19 pandemic. ## 3.2. Changes in AN Symptomatology The patients’ current mean BMI was significantly lower than that before the onset of confinement. There was also a significant increase in scores from pre-measures to current measures, indicating an increase in the symptom burden for all ED domains, except that represented by F2, “changes in eating style” (Table 2). ## 3.3. Changes in Social Media Use, Mirror Checking, Cooking, and Conflicts with Parents There was a significant increase in the amount of overall social media use. However, the difference in the amount of engagement with social media actively glorifying AN before and during the pandemic did not remain significant after correction for multiple comparisons. Patients reported an increase in mirror checking, engaging with cooking recipes, and conflicts with their parents due to eating. The frequency of cooking, as well as conflicts with parents not due to eating, remained unchanged (Table 3). ## 3.4. Remote Treatment Eight out of thirty-eight patients received remote treatment during the pandemic. The evaluation is depicted in Figure 1. As challenging aspects of the remote treatment, the participants mentioned a lack of privacy at home, digital obstacles, the missing division between everyday life and the therapeutic setting, and greater personal distance, leading to less open interaction and more opportunities to dissimulate weight loss issues or other problems. As advantages, the opportunity to continue treatment during lockdown and the lack of a need to drive to the treatment setting were mentioned. ## 4. Discussion This study is the only study that examined changes in AN symptomatology in adolescent patients during the COVID-19 pandemic using a validated questionnaire, asking for direct pre-/post-comparisons and focusing on the adolescents’ perspective. Overall, we found a detrimental impact of COVID-19 pandemic-associated changes on the psychopathology of adolescent patients with AN. As hypothesized, we found a significant increase in ED-related symptoms. This finding is supported by most of the comparable studies (e.g., [9]), but contrasts with the results of Fernandez-Aranda et al. [ 19], who developed the CIES in an adult sample and found a decrease in ED symptomatology. This discrepancy might be due to a difference in the participants’ ages or to the temporal gap of one year between the studies, meaning the investigation periods during the first in contrast to the second lockdown, respectively. Younger patients were shown to be especially prone to developing AN during the COVID-19 pandemic, hinting at the greater vulnerability for this age group [5,6]. There was no difference in eating-related style, which is not surprising since the items belonging to this factor measure bingeing/grazing/craving behaviors, and all included patients wo suffered from the restrictive subtype of AN; therefore, binging/craving/grazing are usually not one of their main concerns. The significant, negative impact of the pandemic on feelings of anxiousness and depression reported by our patients mirrors well the emotional burden caused by confinement, not only for patients with AN [11,16] but also for youths in general [1,2,20]. In a German nationwide longitudinal survey, over two thirds of the participating children and adolescents between 11 and 17 years reported being burdened by the pandemic and suffering from a significantly lower health related quality of life [1]. Accordingly, we found an increase in emotional dysregulation, including irritability, which has also been described previously by Tombeau Cost et al. [ 21] in children and adolescents with and without mental health problems during the pandemic. However, depressive and anxiety disorders are also frequent comorbid disorders in AN; thus, a differentiation between the effects of the pandemic and AN-associated comorbid symptomatology is difficult, although the patients reported a pandemic-induced increase. The digital media consumption of patients with AN, especially associated with body weight and shape, increased distinctly between the pre-pandemic and peri-pandemic times. This finding was not unexpected since more spare time and fewer activities might lead to a higher engagement in screen time [22,23]. Among the major physical health concerns in the Western world related to the COVID-19 pandemic-associated lockdowns were weight gain and a subsequent increase in obesity, for both children and adolescents [24,25]. Accordingly, the number of social media platforms with dietary and workout recommendations increased and gained broader interest, and warnings regarding “quarantine-15”, referring to a typical weight gain of 15 pounds due to the quarantine, could be found on several internet and social media pages [26]. Thus, children and adolescents susceptible to AN not only had more time to indulge in screen time but were also at a substantially increased risk of being confronted with the topic of weight loss in the digital world. The etiological contribution of AN-glorifying websites (“Pro-ANA”) to the development of AN, especially in female teenagers, has already been established [27]. In addition to the digitally fueled fears of weight gain due to a lack of physical activity, we suspect the changes in social media consumption to have been an important mediating factor in the development and deterioration of AN in children and adolescents during the pandemic. Furthermore, our participants reported more mirror checking, more engaging with recipes and more eating-related conflicts with their parents. This outcome is likely due to them spending more spare time at home. There was no relevant increase in conflicts other than eating-related conflicts. These findings support some of the pathways proposed by Rodgers et al. [ 28] through which the COVID-19 pandemic might increase ED risks and symptoms, the pathways namely being social media consumption, negative affect, disruption to daily activities, and social isolation. The disruption of daily routines in combination with the social isolation resulting from confinement is thought to lead to a decrease in resilience and adaptive coping strategies, and an increase in time spent with possibly harmful social media consumption and worrying thoughts regarding body image and weight. Furthermore, fears of contagion might lead to changes in diet with the aim of boosting immunity, simultaneously and involuntarily increasing the risk of developing an ED. Finally, stress and negative affect in general might contribute to the increase in risk for an ED [28]. There was a significant difference in self-reported BMI before and during confinement, with the BMI “before” being within the normal range and that “during” indicating being underweight. In principle, BMI could be interpreted as a medical marker of the disease severity of AN [8,10]. Although the pandemic had already lasted for one year at the time of our study, only approximately one-fifth of the participants had received remote treatment. Furthermore, satisfaction with digital treatment was mediocre, and was not regarded as a good substitute for in-person care, neither was it seen as a fit substitute. This result corresponds to [17] who found that patients with AN were the least comfortable with remote treatment, when compared to patient groups with other EDs, such as bulimia nervosa or obesity. [ 18] also confirmed that adolescent patients with AN preferred face-to-face appointments, also with regard to being able to leave difficult subjects “in the doctor’s office”. When deprived of possibility of in person therapy, patients chose a video-connection over an audio-only connection, because they regarded facial expressions and gestures as important for the therapeutical relationship. However, the overall willingness to continue receiving remote treatment when there was not alternative was good. This study has several limitations. The CIES was originally developed and validated for adults in a Spanish sample and was supposed to distinguish between participants with different ED diagnoses, such as AN, bulimia nervosa, and obesity. Our sample only comprised adolescents with restrictive AN. Moreover, the questionnaire was not validated in a German sample. However, it had been used in an international sample with 829 participants from 11 countries including 146 German-speaking patients and differentiated well between pre- and post-COVID eating disorder and non-eating disorder symptoms [29]. Furthermore, the participants’ assessments of the time before the COVID-19 pandemic were made post hoc by memory recall at the same time as the assessment of the current situation. In addition, the comorbid diagnoses were only reported by the patients themselves, not assessed by experts. However, when we compared the self-reported diagnoses of those participants, who had also been treated as inpatients, to those of the clinicians, there was no significant difference. Finally, the sample size was rather small, and only a minority of the participants received video treatment. ## 5. Conclusions In summary, we found a deterioration of AN symptomatology and general psychopathology during the COVID-19 pandemic. Mediating factors seemed to include the general psychological burden caused by pandemic-associated restrictions, in addition to fears of weight gain, increased exposure to media glorifying a low body weight, mirror checking, and the medial topic of healthy and low carb foods. 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--- title: Maternal Pre-Pregnancy Nutritional Status and Infant Birth Weight in Relation to 0–2 Year-Growth Trajectory and Adiposity in Term Chinese Newborns with Appropriate Birth Weight-for-Gestational Age authors: - Fengxiu Ouyang - Xiaobin Wang - Jonathan C. Wells - Xia Wang - Lixiao Shen - Jun Zhang journal: Nutrients year: 2023 pmcid: PMC10005412 doi: 10.3390/nu15051125 license: CC BY 4.0 --- # Maternal Pre-Pregnancy Nutritional Status and Infant Birth Weight in Relation to 0–2 Year-Growth Trajectory and Adiposity in Term Chinese Newborns with Appropriate Birth Weight-for-Gestational Age ## Abstract Being born with appropriate weight-for-gestational age (AGA, ~$80\%$ of newborns) is often considered as low risk for future obesity. This study examined differential growth trajectories in the first two years by considering pre- and peri-natal factors among term-born AGA infants. We prospectively investigated 647 AGA infants and their mothers enrolled during 2012–2013 in Shanghai, China, and obtained repeated anthropometric measures at ages 42 days, 3, 6, 9, and 18 months from postnatal care records, and onsite measurements at age 1 and 2 years (skinfold thickness, mid-upper arm circumference (MUAC)). Birthweight was classified into sex-and gestational age-specific tertiles. Among mothers, $16.3\%$ were overweight/obese (OWO), and $46.2\%$ had excessive gestational weight gain (GWG). The combination of maternal prepregnancy OWO and high birthweight tertile identified a subset of AGA infants with 4.1 mm higher skinfold thickness ($95\%$ CI 2.2–5.9), 1.3 cm higher MUAC (0.8–1.7), and 0.89 units higher weight-for-length z-score (0.54, 1.24) at 2 years of age with adjustment for covariates. Excessive GWG was associated with higher child adiposity measures at 2 years of age. AGA infants manifested differential growth trajectories by the combination of maternal OWO and higher birthweight, suggesting that additional attention is needed for those “at increased risk” of OWO in early intervention. ## 1. Introduction The first 1000 days of life (from conception to age 24 months) represents a critical period for developing lifelong metabolic health, as well as an optimal window for early-life obesity intervention [1]. In utero exposure to maternal adverse conditions has permanent programming effects [2,3,4]. Both small and large for gestational age (SGA, and LGA) infants have been linked to an increased risk of later cardiometabolic diseases [5,6]. In contrast, infants born with appropriate birth weight-for-gestational age (AGA), consisting of ~$80\%$ of newborns, are often considered as conferring a low risk for future obesity and are, thus, overlooked in obesity prevention [7]. An unsettled clinical question is whether factors assessed prenatally could help to refine infant obesity risk assessment at postnatal care for AGA infants during early childhood. There is limited evidence exploring this topic. It has been recognized that maternal prenatal nutritional characteristics including maternal prepregnancy obesity, excessive gestational weight gain (GWG) and gestational diabetes mellitus (GDM) are risk factors for high birthweight and child obesity [8,9], while inadequate GWG is associated with an increased risk of infant SGA [9,10]. Women with obesity and excess GWG are commonly identified as being at increased risk of GDM and infant LGA [11]. However, these maternal prenatal factors are seldom taken into account by pediatricians in the risk assessment of obesity for infants, and postnatal child care is relatively separate from maternal prenatal care in the typical clinical model. Fetal exposure to the “in utero” unfavored maternal nutritional environment has permanent programming effects on later obesity and adult cardio-metabolic diseases [2,3,4]. In fact, the developmental origins of health and disease (DOHaD) approach has been adopted in the early intervention of childhood obesity at ongoing trials, for example, the Healthy Life Trajectories Initiative (HeLTI) project [12]. The intervention was adapted to individual risk status assessed in each phase from preconception to prenatal, postnatal, infancy and childhood [7,12]. A clinical question raised is whether pre-pregnancy/prenatal maternal nutritional status should be considered in the identification of newborns at increased risk of future obesity among AGA infants during early childhood. This study aimed to examine whether there is a differential growth trajectory in the first two years and a heterogeneity of obesity risk by considering pre- and peri-natal factors among AGA infants, which can be an opportunity for early risk assessment and obesity prevention. As preterm birth infants may have very different growth trajectories [13], this study was limited to term-born children. ## 2.1. Study Population This study used data from the Shanghai Obesity and Allergy Birth Cohort Study. The primary objective of this birth cohort study was to examine early life environmental exposure and maternal risk factors of childhood obesity and allergic diseases. The baseline study was conducted between June 2012 and February 2013 at two large tertiary hospitals in Shanghai, China. Pregnant women were recruited when they were admitted to our study hospitals for deliveries. Eligibility criteria included: [1] having routine prenatal care at the study hospitals; [2] singleton pregnancy; [3] planning to stay in Shanghai for the next 2 years; and [4] a willingness to participate in this study and sign the consent form. After enrollment, trained study nurses conducted a face-to-face maternal questionnaire interview to collect information including prepregnancy weight, education, and smoking and passive smoking during pregnancy, etc. The women normally gave birth to their babies in the next 1–2 days after our investigation. After mothers delivered their babies and before their discharge from the hospital, our study nurses reviewed their medical records to abstract data including prenatal care, laboratory reports, pregnancy complications, labor and delivery course, and birth outcomes (gestational age, infant sex, birth weight, and birth length). We aimed to recruit 500 women in each of the two hospitals, with a total sample of 1000 pregnant women. At the end of February 2013, a total of 1243 women were eligible and enrolled. Among them, 829 of the children had a postnatal follow-up visit. The 23 children born preterm (gestational age <37 weeks) were excluded from this report (Figure S1). We followed the mother-child pairs using a web-based questionnaire investigation at the age of 6 months, and invited them for follow-up visits at Xinhua Hospital at age 1 and 2 years, during June 2014 and April 2015. The follow-up visit at 2 years of age included a face-to-face questionnaire interview of the mother, and a physical examination and blood draw of the child. We obtained signed informed consent from all mothers. The study was approved by the Institutional Review Boards of Xinhua Hospital and the International Peace Maternity and Child Hospital. ## 2.2. Maternal Prenatal Nutritional Factors Prepregnancy weight was self-reported by women at the baseline investigation. Maternal height, GDM, the last weight before delivery, and the mode of delivery were abstracted from hospital medical records. Maternal prepregnancy BMI (kg/m2) was calculated as prepregnancy weight (kg)/height squared (m2), and categorized as: underweight BMI < 18.5 kg/m2, normal weight 18.5–23.9 kg/m2, overweight 24–28 kg/m2 and obese ≥ 28 kg/m2, based on the Chinese BMI classification standards for the adult population [14]. GWG was defined as the difference in maternal weight between prepregnancy weight and the last measure before delivery. The last measure of maternal weight was usually performed at the time of hospital admission, about 1–2 days before delivery. As we described previously, GWG was categorized as excessive, appropriate, or insufficient based on GWG above or below the recommended IOM 2009 guidelines without adjustment and according to prepregnancy BMI: 12.5–18 kg (BMI < 18.5 kg/m2); 11.5–16 kg (BMI 18.5–24.9 kg/m2); 7–11.5 kg (BMI 25–29.9 kg/m2); and 5–9 kg (BMI ≥ 30 kg/m2) [9,15,16]. The diagnosis of GDM followed the recommendation of the International Association of Diabetes and Pregnancy Study Groups (IADPSG) [17,18]. Specifically, a 75-g oral glucose tolerance test (OGTT) was performed at 24–28 weeks of gestation on all pregnant women. GDM was defined if any of the following plasma glucose values reached: [1] fasting: ≥5.1 mmol/L; [2] 1 h: ≥10.0 mmol/L; and [3] 2 h: ≥8.5 mmol/L. ## 2.3. AGA Definition and Sex- and Gestational Age-Specific Birthweight Tertiles among AGA Infant birthweight, gestational age, and sex were abstracted from hospital medical records. The gestational age at delivery was estimated by using maternal last menstrual period (LMP), with supporting information from early ultrasound measures (<20 weeks). If the gestational age estimated by ultrasound measures differed by >7 days from that by LMP, then we used the ultrasound assessment. Birthweight-for-gestational age status was categorized as LGA, AGA, or SGA based on Chinese references for birthweight at each gestational week in boys and girls [19]. LGA was defined as birthweight >90th percentile, AGA as the 10th–90th percentile, and SGA as <10th percentile, respectively. Among AGA infants, gestational age-specific birthweight was then classified into low, medium and high tertile in boys and girls, respectively. Among 806 term born children, 647 were born AGA. This report used data from the 0–2 years of 647 term born AGA infants. ## 2.4. Child Anthropometric Measures At age 12 and 24 months, weight, length, mid-upper arm circumference (MUAC), and skinfold thickness were measured by study nurses according to the WHO protocol, and described elsewhere [20]. With 70–$90\%$ of total adipose tissue located subcutaneously, skinfold thickness can be used to reflect total body fat [21]. We also prospectively obtained repeated anthropometric measures (weight and length) at ages 42 days, 3, 6, 9, and 18 months from postnatal care records, which were measured by trained nurses according to the WHO protocol at community health care centers in Shanghai. Sex-specific z-scores weight-for-length (ZWFL) were calculated using WHO Child Growth Standards (WHO 2006). Z-score = (observed value—median value of WHO growth standards)/standard deviation (SD) of the WHO growth standard (http://www.who.int/childgrowth/standards/en/; accessed on 10 February 2023) [22]. ## 2.5. Postnatal Covariates Infant feeding type (formula feeding, exclusive breastfeeding, and mixed breastfeeding) at age 0–6 months was obtained based on the mother’s report at the postnatal 6 months online survey. Child exposure to passive smoking (yes, no) during age 0–24 months was obtained based on a questionnaire interview at each postnatal follow-up at 6, 12 and 24 months, and it constituted exposure to passive smoking if the mother reported a “yes” at any of the follow-up time points. ## 2.6. Data Analysis and Statistics This study examined the combined association of maternal pre-pregnancy nutritional status and infant birth weight-for-gestational age tertiles (low, medium, and high) with child age 0–2 year-growth trajectory and adiposity measures in term-born Chinese children. Among AGA infants, we present ZWFL (the index used to define OWO by WHO standards) from age 0–2 years (a) by prenatal factors (prepregnancy BMI categories, GWG, and GDM status, respectively); and (b) by infant birthweight for gestational age tertiles (low, medium, and high) in boys, girls, and the combination of both, respectively, using locally weighted smoothing (LOWESS) plots (Figure 1). We also used a Spaghetti plot and LOWESS plots to present length, weight and ZWFL across 0–2 years by the six groups of maternal OWO status (yes, no) and birth weight-for-gestational age tertiles (low, medium, and high) combinations in boys and girls (Figure S2 and Figure 2). To evaluate these associations, generalized estimating equation (GEE) linear models were used with an exchangeable correlation structure assumed to accommodate repeated postnatal outcomes (child anthropometric adiposity measures) during 0–6 months, and 7–25 months, respectively. Multivariate linear regression models were then used to examine the association of pre- and perinatal factors with child anthropometric adiposity measures at 2 years of age. All regression models included the same covariates, which included mode of delivery, infant age, sex, feeding type at age 0–6 months (formula feeding, exclusive breastfeeding, and mixed breastfeeding), maternal passive smoking during pregnancy (yes, no), and child passive smoking (yes, no). The approach to the selection of covariates for the regression models was to control for potential confounders and gain estimate precision. All analyses were performed using SAS 9.2 software (SAS Institute, Cary, North Carolina) and STATA 15.1 (Corp, College Station, TX, USA). ## 3. Results Of 806 term newborns, $12.4\%$ were LGA, and $4.0\%$ were SGA (Figure S1). This study focused on 674 AGA infants. The mean maternal age was 29.4 years (SD 3.5 years) at childbirth. Mean prepregnancy BMI was 21.2 kg/m2 (SD 3.0 kg/m2). The proportion of maternal prepregnancy overweight was $13.2\%$, obesity $3.1\%$, and underweight $17.8\%$. During pregnancy, $46.2\%$ had excessive GWG, $15.4\%$ had inadequate GWG, and $12.5\%$ ($$n = 84$$) had GDM. Gestational age ranged 37–41 weeks (Table 1 and Table S1). ## 3.1. Longitudinal Data Analysis of Birthweight-for-Gestational Age Tertiles, Prenatal Factors and Adiposity Measures among AGA Infants at Age 0–2 Years As shown by Figure 1, Table 2 and Figure S2, AGA infants vary in their growth trajectory and adiposity, depending on maternal characteristics. AGA infants in the high birthweight tertile had higher ZWFL at age 0–2 years compared to those in the low tertile (Figure 1). Newborns in the top tertile of birthweight-for-gestational age and with maternal OWO had the highest weight (β = 0.87; $95\%$ CI: 0.45–1.29 kg) and ZWFL (0.62; 0.31–0.93 unit) at 7 to 25 months with adjustment for mode of delivery, infant age, sex, feeding type at age 0–6 months, maternal passive smoking during pregnancy, and child passive smoking (both $p \leq 0.001$, Table 2, Figure 2). In AGA infants, children of mothers with excessive GWG had on average a 0.19 (0.08, 0.31) unit higher ZWFL at age 0–6 months, and a 0.18 (0.04, 0.31) unit higher ZWFL at 7–25 months than those with adequate GWG with adjustment for the same covariates as in Table 2. GDM was not associated with ZWFL among children born AGA in this study (Supplemental Table S2). ## 3.2. Prenatal Factors, Birthweight-for-Gestational Age Tertiles, and Body Composition among AGA Infants at 2 Years of Age In the follow-up onsite visit at 2 years of age, skinfold thickness and MUAC were measured. Among AGA babies, the combination of a high tertile birthweight-for-gestation and maternal OWO identified a subset of infants with 4.1 mm higher skinfold thickness ($95\%$ CI 2.2–5.9), 1.3 cm higher MUAC (0.8–1.7), and 0.89 units higher ZWFL (0.54, 1.24) at 2 years of age compared to those with low tertile birthweight and without maternal OWO after the adjustment for covariates (Table 3). As shown in Table S3, excessive GWG was associated with higher ZWFL, skinfold thickness, and MUAC in children at age 2 years with adjustment for covariates, but the magnitudes of these associations were lesser than maternal OWO related associations. GDM was not associated with child adiposity measures among children born AGA in this study. ## 4. Discussion This prospective cohort study examined differential growth trajectories in the first two years by considering pre- and peri-natal factors among newborns of AGA, which is novel. We found that AGA infants with a combination of high birthweight tertile and maternal OWO identified a subset of the elevated adiposity among AGA infants. In addition, excessive GWG was associated with higher values in all adiposity measures in AGA children at 2 years of age. Previous studies reported that AGA neonates with reduced antenatal/fetal growth velocity exhibited significant postnatal catch-up growth [23]. Our results in AGA infants with low tertile of birthweight-for-gestational age is in agreement with previous findings [23]. Of note, within the AGA group, a combination of high birthweight tertile and maternal OWO was strongly associated with elevated adiposity values at age 2 years with adjustment for covariates. In this study, the definition of maternal overweight (BMI 24–28) and obese (BMI > 28) were based on the Chinese adult BMI cut points, which has been found to be associated with an increased risk of non-communicable diseases (NCDs) including hypertension, diabetes, and dyslipidemia in the adult population [24,25]. GWG was categorized based on IOM 2009 guidelines [16]. The results suggest that AGA infants with maternal OWO and excessive GWG were still at increased risk of obesity despite their “normal” birth weight, and should receive additional attention regarding obesity prevention/interventions at postnatal child care visits. This proposal can be tested in our ongoing randomized trial [12]. The research question addressed by this study was raised as a result of observations during clinical practice. In current practice, AGA term-born infants are often considered at low risk of future obesity and are thus overlooked for obesity intervention efforts at postnatal care visits. This is also due to a gap between obstetric/maternity and pediatric/child care at the transition point from the pre- and perinatal to the postnatal phase in clinical practice. The present study revealed that even for AGA term-born infants, modifiable maternal prenatal risk factors of child obesity including maternal OWO and GWG are not only intervention target points before conception and during pregnancy, but also biomarkers of risk factors and predictors of child obesity. In Shanghai, children at age 0–3 years had standard postnatal care (including growth monitoring) according to established Shanghai child healthcare working protocols, which were provided by nurses and pediatricians from local Community Healthcare Centers. Low birthweight/SGA and infant overweight/obesity are identified as “high risk” for additional attention and consultation. By identifying predisposing prenatal factors of obesity among AGA infants, it can inform evidence-based practice in the development of early interventions to prevent obesity among children “at increased risk” of obesity and reinforce postnatal intervention. Future risk model analysis will allow us to estimate the childhood overweight and obesity risks associated with the individual predictor variables in a larger cohort study. Cumulative evidence suggests that childhood obesity and its related cardiometabolic risk factors should have their roots in the pre-pregnancy and/or pregnancy period [7,26]. Evidence from this study supported the hypothesis that obesity prevention should start prior to conception and concurrently address multiple prenatal risk factors of adverse birth outcomes [1,7,27]. A recent meta-analysis reported that maternal pre-pregnancy BMI and GWG were associated with risk for GDM, LGA and SGA, and GWG associations with the adverse outcomes assessed were to a lesser degree [28]. Maternal prepregnancy OWO and GWG should both be considered as targets for the prevention of high or low birthweight [29]. In this study, we focused on AGA infants and their mothers. Maternal underweight can be associated with increased risk of SGA and lower values in child BMI and ZWFL at the age of 2 years. The positive associations between maternal BMI and infant birthweight [30] and child adiposity might be due to both a genetic contribution and shared family environment conditions (dietary patterns, physical activity, sedentary lifestyle, screen time, etc.) [ 31,32]. In contrast, GDM was not associated with child adiposity measures at 2 years of age among term-born AGA infants in this study. This might be partly attributable to the routine screening and timely treatment of GDM in prenatal care in Shanghai, one of the most developed areas in China. A recent meta-analysis also revealed that the association between GDM and the risk of child OWO attenuated towards the null after additional adjustment for maternal BMI, suggesting that it was largely explained by maternal BMI [33]. This prospective study has important strengths. We collected high quality clinical data on multiple pre-, peri- and postnatal risk factors, and repeated anthropometric measures and skinfold thickness in young children. All women received routine prenatal care at the study hospitals, and women with GDM received treatment at the time of diagnosis. In this study population, the initial prenatal care was usually registered at about 6–14 gestational weeks. The routine prenatal care included the monitoring of maternal GWG and screening for gestational diabetes at 24–28 gestational weeks, etc. Caution is needed related to the generalizability of our finding on GDM to other populations. This study also had limitations. Prepregnancy weight was self-reported by pregnant women at the baseline investigation. However, at the population level, the judgements about excessive, adequate or inadequate GWG in this study should be able to reflect overall categories upon current available guidelines and tools. Any measurement errors associated with weight at conception, GWG and compliance with IOM 2009 guidelines, if it existed, would be unlikely to increase measures of association. Instead, the bias was more likely to hide associations that might exist toward a finding of no association. A future cohort study starting from preconception or at early pregnancy, with weighing scales calibrated in study clinics or using specific study scales by mothers, might harvest more precise measurements for prepregnancy BMI and GWG. Furthermore, this report only examined children up to 2 years of age; long-term follow-up of these children would allow us to explore the long-term health impacts of prenatal factors and birthweight. In summary, AGA infants manifested differential growth trajectories by the combination of birthweight for gestational age and maternal BMI. This finding underscores that AGA term born infants, who represent the majority of newborns, are heterogenous in their future risk of OWO and present opportunities for early obesity intervention. ## References 1. Blake-Lamb T.L., Locks L.M., Perkins M.E., Baidal J.A.W., Cheng E.R., Taveras E.M.. Interventions for Childhood Obesity in the First 1000 Days a Systematic Review. Am. J. Prev. Med. (2016) 50 780-789. 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--- title: The Effect of a Vegan Diet on the Coverage of the Recommended Dietary Allowance (RDA) for Iodine among People from Poland authors: - Agata Zaremba - Anna Gramza-Michalowska - Kunal Pal - Krystyna Szymandera-Buszka journal: Nutrients year: 2023 pmcid: PMC10005417 doi: 10.3390/nu15051163 license: CC BY 4.0 --- # The Effect of a Vegan Diet on the Coverage of the Recommended Dietary Allowance (RDA) for Iodine among People from Poland ## Abstract The aim of this research was to estimate the effect of a vegan diet on the Recommended Dietary Allowance (RDA) coverage for iodine in people from Poland. It was hypothesized that the problem of iodine deficiency is a concern, especially among vegans. The survey study was conducted in the years 2021–2022 on 2200 people aged 18–80 with omnivore and vegan diets. The exclusion criteria in the study were pregnancy and lactation. The study found that the coverage of RDA for iodine among people with a vegan diet was lower than among people with an omnivore diet ($p \leq 0.05$); $90\%$ of the participants with a vegan diet had an iodine intake below 150 µg/day. Plant-based dairy and meat analogs were consumed by vegans frequently and in large portions, but none were fortified with iodine. It was found that iodized salt was each group’s primary source of iodine. However, it was observed that the iodine supply from this source was limited among vegans, especially in female subjects, who consumed less salt and smaller portions of meals. That is why consideration should be given to the iodine fortification of plant-based foods commonly consumed by vegans. ## 1. Introduction In recent years, there has been an increase in interest in plant-based diets. Earlier consumer studies showed that from 2019 to 2020, as many as 5 million consumers in the United States shifted to avoiding meat altogether, becoming either vegetarians or vegans [1]. The popularity of vegan cuisine is most evident in the UK, Australia, Israel, Austria, New Zealand, and Germany. The Swedes, the Swiss, and Canadians are also more likely to follow a plant-based diet [2]. According to the Vegan Society, 2021, $6\%$ of the US population follows a strict vegan diet, compared with up to $4\%$ in Europe and $13\%$ in Asia [3]. In Israel, $5\%$ of the population declares themselves vegans [4]. In Poland, the sale of meat products fell by $7.5\%$ over the last three years, whereas the sale of plant-based foods grew by $30\%$ for dairy products and $60\%$ for meat products [5]. Among the reasons for switching to a plant-based diet are environmental protection, altruism towards animals, or the desire to improve wellbeing [6]. Veganism is considered the most stringent form of vegetarianism [7]. Due to sometimes very significant differences in the selection of products, the nutritional status of individual nutrients in people who adhere to plant diets differs from that in people who consume meat and other animal products [8]. The health benefits of using plant-based diets include reducing the risk of sclerosis, metabolic syndrome, or certain cancers [8,9]. At the same time, despite the many positive aspects of limiting animal products in the diet, risk factors should also be considered. Numerous studies show that a plant-based diet, especially vegan, is characterized by an insufficient supply of ingredients such as protein, ω-3 fatty acids, vitamin B12, iron, vitamin D, and calcium [9,10,11]. Less numerous studies also indicate deficiencies in zinc and iodine [12,13]. Iodine is necessary for the proper development and functioning of the human body due to its participation in the synthesis of thyroid hormones (T3, T4). Moreover, it affects basic metabolism [14,15,16]. A deficiency of an element that affects so many processes in the body leads to a spectrum of symptoms classified as iodine deficiency disorders (IDD), including goiter and hypothyroidism. Chronic, severe iodine deficiency in children causes cretinism or developmental delay [17,18]. The intake of stable iodine is also considered an effective countermeasure for reducing the risk of thyroid cancer in the case of a possible release of radioactive iodine following a nuclear accident [19]. The recommended daily iodine intake for adults was established at 150 µg [20]. Studies suggest that iodine content in vegetarian diets may be inadequate, but adherence to a balanced vegetarian diet need not lead to iodine deficiency. The iodine intake by the vegan Japanese population is among the highest in the world [21]. However, younger Japanese vegans tend to consume food with low iodine content [22,23]. The latest statistics indicate that $56.9\%$ of Europeans have an insufficient intake of iodine [20,24,25]. The diet of Poles can also be generally low in iodine [17]. This is because primary sources of iodine are excluded from the diet. The primary naturally occurring sources of iodine in the diet are animal products such as fish, seafood, eggs, milk, and dairy products [26]. In contrast, the content of iodine in plant products is strongly correlated with the content of this element in the soil [25]. The primary plant source of iodine may be algae, as the content of this element is high in them [25]. Earlier research confirms that the highest iodine intake was recorded for females following vegan diets with regular consumption of seaweed [27,28]. However, seaweed is not customarily consumed in the Western diet, although its popularity is increasing. As there is also no culture of consuming these types of products in Poland, they are not present in traditional gastronomy. In addition, the possibilities of enriching food with algae are limited due to legal restrictions [29]. Plant-based dairy and meat analogs are increasingly popular, but they are naturally low in iodine, so they cannot be considered nutritionally equivalent to milk. To minimize the risk of deficiency of this element caused by insufficient amounts in the diet, programs to fortify food with iodine are carried out in many countries around the world. One of the most common fortification strategies, also used in Poland, is salt iodization. The salt’s iodine fortification consists of soaking the product in a solution of potassium iodide or potassium iodate at an appropriate concentration. In Poland, these concentrations are legally regulated and amount to (30 ± 10) mg/kg of potassium iodide (KI) or (39 ± 13) mg/kg of potassium iodate (KIO3). The program of obligatory salt iodization in Poland has had very good results. In 2002, Poland was classified by the WHO as a country with sufficient iodine supply at the population level [20,30]. The analysis of iodine consumption shows that iodized salt was the primary source of iodine [26]. However, in 2006, the World Health Organisation introduced a recommendation to limit salt intake to 5 g/day, as it is a risk factor for atherosclerosis and hypertension. Consequently, the iodine supply from this source can be limited. Therefore, appropriate activities must be undertaken to promote the joint implementation of programs for reducing sodium intake and eliminating IDD through salt iodization [31]. Considering the above, this research aimed to estimate the effect of a vegan diet on the coverage of the RDA for iodine and evaluate the prevalence of the risk of inadequate iodine intake (below $41\%$ of RDA for iodine) among people aged 18–80 from Poland. It was hypothesized that the problem of iodine deficiency in the diet especially concerns the vegan group. Our study also assessed the impact of variables concerning age, sex, and education level. We estimated the relative contributions of iodized salt and iodine introduced by foods to the total iodine intake. ## 2.1. The Survey Questionnaire The study was conducted using an anonymous questionnaire based on the KomPAN questionnaire [32] and the author’s questions. The author’s questions were validated. The average correlation coefficients for the intake frequency of individual products were $r = 0.81$, and for product quantities, the coefficients were $r = 0.79.$ The survey questionnaire was completed from January 2021 to June 2022. A total of 2341 questionnaires were sent in and registered in the system; of these, 2200 questionnaires were correct ($94\%$). The questionnaire was made available in electronic form via Google Forms. The surveys were distributed on Polish internet forums for vegans, nutrition, and nutrition for the elderly. Questionnaires were also completed in outpatient family doctor clinics using the investigator’s computer. No ethical approval was required for this study. Participants were informed about the study’s aim and that their participation was entirely voluntary; therefore, they could stop the analysis at any point, and the responses were anonymous. The authors did not ask for sensitive data and personal information. Formal dependence was not used in recruiting subjects for the study. The questionnaire contained questions about the type of diet, the frequency and amount of consumed products, and the types of culinary processes used when preparing products with iodized salt (allowing for a complete determination of the iodine content in the diet from salt) (Table S1 and S2). The information on the method of food preparation (the types of culinary processes used) consisted of how salt was added to the food (or whether salt was added at all) at the end of heating, halfway through heating, and at the beginning of water heating. Based on the authors’ preliminary research (unpublished data) on the content of iodine in iodized salt cooked under varying conditions, iodine losses were assumed. Adding salt at the end of heating (losses of $0\%$), adding salt halfway through heating (losses of $25\%$), and adding salt at the beginning (losses of $50\%$) of water heating. The conversion factor assumed the cooking of meats (for approximately 30 min), potatoes (for approximately 20 min), pasta, rice, and groats (for approximately 10 min), and losses were taken into account only for these culinary processes. For the purpose of calculations of iodine from salt, only salt as a household additive was used. Food products for which the frequency and amount of consumption were asked in the questionnaire were: eggs; dairy products (butter, milk, yogurt, kefir, buttermilk, sour milk, sour cream, cottage cheese, cheese, e.g., Emmental, Mozzarella, Cheddar, Gouda, Brie, Gorgonzola); vegetable drinks; vegan products and dishes (vegan burgers, vegan sausages, bacon, and meat free ham); soy and its products (tofu, tempeh); legume seeds (peas, beans, chickpeas, lentils); vegetables (cabbage, Brussels sprouts, leek, garlic, onion, pepper, tomato, asparagus, and spinach); fruits (banana, apple, blueberries, strawberries, raspberries, peaches, apricots, tangerines, figs, plums); algae; poultry (chicken, turkey, duck, goose); red meat (pork, beef); cold cuts; fish (salmon, mackerel, tuna, cod, haddock, plaice, pollock, sprat, herring, trout); sushi; bread (light, wholemeal, graham, rye, wheat bread); and grain products (buckwheat, millet, barley, white rice, bran, and flakes). Based on the frequency and amount of consumption of particular products as declared by the respondents, as well as on the iodine content in the products, the weekly iodine consumption and the percentage coverage of the demand for this element were estimated. Iodine content in the products was determined based on the tables of composition and nutritional value [33,34]. For iodine calculations from salt, only salt as a household additive was used. For iodine calculations from plant-based dairy and meat analogs—none of them were fortified with iodine, and no iodine content was assumed. Taking into account the average iodine content in 100 g of the product, the content of this element was converted into the usual portion size provided by the respondents. Then, the content of iodine was converted into weekly consumption based on the frequency of consumption, according to an 8-point scale declared by the respondents. The following conversion scheme was adopted: 1—‘less often/never’ means consumption of 0 servings per week; 2—‘several times a year’ means consumption of 0.1 servings per week; 3—‘once a month’ means consumption of 0.25 servings per week; 4—‘several times a month’ means consumption of 0.75 servings per week; 5—‘once a week’ means consumption of 1 serving per week; 6—‘several times a week’ means consumption of 3 servings per week; 7—‘once a day’ means consumption of 7 servings per week; 8—‘several times a day’ means consumption of 21 servings per week. When calculating the coverage of iodine demand, the daily dose of this element was assumed to be 150 µg, i.e., 1050 µg per week. ## 2.2. The Group of Respondents The group selection was random, with stratified sampling taking into account the sampling of Polish society in terms of the type of diet, sex, and age. The exclusion criteria in the study were age (under 18) and pregnancy and lactation. The study population consisted of 2200 people of both sexes (i.e., $49\%$ males and $51\%$ females) aged 18–80 years in different areas of Poland. The subjects were on an omnivore diet and a vegan diet (Table 1). Regarding education, the largest group was people with higher education ($41.6\%$); people with vocational education accounted for $30.36\%$ of the study group, and people with secondary education $28.05\%$. The subjects were divided into age groups: 18–30; 31–40; 41–50; 51–60; 61–70; and 71–80 (Table 1). ## 2.3. Statistical Analysis The data were analyzed with Statistica (Software v. 13, StatSoft, Tulsa, OK, USA). The calculations were performed at a confidence level of 0.95, and the maximum error rate was set at 0.05. One-way analysis of variance (ANOVA) was carried out to compare the means of the groups (covering RDA for iodine) at a significance level of $p \leq 0.05.$ Then, post hoc Tukey’s test was applied. Interdependencies between the qualitative variables (diet, age, sex, education level) were determined with the chi-squared independence test at α = 0.05, which showed differences between the diet and sex. The logistic regression analysis (the odds ratio—OR) was applied to predict the covering of the RDA for iodine among people with a vegan diet. The ranges assumed were: high (above $100\%$ RDA); moderately high (81–$100\%$ RDA); medium (60–$80\%$ RDA); low (41–$60\%$ RDA); very low (21–$40\%$ RDA); or alarmingly low (below $20\%$ RDA) RDA for iodine depending on the people with a vegan diet and among women. ## 3.1. Iodine Intake The recommended amount of iodine that adults should consume each day is 150 µg. Unfortunately, approximately $50\%$ of respondents from Poland declared the consumption of food products that allowed for the coverage of RDA for iodine below $100\%$. The research results showed that the intake estimate of iodine from foods in the Polish population ranged from 9 to 160 µg/day, i.e., 67–1120 µg/weekly, which is 8 to $152\%$ of RDA for iodine intake (Figure 1). The covariance analysis (Table 2) showed a statistically significant influence ($p \leq 0.05$) of the predictors, i.e., the type of diet, sex, and age, on the amount of coverage of the RDA for iodine. Taking the strength of statistically significant influence (Table 2) into account, the predictors can be ranked from the most to the least impact in the following order: the type of diet > sex > age > education level. The statistical analysis showed that the diet’s most significant effect was confirmed. A chi-squared independence test (Table 3) also confirmed the strongest relationship between the type of diet ($p \leq 0.05$) and the coverage of RDA for iodine. People with a vegan diet had a significantly lower intake of iodine from food than people with an omnivore diet ($p \leq 0.05$; Table 3). In vegan people (Figure 1), the estimated iodine intakes ranged from 8 µg/day to 114 µg/day, but on average, $51\%$ RDA for iodine (Median value = 48 µg/day). In people with an omnivore diet, the intake was $90\%$ RDA for iodine on average (Median value = 79 µg/day). In the group of participants with a vegan diet, $90\%$ had an iodine intake below 150 µg/day. Only $1\%$ of vegans had an intake of iodine above 150 µg/day (Figure 2). For omnivore participants, the percentage with an iodine intake below 150 µg/day was $76\%$. The estimated iodine intake above 150 µg/day was $24\%$ of omnivores. To maintain homeostasis and hormone synthesis, the thyroid absorbs 60 µg/day of iodine when the supply is sufficient [35]. Therefore, logistic regression analysis was applied to predict very low (21–$40\%$) or alarmingly low (below $20\%$) coverage of the RDA for iodine among people with a vegan diet (Table 4; Figure 2). The logistic regression analysis indicated a strong relationship between alarmingly low (below $20\%$) and very low (21–$40\%$) coverage of RDA for iodine and a vegan diet (Table 4). A chi-squared independence test also showed a relationship between the sex of the respondents and the covering RDA for iodine, especially in the vegan group (Table 3). It was found that $64\%$ of men vegan participants had an iodine intake below $61\%$ of RDA for iodine, and $94\%$ of vegan women had an iodine intake below $61\%$ of RDA iodine, including $43\%$ who had below $41\%$ RDA iodine. ## 3.2. Iodine Sources Figure 3 and Figure 4 present the sources of iodine in people with a vegan diet and the omnivore diet (men and women). It was found that estimated iodine intake from food products (without iodine salt) contributed $31\%$ among women and men with vegan diets (Figure 3a). In women and men with an omnivore diet, this intake contributed $53\%$ of RDA coverage for iodine (Figure 3b). It was found that dairy products were only consumed by the people surveyed with an omnivore diet, both in women and men (Figure 3b and Figure 4b,d). These products met $17\%$ of the iodine-recommended daily allowance among women and $19\%$ among men. Sea fish and seafood (rich iodine sources) contributed to the iodine intake of only $23\%$, but only among people with an omnivore diet. A low frequency of consumption of these products (twice a month on average) was found in both women and men. In women with an omnivore diet, egg products contributed $2\%$ of total iodine intake and meat products $0.5\%$ (Figure 4b,c), while in men, $4\%$ and $0.6\%$ of intake, respectively. Fruit and vegetables contributed approximately $8\%$ of the total iodine intake in both women and men with an omnivore diet. Our studies showed a high prevalence of iodine deficiency among vegans, especially in women of all ages (Figure 4a,c). Iodine content in food of plant origin is lower than in animal origin due to the low iodine concentration in soil. Vegan dairy products were frequently consumed by the surveyed women with a vegan diet but could satisfy only $4\%$ of the RDA for iodine. These products were not iodine-fortified. A total of $40\%$ of women and $10\%$ of men consumed them daily, an average of 200 g/day. Similarly, vegan meat substitutes (i.e., vege-ham, vege-sausage, vege-burger) were frequently consumed by the surveyed vegan population, especially men. Unfortunately, with these products, women could meet a mere $2\%$ of the recommended daily allowance and men $3\%$. These products were not iodine-fortified. Legumes and their fermented products contributed $2\%$ of total iodine intake among vegan women and $3\%$ among men. These products were frequently and in large portions consumed by the surveyed women and men but were not iodine-fortified. Seaweed (rich iodine sources) contributed to an iodine intake of only $1\%$. A low frequency of consumption of these products (once a month on average) was found. Fruit and vegetables contributed approximately $11\%$ of the total iodine intake in both women and men with vegan diets. High consumption of cruciferous vegetables, especially broccoli, cabbage, and cauliflower, was found. Unfortunately, a high frequency of consumption of these products was found (daily), especially among women with a vegan diet. This can decrease iodine absorption and also contribute to the incidence of thyroid cancer [36]. In vegan women, cereal products (groats, pasta, bread) contributed $8\%$ of total iodine intake, and among men, $9\%$ (Figure 4). Whereas in both women and men with an omnivore diet, cereal products (groats, pasta, bread) intake contributed $5\%$ of total iodine intake. The iodine intake from these products contributed to the covering of RDA for iodine $14\%$ and $8\%$ among women and men with vegan diets, respectively, and $4\%$ with an omnivore diet. It was found that iodized salt was each group’s primary source of iodine. However, the iodine supply from this source was found to be limited among vegans, especially concerning women. Women (vegan and omnivore) declared average salt consumption at the level of 3 g/day. Among men, salt consumption was similar to women, 3 g/day. However, it is known that a significant amount of iodine (10–$40\%$) in salt may be lost during culinary processing [37,38,39,40,41,42]. Therefore, our research (Figure 5) was also concerned with analyzing habits related to salt being added to the food (at the end of heating, halfway through heating, at the beginning of water heating, or no salt was added). Our study found that $96\%$ of vegan women and $26\%$ with an omnivore diet declared adding iodized salt at the beginning of water heating, e.g., to potatoes or pasta. It is not a good practice because the loss of iodine can be as high as 20–$50\%$. Therefore, for those who indicated adding salt at the beginning of cooking, the iodine content was calculated at $50\%$ of the initial content. Unfortunately, only $36\%$ of women with an omnivore diet declared adding salt at the end of heating. None of the women with a vegan diet declared adding salt at the end of heating. Hence, the differences in RDA for iodine from salt. Among the vegan women group, it was found that $22\%$ of RDA for iodine was from iodized salt, and among women with an omnivore diet, $36\%$ (Figure 4). Similarly, it was found that $96\%$ of vegan men and $57\%$ of omnivore men also declared adding iodized salt at the beginning of water heating. Therefore, our studies showed that in the Polish population average daily consumption of iodized salt met $28\%$ of the recommended allowance of iodine. It was found that those who did not consume iodized salt ($$n = 10$$) ingested an estimated $14\%$ of the RDA for iodine. ## 4. Discussion The present study found that people with a vegan diet had a significantly lower intake of iodine from food than people with an omnivore diet. Low iodine intakes have also been reported in other European populations, especially vegans [43,44,45,46]. Iodine nutrition is also a growing issue within industrialized countries, including the US, due to declining iodine intake, partly attributable to changing dietary patterns and food manufacturing practices [26]. A recent national iodine survey found Israel’s population to be mildly iodine deficient, possibly due to unmonitored changes in the food content of dietary iodine [47]. In our research, we confirmed that vegan women and men from Poland have low iodine intake, regardless of age and education level. Krajcovicová–Kudlacková et al. [ 2003] and Groufh–Jacobsen [2020] confirmed similar tendencies in their research. They found that vegetarian groups from Europe, mainly vegan, tended to have the lowest iodine intake [48,49]. In our research, we confirmed that women, especially those with a vegan diet, have worse compliance with the iodine recommendations than those with an omnivore diet. The trend of lower iodine intake in women than men, especially vegans, was confirmed by Groufh–Jacobsen [2020] and Hatch–McChesney [2022] [26,48]. The low intake of iodine in women is in agreement with earlier studies showing a low iodine intake among women in Norway [44,50,51,52,53,54,55]. According to Eveleigh et al. [ 2020], iodine intakes for moderate vegans (vegetarians), vegetarians, and pescatarians from some populations were similar between sexes but also were below RDA for iodine [43]. Our studies confirmed that in people with an omnivore diet, the most important iodine sources were dairy products, especially yogurt, cheeses, and fish, especially sea fish. Similar trends were confirmed among Norwegians by Carlsen [2018] [56,57,58,59]. In the UK, the main sources of dietary iodine are cow’s milk and dairy products [2022]. Similarly, Israel’s milk and dairy products are iodine rich [42]. Brzóska et al. [ 2015], after analyzing the iodine content in milk in various regions of Poland, found that the average iodine content in drinking milk in Poland, taking into account its losses as a result of pasteurization, is at the level of about 100–200 µg/l. The authors confirm that Polish milk may be an important element of IDD prevention [60]. Thanks to the iodine content in milk, its products (cheese, fermented milk drinks) are also a source of this element. These have a higher iodine content due to the higher content of dry matter [61,62]. On average, cheese contains 37.5 mcg of iodine per 100 g of cheese; 100 g of Cheddar cheese provides $32\%$ of the daily requirement for iodine. Our research confirmed an earlier study that while the intake of white cheese has increased, there has been a decrease in the intake of milk [62]. Our research also drew attention to the tendencies of lower frequency and amount of milk consumed among young people. These trends and even stronger ones were confirmed in earlier studies [63,64,65,66,67,68]. However, an increasing frequency of consumption of those gaining in popularity, such as plant-based alternatives, was observed. Our study showed that people on a vegan diet consumed significant amounts of milk-alternative drinks. Dinewa et al. [ 2021], analyzing the consumption of cow’s milk and milk-alternative drinks in the UK population, found that cow milk consumers have higher UIC medians than those consuming milk-alternative drinks. On the basis of the collected data, it was proved that people who consumed milk-alternative drinks were at risk of deficiency of this element [26,69,70]. These authors also point to the need to fortify milk-alternative drinks so that they provide at least as much iodine as cow’s milk [71]. Due to their high consumption, chicken eggs can be a source of iodine in the diet [72]. This was confirmed by our research among omnivore subjects. On the basis of our data, it was proved that people who consumed eggs (a few times a week) were less at risk of deficiency of this element. However, the authors point to the seasonal variability of iodine content in eggs due to feed. Sea fish are a rich source of iodine [34]. Lower iodine intake was found among women compared to men, which can also be seen in an earlier study [73,74]. These studies showed that the consumption of fish products was so low that it only covered $10\%$ of the iodine requirement [73]. Vegans consume plant-based foods and omit all types of animal products. Algae are a plant product that provides significant amounts of iodine. Earlier research confirms that the highest iodine intake was recorded for females following a vegan diet with regular (daily) consumption of seaweed [75,76]. It has recently become popular in UK food products as a whole and as a functional ingredient [27]. However, in our study, a low frequency of consumption of these products (once a month on average) was found. Moreover, iodine content significantly differs between seaweed species consumed [28]. Unfortunately, in the study group, vegans were found to have a high consumption of plant-based food groups (fruit, vegetables, legumes, tubers, cereals, and grains) [55,77,78,79], along with tofu and soya-based products that naturally have a low iodine content. The Polish market also does not offer biofortified vegetables. In our research, a high frequency of consumption of cereal products was found (several times a day), especially among men. In vegan women, cereal products (groats, pasta, bread) contributed $20\%$ of total iodine intake, and among men, $22\%$. However, these were not as high iodine sources as in the research among Norwegians [55]. The Norwegian and American markets offer a wide range of iodized bread with iodized salt [34,55]. In Switzerland, iodine-fortified bread is a significant contributor to dietary iodine and can be consumed by all dietary groups [80]. Unfortunately, bread does not introduce high iodine amounts into the diet though it is a staple food in Poland’s population. Our research confirms an earlier study [46] which indicated that the population could not reach the recommended iodine intake from food sources [37,55,62,81]. The most practical and cost-effective way to provide iodine supplementation to deficient populations is with iodized salt, as advocated by several international organizations such as the WHO, United Nations Children’s Fund, and International Council for Control of Iodine Deficiency Disorders [82,83]. However, it was found that the iodine supply from this source is limited, especially concerning vegan women. This follows a reduction in iodized salt consumption, possibly influenced by efforts to reduce salt consumption in the general public [84,85]. Additionally, bad habits exist related to the use of salt in the household. Our study found that over $80\%$ of the respondents declared adding iodized salt at the beginning of water heating, e.g., to potatoes or pasta. Therefore, in our research, the amount of salt (for declaring adding iodized salt at the beginning of water heating) added during the processing was reduced to $50\%$. A strength of this study was the sample size of over 2202 participants (1000 vegan, aged 18–80), compared to previously conducted studies on vegans and vegetarians [51,86,87]. Another strength is the assessment of iodine intake with methods including the assessment of iodine intake from iodized salt. The earlier study did not identify adjusting iodine intakes to account for the types of culinary processes used. The main limitations were that the presented study did not take into account the place of residence of the respondents and regional differences in the frequency of consumption of products [88], and the lack of measurements of urinary iodine concentration. Therefore, further research should focus on confirming the obtained dependencies using such measurements. ## 5. Conclusions The study found that the coverage of the Recommended Dietary Allowance (RDA) for iodine among people with a vegan diet was lower than among people with an omnivore diet. A total of $90\%$ of the participants with a vegan diet had an iodine intake below 150 µg/day. The results also showed a relationship between the vegan diet of women and the lowest coverage of the RDA for iodine, below $41\%$. Plant-based dairy and meat analogs were consumed frequently and in large portions by vegans, but none were fortified with iodine. It was found that iodized salt was each group’s primary source of iodine. 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--- title: New Anti-Glycative Lignans from the Defatted Seeds of Sesamum indicum authors: - Gyeong Han Jeong - Tae Hoon Kim journal: Molecules year: 2023 pmcid: PMC10005424 doi: 10.3390/molecules28052255 license: CC BY 4.0 --- # New Anti-Glycative Lignans from the Defatted Seeds of Sesamum indicum ## Abstract Seven known analogs, along with two previously undescribed lignan derivatives sesamlignans A [1] and B [2], were isolated from a water-soluble extract of the defatted sesame seeds (Sesamum indicum L.) by applying the chromatographic separation method. Structures of compounds 1 and 2 were elucidated based on extensive interpretation of 1D, 2D NMR, and HRFABMS spectroscopic data. The absolute configurations were established by analyzing the optical rotation and circular dichroism (CD) spectrum. Inhibitory effects against the formation of advanced glycation end products (AGEs) and peroxynitrite (ONOO−) scavenging assays were performed to evaluate the anti-glycation effects of all isolated compounds. Among the isolated compounds, [1] and [2] showed potent inhibition towards AGEs formation, with IC50 values of 7.5 ± 0.3 and 9.8 ± 0.5 μM, respectively. Furthermore, the new aryltetralin-type lignan 1 exhibited the most potent activity when tested in the in vitro ONOO− scavenging assay. ## 1. Introduction Among naturally occurring bioactive polyphenolics, lignan is a class of diphenolic secondary metabolites that are distributed in the plant kingdom and biosynthesized by oxidative dimerization of two phenylpropanoid units [1]. This class of natural polyphenols possesses a broad range of structural diversity and biological potencies, including anti-tumor, antioxidant, anti-inflammatory, anti-neurodegenerative, and antiviral activities [2]. Recent interest in novel lignans originated from natural foodstuffs has continuing increase due to their biological benefits correlated with disease prevention and health promotion. Sesame (Sesamum indicum L.), one of the most important oilseed crops, is a rich source of lignans such as sesamol, sesamin, sesamolin, and sesaminol [3]. Sesame seeds produce a highly stable oil and provide several food nutritional benefits [4]. Studies have reported good antioxidant activities [5] as well as numerous valuable biological effects [6]. Sesame cake obtained from oil extraction is mainly used as a feed ingredient for domestic animals or is discarded. However, defatted sesame seeds are reported to exert various biological properties, including antioxidant [7], anti-diabetic [8,9], and inhibition of brain edema formation [10], and were found to be rich in lignan and polyphenolic constituents [11,12]. Especially, the major lignan glucoside (sesaminol triglucoside) isolated from defatted sesame cake exhibits potent biological activities such as radical scavenging and cytochrome P450 enzyme inhibition [13,14,15]. Current research interests in the biologically active lignans of S. indicum continue because of their inherent ability to prevent diseases and improve health [16,17]. Persistent hyperglycemia is a critical cause associated with the pathogenesis of diabetic complications [18], which can arise from protein kinase C (PKC) isoform activation, increased aldose reductase (AR)-related polyol pathway flux, increased hexosamine pathway flux, and the formation of advanced glycation end products (AGEs) [19]. The formation and accumulation of AGEs are closely implicated in diabetic complication-associated diseases such as arteriosclerosis, kidney disease, neuropathy, osteoporosis, and Alzheimer’s disease [20]. The AGE accumulation reaction is further accelerated with the increased formation of reactive oxygen species (ROS) and free radicals [21]. Thus, suppressing the formation of AGEs and oxidative stress is recognized as an effective therapeutic strategy for diabetic complications in humans. As part of our continuing search to discover naturally occurring anti-glycation compounds from various natural sources, the ethyl acetate-soluble extract of defatted sesame seeds was found to inhibit activities against the in vitro formation of AGEs and radical scavenging assays. The subsequent bioactivity-guided isolation of this extract led to the isolation of a new aryltertralin-type [1] and a tertrahydrofuran-type lignan [2] along with seven known compounds (3−9), using successive column chromatography over Toyopearl HW-40 gel and ODS AQ gel together with semipreparative HPLC. The new structure elucidation, inhibition of AGEs formation, and ONOO− scavenging activity of compounds 1 and 2 were conducted and are presented in this study (Figure 1). ## 2.1. Structure Elucidation of New Compounds and Characterization of Known Compounds The new structure compound 1 was isolated as a yellow amorphous powder, [α]20D +21.6° (c 0.1, MeOH). A pseudomolecular ion peak at m/z 360.1206 [M]+, observed in the HRFABMS spectrum of 1 in conjunction with 13C NMR spectroscopic data, corresponded to the molecular formula C19H20O7. The 1H NMR spectrum of 1 displayed four aromatic proton signals at δH 6.52 (1H, s, H-2), 6.40 (1H, s, H-3′), 6.29 (1H, s, H-6′), and 6.18 (1H, s, H-5), revealing the presence of two 1,3,4,6-tetrasubstituted aromatic rings. Signals for a methylenedioxy group at δH 5.78 (2H, s, H-10′), two oxygenated methylene proton signals at δH 3.69 (1H, m, H-9′), 3.64−3.62 (2H, m, H-9), and 3.42 (1H, dd, $J = 11.4$, 4.8 Hz, H-9′), one methylene at δH 2.68 (2H, d, $J = 7.8$ Hz, H-7), and three methines at δH 4.17 (1H, d, $J = 10.2$ Hz, H-7′), 1.94 (1H, m, H-8), and 1.73 (1H, m, H-8′) were also obtained on the 1H NMR spectrum (Table 1). The 13C NMR and HSQC spectra of 1 showed 19 carbon signals: 12 aromatic carbon signals at δC 151.2 (C-2′), 147.5 (C-5′), 144.3 (C-4), 144.2 (C-3), 142.4 (C-4′), 132.1 (C-1), 129.4 (C-6), 125.3 (C-1′), 117.2 (C-5), 115.7 (C-2), 109.9 (C-3′), and 98.4 (C-6′), one methylenedioxy group at δC 102.0 (C-10′), two oxygenated methylenes at δC 66.2 (C-9) and 63.3 (C-9′), one methylene at δC 33.4 (C-7), and three methine carbons at δC 47.8 (C-8′), 40.9 (C-7′), and 40.8 (C-8) (Table 1). Analogous resonances consistent with the presence of these functionalities were displayed in the 13C NMR data of 1 (Table 1) [22,23]. The 1H-1H COSY correlations of H-7/H-8/H-9, H-7′/H-8′/H-9′, and H-8/H-8′, and the HMBC correlations of H-8 to C-1,-7,-7′ and H-7′ to C-1′,-2,-2′,-8,-8′,-9′, indicate that 1 is an aryltetralin-type lignan [24]. The HMBC correlations from the methylenedioxy moiety (H-10′) indicate that this is located at C-4′ (δC 142.4) and -5′ (δC 147.5) (Figure 2A) [24]. The relative configuration of 1, established on the basis of analyzing the NOESY correlations observed of H-6′/H-8′, H-7′/H-8, and H-7′/H-9′ along with the large-coupling constants (J7′, 8′ = 10.2 Hz), confirmed the trans-trans arrangement (Figure 2B) [25,26]. Compared to previous reports, the circular dichroism (CD) spectrum of 1 showed positive Cotton effects at 238 (Δε + 4.7) and 282 (Δε + 2.4) nm and a negative Cotton effect at 301 (Δε −8.7) nm (Figure S9) [27]. Consequently, the absolute configuration of 1 was determined to be a 7′S, 8R, 8′R-configuration. Compound 1 was identified as a new naturally occurring arytetralin-type lignan, and this compound was assigned the trivial name sesamlignan A (Figure S30). Compound 2 was isolated as a brown amorphous optically active powder ([α]20D +58.9°) and showed a pseudomolecular ion at m/z 344.1262 [M]+, corresponding to the molecular formula C19H20O6 in the HRFABMS. The 1H NMR data of 2 exhibited resonances for two sets of ABX-type aromatic rings at δH 6.82 (1H, d, $J = 2.4$ Hz, H-2′), 6.77 (1H, dd, $J = 7.8$, 2.4 Hz, H-6′), 6.74 (1H, d, $J = 7.8$ Hz, H-6′), 6.67 (1H, d, $J = 8.4$ Hz, H-5), 6.62 (1H, d, $J = 2.4$ Hz, H-2), and 6.51 (1H, dd, $J = 8.4$, 2.4 Hz, H-6), and one methylenedioxy group at δH 5.90 (2H, s, H-10′). The spectrum also included signals attributable to three methine protons at δH 4.76 (1H, d, $J = 6.0$ Hz, H-7′), 2.68 (1H, m, H-8), and 2.29 (1H, m, H-8′), one methylene proton at δH 2.81 (1H, dd, $J = 13.8$ Hz, 5.4, H-7) and 2.42 (1H, dd, $J = 13.8$, 10.2 Hz, H-7), one oxygenated methylene protons at 3.97 (1H, dd, $J = 8.4$, 6.6 Hz, H-9), 3.69 (1H, dd, $J = 8.4$, 6.6 Hz, H-9), and one hydroxymethyl group at δH 3.81 (1H, dd, $J = 10.8$, 7.2 Hz, H-9′) and 3.61 (1H, dd, $J = 10.8$, 7.2 Hz, H-9′) (Table 1). These NMR data observations indicate that 2 contains a three-ring system: two aromatic rings and one tetrahydrofuran moiety. The above data, together with the observation of 13C NMR and HSQC spectra, implied that 2 is a tetrahydrofuran-type lignan [28]. The connective positions of each methylenedioxyphenyl and a dihydroxyphenyl group by the HMBC technique, which demonstrated a three-bond correlation between the oxygenated methine proton (H-7′) to C-1′ (δC 138.6), -2′ (δC 107.2), and -6′ (δC 121.0), and methylene proton signals (H-7) to C-1 (δC 133.5), -2 (δC 108.6), -6 (δC 120.3), -8 (δC 43.7), and -9 (δC 73.7) positions, respectively (Figure 2A). The linkage point of hydroxymethyl residue (H-9′) on the furan moiety (C-7′, -8, -8′) in 2 was determined unambiguously from key HMBC correlations in Figure 2A. These spectroscopic features are comparable to those reported for acuminatin [29], previously isolated from the aerial parts of Helichrysum acuminatum, except for the signals of a methoxyl group at C-3 present in 2. Moreover, the proposed relative stereochemistry of 2 in the tetrahydrofuran moiety was confirmed by the spatial correlations between H-7′, H-7, and H-9′, H-8 and H-8′, and H-8 and H-9 as observed in the NOESY spectrum (Figure 2B) [30,31]. The absolute configuration of the tetrahydrofuran moiety in 2 was determined as a 7′S, 8R, 8′R-configuration based on the positive specific optical rotation value {[α]20D +58.9° (c 0.1, MeOH)} as well as negative Cotton effects at 229 (Δε −0.3) and 288 (Δε −0.7) nm in the CD spectral comparison using authentic analogs (Figure S17) [32]. Although the planar structure of 2 has previously been reported as an intermediate of sesamin metabolites identified in rat urine by GC-MS data [33], this is the first report of isolation and determination of the absolute structure using spectroscopic interpretation (Figure S30). Based on the spectroscopic analysis and comparison of the data with literature values, the previously reported compounds 3−9 were identified as (+)-sesaminol 2′-O-glucopyranosyl(1→2)-O-[glucopyranosyl(1→6)]-O-glucopyranoside [3] [11,34], (+)-sesaminol 2′-O-glucopyranosyl(1→2)-O-glucopyranoside [4] [13,35], (+)-sesaminol [5] [36], (+)-epipinoresinol 4′-O-glucopyranoside [6] [37], (+)-epipinoresinol [7] [38], apocynin [8] [39], and vanillic acid [9] [10] (Figures S18–S29). The methyl-substituted vanillic acid, apocynin [8], was first isolated from Sesamum indicum (Figure 1). ## 2.2. Inhibition of Formation of AGEs and ONOO− Scavenging Effects All pure isolated compounds 1−9 were evaluated for their capacity to inhibit the formation of AGEs using aminoguanidine as the positive control (Table 2). Compared to the positive control aminoguanidine (IC50: 995.3 ± 3.6 μM), the most potent inhibitory effects against AGEs formation were exhibited by the new lignans sesamelignans A [1] and B [2] with IC50 values of 7.5 ± 0.3 and 9.8 ± 0.5 μM, respectively. The IC50 values of the furfuran-type lignans 3−7 were obtained in the range 17.8 to 65.8 μM for AGEs formation ability. The simple phenolic compounds 8 and 9 were considerably less effective compared to other lignan derivatives. In addition, we further evaluated the anti-oxidant effects of the isolated compounds using the previously reported ONOO− scavenging assay [40]. The novel aryltetralin lignan 1 showed maximum scavenging activity against the ONOO− scavenging assay (IC50: 8.1 ± 0.5 μM) compared to the positive control L-penicillamine (IC50: 15.0 ± 1.0 μM). Vanillic acid [9] has previously been described as a powerful ONOO− scavenging substance isolated from Panax ginseng, and our results are in agreement with those findings [41]. Although various lignan analogs from natural products have been reported as anti-glycation inhibitors [42], the current study is the first to validate the new aryltetralin-type lignan 1 with potent inhibitory effects of AGEs formation and ONOO− scavenging activity. Taken together, our results indicate the potential to develop sesamelignan A [1] as a therapeutic for diabetic complications and related diseases. ## 3.1. General Experimental Procedures The ultraviolet (UV) spectrum was measured on a T-60 spectrophotometer (PG Instrument, Leicestershire, UK), and the circular dichroism (CD) spectrum was run on a JASCO J-1500 spectrometer (JASCO, Tokyo, Japan). 1H-, 13C-NMR, 1H-1H COSY, HSQC, HMBC, and NOESY spectra were measured on a Varian VNS-600 MHz spectrometer (Varian, Palo Alto, CA, USA) equipment using CD3OD (δH 3.35, δC 49.0) as the solvent and tetramethylsilane (TMS) as the internal standard. Fast atom bombardment mass spectrometer (FABMS) was recorded on a JMS-700 GC-HRMS spectrometer (JEOL, Tokyo, Japan), and optical rotation was obtained using a JASCO P-2000 polarimeter. Toyopearl HW-40C gel (Tosho Co. Tokyo, Japan) and ODS gel (ODS AQ 120-50S, YMC Co., Kyoto, Japan) were used for column chromatography. ## 3.2. Plant Material and Preparation Sesame seeds (Sesamum indicum L.) were collected in June 2017 from Yecheon-gun, Republic of Korea, and identified by Prof. Tae Hoon Kim. A voucher specimen was deposited at the Natural Products Chemistry Laboratory of Daegu University. The dried sesame seeds (20 kg) were roasted in an electric frying pan (D-1692, Dongkwang oil machine Co., Seoul, Korea) at 300 °C for 12 min. Oil was extracted from the roasted sesame seeds using an electric oil squeezer (D-1880, Dongkwang oil machine Co., Seoul, Korea), and the remaining sesame byproducts were used in the experiment. ## 3.3. Extraction and Isolation Defatted sesame seeds (8.0 kg) were powdered and extracted with distilled water (40 L) at 70 °C for 3 h, after which the extract solution was concentrated in vacuo to yield the solid extract (726.0 g). The dried extract (720.0 g) was suspended in $10\%$ MeOH in H2O (1 L) and partitioned sequentially using organic solvents to yield n-hexane—(2.3 g), EtOAc—(32.2 g), n-BuOH—(66.9 g), and H2O—(425.1 g) soluble fractions. The EtOAc−soluble fraction was found to be active in the AGEs formation inhibition assay, with an IC50 value of 154.8 ± 2.4 μg/mL (Table S1). One portion of the EtOAc−fraction (23.5 g) was chromatographed over a Toyopearl HW-40 column (4 cm i.d. × 40 cm, coarse grade) eluted with gradient systems of H2O-MeOH increasing polarity ($0\%$ to $100\%$, followed by $70\%$ acetone) to yield eleven sub-fractions (SE01-SE11). Fraction SE03 (480.1 mg) was subjected to ODS gel column chromatography (1 cm i.d. × 40 cm, particle size 50 μm) with a MeOH/H2O system, resulting in the isolation of compounds 3 (55.3 mg, tR 20.9 min), 4 (54.1 mg, tR 23.2 min), and 6 (37.4 mg, tR 19.0 min). Similar fractionation of SE04 (306.6 mg) on ODS gel chromatography (1 cm i.d. × 42 cm) yielded the pure compounds 8 (13.8 mg, tR 16.4 min) and 9 (15.5 mg, tR 10.9 min). Finally, the sub-fraction SE08 (155.9 mg) was subjected to ODS gel column chromatography (1 cm i.d. × 42 cm) with aqueous MeOH to give the pure compounds 1 (4.7 mg, tR 16.8 min), 2 (2.6 mg, tR 22.6 min), 5 (3.4 mg, tR 31.0 min), and 7 (3.9 mg, tR 25.0 min). HPLC (Shimadzu, Tokyo, Japan) analysis was performed using the YMC-Pack ODS A-302 column (4.6 mm i.d. × 150 mm, particle size 5 μm; YMC Co., Kyoto, Japan) and mobile phase comprising $0.1\%$ HCOOH in H2O (Solvent A) and MeCN (Solvent B). A gradient system was performed with a linear gradient of $5\%$ to $100\%$ solvent B for 35 min with the flow rate set at 1.0 mL/min. Sesamlignan A [1]: Yellow amorphous powder. [ α]20D +21.6° (c 0.1, MeOH). UV λmax MeOH (log ε): 205 (3.54), 235 (sh), 295 (1.19) nm. CD (MeOH) Δε (nm): 211 (+21.3), 238 (+4.7), 282 (+2.4), 301 (−8.7) nm. 1H- and 13C-NMR: see Table 1. FABMS m/z 360 [M]+. HRFABMS m/z 360.1203 [M]+ (calc. for C19H20O7, 360.1209) (Figures S1–S9). Sesamlignan B [2]: Brown amorphous powder. [ α]20D +58.9° (c 0.1, MeOH). UV λmax MeOH (log ε): 204 (3.75), 234 (sh), 285 (1.10) nm. CD (MeOH) Δε (nm): 210 (−4.3), 229 (−0.3), 288 (−0.7) nm. 1H- and 13C-NMR: see Table 1. FABMS m/z 344 [M]+. HRFABMS m/z 344.1262 [M]+ (calc. for C19H20O6, 344.1260) (Figures S10–S17). (+)-Sesaminol 2′-O-glucopyranosyl(1→2)-O-[glucopyranosyl(1→6)]-O-glucopyrano side [3]: Yellow amorphous powder. [ α]20D −47.0° (c 0.1, MeOH). 1H-NMR (CD3OD, 600 MHz): δ 6.91 (1H, s, H-3′), 6.85 (1H, d, $J = 1.2$ Hz, H-2″), 6.83 (1H, s, H-6′), 6.81 (1H, dd, $J = 7.8$, 1.2 Hz, H-6″), 6.76 (1H, d, $J = 7.8$ Hz, H-5″), 5.91 (2H, s, H-7″), 5.89 (2H, s, H-7′), 5.20 (1H, d, $J = 4.8$ Hz, H-2), 5.02 (1H, d, $J = 7.2$ Hz, H-1″′), 4.87 (1H, d, $J = 7.2$ Hz, H-1″″), 4.69 (1H, d, $J = 4.8$ Hz, H-6), 4.34 (1H, d, $J = 7.2$ Hz, H-1″″′), 4.24 (1H, dd, $J = 9.0$, 6.6 Hz, H-4eq), 4.21 (1H, d, $J = 10.8$ Hz, H-8ax), 4.19 (1H, d, $J = 10.8$ Hz, H-8eq), 4.13 (1H, dd, $J = 10.8$, 1.2 Hz, H-6″′), 3.81 (1H, overlap, H-4ax), 3.86–3.19 (17H, overlap, glucose), 3.00 (1H, m, H-5), 2.90 (1H, m, H-1); 13C-NMR (CD3OD, 150 MHz): δ 149.9 (C-2′), 149.3 (C-5′), 148.5 (C-4″), 148.4 (C-3″), 144.0 (C-4′), 136.5 (C-1″), 125.5 (C-1′), 120.7 (C-6″), 109.0 (C-5″), 107.6 (C-2″), 105.9 (C-6′), 104.9 (C-1″″′), 104.5 (C-1″″), 102.6 (C-7′), 102.4 (C-7″), 101.2 (C-1″′), 99.3 (C-3′), 86.5 (C-6), 82.8 (C-2), 81.8 (C-2″′), 78.4 (C-3″′), 78.1 (C-4″″), 78.0 (C-3″′), 77.9 (C-3″″′), 77.8 (C-5″″), 76.9 (C-5″′), 76.0 (C-2″″′), 75.1 (C-2″″), 73.8 (C-8), 72.8 (C-4), 71.6 (C-4″″′), 71.2 (C-5″″′), 71.1 (C-4″′), 70.3 (C-6″′), 62.8 (C-6″″′), 62.2 (C-6″″), 55.8 (C-1), 55.6 (C-5). FABMS m/z 856 [M]+ (Figures S18 and S19). (+)-Sesaminol 2′-O-glucopyranosyl(1→2)-O-glucopyranoside [4]: Yellow amorphous powder. [ α]20D −26.7° (c 0.1, MeOH). 1H-NMR (CD3OD, 600 MHz): δ 6.91 (1H, s, H-3′), 6.85 (1H, d, $J = 1.8$ Hz, H-2″), 6.81 (1H, s, H-6′), 6.80 (1H, dd, $J = 7.8$, 1.8 Hz, H-6″), 6.75 (1H, d, $J = 7.8$ Hz, H-5″), 5.90 (2H, s, H-7″), 5.89 (2H, s, H-7′), 5.18 (1H, d, $J = 4.8$ Hz, H-2), 4.84 (1H, d, $J = 7.8$ Hz, H-1″′), 4.62 (1H, d, $J = 4.8$ Hz, H-6), 4.34 (1H, d, $J = 7.8$ Hz, H-1″″), 4.27 (1H, dd, $J = 9.6$, 7.8 Hz, H-8eq), 4.17 (1H, dd, $J = 9.6$, 6.0 Hz, H-4eq), 4.13 (1H, m, H-6″′), 4.06 (1H, dd, $J = 9.6$, 4.8 Hz, H-8ax), 3.84 (1H, dd, $J = 9.6$, 4.8 Hz, H-4ax), 3.86–3.21 (11H, overlap, glucose), 3.00 (1H, m, H-5), 2.95 (1H, m, H-1); 13C-NMR (CD3OD, 150 MHz): δ 150.4 (C-2′), 149.3 (C-5′), 148.6 (C-4″), 148.5 (C-3″), 144.1 (C-4′), 136.4 (C-1″), 125.5 (C-1′), 120.7 (C-6″), 109.0 (C-5″), 107.6 (C-2″), 106.0 (C-6′), 104.8 (C-1″″), 103.2 (C-7′), 102.6 (C-7″), 102.4 (C-1″′), 99.9 (C-3′), 86.9 (C-6), 82.9 (C-2), 78.1 (C-5″′), 78.0 (C-5″″), 77.1 (C-3″′), 75.0 (C-4″′), 74.9 (C-2″″), 74.2 (C-2″′), 72.4 (C-3″″), 71.6 (C-4″″), 71.3 (C-4), 71.2 (C-8), 70.8 (C-6″′), 62.8 (C-6″″), 55.6 (C-1), 55.2 (C-5). FABMS m/z 694 [M]+ (Figures S20 and S21). (+)-Sesaminol [5]: White amorphous powder. [ α]20D +73.6° (c 0.1, MeOH). 1H-NMR (CD3OD, 600 MHz): δ 6.86 (1H, d, $J = 1.2$ Hz, H-2″), 6.82 (1H, dd, $J = 7.2$, 1.2 Hz, H-6″), 6.76 (1H, d, $J = 7.2$ Hz, H-5″), 5.91 (2H, s, H-7″), 6.75 (1H, s, H-3′), 6.35 (1H, s, H-6′), 5.81 (2H, s, H-7′), 4.98 (1H, d, $J = 4.2$ Hz, H-2), 4.67 (1H, d, $J = 4.2$ Hz, H-6), 4.21 (1H, dd, $J = 9.6$, 7.8 Hz, H-8eq), 4.24 (1H, dd, $J = 9.6$, 6.0 Hz, H-4eq), 4.01 (1H, dd, $J = 9.6$, 4.2 Hz, H-8ax), 3.85 (1H, dd, $J = 9.6$, 4.2 Hz, H-4ax), 3.00 (1H, m, H-5), 2.95 (1H, m, H-1); 13C-NMR (CD3OD, 150 MHz): δ 151.2 (C-2′), 148.2 (C-5′), 147.7 (C-4″), 146.4 (C-3″), 145.0 (C-4′), 135.4 (C-1″), 126.1 (C-1′), 122.1 (C-6″), 109.2 (C-5″), 107.8 (C-2″), 106.5 (C-6′), 102.9 (C-7′), 101.4 (C-7″), 99.8 (C-3′), 85.8 (C-6), 81.0 (C-2), 71.6 (C-4), 70.5 (C-8), 53.2 (C-1), 51.0 (C-5). FABMS m/z 370 [M]+ (Figure S22). (+)-Epipinoresinol 4′-O-glucopyranoside [6]: Brown amorphous powder. [ α]20D +37.7° (c 0.1, MeOH). 1H-NMR (CD3OD, 600 MHz): δ 7.13 (1H, d, $J = 8.4$ Hz, H-5″), 7.02 (1H, d, $J = 1.8$ Hz, H-2″), 6.63 (1H, d, $J = 1.8$ Hz, H-2′), 6.91 (1H, dd, $J = 8.4$, 1.8 Hz, H-6″), 6.80 (1H, dd, $J = 7.8$, 1.8 Hz, H-6′), 6.75 (1H, d, $J = 7.8$ Hz, H-5′), 4.82 (1H, d, $J = 7.8$ Hz, H-1″′), 4.74 (1H, d, $J = 4.8$ Hz, H-2), 4.69 (1H, d, $J = 7.2$ Hz, H-6), 4.23 (1H, overlap, H-8eq), 4.22 (1H, overlap, H-4eq), 3.86 (3H, s, OCH3-3″), 3.84 (3H, s, OCH3-3′), 3.82 (1H, m, H-8ax), 3.80 (1H, m, H-4ax), 3.69 (1H, dd, $J = 11.4$, 4.2 Hz, H-6″′), 3.68 (1H, m, H-6″′), 3.46 (1H, m, H-2″′), 3.45 (1H, m, H-4″′), 3.30 (1H, m, H-3″′), 3.29 (1H, m, H-5″′), 3.12 (1H, m, H-5), 3.10 (1H, m, H-1); 13C-NMR (CD3OD, 150 MHz): δ 151.1 (C-3″), 149.1 (C-3′), 147.5 (C-4″), 147.3 (C-4′), 137.5 (C-1″), 133.8 (C-1′), 120.1 (C-6′), 119.8 (C-6″), 118.1 (C-5″), 116.1 (C-5′), 111.7 (C-2′), 111.0 (C-2″), 102.9 (C-1″′), 87.5 (C-6), 87.1 (C-2), 78.2 (C-5″′), 77.9 (C-3″′), 74.9 (C-2″′), 73.1 (C-4), 72.7 (C-8), 71.3 (C-4″′), 62.5 (C-6″′), 56.8 (OCH3-3′), 56.4 (OCH3-3″), 55.5 (C-1), 55.4 (C-5). FABMS m/z 520 [M]+ (Figures S23 and S24). (+)-Epipinoresinol [7]: Brown amorphous powder. [ α]20D +84.5° (c 0.1, MeOH). 1H-NMR (CD3OD, 600 MHz): δ 6.94 (1H, d, $J = 1.8$ Hz, H-2″), 6.96 (1H, d, $J = 1.8$ Hz, H-2′), 6.81 (1H, dd, $J = 8.4$, 1.8 Hz, H-6″), 6.80 (1H, d, $J = 8.4$ Hz, H-5′), 6.77 (1H, d, $J = 8.4$ Hz, H-5″), 6.76 (1H, dd, $J = 8.4$, 1.8 Hz, H-6′), 4.85 (1H, d, $J = 4.8$ Hz, H-2), 4.41 (1H, d, $J = 7.2$ Hz, H-6), 4.09 (1H, d, $J = 9.6$ Hz, H-8eq), 3.83 (1H, d, $J = 9.6$ Hz, H-8ax), 3.86 (3H, s, OCH3-3″), 3.85 (3H, s, OCH3-3′), 3.78 (1H, t, $J = 9.0$ Hz, H-4eq), 3.38 (1H, m, H-4ax), 3.28 (1H, m, H-5), 2.93 (1H, m, H-1); 13C-NMR (CD3OD, 150 MHz): δ 149.1 (C-3″), 148.8 (C-3′), 147.4 (C-4″), 146.6 (C-4′), 133.9 (C-1″), 131.3 (C-1′), 120.1 (C-6′), 119.4 (C-6″), 116.1 (C-5″), 116.0 (C-5′), 110.9 (C-2′), 111.6 (C-2″), 89.5 (C-6), 83.5 (C-2), 71.9 (C-4), 70.6 (C-8), 56.4 (OCH3-3′), 55.6 (OCH3-3″), 51.2 (C-1), 59.8 (C-5). FABMS m/z 358 [M]+ (Figures S25 and S26). Apocynin [8]: White amorphous powder. 1H-NMR (CD3OD, 600 MHz): δ 7.56 (1H, dd, $J = 8.4$, 1.8 Hz, H-6), 7.55 (1H, d, $J = 1.8$ Hz, H-2), 6.85 (1H, d, $J = 8.4$ Hz, H-5), 3.86 (3H, s, OCH3-3), 2.53 (3H, s, CH3-8); 13C-NMR (CD3OD, 150 MHz): δ 199.4 (C-7), 153.4 (C-4), 149.0 (C-3), 130.6 (C-1), 125.2 (C-6), 115.8 (C-5), 112.0 (C-2), 56.3 (OCH3-3), 26.2 (CH3-8). FABMS m/z 166 [M]+ (Figures S27 and S28). Vanillic acid [9]: White amorphous powder. 1H-NMR (CD3OD, 600 MHz): δ 7.55 (1H, d, $J = 1.8$ Hz, H-2), 7.55 (1H, dd, $J = 8.4$, 1.8 Hz, H-6), 6.83 (1H, d, $J = 8.4$ Hz, H-5), 3.87 (3H, s, OCH3-3). FABMS m/z 168 [M]+ (Figure S29). ## 3.4. Evaluation of AGEs Formation Inhibitory Effects Using a previously reported method [43] with minor modification, we evaluated the AGEs formation inhibitory potential of the isolated compounds. Briefly, the reaction mixture was prepared by adding 10 mg/mL BSA in 50 mM phosphate buffer (pH 7.4) containing $0.02\%$ sodium azide to a sugar solution (200 mM D-fructose and 200 mM D-glucose). The reaction mixture (800 μL) was then combined with various concentrations of either the test compounds (200 μL) or the positive control (aminoguanidine) dissolved in $5\%$ DMSO. After incubation at 37 °C for 7 days, the fluorescent reaction products were determined using an ELISA reader (Infinite F200; Tecan Austria GmBH, Grodig, Austria), with excitation and emission maxima at 350 and 450 nm, respectively. The concentration required for $50\%$ inhibition (IC50 value) of the fluorescence intensity was determined by linear regression analysis. All measurements were obtained in triplicate. ## 3.5. Evaluation of ONOO− Scavenging Activities The ONOO− scavenging ability was evaluated by observing the extremely fluorescent dihydrorhodamine 123 (DHR 123) that is rapidly generated from non-fluorescent DHR 123 in the presence of ONOO− [40]. The dihydrorhodamine buffer (pH 7.4) comprises 50 mM sodium phosphate monobasic, 50 mM sodium phosphate dibasic, 90 mM sodium chloride, 5 mM potassium chloride, and 100 μM DTPA, and the final DHR 123 concentration used was 5.0 μM. The test sample was dissolved in $5\%$ DMSO. The final fluorescent intensities of the treated samples were observed 5 min after treatment with and without the addition of authentic ONOO− (10 μM) dissolved in 0.3 N NaOH. The fluorescence intensity of the oxidized DHR 123 was estimated with a fluorescence ELISA reader at emission and excitation wavelengths of 530 and 480 nm, respectively. Results of the ONOO− scavenging effect were evaluated as the final fluorescence intensity minus the background fluorescence, determined by the detection of DHR 123 oxidation. The $50\%$ inhibition (IC50 value) was measured by linear regression analysis of the scavenging activity under the above assay conditions. L-Penicillamine was used as a positive control. All measurements were obtained in triplicate. ## 3.6. Statistical Analysis Data for the in vitro analyses of AGEs formation and ONOO− scavenging activity were analyzed using the Proc GLM procedure of SAS software (version 9.3, SAS Institute Inc., Cary, NC, USA). The results are reported as the least square mean values and standard deviation. Statistical significance was considered at $p \leq 0.05.$ ## 4. Conclusions This paper reported two previously undescribed lignans (1 and 2) along with seven known compounds (3−9) isolated from the defatted sesame cake. 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--- title: 'The Effect of Weekly 50,000 IU Vitamin D3 Supplements on the Serum Levels of Selected Cytokines Involved in Cytokine Storm: A Randomized Clinical Trial in Adults with Vitamin D Deficiency' authors: - Dana A. Bader - Anas Abed - Beisan A. Mohammad - Ahmad Aljaberi - Ahmad Sundookah - Maha Habash - Ahmad R. Alsayed - Mohammad Abusamak - Sami Al-Shakhshir - Mahmoud Abu-Samak journal: Nutrients year: 2023 pmcid: PMC10005440 doi: 10.3390/nu15051188 license: CC BY 4.0 --- # The Effect of Weekly 50,000 IU Vitamin D3 Supplements on the Serum Levels of Selected Cytokines Involved in Cytokine Storm: A Randomized Clinical Trial in Adults with Vitamin D Deficiency ## Abstract This research aimed to evaluate the effects of high-dose cholecalciferol (VD3) supplements (50,000 IU/week) on selected circulating cytokines associated with cytokine storms in adults with vitamin D deficiency. This clinical trial, based in Jordan, included 50 participants receiving vitamin D3 supplements (50,000 IU/week) for 8 weeks; the exact number was assigned to the control group. Interleukin-6 (IL-6), interleukin-1β (IL-1β), interleukin-10 (IL-10), tumor necrotic factor-α (TNF-α), and leptin were measured in serum at baseline and 10 weeks (wash out: 2 weeks). Our results revealed that vitamin D3 supplementation significantly increased the serum levels of 25OHD, IL-6, IL-10, IL-1β, and leptin compared with baseline. In contrast, the serum level of TNF-α insignificantly increased in the group receiving vitamin D3 supplementation. Although the observations of this trial may refer to a potential negative effect of VD3 supplementation during cytokine storms, further trials are required to clarify the potential benefits of VD3 supplement during cytokine storms. ## 1. Introduction The new coronavirus infection (COVID-19) is a global pandemic that has aggressively propagated worldwide [1]. Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) is responsible for the pandemic viral pneumonia known as COVID-19. A recent study observed elevated serum levels of specific cytokines, such as those seen during the COVID-19 cytokine storm (CS), which may be associated with severe complications resulting from the infection [2]. These cytokines include tumor necrosis factor-alpha (TNF-α), interleukin-1 beta (IL-1β), IL-6, IL-, 10, IL-17, interferon-gamma (IFN-γ), and many other cytokines [1]. Accordingly, hypercytokinemia has been recently suggested to be one of the main hallmarks of COVID-19 [1]. However, many COVID-19 symptoms can be treated based on the patient’s clinical condition. Hence, because there is no specific treatment for COVID-19 yet, supportive care, including dietary vitamins and minerals for infected persons, can be highly effective. In this manner, vitamins D (VD) and C, in addition to zinc supplementations, are recommended by the Jordan Ministry of Health as a part of the treatment protocol for COVID-19 patients. VD increases innate cellular immunity and provides direct antibacterial activity against various microorganisms, including enveloped and nonenveloped viruses [3]. It has also been shown that VD may mitigate the CS induced by the innate immune system [4]. A previous study [5] concluded that VD modulates the immune response via its effects on dendritic cells (DCs) and T cells. This may enhance the clearance of the virus and decrease inflammatory reactions associated with symptoms. However, vitamin D deficiency (VDD) is still a global problem; over one billion people are either VD deficient or insufficient, and the incidence of VDD was reported to be higher in Mediterranean countries such as Jordan [6,7,8]. VDD is known to be directly associated with bone disorders, but nonskeletal outcomes grabbed the most attention [9]. It was linked with different disorders, including diabetes, cardiovascular disease (CVD), atherosclerosis, and cancer [10,11], and certain abnormal immune conditions, including infections [5]. Recent reviews indicated the ability of VD to reduce the risk of microbial infections through different potential mechanisms: physical barrier, natural cellular, and adaptive immunity [12,13]. Consequently, raising 25-hydroxyvitamin D (25OHD) levels by 1,25(OH)2D3 (VD3) supplementation is highly recommended to reduce the risk of infection and is advised as part of the treatment protocol for people who are sick with influenza [4]. However, the modulatory effects of VD on proinflammatory and anti-inflammatory, as well as cellular and humoral immune, responses are mixed and unclear. There is a study that showed that VD could suppress the production of T-helper (Th)1 proinflammatory cytokine [14] and augment Th2 cell development [15]. Furthermore, VD3 enhances T regulatory cell induction, thus inhibiting inflammatory processes [16]. A previous study [17] showed the safety of VD and a protection activity against acute respiratory tract infection. VDD has been correlated with acute respiratory distress syndrome (ARDS), and case fatality rates (CFRs) increase with age and comorbidity with chronic diseases, both of which are associated with lower 25OHD concentration [4]. Therefore, this randomized clinical trial (RCT) was designed to measure serum levels of IL-1β, IL-6, IL-10, and TNF-α as part of the immune response during CS before and 8 weeks after high-dose VD3 50,000 IU in adults with VDD. ## 2.1. Patient Characteristics This RCT was approved by the Institutional Review Board of Applied Science Private University (ASU) (protocol number 2020-PHA-16) and undertaken between October 2020 and December 2020. The clinical trial was conducted following the Helsinki Declaration. Each individual who was enrolled provided informed consent for this clinical trial. With an average baseline age of 38.37 ± 9.77 years, volunteers included were Jordanian and from ASU staff and their families (ranging from 30 to 66). Eligible participants were included in the trial depending on a diagnosis of VDD confirmed by medical consultants at Ibn Al-Haytham clinical laboratories. Because prolonged VD3 administration is related to the formation of kidney stones, patients with kidney abnormalities were excluded from the study [18]. COVID-19 or chronic medical conditions, such as osteoporosis, cancer, endocrine disorders, and a history of allergic responses to VD3 supplements, were also among the exclusion criteria from this study. ## 2.2. Intervention Before and after the VD3 supplement, baseline and follow-up values of anthropometric and clinical parameters were collected. At the conclusion of the 8-week interventional phase, the subjects underwent a 2-week washout phase before and following VD3 administration. VD3 is a fat-soluble vitamin with a long half-life; a washout period was achieved to avoid the potential effect of its cumulative dose. Then, all participants’ follow-up measurements were obtained. An independent statistician developed a computer-generated randomization process. According to the consortium chart (Figure 1), hundreds of eligible participants were divided into two groups: group 1 received once weekly 50,000 IU of VD3 in a Hi-Dee soft gelatin capsule (United Pharmaceuticals Company, Amman, Jordan). Participants in group 2 did not receive any supplementation and acted as the control group. In compliance with the Endocrine Society’s clinical guidelines for treating VDD in adults, therapeutic protocols for VD3 supplements were approved [19]. Similarly, administering VD3 to individuals throughout 12 months produced no toxicity [19]. All participants’ adherence to the therapy protocol was monitored by periodic text messages sent to their mobile phones. ## 2.3. Anthropometric Measurement This RCT was conducted throughout the winter of 2020 at the ASU Pharmacy school laboratories to minimize seasonal fluctuations in vitamin D assays in the blood [20]. At the beginning and end of the experiment, anthropometric measurements, such as body mass index (BMI), body weight (BW), hip (H) circumference, waist/hip ratio (WHR), height (Ht), and waist (W) circumference, were recorded. ## 2.4. Clinical Parameter Assays Serum assay of clinical parameters was collected into labeled Eppendorf tubes at Ibn Al-Haytham Hospital, Clinical Laboratories Department, Jordan. The chemiluminescence immunoassay LIAISON 25OHD assay (DiaSorin, Saluggia, Italy) measured total serum 25OHD. The assay quantifies serum 25OHD and is cross-reactive with 25OHD2 and 25OHD3. Its lower limit was 4 ng/mL. An enzyme immunoassay kit measured serum leptin levels (leptin EIA-5302, DRG Diagnostics, Marburg, Germany). Test sensitivity was 0.1 ng/mL. An enzyme immunoassay kit tested serum PTH levels (PTH Intact EIA-3645, DRG Diagnostics, Marburg, Germany). The sensitivity was 1.57 pg/mL. The calcium-ARSENAZO kit (M11570i-15) and the phosphorus phosphomolybdate/Uv kit (M11508i-18, BioSystems, Barcelona, Spain) were used to measure the levels of calcium and phosphorus (PO4) in serum. Serum IL-6 concentration was measured using the Human ELISA KIT (ab178013, Abcam, Newark, NJ, USA). Using a Human ELISA Kit, serum IL-10 was measured (ab185986, Abcam). Human ELISA KIT assessed serum IL-1β (ab214025, Abcam). The Human ELISA KIT assay measured serum TNF-α (ab181421, Abcam). ## 2.5. Statistical Analysis SPSS version 27 for Windows was used to execute the statistical analysis. A paired t-test was performed to determine any significant variations in each trial group before and after the delivery of the VD3 supplementation. Two independent sample t-tests were utilized to identify whether there were significant differences between all items of distinct groups (control, D3). Using correlation analysis, correlations were investigated between the serum levels of TNF, IL-1β, IL-6, IL-10, and 25-OHD, as well as between their ratios (TNF-α /IL-10, IL-1β /IL-10, and IL-6/IL-10). Simple linear regression was used to investigate the effect of VD3 supplementation on the items (TNF-α, IL-1β, IL6, IL10, ratio (TNF-α /IL-10), ratio (IL-1β/IL-10), ratio (IL-6/IL-10)), while multiple linear regression analysis was used to determine the predictors of the items (TNF-α, IL-1β, IL-6, IL-10, ratio (TNF-α /IL-10), ratio (IL-1β/IL-10), and ratio (IL-6/IL-10)) at the follow-up level for the (D) group. The Kolmogorov–Smirnov test was utilized to test the normality of distribution for laboratory measurements. The results displayed a normal distribution curve. ## 3.1. Baseline Values of the Participants A total of 75 out of 127 ($59.1\%$) participants in the trial adhered to the protocol and finished the intervention period that lasted for 8 weeks. As indicated in Figure 1, the reasons why participants dropped out ($$n = 27$$) included noncompliance, not fulfilling inclusion criteria, and dropping out from the intervention group ($$n = 8$$) and the control group ($$n = 17$$). In this trial, $50.7\%$ were female, and $49.2\%$ were male. Morning sun exposure was practiced by $57.3\%$ of participants. Other baseline percentages and frequencies of analyzed anthropometric and lifestyle variables are displayed in Table 1. Participants’ average age was (38.37 9.77). When measured at baseline, all other serum markers had mean values that were within normal limits. Descriptive analysis for anthropometric parameters, including BMI (27.90 ± 4.76), waist and hip circumferences, and WHR, are shown in Table 2. ## 3.2. Baseline Clinical Characteristics The baseline mean value for serum 25OHD was (17.29 ± 6.18) ng/mL (all participants were VD deficient). None of the participants presented in this trial with a baseline serum 25OHD level equal to or greater than 30 ng/mL. All participants’ baseline values of all serum parameters, including PTH, Ca, and PO4, were within normal ranges. A descriptive analysis of the clinical parameters is presented in Table 3. Table 4 shows baseline mean values for the serum levels of IL-1β, IL6, IL10, and TNF-α and their ratios. ## 3.3. Connection between Selected Cytokine Variables and 25OHD Concentrations In this trial, selected proinflammatory cytokines (IL-1β, TNF-α, and IL-6) and anti-inflammatory cytokine (IL-10) showed statistically significant intercorrelations (Table 5). At baseline, serum 25OHD levels showed a significant inverse correlation with serum IL-1β levels (R= −0.280, $$p \leq 0.015$$). Serum IL-1β levels also showed significant positive correlations with IL-6 ($R = 0.236$, $$p \leq 0.041$$) and IL-10 ($R = 0.239$, $$p \leq 0.039$$). Pearson correlation analysis showed no correlation between serum IL-6 and IL-10 levels. Other baseline intercorrelations between studied cytokines ratios are listed in Table 5. The Pearson correlation analysis showed no intercorrelation between serum cytokine levels in the VD3 group, as shown in Table 5. Serum 25OHD levels showed a weak but insignificant positive correlation with serum IL-1β levels. However, this trial has shown a significant reverse correlation between proinflammatory cytokines and anti-inflammatory cytokines. Table 5 shows the correlation between each cytokine and 25OHD level. ## 3.4. Changes in the Serum Levels of 25OHD and PTH Paired sample t-tests showed a significant difference in the follow-up mean 25OHD and PTH levels among participants of the D3 group (41.39 ± 12.19 vs. 16.41 ± 4.99 and 16.69 ± 8.72 vs. 37.88 ± 6.82, PA < 0.001, respectively). Independent sample t-tests determined significant differences in 25OHD and PTH levels between the control and D3 groups. There were significant differences in serum 25OHD and PTH between the control and D3 group at follow-up (17.31± 6.74 vs. 41.39 ± 12.19 and 33.85 ± 10.62 vs. 16.69 ± 8.72, PC < 0.001, respectively, PC < 0.001), as shown in Table 6. ## 3.5. Changes in the Serum Levels of Selected Cytokines Associated with Cytokine Storm at Baseline and 10-Week Follow-Up At the end of this study, a paired t-test showed a significant difference in mean IL-1β, IL-6, and IL-10 levels. Mean IL-1β significantly increased with a change to 4.41 ng/mL (7.63 ± 2.36 vs. 3.22 ± 0.99, PA < 0.001) in the D3 group. The application of the statistically independent t-test showed a significant difference in mean IL-1β between D3 and the control group (3.59 ± 2.71 vs. 7.63 ± 2.36, PC < 0.001). There was a significant change between the mean IL-6 at baseline and follow-up among those in the D3 group (5.5 ± 6.51 vs. 26.99 ± 14.47, PC < 0.001). At the 10-week follow-up, mean IL-10 levels were significantly increased with a change to 2.45 ng/mL (2.01 ± 0.59 vs. 4.46 ± 4.67, PA = 0.001) in the D3 group. IL-10 levels were significantly higher in the D3 group compared with the control group, 4.46 ± 4.67 and 2.39 ± 1.39, respectively, with a p-value of PC = 0.016. Table 7 presents the baseline results and follow-up changes of the clinical variables studied in this trial. ## 3.6. Stepwise Regression Analysis The multivariate stepwise regression analysis revealed significant mediating factors (IDVs) on the circulatory levels of selected cytokines associated with CS at the 10-week follow-up supplementation of VD3 50,000 IU once a week. TNF levels were only mediated by age factor ($R = 0.413$, R2 = 0.170, $$p \leq 0.007$$). Changes in IL-1 level values observed in the VD3 interventional group were significantly mediated by body weight ($R = 0.311$, R2 = 0.097, $$p \leq 0.045$$). Regarding the TNF/IL-10 ratio, WHR only was selected by the stepwise regression model among all IVDs to be involved in the positive relationship between elevated 25OHD levels and the TNF/IL-10 ratio ($R = 0.348$, R2 = 0.121, $$p \leq 0.024$$), as observed in Table 8. ## 4. Discussion At the end of the trial, high doses of VD3 supplementation (50,000 IU/week) significantly increased serum IL-6, IL-1β, and IL-10 levels. These findings may refer to potential adverse effects during CS. High doses of VD3 significantly raised IL-6 levels, an important marker since an increase in its concentration is associated with an increase in the levels of CS. These findings confirmed that high or/and extensive doses of VD3 may potentially affect proinflammatory immune responses [21]. It has been demonstrated that 25OHD levels are lower in patients with many inflammatory diseases [22,23]. Further, inconclusive findings on the effects of VD3 supplementation for inflammatory conditions, including cytokines changes, were noted. Results of the current trial were consistent with a prior RCT [24], showing that daily supplementation with 2000 IU of VD3 from baseline to 1 year had an $8\%$ increase in IL-6 concentration in the intervention group compared with the placebo. Elevated IL-6 was also detected in children with multiple sclerosis who received VD3 [25]. Remarkably, the IL-10 findings of this trial were also consistent with other research reporting an elevation in IL-10 with no changes in IFN-γ levels in VD3-supplemented individuals [26]. Another study [27] also reported that after 6 months of VD3 supplementation, the levels of IL-6 were significantly elevated compared with baseline. Previous clinical trials have typically been conducted under a treatment protocol close to or similar to our protocol, but they have been scarce. After extensive review, some clinical research studies were conducted under a protocol similar to this trial: four trials. In a trial conducted for 12 weeks on early chronic kidney disease [28], there were no changes in IL-6 levels. Conversely, dialysis patients, also studied for 12 weeks [29], showed a significant decrease in IL-6. Patients with chronic renal impairment, such as hemodialysis patients, have elevated plasma IL-6 levels due to chronic inflammation and fluid overload. Reduced IL-6 clearance is noted with compromised kidney function, contributing to its retention. Therapeutic hemodialysis triggers inflammatory responses and increases IL-6 production [30,31]. The same dose of VD3 (50,000 IU per week) for 12 weeks lowered IL-6 levels in another RCT that aimed to examine the effect of VD3 and omega-3 fatty acid cosupplementation as an adjuvant chemotherapy [32]. It is important to note here that the observations of Al-Haidari and Khalighi were from trials conducted on patients under the influence of the treatment protocol for chronic diseases. In the Khalighi trial, all IBS patients received antispasmodic medication (Mebeverine, 135 mg twice daily) besides VD3 supplementation. Previous research has shown that the level of IL-6 in patients with diarrhea-predominant IBS was much greater than in healthy controls [33]. Therefore, the independent effect of VD3 supplementation on IL-6 has not been accurately evaluated. Some studies have linked changes in the serum levels of IFN-γ and IL-10 observed after VD3 supplementation to the severity of VDD [34], suggesting that VD3 supplementation exerts the most influence on human immunity in the context of severe VDD. This is in contrast to many observational studies that support a potential inhibitory effect of VD3 supplementation on proinflammatory cytokines such as IL-6, IL-1β or/and TNF levels [35]. The effects of VD3 supplementation on human immunology have now been evaluated by large-scale RCTs that reported an absence of any effect of VD3 supplementation on IL-6 [36,37]. Notably, the majority of data are from Western countries. Ours is the first study to examine the effects of VD3 supplementation on the levels of CS-associated cytokines in the bloodstream of Jordanians with VDD. Similarly, VDD has been associated with elevated IL-6 levels [38], and VD3 downregulated IL-6 in some studies [39,40]. Contradictory results regarding IL-6 may be attributable to assessing the effects of VD3 supplementation on these cytokines in specific populations, multiple confounders, and discrepancies between research. These confounders include the duration and amount of VD3 supplementation, genetic background of patients, underlying clinical problems, impact of clinical therapy, and degree of VDD and insufficiency at baseline. RCTs conducted on diabetic hemodialysis (HD) patients [41] or postmenopausal women without VDD [42] have revealed inconsistent results. Remarkably, results were quite different when smaller doses of VD3 over longer durations were used. Considering the impact of a given dose in the different protocols, past clinical trials utilized different doses and durations of VD3 supplementation. Studies have shown that the effects of VD3 on reducing systemic inflammation may be greater in people who are overweight and have chronic inflammation and with more prolonged use [43,44]. Circulating IL-6 increases with age, BMI, and percentage of body fat mass. These factors such as being overweight with a slight elevation in serum leptin (approximately 8 ng/mL) and a mean age of around 40 years were detected in this trial. Nevertheless, stepwise regression did not show significant effects for these factors to be potential mediators in the association between 25OHD and IL-6 levels. On the other hand, age and body weight factors are separately involved in the association between 25OHD and other proinflammatory cytokines (IL-1β and TNF). Elevated IL-6 and other cytokines observed in this trial contradict our previous hypothesis and are challenging to explain biologically. Considering whether the study sample is healthy people or patients, the effect of VD3 on those cytokines seems to be influenced by several factors, including baseline 25OHD levels and the dose, duration, and treatment protocol of VD3 [4,45]. The National Academy of Medicine of the U.S. deems a 600–800 IU VD daily intake adequate for most of the population. However, the U.S. Endocrine Society suggests daily 1500–2000 IU [46]. A total of 400 IU VD3 per day was suggested to treat individuals aged between 18 and 28 years [47]. This dose is approximately one-tenth the dose used in this trial, which is the most common treatment protocol in Jordan for patients with VDD. In the same context, 4000 IU VD3/day is the dose at which the risk of toxicity increases [48]. Therefore, a U-shaped association between serum 25OHD level and CVD risk has been proposed [49,50]. Further, the presence or absence of typical risk factors did not obscure the U-shape association [50]. Therefore, a U-shape association with extreme fluctuations in serum 25OHD levels may influence cytokine levels via its effects on the expression of their receptors. In this manner, we can explain the unexpected findings shown in this trial and previously [24]. Converse to previous studies and RCTs that reported the presence of a hypercytokiemia-reducing effect in VD3 therapy, Costenbader showed that an extensive VD3 dosage (2000 IU/day over 1 year) elevated $8\%$ of IL-6 levels. According to these findings, high or/and extensive doses of these supplements, which are widespread in the community and a part of COVID-19 protocol treatment, require reconsideration, particularly during CS. Hence, it is improbable that these data can answer crucial issues about the possible effects of these supplements on the inflammatory pathway, even though they are frequently consumed by the general population [24]. Instead, it may induce a hypersensitive reaction accompanied by acute harmful consequences in people at risk of acute respiratory distress syndrome (ARDS), as observed in COVID-19 patients [51]. Although it has been established that VD3 supplementation reduces the incidence of influenza A [52], large amounts of IL-6 and IL-1β have been observed during CS [53]. There is new evidence that VDD is connected with higher levels of IL-6 in HIV patients [54]. There is currently no explanation for the variance in CS severity across COVID-19 patients. Accordingly, the results of this trial may point to a potential role for the sudden onset of 25OHD levels caused by high doses of VD3 supplements for this severity. Another piece of evidence that came from a recent study showed that VD3 and IL-6 blockade (Tocilizumab) synergistically regulate rheumatoid arthritis by suppressing IL-17 [55]. Intriguingly, in the absence of serum VD3, the expression of IL-17A exhibited a positive feedback impact on the expression of IL-6. In contrast, under adequate conditions, IL-10 expression negatively impacted IL-17A and IL-6 expression; it raised the level of IL-10 mRNA expression in all groups. However, these effects were more pronounced in people with multiple sclerosis (MS). Eight weeks of treatment with 50,000 IU VD3 led to the downregulation of IL-6 and overexpression of IL-10 in $80\%$ of MS patients [44]. Before this evidence, VD3 acted in synergy with Toll-like receptor (TLR) agonists and peptidoglycan (PGN) in inducing IL-6 and IL-10, whereas VD3 completely inhibited lipopolysaccharide (LPS) [55,56]. IL-6 and TGF-b may both have a role in developing Th17 cells that may play a vital function in antimicrobial immunity at mucosal barriers [57]. In response to the TLR activation of dendritic cells (DCs), it is well known that IL-6 blocks the inhibitory action of CD4, CD25, and regulatory T cells. It may interact with innate and adaptive immune responses [58]. Nevertheless, IL-6 decreases DC maturation and chemokine-receptor 7 expressions and may sometimes operate as an anti-inflammatory modulator [59]. Instead of exerting a general inhibitory impact on DCs, it has been suggested that VD3 promotes a delicate immunomodulation that inhibits adaptive immune responses while increasing innate immunological processes [60,61]. It is difficult to draw firm conclusions from this study due to its small sample size. Confirming these results, validating the reported cytokines as biomarkers of VD-mediated immune responses, and establishing the linkages between crucial immunological pathways and clinical outcomes all call for a larger clinical trial. Contraindicating results might contribute to the varying doses and durations of VD3 supplements used in past trials. Therefore, based on the U-shaped curve, we hypothesize that high or extensive doses of VD3 may worsen serum cytokines associated with CS. ## 5. Conclusions High doses of VD3 significantly raised IL-6 levels, which is an essential marker since elevated levels are linked to an increase in the severity of cytokine storms. 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--- title: Elateriospermum tapos Yogurt Supplement in Maternal Obese Dams during Pregnancy Modulates the Body Composition of F1 Generation authors: - Ruth Naomi - Rusydatul Nabila Mahmad Rusli - Fezah Othman - Santhra Segaran Balan - Azrina Zainal Abidin - Hashim Embong - Soo Huat Teoh - Azmiza Syawani Jasni - Siti Hadizah Jumidil - Khaled Salem Yaslam Ba Matraf - Zainul Amiruddin Zakaria - Hasnah Bahari - Muhammad Dain Yazid journal: Nutrients year: 2023 pmcid: PMC10005445 doi: 10.3390/nu15051258 license: CC BY 4.0 --- # Elateriospermum tapos Yogurt Supplement in Maternal Obese Dams during Pregnancy Modulates the Body Composition of F1 Generation ## Abstract Maternal obesity is a key predictor of childhood obesity and a determining factor for a child’s body composition. Thus, any form of maternal nutrition during the gestational period plays a vital role in influencing the growth of the fetus. Elateriospermum tapos (E. tapos) yogurt has been found to comprise many bioactive compounds such as tannins, saponins, α-linolenic acid, and 5′-methoxy-bilobate with apocynoside I that could cross the placenta and exhibit an anti-obesity effect. As such, this study aimed to investigate the role of maternal E. tapos yogurt supplementation on offspring body composition. In this study, 48 female Sprague Dawley (SD) rats were induced with obesity using a high-fat diet (HFD) and were allowed to breed. Upon confirmation of pregnancy, treatment was initiated with E. tapos yogurt on the obese dams up to postnatal day 21. The weaning offspring were then designated into six groups according to their dam’s group ($$n = 8$$) as follows; normal food and saline (NS), HFD and saline (HS), HFD and yogurt (HY), HFD and 5 mg/kg of E. tapos yogurt (HYT5), HFD and 50 mg/kg of E. tapos yogurt (HYT50), and HFD and 500 mg/kg of E. tapos yogurt (HYT500). The body weight of the offspring was accessed every 3 days up to PND 21. All the offspring were euthanized on PND 21 for tissue harvesting and blood sample collection. The results showed that both male and female offspring of obese dams treated with E. tapos yogurt showed growth patterns similar to NS and reduced levels of triglycerides (TG), cholesterol, LDL, non-HDL, and leptin. Liver enzymes such as ALT, ALP, AST, GGT, and globulin, and renal markers such as sodium, potassium, chloride, urea, and creatinine levels significantly reduced ($p \leq 0.05$) in the offspring of E. tapos yogurt-treated obese dams with the normal histological architecture of the liver, kidney, colon, RpWAT, and visceral tissue that is comparable to NS. In toto, E. tapos yogurt supplementation of obese dams exerted an anti-obesity effect by preventing intergenerational obesity by reversing HFD-induced damage in the fat tissue of the offspring. ## 1. Introduction Maternal obesity, also termed metaflammation [1], is a chronic low-grade inflammation that appears when the body mass index is more than 30 kg/m2 during pregnancy or the gestational period [2]. Being overweight or obese during the gestational period negatively impacts the metabolism of both the mother and the growing fetus. The child that is born to an obese mother will most likely develop obesity sooner or later [3]. Global data show that approximately 38.9 million obese pregnant subjects existed worldwide in the year 2014 [4], and it is expected that there will be at least 2.7 billion obese adults by the year 2025 [5]. According to a recent study published by Moschonis et al. in 2022, the prevalence of obesity has tripled over the last decade, and one in four children is now classified as obese [6]. Consumption of a high-fat diet (HFD) during the gestational period is one of the key factors for maternal obesity. In this, HFD intake remodels the placenta by inducing gut dysbiosis and stimulating the production of excessive oxidative stress in the placenta. In such conditions, lipoprotein lipase will be expressed in the placenta leading to a dysregulation in lipid metabolism. Thus, the offspring has a high possibility of inheriting obesity from its mother [7]. Thereby, maternal obesity is considered to be a vicious intergenerational cycle [8]. Maternal overnutrition and the presence of excessive inflammatory markers are the main factors influencing a child’s adiposity level. As such, the quality of the maternal diet plays a pivotal role in the body composition of the offspring. In consideration of this situation, maternal obese subjects need to follow anti-inflammatory dietary patterns during the gestational period to prevent the emergence of childhood obesity by suppressing maternal inflammation via dietary optimization [9]. Recent discoveries have shown that the consumption of modern medicines could stimulate adverse effects such as colorectal bleeding, increased blood pressure, and headaches. Hence, plants are known to be the best natural alternative to cater to maternal obesity and metabolism dysregulation in maternal obese subjects. This is mainly due to the presence of natural bioactive compounds in plants and the absence of documented cases of toxicity [10]. In this, a local tropical plant known as Elateriospermum tapos (E. tapos) has been shown to reverse adipose tissue hypertrophy caused by HFD intake during the gestation period in maternal obese subjects. In addition, mothers who supplemented with the raw extract of E. tapos during pregnancy showed a gradual reduction in body weight and calorie intake, while the activity of lipoprotein lipase was highly suppressed. This effect has been witnessed not only in the mothers but also in the offspring [11], which proves that E. tapos extract prevents the transgenerational inheritance of obesity and the obese gene product known as leptin in the offspring [12]. Studies claim that the natural anti-obesity effect of E. tapos could be due to the presence of various phytochemical compounds such as α-glucosidase, α-amylase, and pancreas lipase inhibitor enzymes, which inhibit fat and carbohydrate absorption. Aside from this, some other known isolated compounds from E. tapos extract include β-carotene, phenols, and flavonoids, which may enhance weight reduction via multiple signaling mechanisms by preventing the absorption of triglycerides and promoting lipid hydrolysis [13]. Simultaneously, yogurt consumption during pregnancy was found to supply an appropriate level of nutrients for both the mother and the child. Current investigations show that the yogurt supplemented group consumes a better quality diet compared to the control group, and there is a strong positive correlation between yogurt intake and weight loss [14]. As a matter of fact, the first medicinal plant-integrated yogurt (E. tapos yogurt) has been proven to suppress weight gain in obese dams and has proven its safety efficacy through in vivo model investigations [15]. Hence, this study has been designed to study the effect of maternal supplementation with medical plant-integrated yogurt (E. tapos yogurt) on the body composition of the male and female offspring using Sprague Dawley (SD) rats. ## 2.1. Collection and Identification of E. tapos Seeds Fresh E. tapos seeds were obtained from the Forest Research Institute of Malaysia (FRIM), Jengka, Pahang, and were sent to the Herbarium Biodiversity Unit at Universiti Putra Malaysia, for verification under the voucher code UPM SK $\frac{3154}{17.}$ ## 2.2. Extraction of E. tapos Seeds Ethanol extraction of E. tapos seeds was achieved by soaking 500 g of E. tapos in 2 L of $95\%$ lab-grade ethanol for 7 days at room temperature. On the 7th day, the filtrate was obtained and filtered under reduced pressure using rotary evaporation. The precipitate was then collected and added to maltodextrin powder in a ratio of 1:1 before being dried in the oven [16]. The dried-powder form of the E. tapos extract was collected and stored at −20 °C until used [17]. ## 2.3. Formulation of E. tapos Yogurt To formulate E. tapos yogurt, 100 mL of full cream (Dutch Lady Purefarm UHT) was boiled using a microwave for 10 min and was allowed to cool at room temperature. Once the temperature dropped, the starter culture comprising Lactobacillus delbrueckii subsp. Bulgaricus ATCC 11842, and Streptococcus thermophilus APC151, purchased from New England Cheesemaking Supply, was mixed in the milk and incubated for 7–8 h in a yogurt maker (Pensonic PYM-700). The formed yogurt was then refrigerated overnight at 2–4 °C. The following day, E. tapos powder was added to the formulated yogurt in a ratio of 2 g to 100 mL [18]. ## 2.4. High-Fat Diet Preparation A high-fat diet (HFD) was prepared using the protocol described elsewhere [15]. For this, $50\%$ of normal rat pellets (Gold Coin Feedmills) was mixed with $20\%$ of milk powder (Dutch lady), $24\%$ of ghee (Crispo), and $6\%$ of corn oil (Vecorn). All the mixed ingredients were baked in an oven at 60 °C for 60 min. The baked HFD was then cut into pieces and stored in a freezer at 2–4 °C before being fed to the rats [15]. ## 2.5. Experimental Animals All experiments were conducted upon obtaining approval from the Institutional Animal Care and Use Committee (IACUC), Universiti Putra Malaysia, with an ethic code of UPM/IACUC/AUP-R$\frac{025}{2022.}$ For this study, 48 female SD rats weighing between 150 and 200 g were used. All rats were acclimatized for one week at 22 ± 3 °C with proper regulation of $\frac{12}{12}$ h light/dark and fed with normal rat food purchased from Gold Coin Feedmills (M) Sdn Bhd, Selangor, Malaysia [19]. ## 2.6. Obesity Induction, Mating, Gestation, and Weaning The rats were fed with the HFD for 16 weeks to induce obesity. Upon successful obesity induction, the rats were divided into positive and negative control groups, and then into groups of 3 different concentrations of E. tapos yogurt treatment. All rats were allowed to mate with male rats in a ratio of (2:1). Manual palpation and vaginal smears were performed early in the morning to detect pregnancy [12]. The first sperm detection was recorded at 0 post-coitum [20]. Treatment was started immediately upon confirmation of pregnancy and up to weaning [12]. All pups were adjusted to 8–12 per dam on PND 1 followed by the initiation of E. tapos treatment for the dams. The treatment groups were as follows ($$n = 8$$); normal food and saline (NS), HFD and saline (HS), HFD and yogurt (HY), HFD and 5 mg/kg of E. tapos yogurt (HYT5), HFD and 50 mg/kg of E. tapos yogurt (HYT50), and HFD and 500 mg/kg of E. tapos yogurt (HYT500). Treatment for the dams was terminated on PND 21. The body weight of the male and female pups was documented every 3 days from PND 1 to PND 21 [21]. ## 2.7. Plasma Biochemistry All rats fasted for 12 h prior to blood collection on PND 21. The rats were then euthanized on PND 21 using a carbon dioxide overdose. About 5 mL of blood was collected using a plain tube. The tube was then centrifuged at a 3500× g force for 15 min. The serum was then collected and used for renal and liver profile analysis. Renal markers consisting of sodium (Na), potassium (K), chloride (Cl−), urea, creatinine, and liver enzymes such as alkaline phosphatase (ALP), alanine aminotransferase (ALT), aspartate transaminase (AST), total protein, albumin, total bilirubin, globulin, gamma-glutamyl transferase, and the albumin–globulin ratio were analyzed using an Alere Cholestech LDX® Analyzer (3230 Bethany Lane Suite 8, Ellicott City, MD 21042, USA), in which the serum was placed in the cassette well of the main body, and the test was run. The value that appeared on the screen was recorded [15]. ## 2.8. Lipid Profile and Leptin Analysis The plasma lipid profile consisting of total cholesterol (TC), triglycerides (TG), low-density lipoprotein (LDL), and high-density lipoprotein (HDL) was analyzed using a diagnostic reagent test kit (Roche, Germany). To ensure complete clotting, the blood in the sealed Vacutainer® was placed at room temperature, and, using a refrigerated centrifuge tube, the samples were centrifuged at a 1500× g force for 30 min followed by immediate incubation in an ice bath to maintain a temperature between 2 and 4 °C. The samples’ values were then read (total cholesterol, HDL, LDL, triglycerides, and non-HDL) using a Hitachi Automatic Analyzer 902 (Tokyo, Japan) [22]. The plasma leptin concentration was analyzed using a rat leptin ELISA kit supplied by MyBioSource. For this protocol, the plasma was centrifuged at a 1000× g force for 20 min. The supernatant was collected, and 50 µL of the sample was added to the sample well, while 50 µL of the standard solution was added to six standard wells as prescribed, while the blank well was left empty. Then, approximately 100 μL of an HRP-conjugate reagent was added to all wells except for the blank well. The plates were then covered with a closure plate membrane and incubated at room temperature for 60 min followed by washing. All the wells were washed four times, and chromogen solution A and chromogen solution B were added to all wells separately. The samples were mixed using gentle shaking, and the plate was wrapped in aluminum foil before incubation at room temperature for 15 min. As a final step, 50 μL of a stop solution was added to all wells, and the absorbance was read using an ELISA reader at 450 nm. The sensitivity was maintained at 1.0 ng/mL while conducting the ELISA protocol as described in the supplier’s guidelines [23]. ## 2.9. Gross Organ Weight and Histological Analysis Upon euthanasia, the gross weight of the liver, kidney, colon, brown adipose tissue, retroperitoneal white adipose tissue (RpWAT), gonadal fat, and visceral fats were documented, and all organs were then preserved in $10\%$ of formalin. Tissue processing was performed on the liver, kidney, colon, RpWAT, gonadal fat, and visceral fats, and microtomes were used for sectioning. Sectioning was maintained at a thickness between 4 and 6 μm. The paraffin was allowed to float in the water bath to remove wrinkles and folds before being embedded into the glass slides. The glass slides containing sections of tissue were stained using the hematoxylin and eosin (H&E) staining protocol described elsewhere. The slides were then observed under a microscope, and the histological sections were captured using a microscope [24]. All histological slides were verified by a certified pathologist from Universiti Putra Malaysia. ## 2.10. Quantitative Analysis of Fat Cells Quantitative analysis of RpWAT and visceral fat tissue was performed by measuring the diameter (µm) of fat cells using a light microscope with an inbuilt ruler (KF2; Carl Zeiss, Hamburg, Germany) [25]. ## 2.11. Statistical Analysis Statistical analysis was performed using SPSS 27.0. Results were expressed as mean ± standard error of the mean (SEM) for body weight, organ weight, leptin profile, and liver and renal enzymes, while a normality test was run for all data. One-way ANOVA and post hoc Tukey tests were used to analyze the significant difference among groups. A probability of $p \leq 0.05$ was defined as a statistically significant result. ## 3.1. Changes in Body Weight in Male and Female Offspring from PND 1 to PND 21 Figure 1A,B shows the changes in body weight (BW) of the male and female offspring of E. tapos yogurt-supplemented obese dams from PND 1 to PND 21. The data in Figure 1A shows that the BW of the male offspring in the HS, HY, HYT5, HYT50, and HYT500 groups is significantly ($p \leq 0.05$) higher compared to the male offspring in the NS group on PND 1 up to PND 21. However, on PND 1, 3, and 6, the BW of the male offspring in the HYT5 group is significantly lower ($p \leq 0.05$) compared to the male offspring in the HS and HY groups, while there is no significant difference ($p \leq 0.05$) in BW of the male offspring in the HYT50 and HYT500 groups compared to the male offspring in the HS group. On PND 3, the BW of the male offspring in the HYT5, HYT50, and HYT500 groups is significantly lower ($p \leq 0.05$) compared to the male offspring in the HS group, while the mean value of the male offspring in the HYT500 group is similar to the male offspring in the HY group. On PND 6, the BW of the male offspring in the HYT5, HYT50, and HYT500 groups is significantly lower ($p \leq 0.05$) compared to the male offspring in the HS and HY groups. On PND 9, the BW of the male offspring in the HYT5, HYT50, and HYT500 groups is significantly lower ($p \leq 0.05$) compared to the male offspring in the HS group, and the mean values of the male offspring in the HYT5 and HYT50 groups are similar to the male offspring in the NS group. There is no significant difference ($p \leq 0.05$) in BW between the male offspring in the HYT5, HYT50, and HYT500 groups on PND 9, but the BW of the male offspring in the HYT500 group remains significantly higher ($p \leq 0.05$) than the male offspring in the NS group and significantly lower ($p \leq 0.05$) than the male offspring in the HS and HY groups. On PND 12 and 15, the BW of the male offspring in the HYT5, HYT50, and HYT500 groups is significantly lower ($p \leq 0.05$) compared to male offspring in the HS group and significantly higher ($p \leq 0.05$) compared to the male offspring in the NS group, while the BW of the male offspring in the HYT50 and HYT500 groups is significantly higher ($p \leq 0.05$) compared to the male offspring in the HYT5 group. On PND 18 and 21, the BW of the male offspring in the HYT5, HYT50, and HYT500 groups is significantly lower ($p \leq 0.05$) compared to the male offspring in the HS group and significantly higher ($p \leq 0.05$) compared to the male offspring in the NS group. However, there is no significant difference ($p \leq 0.05$) in BW between the male offspring in the HYT5, HYT50, and HYT500 groups on PND 21. The data from Figure 1B shows that the BW of the female offspring in the HS group is significantly higher ($p \leq 0.05$) compared to the female offspring in the NS group on PND 1 up to PND 21. A similar trend is observed in the female offspring in the HY group from PND 1, 3, 9, 12, 18, and 21; the BW of the female offspring in the HY group is significantly higher ($p \leq 0.05$) compared to the NS group, while no significant ($p \leq 0.05$) difference is observed between the HY and HS groups from PND 1, 3, 9, 12, 18, and 21. However, on PND 6 and 15, the BW of the female offspring is significantly lower ($p \leq 0.05$) compared to the HS group, and the mean value in the HY group is similar to the NS group on PND 6 and 15. Meanwhile, the BW of the female offspring in the HYT5, HYT50, and HYT500 groups is significantly lower ($p \leq 0.05$) compared to the female offspring in the HS and HY groups from PND 1 up to PND 21. In this, the data show no significant difference ($p \leq 0.05$) between the BW of the female offspring in the HYT5, HYT50, and HYT500 groups, and the mean values of the HYT5, HYT50, and HYT500 groups are similar to the BW of the female offspring in the NS group. ## 3.2. Gross Organ Weight of Male and Female Offspring on PND 21 Table 1 shows the changes in gross organ weight of the male and female offspring of dams supplemented with E. tapos yogurt on PND 21. As shown in Table 1, the gross organ weight of brown adipose tissue (BAT), liver, kidney, colon, and retroperitoneal white adipose tissue (RpWAT), and visceral and gonadal fat, including the length of the colon in male offspring, is significantly higher ($p \leq 0.05$) compared to the NS group. In male offspring in the HY group, the gross organ weight of the BAT, liver, kidney, and colon, and the length of the colon show no significant difference ($p \leq 0.05$) compared to the HS group. In this, the mean value of the male offspring’s liver is similar to the NS group as well. Meanwhile, the weight of RpWAT, visceral, and gonadal fat in the male offspring in the HY group is significantly lower ($p \leq 0.05$) compared to the HS group while significantly higher ($p \leq 0.05$) compared to the NS group. There is no significant difference ($p \leq 0.05$) for the HYT5, HYT50, and HYT500 groups in BAT, liver, kidney, colon, RpWAT, visceral, and gonadal fat, including the length of the colon compared to the NS group. Meanwhile, the weight of the colon, including colon length, and visceral and gonadal fat of the male offspring in the HYT5, HYT50, and HYT500 groups shows no significant difference ($p \leq 0.05$) compared to the HY group. The RpWAT in the male offspring in the HYT50 group shows no significant difference ($p \leq 0.05$) compared to the HY group. The weight of the liver in the male offspring in the HYT50 and HYT500 groups and of the colon in the HYT50 group shows no significant difference ($p \leq 0.05$) compared to the HS group. There is no significant difference ($p \leq 0.05$) between the male offspring in the HYT5, HYT50, and HYT500 groups in gross organ weight. For female offspring, the gross weight of the BAT, liver, kidney, RpWAT, visceral, and gonadal fat, including the length of the colon, is significantly higher ($p \leq 0.05$) compared to the NS group, as shown in Table 1. There is no significant difference ($p \leq 0.05$) in the female offspring’s colon weight between the NS, HS, HY, HYT5, HYT50, and HYT500 groups. Meanwhile, the organ weight of the BAT, liver, kidney, RpWAT, visceral, and gonadal fat in the HY group shows no significant difference ($p \leq 0.05$) in comparison with the HS group, while the length of the colon is significantly lower ($p \leq 0.05$) compared to the HS group. With the exception of a similar mean value for visceral fat in the HY and NS groups, all organs such as the BAT liver, kidney, colon, RpWAT, and gonadal fat in the HY group were significantly heavier ($p \leq 0.05$) compared to the NS group. The organ weight of the BAT, liver, kidney, RpWAT, visceral, and gonadal fat, including the length of the colon, is significantly lower ($p \leq 0.05$) in the HYT5, HYT50, and HYT500 groups of female offspring compared to the HS group, but there is no significant difference ($p \leq 0.05$) compared to the NS group. However, there is also no significant difference ($p \leq 0.05$) in the female offspring’s liver weight in the HYT500 group or the kidney weight in the HYT50 group in comparison with the HY group. There is no significant difference ($p \leq 0.05$) between the female offspring in the HYT5, HYT50, and HYT500 groups in gross organ weight. ## 3.3. Liver Profile of Male and Female Offspring on PND 21 Table 2 shows the liver profiles of the male and female offspring on PND 21. As shown in Table 2, there is no significant difference ($p \leq 0.05$) between the NS, HS, HY, HYT5, HYT50, and HYT500 groups in the total protein content of the male offspring. The plasma content of the globulin–albumin ratio, ALP, AST, ALT, and GGT is significantly higher ($p \leq 0.05$) in the male offspring in the HS group compared to the NS group, while there is no significant difference ($p \leq 0.05$) in plasma albumin content between the HS and NS groups. Meanwhile, the plasma concentration of albumin and bilirubin is significantly lower ($p \leq 0.05$) in the male offspring in the HS group compared to the NS group. In the male offspring in the HY group, there is no significant difference ($p \leq 0.05$) in plasma albumin, globulin, ALT, GGT, and bilirubin compared to the HS group. The mean value of the male offspring for albumin, globulin, the albumin–globulin ratio, and AST shows no significant difference ($p \leq 0.05$) compared to the NS group. However, the plasma content of ALP for the male offspring is significantly higher ($p \leq 0.05$) compared to the NS group, while significantly lower ($p \leq 0.05$) compared to the HS group. In the male offspring in the HYT5, HYT50, and HYT500 groups, the plasma content of albumin, the albumin–globulin ratio, AST, GGT, and bilirubin shows a significant reduction ($p \leq 0.05$) compared to the HS group, while no significant difference ($p \leq 0.05$) is observed compared to the NS group. The albumin content of the male offspring in the HYT50 group shows no significant difference ($p \leq 0.05$) compared to the HS group, while the HYT5 and HYT50 groups show no significant ($p \leq 0.05$) difference in plasma globulin content compared to the HS group. There is no significant difference ($p \leq 0.05$) between the HYT5, HYT50, and HYT500 groups for ALP content in male offspring; however, ALP is significantly lower ($p \leq 0.05$) compared to the HS group, while it is significantly higher ($p \leq 0.05$) compared to the NS, HYT50, and HYT500 groups. The mean value for ALP content in the male offspring in the HYT5 group is similar to the NS group, while the mean value for the HYT500 group is similar to the HY group. For ALT, the male offspring in the HYT5 group shows a similar mean value to the NS group, the HYT50 group shows a similar mean value to the HS group, while the HYT500 group shows a similar mean value to the HY group. There is no significant ($p \leq 0.05$) difference in plasma ALT content in the male offspring in the HYT5 and HYT500 groups. For the plasma concentration of GGT, the male offspring in the HYT5, HYT50, and HYT500 groups show no significant ($p \leq 0.05$) difference compared to the NS group. There is no significant ($p \leq 0.05$) difference in the male offspring in the HYT50 group compared to the HY and HYT500 groups compared to the HS and HY groups for plasma concentration of GGT. As shown in Table 2, there is no significant difference ($p \leq 0.05$) between the NS, HS, HY, HYT5, HYT50, and HYT500 groups in the total protein content of the female offspring. The plasma content of albumin and total bilirubin is significantly lower ($p \leq 0.05$) in the female offspring in the HS group compared to the NS group, while the levels of globulin, the albumin–globulin ratio, ALP, AST, ALT, and GGT are significantly higher ($p \leq 0.05$) in the female offspring in the HS group compared to the NS group. In the female offspring in the HY group, the levels of globulin, the albumin–globulin ratio, ALT, AST, ALP, GGT, and bilirubin show no significant difference ($p \leq 0.05$) compared to the HS group. However, the mean values of GGT and ALT for the female offspring in the HY group show no significant difference ($p \leq 0.05$) compared to the NS group. The mean value of albumin is significantly higher ($p \leq 0.05$) compared to the HS group while significantly lower ($p \leq 0.05$) compared to the NS group. Meanwhile, the level of the albumin–globulin ratio is significantly higher ($p \leq 0.05$) compared to the NS group. For all female offspring in the HY, HYT50, and HYT500 groups, the plasma concentration of albumin, globulin, the albumin–globulin ratio, ALT, AST, ALP, GGT, and total bilirubin shows no significant difference ($p \leq 0.05$) compared to the NS group. In this, the mean values of total bilirubin and globulin in the HYT5, HYT50, and HYT500 groups show no significant difference ($p \leq 0.05$) compared to the HS group. The mean values of albumin and the albumin–globulin ratio in the female offspring in the HYT50 and HYT500 groups is similar to the HY group, while the mean value of albumin in the female offspring in the HYT500 group shows no significant difference ($p \leq 0.05$) compared to the HS group. ## 3.4. Renal Profiles of Male and Female Offspring on PND 21 Table 3 shows the renal profiles of the male and female offspring of E. tapos yogurt-supplemented obese dams on PND 21. As shown in Table 3, the level of sodium (Na) in the male offspring in the HS group is significantly lower ($p \leq 0.05$), while the levels of potassium (K) and creatinine are significantly higher ($p \leq 0.05$), compared to the NS group. Meanwhile, there is no significant difference ($p \leq 0.05$) in plasma chloride and urea concentration in the male offspring in the HS group compared to the NS group. In the male offspring in the HY group, the mean values of Na, K, chloride, and creatinine show no significant difference ($p \leq 0.05$) compared to the HS and NS groups. On the other hand, the plasma concentration of urea in the male offspring in the HY group is significantly lower ($p \leq 0.05$) compared to the HS group, with a mean value similar to the NS group. In the male offspring in the HYT5, HYT50, and HYT500 groups, the plasma concentration of Na is significantly higher ($p \leq 0.05$) compared to the HS group, and there is no significant difference ($p \leq 0.05$) compared to the NS group. In this, the male offspring in the HYT50 group shows no significant difference ($p \leq 0.05$) compared to the HY group. Regarding the plasma K concentration, the level is significantly lower ($p \leq 0.05$) in the male offspring in the HYT5 and HYT500 groups compared to the HS group, and there is no significant difference ($p \leq 0.05$) compared to the NS group. The plasma concentration of K in the male offspring in the HYT50 group shows no significant difference ($p \leq 0.05$) compared to the HS and NS groups. There is no significant difference ($p \leq 0.05$) in plasma chloride levels in the male offspring in the HYT5, HYT50, and HYT500 groups compared to the HS, HY, and NS groups. The plasma urea concentration shows no significant difference ($p \leq 0.05$) in the male offspring in the HYT5 and HYT50 groups compared to the HS and NS groups, while the urea level in the male offspring in the HYT500 group is significantly lower ($p \leq 0.05$) compared to the HS group and shows no significant difference ($p \leq 0.05$) between the HY and NS groups. The creatinine level in the male offspring in the HYT5, HYT50, and HYT500 groups is significantly lower ($p \leq 0.05$) compared to the HS group, with a similar mean value to the NS group. Similarly, the creatinine level in the male offspring in the HYT5 and HYT500 groups is significantly lower ($p \leq 0.05$) compared to the HY group, while there is no significant difference ($p \leq 0.05$) between the HYT50 and HY groups. As shown in Table 3, there is no significant difference ($p \leq 0.05$) between the NS, HY, HS, HYT5, HYT50, and HYT500 groups for plasma chloride and Na concentrations in the female offspring. The levels of urea and creatinine are significantly higher ($p \leq 0.05$) in the female offspring in the HS group compared to the NS group. In the female offspring in the HY group, the creatinine level shows no significant difference ($p \leq 0.05$) compared to the HS group, while the urea level shows a significant reduction ($p \leq 0.05$) compared to the HS group. In this, the creatinine level in the female offspring in the HYT5, HYT50, and HYT500 groups is significantly lower ($p \leq 0.05$) compared to the HS group, while the level of urea in the HYT5 and HYT500 groups is significantly lower ($p \leq 0.05$) compared to the HS group, with a similar mean value to the NS group. There is no significant difference ($p \leq 0.05$) between the HYT50 and HS groups in the female offspring’s urea concentration in plasma. The level of K is significantly higher ($p \leq 0.05$) in the female offspring in the HS group compared to the NS group, while the female offspring in the HY group show no significant difference ($p \leq 0.05$) in plasma K concentration compared to both the HS and NS groups. Meanwhile, the level of K is significantly reduced ($p \leq 0.05$) in the female offspring in the HYT5 and HYT50 groups compared to the HS group. The mean value of the female offspring in the HYT5 and HYT50 groups for K is similar to the NS group. There is no significant difference ($p \leq 0.05$) in plasma K concentration between the female offspring in the HYT500, HS, and NS groups. ## 3.5. Lipid Profiles of Male and Female Offspring on PND 21 Table 4 shows the lipid profiles of the male and female offspring of E. tapos yogurt-supplemented obese dams on PND 21. As shown in Table 4, the levels of total cholesterol, non-HDL, LDL, and triglycerides are significantly higher ($p \leq 0.05$) in the male offspring in the HS group compared to the NS group. The level of HDL is significantly reduced ($p \leq 0.05$) in the HS group compared to the NS group. However, the male offspring in the HYT5, HYT50, and HYT500 groups show a significant ($p \leq 0.05$) reduction in plasma total cholesterol, HDL, and triglycerides, and a significant increase ($p \leq 0.05$) of HDL, on PND 21 in comparison with the HS group; the mean value is similar to the NS group. For plasma LDL concentration, a significant reduction ($p \leq 0.05$) is observed in the male offspring in the HYT5 and HYT50 groups compared to the HS group. However, the plasma LDL level remains significantly higher ($p \leq 0.05$) in the male offspring in the HYT500 group compared to the NS group, with a similar mean value as the HS group. Thus, there is no significant difference ($p \leq 0.05$) between the male offspring in the HYT500 and HS groups for plasma LDL concentration. In this, the plasma levels of triglycerides, HDL, total cholesterol, non-HDL, and LDL show no significant difference ($p \leq 0.05$) between the male offspring in the HY group compared to the HS group and remains significantly higher ($p \leq 0.05$) compared to the NS group on PND 21. Meanwhile, as demonstrated in Table 4, the lipid profile of the female offspring of obese dams shows that the plasma concentrations of total cholesterol, non-HDL, LDL, and triglycerides are significantly higher ($p \leq 0.05$) in the HS group compared to the NS group. The level of HDL is significantly reduced ($p \leq 0.05$) in the HS group compared to the NS group. Meanwhile, the mean value for LDL in the female offspring in the HY group shows no significant difference ($p \leq 0.05$) in comparison with the HS group. However, the levels of total cholesterol, non-HDL, and triglycerides are significantly higher ($p \leq 0.05$) in the HY group compared to the NS group, but show no significant difference ($p \leq 0.05$) in comparison with the HS group. The mean value of HDL in the female offspring in the HY group is significantly higher ($p \leq 0.05$) compared to the HS group and shows no significant difference ($p \leq 0.05$) in comparison with the NS group. In the female offspring in the HYT5, HYT50, and HYT500 groups, the mean values for total cholesterol, HDL, non-HDL, LDL, and triglycerides for the HY group are significantly reduced ($p \leq 0.05$) compared to the HS group and show no significant difference ($p \leq 0.05$) in comparison with the NS group. ## 3.6. Leptin Levels in Male and Female Offspring on PND 21 Figure 2A,B shows the plasma concentrations of leptin in the male and female offspring of E. tapos yogurt-supplemented obese dams on PND 21. As shown in Figure 2A, the plasma leptin concentration of the male offspring is significantly higher ($p \leq 0.05$) in the HS group compared to the NS group on PND 21. However, the male offspring in the HY group show a gradual decrease in plasma leptin concentration but show no significant difference ($p \leq 0.05$) compared to the HY group and show a similar mean value to the HY and NS groups. Meanwhile, the male offspring in the HYT5, HYT50, and HY500 groups show a gradual reduction in plasma leptin concentration but show no significant difference ($p \leq 0.05$) between the HS and HY groups; yet the mean value for plasma leptin concentration in the HYT5, HYT50, and HY500 groups is similar to the NS group. As shown in Figure 2B, the plasma leptin concentration of the female offspring is significantly higher ($p \leq 0.05$) in the HS group compared to the NS group on PND 21. Similarly, the plasma leptin concentration of the female offspring in the HY group is significantly higher ($p \leq 0.05$) in the HS group compared to the NS group on PND 21 but shows no significant ($p \leq 0.05$) difference compared to the HS group. Meanwhile, the plasma leptin concentration of the female offspring in the HYT5, HYT50, and HYT500 groups shows a gradual reduction compared to the HS group, while the mean plasma leptin concentration of the female offspring in the HYT50 group is significantly lower ($p \leq 0.05$) compared to the HS group and similar to the NS group. However, there is no significant ($p \leq 0.05$) difference in the plasma leptin concentration of the female offspring in the HYT5 and HYT500 groups compared to the HS group; yet the mean value of plasma leptin concentration of the female offspring in the HYT5 and HYT500 groups is similar to both the NS and HS groups. ## 3.7. Histological Analysis of the Liver, Kidney, Colon, RpWAT, and Visceral Tissue of Male and Female Offspring on PND 21 Figure 3A,B shows the histological analysis of the liver, kidney, colon, RpWAT, and visceral tissue of male and female offspring of dams supplemented with E. tapos yogurt on PND 21. Similar histological features were observed in both the male and female offspring’s histological changes. As shown in Figure 3A,B, the male and female offspring in the HS group show abnormal strands of hepatocytes (H), sinusoids (S), and central veins (CVs). Cell ballooning, steatosis, and >$30\%$ of lobular inflammation were observed. Thus, for both the male and female offspring in the HS group, the liver scoring has been graded as 2. Similarly, the liver histology of the male and female offspring in the HY group shows abnormal hepatocytes (H), sinusoids (s), and central veins (CVs) with hepatocyte ballooning with lipid droplets. However, there is no presence of steatosis or lobular inflammation in the HY group. As such, for both the male and female offspring in the HS group, the liver scoring has been graded as 1. In this, the liver histology for the male and female offspring in the NS, HYT5, HYT50, and HYT500 groups shows a completely normal architecture. Thus, for the livers of both the male and female offspring in the NS, HYT5, HYT50, and HYT500 groups, the liver scoring has been graded as 0. Similarly, the kidneys of the male and female offspring in the HS and HY groups show slight tubular dilation, the presence of abnormal lesions, and slight abnormalities of the renal corpuscle, while the liver histology for the male and female offspring in the NS, HYT5, HYT50, and HYT500 groups shows a completely normal architecture. There is no difference in the histological structure of the kidneys of males and females in the HYT5, HYT50, and HYT500 groups compared to the NS group. Meanwhile, the colons of both the male and female offspring in the HS and HY groups show a de-attachment of epithelial cells, reduced mucosal content in the colonic wall, severe infiltration in the lamina propria, the presence of inflammation, and fat deposition in the muscle layer. However, the colon structure of the male and female offspring in the HYT5, HYT50, and HYT500 groups shows a completely normal architecture, which is comparable to the NS group. There is no difference in the histological structure of the colon of males and females in the HYT5, HYT50, and HYT500 groups compared to the NS group. There is severe fat hypertrophy in the male and female offspring in the HS group in retroperitoneal white adipose tissue (RpWAT) and visceral fat tissue. The degree of fat hypertrophy is less in the male and female offspring in the HY group in RpWAT and visceral tissue compared to the HS group. However, there is no adipocyte hypertrophy in the male and female offspring in the HYT5, HYT50, and HYT500 groups. There is no difference in the histological structure of RpWAT and visceral tissue of males and females in the HYT5, HYT50, and HYT500 groups compared to the NS group. ## 3.8. Quantitative Analysis of Fat Hypertrophy of Male and Female Offspring on PND 21 Table 5 shows a quantitative analysis of the fat hypertrophy of the RpWAT and visceral tissue of the female and male offspring on PND 21. The data show that the RpWAT and visceral tissue of the male and female offspring in the HS and HY groups is significantly heavier ($p \leq 0.05$) compared to the NS, HYT5, HYT50, and HYT500 groups. However, the RpWAT and visceral tissue of the male and female offspring in the HY group is significantly lower ($p \leq 0.05$) compared to the HS group. There is no significant difference ($p \leq 0.05$) in the quantitative analysis of the RpWAT and visceral tissue of the HYT5, HYT50, HYT500 groups compared to the NS group in either the male or female offspring on PND 21. ## 4. Discussion Maternal obesity is a vicious intergenerational cycle. Pre-pregnancy weight-gain greatly influences childhood obesity. This usually occurs through epigenetic modifications or inheritance of an obese gene, the melanocortin 4 receptor (MC4R). Being one of the key regulators for energy homeostasis, MC4R is the key predictor for body weight and food intake, and comprises the coding regions for leptin in offspring [26]. Practically, different growth patterns make it difficult to rule out obesity in children. However, a high level of adiposity in offspring is one of the vital indicators for developing obesity in later life [27]. Similarly, both male and female offspring of obese dams in the HS group showed significantly increased body weight from PND 1 to PND 21 compared to the NS group, proving the hypothesis of a vicious intergenerational cycle of maternal obesity. Similarly, dietary intake during pregnancy influences the body composition of offspring [28]. A recent toxicity study showed that E. tapos yogurt is safe to consume in amounts up to 2000 mg/kg/day [15]. Thus, this study was designed to study the effect of maternal E. tapos yogurt supplementation on the body composition of male and female offspring using Sprague Dawley (SD) rats. As shown in Figure 1A, the male offspring showed fluctuating changes in body weight in the HY5, HYT50, and HYT500 groups over time, yet remained lower compared to the HS group. This is relevant in regard to the previous claim of a gender-specific study that male offspring are more prone to developing obesity compared to female offspring [29]. In this, maternal prenatal supplementation plays a vital role in the weight gain of the child [30]. It was proven in this experimental study that supplementation with E. tapos yogurt during the gestational period influences the body composition of male and female offspring, in which the offspring of dams supplemented with E. tapos yogurt showed normal growth rates (body mass) regardless of varying concentrations (HYT5, HYT50, and HT500) and comparable to the offspring of the NS group. This is mainly due to the presence of bioactive compounds in the E. tapos yogurt. In addition, studies claim that maternal nutrition supplements during the gestational period will be transported through the umbilical cord to the growing fetus. In this regard, any compounds ranging between the molecular weights of 500 and 1000 g/mol can efficiently cross the placenta and reach the fetus easily, thereby exhibiting its effects [31]. Some of the identified bioactive compounds in E. tapos yogurt, such as 5′-methoxy-bilobate with a molecular weight of 582.5 g [32] and the lipophilic nature of apocynoside I [33], can cross the placenta and exhibit their anti-obesity effects on the growing fetus. This is because bilobetin is considered to be one of the strong inhibitors of pancreas lipase [34], which may result in a drastic reduction in fat absorption, eventually leading to weight loss [35]. Similarly, apocynoside I positively impacts the lipid profile of obese subjects by promoting weight loss [36]. Thus, this unrevealed and possible underlying cause of similar body mass in the male and female offspring in the HYT5, HYT50, and HYT500 groups is similar to the NS group. In addition, high-fat diet supplements during the gestational period tend to influence the mass of internal organs. The organ weight of the liver and kidney will increase by 5–$7\%$ in HFD-supplemented subjects due to fat tissue accumulation [37], while adipocytes hypertrophy can increase the mass of RpWAT, BAT, gonadal, and visceral fat tissue [38]. Similarly, prolonged HFD feeding could increase the crypt length of the colon, inflammatory infiltration [39], detachment of epithelial cells, and reduced mucosal content in the colonic wall [40,41]. Similar features as those described were observed in the organ weight as well as in the histological analysis of the liver, kidney, RpWAT, BAT, gonadal, and visceral fat tissue and the colon length of the male and female offspring in the HS group, further proving the establishment of the intergenerational obesity model in this study. However, these features were completely absent in the E. tapos yogurt-treated dams’ offspring in both organ weight and histological changes. One of the reasons could be due to the presence of tannins in E. tapos yogurt [15]. Tannins are known for their natural anti-inflammatory properties [42], and in the case of a low-grade condition such as obesity, tannins could potentially suppress inflammation by modulating the expression of cytokines and the formation of inflammatory molecules [43], particularly in adipocytes. Tannins have been proven to inhibit lipid accumulation, suppress the differentiation of adipocytes [44], and prevent the infiltration of fats into the liver [45]. Thence, the anti-inflammatory and antioxidant activity of tannins in E. tapos yogurt significantly explains the normal features and mass of the female and male offspring’s colon, kidney, liver, RpWAT, BAT, gonadal, and visceral fat tissue. Further, tannins are proven to increase the level of satiety and decrease the frequency of voluntary food intake. In reality, tannin exposure leads to an improvement in lipid profile [46]. An HFD load in the long term could result in increased plasma concentrations of TG, LDL, non-HDL, and total cholesterol [47]. Those characteristics have been observed in both genders of offspring of the HS and HY groups in this study, further proving the link between HFD and an alteration in lipid profile markers; yet those features seem to be absent in E. tapos-exposed groups (HYT5, HYT50, and HYT500). Again, the underlying reason could be due to tannins’ ability to interfere with the absorption of cholesterol, leading to a reduction of the cholesterol concentration in plasma [45]. Another study claims that the presence of tannins in fruit could express inhibitory activity against lipoprotein lipase, an enzyme that modulates the levels of HDL and LDL in plasma. In such conditions, hepatic lipase promotes the degradation of HDL cholesterol, causing the concentration level of HDL in plasma to rise [48]. This hypothesis is further supported by Gato et al. who, in 2012, witnessed similar data on the lipid profile of humans supplemented with tannin-rich fruit [49]. On the contrary, the plasma concentration of leptin in the placenta indicates the nutritional status of the child and is a marker for a high level of adiposity. A high level of leptin will stimulate neuropeptide Y (NPY), which will suppress activation of the hypothalamic–pituitary–adrenal (HPA) axis, causing the lipogenesis signaling mechanism to be stimulated [50]. Such a defect will be inherited from dams by their offspring, and is one of the primary reasons for familial causes of obesity. As a result, the offspring tend to develop resistance towards leptin and will possess an excessive level of leptin in their plasma [51]. This theory has been proven in this study, where the leptin concentration was significantly higher in the male and female offspring in the HS and HY groups. However, exposure to E. tapos yoghurt is able to reduce the plasma leptin concentration gradually, and the mean value was comparable to the offspring of the NS group. The presence of linolenic acids in E. tapos yogurt [15] could potentially stimulate lipolysis in the adipose tissue of the fetus [52], as the low plasma concentration of leptin in the offspring of dams treated with E. tapos yogurt evinces. In addition, a low Na+ plasma concentration correlates with fat mass and obesity. Preliminary studies show that obesity damages renal pressure natriuresis by stimulating the reabsorption of Na+ back into the kidneys. As a result, the plasma concentration of Na+ drops [53]. In this study, this phenomenon was observed in the male offspring of the HS group, while there was no effect on Na+ plasma concentration in the female offspring of the HS group. This might be due to the high body mass of the male offspring compared to the female offspring in this study. Meanwhile, a reduced level of serum potassium (K+) is associated with an increased level of serum TG, low HDL, and a large waist circumference. In short, metabolic component changes could induce electrolyte imbalance, and one of the common manifestations is reduced serum K+ [54]. In this study, both the male and female offspring of untreated obese dams showed a drastic reduction in K+ on PND 21, proving the speculation regarding an electrolyte imbalance in obesity. Electrolytes appear to be normally regulated in E. tapos-treated obese dams’ offspring, and this could be due to the presence of bioactive compounds such as saponins in E. tapos [12], as saponin can modulate Na+ and K+ levels by increasing the activity of Na+ and suppressing the activity of K+ [55]. Therefore, obesity increases the workload of the kidney, thereby stimulating the excessive release of tubular secretions, leading to a rise in plasma creatinine and urea [56]. This theory was proven in this study, as the male and female offspring of untreated obese dams possessed a high level of creatinine, but there was no effect on the serum urea level in the male offspring of obese dams on PND 21. This is again due to the ability of saponins to improve kidney function. According to the preliminary study performed by Kim et al., [ 2013], saponin removes excess levels of urea and creatinine from the body, thus decreasing their concentration in plasma [57]. Changes in liver enzymes are not an exception in the case of intergenerational obesity inheritance. Decreased levels of albumin in plasma have a positive correlation with body fat and inflammation in adipose tissue [58]. Other liver enzymes such as ALT, AST, ALP, and GGT were found to be increased in obese subjects. This could be due to the hepatic pathology arising due to HFD supplements and the presence of steatosis or hepatocyte ballooning due to lipid droplets [59]. Such pathological changes could induce oxidative stress, and the manifestation of decreased levels of serum bilirubin is common in obesity [60]. Laboratory assessments on the liver profiles of the male and female offspring of untreated obese dams (HS and HY) further support the hypothesis in this study. However, the presence of bioactive compounds in E. tapos yogurt is proven to reverse these pathological implications in offspring with HFD-induced obesity. Thus, all three different concentrations (HYT5, HYT50, and HYT500) of E. tapos yogurt-treated obese dams’ offspring showed comparable liver enzymes with NS offspring. ## 5. Conclusions In toto, supplementation with E. tapos yogurt during the gestation period up to PND 21 of the obese dams resulted in significant changes in the body composition of their offspring regardless of gender. These include body weight, liver enzymes, renal markers, lipid profiles, leptin concentration, gross organ weight of fat tissues, and histopathological changes in vital organs. Thus, the outcome of this study gives preliminary data that the pre-treatment with E. tapos yogurt of obese dams during pregnancy could prevent the intergenerational transmission of obesity in the family, thereby proving the anti-obesity effects of E. tapos yogurt. Therefore, further study must be conducted to access the long-term effects of E. tapos yogurt on the transmission of the obesity gene. ## References 1. Denizli M., Capitano M.L., Kua K.L.. Maternal obesity and the impact of associated early-life inflammation on long-term health of offspring. Front. Cell. Infect. Microbiol. (2022.0) 12 1348. DOI: 10.3389/fcimb.2022.940937 2. Forno A.E., Young O.M., Kumar R., Simhan H., Celedón J.C.. Maternal Obesity in Pregnancy, Gestational Weight Gain, and Risk of Childhood Asthma. Pediatrics (2014.0) 134 535-546. 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--- title: 'Eating Spicy Food, Dietary Approaches to Stop Hypertension (DASH) Score, and Their Interaction on Incident Stroke in Southwestern Chinese Aged 30–79: A Prospective Cohort Study' authors: - Liling Chen - Wenge Tang - Xiaomin Wu - Rui Zhang - Rui Ding - Xin Liu - Xiaojun Tang - Jing Wu - Xianbin Ding journal: Nutrients year: 2023 pmcid: PMC10005455 doi: 10.3390/nu15051222 license: CC BY 4.0 --- # Eating Spicy Food, Dietary Approaches to Stop Hypertension (DASH) Score, and Their Interaction on Incident Stroke in Southwestern Chinese Aged 30–79: A Prospective Cohort Study ## Abstract Little is known about the association between spicy food intake, dietary approaches to stop hypertension (DASH) score, and incident stroke. This study aimed to explore the association of eating spicy food, DASH score, and their interaction with stroke incidence. We included 22,160 Han residents aged 30–79 in southwest China from the China Multi-Ethnic Cohort. Three hundred and twelve cases were newly diagnosed with stroke by October 8, 2022, during a mean of 45.5 months of follow-up. Cox regression analyses showed that eating spicy food reduced stroke risk by $34\%$ among people with low DASH scores (HR 0.66, $95\%$CI 0.45–0.97), while individuals with high DASH scores versus low DASH scores had a $46\%$ lower stroke incidence among spicy food nonconsumers (HR 0.54, $95\%$CI 0.36–0.82). The HR of the multiplicative interactive term was 2.02 ($95\%$CI 1.24–3.30) and the overall estimates of relative excess risk due to interaction (RERI), attributable proportion due to interaction (AP), and the synergy index (S) were 0.54 ($95\%$CI 0.24–0.83), 0.68 ($95\%$CI 0.23–1.14), and 0.29 ($95\%$CI 0.12–0.70), respectively. Consuming spicy food seems to be associated with lower stroke risk only in people who have a lower DASH score, while the beneficial effect of higher DASH scores seems to be found only among nonconsumers of spicy food, and a negative interaction may exist between them in southwestern Chinese aged 30–79. This study could provide scientific evidence for dietary guidance to reduce stroke risk. ## 1. Introduction Stroke is the leading cause of death and disability-adjusted life-years (DALYs) in the world [1,2]. From 1990 to 2019, the absolute number of cases significantly increased with a $70.0\%$ increase in incident strokes, an $85.0\%$ increase in prevalent strokes, a $43.0\%$ increase in deaths from stroke, and a $32.0\%$ increase in DALYs due to stroke [2]. In China, the stroke incidence rate reached 276.7 per 100,000, and the mortality rate reached 153.9 per 100,000 in 2019 [3]. Therefore, the disease burden of stroke remains severe around the world and in China. Implementing effective prevention strategies is essential to mitigate the stroke burden. A balanced diet, as one of the four cornerstones of health, has been widely studied for disease prevention and health improvement. Current internationally recommended dietary patterns tend to be based on a variety of healthy foods, and the benefits for cardiovascular disease are generally better than when controlling single isolated nutrients [4]. The Dietary Approaches to Stop Hypertension (DASH) diet is a well-accepted dietary pattern recommendation targeting blood pressure [5] and offers superior dietary guidance compared to an alternative Mediterranean diet for reducing cardiometabolic risks in less developed ethnic minority regions (LEMRs) [4]. In addition to lowering blood pressure, following the DASH diet has been found to improve cardiovascular disease (CVD) risk factors such as body weight, blood lipids, lipoproteins, inflammatory markers, and glucose–insulin homeostasis [6]. Previous research indicated that higher adherence to the DASH diet (i.e., a higher DASH score) is associated with lower stroke mortality (hazard ratios: 0.62, 0.97, respectively) [7,8] and incidence (hazard ratios: 0.61–0.88) [6,9,10]. Spicy food, defined mainly by chili pepper content, is widely consumed in many parts of the world [11,12,13]. The bioactive ingredients of spicy food, such as capsaicin, have been found to have antithrombotic and vasodilatory properties, anti-inflammatory properties, antioxidant properties, etc. [ 14,15]. A meta-analysis of four prospective cohort studies (from the United States, China, Italy, and Iran) showed that regularly consuming (≥ 1 day/week) spicy food was associated with a $12\%$ lower risk of all-cause mortality [12]. Meta-analyses and prospective studies suggested that spicy food could reduce diastolic blood pressure (weighted mean differences of −1.90 mmHg) [16], reduce total cholesterol level (overall standardized mean difference: −0.52) [17], and reduce the risk of overweight/obesity (hazard ratio: 0.73–0.81) [18]. According to previous studies, spicy food has the ability to control metabolic syndrome and related disorders such as high blood pressure, obesity, and disordered lipid and glucose profiles [19,20], which are risk factors for stroke [2]. Therefore, we hypothesize that spicy food may have a protective effect on stroke onset. Chongqing is located in southwest China, where the climate is humid and almost $50\%$ of the residents consume spicy food daily [21,22]. The incidence of stroke is lower in Chongqing than the average level in China [23]. Is this lower rate related to consuming spicy food? Although the benefits of a higher DASH score for stroke are well established, the effects may vary across regions with different economic development levels, dietary habits, and lifestyles. How does it work in a region where spicy food is preferred? Reliable evidence from large-scale epidemiological studies on this topic is scarce. As spicy food and DASH score are both closely related to the daily diet and have cardiovascular-protective effects, we are interested in whether there is an interaction between them in terms of incident stroke. This study aimed to explore the association between spicy food intake, DASH score, and stroke incidence using the Han cohort aged 30–79 established by the China Multi-Ethnic Cohort (CMEC) study in Chongqing. ## 2.1. Study Population This is an ongoing community-based prospective study from Chongqing Municipality in southwest China, based on the China Multi-Ethnic Cohort (CMEC) study. Details of the CMEC study design have been described elsewhere [24]. In brief, a total of 23,308 Han Chinese participants, aged 30–79 years, who had lived in the local area for half a year or more, were recruited by multi-stage, stratified cluster sampling in consideration of both sex ratio and age ratio between September 2018 and February 2019. Baseline assessment consisted of an electronic questionnaire with face-to-face interviews (e.g., sociodemographics, diet and lifestyle, medical history), medical examinations (e.g., height, body weight, and blood pressure), and clinical laboratory tests (e.g., blood and urine specimens). Figure 1 shows the data cleaning flowchart. In this study, 22,160 participants were included in the final analysis. The inclusion criteria were: [1] completed the baseline ($$n = 23$$,308); [2] without self-reported stroke diagnosed by a physician at enrolment ($$n = 23$$,139). The exclusion criteria were: [1] newly discovered to have had a stroke prior to recruitment via follow-up ($$n = 62$$); [2] missing data for spicy food consumption ($$n = 17$$); [3] missing data for seven food group components of DASH score ($$n = 3$$); [4] missing data for covariates ($$n = 917$$). Ethic approvals from the Medical Ethics Committee of Chongqing Center for Disease Control and Prevention (2021[006],2017[001]) and the Sichuan University Medical Ethical Review Board (K2016038) were obtained, and informed consent was signed by all study participants. ## 2.2. Follow-Up and Outcome Assessment Follow-up of the stroke was conducted in both passive and active modes. The passive follow-up was performed via linkage to the national cardiovascular event registration report system and the Chongqing death registry system. In active mode, a resurvey among $10\%$ of surviving participants was conducted in late 2020 using the same measurements as the baseline survey. In addition, all survivors received telephone follow-ups every year since they entered the cohort. The main outcome examined in the present study was the incidence of stroke. According to the 10th version of the International Statistical Classification of Diseases (ICD-10), disease codes I60 to I64 were used to identify cases diagnosed with a stroke, including subarachnoid hemorrhage, intracerebral hemorrhage, ischemic stroke, and unclassified stroke, and excluding transient ischemic attack (TIA) and chronic cerebral arteriosclerosis [25]. Participants contributed person-months from their enrolment date until the onset of stroke event, death (from any cause), loss to follow-up, or the final follow-up assessment date (8 October 2022, for this current study), whichever came first. ## 2.3. Assessment of Spicy Food Consumption and DASH Score Food intake was collected by a quantitative food frequency questionnaire (FFQ) of the CMEC study, and its validity and reproducibility were both assessed by conducting repeated FFQ and 24 h dietary recalls (24 HDRs) in a resurvey in 2020 [5]. As with the China Kadoorie Biobank study [26], spicy food intake refers to the consumption of any “hot” spices when cooking or eating, including fresh or dried chili pepper, chili sauce, chili oil, or other hot spices. Participants were asked about their consumption frequency (never or rarely, only occasionally, 1–2 days/week, 3–5 days/week, or 6–7 days/week) in the past month at baseline. Those who selected the last 3 categories (1–2 days/week, 3–5 days/week, or 6–7 days/week) were defined as regular spicy food consumers and nonconsumers (never or rarely, only occasionally) as the reference group in this study. A modified DASH diet was verified to be more effective at reducing cardiovascular and cerebrovascular risks in a randomized controlled trial [27]. To assess adherence to the modified DASH diet, we calculated the DASH score following scoring methods developed by Chiu et al. [ 27] and Fung et al. [ 28] with slight adaption according to the CEMC data [4]. Each of the seven food groups, including whole grains, fresh fruits, vegetables, beans, red meat products, dairy, and sodium, was assigned a score of 1 to 5 based on the quintile of the average food intake. For whole grains, fresh fruits, vegetables, beans, and dairy, a score of 5 was given for the highest quintile, while a score of 1 was given for the lowest quintile. For red meat products and sodium, this pattern of scoring was inverted. The sum of the seven component scores resulted in an overall DASH score ranging from 7 (minimal adherence) to 35 (maximal adherence). Based on the lowest tertile of DASH score as the cutoff point, all participants were categorized into two groups (low DASH score: <19; high DASH score: ≥19), with the low DASH score group as the reference. Spicy food consumption (no and yes) and DASH score (low DASH score and high DASH score) were both dichotomous factors, in which case we have four combinations by combining them, i.e., “Not spicy/Low DASH score”, “Not spicy/High DASH score”, “Spicy/Low DASH score”, and “Spicy/High DASH score”, using “Not spicy/Low DASH score” as reference. ## 2.4. Assessment of Covariates Sociodemographic covariates included gender (male and female), age (<60 years and ≥60 years), and education level (primary school and below, middle school, high school, college or university and above). Smoking status and drinking status were both classified as no and yes. Physical activity was estimated by summing up the corresponding metabolic equivalent values (METs) of four domains including leisure, work, transportation, and housework [29]. According to the tertiles, physical activity was categorized into <21.94 METs h/day, 21.94–35.55 METs h/day, and >35.55 METs h/day. Body mass index (BMI) was calculated using weight in kilograms divided by the square of height in meters (kg/m2) and grouped into <18.5 kg/m2, 18.5–23.9 kg/m2, 24–27.9 kg/m2, and ≥28 kg/m2 according to the weight criteria for adults in China [30]. Hypertension was defined as an average of three measurements for systolic or diastolic blood pressure (SBP/DBP) ≥$\frac{140}{90}$ mmHg or a self-reported diagnosis of hypertension by a physician [31]. Participants having any one of the following conditions were regarded as having dyslipidemia: [1] triacylglycerol (TG) ≥2.26 mmol/l; [2] serum total cholesterol (TC) ≥6.22 mmol/L; [3] low-density lipoprotein cholesterol (LDLC) ≥4.14 mmol/L; [4] and high-density lipoprotein cholesterol (HDLC) <1.04 mmol/L; [5] a self-reported diagnosis of hyperlipemia by a physician [32]. Diabetes was diagnosed as fasting blood glucose (FBG) ≥7.0 mmol/L, glycosylated hemoglobin percentage of ≥$6.5\%$, or self-reported diagnosis of diabetes by a physician [33]. A family history of stroke-related diseases was defined as a self-reported family history of hypertension, diabetes, stroke, or acute myocardial infarction (AMI). In addition to the 11 covariates listed above, spicy food and DASH scores were adjusted for each other. ## 2.5. Statistical Analysis Continuous data were described as the mean ± SD, and statistical significance was assessed by the independent samples t-test. Categorical variables were described as numbers (percentages), and statistical significance was assessed by the chi-square test. Cox proportion hazard regression models were applied to calculate the hazard ratios (HRs) and confidence intervals (CIs) of spicy food consumption, DASH score, and their interaction with stroke incidence. Subgroup analyses by spicy food consumption and DASH score were performed to examine the potential interaction. The relative excess risk due to interaction (RERI), the attributable proportion due to interaction (AP), and the synergy index (S) were used to assess the additive interaction between spicy food and DASH score [34]. These measures were output according to the following equations: [1] RERI = HR11-HR10-HR01 + 1; [2] AP = RERI/HR11; [3] S = (HR11-1)/[(HR10-1) + (HR01-1)]. In this study, with “Not spicy/Low DASH score” (HR00 = 1) as the reference category, HR01, HR10, and HR11 were the hazard ratios for groups of “Not spicy/High DASH score”, “Spicy/Low DASH score”, and “Spicy/High DASH score”, respectively. If there is no biological interaction, RERI and AP are equal to 0 and S is equal to 1. All analyses were performed using SPSS (Version 25.0. IBM Corp., Armonk, NY, USA) and Excel 2010 (Microsoft Office 2010, Microsoft Corporation, USA). A two-sided p-value < 0.05 was considered statistically significant. ## 3.1. General Characteristics Among the 21,160 participants, the average age was 51.4 ± 11.7 years and $53.2\%$ were females (Table 1). Seventy-six percent of them consumed spicy food in the past month and $69.5\%$ had high DASH scores. Participants who ate spicy food were more likely to be males, younger, smokers, drinkers, and have a family history of stroke-related diseases; to have higher education levels, physical activity levels, BMI levels, DASH score, and dyslipidemia prevalence; and to have lower hypertension and diabetes prevalence than those who did not eat spicy food (all $p \leq 0.05$). Conversely, compared to those with low DASH scores, individuals with high DASH scores were more likely to be females, nonsmokers, and spicy food consumers; and have lower BMI levels and dyslipidemia prevalence (all $p \leq 0.05$). ## 3.2. Independent Effect of Spicy Food Consumption and DASH Score The mean follow-up time was 45.5 (4.0) months, and 312 individuals ($1.4\%$) were diagnosed with stroke during the follow-up period, equivalent to an incidence rate of 371.68 per 100,000 person-years (Table 2). In the crude Cox regression model, except for the elderly (all $p \leq 0.05$), both spicy food consumption and DASH score were inversely associated with stroke risk in the overall population and among both males and females, and those <60 years of age (all $p \leq 0.05$). However, after adjusting for covariates in model 1, neither spicy food consumption nor DASH score was significantly correlated with stroke incidence (all $p \leq 0.05$). Separate subgroup analyses by spicy food consumption and DASH scores implied that there may be an interaction between them (Figure 2). Figure 2A shows that eating spicy food reduced the risk of developing stroke by $34\%$ among people with low DASH scores (HR 0.66, $95\%$CI 0.45–0.97, $$p \leq 0.03$$), whereas the protective effect was not found among people with high DASH scores ($p \leq 0.05$). After stratifying by spicy food consumption (Figure 2B), individuals with high DASH scores versus low DASH scores had a $46\%$ decrease in stroke incidence among spicy food nonconsumers (HR 0.54, $95\%$CI 0.36–0.82, $p \leq 0.01$). However, similarly, this inverse association was not discovered among those who consumed spicy food ($p \leq 0.05$). ## 3.3. Interaction Effect of Spicy Food Consumption and DASH Score on Stroke Incidence In model 2 (Table 3), the interaction term was created by combining spicy food consumption and DASH score. Compared to the group of ”Not spicy/Low DASH score”, participants in the group of “Not spicy/High DASH score” had the lowest risk of stroke (HR01 0.58, $95\%$CI 0.39–0.86, $p \leq 0.01$), followed by the group of “Spicy/Low DASH score” (HR10 0.67, $95\%$CI 0.46–0.97, $$p \leq 0.03$$), but little difference was found in the group of “Spicy/High DASH score” (HR11 0.78, $95\%$CI 0.57–1.08, $$p \leq 0.14$$). This interaction term was also found to be significantly associated with incident stroke among males (HR01 = 0.57, $95\%$CI 0.39–0.86, $p \leq 0.01$; HR10 = 0.62, $95\%$CI 0.39–1.01, $$p \leq 0.05$$) and those < 60 years of age (HR01 0.43, $95\%$CI 0.19–1.00, $$p \leq 0.05$$; HR10 0.43, $95\%$CI 0.22–0.86, $$p \leq 0.02$$; HR11 0.44, $95\%$CI 0.237–0.831, $$p \leq 0.01$$). Similar trends were also observed among females and those ≥ 60 years of age ($p \leq 0.05$). In model 3 (Table 3), a multiplicative interaction effect between spicy food consumption and DASH score was revealed. The corresponding HRs of the multiplicative interaction term for the overall population and males as well as the elderly were 2.02 ($95\%$CI 1.24–3.30, $$p \leq 0.01$$), 1.99 ($95\%$CI 1.03–3.85, $$p \leq 0.04$$), and 1.89 ($95\%$CI 1.07–3.34, $$p \leq 0.03$$), respectively (Table 3). HRs of the multiplicative interaction term among females and those < 60 years of age were both marginally significant ($p \leq 0.05$). Based on adjusted HR10, HR01, and HR11, additive interaction indicators were calculated and showed a negative additive interaction between spicy food consumption and DASH score (Table 4). RERIs of the overall population, males, females, those <60 years of age, and the elderly were 0.54 ($95\%$CI 0.24–0.83), 0.52 ($95\%$CI 0.13–0.90), 0.52 ($95\%$CI 0.02–1.02), 0.58 ($95\%$CI 0.15–1.00), and 0.51 ($95\%$CI 0.11–0.90), respectively. APs were observed to be statistically significant in the overall population, and among males and the elderly, with corresponding measures of 0.68 ($95\%$CI 0.23–1.14), 0.73 ($95\%$CI 0.08–1.38), 0.54 ($95\%$CI 0.06–1.02), respectively. S estimates were 0.29 ($95\%$CI 0.12–0.70) in the overall population, 0.36 (0.17–0.78) for males, and 0.50 (0.34–0.74) for those <60 years of age. ## 3.4. Summary of Main Results Figure 3 summarizes the association between spicy food consumption, a higher DASH score, and incident stroke in the current study. A negative interaction between spicy food consumption and DASH score was suggested by both multiplicative interaction (HR 2.02, $95\%$CI 1.24–3.30) and additive interaction (RERI 0.54, $95\%$CI 0.24–0.83; AP 0.68, $95\%$CI 0.23–1.14; S 0.29, $95\%$CI 0.12–0.70). It was found that consuming spicy food reduced the risk of developing stroke by $34\%$ among people with low DASH scores but not among those with high DASH scores. A higher DASH score was linked to a $46\%$ lower stroke incidence for spicy food nonconsumers but not for spicy food consumers. ## 4. Discussion In this study, we found that consuming spicy food reduced the risk of developing stroke by $34\%$ among people with low DASH scores, whereas the protective effect was not found among those with high DASH scores. A higher DASH score was linked to a $46\%$ lower stroke incidence among those who did not regularly consume spicy food, whereas the protective effect was not found among spicy food consumers. The beneficial effect of a higher DASH score appears to be greater than that of spicy food intake for incident stroke and a negative interaction was discovered between them in terms of multiplicative interaction as well as additive interaction. ## 4.1. Spicy Food May Be an Effective Nutritional Strategy for Preventing Stroke To our knowledge, this is the first prospective study assessing the association between spicy food and stroke incidence. Spicy food consumption was found to be linked with lower stroke risk in people with low DASH scores, whereas the protective effect was not found among those with high DASH scores. In line with our results, Bonaccio et al. [ 35] demonstrated that using chili pepper was associated with a lower risk of cerebrovascular mortality in an Italian cohort comprising 23,811 participants aged more than 35 years old. In 2017, the U.S. National Health and Nutrition Examination Survey (NHANES) trial [36] reported a lower risk of total mortality associated with the consumption of hot chili peppers, and similar trends of reduction (statistically nonsignificant) were seen for death from stroke. However, a Chinese population-based study ($$n = 487$$,375, aged 30–79 years) [37] found no statistically significant link between eating spicy food daily and lower stroke mortality. It may be attributed to some risk factors that were not adjusted in the final analysis, such as salt intake, which is high in China and could increase stroke risk [38]. In this study, among residents consuming spicy food in Chongqing, about $98.8\%$ of them consumed chili peppers. Capsaicin, one of the main components of chili peppers, was confirmed to have cardiovascular-protecting effects by activating transient potential vanilloid 1 (TRPV 1) and substance p and the release of calcitonin gene-related peptide (CGRP) [11,14]. Moreover, capsaicin can prevent platelet aggregation and the activity of clotting factors VIII and IX, resulting in decreasing stroke events [14,39]. Additionally, spicy food may reduce stroke risk by decreasing metabolic risk factors such as hypertension, obesity, hyperglycemia, and hyperlipidemia [14,40]. Thus, it is also biologically plausible that spicy food may be an effective nutritional strategy for preventing stroke, especially in people who have a lower DASH score. ## 4.2. A Higher DASH Score Plays a Beneficial Role in Incident Stroke In this study, it was found that a higher DASH score, indicating greater adherence to the DASH diet, was associated with a lower stroke incidence among spicy food nonconsumers, whereas the protective effect was not found among spicy food consumers. As far as we know, few studies have considered spicy food consumption when analyzing the association between DASH score and stroke onset. Previous studies still found a protective effect of adherence to the DASH diet for incident stroke, although they did not adjust for spicy food consumption [8,9,10]. It may be because the proportion of respondents eating spicy food in these studies was not as high as ours in Chongqing which is famous for its spicy food preference [41,42]. The DASH diet is rich in fruits, vegetables, whole grains, legumes, and dairy products, and high consumption of them either together or alone was confirmed to be inversely associated with stroke and CVD incidence by systematic reviews and meta-analyses of prospective cohort studies, while the restricted red meat and dietary sodium in DASH diets were positively correlated with the increased incidence of stroke and CVD [8,38]. Further, dietary fiber and other essential nutrients richly found in the DASH diet, such as magnesium, potassium, and phytochemicals, have anti-inflammatory and antioxidant activity and reduce angiogenesis, which has stroke benefits [43,44,45]. In addition, adherence to the DASH diet was identified to improve blood pressure, lipids, body weight, and blood glucose, which is also beneficial for the prevention and control of stroke [8,46]. Therefore, it is biologically tenable that a higher DASH score reduces stroke risk in people who do not regularly eat spicy food. However, the reason for the lack of protective effect among consumers of spicy food needs to be studied further. ## 4.3. A Negative Interaction May Exist between Spicy Food Consumption and DASH Score As far as we know, the interaction between spicy food consumption and DASH score on stroke incidence was found for the first time. Subgroup analyses showed that consuming spicy food is associated with a lower stroke incidence only in people who have a lower DASH score, while the beneficial effect of a higher DASH score is found only among nonconsumers of spicy food. Compared to the group of ”Not spicy/Low DASH score”, people in the group of “Not spicy/High DASH score” had the lowest risk of stroke, followed by the group of “Spicy/Low DASH score”, and then the group of “Spicy/High DASH score”. Moreover, the HR of the multiplicative interaction term was significantly greater than 1, whereas those of spicy food and DASH score were both less than 1. Regarding additive indicators, both RERIs and APs were more than 0, and S estimates were less than 1. All these results demonstrated that a negative interaction may exist between spicy food consumption and DASH score. Why is the interaction negative? Little is known about this. Perhaps spicy food and the DASH diet both contain certain nutrients, such as magnesium or potassium. The beneficial effects may diminish once the shared nutrients reach a specific amount when people consume spicy food and follow a DASH diet. In addition, some of the active ingredients in spicy food and the DASH diet may be antagonistic to each other. These speculations need to be explored in further studies. In this study, the fact that the distribution of some risk factors differed between spicy food intake and DASH score may also provide some clues: people with high DASH scores were more likely to be females, nonsmokers, and to have lower BMI levels and dyslipidemia prevalence, whereas those characteristics were distributed conversely for those who consumed spicy food. The incidence of stroke is always found to be higher for males and smokers, and higher BMI and dyslipidemia are the common risk factors for stroke [2]. Additionally, some overlaps may exist between spicy food and DASH score. The main source of spicy food in this population is chili pepper (about $98.8\%$) which would also be captured in the vegetable section of the DASH score. However, this overlap would be limited because vegetables refer to many categories and the intake of other vegetables is usually much greater than that of chili peppers. The correlation coefficient ($r = 0.10$) between spicy food intake and DASH score in this study is small, also suggesting that the overlap is limited. In brief, a negative interaction may exist between spicy food consumption and DASH score. Further research is needed on the mechanisms of this interaction. ## 4.4. Strengths and Limitations To our knowledge, this is the first prospective cohort study to comprehensively examine the interaction between spicy food consumption and DASH score on stroke incidence, including both the multiplicative and additive interactions. Furthermore, our study explored the association of spicy food consumption with the risk of developing stroke, which has been rarely performed. Nonetheless, limitations are worth noting. First, due to the preponderance of ischemic stroke versus hemorrhagic stroke and short follow-up time, the number of participants with specific types of stroke was insufficient to conduct separate analyses, but preliminary analyses showed similar results between the two types of stroke (data not shown). Second, the dietary data were self-reported at baseline, likely leading both to measurement error and the possibility of unaccounted changes in dietary behavior before the incident stroke. Furthermore, the FFQ used in this study did not provide information in sufficient detail to allow for computing other indices, such as the dietary inflammatory index [47], which would help to pinpoint the mechanisms of action. Third, exposure factors were only roughly grouped according to whether participants ate spicy food and whether their DASH score was ≥19, thus the dose-response relationship between exposure and outcome needs further research. Fourth, these results were obtained among Han ethnicity residents aged 30–79 years, from areas eating spicy foods relatively often, so extrapolating the findings to other areas or populations should be noted. ## 5. Conclusions In conclusion, consuming spicy food seems to be associated with a lower stroke incidence only in people who have a lower DASH score, while the beneficial effect of a higher DASH score seems to be found only among nonconsumers of spicy food. The beneficial effect of a higher DASH score appears to be greater than that of spicy food consumption for incident stroke, and a negative interaction may exist between them in southwestern Chinese aged 30–79. This study could provide scientific evidence for dietary guidance to reduce stroke risk. 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--- title: 'Clustering of cardiovascular disease risk factors among first-year students at the University of Ibadan, Nigeria: a cross-sectional study' authors: - Olumide Ebenezer Olufayo - Ikeoluwapo Oyeneye Ajayi - Samuel Osobuchi Ngene journal: São Paulo Medical Journal year: 2022 pmcid: PMC10005463 doi: 10.1590/1516-3180.2021.0998.11052022 license: CC BY 4.0 --- # Clustering of cardiovascular disease risk factors among first-year students at the University of Ibadan, Nigeria: a cross-sectional study ## ABSTRACT ### BACKGROUND: Cardiovascular disease (CVD) is the second leading cause of death in sub-Saharan Africa. Globally, there is substantial evidence that modifiable risk factors for CVD are increasing in adolescents. Unfortunately, there is a paucity of information on the prevalence and clustering of these risk factors in adolescents. ### OBJECTIVES: This study explores the modifiable risk factors for CVD among first-year students at the University of Ibadan, Nigeria. ### DESIGN AND SETTING: This cross-sectional study was conducted at the University of Ibadan, Nigeria. ### METHODS: A total of 546 newly admitted students at the University of Ibadan, Nigeria, were recruited using stratified random sampling. An interviewer-administered questionnaire was used to obtain information from study participants between January and February 2016. ### RESULTS: The mean age of respondents was 19 ± 2.2 years with a male-to-female ratio of 1:1. The reported risk factors for CVD were smoking ($1.6\%$), abdominal obesity ($3.3\%$), alcohol consumption ($3.7\%$), overweight/obesity ($20.7\%$), unhealthy diet ($85.3\%$), and physical inactivity ($94.5\%$). Clustering of ≥ 2 risk factors was reported in $23.4\%$ of students. Female students were twice as probably overweight/obese as male students (adjusted odds ratio [AOR] = 2.2; confidence interval [CI] = 1.41–3.43). Students whose fathers were skilled workers were 3.5 times more likely to be physically inactive (AOR = 1.7; CI = 0.97–2.96). The clustering of ≥ 2 risk factors was significantly higher among women and Muslims in bivariate analysis, whereas no significant association was found in multivariate analysis. ### CONCLUSIONS: Public health strategies to prevent CVD risk factors should begin in schools and extend to the entire community. ## INTRODUCTION Cardiovascular disease (CVD) is a global public health problem and a leading cause of disability-adjusted life years in 2019. 1 Most of these risk factors are caused by unhealthy lifestyles and habits; therefore, they are sometimes referred to as lifestyle risk factors and include smoking, tobacco, and excessive alcohol use, poor dietary patterns, and physical inactivity. Adolescents and young adults are particularly susceptible to these CVD risk factors in both developing and developed countries. 2,3 Nearly all deaths from CVD occur among young people in Africa than in Europe and North America. 4 Modifiable behaviors like physical inactivity, tobacco use, unhealthy diet and harmful alcohol consumption increase the risk of CVDs. 5 About $38\%$ of men and $40\%$ of women aged at 18 years or older were overweight in 2014, and this figure is more than double the rate between 1980 and 2015. 4 In Nigeria, the prevalence of overweight and obesity is $26.8\%$ and $6.5\%$, respectively according to WHO. 6 In southwestern Nigeria, a study revealed that only $60\%$ of university undergraduates consumed the minimum recommended number of servings of grain (cereal) foods, while $60\%$, $85\%$, and $40\%$ of students did not meet the recommended daily allowance for protein, calcium, and iron respectively. 7 Globally, $23\%$ of men and $32\%$ of women over the age of 18 years were insufficiently physically active in 2016. 8 Not having sufficient physical activity is one of the ten leading risk factors for global mortality. These people have at $20\%$–$30\%$ increased risk in all-cause mortality compared with those who engage in at least 150 minutes in moderate-intensity physical activity per week, or equivalent, as recommended by the World Health Organization. 9 Physical inactivity causes $6\%$ in the burden of disease from coronary heart disease, $30\%$ of ischemic heart disease, $7\%$ of type 2 diabetes, $10\%$ of breast cancer, and $10\%$ of colon cancer. 9 Excessive fat accumulation produces an accumulation of lipids around the visceral adipose tissue, which is another risk factor for developing CVDs. 10 A study also shows that the prevalence of abdominal obesity was low among young adults in a tertiary institution. 11 A study among Nigerian university students found a higher proportion of abdominal obesity ($5.9\%$) among female undergraduate students compared with their male counterparts ($0.8\%$) 12 The clustering of CVD risk factors has an amplifying effect that induces increased CVD risk. 13,14 These risk factors can be observed in early adolescence and continue into adulthood. 15 Multiple clustering of these risk factors in adolescents and young adults leads to an initial stage of CVD such as atherosclerosis. 13 The accumulation of cholesterol, lipids and fibrous plagues begins in arterial walls at the age of 10 years and increases over time until it manifests overtime and manifests as an atherosclerotic lesion in adulthood. 13,14 Therefore, tracking of the clustering of multiple CVD risk factors is highly essential and is a sine qua non for mitigating the threat of CVD in adolescents and young adults. Clustering of CVD risk factors among young people has been well explored in the literature, with interesting findings in low-, middle-, and high-income countries. 16–19 However, there is a paucity of information on this subject matter among university students in Nigeria, particularly newly admitted students who will most likely experience a significant change in their lifestyle. Therefore, this study examined the risk factors for CVD and their clustering in first-year undergraduate students at the University of Ibadan. ## OBJECTIVE Against this background, this study investigated CVD risk factors and their clustering in first-year undergraduate students at the University of Ibadan. ## Study site The University of Ibadan has 13 faculties and enrolls at least 3,000 students annually. The University of Ibadan maintains a well-rounded program of sport and athletic activities on campus under the supervision of the Director of Sports. Aside from maintaining a sound body, which is beneficial for progressive thinking and rigorous academic pursuits, students have the added benefit of being exposed to modern facilities and techniques through active participation in various sports. ## Study design and population This was a cross-sectional study among the first-year students of the $\frac{2014}{2015}$ academic year at the University of Ibadan, Oyo State, Nigeria. All consenting first-year students at the University of Ibadan aged 15–35 years were eligible to participate in the study while those with physical deformities were excluded. ## Sample size and sampling procedure The sample size was calculated using the Leslie-Kish formula, representing $23.7\%$ of adolescents with a cluster of three CVD risk factors, 3 and a sampling error of $5\%$. A stratified random sampling technique was used to recruit eligible respondents. The University of Ibadan has academic programs in 13 faculties. Out of the nine faculties, six faculties were randomly selected while all faculties in the College of Medicine, University of Ibadan were selected for the study. In each randomly selected faculty, $50\%$ of the departments were considered except in the Faculty of Dentistry and Public Health, where only one department was chosen while in Clinical Sciences, $100\%$ of the departments were admitted for the $\frac{2014}{2015}$ academic session were used. The total number of first-year students (study population) in the randomly selected departments was determined. Then, a proportional allocation of the sample was carried out to determine the number of first-year students in each department. Then, systematic random sampling was used to select the study participants (students) from each department based on the sampling interval. Each person (student) in each department was then assigned a number, and each Kth person was taken from the total number of first-year undergraduate students in each randomly selected department, and the starting point was randomly selected. ## The data collection instrument A semi-structured questionnaire was used to obtain information on the socio-demographic, anthropometric, and lifestyle characteristics of the respondents. Data were collected from January 2016 to February 2016. The questionnaire was validated by experts and then tested among 20 first-year students at another faculty that was not selected for the study. A Cronbach's alpha of 0.8 was obtained. These students had a similar age range to the study participants. Scale and meter rules, respectively, measured weight and height. The waist circumference (WC) of each participant was measured with a nonelastic tape measure. WC was measured midway between the lowest rib and the superior border of the iliac crest at the end of normal exhalation to the nearest 0.1 cm. 6 The validated International Physical Activity Questionnaire Short Form (IPAQ-SF) was used to measure students' level of physical activity. Respondents with less than 600 metabolic equivalent minutes of work/week were classified as not physically active. 20 Respondents with body mass index ≥ 29.9 kg/m 2 were classified as overweight/obese. 21 Respondents with waist circumference greater than or equal to 88 cm (women) and greater than or equal to 102 cm (male) were classified as abdominally obese. 21 Dietary patterns were assessed using eating habit questionnaires. Respondents who consumed fewer than five servings of fruits and vegetables per day on at least five days per week were classified as having an unhealthy diet. 22 Alcohol consumption of more than three standard units/day for men or more than two standard units/day for womenwas classified as excessive alcohol consumption. 23 Current Smoking status was measured as use of tobacco (smoke and/or smokeless) within the past month. 24 ## Data analysis Data were entered and analyzed using SPSS version 24 (IBM Corp. Released 2016. IBM SPSS Statistics for Windows, version 24.0. Armonk, New York: IBM Corp). The general characteristics of the respondents are presented using descriptive statistics. Factors associated with CVD risk factors and their clusters (≥ 2) were assessed using the chi-square test. Binary logistic regression was used to analyze CVD predictors considering a CI of $95\%$. The significance level was set at $P \leq 0.05.$ ## Ethical considerations This study was approved by the Ethics Committee of the of Ibadan on October 23, 2015 under the approval number: NHREC/$\frac{05}{01}$/2008a. The chairman of this committee can be contacted at the Biode Building, Room 210, 2nd Floor, Institute for Advanced Medical Research and Training, College of Medicine, University of Ibadan. e-mail: [email protected] and, [email protected]. ## RESULTS A total of 546 first-year students (first-year students) participated in the survey. Table 1 shows that most respondents ($81.7\%$) were between 20 years old and younger, while the mean age of the respondents was 19 ± 2.2 years. Table 1 shows the socio-demographic characteristics of the study participants. More than half of the respondents were female ($55.1\%$) and the majority ($99.3\%$) were single. Christianity ($86.1\%$) was the predominant faith. Most students ($93.0\%$) lived in university dormitories. The majority ($49.6\%$) of the participants had fathers who held skilled occupations, others ($38.3\%$) had fathers who held semi-skilled occupations, and a few ($12.1\%$) of the respondents' fathers had unskilled occupations. About ($62.5\%$) of the respondents received monthly allowances between N10,001 and N20,000, while ($31.3\%$) received monthly allowances between N10,000 and below. Table 1 \| Variables \| Variables.1 \| Frequency \| Percent (%) \| \| --- \| --- \| --- \| --- \| \| Gender \| Gender \| Gender \| Gender \| \| \| Male \| 245 \| 44.9 \| \| \| Female \| 301 \| 55.1 \| \| Age group (years) \| Age group (years) \| Age group (years) \| Age group (years) \| \| \| ≤ 20 \| 446 \| 81.7 \| \| \| ≥ 21 \| 100 \| 18.3 \| \| Marital status \| Marital status \| Marital status \| Marital status \| \| \| Single \| 542 \| 99.3 \| \| \| Married \| 4 \| 0.7 \| \| Religion \| Religion \| Religion \| Religion \| \| \| Christianity \| 470 \| 86.1 \| \| \| Islam \| 76 \| 13.9 \| \| Residence \| Residence \| Residence \| Residence \| \| \| University hostel \| 508 \| 93.0 \| \| \| Off campus \| 38 \| 7.0 \| \| Fathers’ occupation \| Fathers’ occupation \| Fathers’ occupation \| Fathers’ occupation \| \| \| Skilled \| 271 \| 49.6 \| \| \| Semi-skilled \| 209 \| 38.3 \| \| \| Unskilled \| 66 \| 12.1 \| \| Mothers’ occupation \| Mothers’ occupation \| Mothers’ occupation \| Mothers’ occupation \| \| \| Skilled \| 277 \| 50.7 \| \| \| Semi-skilled \| 236 \| 43.2 \| \| \| Unskilled \| 33 \| 6.0 \| \| Monthly allowance (N) \| Monthly allowance (N) \| Monthly allowance (N) \| Monthly allowance (N) \| \| \| ≤ 10,000 \| 171 \| 31.3 \| \| \| 10,001-20,000 \| 341 \| 62.5 \| \| \| ≥ 20,001 \| 34 \| 6.2 \| \| Cardiovascular disease risk factors \| Cardiovascular disease risk factors \| Cardiovascular disease risk factors \| Cardiovascular disease risk factors \| \| \| Overweight/obese \| 113 \| 20.7 \| \| \| Unhealthy diet \| 466 \| 85.3 \| \| \| Currently smoking \| 9 \| 1.6 \| \| \| Physical inactivity \| 516 \| 94.5 \| \| \| Abdominal obesity \| 18 \| 3.3 \| \| \| Alcohol use \| 20 \| 3.7 \| \| \| Clustering risk factors (≥ 2) \| 128 \| 23.4 \| The various CVD risk factors and their clustering are shown in Table 1. These included current smoking ($1.6\%$), abdominal obesity ($3.3\%$), alcohol consumption ($3.7\%$), overweight/obesity ($20.7\%$), unhealthy diet ($85.3\%$), and physical inactivity ($94.5\%$), whereas $23.4\%$ had at least two of these CVD risk factors. Figure 1 shows the number of CVD risk factors and their clustering by sex. Most respondents had one CVD risk factor ($70.1\%$), followed by two risk factors ($20.7\%$), three risk factors ($2.4\%$), and four risk factors ($0.4\%$); $6.4\%$ had none of the risk factors studied. Figure 1: Clustering of cardiovascular diseases (CVD) risk factors among newly admitted undergraduate students at the University of Ibadan, Nigeria. Table 2 shows the bivariate analysis of the factors associated with CVD risk factors and their clusters. The clustering of CVD risk factors was significantly higher in women than in men ($P \leq 0.05$) and higher in Christians than in Muslims ($P \leq 0.05$). Table 3 shows the multivariate analysis of the predictors of CVDs and their clusters. Women were twice as likely as male respondents to be overweight/obese (adjusted odds ratio, AOR = 2.2; $95\%$ CI = 1.41–3.43; P value = 0.001). Muslims were 5.1 times more likely to smoke than Christians (AOR = 5.1; $95\%$ CI = 1.32–19.37; P value = 0.018). Respondents whose parents were skilled workers were 3.5 times more likely to be physically inactive than respondents whose parents were unskilled workers (AOR = 3.5; $95\%$ CI = 1.24–9.85; P value = 0.018). ## DISCUSSION To our knowledge, our study is one of the first to explore the clustering of CVD risk factors in newly admitted students in this part of the continent. We found that clustering of two CVD risk factors was observed in one-fifth of the students. The most common of these risk factors were physical inactivity, unhealthy diet, and overweight/obesity, whereas alcohol consumption, smoking, and abdominal obesity were rare in our study population. The high response rate ($98.0\%$) observed in this study is consistent with similar studies in Nigeria., 25 and Ghana. 12 The proportion of women who participated in this study was higher than that of male respondents. The female predilection in our study corresponds with the reports of Ekerand colleagues among high school students in Turkey. 26 and a national survey of students in various tertiary institutions between 2010 and 2015 in Nigeria. 27 An unhealthy dietary pattern was evident among undergraduate students in this study, which corresponds to previous studies in Nigeria 28,29 A higher rate of unhealthy dietary lifestyle among women supports the report by Omage and Omuemu. 29 In line with the report on students in Bangladesh, 30 we found that students who lived off campus had poorer dietary patterns than students who lived in a university dormitory. One plausible reason for this is that students who live off campus prepare their own food, which is better than what is available in school cafeterias. Others live with family members or relatives who prepare the food for them. The prevalence of current smoking was low in our study ($1.6\%$), compared with previous studies among adolescents and young adults that found $6.8\%$ in Ethiopia, 31 We found that students who lived off campus had lower dietary behaviors than students who lived in a university dormitory. One plausible reason is that students living off campus prepare their own food, which is better than what is available in school cafeterias. Others live with family members or relatives who prepare the food for them. The prevalence of current smoking in our study was low ($1.6\%$), compared with previous studies among adolescents and young adults that found $6.8\%$ in Ethiopia, 32 $9.0\%$ in Oman, 33 $11.1\%$ in New Zealand 34 and $27.9\%$ in Turkey. 35 The low prevalence of smoking observed in our study is likely due to risky behaviors such as smoking are reportedly more common among students in higher grades students than newly admitted students. 7 Muslims were more likely to smoke than Christians in our study, which contradicts the report by Hussain and colleagues. 36 Nonetheless, the teachings of both religions have been reported to influence the behavior of their believers and to condemn smoking and alcohol consumption. 37,38 Some authors have argued that people tend not to disclose their correct smoking status despite assurances of confidentiality of data collected. 39 Hence, the reported smoking status should be interpreted with this in mind. This study also show that a small proportion of the study population was highly engaged in physical activity. This is very similar to the findings in the study by Eleojo et al., who proved that only a small proportion of the study population was physically active. 40 A previous multicentre study revealed that a proportion of male respondents were physically inactive compared with female respondents, 40 which corresponds to our findings. Our study found an association between physical inactivity and father's occupation. This supports the assertion that parental factor influences the level of physical activity of their children. Parents' occupation and type of living environment have been seriously implicated. 41,42 The low prevalence of obesity in our study is in contrast to the report of Sabageh and Ojofeitimi with higher prevalence. 43 A study also showed that the prevalence of abdominal obesity was high in the study population. 11 A study showed that the prevalence of abdominal obesity as determined by the waist circumference, was higher in male respondents than in female respondents. 44 This present study also revealed that abdominal obesity is significantly related to gender of which male respondents have a higher proportion of central obesity than female respondents. This study revealed a low prevalence of alcohol intake among the study population. Another study was done by Alex-Hart and colleagues showed that the prevalence of alcohol consumption was $28.6\%$ significantly higher prevalence from this study. 45 Another study reported a higher proportion of alcohol consumption among males students compared to their female colleagues. 46 Several studies revealed that excessive alcohol consumption is much more common among undergraduate students who reside in the university hostels away from their permanent domicile. This is very similar to the findings reported in this study which shows that students who live on campus had a higher proportion of alcohol intake compared to those who live off campus. 47 This study revealed that most of the respondents had at least two risk factors. This study also corresponds to another study done by Olawuyi and Adeoye, which revealed that a higher proportion of the population had at least two non-communicable disease risk factors. 48 The clustering risk factors have been associated with a higher risk of developing CVDs. 49 A study conducted among young adults in southwest Nigeria reported that there is no significant difference in clustering risk factors for CVDs between the males and females who participated in the study, which is in contrast with the finding of this study. Another study conducted among university students in Libya revealed that there was a significant relationship between clustering risk factors and socio-demographic characteristics of university students. 50 A study conducted among young adults in Yaoundé, Cameroon revealed that the prevalence of some major CVD risk factors increase due to a lack of a appropriate behavioral approach towards healthy living. 51 A previous study also showed a higher proportion of obesity among the females' respondents compare to the male respondents. 52 It is vital to know the relationship between socio-demographic characteristics such as age, gender, and clustering risk factors for CVDs explicitly because it will help to control and prevent CVDs especially among undergraduate students. 53 A previous study revealed that male undergraduate students had lower awareness of the clustering risk factors to CVD compared to their female counterparts. 54 A study conducted among Nigeria undergraduate students revealed that there was no significant difference between the risk factors for CVD among the gender stratification. 55 The findings of our study suggest there is urgent need for public health strategies that will improve physical activity and consumption of healthy diets. This should be done in corroboration with the university management. ## Implications of the findings of the study Note that most young adults do not take care of their health before coming to university. The missed opportunities that result from poor health facilities for young people could be addressed at the university where health facilities exist, through the approach prevention strategy developed by Leavell: ## Strengths and limitations The strength of our study was that it was interviewer-administered, which explains the high response rate and limited missing data. Nevertheless, like any other cross-sectional study, our study shows an association and not a causal relationship. Additionally, our study investigated the level of clustering in a selected university in Ibadan, Nigeria. Additionally, this study focused on first-year students from the selected departments. Therefore, these results may not be generalizable to other universities in Ibadan or Nigeria. ## CONCLUSION Our study's clustering of cardiovascular risk factors was unexpectedly high, with high levels of physical inactivity and an unhealthy diet. The results of this study underscore several issues that need to be considered in reducing the risk of CVD in first-year students at the University of Ibadan. ## References 1. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet (2020.0) 396 1204-1222. DOI: 10.1016/S0140-6736(20)30925-9 2. Andersson C, Vasan RS. Epidemiology of cardiovascular disease in young individuals. Nat Rev Cardiol (2018.0) 15 230-240. DOI: 10.1038/nrcardio.2017.154 3. Odunaiya NA, Grimmer K, Louw QA. High prevalence and clustering of modifiable CVD risk factors among rural adolescents in southwest Nigeria: implication for grass root prevention. 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Gender Differences in Physical Activity among the University Students in the Visegrad (V4) Countries. Studia Educatio Artis Gymnasticae (2017.0) 62 5-17. DOI: 10.24193/subbeag.62(1).01 41. Pyky R, Puhakka S, Ikäheimo TM. Parental Factors Related to Physical Activity among Adolescent Men Living in Built and Natural Environment: A Population-Based MOPO Study. J Environ Public Health (2021.0) 2021 3234083-3234083. DOI: 10.1155/2021/3234083 42. Pluta B, Korcz A, Krzysztoszek J, Bronikowski M, Bronikowska M. Associations between adolescents’ physical activity behavior and their perceptions of parental, peer and teacher support. Arch Public Health (2020.0) 78 106-106. DOI: 10.1186/s13690-020-00490-3 43. Sabageh A, Ojofeitimi EO. Prevalence of obesity among adolescents in Ile-Ife, Osun state, Nigeria using body mass index and waist hip ratio: A comparative study. Niger Med J (2013.0) 54 153-156. DOI: 10.4103/0300-1652.114566 44. Csongová M, Volkovová K, Gajdoš M. Gender-associated differences in the prevalence of central obesity using waist circumference and waist-to-height ratio, and that of general obesity, in Slovak adults. Cent Eur J Public Health (2018.0) 26 228-233. DOI: 10.21101/cejph.a4719 45. Alex-Hart BA, Opara PI, Okagua J. Prevalence of alcohol consumption among secondary school students in Port Harcourt, Southern Nigeria. Niger J Paed (2015.0) 42 39-45. DOI: 10.4314/njp.v42i1.9 46. Ajayi AI, Owolabi EO, Olajire OO. Alcohol use among Nigerian university students: prevalence, correlates and frequency of use. BMC Public Health (2019.0) 19 752-752. DOI: 10.1186/s12889-019-7104-7 47. Reznik A, Isralowitz R, Gritsenko V, Khalepo O, Kovaleva Y. Russian Federation university student alcohol use: Smolensk City-a case example. J Ethn Subst Abuse (2019.0) 18 549-557. DOI: 10.1080/15332640.2017.1417188 48. Olawuyi AT, Adeoye IA. The prevalence and associated factors of non-communicable disease risk factors among civil servants in Ibadan, Nigeria. PloS One (2018.0) 13. DOI: 10.1371/journal.pone.0203587 49. Peters SAE, Wang X, Lam TH. Clustering of risk factors and the risk of incident cardiovascular disease in Asian and Caucasian populations: results from the Asia Pacific Cohort Studies Collaboration. BMJ Open (2018.0) 8. DOI: 10.1136/bmjopen-2017-019335 50. El Ansari W, Khalil KA, Ssewanyana D, Stock C. Behavioral risk factor clusters among university students at nine universities in Libya. AIMS public health (2018.0) 5 296-311. DOI: 10.3934/publichealth.2018.3.296 51. Nansseu JR, Kameni BS, Assah FK. Prevalence of major cardiovascular disease risk factors among a group of sub-Saharan African young adults: a population-based cross-sectional study in Yaoundé, Cameroon. BMJ Open (2019.0) 9. DOI: 10.1136/bmjopen-2019-029858 52. Fawibe AE, Shittu AO. Prevalence and characteristics of cigarette smokers among undergraduates of the University of Ilorin, Nigeria. Niger J Clin Pract (2011.0) 14 201-205. DOI: 10.4103/1119-3077.84016 53. Peltzer K, Pengpid S. Prevalence, risk awareness and health beliefs of behavioural risk factors for cardiovascular disease among university students in nine ASEAN countries. BMC Public Health (2018.0) 18 237-237. DOI: 10.1186/s12889-018-5142-1 54. Güneş FE, Bekiroglu N, Imeryuz N, Agirbasli M. Awareness of cardiovascular risk factors among university students in Turkey. Prim Health Care Res Dev (2019.0) 20 e127-e127. DOI: 10.1017/S146342361900063X 55. Johnson OE, Adedoyin RA, Awotidebe TO. Cardiovascular risk among undergraduates in a Nigerian University. International Journal of Public Health and Epidemiology (2013.0) 2 85-89
--- title: 'The impact of bariatric and metabolic surgery on the morbidity and mortality of patients infected during the COVID-19 pandemic: a retrospective cohort study' authors: - Luiz Henrique Sala de Melo Costa - Luiz Filipe Sala de Melo Costa - Gabriela Rezende Kachan - João Kleber de Almeida Gentile - Raul Andrade Mendonça - Marcela Ralin de Carvalho Deda Costa - Jurandir Marcondes Ribas journal: São Paulo Medical Journal year: 2022 pmcid: PMC10005465 doi: 10.1590/1516-3180.2021.0952.R2.11052022 license: CC BY 4.0 --- # The impact of bariatric and metabolic surgery on the morbidity and mortality of patients infected during the COVID-19 pandemic: a retrospective cohort study ## ABSTRACT ### BACKGROUND: Since the impact of the coronavirus disease 2019 (COVID-19) pandemic in March 2020, several studies have shown a strong relationship between obesity and severe cases of COVID-19. It is imperative to assess whether bariatric surgery exerts a protective effect in such cases. ### OBJECTIVE: This study aimed to assess the impact of bariatric surgery on the morbidity and mortality in obese patients during the COVID-19 pandemic. A comprehensive search was performed using the PubMed and Cochrane Library databases. ### DESIGN AND SETTING: Retrospective cohort studies conducted in the Faculdade de Medicina da Universidade Cidade de São Paulo, São Paulo (SP), Brazil. ### METHODS: The search comprised the following descriptors: “bariatric, surgery, COVID-19”. Current retrospective cohort studies that examined the influence of bariatric surgery on the morbidity and mortality of obese patients during the COVID-19 pandemic were considered eligible. ### RESULTS: After removing duplicates, 184 studies were obtained from the databases. Of these, 181 were excluded from the analysis as they did not meet the eligibility criteria. Patients undergoing postoperative follow-up of bariatric surgery had a similar probability of SARS-CoV-2 infection compared to the general population, and persistent comorbidities were associated with an increased risk and severity of infection. ### CONCLUSION: Bariatric surgery has a protective effect against severe COVID-19 in the obese population, bringing the prevalence of severe disease cases to levels equivalent to those of the nonobese general population, with a positive impact on morbidity and mortality. ## INTRODUCTION In March 2020, the World Health Organization (WHO) declared the coronavirus disease 2019 (COVID-19) a pandemic. Since then, the impact of this infection on the public and private health systems of many countries has become evident. 1 The overcrowding of intensive care beds has led to the cancellation of elective surgeries, as there has been an increasing demand for professionals and and resources to treat infected patients. 2,3 In this context, Hussain et al. 4 presented a flowchart scaling priority among candidates for elective and revision procedures during the pandemic. Patients with severe obesity, comorbidities, or surgical complications should be prioritized when performing procedures. Outpatient activities began to be performed through tele-medicine, and only urgent procedures such as early and late surgical complications remained in the usual routine. Studies indicate obesity as an isolated risk factor for severe cases of COVID-19. 4–6 In addition, biochemical and endocrine factors related to obesity, such as type 2 diabetes and insulin resistance, are worse prognostic factors in infected patients. 7,8 Therefore, it has become imperative to evaluate whether bariatric surgery exerts a protective effect against severe covid-19 conditions. Retrospective studies have evaluated outcomes in patients with previous bariatric surgery infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) regarding the severity of the disease, need for intensive care and impact on mortality. 6,9,10 However, there remains a lack of controlled clinical trials or other prospective studies evaluating such parameters. ## OBJECTIVE The present study aimed to evaluate, through a literature review, the impact of bariatric surgery on the morbidity and mortality of obese patients during the COVID-19 pandemic in reference centers inside and outside Brazil. ## Data sources and surveys A comprehensive search was conducted using the PubMed and Cochrane Library databases. The search strategies comprised the following descriptors: “bariatric, surgery, COVID-19”. These have been adapted for use in various databases. The access routes to the descriptions of the studies used in this article are presented in Table 1. Table 1 \| Author (year) \| Date searched \| Article title \| Journal \| Search terms \| Databases \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Aminian et al. 9 (2020) \| September 15, 2021 \| Association of prior metabolic and bariatric surgery with severity of coronavirus disease 2019 (COVID-19) in patients with obesity \| Official Journal of the American Society for Bariatric Surgery \| Bariatric surgery;Obesity;COVID-19;Body mass index \| PubMed \| \| Bel Lassen et al. 10 (2021) \| September 15, 2021 \| COVID-19 and its Severity in Bariatric Surgery-Operated Patients \| Obesity (Silver Spring) \| Bariatric surgery;Obesity;COVID-19;Body mass index \| PubMed \| \| Uccelli et al. 6 (2020) \| September 15, 2021 \| COVID-19 and Obesity: Is Bariatric Surgery Protective? Retrospective Analysis on 2,145 Patients Undergone Bariatric-Metabolic Surgery from High Volume Center in Italy (Lombardy) \| Obesity Surgery \| Bariatric surgery;Obesity;COVID-19;Body mass index; \| PubMed \| Current retrospective cohort studies that examined the influence of bariatric surgery on the morbidity and mortality of obese patients during the COVID-19 pandemic were eligible for this review without restrictions on dates and languages. Further inclusion criteria included studies that evaluated adult patients over 18 and under 65 years of age, obese patients who underwent bariatric surgery, and those infected by SARS-CoV-2, in reference centers inside and outside Brazil. Studies with patients outside the age group of 18 to 65 years, those that did not deal with bariatric surgery, and those performed outside the pandemic period were excluded. ## Data extraction Data extraction was performed using a standardized data extraction form. The data extracted from all studies included study details, demographic data of participants, and available information on the interventions used. ## Search results A total of 186 studies were obtained from the surveyed databases. After removing duplicates, 184 studies were retained for the analysis. Of these, 181 were excluded after analyzing titles, abstracts, and full texts because they did not meet the eligibility criteria. Only three studies were included in this review (Figure 1). The characterization of the participants included in the studies is shown in Table 2. Figure 1: Flow diagram of the results. TABLE_PLACEHOLDER:Table 2 A description of studies evaluating the impact of bariatric surgery on the morbidity and mortality of obese patients during the COVID-19 pandemic is shown in Table 3. Table 3 \| Study \| Aminian et al. 9 \| Bel Lassen et al. 10 \| Uccelli et al. 6 \| \| --- \| --- \| --- \| --- \| \| Participants \| n = 363 tested positive for COVID-19Group with previous surgery: 33; Group of non-operated: 330 \| n = 738; All underwent bariatric surgeryGroup “probably infected”: 62;Group “probably not infected”: 676 \| n = 2,145; All underwent bariatric surgery \| \| Goals \| Investigate the relationship between previous metabolic surgery and the severity of COVID-19 in patients with severe obesity. \| Estimate the prevalence of COVID-19 and evaluate factors associated with the incidence and severity of the disease in patients who underwent bariatric surgery. \| Investigate the incidence of SARS-CoV-2 infection and its severity in patients who underwent bariatric surgery. \| \| Collection procedures \| A search was performed in medical records of the institution that conducted the study for patients who tested positive in RT-PCR for COVID-19, evaluating the rate and time of hospitalization, need for ICU, mechanical ventilation, dialysis, and mortality in patients who tested positive in RT-PCR for COVID-19, evaluating the rate and time of hospitalization, need for ICU, mechanical ventilation, dialysis and mortality. \| A standardized questionnaire was conducted through telephone calls in which probable symptoms of COVID-19 were questioned, such as anosmia, fever, rhinorrhea, odynophagia, or patients who tested positive for the disease. In addition, a medical record search was performed for anthropometric and laboratory data before and after the patients. \| A questionnaire was sent to patients previously submitted to bariatric surgery in which age, gender, BMI, origin, comorbidities, and type of surgery were questioned, and they were asked about the main symptoms of COVID-19, and occurrence of hospitalization and ICU admission. \| \| Main findings \| The mean preoperative BMI in the group with previous surgery was 49.1 ± 8.8kg/m2, decreasing to 37.2 ± 7.1 kg/m2 at the time of testing for COVID-19. The mean BMI of the non-operated group was 46.7 ± 6.4 kg/m2.Six patients (18.2%) from the group submitted to surgery, and 139 patients (42.1%) from the non-operated group were admitted to the hospital (P = 0.013).43 patients (13%) from the non-operated group required ICU admission (P = 0.021). 22 patients (6.7%) required mechanical ventilation.Five patients (1.5%) underwent dialysis.Eight patients (2.4%) died.In the group with previous surgery, none of these four outcomes were identified. \| Patients had a mean age of 50 ± 12.3 years, with most being female (78.3%) and 44% having type 2 diabetes before surgery. The most used surgical technique was gastric bypass (54.4%), followed by sleeve gastrectomy (45.0%). The mean postoperative time at collection was 3.7 ± 2.7 years.There was no difference in the surgical technique outcomes between the groups. The mean postoperative time was significantly longer in the “probably infected” group, with a considerably higher proportion of persistently diabetic patients than in the “probably not infected” group. \| All patients underwent elective bariatric surgery. The mean preoperative BMI was 44 ± 6.8 kg/m2 with a reduction to 29.3 ± 5.5 kg/m2 in the postoperative period. The main technique used was laparoscopic sleeve gastrectomy (82.4%). The reduction in the number of comorbidities was almost entirely statistically significant.A total of 181 patients (8.4%) reported at least one symptom related to COVID-19. Nevertheless, only 26 cases (1.2%) were tested, and only 13 individuals (0.6%) tested positive. Six patients (0.3%) were admitted to hospital units; two patients (0.1%) required ICU with mechanical ventilation. The mean length of hospital stay was 23 ± 13 days. \| \| Conclusions \| The study identified that previous bariatric surgery is associated with lower hospitalization rates and the need for ICU for patients infected with SARS-CoV-2. \| Patients under postoperative follow-up of bariatric surgery presented a probability of SARS-CoV-2infection similar to that of the general population. The persistence of type 2 diabetes and the presence of lower BMI are associated with increased risk and severity of SARS-CoV-2 infection. \| Because the rate of hospitalization and need for ICU of the patients evaluated was equivalent to those of the general nonobese population, the study concludes that bariatric surgery can be considered a protective factor for severe acute respiratory syndrome caused by SARS-CoV-2 infection \| ## DISCUSSION Studies indicate obesity as an isolated risk factor for severe cases of COVID-19. 4–6 In addition, biochemical and endocrine factors related to obesity, such as type 2 diabetes and insulin resistance, are associated with a worse prognosis in infected patients. 7,8,11 In this context, the publications evaluated in this study explore bariatric surgery as an intervention capable of serving as a protective factor against severe cases of COVID-19. 6,9,10 There is great heterogeneity between the methodology of the studies since the situation of social isolation itself made it impossible to conduct controlled clinical trials. The publication by Uccelli et al., 6 whose data collection was carried out from March to May 2020, presented many participants from several different areas of Italy, which allowed a global analysis of the involved population. However, there was a population bias as only patients who had already undergone surgery answered the questionnaire, and there was no control group of non-surgical patients. There was also a low testing rate with reverse transcription polymerase chain reaction (RT-PCR) ($1.2\%$), which may have underestimated the number of infected patients. Moreover, as the questionnaire was self-applicable online, seeking the most common symptoms of COVID-19, there was bias in the collection not being performed by an examiner trained to perform the necessary anamnesis. The study conducted by Aminian et al., 9 whose data collection was carried out between March and July 2020, analyzed patients who tested positive for COVID-19 through RT-PCR and anthropometric data extracted from the institution's medical records confirmed the reliability of the research. However, the major limitation of this study was the small number of patients with a history of previous bariatric surgery, which resulted in a longer confidence interval and may have influenced the statistical analysis of the results. Moreover, as only six operated patients were hospitalized for COVID-19, laboratory, radiological, and oxygenation data were unavailable for most patients in this group; therefore, they were not included in the statistical analysis. Bel Lassen et al. 10 performed data collection between March and May 2020. Similar to the study by Aminian et al., 9 this study used anthropometric data collected from medical records with good reliability. Additionally, a large number of participants were included in the study. However, the postoperative time among the patients was extremely heterogeneous, with an interval of up to 16 years. This introduced a population bias that may have interfered with the results. Similar to the study by Uccelli et al., 6 a self-administered questionnaire was made available, which may have been subject to different interpretations by individuals regarding the symptoms of COVID-19. Despite the heterogeneity in the methodology employed by the different authors and the complicating factors between data collection and statistical analysis of results, the three publications concluded that the prevalence of severe COVID-19 conditions in patients in the postoperative period of bariatric and metabolic surgery does not differ from the prevalence in the general nonobese population. From the perspective of countries' health systems that have managed COVID-19 in the long term, it is necessary to develop controlled clinical trials with a good methodology to assess whether such results are reproducible and whether there are other clinical implications in carrying out such procedures. ## CONCLUSION Based on the results of the analyzed studies, even with the reservations described regarding the methodological limitations employed, it can be concluded that bariatric surgery exerts a protective effect against severe cases of COVID-19 in the obese population, with a positive impact on morbidity and mortality. ## References 1. 1 World Health Organization Coronavirus disease 2019 (COVID-19): Situation Report - 52Available from: https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200312-sitrep-52-covid-19.pdf Accessed in 2022 (May 4). Coronavirus disease 2019 (COVID-19): Situation Report - 52 2. Spinelli A, Pellino G. COVID-19 pandemic: perspectives on an unfolding crisis. Br J Surg (2020.0) 107 785-787. DOI: 10.1002/bjs.11627 3. Iacobucci G. Covid-19: all non-urgent elective surgery is suspended for at least three months in England. BMJ (2020.0) 368. DOI: 10.1136/bmj.m1106 4. Hussain A, Mahawar K, El-Hasani S. The Impact of COVID-19 Pandemic on Obesity and Bariatric Surgery. Obes Surg (2020.0) 30 3222-3223. DOI: 10.1007/s11695-020-04637-7 5. Nakeshbandi M, Maini R, Daniel P. The impact of obesity on COVID-19 complications: a restrospective cohort study. Int J Obes (Lond) (2020.0) 44 1832-1837. DOI: 10.1038/s41366-020-0648-x 6. Uccelli M, Ceasana GC, De Carli SM. Covid-19 and Obesity: Is Bariatric Surgery Protective? Retrospective Analysis on 2145 Patients Undergone Bariatric-Metabolic Surgery from High Volume Center in Italy (Lombardy). Obes Surg (2021.0) 31 942-948. DOI: 10.1007/s11695-020-05085-z 7. Vas P, Hopkins D, Feher M, Rubino F, B Whyte M. Diabetes, obesity and COVID-19: A complex interplay. Diabets Obes Metab (2020.0) 22 1892-1896. DOI: 10.111/dom.14134 8. Finucane F, Davenport C. Coronavirus and Obesity: Could Insulin Resistance Mediate the Severity of Covid-19 Infection?. Front Public Health (2020.0) 8 184-184. DOI: 10.3389/fpubh.2020.00184 9. Aminian A, Fathalizadeh A, Tu C. Association of prior metabolic and bariatric surgery with severity os coronavirus disease 2019 (COVID-19) in patients with obesity. Surg Obes Relat Dis (2021.0) 17 208-214. DOI: 10.1016/j.soard.2020.10.026 10. Bel Lassen P, Poitou C, Genser L. COVID-19 and its Severity in Bariatric Surgery-Operated Patients. Obesity (Silver Spring) (2021.0) 29 24-28. DOI: 10.1002/oby.23026 11. Lockhart S, O’Rahilly S. When Two Pandemics Meet: Why Is Obesity Associated with Increased COVID-19 Mortality?. Med (N Y) (2020.0) 1 33-42. DOI: 10.1016/j.medj.2020.06.005
--- title: 'Consumption of Coffee and Tea Is Associated with Macular Retinal Nerve Fiber Layer Thickness: Results from the UK Biobank' authors: - Yixiong Yuan - Gabriella Bulloch - Shiran Zhang - Yanping Chen - Shaopeng Yang - Wei Wang - Zhuoting Zhu - Mingguang He journal: Nutrients year: 2023 pmcid: PMC10005476 doi: 10.3390/nu15051196 license: CC BY 4.0 --- # Consumption of Coffee and Tea Is Associated with Macular Retinal Nerve Fiber Layer Thickness: Results from the UK Biobank ## Abstract Coffee and tea drinking are thought to be protective for the development and progression of neurodegenerative disorders. This study aims to investigate associations between coffee and tea consumption with macular retinal nerve fiber layer (mRNFL) thickness, a marker of neurodegeneration. After quality control and eligibility screening, 35,557 out of 67,321 United Kingdom (UK) Biobank participants from six assessment centers were included in this cross-sectional study. In the touchscreen questionnaire, participants were asked how many cups of coffee and tea were consumed daily on average over the last year. Self-reported coffee and tea consumption were divided into four categories including 0 cup/day, 0.5–1 cups/day, 2–3 cups/day, and ≥4 cups/day, respectively. The mRNFL thickness was measured by the optical coherence tomography (Topcon 3D OCT-1000 Mark II) and automatically analyzed by segmentation algorithms. After adjusting for covariates, coffee consumption was significantly associated with an increased mRNFL thickness (β = 0.13, $95\%$ CI = 0.01~0.25), which was more prominent in those who drank 2~3 cups coffee per day (β = 0.16, $95\%$ CI = 0.03~0.30). The mRNFL thickness was also significantly increased in tea drinkers (β = 0.13, $95\%$ CI = 0.01~0.26), especially for those who drank more than 4 cups of tea per day (β = 0.15, $95\%$ CI = 0.01~0.29). The positive associations with mRNFL thickness, indicating that both coffee and tea consumptions had likely neuroprotective potentials. Causal links and underlying mechanisms for these associations should be explored further. ## 1. Introduction Coffee and tea have been enjoyed for centuries around the world [1], and it is estimated that more than ten and six billion tons of coffee [2] and tea [3] are consumed worldwide in 2021. Considering their volume of intake, if coffee or tea had any positive or negative medical benefit, they would impact public health enormously. Both coffee and tea are known to contain caffeine which is best known for its stimulating effects on cognition, attention, and wakefulness [4]. The discovery of other antioxidants such as flavonoids and polyphenols in coffee and tea [5] have led to the hypothesis that their intake may have neuroprotective benefits. Epidemiological studies have discovered coffee and tea drinkers have reduced odds of Parkinson’s disease [6] and dementia [7] but unfortunately medical evidence to support these benefits are limited [8]. To complicate things further, magnetic resonance imaging (MRI) studies show inconsistent results when comparing brain volume between consumers and non-consumers [9,10,11]. The retina is a tissue that extends from the central nervous system (CNS) and presents itself as a unique window for non-invasively detecting brain and vascular disease through optical coherence tomography (OCT) [12]. OCT studies indicate that retinal nerve fiber layer (RNFL) thinning is significantly associated with cognition [13], and states of neurodegeneration such as glaucoma [14], Parkinson’s disease [15], Alzheimer’s disease [16], and mild cognitive impairment [17]. With OCT now being performed routinely in hospital and community settings, and its ability to image the RNFL at a micron level, OCT’s potential dual purpose as a risk-stratification tool for neurodegenerative states should be considered [18]. Given that the RNFL reflects neurodegenerative changes, we intend to investigate associations between self-reported coffee and tea consumptions with OCT-measured macular retinal nerve fiber layer (mRNFL) thickness in a subgroup of United Kingdom (UK) Biobank participants. The combination of self-reported information and objective retinal measurements might bring additional evidence to support the neuroprotective potentials of these two beverages and provide novel insight into the prevention and treatment of neurodegenerative disorders. ## 2.1. Study Population The UK Biobank is a population-based cohort study, with more than half a million participants recruited from England, Scotland, and Wales. All participants were aged 40–69 years old at the time of recruitment and lived within twenty-five miles of assessment centers. The baseline visit (2006–2010) consisted of touchscreen questionnaires, verbal interviews, physical measurements, blood, and urine assays. From June 2009 to July 2010, a subgroup of participants from six designated assessment centers (Sheffield, Liverpool, Hounslow, Croydon, Birmingham, and Swansea) were invited to receive additional eye examinations including intraocular pressure, autorefraction, visual acuity, and macular OCT at baseline. The UK Biobank was conducted with ethics approval from the National Information Governance Board for Health and Social Care and North West Multicenter Research Ethics Committee (11/NW/0382), and was carried out in accordance with the Declaration of Helsinki. Informed consents and authorizations to access anonymous health records were obtained from all participants. Deidentified data were stored in the UK Biobank database, with personal identifiers kept separately under strict control with restricted access. Participants who completed baseline OCT measurements were included in this study. According to established standards for quality control, eyes with low signal strength (Q < 45), weak centration or segmentation indicators (poorest $20\%$) were excluded from analyses. To avoid interference from other ocular parameters, eyes with high refractive error (spherical equivalent [SE] >6 or <−6 diopters [D]), visual impairment (>0.1 logarithm of the minimum angle of resolution [logMAR]), or abnormal intraocular pressure (IOP) (≥22 or ≤5 mmHg) were also excluded. Considering the probable RNFL destructions secondary to neurodegenerative diseases, patients with glaucoma, other retinal disorders, multiple sclerosis, dementia, and Parkinson’s diseases were identified from participants’ medical history consisting of questionnaires, interviews, and inpatient diagnoses before baseline (Detailed in Table S1) and excluded. In addition, participants refusing to answer questions about coffee or tea consumption were further excluded. As for those with both eyes being deemed high quality, one eye was selected for analysis. ## 2.2. Coffee and Tea Consumption In the baseline questionnaire, participants were asked how many cups of coffee and tea were consumed daily on average over the last year. Participants’ answers were limited in the range of 0 to 99, and those who consumed more than ten cups of coffee or tea per day were required to confirm the accuracy of their answers. In the current study, daily amounts of coffee and tea consumption were further divided into four categories; 0 cup/day, 0.5–1 cups/day, 2–3 cups/day, and ≥4 cups/day. Additionally, coffee drinkers were asked which type of coffee was usually consumed or consumed the most. Participants accustomed to drinking instant coffee were categorized separately. Type of tea was not implicated in the touchscreen questionnaire. ## 2.3. Eye Examinations and OCT Measurements All participants in this study underwent visual acuity, autorefraction, IOP, and macular OCT measurements. Examinations were performed on both eyes, beginning with the right. Visual acuity was tested with 4-metre traditional LogMAR charts with refractive correction (spectacles or contact lens), if any. The refractive error was measured by autorefraction (Tomey RC5000; Nagoya, Japan). SE values were calculated based on autorefraction results (sphere degree + 0.5 × cylinder degree). IOP values were measured by the Ocular Response Analyzer (ORA, Reichert, Corp., Buffalo, NY, USA) which consisted of two consecutive measurements in one single test. The corneal-compensated IOP, a linear combination of the two measurements, was recommended by the previous literature and used in the current study [19]. Commercial spectral-domain OCT (Topcon 3D OCT-1000 Mark II; Topcon, Inc., Tokyo, Japan) obtained 6 × 6 mm2 macular volume scans on non-dilated eyes in dark rooms, with axial resolution of 6 µm. Each volume scan consisted of 512 A-scans × 128 B-scans and required about 3.7 s (18,000 A-scans/second). Six assessment centers used the same model of OCT devices and at least three trained technicians were assigned to each center. After acquisition, OCT images were submitted to UK Biobank servers and stored in a central repository. A custom image segmentation software, Topcon Advanced Boundary Segmentation (TABS) algorithm Version 1.6.1.1 (Topcon Advanced Biomedical Imaging Laboratory, Oakland, CA, USA), was used to perform automated location of fovea and segmentation of retinal layers. Validity (Overall border position differences: 0.82~3.45 µm) and reliability (Intraclass correlation: 0.942–0.993) of the TABS algorithm were reported in previous studies [20]. The mRNFL referred to bright zones between the inner limiting membrane and ganglion cell layer (Figure 1). In this study, the average mRNFL thickness across six subfields (superior, superior-temporal, superior-nasal, inferior, inferior-nasal, and inferior-temporal) was analyzed. ## 2.4. Covariates To control for potentially confounding variables, demographic, socioeconomic, lifestyle, and health-related covariates were included in this study. In brief, age at baseline assessment were divided into five categories, including <50 years, 50–54 years, 55–59 years, 60–64 years, and >64 years. The UK Biobank assessment center at which participant attended were automatically acquired. Townsend deprivation index (TDI) were assigned according to participants’ postal codes, which reflected the proportions of unemployment, crowding household, non-car ownership, and homelessness in corresponding output areas. Four quantiles were categorized in the ascending order for TDI (<−3.6, −3.6~−2.1, −2.1~0.6, and >0.6). Body mass index (BMI) were constructed from height and weight, which were measured by Seca 240 cm height measure (Seca Gmbh & Co. KG., Hamburg, Germany) and Tanita BC418MA body composition analyzer (Tanita Corp., Tokyo, Japan) at baseline respectively. In the touchscreen questionnaire, UK Biobank participants were asked about their ethnic group, including White, Mixed, Asian, Black, Chinese. Due to the small number of participants, the last five alternatives were assembled into others. The average total household income before tax were directly derived from questionnaires, including <GBP 18,000, GBP 18,000 to GBP 30,999, GBP 31,000 to GBP 51,999, GBP 52,000 to GBP 100,000, and >GBP 100,000. Educational qualifications reflected the highest diploma achieved which were divided into three categories including O levels or equivalent, A levels or equivalent, and college or university degree. Time spent on moderate to vigorous activity (MVPA) was categorized into four quantiles based on adapted questions from the short International Physical Activity Questionnaire [21]. Weighted by expended energy, MVPA time were transformed into metabolic Equivalent Task (MET) minutes/week and categorized into four quantiles. Sleep duration was derived from the average hours spent on both nocturnal sleep and daytime naps for a 24 h day in the last 4 weeks, which were further divided into four categories including <7 h, 7 h, 8 h, and >8 h. For smoking status, previous smokers and current smokers were distinguished from those who never smoked tobacco. Similarly, previous and current drinkers were also separated from those who never consumed alcohol. According to the consumption of different foods over the last year, diet patterns were determined as healthy or unhealthy diet in accordance with previous studies [7]. Seven components of healthy diet were defined (fruits, vegetables, and whole grains ≥ 3 servings/day; fish ≥ 2 servings/week; unprocessed red meats and refined grains ≤ 1.5 servings/week; processed meats ≤ 1 serving/week). Participants who met the definitions of four or more components were considered to have a healthy diet. In addition, habitual intake of sweeten beverages or foods were determined based on participants’ replies and included in this study. For health-related covariates, systemic comorbidities including cardiovascular diseases, hypertension, and diabetes which were likely associated with coffee and tea consumption were also identified based on participants’ inpatient records before baseline using International Classification of Diseases-10 (ICD-10) codes. As the complement to inpatient records, systemic comorbidities were also identified if there were corresponding medical history in the touchscreen questionnaire or verbal interview. Using non-fasting venous blood samples, baseline high density liptein (HDL) cholesterol and low density liptein (LDL) cholesterol concentrations were analyzed by Beckman Coulter AU5800 (Beckman Coulter Inc., Brea, CA, USA). Participants with excessive low HDL cholesterol level (<1.04 mmol/L) and high LDL cholesterol level (>3.37 mmol/L) were categorized into abnormal groups. For covariates containing missing or unavailable values, an independent category was set and kept in the analysis. All UK Biobank fields used to retrieve baseline covariates are described in Table S1. ## 2.5. Study Limitations The main weakness of this study was its retrospective and cross-sectional design, which limited any casual inference. Self-reported coffee and tea consumption obtained from participants’ questionnaires determined that recall bias was inevitable and it was difficult to quantify the exact intake of caffeine or other antioxidants using precise units such as milligram. This limitation also existed in other covariates derived from questionnaires and interviews. Furthermore, the outcome, mRNFL thickness, was measured by different devices and examiners in six assessment centers. Despite standardized training and supervision, the distributed measurement might still magnify random errors and weaken statistical significance. ## 2.6. Statistical Analyses Baseline characteristics were expressed as number (percentage) for categorical covariates and mean (standard deviation, SD) for continuous covariates. Chi-square tests, Student t-tests, and analyses of variance compared categorical and continuous characteristics among participants with differing frequency of coffee and tea consumption (0, 0.5–1, 2–3 and ≥4 cups/day). After adjusting for demographics, socioeconomic, medical factors, ocular parameters, and lifestyle, multivariable linear regression models evaluated the association of coffee and tea consumption with average mRNFL thickness, respectively. Due to the addition of non-dairy creamers and hydrogenated vegetable oils in some instant coffee ingredient lists, it was a concern that instant coffee could contain more trans fatty acids (TFA), which is associated with numerous systemic diseases [22]. Therefore, regular intake of instant coffee was further included in the multivariable models as a confounding factor. Restricted cubic spline (RCS) model explored potential non-linear associations between coffee and tea consumption (cups/day) with mRNFL thickness, with three knots at the 10th, 50th, and 90th percentiles. Sensitivity analyses were performed in different age subgroups (≤60 and >60 years) and gender subgroups (female and male). All p values were two-sided and significance was considered when $p \leq 0.05.$ All statistical analyses were carried out using STATA 15.1 (StataCrop, College Station, TX, USA). ## 3. Results Of the 67,321 participants who completed OCT examinations at baseline, mRNFL thicknesses were available in 67,135 participants. After quality control and exclusion of diseases which may cause mRNFL thinning, a total of 35,557 eligible participants were included in this study (Figure 2). Coffee and tea were consumed by $78\%$ and $86\%$ of participants respectively. Distributions of covariates across coffee and tea drinkers, and non-coffee and non-coffee drinkers are outlined in Table 1. Comparison of covariates stratified by daily cups of coffee and tea are provided in Table S2. Most covariates were significantly different between coffee and non-coffee drinkers, except for CVD and SE. As for tea drinkers, no significant differences were detectable among sex, income, ethnic background, education achievement, diabetes, CVD, hypertension, LDL, SE, and IOP with reference to non-tea drinkers. Multivariable linear models found that coffee consumption was not associated with mRNFL thickness after adjusting for demographic (age, sex, and assessment center) and socioeconomic covariates (TDI, household income, ethnic background, and educational qualification) in Model 1 (Table 2). Further adjustments in life-style and health-related covariates in Model 2 confirmed no significant association between coffee drinking and mRNFL thickness (Table 2). In Model 3, coffee consumption was found to be significantly associated with an increased mRNFL thickness (β = 0.13, $95\%$ CI = 0.01~0.25; Table 3). Within coffee drinkers, the association with mRNFL thickness was only significant in those who drank 2~3 cups of coffee per day (β = 0.16, $95\%$ CI = 0.03~0.30). These findings were supported by RCS models (Figure 2), with an inverted U-shape association found between coffee drinking and mRNFL thickness (p for non-linear = 0.01). Sensitivity analyses (Tables S3 and S4) indicated associations between coffee consumption and mRNFL thickness was not affected by age and gender groups (All p for interaction > 0.05). Comparison of participant characteristics stratified by habitual intake of instant coffee is provided in Table S5. The mRNFL thickness was significantly thinner in instant coffee drinkers than those that did not drink instant coffee (28.36 µm vs. 28.63 µm, $p \leq 0.05$). On the basis of Model 2, multivariable linear models further took into account the habitual intake of instant coffee (Model 3; Table 3), which was significantly associated with a reduced mRNFL thickness (β = −0.19, $95\%$ CI = −0.29~−0.10). In Model 1 adjusting for demographic and socioeconomic factors, tea consumption was associated with an increased mRNFL thickness (β = 0.17, $95\%$ confidence intervals [$95\%$ CI] = 0.04~0.29), particularly in those who consumed ≥4 cups of tea per day (β = 0.16, $95\%$ CI = 0.01~0.30) (Table 2). Its association with mRNFL thickness remained statistically significant in Model 2 (β = 0.14, $95\%$ CI = 0.01~0.26). Tea consumption was also significantly associated with an increased mRNFL thickness in Model 3 (β = 0.13, $95\%$ CI = 0.01~0.26), and particularly for those who consumed ≥4 cups of tea per day (β = 0.15, $95\%$ CI = 0.01~0.29, p for trend = 0.03). The RCS model (Figure 3) indicated mRNFL thickness linearly increased with tea intake (p for non-linear = 0.29). Sensitivity analysis (Tables S3 and S4) indicated associations between tea consumption and mRNFL thickness were not affected by age and gender groups (All p for interaction > 0.05). No significant interaction was observed between coffee and tea consumption with the mRNFL thickness in Model 3 (p for interaction = 0.84). ## 4. Discussion Based on OCT measurements, this study is the first to link self-reported coffee and tea consumption with retinal markers of neurodegeneration in a large real-world population. Our results demonstrate mRNFL was significantly thicker amongst coffee and tea drinkers, which was most prominent in participants drinking 2~3 cups of coffee per day and four or more cups of tea per day. In particular, an inverted U-shape association was observed between daily coffee consumption and mRNFL thickness in RCS analysis. We also found that the intake of instant coffee was associated with a reduced mRNFL thickness, which was independent from the magnitude of coffee and tea consumption. The significant findings support neuroprotective potentials of these two beverages and warrant further studies to validate their effects on neurodegenerative diseases. Considering that mRNFL correlates with neurodegenerative states [14,15,16] and cognition [17], this study provides evidence for the assertion that coffee and tea may have neuroprotective potential. As earlier stated, 2–3 cups of coffee per day or >4 cups of tea were significantly associated with thicker mRNFL. This is in accordance with numerous previous studies, including a population-based cohort in Finland that reported a $60\%$ risk reduction for Parkinson’s diseases in participants who drank more than five cups of coffee or three cups of tea per day [6]. The inverted U-shape association between coffee and mRNFL thickness further corroborates results from a previous meta-analysis, with the lowest risk of Parkinson’s diseases found in those who drank three cups of coffee per day [23]. In addition, large-scale studies in Japan [24] and the UK [7] observed a lower risk of dementia for moderate coffee and tea drinkers. Despite reduced risks of neurodegenerative disease, associations with brain imaging findings are still controversial. This may be attributed to the small sample sizes in these studies, resolution of the MRI, and heterogeneity among different measurement modalities [9,10,11]. Contrary to the increased mRNFL thickness in coffee drinkers, the current study found that instant coffee was negatively associated with mRNFL thickness. Overall, this study suggests that the intake of instant coffee increases risk for glaucoma, and other neurodegenerative disorders such as dementia and Parkinson’s diseases. Especially for glaucoma, coffee drinking has conflicting evidence regarding neuroprotective functions [8], which is comparison with the beneficial effect of tea leaves consistently reported in previous studies [25,26]. In Korean coffee drinkers, Bae et al. observed a 2.4 greater risk for open angle glaucoma [25] which agrees with the results of Li et al. ’s UK Biobank Medelian study [27]. In contrast, Kim et al. failed to observe any significant association despite using participants from the same cohort as Li et al. [ 28]. Additionally, some studies indicated that caffeine intake increased the resistance of blood vessels and reduced ocular blood flow, which also could exacerbate glaucomatous optic neurodegeneration [29,30]. Trans-fatty acids (TFAs) and acrylamide (a carcinogen) have been associated with cognitive change in the past and are known to be present in some premixed coffee and instant coffee powders, but not in tea leaves. It is our opinion that their presence may account for the findings observed in instant coffee, and explain inconsistencies reported by previous studies about glaucoma [27,28]. It has been reported that TFA and acrylamide are facilitators of neurodegeneration as evidenced by human exposure cases [31,32] and animal studies with high grade exposures [33]. While this is a possible hypothesis, the few existing observational studies involving instant coffee suggest coffee is neuroprotective [7], and lab studies observe instant [34] and brewed coffee [35] reduce amyloid production. The only study suggesting adverse effects of instant coffee on cognitive impairment was in coffee drinkers who had >6 cups per day, but excessive intake of other types of coffee had similar associations [36]. Unfortunately, most studies assessing for the impact of coffee intake on degenerative diseases do not stratify for instant coffee intake, so it is difficult to contextualize these findings. Nonetheless, this large-scale study suggests instant coffee drinkers should be defined, as they are observed to impact mRNFL, and likely impact the direction of an association with neurodegenerative diseases. Importantly, these findings may have implications to public health considering coffee and tea intake occur daily and this study suggests it could have concerning implications for instant coffee drinkers. Several explanations for the protective potentials of coffee and tea on mRNFL thickness are worth exploring. First, the intake of caffeine regulates activation of microglia and inhibit excessive neuroinflammation which plays important roles in the development of neurodegenerative diseases [37]. Maderia et al. found caffeine-attenuated microglia-mediated inflammatory responses and reduced rat RGC loss following acute ocular hypertension insults [38]. In addition, anti-oxidants in coffee and tea are protective against reactive oxygen species and prevents ischemia-related neurodegeneration. For example, both chlorogenic acids and catechin polyphenols which are extracts from coffee and tea, alleviated oxidative-induced RGC apoptosis in rodent ischemic models [39,40]. Furthermore, catechin polyphenols extracted from green tea, especially epigallocatechin gallate, inhibited atherosclerosis [41], regulated blood lipids, and prevented insulin resistance [42]. These maintain hemostasis and lower the vulnerability of the brain to neuroinflammatory change. As the retina is an extension of the brain, these components in coffee and tea likely impact mRNFL thickness, although it is difficult to distinguish whether these theories can be related to humans as currently only laboratory studies can provide causal insight to the effects of coffee and tea on neurodegenerative changes. To the best of our knowledge, this is the first comprehensive investigation into associations between coffee and tea consumption with mRNFL thickness, which supports the neuroprotective potentials of these two beverages from the perspective of retinal integrity. Strengths of the current study lies in its combination of real-world coffee and tea consumption with the retinal marker for neurodegenerative changes, which could be conveniently and objectively measured in large-scale population by OCT examinations. However, there are still several weaknesses in this study. First, the cross-sectional study design inhibits the formation of causal conclusions and might lead to confounding bias. Therefore, the current findings should be further validated in longitudinal studies and clinical trials. Second, it was difficult to standardize the exact amount of a cup of coffee or tea in self-reported questionnaires, which suffered from recall bias. Although coffee and tea intake were unlikely to change greatly day to day, their quantitative analyses were still limited by the lack of serum and urine caffeine concentrations or other active ingredients. Third, OCT examinations were completed by different devices and technicians in distinct assessment centers, which would magnify the measurement error and weaken possible associations with mRNFL. Although all technicians underwent structured training for OCT image acquisition and experienced ophthalmologists were responsible for quality control, this study further adjusted the assessment center as covariates to control potential bias in OCT measurements. Fourth, we could not account for the subclinical retinal and neurodegenerative diseases. Due to the lack of ophthalmic and neurological examinations, normal tension glaucoma and other insidious diseases could be misdiagnosed in self-reported questionnaires and interviews. To avoid selection bias, history of inpatient diagnosis was also considered in this study. Last but not least, it should be noted that eligible participants were selected from a subgroup of the UK Biobank population who completed additional eye examinations at baseline, which made current findings less generalizable to general populations. Furthermore, most participants were Caucasian and came from the UK. Ideally, the study should be repeated in other geographic regions and among various ethnicities to determine a universal link. ## 5. Conclusions In summary, this study suggests the intake of coffee and tea are associated with increased mRNFL thickness. These associations were significant in those who consumed 2–3 cups of coffee and ≥4 cups of tea daily. In contrast, mRNFL was significantly thinner in instant coffee drinkers, which highlights the need for future associative studies to adjust for coffee types. Overall, this study provides novel evidence on the neuroprotective function of coffee and tea. The roles of these two beverages for the prevention and treatment of neurodegenerative diseases should be explored. ## References 1. Landais E., Moskal A., Mullee A., Nicolas G., Gunter M.J., Huybrechts I., Overvad K., Roswall N., Affret A., Fagherazzi G.. 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--- title: Probiotic Supplementation Attenuates Chemotherapy-Induced Intestinal Mucositis in an Experimental Colorectal Cancer Liver Metastasis Rat Model authors: - Matas Jakubauskas - Lina Jakubauskiene - Bettina Leber - Angela Horvath - Kestutis Strupas - Philipp Stiegler - Peter Schemmer journal: Nutrients year: 2023 pmcid: PMC10005486 doi: 10.3390/nu15051117 license: CC BY 4.0 --- # Probiotic Supplementation Attenuates Chemotherapy-Induced Intestinal Mucositis in an Experimental Colorectal Cancer Liver Metastasis Rat Model ## Abstract The use of chemotherapeutic agents is of paramount importance when treating colorectal cancer (CRC). Unfortunately, one of the most frequent chemotherapy (CTx) side effects is intestinal mucositis (IM), which may present with several clinical symptoms such as nausea, bloating, vomiting, pain, and diarrhea and even can result in life-threatening complications. There is a focused scientific effort towards developing new therapies to prevent and treat IM. The aim of this study was to assess the outcomes of probiotic supplementation on CTx-induced IM in a CRC liver metastasis rat model. Six-week-old male Wistar rats received either a multispecies probiotic or placebo mixture. On the 28th experiment day, rats received FOLFOX CTx, and afterwards, the severity of diarrhea was evaluated twice daily. Stool samples were collected for further microbiome analysis. Additionally, immunohistochemical stainings of ileum and colon samples with were performed with MPO, Ki67, and Caspase-3 antibodies. Probiotic supplementation alleviates the severity and length of CTx-induced diarrhea. Additionally, probiotics significantly reduced FOLFOX-induced weight and blood albumin loss. Furthermore, probiotic supplementation mitigated CTx-induced histological changes in the gut and promoted intestinal cell regeneration. This study shows that multispecies probiotic supplementation attenuates FOLFOX-induced IM symptoms by inhibiting apoptosis and promoting intestinal cell proliferation. ## 1. Introduction Colorectal cancer (CRC) is a major oncologic burden responsible for around $10\%$ of new cancer cases and deaths worldwide [1]. As in many types of cancers, the use of chemotherapeutic agents is of paramount importance when treating CRC [2]. Unfortunately, one of the most frequent chemotherapy (CTx) side effects is intestinal mucositis (IM). It can affect 40 to $100\%$ of cancer patients depending on the drug and its dosing [3,4]. IM develops due to the direct cytotoxicity inflicted by the antineoplastic drugs on the intestine epithelial cells, and this further promotes inflammation and indirect injury such as villus blunting or loss of the mucus layer [5,6]. This intestinal injury may present with various clinical symptoms such as nausea, vomiting, bloating, pain, and diarrhea and even can lead to life-threatening complications [4,7]. Furthermore, severe IM may result in suboptimal cancer treatment as the CTx doses need to be reduced or postponed. There is a focused scientific effort towards developing new therapies to prevent and treat IM [6]. One of the main IM pathophysiological mechanisms is the gut microflora balance alteration; thus, probiotics and antibiotics are being tested to prevent the formation of a harmful environment within the intestine [8,9]. Probiotics are described as “non-pathogenic live microorganisms that, when administered in adequate amounts, confer a health benefit on the host” [10]. They have a very wide variety of actions on the host, such as direct interaction with pathogens, promotion of intestinal barrier function, or immunomodulation [11]. As pointed out by a recent review, several studies report positive results of different probiotic strains in treating IM; however, further research for novel probiotic strains and their action mechanisms is essential [8]. The aim of this study was to assess the outcomes of a novel multispecies probiotic combination on CTx-induced IM in a CRC liver metastasis rat model. ## 2.1. Animals In this experimental model, we used six-week-old Wistar rats (Janvier Labs, Le Genest-Saint-Isle, France) ranging in weight from 200 to 280 g at the start of the protocol. According to the experimental groups, rats were housed two to four per enclosure, having ad libitum pelleted chow and tap water. The experimental protocol was approved by the Austrian Committee for Animal Trials (Approval number: BMWF-$\frac{66.010}{0158}$-V/3b/2019) and performed according to the 3R guidelines. ## 2.2. Experiment Design Overall, 90 rats were separated into six different study groups (Table 1). The detailed study design is presented in Figure 1. The whole study protocol lasted for 34 days. For the first two weeks, rats received daily gavage with probiotics or placebo mixture. On day 14, rats underwent CRC liver metastasis implantation that used a rat colorectal cancer cell line (CC531) or sham surgery. On days 28 and 29, CTx drugs or placebo were administered. After administering the first dose of CTx, diarrhea assessment started. On day 34, after performing necessary imaging, modalities rats were culled, and samples were collected. ## 2.3. Probiotics A multispecies probiotic mixture (provided by Institut Allergosan, Graz, Austria) composed of eight bacterial strains was used. Lactobacillus casei W56; Lactobacillus acidophilus W37; Lactobacillus brevis W63; Lactococcus lactis W58; Bifidobacterium lactis W52; Lactococcus lactis W19; Lactobacillus salivarius W24; and Bifidobacterium bifidum W23 were combined with 1 g of matrix (maize starch, maltodextrins, vegetable protein, potassium chloride, magnesium sulphate, amylases, and manganese sulphate). The placebo contained only the matrix. Either the probiotic or placebo powder was dissolved freshly every morning using tap water approximately 15 min before gavaging. Each rat received 1 mL of probiotic (1.2 × 109 CFU/mL) or placebo suspension. ## 2.4. Chemotherapy In this study, we used the FOLFOX regimen, as it is used for colorectal cancer metastasis treatment and has a proven capability to induce gastrointestinal damage [12,13,14]. The doses were adjusted using a previously described methodology according to the animals’ skin surfaces [15]. All CTx drugs were injected intraperitoneally under general $2\%$ isoflurane anesthesia. The CTx administration protocol is presented in Figure 2. ## 2.5. Diarrhea Evaluation All animals were examined twice daily after the first injection of the chemotherapeutical agents. Diarrhea was graded using a published scale: grade 0, no diarrhea; grade 1, mild diarrhea (staining of anus); grade 2, moderate diarrhea (staining of the lower abdomen) and; grade 3, severe diarrhea (staining over legs and higher abdomen or continual oozing) (Figure 3) [16]. ## 2.6. Blood Tests The first three samples were acquired by drawing blood from the subclavian vein. The last blood sample was drawn on day 34 from the inferior vena cava just before final organ sample collection. Complete blood count was calculated using a V-Sight hematology analyzer (A. Menarini Pharma GmbH, Vienna, Austria). Biochemical blood measurements were performed with a Spotchem EZ (A. Menarini Pharma GmbH, Vienna, Austria) analyzer. ## 2.7. Immunohistochemical Staining Organ samples were fixed in $4\%$ buffered formaldehyde solution, rinsed with distilled water, and dehydrated with ascending ethanol series. After incubating at 60 °C, samples were embedded in paraffin. Using a rotary microtome, 2 μm thick tissue sections were cut. Slides were stained using the following primary antibodies: Anti-MPO (Dako, Via Real Carpinteria, CA, USA, A0398, dilution 1:800), Anti-Ki67 (Abcam, Cambridge, UK; ab16777, dilution 1:200), and Anti-Caspase-3 (Abcam, Cambridge, UK; ab4051, dilution 1:200). The UltraVision LP Detection System: HRP Polymer (Thermo Fisher Scientific, Waltham, MA, USA) and DAB Chromogen (Dako, Via Real Carpinteria, CA, USA) were used to visualize the target antigen. Sections were counterstained with hematoxylin. All stained slides were scanned and analyzed using the open-source QuPath software (v0.3.0) [17]. ## 2.8. Intestine Crypt and Villi Length Analysis Crypt and villi length in both ileum and colon were measured according to a publication by Adelman et al. [ 18]. Five random crypt and villi lengths of the ileum were measured, and a villi/crypt length ratio was calculated for further data analysis. The median value of five random crypt lengths of the colon for each rat was used. ## 2.9. Microbiome Analysis DNA isolation from fecal samples was performed using the Magna Pure LC DNA III Isolation Kit (Bacteria, Fungi) (Roche, Mannheim, Germany) according to previously reported protocols [19,20]. A stool pellet was mixed with 500 µL phosphate-buffered saline (PBS) and 250 µL bacterial lysis buffer. Afterward, the sample was homogenized using the MagNA Lyser instrument (Roche Life Science. Mannheim, Germany) at 6500 rpm for two 30 s cycles. Enzymatic lysis was performed using 25 µL lysozyme (100 ng/mL, 37 °C for 30 min) and 43.4 µL proteinase K (20 mg/mL, 65 °C for 1 h). After the samples were heat inactivated at 95 °C for 10 min, DNA was extracted using a MagnaPure LC instrument (Roche, Mannheim, Germany) according to the manufacturer’s instructions. Extracted DNA was eluted in 100 μL elution buffer and stored at −20 °C until analysis. Then, 2 μL of total DNA was used in a 25 μL PCR reaction in triplicates using a FastStart High Fidelity PCR system (Sigma, Darmstadt, Germany) according to the manufacturer’s instructions and the target specific primers 515F (5′-GTGYCAGCMGCCGCGGTAA-3′) and 806R (5′GGACTACNVGGGTWTCTAAT-3′) for 30 cycles. Triplicates were pooled, normalized, indexed, and purified according to a published protocol [19]. The final pool was sequenced on an Illumina MiSeq desktop sequencer at 9 pM and v 3 600 cycles chemistry. FASTQ raw reads were processed using QIIME2 tools implemented on a local galaxy instance (https://galaxy.medunigraz.at). Taxonomic assignment was carried out using a naïve Bayesian classifier trained on the SILVA V132 database. Features were summarized on genus level for further analysis. Using the web-based analysis platform “Calypso”, group-specific general linear models identified genera that significantly changed during the course of the study. For the selected genera, differences between day 28 and day 34 were calculated and entered into a Spearman correlation analysis with diarrhea characteristics and albumin and weight changes to assess the associations between microbiome changes and the effects of the probiotics. p-values were adjusted with Benjamini–Hochberg correction and visualized using the R package “corrplot” (Version 0.92). ## 2.10. Statistical Analysis Statistical analysis was executed using SPSS 23.0 (IBM Corp., Armonk, New York, NY, USA) and GraphPad Prism 9 (GraphPad Software, La Jolla, CA, USA). Data distribution was evaluated with the Shapiro–Wilk test. Normally distributed data were further analyzed using t-test and one-way ANOVA with Tukey’s post hoc test. Non normally distributed data were investigated with Mann–Whitney U and Kruskal–Wallis with Dunn’s post hoc tests. A p-value of 0.05 or lower was considered significant. Data is provided as median and quartiles (Q1; Q3). ## 3.1. Response to Chemotherapy During the whole study, we had a single death due to CTx toxicity (Table 1). The administration of FOLFOX CTx induced severe leukopenia for both CTx-receiving rat groups. No probiotic influence was observed on the severity of FOLFOX-induced leukopenia. ## 3.2. Diarrhea Assessment A total of 15 animals ($75\%$) receiving placebo and 19 animals ($100\%$) receiving probiotics developed some degree of diarrhea (Figure 4). The peak incidence of diarrhea was observed 96 h after the first CTx injection in both groups. At the peak incidence, seven animals ($35\%$) receiving placebo and four ($21\%$) receiving probiotics developed severe diarrhea. Furthermore, severe diarrhea tended to resolve quicker for animals receiving probiotic supplementation (24 h (12.0; 30.0) vs. 12 h (12.0; 12.0); $$p \leq 0.026$$). ## 3.3. Weight Change At the start of the study, rat weight increased evenly in all groups. This tendency continued for rats not receiving CTx, yet rats that received FOLFOX significantly lost weight (Figure 5A). Probiotic supplementation managed to significantly limit weight loss caused by CTx ($83.97\%$ (79.65; 86.61) vs. $86.76\%$ (84.29; 88.46); $$p \leq 0.016$$) (Figure 5B). ## 3.4. Blood Albumin Levels Rat blood albumin levels were consistent across all groups at the start of the study (Figure 6A). FOLFOX CTx significantly reduced blood albumin levels in both the placebo and probiotic groups. We further analyzed blood albumin level changes between protocol days 28 and 34. Figure 6B shows that probiotic supplementation managed to significantly reduce CTx-induced blood albumin loss ($80.35\%$ (68.03; 84.88) vs. $83.60\%$ (80.28; 89.48); $$p \leq 0.021$$). ## 3.5. Histopathological Examination Analysis of rat terminal ileum villi length and crypt depth ratio showed that probiotics helped to alleviate CTx-induced intestinal damage (1.59 (1.44; 1.76) vs. 1.93 (1.73; 2.09); $p \leq 0.001$)) (Figure 7A). A similar tendency was seen with the rat colon crypt depth analysis (366.00 µm (331.70; 402.20) vs. 309.80 µm (286.20; 345.10; $p \leq 0.001$)) (Figure 7B). The percentage of MPO-positive cells was significantly lower in both FOLFOX-receiving groups in comparison to rats that did not receive CTx, and this was observed both in colon and ileum tissues. There were no differences between both CTx groups (Figure 7C,D). Probiotic supplementation greatly increased intestinal cell proliferation, and the differences between both CTx groups were statistically significant (Figure 7E,F). Furthermore, it seems that probiotic supplementation also managed to have a protective effect from CTx-induced apoptosis on ileum cells ($9.57\%$ (7.98; 11.07) vs. $7.58\%$ (6.50; 9.30); $$p \leq 0.001$$) (Figure 7G). However, the apoptosis index in the colon samples was similar across all experiment groups (Figure 7H). ## 3.6. Associations between Adverse Effects and the Microbiome Correlation analysis results are summarized and presented in Figure 8. The length of diarrhea shows a strong correlation with an increase in Bacteroides in stool samples during CTx (rs = 0.76; padj = 0.002). Additionally, our correlation analysis shows that the higher abundance of Ruminococcaceae NK4A214-group bacteria may further promote albumin loss during CTx (rs = −0.68; padj = 0.015). Further microbiome representation data are presented in the Supplementary File. ## 4. Discussion One of the most common CTx side effects is IM, and its occurrence can affect 40 to $100\%$ of cancer patients depending on the CTx regimen used [3,4]. IM interferes with optimal cancer treatment as the CTx doses need to be reduced or postponed; furthermore, it may even cause life-threatening complications [4,7]. Pathophysiology of IM is very complex but mainly involves five phases: [1] direct DNA damage and tissue cytotoxicity; [2] primary damage response, leading to inflammation and apoptosis; [3] signal amplification, resulting in exacerbated tissue injury; [4] inflammation and ulceration, leading to villus atrophy and barrier disruption; and [5] healing, with epithelial proliferation and intestine barrier regeneration [5,6,21]. Although this is a great IM development summary, various CTx regimes act differently on the gut barrier. Currently, only few studies report the impact of FOLFOX CTx on the development of IM and subsequent diarrhea [13,22,23]. Therefore, the aim of this study was to assess the outcomes of probiotic supplementation on FOLFOX CTx-induced IM. Our study shows that a previously never-tested multispecies probiotic combination alleviates the severity and length of CTx-induced diarrhea. Additionally, probiotics significantly reduced FOLFOX-induced weight and blood albumin loss. Furthermore, probiotic supplementation mitigated CTx-induced histological changes in the gut and promoted intestinal cell regeneration. Lastly, we managed to identify few bacteria groups that may play a role in the pathogenesis of severe diarrhea development and CTx-induced blood albumin level loss. Similarly, FOLFOX CTx-induced diarrhea attenuation results were reported by Chang et al. [ 22]. After administering increasing doses of Lactobacillus casei variety rhamnosus, authors observed lower diarrhea severity scores, with the peak being reached 6 days after the first chemotherapeutical agent injection. The peak diarrhea incidence differs slightly from the one we report in our study (96 h), but this may be mainly explained by the different FOLFOX CTx injection timing and dosage. One of the most common CTx side effects is weight loss, and on some occasions, it can be associated with worse patient survival [24]. Similarly, to our study, several other studies showed that probiotics containing Lactobacillus and Bifidobacterium manage to decrease CTx-induced weight loss [25,26]. Additionally, a study by Bowen et al. reported that multispecies probiotic VSL#3 can prevent CTx-caused weight loss. Probiotics preserve weight by preserving the intestinal integrity and alleviating CTx-induced diarrhea [27]. Additionally, the development of hypoalbuminemia has been associated with increased rates of chemotherapy failure and mortality [28,29]. To our knowledge, our study is the first to show that multispecies probiotic supplementation can help to preserve albumin levels in a cancer model. Probiotic safety and their interaction with the CTx itself for immunocompromised cancer patients is a very important and often debated issue [30,31,32]. In this study, we did not observe an increase in severe complications or premature deaths attributed to probiotic use. Moreover, our other study analyzing the impact of probiotics on tumor growth revealed that the used probiotic supplementation does not decrease the efficiency of FOLFOX CTx on CRC liver metastasis [33]. The histopathological investigation of ileum and colon tissue sheds some light on the underlying probiotic action mechanisms. Probiotic supplementation managed to alleviate CTx damage to the intestine, and this was indicated by the preserved ileum villi/crypt length ratio and crypt depth in the colon. This finding was in line with the results reported by Chang et al. in a CRC model; however, other studies report inconsistent results [22,25,34]. The analysis of anti-MPO-positive cells revealed an unexpected finding. The percentage of anti-MPO-positive cells was significantly lower in both chemotherapy groups both in the ileum and colon. This result is a bit counterintuitive, as usually, various inflammatory cells play an important role in the development of IM [6]. This result may be mostly influenced by the fact that the samples were gathered 6 days after initial CTx administration, when the course of IM starts to shift towards regeneration, especially in rats [5]. Furthermore, rats were in severe leukopenia at that time, theoretically leaving fewer neutrophils for tissue infiltration. A critical event for IM development is increased intestinal cell apoptosis, which was measured using Caspase-3 staining [5]. Interestingly, our results show that probiotics managed to decrease apoptosis in the ileum; however, no positive effects were observed in the colon. This may be mostly explained by the 10-times-lower large intestine apoptotic activity and thus its lower susceptibility to CTx-induced damage [35]. Moreover, probiotic supplementation significantly increased intestinal cell regeneration in both the colon and ileum. Various other probiotic strains have previously shown intestinal-healing effects in CTx-induced IM models [22,27]. Administration of CTx is known to dramatically alter the gut microbiome [36]. This includes the overall decrease in diversity and a relative increase of proteobacteria [37,38,39]. Our performed microbiome correlation analysis revealed that a relative increase of Bacteroides group bacteria was associated with increased diarrhea length. Results in the literature are quite inconsistent, as some studies report an increase and some a decrease of Bacteroides abundance when administering CTx [37,38]. Furthermore, although Ruminococcaceae NK4A214-group bacteria are generally known for their short-chain fatty acid production and anti-inflammatory effects, our analysis indicated contradictive results, showing that the higher abundance of Ruminococcaceae NK4A214-group bacteria may further promote albumin loss during CTx [40,41,42]. We could not identify other articles supporting this result; thus, it should be used cautiously and re-evaluated in a further study. One potential drawback of this study is the adoption of an animal model. Although successful probiotic effect translation from rodent to human has been published, we should note that the multispecies probiotics used in our study may have a different interaction with the human gut microbiome, resulting in altered outcomes [43]. ## 5. 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--- title: Association between Intra- and Extra-Cellular Water Ratio Imbalance and Natriuretic Peptides in Patients Undergoing Hemodialysis authors: - Yui Nakayama - Yosuke Yamada - Shingo Ishii - Mai Hitaka - Keisuke Yamazaki - Motoyuki Masai - Nobuhiko Joki - Ken Sakai - Yasushi Ohashi journal: Nutrients year: 2023 pmcid: PMC10005491 doi: 10.3390/nu15051274 license: CC BY 4.0 --- # Association between Intra- and Extra-Cellular Water Ratio Imbalance and Natriuretic Peptides in Patients Undergoing Hemodialysis ## Abstract Natriuretic peptides are associated with malnutrition and volume overload. Over-hydration cannot simply be explained by excess extracellular water in patients undergoing hemodialysis. We assessed the relationship between the extracellular and intracellular water (ECW/ICW) ratio, N-terminal pro-B-type natriuretic peptide (NT-proBNP), human atrial natriuretic peptide (hANP), and echocardiographic findings. Body composition was examined by segmental multi-frequency bioelectrical impedance analysis in 368 patients undergoing maintenance dialysis (261 men and 107 women; mean age, 65 ± 12 years). Patients with higher ECW/ICW ratio quartiles tended to be older, were on dialysis longer, and had higher post-dialysis blood pressure and lower body mass index, ultrafiltration volume, serum albumin, blood urea nitrogen, and creatinine levels ($p \leq 0.05$). The ECW/ICW ratio significantly increased with decreasing ICW, but not with ECW. Patients with a higher ECW/ICW ratio and lower percent fat had significantly higher natriuretic peptide levels. After adjusting for covariates, the ECW/ICW ratio remained an independent associated factor for natriuretic peptides (β = 0.34, $p \leq 0.001$ for NT-proBNP and β = 0.40, $p \leq 0.001$ for hANP) and the left ventricular mass index (β = 0.20, $$p \leq 0.002$$). The ICW-ECW volume imbalance regulated by decreased cell mass may explain the reserve capacity for fluid accumulation in patients undergoing hemodialysis. ## 1. Introduction Chronic fluid volume overload occurs in 27–$46\%$ of patients undergoing hemodialysis [1,2,3] and is a major risk factor for cardiovascular events [4] and mortality [2,3]. Hypovolemia leads to intra- and post-dialysis hypotensive symptoms (muscle cramps, yawning, nausea, vomiting, dizziness, and syncope). Therefore, controlling the fluid volume within an optimal range is crucial for improving cardiovascular stress, quality of life, and survival. Optimal fluid volume status is usually described as dry weight in patients undergoing hemodialysis; it is the lowest tolerated post-dialysis weight achieved via gradual change, at which there are minimal signs or symptoms of hypovolemia or hypervolemia [5]. Total body water (TBW) is measured using the dilution method with deuterium (2H) or heavy oxygen (18O). TBW changes have been examined during short-term (≤10 days) weight loss or weight gain using the dilution method [6,7,8,9]. However, the dilution method is not practical for monitoring daily fluid volume changes in patients undergoing hemodialysis. Multi-frequency bioelectrical impedance analysis (MF-BIA) or bioelectrical impedance spectroscopy (BIS) are alternative methods for estimating fluid volume status, determining body composition, and monitoring changes over time in body composition [10,11]. MF-BIA- or BIS-guided fluid volume management reduces blood pressure (BP) and post-dialysis weight; however, this does not seem to improve patient survival [12]. Therefore, fluid volume overload cannot be simply explained as excess extracellular water (ECW) due to oral sodium and water intake, which presents as inter-dialysis weight gain (IDWG). The balance between intracellular water (ICW) and ECW might change when the hydration component gradually decreases with aging or muscle attenuation, primarily due to decreased cell volume [13,14]. Sodium retention typically causes extracellular volume expansion and compensatory release of natriuretic peptides due to stretching of the cardiac wall. Elevated cardiac peptide levels might also be associated with malnutrition and fluid volume overload [15,16]. We hypothesized that individuals with fluid volume imbalance might have different reserve capacities for fluid accumulation. Recognizing these changes in body composition due to aging and sarcopenia can aid clinical decision-making for dry weight. Thus, we aimed to assess the relationship between the ECW/ICW ratio and natriuretic peptide levels to explore a novel marker for assessing the reserve capacity for fluid accumulation in patients undergoing hemodialysis. ## 2.1. Study Design and Participants A multicenter, cross-sectional study was conducted in four maintenance hemodialysis clinics (Seijinkai Mihama Hospital, Seijinkai Mihama Sakura Clinic, Seijinkai Narita Clinic, and Seijinkai Katori Clinic) between 2019 and 2022. During this period, 1094 patients underwent hemodialysis in these centers (average age, 68 ± 13 years; 777 men and 317 women). Eligible participants were selected from patients undergoing daytime dialysis and identified from the electronic medical records. Adults (aged ≥ 20 years) on maintenance dialysis for ≥90 days who had a stable dialysis prescription for at least 30 days at the time of recruitment were eligible. Ultimately, 368 patients who provided informed consent were included in the present study. Patients were excluded if they met any of the following criteria: had undergone coronary and/or valvular intervention or had suffered a myocardial infarction within the last 6 months; had been hospitalized for unscheduled dialysis for the treatment of heart failure in the last 6 months; had echocardiographic evidence of a left ventricular ejection fraction (LVEF) < $40\%$; had a contraindication to bioimpedance measurement, such as a pacemaker, joint replacement, or mechanical heart valve; were pregnant; or had major amputations, advanced malignancy, or dementia. This study was approved by the Ethics Committee of Toho University Sakura Medical Center, Tokyo, Japan (Approval Number: S21073\|Date: 26 April 2022 [S18086\|27 December 2018]) and was conducted in accordance with the Declaration of Helsinki. Informed consent was obtained from all the participants. ## 2.2. Data Collection The following baseline data were recorded: age, sex, anthropometric measurements, presence of diabetes mellitus (DM), hemodialysis vintage, body weight, and pre- and post-dialysis BP. The following standard laboratory parameters were collected during the long-interval hemodialysis session at the beginning of the month: serum albumin, blood urea nitrogen, serum creatinine, sodium, potassium, chloride, calcium, phosphorus, uric acid, glucose, C-reactive protein, hemoglobin, and intact parathyroid hormone (iPTH) levels. Dialysis adequacy, assessed in terms of the urea reduction ratio and single-pool Kt/Vurea, was measured using the Shinzato formula [17]. The geriatric nutritional risk index (GNRI) was calculated as (14.89 + albumin (g/dL)) + (41.7 × body weight/ideal body weight). The ideal body weight was calculated using height and an idealized body mass index (BMI) of 22 kg/m2 [18]. Pre-dialysis N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels were determined using an electrochemiluminescence immunoassay system (Cobas8000 e801 module; Roche Diagnostics K.K., Tokyo, Japan). Post-dialysis human atrial natriuretic peptide (hANP) levels were determined using a chemiluminescent enzyme immunoassay system (CL-JACK RK; Minaris Medical Co., Ltd., Tokyo, Japan). Pre-dialysis chest X-ray was obtained to evaluate the cardio-thoracic index (CTR). CTR was calculated as the ratio of the maximum transverse cardiac diameter (mm) to the maximum thoracic diameter (mm). As a routine clinical practice, annual transthoracic echocardiographic examinations were performed by a single experienced cardiologist [date difference between body fluid composition analysis and echocardiographic examinations, median (10th–90th percentile) of −70 days (−275 to 68 days)]. The left atrial diameter (LAD), left ventricular end-diastolic diameter (LVDd), left ventricular end-systolic diameter (LVDs), left ventricular posterior wall thickness (PWT), interventricular septum thickness (IVST), and LVEF were measured. Left ventricular mass (LVM) was calculated using the Devereux Equation [19], and the LVM index (LVMI) was calculated as the ratio of LVM to body surface area (BSA). ## 2.3. Assessment of Body Fluid Composition Standard MF-BIA was performed with the patient in the supine position on a flat non-conductive bed after hemodialysis. For body composition measurements, we used a segmental MF-BIA instrument (Inbody S10®; InBody Co., Ltd., Seoul, Korea; https://inbodyusa.com/ (accessed on 26 April 2022)) with eight tactile electrodes. The microprocessor-controlled switches and impedance analyzer were activated and the segmental resistances of the arms, trunk, and legs were measured at four frequencies (5, 50, 250, and 500 kHz). A total of 20 segment resistances were obtained for each individual. Subsequently, the sum of each body segment measurement was used to calculate the TBW, ICW, and ECW using MF-BIA software (Inbody S10®; InBody Co., Ltd., Seoul, Korea; https://inbodyusa.com/ (accessed on 26 April 2022)). Participants were categorized according to the ECW/ICW ratio quartiles. ## 2.4. Statistical Analyses Data were analyzed using JMP pro (version 16.0; SAS Institute, Inc., Cary, NC, USA). The measured values were expressed as mean ± standard deviation or median (interquartile range). Statistical significance was assessed using a linear regression model for the continuous variables and Pearson’s chi-square test for the categorical variables. Correlations between the variables were determined using the Pearson product-moment correlation coefficient. Linear regression analysis was used to identify associations between the ECW/ICW ratio and natriuretic peptides and the left ventricular mass index. The explanatory variables for multivariate analysis were based on Japanese Society for Dialysis Research guidelines [20]: factors that increased the risk of developing new heart failure in patients undergoing dialysis included age, diabetes, history of coronary artery disease, decreased left ventricular systolic function, increased diastolic BP, hypoalbuminemia, and decreased Hb concentration. Alternatively, variables that had a significant correlation with these cardiac markers were analyzed to evaluate independent associations. Receiver operating characteristic (ROC) curve analysis was used to identify the best prognostic value of the ECW-to-ICW ratio for NT-proBNP (≥4000 or ≥8000 pg/mL) [21] and hANP (≥75 or ≥100 pg/mL). These were equivalent to the NT-proBNP cutoff values based on the regression equation between NT-proBNP and hANP [log10-transformed hANP = 0.3065 + 0.4283 × log10-transformed NT-proBNP]. $p \leq 0.05$ was considered statistically significant. ## 3.1. Population Characteristics The population characteristics of the patients (261 men and 107 women; mean age, 65 ± 12 years) are presented by the ECW/ICW ratio quartiles in Table 1. The quartile values for men and women were 0.611, 0.638, and 0.663, and 0.628, 0.650, and 0.674, respectively. Patients in the higher ECW/ICW ratio quartiles tended to be older and had a longer dialysis vintage, lower BMI, ultrafiltration volume, pre-dialysis diastolic BP, pulse rate, serum albumin, blood urea nitrogen, serum creatinine, serum phosphorus, total cholesterol, serum triglyceride, serum uric acid, hemoglobin levels, and GNRI, and higher post-dialysis systolic BP, CTR, serum chloride, C-reactive protein, NT-proBNP, and hANP levels in both men and women ($p \leq 0.05$). As shown in Supplementary Tables S1 and S2, both men and women in the higher ECW/ICW ratio quartiles tended to have lower body weight, body surface area, TBW, ICW, ICW per BSA, and muscle mass, and had higher ECW per BSA. As shown in Supplementary Figure S1, both men and women in the higher ECW/ICW ratio quartiles tended to have a higher percentage of ECW content in TBW content at both times of pre- and post-hemodialysis. This trend was similar even if the ECW content in normal hydrated adipose tissue was excluded. ## 3.2. Association between Body Fluid Imbalance and Natriuretic Peptides Body fluid composition and log10-transformed natriuretic peptide levels according to the ECW/ICW ratio quartiles are shown in Figure 1. The ECW/ICW ratio had a significant negative correlation with the ICW content (men: r = −0.42 in men, $p \leq 0.001$; women: r = −0.38, $p \leq 0.001$), but not with the ECW content (men: r = −0.05, $$p \leq 0.60$$; r = −0.09, $$p \leq 0.13$$). Thus, the ECW/ICW ratio was primarily derived from decreased intracellular volume and not excess extracellular volume. The ECW/ICW ratio had significant positive correlations with the log10-transformed NT-proBNP (men: $r = 0.49$, $p \leq 0.001$; women: $r = 0.32$, $p \leq 0.001$) and log10-transformed hANP (men: $r = 0.53$, $p \leq 0.001$; women: $r = 0.49$, $p \leq 0.001$). The correlations between the ECW/ICW ratio, ECW per BSA, ICW per BSA, and log10-transformed natriuretic peptides are shown in Figure 2. In comparison to the ECW per BSA or ICW per BSA, the ECW/ICW ratio had a stronger correlation with the log10-transformed natriuretic peptides. As shown in Figure 3, patients with a higher ECW/ICW ratio in the fat percentage quartiles 1 and 2 tended to have higher natriuretic peptide levels. However, the trend was lessened in the fat percentage quartile 3. In addition, the highest fat percentage quartile with a higher ECW/ICW ratio, which indicates sarcopenic obesity, also tended to have higher natriuretic peptide levels. The quartile values of fat percentage for men and women were 18.3, 24.2, and 29.5, and 21.8, 29.9, and 38.5, respectively. ## 3.3. Association between Body Fluid Imbalance and Echocardiographic Findings Echocardiography findings performed within one year according to the ECW/ICW ratio quartiles are shown in Table 2. Patients in the higher ECW/ICW ratio quartile tended to have a wider LAD, narrower LVDd, thicker PWT and IVST, and heavier LVMI ($p \leq 0.05$). ## 3.4. Body Fluid Imbalance Is an Independent Associated Factor for Natriuretic Peptides and LVMI In the linear regression analysis, the ECW/ICW ratio remained an independent associated factor for log10-transformed natriuretic peptides and LVMI (Table 3). We constructed receiver-operating characteristic curves to derive the cut-off values of the ECW/ICW ratio for these cardiac biomarkers. The best cutoff values of the ECW/ICW ratio for NT-pro BNP ≥ 4000 and ≥8000 pg/mL were 0.638 and 0.637 in men and 0.652 and 0.660 in women, respectively. The best cutoff values of the ECW/ICW ratio for hANP ≥ 75 and ≥100 pg/mL were 0.624 and 0.632 for men and 0.653 and 0.685 for women, respectively. ## 4. Discussion This study revealed that the ICW-ECW fluid imbalance in patients undergoing hemodialysis was significantly associated with natriuretic peptides released from the heart in response to pressure and fluid volume and LVMI, an indicator of concentric left ventricular remodeling, despite the ECW/ICW ratio being primarily driven by decreased intracellular volume rather than by excess extracellular volume. That is, volume overload in patients undergoing hemodialysis may be characterized by a relative increase in ECW content with a decrease in ICW content by aging or muscle attenuation as well as an absolute increase in ECW content by sodium retention. Obesity and metabolic syndromes affect the onset and progression of chronic kidney disease (CKD). Nutrition-related health problems in advanced CKD cause malnutrition in association with sodium retention, arterial stiffness, and inflammation. MIA syndrome is a serious clinical syndrome occurring in patients with advanced CKD [22]. Furthermore, malnourished patients on dialysis are prone to fluid accumulation. In the intermittent hemodialysis regimen, fluid accumulates between dialysis sessions and is removed during dialysis sessions, just like the ebb and flow cycle of tides. Clinicians primarily set the fluid surface of the ebb tide after dialysis. The flow (IDWG) is determined primarily by oral sodium and water intake, residual urine output, and insensible fluid losses; it is often used as a marker of better nutrition or nonadherence to sodium and water restrictions [23]. Approximately 25–$50\%$ of patients undergoing dialysis do not achieve an adequate fluid volume status even after dialysis, leaving the patient in persistent chronic fluid overload status [2,23]. Hecking et al. emphasized that chronic fluid overload is more strongly associated with mortality risk than IDWG [24]. However, we often experience patients who have clinical signs of fluid overload but cannot lower their dry weight. In spite of patients in the higher ECW/ICW ratio quartiles having several clinical signs of fluid overload, such as higher post-dialysis systolic BP and larger CTR, clinical physicians seem to consider their post-dialysis weight as clinical dry weight. Nevertheless, natriuretic peptide levels in the higher ECW/ICW ratio quartiles tend to be higher. The total fluid volume depends on age, sex, and body size. The ICW content may be associated with the muscle component in healthy individuals. In adulthood, the TBW content is relatively constant until the age of 40 years; thereafter, it gradually declines at the rate of 0.1–0.3 kg/yr until 70–80 years of age. The decreasing ICW slope with age is steeper than the decreasing ECW slope. In particular, there is a dramatic change in the balance between the ICW and ECW content after the age of 70 [13]. Approximately $75\%$ of the muscles and viscera are composed of water [25]; organ aging [26] and sarcopenia [27,28] are associated with ICW loss. This universal cell volume loss expands the interstitial area, which leads to a mismatch in ICW-ECW content balance. We believe that this apoptotic fluid volume imbalance is less likely to preserve excess fluid because the cell area is replaced by interstitium in lean tissue; just as a small bottle already filled with water cannot store more water. In addition, this replaced fluid filling the interstitium probably cannot be removed by ultrafiltration. Indeed, in this present study patients with the higher ECW/ICW ratio quartiles tended to have a higher percentage of ECW content in TBW content at both times of pre- and post-hemodialysis. In our daily clinical practice, we think that a two-pronged approach is very important for normalizing the ECW/ICW ratio. First, we should try to remove excess ECW in patients who have clinical signs of fluid overload. Next, we should supply nutritional support to gain muscle mass for patients with elevated natriuretic peptides and higher ECW/ICW ratio along with a downward resetting of dry weight. A previous study has reported that the ECW/ICW ratio provides a prognostic value superior to that of B-type natriuretic peptide for the incidence of heart failure-related re-hospitalization and all events in patients hospitalized due to acute heart failure [29]. This study suggests that correcting excess ECW alone is not enough to prevent future heart failure. On the other hand, optimal dry weight setting in obese patients is difficult in a different way. ECW content in obese patients is increased even in the absence of fluid accumulation because 15–$20\%$ of adipose tissue is composed of extracellular fluid area. Furthermore, we speculate that adipose tissue may work as a buffer for fluid accumulation because adipose tissue expands the extracellular fluid area. For example, we often experience obese patients who have no symptoms even in fluid accumulation of 6.0–7.0 L. In addition, obese patients undergoing hemodialysis often have myocardial damage, which may affect natriuretic peptide levels. In the present study, the positive relationship between the ECW/ICW ratio and natriuretic peptides was uncertain in the higher fat percentage quartile. As a result, patients with the ICW-ECW fluid imbalance in the lower fat percentage were likely to have a noticeable clinical significance. Natriuretic peptides are neurohormones synthesized in cardiac myocytes in response to increased left ventricular wall stress and stretching, which are reportedly associated with fluid volume [30,31]. However, several cohorts have reported that natriuretic peptides are inversely associated with BMI [32,33]. In addition, natriuretic peptide accumulation is associated with poor nutritional status and reduced survival among patients undergoing hemodialysis [16,34,35]. Lower natriuretic peptide levels might result from: [1] increased receptors in the adipose tissue or [2] suppression of either the synthesis or release of natriuretic peptides by a substance produced in the lean mass. Natriuretic peptides are correlated with high-sensitive C-reactive protein (hsCRP) and Interleukin-6 (IL-6) levels; hsCRP predicts the future loss of lean mass. In the present study, the ECW/ICW ratio was associated with the natriuretic peptide levels, LAD, and LVMI. These results suggest that the ICW-ECW fluid imbalance in patients undergoing hemodialysis may have an adverse effect on the reserve capacity for fluid accumulation. Patients undergoing hemodialysis with a low BMI reportedly have a higher prevalence of hypertension, poorer control of BP, and greater left ventricular hypertrophy [36]. The present study has several limitations. First, it was a retrospective analysis of 368 of 1094 patients ($33.6\%$) in a four-center study in maintenance hemodialysis clinics; hence, generalization to the general population undergoing evaluation may not be valid. Second, echocardiographic findings were obtained from annual transthoracic echocardiographic examinations instead of the time of recruitment or the BIA measurements. Third, the quantitative assessment of overhydration and malnutrition using the ECW/ICW ratio was difficult. Fourth, natriuretic peptide levels are influenced by various factors, such as age and BMI. These confounding factors might have affected the association between the ECW/ICW ratio and natriuretic peptide levels; therefore, future studies are required to clarify these issues. ## 5. Conclusions The fluid volume imbalance between ICW and ECW in patients undergoing hemodialysis was significantly associated with natriuretic peptide levels. Additionally, such patients may have different reserve capacities for fluid accumulation. We recommend that both sides of retention of cell volume and correction of the increase in ECW content might be important for normalizing fluid volume balance. Recognizing these changes in body composition due to aging and sarcopenia can then aid clinical decision-making for determining dry weight. ## References 1. Dekker M.J.E., Marcelli D., Canaud B.J., Carioni P., Wang Y., Grassmann A., Konings C.J., Kotanko P., Leunissen K.M., Levin N.W.. MONDO Initiative. 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--- title: The Associations between Multiple Essential Metal(loid)s and Gut Microbiota in Chinese Community-Dwelling Older Adults authors: - Jianghui Zhang - Yuan Wang - Guimei Chen - Hongli Wang - Liang Sun - Dongmei Zhang - Fangbiao Tao - Zhihua Zhang - Linsheng Yang journal: Nutrients year: 2023 pmcid: PMC10005492 doi: 10.3390/nu15051137 license: CC BY 4.0 --- # The Associations between Multiple Essential Metal(loid)s and Gut Microbiota in Chinese Community-Dwelling Older Adults ## Abstract Several experimental studies have suggested that individual essential metal(loid)s (EMs) could regulate the gut microbiota. However, human studies assessing the associations between EMs and gut microbiota are limited. This study aimed to examine the associations of individual and multiple EMs with the compositions of the gut microbiota in older adults. A total of 270 Chinese community-dwelling people over 60 years old were included in this study. Urinary concentrations of selected EMs, including vanadium (V), cobalt (Co), selenium (Se), strontium (Sr), magnesium (Mg), calcium (Ca), and molybdenum (Mo), were examined by inductively coupled plasma mass spectrometry. The gut microbiome was assessed using the 16S rRNA gene sequencing analysis. The zero-inflated probabilistic principal components analysis PCA (ZIPPCA) model was performed to denoise substantial noise in microbiome data. Linear regression and the Bayesian Kernel Machine Regression (BKMR) models were utilized to determine the associations between urine EMs and gut microbiota. No significant association between urine EMs and gut microbiota was found in the total sample, whereas some significant associations were found in subgroup analyses: Co was negatively associated with the microbial Shannon (β = −0.072, $p \leq 0.05$) and the inverse-Simpson (β = −0.045, $p \leq 0.05$) indices among urban older adults; Ca (R2 = 0.035) and Sr (R2 = 0.023) exhibited significant associations with the altercations of beta diversity in females, while V (R2 = 0.095) showed a significant association with altercations of beta diversity in those who often drank. Furthermore, the associations between partial EMs and specific bacterial taxa were also found: the negative and linear associations of Mo with Tenericutes, Sr with Bacteroidales, and Ca with Enterobacteriaceae and Lachnospiraceae, and a positive and linear association of Sr with Bifidobacteriales were found. Our findings suggested that EMs may play an important role in maintaining the steady status of gut microbiota. Prospective studies are needed to replicate these findings. ## 1. Introduction The human gut microbiome comprises 10 trillion diverse symbionts (50 bacterial phyla and about 100–1000 bacterial species) [1], which maintain a close symbiotic relationship with the human body [2]. The gut microbiota remains relatively stable during adulthood, but its compositions are constantly changed during infancy and old age [3]. The alterations of gut microbiota with age are characterized by progressive decreases of overall diversity, core microbiota, and other health-associated bacteria, ultimately leading to gut microbiota dysbiosis [4,5,6,7,8]. Gut dysbiosis may trigger the innate immune response and result in chronic low-grade inflammation, which consists of the basic mechanisms underlying age-related diseases, such as atherosclerosis, diabetes, hypertension, cancer, Alzheimer’s diseases, et al. [ 9]. Therefore, an in-depth exploration of modifiable risk factors for gut dysbiosis in older adults is necessary to promote healthy aging. Of the modifiable factors, diet emerges as one of the pivotal determinants. Nutrients in food can directly interact with microorganisms and reshape the gut microbiota [10], which may be especially true for older adults. The physical changes with age such as reduction in dentition, impairment of taste and olfaction, and an increased level of satiation may decrease food intakes and limit the choices for food diversity, which in turn cause malnutrition and alterations of gut microbiota in older adults [11,12]. Essential metal(loid)s (EMs), such as vanadium (V), cobalt (Co), selenium (Se), strontium (Sr), magnesium (Mg), calcium (Ca), and molybdenum (Mo) et al., are essential for biological functions. EMs cannot be produced endogenously and mainly rely on dietary intake [13]. As a result, older adults are more prone to deficiencies of EMs due to their reduced food intake. A recent systematic review found that there were $31\%$ of women and $49\%$ of men living with zinc deficiencies and $49\%$ of women and $37\%$ of men living with selenium deficiencies in community-dwelling older adults [14]. In recent years, an emerging biologic pathway by which EMs play biological functions is that EMs could reshape and modulate the gut microbiota [15]. For instance, a previous animal study found that calcium (Ca) had a prebiotic-like effect. That is, Ca supplementation can increase the abundance of Bifidobacterium and Bacteroides [16]. Another animal study also showed that both low and high Ca concentrations led to changes in microbial composition in mice, and the high level of Ca supplement even significantly decreased plasma biomarkers for the metabolic disorder [17]. In addition, dietary Se supplementation at a dose range of 0.1 μg/g through 2.25 μg/g in mice could increase microbial diversity [18]. A similar study indicated that the Se supplement had a beneficial impact on the proliferation of lactic acid bacteria and other beneficial bacteria such as Bacteroides, Prevotella, and Roseburia [19]. Despite experimental studies that have provided convincing evidence, epidemiological studies on the association between EMs and the gut microbiota were scarce and did not yield consistent findings [20,21]. The reasons for these mixed results remained unclear. One possibility is that single EMs may have weaker effects and/or their protective effects may depend on other EMs. Thus, the analyses of single EMs may underestimate the effects of EMs on gut microbiota, and the mixture analyses are warranted to disentangle joint effects of EMs on gut microbiota in older adults. In this study, we aimed to examine the associations of single EMs and EM mixtures with the compositions of the gut microbiota using a sample of older adults in China. ## 2.1. Data Source and Population This study utilized data and biological specimens from the baseline survey of a cohort study: Older Adult Health and Modifiable Factors, which was launched in Fuyang city, Anhui province, China, from July to September 2018. Details on subject recruitment and sampling have been described elsewhere [22]. Briefly, a total of 6000 older adults aged 60 or over were randomly selected from 8 counties in Fuyang, and 5186 older adults agreed to participate in this survey. Each participant was invited to finish a structured questionnaire and undergo a physical examination in the local community hospital. Morning urine and stool samples were obtained from older adults when they underwent the physical examination. Only 300 fecal samples were collected due to the lack of refrigeration equipment at the investigation sites. Of 300 older adults with fecal samples, 30 were excluded because of insufficient feces ($$n = 17$$) or no urine sample ($$n = 13$$). Finally, a total of 270 older adults were included in the analysis. The protocol for this study was approved by the biomedical ethical committee of Anhui Medical University (No. 20190288), and all participants have provided written informed consent. ## 2.2. Measurement of Urinary EMs The urine samples were removed from −80 °C to room temperature and melted. Then they were diluted to 10 times after mixed with diluent ($0.05\%$TritonX-100 + $1\%$HNO3, Sigma, St. Louis, MO, USA) thoroughly. The Inductively Coupled Plasma Mass Spectrometer (ICP-MS; Nexion350X, Perkin-Elmer, Shelton, CT, USA) was used to measure urinary concentrations of seven EMs (including V, Co, Se, Sr, Mg, Ca, and Mo). The limits of detection (LODs) for V, Co, Se, Sr, Mg, Ca, and Mo were 0.005 µg/L, 0.001 µg/L, 0.274 µg/L, 0.015 µg/L, 0.002 mg/L, 0.0127 mg/L, and 0.002 µg/L, respectively. Urinary EM concentrations were corrected for urine dilution by urinary creatinine concentrations and expressed as µg/g creatinine. The urine creatinine concentrations were measured by using the picric acid assay. ## 2.3. 16S rRNA Gene Sequencing and Data Analyses The 16S rRNA gene sequencing process was carried out in an entirely sterile environment. MagPure Stool DNA KF kit B was used to extract bacterial DNA in accordance with the manufacturer’s instructions (Magen, Guangzhou, China). The V4 hypervariable region of the 16S rRNA was amplified with degenerate PCR primers, 515F (5′-GTGCCAGCMGCCGCGGTAA-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′). The Illumina adapter, pad, and linker sequences were attached to both the forward and reverse primers. With the use of Agencourt AMPure XP beads and elution buffer, the PCR products were purified. Agilent Technologies’ 2100 bioanalyzer was used to qualify libraries. The validated libraries were used to generate 2 × 250 bp paired-end reads on the Illumina HiSeq 2500 platform (BGI, Shenzhen, China) using the standard Illumina pipelines. The Fast Length Adjustment of Short Reads software (FLASH, v1.2.11) [23] was used to add paired-end reads to tags after raw reads had been filtered to eliminate adaptors and low-quality and ambiguous bases. The tags were grouped into operational taxonomic units (OTUs) with a cutoff value of $97\%$, and chimera sequences were compared with the Gold database using UCHIME (v4.2.40) [24]. Then, using the Ribosomal Database Project (RDP) Classifier v.2.2 and QIIME v1.8.0 with training data from the Greengenes database v201305, sample OTU sequences were taxonomically categorized [25]. The OTU abundance statistics table for each sample was obtained by comparing all Tags back to OTU using the USEARCH global [26]. ## 2.4. Outcome Assessments Based on the previous study [27], gut microbial alpha diversity, beta diversity, and the top 5 taxa in abundance at five levels from phylum to genus were reported, respectively. The Shannon index and inverse-Simpson were used to measure the richness and diversity of special taxon, respectively, which could be combined to comprehensively interpret the alpha diversity of gut microbiota. Beta diversity was measured by the Euclidean distance, and R2 of the analysis result was utilized to interpret the differences between samples. ## 2.5. Covariates Covariates included demographic characteristics (age, gender, residence, education, economic condition, and body mass index (BMI)), behavioral factors (smoking, drinking, and antibiotic use), chronic diseases (hypertension, diabetes, and chronic kidney disease), and diet patterns. Residence was dichotomized into rural or urban areas. Education was classified as illiteracy (without formal education), primary school (1–6 years of education) and junior school or above (>6 years of education). Economic condition was grouped into 2 categories (low and high) based on the self-perception. Behavioral factors were defined as follows: smoking status (non-smoker, former smoker, and current smoker), drinking status (never, often, and always), and antibiotic use (yes or no). In the process of physical examination, all participants were queried about their medical history and recent drug use. Medical histories included hypertension (yes or no), diabetes (yes or no), and chronic kidney disease (yes or no). We have double-checked the clinical history sheets, laboratory data, and other medical reports of each participant for ensuring the accuracy of the data. Additionally, diet consumption was measured by Food Frequency Questionnaire. All participants were asked whether they had eaten pork, vegetables, fruits, fungi, eggs, milk, and coarse cereals et al. in the past 12 months. If the respondents’ answer was “no”, the consumption frequency of this kind of food was recorded as “0”. If the respondents’ answer was “yes”, the consumption frequency was recorded according to how many times they eat every day/week/month/year. Diet consumption was clustered into 5 diet patterns based on factor analysis: factor 1 (mainly included Livestock meat, Fish meat, and Poultry), factor 2 (mainly included Soya, Animal viscera, Coarse cereals), factor 3 (mainly included Eggs, Milk, Nut, Sugary drinks, Fruits), factor 4 (mainly included Fungus, Pork), and factor 5 (mainly included Fruits, Animal oil, Vegetables), respectively. Details are provided in Supplementary Materials. ## 2.6. Statistical Analysis The categorical variables, continuous variables, and the factor scores of diet patterns were described using frequencies and proportions, mean and standard deviation (SD), as well as range and median, respectively. The correlations between EMs, which had been adjusted by creatinine and log-transformed, were performed by Pearson correlation analysis. We performed zero-inflated probabilistic principal component analysis (ZIPPCA) [28] using the mbDenoise R package to denoise substantial noise in microbiome data. More specifically, microbial count matrices contained a large proportion of zero values, and parts of them were caused by the low sequencing depth and sampling variations (technical zeros). The ZIPPCA denoises microbiome data by learning the latent features, which effectively deals with the data sparsity problem, distinguishes between technical and biological zeros, and then recovers the true abundance levels using the posterior mean. We then analyzed the alpha diversity (Shannon and inverse-Simpson indices), beta diversity (based on Euclidean distance), and the abundance of the taxon (the denoised counts of microbiome data). Single- and multiple-element linear regression models were performed to examine the associations between single EMs and alpha diversity. Single-element linear models only included single EMs with and without adjusting covariates (age, gender, BMI, residence, education, economic condition, smoking, alcohol consumption, antibiotic use, medical history, and diet patterns). Multiple-element linear regression model included aforementioned covariates and all EMs. Furthermore, to assess the associations between single EMs and the beta diversity of gut microbiota, we divided each EM into two groups (high- and low-level groups) based on its median, and then compared the differences in beta diversity of gut microbiota between the two groups using the permutational analysis of variance (PERMANOVA) after adjustment for other EMs and aforementioned covariates. To more accurately estimate the relationship between each EM and the abundance of special taxon, we added pseudo counts of 1 to denoised counts of microbiome data before log transformation. The abundances of special taxon were then calculated and used as the dependent variables in subsequent multiple linear regression models. Independent variables and covariates were the same as the adjusted multiple linear regression models of alpha diversity. Lastly, to examine joint associations of EMs with microbial metrics, we utilized Bayesian kernel machine regression (BKMR) [29] to flexibly model the associations of EMs with alpha diversity and the top five most abundant taxa in five levels from phylum to genus. Before fitting the BKMR models, the EMs concentrations were subtracted by the mean and then divided by the standard deviation. Given the high correlations between EMs, we divided EMs into two groups based on principal component analysis, and used a hierarchical variable selection approach to estimate the posterior inclusion probabilities (PIPs) for two groups as well as conditional PIPs (condPIPs) for each EM within the group, with the condPIPs > 0.5 indicating that the corresponding EMs were significant contributors to the variability of the outcomes. A p value < 0.05 was considered statistically significant in the current study, and all analyses were conducted in SPSS 26.0 and R 4.2.0. ## 3.1. Population Characteristics The study population consisted of 270 older adults with a mean age of 71.42 years old (SD = 4.91) and a mean BMI of 24.68 kg/m2 (SD = 3.74). Of 270 older adults, $51.9\%$ were males, $75.2\%$ resided in rural areas, $75.5\%$ had lower education (≤primary school), $71.1\%$ had never been drinking, $78.5\%$ had never been smoking, and $70.0\%$ had not used antibiotics in the past month. The factor scores of five diet patterns ranged from −8.173 to 10.792 (Table 1). ## 3.2. Gut Microbiota Compositions A total of 1416 distinct OTUs were observed in the raw data. After excluding the OTUs that total counts were zero, the OTUs were assigned to 16 phyla, 28 classes, 51 orders, 84 families, and 178 genera, respectively. The denoised models were subsequently performed at different levels. Figure 1 shows the denoised relative taxon abundance at the phylum level. Firmicutes had the highest relative abundance in all bacterial phylum, followed by Bacteroidetes and Proteobacteria. ## 3.3. Distributions of Urinary EM Concentrations Seven EMs, including V, Co, Se, Sr, Mg, Ca, Mo, were detected in all urine samples, and the corresponding median concentrations were 1.804 μg/L, 0.369 μg/L, 16.795 μg/L, 215.848 μg/L, 121.837 mg/L, 149.939 mg/L, and 116.160 μg/L, receptively (Table 2). The Pearson correlation coefficients between EMs are shown in Figure S1. There were significant positive correlations between all EMs, in which the associations between Sr, Mg, and Ca were accentuated, ranging from 0.56 to 0.85 (all p value < 0.001). ## 3.4. Associations of Single EMs with α-Diversity and β-Diversity The single-element and multiple-element linear regression models (Table S2) did not exhibit any statistically significant association between single EMs and α-diversity metrices (Shannon and inverse-Simpson indices) in total sample. BKMR models also showed that no EM significantly contributed to any metric of α-diversity (all condPIPs < 0.5; Table S6, Figure S2). However, stratified analyses found several significant associations in the subgroup population (Figure 2, Tables S3 and S4). Urinary V in non-smokers [β = 0.037,$95\%$CI = (0.006, 0.068)], Co in urban older adults [β = −0.072, $95\%$CI = (−0.126, −0.019)], Mg in older adults age 75 years and above [β = −0.066, $95\%$CI = (−0.123, −0.003)], and Mg in those without diabetes [β = −0.037, $95\%$CI = (−0.069, −0.005) exhibited significant associations with the Shannon index. while urinary Co in urban older adults [β = −0.045, $95\%$CI = (−0.081, −0.008)] was also associated with the inverse-Simpson index. Similarly, no significant association between single EMs and β-diversity was found in the total sample, whereas significant differences in β-diversity were found between high- and low- level groups of urinary Sr levels (R2 = 0.023) and Ca levels (R2 = 0.035) in females, and of V levels (R2 = 0.095) in older adults who often drank (Table S5). ## 3.5. Associations between Single EMs and Specific Taxons In multivariable-adjusted regression models (Table S7), at least one element was detected in an association with the selected taxon in different levels except class level. The strongest negative association was detected between Mo and Tenericutes at the phylum level [β = −1.115, $95\%$CI = (−1.751, −0.479)], followed by the associations of Mo with RF39 at the order level [β = −0.598, $95\%$CI = (−1.123, −0.073)] and Sr with Bacteroides at the order level [β = −0.408, $95\%$CI = (−0.754, −0.061)]. The strongest positive association was found between Mo and Megamonas at the genus level [β = 0.681, $95\%$CI = (0.248, 1.113)], followed by the associations of Sr with Bifidobacteriales at the order level [β = 0.412, $95\%$CI = (0.150, 0.673)], Mo with Bacteroides at the genus level [β = 0.385, $95\%$CI = (0.066, 0.703)], and Ca with Bacteroidales at the order level [β = 0.373, $95\%$CI = (0.078, 0.668)]. Additionally, although no association between the selected taxon whose abundances ranked as the top 5 at class level and EMs was significant, the p values for the associations between Mg and each taxon were all smaller than 0.07. We next used BKMR to replicate the results identified in multiple regression models. The CondPIPs for significant EMs that were identified using BKMR models ranged from 0.110 for Sr contributing to the abundance of Enterobacteriaceae at family level to 0.975 for Sr contributing to the abundance of Bacteroides at genus level (Table S8). Of these EMs, Sr (in an association with Bacteroides at the genus level and with Bifidobacteriales and Bacteroidales at the order level), Mo (with Tenericutes at the phylum level), and Ca (with Lachnospiraceae and Enterobacteriaceae at the family level) had condPIP values of >0.5 and exhibited the same association direction as those described in multiple regression models (Figure 3 and Figure S3–S7). Furthermore, dose–response curves from BKMR suggested a nonlinear (inverted U-shaped) association between Sr and Bacteroides at the genus level. ## 3.6. The Cumulative Effects of EMs on the Compositions of the Gut Microbiota The overall effect of the EM mixture on α-diversity is shown in Figure S8. There was a linear increase in the Shannon index or inverse-Simpson index with the elevated levels of the mixture, although no statistical significance was found. The overall effect of the EM mixture on specific taxon in different levels exhibited complex pictures (Figure S9). Of these associations, the EM mixture exhibited significantly negative associations with the abundance of Bacteroides and Megamonas at the genus level when all EMs were fixed at the 80th or above percentile (Figure 4). ## 3.7. The Interaction Effects of EMs on the Composition of the Gut Microbiota The bivariate exposure response functions, provided by BMKR, were used to identify the potential interactions. Although there was no obvious interaction between EMs on α-diversity (Figures S10 and S11) and the above-mentioned five significant taxons (Figures S12–S16), we found there were interactions between Sr and Mo in their associations with Bacteroides and between V and Mo in their associations with Megamonas (Figures S17 and S18). We then applied multi-variable linear regression models to confirm these interaction effects. Given the limited power of this model, the p value of interaction terms (Pint) < 0.3 could be considered as significant. We found that the effect of interaction between Sr and Mo on Bacteroides was significant when the Mo exposure was relatively high (Pint = 0.132–0.274), and the direction of the interaction suggested that Sr and Mo had a negative synergistic relationship (Figure 5A1–A3). The p values of the interaction term of V and Mo on Megamonas ranged from 0.082 to 0.098. Those models all showed significant interaction effects of V and Mo on the abundance of Megamonas when the V exposure was relatively high and with a positive synergistic relationship between them (Figure 5B1–B3). ## 4. Discussion The main findings of our study were as follows: [1] no single EMs were associated with the altercations in the gut microbiota diversity and community structure in the total sample, individually, and as a mixture, whereas several significant associations of single EMs (V, Co, Mg, Sr, and Ca) with α-diversity and/or β-diversity were found in subgroups of older adults; [2] both multiple linear regression and BKMR showed that Sr, Mo, and Ca significantly contributed to the abundance of several bacterial taxons at different levels: Sr (in an association with Bacteroides at the genus level and with Bifidobacteriales and Bacteroidales at the order level), Mo (with Tenericutes at the phylum level), and Ca (with Lachnospiraceae and Enterobacteriaceae at the family level); [3] the EM mixture exhibited a linear dose–response association with the Shannon or inverse-Simpson indices, although no significance was found; [4] the EM mixture showed significantly negative associations with the abundance of Bacteroides and Megamonas at the genus level, in which Sr and Mo had an interaction on Bacteroides, and V and Mo had an interaction on Megamonas. To our best knowledge, this is the first study to explore the associations between multiple EMs and gut microbiota in older adults. Our study suggested that EMs may play an important role in maintaining the steady status of gut microbiota. Urine samples have been widely used to assess individual exposure to EMs. The median urine concentrations of the EMs in our sample (μg/L) were comparable to those found in other research. For instance, the urine Co concentration of 0.369 μg/L was marginally lower than the reported value of Xiangdong Wang (0.389 μg/L) [30], while the urine V concentration of 1.804 μg/L was marginally higher than the value reported by Shunli Jiang (1.27 μg/L) [31]. These differences could result from the specific population, diet, lifestyle, and so on. Given that diets, lifestyles, and daily activities in older adults are more stable than those of other populations, such as teenagers, we believe that the urine EMs concentrations based on the cross-sectional investigation may be used to estimate or replace the exposure levels in the past period. Although both single- and multiple-element linear regression models did not find a significant association between Sr and alpha diversity, the BKMR models exhibited that Sr was the most important contributor within the EM mixture to alpha diversity, which was similar to the report from a prospective cohort study of Chinese pregnant women [32]. Furthermore, Sr showed a negative association with Bacteroidales (at the order and genus levels) and a positive association with Bifidobacteriales (at the order level). So far, no comparable study has been found. It is well known that Sr ions (Sr2+), resembling Ca ions (Ca2+), are bound to phosphate in the bones and have the potential for preventing osteoporosis. A recent study [33] found that the order Bacteroidales and family Lachnospiraceae were negatively associated with bone mass, which, together with our findings, suggests a possible link between Sr and Bacteroidales. Whether Bacteroidales mediates the effect of the Sr on bone health is warranted to be investigated. Excessive Mo intake could lead to diarrhea, which suggested that Mo may interfere with gut microbial metabolism. A recent animal study using a 2 × 2 factorial design to examine the effect of diet with Mo on the gut microbiota in laying hens found that high Mo levels in the diet led to lower Firmicutes and higher Proteobacteria abundance, possibly disrupting redox balance and reducing production performance [34]. However, high Mo level in experimental animals is unlikely to occur in the human body. The relationship between Mo and gut microbiota in the human body remains unknown. In this study, Mo was not associated with gut microbiota diversity as a whole, whereas it exhibited a negative association with Tenericutes and positive associations with Bacteroides and Megamonas in both the linear and BKMR models. Tenericute, Bacteroides, and Megamonas are all the dominant bacteria in the gut, and the latter two could produce butyric acid. Our findings suggested that moderate Mo supplements in older adults may help the growth of beneficial bacteria in the gut. Animal studies have reported increased alpha diversity in those rats or mice fed with a high Ca diet [17,35]. The possible mechanism by which Ca beneficially modifies the gut microbiota is via precipitating bile acids and fatty acids and reducing cytotoxicity to the intestinal mucosa [36,37]. However, human studies on the association of Ca with gut microbiota did not yield consistent findings. For instance, Falak Zeb et al. [ 38] conducted a randomized controlled trial and reported that there was a negative association between Ca and alpha diversity. Similarly, Lara S. Yoon et al. [ 21] performed a randomized crossover design where three groups of study participants with a supplement of calcium alone, inulin alone, or both calcium and inulin did not exhibit significant differences in alpha diversity and composition of gut microbiota. The reasons for the mixed results remain unclear. In addition to sample sizes, supplemental dose, and intervention duration, another possibility is that the effect of Ca on gut microbiota is dependent on other EMs. In this study, both single-element and mixture models were used to examine the association between Ca and gut microbiota. We observed that Ca was positively associated with the Shannon and inverse-Simpson indices, individually and as a mixture, although no significance was found. Moreover, a small but significant difference was found in β-diversity between high and low levels of urine Ca exposure in females. Firmicutes and Bacteroidetes were the dominant bacteria of the human gut microbiota, and the Firmicutes/Bacteroidetes (F/B) ratio was related to obesity in recent years [39,40]. In our study, we found that urinary Ca had a negative association with the F/B ratio, which aligns with an animal study [16] in which dietary calcium promoted a significant increase in Bacteroidetes and Actinobacteria. Additionally, there was a negative and significant association between urinary Ca and the abundance of Lachnospiraceae in our results, which was similar to reports by Li et al. [ 41], where the abundance of Lachnospiraceae significantly decreased in the mice fed normal Ca as compared with those fed with low-level Ca. A previous study indicated that dietary calcium intake could increase the intraluminal calcium concentration to stimulate gastrin release and acid secretion, and further prohibit the growth of acid-intolerant bacteria, such as Lachnospiraceae [36]. A major strength of our study is that we utilized a new denoised method to deal with the count data of gut microbiota, which effectively avoided the troublesome zero-inflated nature of microbiome data and ensured the quality of downstream analysis. Secondly, applying the BKMR model helped us to identify which EMs provided the greatest contribution to the outcomes, and the revealed non-linear association and interaction may be more meaningful for future validation studies. Likewise, the limitations of this study also need to be acknowledged and discussed. First, food consumption frequencies rather than consumption quantities were obtained, which may not be accurate measurements for dietary intakes and could lead to residual confounding. Second, the cross-sectional nature of this study restricted causal inferences. Finally, the 16S rRNA amplicon sequencing cannot identify the bacteria at the genus and the species level, which may result in erroneous conclusions. ## 5. Conclusions In summary, our study found some novel associations of common essential nutrient elements with the gut microbiota diversity and specific taxons with a higher abundance in older adults: The negative and linear associations of Mo with Tenericutes, Sr with Bacteroidales, and Ca with Enterobacteriaceae and Lachnospiraceae, and a positive and linear association of Sr with Bifidobacteriales were found. Our findings suggested that EMs may play an important role in maintaining the steady status of gut microbiota. Future prospective epidemiological studies with metagenomic sequencing are needed to replicate these findings, as well as to further elucidate the additional contribution of the EMs to the gut microbiota. ## References 1. Adak A., Khan M.R.. 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--- title: Stimuli-Responsive and Antibacterial Cellulose-Chitosan Hydrogels Containing Polydiacetylene Nanosheets authors: - Edwin Shigwenya Madivoli - Justine Veronique Schwarte - Patrick Gachoki Kareru - Anthony Ngure Gachanja - Katharina M. Fromm journal: Polymers year: 2023 pmcid: PMC10005511 doi: 10.3390/polym15051062 license: CC BY 4.0 --- # Stimuli-Responsive and Antibacterial Cellulose-Chitosan Hydrogels Containing Polydiacetylene Nanosheets ## Abstract Herein, we report a stimuli-responsive hydrogel with inhibitory activity against Escherichia coli prepared by chemical crosslinking of carboxymethyl chitosan (CMCs) and hydroxyethyl cellulose (HEC). The hydrogels were prepared by esterification of chitosan (Cs) with monochloroacetic acid to produce CMCs which were then chemically crosslinked to HEC using citric acid as the crosslinking agent. To impart a stimuli responsiveness property to the hydrogels, polydiacetylene-zinc oxide (PDA-ZnO) nanosheets were synthesized in situ during the crosslinking reaction followed by photopolymerization of the resultant composite. To achieve this, ZnO was anchored on carboxylic groups in 10,12-pentacosadiynoic acid (PCDA) layers to restrict the movement of the alkyl portion of PCDA during crosslinking CMCs and HEC hydrogels. This was followed by irradiating the composite with UV radiation to photopolymerize the PCDA to PDA within the hydrogel matrix so as to impart thermal and pH responsiveness to the hydrogel. From the results obtained, the prepared hydrogel had a pH-dependent swelling capacity as it absorbed more water in acidic media as compared to basic media. The incorporation of PDA-ZnO resulted in a thermochromic composite responsive to pH evidenced by a visible colour transition from pale purple to pale pink. Upon swelling, PDA-ZnO-CMCs-HEC hydrogels had significant inhibitory activity against E. coli attributed to the slow release of the ZnO nanoparticles as compared to CMCs-HEC hydrogels. In conclusion, the developed hydrogel was found to have stimuli-responsive properties and inhibitory activity against E. coli attributed to zinc nanoparticles. ## 1. Introduction Biosensors play an important role in clinical diagnostics, point-of-care testing, personalized medicine, and pharmaceutical research [1]. In this area, highly sensitive detection systems that have excellent specificity are required for their ability to provide useful insights into an individual’s health [2]. Over the last decade, the scientific community has focused much of its research on biosensor systems applicable to point-of-care (POC) diagnostic devices which can rapidly assess the cause of various illnesses among patients [1,2,3,4,5]. These diagnostic devices facilitate faster and more accurate identification of diseases, which leads to better treatment of patients [1,2]. Among these devices, hydrogels are attracting attention due to their versatility in chemical modification which makes them responsive to external stimuli such as pH [6,7,8,9], or temperature [1,10] and the prospect of encapsulating therapeutic agents such as antimicrobials, e.g., zinc and silver, containing nanoparticles within their matrix [2]. They are developed from both synthetic polymers and biopolymers, and are utilized in tissue engineering, artificial biomedical scaffolds, soft actuators, wound dressings and environmental remediation [6,11]. Chitosan [12,13], cellulose [14,15,16], pectin [17], alginate [18,19], and carrageenan [20,21] are the most attractive surrogates to petroleum-based starting materials due to their relative abundance and availability. Moreover, their biodegradability, biocompatibility, ease of functionalization and gelling properties make them ideal starting materials for developing hydrogels [22,23]. These features can also be enhanced through chemical and physical crosslinking of two different hydrogels to create dual crosslinked polymers [24,25]. In addition, chitosan is a polyelectrolyte that has mild swelling capacity in acidic media but its functionalization by the introduction of a carboxymethyl group and crosslinking to another high swelling capacity polymer such as hydroxyethyl cellulose will further enhance its properties. Moreover, the incorporation of conjugated polymers and other chemical moieties within their matrices further enhances their functionality as they impart the ability to respond to external stimuli [2,26,27]. Conjugated polymers such as polyaniline, polypyrrole, and polydiacetylene (PDA) enable them to function as electrochemical biosensors since the electrochemical properties of the conjugate systems are associated with visible colorimetric changes [26,28]. PDAs are especially attractive as they exhibit a blue to red color transition visible to the naked eye when they are subjected to external stimuli such as changes in temperature, pH, bacterial cells, and aromatic compounds [7,29]. They have also been used in the development of highly sensitive colorimetric probes for the detection of cholesterol [5], ammonia [30], glucose [31], microorganisms [7,32], volatile organic compounds [33], active pharmaceutical excipients, and small and large biomolecules among other compounds [28]. In this study, a hydrogel prepared from polydiacetylene-zinc oxide-carboxymethyl chitosan-hydroxyethyl cellulose (PDA-ZnO-CMCs-HEC) was evaluated for its pH responsiveness, colorimetric transitions and inhibitory activity against E. coli. First, chitosan (Cs) was esterified with monochloroacetic acid to obtain carboxymethyl chitosan (CMCs) which was subsequently chemically crosslinked with hydroxyethyl cellulose (HEC) using citric acid as the crosslinking agent to obtain CMCs-HEC hydrogels. To enhance the pH responsiveness of the hydrogels, PDA-ZnO was introduced into the CMCs-HEC hydrogel to obtain a colorimetric pH-responsive PDA-ZnO-CMCs-HEC hydrogel. To better understand the properties such as pH responsiveness, thermal profile, crystallinity, functional group changes upon crosslinking and antimicrobial activity of the hydrogels against E. coli, the hydrogels were then analyzed using a Fourier transform infrared spectrophotometer (FT-IR), powder X-ray diffraction (XRD), scanning electron microscopy (SEM), thermal gravimeter analyzer (TGA), and UV-Vis techniques as well as by antibacterial assays. ## 2.1. Materials Chitosan (medium molecular weight: 100,000–300,000 Da), 10,12-pentacosadiynoic acid ($97.0\%$, HPLC grade), and isopropanol (ACS reagent, ≥$99.5\%$), glacial acetic acid (ACS reagent, ≥$99.7\%$), sodium hydroxide (ACS reagent, ≥$97.0\%$, pellets), and ethyl alcohol ($95\%$) were procured from Sigma Aldrich Co. (Buchs, Switzerland). ## 2.2. Synthesis of CMCs-HEC Hydrogels Carboxymethyl chitosan (CMCs) was synthesized by esterification using monochloroacetic acid in aqueous media (Scheme 1) [15,34,35]. First, 1 g chitosan (Cs) was dispersed in 20 mL 25 µM NaOH solution followed by the addition of 50 mL isopropyl alcohol and monochloroacetic acid (11–32 mmol/g), respectively. The reaction mixture was heated to 80 °C for 2 h under constant stirring, after which the product was purified by washing repeatedly with $98\%$ ethanol and dried in an oven to constant weight [36]. This was followed by Soxhlet extraction of sodium chloride using methanol as the extracting solvent for 6 h at 70 °C [10,14,37]. To prepare CMCs-HEC hydrogels, different formulations containing HEC and CMCs in the ratios between 1:1–1:5 were prepared in petroleum ether as follows [36]. First, to a series of solutions containing 1 g CMCs in petroleum ether, between 1 and 5 g of HEC were added and stirred for 2 h at room temperature. This was followed by the addition of citric acid (1.04 mmol/g CMCs-HEC) to the solution, which was then stirred for 16 h at 60 °C for the crosslinking to occur, filtered, washed with $98\%$ ethanol and dried to constant weight at 105 °C to obtain CMCs-HEC hydrogels [15,34,35,36,38]. To obtain PDA-ZnO-CMCs-HEC hydrogels, the composite was prepared in situ by crosslinking CMCs and HEC in the presence of 2.67 mmol 10,12-pentacosadiynoic acid containing 2.5 mmol ZnO followed by UV irradiation at 254 nm to form a composite (Scheme 2) [7,30,32]. ## 2.3. Swelling Measurements To evaluate the swelling capacity of the hydrogels as a function of HEC:CMC ratio and pH, 100 mg of the samples were pressed using a laboratory hand press (International Crystal Laboratories, Garfield, NJ, USA) to obtain 7 mm discs. To evaluate the effect of CMC:HEC ratio on the swelling capacity of the hydrogels, the discs were prepared from samples containing HEC-CMC crosslinked in the ratios between 1:1 and 1:5 g/g. To study the effect of pH of the swelling media on the swelling capacity, 7 mm disc of HEC:CMC (1:4 g/g) were then immersed in ultrapure water whose pH was adjusted to 3, 5, 7, 11, and 14 at 20 °C [27,38,39]. The swelling capacity was then determined in triplicate by removing the discs from the swelling media at 1 min intervals, blotting to remove excess water and recording the weight gained. The degree of swelling was then calculated using the following equation and reported as mean ± SD:[1]Swelling capacity=Mt−MiMi × 100 where Mi and Mt are the weights of the hydrogels before and after swelling, respectively [38]. ## 2.4. Colorimetric Response to pH Change The colorimetric response to changes in pH was evaluated by preparing different solutions of PDA-ZnO-CMCs-HEC at 3.3 g/L in distilled water basified with 1 M NaOH to adjust the pH to 7, 8, 10, 12, and 14, respectively. These solutions were then analyzed with a fluorimeter using excitation wavelengths 320 and 365 nm, and measuring from 360 to 620 nm and 400 to 700 nm, respectively. The solutions were then diluted to 400 µL in 2.0 mL of pH-adjusted distilled water and their absorbance was measured using a UV-Vis spectrophotometer from 800 to 200 nm [7,28,29,40]. ## 2.5. Characterization of PDA-ZnO-CMCs-HEC The IR spectra of the PDA-ZnO-CMCs-HEC hydrogels were acquired in the frequency range of 4000–400 cm−1 using a Bruker Tensor II FT-IR spectrophotometer model (Bruker, Ettlingen, Germany) [41,42]. UV measurements, fluorescence measurements, and bacterial optical density measurements were measured using Perkin Elmer Lambda 40 UV-Vis Spectrophotometer (Perkin Elmer, Waltham, MA, USA), Perkin Elmer Luminescence spectrometer LS50B (Perkin Elmer, Waltham, MA, USA) and Spark 10 M multimode microplate reader, (Tecan Austria GmbH, Grodig, Austria), respectively. The X-ray diffractograms were obtained using a Bruker D8 Advance Diffractometer (Bruker, Ettlingen, Germany) with a copper tube operating at a voltage and current of 40 kV and 40 mA. The samples were irradiated with a monochromatic CuKα radiation of 0.1542 nm and the diffractograms were acquired between 2θ values of 5°–90° at 0.05° intervals with a measurement time of 1 s per 2θ intervals. The thermal profile of the hydrogels was evaluated using a Mettler Toledo TGA/DSC (Mettler-Toledo GmbH, Greifensee, Switzerland) [41,43]. Approximately 5 mg samples were weighed into 40 µL aluminium crucibles which were then heated from 25–500 °C at 10 °C/min and cooled to 25 °C. Morphological analysis of the composite hydrogels was observed using a Tescan Mira3 LM FE scanning electron microscope (Tescan, Brno-Kohoutovice, Czech Republic) operating under an accelerating voltage of 3 kV. The samples were sputter coated with 4 nm gold before analysis to avoid charging using AGB7340 Agar Sputter Coater (Agar Scientific, Essex, UK) [42,43]. The elemental mapping of the hydrogel was obtained with a Thermo Fischer SEM FEIXL30SFEG equipped with an Oxford Aztec advanced system equipped with an X-MAX 150 mm2 Silicon Drift Detector. The EDX mapping was obtained with an accelerating voltage of 15 kV and an acquisition time of 2 h. ## 2.6. Zinc Ion Release Experiments To determine the concentration of Zn2+ ions diffusing from the hydrogels [44], 1 g of the composite containing 0.6 mmol ZnO was immersed in 300 mL distilled water to allow swelling of the hydrogels [45]. Aliquots of 15 mL were subsequently drawn from the solution at 30 min intervals, acidified with 1 mL concentrated nitric acid and the concentration of the solution was measured using a Perkin Elmer ICP-OES Optima7000 DV (Perkin Elmer, Waltham, MA, USA) [46]. ## 3. Killing Curve Assay To test the minimum inhibitory concentration (MIC) and minimum bacterial concentrations (MBC), a colony of Escherichia coli 25,922 was picked up from a Muller Hinton (MH) agar plate and incubated in 5 mL of MH broth overnight at 37 °C and 180 rpm. Then, a 24 g/L solution of PDA-ZnO-CMCs-HEC in H2O was prepared and agitated at 10 rpm overnight [45,46]. A 96 well-plate was filled with different volumes of the hydrogel solutions, and total volumes were completed to 100 uL with MH broth. Bacteria preculture was diluted to 2.0 × 104 cfu/mL MH broth [45,46] after which 100 uL of this culture was inoculated in each well except sterility controls where 100 uL of MH broth was added. The plate was incubated in a humidity cassette at 37 °C and stirred at 72 rpm with absorbance monitoring for 23 h. MIC was then determined as the first concentration for which the absorbance did not increase during the incubation. Then serials of dilutions were performed for all wells, and 100 μL were spread on MH agar plates. They were incubated at 37 °C for 20 h, and the bacterial colonies were counted. MBC was then determined as the first concentration for which no colony grew at all. The same procedure was applied for the negative control [45,46]. ## 4. Statistical Analysis The results obtained in this study were analyzed using Originlab statistical software package (V. 22, OriginLab Corp, Northampton, MA, USA) and reported as their mean ± standard deviations while the least significance difference test (ANOVA, LSD test p ≤ 0.05) was used to evaluate the influence of pH and CMC/HEC ratio on the swelling capacity of the hydrogels. ## 5.1. Swelling Behaviour of CMC-HEC Hydrogels The swelling capacities of the CMCs-HEC hydrogels (Scheme 1) are depicted in Figure 1. As can be observed from Figure 1, while CMCs had a higher swelling capacity as compared to Cs, chemical crosslinking of CMCs with HEC using citric acid resulted in a subsequent increase in the swelling capacity of the resultant hydrogels [36]. Higher swelling capacities were observed when the amount of HEC present in the hydrogels was increased from 0 to 5 g with the highest swelling capacity observed when the ratio of CMCs-HEC was 1:4 g/g, respectively. It is worth noting also that maximum water uptake was attained after 20 min in which 1 g of the hydrogel absorbed up to 7 g of water (≈700 %) to attain equilibrium (Figure 2). This fluid uptake is relatively higher as compared to carboxymethyl cellulose (CMC)-hydroxyethyl cellulose (HEC) hydrogels crosslinked with fumaric acid which exhibited a $400\%$ swelling capacity [15,35]. The swelling capacity is dependent on the behaviour of individual components making up the hydrogels, the degree of crosslinking and the type of crosslinker used in the synthesis. For instance, CMC and HEC have been reported to have lower swelling capacities when individually crosslinked with citric acid as compared to when they are crosslinked together [35,36,47,48]. In HEC, the decrease in the swelling capacity was attributed to a lower potential for citric acid adsorption leading to a lower degree of crosslinking brought about by fewer OH groups [36,49,50]. While in CMC the observed lower swelling capacity of $200\%$ was attributed to an increase in electrostatic repulsion between the charged macromolecules of the polyelectrolyte chains that limit water uptake [10,36,51]. As such, the increase in the swelling capacity in CMCs-HEC can be associated with the formation of intermolecular rather than intramolecular crosslinks when HEC is added to the citric acid solution containing CMCs [36]. Moreover, higher swelling capacities are normally observed when the ratio of the constituent polymers favours a higher degree of crosslinks between individual polymers and the crosslinking agent increases hydrogen bond interactions [36,50]. For instance, when crosslinked with divinyl sulfone, CMC and HEC have been reported to have a swelling capacity of $200\%$ as compared to citric acid crosslinked CMC-HEC which had an equilibrium swelling capacity of $900\%$ [52]. The effect of pH on the swelling capacity of the hydrogels was evaluated at pH 3, 5, 7, 10, and 12 (Figure 1b). At low pH values, the swelling capacity of the hydrogels was higher but it gradually decreased as the pH of the swelling media became more basic. The highest swelling capacity of $625\%$ (6g of water per gram of hydrogels) was observed when the pH of the swelling media was 3 while the lowest swelling capacity was observed at pH 12. This phenomenon was attributed to the strong interactions between the two polymers and citric acid, including hydrogen bonding between –COOH and –OH groups and electrostatic interactions between –NH3+ and –COO− groups. Indeed, as the pH of the swelling media increased, hydrogen bond interactions between –NH2 groups and –OH groups and repulsions in the polymer chains resulted in a decline in the swelling capacity. Moreover, it should be noted that using citric acid as the crosslinker leads to a formation of an ester bond (C–O–C) between OH groups present in the two polymers and COOH groups present in citric acid [52]. In the presence of OH− ions, this bond is cleaved forming carboxylic acid salts thereby reducing hydrogen bond interactions between water molecules and the hydrogels. However, at low pH, the protonation of amino groups in chitosan, a water-soluble cationic polyelectrolyte, led to repulsion in the polymer chains thus dissociation of intramolecular hydrogen bond interactions allowing more water uptake into the gel network. Moreover, displacement of Na+ present in COONa of carboxymethyl chitosan enhances hydrogen bond formation hence the observed increase in swelling capacity at low pH values. Thus, the ease of solution uptake into the hydrogel at low pH values is attributed to the protonated amine groups in chitosan while the “charge screening effect” of Na+ that occurs at higher pH values accounts for the decrease in swelling capacity in a basic medium [38]. In addition, at higher pH values, the electronegative oxygen atom of the water molecule is electrostatically attracted to the positive charge of the metal ions having a larger hydration sphere thereby deswelling the hydrogel. The pH value of normal skin is usually below 5 but once the surface is damaged, the underlying tissue with a pH value of 7.4 is normally exposed. During the early stages of wound healing where the pH is slightly acidic, the PDA-CMC-HEC hydrogels can accelerate cell infiltration and proliferation and facilitate oxygen osmosis thereby aiding wound healing [39,53]. The potential applications of such a hydrogel include releasing antimicrobial agents such as metallic nanoparticles during the initial wound healing phase, which will reduce the extent of inflammation in the inflammation stage and avoid bacterial overgrowth in the fibroblast proliferation phase [54,55,56]. ## 5.2. IR Results of CMCs, CMCs-HEC Hydrogels FTIR spectroscopy was used to follow the introduction of the carboxymethyl group in Cs (Figure 3) and the subsequent crosslinking of HEC and CMCs with citric acid monomers (Figure 4). The introduction of the carboxymethyl group on CS to form CMCs was manifested by a weak band associated with C=O bonds at 1727 cm−1 (Figure 3b). Initially, this band was not observed in both CS and CMCs but ion exchange of CMCs in acetic acid (Figure 3a) resulted in the conversion of RCOONa groups to RCOOH observed by the characteristic C=O vibrational band (Figure 3b) [52]. Characteristic bands of chitosan were observed at 3740 cm−1 attributed to NH stretching, OH stretching between 3000–3500 cm−1, CH stretching at 2900–2879 cm−1, NH bending at 1599 cm−1, CH bending at 1401 cm−1 and C–O stretching at 1030 cm−1 [35,38,47,57]. The band at 1628 cm−1 assigned to the NH2 group overlapped with the asymmetrical stretching vibration of COONa groups. Treatment of CMCs with acetic acid converted the COONa group to the COOH group evidenced by the formation of a strong characteristic band of C=O at 1738 cm−1 present in the free protonated carboxylic groups (Figure S2) [49,57]. Note that, the substitution of carboxymethyl groups on the oxygen in the C6 position imparts a pH-dependent anionic character to the molecule [49,52]. From Figure 4, the chemical crosslinking of CMCs with HEC using citric acid as the crosslinking agent led to the formation of a sharp band associated with C=O vibration at 1737 cm−1 [57]. This band was attributed to the ester linkage formed when the hydroxyl groups of CMCs and HEC reacted with the carboxyl group of citric acid. On the other hand, the intensity of the NH peak observed at 1599 cm−1 decreased as the amount of HEC was increased during the crosslinking reaction (Figure S3) [35,38,52]. PDA nanosheets synthesized in the absence of the hydrogels were also characterized by IR (Figure S4). The vibrational bands originating at IR νmax (cm−1): 2918−2847 (C−H stretch), 1690 (C=O stretch), and 722 (C−H bend) were observed [7]. In the presence of CMCs-HEC, a doublet was observed at 2922 cm−1 and 2852 cm−1 (C−H stretch), while the bands at 1419 cm−1 in CS shifted to 1422 cm−1 with a shoulder at 1394 cm−1. ## 5.3. XRD Results of CMCS-HEC Composite The X-ray diffractograms of CMCs-HEC and PDA-ZnO-CMCs-HEC hydrogels are depicted in Figure 5. From Figure 5, it can be observed that there were no remarkable changes in the diffractograms after chemical crosslinking of CMCs and HEC with citric acid. Typically, chitosan exhibits broad peaks centred at about 2θ = 18°and 32° attributed to the presence of OH and NH2 groups which form stronger interactions that impart some degree of crystallinity. On the other hand, being a precursor of cellulose, HEC exhibits diffraction peaks that are typically observed in cellulose nanofibers at 2θ angles of 16°, 22°, and° which have been attributed to the diffraction planes of [101] and [002] characteristic of cellulose I (Figure S5) [43,58]. Chemical crosslinking CMCs and HEC with citric acid resulted in one peak centred at 2θ = 21 being observed with sharp peaks at 2θ values of 31°, 37°, 45°, 56°, and 75°. The broad peak was attributed to the overlap of broad peaks of Cs and HEC while the sharp peaks were attributed to citric acid used as a crosslinking agent in the hydrogels (Figure S5). The incorporation of PDA-ZnO within the hydrogels was linked to the sharp peaks observed at 2θ values of 32°, 36°, 48°, 57°, 68°, 75°, and 85° associated with [100], [101], [102], [110], [103], and [113] crystal planes of crystalline ZnO nanoparticles which had an average crystallite size of 18.22 ± 4.7 nm calculated from the Scherrer equation [7,59]. ## 5.4. Thermal Profile of Cs, CMCS, and Hydrogels The thermal profiles of Cs, CMCs, and the prepared hydrogels were investigated using TGA/DTGA and the results are depicted in Figure 6 and Figure 7. Both CMCs and Cs had two degradation stages in which, the first degradation stage accounted for water loss in CMCs and the hydrogels, which was slower than for Cs particles (Figure 6 and Figure 7). The second and third mass loss in the DTGA thermograms of the hydrogels was observed in the range between 175–300 °C. The onset degradation temperature and ash content for Cs was higher as compared to that of CMCs and the hydrogels. The first degradation stage observed below 100 °C was attributed to the evaporation of water molecules adsorbed on the surface of the Cs, CMCs, and hydrogels [15,35]. For Cs and CMCs, the second degradation stage that is linked to the breaking down of the anhydroglucose units was observed to occur at 286 and 259 °C, respectively [60]. As for the hydrogels, this degradation stage was observed to occur at 273 °C since a second degradation stage associated with the degradation of the crosslinking agent was observed at 175 °C. In this study, the CMCs-HEC hydrogel possessed lower thermal stability than the corresponding precursors as CMCs and HEC have maximum degradation temperatures of 284 and 280 °C, respectively [15,35]. ## 5.5. UV-Vis Spectra of Polydiacetylene Figure 8 depicts the UV-Vis spectra of the photopolymerization reaction that converted PDCA to PDA and the subsequent thermochromic shift. As illustrated in Figure 8, the photopolymerization of 10,12-pentacosadiynoic acid (PCDA) upon irradiation with UV radiation occurred as a function of time. The plot shows a sharp increase in the absorbance at the onset of the photopolymerization which starts to level off after irradiation for 6 min, an indication of the formation of PDA nanosheets (Figure 8b inset). The colour of the solution, which changed from clear colourless to dark blue, was also another indicator of the complete polymerization, but upon exposure of this solution to higher temperatures, it changed to red (Figure 8b inset) [7,33,59,61]. Interaction of the monomers upon radiation resulted in the formation of a peak centred at 650 nm that increased with longer photopolymerization time as the nanosheets are formed (Figure 8a). However, due to the presence of an extended π-electron system composed of alternating carbon multiple bonds, this peak can shift to higher or lower wavelengths when the sheets are exposed to external stimuli such as thermal, chemical, and mechanical. At higher temperatures, however, the hypsochromic shift observed at 550 nm when blue PDA nanosheets change to red PDA nanosheets is a result of the conjugation effect which endows the chromatic properties to the material giving rise to the observed shift in λmax (Figure 8b) [2]. ## 5.6. Colorimetric Response of the Hydrogels to pH Figure 9 and Figure 10 depict the fluorescence and UV spectra of the hydrogel solutions as a function of pH. From Figure 9, it was observed that PDA-ZnO-CMCs-HEC was only slightly fluorescent, and no major changes were observed when the pH was varied. Nevertheless, the hydrogel depicted negative solvatochromism when in contact with basic media which is illustrated in the UV absorbance spectra (Figure 10). The observed hypsochromic shift upon changes in pH between pH 5 (max at 364 nm) and pH 14 (max at 364 nm) was also visually observed as the hydrogel changed its colour from pale purple to pale pink (Figure 10 inset). This colorimetric response was also evident when PDA-ZnO was exposed to pH changes, though in this case, it resulted in a hypsochromic shift on exposure to basic pH [7,28,29,40]. Similar observations have been made in the literature as various composite materials containing PDA showed colorimetric transition when they were exposed to basic pH [7,28,29,40]. This transition has been reported to be important as it can be used as a means to detect the onset of infection in wounds as, during infection, the pH around the wound area has been reported to be basic [7,28,29,40]. ## 5.7. SEM Micrographs The nanostructures formed during the formation of PDA-ZnO-CMCs-HEC were studied using SEM and the results are depicted in Figure 11 and Figure 12. In this study, the presence of amine groups and a plethora of OH functional groups in the hydrogel network are instrumental in the structuring and stabilization of the PDA assemblies within the hydrogel network. The nanocomposite comprised a mixture of nanorods, nanosheets and spherical hollow rings which was attributed to the composition making up its matrix (Figures S6–S8) [62]. Before chemical crosslinking, HEC appeared as thick fibres while CMCs appeared as sponge-like amorphous materials (Figure S7) but after chemical crosslinking and in situ synthesis of PDA-ZnO, the composition of the resultant hydrogel changed (Figure S8). ZnO nanoclusters which appeared as rods of different sizes and lengths within the hydrogel network were confirmed by EDS spectra (Figure 12) and elemental mapping of the composite (Figure S9). These nanorods were also observed when the hydrogels were used to prepare composite films through dissolution and solvent casting in PET moulds. In this case, while the surface of the resultant dried hydrogel films was smooth, observation of its cross-section revealed that it comprised multiple layers stacked together in a repeating pattern (Figure 11) similar to multi-layered hydrogels reported in the literature. These highly porous and interconnected multiple layers are a result of strong ionic and electrostatic interactions within the hydrogel network which have been shown to be responsible for the formation of interlayer spaces in a composite hydrogel [63,64]. The formation and growth of each layer are related to the diffusion of the crosslinker with the interlayer spaces fine-tuned by changing the degree of crosslinking between layers [63,64,65]. For the case of PDA-ZnO-CMCs-HEC, the interaction between zinc ions and the hydroxyl and amine functional groups found in CMCs-HEC induced a chelation effect which lead to the formation of multi-layered hydrogels [63,64,65]. In addition, polyanions such as Ca2+, Al3+, Cu2+ and other polyvalent inorganic cations have been used as crosslinkers to prepare multi-layered hydrogels with arbitrary shapes, including onion-like, tubular, and star-like from combinations of different polymers such as alginate, carboxymethyl cellulose, chitosan, and agar, among others [63,64,65]. ## 5.8. Kinetics of Zn2+ Release from the Nanocomposites Figure 13 depicts the concentration of zinc ions released from the hydrogels over a period of 120 h as determined by ICP-OES measurements. From Figure 13, it can be observed that the percent concentration of zinc ions released increases over time with the highest amount released being observed after 72 h before it gradually became constant. From the experimental data obtained in this study, it was observed that the amount of zinc ions released was initially low during the first few hours after the hydrogels were immersed in the absorbing media but it gradually increased with time. After 1 h, the concentration of Zn2+ ions in solution was found to be 6.74 ppm but it gradually increased to 121 ppm ($93\%$ Zn2+) after 96 h (Figure S10) remaining constant afterwards [46,66]. Similar observations were made when AgNPs were embedded within chitosan-PEG hydrogels and titanium dioxide nano-capsules in which there was a gradual increase in the concentration of AgNPs being released over several days [46,66]. It is worth mentioning that diffusion of Zn2+ ions loaded into a polymer matrix usually implies water penetration into the matrix, hydration, swelling, diffusion of the dissolved substance and or erosion of the gelatinous layer. The amount of encapsulated moiety released from the matrix is dependent on the loading efficiency, the solution pH, and the nature of the encapsulated substance and polymer used [46,66]. ## 5.9. Antimicrobial Assays The inhibitory activity of the hydrogel was evaluated by killing curve assay and the results are depicted in Figure 14. As can be observed in Figure 14, the bacterial concentration at the start was 1.0 × 10−4 cfu/mL but PDA-ZnO-CMCs-HEC was able to inhibit the growth of E. coli as compared to CMCs-HEC that was used as a control (Figure 15 and Figure 16). The composite hydrogels were able to absorb liquid from the culture media which necessitated the flow of Zn2+ ions from the hydrogels into the surrounding medium thereby preventing the growth of bacteria [66]. As for the case of CMCs-HEC, the low inhibition activity observed was a result of the absence of ZnO nanoparticles which were present in PDA-ZnO-CMCs-HEC [66]. The PDA-ZnO-CMCs-HEC scaffolds showed excellent antibacterial activity toward clinical E. coli compared with CMCs-HEC scaffolds in vivo attributed to the release of Zn2+ ions from the composite hydrogels. From kinetic measurements (Figure 13), it was observed that after 24 h, the amount of zinc ion released from the composite hydrogels into swelling media was 74 ppm which was linked to the antibacterial activity being observed during bacterial studies (Figure 14 and Figure 15) as the composite hydrogel without ZnO that did not inhibit bacterial growth (Figure 16). This strain of E. coli has an MBC (Figure 14) and an MIC (Figure 15) of 1.5 g/L for PDA-ZnO-CMCs-HEC as compared to CMCs-HEC which did not exhibit any inhibitory activity against the bacteria. It has been shown that the incorporation of metal nanoparticles such as ZnO and AgNPs in a nanocomposite enhances their inhibitory activity thereby preventing the onset of infections. This inhibitory activity is associated with the gradual and sustained release of the antimicrobial ZnO nanoparticles which interact with the bacterial cell causing cell membrane disruption, the infiltration of cell components and, ultimately, bacterial cell lysis (Figure 17) [45,46,66]. Moreover, chitosan alone and chitosan functionalized with cyclohexanone and 2-N-methylpyrrolidone have been shown to have a minimum inhibitory concentration of 50 g/mL for E. coli [39]. ## 6. Conclusions We rationally combined PDA-ZnO nanosheets as a stimuli-responsive matrix into CMCs-HEC hydrogel to obtain a pH- and thermal-responsive PDA-ZnO-CMCs-HEC hydrogel with inhibitory activity against E. coli. The swelling capacity of the hydrogels was pH dependent as it had a higher swelling capacity in acidic pH as compared to basic pH, though the presence of PDA-ZnO within the hydrogel imparted a colorimetric response triggered by a change in pH from acidic to basic pH. This response resulted in a colour transition from purple to pale pink visible to the naked eye and as such, it is hypothesized that this property can be utilized as a sensing mechanism to indicate the onset of infection in a wound. Moreover, the incorporation of ZnO within the hydrogel ensured that the composite had inhibitory activity against E. coli as compared to the composite without nanoparticles. ## Figures and Schemes Scheme 1: Mechanism of thermochemical crosslinking of CMCs and HEC using citric acid. Scheme 2: Synthesis of PDA-ZnO-CMCs-HEC. Figure 1: Swelling capacity as a function of (a) HEC content and (b) pH of the swelling medium. Figure 2: Swelling capacity as a function of time. Figure 3: IR spectra of (a) RCOOH, (b) RCOONa. Figure 4: IR spectra of (a) PDA-ZnO-CMCs-HEC, (b) CMCs-HEC, and (c) CMCsNa. Inset CMCs-HEC hydrogels and PDA-ZnO-CMCs-HEC hydrogels. Figure 5: X-ray diffractograms of (a) CMCs-HEC and (b) PDA-ZnO-CMCs-HEC hydrogels. Figure 6: TGA thermograms of (a) Cs, (b) CMCsNa, and (c) hydrogels. Figure 7: DTGA thermograms of (a) Cs, (b) CMCsNa, and (c) hydrogels. Figure 8: Absorption spectra of (a) PDA-ZnO, (b) plot of A670nm vs. time. Inset thermochromic shift at 65 °C and PDA nanosheets micrograph. Figure 9: Fluorescence spectra of PDA-ZnO-CMCs-HEC as a function of pH. Figure 10: Absorbance spectra of PDA-ZnO-CMCs-HEC as a function of pH. Inset colour transition at higher pH. Figure 11: SEM micrographs of hydrogels (a) surface and (b) cross-section. Inset hydrogel cross-section. Figure 12: EDS spectra showing the elemental composition of the composite films. Figure 13: Percent Zn2+ ions diffusing from the hydrogels per unit time (h). Figure 14: Minimum bacterial concentration (MBC) of alive E. coli 25,922 isolates after 23 h in the presence of (a) PDA-ZnO-CMCs-HEC and (b) CMCs-HEC hydrogels. Figure 15: Growing bacteria in the presence of (MIC) of PDA-ZnO-CMCs-HEC. 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--- title: High Levels of Glycated Hemoglobin (HbA1c) Are Associated with Physical Inactivity, and Part of This Association Is Mediated by Being Overweight authors: - Samara Silva de Moura - Luiz Antônio Alves de Menezes-Júnior - Ana Maria Sampaio Rocha - Aline Priscila Batista - Mariana Carvalho de Menezes - Júlia Cristina Cardoso Carraro - George Luiz Lins Machado-Coelho - Adriana Lúcia Meireles journal: Nutrients year: 2023 pmcid: PMC10005515 doi: 10.3390/nu15051191 license: CC BY 4.0 --- # High Levels of Glycated Hemoglobin (HbA1c) Are Associated with Physical Inactivity, and Part of This Association Is Mediated by Being Overweight ## Abstract The COVID-19 pandemic has generated substantial changes in the lives of the population, such as increased physical inactivity, which can lead to being overweight and, consequently, repercussions on glucose homeostasis. A cross-sectional study based on the adult population of Brazil was conducted by stratified, multistage probability cluster sampling (October and December 2020). Participants were classified as physically active or inactive during leisure time according to the recommendations of the World Health Organization. HbA1c levels were categorized as normal (≤$6.4\%$) or with glycemic changes (≥$6.5\%$). The mediating variable was being overweight (overweight and obese). Descriptive, univariate, and multivariate logistic regression analyses examined the association between physical inactivity and glycemic changes. Mediation was analyzed using the Karlson–Holm–Breen method to verify the influence of being overweight on the association. We interviewed 1685 individuals, mostly women ($52.4\%$), 35–59 years old ($45.8\%$), race/ethnicity brown ($48.1\%$), and overweight ($56.5\%$). The mean HbA1c was $5.68\%$ ($95\%$ CI: 5.58–5.77). Mediation analysis verified that physically inactive participants during leisure time were 2.62 times more likely to have high levels of HbA1c (OR: 2.62, $95\%$ CI: 1.29–5.33), and $26.87\%$ of this effect was mediated by over-weight (OR: 1.30: $95\%$ CI: 1.06–1.57). Physical inactivity at leisure increases the chances of high levels of HbA1c, and part of this association can be explained by being overweight. ## 1. Introduction The COVID-19 pandemic promoted profound changes in the daily lives of citizens around the world; the social restriction, although necessary, negatively altered some health behaviors, especially those related to lifestyle habits, such as food consumption and physical activity practice [1]. Studies conducted during the pandemic showed that physical inactivity (PI) increases considerably during home confinement [2,3,4,5]. In addition, physical inactivity and the adoption of unhealthy eating habits may have influenced weight gain during the pandemic [6], which may have affected glycemic levels [7]. Although studies have shown the negative effects of the pandemic on the general health of the population [7,8], it is important to understand how physical inactivity during social restrictions can be associated with glycemic changes, thus posing a threat to the increased incidence of type 2 diabetes mellitus (T2DM) worldwide. T2DM has emerged as a serious public health problem due to a modern lifestyle characterized by increased sedentary behavior and the consumption of ultra-processed foods [9]. The International Diabetes Federation has reported that approximately 422 million people worldwide live with diabetes mellitus [9,10]. One of the measures used to determine glycemic control is the HbA1c test, considered the gold standard for assessing glycemic control and an indicator of the average plasma glucose level during the previous 120 days [11]. Abnormalities in serum HbA1c concentrations are implicated in disease progression and physical disability, resulting in microvascular and macrovascular risks, neuropathy, retinopathy, renal dysfunction, and cardiovascular disease [9,12]. Some behaviors increase the risk of HbA1c changes, including smoking, poor eating habits, being overweight, and physical inactivity [13]. However, studies suggest that physical activity is an important non-pharmacological intervention for the treatment of diabetes mellitus, acting significantly on insulin resistance, glycemic control, and reducing HbA1c [14,15]. Therefore, the recommendation for adults to involve in regular physical activity is at least 150 min of moderate physical activity or 75 min of vigorous physical activity weekly [13]. Faced with the scarcity of biochemical data during the period of social restriction due to the COVID-19 pandemic and the prospect of intense modification in lifestyle and health behavior, especially physical activity, which may have influenced weight gain and, consequently, glycemic change, it is important to understand the mechanisms associated with this clinical condition to try to minimize negative health impacts, in addition to a higher prevalence of diabetes in the medium/long term. Therefore, this study aimed to evaluate serum HbA1c levels and their association with overweight-related physical inactivity. We hypothesized that high levels of HbA1c may be associated with physical inactivity at leisure during the social restriction and that being overweight may be a mediating variable in this association. ## 2.1. Study Design and Sampling This was a cross-sectional study using data from the COVID-Inconfidentes project, a population-based household epidemiological survey conducted between October and December 2020 in two municipalities (Ouro Preto and Mariana) in Minas Gerais, Brazil. Participants eligible for the study were permanent residents of households in the urban area of the municipalities, aged 18 years or older, and who consented to participate in the study. Individuals with impaired cognitive function, difficulty answering the questionnaire, or inability to provide blood samples due to difficulties in venous access were excluded. For data collection, a sample calculation was performed based on the 2010 population census for the urban area of each city, adopting a confidence level of $95\%$ and an effect size equal to 1.5. Furthermore, $20\%$ was added to the sample size of each city for possible refusals, absence of the resident selected, or people not at home during the visit. The sample size was calculated using OpenEpi (https://www.openepi.com/Menu/OE_Menu.htm, accessed on 7 February 2023), the minimum number of volunteers after the sample calculation for both municipalities (Ouro Preto and Mariana/MG) with 732 interviews, respectively. A three-stage sampling was used: census sector (selected with probability proportional to the number of households), household (selected from systematic sampling), and residents (≥18 years old, randomly selected by applying Sorteador de Nomes). The sample weight of each selected unit (census sector, household, and individual) was calculated to correlate with the 2019 population projections (DATASUS) [16]. In this calculation, adjustments were made to compensate for interview losses due to non-response. For more details on sample calculation and field logistics, see Meireles et al. 2021 [17]. ## 2.2. Data Collection In each municipality, data were collected on three weekends (Friday, Saturday, and Sunday), with intervals of 21 days between each. In the week preceding the data collection weekend, actions were carried out to disseminate the survey in selected census sectors, draw households, draw lots, and approach households to increase awareness and adherence to the survey. On the days of data collection, a resident was selected, followed by blood collection and face-to-face interviews. For venous blood collection, a 2.7 mL S-Monovette (Sarstedt) tube containing sodium fluoride/EDTA was used to analyze serum HbA1c levels. Face-to-face interviews lasted 30 to 45 min, using the questionnaire in the DataGoal application via tablets. The interviewers maintained a minimum distance of 1.5 m from the interviewee as a protective measure against COVID-19, and physical contact was restricted to the point of collection of biological material. The questionnaire included sociodemographic and economic variables, lifestyle habits, and general health status. ## 2.3. Outcome Variable: Glycated Hemoglobin (HbA1c) HbA1c was measured in the Clinical Analysis Pilot Laboratory (LAPAC) of the School of Pharmacy/Federal University of Ouro Preto using the immunoturbidimetry method in the COBAS INTEGRA 400 plus automatic analyzer (Roche, Germany), following a protocol standardized by the manufacturer. Before each analysis, the device was calibrated with quality controls (HbA1c Control N and HbA1c Control P, Roche). A minimum volume of 400 µL of whole blood was used for the samples. The normal range adopted for the HbA1c level was ≤$6.4\%$, and levels ≥ $6.5\%$ were classified as having high levels of HbA1c [18]. ## 2.4. Explanatory Variable: Physical Inactivity in Leisure Time The self-reported physical activity during leisure time was evaluated by weekly frequency, duration, and type of physical exercise. Moderate physical activity was defined as walking, treadmill walking, weight training, water aerobics, Pilates, volleyball, and dancing, and vigorous physical activity included running, cycling, swimming, treadmill running, aerobics in general, wrestling, soccer/futsal, basketball, and tennis [19,20,21,22]. Then, the weekly frequency (0 to 7 days) was multiplied by the daily time (in minutes) to obtain the weekly amount of physical activity during leisure time in minutes. This amount was categorically evaluated using the cut-off points established by the World Health Organization (WHO) and the Physical Activity Guide for the Brazilian Population [23,24] into physically active (≥150 min/week of moderate physical activity, ≥75 min/week of vigorous physical activity, or a combination of both) or physically inactive (<150 min/week of moderate physical activity or <75 min/week of vigorous physical activity). ## 2.5. Mediating Variable: Overweight Overweight was used as a mediation variable, measured by body mass index (BMI), calculated from self-reported weight and height according to the following formula: BMI = body weight (kg)/height (m2). kg/m2 if <60 years to WHO cut-off points into “not overweight” (BMI < 25.0 kg/m2 if <60 years or BMI < 28.0 kg/m2 if ≥60 years) and “overweight” (BMI ≥ 25.0 kg/m2 if <60 years or BMI ≥ 28.0 kg/m2 if ≥60 years) [25,26]. ## 2.6. Adjustment of Variables Sociodemographic covariates, tobacco use, and morbidities were included. The sociodemographic covariates determined were sex (women and men), age group (18–34 years; 35–59 years; ≥60 years), race/ethnicity categories were self-reported (white, black, brown, and others: yellow and indigenous), marital status (single or married), current family income (≤2 wages; >2 to ≤4 wages; >4 wages), and education (non-literate; <9 years; ≥9 years). Tobacco consumption was determined using the question, “Do you smoke, or have you ever smoked cigarettes or any other tobacco product?” The answer options were yes and no. Reported morbidity was determined by self-reporting the following diseases: high blood pressure, asthma, lung disease, chronic kidney disease, depression, anxiety disorder, obstructive sleep apnea, cancer, heart disease, or thyroid disease. Individuals who reported having at least one of the diseases were classified as having morbidity, and those with no disease had no morbidity. ## 2.7. Ethical Considerations This study was approved by the Ethics Committee on Human Research of the Federal University of Minas Gerais (protocol number:32815620.0.1001.5149). All procedures followed the Brazilian guidelines and standards for human research. Participants were informed about the research objectives, the steps to be taken, and the risks and benefits of their participation. Those who agreed to participate signed an informed consent form. ## 2.8. Statistical Analysis The study population was characterized using descriptive calculations, such as relative frequencies, mean values, and $95\%$ confidence intervals (CI). Pearson chi-square test was used to verify the relationship between high levels of HbA1c and the sociodemographic characteristics of the study population. Univariate and multivariate logistic regression analyses assessed the association between physical inactivity and glycemic changes. A theoretical causality model based on a directed acyclic graph (DAG) was developed based on exposure (physical inactivity), outcome (glycemic changes, HbA1c ≥ $6.5\%$), and covariate variables using online Dagitty software, software version 3.2 [27] (Figure 1). DAG was used to avoid unnecessary adjustments, spurious associations, and estimation errors. The backdoor criterion selected a minimum set of confounders to fit the analyses [28]. The model was fitted using the following minimal and sufficient variables: sex, age, family income, race, and referred morbidities. To verify whether overweight could be a mediating variable between the association of PI and glycemic changes, we used mediation analysis using the Karlson–Holm–Breen method, package “khb” in Stata [29]. This method estimates total, direct, and indirect associations between the explanatory variable (leisure time PI) and the outcome variable (glycemic changes: serum HbA1c levels ≥ $6.5\%$). Using logistic regression models, the method decomposed the total effect of a variable into a direct effect (direct association of leisure time physical inactivity and glycemic changes) and an indirect effect (the mediating effect of overweight on glycemic changes). Results were analyzed using the Stata statistical program, version 15.1, operating the “svy” command, which considers a complex sample design. Statistical significance was established at $p \leq 0.05.$ ## 3. Results We evaluated 1685 individuals, most of whom were women ($52.4\%$; $95\%$ CI: 40.5–54.8), aged 35–59 years old ($45.8\%$; $95\%$ CI: 41.2–50.5), married ($52.9\%$; $95\%$ CI: 46.8–58.9), self-reported as brown ($48.1\%$; $95\%$ CI: 41.7–54.5), less than nine years of schooling ($69.1\%$; $95\%$ CI: 64.3–73.6), income less than or equal to two minimum wages ($41.2\%$; $95\%$ CI: 35.6–47.1), and overweight ($56.5\%$; $95\%$ CI: 49.8–63.0). In addition, most of the respondents reported having one or more morbidities ($55.5\%$; $95\%$ CI: 48.3–62.4), as shown in Table 1. The mean HbA1c was $5.68\%$ ($95\%$ CI: 5.58–5.77). We observed that $92.9\%$ ($95\%$ CI: 90.6–94.7) of the subjects had normal levels of HbA1c, and $7.1\%$ ($95\%$ CI: 5.3–9.4) had glycemic changes. In addition, $69.2\%$ ($95\%$ CI: 64.2–73.7) of the participants were physically inactive at leisure (Figure 2). Table 1 presents the characteristics of the participants according to their serum HbA1c levels. Variables related to the presence of high levels of HbA1c according to the Pearson chi-square test ($p \leq 0.05$) were age ≥ 60 years, female, education ≤ 9 years, overweight, black race/ethnicity, and presence of morbidities (Table 1). When examining the results of the logistic regression, it was observed in the multivariate analysis (adjusted for sex, age, race/ethnicity, family income, and reported morbidity) that individuals who were physically inactive during leisure time were 2.62 times more likely to have high levels of HbA1c (OR: 2.62, $95\%$ CI: 1.31–5.24), as presented in Table 2. In the mediation analysis by overweight, it was possible to verify that physically inactive individuals during leisure time had 2.62 times greater chance of having high levels of HbA1c (OR: 2.62,$95\%$ CI: 1.29–5.33) and $26.87\%$ of this effect was mediated by overweight (OR: 1.30, $95\%$ CI: 1.06–1.57). The mediation calculation is obtained by total Beta (β) divided by indirect Beta (β), multiplied by 100. Therefore, β total/β indirect* 100 = mediated effect (Figure 3). ## 4. Discussion This study investigated the association between physical activity and high levels of HbA1c, as well as the mediation of overweight in this association. Our findings confirm the initial hypothesis that adults who accumulate 150 min of moderate or 75 min of vigorous physical activity per week are less likely to have glycemic changes than individuals who do not reach the weekly minutes of physical activity recommended by the guidelines [23,24]. To our knowledge, this is the first major epidemiological survey to investigate HbA1c and physical inactivity during leisure time during social restrictions caused by the COVID-19 pandemic. These results are important regarding public health and clinical practice, as approximately $70\%$ of the population in this study did not meet the minimum guidelines for physical activity. Although our study did not allow us to assess the change in physical activity practice during the COVID-19 pandemic, it is believed that the pandemic may have contributed substantially to the increased prevalence of physical inactivity [3] due to the closure of recreational facilities, urban parks, and gyms to mitigate the spread of the virus. However, chronic damage caused by reduced levels of physical activity in the face of prolonged home stays has become a significant challenge [3,4]. The health consequences are inevitable, with deterioration of the physical and mental conditions of the population observed due to the confinement period [30]. Thus, some studies show a worsening prevalence of physical inactivity and overweight due to social restrictions and reduced urban mobility worldwide [4,6,31,32,33]. This panorama exacerbates the risks for the incidence and prevalence of chronic non-communicable diseases [34] and the increased occurrence of certain disorders, such as changes in HbA1c levels. These changes in glycemic homeostasis may reflect the progression and/or worsening of the clinical picture to type 2 diabetes mellitus [35]. Another concern is the susceptibility to infection and the risk of COVID-19 in individuals with high levels of HbA1c [36]. This highlights the importance of maintaining good control of serum Hb1Ac levels for overall health and to prevent worsening and complications associated with SARS-CoV-2 infection (Severe Acute Respiratory Syndrome Coronavirus 2). Regular physical activity has long been considered a non-pharmacological way to improve general health, manage glycemic control, and treat T2DM, and its low cost has increased its appeal [37,38]. According to the American Diabetes Association [39], physical activity is essential for preventing the harmful effects of T2DM [40]. Musculoskeletal contraction stimulates the displacement of glucose transporter protein (GLUT-4) to the plasma membrane; this signaling promotes an acute increase in glucose transport and metabolization [15,41]. Thus, physical activity acts in several mechanisms that minimize the deleterious effects of high glucose levels, indirectly in fatty acid metabolism, helping the energy balance and consequently attenuating adiposity; being overweight is one of the main risk factors for the development of T2DM and, directly promotes improvement in glycemic control and insulin sensitivity [42]. A study of Brazilian women before (January and February 2020) and after 16 weeks of the COVID-19 pandemic (June and July 2020) showed that confinement promoted important changes in health-related parameters. HbA1c levels increased significantly by $9.7\%$, which was explained by reduced physical activity [35]. Consequently, a cross-sectional study conducted in India revealed the negative impact of social restrictions on glycemic control. The mean value of serum blood glucose concentrations worsened during the period of social restriction compared to the pre-pandemic time [7]. Furthermore, a systematic review concluded that the COVID-19 pandemic caused worsening glycemic control and complications related to T2DM; this observed worsening may be provinient to the closure of outdoor public places (such as parks and squares) that encourage the practice of physical activity [36]. Our results show an association between physical inactivity and high levels of HbA1c, and a part of this association can be explained by being overweight. These findings demonstrate clinical relevance, with physical activity effectively mitigating the risk of developing T2DM, both for body weight maintenance and through metabolic mechanisms that help glycemic control. A similar effect to ours was found in a systematic review and dose-response meta-analysis, which compared risk estimates of T2DM in relation to leisure time physical activity, with and without adjustment for the BMI covariate. The results with adjustment weakened the association by about $20\%$-$30\%$ when compared to the results unadjusted for BMI [42], thus demonstrating the mediating effect of BMI on the association between physical inactivity and glycemic changes. Thus, a consistent justification for this association may be positive energy balance, that is, increased habitual food intake compared to the period before the COVID-19 pandemic (mainly of ultra-processed foods) and reduced energy expenditure by reducing urban mobility and physical activity. This may lead to weight gain and increase the risk of T2DM in individuals who are physically inactive [43,44]. Considering these findings, it is possible to postulate the lasting impact of the increased physical inactivity caused by the pandemic on the health of the population. The findings of this study are of great value to the scientific community, leaders, and health professionals to better understand the side effects on short- and long-term health conditions of the COVID-19 pandemic and to devise strategies to mitigate these impacts. The COVID-19 pandemic may have provided a favorable scenario for morbidity and a poorer health prognosis. Furthermore, there are no structured educational programs for the self-management of T2DM in Brazil [45]. PA acts in the prevention and mitigation of complications from T2DM, in addition to mitigating the high costs to the public system, which is a burden on global health [12]. Health professionals operating in the treatment of T2DM should encourage the practice of PA, promoting the population’s understanding of the acute and chronic physiological responses to PA in T2DM. Although robust and with relevant results in the scientific literature, this study had some limitations. The level of physical activity was obtained by self-reporting; therefore, it is subject to memory and other bias, which may underestimate or overestimate the data. Furthermore, calorie intake and added sugars, important covariates in the present association, were not assessed. Methodologically, the cross-sectional design of this study did not allow the establishment of causalities. However, using the counterfactual approach from directed acyclic graphs (DAG), it is possible to infer and verify the causal effect of associations from the observed variables and the assumptions in the diagram. It is emphasized that the hypotheses were carefully defined according to the current scientific literature and articulated in counterfactual terms to build theories that can underpin the driving assumptions of the analyses. In this regard, incorporating directed graphical models is of great importance and brings robustness to the study [46]. However, in observational studies, the assumptions necessary to estimate the causal effect are not empirically testable. Therefore, observational analyzes will always be subject to errors in the direction of the analysis. In addition, our study has other strengths, such as a robust sample methodology with probability selection and sample weight, providing statistical power to the study. Furthermore, the interviews were conducted face-to-face, allowing greater accuracy in the information obtained. In addition, assessing glycemic homeostasis through Hb1Ac is noteworthy, as it represents glycemic levels during the last four months and not only at the current time. ## 5. Conclusions Leisure time physical activity is associated with greater high levels of HbA1c and part of this association is explained by being overweight. Therefore, our data suggest that physical activity may attenuate the risk of developing T2DM, partly by controlling overweight but also independently of adiposity. In addition, glycemic changes are metabolic changes that require regular attention, given their implications for the worsening of population health and high costs to the public health system. Therefore, we believe that maintaining regular physical activity is essential for the management of glycemic control in periods of restricted social mobility. During the pandemic, there was a great deal of uncertainty and fear around physical activity, especially in open areas, since little was known about the forms of contamination. This favored an increase in physical inactivity during data collection. However, with current knowledge, encouraging physical activity in open environments should be encouraged in times of restricted social mobility. Taking the necessary precautions to reduce contagion, or at least the encouragement of physical activity at home, through online classes and simple exercises with body weight, will prevent the harmful effects on health since increased physical inactivity can lead to an increase in weight and an increased risk of T2DM together with other pathophysiology. ## References 1. Romero-Blanco C., Rodríguez-Almagro J., Onieva-Zafra M.D., Parra-Fernández M.L., Prado-Laguna M.D.C., Hernández-Martínez A.. 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--- title: Fermented Lettuce Extract Containing Nitric Oxide Metabolites Attenuates Inflammatory Parameters in Model Mice and in Human Fibroblast-Like Synoviocytes authors: - Jisu Park - Ji Hyeon Ryu - Bo-Young Kim - Hyun Soo Chun - Min Sun Kim - Yong-Il Shin journal: Nutrients year: 2023 pmcid: PMC10005524 doi: 10.3390/nu15051106 license: CC BY 4.0 --- # Fermented Lettuce Extract Containing Nitric Oxide Metabolites Attenuates Inflammatory Parameters in Model Mice and in Human Fibroblast-Like Synoviocytes ## Abstract Lettuce (Lactuca sativa L.) contains various bioactive compounds that can reduce the severity of inflammatory diseases. This study aimed to identify therapeutic effects and underlying mechanisms of fermented lettuce extract (FLE) containing stable nitric oxide (NO) on collagen-induced arthritis (CIA) in mice and fibroblast-like synoviocytes (MH7A line) from patients with rheumatoid arthritis (RA). DBA/1 mice were immunized with bovine type II collagen and orally administered FLE for 14 days. On day 36, mouse sera and ankle joints were collected for serological and histological analysis, respectively. Consuming FLE inhibited RA development, suppressing pro-inflammatory cytokine productions, synovial inflammation, and cartilage degradation. The therapeutic effects of FLE in CIA mice were similar to those of methotrexate (MTX), which is typically used to treat RA. In vitro, FLE suppressed the transforming growth factor-β (TGF-β)/Smad signaling pathway in MH7A cells. We also demonstrated that FLE inhibited TGF-β-induced cell migration, suppressed MMP-$\frac{2}{9}$ expression, inhibited MH7A cell proliferation, and increased the expression of autophagy markers LC3B and p62 in a dose-dependent manner. Our data suggest that FLE could induce autophagosome formations in the early of stages of autophagy while inhibiting their degradation in the later stages. In conclusion, FLE is a potential therapeutic agent for RA. ## 1. Introduction Rheumatoid arthritis (RA) is an autoimmune disease that mainly manifests as the chronic inflammation of joints. Its cause is a complex interaction between genetic, environmental and immunological factors [1]. Fibroblast-like synoviocytes (FLS) are located at the synovial intimal edge and play a critical role in RA pathogenesis. Activated FLS have similar characteristics, such as resistance to proliferation, invasion, and apoptosis of tumor cells, and they directly participate in synovial proliferation by migration to cartilage and bone to attach and invade [2]. Stimulation by inflammatory cytokines induces FLS proliferation and releases several factors, including inflammatory cytokines, chemokines, and matrix-degrading enzymes [3]. In addition, FLS proliferation interferes with immune cell regulation and leads to the destruction of the extracellular matrix, cartilage, and bone [4]. Therefore, it is important to modulate the biological behavior of FLS to ameliorate symptoms of RA. The MH7A cells are an FLS immortalized cell line transfected with the SV40 T antigen and are widely used to study the mechanism of RA. Transforming growth factor-β (TGF-β) is an important growth factor involved in the induction and proliferation of fibrosis associated with RA [5]. TGF-β has also been shown to be upregulated in FLS from RA patients in some studies [6]. TGF-β is a cytokine with diverse functions in proliferation, differentiation, angiogenesis, extracellular matrix production, and apoptosis. Activated TGF-β signaling phosphorylates and induces Smad, including pro-fibrotic Smad$\frac{2}{3}$ and anti-fibrotic Smad$\frac{1}{5}$/9 in progressive fibrosis [7]. Therefore, the TGF-β/Smad signaling pathway may contribute to RA-FLS. The pathogenesis of autoimmune diseases, including RA, is associated with the dysregulation of programmed cell death [8]. Cell death involves degrading unnecessary or non-functional cellular components, and thus, it can be destructive or protective [9]. Several studies have demonstrated an association between cell death and RA in various cell types, including FLS. In RA, a decrease in FLS cell death leads to excessive synovial cell proliferation, promoting synovitis [10]. Therefore, novel therapeutic targets for RA should include pathways associated with cell death processes, including apoptosis, necroptosis, pyroptosis and autophagy. In particular, the correlation between RA pathogenesis and autophagy is entirely unknown. Autophagy is a degradation pathway that isolates and eliminates unnecessary cytoplasmic material such as damaged organelles and abnormal/non-functional proteins [11]. The target component is separated from other cellular components surrounded by an autophagosome and fused with a lysosome for decomposition [12]. Multiple proteins are involved in autophagy, with p62 and LC3 in particular being widely used to monitor autophagic flux, which is an indicator of overall autophagic degradation [13]. Cytoplasmic LC3 (LC3-I) conjugates with phosphatidylethanolamine to form LC3-II, which is recruited to the autophagosome membrane [14]. Autophagy targeting is completed when p62 binds to LC3-II, triggering lysosomal proteolysis and degrading p62 together with autophagosomes [15]. Disruptions in autophagy may be related to RA pathogenesis and may be targeted during therapy. Lettuce (family Asteraceae) is a globally consumed vegetable containing numerous vitamins, minerals, carotenoids, and polyphenols [16]. As a natural source of phytochemicals and bioactive nutritional compounds, lettuce has important health benefits, including cytoprotective and cholesterol lowering [17,18]. Moreover, some nutritional compounds in lettuce have been reported to protect hippocampal neurons against amyloid-α-mediated oxidative stress and apoptosis [19]. Fermentation improves the biological properties of raw materials by producing new bioactive compounds [20]. Some studies have shown that natural products such as fermented garlic, lettuce, and beans can improve menopausal symptoms and peripheral and central blood flow [21,22]. Additional studies have shown that fermented lettuce extracts (FLE) have an antidiabetic effect [23]. Thus, FLE has potential as an RA treatment; however, its application needs to be explored empirically. In this study, we investigated the therapeutic efficacy of FLE, a naturally derived compound containing NO, stabilized for a long time. Using a collagen-induced arthritis (CIA) mouse model, we assessed whether RA prognosis improved after FLE consumption. We explored the effect or FLE on TGF-β and investigated the underlying mechanism of FLE-induced autophagy in MH7A cells. ## 2.1. Preparation of FLE The study used FLE manufactured by food manufacturing company (HumanEnos LLC., Wanju, Republic of Korea) [21]. Ground lettuce was mixed with distilled water in 1:1 ratio, and then, approximately $1\%$ (1.0 × 108 cfu/mL) of generally recognized as safe (GRAS) grade microorganisms were added. Fermentation occurred at 30 °C for 21 days. Every factor that can affect fermentation was managed to maintain optimal conditions, including aeration, temperature, and pH. At the end of 21 days, the liquid product contained NO metabolites and antioxidants. The supernatant was then separated with a centrifuge (1.5 ton/Hr, disc separator, Alfatechkorea Corp., Seongnam, Republic of Korea) and then condensed with an evaporator (1.5 ton, Vacuum Evaporator, BDMPLANT, Gwangju, Republic of Korea), yielding brix = $4\%$. The condensed material was then frozen at –40 °C for 48 h and placed in a freeze dryer (1.5 ton, Vacuum Freeze Drier, Ajin E.S.R Co. Ltd., Daegu, Republic of Korea) for 72 h until reaching powder form. Nitrite levels in FLE were determined using Griess reagent (Promega, Madison, WI, USA), following manufacturer protocol (Table 1). The FLE used in this study was fermented for 21 days. ## 2.2. Animal Care Male DBA/1J mice aged 7–8 weeks were obtained from Central Lab Animal Inc. (Seoul, Republic of Korea). The mice were housed in a specified pathogen-free animal facility under a 12 h light/dark cycle. They were fed ad libitum with a breeding diet (A03, SAFE, Rosenberg, Germany) containing $52.0\%$ carbohydrates, $21.4\%$ protein, $5.1\%$ lipids, $4.0\%$ fiber, $5.7\%$ minerals, and $12.1\%$ moisture. All experiments were approved by the Institutional Animal Care and Use Committee of Pusan National University, conducted in accordance with the National Institutes of Health Guidelines (PNU-2020-2757), and registered at preclinicaltrials.eu (PCTE0000355). ## 2.3. Generation of CIA Mice and Experimental Intervention The CIA tends to be more severe in male than in female mice [24,25]. The incidence of arthritis may reach $100\%$ in males [26] and >$80\%$ in females in the CIA mouse model. The induction of CIA was as previously described [27]. Mice were immunized intradermally on day 0 via a tail-vein injection of 100 μg bovine type II collagen (CII; Chondrex inc., Redmond, WA, USA) emulsified with an equal volume of complete Freund’s adjuvant (CFA; Sigma-Aldrich, St. Louis, MO, USA). Immunization was boosted with an equal volume of CII emulsion and incomplete Freund’s adjuvant (IFA; Chondrex) on day 21. Normal mice were treated with Freund’s adjuvant only. After boosters, mice were randomly divided into four groups ($$n = 8$$): Normal, CIA, CIA + MTX, and CIA + FLE. Methotrexate (MTX) is a common RA drug, which is selected as the positive control. Oral administration is a common and cost-effective method of treatment. In addition, it is attractive because it is non-invasive, convenient, and enables long-time treatment [28]. Therefore, we accessed the oral administration of FLE to CIA mice. From days 22 to 35, FLE (75 μg nitrite/kg/day) was orally administered daily in the CIA + FLE group. Across the same period, MTX (1 mg/kg) was injected intraperitoneally once every 3 days for the CIA + MTX group. Normal mice were orally administered distilled water daily from day 22 to day 25 at the time of FLE administration. ## 2.4. Clinical Assessment of Arthritis Mice were closely monitored and scored three times a week after the primary collagen injection. Scoring was performed by three independent investigators for each limb on a four-point scale [27]: 0 = normal paw; 1 = erythema and mild swelling confined to the tarsal or ankle joint; 2 = erythema and mild swelling extending from the ankle to the tarsal; 3 = erythema and moderate swelling extending from the ankle to the metatarsal joints; 4 = erythema and severe swelling encompassing the ankle, foot and digits, or limb ankylosis. Final arthritis scores were obtained from summing scores of all four paws. Paw thickness was measured using an electric caliper placed across the ankle joint at its widest point. ## 2.5. Enzyme-Linked Immunosorbent Assay (ELISA) Mouse anti-CII antibodies (IgG, IgG1, and IgG2a; Chondrex) and mouse TNF-α, IL-1β/12, and IL-6 (R&D Systems, Minneapolis, MN, USA) were measured using ELISA kits according to the manufacturer’s instructions. Absorbance was read at 450 nm using a microplate reader (Tecan, Infinite M200, Austria). ## 2.6. IHC On day 36, mice were euthanized to collect ankle joints. The samples were fixed in $10\%$ phosphate-buffered formaldehyde solution for 24 h, then decalcified in a $10\%$ ethylenediaminetetraacetic acid solution for 1 month, dehydrated and embedded in paraffin. TGF-β expression in ankle joints was measured as previously described [29]. Tissue sections (thickness, 3 μm) were incubated with primary antibodies against TGF-β (Abcam, Cambridge, MA, USA) overnight at 4 °C. Subsequently, they were incubated with HRP-conjugated goat anti-rabbit IgG antibody (DAKO, Carpinteria, CA, USA) to detect immunoactivity, which was followed by detection using a DAB solution kit (DAKO). The counterstain was hematoxylin. Stained specimens were visualized under a virtual microscope (Axio Scan. Z1; Carl Zeiss, Heidenheim, Germany). Histological results were the average of scores from three independent observers blinded to the experimental conditions. TGF-β expression was analyzed Image J (NIH; National Institutes of Health). ## 2.7. Histochemical Analysis Tissue sections were prepared and stained with hematoxylin and eosin (H&E; Sigma-Aldrich) and safranin O (Sigma-Aldrich). Synovial inflammation and hyperplasia were determined based on the results of H&E staining [29]: 0, no signs of inflammation; 1, slight thickening of the lining layer or some infiltrating cells in the underlying layer; 2, slight thickening of the lining layer plus some infiltrating cells in the underlying layer; 3, thickening of the lining layer, cell influx in the underlying layer, and cell presence in the synovial space; 4, synovium highly infiltrated with many inflammatory cells. Cartilage damage was determined based on the results of safranin O staining: 0, no destruction; 1, minimal erosion limited to single spots; 2, slight-to-moderate erosion in a limited area; 3, more extensive erosion; and 4, general destruction. Stained specimens were visualized under a virtual microscope (Axio Scan. Z1; Carl Zeiss). Histology was assessed by three independent, blinded observers, and their scores were averaged for the final results. ## 2.8. Micro-Computed Tomography (CT) Hind-paw images from mice in all four groups ($$n = 4$$) were acquired on day 36 using micro-CT (Quantum FX, Perkin Elmer, MA, USA) [30]. Ankle joints were scanned at a tube voltage of 90 kV, tube current of 160 μA, resolution of 20 μm scan time of 2 min. ## 2.9. In Vivo Safety Evaluation Preliminary safety tests for FLE were performed as previously described [31]. Healthy 6-week-old male C57BL/6 mice were randomly divided into two groups ($$n = 5$$–6), Control ($$n = 5$$) and FLE ($$n = 6$$). Each group received a single orally administered dose of FLE (75 μg nitrite/kg/day) or distilled water (control) daily for 14 days. Body weight was measured daily. Blood and tissue samples were collected 24 h after the final administration for hematologic and histochemical analyses. Levels of serum aspartate transaminase (AST), alanine transaminase (ALT), blood urea nitrogen (BUN), and creatinine were measured by GC Labs (Yongin, Republic of Korea) using the International Federation of Clinical Chemistry standard. Brain, lung, heart, liver, kidney, spleen, thymus, and testis tissues were fixed with $4\%$ paraformaldehyde for 24 h and embedded in paraffin. Each sample was cut into 5 μm sections, processed for routine H&E staining, and visualized under a virtual microscope (Axio Scan. Z1; Carl Zeiss). ## 2.10. Cell Culture Human synovial fibroblast cell line MH7A was obtained from the Prof. Sang-Il Lee (Gyeongsang National University, Jinju, Republic of Korea). The MH7A cells were cultured in Roswell Park Memorial Institute 1640 medium (RPMI 1640; Welgene, Gyeongsan, Republic of Korea) supplemented with $10\%$ fetal bovine serum (FBS; Welgene) and penicillin/streptomycin (Welgene). Culture conditions were 37 °C in a humidified atmosphere with $5\%$ CO2. Cells were treated with various FLE doses in the absence or presence of TGF-β (10 ng/mL; R&D Systems) or of chloroquine (CQ, 20 μM; Cayman, Ann Arbor, MI, USA) for 72 h. ## 2.11. Cell Viability Assay Cell viability was determined colorimetrically using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT; Duchefa, Haarlem, Netherlands) as previously described [32]. Cells were seeded in 96-well plates (1 × 104 cells/well) and incubated for 24 h. After treatment with 62.5, 125, 250, and 500 μg nitrite/mL FLE for 24, 48, and 72 h, MTT (5 mg/mL) was added to each well, which was followed by a 2 h incubation. Supernatants were aspirated before 100 μL of DMSO was added to dissolve formazan crystals remaining in each well. Optical density per well was read at 570 nm using a microplate reader (Tecan). ## 2.12. Cell Proliferation Assay Cell proliferation was assessed using the 5-bromo-2′-deoxyuridine (BrdU) cell proliferation ELISA kit (Abcam, Cambridge, MA, USA), as previously described [33]. Cells (1 × 104) were seeded in a 96-well plate and treated with various doses of FLE for 72 h. BrdU was added to the appropriate wells, and cells were incubated for 4 h before being subjected to fixing solution. The plate was then washed and incubated with BrdU detector antibody for 1 h at room temperature. Peroxidase-conjugated secondary antibodies were added, which was followed by a 30 min incubation. Finally, cell proliferation was assessed using a spectrophotometer at 450 nm (Tecan). ## 2.13. Reverse-Transcriptase PCR (RT-PCR) Total RNA was isolated from MH7A cells using Trizol reagent and synthesized into cDNA with the antiRivert Platinum cDNA synthesis master mix (GenDepot, Barker, TX, USA), following manufacturer protocol [31]. Target genes were amplified with RT-PCR using TOPsimple PCR DyeMix-Tenuto (Enzynomics, Daejeon, Republic of Korea) and the following primers: ACTA1, forward: 5′-GTGTTGCCCCTGAAGAGCAT-3′, reverse: 5′-GCTGGGACATTGAAAGTCTCA-3′; COL1A, forward: 5′-CCCGGGTTTCAGAGACAACTTC-3′, reverse: 5′-TCCACATGCTTTATTCCAGCAATC-3′; MMP2, forward 5′-AGCGAGTGGATGCCGCCTTTAA-3′, reverse: 5′-CATTCCAGGCATCTGCGATGAG-3′; MMP9, forward: 5′-GCCACTACTGTGCCTTTGAGTC-3′, reverse: 5′-CCCTCAGAGAATCGCCAGTACT-3′; GAPDH, forward: 5′-AAAATCAAGTGGGGCGATGC-3′, reverse: 5′-GATGACCCTTTTGGCTCCCC-3′. The cDNA was amplified by PCR with 25–30 cycles of denaturation at 95 °C for 30 s, annealing at 60 °C for 30 s, and elongation at 72 °C for 40 s. GAPDH was selected as the reference gene. The RT-PCR products (10 μL each) were resolved in a $1.5\%$ agarose gel in Tris-acetic acid–EDTA (TAE) buffer and visualized with ethidium bromide under UV light. Relative expression of the target genes was quantified in Image J. ## 2.14. Western Blotting Western blot analysis was performed as previously described [34]. MH7A cells were treated with IP lysis buffer (Thermo Fisher Scientific, Rockford, IL, USA) containing protease and phosphatase inhibitor cocktails (GenDEPOT) following manufacturer protocol. Lysates were centrifuged at 16,609× g (Hanil, Incheon, Republic of Korea) and 4 °C for 15 min. Protein concentrations were measured using at Bicinchoninic Acid Protein Assay Kit (Thermo Scientific). Samples were separated with sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and transferred to polyvinylidene difluoride (PVDF) membranes (Millipore, Darmstadt, Germany). Membranes were incubated overnight at 4 °C with primary antibodies phospho-Smad$\frac{2}{3}$ (1:1000, Cell Signaling, Danvers, MA, USA), phospho-Smad$\frac{1}{5}$/9 (1:1000, Cell Signaling), LC3B (1:1000; Cell Signaling), p62 (1:1000; Cell Signaling) and Beclin-1 (1:1000; Cell Signaling). Next, they were incubated with HRP-conjugated anti-rabbit (1:50,000; ENZO Life Science, Farmingdale, NY, USA) secondary antibodies for 1 h. An enhanced chemiluminescence kit (Amersham Pharmacia, Piscataway, NJ, USA) and ABI680 Analyzer (Amersham) were used to detect protein bands; signals were quantified in Image J. The loading control was β-actin antibody (1:5000; Sigma-Aldrich). ## 2.15. Cell Migration Assay Cell migration was assessed using transwell chambers (8 μm pore size membrane; Cell Biolabs, San Diego, CA, USA), as previously described [35]. Cells were seeded in the upper chamber (1 × 105 cells/well) with serum-free medium. To the lower chamber, medium containing $10\%$ FBS was added, which was followed by FLE and recombinant human TGF-β (R&D Systems). After 24 h, cells found on the lower side of the transwell culture insert were considered migratory. These cells were fixed with $4\%$ paraformaldehyde and stained with $0.1\%$ crystal violet. Non-migratory cells (inside the membrane) were removed. Stained cells were photographed under a microscope (Nikon, Tokyo, Japan) and analyzed in ImageJ. The average number of migratory cells was calculated. ## 2.16. Detection of Autophagosome Formation Autophagosome formation was analyzed using a CYTO-ID Autophagy Detection Kit (ENZO Life Science), which selectively labels accumulated autophagic vacuoles and autophagy flux with a green fluorescent dye [36]. Cells were cultured in 24-well plates with coverslips. Next, FLE, rapamycin (positive control), and CQ (negative control) were added, which was followed by incubation for 72 h. Hoechst and CYTO-ID detection reagents were dispensed into each sample for microscopic analysis. Images were obtained using confocal laser scanning (K1-Fluo; Nanoscope Systems, Daejeon, Republic of Korea). ## 2.17. Statistical Analysis After checking for normality, one-way ANOVA/Bonferroni correction Kruskal–Wallis tests, or Mann–Whitney U tests were used to analyze between-group differences (OriginPro2020, OriginLab, Northampton, MA, USA). Significance was set at $p \leq 0.05.$ ## 3.1. FLE Treatment Reduced RA Severity in CIA Mice To investigate the effect of FLE on RA, we conducted an experiment as shown in Figure 1A. Histological observations in the hind paws of CIA mice revealed swelling and erythema. However, these symptoms decreased significantly in the CIA + FLE and CIA + MTX groups (Figure 1B). Regardless of MTX or FLE administration, mean body weight decreased in collagen-induced CIA mice (Figure 1C). The CIA group showed a significant increase in the arthritis score when compared with the Normal group at day 36. This increase was reduced by $45.94\%$ and $28.44\%$, respectively, by MTX and FLE treatment ($p \leq 0.05$; Figure 1D). Similarly, treatment with MTX and FLE showed reduced hind paw thickness by $28.82\%$ and $12.62\%$, respectively, when compared to that of the CIA group ($p \leq 0.05$; Figure 1E). Next, we observed significantly higher serum levels of CII-specific IgG, IgG1, and IgG2a in CIA mice than in the normal group; MTX or FLE treatment attenuated this increase ($p \leq 0.05$; Figure 1F). The average absorbance value for each autoantibody of Normal, CIA, CIA + MTX, and CIA + FLE groups was IgG, 0.07 ± 0.00, 2.29 ± 0.15, 1.14 ± 0.17, and 1.50 ± 0.19; IgG1, 0.09 ± 0.00, 2.36 ± 0.15, 1.35 ± 0.16, and 1.66 ± 0.14; IgG2, 0.07 ± 0.01, 1.76 ± 0.08, 0.89 ± 0.13, and 1.31 ± 0.12. We then measured serum cytokine levels to determine how FLE affected inflammatory responses in CIA mice. ELISA results indicated that IL-6, IL-1β, and TNF-α levels were higher in CIA mice than in normal mice; the increase was attenuated by treatment with MTX and FLE ($p \leq 0.05$; Figure 1G). The average concentration for each pro-inflammatory cytokine of Normal, CIA, CIA + MTX, and CIA + FLE groups was IL-6 (pg/mg), 51.73 ± 4.09, 345.55 ± 33.12, 127.00 ± 29.75, and 236.15 ± 18.21; IL-1β (pg/mg), 33.29 ± 4.63, 450.88 ± 41.23, 219.07 ± 15.09, and 275.56 ± 23.88; TNF-α (pg/mg), 53.22 ± 6.77, 353.38 ± 23.72, 160.95 ± 23.36, and 235.52 ± 23.69. In the CIA group, the relative expression level of TGF-β in the synovial tissue was high when compared with that observed in the Normal group (Figure 1H). The expression level of TGF-β showed a significant decrease of $54.37\%$ and $46.28\%$ in the CIA + MTX and CIA + FLE groups, respectively ($p \leq 0.05$). ## 3.2. Administering FLE to CIA Mice Alleviated Histological Signs of RA Histological analysis of ankle joints and paws from CIA mice revealed characteristics of RA inflammation, such as edema, pannus formation, as well as cartilage and bone damage. These signs decreased significantly in the CIA + FLE and CIA + MTX groups ($$n = 8$$, $p \leq 0.05$; Figure 2A,B). Compared to the CIA mice, both MTX and FLE treatments significantly attenuated the synovial inflammation by 55.10 % and $48.21\%$, respectively. Similarly, the cartilage damage score showed a decrease of $53.33\%$ and $50.00\%$ in the CIA + MTX and CIA + FLE groups, respectively ($p \leq 0.05$). Pathological changes were confirmed using micro-CT, with CIA mice exhibiting more bone and cartilage degradation than normal mice ($p \leq 0.05$; Figure 2C). Furthermore, both MTX and FLE treatment significantly limited this degradation ($p \leq 0.05$). ## 3.3. Safety Assessment of FLE in Mice To verify FLE safety, we performed a systemic toxicity test in healthy C57BL/6 mice after they consumed FLE. Histopathological observation of major tissues (brain, lung, heart, liver, kidney, spleen, thymus, and testis) confirmed the lack of abnormalities in either the control group or the FLE group (H&E staining, Figure 3A). In addition, serum AST, ALT, BUN, and creatinine levels did not differ between the two groups ($$n = 5$$−6, $p \leq 0.05$; Figure 3B). These results indicate that FLE does not cause systemic toxicity in mice. ## 3.4. FLE Attenuated TGF-β/Smad Mediated Migration in MH7A Cells Fibroblast-like synoviocytes (FLS) are important in RA pathogenesis because of their resistance to proliferation, migration, and apoptosis [37]. Regulating FLS biological behavior can improve RA symptoms. Here, we used the human FLS line MH7A to determine FLE cytotoxicity in vitro. The results of MTT assays indicated a significant decrease in cell viability at all FLE concentrations by 72 h ($$n = 3$$, $p \leq 0.05$; Figure 4A). Next, BrdU labeling revealed that 500 μg nitrite/mL of FLE significantly decreased MH7A proliferation by 72 h ($$n = 3$$, $p \leq 0.05$; Figure 4B). We next stimulated MH7A cells with TGF-β (10 ng/mL) to determine the protective effects of FLE in an inflammatory environment. By 72 h, FLE had significantly suppressed cell viability in a dose-dependent manner ($$n = 3$$, $p \leq 0.05$; Figure 4C). TGF-β alone tended to increase cell viability but not significantly. Next, we investigated whether FLE is involved in the TGF-β-induced Smad signaling pathway. Cells were treated with different doses of FLE and 10 ng/mL TGF-β for 72 h. Next, Western blotting was used to determine phospho-Smad$\frac{2}{3}$ and phospho-Smad$\frac{1}{5}$/9 expression. After TGF-β stimulation, p-Smad$\frac{2}{3}$ and p-Smad$\frac{1}{5}$/9 expression increased and decreased, respectively, but FLE significantly reversed these changes ($$n = 2$$−3, $p \leq 0.05$; Figure 4D). To evaluate whether FLE could regulate TGF-β-induced fibrosis and the degradation of non-collagen matrix components in the joint, we used RT-PCR to determine ACAT1, COL1A, MMP2, and MMP9 mRNA expression, and we found that COL1A, MMP2, and MMP9 decreased significantly under FLE ($$n = 3$$, $p \leq 0.05$; Figure 4E). Consistent with these results, FLE also limited TGF-β-induced cell migration ($$n = 3$$, $p \leq 0.05$; Figure 4F). ## 3.5. FLE Induces Autophagosome Formation via Inhibiting Degradation in MH7A Cells We next investigated whether FLE regulates autophagy in MH7A cells. Western blotting indicated that FLE significantly upregulated autophagy markers LC3-II and p62 in a dose-dependent manner ($$n = 3$$, $p \leq 0.05$; Figure 5A). These results suggest that FLE induces autophagosome accumulation by suppressing their fusion with lysosomes. Treatment with FLE also did not alter Beclin-1 expression level, which is associated with autophagy induction. We next determined FLE involvement in the inhibition of autophagic flux. The CYTO-ID assay revealed a dose-dependent decrease in green fluorescence, reflecting reduced autophagy under FLE treatment (Figure 5B). Rapamycin-treated cells yielded a high green-fluorescence signal, whereas CQ-treated cells yielded a lower signal. We then measured LC3-II expression levels in MH7A cells after treatment with FLE and CQ. Because CQ blocks LC3-II degradation via inhibiting autophagosome-lysosomal fusion, we expect that combining CQ with FLE will have an additive effect on autophagic flux. Indeed, we observed elevated LC3-II expression under FLE treatment and even higher expression when CQ was also added ($$n = 3$$, $p \leq 0.05$; Figure 5C). Consequently, we suggest that FLE and CQ suppress late autophagy flux in a similar a manner. ## 4. Discussion In our study, daily FLE treatment attenuated RA severity in CIA mice. FLE improved RA clinical symptoms, including hind-paw thickness and arthritis score. We also confirmed through histological analysis that the hind paws of FLE-treated mice experienced lower inflammation and cartilage damage. Furthermore, FLE consumption decreased the amount of IgG isotypes and pro-inflammatory cytokines, indicating that FLE can influence IgG-induced cytokine production in RA. Next, we performed in vivo tests using MH7A cells to elucidate the underlying mechanisms. The treatment of TGF-β-stimulated MH7A cells with FLE revealed that affected the TGF-β/Smad signaling pathway, decreasing COL1A1 and MMP-$\frac{2}{9}$ expression, and suppressed cell migration. Additionally, FLE inhibited MH7A survival and proliferation in a dose- and time-dependent manner. We then investigated how autophagy pathways were affected and found that FLE upregulated autophagy markers LC3-II and p62 but did not alter Beclin-1 expression. Moreover, FLE appeared to have a similar mechanism of action as CQ, which is an autophagy inhibitor. Treatments for RA aim to relieve joint pain and inflammation, limit joint destruction or deformities, improve joint function, and induce sustained remission [38]. Nonsteroidal anti-inflammatory agents (NSAIDs), corticosteroids, and disease-modifying anti-rheumatoid drugs (DMARDs) are widely used drugs for RA [39]. Notably, DMARDs with high rheumatoid factor titers, such as methotrexate (MTX), sulfasalazine (SSZ), and hydroxychloroquine (HCQ), are used in the early stages of severe RA to reduce inflammation and prevent joint destruction [40]. However, these drugs have a variety of side effects, including hepatotoxic, pulmonary, and gastrointestinal problems [41]. In the effort to find treatments with fewer side effects, researchers have turned to natural food extracts that have a long history of medical applications [42,43]. Natural plant extracts used for RA typically act through several mechanisms, including anti-inflammatory activity, chondroprotection, angiogenesis inhibition, and antioxidant activity [44]. Advantages of natural plant extracts include greater customizability based on individual patient condition [45]. Moreover, many of the commonly used options are less toxic than existing drugs while containing bioactive compounds that affect signal transduction pathways relevant to diseases [46,47]. In this study, we selected lettuce as a natural medicine for RA because of previous reports regarding its anti-inflammatory, anti-diabetic, and antioxidant activity [17,18,23]. We specifically used FLE because the fermented product generates NO at a constant concentration for a long period of time. Treatment with FLE significantly limited hind-paw swelling, attenuated clinical symptoms, and inhibited IgG and pro-inflammatory cytokine expression in the serum of CIA mice. Additionally, FLE limited bone destruction, invasion, and inflammation. Overall, FLE significantly ameliorated RA symptoms. In synovial tissue, FLS differentiate into myofibroblasts that produce and secrete extracellular matrix components, such as collagen. The transition to myofibroblasts drives increased proliferation, migration, and invasion [48]. The cytokine TGF-β plays a pivotal role in fibrosis and is upregulated in RA, with elevated expression in the synovial fluid and fibroblasts of patients. Specifically, TGF-β activates Smad phosphorylation in the Smad signaling pathway. The phosphorylation of Smad$\frac{2}{3}$ promotes fibrosis, whereas Smad$\frac{1}{5}$/9 exert anti-fibrotic effects [7]. Therefore, balancing Smad$\frac{2}{3}$ and Smad$\frac{1}{5}$/9 activation via TGF-β may be a viable strategy for RA treatment. Smad$\frac{2}{3}$ activation increases the expression of pro-fibrotic genes (e.g., collagen) and regulates the expression of matrix metalloproteases (MMPs) [48], which are major zinc-dependent endopeptidases that are involved in the invasion and degradation of the extracellular matrix. In RA synovial fibroblasts, increased MMP-2 and -9 production is especially associated with cartilage invasion [49]. Here, we investigated the effects of FLE on TGF-β-treated MH7A cells. We demonstrated that FLE affects TGF-β-induced Smad$\frac{2}{3}$ and Smad$\frac{1}{5}$/9 phosphorylation, downregulating COL1A, MMP-2, and MMP-9 expression to inhibit cell migration. Taken together, our data provide evidence that FLE relieves RA symptoms via the TGF-β/Smad signaling pathway. An increase in RA-FLS causes synovial proliferation and is important in RA pathogenesis. Because of its resistance to cell death, activated RA-FLS exhibits tumor-like uncontrolled proliferation [4] that contributes to RA pathogenesis and progression [50]. In addition to resisting apoptosis, RA-FLS also appears capable of inducing autophagy. Studies have shown that autophagy increases in RA synovial tissues [51] and is implicated in protecting fibroblasts from cell death [52]. These findings underscore the importance of autophagy in controlling RA-FLS survival and proliferation. To investigate autophagy, we chose to measure LC3 and p62, two representative autophagy regulators. LC3 and p62 are essential proteins in autophagy, specifically autophagosome formation and fusion with lysosomes [53]. Here, we showed that the autophagy regulation of FLE inhibited MH7A proliferation and survival in a dose- and time-dependent manner. The dose-dependent increase in LC3B and p62 suggested that FLE regulates FLS proliferation and survival through inhibiting autophagosome–lysosomal fusion. In addition, our monitoring of autophagic flux and LC3B turnover revealed that FLE inhibited autophagy in a manner similar to CQ. The main limitation of our study is that lettuce consists of various components, particularly after fermentation, and the influence of these components cannot be excluded. In addition, we have little data on whether RA is actually correlated with NO, which is the primary by-product of FLE. The exact reasons for the health benefits of fermented lettuce remain unclear, but some evidence suggests that NO production may play a role. Various studies have shown that the serum and synovial fluid of RA patients have high nitrite concentrations. Local inhibitors of nitric oxide (NO) synthesis could therefore be a therapeutic strategy for RA [54], but other studies have suggested that NO actually protects against inflammation. The release of pro-inflammatory mediators is regulated through local NO production, and in patients with RA, NO donors increase hyaluronic acid production by synovial cells [55,56]. Some evidence indicates NO does not mediate symptoms of late-stage RA (e.g., chronic inflammation and joint destruction) nor is its production fundamentally associated with arthritis susceptibility or severity [57]. Therefore, experiments directly introducing NO could help address these apparent contradictions. Whether TGF-β contributes to RA progression also remains controversial. We further note that a recent study on autophagy in RA contradicted our findings, suggesting that autophagy may have a dual role in RA-FLS survival [58]. To clarify the effect of FLE on RA, future research should investigate the molecular properties of FLE components and identify the signaling pathways involved in autophagy. Despite these limitations, our findings in a mouse model suggest that FLE ameliorates severe clinical symptoms of RA. Moreover, we confirmed that FLE regulates fibrosis and cell migration through the TGF-β/Smad signaling pathway and inhibits RA-FLS proliferation and survival via downregulating autophagy. Thus, FLE administration has strong potential for use as an RA treatment ## 5. Conclusions Collectively, our results demonstrate the effects of FLE on an RA mouse model and the human fibroblast synoviocyte line MH7A. Treatment of CIA mice with FLE had a similar effect as a standard RA drug (MTX), attenuating symptom severity. Molecular analyses suggest that the mechanism underlying FLE efficacy is the inhibition of cell migration and proliferation through the TGF-β/Smad signaling pathway and autophagy. 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--- title: 'Systematic Review and Meta-Analysis: Malnutrition and In-Hospital Death in Adults Hospitalized with COVID-19' authors: - Mona Boaz - Vered Kaufman-Shriqui journal: Nutrients year: 2023 pmcid: PMC10005527 doi: 10.3390/nu15051298 license: CC BY 4.0 --- # Systematic Review and Meta-Analysis: Malnutrition and In-Hospital Death in Adults Hospitalized with COVID-19 ## Abstract Background: Malnutrition and increased malnutrition risk are frequently identified in hospitalized adults. The increase in hospitalization rates during the COVID-19 pandemic was accompanied by the documentation of adverse hospitalization outcomes in the presence of certain co-morbidities, including obesity and type 2 diabetes. It was not clear whether the presence of malnutrition increased in-hospital death in patients hospitalized with COVID-19. Objectives: To estimate the effect of malnutrition on in-hospital mortality in adults hospitalized with COVID-19; and secondarily, to estimate the prevalence of malnutrition in adults hospitalized with malnutrition during the COVID-19 pandemic. Methods: EMBASE, MEDLINE, PubMed, Google Scholar, and Cochrane Collaboration databases were queried using the search terms malnutrition and COVID-19 and hospitalized adults and mortality. Studies were reviewed using the 14-question Quality Assessment Tool for Studies with Diverse Designs (QATSDD) (questions appropriate for quantitative studies). Author names; date of publication; country; sample size; malnutrition prevalence; malnutrition screening/diagnostic method; number of deaths in malnourished patients; and number of deaths in adequately nourished patients were extracted. Data were analyzed using MedCalc software v20.210 (Ostend, Belgium). The Q and I2 tests were calculated; a forest plot was generated, and the pooled odds ratio (OR) with $95\%$ confidence intervals ($95\%$CI) were calculated using the random effects model. Results: Of the 90 studies identified, 12 were finally included in the meta-analysis. In the random effects model, malnutrition or increased malnutrition risk increased odds of in-hospital death by more than three-fold: OR 3.43 ($95\%$ CI 2.549–4.60), $p \leq 0.001.$ The pooled prevalence estimate for malnutrition or increased malnutrition risk was $52.61\%$ ($95\%$ CI 29.50–$75.14\%$). Discussion and Conclusions: It is clear that malnutrition is an ominous prognostic sign in patients hospitalized with COVID. This meta-analysis, which included studies from nine countries on four continents with data from 354,332 patients, is generalizable. ## 1. Introduction Malnutrition, or more specifically, undernutrition, can be defined as the adverse clinical, functional, metabolic, and anthropometric outcomes of nutrient deficiency [1]. Malnutrition can also be the result of disease and can interact with the disease to produce unfavorable outcomes [2]. While prevalence estimates vary, malnutrition has been reliably shown to increase with age, disease, and disability [3]. Malnutrition prevalence estimates in older adults range from less than $5\%$ among healthy community-dwelling seniors [4] to more than $20\%$ among nursing home residents [5] and more than $50\%$ among hospitalized adults [6]. Among older hospitalized adults, inadequate protein intake during hospitalization predicts in-hospital death even more strongly than inadequate energy intake [7]. Malnutrition in hospitalized adults is associated with greater in-hospital mortality compared to rates among well-nourished and obese patients [8]. Malnutrition has been associated with multimorbidity, frailty, sarcopenia, inflammation, and insulin resistance, leading to increased mortality [9]. The importance of nutrition status cannot be overstated, as evidenced by the directive from the Norwegian Directorate of Health to screen all adults for malnutrition [10]. This position paper recommends that all adults aged 18 years or older undergo evaluation for nutrition status in most healthcare environments, including in-patient and out-patient settings, as well as individuals receiving assistance with food and/or feeding. The screening tool selected by the advisory committee was the Malnutrition Screening Tool (MST), based on a systematic review of its reliability and validity. The Global Leadership Initiative on Malnutrition (GLIM) acknowledged that while undernutrition is associated with inadequate nutrient intake and/or absorption, it is also associated with impaired health conditions such as illness, trauma and/or inflammatory states, which may lead to increased healthcare costs, functional decline, morbidity and death. Despite the universality of concern about malnutrition, a globally accepted definition of this condition, screening or diagnostic approach was not available. Thus, participants in the GLIM project endeavored to create a standardized, globally acceptable consensus on malnutrition diagnosis. GLIM has determined that malnutrition diagnosis is a two-step process which begins with screening for malnutrition using an accepted, valid malnutrition screening tool, followed by a nutrition assessment through which diagnosis and determination of severity are made [11]. Screening tools thus represent the first step in diagnosing malnutrition. Frequently used among these are the Mini Nutrition Assessment (MNA); the Nutrition Risk Screening 2002 (NRS2002); the Malnutrition Universal Screening Tool (MUST); Controlling Nutritional status (CONUT); and Prognostic Nutrition Index (PNI). The MNA, often used in its short form (MNA-SF), screens for malnutrition in the elderly and includes queries on food intake, involuntary weight loss, mobility, psychological stress or acute disease, neurocognitive disorders, and current BMI [12]. The NRS2002 was designed for use in hospitalized adults and includes measures of involuntary weight loss, reduced food intake, and disease severity [13]. Designed for use in the community, MUST has nevertheless been employed by many hospitals [14]. MUST considers current BMI, involuntary weight loss, and disease severity and provides nutrition management guidelines based on score [15]. CONUT utilizes n total peripheral lymphocytes, serum albumin, and total cholesterol to categorize hospitalized patients as low, medium, or high risk for malnutrition. These data can be uploaded daily, providing real-time monitoring or patient nutrition status throughout the hospital confinement [16]. Similarly, the PNI uses the equation 10 × serum albumin (g/dL) + 0.005 × lymphocyte count (per mm3) to develop a patient score where a lower score indicates more severe nutrition risk [17]. This plethora of diagnostic tools available gives great freedom to the clinician, but lack of between-method agreement has been documented [18]. On the other hand, validity and inter-rater reliability has been demonstrated between various methods and combinations of methods [19]. In the absence of a gold standard for screening for and/or diagnosing malnutrition, the current study will assume that the method used in a given study identified malnutrition with a reasonable degree of accuracy; however, this heterogeneity inserts variability into data summarization. ## COVID-19 Worldwide, many governments managed the COVID-19 outbreak by mandating lockdowns. These curfews, quarantines, and school and business closures had a negative psychological impact on the populations subjected to them [20]. Specifically, population surveys revealed elevated levels of psychological stress, depression, and anxiety [21], which were reported across cultures [22]. Suicidal ideation also increased during lockdowns [23], exemplified by increased calls to suicide hotlines during the same periods [24]. Indeed, an increase in suicide attempts was observed during the COVID-19 epidemic [25]. Associations between emotional distress, including anxiety and depression and eating behaviors are well documented. A direct association between binge eating and anxiety has been reported [26], and poor diet quality has been associated with increased depression and suicidal ideation [27]. During the COVID-19 pandemic in general and during lockdowns in particular, when anxiety, depression, and psychological stress were increased, alterations in dietary intake were noted, though the direction of that change is not entirely clear. For example, an increase in fast food and sugary food was concomitantly reported with an increase in fresh foods among UK adults with diabetes [28]. On the other hand, Korean adults, particularly seniors, experienced a worsening in diet quality during the pandemic [29], and findings from an international survey identified an association between self-reported anxiety and diet quality in community-dwelling adults [30]. Given the worsening in diet quality during the pandemic, it is possible that adults were hospitalized for COVID-19 with greater malnutrition risk than they might have during other historical periods. The adverse impact of malnutrition on hospitalization outcomes is well established [31]. Thus, the present meta-analysis sought to quantify the impact of malnutrition on in-hospital mortality in adult patients hospitalized with COVID-19. The primary objective of the present study is to estimate the effect of malnutrition on in-hospital mortality in adults hospitalized with COVID; and secondarily, to estimate the prevalence of malnutrition in adults hospitalized with malnutrition. ## 2.1. Protocol and Registration The present meta-analysis followed the PRISMA guidelines [32]. The protocol was registered on the National Institute for Health Research (NIHR) PROSPERO site registration number CRD42023392009 [33]. ## 2.2. Inclusion Criteria Studies were eligible for inclusion in the present meta-analysis if they were observational, including cross-sectional, retrospective, or prospective designs. Study populations in the included studies were limited to adults aged 18 years or older hospitalized with COVID-19. All included articles were published in the period 2021–2023 to provide coverage of legacy and more recent COVID variants. All publications were published in the English language in peer-reviewed journals. Published and in-press articles were acceptable. For all included studies, malnutrition was the stated exposure and in-hospital morality was the stated outcome, though not necessarily the primary outcome of the study. In-hospital mortality was identified as an outcome (not necessarily a primary outcome). ## 2.3. Exclusion Criteria Studies were only eligible for inclusion in the present analysis if they provided the full information necessary for data extraction needed for the analysis. Additionally, studies in which malnutrition prevalence was not stated were not included. Finally, narrative reviews, systematic reviews, and meta-analyses were not eligible for inclusion in the present meta-analysis. ## 2.4. Databases The following databases were queried: EMBASE, MEDLINE, PubMed, Google Scholar, and Cochrane Collaboration. ## 2.5. Search Words Search words used in each of the databases were: malnutrition and COVID-19 and hospitalized adults and mortality. ## 2.6. Study Quality Prior to inclusion, studies were reviewed using the 14-question Quality Assessment Tool for Studies with Diverse Designs (QATSDD) (questions appropriate for quantitative studies). Using this tool, a score on a given item can range from 0 (not at all or not mentioned) to 3 (complete). Scores for each of the items were compared between the two reviewers (MB, VKS), and agreement was defined as a score from one reviewer within one point of the other reviewer’s score. A $75\%$ agreement was set as the minimum level of between-reviewer concordance. Disagreement beyond this was to be settled by discussion, but all articles reviewed met this criterion. Total scores were also compared between reviewers. According to protocol, an article with a total score less than 21 points (half of the total possible points overall for a quantitative study) were to be omitted. None of the articles reviewed at the full manuscript level were omitted for this reason. ## 2.7. Search Strategy The study examined all published and in-publication articles from 2020 to 2023, in order to capture hospitalizations for COVID. Only English-language publications were included. Search terms in each of the databases included malnutrition, COVID-19, mortality, and hospitalized adults. ## 2.8. Data Extraction Data extracted from each included article were author names; date of publication; country; sample size; malnutrition prevalence; malnutrition screening/diagnostic method; number of deaths in malnourished patients; number of deaths in adequately nourished patients. Data were extracted to an Excel spreadsheet. ## 2.9. Data Analysis Data were analyzed using MedCalc software v20.210 (Ostend, Belgium). The Q and I2 tests were calculated, indicating a great deal of heterogeneity between studies. Egger’s test and Begg’s test were used to assess publication bias. A forest plot was generated and the pooled odds ratio (OR) with $95\%$ confidence intervals ($95\%$CI) were calculated using the random effects model. ## 3.1. Selection of Studies The flow of study selection is presented in Figure 1. As can be seen, 90 studies were originally identified, and their abstracts reviewed. From these, 45 studies were eliminated due to title or abstract screening. A total of 26 studies were omitted because the exposure was not malnutrition, or the outcome did not include in-hospital mortality. Another 16 studies were omitted because the study population was not appropriate: specifically, the study was conducted in children ($$n = 5$$); the study was conducted in non-COVID patients ($$n = 6$$); and the study was conducted in non-hospitalized patients ($$n = 5$$). Another three manuscripts were omitted because the article was not a study, but rather a set of treatment guidelines or a case report. Of the remaining 45 studies that were retrieved for full text review, 22 were eliminated because the exposure was not malnutrition and/or the outcome was not in-hospital death. Another three were conducted in non-hospitalized individuals; seven were judged to be off topic, for example, a study designed to understand barriers to physician evaluation of nutrition status; and one was a meta-analysis. ## 3.2. Description of Included Studies The included studies are summarized in Table 1. A total of twelve studies were included, of which two each were from the USA, Turkey, and Brazil; and one each were from China, Switzerland, Iran, Italy, Spain, and Sweden. Of the studies, five were prospective (of which one was historical prospective); three were retrospective; and four were cross sectional. The included studies were published between April 2021 and December 2022. Sample sizes ranged from 75 to 343,188. Of the 354,332 total participants, 34,437 (unweighted fixed percentage, $9.7\%$) were classed as malnourished or at increased risk of malnutrition. All studies were conducted in hospitalized adults diagnosed with COVID-19; however, COVID-19 was not necessarily the reason for hospitalization. All of the studies reported malnutrition prevalence. Methods of diagnosing malnutrition risk or malnutrition itself included: NRS 2002 ($$n = 6$$ studies); modified NRS 2002 ($$n = 1$$ study); MNA-SF ($$n = 2$$ studies); MUST ($$n = 2$$ studies); SGA ($$n = 1$$ study); PNI ($$n = 1$$ study); anthropometric or biochemical measures ($$n = 2$$ studies); CONUT ($$n = 1$$); diagnostic code ($$n = 1$$); self-reported food reduction ($$n = 2$$). Of these, five studies incorporated more than one method of screening for/diagnosing malnutrition. The meta-analysis of the impact of malnutrition on risk of in-hospital death is summarized in Table 2. The Q and I2 statistics indicate a high degree of heterogeneity between studies: $Q = 56.5$% and I2 = $80.53\%$, $95\%$ CI 66.91–$888.54\%$, $p \leq 0.001.$ For this reason, the random effects model is used (though the fixed effects model is also presented). The odds for in-hospital mortality in patients with malnutrition range from the non-significant OR 1.46 in the Kananen et al., study to OR 26.58 in the Nunes et al., study and OR 31.010 in the Zhang et al., study. The Kananen study was retrospective and conducted in geriatric hospitals in Sweden. The Zhang et al., study was prospective, while the Nunes et al., study was cross-sectional. Three studies did not have significant OR values in the random effects model: Shabanpur et al., ( OR 1.58, $95\%$ CI 0.09–27.55); Nicolau et al., ( OR 9.51, $95\%$ CI 0.44–205.69; and Kananen et al., ( OR 1.46, $95\%$ CI 0.0.88–2.42). Both the Shabanpur et al., and the Nicolau et al., studies were cross-sectional, while the Kananen et al., study, as stated previously, was retrospective. Overall, in the random effects model, OR 3.43 ($95\%$ CI 2.55–5.40), $p \leq 0.001$, indicating that increased malnutrition risk or a diagnosis of malnutrition nearly quadruples the risk of in-hospital death. The forest plot of the association between malnutrition and in-hospital death is presented in Figure 2. Publication bias was not present according to Egger’s test: intercept = 0.21, $95\%$ CI −1.47–1.89, $$p \leq 0.79$$; and Begg’s test: Kendall’s Tau = 0.24, $$p \leq 0.27.$$ Malnutrition prevalence is summarized in Table 3. Malnutrition prevalence estimates range from 7.843 % ($95\%$ CI 7.753–7.934) in the Ponce et al., study to 96.750 ($95\%$ CI 94.507–98.258) in the Shabanpur et al., study. The I2 statistic indicates a high degree of between-study heterogeneity, necessitating the use of the random effects model: I2 = $99.93\%$, $95\%$ CI 99.92–$99.94\%$, $p \leq 0.001.$ The pooled prevalence estimate using the random effects model is $52.61\%$ ($95\%$ CI 29.50–$75.14\%$). Egger’s test indicated publication bias: intercept = 31.82, $95\%$ CI 15.09–48.55, $$p \leq 0.002.$$ Though Begg’s test was not significant: Kendall’s Tau −0.125, $$p \leq 0.58$$, the findings nevertheless suggest publication bias. ## 4. Discussion The purpose of the present study was to estimate the impact of malnutrition on in-hospital mortality in adults hospitalized with COVID-19. The pooled random effects model indicates that malnutrition almost quadruples the odds of in-hospital mortality in this population, supporting the study hypothesis. The analysis indicates that malnutrition increases odds of death by more than three-fold. This contrasts with the odds of death reported in the meta-analysis by Abate et al, who found that malnutrition increased odds of death by a factor of 10 (OR 10.14, $95\%$ CI 6.49–15.82) [46], significantly higher than the odds of death found in the present report (OR 3.43 ($95\%$ CI 2.55–5.40). The influence of malnutrition on mortality in patients with COVID-19 was among the associations examined in a large, U.S. retrospective cohort study designed to identify risk factors for adverse outcomes of COVID-19, employing the National Inpatient Sample (NIS) 2020. Part of the Healthcare Cost and Utilization Project, the NIS 2020 includes data on patients hospitalized from all healthcare payers and includes unweighted data from approximately 7 million hospitalizations annually on a national level, providing data on more than $97\%$ of the U.S. population. This study identified that malnutrition approximately doubled risk of mortality in patients with COVID-19, a risk increase similar to that of stroke and liver disease, and almost twice that of the increased risk conferred by obesity, uncomplicated diabetes and chronic obstructive pulmonary disease [47]. This finding is of particular interest given the amount of attention devoted to the risk of overweight and obesity in patients with COVID-19. In a prospective study of almost 200 consecutive adults hospitalized with COVID-19, body composition was measured using bioipedence analysis together with anthropometric and biochemical measures of inflammation and immune response. While measures of abdominal fatness increased risk for ventilatory support, it did not predict mortality [48]. An umbrella review of systemic reviews and meta analyses on the association of obesity and mortality in patients with COVID-19 detected an increased risk of 1.14–3.52 in 16 of 24 included studies, but no increase in risk in eight studies. The authors concluded that the high degree of bias prevented making definitive statements about this association [49]. Malnutrition diagnosed using bioimpedance vector analysis (BIVA) was shown to more than quadruple the risk for adverse outcomes including mechanical ventilation and 60-day mortality in adults with COVID-19 pneumonia [50]. Elevated malnutrition risk and diagnosed malnutrition have been shown to increase risk of in-hospital mortality and early post-discharge mortality in a number of disease states. For example, a retrospective study of elderly patients (mean age > 80 years) hospitalized in an acute care geriatric department defined malnutrition as MNA score < 17. At the three-month follow-up point, malnutrition was associated with increased risk of death: OR 3.519, $95\%$ CI: 1.254–9.872, $$p \leq 0.017.$$ The MNA score also predicted incident geriatric syndrome, discharge location (home vs. institution) and functional status [51]. Malnutrition also predicted short term post-hospital discharge mortality. In a northern Italian cohort of 1451 consecutively enrolled adult patients (median age 80 years) admitted to internal medicine departments in a large tertiary care hospital, nearly $16\%$ of participants died within four months of hospital discharge. Malnutrition was defined as BMI < 18.5 kg/m2. Using this definition, investigators found that malnutrition more than doubled the risk of post-discharge mortality compared to patients with normal or elevated BMI. Among patients who died in-hospital, $3.2\%$ were classed as having malnutrition compared to $8\%$ of patients who were discharged home. This surprising finding was explained by the investigators as a reflection of the efficacy of the nutrition intervention provided to patients with malnutrition, improving survival [52]. It is also possible that the use of BMI < 18.5 kg/m2 as the measure of malnutrition explains these findings. If individuals with BMI in the normal, overweight and obese ranges have elevated malnutrition risk or a diagnosis of malnutrition, they will be misclassified as adequately nourished or at minimal risk. Further, if overweight and obesity are associated with improved survival, as has been demonstrated in acutely and chronically ill patient populations [53,54,55], then the estimated in-hospital mortality rate will be underestimated. Malnutrition does not predict mortality in all patient populations. A study of the association between malnutrition risk screened using the NRS 2002 and in-hospital mortality was conducted in patients admitted to intensive care units following a cardiac arrest. This retrospective study found no difference in either the NRS 2002 score or BMI between patients who survived and those who expired; however, both serum albumin and total cholesterol levels, both markers of malnutrition, were significantly lower in patients who died. Nevertheless, the NRS 2002 score did not predict in-hospital mortality in patients after cardiac arrest [56]. Included studies were characterized by a great deal of heterogeneity, evidenced by the very large I2 values. The heterogeneity in malnutrition prevalence undoubtedly reflects the variability in malnutrition risk screening and malnutrition diagnosis methods. Further, this heterogeneity in prevalence likely contributes to the between-study heterogeneity in odds of in-hospital death. Other potential contributors to malnutrition prevalence estimate variability may be patient characteristics associated with malnutrition risk, such as advanced age and number of comorbidities. Differences in odds estimates for death can arise from the diversity of malnutrition ascertainment methods employed in the included studies. Different screening and/or diagnostic tools can lead to differences in malnutrition prevalence estimates as well as differences in estimates of the impact of malnutrition on mortality [57]. However, if misclassification for exposure is non-differential (the probability of classifying a well-nourished individual as a malnourished individual is approximately equal to the probability of classifying a malnourished individual as a well-nourished individual), the result is bias toward the null hypothesis. This would imply that if there is an effect on the risk estimates, it is that the pooled OR is an underestimate of true risk. The prevalence-estimate of $57.145\%$ is somewhat greater than the prevalence estimate reported in the meta-analysis by Abate et al., which included studies of malnutrition published between 2019 and 2020 and estimated prevalence to be $49.11\%$ ($95\%$ CI: 31.67 to 66.54) [46]. While this difference is not significant, it may nevertheless reflect changes in hospitalized patient mix as the pandemic continued. Indeed, it has been reported that the hospitalized patient mix skewed toward more seriously debilitated individuals as the pandemic progressed [58]. Additionally, it is possible that the prevalence of malnutrition in study participants is biased, since hospital personnel may have avoided the more prolonged contact with these highly contagious patients necessary to perform a thorough nutrition status assessment. Indeed, health care worker stress in caring for patients hospitalized with COVID-19 has been documented [59]. In the present meta-analysis, we sought to estimate the association between malnutrition and risk of in-hospital death in adults hospitalized with COVID-19. Our study only included those studies published between 2021 and 2023, permitting an estimation of this association in patients hospitalized with legacy as well as current variants of the virus. It is clear that malnutrition is an ominous prognostic sign in patients hospitalized with COVID. This meta-analysis, which includes studies from nine countries on four continents with data from 350,021 patients, seems generalizable. ## 4.1. Limitations A potentially important study limitation was the identification of publication bias in the pooled prevalence estimates of malnutrition. While none were included in the present meta-analysis, an examination of unpublished but completed studies in study registries identified four such projects, all inClinicalTrials.gov: Nutritional Assessment of Hospitalized Patients With COVID-19 (DenutCOVID), which recruited ninety participants and did not post results, and for which malnutrition was the outcome variable (NCT04503525); Minimizing the Effects of COVID-19 Hospitalization With the COVID Rehabilitation Program for the Elderly (CORE) enrolled 124 participants in a randomized clinical trial in which oral nutrition supplements were included to prevent malnutrition, but in-hospital mortality was not a study endpoint (NCT04771052); an observational study in which 986 participants were enrolled, Investigating the Role of Vitamin D in the Morbidity of COVID-19 Patients (NCT04386044) examined the role of blood vitamin D levels (but not overall nutrition status) on several clinical outcomes; and Increased Risk of Severe Coronavirus Disease 2019 in Patients With Vitamin D Deficiency (COVIT-D), another observational study with 300 enrolled patients also looked at vitamin D nutriture (but not overall nutrition status) on COVID-19 severity (NCT04403932). Another important limitation is the high degree of heterogeneity in the included studies. This was remedied by using the random effects model; however, it cannot be assumed that the heterogeneity was fully neutralized. Other limitations include between-study variability in patient mix; between-study variability in COVID-19 variant (variant was not reported in any of the studies); and between-study variability in nutrition status screening/malnutrition diagnosis. This overall heterogeneity was remedied by using the random effects model, but residual variability may still influence findings. ## 4.2. Implications Findings of the present meta-analysis stress the importance in nutrition screening in adults hospitalized with COVID-19. While none of the included studies examined the impact of nutrition interventions, this is clearly an important future research objective. 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--- title: The Discriminant Power of Specific Physical Activity and Dietary Behaviors to Distinguish between Lean, Normal and Excessive Fat Groups in Late Adolescents authors: - Jarosław Domaradzki journal: Nutrients year: 2023 pmcid: PMC10005529 doi: 10.3390/nu15051230 license: CC BY 4.0 --- # The Discriminant Power of Specific Physical Activity and Dietary Behaviors to Distinguish between Lean, Normal and Excessive Fat Groups in Late Adolescents ## Abstract Physical activity (PA) and dietary behaviors (DBs) are crucial determinants of body mass composition. This work is a continuation of the previous study of PA and DBs patterns in late adolescents. The main aim of this work was to assess the discriminant power of PA and dietary behaviors and to identify the set of variables that discriminated participants with low, normal, and excessive fat the most. The results were also canonical classification functions that can allow the classification of individuals into adequate groups. A total of 107 individuals ($48.6\%$ male) participated in examinations, which used the International Physical Activity Questionnaire (IPAQ) and Questionnaire of Eating Behaviors (QEB) to assess PA and DBs. The participants self-reported body height, body weight, and BFP, with the accuracy of the data confirmed and empirically verified. Analyses included the metabolic equivalent task (MET) minutes of PA domains and intensity, and indices of healthy and non-healthy DBs, calculated as a sum of the intake frequency of specific food items. At the beginning, Pearson’s r-coefficients and chi-squared tests were calculated to study various relationships between variables, while the main considerations were based on discriminant analyses conducted to determine the set of variables with the most power to distinguish between lean, normal, and excessive body fat groups of participants. Results showed weak relationships between PA domains and strong relationships between PA intensity, sitting time, and DBs. Vigorous and moderate PA intensity related positively to healthy behaviors ($r = 0.14$, $r = 0.27$, $p \leq 0.05$), while sitting time related negatively to unhealthy DBs (r = −0.16). Sankey diagrams illustrated that lean persons displayed healthy DBs and low sitting time, while those with excessive fat had non-healthy DBs spent more time sitting. The variables that effectively distinguished between the groups include active transport and leisure time domains alongside low-intensity PA, represented by walking intensity and healthy dietary behaviors. The first three variables participated significantly in the optimal discriminant subset ($$p \leq 0.002$$, $$p \leq 0.010$$, $$p \leq 0.01$$, respectively). The discriminant power of the optimal subset (contained four above-mentioned variables) was average (Wilk’s Λ = 0.755) and determined that weak relationships between PA domains and DBs resulted from heterogeneous behaviors and mixed patterns of behaviors. Identifying the trajectory of the frequency flow through specific PA and DBs allowed for well-designed tailored intervention programs to improve healthy habits in adolescents. Therefore, identifying the set of variables that discriminate the most between lean, normal, and excessive fat groups is a suitable target for intervention. The practical achievements are canonical classification functions that can be used to classify (predict) participants in groups based on the three the most discriminating PA and DB variables. ## 1. Introduction Excessive body fat results from several factors, which include weak physical activity (PA), a sedentary lifestyle, and poor dietary behaviors (DBs). Obesity is still growing in prevalence [1] and is considered a global pandemic [2], with evidence suggesting that the extent of the problem has tripled since 1970 [3]. The consequences of obesity on health are multidimensional and include several outcomes such as metabolic disorders, cardiovascular diseases, poor mental health, and deterioration of physical performance [4,5,6,7,8]. Many studies have focused on the relationship between PA and DBs [1,9,10,11], with most highlighting a mixture of healthy and unhealthy behaviors through assessing patterns of PA (or inactivity) and eating behaviors in young people [12,13]. Moreover, some studies demonstrated associations between specific PAs and food intake, suggesting a relationship between the time spent on PA and eating behaviors. In particular, increased consumption of healthy food items, such as fruits and vegetables and lower amounts of soft drinks, relates to higher levels of exercise [14,15]. PA can be considered through the prism of domains, intensity levels, and global nutrition indicators. However, a gap exists in the literature as no studies have examined the relationship between PA and DBs and their combined effects in body mass composition. In addition, studies attempting to indicate the variables most related to body mass composition, particularly lean, normal, and excessive body fat, are lacking. Furthermore, no studies have identified the PA and DB variables that discriminate the most between groups of adolescents with different levels of body fat. Despite many works showing PA and DB patterns using principal component analysis (PCA), approaches using analysis of variance (ANOVA) or regression models [16,17,18] are unclear on which variables have the most power in distinguishing between groups with differing body fat. Therefore, this study presents a novel discriminant analysis approach to exploring the relationships between PA and DBs. Control functions of PA and dietary habits used for regulating body weight as a whole, and body fat in particular, are well documented [19,20,21]. Some studies using the International Physical Activity Questionnaire (IPQA), for example, suggest that the PA task domains appear to be less effective than PA intensity. Indeed, moderate to vigorous PA (MVPA) provides more substantial health benefits than low-intensity PA [16,18]. Similarly, the results regarding the assessment of importance of healthy and unhealthy dietary behaviors for body mass composition are still unclear [16,17]. The additive influence and hierarchy of the strength of effect on body fat percentage (BFP) within sets of PA and dietary components remain unknown. The most efficient intervention programs created for the fight against obesity combine PA and dietary recommendations [22,23]. However, lifestyle changes, particularly the increased consumption of food containing oils in late adolescents and increased use of sugar-sweetened beverages in college students [24,25], suggest a need to continue searching for variables related to BFP that can potentially distinguish between individuals with low and excessive levels of fat. Therefore, the main aim of this work was to assess the discriminant power of PA and dietary behaviors and to identify the set of variables that discriminated participants with low, normal, and excessive fat the most. The results were also canonical classification functions, which can allow to classify individuals to adequate groups. ## 2. Materials and Methods This work constitutes the second part of a study on the effects of PA and DBs on body mass composition in late adolescents. A detailed description of the sample size estimation, study design, participant recruitment, data collection, data handling, input of missing data, validation, and assessment of the consistency between self-reported and empirically measured body weight and BFP, as well as the Yeo–Johnson power transformation of data to a normal distribution, were published previously [13]. Herein, a summary of the data is given. ## 2.1. Ethics The Research Bioethics Committee of the Faculty Senate of the Wroclaw University of Health and Sport Sciences approved the study (consent numbers $\frac{33}{2018}$ and $\frac{13}{2022}$). The study followed the ethical principles for medical research involving human subjects contained in the Declaration of Helsinki published by the World Medical Association. ## 2.2. Participants and Study Design Power analysis conducted for clustering procedures, presented in the first part of the study, indicated a requirement for a minimum of 60 males and 60 females. However, the final analysis included 107 participants. Participants included 107 healthy individuals (52 males ($48.6\%$)) recruited in 2022 from students in their 1st year of study at the Faculty of Physical Education and Sport at Wroclaw University of Health and Sport Sciences. A flowchart describes the sampling procedure (Figure 1). Of 275 students recruited to the university in 2022, 147 took part in the examinations. After eliminating students rejected from the university ($$n = 25$$), those who met exclusion criteria ($$n = 9$$), and those who did not answer the questionnaires ($$n = 6$$), 107 participants took part in the study. ## 2.3. Data Collection and Measurements Data collection utilized the local Student Health Behaviors Studies (STUHB22) project, which evaluates PA, DBs, attitudes towards health, lifestyle, intrinsic and extrinsic risk factors for injuries during PA, and risk factors for overweight and obesity. Sports field studies students completed online questionnaires using Google Forms immediately after an academic lecture (Human Anatomy taught by the author) during the 2022 academic year. The author of this article conducted recruitment, data collection, and data entry. The study used the Polish version of the International Physical Activity Questionnaire (IPAQ) (long version) [26] and the Questionnaire of Eating Behaviors (QEB) [27]. IPAQ showed moderate validity and was similar to studies when compared to accelerometers (r ≈ 0.4), while QEB showed great internal reliability (Fleiss’ kappa: 0.64–0.84). The analysis also included self-reported body height, weight, and BFP data. In addition, the R sample function randomly selected a subset of 19 participants, including nine males and ten females, from an alphabetical list for manual anthropometric measurement. In this work, four domains and three intensity levels of PA derived from IPAQ were considered. Domains were: work/school—PA related to activity in work or/and school; active transport—PA related to the way of commuting during the day (on foot, cycling, or using private or public transportation); domestic/gardening—PA related to housework and yard activities; leisure time—PA related to activities during leisure time. Intensity levels were: vigorous, moderate, and walking. Activities during the last 7 days were considered. The physical activity level was expressed as a standard Metabolic Energy Turnover (MET) in METminutes/week [26]. Calculations were based on corresponding MET values assigned to various activities: walking = 3.3, moderate = 4.0, cycling = 6.0, and vigorous = 8.0. The result for each participant was METminutes/week scores computed by multiplying the MET value by the time spent on these activities. In addition, sitting time, as an inactivity domain, was collected. The minimal set of 16 questions recommended in the instruction of the QEB was used in this work [27]. Two indices on diet quality (modules) were calculated into a healthy dietary habits index (8 items: whole-grain bread, milk, fermented milk drinks, curd cheese (including homogenized cheese), fish and fish dishes, bean and pea dishes, fruits, and vegetables) and an unhealthy dietary habits index (fast food, fried foods, cheese (including cream cheese), sweets, confectionery, canned meat, canned fish or canned vegetable-meat, sweetened carbonated beverages, energy drinks, and alcoholic drinks). Anthropometrical and body composition measurements used standard procedures and included two body height measurements with an accuracy of 0.1 cm using an anthropometer (GPM Anthropological Instruments, DKSH Ltd., Zürich, Switzerland). A bioelectric impedance method assessed body weight and BFP using an InBody230 device (InBody Co., Ltd., Cerritos, CA, USA). A statistical approach assessed the reliability of the self-reported BFP and body weights used in the analyses. A comparison of the slopes and intercepts of simple regression analyses of the empirical and self-reported data confirmed the reliability of the latter [28]. Although there were no missing anthropometrical, body composition, or PA data, there were missing data for the QEB questionnaire ($$n = 13$$). The propensity for a data point to be missing was random, also known as missing completely at random (MCAR) [29,30]. All measurements were preprocessed by applying multiple imputations in the R language using the software RStudio v.2022.7.1.554 (RStudio Team [2022]. RStudio: Integrated Development Environment for R. RStudio, PBC, Boston, MA, USA URL http://www.rstudio.com/ (accessed on 15 November 2022)) with the mice package (v.3.14.0). ## 2.4. Statistics The Shapiro–Wilk test evaluated the normality of data distribution, with the variables identified as non-normally distributed transformed to a normal distribution shape using the Yeo–Johnson power transformation [31]. Transforming the data to bring them closer to normality was required to meet the assumptions of the statistical methods, with all variables standardized to mean = 0 and standard deviation (SD) = 1 for each sex. This procedure allowed for the analysis of the whole group without dividing them by gender. Continuous variables were presented as means and $95\%$ confidence intervals (CI), while categorical variables were presented as numbers and percentages. The procedures of a discriminant analysis need knowledge about simple associations between variables and relations between subgroups. Therefore, as a first stage of analysis, Pearson’s product–moment correlation coefficients were calculated. Graphically correlation matrices were presented in chord diagrams using the circlize package in R [32]. Chord diagrams are a specific type of flow diagram that illustrate relationships between variables, with the width of the link being proportional to the strength of associations. Chord diagrams can be particularly useful for displaying inter-relationships between two or more groups of items, whereas the numbers of individuals in various subgroups, such as those preferring specific PA domains or healthy DBs, were presented as percentages, with quantiles used to represent sitting time. The interrelationship between PA domains, PA intensity, sitting time, DBs, and the corresponding BMI, BFP, and FMI categories, was assessed based on the prevalence of participants and their choice of PA and DBs. The preferred behavior was considered to be the one for which participants scored the most points. Differences in proportions between various subgroups were tested with the chi-squared test (χ2). Graphically, trajectories of the number of males and females flowing from PA domains or intensity, through sitting quintiles, and DBs, to body composition categories, were presented as Sankey diagrams. Sankey diagrams are not a statistical method, but visually depict a flow from one set of values to another. In this work, Sankey diagrams present the interrelationships between different subcategories of participants. The graphs were prepared in Google Sheets using the ChartExpo add-on. BFP used in Sankey diagrams was categorized according to the American Journal of Clinical Nutrition, which states that males should have 8–$19\%$ body fat and females should have 21–$32\%$. A BFP of less than $8\%$ in males is considered a fat deficit, with 8–$14\%$ considered lower normal (1st), 14–$19\%$ upper normal (2nd), and above $19\%$ considered excessive fat. Meanwhile, less than $21\%$ body fat is a deficit in females, with 21–$27\%$ considered lower normal (1st), 27–$32\%$ upper normal (2nd), and above $32\%$ considered excessive. The main approach was based on discriminant analysis (DA). This method allows for identifying the set of variables that distinguish between the four BFP categories the most, which, unlike analysis of variance, allows for multivariate analyses. Discriminant analysis procedures allow for building canonical discriminant functions that are the linear combination of independent variables that will discriminate between the categories (e.g., lean, normal and excessive fat groups of participants) in a perfect manner [28]. DA also allows one to determine the classification functions that can be used to classify participants into specific groups. It is a practical application of the DA. Discriminant analysis with the best subsets selection of predictor effects was conducted, with Wilks’ Lambda value set as the criterion for choosing the best subset of predictor effects, and searching of the four-element subsets was ordered. Wilk’s Lambda is the primary statistic usually used to assess the discriminant power of the subset of the identified variables, whereas the ANOVA tests were used to test the significance of every single variable in subset. Tolerance values were calculated as a 1-R2 for each variable in relation to the rest of the variables. Tolerance is the proportion of a variable’s variance not accounted for by other independent variables in the equation. Raw and standardized coefficients (β) of the canonical discriminant functions were calculated. Raw values are used to build discriminant equations, while β–values allow for a comparison of the discriminant power between variables. Coefficients of the classification functions were calculated in addition. These can be used to predict subjects’ group membership. At the end, hierarchical cluster analysis (HCA) assessed the similarities and dissimilarities between BFP categories, with Mahalanobis distances and the Ward method of linkage used to agglomerate the set of variables that best discriminated between groups. All statistical tests and procedures used a p-value equal to 0.05 to determine the significance level, and all calculations (except those using RStudio) employed Statistica 13.0 (StatSoft Poland 2018, Cracow, Poland). ## 3. Results The basic characteristics of the males and females were presented previously [13]. Sex differences in the active transport domain and non-healthy dietary behaviors revealed that males had significantly lower mean values than females ($$p \leq 0.027$$ and $$p \leq 0.036$$, respectively). In addition, a lower value for total sitting time in males was very close to significance ($$p \leq 0.076$$). Comparing PA and DB patterns, several similarities and dissimilarities emerged between both sexes. One of the common phenomena included the relationship between some unhealthy PA and DBs (low-intensity PA, longer sitting time, and poor food consumption). Moreover, the preliminary analysis suggested a possible combined effect of such behaviors on body composition. Therefore, this work analyzed the multivariate relationship between PA and dietary behaviors and their impact on body fat percentage in-depth. The analysis in this work was divided into two parts. The first stage for discriminant analysis was to study the interrelationship between PA domains, sitting time, and dietary behaviors. The approach was based on simple Pearson correlations and χ2 tests. Results were illustrated with chord and Sankey diagrams. The second stage, as a main analysis, was descriptive discriminant analysis conducted in order to identify a set of variables. ## 3.1. The Interrelationship between Physical Activity Domains/Intensity, Sitting Time, and Dietary Behaviors Initial examinations included the interrelationships between PA domains and PA intensity, with sitting time and dietary behaviors, by calculating Pearson-product correlation matrices and producing chord diagrams (Figure 2 and Figure 3). Meanwhile, Sankey diagrams summarized the relationship between the prevalence of the participant’s preference for PA domains or PA intensity concerning sitting time, healthy DBs, unhealthy DBs, and corresponding BFP categories (Figure 4 and Figure 5). These mainly visual presentations of the relationships were the starting point in designing discriminant analysis and multivariate multiple linear regression models. Figure 2a shows the correlations between dominant PA domains, sitting time, and DBs. PA related to the work/school and domestic/gardening domains relate to sitting time compared to active transport and leisure time. However, none of those correlations were statistically significant ($p \leq 0.05$). On the other hand, sitting time was negatively related to unhealthy DBs (r = −0.16), which suggests a minor relationship between time spent sitting and the frequency of eating unhealthy foods. PA leading to leisure time and active transport are the domains most related to healthy eating behaviors ($r = 0.14$ and $r = 0.16$, respectively), indicating that more PA supports healthy behaviors. Figure 2b shows the correlations between dominant PA intensity domains, sitting time, and DBs. Vigorous-intensity PA is mainly related to healthy DBs ($r = 0.14$), although this relationship was not significant. Thus, there was a significant relationship observed for moderate-intensity PA ($r = 0.23$, $p \leq 0.05$), while walking (low intensity) was also positively related ($r = 0.14$). The strongest, negative, and significant relationship with sitting time presented PA vigorous intensity (r = −0.21, $p \leq 0.05$). Figure 3 shows the trajectory of the changes in the prevalence of the participants in relation to dominating PA domains, sitting time quantiles and dietary behaviors in relation to the body fat percentage categories. As it is seen, in females, dominating PA domains are active transport and domestic/gardening domains. While in males it is domestic/gardening and leisure time domains. More interesting information can be seen by reading the diagram from the body fat categories. Opposite categories are marked in color. Lean persons ($62\%$—both males and females) prefer healthy dietary behaviors and excessive fat persons ($44\%$). The difference, however, was not statistically significant (χ2 = 1.07, $$p \leq 0.785$$) with small non-healthy behaviors. Healthy behaviors are related to lower sitting time ($41\%$—quantiles Q1 and Q2), while negative behaviors mostly to higher sitting time ($44\%$—quantiles Q4 and Q5). However, differences in proportions were not significant (χ2 = 2.64, $$p \leq 0.620$$). Persons with excessive fat, to a large degree, prefer non-healthy dietary behaviors, and they are related to more time spent sitting. On the other hand, tracking yellow lines leading from sex to excessive fat persons, as well as green lines leading to lean persons, showed that more males are included in excessive fat categories, while females mostly agglomerated in lean categories. Differences were statistically significant (χ2 = 35.28, $p \leq 0.001$). Figure 4 shows the trajectory of changes in the prevalence of participants in relation to dominant PA intensity and DBs with BFP categories. PA intensity flow demonstrated similar proportions in both sexes for vigorous and moderate walking intensity. Interestingly, more individuals with less intense PA related to non-healthy DBs ($56.7\%$) and the upper normal and excessive fat categories. In contrast, healthy dietary behaviors related to vigorous-intensity PA in most participants (both males and females ($46.8\%$). However, the differences in proportions were not statistically significant (χ2 = 0.13, $$p \leq 0.938$$). Indeed, most participants with excessive fat preferred non-healthy eating ($56.0\%$) and moderate-intensity PA. ( $42.9\%$). Differences in proportions were not significant (χ2 = 3.24, $$p \leq 0.198$$). ## 3.2. The Set of Physical Activity and Dietary Behaviors That Effectively Distinguished between Lean, Normal, and Excessive Fat Groups Based on the results so far, it was reasonable to look for a set of PA and DB variables that differentiate individuals with different levels of adiposity the most. Standardized mean values with $95\%$ CI are presented in Table 1. The results of the discriminant analysis are presented in Table 2. The results of discriminant analysis are limited to the indication of the set of variables that most discriminate between the four groups of participants. In-depth analysis of the discriminating functions separating groups of participants, their eigenvalues, structural coefficients, and predicted classifications were omitted. The set of variables best differentiating between the groups contained active transport and leisure time domains, low-intensity PA (walking intensity), and healthy dietary behaviors. Table 2 presents the calculated tolerances, which are 1-R2 for each variable in relation to the rest of the variables. Tolerance is the proportion of a variable’s variance not accounted for by other independent variables in the equation. All three PA domains were statistically significant, while dietary behaviors were not. All four variables explained $25\%$ of the between-group variance, which confirmed Wilks’ lambda (Wilk’s Λ = 0.755). The first three variables were found in all earlier steps of analysis, indicating their high discriminatory power. Nonetheless, healthy DBs replaced unhealthy behaviors in the second last step and the vigorous PA domain in the previous step. The first and the second discriminant functions (eigenvalues: 0.232, 0.088, respectively), which separated groups in the best way, explained overall $89\%$ of the between-groups variance. The raw coefficients of the functions presented in Table 3 can be used to construct equations to separate participants based on four discriminant variables. The load of each variable to the overall discriminant power of the whole optimal subset was assessed based on standardized () coefficients (Table 3). As it is seen, the greatest load to the first function was active transport (β =−1.15) and low intensity represented by the walking domain (β = 1.42). The second function is determined mainly by leisure time (β = 0.91). All three PA variables occurred more diagnostic than dietary healthy behaviors. The first function separates mainly participants who are active commuters from those who use privet or public transport, while the second function distinguishes between physically active and non-active during leisure time. ANOVA one-way tests for each variable confirmed the highest and statistically significant discriminant power of the active transport ($F = 5.49$, $$p \leq 0.002$$) and the lowest and nonsignificant (but quite close to significance) of the healthy dietary behaviors ($F = 2.17$, $$p \leq 0.095$$). Based on the best subset four canonical classification functions were calculated, and four classification equations are presented in Table 4. It can be used to construct the equation formulas to classify participants into the adequate BFP subgroup. The example equation for the prediction of the individuals to lean group (Clean) can be written as: Clean = −1.80-at + 0.27 × lt + 1.13 × walk − 0.59 × hdi, where Clean—1st classification function; at—PA value (MET-min/week) from active transport domain; lt—PA value (MET-min/week) from the leisure time domain; walk—PA value (MET-min/week) from walking domain; hdi—scores from healthy dietary behaviors. Moreover, HCA allowed for a visual assessment of the similarities between the four groups of participants based on the four variables in the best-discriminating set, the results of which are presented in Figure 5. As expected, persons with lean and lower normal body fat had more similar behaviors than people with higher fatness (upper normal and excessive fat, which were similar to each other). ## 4. Discussion This work mainly aimed to identify the set of variables that most discriminated between participants separated into subgroups based on BFP. In addition, an initial part of the main analysis was to assess interrelations between PA domains, intensity, time spent sitting, and healthy and non-healthy DBs. The study demonstrated a minor relationship between PA domains, sitting time, and DBs. On the contrary, stronger associations were observed for PA intensity and DB. Sankey diagrams provided a well-illustrated trajectory of the frequency flow of participants through specific PA and DB areas and clearly showed that the participants with excessive fat displayed non-healthy DBs and spent more time sitting. Individuals with a lean body mass preferred vigorous-intensity PA, while persons with excessive body fat preferred low or moderate-intensity PA. The main achievement, received with discriminant analysis, was to identify the set of four variables that most effectively distinguished between lean, normal, and excessive fat groups of participants. The set contained active transport, leisure time, low-intensity PA represented by walking intensity, and healthy dietary behaviors. As a practical result, the canonical discriminant functions were determined. They can be used for classification of individuals into groups with lean, normal, and excessive fat mass. The initial stage of analysis was to assess the associations between PA and DB variables and the relationship between subgroups of participants preferring specific PA and DB domains. It occurred that healthy DBs increased modestly in proportion to increased leisure time and active transport. Moreover, an even stronger relationship was observed between healthy DBs and vigorous-intensity PA. Meanwhile, sitting time was related negatively to unhealthy DBs. These results are in agreement with Mitchell et al. [ 33], who studied associations between PA and other health behaviors during the coronavirus disease (COVID-19) pandemic. The authors observed modest improvements in nutritional behaviors in parallel with an increase in PA. This small, but noticeable, link appears to be a common observation after the lockdown experienced globally [34,35,36]. Furthermore, these results are in line with pre-pandemic studies showing that physically active persons preferred the consumption of healthier food (fruits, vegetables, lower fat savory foods, and water), while less active or non-active people preferred non-healthy foods (meat, fast food, sweetened beverages, or energy drinks) [37,38]. The mechanism that inhibits the need for calorific food is linked to reduced ghrelin levels (the hormone responsible for appetite), due to the acute effects of exercise [39,40]. The increase in non-healthy foods in the diet of physically inactive persons, including adolescents, can be related to common changes in dietary habits and the exchange of good food with poor food, especially fast-food. The results of Pearson’s correlations were in line with the analysis of the frequencies of participants’ preferred PA (called PA domains) and PA intensity in relation to their BFP classification by tracking changes in those frequencies. Using diagrams to visualize time spent sitting, healthy DBs, and non-healthy DBs made it possible to assess their role. Focusing on PA intensity and considering both sexes, males preferred vigorous and moderate intensity slightly more often, while females preferred moderate and low intensity. However, there were no differences in PA domain preferences, except for leisure time. Moreover, most persons preferring vigorous PA spent less time sitting and had healthy DBs. Regarding sitting time, there were no differences when comparing the numbers of less active participants, which was surprising but was also noted in previous literature [41,42]. More persons with healthy DBs were moderately or vigorously physically active, with less than $30\%$ preferring low-intensity PA. This is in line with observations before the pandemic, which suggested that $27.5\%$ of young adults do not achieve the levels of PA necessary for health [43]. In addition, other studies indicated that those who were not active preferred non-healthy food, which is in line with the frequency analysis in this study. The main stage of analysis was to identify the set of variables that might efficiently discriminate participants between subgroups previously separated based on body fat percentage (namely lean, normal, and excessive fat). This kind of analysis, which is a novel approach to the problem, has two main benefits. The first is a set of variables identified as the most discriminated subgroups. This set can be treated as variables worth targeting to prevent overweight (i.e., stimulating PA domains and shaping attitudes related to dietary behaviors). The second one is the determination of canonical discriminatory functions for classifying individuals into groups differing in BFP. To the authors’ best knowledge, no works in the field of PA and DB relationships have attempted to solve the aforementioned problem. Most analyses aimed at assessing the variability between groups of subjects with different PA levels, body composition, or DBs used ANOVA or various regressions. This approach made it possible to highlight individual variables for which groups significantly differed and identify predictors of dependent variables. The multidimensional approach indicated the four-element subset that best differentiates the selected groups. The subset contained active transport, leisure time, walking intensity (lowintensity), and healthy dietary behaviors. HCA of the groups with different BFP confirmed similarities between lean and lower normal groups and between upper norm and excessive fat groups, separately, as well as dissimilarities between these clusters. Participants (both male and female) who are active commuters, active during leisure time even with low-intensity and healthy eating were more likely lean or have normal body fat than participants who are not related to these domains. The coexisting components found in this study are partially in line with other authors. Most studies have shown a beneficial effect of leisure time on body weight and body fat mass, which was confirmed in systematic reviews [44,45]. Leisure time occurred as the most important variable (0.91, $p \leq 0.05$). This is convergent with results suggesting significant risk factor for being overweight and obese included no frequent leisure time physical activity [46]. However, contrary to these studies two facts: the level of intensity of the PA in leisure time and the prevalence of overweight participants who are not active during leisure time, but in relation to sex. Our own results showed low-intensity PA as a one of the key variables differentiating the groups, while Winkvist et al., showed moderate and vigorous intensity as more significant. Apart from that, own results showed also more overweight, no-active females than males, while Winkvist et al., showed opposite proportions ($9.9\%$ vs. $15.1\%$, respectively). The explanation for the difference could be the age of the participants. Winkvist et al., research covered younger groups of participants (pre- and just after maturation), while participants in our own studies were in the late phase of adolescence. Some results showed that girls change their BMI categories more often than boys with age [47]. Regardless of these differences, consistently with other studies, combined leisure time behaviors and intensity of after-school PA can strengthen the effect. [ 48]. This shows, on the one hand, the additive impact of positive or negative behaviors on body mass composition, and, on the other hand, justifies analyses based on multivariate statistical procedures as a more in-depth study of relationships [49]. Nonetheless, it is well-documented that more confounders support the effects of PA in poor body mass composition [50,51]. Our own results, consistently with others, indicated the importance of active commuting as a factor related to body mass composition. Many studies indicate that using active transport in work, school, or for private purposes constitutes an essential element of overall individual PA [52]. The great importance of active commuting may be related to the fact that adolescents must systematically, every day, spend some time getting to school or work. Hence, the large contribution of the active transport domain to the overall PA. Regular walking or cycling is a significant energy expenditure and contributes to the reduction in body fat [53]. Unfortunately, many studies demonstrated that declining rates of active transport is one of the consequences of, as well as a reason for, the growing inactivity in populations, and the evidence confirms that the increasing frequency of obese people is related to a decline in active transportation [54,55]. The current work showed active transport was the second most common source of PA volume (in overall PA MET-min/week) after leisure time. Its presence within the set of variables discriminating groups with different BFPs was not accidental. Systematic reviews demonstrated that active commuters are fitter and have a lower risk of obesity [56,57]. The results of the current study corroborate findings from other studies showing that walking or cycling to work is associated with lower body weight [58,59,60] and lower BFP [61]. A healthy diet is a foundation for health and well-being, while an unhealthy diet is a risk factor for various diseases [62]. The algorithm of discriminant analysis used combinations of the variables to search for the optimal set. Among the two DB indices, healthy and non-healthy, the importance of healthy behaviors in discriminating between the groups turned out to be greater. In this regard, more frequent healthy food consumption and eating unhealthy food less often were in competition. Nonetheless, eating healthy products more often was more specific and distinct when considered alongside the other three variables, which may explain the partial inconsistencies with previously noted results. A study on healthy and non-healthy dietary patterns suggested a crucial role for unhealthy DBs in obesity, and indicated that they were indirectly responsible for non-communicable diseases [63]. Furthermore, odds ratio (OR) data indicated that an unhealthy diet was more likely to increase overweight/obesity risk (OR = 1.65) compared to a prudent/healthy dietary pattern, which was more likely to lead to decreased body weight and fat mass (OR = 0.64) [64]. On the other hand, combining diet and PA improves weight loss more than every lifestyle component [65]. Indeed, those who changed their diet from unhealthy to healthy were 7.2 times more likely to lose weight than the control group. Moreover, improved dietary habits combined with increased PA enhanced the odds of losing weight (17.5-fold). Thus, these results are in agreement with the results presented in this work and confirm that healthy DBs are more vital than unhealthy behaviors when combined with other variables in a multidimensional set. Our own results are consistent also with findings in the Polish population suggesting a major role of healthy dietary behaviors combined with ‘Yard activity’ on BMI [9]. The findings showed, in addition, that participants with this kind of behavior were less likely to adhere to ‘Fast food and sweets’ dietary patterns. It is consistent with our previous findings [13]. Involvement in housework fills the free time of young people and can be a kind of protection against unfavorable factors (e.g., non-active sitting time or fast food or chips consumption) that affect body weight. The role of being involved in house chores was previously examined and results showed the associations with authoritative parenting style and dietary intake patterns [66]. A practical result of many studies is the set of regression equations to predict the value of dependent variables such as body weight components based on significant predictors. These equations can be used in various groups of people. Usually, different kinds of regression are used. In this article, the practical achievement was to determine the classification functions, which can be used to classify individuals based on a set of variables the most discriminated participants into 4 groups of BFP. Although our own methodology is quite different to regression analyses and classification equations cannot be directly compared or discussed with regression equations, our own results are consistent across identified variables. Taking into account the studied problem, the most often indicated predictors of overweight and obesity were the intensity of PA and healthy dietary behaviors (particularly consuming fruits and vegetables). Both domains were negatively related to body weight or body fat (the more PA or fruits and vegetables, the less body weight or fat) [46,67]. Moreover, infrequent physical activity was significantly associated with an almost $60\%$ increased risk of being overweight or obese [46]. The same study showed that a low reported frequency of intake of vegetables was significantly associated with a $20\%$ increased risk of being overweight or obese, and a low consumption of fish was associated with a $17\%$ increased risk of being overweight or obese. Limitations of this study include its cross-sectional design, which limited causal inference analysis. Another limitation is the inclusion of self-reported data. Despite empirical verification of the data reliability, the error resulting from such data acquisition is less robust than measuring directly. Moreover, collecting data through a web-based survey can affect accuracy. Nonetheless, a better response is expected from an online survey as respondents are more likely to complete it. ## 5. Conclusions In conclusion, weak relationships between PA domains and DBs resulted from heterogeneous behaviors and mixed behavior patterns. Identification of the trajectory of the frequency flow through specific PA and DB areas allows for well-designed, tailored intervention programs that can promote healthy habits in adolescents. Thus, identifying the set of variables that most discriminate between lean, normal, and excessive fat groups is a reliable target for developing interventions. Intervention programs should use a combination of increased physical intensity, especially during leisure time, and healthy eating. It is also advisable to promote active transport on foot or by bicycle. Authorities and policy-makers could include a prescription for lowering body fat in education through a combined program of nutrition and PA, which could shape people’s awareness of the need to change their lifestyle habits. A practical achievement are the equations classifying the individuals based on a set of four variables (PA and DB variables) into four groups with different levels of body fat. These equations can be useful in conducting interventions. ## References 1. Leis R., Jurado-Castro J.M., Llorente-Cantarero F.J., Anguita-Ruiz A., Iris Rupérez A., Bedoya-Carpente J.J., Vázquez-Cobela R., Aguilera C.M., Bueno G., Gil-Campos M.. Cluster Analysis of Physical Activity Patterns, and Relationship with Sedentary Behavior and Healthy Lifestyles in Prepubertal Children: Genobox Cohort. Nutrients (2020.0) 12. DOI: 10.3390/nu12051288 2. Swinburn B.A., Sacks G., Hall K.D., McPherson K., Finegood D.T., Moodie M.L., Gortmaker S.L.. 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--- title: 'Differences in Kinanthropometric Variables and Physical Fitness of Adolescents with Different Adherence to the Mediterranean Diet and Weight Status: “Fat but Healthy Diet” Paradigm' authors: - Adrián Mateo-Orcajada - Raquel Vaquero-Cristóbal - Jesús Miguel Montoya-Lozano - Lucía Abenza-Cano journal: Nutrients year: 2023 pmcid: PMC10005536 doi: 10.3390/nu15051152 license: CC BY 4.0 --- # Differences in Kinanthropometric Variables and Physical Fitness of Adolescents with Different Adherence to the Mediterranean Diet and Weight Status: “Fat but Healthy Diet” Paradigm ## Abstract The present investigation provides a new paradigm, the fat but healthy diet, through which to analyze the importance of adherence to the Mediterranean diet (AMD) in the adolescent population. To this end, the objectives were to analyze the existing differences in physical fitness, level of physical activity, and kinanthropometric variables in males and females with different AMD and to determine the existing differences in physical fitness, level of physical activity, and kinanthropometric variables in adolescents with different body mass index and AMD. The sample consisted of 791 adolescent males and females whose AMD, level of physical activity, kinanthropometric variables, and physical condition were measured. The results showed that when analyzing the whole sample, the differences were only significant in the level of physical activity among adolescents with different AMD. However, when considering the gender of the adolescents, the males also showed differences in the kinanthropometric variables, while the females did so in the fitness variables. In addition, when considering gender and body mass index, the results showed that overweight males with better AMD showed less physical activity and higher body mass, sum of three skinfolds, and waist circumference, and females did not show differences in any variable. Therefore, the benefits of AMD in anthropometric variables and physical fitness of adolescents are questioned, and the fat but healthy diet paradigm cannot be confirmed in the present research. ## 1. Introduction In the adolescent population, nutritional habits are one of the most important factors for the establishment of healthy lifestyles [1,2]. Thus, a correct diet facilitates the prevention of chronic diseases such as obesity [3], contributes to better glycemic control [4], and has a fundamental anti-inflammatory and antioxidant effect in this population [5]. In recent decades, the adherence to the Mediterranean diet (AMD) of adolescents has been used in Europe as a criterion for assessing their diet because it is one of the healthiest dietary patterns known to date [6,7]. Previous research conducted in adolescents has tried to establish differences in AMD according to gender [8,9] and to analyze the relationship between AMD and other determinant variables for health such as body composition [10,11], level of physical activity [11,12], or physical fitness [13]. Regarding some components of physical fitness and physical activity levels of adolescents according to AMD, the results found were very disparate. Some of the results found in previous research were: [1] higher values in handgrip strength and vertical jump in males with a higher AMD but not in females [13]; [2] a higher performance in cardiorespiratory endurance tests in males and females with moderate-high AMD [10,13]; [3] a higher level of physical activity in adolescents with a higher AMD [12]; or [4] absence of significant differences in the level of physical activity and physical fitness among adolescents with different AMD [11]. Similarly, the existing relationship between body composition and AMD shows contradictory results. Some previous research found: [1] no significant differences in males or females in body composition when considering the level of AMD [10,11]; and [2] significant differences in fat percentage when considering AMD, with males with moderate-high AMD showing the lowest fat percentage [13,14]. Although previous research has investigated the differences between males and females in AMD as well as the differences in body composition, level of physical activity, and physical fitness of adolescents according to AMD, the conclusions are not clear. In the field of physical activity, a paradigm known as “fat but fit” has been considered in recent years [15,16]. In this paradigm, overweight and obese adolescents with a better level of physical fitness showed lower cardiometabolic risks than adolescents with the same weight status but with a worse level of physical fitness [15,16]. Extrapolating this theory to the field of nutrition, it could be that a similar phenomenon occurs, whereby differences in body composition, physical fitness, and physical activity level of adolescents could differ according to AMD and adolescent weight. Thus, a paradigm called “fat but healthy diet” could be proposed in which, hypothetically, adolescents with optimal AMD would present higher levels of physical activity, better kinanthropometric variables, and higher performance in fitness tests compared to adolescents with worse AMD within the same weight status group. It could provide key information on the relevance of diet in the adolescent population regardless of weight status. However, no known study has addressed this joint approach to AMD and weight status. For this reason, the main objectives of the present investigation were (a) to analyze the existing differences in physical fitness, level of physical activity, and kinanthropometric variables in males and females with different AMD and (b) to determine the existing differences in physical fitness, level of physical activity, and kinanthropometric variables in adolescents with different body mass index (BMI) and AMD. Based on previous research, the following research hypotheses are posed: (H1) differences will be significant in physical fitness, physical activity level, and fat-mass-related variables in adolescents according to AMD level, although there will be differences according to gender; and (H2) adolescents with higher AMD will perform more physical activity, present better body composition, and higher performance in physical fitness tests regardless of their weight status. ## 2.1. Design A descriptive and cross-sectional design with non-probabilistic convenience sampling was carried out. Before starting the study, the institutional ethics committee of the Catholic University of Murcia approved the protocol designed for the research study (protocol code: CE022102, 26 February 2021) according to the Helsinki declaration. The STROBE statement was followed for the development of the manuscript [17]. ## 2.2. Participants The minimum sample size was calculated using the statistical software Rstudio 3.15.0 (Rstudio Inc., Boston, MA, USA) using the standard deviations from previous studies that analyzed diet in adolescents (SD = 2.32) [10]. The minimum sample size for the development of the present research was 750 adolescents considering an estimated error (d) of 0.22 for a $99\%$ confidence interval. The final sample consisted of 791 adolescents (404 males and 387 females; mean age: 14.39 ± 1.26 years) who decided to voluntarily participate in the study. Informed consent was signed before the start of the study by the adolescents and their parents, accrediting their participation in the study. The participants were enrolled in four secondary schools in the (Region of Murcia) (two located in the north, one in the center, and one in the southeast). These schools were selected because they had a high number of students enrolled in compulsory secondary education. In Spain, during this formative stage, students receive notions of nutrition and dietetics in the subjects of Biology and Geology as well as in Physical Education [18]. Adolescents learn about the importance of macronutrients and micronutrients of general importance for the functionality of the body as well as the importance of a healthy diet in lifestyle habits and health improvement, but this is always learned in a secondary and complementary way and not as the main content of any of the aforementioned subjects. The inclusion criteria of the sample were as follows: (a) enrolled in compulsory secondary education with ages between twelve and sixteen years old and (b) not having any incapacitating disease that would make it impossible to complete the questionnaires and physical tests. ## 2.3.1. Questionnaire Measurements The KIDMED questionnaire [6] was used to determine the AMD of these adolescents. This questionnaire presents moderate reliability and reproducibility values for use in adolescents (α = 0.79 and kappa: 0.66). The questionnaire is composed of 16 questions that were rated by the adolescents with a score of 1 or 0 depending on whether the criterion was met. Subsequently, the scores obtained were added up considering that twelve of the questions had a positive connotation (+1) (favoring a good adherence), and four had a negative connotation (−1) (favoring an inadequate adherence). The final score was between 0 and 12 points for all the participants, establishing three classifications: poor adherence to the Mediterranean diet (0–3 points), need to improve adherence (4–7 points) or optimal adherence (8–12 points) [6]. The level of physical activity was determined using the Spanish version of the “Physical Activity Questionnaire for Adolescents” (PAQ-A) [19]. This questionnaire has an intraclass correlation coefficient of 0.71 and an internal consistency of 0.74 for the final score. It is composed of nine questions that provide information on the physical activity performed in the last seven days, considering different time slots during the day. A Likert scale of 1 to 5 points is used for its completion, with 1 being an absence of physical activity and 5 a high level of physical activity [19]. ## 2.3.2. Kinanthropometric Measurements The kinanthropometric analysis was composed of [1] three basic measurements (body mass, height, and sitting height); [2] three skinfolds (triceps, thigh, and calf); and [3] five girths (arm relaxed, waist, hip, thigh, and calf). To carry them out, the protocol established by the International Society for the Advancement of Kinanthropometry (ISAK) was followed [20]. ISAK-accredited anthropometrists (levels 2 to 4) measured each variable twice, performing a third measurement when the differences between the first and second measurements were greater than $5\%$ in the skinfolds and $1\%$ in the rest of the measurements. The mean of the measured values was used when two measurements were performed, while the median was used when a third measurement was performed [20]. The variables from the measurements were used to calculate BMI, Σ3 skinfolds (triceps, thigh, and calf), corrected girths of the arm [arm relaxed girth − (π × triceps skinfold)], thigh [middle thigh girth − (π × thigh skinfold)], and calf [calf girth − (π × calf skinfold)], fat mass (%) [21], muscle mass [22], and waist-to-hip ratio (waist girth/hip girth). The intra- and inter-evaluator technical error of measurements (TEM) were calculated in a sub-sample. The intra-evaluator TEM was $0.02\%$ for the basic measurements; $1.21\%$ for skinfolds, and $0.04\%$ for the girths; and the inter-evaluator TEM was $0.03\%$ for the basic measurements; $1.98\%$ for skinfolds, and $0.06\%$ for the girths. The kinanthropometric equipment used was calibrated prior to the measurements. A TANITA BC 418-MA Segmental (TANITA, Tokyo, Japan), with an accuracy of 100 g, was used for body mass. For height and sitting height, a SECA stadiometer 213 (SECA, Hamburg, Germany) with an accuracy of 0.1 cm was used. A skinfold caliper (Harpenden, Burgess Hill, UK) was used for measuring skinfolds, with an accuracy of 0.2 mm. An inextensible tape (Lufkin W606PM, Missouri City, TX, USA) was used to measure girths with a 0.1 cm accuracy. ## 2.3.3. Physical Fitness Test The familiarization and correct performance of the physical fitness tests by the adolescents was supervised by four investigators with previous experience in the field. Each investigator oversaw the same physical fitness tests during all measurements to avoid inter-evaluator error. Three physical fitness tests were performed, which were chosen for their validity and reliability in this population [23,24,25]. The 20 m shuttle run test was chosen to assess cardiorespiratory endurance in adolescents. This is an incremental test that consists of running twenty meters as many times as possible. This test ends when the distance is not covered two consecutive times before the allotted time ends or when the adolescent reaches exhaustion. The formula by Leger et al. [ 26] was used to determine the maximum oxygen consumption (VO2 max) of each adolescent. Handgrip strength was assessed using the handgrip strength test, in which the adolescents had to apply the greatest possible force on a Takei Tkk5401 digital handheld dynamometer (Takei Scientific Instruments, Tokyo, Japan). The adolescents’ elbow was fully extended, as this is the optimal position for applying the maximum force [27]. The countermovement jump (CMJ) was used to assess the explosive power of the lower limbs. For its execution, following the protocol by Barket et al. [ 28], the adolescents had to perform a maximal vertical jump. The adolescents’ hands were to be placed at the waist, and the legs and back must be fully extended during the flight phase. In the starting position, the adolescents had to stand on the force platform (MuscleLab, Stathelle, Norway) with hands on their waist and feet hip-width apart. Subsequently, they performed a knee flexion to 90° as quickly as possible, followed by a full knee extension to reach the maximum possible height in the vertical jump. ## 2.4. Procedure The tests were carried out during Physical Education class time using covered sports pavilions of the participating compulsory secondary education centers to reduce contaminating variables that could affect the results. First, all the adolescents completed the KIDMED and PAQ-A questionnaires. Subsequently, the kinanthropometric measurements were taken. Next, the correct execution of the handgrip strength and CMJ tests was explained to the students so that they became familiar with them. Once the familiarization process was completed, a warm-up consisting of running and joint mobility exercises was carried out, after which the tests were performed. Finally, the 20 m shuttle run test was performed. All the physical condition tests were performed twice, leaving two minutes of recovery time between the two measurements of each test and five minutes between the different tests. The best value of each test was recorded, except for the 20 m shuttle run test, which was performed only once. The testing protocol was established based on previous research [29] and following the recommendations of the National Strength and Conditioning Association (NSCA). These recommendations consider the fatigue generated by each test and establish sufficient recovery time between them to minimize possible interferences [30]. ## 2.5. Data Analysis The Kolmogorov–Smirnov test was used to assess the normality of the data. As all variables showed a normal distribution, parametric tests were used to analyze them. Descriptive statistics were used to find the mean and standard deviations. An ANOVA was performed to establish the existing differences in the physical activity level, physical condition, and kinanthropometric variables according to the AMD of adolescents. Next, an ANCOVA was performed to determine the existing differences in the measured variables as a function of AMD, considering gender and BMI as covariates of the model. Subsequently, a MANOVA was performed to determine the differences in the variables measured between males and females according to AMD and to establish the differences between the different weight statuses according to AMD in general and for males and females. The Bonferroni post hoc analysis was used to determine the differences between groups. Partial eta squared (η2) was used to establish whether the effect size was small (ES ≥ 0.10), moderate (ES ≥ 0.30), large (ES ≥ 1.2), or very large (ES ≥ 2.0), with an error of $p \leq 0.05.$ A $p \leq 0.05$ value was used to determine statistical differences [31]. The SPSS statistical software was used to perform the statistical analysis (v.25.0; SPSS Inc., Chicago, IL, USA). ## 3.1. Differences in the Study Variables According to the AMD Level The differences in the level of physical activity, kinanthropometric variables, and physical fitness of adolescents with different levels of AMD are shown in Table 1. The differences were significant only in the level of physical activity, with the adolescents with an optimal adherence being those who practiced sports the most ($p \leq 0.001$). The inclusion of the covariates gender and BMI in the model showed significant differences for gender ($p \leq 0.001$–0.004) in all analyzed variables, except for BMI ($$p \leq 0.064$$) and hip girth ($$p \leq 0.121$$); however, when considering BMI, significant differences were found in all the variables ($p \leq 0.001$–0.013) except for height ($$p \leq 0.081$$). Figure 1 shows the differences among males with poor AMD, males that need to improve AMD, and males with an optimal AMD as well as among females with poor AMD, females that need to improve their AMD, and females with an optimal AMD. With respect to males, the differences were significant in the level of physical activity and kinanthropometric variables but not in physical fitness. Females showed differences in the level of physical activity and physical fitness variables but not in kinanthropometric variables. Bonferroni’s pairwise comparison showed that males with a poor AMD had a lower level of physical activity ($p \leq 0.001$–0.039), body mass ($$p \leq 0.032$$), BMI ($$p \leq 0.030$$), hip girth ($$p \leq 0.021$$), corrected thigh girth ($$p \leq 0.044$$), fat mass ($$p \leq 0.031$$), and muscle mass ($$p \leq 0.050$$) than males with an optimal and/or need to improve AMD. Regarding the females, whose who showed a poor AMD or need to improve AMD had a lower level of physical activity ($$p \leq 0.001$$–0.003) and VO2 max ($$p \leq 0.037$$) than females with an optimal AMD. ## 3.2. Differences in the Study Variables According to the Gender, AMD Level, and Weight Status The differences in the analyzed variables according to the AMD and the BMI of the adolescents are shown in Figure 2. In the normal weight ($p \leq 0.001$) and underweight ($$p \leq 0.007$$–0.026) groups, adolescents with an optimal AMD showed a significantly higher level of physical activity. In the overweight group, adolescents with an optimal AMD showed significantly higher values in body mass ($$p \leq 0.014$$). Table 2, Table 3 and Table 4 show the differences in the level of physical activity, kinanthropometric variables, and physical fitness in males and females who were normal weight, overweight and underweight with different levels of AMD. In normal weight males and females, differences were significant in the level of physical activity ($$p \leq 0.001$$–0.011), with males and females with an optimal AMD showing higher scores in both groups (Table 2). In the overweight group, significant differences were found in BMI ($$p \leq 0.027$$), sum of three skinfolds ($$p \leq 0.044$$), and waist girth ($$p \leq 0.016$$), with males with an optimal AMD showing higher values in all these variables (Table 3). In the underweight group, males with optimal AMD showed higher scores in the level of physical activity ($$p \leq 0.004$$), and males with a need to improve their AMD showed higher values in the CMJ test ($$p \leq 0.003$$) as compared to males with a poor AMD. Also in this group, males with an optimal AMD showed higher values of hip girth ($$p \leq 0.040$$) as compared to males with a need to improve AMD (Table 4). Females in the overweight and underweight groups did not present significant differences in any variable. ## 4. Discussion The main objectives of the present investigation were (a) to analyze the existing differences in physical fitness, level of physical activity, and kinanthropometric variables in males and females with different AMD and (b) to determine the existing differences in physical fitness, level of physical activity, and kinanthropometric variables in adolescents with different BMI and AMD. Based on these objectives and on previous scientific literature, the following research hypotheses were established: (H1) differences will be significant in physical fitness, physical activity level, and fat-mass-related variables in adolescents according to AMD level, although there will be differences according to gender; and (H2) adolescents with higher AMD will perform more physical activity, present better body composition, and have higher performance in physical fitness tests regardless of their weight status. According to the first objective of the present investigation (to analyze the existing differences in physical fitness, level of physical activity, and kinanthropometric variables in males and females with different AMD), the results showed only significant differences in the level of physical activity of the adolescents; adolescents with an optimal AMD practiced sports to a greater extent than those with a poor AMD. No differences were found in anthropometry and physical fitness variables. However, when considering the gender of the adolescents, both males and females with an optimal AMD presented a significantly higher level of physical activity. In addition, males with and optimal AMD showed greater muscle mass, especially in the thigh area, but also greater values in body mass, BMI, and fat mass, especially in the hip area, with respect to the poorer AMD group. Among females with optimal AMD, only differences were found in VO2 max with respect to the poorer AMD group. Previous research does not provide conclusive results in this regard, as some studies showed that there was no relationship between AMD, physical activity level, and kinanthropometric variables [11], while other studies showed that adolescents with better AMD performed more physical activity [12] and presented a higher VO2 max [10,13]. More specifically, the higher fat percentage of males with higher adherence to the Mediterranean diet may be because previous studies have suggested that high-fat diets such as the Mediterranean diet may promote obesity and fat accumulation when there is a positive energy balance [32]. Indeed, males with optimal AMD showed a greater increase in fat mass compared to muscle mass, which could be the origin of the changes in body mass and BMI [33]. In addition, this would explain why no differences were found in the fitness tests related to strength [34,35]. On the other hand, the isolated improvement in VO2 max in females could be due to the fact that adolescents with a better diet are those who are more aware of the importance of healthy habits [1,2], thus leading them to practice more physical activity. Therefore, it could be the greater practice of physical activity that is responsible for the higher VO2 max compared to the rest of the AMD groups [36,37]. Therefore, differences obtained in the present study could indicate that AMD as an isolated factor is not a determinant in the changes in kinanthropometric variables or in the fitness of adolescents. Despite these results, questions remain for future studies. The results obtained in the present study partially confirm the first research hypothesis (H1) since adolescents with better AMD had a higher level of physical activity. However, the differences were not significant in the kinanthropometric or physical condition variables. When analyzing the results according to the gender of the adolescents, the differences were significant in males and females with optimal AMD compared to those with worse AMD. In this regard, males and females with optimal AMD showed a higher level of physical activity, but only males showed differences in body composition (increasing fat mass to a greater extent than muscle mass), and only females showed differences in physical fitness (increasing VO2 max) but not being able to claim that this was due to better AMD. The second objective of the present study was to determine the existing differences in physical fitness, level of physical activity, and kinanthropometric variables of adolescents with different BMI and AMD, which could be termed the “fat but healthy diet” paradigm. Following the line of the “fat but fit” paradigm [15,16], it was hypothesized that adolescents with a better AMD would show a higher level of physical activity, better kinanthropometric variables, as well as a higher performance in the fitness variables compared to adolescents presenting worse AMD within the same weight status. Thus, the obtained results showed higher levels of physical activity in the optimal AMD group of normal weight and underweight adolescents. However, this was not the case in the overweight group, where a higher body mass was also found in the optimal AMD group. Previous research that considered adolescent AMD showed a higher level of physical activity in the group of adolescents with a higher AMD [12]. It is important to note that adolescent BMI and AMD have not been previously considered together, so the results obtained in this regard are novel. The fact that the overweight group was the only one that did not show significant differences in the level of physical activity among adolescents with different AMD could be explained by the frequent alterations in body image suffered by overweight and obese adolescents. This has very negative consequences during adolescence, mainly related to dietary alterations and avoidance of sports participation [38]. Regarding the greater body mass in adolescents with an optimal AMD in the overweight group, a possible explanation would be that the type of food ingested was not as decisive as the quantity ingested [39]. Thus, the lack of physical activity in this population, linked to excessive intake, would favor the increase in body mass, although future research that analyzes the specific daily intake of adolescents is necessary to corroborate this conclusion. When analyzing the results considering the BMI and gender of the adolescents, it should be noted that the differences in physical fitness were significant in males and females, while kinanthropometric variables only showed differences in the group of males. Regarding physical fitness, the CMJ of underweight males with a poor AMD was significantly lower than the rest of the males with a better AMD. Previous research analyzing CMJ performance found no significant differences in either males or females based on AMD [13]. These results suggest that AMD may not be particularly relevant in this variable, so the observed differences could be due to the fact that males in the underweight group have less muscle mass and corrected thigh and calf girth than males in the normal weight and overweight groups. This could be a determining factor in the relationship between the amount of muscle mass and CMJ performance [40]. In the group of overweight males with an optimal AMD, regarding the kinanthropometric variables measured, the sum of three folds and waist girth were significantly higher as compared to the males with poor AMD. These results are in line with previous research, which showed that adolescent males and females with better AMD had a higher body fat than those with worse AMD, although the differences were not statistically significant in this case [13]. These results could be explained by the fact that, although the adolescents have an optimal AMD, the level of physical activity in the overweight group is very low (≤2.75). Thus, most of the adolescents in this group are considered physically inactive, which would make it difficult to achieve the caloric deficit necessary to reduce body fat [41]. However, the results obtained should be taken with caution, as the sample size of the overweight groups of males and females was very small, which makes it difficult to extrapolate the results. It should also be noted that, together with the results obtained in the group of overweight males, the absence of significant differences in the females and in the group of normal weight males is relevant. This could indicate that AMD alone is not so important in producing modifications in the kinanthropometric variables of adolescents, which would grant greater relevance to other elements of the diet (e.g., quantity or caloric deficit) and to other healthy lifestyle habits, such as the practice of physical activity at this age. Nevertheless, this should be confirmed in future research in which the contribution of dietary variables and physical activity to the kinanthropometric variables of adolescents is analyzed. Regarding the second research hypothesis (H2), the results obtained allow us to partially accept it since the differences when considering BMI and AMD were significant in adolescents with optimal AMD compared to those with worse AMD. Thus, the level of physical activity practiced in males and females of the normal weight group with optimal AMD was higher. The level of physical activity and CMJ performance was higher in the males of the underweight group with optimal AMD. However, in the overweight group, the differences were significant in the kinanthropometric variables, with the males with optimal AMD showing greater body fat. Furthermore, in the females, there were only differences in the level of physical activity in the normal weight group, so future research is needed to explore the “fat but healthy diet” paradigm. It should be noted that the findings of the present research regarding differences in physical fitness could be also influenced by the biological maturation of adolescents [42]. Adolescence is a stage in which physical and anthropometric changes occur that are determinant in the development of physical capacities. Thus, previous research has found that adolescents who mature earlier present better performance in physical condition tests, independently of the physical activity performed [42,43]. Therefore, it would be necessary that future research studying the differences in the level of physical activity practiced, kinanthropometric variables, and physical fitness variables of adolescents according to their AMD, BMI, and gender also analyze the effect of biological maturation on the changes found. The present investigation is not free of limitations. The sample was selected by convenience in the educational centers to which we had access. It should be noted that this is a cross-sectional study in which the data were measured at a single point in time. In addition, the use of questionnaires to assess the AMD and the level of physical activity always involves a risk that adolescents will not complete the questionnaire with complete accuracy, so this is a factor to be highlighted. The classification provided by the KIDMED questionnaire makes it possible to obtain a score on the AMD, but it has gaps in terms of knowing the food intake and the quantities ingested by adolescents. Changes in physical fitness, mainly in strength, power, and cardiorespiratory fitness, could be influenced by the biological maturation of adolescents. Finally, when analyzing the results according to BMI, AMD, and gender of the adolescents, the sample sizes of some groups were too small. Regarding the practical applications derived from the present investigation, although AMD does not seem to exert great influence on kinanthropometric variables and physical fitness in adolescents, it does seem to be related to the adoption of other healthy lifestyle habits in adolescent males and females, including a higher level of physical activity. However, the novelty of the present article with respect to previous scientific literature is that it shows the need to consider gender and BMI in the study of AMD since needs change between groups. The results obtained show that in overweight males, the optimal AMD seemed not to be so relevant for the practice of physical activity and kinanthropometric variables, and other healthy habits may be more determinant in this population, but future research is required to corroborate this. Furthermore, AMD does not seem to be a relevant variable in the improvement of the physical condition and body composition of females since the only differences were found in the group of normal weight females in the level of physical activity, but not in body composition or physical condition, independently of weight status. The second major novelty of the present study is that the “fat but healthy diet” paradigm cannot be confirmed since differences in the level of physical activity, anthropometric variables, and physical condition according to AMD were observed only in the underweight and normal weight males. In addition, the results showed that in the overweight group, males with optimal AMD had worse body composition, and no differences were found in kinanthropometrics and physical fitness variables in any weight status group in females with different AMD. Therefore, future research is needed to determine whether AMD is a sufficient determining factor to compensate for inadequate weight status. In this sense, more scientific literature is needed to determine whether adolescents with better AMD show better body composition and physical condition, independently of their weight status, as occurs in the fat but fit paradigm [15]. Furthermore, future studies should also consider monitoring the degree of AMD during adolescence with a longitudinal and observational design mainly from pre-adolescence. This is because adolescence is a stage in which changes occur in the factors that are most determinant for the acquisition of healthy behaviors, with the influence exerted by peers increasing considerably and lessening the influence of their parents and teachers [44]; moreover, they obtain more information from other sources such as the internet or social networks [45,46], which is not always correct and can influence AMD. ## 5. Conclusions Based on the results obtained, it can be concluded that greater AMD does not seem to produce beneficial effects in the adolescent population. Thus, only the level of physical activity showed significant differences as a function of the adolescents’ AMD, while there were no significant differences in the kinanthropometrics variables and the physical condition variables of the adolescents according to their AMD when considering the whole sample. Considering the gender of the adolescents, it was observed that the males with better AMD had a higher level of physical activity and a greater muscle mass, but also showed a greater fat mass, body mass, and BMI. As for the females with better AMD, they presented only a higher level of physical activity and higher VO2 max. In addition, when considering BMI and gender together, the view of the “fat but healthy diet” paradigm could not be confirmed. This is because overweight and obese males with optimal AMD showed greater body mass, sum of three skinfolds, and waist circumference, and they did not practice more physical activity than overweight males with worse AMD. 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--- title: 'Taste Function in Adult Humans from Lean Condition to Stage II Obesity: Interactions with Biochemical Regulators, Dietary Habits, and Clinical Aspects' authors: - Alessandro Micarelli - Alessandra Vezzoli - Sandro Malacrida - Beatrice Micarelli - Ilaria Misici - Valentina Carbini - Ilaria Iennaco - Sara Caputo - Simona Mrakic-Sposta - Marco Alessandrini journal: Nutrients year: 2023 pmcid: PMC10005537 doi: 10.3390/nu15051114 license: CC BY 4.0 --- # Taste Function in Adult Humans from Lean Condition to Stage II Obesity: Interactions with Biochemical Regulators, Dietary Habits, and Clinical Aspects ## Abstract Differences in gustatory sensitivity, nutritional habits, circulating levels of modulators, anthropometric measures, and metabolic assays may be involved in overweight (OW) development. The present study aimed at evaluating the differences in these aspects between 39 OW (19 female; mean age = 53.51 ± 11.17), 18 stage I (11 female; mean age = 54.3 ± 13.1 years), and 20 II (10 female; mean age = 54.5 ± 11.9) obesity participants when compared with 60 lean subjects (LS; 29 female; mean age = 54.04 ± 10.27). Participants were evaluated based on taste function scores, nutritional habits, levels of modulators (leptin, insulin, ghrelin, and glucose), and bioelectrical impedance analysis measurements. Significant reductions in total and subtests taste scores were found between LS and stage I and II obesity participants. Significant reductions in total and all subtests taste scores were found between OW and stage II obesity participants. Together with the progressive increase in plasmatic leptin levels, insulin, and serum glucose, decrease in plasmatic ghrelin levels, and changes in anthropometric measures and nutritional habits along with body mass index, these data for the first time demonstrated that taste sensitivity, biochemical regulators, and food habits play a parallel, concurring role along the stages evolving to obesity. ## 1. Introduction Since the sense of taste may impact nutrient selection and dietary intake [1], studies in the last decades have demonstrated an increasing interest in deepening the effect of the taste on energy balance, satiety, and long-term health [2,3]. Rising findings evidence that the gustatory system is also possibly involved in many other important metabolic processes such as energy homeostasis and appetite control, and in turn conditioning health and body weight [4,5]. Considering that sweet taste has an important hedonic appeal, the choice of sweet nutrients has been considered crucially involved in the control of weight and the development of obesity [6]. Given these aspects, obesity has been considered a global health concern of great magnitude [7], and efforts devoted to mitigating the epidemic have been almost unsuccessful [8]. Despite the underpinnings of obesity being various, key factors—together with unusual daily physical activity [9]—consist of overconsumption of cheap, highly palatable, energy-dense, and nutrient-poor foods and beverages with high concentrations of sugar [10]. In the field of sweet perception—for example—different psychophysical methods to estimate the sense of taste are used to depict different features of the tastant [11]. A widely used technique consists of threshold testing, which may determine either the lowest sweet tastant concentration of detection or recognition [12]. In this vision, those persons having a lower intensity perception of sweetness are more prone to obtaining higher quantities of a sweet tastant to be satisfied and their higher hedonic liking of sweetness at elevated concentrations may induce them to increase the usage of sweet foods [13]. Interestingly, if on one side the sense of taste contributes to obesity development, on the other one it seems to be itself also affected by obesity given that evidence suggests that the primary gustatory tissue, the tongue, is an obesity target organ [14]. Furthermore, different modulators such as leptin, ghrelin, and insulin and the plasma concentration of glucose have been demonstrated to be involved in the sweet sensation of sweet-sensing taste cells [15,16] (for example, a certain degree of reduction in taste bud percentage has been associated with obesity [14]) and to modulate gustatory pathways at higher levels [16,17]. However, the literature did not fully elucidate if differences in taste sensitivity might reflect the different stages of obesity and their relationship with dietary habits, anthropometric aspects, circulating levels of modulators of feeding and taste behavior, and metabolic assays. Thus, since obesity stages have been demonstrated to be related—in terms of body mass index (BMI)—with different risks of metabolic diseases, mortality, and survival [18,19] and to be involved in nutritional choices [20], the aim of the present study was (i) to evaluate the changes in gustatory sensitivity, dietary habits, circulating levels of biochemical regulators of feeding and taste behavior, anthropometric aspects, and metabolic assays between healthy lean subjects (LS) and participants affected by overweight (OW) and stage I and II obesity and (ii) to estimate the possible impact of these factors on total taste score when evaluating all the participants together. ## 2.1. Participants The sample consisted of four groups: 60 LS (29 female), 39 OW participants (19 female), 18 participants with stage I obesity (11 female), and 20 participants with stage II obesity (10 female). All the participants were Caucasian adults and were recruited from the University Hospital of Rome “Tor Vergata”. All subjects underwent a general clinical and ear-nose-throat (ENT) examination. Current or recent smokers (<3 years of abstinence) and individuals affected by allergies, metabolic diseases, and a history of ENT surgery were excluded. Individuals with endocrinological disorders or suffering from chronic renal disease and other systemic or organ failure disorders including neuropsychiatric and cardiovascular disorders, as evaluated by medical history, physical examination, and routine blood tests were further excluded. Conditions of vegetarian/vegan diet, history of gustatory disturbances, ongoing use of medication (except oral contraceptives), and drug/alcohol abuse were considered as exclusion criteria. Gastrointestinal/eating disturbances and surgery were considered dropout conditions. Pregnant and currently breastfeeding females were excluded [21,22]. After inclusion, participants provided written informed consent. The study was performed in agreement with the Declaration of Helsinki and was approved by the Institutional Ethics Committee (Reference number RS $\frac{60}{20}$, date of vote: 24 July 2020). Following other research and clinical experiences all the participants filled out a food frequency questionnaire (FFQ) defined according to local dietary habits [23]. The final report recorded the intakes of calories (energy intake; EI), fats (saturated, monounsaturated, unsaturated), carbohydrates, proteins, fibers, and relevant micronutrients [23]. Thus, following the Goldberg cut-off method, which was developed to identify individuals whose reported energy intake would be considered a plausible habitual intake and to exclude participants who report intakes that are unlikely to represent true habitual intake [24], the ratio of EI:basal metabolic rate (BMR) for each participant was calculated by dividing self-reported FFQ energy intake by BMR estimated by means of the equations of Schofield [25]. Therefore, subjects with calculated values of the ratio EI: BMR in the interval 1.10–2.19 were classified as “plausible or adequate energy reporters”. Subjects with individual EI: BMR < 1.10 and/or individual EI: BMR > 2.19 were categorized as implausible reporters [26]. ## 2.2.1. Anthropometric Parameters and Bioelectrical Impedance Analysis (BIA) Measurements In line with other experiences, height and waist circumference (WC) were respectively measured to the nearest 0.01 m using a Stadiometer (Holtain Ltd., Crymych, UK) [27] and in cm [28]. Following previous experiences and BIA devices (Omron HBF-500 BIA, Omron Medizintechnik, Mannheim, Germany) [29,30], weight was measured in kilograms (Kg) and fat mass (FM, in %), skeletal muscle mass (MM, in %), grade of visceral fat (VF level), and resting energy expenditure (REE, in kilocalories (Kcal)) were calculated by means of the manufacturers’ equations [31,32,33]. ## 2.2.2. Taste Function Testing Participants’ taste functions were tested between 7.00 to 9.30 a.m. after abstaining from food or non-water beverage since 10 p.m. of the previous night [34]. One hour prior to testing, all the participants did not eat or drink anything except water and they did not brush their teeth. The taste test—consisting of filter paper strips (“Taste Strips”, Burghart, Wedel, Germany) impregnated with four concentrations of the four basic taste qualities: sweet, sour, salty, and bitter (for details see [35])—was administered in increasing concentrations (0.4, 0.2, 0.1, and 0.05 g/mL sucrose; sour: 0.3, 0.165, 0.09, and 0.05 g/mL citric acid; salty: 0.25, 0.1, 0.04, and 0.016 g/mL sodium chloride; bitter: 0.006, 0.0024, 0.0009, and 0.0004 g/mL quinine hydrochloride) and by means of a randomized method on the left or right side of the anterior third of the extended tongue, resulting in a total of 32 trials. Before each administration of a strip, the mouth was rinsed with water. With their tongue still extended, participants were asked—by means of a multiple forced choice method—to identify the taste from a list of the four qualities. After the number of correctly identified tastes per side was summed, the left and right sides scores were added up in order to obtain the total number of identified tastants [35]. The procedure lasted about 20 min for lateralized testing [36]. ## 2.2.3. Biochemical Assays After a 12 h fast, baseline laboratory parameters, including serum glucose, creatinine, alanine aminotransferase (ALT), aspartate aminotransferase (AST), total cholesterol, triglycerides (TGs), high-density lipoprotein (HDL) cholesterol, and low-density lipoprotein (LDL) cholesterol, were measured using the auto analyzer system (Aeroset System Abbott, Abbott Laboratories, Diagnostic Division, Chicago, IL, USA) [37]. Further, once plasma samples were collected following previous procedures described elsewhere [21], leptin levels were measured by means of an enzyme immunoassay (ELISA) kit (cat. No. EH0216; FineTest, Wuhan, China), insulin was analyzed by an immunoradiometric assay (BioSource International, Camarillo, CA, USA), and ghrelin was determined by an enzyme-linked immunosorbent assay kit (cat. No. EH0355; FineTest, Wuhan, China). The methods of analysis and concentration measurements followed established procedures [21]. ## 2.2.4. Data Handling and Statistical Analysis In line with those works powerfully associating obesity alterations and BMI [17,38], the sample size was calculated to detect intergroup differences in the BMI results. The sample size for the test hypothesis was calculated in accordance with the context (independent samples and continuous variables), using a statistical power of $80\%$ (1 − β) for an error probability of 0.05. The t-test for independent samples and an effect size of 0.80 was used. At least 12 participants per group were thus determined to be included and the sample size was finally in accordance with previous experiences [17,21,38]. The X2 test was performed to assess associations between categorical factors and groups. Descriptive data were computed as mean ± standard deviations (SDs) for FFQ scores, gustatory testing, biochemical assay parameters, and BIA measurements. To define that data for independent samples were of Gaussian distribution, D’Agostino K squared normality and Levene’s homoscedasticity tests were performed (under the null hypothesis that the data were normally and homogeneously distributed). A between-group analysis of variance was carried out for each of the FFQ values, taste testing scores, biochemical assay parameters, and anthropometric/BIA measurement variables. BMI degree and gender were treated as categorical predictors while age was treated, where possible, as a continuous predictor. The significant cut-off level (α) was set at a p-value of 0.05. Bonferroni correction for multiple comparisons was used for the post hoc test of the significant main effects, and the corrected level of significance was set at 0.008 ($\frac{0.05}{6}$). Given the exploratory nature of the study and previous biomedical retrospective approaches [39], the associations between the total taste score and three groups of prognostic factors (anthropometric, demographic and BIA variables, FFQ-based nutrient intakes, and biochemical assays) were examined by three different multiple regression analysis models. p-values less than 0.05 were considered statistically significant [40] (STATISTICA 7 package for Windows). ## 3. Results Socio-demographic aspects of the four groups of participants are reported in Table 1. Regarding anthropometric and BIA measurements, participants affected by stage II obesity had a significantly increased WC ($p \leq 0.001$), weight ($p \leq 0.001$), BMI ($p \leq 0.001$), VF level ($p \leq 0.001$), and REE ($$p \leq 0.0059$$) when compared to stage I obesity participants (Table 1). Both these groups had significant ($p \leq 0.008$) increases when compared with both LS and OW participants in WC, BMI, FM, VF level, REE, and reduction in MM. A similar significant behavior in these anthropometric and BIA measurements was found also found between LS and OW participants (Table 1). Once we applied the Goldberg cut-off method and given the estimated BMR in the total subjects (1651.83 ± 270.64 Kcal in LS; 1858.97 ± 525.83 Kcal in OW; 2753.66 ± 1057.43 Kcal in stage I obesity; 2892.9 ± 833.05 Kcal in stage II obesity), out of the 137 participants, 58 were found as implausible FFQ reporters (29 LS; 13 OW; 7 stage I obesity; 9 stage II obesity). Significant ($p \leq 0.008$) differences in terms of FFQ scores were found between the four groups of plausible reporters, depicting a progressive intake increase in nutrients along the BMI stages (Table 2), with no significant differences between stage I and stage II obesity participants. A significant reduction in total and all subtests taste scores between LS and stage II obesity participants ($p \leq 0.001$, $$p \leq 0.007$$, $$p \leq 0.002$$, $p \leq 0.001$, and $$p \leq 0.001$$, respectively) (Figure 1). Similarly, a significant reduction in total, salty, and bitter taste scores was found between OW and stage II participants ($p \leq 0.001$, $p \leq 0.001$, and $$p \leq 0.008$$, respectively). No significant differences in total (and subtests) taste scores were found between stage I and II obesity participants nor between lean and OW participants, between this latter group and stage I obesity participants, and between LS and stage I obesity (Figure 1). A significant interaction was found for the total taste and sour taste scores with factor “gender” (F[1, 135] = 8.18, $$p \leq 0.004$$; F[1, 135] = 8.76, $$p \leq 0.003$$), depicting that female participants outperformed male participants along these tests. Further, stage II obesity participants demonstrated significantly higher levels of plasmatic leptin, serum glucose, and lower levels of plasmatic ghrelin with respect to stage I obesity ($p \leq 0.001$, $$p \leq 0.001$$, and $p \leq 0.001$, respectively) and significantly higher levels of plasmatic leptin, insulin, and serum glucose and lower levels of plasmatic ghrelin with respect to OW ($p \leq 0.001$, $$p \leq 0.002$$, $p \leq 0.001$, and $p \leq 0.001$, respectively) and LS ($p \leq 0.001$, $p \leq 0.001$, $p \leq 0.00$,1 and $p \leq 0.001$, respectively). Stage I obesity participants had significantly higher levels of plasmatic leptin ($p \leq 0.001$) and serum glucose ($$p \leq 0.001$$) and lower levels of plasmatic ghrelin ($p \leq 0.001$) with regard to LS and lower levels of plasmatic ghrelin ($p \leq 0.001$) with respect to OW participants. The latter group was found to have significantly higher levels of plasmatic leptin ($p \leq 0.001$) and serum glucose ($$p \leq 0.001$$) and lower levels of plasmatic ghrelin ($p \leq 0.001$) with regard to LS (Table 3, Figure 2). Further significant differences in biochemical results depicting a worsening of metabolic pattern in the four sub-groups are depicted in Table 3. When performing multiple regression analysis within all the participants in order to determine the total taste score in relation to the three prognostic factors (anthropometric, demographic and BIA variables, FFQ-based nutrients intakes, and biochemical assays) the multiple correlation coefficient was, respectively, 0.75, 0.54, and 0.45, with a p-value less than 10−4. The regression was statistically significant only for age, VF level, and gender with partial correlation coefficients of −0.45, −0.17, and −0.2, respectively; for monounsaturated fatty acids with a partial correlation coefficient of -0.20; for plasmatic leptin levels with a partial correlation coefficient of −0.36 (Table 4, Figure 3). ## 4. Discussion The main interesting findings of the present study reside in the significantly decreased taste sensitivity when comparing stage II obesity participants to LS and OW. For the first time, all the taste sensitivities were found to be significantly reduced—as well as the total taste score—when comparing LS to the advanced stage of obesity, while the salty and bitter—and their sum sensitivities were significantly reduced when comparing OW and stage II obesity (Figure 1). On the other side, participants exhibited significant alterations in biochemical assays connected with both taste sensitivity and the worsening of metabolic status along with the BMI increase (Table 3, Figure 2). As expected, significant progressive increases in energy intake and in the amount of the main nutrients involved in obesity development were found (Table 2) [41]. All these aspects together tend to corroborate all previous studies evidencing that obesity is fostered by individual, nutritional, and sedentary lifestyle factors that may in turn provoke an excess of caloric intake [42]. Within individual factors, taste identification plays a pivotal role in food preferences, choices, consumption [43], and—as a consequence—body weight regulation. This evidence is supported by those studies linking taste to food selection and obesity [16,44] and recent reviews remarking that nutrient intakes or food habits may in turn impact taste sensitivity [1]. Although the association between BMI and taste perception has been studied extensively with heterogeneous results over the past decade, it has been repeatedly reported—in line with the results of the present study—that BMI increase is associated with a reduction in the perceived intensity of different tastants and weakened sense of taste [42,44,45,46,47]. In light of this, a reduction in salty sensitivity was found to be associated with higher BMI [47]. Other works supported this evidence, demonstrating that obese adults have a less intense sweet and salty taste perception [46] or that women affected by obesity perceive the monosodium glutamate detection threshold as increased (less sensitive) [45]. However, in the latter study, no differences in sucrose threshold were found between obese and lean participants [45]. Of note, the work of Hardikar et al. [ 44] contrasted such evidence when observing in obese participants a lower sucrose and sodium chloride threshold when compared to lean subjects. Similarly, Bartoshuk and coworkers [48] found that the perception of sweet and fatty tastes was increased in those subjects with higher BMI, concluding that individuals with obesity might have an increased sensitivity for these tastants. The inverse relationship between weight and taste sensitivity has been further confirmed by all those studies demonstrating increased sweet, salt, sour, and bitter sensitivity in patients undergoing bariatric surgery, both after Roux-en-Y gastric bypass and vertical sleeve gastrectomy [9,49]. Many of these studies concluded that post-surgical taste changes could have been exacerbated by weight loss, suggesting a causal relationship between weight and taste perception [49,50] and positing that reduced sweet taste threshold could be the consequence of the reduced intake of sweet and energy-dense foods [1,51,52]. In light of these data, some authors claimed that the greater the ability to perceive a certain taste, the lower the preference for it, resulting in a lower intake of that food [12,53]. However, although associations between regulation of body weight and alteration in taste seem to exist and may underpin the variety of responses to weight loss interventions, it cannot be excluded—also due to the large inter-individual taste changes differences—that other factors than weight loss per se, such as reward value and gut–brain interaction, might drive the observed changes in taste perception [1,16]. At hormonal levels—in line with the current literature—patients affected by OW and stages I–II of obesity demonstrated a progressive increase in leptin and insulin and a reduction in ghrelin (Figure 2), possibly impacting the imbalance of the taste signaling and food reward [54,55,56]. The latter hormone, indeed, a regulator produced in the stomach, has been found to have orexigenic effects at the peripheral and central levels [57]. If it has been demonstrated that ghrelin is able to control the response to food cues via a neural pathway involved in the regulation of feeding and, most importantly, in the appetitive response to food cues by increasing hedonic and incentive responses to adequately influence food intake [57,58,59], specific knock-out of ghrelin, growth hormone secretagogue receptor, and the ghrelin O-acetyltransferase (needed for the post-transcriptional activation of ghrelin) in mice resulted in altered responses to salty and sour food stimuli [60,61]. This resulted in lower consumption of sweet solutions (sucrose and maltodextrin), reduced weight gain, and improved glucose and insulin homeostasis [61]. On the other side, given the fact that (i) insulin was found to down-regulate taste buds’ cell expression in a dose-dependent manner [62] and that (ii) obesity is associated with the development of insulin resistance, alterations in the sense of taste have been speculated to be a consequence of metabolic disturbances related to extreme overweight [16]. Indeed, although genetic variations in taste receptors have been ascertained as driving genetic–environment interaction in the development of taste behavior and obesity [63,64], an increasing body of evidence demonstrated that different hormones may also modulate taste bud activity. In this vision, leptin acts via binding to its receptor obese receptor in type II taste buds cells and interferes with local KATP channels [15,65]. Thus, beyond one of the pivotal roles of leptin in energy homeostasis being to boost hunger in response to its decreased or absent circulating levels [66], it has been found that the activation of such channel results in reduced sweet response signaling to the afferent nerve fiber in the taste cell and dampens sweet perception [15]. Considering that leptin levels in the bloodstream may strongly correlate with BMI [16] and that leptin may mitigate sweet taste sensitivity, it may account for the reduction in sweet taste sensitivity often observed in people with obesity [48,67]. Anecdotally, such hormones are also present in saliva which is not a simple byproduct of the plasma and different studies demonstrated that the concentrations of these (and other) regulators are mainly due to transport from the blood vessels into the glandular cell [68]. However, recent studies also demonstrated that salivary gland production is present and may modulate taste responses in relation to physiological status [69]. Some of these aspects have also been confirmed in the present study by the regression model, which highlighted a negative association between total taste score and plasma leptin levels among all the subjects and—even if beyond the level of significance—the insulin plasma level, possibly due to the downregulation of several taste bud cell genes demonstrated in the literature [62] (Table 4, Figure 3, Table S1). Further, according to previous experiences [42], the regression models evidenced that other variables impacting taste sensitivity are age (negative association), gender (women generally show higher sensitivity), and VF level (negative association) (Table 4, Figure 3). These aspects tend to corroborate those previous experiences positing that age is associated with an overall reduction in the number of taste buds and the number of taste cells per taste bud, especially in men [42,70,71], and that such taste sensitivity reduction is the effect or the cause of the BMI increase, on which constitution visceral fat has a contributing effect [42]. This result is noteworthy since it supports previous findings in mice [14] showing that chronic low-grade inflammation brought on by obesity reduces the number of taste buds in gustatory tissues of mice and is likely to be the cause of taste dysfunction seen in obese populations [72]. In this vision, VFlevel—more than the other anthropometric measures that collectively depict obesity (i.e., BMI and FM%) and that have been found to be negatively (even if not significantly) associated with total taste score (see Table S1)—is more metabolically active than subcutaneous fat [73] and affects the development of metabolic disturbances by contributing to the pro-inflammatory milieu [74], possibly impacting taste bud decrease. Further, together with the interaction found between total taste and sour taste score with the gender factor, the regression model confirms the general trend in the literature suggesting that women exhibit higher gustatory sensitivity than men [36,75,76,77]. Similar findings have been reported for the other chemical sense, olfaction, where women also outperform men [21,78,79,80]. Besides the fact that the exact reason for female superiority in olfaction and taste remains unexplained, one possible explanation relates to a hormonal influence and protective effect on the chemical senses [36,81,82,83]. However, in contrast to olfactory function, taste function seems to be more influenced by hormonal changes as shown during pregnancy [84]. Finally, with regard to relationships between total taste scores and FFQ-based nutrient intake, a negative association between monounsaturated fats and taste sensitivity was found, corroborating those theories evidencing that the consumption of enriched in sugar and fat diets is associated with a reduction in taste stimuli sensitivity, thus impacting food choices and fostering food intake [14,46,48,85,86]. At the same time, such a negative association reinforces those previous studies in which significant increases in fat perception were observed following a low-fat diet [87,88], indicating that differences in taste sensitivity to fatty acids may be a result of gustatory adaptation to a high-fat diet and may contribute to excess fat intake because of an attenuated taste response to fatty acids among individuals who habitually consume a high-fat diet, as happens in overweight/obese subjects [88,89]. These phenomena have been attributed to a downregulation in the expression of specific subunits of sensing G-protein coupled receptors of different nutrients, which in some cases, given their cross-sensitivity, may also account for a downward trend for the sensitivity regarding other tastants [90,91]. With the premise that specific tests targeted on different nutritional stimuli have not been used in the present study, the complexity of these phenomena could be one of the underpinnings of those (not significant) associations found in the present study (see Table S1), corroborating previous works which highlighted that one taste quality threshold might be affected not only by the deprivation of its stimulus but also by the exposure to another one [92,93,94]. In conclusion, the present large-scale study for the first time demonstrated that—among individual factors—taste sensitivity, biochemical regulators, and food habits play a parallel, concurring role along the stages evolving to obesity (Figure 4). Considering that taste plays a pivotal role in the mosaic pathways influencing food preferences, choices, and thus, consumption [43,95] and that it has been found to be clearly associated with certain hormonal and anthropometric aspects and food habits, further studies deepening the role of taste in food choices and eating behavior are pivotal to enlarging the comprehension of the factors involved in body weight maintenance and the risk of chronic diseases including obesity, atherosclerosis, cancer, diabetes, liver disease, and hypertension. ## 5. Limitations of the Study Although the current study addresses a number of methodological gaps in the literature by controlling important potential confounders, there are limitations to consider. First, the biochemical regulator assay considered in the study is restricted and it needs to be enlarged in future protocols to other new hormonal compounds and targets, such as those that could partially depict the association between gustatory behavior and the brain reward system. Secondly, the authors are aware that sensory processing involved in overeating and obesity development is a complex mosaic of underpinning factors including individual genetic variations, cultural and psychological factors, and other chemosensory pathways, and that taste “per se” could not explain the whole process [96]. In light of this, despite the present study sample being one of the largest in which the gustatory sensitivity has been analyzed in overweight and obesity so far when compared with healthy eating lean subjects, the cross-sectional nature of the study does not allow us to determine causality, and it is unclear whether taste dysfunction in obese patients is a consequence or a cause of abnormal nutritional and metabolic patterns. Further, for future perspectives—especially enlarging the study cohort—it could be of interest to deepen the interaction between single taste, biochemical regulators, routine blood samples, and nutrient intake. However, to better achieve such aims, attention should be paid to meal intervention and/or dietary habits and taste tests should be specifically devised in terms of tastant qualities and sensorial modalities [1]. Thus, given these assumptions, the present data have to be considered preliminary and should be replicated in further cohorts of patients. 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--- title: 'Parent Perception of School Meals in the San Joaquin Valley during COVID-19: A Photovoice Project' authors: - Tatum M. Sohlberg - Emma C. Higuchi - Valeria M. Ordonez - Gabriela V. Escobar - Ashley De La Rosa - Genoveva Islas - Cecilia Castro - Kenneth Hecht - Christina E. Hecht - Janine S. Bruce - Anisha I. Patel journal: Nutrients year: 2023 pmcid: PMC10005543 doi: 10.3390/nu15051087 license: CC BY 4.0 --- # Parent Perception of School Meals in the San Joaquin Valley during COVID-19: A Photovoice Project ## Abstract School-based nutrition programs are crucial to reducing food insecurity. The COVID-19 pandemic adversely impacted students’ school meal participation. This study seeks to understand parent views of school meals during COVID-19 to inform efforts to improve participation in school meal programs. Photovoice methodology was used to explore parental perception of school meals in San Joaquin Valley, California, a region of predominately Latino farmworker communities. Parents in seven school districts photographed school meals for a one-week period during the pandemic and then participated in focus group discussions and small group interviews. Focus group discussions and small group interviews were transcribed, and data were analyzed using a team-based, theme-analysis approach. Three primary domains emerged: benefits of school meal distribution, meal quality and appeal, and perceived healthfulness. Parents perceived school meals as beneficial to addressing food insecurity. However, they noted that meals were unappealing, high in added sugar, and unhealthy, which led to discarded meals and decreased participation in the school meal program. The transition to grab-and-go style meals was an effective strategy for providing food to families during pandemic school closures, and school meals remain an important resource for families experiencing food insecurity. However, negative parental perceptions of the appeal and nutritional content of school meals may have decreased school meal participation and increased food waste that could persist beyond the pandemic. ## 1. Introduction Food insecurity (FI), defined as reduced access to affordable and nutritious food, had been decreasing in U.S. households with $14.9\%$ of households experiencing FI in 2011 to $10.5\%$ reporting FI in 2020 [1]. Overall, during 2020, the first year of the COVID-19 pandemic, prevalence of FI was $14.8\%$ among U.S. households with children [1]. Due to the social and economic disruption from the pandemic, by some estimates FI in households with children climbed upwards of $27.5\%$ at one point during the pandemic [2,3]. FI has many adverse consequences for children’s physical and mental health, with increased risk for obesity [4], diabetes [5,6], and depression [7]. It has also been shown to be associated with poor maternal health, increased parental depressive symptoms, and increased conflict between parents [8], all of which affect the social and emotional development of children. FI disproportionately affects families of color [1], and thus addressing childhood FI is crucial to achieving health equity. The National School Lunch Program was established in 1946 to address food insecurity and continues to do so, serving ~30 million US children daily. The 2010 Healthy, Hunger-Free Kids Act aligned nutrition standards for school meals with the Dietary Guidelines for Americans, increasing whole grains, eliminating trans fats, establishing appropriate calories by age, increasing required servings of fruits and vegetables, and reducing sodium. Thus, school meal programs not only reduce FI, but also promote higher overall diet quality, may decrease obesity and related health consequences, and have the potential to reduce learning losses stemming from the COVID-19 pandemic [9,10,11]. Despite the numerous benefits of school meals, there was a decrease in participation in school meal programs early in the pandemic [12]. During the COVID-19 pandemic when schools closed and switched to remote learning, the USDA granted waivers that allowed for flexibilities both in the logistics of distributing school meals as well as existing nutritional standards so schools could more easily distribute food to children given local COVID-19 safety protocols and remote schooling during the pandemic [13,14,15]. School districts developed creative solutions in their distribution of school meals for consumption at home, some delivering meals via school buses while others provided “grab-and-go” meals which parents picked up at designated schools [16,17]. For many parents, this was the first glimpse of what their children eat at school. Parental perceptions of school meals affect whether students participate in school meal programs [18]. Conducting this study during the period of the pandemic when schools were closed for in-person instruction, and meals were consumed at home allowed unprecedented insight into parent experience of school meals, which could persist beyond the pandemic period. This study was conducted in California’s San Joaquin Valley (SJV), which is a predominately rural area home to a majority Latino population including many agricultural workers [19] among the hardest hit economically by the COVID pandemic [20,21,22]. Rural areas produce most of the nation’s food, yet 9 of 10 counties with the highest rates of food insecurity are rural. Families who are of Latino background are also most affected; 1 in 6 Latinos experience food insecurity, a rate 2.5 times that of Whites [23,24]. One study of accessibility to produce in the SJV found that while most participants reported physical access to produce was not a problem in their communities, $65\%$ reported concerns about affordability of fruits and vegetables in grocery stores and other retail locations [25]. The pandemic only further limited access to affordable healthy produce, making school meal programs an especially important source of nutrition for children in SJV [26]. The goal of this study was to explore parent perspectives of the school meal programs and to empower parent participants to use study findings to influence local school meal programs and enhance participation in the program. To our knowledge, the current study, which stems from concerns about meal quality and appeal mentioned in earlier parent focus group discussions conducted by our community-academic partnership [17], is one of the first U.S. studies to examine parents’ concerns regarding school meals provided during the COVID-19 pandemic and thus offers important insight that may inform efforts to address decreased school meal participation [27]. Moreover, this study provides perspectives of Latino families from rural communities who face unique challenges that are rarely explored. ## 2.1. Community-Academic-Policy Partnership This study was conceptualized and conducted by a community-academic-policy partnership. The partnerships included two community-based non-profit organizations, Dolores Huerta Foundation and Cultiva la Salud, that work to advance health equity and social justice for predominately Latino farmworker communities in SJV, academic research partners at Stanford University, and policy partners at the University of California’s Nutrition Policy Institute (NPI). The study was conducted in the SJV where the two community partners are based. Community and policy partners collaborated with the academic partners on the entire research study. They identified the study objectives, recruited participants, and helped shape focus group discussion guides. All community, academic, and policy partners met regularly to inform the study design, data collection, and analysis processes and to review updates and provide feedback. ## 2.2. Study Design Photovoice is a community-based participatory research method through which photographs are used to promote dialogue and give voice to community members’ lived experiences [28]. This methodology was selected for the current study as it was identified as an effective way to engage meaningfully with community members and to amplify their voices to advocate for changes in practices and policies. This study received approval from the Stanford University IRB. ## 2.3. Data Collection Participants were recruited by community partners. Inclusion criteria included [1] being a parent or caregiver of a child enrolled in a public elementary, middle, or high school in SJV, [2] speaking either English or Spanish, and [3] participating in the school meals program during the COVID-19 related school closures. The convenience sample consisted of parents of children enrolled in eight school districts across the SJV region. Parents attended a training webinar (Spanish/English) hosted via Zoom by the research team to receive instructions for photographing school meals and to review the project. Interested parents then signed up to take photographs and participate in the subsequent focus group discussions or interviews. Parents were asked to take photographs of all food provided by the school during the week of $\frac{11}{2}$/20 to $\frac{11}{6}$/20 and were instructed to have any food labels with nutrition information visible in the photographs. Parents were provided with cards to include in the photos on which they filled out the date, number of meals displayed in photographs, type of meals (breakfast, lunch, or snack), school where they picked up the meal, and any other comments for the photos. Photos taken by participants were sent to the study team by email or text message and then uploaded to a centralized database for the research team to organize and label. Thirty-seven participants from eight school districts provided photographs. Six focus group discussions and two small group interviews [29] were then conducted virtually over video conference. Each discussion was led by a member of the Stanford research team with a community partner representative present in each group. Each focus group and small group interview began with the virtual sharing of one photograph selected by each parent and the open-ended question, “Please tell us about the photo you selected and what it means to you.” The facilitator then moved on to questions about school meals during the pandemic following a focus group guide, which was developed in collaboration with community partners and was informed by prior discussions with community members about their experiences with school meals. Each focus group and small group interview consisted of parents from one school district and was conducted in English or Spanish. The groups were divided in this manner to accommodate language preference and encourage discussion among parents in the same districts. This allowed for in-depth conversation about parent experiences [30] within a district for eventual action to influence policy and practice, which is a key component of photovoice methodology [28,31]. The small and large group discussions achieved thematic saturation, so no additional groups were conducted. Themes addressed in the discussions included child opinions of meals, parent opinions about quality of food, food waste, packaging and presentation of meals, and ideas for improvements. All participants received a $50 gift card. ## 2.4. Analysis Focus group discussions and small group interviews were audio-recorded and transcribed. Transcripts in Spanish were translated into English. All transcripts were uploaded into Dedoose, a qualitative software used to share transcripts, code data, and test interrater reliability [32]. Thematic analysis was conducted using an iterative approach. Initially, two coders developed a draft codebook, which was edited with input from team members and community partners. The two researchers then independently coded the transcripts using the code book. Discussions regarding discrepancies in coding were held until a final code book was agreed upon. The pooled Cohen’s κ score on the final code book was 0.89. ## 3. Results A total of 27 parents, all female, from seven school districts in the SJV participated in six focus group discussions and two small group interviews to converse about their photographs and school meals during the pandemic. Five group discussions were conducted in Spanish, and one was in English. One small group interview was in Spanish, and one was in English. Focus group size ranged from four to seven participants. The small group interviews had two to three participants each. Participants were parents of elementary, middle, and high school children with ages ranging from 5 to 17 years old. Three primary themes emerged from analysis of the discussions: [1] benefits of meal distribution during school closure, [2] meal quality and appeal, and [3] perceived healthfulness of meals. Representative quotes and photographs for each theme are presented in Table 1 (Theme 1), Table 2 (Theme 2), and Table 3 (Theme 3). Theme 1: Parent Descriptions of the Benefits of School Meal Distribution during COVID-19 Pandemic School Closures Parents described several benefits of the transition to grab-and-go meal distribution including schools’ efforts to ensure that the logistics of picking up meals were streamlined, financial impact of schools providing meals for the family, and COVID-19 safety protocols enforced by schools providing meals. Parents appreciated that they could pick up meals at any nearby local school. Many parents mentioned the convenience of being able to pick up batched meals, meaning they could pick up several days’ meals at the same time, which meant fewer trips to the pick-up sites. They also valued efforts by schools to communicate about their meal distribution through multiple platforms including phone, email, banners, and flyers. Parents reported that the school meals saved them money on groceries during the pandemic, and they specifically valued receiving bulk items such as cartons of milk, dried beans, and rice (Table 1b). Parents appreciated the reliable supply of milk that they received with batched meals as they perceived it as a healthy, yet costly, food staple. One parent even stated that the milk was the main reason they continued to pick up meals for their family. Parents described bulk dried items, primarily beans and rice, such as those in Table 1b(ii), as enabling them to cook culturally preferred foods at home. One parent noted that she was experiencing unemployment during the pandemic and that she felt relief that the school meal program provided her children with consistent access to food. Parents also reported feeling safe collecting meals from the school sites during the pandemic. The photo in Table 1c shows meals picked up in plastic bags, which parents equated to schools’ strong safety and cleanliness standards when packaging and distributing foods. Theme 2: Parents Perceptions of the Quality and Appeal of School Meals during the COVID-19 Pandemic Issues of meal quality and appeal were prominent during the photovoice discussion sessions. Parents reported throwing away meals because their children refused to eat the food. Even when children were hungry, parents said they declined to eat much of the food provided by school because it did not look appetizing or appealing. Parents described food as soggy, bruised, squished, greasy, or frozen. The photo in Table 2d depicts a pizza roll that was squished by the time it made it home. Pizza was one of the most common food items reported to be discarded and was described as greasy, rubbery, and tasteless. Both photos in row a of Table 2 demonstrate slices of pizza that were unappealing for different reasons. Photo (i) reveals grease pooled beneath the slice while photo (ii) shows a shrink-wrapped slice that is squished and misshapen. Several parents also described produce as inedible due to being bruised, discolored, or even moldy. Many parents reported that children did not like the taste of the food, and several parents described tasting the food themselves and agreeing with their children. Parents consistently reported guilt about the amount of food thrown away. Multiple parents stopped picking up meals because of the waste. Other concerns about school meals included lack of variety in foods. Parents perceived that repetitive meals each week made their children less likely to eat the food. Parents described putting in additional preparation to improve variety and appeal of meals. Food often required defrosting, cooking, or adding extra ingredients. Parents repeatedly expressed concern about safety of children needing to microwave food, especially with some foods being packaged in non-microwave safe packaging. The sandwich pictured in Table 2c has frozen filling that required disassembling the sandwich to defrost. These added steps to prepare meals were barriers to student consumption of school-provided meals. Theme 3: Parents’ Perceptions of the Healthfulness of School Meals Provided during the COVID-19 Pandemic Nearly every parent expressed concern about unhealthy food provided in school meals. Parents believed the food was “too sweet” and unhealthy. Table 3b(i) shows waffles and pop-tarts provided for breakfast that were perceived as too sugary. Pop-tarts and breakfast cereals were common items about which parents expressed concern. Parents also noted that many of the snack foods received as a part of lunch were unhealthy (e.g., Cheetos, chips, cookies). Several parents went as far as to describe the food their child received as “junk”. A few parents also described the cheese in several foods, specifically in macaroni and cheese, cheese sandwiches, and pizza, as greasy and fatty. Parents reported that their children preferred the fresher, healthier components of the meals such as whole fruits and vegetables. Children reportedly preferred whole produce over packaged sliced produce; they often described the pre-sliced and prepackaged vegetables as slimy. The photo in Table 3a contrasts sliced packaged cucumbers and carrots that children perceive as slimy or spoiled next to whole apples that were more appealing. Many school districts did provide whole apples and oranges; however, these did not make up the majority of fruit and vegetable servings. Overall, parents expressed concern that school meals set a poor example of a healthy lifestyle for children. Parents felt that the school was missing an opportunity to teach children healthy habits through the school meals program. Several parents expressed concern that children were not getting appropriate nutrients, and those unhealthy meals would affect children’s ability to learn in school. Multiple parents also worried that children would not develop healthy habits since they lacked access to nutritious food at school. Parents were hopeful about bringing change to the food provided by their school districts. One parent stated that the district leadership is likely unaware of the poor quality and nutritional content of food being sent home, and parents expressed that they felt photographs from this study would be effective to inform school district leadership of their concerns, as was intended from the outset of this photovoice project. ## 4. Discussion Grab n’ go school meals provided during pandemic-related school closures provided a rare opportunity for parents to see the types of foods and beverages provided in school meals. We found that parent perceptions of quality, appeal, and healthfulness of meals may have contributed to the lower participation in school meals observed during the pandemic. Our findings are particularly salient given that effective this school year, California provides all public-school children with school breakfast and lunch daily, at no charge, regardless of family income [33]. While it is important to acknowledge that school meals may have changed during the pandemic due to supply chain disruptions and USDA waivers for school meal nutrition standards, parents’ perspectives of school meals during the pandemic could persist even after school meals return to normal, thereby limiting the participation in California’s universal school meals program unless action is taken to address parent concerns. In our study, parents perceived meals provided during the pandemic as unhealthy, and specifically voiced that meals were too sugary. Parents in one other U.S. study with 101 parents of children ($78.2\%$ were non-Hispanic Black or Latino/Hispanic) receiving meals from New York City public schools during the pandemic expressed some similar concerns to those raised in our SJV focus group discussions about healthfulness of meals, noting foods that were more snacks than meals and often included ultraprocessed items [27]. However, in contrast to our study, parents in this New York City sample identified Nutritional Quality as a positive facilitator of participation in school meals [27]. Another study of parent perspectives of school meals conducted in a majority Non-Hispanic Black population ($51.6\%$) found that, after the implementation of HHFKA, nearly $20\%$ of parents perceived school meals as unhealthy while $80\%$ viewed the meals as healthy [34]. The study populations and locations are very different among these two studies and our study, with the two existing studies examining urban populations, which may contribute to differences in food provided or affect parental perceptions of food provided by school meal programs. Federal nutrition guidelines for school meals, according to the HHFKA, include limitations on overall calories, sodium, added trans fats, and saturated fats. They also require meals to be whole grain-rich, offer one cup of low fat or fat-free milk, and include fruit and vegetable servings. Despite the fact that HHFKA requires meal program nutrition standards to align with the Dietary Guidelines for Americans (DGA), there are no existing limitations or guidelines regarding sugar content of school meals [35]. The DGA recommend that sugar should account for less than $10\%$ of total daily calories [36]. According to the American Academy of Pediatrics (AAP), all children should consume less than 6 teaspoons of added sugar per day, and younger children should consume even less with 3–8-year-olds being limited to 3 teaspoons of sugar per day [35]. While a complete nutrient analysis was beyond the scope of this initial project, the photographs did demonstrate that many of the school meals served during our study period were not meeting this daily sugar recommendation. According to the photographed nutrition labels, one pop-tart pastry, a common item provided in school breakfasts in the study, contains 15 g or nearly 4 teaspoons of added sugar—$60\%$ of the AAP’s daily recommendation. One previous study conducted before the pandemic found that $92\%$ of U.S. schools exceeded the DGA for sugar in breakfast, and $69\%$ of schools exceeded it for lunches [37]. A report published in November 2021 states that most companies supplying school food had about $75\%$ compliance to DGA for added sugars in their foods [38]. However, parents in our study school districts perceived that school meals contain too much sugar and are unhealthy. Similarly, in a study of elementary school parents in Oregon, improving nutrition standards for school meals was noted as the top priority for supporting children’s healthy eating [39], so this concern is not unique to our study population. School districts must seize the opportunity to model healthy habits by providing meals that are healthy and in accordance with nutritional guidelines. Parent perceptions in this study strongly support the inclusion of a standard for added sugars in Child Nutrition Reauthorization, which is expected to be considered by Congress in the coming year. Our findings also indicated that meal appeal and quality were major concerns of parents. While some concerns raised in focus group discussions were specific to the pandemic constraints (e.g., need for individual packaging, transportation of meals from school to home), other concerns apply to school meals served during more routine school periods. Parents said that a lack of variety in food led to children refusing to eat the meals, which was remedied at home by introducing additional fresh ingredients that improved taste, presentation, and nutrition. For example, one parent added tomatoes, lettuce, and peppers to sandwiches provided by the school. In the New York City study cited earlier, parents expressed similar concerns to those identified in our study about meal appeal, noting repetitive and limited food options and unappealing presentation and texture of foods [27]. Similarly, in a prior study of parents of students in rural Midwestern middle schools, parents stated that the top reason why their child did not participate in school meals was because they did not like what was being served [40]. In contrast to other studies that describe advantages to serving pre-sliced fruit [41], parents in our study stated that their children desired more whole fresh produce as packaged, pre-sliced fruits and vegetables were often slimy, moldy, or otherwise unappealing. One innovative way to increase the variety of healthy, fresh foods is to source locally grown produce rather than mass-produced, packaged sliced fruits and vegetables. Research demonstrates that student and teacher school meal participation increased from 1.3 percent to 16 percent in school districts that implemented farm to school programs that increased local produce in the school meals [42]. Prior studies demonstrate that parents’ perception of school meals directly affects their child’s meal participation [43]. This was also reflected in our findings, as in discussion of wasted food, multiple parents stated they had either already stopped or planned to stop picking up school meals. Limited research suggests that parents may not know that meals must meet federal nutrition standards; they misperceive home meals as healthier than school meals, have concerns about the appeal and taste of food, or do not trust school meal quality [18,34,40,44]. While these findings are useful, generalizability is limited, as most studies do not capture rural areas like SJV, which face unique challenges. Though one in four US children are Latino, few studies explore such perspectives, and those that do note concerns about the cultural mismatch between Western foods offered at school and more traditional foods at home [45]. Our study adds the unique perspective of rural, mostly Spanish-speaking, Latino parents to the existing literature about parent perceptions of school meals, which is critically important to understand given this population’s high risk of food insecurity. Given the unprecedented policy window with California’s new universal school meals policy and its capacity to reduce persistently high rates of FI and obesity stemming from the pandemic [34], the community-academic-policy team has worked to develop policy briefs based on study findings that recommend [1] including guidelines limiting added sugars in school meals in the upcoming Child Nutrition Reauthorization and [2] supporting programs that incentivize use of local fresh produce in school meals. The briefs were circulated among community members and nutrition policy advocates to support these efforts [46]. Additionally, our research and community partners are working to present photographs and findings to school districts and other stakeholders to advocate for improvements in school meals locally. Limitations of our study include the fact that, due to safety constraints during the COVID-19 pandemic, focus group discussions were conducted virtually and required access to phone or computer and Wi-Fi. While this had the potential to bias our sample towards those with internet access, our community partners worked to help those with limited technology skills participate. The sample size of some of the small group interviews, which were as small as two participants, is a limitation for traditional focus group type discussions. Despite this, our facilitators encouraged highly interactive discussions of the designated photos. We believe that, in some cases, the smaller group discussions further accommodated language of choice and the ability to organize groups by school district. This facilitated presentation of focus group findings at the district level, which was important for advocacy at the local-district level in keeping with the priorities of photovoice methodology [28]. When reviewed separately from aggregate data, themes from the two to three person small group interviews were consistent with overall findings, and all major themes discussed in the group were also discussed in other larger focus groups. Lastly due to pandemic USDA school meal program waivers that facilitated meal distribution in different formats (e.g., grab-and-go) and provided more flexibility around nutrition standards, parents’ perceptions of foods as being unhealthy may not be representative of typical school meals served in a non-pandemic time period. 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--- title: Integrated Analysis of Gut Microbiome and Liver Metabolome to Evaluate the Effects of Fecal Microbiota Transplantation on Lipopolysaccharide/D-galactosamine-Induced Acute Liver Injury in Mice authors: - Chunchun Yuan - Jinghui Fan - Lai Jiang - Wenxin Ye - Zhuo Chen - Wenzi Wu - Qixin Huang - Lichun Qian journal: Nutrients year: 2023 pmcid: PMC10005546 doi: 10.3390/nu15051149 license: CC BY 4.0 --- # Integrated Analysis of Gut Microbiome and Liver Metabolome to Evaluate the Effects of Fecal Microbiota Transplantation on Lipopolysaccharide/D-galactosamine-Induced Acute Liver Injury in Mice ## Abstract Acute liver failure (ALF) refers to the occurrence of massive hepatocyte necrosis in a short time, with multiple complications, including inflammatory response, hepatic encephalopathy, and multiple organ failure. Additionally, effective therapies for ALF are lacking. There exists a relationship between the human intestinal microbiota and liver, so intestinal microbiota modulation may be a strategy for therapy of hepatic diseases. In previous studies, fecal microbiota transplantation (FMT) from fit donors has been used to modulate intestinal microbiota widely. Here, we established a mouse model of lipopolysaccharide (LPS)/D-galactosamine (D-gal) induced ALF to explore the preventive and therapeutic effects of FMT, and its mechanism of action. We found that FMT decreased hepatic aminotransferase activity and serum total bilirubin levels, and decreased hepatic pro-inflammatory cytokines in LPS/D-gal challenged mice ($p \leq 0.05$). Moreover, FMT gavage ameliorated LPS/D-gal induced liver apoptosis and markedly reduced cleaved caspase-3 levels, and improved histopathological features of the liver. FMT gavage also restored LPS/D-gal-evoked gut microbiota dysbiosis by modifying the colonic microbial composition, improving the abundance of unclassified_o_Bacteroidales ($p \leq 0.001$), norank_f_Muribaculaceae ($p \leq 0.001$), and Prevotellaceae_UCG-001 ($p \leq 0.001$), while reducing that of Lactobacillus ($p \leq 0.05$) and unclassified_f_Lachnospiraceae ($p \leq 0.05$). Metabolomics analysis revealed that FMT significantly altered LPS/D-gal induced disordered liver metabolites. Pearson’s correlation revealed strong correlations between microbiota composition and liver metabolites. Our findings suggest that FMT ameliorate ALF by modulating gut microbiota and liver metabolism, and can used as a potential preventive and therapeutic strategy for ALF. ## 1. Introduction Acute liver failure (ALF) leads to hepatocytes apoptotic, large amounts inflammatory response, and liver damage, thereby leading to multiple organ failure [1,2]. Numerous factors can stimulate acute liver injury, including hepatitis viruses, drugs, and toxins [3]. At present, the effective therapeutic strategies for ALF are still lacking [4], of which liver transplantation is still the most effective therapy for ALF. There is an urgent need for new therapies and drugs due to the lack of liver donors and extremely high costs [5]. Lipopolysaccharide (LPS), a characteristic component of Gram-negative bacteria, stimulates Kupffer cells, leading to the release of numerous pro-inflammatory cytokines including interleukin-1β (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α) [6,7]. D-galactosamine (D-gal) is a hepatocyte disrupting agent, which used to enhance the liver toxicity of LPS [8]. Therefore, the LPS/D-gal-challenged animal model has been well established to investigate the mechanisms and potential therapeutic strategies of ALF [8,9]. Human gut microbiota composition is linked to various diseases, including respiratory, neurological, hepatic, gastroenterological, and cardiovascular disorders [10]. Consequently, many studies have suggested that changes in gut microbiome affect the disease development. As the first organ to come into contact with microbial products that enter the portal circulation across the intestinal epithelial, the liver is likely influenced by microbiome and its metabolites [10,11]. Various acute and chronic hepatic diseases affect the gut microbiota composition, which has impact on the pathogenesis of liver diseases, the pathophysiology of this bidirectional relationship has recently been the subject of several studies [5,12,13]. The use of bacteria as probiotics has a long history. Fecal microbiota transplantation (FMT) refers to transfer intestinal microbes from a fit donor and possible implantation into the intestinal tract of recipients to rebuilt a healthy intestinal microbial ecosystem [14,15]. FMT has been ratified as standard treatment for Clostridia difficile infections, and has provided evidence for the effect of the microbiota in various diseases and has become a study hotspot in clinical medicine and biomedicine recently [16,17]. FMT also received interest for its therapeutic potential in autoimmune, cardiac, and other extraintestinal diseases [18,19], including as a viable and safe treatment for a variety of liver diseases. Studies by Schneider KM et al. showed that intestinal microbiome is a potentially modifiable risk factor for ALF, and that ALF can change the composition of intestinal microbiome, leading to intestinal barrier damage and bacterial translocation [20]. Ferrere et al. found that fecal transplantation from alcohol-resistant donor mice prevented dysbiosis and alcohol-caused liver injury [21]. Wang et al. revealed that FMT prevented hepatic encephalopathy in rats with acute hepatic dysfunction evoked by carbon tetrachloride [18]. The function of FMT in the therapy of chronic hepatic disease and delaying the progression of liver cancer provides new horizons for the clinical therapy of hepatic disease [22]. FMT also has significant advantages in reducing medical and social costs. Therefore, we hypothesized that there are probiotics against liver injury in the gut microbes of animals, and that FMT can effectively treat ALF. We assessed the preventive and therapeutic functions of FMT from healthy mice on LPS/D-gal caused ALF in C57BL/6 mice. Additionally, 16S rDNA and metabolomics technology were used to analyze FMT-mediated changes in gut microbiota and liver metabolome, and the potential mechanisms of hepatoprotective effects of FMT. ## 2.1. Animals Male C57BL/6 mice (SPF-grade, Hans Biotechnology Co., Ltd., Hangzhou, China), 6 weeks old, were provided with fixed temperature (21–24 °C), humidity (50–$60\%$), and natural lighting, and given water ad libitum. All experiment were carried with the guidelines for animal care and use, and were approved by the Committee for Animal Research at Zhejiang University (Hangzhou, China) (ZJU20220438). ## 2.2. Experimental Protocol Mice were intraperitoneally injected with LPS and D-gal, purchased from Sigma-Aldrich, to induce ALF. After 1 week of acclimation, the animals were separated into six groups randomly with 10 mice in each group: [1] control, [2] LPS/D-gal, [3] LPS/D-gal combined with FMT preventive treatment for 7 days (FMT-pre7d group), [4] LPS/D-gal + FMT preventive treatment for 14 days (FMT-pre14d group), [5] LPS/D-gal + FMT treatment for 7 days (FMT-treat7d group), and [6] LPS/D-gal + FMT treatment for 14 days (FMT-treat14d group). Prevention groups were gavaged with FMT (0.2 mL, 1 × 109 CFU/mL) once a day for 7 or 14 days before LPS/D-gal administration. Additionally, the treatment groups were gavaged with FMT (0.2 mL, 1 × 109 CFU/mL) once a day for 7 or 14 days after LPS/D-gal administration. The preventive and therapeutic effects of FMT were assessed over two time periods to determine its effect on acute liver injury over time. Correspondingly, the control and LPS/D-gal model groups were gavaged 0.2 mL PBS once a day. After 14 days, the preventive and therapeutic groups were injected with D-gal and LPS intraperitoneally at the doses of 400 mg/kg and 50 μg/kg body weight, respectively. Meanwhile, the control group was injected the same dose of PBS. On day 14, the mice were anesthetized and sacrificed 6 h after injection. Professors with Medical Laboratory Animal and animal Experiment Certificate of Zhejiang Province handled the experimental animals. The serum, colonic content, and liver tissues were collected immediately from all mice. A part of the liver was fixed using $4\%$ paraformaldehyde solution, and the rest of liver tissue was saved at −80 °C. Feces from control mice were collected daily as donors. Then, we placed the collected fresh fecal material in sterile tubes, and homogenized in sterile normal saline. Centrifuged at 6000× g for 10 min, pass the homogenate through 70 μm filter, and quantified (OD 600 value of 1.0, predicted to be 109 CFU/mL). Eventually, the fecal bacterial slurry was diluted to 5.0 × 109 CFU/mL using $30\%$ sterile medical glycerin PBS, and stored at −80 °C [23]. ## 2.3. Determination of Liver Enzymes and Biochemical Indexes A ratio of 1:9 was used for homogenizing $10\%$ liver tissue homogenates, which was then centrifuged at 3000 r/min (4 °C) for 15 min. The activities of alanine aminotransferase (GPT/ALT), aspartate aminotransferase (GOT/AST), and total bilirubin (TBIL) in mice livers were determined according to the instructions (Jiancheng Institute of Biological Engineering, Nanjing, China). Concentrations of cytokines, namely, serum TNF-α, IL-1β, IL-6, IL-10, and IL-12, using mouse ELISA kits (Jiancheng Institute of Bioengineering, Nanjing, China) to assay. ## 2.4. Terminal Deoxynucleotidyl Transferase-Mediated Nucleotide Nick-End Labeling (TUNEL) Assay TUNEL assays detected hepatocyte apoptosis by terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling. To label nuclei, liver sections were counterstained with 4′-6-diamidino-2-phenylindole after TUNNEL labeling. Using a fluorescence microscope to acquired images (Olympus, Shanghai, China). ## 2.5. Histology and Immunohistochemistry The livers were fixed using $4\%$ paraformaldehyde, and embedded using paraffin, and stained using hematoxylin and eosin (H and E) or myeloperoxidase (MPO) antibodies. Histopathological changes in each mouse were examined using an optical microscope (Nikon, Tyoko, Japan). ## 2.6. Microbial 16S rRNA Gene Sequencing Analysis Colon contents were taken on day 14 for microbiota analysis. Extracted fecal DNA from the microbial community using DNA Kit (Omega Biotek, Norcross, GA, USA). The, we utilized 338F and 806R universal PCR amplification to change the regions of 16S rRNA genes V3-V4. The activation was 2 min at 95 °C, denaturation of 30 s at 95 °C, annealing of 30 s at 50 °C, and extension of 30 s at 72 °C, then extension of 10 min at 72 °C followed by 27 cycles. PCR amplicon products were isolated using a $2\%$ agarose gel and a DNA gel extraction kit to purify. We used the QuantiFluor™-ST system (Promega, Madison, WI, USA) to quantify the PCR products. Sequencing was performed on an Illumina MiSeq platform (Illumina, San Diego, CA, USA) at Majorbio, Shanghai, China. The following criteria were used for demultiplexing and filtering raw reads by QIIME (version 1.9.1): [1] At each site with a mean quality score < 20, 300 bp reads were trimmed, and reads less than 50 bp were deleted after a sliding window of 10 bp; [2] Reject sequences with indistinct reads or two nucleotide mismatches in primer mates. Sequences with at least $97\%$ of matching nucleotides are clustered in the operational taxonomic units (OaTUs). Every 16S rRNA gene sequence was explained using the RDP classifier (version 2.11), compared to the SILVA 16S rRNA database (version 132), and set a comparison threshold of $70\%$ [24,25]. ## 2.7. Metabonomics Non-targeted profiling of metabolites in mouse liver tissues were performed on the Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China). First, liver samples (50 mg) were spiked with 400 μL of cold methanol (LC/MS grade, A456−4) solution ($80\%$, v/v). Homogenized tissue sample matrix using an automated homogenizer (FastPrep-24TM5G; MP Biomedicals, Santa Ana, CA, USA). Then, the samples were vortexed 30 s and extracted metabolites by sonication for 10 min in an ice water bath. The extraction process was repeated thrice. Then, incubated samples 30 min (−20 °C) and centrifuged 13,000× g 15 min at 4 °C (Centrifuge 5424-R, Eppendorf, Hamburg, Germany). Then, collected 200 μL of supernatant for liquid chromatography–mass spectrometry (LC–MS). LC–MS uses a Waters ACQUITY ultraperformance liquid chromatography system with a triple time-of-flight mass spectrometer (Waters, Milford, CT, USA; AB SCIEX TripleTOF 5600 System, Framingham, MA, USA). Separate chromatographic on Waters ACQUITY UPLC R BEH C18 (100 mm × 2.1 mm, 1.7 μm) at a flow rate of 0.4 mL/min preheated to 40 °C. The mobile phase consists of ultrapure water including $0.1\%$ formic acid (phase A), and a mixture of acetonitrile: isopropanol with $0.1\%$ formic acid according 1:1 volume (phase B), and injection volume is 20 μL. The gradient started with $5\%$ phase B and $95\%$ phase A, increases to $20\%$ phase B in 3.0 min, increases to $95\%$ in 6.0 min, and holds for 4.0 min, respectively. Thereafter, the gradient turned to the original chromatographic status within 0.1 min and hold 2.9 min. The scanning scope was m/z 50–1000 and the resolution was 30,000 resolutions for the full-scan mode. Using nitrogen as the carrier and a rate of 900 L/h. Quality parameters are set to ion original temperature is 120 °C, desolvation temperature is 500 °C, electrospray capillary voltage is 1.0 kV, collision voltage is 6 eV, and injection voltage is 40 V. We prepared quality control (QC) samples by mixing all sample extraction aliquots to evaluate system stability and were analyzed after each of the five samples throughout the analytical run. Principal component analysis suggested that the QC samples were clustered closely, verifying the good repeatability of UPLC-triple TOF-MS/MS method. Metabolomics raw data from LC–MS were analyzed, and calibration was done using the Progenesis QI software (Waters, Milford, USA). Peak intensities were normalized to exclude peaks with the relative standard deviation > $30\%$ in QC samples. Comparing its mass spectrometry information to public and commercial databases (http://www.hmdb.ca/; https://metlin.scripps.edu/; https://i-sanger.com) (accessed on 23 August 2021), annotating metabolites. The data from the positive and negative ion modes were then integrated into the SIMCA-P 14.0 (Umetrics, Umeå, Sweden) for further exploration. Principle component analysis (PCA) using an unsupervised method was applied to obtain an overview of the metabolic data, general clustering, trends, or outliers were visualized. Overall differences between treatments were distinguished using orthogonal partial least squares discriminant analysis (OPLS-DA) and Student’s t-test. The changed metabolites among treatments were analyzed using the importance in the projection (VIP) values > 1.0 and p-value < 0.05. To evaluate the potentially affected metabolic pathways, metabolic pathways enrichment, and topological analysis were conducted on the Kyoto Encyclopedia of Genes and Genomes (KEGG) library [26,27]. ## 2.8. Western Blot Analysis Separate proteins electrophoretically on $10\%$ SDS-polyacrylamide gels and the bands were transferred on polyvinylidene difluoride membranes. We blocked the membrane for 1 h using $5\%$ defatted skim milk and incubated with Tris-buffered saline containing anti-caspase-3 at 1:1000 dilution overnight, then incubated with secondary antibody. Finally, we used the ChemiDoc MP (Bio-Rad, Hercules, CA, USA) imaging system to obtain images [28]. ## 2.9. Statistical Analysis Statistical calculations using the GraphPad Prism (version 8.0, San Diego, CA, USA), and expressed as mean ± SEM. We used one-way ANOVA and Tukey’s multiple comparison test to analyze. We used Spearman’s rank correlation test to analyze the correlation of variants. The statistical significance was set at $p \leq 0.05.$ ## 3.1. FMT Relieved LPS/D-Gal-Induced Liver Injury in Mice To evaluate liver injury, several hepatic enzymes in liver homogenates were examined. Hepatic plasma ALT and AST, indicators of ALF [29], were remarkably improved in the LPS/D-gal group. Interestingly, FMT prevention and treatment decreased the elevated ALT and AST levels significantly (Figure 1A,B). Furthermore, total bilirubin (TBIL) level, an indicator of liver function, were decreased significantly in the FMT treatment groups than that in the LPS/D-gal group (Figure 1C). As the H and E staining showed, the LPS/D-gal challenge acute hepatic injury accompanied by a range of pathological manifestations, such as hemorrhagic necrosis, hepatic structural destruction, and numerous inflammatory cell infiltration. However, these morphological changes improved in the livers of the FMT prevention and treatment group mice (Figure 1D). In conclusion, these results indicate that pretreatment and treatment with FMT ameliorated ALF in LPS/D-gal-caused mice. Moreover, we also found that the effect of the 14 d groups was better than that of the 7 d groups, and the 14 day treatment group had the best results. ## 3.2. FMT Ameliorated Inflammatory Response in LPS/D-Gal-Challenged Mice The hepatocellular injury caused by LPS/D-gal is linked with liver and systemic inflammation [30]. Serum IL-6, IL-1β, and TNF-α levels were improved significantly in the LPS/D-gal group, and pretreatment and treatment with FMT effectively suppressed these pro-inflammatory cytokines levels, which further decreased with prolongation of FMT gavage (Figure 2A–C). Moreover, the anti-inflammatory cytokines IL-10 and IL-22 decreased significantly in the LPS/D-gal group and recovered in FMT prevention and treatment groups (Figure 2D,E). Thus, FMT by gavage suppressed LPS/D-gal caused systemic and liver pro-inflammatory responses in mice. A notable increase in MPO, a marker of neutrophil infiltration, was found in the LPS/D-gal group, and treatment with FMT repressed neutrophil infiltration caused by LPS/D-gal (Figure 2F). ## 3.3. FMT Inhibited Apoptosis of Hepatocytes in LPS/D-Gal-Induced Mice LPS/D-gal caused by ALF is characterized by excessive apoptosis of hepatocytes [31]. Hence, hepatocyte apoptosis was detected using TUNEL staining and caspase activation in the liver. As shown in Figure 3A, numerous TUNEL-positive hepatocytes were observed in LPS/D-gal-treated liver tissues. However, the improved number of positive hepatocytes was lower in the FMT prevention and treatment groups. Similarly, the level of active caspase-3 was upregulated in LPS/D-gal-induced mice, whereas this induction was inhibited in the FMT prevention and treatment groups (Figure 3B). Therefore, FMT by gavage suppressed LPS/D-gal-treated hepatocyte apoptosis and protected against ALF. Moreover, the FMT treatment 14 d group showed a lower positive TUNEL than the FMT treatment 7 d group, suggesting that prolonged FMT administration have better therapeutic effects against liver injury. ## 3.4. FMT Modulated Gut Microbiota Composition in LPS/D-Gal-Induced Mice We further analyzed the influence of FMT therapy on the colonic microbial composition of LPS/D-gal challenged mice by 16S rDNA gene sequencing. We found decreased α-diversity indices, including Shannon, Sobs, Chao, and ACE indices of the gut microbiota of LPS/D-gal group than those of control group ($p \leq 0.05$). Aforementioned indices were increased in FMT prevention and treatment groups than in the LPS/D-gal group, although not obviously (Figure 4A–D), suggesting that FMT increased bacterial richness (as determined by rising Sobs, Chao, ACE, and Shannon indices) and evenness (as determined by rising Shannon index) in LPS/D-gal-induced mice. Then, we analyzed the β-diversity with the Bray–Curtis principal coordinate analysis (PCoA). PCoA exposed remarkable differences in gut microbiota between the LPS/D-gal and control groups (R2 = 0.4343, $$p \leq 0.002$$, Figure 4E). The LPS/D-gal and FMT treatment groups also showed different gut microbiota (R2 = 0.3216, $$p \leq 0.001$$, Figure 4G), but there was no significant difference between LPS/D-gal and FMT prevention groups (R2 = 0.2974, $$p \leq 0.001$$, Figure 4F). These results indicated that FMT modulates gut microbiota composition of the ALF mice are induced by LPS/D-gal. Subsequently, we assessed the relative abundance of bacteria on phylum and genus levels (Figure 4H,I). Bacteroides and Firmicutes were the most abundant, accounting for over $80\%$ of all microorganisms in all groups. In Figure 4J, the relative abundance of Bacteroidota was reduced ($p \leq 0.01$), and those of Firmicutes ($p \leq 0.01$) and Desulfobacterota ($p \leq 0.01$) were increased in LPS/D-gal-challenged mice. FMT treatment inverted this change by improving the relative abundance of Bacteroidetes ($p \leq 0.001$) and reducing the abundance of Firmicutes ($p \leq 0.01$) and Desulfobacterota ($p \leq 0.05$). Compared with the FMT treatment groups, FMT prevention groups mainly showed an increased abundance of Bacteroidetes ($p \leq 0.001$) and a reduced abundance of Firmicutes ($p \leq 0.01$). Furthermore, within the phylum Bacteroidota (Figure 4K), the abundance of norank_f_Muribaculaceae ($p \leq 0.01$), Alistipes ($p \leq 0.05$), and Prevotellaceae_UCG-001 ($p \leq 0.01$) was significantly decreased in LPS/D-gal than control group and accounted for the majority of reduced abundance of Bacteroidetes in LPS/D-gal group. Improved abundance of Firmicutes in LPS/D-ga-induced mice was mostly explained by the improved abundance of Lactobacillus ($p \leq 0.05$) and unclassified_f_Lachnospiraceae. As shown in Figure 4L, FMT prevention and treatment mitigated LPS/D-gal-induced changes in bacterial abundance. FMT prevention improved the abundance of norank_f_Muribaculaceae ($p \leq 0.01$) and reduced that of Lactobacillus ($p \leq 0.05$). FMT treatment improved the abundance of norank_f_Muribaculaceae ($p \leq 0.001$), Alloprevotella ($p \leq 0.05$), unclassified_o_Bacteroidales ($p \leq 0.001$), and Prevotellaceae_UCG-001 ($p \leq 0.001$), and reduced that of unclassified_f_Lachnospiraceae ($p \leq 0.05$) (Figure 4M). Therefore, FMT treatment alleviated gut dysbiosis by modulating gut microbiota composition in LPS/D-gal challenged mice. ## 3.5. FMT Altered Liver Metabolome in LPS/D-Gal-Induced Mice Analysis of the liver metabolome using LC-MS-based non-targeted metabolomics. PCA showed a distinct variation in liver metabolome of the control and LPS/D-gal groups (Figure 5A,B), suggesting that hepatic injury induced metabolic disturbances. The liver metabolome of FMT prevention and treatment group mice were slightly overlapped with those of control group and were distinct from that of LPS/D-gal group, clearly indicating the role of FMT in preventing and treating hepatic injury. Furthermore, PLS-DA exhibited a better discriminative ability than PCA and was used to analyze the metabolic profiles. The OPLS-DA score plot showed significant distinctions in the liver metabolite content of the six groups (Figure 5C,D). The hierarchical clustering heatmap in Figure 5E shows the changing trends of liver metabolites, and indicating significant differences in the control, LPS/D-gal, and FMT prevention and treatment groups. Based on variable VIP > 1, fold change (FC) ≥ 1.2 or ≤0.8333, $p \leq 0.05$, and with KEGG annotations. As shown in Table 1, compared with the control group, there were 25 significantly differentially expressed metabolites in the LPS/D-gal group; of which, 11 were upregulated and 14 were downregulated—vs. (Table 1). The levels of these metabolites tended to restore in the FMT prevention and treatment groups. We found that caffeine, L-2-Aminoadipic acid, cortolone, thymine, and 12(R)-HETE were significantly downregulated in the FMT-pre7d group, and cortolone, N-acetyl-alpha-D-glucosamine 1-phosphate, and taurocholate were significantly down-regulated in the FMT-pre14d group. Ne-acetyllysine, trichloroethanol glucuronide, thiamine, 2-(S-glutathionyl)acetyl glutathione, stachyose, and 5-L-Glutamyl-L-alanine were significantly up-regulated in both the FMT-pre7d and 14d groups. These metabolites were further studied. KEGG enrichment analysis revealed that FMT prevention mainly affected lysine degradation, metabolism of xenobiotics by cytochrome P450, the biosynthesis of plant secondary metabolites and other pathways (Figure 5G,H). Compared with the LPS/D-gal group, both the FMT-treat7d and FMT-treat14d groups exhibited a decrease in metabolites of nucleotide sugar and amino sugar metabolites, such as UDP-N-acetyl-alpha-D-glucosamine, N-acetyl-alpha-d-glucosamine 1-phosphate, and CDP-glucose, and an increase in metabolites issued from xenobiotic metabolism by cytochrome P450 (trichloroethanol glucuronide, 2-(S-Glutathionyl)acetyl glutathione, and 1-methylguanosine), and starch and sucrose metabolites (UDP-glucose, CDP-glucose, and maltose). In addition, we found that cholesterol metabolites (number of metabolites = 2, $$p \leq 0.0005$$) were down-regulated in the FMT-treat 7 d group significantly and arachidonic acid metabolites (number of metabolites = 4, $$p \leq 0.0002$$) were notably down-regulated in the FMT-treat 14 d group (Figure 5I,J). ## 3.6. Gut Microbiota Is Associated with Liver Metabolites and Inflammatory Markers The correlations between gut microbes, liver metabolites, and markers of inflammation were analyzed to further explore the potential mechanism of action of FMT. Figure 6A,B displayed the correlation between differential gut microbes on phylum, and genus and inflammatory markers. Firmicutes and Desulfobacterota showed a positive relationship with AST, ALT, TBIL, IL-6, IL-1β, and TNF-α, and a negative correlation with IL-10 and IL-22 levels. Similar trends were observed at the genus level, including Lactobacillus, Lachnospiraceae_UCG-006 unclassified_f_Lachnospiraceae, Desulfovibrio, etc. Opposite correlations were observed in Bacteroides, unclassified_o__Bacteroidales, norank_f__Muribaculaceae, and Prevotellaceae_UCG-001, etc. Figure 6C showed the correlation between metabolic biomarkers and inflammatory markers. Thymine, CDP-glucose, and N-acetyl-alpha-D-glucosamine 1-phosphate were negatively correlated with anti-inflammatory cytokines and direct correlation with pro-inflammatory cytokines. In particular, unclassified_o__Bacteroidales, unclassified_f__Prevotellaceae, and norank_f__Muribaculaceae, among others positively correlated with metabolites, such as trichloroethanol glucuronide, stachyose, and maltose, that were notably downregulated in LPS/D-gal group, and negatively correlated with some metabolites that were significantly upregulated in LPS/D-gal group. Conversely, Lachnoclostridium, Faecalibaculum, Dubosiella, and Lachnospiraceae_UCG-006 showed opposite trends (Figure 6C). ## 4. Discussion ALF, fulminant hepatic failure, has a high mortality rate and resource cost, which is caused by the stimulation of drugs and toxins. Clinical manifestations of ALF include rapid hepatic injury, derangements in coagulopathy, hepatic encephalopathy, and multi-organ failure, which pose severe threats to a patient [32,33]. D-Gal, a hexosamine derived from galactose, is a hepatotoxic poison, and intraperitoneal administration of which will induce multiple hepatocyte damage and inflammation, is analogous to the pathological status of clinical viral hepatitis [34,35]. Combined use of LPS and D-gal has been diffusely applied to set up animal models of ALF [36]. Considering the limitations of the existing ALF treatments, there is urgent to find effective treatments to ameliorate ALF-related treatment difficulties. There is increasing evidences that the dynamic changes of intestinal microbiota perform an important role in the occurrence and development of liver injury. In view of this, the regulation of intestinal microbiota are promising diagnostic, prognostic, and therapeutic tools. In this regard, FMT is the most powerful tool for resetting intestinal microbiome disturbances caused by liver diseases. [ 37,38]. Our study mainly explored the guarding role of FMT and its potential mechanism of action in an LPS/D-gal-induced ALF, and found that FMT can be used as a safe, effective, and low-cost treatment for ALF. However, there is no clear protocol for the standardized donor screening, preparation, pretreatment, administration, or long-term stability of FMT, and the exact mechanism involved in its action has not been elucidated. Furthermore, histopathological assessment and aminotransferase activities revealed that FMT intervention also alleviated LPS/D-gal-challenged hepatic failure. FMT decreased hepatic ALT and AST levels significantly to attenuate liver failure, which was confirmed by the decrease in TUNEL-positive hepatocytes, indicating reduced hepatocyte apoptosis. LPS, on administration, binds TLR4 to activate it, which in turn activates the NF-κB via intermediate proteins, which then induces the expression of inflammatory cytokines [39,40]. We found that FMT restrained LPS/D-gal-caused hepatic neutrophil infiltration, and the making of pro-inflammatory cytokine and chemokine. In ALF, hepatocytes and other cells produce excessive inflammatory mediators, resulting partial and systemic inflammation [41]. Sustained making of cytokines including TNF-α, IL-1β, IL-6, IL-10, and IL-22, causes hepatocellular inflammation and apoptosis, and eventually leads to ALF [42,43]. According to the research, inhibition of inflammatory cytokines making may be a potential tactic for the therapy of ALF [43,44]. We found that FMT intervention downregulated the pro-inflammatory cytokines levels significantly and upregulated the anti-inflammatory cytokines, thereby reducing LPS/D-gal-induced inflammation, consistent with a previous study [45,46]. Studies have shown that the TNF-α is the main pro-apoptotic factor inducing extensive hepatocyte apoptosis in LPS/D-gal caused ALF [47,48]. TNF-α induces the neutrophil migration, that casts an important role in hepatocyte necrosis in the late stage of ALF [49]. TNF-α administration has been improved to accelerate hepatic failure [50], while TNF-α production inhibition [51] or TNF-α knockout [48] efficiently prevents acute liver injury. We found that FMT inhibited TNF-α production, a key regulator of apoptosis, to ameliorate the development of LPS/D-gal-induced ALF. Daubioul et al. [ 52] demonstrated in an initial research of seven patients with nonalcoholic steatohepatitis (NASH), prebiotic feeding for 8 weeks significantly reduced levels of liver inflammatory markers. Another study found the modulation of gut microbiota significantly decreased inflammatory markers (TNF-α and C reactive protein) [53]. To sum up, these data suggest that gut microbiota composition and metabolic activity may help suppress hepatic and systemic inflammation. The infiltration of neutrophils into liver tissue increases after the development of ALF, which can promote liver injury by producing inflammatory mediators [54]. Therefore, we measured the level of the neutrophil infiltration index MPO. We observed that FMT reduced MPO activity, indicating decreased neutrophil filtration. TNF-α induces apoptosis and necrosis of hepatocytes by activating caspase-3, which is a key executive molecule in apoptotic pathways [55,56]. FMT inhibited the expression of caspase-3, which was raised in LPS/D-gal challenged liver, indicating that FMT may adjust cellular apoptosis of ALF by suppressing caspase-3 expression. Finally, we found that prolonged intragastric administration of FMT moderated ALF caused by LPS/D-gal, and the effect was slightly better in the treatment groups than in the prevention groups. To probe the mechanism of action of FMT, we evaluated the FMT-mediated changes in intestinal microbiota and liver metabolic pathways in mice. The intestinal microbiota is closely associated with health and numerous host functions, including growth, development, immunity, metabolism, and disease [57,58,59]. The liver secretes bile acids that end up in the small intestine. The portal vein that enters the liver is rich in nutrients, and is the main blood provide to liver. The gut-liver axis makes reference to the interplay between the gut (including the microbes in it) and the liver [11]. Accumulating studies revealed that the composition and metabolites of gut microbes can regulate and promote the development of the human immune system, such as through probiotics, prebiotics, antibiotics, FMT, diet regulation, and management of bacterial complexes [60,61]. FMT can replenish a healthy colonic microbial condition and restore colonization [62]. It is currently an established clinical therapy for Clostridium difficile infection [63] and was explored as a therapy for ameliorating inflammatory bowel disease [64], metabolic syndrome [65], and several liver diseases [66]. To elucidate the mechanisms by which FMT manifests its effects, we studied the alterations of colonic microbiota composition and diversity. We found that FMT significantly altered the relative abundance of some phylum and genera in intestinal microbiota, some of which are supported by previous studies. Using 16S rDNA sequencing, it found that LPS/D-gal decreased the abundance of Bacteroidota and improved that of Firmicutes and Desulfobacterota significantly. In contrast, gavage of FMT decreased the Firmicutes/Bacteroidetes ratio, which can assess the gut microbial community stability [67]. Therefore, FMT may alleviate liver injury by modulation of gut microbiota composition. Yu et al. [ 68] also identified an improved abundance in Firmicutes, and a reduction in Bacteroidetes after S. boulardii intervention. Firmicutes and Bacteroidetes phyla comprise the vast majority of the dominant human gut microbiota [69]. Members of Bacteroidota are widely considered as candidate probiotics for the treatment of immune dysfunction, intestinal colitis, metabolic disorders, and cancer [67,70]. The abundance of norank_f_Muribaculaceae increased in the FMT prevention groups, while the abundance of norank_f_Muribaculaceae, unclassified_o_Bacteroidales, and Prevotellaceae_UCG-001 increased in the FMT treatment groups. Muribaculaceae have favorable influences on gut dysregulation through immune modulation and regulation of intestinal homeostasis [71]. The LPS/D-gal-induced reduction in Muribaculaceae, which was negatively correlated with AST, ALT, and pro-inflammatory cytokines, was significantly improved after treatment with FMT. Although some strains of the Prevotellaceae appear to be inflammatory pathogens, they act a key role in immunomodulatory, especially those of Prevotella [12]. Thus, the reduced abundance of Prevotellaceae_UCG-001 in the FMT treatment groups suggested that FMT promoted the abundance of microbes regulating immunity. Alistipes, a comparative new genus of Bacteroidetes, is highly relevant to immune modulation, dysbiosis, and inflammation [72,73]. Studies have revealed that Alistipes abundance is decreased in liver fibrosis diseases, including NASH and nonalcoholic fatty liver disease (NAFLD) [73], as we found. In this research, Alistipes likely contributed to worsening liver injury and inversely correlated with IL-1β level. The reduction in member of Bacteroidota induced by LPS/D-gal was negatively related to pro-inflammatory cytokine levels, which may affect the host immune system. After treatment with FMT, LPS/D-gal caused the depletion of Muribaculaceae, Bacteroidales, and Prevotella in mice to improve. Therefore, FMT may promote immune homeostasis by regulating the intestinal microbiota, which, in turn, modifies the making of pro- and anti-inflammatory cytokines. Firmicutes abundance shows positively correlated with pro-inflammatory cytokines and marker enzymes of liver injury. The increase in Firmicutes was mainly manifested through the increased abundance of Lactobacillus and unclassified_f_Lachnospiraceae in the LPS/D-gal group. Lachnospiraceae and Lactobacillaceae can sugars and hydrolyze starch to generate short-chain fatty acids (SCFAs) [74,75]. Large amounts of SCFAs do not ever confirm favorable effects [76]. The gut microbiota of patients with NAFLD is enriched with Lachnospiraceae, as do patients with NASH or obvious hepatic fibrosis [77]. In our research, the gavage treatment of FMT obviously attenuated the abundance of Lachnospiraceae, which was positively correlated with pro-inflammatory cytokines. Lactobacillus provides the host with multiple benefits, such as suppression of pathogens and improvement of immune response [78]. Jiang et al. [ 12] found that pretreatment with probiotic Lactobacillus reuteri DSM 17,938 in rats could alleviate intestinal dysbiosis, reduced the inflammatory factors, and alleviated D-gal caused liver injury. However, our results showed a negative impact of Lactobacillus for it was positively correlated with pro-inflammatory cytokines, and a significantly negative correlation with IL-10 and IL-22. FMT pretreatment significantly attenuated the abundance of Lactobacillus, which is the same as that observed by Yan R et al. [ 79], who found Lactobacillus was heavily enriched in the D-gal-induced group and depleted in Lactobacillus casei strain Shirota (LcS) group. The metabolomic PLS-DA analysis showed the mice treated with LPS/D-gal had unique metabolic characteristics compared to control group. A total of 25 differentially accumulated metabolites were identified. Through KEGG enrichment analysis, these metabolites were mainly enriched in primary bile acid biosynthesis, bile secretion, cholesterol metabolism, amino sugar and nucleotide sugar metabolism, secondary bile acid biosynthesis, and starch and sucrose metabolism. KEGG analysis revealed that prevention with FMT mainly improved lysine degradation, the metabolism of xenobiotics by cytochrome P450, and the biosynthesis of plant secondary metabolites. The levels of L-2-aminoadipic acid and N-acetyl-alpha-D-glucosamine 1-phosphate, intermediate products of lysine metabolism, were elevated in the LPS/D-gal induced live. Several amino acids are known to have favorable influences on the treatment of liver steatosis, and lysine is known to regulate lipid metabolism in liver [80]. FMT significantly restored the levels of these two metabolites by altering lysine metabolism to alleviate LPS/D-gal-induced liver failure. Recent studies reported the active role of the hexosamine biosynthesis pathway (HBP) in host antiviral immunity of hepatitis B and C virus [81,82]. About 2–$5\%$ of the total glucose is transformed to uridine diphosphate N-acetylglucosamine (UDP-GlcNAc)—the last product of HBP [83]. D-gal can exhaust the uridine phosphate in hepatocytes, reducing the production of nucleic acid and protein synthesis [84]. We also found that N-acetyl-alpha-D-glucosamine 1-phosphate, UDP-N-acetyl-alpha-D-glucosamine, and CDP-glucose were markedly downregulated in the FMT treatment groups than LPS/D-gal group; therefore, FMT modulates HBP markedly. Pearson’s correlation analysis revealed that the three metabolites mentioned above positively correlated with pro-inflammatory cytokines. The cytochrome P450 metabolism pathway has important function in detoxification, cell metabolism, and homeostasis of exogenous drugs [85]. Metabolomics results showed that the metabolites of xenobiotics metabolism by cytochrome P450, including 2-(S-Glutathionyl) acetyl glutathione and trichloroethanol glucuronide, which were negatively correlated with pro-inflammatory cytokines, were downregulated remarkably in LPS/D-gal group, and were restored in FMT prevention and treatment groups. Several studies have revealed that CYP450 enzyme are essential in the detoxification of Aristolochic acid I (AAI)-induced liver injury in mice [87]. We also found that arachidonic acid metabolism was obviously down-regulated in FMT-treat14d group. As a plentiful lipid mediator in the human, arachidonic acid is essential in the inflammatory metabolic network, and its metabolites have pro-inflammatory features [86,87]. CYP450 hydrolyzes arachidonic acid, and the main metabolite of which is important in pro-inflammatory reactions [88,89]. Correlation analysis revealed arachidonic acid metabolites were positively related to IL-1β, IL-6, and TNF-α levels. ## 5. Conclusions In summary, this study demonstrated the preventive and therapeutic effects of FMT against ALF caused by LPS/D-gal; indeed, we found that with prolongation of intragastric administration of FMT, liver failure caused by LPS/D-gal had markedly improved, and the results of treatment were significantly better than those of prevention. Furthermore, changes in the gut microbiome and hepatic metabolism have also been shown to contribute to understanding of the potential mechanism of FMT therapy for ALF. In conclusion, this study suggests that FMT could be a potential therapeutic approach to treat ALF and that the gut microbiome may be a potential therapeutic target for ALF. ## References 1. Arroyo V., Moreau R., Jalan R.. 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--- title: Preparation of Cu/Sn-Organic Nano-Composite Catalysts for Potential Use in Hydrogen Evolution Reaction and Electrochemical Characterization authors: - Nezar H. Khdary - Gaber El Enany - Amani S. Almalki - Ahmed M. Alhassan - Abdullah Altamimi - Saeed Alshihri journal: Nanomaterials year: 2023 pmcid: PMC10005550 doi: 10.3390/nano13050911 license: CC BY 4.0 --- # Preparation of Cu/Sn-Organic Nano-Composite Catalysts for Potential Use in Hydrogen Evolution Reaction and Electrochemical Characterization ## Abstract In this work, the solvothermal solidification method has been used to be prepared as a homogenous CuSn-organic nano-composite (CuSn-OC) to use as a catalyst for alkaline water electrolysis for cost-effective H2 generation. FT-IR, XRD, and SEM techniques were used to characterize the CuSn-OC which confirmed the formation of CuSn-OC with a terephthalic acid linker as well as Cu-OC and Sn-OC. The electrochemical investigation of CuSn-OC onto a glassy carbon electrode (GCE) was evaluated using the cyclic voltammetry (CV) method in 0.1 M KOH at room temperature. The thermal stability was examined using TGA methods, and the Cu-OC recorded a $91.4\%$ weight loss after 800 °C whereas the Sn-OC and CuSn-OC recorded 16.5 and $62.4\%$, respectively. The results of the electroactive surface area (ECSA) were 0.5, 0.42, and 0.33 m2 g−1 for the CuSn-OC, Cu-OC, and Sn-OC, respectively, and the onset potentials for HER were −420, −900, and −430 mV vs. the RHE for the Cu-OC, Sn-OC, and CuSn-OC, respectively. LSV was used to evaluate the electrode kinetics, and the Tafel slope for the bimetallic catalyst CuSn-OC was 190 mV dec−1, which was less than for both the monometallic catalysts, Cu-OC and Sn-OC, while the overpotential was −0.7 vs. the RHE at a current density of −10 mA cm−2. ## 1. Introduction Burning non-renewable energy sources such as fossil fuels to produce energy has several problems, such as increasing carbon dioxide emissions, which is a serious factor contributing to the greenhouse effect. It is important to recognize that global warming is closely related to using fossil fuels as energy resources, and further, one of the main energy resources, namely, crude oil, could be exhausted by the mid-21st century [1,2]. Consequently, there is an urgent need to find renewable energy resources free from environmental pollution. Hydrogen has been referred to as the fuel of the future with water as an oxidation product with no carbon and a higher calorific value than any other chemical fuel [3,4,5]. At present, hydrogen is produced worldwide from various sources, e.g., steam reforming of methane, coal gasification, and water electrolysis. Hydrogen produced from electrocatalytic water splitting has been proven as an alternative clean and sustainable fuel to finite fossil fuels [6]. Although water splitting is a cleaner method for H2 synthesis, economic limitations have prevented it from playing a more significant role in H2 production [2,7]. Water splitting comprises two half-reactions, the oxygen evolution reaction (OER) at the anode and the hydrogen evolution reaction (HER) at the cathode. HER kinetics are significantly faster in acidic media than in alkaline media, but the process is expensive due to many factors such as electrode corrosion. However, the electrolysis of alkaline water is of paramount importance as it is one of the most widely used techniques in the industry [8], while alkaline media also enable the use of non-noble metals as catalysts at low prices [9], also provide better stability to non-noble metals by avoiding their corrosion and dissolution, hence resulting in prolonged catalysis and making it an attractive alternative to acidic catalysis [10]. In a basic solution, adsorbed H can only be produced from water molecule reduction by transferred electrons [11,12,13,14] (Volmer step):[1]H2O+e⇌Hads+OH− Volmer followed by the recombination of adsorbed hydrogen atoms, Hads, which occurs via the Heyrovsky or the Tafel step:[2]H2O+Hads+e⇌H2+OH− Heyrovsky [3]2Hads⇌H2 Tafel Generally, in a basic medium, a catalyst is required to break the stronger covalent H-O-H bond prior to adsorbing H, whereas the OH− group in the basic medium competes with H to absorb on the active site of the catalyst. The high catalytic activity of Noble metals such as Pt for HER is attributed to their affinity for hydrogen adsorption and to facilitating the Hads recombination; however, they tend to be poor materials for cleaving the H-OH bond required for the Volmer step. On the other hand, non-noble oxophilic transition metals, while being poor catalysts for the recombination step, are quite efficient for water dissociation [15]. Therefore, moderate adsorption energies for water and hydrogen on the active sites with a low attraction to hydroxyl ions are required to proceed with a reaction in an alkaline medium at lower overpotentials and better efficiencies. Yan et al. demonstrated that the relation between the HER exchange current density in an alkaline medium and the H-binding energy values can be correlated via a volcano-type of relationship, supported both by the experimental and DFT studies [16]. It is clear that in alkaline media, Cu is located on the right branch of the volcano curve because it binds to hydrogen weak [17]. As pointed out by the Sabatier principle, neither too strong nor too weak binding would favor the overall reaction because strong or weak binding leads to either difficulty in removing the final product or poor adsorption of the reactant and this principle appears to apply in both basic and acidic electrolytes. Hydrogen and hydroxyl are important surface-bonded intermediates during many reactions in advanced energy conversion systems, in particular hydrogen evolution, hydrogen oxidation, oxygen evolution, oxygen reduction, and the oxidation of small organic molecules [18,19]. Many previous studies have been undertaken to achieve these requirements by using more than one metal compound to prepare the hybrid catalysts for HER, and most of them used transition metals (d-Blook element) [20,21,22] such as Cu, Ni, Co, and Ag. Sn, as one of the sp metals of the group four elements, has two oxidation states, namely, Sn (II) and Sn (IV), and the stability of the +2 oxidation state results from the relativistic contraction [23] of the electrons, which tends to draw the electrons closer to the nucleus than you would expect, affecting the s electrons much more than p electrons. For this reason, Sn and concentrated hydrochloric acid are traditionally used to reduce nitrobenzene to phenylamine (aniline). This reaction involves the tin first being oxidized to Sn (II) ions and then further to the preferred Sn (IV) ions. Consequently, one can conclude that the presence of Sn or Sn (II) in the catalyst might enhance the absorption and ionization of water molecules in a basic medium and could also act as a self-reducing agent for hydrogen ions. From this point, a homogenous organic nano-composite of Cu, Sn, and bimetallic CuSn were papered by a solvothermal solidification process and thermal annealing, for investigation of its catalytic activity in HER. ## 2.1. Catalysts Preparation Synthesis of a metal-organic nano-composite (M-OC). Typically, a known amount of a metallic compound, as shown in Table 1, was dissolved in a known volume of DMF (Dimethylformamide) with sonication, then 3 mmol of terephthalic acid C6H4(COOH)2 was added and the mixture was sonicated until a clear solution was observed. The resulting mixture was transferred to an autoclave reactor for 2 days at 90 °C and the obtained precipitate was then washed several times with DMF and isopropanol and then dried under a vacuum. ## 2.2. Catalysts Characterizations The crystal structures of the samples were characterized using an X-ray diffractometer (D8 Advance, Bruker, Karlsruhe, Germany) with a Cu-Kα X-ray 2.2 kW source. Infrared absorption spectra were taken by an FT-IR spectrometer with a resolution of 4 cm−1 and 64 scans (Vertex 70—Bruker, Mannheim, Germany). The weight loss of the samples was measured using a TGA thermal analyzer (STD-Q600, New Castle, DE, USA) from room temperature to 800 °C at a heating rate of 10 °C/min in a nitrogen atmosphere. A scanning electron microscope (SEM) JSM-7800F (JEOL, Akishima, Japan), combined with energy dispersive X-ray spectroscopy (EDS), was performed to investigate the morphology and microstructure analysis of the prepared catalysts. ## 2.3. Electrochemical Measurements The electrochemical characterization was performed with a multichannel potentiostat/galvanostat VSP 150 (BioLogic) connected to a computer with the EC-Lab software using three electrode configurations. Platinum mesh (Pt) and sodium saturated calomel electrodes (SSCE) were used as the counter and reference electrodes, respectively. Working electrodes were fabricated by depositing the catalyst on glassy carbon (GC) electrodes (3 mm diameter). The GC electrodes were polished using an aqueous alumina Al2O3 suspension (5 and 0.25 μm, Allied High-Tech Products Inc., Compton, CA, USA) on polishing pads. The GC electrode was then sonicated in 1 M KOH for 5 min to dissolve any embedded alumina and then rinsed with water and dried in air. To prepare the catalyst ink, 10 mg of the catalyst was dispersed in 500 μL of isopropanol, then, the ink was pipetted into a pretreated GC surface to give a loading of 1.5 μg/cm and dried under an ambient environment. The electrochemical measurements were conducted in 0.1 M KOH electrolyte at room temperature (25 ± 2 °C). The electrolyte was prepared by using KOH (≥$85\%$ KOH basis, Sigma Aldrich, MO, USA) and water (18.2 Ω·cm, Milli-Q water). The electrolyte was purged for 30 min with N2 gas before usage and during the experiment to remove any dissolved gases during the electrochemical measurements. The LSV polarization curves were recorded in a potential range of −0.8 to −1.5 V vs. SSCE at a sweep rate of 10 mV/s. The electrochemically active surface area (ECSA) for each system was estimated from the electrochemical double-layer capacitance of the catalytic surface. The CV used for the electrochemical double-layer capacitance (Cdl) calculation was acquired in a potential window where no faradaic process occurred from −0.2 to −0.5 V vs. the SSCE at 5, 10, 30, 50, 70, 100, and 200 mV/s, while the charging current density (jc) was captured at −0.45 V vs. the SSCE. The double-layer capacitance of the electroactive materials (Cdl) was calculated from the slope of the relation between the scan rate and the charging current density. The ECSA for each system was estimated according to the relation of ESCA = Cdl/Cs, where Cs is the specific capacitance (0.040 mF/cm2) [24]. All measured potentials we reconverted to a reversible hydrogen electrode (RHE) using the following equation: E vs. RHE = E vs. SSCE + 0.236 + 0. 059 pH. ## 3.1. Synthesis and Microstructural Characterization The metal-OCs were prepared by the solvothermal solidification method using Cu (II), Sn (IV), and Cu (II)/Sn (IV) as the central metals while terephthalic acid was the linker molecule. The as-synthesized Sn-OC, Cu-OC, and CuSn-OC were obtained as light yellowish-white color, light blue, and light green powders, respectively (Figure 1). The morphology of the prepared OCs samples is presented in the SEM images in Figure 2, and it is obvious that the method of synthesis led to three different shapes, namely, spherical with around 300 nm in the case of the Sn-OC, cubic-like in the case of the Cu-OC, and flower-like in the case of the CuSn-OC with an open structure. The EDX data, (Table 2) illustrates the weight and atomic percentages of each element, and the ratio between the Cu to Sn was around 3:1. This result reflects the high susceptibility of copper to chelating with terephthalic acid, as the proportion of copper in the resulting compound was three times that of tin. Terephthalic acid had a crystalline nature as maintained in file no. [ 311916, 211919] at the ICCD, but when Sn was reacted with terephthalic acid, an amorphous complex was formed as Figure 3c shows. Figure 3b shows different behavior when Cu reacted with terephthalic acid, where a crystalline complex of Cu–terephthalic acid was formed, (see file no. [ 381629] at the ICCD), revealing the existence of strange peaks between 2θ angles (10–16 degrees), marked by (), which may refer to the formation of Cu(OH)2.H2O (see file no. [ 420746] at the ICCD). Figure 3a shows a strong peak at 2θ = 19°, which confirms the formation of a complex between Cu and terephthalic acid. Additionally, Figure 3a does not deny the formation of an amorphous complex between Sn and terephthalic acid because the Sn and terephthalic acid formed an amorphous complex as mentioned at the beginning of this paragraph; therefore, we can expect the presence of two phases in this state where one is crystalline with Cu and the other with Sn. According to the Debye–Scherrer formula, the average particle sizes D of the samples were given by: [25,26] Dhkl = 0.9λ⁄βcosθ, whereby β is the full width at a half-maximum (FWHM) value of the XRD diffraction lines (see Figure 3a,b), λ is the wavelength whose value was 0.154056 nm, and θ is the half diffraction angle of 2θ. The crystalline sizes were determined at the highest peaks at ca. 2θ = 17.18° and 19.23°, and this is marked by (▼) in both Figure 3a,b. They were 24.34 and 62.68 nm for the Cu-OC and CuSn-OC, respectively. The microstructure of the prepared samples was further investigated by FTIR spectroscopy as shown in Figure 4. It was found that at high-frequency regions, the nearly identical broad band located from 2000 to 3500 cm−1 for both Sn-OC and CuSn-OC samples may have been assigned to those carboxylate groups located in the big pores. Water may have been penetrating the carboxylic acid groups only in the large pores, where it protonated the carboxylate groups, and signals around 1700 and 1200 cm−1, characteristic of carboxylic acid groups, appeared [27]. These results reflect the affinity of the sample containing the Sn cation to absorb more water during the washing process, and the result was supported by the results of the TGA in the next section. Additionally, the broadband may be explained in the base of the formation of a dimeric form of terephthalic acid, which gave rise to a broad absorption region with many sub-maxims [28] at 2500–3000 cm−1. In the region of 2500–3500 cm−1, we would expect also to find the C-H stretching modes of the benzyl group. The band at 578 (Figure 3c), 573 (Figure 3b), and 548 cm−1 (Figure 3a), indicate the presence of a M-O bond in the Sn-OC, Cu-OC, and CuSn-OC, respectively. The band at about 3509 cm−1 for the carboxylic acid virtually disappeared from the spectra of the prepared OC samples. The OC samples exhibited a more broadened band in the region near 2970–2980 cm−1, indicating the presence of coordinated water molecules. The coordinated water in all the oligomeric metal (II) complexes presented different peaks at 1000 cm−1 (rocking) and 750 cm−1 (wagging), whereas none of these vibrations appeared in the spectra of uncoordinated ligands [29]. The thermal behavior was investigated by a TGA thermal analyzer at a heating rate of 10 °C min−1 in the temperature range of 50–800 °C under nitrogen. The Cu-OC lost its weight in four stages, with the initial weight loss occurring in the range of 30 to 170 °C due to the evaporation of moisture content, and the total mass loss was about ($9.66\%$). In the second and third steps, the weight loss where the ca. was $84\%$, which occurred between 170 and 440 °C, exhibited a mass loss corresponding to the loss of coordinated water molecules to the metal ion and decomposition of the ligand, as shown in Figure 5a. The decomposition of the Sn-OC, shown in Figure 5b, occurred in four steps as in the case of the Cu-OC. The first step occurred between 30 and 110 °C, which might be attributed to the mass loss corresponding to the absorbed water molecules. In steps two and three, a very low weight loss occurred in the range of 110 to 450 °C, where an observed ca. of $8\%$ reflected the low loss of coordinated water molecules and good thermal stability for the obtained sample with a total mass loss of $16\%$. In the case of the CuSn-OC, the three steps of decomposition were observed, as shown in Figure 5c, and the presence of a Sn ion in the sample made it more thermally stable than in the case of using a Cu ion only, where the total mass loss was $26\%$. ## 3.2.1. Cyclic Voltammetric Studies For the Cu-OC, as shown in Figure 6a, a large diminishing of the peek current appeared, and oxides of copper were produced with limited solubility as the potential was increased in the case of alkali metal hydroxide solutions [30]. Additionally, soluble species such as Cu(OH)4−2 were also speculated to form [31], which explained the large decay in the peak current. In Figure 6b, peak 1 may have been attributed to the formation of Cu2O whereas peak 2 may have been attributed to the soluble product of Cu(OH)4−2. The two peaks in the reduction region may have been due to the formation of Cu from CuO, at a potential of −0.12(V) and Cu(OH)4−2, or at a more negative optional up to −1 (V). As seen in Figure 6c, the anodic peak current increased linearly with an increasing scan rate, which indicates that the redox process was non-diffusional. For the cyclic voltammograms of the Sn-OC electrode in 0.1 M KOH at scan rates of 200 mVs−1, as represented in Figure 7a, it can be observed that peak S was only observed in a fresh sample on the first scan cycle, then disappeared with successive scans and was not observed again. This peak may have resulted from the reduction of Sn IV to Sn (II) or Sn particles. Peak S3, which appeared in the second cycle, could be attributed to the oxidation of metallic tin to Sn (II), probably forming Sn(OH)2 or SnO, whereas peak S4 may be attributed to the oxidation of the previously formed Sn (II), giving Sn(OH)4. The passivation region after peak S4 has been attributed to the progressive conversion of Sn(OH)4 to SnO2. At the reduction region, peaks S1 and S2 were associated with the simultaneous reduction of the Sn(IV) and Sn(II) to Sn particles. Generally, by increasing the cycle number the oxidation charge becomes much more than the reduction one, which indicates the formation of passive oxides or hydroxides, and this results in a decrease in hydrogen and oxygen evolution. At cycle 25, peaks S3 and S4 diminished, and small broadband formed, as shown in Figure 7b. The peak current for peaks S2 and S5 increased linearly with an increasing scan rate, as shown in Figure 7c, indicating that the redox process was non-diffusional, whereas the peak current for peak S1 increased linearly with increasing the square root of the scan rate, indicating that the redox process was diffusional, as shown in Figure 7d. For the CuSn-OC, Figure 8 represents the response of the CuSn-OC sample in a 0.1 M KOH solution at a scan rate of 200 mV/s, where the behavior of the first reduction peaks may be attributed to the transition of Sn (IV) to Sn(II), and then to the Sn metal and deposition of Cu to form a Sn-Cu bimetal onto the surface of the GC substrate. In the oxidation region, the dissolution of the Cu and Sn was started and the formation of a thin layer from passive SnO2 may have been formed. The decrease in the peak’s current and charge in the next cycles may have been due to this layer. ## 3.2.2. HER Performance Measurements The HER performances of the Cu-OC, Sn-OC, and CuSn-OC catalysts were evaluated in a 0.1 M KOH electrolyte via a conventional three-electrode system. Linear sweep voltammetry, as shown in Figure 9 for the prepared catalyst samples, revealed that the overpotential at −10 mA cm−2 was in the order of Sn-OC > CuSn-OC > CuSn-OC, where the Cu-OC required a lower overpotential than the CuSn-OC of only −30 mV at −10 mA cm−2. The overpotential values at −10 mA cm−2 are shown in Table 3. We have taken the onset potential from Figure 9 and the values were −420, −900, and −440 mV vs. the RHE for the Cu-OC, Sn-OC, and CuSn-OC, respectively. In the case of the Sn-OC, the d-band of the Sn sp metal, would be expected to lie low, thus it would play no role in the bonding of hydrogen nor electrocatalysis [32]. The difficulty with Sn-OC is that measurable currents can only be obtained at high overpotentials (i.e., an onset potential = −900 mV), therefore, the determination of the exchange current density jo requires an extrapolation over a large potential range. The Fermi level of the d metals lies within the d band as a result, and the hydrogen adsorption ability on the Cu surface is more than that on the Sn surface; therefore, the onset potential of the catalysts containing Cu, Cu-OC, and CuSn-OC, were much less than in the case of the Sn-OC, as shown in Table 3. The electrochemically-active surface area (ECSA) for each system was estimated from the electrochemical double-layer capacitance of the catalytic surface (Figure 10a), as shown in Table 3. The result reflects more ECSA for the bimetal ions catalyst of CuSn-OC, with 0.5 m2 g−1, compared with the monometallic catalysts Cu-OC and CuSn-OC. The HER process in an alkaline medium is governed by the dissociation of water molecules, where two intermediate species form, namely, OH− and H+, which need to adsorb and desorb on the catalyst surface. Adsorbed H, in an alkaline medium, can only be produced from water molecule reduction by transferred electrons (Volmer step), and the catalyst requires the breaking of a stronger covalent H-O-H bond prior to adsorbing H, taking into consideration the fact that hydroxide ions present in a solution tend to adsorb onto a catalyst surface, thereby competing with hydrogen and reducing the number of available adsorption sites needed for HER progress [17]. To evaluate the HER kinetics of these electrocatalysts, the Tafel slope was calculated and plotted as shown in Figure 10b. the Tafel slope of the CuSn-OC was 195 mV dec−1, which was lower than for both the Cu-OC at 335 mV dec−1, and the Sn-OC at 250 mV dec−1, indicating the synergetic effect of Cu and Sn ions in its binary catalyst. The exchange current density, jo, and the transfer coefficient, α, were also estimated as shown in Table 3. The Tafel slope in the case of a catalyst containing Sn was less than that of a Cu-OC catalyst, even though a catalyst containing Cu had a higher ability to absorb hydrogen than tin, and an ECSA of Cu-OC was more than for Sn-OC. This indicates that the low bonding of hydrogen in the Sn-OC catalyst did not imply that the interaction of the d band with the adsorbed hydrogen was weak. The bimetal ions catalyst CuSn-OC had a lower Tafel slope value and higher ECSE; therefore, the CuSn-OC catalyst had much more catalytic activity for HER in the basic medium than both the Cu-OC and Sn-OC. This caused us to assume that the Cu form in the catalyst CuSn-OC enhanced the dissociation of water molecules and broke the stronger covalent H-O-H bond prior to adsorbing H, whereas the Sn form facilitated the reduction of the formed H+ to weakly adsorb H. ## 4. 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--- title: Adaptation and Validation of the Well-Being Related to Food Questionnaire (Well-BFQ©) for the French-Speaking General Adult Population of Québec, Canada authors: - Andrée-Anne Doyon - Alexandra Bédard - Catherine Trudel-Guy - Louise Corneau - Simone Lemieux journal: Nutrients year: 2023 pmcid: PMC10005551 doi: 10.3390/nu15051128 license: CC BY 4.0 --- # Adaptation and Validation of the Well-Being Related to Food Questionnaire (Well-BFQ©) for the French-Speaking General Adult Population of Québec, Canada ## Abstract Efforts to develop effective strategies that improve dietary intake are needed; however, this improvement in diet quality must not be at the expense of well-being. The Well-Being related to Food Questionnaire (Well-BFQ©) is a tool that has been developed in France to comprehensively measure food well-being. Even though the same language is spoken in France and in Québec, cultural and linguistic differences are present, which supports the importance of adapting and validating this tool before its use in the Québec population. This study aimed to adapt and validate the Well-BFQ© for the French-speaking general adult population of Québec, Canada. The Well-BFQ© underwent a full linguistic adaptation process, including an expert panel adaptation step, a pretest among 30 French-speaking adult (18–65 years) Quebecers, and a final proofreading. The questionnaire was thereafter administered to 203 French-speaking adult Quebecers ($49.3\%$ females, MAGE = 34.9, SD = 13.5; $88.2\%$ Caucasians; $54.2\%$ with a university degree). The exploratory factor analysis showed a two-factor structure: [1] food well-being related to physical and psychological health (27 items) and [2] food well-being related to symbolic/pleasure of food (32 items). Internal consistency was adequate, with a Cronbach’s α of 0.92 and 0.93, respectively, for the subscales, and 0.94 for the total scale. The total food well-being score, as well as the two subscale scores, were associated with psychological and eating-related variables in expected directions. Overall, the adapted version of the Well-BFQ© was found to be a valid instrument to measure food well-being in the French-speaking general adult population of Québec, Canada. ## 1. Introduction It is widely recognized that healthy eating can prevent the development of chronic diseases and that a substantial proportion of cardiometabolic deaths is associated with a suboptimal diet [1,2,3,4]. Multiple disease states and their detrimental effects on morbidity and mortality can be prevented or minimized with effective dietary and lifestyle interventions [5,6]. This body of knowledge has contributed to the emergence of a “food as medicine” paradigm, where healthy eating is identified as an adequate food and nutrient intake to prevent, manage, and treat illness [7,8,9,10,11,12]. In this paradigm, some foods may be considered to possess medicinal qualities that provide health benefits beyond their basic nutritional contributions. Considering the “food as medicine” paradigm, various tools based on food and nutrient intake have been developed to measure diet quality [13,14]. However, in addition to providing the necessary nutrients, foods also have emotional, social, symbolic, and hedonic values [15]. Eating is more than the amount of food we eat; it is also about how we eat it. In this regard, a paradigm shift from “food as medicine” to “food as well-being” has been claimed by an increasing number of researchers and health professionals [16,17,18,19,20]. According to the World Health Organization definition of health, healthy eating could be more comprehensively defined as eating behaviors that can enable the person to achieve “a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity” [21]. Accordingly, the “food as well-being” paradigm considers the psychological and social dimensions of food consumption, and not merely the biomedical orientation of food consumption. This paradigm shift is not only appearing in the scientific literature, but also in recent dietary guidelines in some countries, who now offer many tips above and beyond the choice of what foods to eat [22,23]. As an example, in addition to food choices, the newest version of the Canada’s food guide now includes recommendations about healthy eating habits, such as being mindful about one’s eating habits, cooking more often, enjoying food, and eating meals with others [24]. Accordingly, efforts to develop effective strategies that improve dietary intake are urgently needed to enhance populational health; however, this improvement in diet quality must not be at the expense of well-being. Namely, restricting food intake may lead to a repetitive pattern of self-deprivation, which can result in disordered eating such as bingeing, lower self-esteem, and weight changes, including weight gain and worsening body dissatisfaction [25]. In this context, the use of food well-being (FWB) as an outcome, in addition to diet quality, is of great interest. Block et al. [ 16] defined FWB as a positive psychological, physical, emotional, and social relationship with food at both individual and societal levels. Even if nearly 100 self-reported measures of well-being have been developed over the past 50 years [26], few tools have been developed to measure FWB [27,28,29,30,31,32]. The development of comprehensive measurement tools and their validation could be useful in research to supplement information provided by diet quality indices, allowing for a broader measurement of the concept of healthy eating. One questionnaire that has been developed in France to measure FWB comprehensively is the Well-Being related to Food Questionnaire (Well-BFQ©) [32]. The 134-item questionnaire is divided into six thematic modules, i.e., “Grocery shopping”, “Cooking”, “Dining places”, “Commensality”, “Eating and drinking”, and “Eating habits and health”. To develop the structure of the questionnaire, 24 focus groups (198 subjects) were conducted with French subjects to collect qualitative data about their definition and experience of well-being in general and more specifically in the context of food and diet. Pleasure and health were the two major domains emerging from these discussions that subjects linked to FWB. After the development of the questionnaire, a preliminary validation was conducted on 444 French subjects. Using principal component analyses, the structure of the questionnaire was determined, with confirmation of the sub-sections “immediate benefits” (pleasure, security, relaxation), “direct short-term benefits” (digestion and satiety, energy and psychology), “deferred long-term benefits” (eating habits and health), and “food behaviors”. In total, thirty-three subscales and 15 single items were identified. Confirmatory factor analyses confirmed the structure, with overall moderate to excellent convergent and divergent validity and internal consistency reliability among the French population [32]. Even if the French population (France) and the French-speaking population in Québec (Canada) have a common ancestral and cultural background, food habits in Québec have been influenced by the North American culture [33]. Nowadays, the eating habits and food-related attitudes of Quebecers are somewhat different from those of the French [33,34,35]. In addition, there are numerous linguistic specificities related to food that differentiate these two nations. In this regard, guidelines recommend that questionnaires be adapted culturally to maintain their content validity at a conceptual level, even if the language is the same [36]. The adaptation and validation of an instrument to consider the cultural and linguistic specificities of a target population ensures that it will be more culturally relevant and easily understood by the people to whom the tool is administered. In this study, we thus aimed to adapt and validate the Well-BFQ©, which was initially developed for the French population (France) for the French-speaking general adult population of Québec, Canada. The structure of the scale was assessed using exploratory factor analysis. We expected to find a similar structure to the one observed for the original version of the questionnaire. The internal consistency and the construct validity of the Well-BFQ© were also assessed. Results indicate that the adapted and validated version of the Well-BFQ© is a suitable instrument which can be used to measure FWB in the French-speaking general adult population of Québec, Canada. More precisely, a two-factor structure was found, mainly FWB related to physical and psychological health and FWB related to symbolic/pleasure of food. ## 2. Materials and Methods The Well-BFQ© underwent a full linguistic adaptation process, including an expert panel adaptation step, a pretest among French-speaking adult subjects from the province of Québec (Canada) and a final proofreading. These three linguistic validation steps were supervised by the authors of the original version of the questionnaire. These steps were followed by a validation study. ## 2.1. Expert Panel Adaptation Three registered dietitians (A.B., L.C., C.T.-G.), who are native speakers of the target language (Québec French) and proficient in the source language (France French), first identified all items that were not suitable to the cultural or linguistic context of Québec (Canada) and suggested alternative formulations. The suggestions were thereafter reviewed by a nutrition researcher (S.L.) with extensive expertise in the development and translation of questionnaires, who decided, after a discussion with the three registered dietitians, if the suggested changes were appropriate. ## 2.2. Pretest After the expert panel adaptation, 30 French-speaking participants (15 men and 15 women), aged between 18 and 65 years old, from the Québec City metropolitan area, were recruited to assess face validity of the adapted questionnaire (MAGE = 47.2, SD = 11.9; $86.7\%$ Caucasians; $63.3\%$ with a university degree). They were recruited from an internal list of people willing to participate in clinical nutrition studies. Each participant provided informed written consent before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the pretest study was approved by the Research Ethics Committee of Université Laval (#2017-045; 2 May 2017). Subjects were asked to complete the questionnaire on a secured online platform (FANI, http://inaf.fsaa.ulaval.ca/fani/) and to comment, using a comment box, each instruction and item of the questionnaire (e.g., ease of completion, comprehension problems, alternative wording for problematic items). The expert panel reviewed the comments and made adjustments to improve the acceptability and comprehension of the problematic instructions/items. ## 2.3. Proofreading To resolve any typing, spelling, or grammatical mistakes, the expert panel reviewed the final version of the questionnaire. They then sent the final proofread version of the questionnaire to the authors of the original questionnaire for approval of the modifications performed. ## 2.4. Validation Study The validation of the Well-BFQ© was achieved within the context of a prior study, which took place between September 2017 and February 2018, and that has been described previously [37]. A total of 213 French-speaking adults (110 men and 103 women) from the Québec City metropolitan area were recruited. Participants had to be aged between 18 and 65 years old and had to perceive that their food habits needed to be improved. Participants were excluded if they met the following exclusion criteria: [1] having dietary behaviors that could significantly affect food choices (eating disorders, vegetarianism), [2] having food allergies or intolerance, [3] working or studying as a nutritionist/registered dietitian, [4] being pregnant or breastfeeding women, or [5] having participated in an intervention study about nutrition in the previous six months. Each participant provided written informed consent before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Research Ethics Committee of Université Laval (#2017-146; 3 July 2017). ## 2.4.1. Questionnaires All questionnaires were completed on the same secured online platform as the pretest (FANI, http://inaf.fsaa.ulaval.ca/fani/). Respondents were first asked to complete the adapted 134-item version of the Well-BFQ©. At the same time, participants also completed other questionnaires presented below. A 29-item questionnaire was used to collect socio-demographic data (e.g., sex, age, ethnicity, marital status, education level, and annual household income). A 26-item questionnaire was used to document participants’ medical antecedents. In this questionnaire, nine specific medical antecedents were directly assessed (i.e., diabetes, cardiovascular diseases, hypertension, dyslipidemia, thyroid gland disorder, gastrointestinal disorders, liver diseases, kidney diseases, and cancer). The last section of the questionnaire asked participants to report other health problems not previously mentioned, if any. Food preoccupation was measured using the following true/false question: “Do you consider yourself to be concerned about food?”. The validated orientation to happiness scale was used to measure individuals’ orientations to happiness through the pursuit of pleasure, engagement, and meaning [38,39]. The scale contains a total of 18 items rated on a 5-point scale, from 1 = ‘very much unlike me’ through 5 = ‘very much like me’. A total score was calculated, with a higher score indicating a higher orientation to happiness. The Health and Taste Attitudes Questionnaire has been developed and validated by Roininen et al. [ 40] to assess consumers’ orientations toward the health and hedonic characteristics of foods. This questionnaire included 38 items rated on a 7-point Likert scale, from 1 = ‘strongly disagree’ through 7 = ‘strongly agree’. Reversed scoring was applied to negatively worded items. The health-related factor labelled “General health interest” (8 items) was used to measure the health orientation of participants, while the taste-related factor named “Pleasure” (6 items) measured the pleasure orientation. A higher score represents a higher orientation toward health or pleasure. The validated 24-item Regulation of Eating Behavior Scale was used to assess the type of motivation used for the regulation of eating behaviors according to the self-determination theory [41,42]. Items included in this questionnaire are rated on a 7-point Likert scale and assess, among others, intrinsic motivation and amotivation. A higher score indicates a higher level for each type of motivation. Three web-based 24-h food recalls were completed using an online application developed by our research team, the R24W, which was specifically developed and validated for the French-speaking population of Québec, Canada [43,44,45]. Data generated by the dietary recalls were used to calculate the Canadian adapted Healthy Eating Index (C-HEI). The C-HEI is a measure of diet quality based on recommendations of the 2007 Canada’s food guide, which was the food guide in effect at the time of the study [40]. It is composed of eight adequacy components, including total fruits and vegetables, whole fruits, dark-green and orange vegetables, grain products, whole-grain products, milk and alternatives, meat and alternatives, and unsaturated fat, and three moderation components, including saturated fat, sodium, and “other foods” that are not part of the foods recommended by Canada’s Food Guide. The total score can range from 0 to 100. A total score of less than 50 was considered as a poor diet, a score of 50 to 80 was considered a diet requiring improvement, and a score of more than 80 was considered a good diet. The Three-Factor Eating Questionnaire (TFEQ) is a 51-item validated questionnaire used for measuring three cognitive and behavioral components associated with eating, namely cognitive dietary restraint, disinhibition, and susceptibility to hunger [46]. The first concept refers to cognitive dietary restraint (Restraint; on a scale of 0 to 21 points), i.e., conscious control of food intake with concerns about body shape and weight. The second refers to disinhibition (Disinhibition; on a scale of 0 to 16 points), i.e., overconsumption of food in response to a variety of stimuli associated with a loss of control over food intake. The third concept is susceptibility to hunger (Hunger; on a scale of 0 to 14 points), i.e., food intake or eating in response to feelings and subjective perceptions of hunger. This questionnaire is divided into two parts, the first part consisting of 36 true/false questions and the remaining 15 items rated on 4- or 5-point Likert scales. A higher score indicates a higher level for each eating behavior evaluated. According to standardized procedures, the research team measured height to the nearest millimeter with a stadiometer and body weight to the nearest 0.1 kg on a calibrated balance [47]. Body mass index (BMI; kg/m2) was then calculated. ## 2.4.2. Statistical Analyses The structure of the scale was assessed using exploratory factor analysis (EFA), with pairwise treatment for missing cases. As the participant to item ratio was below 5:1, the ULS method was used [48]. EFA was chosen instead of principal component analysis (PCA) or confirmatory factor analysis (CFA) because we wanted to explore the possible underlying factor structure in our target population without imposing any preconceived structure on the outcome [49]. To assess the number of factors to retain for the structure, the eigenvalue-greater-than-one rule, the analysis of the variance explained, and the scree plot were used. More specifically, only factors with an eigenvalue of 1 or greater and with a variance of more than $5\%$ were retained [50]. According to the scree plot, the “elbow” of the graph where the eigenvalues seem to level off was found and factors to the left of this point were retained as significant [50]. Items with a contribution ≤0.4 on all factors, as well as items with a contribution > 0.4 on at least two factors, were eliminated. Once this first step was completed, EFA were performed again based on the remaining items, and adjustments were made in successive iterations to achieve the final structure of the questionnaire. Internal consistency was assessed with Cronbach’s α coefficients. Once the final structure of the questionnaire was defined, a total score, as well as a score for each factor, were calculated by adding up the scores of each scale’s items. Each item was rated on a 5-point scale from 0 = ‘never’ to 4 = ‘always’ and negative items were reverse-coded. All scores were linearly transformed to be presented on a scale from 0 to 100. Higher scores indicated higher FWB. The score was not calculated if participants completed less than $66\%$ of items included in the scale/subscale. Floor or ceiling effects were considered to be present if more than $15\%$ of participants achieved the lowest or highest possible scale score, respectively [51]. If floor or ceiling effects were present, it was likely that extreme items were missing in the lower or upper end of the scale, indicating limited content validity for our population [51]. To assess construct validity, Pearson’s correlation analyses were conducted to investigate the association between Well-BFQ© scores and various psychological and eating-related variables, namely happiness orientation, pleasure and health orientations toward food, motivations for regulating eating behaviors (intrinsic motivation and amotivation), diet quality (C-HEI), as well as eating behaviors (cognitive dietary restraint, disinhibition, susceptibility to hunger), which are expected to be associated with the concept of FWB. Happiness has been identified as a central component of well-being in previous research [52]. In addition, since health and pleasure are the two main factors of FWB identified in the conceptual model of Guillemin et al. [ 32] used to develop the Well-BFQ©, health and pleasure orientations toward food should also be positively associated with the Well-BFQ© scores. With regard to motivation types, according to the self-determination theory, FWB should be positively associated with intrinsic motivation (which refers to engaging in an activity for its own sake and experience of pleasure and satisfaction derived from participation) and negatively associated with amotivation (which pertains to the lack of intentionality and therefore refers to the relative absence of motivation) to regulate eating behaviors [53]. Finally, well-being has also been previously associated with better diet quality (e.g., higher C-HEI, higher intake of fruits and vegetables), as well as with healthy eating behaviors (e.g., lower disinhibition and susceptibility to hunger) [54,55,56]. The classification of Cohen, i.e., a correlation coefficient of 0.1 being classified as small, of 0.3 being classified as moderate and of 0.5 being classified as strong, was used to interpret effect size [57]. A known-group approach was also used to measure construct validity. Differences in Well-BFQ© scores between subgroups were assessed using the Student’s t-test procedure (for two subgroups; variables: sex (men/women), medical antecedents (yes/no) and preoccupation toward food (yes/no)) and the generalized linear model (GLM) procedure (for more than two subgroups; variables: age and BMI). According to the literature related to well-being, FWB should be negatively affected by food preoccupation [58], BMI [59,60,61], and the presence of medical antecedents [62], and positively associated with age [63] and not affected by sex [64,65]. All statistical analyses were performed using the SAS software, version 9.4 (SAS Institute Inc., Cary, NC, USA). A $p \leq 0.05$ was considered significant. ## 3.1.1. Expert Panel The expert panel identified one title section, two section instructions, and 24 items that were not suitable to the cultural or linguistic context of our population. Problems identified were due to [1] expressions rarely used in Québec, [2] expressions with different meanings in France and in Québec and [3] wording that may not be clear for the target population. The main changes to the questionnaire made by the expert panel are listed in Table 1. ## 3.1.2. Pretest Face validity of the scale was assessed by pretest participants who formulated comments about the acceptability and comprehension of the scale. Based on the participants’ comments, three items were modified. The problems were due to words that were confusing and/or not clear for our population. We also added some explanations to certain items that were unclear for the participants. The main changes made following the pretest are also summarized in Table 1. ## 3.1.3. Proofreading The expert panel proofread the final version of the questionnaire. No typing, spelling, or grammatical mistakes were found. ## 3.2. Validation Study Of the 213 participants, ten (seven men and three women) were excluded from the analyses since they dropped out before the completion of the Well-BFQ©. Table 2 shows descriptive characteristics of participants. Participants included in this study were men and women (women: $49.3\%$; men: $50.7\%$), with a mean age of 34.9 (SD = 13.5) and a mean BMI of 26.2 kg/m2 (SD = 5.5), mostly Caucasians ($88.2\%$) and singles ($53.2\%$). The majority of participants had a household income of less than $60,000 ($53.7\%$) and a university degree ($54.2\%$). Mean C-HEI score was 54.1 (SD = 13.6), indicating a diet requiring improvement. Mean time for completion of this 134-item questionnaire was 20 min (SD = 15). A total of 154 participants ($76\%$) responded to all items of the questionnaires. Thirty-nine items showed one missing data ($0.49\%$), 12 items showed two missing data ($0.99\%$), three items showed three missing data ($1.48\%$), and one item showed four missing data ($1.97\%$). ## 3.2.1. Exploratory Factor Analysis The EFA was performed on the 134 questionnaire items. The result from the Kaiser–Meyer–Olkin test of sampling adequacy was more than 0.5 (measure of sample adequacy = 0.57), a value that is considered suitable for factor analysis [66,67,68]. The significance of Bartlett’s test of sphericity was also considered suitable for factor analysis (khi-2 = 18694.5833, $p \leq 0.0001$) [66,67]. These two results justified the use of an EFA given the common variance of the set of items. Thirty-one factors were retained with eigenvalues greater than 1. However, the explained percentage variance showed a two-factor structure with a variance in the data of more than $5\%$ for only two factors (O’Rourke and Hatcher, 2013), accounting, respectively, for $16.5\%$ and $9.7\%$ of the variance. In addition, according to the scree plot, the “elbow” of the graph identified only two significant factors. It was therefore decided to use a two-factor solution for the structure of the questionnaire. We made a factor rotation to help the interpretation of the factor structure. In order to decide between an orthogonal and an oblique rotation, an oblique (promax) rotation was first requested to obtain a correlation matrix. Because the correlation between the two factors ($r = 0.27$) was below 0.32 [67], we decided to use an orthogonal (varimax) rotation, which was more suitable considering the structure of the questionnaire. Using a cut-off value of 0.4 for factor loading, we removed 75 items from the questionnaire as they loaded too poorly on both factors. No retained item loaded simultaneously on the two factors. The final scale consisted of two subscales: one related to the “Physical and psychological health” (27 items; factor 1) and the other related to the “Symbolic/pleasure of food” (32 items; factor 2). The first factor refers to the impact that psychological and physical health can have on FWB, while the second factor refers to the symbolic value of food and pleasure that we can derive from it. Factor loadings and items are presented in Table 3. For these two factors, none of the participants had the highest or the lowest score, suggesting no floor and ceiling effects (score range: factor 1: 7.4 to 99.1; factor 2: 10.9 to 93.8). The same result was observed for the total score (score range: 10.6 to 82.6). These results indicate that scales used in this questionnaire are sensitive in capturing the variation in FWB in our target population. No participant completed less than $66\%$ of the items included in each scale/subscale, allowing for scale/subscale scoring for each participant. ## 3.2.2. Internal Consistency After removing items that loaded poorly on both factors, internal consistency was adequate, with Cronbach’s α coefficient of 0.92 for factor 1 (i.e., FWB-related physical and psychological health), 0.93 for factor 2 (i.e., FWB-related to the symbolic/pleasure of food), and 0.94 for all the retained items (see Table 3). ## 3.2.3. Construct Validity Table 4 shows Pearson correlations between Well-BFQ© scores and psychological and eating-related variables. The total FWB score, as well as the two subscales, were positively associated with happiness orientation (small to moderate correlations; $r = 0.29$ to 0.39, $p \leq 0.0001$), health orientation toward food (moderate correlations; $r = 0.32$ to 0.40, $p \leq 0.0001$), intrinsic motivation for regulating eating behaviors (moderate to strong correlations; $r = 0.44$ to 0.59, $p \leq 0.0001$), and C-HEI (small to moderate correlations; $r = 0.19$ to 0.30, p ≤ 0.007), and were inversely associated with amotivation for regulating eating behaviors (small correlations; r = −0.18 to −0.23, p ≤ 0.01). The FWB total score and the FWB related to symbolic/pleasure subscale score were also positively associated with pleasure orientation toward food (small to moderate correlations; $r = 0.29$, $p \leq 0.0001$ and $r = 0.38$, $p \leq 0.0001$, respectively). For eating behaviors, total FWB score and the FWB related to physical and psychological health subscale score were both inversely associated with susceptibility to hunger (small to moderate correlations; r = −0.16, $$p \leq 0.03$$ and r = −0.30, $p \leq 0.0001$, respectively), whereas only the FWB related to physical and psychological health subscale score was negatively associated with disinhibition (small correlation; r = −0.24, $$p \leq 0.0005$$). FWB total and subscale scores were similar between age groups (Table 5). However, FWB related to physical and psychological health subscale score was higher in men than in women ($$p \leq 0.02$$). The total FWB score and the FWB related to physical and psychological health subscale score were both significantly lower in participants with obesity than in normal weight and overweight participants ($$p \leq 0.003$$ and $p \leq 0.0001$, respectively). Furthermore, those with medical antecedents reported lower FWB on the physical and psychological health subscale than those without personal medical antecedents ($$p \leq 0.01$$). In addition, participants who considered themselves as being preoccupied with food tended to report a lower total FWB score and FWB related to physical and psychological health subscale score than those not preoccupied about food ($$p \leq 0.08$$ and $$p \leq 0.06$$, respectively). ## 4. Discussion The Well-BFQ© is a questionnaire that has been developed in France to comprehensively measure FWB [32]. Despite the fact that the same language (i.e., French) is spoken in France and in Québec, cultural and linguistic differences exist between these two populations [34,69]. This supports the importance of adapting and validating the Well-BFQ© before its use in Québec, Canada. In this study, we first modified the Well-BFQ© to obtain a version that is culturally relevant and easily understood by our adult population, which was followed by a validation study. Results indicate that the adapted version of the Well-BFQ© has good psychometric properties, and thus that this is a suitable instrument which can be used to measure FWB in the French-speaking adult population of Québec, Canada. We obtained a different questionnaire structure from the one observed in the original validation study. In fact, Guillemin et al. [ 32] reported a 7-factor structure in the French population, namely “immediate pleasure benefits”, “immediate security benefits”, “immediate relaxation benefits”, “direct digestion and satiety benefits”, “direct energy and psychology benefits”, “well-being food behaviors”, and “deferred health benefits”. In the present validation study, however, we observed a two-factor structure related to FWB: “Physical and psychological health” and “Symbolic/pleasure of food”. It is worth mentioning that different analyses were conducted in these two validation studies (principal component analysis in the previous study and exploratory factor analysis in the present study), which may partly explain the differences observed in the structure of the questionnaire. However, this two-factor structure is in concordance with the two main factors of FWB that have been previously identified in the conceptual model of Guillemin et al. [ 32], and which served as the basis for the development of the Well-BFQ©. In fact, their analysis using focus groups with 198 French subjects indicated that FWB articulates around two central domains that are health and pleasure. These results are also in line with those of Ares et al. [ 52], suggesting that the influence of food on well-being is strongly associated with physical and psychological health as well as with pleasure. In our study, the “Physical and psychological health” factor contains several items related to physical health (e.g., back, joints, bones, arteries, immune system), which provides a fairly comprehensive measure of this concept. Although they are fewer in number, it also contains items documenting the impact of eating habits on mental health, such as how eating habits impact individuals’ morale and feelings. Interestingly, this factor allows for documenting immediate and direct health benefits of eating habits (e.g., energy throughout the day, digestion, breathing), but also for deferred health benefits (e.g., life expectancy, bone strength, avoidance of cholesterol- and diabetes-related problems). For the “Symbolic/pleasure of food” factor, some items directly document the pleasure that we can derive from food (e.g., going shopping, preparing meals, discovering new foods), while some others are related to values that people may have about foods (e.g., eating local food, fresh food, seasonal food, organic food, and food of known origin/provenance). Overall, the Well-FBQ© presented good psychometric properties in our population. In fact, internal consistency for each of the two factors is considered excellent (≥0.92), indicating that items in each subscale are strongly correlated and support the structure of the questionnaire. Internal consistency for the total score was also excellent, which means that the questionnaire measures a single overall concept and can be used to measure a total score of FWB. To evaluate the construct validity of the questionnaire, we investigated the associations between FWB scores and some psychological and eating-related outcomes that are expected to be associated with the concept of well-being. First, we found moderate to strong associations between FWB scores and intrinsic motivation to regulate eating behaviors, with participants with greater FWB being characterized by a higher level of intrinsic motivation. These results are in line with the self-determination theory that suggested that well-being is increased when individuals present intrinsic motivation for regulating their eating behaviors [53]. Intrinsic motivation is defined as the doing of an activity for its own sake and experience of pleasure and satisfaction derived from participation rather than from expectations about other consequences [53]. Thus, participants with this type of motivation seem more likely to make food choices for their own satisfaction and pleasure, which may in turn increase the FWB experienced. Higher FWB scores were also associated with a lower amotivation to regulate eating behaviors, i.e., a lack of intentionality due to the relative absence of motivation. This lack of motivation can result in individuals not changing eating behaviors, even if they are not comfortable with them. In addition, we found small to moderate associations between FWB and the health and pleasure orientations toward food. These results are in line with the structure of the scale, with the two main factors of FWB being health and pleasure. As expected, the physical/psychological health subscale was positively associated with the health orientation toward food. However, the symbolic/pleasure subscale was positively associated with both health and pleasure orientations toward food. These results may be explained by the fact that the symbolic/pleasure subscale has many items related to the importance of pleasure, but also items related to the symbolic of food, including some that may be associated with better health (e.g., importance to eat fresh, local, organic and seasonal foods). A higher FWB was also slightly to moderately associated with a greater orientation to happiness, which is known as a central component of well-being [70]. Results also showed that higher FWB scores were slightly to moderately associated with a better diet quality, as measured by the C-HEI. Research suggests that the levels of well-being can influence our responses to food [71]. However, foods may also affect the consumer’s perceived well-being [52,54,56]. In a study by Ares et al. [ 54], participants reported that foods that have a favorable impact on well-being were those recognized as being healthy, such as fruits and vegetables, while fatty, fried, and sugary food were perceived as reducing well-being. In addition, Holder [56] reviewed the contribution of food consumption to well-being. This paper suggested that healthy eating, particularly the intake of fruits and vegetables, is associated with higher levels of well-being, and that an increased fruit and vegetable intake results in increased well-being in a dose–response fashion. Taken together, these results are in line with ours, suggesting a link between well-being and diet quality. In addition, in our study, small to moderate associations have been observed between FWB and eating behaviors, suggesting that FWB is linked to more healthy eating behaviors (lower levels of susceptibility to hunger and disinhibition). This is consistent with findings from Provencher et al. [ 72], which showed a significant negative relationship between psychological well-being, and disinhibition, susceptibility to hunger, and all their subscales in a population of postmenopausal women using the same questionnaire as we did (Three-Factor Eating Questionnaire). Overall, these results are supportive of a good construct validity of the scale, since associations with psychological and eating variables occurred in expected directions. Regarding subgroups analyses, the total score and subscale scores were similar between age groups. Comparison with other studies is difficult, considering that research about the concept of FWB is still at a nascent stage. A U-shape association is generally observed between well-being and age groups [63]; however, the relatively narrow age range limit used in the present study may have impeded the observation of this association. Results were also similar between men and women, except for FWB related to physical and psychological health, with men reporting higher score than women. These results are in line with the literature, suggesting no clear sex differences related to the general concept of well-being [64,65]. We also found that those with medical antecedents reported lower FWB related to physical and psychological health subscale score than those without personal medical antecedents. These results are in accordance with those of Stewart et al. [ 62], which showed adverse effects of chronic diseases on most aspects of functioning and well-being. Total FWB and FWB related to physical and psychological health scores were significantly lower for participants with obesity compared to normal weight and overweight participants. In addition, participants who considered themselves as being preoccupied with food also tended to report lower scores for these two scales. A culture based on worry and preoccupation with weight and food can have detrimental effects on well-being [73]. Concern about weight may lead to preoccupation with weight gain and appearance, and behaviors such as dieting [58,74], which may thereafter impede FWB. As previously suggested, obsessive thoughts about food and eating may also lead to a lower well-being [58]. The major strengths of the study include the rigorous adaptation process of the questionnaire for the target population, which was based on a three-step method proposed by the developer of the Well-BFQ©. Another strength was that we recruited a well-distributed sample in terms of sex, age, and BMI. However, this study also has some limitations. The sample size may be viewed as slightly small. Generally, EFA procedures require fairly large sample sizes. However, although different authors give different guidelines, it is well accepted that a minimum of 100 participants is required [75]. Therefore, considering these guidelines, our sample size of more than 200 participants is adequate. Nevertheless, a confirmatory factor analysis should be performed to confirm the structure of the questionnaire in our population in the next study. Additionally, a test–retest should be done in order to evaluate whether this questionnaire could be reliably replicated more than once in the same situation and population. ## 5. Conclusions The adapted and validated version of the Well-BFQ© was found to be a suitable instrument, which can be used to measure FWB in the French-speaking general adult population of Québec, Canada. Results showed a two-factor structure, namely [1] FWB related to physical and psychological health and [2] FWB related to symbolic/pleasure of food. Internal consistency was adequate, and the total food well-being score, as well as the two subscale scores, were associated with psychological and eating-related variables in expected directions, highlighting the good construct validity of the scale. The fact that recent Canadian dietary guidelines now offer a more comprehensive view of healthy eating, including recommendations not only about healthy food choices but also about a healthy relation with food, underlines the relevance of validating tools such as the Well-BFQ© in the Canadian population. Therefore, the validation of the Well-BFQ© sets an exciting path for researchers, allowing for the combination of diet quality and this FWB tool to comprehensively measure the concept of healthy eating in different segments of our population. 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--- title: Mid-Luteal Progesterone Is Inversely Associated with Premenstrual Food Cravings authors: - Ajna Hamidovic - Fatimata Soumare - Aamina Naveed - John Davis journal: Nutrients year: 2023 pmcid: PMC10005553 doi: 10.3390/nu15051097 license: CC BY 4.0 --- # Mid-Luteal Progesterone Is Inversely Associated with Premenstrual Food Cravings ## Abstract It is not clear whether progesterone and estradiol associate with premenstrual food cravings, which significantly contribute to cardiometabolic adverse effects associated with obesity. We sought to investigate this question in the present study based on the prior literature showing a protective effect of progesterone on drug craving and extensive neurobiological overlaps between food and drug cravings. We enrolled 37 non-illicit drug- or medication-using women in the study to provide daily ratings of premenstrual food cravings and other symptoms across two-three menstrual cycles, based on which we classified them as premenstrual dysphoric disorder (PMDD) or control participants. In addition, the participants provided blood samples at eight clinic visits across the menstrual cycle. We aligned their mid-luteal progesterone and estradiol using a validated method which relies upon the peak serum luteinizing hormone and analyzed estradiol and progesterone using ultraperformance liquid chromatography tandem mass spectrometry. Hierarchical modeling, adjusted for BMI, showed a significant inverse effect of progesterone ($$p \leq 0.038$$) but no effect of estradiol on premenstrual food cravings. The association was not unique to PMDD or control participants. Results of research to date in humans and rodents showing that progesterone has dampening effects on the salience of the reinforcer translate to premenstrual food cravings. ## 1. Introduction Premenstrual food craving is a widespread problem in reproductive-age women [1,2,3], confirmed in both retrospective [4,5,6] and prospective [7,8,9] studies. They significantly contribute to cardiometabolic adverse effects associated with obesity. For example, self-reported premenstrual food cravings were found to be higher in the abdominally obese (defined as having waist circumference >80 cm) relative to the abdominally lean women [10], and the Study of Women’s Health Across the Nation (SWAN) identified a positive correlation between self-reported premenstrual appetite increases and high sensitivity C-reactive protein (hs-CRP) [11]. Despite the significance of premenstrual food cravings to health in reproductive age women, no study to date examined a potential effect of sex hormones (progesterone and estradiol) at their peak in the luteal (premenstrual) phase. We sought to investigate this question in the present study based on the prior literature showing a significant effect of progesterone on appetitive behaviors. Specifically, evaluations across several addictive substances show that drug craving and intake are associated with low levels of progesterone. During early abstinence, postmenopausal females with alcohol use disorder report higher alcohol cravings than premenopausal females, and in postmenopausal women, higher baseline progesterone levels correlate with lower alcohol cravings [12]. High progesterone during the mid-luteal phase of the menstrual cycle is associated with decreased stress- and drug cue-induced cocaine cravings [13]. High levels of endogenous progesterone attenuate subjective responses to cocaine cues that are preceded by a stressor (i.e., yohimbine) [14]. The administration of twice daily micronized progesterone in a double-blind, placebo-controlled manner increased smoking cessation in women during an 8 week postnatal period [15], and increases in progesterone level in non-pregnant premenopausal women is associated with increased odds for being abstinent within each week of treatment [16]. The study of the menstrual cycle (Figure 1) is complicated by its natural duration variability [17], which makes the scheduling of the menstrual cycle visits difficult. Data from the study visits should correspond to the six menstrual cycle subphases that capture distinct changes in the circulating sex hormone levels: [1] early follicular, [2] mid-follicular, [3] periovulatory phase, [4] early luteal, [5] mid-luteal, and [6] late luteal [18]. An accurate and validated method for dealing with this complexity was developed in the BioCycle study [18] and it involves collecting data from eight clinic visits at the estimated menstrual cycle subphases, following which a realignment of the data is carried out based on the periovulatory LH peak. We implemented the Biocycle protocol in the present study to accurately stage the mid-follicular and mid-luteal subphases of the menstrual cycle, and determine the effects of the sex hormones progesterone and estradiol on premenstrual food cravings. Importantly, we analyzed progesterone and estradiol using mass spectrometry, which is preferable over the immunoassay technology because it more precisely recognizes similar structures [19,20,21]. Based on the line of research that progesterone inversely associates with drug craving reviewed above and on the significant neurocircuit overlaps between drug and food cravings [22], we hypothesized that progesterone would be associated with a premenstrual increase in food cravings, and that this effect would be specific to the mid-luteal but not the mid-follicular subphase. Second, we examined whether this hypothesized association varies according to group (i.e., premenstrual dysphoric disorder vs. controls). ## 2.1. Study Design Premenstrual Hormonal and Affective State Evaluation (PHASE) is a longitudinal study designed to increase our understanding of normal menstrual cycle physiology and its dysregulated states. PHASE is a registered clinicaltrials.gov study (NCT03862469). It enrolls women with regular menstrual cycles to chart their symptoms using the Daily Record of Severity of Problems (DRSP) [23] over two to three menstrual cycles. In the last menstrual cycle of the study, the participants complete blood and salivary sample collection visits at eight different times during the menstrual cycle, as well as psychosocial stress testing in the luteal phase and urinary LH testing. ## 2.2. Study Sample We recruited reproductive age women (ages of 18 and 35), with regular menstrual cycles lasting 21 to 35 days [24], from the general population using flyers, word-of-mouth referrals, and electronic media (Facebook, Instagram, and Craigslist). Study participants first completed an online survey, following which they were scheduled to complete an in-person screening. Before any collection of data, they signed a consent form, approved by the University of Illinois Human Research Protection Office. Study exclusion criteria were: (a) lifetime DSM-5 Axis I disorder, except anxiety and depression (based on the Structured Clinical Interview for DSM Disorders (SCID)); (b) current (i.e., within the past 12 months) DSM-5 Major Depressive Disorder or an anxiety disorder (based on SCID); (c) positive urine drug screen test; (d) breath alcohol concentration >$0.00\%$; (e) Alcohol Use Disorders Identification Test (AUDIT) score >7; (e) self-reported smoker or carbon monoxide concentration ≥6 ppm; (f) irregular menstrual cycle; (g) current pregnancy (urine test-verified), lactation, or planning to become pregnant; (h) moderate or high suicide risk; (i) Shipley IQ (vocabulary standard score) <80; (j) prescription medications; and (k) hormonal contraception. ## 2.3. Study Procedures Study participants conducted urinary self-testing of the luteinizing hormone (LH) using Clearblue urine tests. They collected the first morning urine on a menstrual cycle day as specified in the Clearblue manual and continued the testing for 10–20 days, depending on whether the peak levels were reached. In order to read the results, study participants took a photo of the strip and uploaded it to the Clearblue phone app, which read the test result and determined LH as “low”, “high”, or “peak”. In the present study, this self-testing was implemented to guide the scheduling of study visits in the last menstrual cycle (last paragraph in this section), not for fertility purposes. On a daily basis, study participants uploaded the result screenshot of the Clearblue app into REDCap. Adherence to the daily urine LH testing procedure is critical as the capturing of the short-lived peak LH surge is based on the daily reading of LH levels [25]. In the present study, we assessed adherence to ovulation testing in real-time. On a daily basis, the study coordinator completed a checklist, ensuring compliance with the testing. In the event that a participant missed a time-point, she was contacted and asked to complete the procedure in a timely manner. During the last menstrual cycle while enrolled in the study, the participants were scheduled to come to the clinic for eight blood draws/saliva collection visits, as described in detail in Hamidovic et al. [ 26]. In the proposed study, we realigned data according to the protocol from Mumford et al. [ 18] and analyzed progesterone and estradiol concentrations from the mid-follicular and mid-luteal subphases in a hierarchical linear regression model, as described in detail in Section 2.6 (the Data Analysis section). ## 2.4. Diagnosis The Diagnostic and Statistical Manual of Mental Disorders 5 (DSM-5) specifies the possible symptoms of PMDD as: [1] affective lability (mood swings); [2] irritability or anger; [3] depressed mood; [4] anxiety or tension; [5] decreased interest in usual activities; [6] difficulty concentrating; [7] a sense of being overwhelmed or out of control; [8] change in appetite, overeating, or specific food cravings; [9] hypersomnia or insomnia; [10] fatigue; and [11] one physical symptom (for example, breast tenderness). PMDD diagnosis requires the presence of at least one affective symptom (symptoms 1–4) to reach the total of 5 required symptoms, which must be confirmed in a prospective manner for at least 2 menstrual cycles. In addition, the symptoms must be associated with clinically significant distress or interference with work, school, usual social activities, or relationships with others. In accordance with DSM-5 criteria, PMDD diagnosis in the proposed study was assessed prospectively by evaluating the participants’ daily symptom ratings using the DRSP scale [23] during two to three menstrual cycles. PMDD diagnosis was defined as a $30\%$ or greater increase in 5 or more symptoms, one of which had to be affective, between the luteal (day −7 to −1) and follicular (day 6 to 12) days relative to the range of the scale of each individual participant across the entire menstrual cycle [27]. They also had to have at least a $30\%$ or greater increase in interference with daily activities due to these symptoms. PMDD participants defined using these criteria were found to have an over-expression of ESC/E(Z) complex genes [27], blunted endoplasmic reticulum stress response [28], and symptom induction upon exogenous sex hormone administration [29], which is seemingly related to specific gene induction and includes Mtf2—a core gene of the ESC/E(Z) complex [30]. ## Food Cravings Food Cravings. The Daily Record of Severity of Problems (DRSP) [23] is a validated questionnaire, which measures 24 symptoms of PMDD. The symptoms are rated on a scale of 1 (not at all) to 6 (extreme). The present analysis evaluated ratings of food cravings (“Had cravings for specific foods”). Once enrolled, study participants notified the research coordinator when their next menstrual cycle started. Upon notification, the research coordinator set up the DRSP surveys to be sent out daily. Study participants received a new survey link every day and were asked to complete it between 7 PM and midnight. They were also asked to use the day’s link and not any previous links to minimize retrospective reporting. In case a participant missed more than two DRSP entries in a row, or four or more in a month, the research coordinator contacted the participant to complete the survey daily. Estradiol and Progesterone. Estradiol and progesterone analyses were performed by the Mass Spectrometry Core in the Research Resources Center at the University of Illinois at Chicago. Analyses were conducted using an AB SCIEX 6500 QTRAP mass spectrometer coupled with Agilent 1290 UPLC system. All samples were eluted from an Agilent Poroshell 120 EC-C18 2.7 µm column (2.1 × 100 mm2) with a flow rate of 200 µL/min. The column compartment was kept at 50 °C. The gradient began with a $95\%$ mobile phase A ($0.1\%$ formic acid in H2O) for 2 min and was followed by a linear gradient increase in the mobile phase B ($0.1\%$ formic acid in MeOH) from $5\%$ to $80\%$ in 2 min and $80\%$ to $90\%$ over 2 min and kept at $90\%$ mobile phase B for 7 min, then re-equilibrated back to the initial condition ($95\%$ A) for 3 min, resulting in a total separation time of 16 min. Mass spectrometry experiments were performed via MRM scan using electrospray ionization in positive ion mode with an ESI spray voltage of 4.5 kV and source temperature of 500 °C. The limit of detection of estradiol and progesterone ranged from 0.05–0.25 pg and 0.5–1.5 pg, respectively. The limit of quantification of estradiol and progesterone ranged from 0.2–0.5 pg and 1.5–2.5 pg, respectively. Each calibration standard’s accuracy was within the acceptable range of $15\%$. The recovery of estradiol and progesterone were assessed for quality control samples at 3 levels (low, mid, and high) during the initial method development. For estradiol, recovery of 1 pg, 4 pg, and 8 pg was $99.3\%$, $98.6\%$, and $99.8\%$, respectively. For progesterone, the recovery of 7.5 pg, 20 pg, and 40 pg was $93.5\%$, $110.1\%$, and $95.8\%$, respectively. Luteinizing Hormone. Serum luteinizing hormone analyses were performed by ARUP Laboratories, as described in Hamidovic et al. [ 26]. The Beck Depression Inventory (BDI). The BDI [31] is a 21-item, self-report rating inventory that measures the characteristic attitudes and symptoms of depression. Internal consistency for the BDI ranges from 0.73 to 0.92 with a mean of 0.86. [ 32]. The BDI demonstrates high internal consistency, with alpha coefficients of 0.86 and 0.81 for psychiatric and non-psychiatric populations, respectively [33]. Study participants completed the BDI at screening, and the purpose was to ensure that our procedure for screening out participants with current MDD was efficient. ## 2.6. Data Analysis All analyses were performed in R software (version 4.0.2) [34]. To derive one summary score of premenstrual food cravings, we calculated the degree to which the DRSP food craving symptom (Section 2.5) demonstrated an elevation in the pre-menstruum (days −6 to −1) relative to post-menstruum (days +5 to +10). The average post-menstrual score was subtracted from the average pre-menstrual score and divided by participant-specific variance for the food cravings symptom across all days of the menstrual cycle. This essentially yielded an “effect size” for each woman for food cravings [7]. The effect size reflects the % increase from the postmenstrual to the premenstrual period. For example, an effect size of 0.5 indicates that there is a $50\%$ increase in food cravings from postmenstrual cycle days +5 to +10 to the premenstrual days −6 to −1. We first realigned the study data to ensure that all women were reclassified to the same subphase. Since the study visits occurred at predetermined timepoints, once serum LH surge was determined post hoc, the visits were realigned, and the data were standardized according to the algorithm described in detail in Mumford et al. [ 18], which had been completed by our investigative team previously and is described in Hamidovic et al. [ 26]. We analyzed the mid-luteal subphase progesterone and estradiol concentrations from the realigned dataset, which reflects the peak luteal phase progesterone and estradiol, and the mid-follicular concentrations of these sex hormones, as this subphase approximates the time of menstrual bleeding cessation. We only analyzed participants with an ovulatory menstrual cycle, defined as luteal phase progesterone ≥5 ng/mL. Next, we assessed the distribution of all study variables using the “shapiro.test” function. The tests of normality indicated that the distributions of mid-follicular progesterone and mid-follicular estradiol were not normal. Hence, these values were log-transformed, which normalized the data, as well as all the remaining variables. Next, we completed the hierarchical linear regression analysis. As our hypothesis was that mid-luteal progesterone would be inversely associated with premenstrual food cravings, we entered mid-luteal progesterone as step 1. We next entered mid-follicular progesterone in step 2, followed by mid-follicular estradiol and mid-luteal estradiol in step 3. Our goal was to first fit ovarian hormones, thereby characterizing endocrine menstrual cycle events (as related to progesterone and estradiol) and further build on that physiological milieu. We corrected the model for BMI (coded as underweight/normal weight and overweight/obese) in step 4 and entered the interaction between mid-luteal progesterone and diagnosis (PMDD vs. control) in step 5. Last, we visually inspected the distribution of model residuals of all the 5 steps using qq plots, and we formally tested their distribution using the “shapiro.test” function in R. ## 3.1. Study Participants Thirty-seven women completed the study, of whom five did not have an ovulatory cycle. As our analytical approach controlled for ovulation, we completed the analysis on the remaining 32 women. Table 1 lists their demographic, anthropomorphic, and psychological characteristics. The PMDD ($$n = 13$$) and control ($$n = 19$$) participants did not differ on any characteristics listed in Table 1. The short-lived peak LH was captured in 26 out of the 32 (~$81\%$) participants. This rate is similar to the finding by Mumford et al. [ 18], which validated the study methodology we implemented in the present study, as well as in our previous publication [26]. The mean and standard deviation periovulatory serum luteinizing hormone values in the 25 participants were 34.55 (14.09) IU/L. ## 3.2. Relationship between Sex Hormones and Food Cravings We present mid-follicular and mid-luteal progesterone in Figure 2a, estradiol in Figure 2b, and the detailed results of the hierarchical analysis in Table 2. Step 1 of the hierarchical linear regression showed that mid-luteal progesterone (β = −0.487; $$p \leq 0.042$$) accounted for $14.9\%$ of the variation in premenstrual food cravings (multiple R2 = 0.1491). Adjusting the model for mid-follicular progesterone in step 2 showed that mid-follicular progesterone is marginally associated with premenstrual food cravings (β = 0.177; $$p \leq 0.053$$), while mid-luteal progesterone remained significant (β = −0.499; $$p \leq 0.041$$). The model accounted for a greater variation (multiple R2: $25.0\%$). Introducing mid-follicular and mid-luteal estradiol in step 3 improved variation by $15\%$ (multiple R2 = $40.0\%$), with mid-luteal progesterone remaining significant (β = −0.599; $$p \leq 0.020$$) and mid-luteal estradiol reaching significance (β = 0.495; $$p \leq 0.038$$). In step 4, the addition of BMI did not increase the variation in the outcome and mid-luteal progesterone remained significant (β = −0.692; $$p \leq 0.038$$), while none of the other predictors, including mid-luteal estradiol, showed significance. In the final model, which included the interaction between mid-luteal progesterone and diagnosis, multiple R2 was $52.5\%$ and mid-luteal progesterone remained significant (β = −1.253; $$p \leq 0.006$$). The diagnosis by mid-luteal progesterone was not statistically significant. In the final model, mid-luteal estradiol was also significant (β = 0.553; $$p \leq 0.043$$). Results of the Shapiro–Wilk normality tests on individual model residuals were not significant for each of the five steps, and the visual evaluations of individual model qq plots were suggestive of normal residual distributions. Supplementary Figure S1a–e shows the individual qq plots for each of the steps. In Figure 3a,b, we show associations between premenstrual food cravings and mid-luteal progesterone and mid-luteal estradiol, respectively. ## 4. Discussion The results of the present study demonstrate a robust inverse relationship between circulating mid-luteal progesterone and premenstrual food cravings. This relationship remained significant following the adjustment for mid-follicular progesterone, mid-follicular and mid-luteal estradiol, and BMI. The association does not appear to be unique in PMDD study participants. Although we observed a positive relationship between estradiol and premenstrual food cravings, the association did not remain significant following the adjustment for BMI. The results of research to date in humans and rodents show that progesterone has protective effects and may dampen vulnerability to addiction [35,36]. For example, Mello [37] observed that women in the follicular phase (when progesterone is low) experience greater drug cravings, while DeVito et al. [ 38] demonstrated that women show diminished subjective effects (“high”, “feel good”, and “want more”) to intravenous nicotine in the luteal (when progesterone is high) relative to the follicular phase of menstrual cycle. Several research domains demonstrate neurobiological overlaps between drug and food cravings. Craving in the laboratory is provoked via cue presentation, which activates the prefrontal cortical regions to drive the process through functional connections with the striatum [39]. The activation in the prefrontal cortical regions following cue presentation correlates with cravings for drugs or food [39,40]. Pharmacological treatments can target both drug and food craving. Buproprion/naltrexone combination—an FDA-approved adjunct medication to a reduced-calorie diet and increased physical activity for chronic weight management in obese adults—produces a significant reduction in food cravings [41,42]. Buproprion, an FDA-approved medication for smoking cessation, reduces nicotine cravings [43,44], while naltrexone, an FDA-approved medication for alcohol use disorder and opioid use disorder, reduces alcohol [45,46] and opioid [47] cravings. Non-pharmacological interventions—repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS)—effectively reduce food and drug cravings [48]. Combined, these studies unequivocally point to a shared neurocircuitry between food and drug cravings, reflected in the results of both experimental and clinical research. Hence, our results—showing progesterone dampening effects on food cravings—are in line with the literature on addiction, demonstrating the same effect across several addictive drugs [12,13,14,15,16]. Cellular and anatomic brain circuits underlying the drive for food are highly controlled and very complex. Reinforcing drugs—as well as palatable foods—may overwhelm these control mechanisms, leading to the abnormally enhanced salience of the reinforcer. The brain systems driving the motivation to promote excessive feeding and uncontrollable drug intake have significant overlaps, although the regulation of food intake is much more complex [49]. While drug intake is predominantly mediated by the rewarding effects of drugs, food intake is controlled not just by its rewarding effects, but also by multiple peripheral and central homeostatic factors [49]. The identified inverse relationship between progesterone and food cravings likely represents a hedonic, not a homeostatic, process, though this hypothesis needs to be tested in the future. Changing sex hormones in the luteal phase are associated with decreased levels of amino acids and lipid species, presenting a physiological milieu of a heightened energy requirement [50,51,52,53,54,55]. Progesterone upregulates cell cycle and growth, resulting in protein biosynthesis for endometrial thickening [56]. If the relationship between progesterone and food cravings reflected this particular homeostatic process, then it would have been positive, as higher progesterone would have resulted in higher food cravings due to a small but significant positive effect of the luteal phase on resting energy expenditure [57]. Whether the heightened food cravings associated with low progesterone identified here actually translate to an increased food intake needs to be investigated in a rigorous study which controls for ovulation, periovulatory subphase (determined by serum luteinizing hormone), a precise subphase alignment, and measurement of sex hormones via mass spectrometry. Roney and Simmons [58] examined within-cycle shifts in total food intake and sex hormones (estradiol and progesterone), showing that progesterone positively and estradiol negatively predicted food intake, especially during the periovulatory subphase when there is a significant drop in food intake. Study participants collected salivary samples for a further concentration analysis of sex hormones using the immunoassay technology. However, relative to the mass spectrometry gold standard for the measurement of progesterone, the immunoassay analysis of salivary progesterone may produce skewed results. For example, Ney and colleagues [59] analyzed salivary samples for progesterone using mass spectrometry and immunoassay, showing that progesterone was highly variable and overall significantly higher when analyzed using immunoassay compared to mass spectrometry, mostly due to its cross reactivity with 17α-hydroxyprogesterone (17α-OHP). The authors concluded that research using salivary progesterone immunoassay techniques should be interpreted cautiously due to a high variability of results from the immunoassay measurement. Mass spectrometry methods are starting to become the reference method for the analysis of both sulfated and non-sulfated steroids in clinical laboratories and research studies, as they provide high accuracy [60]. Though the cost of mass spectrometry techniques is currently high, this is expected to decrease over time. Hence, the relationship between sex hormones and food intake, in particular in the luteal phase of the menstrual cycle, is still an inconclusive area which needs to be further explored. We suspect that the inverse effect of progesterone on food craving had not been captured until now due to the immunoassay measurement of progesterone in studies to date, and also because the mid-luteal subphase may not have been properly aligned. As endogenous progesterone kinetics follow an inverted u trajectory across the luteal phase, if the subphases are not perfectly aligned, women in the study may be misclassified for being in the low or high progesterone group. We further suspect that the inverse association identified in the present study reflects the dampening effect of progesterone on rewarding processes. Indeed, the luteal phase of the menstrual cycle is marked by an increase in palatable (hedonic and rewarding) food intake, as shown in the study by Gorczyca et al. [ 61], which identified increased cravings for chocolate, sweets, salty flavor, and general food cravings. With respect to the mediation of reward circuits, the attenuating effects of progesterone involve endocannabinoid, γ-aminobutyric acid, dopamine, and glutamate transmission in the medial prefrontal cortex and striatum [62]. The neurobiological bases for the identified association in the present study warrants a further investigation. Our study had strengths and limitations. Ideally, the present study should be replicated in a larger sample of study participants. However, any sample size needs to be weighed against sources of bias. The present study recruited a sample of women with no illicit drug- or prescription drug-use, who did not smoke or consume heavy amounts of alcohol, all of which may distort circulating sex hormone levels, thereby removing a number of potential confounding effects. We excluded individuals with positive drug urine screenings, heavy alcohol drinking, and current smoking to better define effect size estimates (i.e., removing significant sources of bias). Similarly, we excluded women with current major depressive disorder, though that may have limited the generalizability of the study findings given the significant comorbidity between the two disorders [63,64]. Moreover, although the Daily Record of Severity of Problems (DRSP) is a validated instrument for the collection of premenstrual symptomatology, food craving is only one item; hence, the measurement of craving ideally should be examined using more powerful methodologies, such as laboratory, cue-induced cravings. The study’s strengths are a prospective measure of premenstrual food cravings, the adjustment of premenstrual food craving relative to the scores from the follicular phase (and variance of all ratings across the entire menstrual cycle), the accurate staging of the mid-luteal subphase, the adjustment for BMI, and the implementation of ultraperformance liquid chromatography tandem mass spectrometry, which demonstrates high steroidal specificity [65]. In summary, the present study shows the protective effects of progesterone on premenstrual food cravings. The significance and the direction of this novel finding is in line with the existing literature demonstrating an inverse relationship between progesterone and drug cravings. Future studies should evaluate the neurobiological mechanisms of this finding. ## References 1. 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--- title: UPLC-ESI-MS/MS Profiling and Cytotoxic, Antioxidant, Anti-Inflammatory, Antidiabetic, and Antiobesity Activities of the Non-Polar Fractions of Salvia hispanica L. Aerial Parts authors: - Afaf E. Abdel Ghani - Muneera S. M. Al-Saleem - Wael M. Abdel-Mageed - Ehsan M. AbouZeid - Marwa Y. Mahmoud - Rehab H. Abdallah journal: Plants year: 2023 pmcid: PMC10005563 doi: 10.3390/plants12051062 license: CC BY 4.0 --- # UPLC-ESI-MS/MS Profiling and Cytotoxic, Antioxidant, Anti-Inflammatory, Antidiabetic, and Antiobesity Activities of the Non-Polar Fractions of Salvia hispanica L. Aerial Parts ## Abstract Salvia hispanica L. is an annual herbaceous plant commonly known as “Chia”. It has been recommended for therapeutic use because of its use as an excellent source of fatty acids, protein, dietary fibers, antioxidants, and omega-3 fatty acids. A literature survey concerning phytochemical and biological investigations of chia extracts revealed less attention towards the non-polar extracts of S. hispanica L. aerial parts, which motivates us to investigate their phytochemical constituents and biological potentials. The phytochemical investigation of the non-polar fractions of S. hispanica L. aerial parts resulted in the tentative identification of 42 compounds using UPLC-ESI-MS/MS analysis with the isolation of β-sitosterol [1], betulinic acid [2], oleanolic acid [3], and β-sitosterol-3-O-β-D-glucoside [4]. GLC-MS analysis of the seeds’ oil showed a high concentration of omega-3 fatty acid, with a percentage of $35.64\%$ of the total fatty acid content in the seed oil. The biological results revealed that the dichloromethane fraction showed promising DPPH radical-scavenging activity (IC50 = 14.73 µg/mL), antidiabetic activity with significant inhibition of the α-amylase enzyme (IC50 673.25 μg/mL), and anti-inflammatory activity using in vitro histamine release assay (IC50 61.8 μg/mL). Furthermore, the dichloromethane fraction revealed moderate cytotoxic activity against human lung cancer cell line (A-549), human prostate carcinoma (PC-3), and colon carcinoma (HCT-116) with IC50s 35.9 ± 2.1 μg/mL, 42.4 ± 2.3 μg/mL, and 47.5 ± 1.3 μg/mL, respectively, and antiobesity activity with IC50 59.3 μg/mL, using pancreatic lipase inhibitory assay. In conclusion, this study’s findings not only shed light on the phytochemical constituents and biological activities of the non-polar fractions of chia but also should be taken as a basis for the future in vivo and clinical studies on the safety and efficacy of chia and its extracts. Further study should be focused towards the isolation of the active principles of the dichloromethane fraction and studying their efficacy, exact mechanism(s), and safety, which could benefit the pharmaceutical industry and folk medicine practitioners who use this plant to cure diseases. ## 1. Introduction The Lamiaceae (Labiatae, Mint) family comprises 245 genera and about 7886 species worldwide. Many genera belonging to this family have important uses in medicine, the culinary arts, and cosmetics [1]. The chemical components of the family members have biological roles with therapeutic value; these chemicals include essential oils, alkaloids, flavonoids, glycosides, steroids, coumarins, tannins, and terpenoids [2]. Salvia hispanica L. is an annual herb that is commonly known as “Chia”, native to southern Mexico and northern Guatemala [3]. Salvia hispanica L. is mainly grown for its seeds, which are widely consumed because of their high nutritional and medicinal value [4,5,6,7,8,9]. Globally, research has been conducted investigating the benefits of chia seeds and oil and their applications in the food, cosmetic, medical, and pharmaceutical industries. A literature survey revealed more concern towards chia seeds’ constituents and biological activities, with less attention to other parts of the plant. Previous phytochemical analyses of S. hispanica seeds’ constituents indicated the presence of flavonoids and phenolic acids that are linked to their antioxidant, antiobesity, antidiabetic, and antimicrobial activities [4,5,6,7,8,9,10,11,12,13]. In contrast, only a few studies have reported on the phytochemical and biological activities of S. hispanica L. aerial parts, which exhibit the presence of neoclerodane-type diterpenoids with the tentative identification of different phenolic compounds [14,15,16]. To the best of our knowledge, there are no bibliographic data in the literature about the phytochemical composition and biological activities of the aerial parts of S. hispanica cultivated in Egypt except our previous work that focused on the investigation of the main bioactive constituents of the polar fraction of the aerial parts, which resulted in the tentative detection of 37 compounds, using UPLC-ESI-MS/MS analysis with the isolation of 1,2,4,5 tetrahydroxy benzene, leucantho flavone, and rhamnetin [17]. The current study focused on the identification of the active constituents of the non-polar fractions of the aerial parts of S. hispanica cultivated in Egypt with the investigation of their potential biological activities, including cytotoxic, antioxidant, anti-inflammatory, antidiabetic, and antiobesity activities, to attract attention and provide evidence for their therapeutic value. ## 2.1. Structural Identification of Constituents by UPLC-ESI-MS/MS UPLC-ESI-MS/MS in positive ionization mode was used to analyze the light petroleum fraction of S. hispanica L. aerial parts (Figure 1). The tentative detection of nine compounds was based on the fragmentation patterns that were compared with the available literature data, as shown in Table 1. Compound 1 (Rt, 23.57) showed a molecular ion peak [M+H]+ at m/z 577, a base peak [M]+ at m/z 576, as well as a fragment ion at m/z 415 [M+H-Glu]+. In accordance with this fragmentation pattern, the compound was classified as β-sitosterol-3-O-β-D-glucoside [18]. Compound 2 (Rt, 23.76) showed a precursor ion [M+H]+ at m/z 301 as well as a fragment ion at m/z 227 [M+H-propene unit-H2O-CH2]+. By this fragmentation pattern, the compound was classified as sugiol [19]. Compound 3 (Rt, 23.77) showed a precursor ion [M+H]+ at m/z 279 as well as fragment ions at m/z 301 [M+Na]+, 279 [M+H]+,and 261 [M+H-H2O]+. The compound [3] was identified as 7α-hydroxy-14,15-dinorlabd-8[17]-en-13-one based on this fragmentation [20]. Compound 4 (Rt, 24.13) showed a molecular ion peak [M]+ at m/z 414 and a fragment ion at m/z 396 [M-H2O]+. In accordance with this fragmentation pattern, the compound was classified as β-sitosterol [21]. Compounds 5, 6, and 9 (Rt, 24.68, 26.54 & 29.68 min) revealed protonated molecular ions at m/z 281, 279, and 257, respectively. These fragments were in good agreement with the characteristics of linoleic acid, linolenic acid, and palmitic acid, respectively. These fatty acids were previously detected in other salvia species [22]. Compound 7 (Rt 29.68 min) showed a molecular ion fragment at m/z 457 [M+H]+ and was tentatively identified as betulinic acid. The HPLC-ESI-MS spectra of this compound showed MS2 fragment ions at m/z 248 [C16H24O2]+, 203 [248-COOH]+, 207 [M-C16H27]+, 189 [207-H2O]+,and 175,which comprise the characteristic fragments for betulinic acid [23]. In the same manner, compound 8 (Rt, 29.68 min) showed a molecular ion fragment at m/z 457 [M+H]+ and prominent ion fragments at m/z 248 and 207 [C14H23O]+; it also showed a fragment ion at 203 [C15H23]+,because of loss of COOH from 248,and another fragment ion at m/z 189 [207-H2O]+.This fragmentation pattern was in good agreement with the previous report of oleanolic acid [24]. For the dichloromethane fraction, the UPLC-ESI-MS/MS in negative and positive ion modes led to the identification of 33 compounds (Figure 2). The compounds were arranged according to retention time (Rt) and classified accordingly into different classes including phenolic acids, flavonoids, diterpenoids, alkaloids, tannins, steroids, triterpenoids, fatty acids, and miscellaneous compounds (Table 2). The dichloromethane fraction is high in diterpenoids (Figure 3A), most of which are abietane quinones. There were 13 diterpenoids compounds tentatively identified as follows. Compound 2 (Rt, 7.26 min) exhibited a precursor ion at m/z 317 [M+H]+ as well as fragment ions at m/z 299 [(M+H-H2O)]+ and 267 [(M+H-2H2O-CH2)]+,which are characteristic of tanshinone V [26]. Compound 10 (Rt, 10.74 min) exhibited a precursor ion at m/z 357 [M+H]+ as well as fragment ions at m/z 293 [(M+H-2H2O-CO)]+ and 181. Accordingly, the compound was identified as salviacoccin [20] (Figure 4). In negative ion mode, compounds 12 and 13 (Rt, 11.33 and 11.34 min) showed a molecular ion peak at m/z 315 [M-H]−. In the case of 12, the fragmentation pattern exhibited a fragment ion at m/z 285 corresponding to [(M+H˗H2O-CH2)]+, but in the case of compound 13, a fragment ion at m/z 243 was formed after the loss of [(M+H˗3CH3-C2H5)]+. The fragmentation patterns are characteristic of cryptanol and royleanone, respectively [20]. Compound 14 (Rt, 11.44 min) exhibited a precursor ion at m/z 341 [M+H]+ as well as fragment ions at m/z 309 [(M+H-H2O-CH2)]+, 295 [(M+H-H2O-2CH2)]+, and 231 [(M+H-H2O-2CH2-CO-2H2O)]+,which were formed after the loss of C3H6O. Accordingly, the compound was tentatively identified as trijuganone C [22] (Figure 4). Compound 17 (Rt, 12.27 min) exhibited a precursor ion at m/z 313 [M+H]+ as well as fragment ions at m/z 249 [(M+H-2H2O-CO)]+ and 193 [(M+H-2H2O-3CO)]+. The compound was tentatively identified as tanshindiol C [35]. Compound 19 (Rt, 12.51 min) showed a precursor ion at m/z 345 [M-H]−, as well as fragment ions at m/z 330 [(M-H˗CH3)]−, 315 [(M-H˗2CH3)]−, and 287 [(M-H˗2CH3˗CO)]−. The compound was tentatively identified as 7α-methoxy royleanone [37] (Figure 4). Compound 22 (Rt, 13.48 min) exhibited a precursor ion at m/z 313 [M+H]+ as well as fragment ions at m/z 249 [(M+H-2H2O-CO)]+ and 197. This compound was tentatively identified as hydroxy cryptotanshinone [39]. Compound 26 (Rt, 14.95 min) exhibited a precursor ion at m/z 339 [M+H]+ as well as a fragment ion at m/z 311 [(M+H-CO)]+, which is characteristic of methyl tanshinonate [35]. Compound 29 (Rt, 15.51 min) exhibited a precursor ion at m/z 299 [M-H]− as well as a fragment ion at m/z 227 [M-H-3CH3-C2H3]−. Thus, the compound [29] was identified as 16-hydroxy-6,7-didehydroferruginol [20]. Compound 31 (Rt, 17.04 min) produced both a precursor ion at m/z 329 [M-H]− as well as a fragment ion at m/z 285 [(M-H˗CO2)]−. This fragmentation is typical for carnosol [45]. Compound 32 (Rt, 25.32 min) showed a precursor ion at m/z 313 [M+H]+, and the presence of a fragment ion at m/z 269 [(M+H-CO2)]+ is characteristic of hydroxy tanshinone VI [33]. Compound 33 (Rt, 27.12 min) exhibited a precursor ion at m/z 279 [M+H]+ and a fragment ion at m/z 261 [(M+H˗H2O)]+, and it was identified as 15,16-dihydrotanshinone I [35]. Moreover, seven flavonoid aglycones were tentatively identified in the dichloromethane fraction (Figure 3B), including compound 16 (Rt, 11.99 min), which showed a molecular ion peak at [M-H]− at m/z 359, as well as fragment ions at m/z 344, 329, and 314, due to successive losses of CH3, and a fragment ion at m/z 195 that formed after cleavage of the flavone skeleton. Based on this result, the compound was classified as 5,7,3′-trihydroxy-6,4′,5′-trimethoxy flavone [34]. Compound 18 (Rt, 12.47 min) showed a molecular ion peak at m/z 345 [M-H]−; the fragment ions formed after the loss of CH3 groups were at m/z 330, 315, and 287, indicating that 18 could tentatively be identified as 5,3′-dihydroxy-7,8,4′-trimethoxy flavanone [36] (Figure 4). Compound 20 (Rt, 12.52 min) showed a molecular ion peak at m/z 345 [M-H]− in addition to fragment ions formed after successive losses of CH3 groups at m/z 330 and 315. The compound was classified as axillarin (methylated flavonol) [38]. Compound 23 (Rt, 13.69 min) presented an [M+H]+ ion at m/z 331.The MS2 spectrum showed fragment ions at m/z 316 [331-CH3]+ and m/z 298 that formed after the loss of H2O. The compound was classified as salvigenin (flavone) [40] (Figure 4). Compound 25 (Rt, 14.93 min) exhibited a sorbifolin (flavone)-specific molecular ion peak at m/z 301 [M+H]+ and a fragment ion at m/z 286 [41].Compounds 27 and 28 (Rt, 15.21 and 15.40 min) showed identical molecular ion peaks at m/z 299 [M-H]− in negative ion mode. In the case of compound 27, the fragment ions at m/z 284 and 283 were characteristic of diosmetin or chryseriol (flavone) [42], whilst compound 28 revealed fragment ions at m/z 284 and 255, characteristic of 3′-O-methylorobol or gliricidin (isoflavone) [43]. Three alkaloids were tentatively identified from the dichloromethane fraction of aerial parts (Figure 3C), including compound 4 (Rt, 8.82 min), which exhibited a precursor ion at m/z 357 [M+H]+ as well as a fragment ion at m/z 311 [(M+˗CH3)2 NH)]+; this fragmentation is characteristic of menisperine (M+:356.4) [28]. Compound 15 (Rt, 11.78 min) exhibited a precursor ion at m/z 339 [M+H]+ as well as a fragment ion at m/z 295 that was formed after the loss of CH3 and CO. Accordingly, jatrorrhizine (M+:338.4) was tentatively identified as this compound [28]. Compound 24 (Rt, 14.50 min) showed a fragment ion at m/z 345 [M+H]+. The MS2 spectrum showed the fragment ion at m/z 312 (M+H˗CH3OH)]+, so the compound was tentatively identified as tembetarine (M+:344.4) [28] (Figure 4). Furthermore, five compounds of phenolic acids and their derivatives were tentatively identified from the dichloromethane fraction of the aerial parts of S. hispanica L. (Figure 5A) and are described as follows: Compound 5 (Rt, 9.00 min) showed a precursor ion at m/z 343 [M+H]+ that was successively subjected to the loss of the hexose sugar moiety to form a fragment ion at m/z 181 [caffeic acid+H]+. Therefore, the compound [5] was identified as caffeic acid hexoside [29]. Compound 6 (Rt, 9.12 min) revealed a precursor ion [M-H]− at m/z 355 and a fragment ion at m/z 193, corresponding to the ferulic acid moiety after losing hexose sugar. This fragmentation is characteristic of feruloyl hexose [30] (Figure 4). Compound 7 (Rt, 9.96 min) showed a precursor ion at m/z 195 [M+H]+ as well as a fragment ion at m/z 180 [(M+H-CH3)]+ and 177 [(M+H-H2O)]+ after losing CH3 and H2O, respectively. This fragmentation pattern is characteristic of ferulic acid [31]. Compound 8 (Rt, 10.16 min) exhibited a fragment ion [M-H]− at m/z 353 in addition to a fragment ion at m/z 191, corresponding to quinic acid, after losing the caffeoyl moiety. Accordingly, 8 was identified as caffeoyl quinic acid [8]. Compound 11 (Rt, 11.10 min) exhibited mainly a precursor ion at m/z 359 [M+H]+ and a fragment ion at m/z 315 [M+H-CO2]+, which is characteristic of przewalskinic acid [33]. Other identified miscellaneous compounds (Figure 5B) were compounds 1 and 3 (Rt, 6.39 and 7.87 min) which exhibited identical precursor ions at m/z 249 [M+H]+. For compound 1, the fragment ion at m/z 137 was characteristic of 6-hydroxy, 7-methoxy tremetone, while compound 3 exhibited fragment ions at m/z 193 [M+H˗2CO]+ and m/z 175 (M+H˗2CO-H2O)+, characteristic of brevifolin [25,27]. Compound 9 (Rt, 10.53 min) exhibited a precursor ion at m/z 314 [M+H]+ as well as fragment ions at m/z 177,which corresponded to ferulic aldehyde, and 121,which corresponded to 4-ethylphenol. Thus, compound 9 was tentatively identified as feruloyl tyramine [32] (Figure 4). Compound 21 (Rt, 13.38 min) showed a fragment ion [M-H]− at m/z 329. The MS2 spectrum showed fragment ions at m/z 314 [(MH˗CH3)]−, 299 [(M-H˗2CH3)]−, and 271 [(M-H˗2CH3˗CO)]−. This compound was identified as dimethyl-O-ellagic acid [27]. Compound 30 (Rt, 15.57 min) showed a fragment ion at m/z 327 [M+H]+. The MS2 spectrum showed fragment ions at m/z 229, 211, and 171. The compound was tentatively identified as 13-Oxo-9,10 dihydroxy-11-octadecenoic acid [44]. ## 2.2. Isolated Compounds from the Light Petroleum Fraction Compounds 1–4 were identified as β-sitosterol, betulinic acid, oleanolic acid, and β-sitosterol-3-O-β-D-glucoside, respectively, through spectral analyses and comparison with the literature data [18,46,47,48,49], as represented in Figure 6 and Table 3. β-sitosterol [1]: white needles; m.p. 137–139 °C; IR (KBr νmax, cm−1): 3416 (O-H), 2932 and 2864 (C-H aliphatic), 1642 (C=C), 1463 (-CH2), 1376 (-CH3), and 1051 (C-O). EI-MS: m/z (relative abundance %) = 414 (M+, 100), 399 (24.19), 397 [13], 396 (31.88), 381 (15.54), 367 (1.19), 329 (6.7), 303 (2.88), 119 (1.3), 109 (1.36), 107 [3], 105 (3.41), 95 (5.23), 69 (17.8), 57 (20.9), 55 (17.41), and 43 (27.34). 1H- and 13C-NMR (CDCl3) spectral data are summarized in Table 3. Betulinic acid [2]: white amorphous powder; IR (KBr νmax, cm−1): 3450 (O-H), 2939 and 2867 (C-H), 1682 (C=O), 1642 (C=C), 1449 (CH2), 1376 (CH3), and 1042 (C-O); EI-MS: m/z (relative abundance %) = 456 (M+, 32), 248 (17.8), 233 (21.3), 220 [100], 207 (14.8), 203 (27.7), 189 (45.7), 175(60.2), 147 (69.8), 91 (18.6), and 79 (19.3). 1H-NMR (CDCl3) data are summarized in Table 3. Oleanolic acid [3]: white amorphous powder; IR (KBr νmax, cm−1): 3391 (O-H), 2930 (C-H aliphatic), 1687 (C=C), 1458 (CH2), 1377 (-CH3), and 1023 (C-O); EI-MS: m/z (relative abundance %) = 456 (M+,100), 248 (75.79), 207 (10.48), 203 (17.13), 189 (6.67), and 119 (15.77). 1H- and 13C-NMR (CDCl3) readings are summarized in Table 3. β-sitosterol-3-O-β-D-glucoside [4]: white crystals; m.p. 272–274 °C; IR (KBr νmax, cm−1): 3391 (O-H), 2931 and 2866 (C-H aliphatic), 1461 (CH2), 1366 (CH3), 1069 (C˗O). ESI-MS: m/z (Relative abundance %) = 577 (M+H+, 14.3), 576 (M+, 100), 415 (M+H–Glu, 8.89), 267 (20.1), and 211 (34.2). 1H- NMR signals of glucose moiety at δ(ppm): δ 4.21 (1H, d, $J = 10$ Hz, H-1‘), δ 2.89 (1H, m, H-2‘), δ 3.12 (1H, m, H-3‘), δ 3.01 (1H, m, H-4‘), δ 3.05 (1H, m, H-5‘), δ 3.46 (1H, m, H-6‘b), and δ 3.60 ppm (1H, m, H-6‘a).13C-NMR signals of glucose moiety at δ (ppm): δ 101.27 (C-1‘), δ 73.94 (C-2‘), δ 77.41 (C-3‘), δ 70.58 (C-4‘), δ 77.21 (C-5‘), and δ 61.57 (C-6‘). 1H- and 13C-NMR (CDCl3) data are summarized in Table 3. ## 2.3. GLC-MS Analysis of Seeds Oil The major fatty acids identified as methyl esters were linoleic acid ($35.64\%$), linolenic acid (23.95), palmitic acid ($14.12\%$), stearic acid ($7.63\%$), lauric acid ($5.87\%$), myristic acid ($2.31\%$), 11,14,17-eicosatrienoic acid ($0.59\%$), arachidic acid ($0.57\%$), caprylic acid ($0.54\%$), and capric acid ($0.42\%$). Polyunsaturated fatty acids (PUFAs) represented $60\%$ of seeds’ oil, while omega-3 fatty acids (linolenic acid) represented $35.64\%$ of the total fatty acids in the seed oil. ## 2.4. Cytotoxic Activity The cytotoxic activity of the dichloromethane fraction was tested using a viability assay with vinblastine as a standard against human lung cancer cell line (A-549), human prostate carcinoma (PC-3), and colon carcinoma (HCT-116). The presence of flavonoids, phenolic compounds, tannin, and glycosides is responsible for cytotoxic activities [50]. The results revealed that the fraction had a moderate cytotoxic activity against A-549, PC-3, and HCT-116 cell lines with IC50 of 35.9 ± 2.1 μg/mL, 42.4 ± 2.3 μg/mL, and 47.5 ± 1.3 μg/mL, respectively, and when compared with vinblastine sulfate as a positive control, the IC50was 24.6 μg/mL, 42.4 μg/mL, and 3.5 μg/mL, respectively (Figure 7A–C). ## 2.5. Antioxidant Activity The promising antioxidant result of the dichloromethane fraction refers to the flavonoids and phenolic contents. The hydroxyl groups in phenolic compounds are responsible for antioxidant activity because of their radical-scavenging properties [51]. The DPPH scavenging percentage of the dichloromethane fraction (IC50 = 14.73 µg/mL) was approximately comparable to that of ascorbic acid (IC50 = 12.50 µg/mL, as shown in Figure 7D. ## 2.6. Anti-Inflammatory Activity The dichloromethane fraction showed stronger anti-inflammatory activity than the light petroleum fraction, with IC50s of 61.8 μg/mL and 458.6 μg/mL, respectively, compared to diclofenac sodium as a positive control, with IC50 of 17.9 μg/mL (Figure 7E). The contents of diterpenes and phenolics in the dichloromethane fraction play important roles in anti-inflammatory activity [52]; sterols, such as β-sitosterol, betulinic acid, oleanolic acid, and β-sitosterol-3-O-β-D-glucoside, are also known to exhibit anti-inflammatory activity [53]. ## 2.7. Antidiabetic Activity The antidiabetic activity of the dichloromethane fraction was tested using the α amylase enzyme and acarbose as a positive standard. The results showed that the dichloromethane fraction inhibited the α-amylase enzyme, with IC50 of 673.25 μg/mL compared to acarbose, which showed IC50 of 34.71 μg/mL (Figure 7F). S. hispanica contains a high concentration of omega-3 fatty acids ($35.64\%$ of total fatty acid content), which have been shown to reduce insulin resistance [54]. ## 2.8. Antiobesity Activity There are numerous reports on the antiobesity activity of S. hispanica L. seeds but none on the activity of the aerial parts. The antiobesity activity was determined using a pancreatic lipase inhibitory assay, and the results showed that the dichloromethane fraction has moderate antiobesity activity, with IC50 59.3 μg/mL, versus orlist, with IC50 23.8 μg/mL (Figure 7G). The antiobesity activity is due to the presence of poly phenolics, flavonoids, and terpenoids [55]. ## 3.1. Instruments for Spectroscopic Analyses Infrared spectral analysis was recorded using the potassium bromide disk technique on a PyeUnicam SP 3000 and IR spectrophotometer of Alpha (I-00523), Jasko, FT/IR-460 plus, Japan. Mass spectra were obtained on Shimadzu GC-MS-QP5050A mass spectrometer at 70 eV. 1H and 13C-NMR spectral analyses were carried out at the faculty of pharmacy, Ain Shams University, Egypt, using Bruker (Zurich, Switzerland) at 400 and at 100 MHz, respectively. Chemical shifts were given in ppm with the TMS as the internal standard. ## 3.2. Plant Material Salvia hispanica L. aerial parts were collected at the flowering stage from Mushtohor farm (Tokh, Egypt) in March 2018. This plant was identified and verified by Dr. Hussein Abdelbaset (Professor of Plant Taxonomy, Faculty of Science, Zagazig University). A voucher specimen (Lam. S-10) was deposited in the herbarium of the pharmacognosy department, faculty of pharmacy, Zagazig University, Egypt. ## 3.3. Extract Preparation The air-dried powdered aerial parts of Salvia hispanica L. (3 kg) were extracted by cold maceration (5 times × 7 L) using $70\%$ aqueous ethanol. The total extract was evaporated under reduced pressure at 50 °C, yielding 540 gm of dark green viscous residue. The residue (400 gm) was dissolved in a methanol: water mixture (1:9) then subjected to fractionation using light petroleum and dichloromethane. The fractions were washed with distilled water and dried over anhydrous sodium sulfate, then the solvent of each fraction was distilled off under reduced pressure at 50 °C to yield a light petroleum fraction (68 gm) and a dichloromethane fraction (4 gm). ## 3.4. Chromatographic Investigations The light petroleum fraction was investigated by normal phase TLC using dichloromethane and methanol 99:1. The TLC plates were visualized with anisaldehyde and sulfuric acid, and the promising fractions were subjected to chromatographic investigations. The light petroleum fraction (33 gm) was chromatographed on a silica gel column packed with light petroleum, and the polarity was increased successively by dichloromethane followed by methanol. Similar fractions were collected according to the TLC profile. Fractions (26–35) eluted by $80\%$ CH2Cl2/light petroleum were combined, concentrated, and crystallized to obtain four compounds (1–4). ## 3.5. LC/MS Instrument and Separation Technique Each fraction (100 μg/mL) solution was prepared using HPLC analytical-grade solvent MeOH, filtered with a membrane disc filter, and then subjected to LC-ESI-MS analysis. Fractional injection volumes (10 μL) were injected into the UPLC instrument equipped with a reverse-phase C-18 column (ACQUITY UPLC—BEH C18 1.7 µm particle size—2.1 × 50 mm column). The mobile phase was prepared by filtering solvents using a filter membrane disc and degassing by sonication before injection. The flow rate was 0.2 mL/min with a gradient mobile phase comprising two eluents: H2O acidified with $0.1\%$ formic acid and MeOH acidified with $0.1\%$ formic acid. The parameters for analysis were carried out using positive ion mode as follows: source temperature 150 °C, cone voltage 30 eV, capillary voltage 3 kV, desolvation temperature 440 °C, cone gas flow 50 L/h, and desolvation gas flow 900 L/h. Mass spectra were detected in the ESI between m/z 100 and 1000. The peaks and spectra were processed using Maslynx 4.1 software and tentatively identified by comparing their retention time and mass spectrum with the reported data. ## 3.6. GLC-MS of Salvia Seeds’ Oil The seeds were pressed using the Ixtaina et al. method [56], and the oil was derivatized using the Metcalfe et al. method [57] and recorded using Shimadzu GCMS-QP2010 (Tokyo, Japan) equipped with Rtx-1MS fused bonded column and a split–splitless injector. The initial column temperature was kept at 45 °C for 2 min (isothermal), programmed to 300 °C at a rate of 5 °C/min, and kept constant at 300 °C for 5 min (isothermal). The injector temperature was 250 °C. The helium carrier gas flow rate was 1.41 mL/min. All the mass spectra were recorded under the following conditions: (equipment current) filament emission current, 60 mA; ionization voltage, 70 eV; ion source, 200 °C. A series of hydrocarbon samples ($1\%$ v/v) were injected in split mode (split ratio 1:15). The components were identified by matching the retention indices and mass spectra with those reported in NIST17-1 libraries and literature. ## 3.7. Cytotoxic Activity The anti-cancer activity was carried out using a cell viability assay [58]. Briefly, the cell lines used were the human lung cancer cell line (A-549), human prostate carcinoma cells (PC-3), and colon carcinoma cells (HCT-116), and they were obtained from VACSERA company (Tissue Culture Unit), Cairo, Egypt) [59,60]. The dichloromethane fraction was used in various concentrations (500 to 0 μg/mL). The IC50 values of the fractions and the standard (vinblastine sulfate) were calculated. ## 3.8. Antioxidant Activity The antioxidant activity was determined using the DPPH method according to the Leaves et al. method [61]. Briefly, the dichloromethane fraction was used at different concentrations, 2.5, 5, 10, 20, 40, 80, 160, 320, 640, and 1280 μg/mL, which were each added to 3 mL of DPPH solution, and the decrease in absorbance at 515 nm was determined continuously, with data being recorded at 1min intervals until the absorbance stabilized (16 min). The $50\%$ inhibitory concentrations (IC50) of the dichloromethane fraction and the standard (ascorbic acid) were determined. ## 3.9. Anti-Inflammatory Activity In vitro histamine release assay was performed on light petroleum and dichloromethane fractions according to Venkata et al. ’s assay [62]. The results were expressed as inhibition percentage, which was calculated using the following formula:Inhibitory activity (%) = (1 − As/Ac) × 10 As is the absorbance in the presence of the test substance and Ac is the absorbance of the control substance. The IC50 value in μg/mL was estimated. ## 3.10. Antidiabetic Activity The α-amylase inhibition method was used to determine the antidiabetic activity [63]. Briefly, 1 mL of the dichloromethane fraction of various concentrations (1000 to 7.81 μg/mL) and 1 mL of the enzyme solution were mixed and incubated at 25 °C for 10 min. After incubation, 1 mL of starch ($0.5\%$) solution was added to the mixture and incubated at 25 °C for 10 min. The reaction was then stopped by adding 2 mL of dinitro-salicylic acid, followed by heating the mixture in a boiling water bath for 5 min. After cooling, the absorbance was measured colorimetrically at 565 nm, and the IC50 values of the dichloromethane fraction and the standard (acarbose) were estimated. ## 3.11. Antiobesity Activity The antiobesity activity was determined by pancreatic lipase inhibitory assay [64]. Briefly, the dichloromethane fraction at different concentrations (1000 to 7.81 μg/mL) was pre-incubated with 100 µg/mL of lipase for 10 min at 37 °C. The reaction was then started by adding 0.1 mL of p-nitrophenyl butyrate substrate after incubation at 37 °C for 15 min. The amount of p-nitrophenol released in the reaction was measured using a multiplate reader (Sigma Aldrich, Burlington, Massachusetts, USA). The IC50 values of the dichloromethane fraction and the standard (orlistat) were determined. ## 4. Conclusions This study represents the first report on the phytochemical constituents of the non-polar fraction of S. hispanica aerial parts cultivated in Egypt as well as their pharmacological potentials. The UPLC-ESI-MS/MS analyses of the non-polar fractions (light petroleum and dichloromethane fractions) resulted in the tentative identification of 42 compounds of different chemical classes, including fatty acids, steroids, di- and tri-terpenoids, flavonoids, phenolic acids, and alkaloids. The phytochemical investigation of the light petroleum fraction resulted in the isolation of four compounds, including β-sitosterol [1], betulinic acid [2], oleanolic acid [3], and β-sitosterol-3-O-β-D-glucoside [4]. The GLC-MS analysis of the seeds’ oil revealed that seeds contain a high concentration of omega-3 fatty acids, with a percentage of $35.64\%$ of the total fatty acids content. Biologically, the dichloromethane fraction showed moderate cytotoxic activity against the human lung cancer cell line (A-549), human prostate carcinoma (PC-3), and colon carcinoma (HCT-116). It also exhibited remarkable antioxidant results that can be attributed to its contents of polyphenolic compounds, in addition to antidiabetic, antiobesity, and anti-inflammatory activities, which are attributed to the fatty acids, steroids, terpenoids, flavonoids, and phenolic acid contents. 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--- title: Prospective Study of Diet Quality and the Risk of Dementia in the Oldest Old authors: - Ashley C. Flores - Gordon L. Jensen - Diane C. Mitchell - Muzi Na - G. Craig Wood - Christopher D. Still - Xiang Gao journal: Nutrients year: 2023 pmcid: PMC10005581 doi: 10.3390/nu15051282 license: CC BY 4.0 --- # Prospective Study of Diet Quality and the Risk of Dementia in the Oldest Old ## Abstract This study examined the associations between overall diet quality and the risk of dementia in a rural cohort among the oldest old. Included in this prospective cohort study were 2232 participants aged ≥ 80 years and dementia-free at the baseline according to the Geisinger Rural Aging Study (GRAS), a longitudinal cohort in rural Pennsylvania. In 2009, diet quality was assessed by a validated dietary screening tool (DST). Incident cases of dementia during 2009–2021 were identified using diagnosis codes. This approach was validated by a review of electronic health records. Associations between diet quality scores and the incidence of dementia were estimated using the Cox proportional hazards models, adjusted for potential confounders. Across a mean of 6.90 years of follow-up, we identified 408 incident cases of all-cause dementia. Having a higher diet quality was not significantly associated with a lower risk for incidents of all-cause dementia (adjusted HR for the highest compared with the lowest tertile: 1.01, $95\%$ CI: 0.79, 1.29, P-trend = 0.95). Similarly, we did not observe a significant association between diet quality and altered risks of Alzheimer’s disease and other forms of dementia. Overall, having a higher diet quality was not significantly associated with a lower risk of dementia among the oldest old during the full follow-up. ## 1. Introduction In the United States (U.S.), individuals of advanced ages are expected to rise as the percentage of individuals aged 65 years and over is predicted to increase from $17\%$, in 2020, to $23\%$, in 2060, with the oldest old adults will comprise a growing proportion of this population [1]. As the number of older persons in the U.S. population continues to escalate, the risk of dementia, including Alzheimer’s disease, will also increase [2,3]. More than 55 million people were estimated to be living with dementia in 2021, and with projections of 78 million people to be afflicted by 2030, the need for modifiable strategies to reduce the risk of dementia is of high importance [4]. Even though the oldest old is a rapidly growing age group in the U.S. [1] and possesses a greater risk of developing dementia [5], epidemiological research on dementia in this population is limited [6]. Therefore, additional studies to further understand the dementia risk factors in the oldest old age group are warranted. Targeting modifiable risk factors to prevent dementia has been suggested as an approach to delay or slow down cognitive decline [7]. For instance, nutrition, including adhering to a healthy and balanced diet is recommended for reducing cognitive decline and the risk of dementia [7]. With respect to the oldest old and dementia risk, very few studies have provided insight into the role of individual nutrients or singular dietary components. Although one longitudinal study in this population identified lifestyle factors such as caffeine consumption and supplemental vitamin C intake were linked to reduced dementia risk [8]. Evidence of such associations has been more thoroughly evaluated among other age groups in the literature. For example, prior studies have investigated the associations between the consumption of vegetables and fruits [9,10], meat [11], whole grains [12], and intake of several nutrients [10,13,14] with the risk of dementia among midlife to older adult age groups. It is important to note, the shift from examining singular nutrients and specific food components to dietary patterns as a gauge of diet quality may better explain the role of diet in chronic diseases [15,16]. Prospective studies on the relationship between diet quality or overall dietary patterns on dementia risk, including Alzheimer’s disease have generated inconsistent results, however, these studies are mainly centered on populations of people 60–80 years of age [17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35]. Limited studies have investigated the influence of diet quality or overall dietary patterns on the subsequent risk of dementia in the oldest old population: aged 80 years and older. Recently, a population-based cohort study, including the oldest old residing in the Varese province, Italy, identified an inverse association between adherence to the Mediterranean diet and dementia prevalence, though, not for incidence [36]. Although granted the promising importance of diet for cognitive health and neurodegenerative disease [37,38,39], the paucity of studies examining the role of diet, particularly overall diet quality, with dementia risk in the oldest old age group highlights the current literature gap among observational studies. By studying the oldest old population, modifiable risk factors may be detected to promote healthy aging, for at-risk, rural populations of advanced age. We previously demonstrated in our validation analysis of 122 oldest old participants that diet quality scores, as captured by the dietary screening tool (DST), were significantly correlated with scores from the Healthy Eating Index-2015 (HEI-2015) [40]. In our recent prospective cohort analysis of diet quality and Parkinson’s disease in older adults in rural Pennsylvania, we observed higher diet quality was linked to a lower risk of Parkinson’s disease [41]. The results from our studies suggest DST is a valid indicator of diet quality in older adults [42] and the oldest old [40] and warrants further investigations concerning neurodegenerative diseases in populations of advanced age. Thus, the current study aimed to prospectively examine the relationship between diet quality, as assessed by the DST, and the subsequent risk of dementia in a longitudinal rural cohort of the oldest old: aged 80 years and older. ## 2.1. Study Population Beginning in 1994, the Geisinger Rural Aging Study (GRAS) recruited a longitudinal cohort of 21,645 older adults (aged ≥ 65 years) enrolled in a Medicare-managed health maintenance organization per the Geisinger Health System [43]. This cohort included community-dwelling individuals residing in rural central and northeastern Pennsylvania [43]. Representative of rural central Pennsylvania, the GRAS cohort participants are almost entirely non-Hispanic white. Detailed information regarding participant recruitment in the GRAS cohort has been previously described [43]. In October 2009 (baseline of the study), surveys including health and demographic questionnaires and the DST were mailed to 3891 surviving GRAS participants, ≥ 80 years. Of the individuals who were sent mailed surveys, 2713 participants returned completed dietary information. Participants that did not follow-up were excluded from the analysis ($$n = 26$$). We further excluded participants enrolled after the study baseline or disenrolled before the study baseline based on the first and most recent Electronic Health Records (EHR) findings ($$n = 391$$). After excluding participants with prevalent all-cause dementia at baseline ($$n = 64$$), the 2232 remaining participants were included in the primary analysis (Figure 1). Participants were followed through until 20 July 2021. The original study conducted in 2009 was approved by the Geisinger Health System Institutional Review Board (Protocol #1999-0112). Approval for participant consent was implied by mailed survey completion. Access to the data used in this study was part of a data use agreement between the Pennsylvania State University and the Geisinger Health System. ## 2.2. Assessment of Diet Quality To assess diet quality, the DST survey questionnaire comprised 25 questions related to food and behavior associated with dietary intake and was mailed in 2009 to the surviving GRAS participants. The DST was developed using data from multiple 24 h dietary recalls, which were administered to a subsample of the GRAS cohort [44]. The DST questions were derived based on detailed dietary intake data and frequency and temporal distribution analyses of food intake [44]. Cognitive interviewing techniques including concurrent and retrospective methods were used to ensure the population of interest understood the intended concepts [44]. Diet quality was examined with markers of nutritional status and health, including biomarkers, anthropometric, and dietary measures [44]. The total possible score for the DST ranged from 0 to 100 points, with 5 bonus points potentially allotted for dietary supplement uses [42]. An example of a question from the DST is “how often do you usually eat whole grain bread?” To capture intake, participants may select from “never,” “less than once a week”, “1 or 2 times a week”, or “3 or more times a week.” The DST has been validated as a measure of diet quality in older adults [42,44] and the oldest old population [40] in rural Pennsylvania as well as middle-aged adults residing in Appalachia [45]. Further detailed information regarding the DST development and validation has been previously recorded [40,42,44]. ## 2.3. Assessment of Incident Cases for All-Cause Dementia All-cause dementia was the primary outcome in the current analysis. EHR data were utilized to derive an electronic algorithm to identify incident cases of dementia. The following International Classification of Diseases and Related Health Problems, Ninth and Tenth Revision (ICD-9 and ICD-10) codes F01 (vascular dementia), F02 (dementia in other diseases classified elsewhere), F03 (unspecified dementia), and G30 (Alzheimer’s disease) were used. The accuracy of deriving cases was tested by comparing the results of the identified cases to those revealed by a physician’s review of the individual EHR records. Consultation on the chart review process by a Geisinger neurologist guided two neurology fellowship trainees in the identification of patients with dementia through an independent review of the Geisinger EHR. Included in the review was a randomized sample of 26 GRAS participants with and without codes for diagnosed dementia (identified by ICD 10 codes F01, F02, and F03). The results of the independent reviews were compared and found to have a good agreement ($\frac{22}{26}$ = $85\%$, κ = 0.67). The two reviewers re-reviewed the four disagreements and came to a consensus. Diagnostic test measures including sensitivity, specificity, proportions of positive and negative results (PPV and NPV), and accuracy were assessed. If we assume a dementia prevalence of $30\%$, we find the following diagnostic test measures: sensitivity = $83\%$, specificity = $88\%$, PPV = $74\%$, NPV = $92\%$, and accuracy = $86\%$. ## 2.4. Assessment of Covariates Descriptive information including age, sex, height, weight, educational level, physical activity, diabetes status, hypertension status, coronary heart disease status, living status, living arrangement, antidepressant medication use, and self- or proxy-reporting were obtained at the baseline from the mailed questionnaire data. Information on sex was cross-referenced from the mailed questionnaire data and EHR data. Body mass index (BMI) was determined by weight (kg)/height (m)2 and classified as indicated by the National Institutes of Health Guidelines [46]. For BMIs < 18.5 kg/m2, participants were categorized as underweight, 18.5–24.9 kg/m2 was considered normal, 25.0–29.9 kg/m2 was identified as overweight, 30.0–34.9 kg/m2 was indicated as obese class I, and the combined obesity classes II and III were set at ≥ 35 kg/m2. Demographic information, including race and smoking status, was collected from EHR data. ## 2.5. Statistical Analysis Baseline descriptive characteristics were presented as means ± standard deviation for continuous variables or as numbers and percentages for categorical variables. Differences between the groups were determined by the Kruskal–Wallis test for non-normally distributed continuous variables, while the chi-square test or Fisher’s exact test was used for the categorical variables. Cox proportional-hazards models were used to estimate the hazard ratios (HRs) and $95\%$ confidence intervals (CIs) used to test the association between diet quality tertiles and the incidence of all-cause dementia within a 12-year follow-up duration (2009–2021). In all analyses, the lowest tertile in diet quality was used as the reference group. The proportional hazards assumption was tested by adding interaction effects for related covariates by time including age, sex, and diet quality. Since time-dependent sex did not satisfy the proportional hazards assumption (p-value < 0.001), sex was included in a strata statement within all Cox proportional-hazards models to permit non-proportionality. Model 1 was adjusted for age and sex. Model 2 was further adjusted for race, BMI, educational level, smoking status, physical activity, diabetes status, hypertension status, coronary heart disease status, living status, living arrangement, use of antidepressant medication, and self- or proxy-reporting. To test for trends between the tertiles of the diet quality and the subsequent risk for all-cause dementia, we designated participants by the median value of their corresponding tertile in the diet quality as a continuous variable. To assess the short- and long-term temporal relationship between diet quality and all-cause dementia, Cox proportional-hazards models were calculated for the development of dementia during the first 4 years of the follow-up (2009–2013), and those after 4 years of the follow-up separately. Potential effect modifiers including age, BMI, educational level, and physical activity level for the diet–dementia relationship were assessed by including multiplicated terms in separate models. All statistical analyses were conducted using SAS version 9.4 (SAS Institute, Cary, NC, USA). Statistical significance was determined at a p-value of < 0.05. ## 3. Results The mean age of the overall cohort was 84.1 years at the study baseline. After a maximum follow-up time of 11.7 years and a mean follow-up time of 6.90 years, 408 cases of incident all-cause dementia were identified. Participants with better adherence to higher diet qualities were more likely to have a higher education level, not ever have been a smoker, and participate in daily physical activities (Table 1). Participants with lower diet qualities were more likely to live alone in either a house, apartment, condominium, or mobile home (Table 1). Additional descriptive or demographic characteristics were not determined to be significant across the tertiles of the diet qualities (Table 1). Having a higher diet quality was not significantly associated with a lower risk of all-cause dementia (fully adjusted HR for the highest compared with the lowest tertile: 1.01, $95\%$ CI: 0.79, 1.29, P-trend = 0.95) (Table 2). When we limited our analysis to dementia onset during the first 4 years of the follow-up (2009 to 2013), a non-significant trend was demonstrated between having a higher overall diet quality and a reduced risk of developing all-cause dementia (fully adjusted HR for the highest compared with the lowest tertile: 0.80, $95\%$ CI: 0.52, 1.22, P-trend = 0.29) (Figure 2). In contrast, our 4-year lag analysis, excluding participants who were diagnosed with all-cause dementia within the first 4 years of the follow-up, did not materially change the non-significance of the results (fully adjusted HR for the highest compared with the lowest tertile: 1.14, $95\%$ CI: 0.84, 1.55, P-trend = 0.41) (Figure 2). Furthermore, we did not observe any significant associations between the diet quality and the risk of Alzheimer’s disease (fully adjusted HR for the highest compared with the lowest tertile: 1.04, $95\%$ CI: 0.68, 1.60, P-trend = 0.86) or the risk of other forms of dementia (fully adjusted HR for the highest compared with the lowest tertile: 1.00, $95\%$ CI: 0.74, 1.35, P-trend = 0.99). There were no significant effect modifications between diet quality and potential confounders of interest including age, BMI, educational level, and physical activity (p-value > 0.05 for all). ## 4. Discussion In our prospective cohort study conducted on 2232 oldest old adults in rural Pennsylvania with a mean follow-up time of 6.90 years, overall higher diet quality was not significantly associated with incident all-cause dementia. Similar non-significant associations were observed for different subtypes of dementia. Having a higher diet quality was non-significantly associated with a lower risk of all-cause dementia development during the first 4 years of the follow-up. Therefore, healthy dietary modifications might be suggestive as a protective factor in preventing dementia during short-term follow-up periods, yet may no longer be considered predictive of the incident of dementia risk beyond the 4 years of follow-up. Several cohort studies have prospectively examined overall dietary patterns or diet quality on the risk of developing dementia, however, the findings are mixed and primarily focused on populations not restricted to the oldest old [17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35]. Notably, a longitudinal investigation of dietary habits and dementia risk among those 80 years and older did not identify an inverse correlation between greater adherence to the Mediterranean diet pattern, as an indication of diet quality, and a reduced incidence of dementia [36]. This finding is consistent with the results from our study indicating no statistically strong associations between diet quality and the incidence of dementia during the follow-up. To the best of our knowledge, our prospective study is the first to examine the association between overall diet quality as assessed by a screening tool rather than dietary patterns with the longitudinal risk of all-cause dementia among a rural cohort of the oldest old. Diet, especially dietary patterns, has gained interest as a modifiable factor to encourage healthy aging, however, studies are few among the oldest old [47,48]. Several studies have examined the associations between tea consumption [49], dietary patterns high in red meat, potato, gravy, and butter [50], and dietary diversity [51] with cognitive decline among the oldest old population. Findings pooled together from prospective cohort studies found adherence to better diet quality or a healthy dietary pattern was significantly associated with reduced overall dementia and Alzheimer’s disease risk [37]. Among our GRAS cohort, the oldest old participants reside in primarily rural settings and, therefore, may be more susceptible to disparities that are associated with poor diet quality and unfavorable health outcomes [52]. Older persons, especially the oldest old population, are at risk of having a poor diet and associated malnutrition accompanied by a decline in physiological functions [47]. Likewise, alterations in eating behaviors along with abnormal dietary changes are more often present in individuals with dementia [53,54]. As such, among the oldest old, a decreased quantity of food intake alongside the intake of less varied foods were found to occur more frequently in persons with dementia [36]. We observed a trend between better diet quality and lower dementia risk after restricting our analysis to include incident cases identified within the first 4 years of the follow-up, although not in our 4-year lag analysis. Therefore, more prospective studies with an earlier baseline and longer follow-up duration are warranted. Our study focused on a rural cohort of the oldest old adults: an age group often understudied in research, where population-based studies with very old age participants are infrequent. Diagnosing dementia in the oldest old can pose several challenges since prospective studies inclusive of this population tend to have smaller sample sizes, in addition to lacking sufficient normative data in the oldest old group [55]. Previous studies on individuals above 85 years of age have found variable results in age-specific incidence rates of dementia and Alzheimer’s disease, thus, implying inconsistencies in determining risk in this age group [56,57]. Further, the age-related likelihood of having mixed dementia increases among community-dwelling older people and the oldest old [58,59,60]. Therefore, we cannot exclude the possibility of other mixed pathologies manifesting among participants diagnosed with Alzheimer’s disease or other dementias in our rural cohort of the oldest old. The results of diet quality and the risk of different subtypes of dementia should be interpreted with caution. Dementia cases were identified by an electronic search algorithm from EHR data based on ICD codes. This search strategy may be considered less extensive in comparison to a comprehensive manual review, in-person clinical assessments, and validation of each case by a panel of neurologists. In a recent large prospective study of about 500,000 middle-aged to late adults, 1051 cases of total incident dementia, including 352 cases of Alzheimer’s disease, were identified using ICD codes, thus, around $33\%$ of Alzheimer’s disease cases contributed to all-cause dementia cases; however, the potential for under-detecting incident cases of dementia was noted [61]. The reported percentage of Alzheimer’s disease is similar to that observed in our study, although, in contrast to our ascertainment of dementia cases, more extensive ICD codes, including specific subtypes of dementia and general dementia were selected [61]. It may be possible that our search strategy under-identified incident cases and lacked the statistical power required to detect a significant association between diet quality and dementia risk. Although there are limitations to using electronic health records for dementia case detection, such linkage to the Geisinger Healthcare database allows for a streamlined method for identifying cases. There are several additional limitations to note in our present study. First, we acknowledge we were unable to adjust for energy intake, a covariate often controlled for in analyses of diet quality and other outcomes. Since the DST is limited to questions that were associated with diet quality in the GRAS population, this survey questionnaire may likely be population specific. Therefore, a quantitative assessment of energy or other nutrients is not feasible, although the DST has shown good agreement with HEI scores among the oldest old [40]. A constraint of using electronic medical record data is that each diagnosis was not confirmed in an EHR review by a neurologist, which may lead to the potential for misclassification. Our study population is primarily non-Hispanic white, so our findings may not be generalizable to other populations. Overall diet quality was assessed only at the baseline and, therefore, repeated diet quality assessments may support a better understanding of the development of dementia. Finally, the survey questionnaire relies on self- or proxy-reporting; thus, the results are exposed to recall bias. Despite the limitations addressed, our study has several strengths. First, our study included a rural population of the oldest old adults, an understudied age group in research. Second, using longitudinal data in our analysis permits an investigation into the long-term relationship between overall diet quality and dementia in an advanced-age population. Our findings add to the limited number of prospective studies regarding this association among the oldest old. Another strength is the use of the DST as a validated measure of diet quality for this population [40]. 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--- title: Methoxyflavones from Black Ginger (Kaempferia parviflora Wall. ex Baker) and their Inhibitory Effect on Melanogenesis in B16F10 Mouse Melanoma Cells authors: - Chen Huo - Sullim Lee - Min Jeong Yoo - Bum Soo Lee - Yoon Seo Jang - Ho Kyong Kim - Seulah Lee - Han Yong Bae - Ki Hyun Kim journal: Plants year: 2023 pmcid: PMC10005586 doi: 10.3390/plants12051183 license: CC BY 4.0 --- # Methoxyflavones from Black Ginger (Kaempferia parviflora Wall. ex Baker) and their Inhibitory Effect on Melanogenesis in B16F10 Mouse Melanoma Cells ## Abstract Kaempferia parviflora Wall. ex Baker (Zingiberaceae), commonly known as Thai ginseng or black ginger, is a tropical medicinal plant in many regions. It has been traditionally used to treat various ailments, including ulcers, dysentery, gout, allergies, abscesses, and osteoarthritis. As part of our ongoing phytochemical study aimed at discovering bioactive natural products, we investigated potential bioactive methoxyflavones from K. parviflora rhizomes. Phytochemical analysis aided by liquid chromatography–mass spectrometry (LC-MS) led to the isolation of six methoxyflavones (1–6) from the n-hexane fraction of the methanolic extract of K. parviflora rhizomes. The isolated compounds were structurally determined to be 3,7-dimethoxy-5-hydroxyflavone [1], 5-hydroxy-7-methoxyflavone [2], 7,4′-dimethylapigenin [3], 3,5,7-trimethoxyflavone [4], 3,7,4′-trimethylkaempferol [5], and 5-hydroxy-3,7,3′,4′-tetramethoxyflavone [6], based on NMR data and LC-MS analysis. All of the isolated compounds were evaluated for their anti-melanogenic activities. In the activity assay, 7,4′-dimethylapigenin [3] and 3,5,7-trimethoxyflavone [4] significantly inhibited tyrosinase activity and melanin content in IBMX-stimulated B16F10 cells. In addition, structure–activity relationship analysis revealed that the methoxy group at C-5 in methoxyflavones is key to their anti-melanogenic activity. This study experimentally demonstrated that K. parviflora rhizomes are rich in methoxyflavones and can be a valuable natural resource for anti-melanogenic compounds. ## 1. Introduction The amount and distribution of melanin, a pigment present in the skin epidermis, are decisive factors in determining skin color. Melanin plays an important role in protecting the skin from ultraviolet rays and harmful external factors [1,2,3]. However, the excessive production and accumulation of melanin in the skin causes spots and freckles. In addition, melanin precursors can cause cell death due to toxicity and diseases, such as skin cancer [4]. The enzymes involved in melanin synthesis include tyrosinase, tyrosinase-related protein-1 (TRP-1), and dopachrome tautomerase (TRP-2). Tyrosinase acts in the initial reaction, the rate-determining step of melanin synthesis, and oxidizes tyrosine to DOPA-quinone via 3,4-dihydroxyphenylalanine (DOPA) [5,6,7]. DOPA-quinone is converted to dopachrome without a catalytic reaction and is converted to 5,6-dihydroxyindole-2-carboxylic acid (DHICA) by the catalyst TRP-2. DHICA is converted to indole-5,6-quinone-2-carboxylic acid by the catalyst TRP-1, which converts it to melanin [5,7,8]. Therefore, the inhibition of tyrosinase, TRP-1, and TRP-2, which catalyze melanogenesis, is an important target for anti-melanogenic activities. Phenol derivatives, such as hydroquinone, resorcinol, l-ascorbic acid and its derivatives, arbutin, lactic acid, glucosamine, and tunicamycin, have been developed as representative melanin production inhibitors; however, their use is strictly limited owing to problems such as skin irritation and safety concerns [9,10,11,12,13,14]. Therefore, research is being actively conducted to identify safe and effective natural whitening agents. Kaempferia parviflora Wall. ex Baker, known as Thai ginseng or black ginger, belongs to the family Zingiberaceae and is widely distributed in northern Thailand [15]. According to past efficacy and safety evaluations, traditional medicines derived from the rhizome of K. parviflora can be used to treat hypertension, inflammation, peptic and colic disorders, allergy, osteoarthritis, and duodenal ulcers [16,17,18]. In addition, K. parviflora extract has a wide range of pharmacological effects, including antioxidant, anti-inflammatory, antitumor, cardioprotective, antiallergic, and antibacterial activities [19]. Phytochemical investigations of K. parviflora have led to the identification of several biologically active compounds, such as isopimarane, labdane- and clerodane-type diterpenoids, phenolic acids, flavonoids, and steroids [20]. Moreover, K. parviflora rhizome extracts have been highlighted to contain flavonoids that exhibit potent biological activities, including antioxidant, neuroprotective, and cognition-enhancing effects [21]. The major components of K. parviflora rhizomes are methoxyflavones, structurally identified as 5,7-dimethoxyflavone, 5,7,4′-trimethoxyflavone, and 3,5,7,3′,4′-pentamethoxyflavone [18,22,23], the pharmacokinetic characteristics of which have been investigated [20,24]. In a previous study, 5,7-dimethoxyflavone was shown to reduce the viability of HepG2 cancer cells with an IC50 of 25 μM by generating reactive oxygen species and significantly reducing the mitochondrial membrane potential, suggesting that it might be considered to be an anti-liver cancer lead compound [25]. In another study, 5,7,4′-trimethoxyflavone exhibited anti-plasmodial activity against Plasmodium falciparum, indicating the possibilities of development as a treatment agent for the malaria parasite [21]. According to a recent study, 3,5,7,3′,4′-pentamethoxyflavone had a relaxing effect on isolated human corpus cavernosum tissue during a sex change operation [26], indicating the potential of this compound as an effective agent to stimulate sexual activity in men. Another methoxyflavone isolated from this plant, 5-hydroxy-3,7,3′,4′-tetramethoxyflavone, was examined for its inhibitory activity against nitric oxide production and exhibited potent anti-inflammatory activity [27]. Considering the biological activities of these methoxyflavones from K. parviflora rhizomes, it is essential to investigate methoxyflavone derivatives from this plant to develop novel therapeutics. As part of continuing natural product discovery research for new bioactive constituents from interesting natural resources [28,29,30,31,32], we investigated potential bioactive flavonoids from K. parviflora rhizomes. In our recent study on K. parviflora rhizomes, we found that methoxyflavones inhibit tumor necrosis factor-α-induced interstitial collagenase (MMP-1) in human dermal fibroblasts. Among them, 3,5,7-trimethoxyflavone inhibits the pro-inflammatory cytokines interleukin (IL)-1β, IL-6, and IL-8, thus counteracting skin damage [33]. As part of an ongoing study on the discovery of bioactive phytochemicals with beneficial cosmetic properties from K. parviflora rhizomes, we isolated six methoxyflavones (1–6) from the methanolic extract of these rhizomes using column chromatography and high-performance liquid chromatography (HPLC) purification coupled with liquid chromatography–mass spectrometry (LC-MS) analysis. The isolated compounds were tested for their anti-melanogenic activity in B16F10 mouse melanoma cells, and their structure–activity relationships (SARs) were investigated. Herein, we describe the separation and structural elucidation of Compounds 1–6, the evaluation of their anti-melanogenic activity, and SARs. ## 2.1. Isolation and Structural Identification of Compounds The extraction of the rhizomes of K. parviflora with $80\%$ MeOH to give the resultant MeOH extract, and then the MeOH extract, was effectively partitioned with four different organic solvents to obtain four main fractions. Each fraction was evaporated to dryness in vacuo to give the following yields: hexane (1.0 g), dichloromethane (CH2Cl2, 3.2 g), ethyl acetate (EtOAc, 0.4 g), and n-butanol (BuOH, 0.5 g)-soluble fractions (Figure 1). Each fraction was analyzed using a house-built UV library database in our LC-MS system, which verified that the hexane fraction was rich in flavonoids. Column chromatography and semi-preparative HPLC separation were efficiently applied, leading to the isolation of six structurally related methoxyflavones (Figure 1). These methoxyflavones were determined to be 3,7-dimethoxy-5-hydroxyflavone [1] [34], 5-hydroxy-7-methoxyflavone [2] [35], 7,4′-dimethylapigenin [3] [36], 3,5,7-trimethoxyflavone [4] [37], 3,7,4′-trimethylkaempferol [5] [22], and 5-hydroxy-3,7,3′,4′-tetramethoxyflavone [6] [38] (Figure 2) by comparing their 1D nuclear magnetic resonance (NMR) spectroscopic data (Figures S1, S3, S5, S7, S9 and S11) with those previously reported and MS data obtained from LC-MS analyses (Figures S2, S4, S6, S8, S10 and S12). ## 2.2. Effects of Methoxyflavones 1–6 on Viability of B16F10 Mouse Melanoma Cells We evaluated the inhibitory effects of the isolated methoxyflavones 1–6 on 3-isobutyl-1-methylxanthine (IBMX)-induced melanogenesis in B16F10 cells. Before the evaluation, the effect of each methoxyflavone on B16F10 cell viability was examined. B16F10 cells were treated with methoxyflavones at 12.5, 25, 50, and 100 μM for 24 h. No differences in cell viability were observed between the methoxyflavone-treated and control groups (Figure 3). Therefore, the concentration range of 25–100 μM was selected for further experiments. ## 2.3. Inhibitory Effect of Methoxyflavones 1–6 on Melanogenesis in B16F10 Mouse Melanoma Cells Melanin increases the l-tyrosine to l-DOPA ratio by activating tyrosinase in melanocytes and synthesizing l-DOPA-quinone, TRP-2, and TRP-1, which are finally transformed into red-type eumelanin or brown-type pheomelanin [39,40]. Melanin hyperproduction is caused by the increased oxidative stress induced by external stimuli. Oxidative stress oxidizes DNA and proteins and causes lipid peroxidation, which plays a major role in increasing the proportion of unsaturated fatty acids. In addition, these stresses excessively increase melanin synthesis and pigmentation in skin melanocytes and contribute to the development of skin cancer [41,42]. Similarly to these oxidative stresses, IBMX inhibits phosphodiesterase, increases cAMP levels, and activates the ERK and PI3K/Akt signaling pathways. These changes promote the production of melanogenesis-related proteins and induce melanin hyperproduction [43]. The anti-melanogenic effects of methoxyflavones 1–6 on IBMX-induced melanogenesis in B16F10 melanoma cells were investigated. As shown in Figure 4, methoxyflavones 3 and 4 decreased cellular tyrosinase activity in IBMX-stimulated B16F10 cells. The IBMX-stimulated group showed a 3.18 ± 0.06-fold ($p \leq 0.01$) increase in tyrosinase activity compared to that in the vehicle group. Tyrosinase activity decreased in the positive control group treated with kojic acid at 12.5 μM (2.13 ± 0.31-fold, $p \leq 0.05$) and 25 μM (1.38 ± 0.06-fold, $p \leq 0.01$) compared with that in the IBMX-treated group. Compound 3 significantly decreased the tyrosinase activity at 25–100 μM (25 μM: 2.25 ± 0.29-fold; 50 μM: 1.88 ± 0.08-fold, $p \leq 0.01$; 100 μM: 1.78 ± 0.07-fold, $p \leq 0.01$) compared to that in the IBMX-stimulated group. In addition, Compound 4 decreased tyrosinase activity at 50 μM (2.16 ± 0.29-fold, $p \leq 0.05$) and 100 μM (1.56 ± 0.13-fold, $p \leq 0.01$) compared to that in the IBMX-stimulated group. These results indicate that 7,4′-dimethylapigenin [3] and 3,5,7-trimethoxyflavone [4] significantly inhibited the IBMX-stimulated hyperactivity of tyrosinase. To investigate whether the inhibitory effects of the compounds on cellular tyrosinase influenced melanogenesis, melanin content was measured. As shown in Figure 5, methoxyflavones 3, 4, and 6 decreased the melanin content in IBMX-stimulated B16F10 cells. The IBMX-stimulated group showed a 4.72 ± 0.15-fold ($p \leq 0.001$) increase in melanin content compared to that in the vehicle group. Melanin content decreased in the positive control group treated with kojic acid at 12.5 μM (1.29 ± 0.24-fold, $p \leq 0.001$) and 25 μM (0.98 ± 0.06-fold, $p \leq 0.001$) compared with that in the IBMX-treated group. Compound 3 significantly decreased the melanin content at 12.5–100 μM (12.5 μM: 3.16 ± 0.28-fold, $p \leq 0.05$; 25 μM: 2.30 ± 0.25-fold, $p \leq 0.01$; 50 μM: 1.41 ± 0.08-fold, $p \leq 0.001$; 100 μM: 1.21 ± 0.06-fold, $p \leq 0.001$) compared to that in the IBMX-stimulated group. In addition, Compound 4 decreased melanin content at 50 μM (3.03 ± 0.26-fold, $p \leq 0.01$) and 100 μM (2.23 ± 0.16-fold, $p \leq 0.001$) compared to that in the IBMX-stimulated group. Compound 6 weakly inhibited melanin synthesis at 100 μM (3.24 ± 0.31-fold, $p \leq 0.001$) compared to that in the IBMX-stimulated group. These results indicate that 7,4′-dimethylapigenin [3] and 3,5,7-trimethoxyflavone [4] significantly inhibited IBMX-stimulated melanin overproduction. Therefore, methoxyflavones derived from K. parviflora rhizomes can be said to be effective in reducing melanogenesis. ## 2.4. SAR Analysis A better understanding of SARs can lead to the comprehension of the structural characteristics of compounds and the discovery of more potent therapeutic agents to treat and prevent some diseases. SARs have been used to investigate the effects of structural features of molecules on their biological activities; thus, they are considered to be a key tool for drug discovery [44,45,46]. While analyzing the results of anti-melanogenic activity tests, we found interesting SARs among the six methoxyflavones (Figure 6). First, the substitution of the methoxy group at C-4′ in the methoxyflavones enhanced the activity; Compound 3 exhibited the strongest activity, whereas Compound 2 lost its activity without the methoxy group, indicating that the methoxy group at C-4′ is key to anti-melanogenic activity. Second, the substitution of the methoxy group at C-5 in methoxyflavones is a key structural element involved in the activity, based on the moderate activity of Compound 4 and the loss of activity of Compound 1 on substituting a hydroxy group at C-5. Third, the substitution of the methoxy group at C-3 in the methoxyflavones decreased the activity, based on the strongest activity of Compound 3 and the loss of activity in Compound 5 upon substituting a methoxy group at C-3. Lastly, according to the results for Compound 4 and Compound 5, the methoxy group at C-5 in methoxyflavones had a greater positive effect on the activity than that of the methoxy group at C-4′. The roles of the methoxy groups in the biological activities of flavonoid derivatives are well-known [47,48,49]. Therefore, the anti-melanogenic activity of methoxyflavones depends not only on the number of methoxy groups but also on their position. ## 3.1. Plant Material K. parviflora rhizomes were purchased at Warorot Market in January 2020 from Chiang Mai City, Northern Thailand. One of the authors (K. H. Kim) authenticated the materials, and the voucher specimen (SKKU-BG 1908) was stored in the herbarium at the School of Pharmacy, Sungkyunkwan University, Suwon, Korea. ## 3.2. Extraction and Separation of Methoxyflavones The dried rhizomes of K. parviflora (132 g) were squashed and macerated separately with MeOH and partitioned with various solvents (n-hexane, CH2Cl2, EtOAc, and n-BuOH, 700 mL) for 24 h three times at ambient temperature. After that, each organic solvent was evaporated under reduced pressure using a rotary evaporator to obtain four fractions. Four fractions with increasing polarity were obtained: hexane (1.0 g), CH2Cl2 (3.2 g), EtOAc (0.4 g), and n-BuOH-soluble fractions (0.5 g). LC-MS analysis of each fraction indicated that the hexane fraction contained high-quality flavonoids; hence, it was selected for further isolation. LC-MS analysis was conducted using an Agilent 1200 Series HPLC system (Agilent Technologies, Santa Clara, CA, USA) equipped with a diode array detector, 6130 Series ESI mass spectrometer, and an analytical Kinetex C18 100 Å column (100 × 2.1 mm, 5 μm; flow rate: 0.3 mL/min; Phenomenex, Torrance, CA, USA). Thin-layer chromatography was carried out on precoated silica gel F254 plates and RP-C18 F254s plates (Merck, Darmstadt, Germany), and the plates were visualized under UV light (254 and 365 nm) by heating after spraying with anisaldehyde–sulfuric acid reagent. A portion of the hexane fraction (1.0 g) was chromatographed on a silica gel column with two gradient solvent systems—n-hexane/EtOAc (10:1, 3:1, 1:1) and CH2Cl2/MeOH (10:1, 1:1)—yielding six fractions (Fr.1–Fr.6). Fr.1 (31.1 mg) was subjected to semi-preparative reverse-phase HPLC with $94\%$ MeOH/H2O at a flow rate of 2 mL/min, yielding Compound 1 (1.8 mg) (Figure 1). Fr.2 (91.5 mg) was subjected to Sephadex LH-20 column chromatography with an isocratic solvent system comprising CH2Cl2/MeOH (2:8), yielding five subfractions (Sfr.2.1–Sfr.2.5). Sfr.2.2 (29.2 mg) was further subjected to semi-preparative reverse-phase HPLC with $78\%$ MeOH/H2O at a flow rate of 2 mL/min, yielding Compound 2 (4.9 mg). Similarly, Fr.5 (112.7 mg) was also performed on a Sephadex LH-20 column eluting with the same solvent system with Fr.2, yielding two subfractions (Sfr.5.1 and Sfr.5.2). Sfr.5.2 (28.2 mg) was further purified using semi-preparative reverse-phase HPLC with an isocratic solvent ($83\%$ MeOH/H2O) at a flow rate of 2 mL/min, yielding Compounds 3 (2.2 mg), 4 (2.5 mg), and 5 (3.4 mg). Finally, Fr.6 (271.8 mg) was also fractionated using Sephadex LH-20 column chromatography with an isocratic solvent system comprising CH2Cl2/MeOH (2:8), yielding four subfractions (Sfr.6.1–Sfr.6.4). Sfr.6.4 (46.3 mg) was efficiently purified using semi-preparative reverse-phase HPLC and eluted with $78\%$ MeOH/H2O at a flow rate of 2 mL/min, yielding Compound 6 (3.0 mg). ## 3.3. Cell Culture Mouse melanoma B16F10 cells (Korean Cell Line Bank, Seoul, Republic of Korea) were cultured in DMEM medium (Corning, Manassas, VA, USA), supplemented with $10\%$ (v/v) fetal bovine serum and $1\%$ penicillin/streptomycin, in a humidified atmosphere containing $5\%$ CO2 at 37 °C. ## 3.4. Cell Viability B16F10 cells were plated in 96-well plates at a density of 5 × 103 cells/well and were grown for 24 h. The following day, cells were treated with each compound (12.5, 25, 50, and 100 μM). After incubation for 24 h, EZ-Cytox solution was added to the culture medium and incubated for 2 h at 37 °C. The absorbance was measured at 450 nm using a microplate reader (SPARK 10M; Tecan, Männedorf, Switzerland). ## 3.5. Measurement of Cellular Tyrosinase Activity Tyrosinase activity was evaluated using a previous method [50]. B16F10 cells were plated in a 60 mm dish at a density of 5 × 105 cells/dish and grown for 24 h. The following day, the cells were treated with each compound (12.5, 25, 50, and 100 μM) and 100 mM IBMX (Sigma-Aldrich, St. Louis, MO, USA). After incubating for 72 h, the cells were collected and centrifuged. The supernatant was mixed with l-DOPA and incubated at 37 °C for 30 min. The absorbance was measured at 475 nm using a microplate reader (SPARK 10M). ## 3.6. Measurement of Cellular Melanin Content The melanin content was evaluated using a previous method [51]. B16F10 cells were plated in a 60 mm dish at a density of 5 × 105 cells/dish and grown for 24 h. The following day, cells were treated with each compound (12.5, 25, 50, and 100 μM) and 100 mM IBMX. After incubating for 72 h, the cells were collected and centrifuged. The pellet was collected and lysed with 1 N NaOH containing $10\%$ DMSO at 90 °C for 30 min. The absorbance was measured at 475 nm using a microplate reader (SPARK 10M). ## 3.7. Statistical Analysis All experiments were conducted in triplicate and are shown as the mean ± SEM. The differences were calculated using one-way analysis of variance, followed by Tukey’s test with GraphPad Prism version 8.0.1 (GraphPad Software Inc., La Jolla, CA, USA). Statistical significance was set at $p \leq 0.05.$ ## 4. Conclusions In summary, six methoxyflavones were isolated from the hexane fraction of the MeOH extract of K. parviflora rhizomes and characterized using LC-MS analysis. The compounds were identified as 3,7-dimethoxy-5-hydroxyflavone [1], 5-hydroxy-7-methoxyflavone [2], 7,4′-dimethylapigenin [3], 3,5,7-trimethoxyflavone [4], 3,7,4′-trimethylkaempferol [5], and 5-hydroxy-3,7,3′,4′-tetramethoxyflavone [6] using 1D NMR spectroscopic methods, MS data, and LC-MS analysis. In the anti-melanogenic activity assays, Compounds 3 and 4 significantly inhibited tyrosinase hyperactivity and melanin overproduction induced by IBMX. Notably, SAR analysis showed that the methoxy group at C-5 in methoxyflavones is key to their anti-melanogenic activity and that the previously unappreciated methoxy group plays a critical role in the anti-melanogenic activity of flavonoid derivatives. This study provides experimental evidence that K. parviflora rhizomes are rich in methoxyflavones and can be a valuable natural resource for anti-melanogenic compounds. ## References 1. Tucker M.A.. Melanoma epidemiology. Hematol. Oncol. Clin. N. Am. (2009) 23 383-395. 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--- title: Oral Treatment with the Extract of Euterpe oleracea Mart. Improves Motor Dysfunction and Reduces Brain Injury in Rats Subjected to Ischemic Stroke authors: - Leonan Lima Teixeira - Helma Maria Negrão da Silva Alencar - Luan Oliveira Ferreira - João Cleiton Martins Rodrigues - Rafael Dias de Souza - Laine Celestino Pinto - Nilton Akio Muto - Hervé Rogez - Arnaldo Jorge Martins-Filho - Vanessa Joia de Mello - Moises Hamoy - Edmar Tavares da Costa - Dielly Catrina Favacho Lopes journal: Nutrients year: 2023 pmcid: PMC10005587 doi: 10.3390/nu15051207 license: CC BY 4.0 --- # Oral Treatment with the Extract of Euterpe oleracea Mart. Improves Motor Dysfunction and Reduces Brain Injury in Rats Subjected to Ischemic Stroke ## Abstract Ischemic stroke is one of the principal causes of morbidity and mortality around the world. The pathophysiological mechanisms that lead to the formation of the stroke lesions range from the bioenergetic failure of the cells and the intense production of reactive oxygen species to neuroinflammation. The fruit of the açaí palm, Euterpe oleracea Mart. ( EO), is consumed by traditional populations in the Brazilian Amazon region, and it is known to have antioxidant and anti-inflammatory properties. We evaluated whether the clarified extract of EO was capable of reducing the area of lesion and promoting neuronal survival following ischemic stroke in rats. Animals submitted to ischemic stroke and treated with EO extract presented a significant improvement in their neurological deficit from the ninth day onward. We also observed a reduction in the extent of the cerebral injury and the preservation of the neurons of the cortical layers. Taken together, our findings indicate that treatment with EO extract in the acute phase following a stroke can trigger signaling pathways that culminate in neuronal survival and promote the partial recovery of neurological scores. However, further detailed studies of the intracellular signaling pathways are needed to better understand the mechanisms involved. ## 1. Introduction Stroke is the second leading cause of death in the world and the primary cause of disability [1] resulting in a loss of quality of life [2] and a significant burden for national and regional health systems. Ischemic stroke involves cellular and molecular events that begin with the bioenergetic failure of cells by processes that include global or focal brain hypoperfusion, excitotoxicity, oxidative stress, neuro-inflammation, blood-brain barrier dysfunction, and microvascular injuries which culminate in the death of neurons, as well as glial and endothelial cells [3,4,5]. While it is necessary to reverse ischemic stroke, tissue reperfusion can worsen the injuries by overloading the reactive oxygen species during the restoration of the blood flow [4]. In this context, many recent studies have focused on the antioxidant and anti-inflammatory properties of natural products in search of complementary therapeutic strategies for the attention of ischemia-reperfusion injuries [6,7]. The açaí palm, Euterpe oleracea Mart. ( family Arecaceae), is widely distributed in the Brazilian Amazon region and neighboring areas of Guyana, French Guyana, Surinam, Venezuela, Colombia, Ecuador, and Panama. This palm is found in both seasonally-flooded swamps (known in Brazil as várzeas) and on higher ground in areas with high precipitation rates [8,9]. The fruit (pericarp) is rich in proteins, fibers, lipids, and saturated and unsaturated fatty acids [10,11,12]. A number of recent studies have shown that the extracts of açaí fruit, leaves, and roots, and the oil of the fruit are rich in substances with antioxidant and anti-inflammatory properties that have also exhibited other biological activities, including antinociceptive, antimicrobial, and anticonvulsant properties [13,14,15,16,17,18,19,20]. These biological effects may be related to the chemical composition of the açaí fruit which is known to contain high concentrations of polyphenolic compounds such as anthocyanins and tocopherols, with the most abundant substances being cyanidin-glucoside, cyanidin-rutinoside, peonidin-glycoside, epicatechin, catechin, homoorientin, orientin, vitexin, isovitexin, taxifolin, and ferulic acid [14,21,22,23]. Polyphenolic compounds, such as flavonoids and proanthocyanidins, are widely reported in the literature to decrease the harmful effects of inflammatory processes, in addition to their antioxidant activity to reduce mainly reactive oxygen species [24]. Based on its known ethnopharmacological properties and potential benefits for human health, and given its growing relevance in human diets, the present study investigated the potential effects of açaí on ischemic events in a murine model of middle cerebral artery occlusion (MCAO). The study also evaluated the influence of the treatment on the histopathology of the area of ischemic lesion and neuronal death, on biochemical parameters, and on motor and behavioral alterations. ## 2.1. Clarified Lyophilized Açaí (Euterpe oleracea) Extract The clarified lyophilized extract of Euterpe oleracea Mart. ( EO) was kindly provided by Amazon Dreams (Belém, Pará, Brazil). The extract is obtained from the fruit (pericarp) using a production process that is patented by Amazon Dreams in collaboration with the Federal University of Pará (PI 8 1003060-3). The phytochemical procedures and the validation of the UHPLC-DAD methods have been described previously, and the analyses have shown that the principal compounds found in the fruit are cyanidin-3-glucoside (180 mg/L), cyanidin-3-rutinoside (450 mg/L), homoorientin (250 mg/L), orientin (380 mg/L), and taxifolin (310 mg/L) [14,20,25,26]. The work extract was reconstituted in filtered water to a concentration of 200 mg/mL, while the dosage used in the present study—200 mg/kg/day—was based on the findings of previous research [27,28]. ## 2.2. Animals and Experimental Design All the experimental procedures were conducted in accordance with the principles of the Brazilian National Council for the Control of Animal Experimentation (CONCEA) and were approved by the Ethics Committee on the Use of Animals of the Biological Sciences Institute of the Federal University of Pará (ICB-UFPA; CEUA No. 1225030320). All necessary precautions were taken to prevent animal suffering and distress. For the present study, 36 adult (8–10 weeks) male Wistar rats weighing 220 g (±30 g) were obtained from the Central Animal Facility of the Biological Sciences Institute of Federal University of Pará (ICB-UFPA). These animals were housed in polypropylene cages covered by a metallic grid in a controlled environment (22 ± 2 °C; $\frac{12}{12}$ h light/dark cycle) with ad libitum access to standard rat chow and water. After a seven-day acclimation period, the animals were allocated randomly to one of four experimental groups ($$n = 9$$ animals/group): (i) sham, (negative control), in which the animals received only the vehicle (water); (ii) sham + EO (positive control), in which the animals received the Euterpe oleracea (EO) extract; (iii) MCAO, the animals were submitted to middle cerebral artery occlusion for 30 min and received only the vehicle; or (iv) MCAO + EO. The surgical procedures were conducted invariably between 08:00 a.m. and 11:00 a.m. These four groups were monitored for 14 days after the surgery. The MCAO + EO animals received a dose of EO extract (200 mg/kg/day) via oral gavage 4.5 h after the beginning of the MCAO and an additional dose every day until the end of the experiment. Neurological scores were obtained every day during the experiment, with the first evaluation taking place 24 h after the first application of the EO extract, and the last record being obtained on the day following the final application. Neurological tests were conducted on the seventh and fourteenth days after the MCAO. Prior to euthanasia, blood samples were collected via cardiac puncture for biochemical analysis. After euthanasia, the brains were extracted, sectioned, and stained with cresyl violet ($0.3\%$) and neuronal antibody (Figure 1). ## 2.3. Middle Cerebral Artery Occlusion Surgery The MCAO surgery was conducted as described by Ferreira et al. [ 29]. For this process, the animals were anesthetized intraperitoneally (i.p.) with ketamine (80 mg/kg) and xylazine (10 mg/kg), then placed on a heated blanket. After the abolishment of the corneal reflex, the common carotid bifurcation was exposed through an incision to the cervical midline, revealing the internal and external carotid arteries. A silicone-coated nylon monofilament (Doccol Corp., Redlands, CA, USA) was then inserted into the stump of the external carotid artery and fed up the internal carotid artery to occlude the origin of the middle cerebral artery. The filament was withdrawn after 30 min to allow for reperfusion. The sham-operated animals were anesthetized and the carotid bifurcation exposed, but no filament was inserted. The incision was sutured, and the animals were returned to their home cages to regain consciousness, receiving dipyrone (500 mg/kg) subcutaneously for analgesia. All the sham-operated rats survived, although mortality was $33.3\%$ in the MCAO group (3 of the 9 animals), and $11.1\%$ (one animal) in the MCAO + EO group. ## 2.4. Neurological Score and Behavioral Test The neurological motor deficit (clinical scores) of the rats was evaluated daily after the MCAO, with the first score being recorded 24 h after first application of the EO extract and the final score being obtained on the day after the last application of the extract. Neurological deficit was assessed based on the five-point scale of Zhang et al. [ 30]: 0 = no neurological deficit; 1 = mild loss of contralateral forelimb muscle tone; 2 = loss of muscle tone in the contralateral forelimb of the lesion and circular movement to the side contralateral to the lesion when suspended by the tail; 3 = spontaneous circular movement to the contralateral side of the lesion; 4 = no spontaneous motor activity. The animals with a score of 4 were euthanized to avoid suffering and distress. The observer that evaluated the rats was blind to the treatments. ## 2.5. Histological Analyses After euthanasia, the animals were perfused transcardially with phosphate buffer saline (PBS, pH 7.4; 4 °C) and then with $4\%$ formaldehyde (pH 7.4). The brain was then extracted from the skull and fixed in $4\%$ formaldehyde for 72 h, followed by cryoprotection in $30\%$ sucrose for a further 72 h before being cut into serial coronal sections (40 μm) in a Leica 1850 UV semi-automatic cryostat (Leica Microsystems, Wetzlar, Germany). The sections were mounted on gelatin-coated microscope slides, air-dried, and then stained with cresyl violet ($0.3\%$) to measure the percentage area of cerebral infarction [31]. The sections were then dehydrated in an increasing ethanol series, cleared in xylene, and coverslipped with Entellan (Merck, Danstadt, Germany). The images were captured using a Toshiba digital camera (Toshiba America Inc., NY, USA), and the percentage of infarction area (PIA) was calculated using the formula [29]: PIA = (area of infarction/area of the ipsilateral hemisphere) × 100. The neuronal cells were immunostained overnight at 4 °C using the free-floating method with the anti-NeuN mouse primary antibody (1:2000, Merck-Millipore, Darmstadt, Germany). After washing with phosphate buffered saline (PBS), a DAKO EnVisionTM + Dual Link System-HRP kit (Carpinteria, CA, USA) was used according to the manufacturer’s protocol. This system is based on an HRP-labeled polymer which is conjugated with secondary antibodies. After washing with PBS, DAB (3,3′-diaminobendine; Sigma-Aldrich) was used as a chromogen to stain the sections. Finally, the sections were mounted on gelatin-coated microscope slides, air-dried, dehydrated in an increasing ethanol series, cleared in xylene, and coverslipped with DPX mounting medium. The images were captured using a Toshiba digital camera (Toshiba America Inc., NY, USA) and analyzed with the Stereoinvestigator (MBF Bioscience, Williston, VT, USA) and ImageJ software (NIH, Bethesda, MD, USA). The NeuN+ cells were counted in eight sections from the lesion and peri-infarction area in the somatosensory cortex, and the striatum ipsilateral to the area of the lesion in sample fields of 200 μm × 200 μm, separated by cortical layers, with $$n = 6$$ animals per group [32]. ## 2.6. Biochemical Analysis For the biochemical assays, the serum was extracted from the blood samples (obtained by cardiac puncture) by centrifugation at 3000 rpm for 10 min. The following nine biochemical parameters were determined using a Wiener CM200 chemical analyzer, following the manufacturer’s instructions: aspartate aminotransferase (AST), alanine aminotransferase (ALT), urea (URE), creatinine (CRE), high-density lipoprotein (HDL), very low-density lipoprotein (VLDL), low-density lipoprotein (LDL), total cholesterol (CHO), and triglyceride (TRY). All the analyses were run in the ICB-UFPA Laboratory of Clinical Analyses (LAC). ## 2.7. Statistical Analyses Prior to the analyses, the normality of the variances was verified using the Kolmogorov-Smirnov test. The data are presented as the mean with the standard deviation (SD), and the F and p values included whenever relevant. A $p \leq 0.05$ significance level was considered for all the analyses. Differences between pairs of groups were analyzed using Student’s t test, while those among three or more groups were evaluated using a two-way Analysis of Variance (ANOVA), followed by Tukey’s post hoc test for pairwise comparisons. The data were analyzed using GraphPad Prism, version 9 (Graph-Pad Software Inc., San Diego, CA, USA). ## 3.1. Açaí Extract Improves the Behavioral Outcome of Animals Submitted to Ischemic Stroke The neurological scores varied significantly among treatment groups and days (F [39, 392] = 2.419; $p \leq 0.0001$; Figure 2). The comparison of the days among treatment groups (F [3, 392] = 1996; $p \leq 0.0001$) showed that there was no significant variation between the sham and sham + EO groups (control groups) over the course of the experiment ($p \leq 0.9999$). The neurological scores of the ischemic animals (MCAO groups) were significantly different from those of the control groups ($p \leq 0.0001$, for all comparisons). The animals of the MCAO group presented high rates of spontaneous circular movement to the contralateral side of the lesion (score 3), while treatment with EO extract (MCAO + EO group) alleviated the neurological dysfunction significantly in comparison with the MCAO group from the ninth day (D9) onward (D9 and D10: $$p \leq 0.0287$$; D11 and D12: $$p \leq 0.0021$$; D13 and D14: $p \leq 0.0001$; for MCAO vs. MCAO + EO groups), with the rats presenting a loss of muscle tone in the contralateral forelimb and circular movements to the contralateral side when suspended by the tail (score 2). Clinical improvement was observed in the MCAO + EO group during the course of the treatment (Supplementary Table S1), with days D9 and D10 being significantly different in comparison with D1 and D2 ($p \leq 0.01$), and D11–D12 significantly different from D1–D3 ($p \leq 0.01$). Days D13–D14 were also significantly different from each of the five first days of the treatment, i.e., D1–D5 ($p \leq 0.01$). ## 3.2. Açaí Extract Reduces the Size of the Area of Infarction and Increases the Number of Surviving Neurons after Ischemic Stroke The behavioral changes observed in the ischemic animals are consistent with lesions in the cortical and subcortical regions and indicate attenuation of the damage by treatment with the EO extract. The ischemic animals presented cortical-subcortical injuries with damage in the primary and secondary somatosensory areas and in the striatum, with a mean area of hemispheric lesion of 31.37 ± $13.32\%$ (MCAO group). After 14 days of EO treatment (MCAO + EO group), the mean area affected was reduced to values 15.02 ± $5.39\%$ ($$p \leq 0.0217$$). No lesions were observed in any of the animals of the two control groups (Figure 3). Cerebral ischemia culminates in cell death, mainly of neurons in the cortical region, which was the reason the NeuN+ cell count was conducted per cortical layer (Figure 4A). As no difference was found between the animals of the two control groups (sham and sham + EO), it appears that the administration of the EO extract does not alter the density of the cortical neurons or induce cell death (Figure 4B–G; Supplementary Table S2). However, the MCAO did cause neuronal death, as indicated by the significant decrease in the number of NeuN+ cells in layers II/III through VI (Figure 4B–G; Supplementary Table S2) of the cortical region of the somatosensory cortex (Figure 3 and Figure 4A). Surprisingly, the animals that received EO extract for 14 days presented significant levels of preservation in the number of NeuN+ cells in these same layers (Figure 4B–G; Supplementary Table S2). This result indicates that the EO treatment may activate neuronal survival pathways. The area adjacent to the ischemic lesion, known as the penumbra, is also of considerable importance, in particular because it is a potentially recoverable area. In the present study, treatment with the EO extract was reflected in a significantly higher number of NeuN+ cells in the treated ischemic animals (MCAO + EO group) in comparison with the MCAO group (Figure 4H, Supplementary Table S2; $p \leq 0.01$). The number of NeuN+ cells of the striatum was also reduced significantly in the animals of the MCAO group (Figure 4I, Supplementary Table S2; $p \leq 0.01$), indicating that the treatment with the EO extract contributed significantly to the preservation of the cells in this region. These findings indicate that the EO treatment offers important protective effects. ## 3.3. Açaí Extract Did Not Cause Biochemical Alterations after Ischemic Stroke No significant variation was found in the biochemical parameters of the animals treated with EO extract following ischemic stroke (Figure 5). ## 4. Discussion The present study is the first to demonstrate that the extract of Euterpe oleracea may attenuate the progression of the damage caused by cerebral ischemia, as demonstrated by the improvement of neurological function, the reduction of ischemic area, and the decrease in neuronal death. We also showed that the application of the extract did not alter either the lipid profile or the liver and kidney functions of the experimental animals. This is an important point because the application does not limit therapy in patients with hepatic or renal impairments. In most experimental studies, the effects of anthocyanins with antioxidant and anti-inflammatory properties have been shown to attenuate injuries caused by ischemia and reperfusion [33,34,35,36]. The literature elucidates the fact that polyphenols found in EO extract have positive effects on modulating oxidative and inflammatory activity in in vivo and in vitro models by decreasing reactive oxygen species, increasing antioxidant activity, and regulating inflammatory mediators [37,38]. Furthermore, these effects have also been found in humans submitted to ingestion of the EO extract consisting of polyphenolic compounds such as cyanidin-3-glycoside and cyanidin-3-rutinoside, as well as monomeric catechin and epicatechin or oligomeric procyanidins [39,40]. Shin et al. [ 33] showed that, after 24 h of reperfusion, pretreatment with anthocyanins extracted from the bilberry (300 mg/kg of anthocyanins) enabled a reduction in the size of the cerebral infarction, a finding which may have been related to the suppression of the JNK and p53 signaling pathways. Cui et al. [ 35] demonstrated that the pretreatment of mice with Myrica rubra, whose principal anthocyanin is cyanidin-3-O-glucoside, for seven days prior to the MCAO, was effective for the reduction of both damage to neurological function and the volume of the cerebral infarction, principally in the middle- and high-dose groups (150 and 300 mg/kg). The neurological scores of the ischemic animals treated with EO extract improved significantly from the ninth day onward. Sunil et al. [ 34] found that the total oligomeric flavonoids of Cyperus rotundus (200 mg/kg, by oral gavage), a traditional Indian herb from the Ayurvedic medicine system, when administered to rats once a day for one week (including four days prior to the MCAO and three days afterward), alleviated the neurological deficit significantly in comparison with untreated ischemic rats. Fu et al. [ 36] also showed that a high dose (250 or 500 mg/kg) of proanthocyanidins extracted from grape seed improved the neurological score and decreased the area of infarction in comparison with the untreated MCAO group. Based on this evidence, our findings indicate that both the behavioral improvement and the reduction of the area of cerebral infarction may be related to the presence of anthocyanins (primarily cyanidin-3-glucoside and cyanidin-3-rutinoside) and non-anthocyanin flavonoids (homoorientin and orientin), which are the principal compounds of the clarified EO extract applied in the present study [14,20,25,26]. We also showed that the ischemic animals presented an important motor deficit and that the therapy with EO extract reduced this dysfunction, in particular after one week of treatment. The neurological deficits exhibited by the animals in the present study correlated with the brain region affected by the MCAO which caused injuries in areas that are important for motor processing, such as the somatosensory cortex and the striatum [32]. We also provided the first evidence that the ingestion of the EO extract at a dose of 200 mg/kg/day reduced the area of infarction and contained the neuronal damage. However, one of the limitations of the present study is the lack of evaluation of other motor parameters affected by stroke, such as balance and walking. Thus, more behavioral tests are needed to measure the potential benefit of EO extract in clinical improvement. Sensorial information is known to have a strong influence on motor processing and is essential for rehabilitation, especially following ischemic stroke [41,42]. Previous studies have shown that changes in the connections between the motor and somatosensory cortexes occur primarily during the acute phase of the stroke. This finding is extremely important because therapies initiated in the acute phase present greater benefits than those initiated later on [43,44,45]. Some studies have also demonstrated that the repair of neuronal connections following ischemic stroke depends on the cellular tissue adjacent to the infarcted area and on other factors, such as glial activation [46,47]. It is also important to note that the motor cortex is able to receive signals directly from the somatosensory cortex, although we cannot rule out the possibility that connections are mediated through the thalamus, especially its posterior medial portion [32,48]. In studies of rodents, these reciprocal connections maintained between the somatosensory and motor cortexes are innervated primarily by cortical layers II/III and V [49,50]. The present study showed a significant level of preservation of the neurons in these layers in the ischemic animals treated with EO extract, indicating that this neuroprotection may be related to the improvement in behavior. From this perspective, the partial integrity of these cortical layers may be important for the maintenance of the afferents/efferents connecting the subcortical and neocortical regions, such as the basal ganglia and the thalamic control centers [51]. This relationship reinforces the importance of the cortical neuroprotection that appeared to be provided by the use of the EO extract. One other important finding of the present study is that the use of EO extract for 14 days did not appear to alter the lipid homeostasis or metabolism and excretion pathways, such as liver and kidney functions. A number of previous studies have provided evidence of the significant therapeutic potential of compounds extracted from açaí for the control of the lipid profile and improvement of atherosclerosis, in addition to the prevention of hepatic steatosis, although this potential is dose-dependent and also affected by the treatment time [52,53]. It is nevertheless important to note that the pathophysiology of ischemic stroke-induced brain lesions is complex and multifactorial. The blockage of blood flow in the principal artery has a rapid effect on the neurons, which die by necrosis forming the nucleus of the ischemia (core of the lesion). However, the peripheral neurons may be supplied by arteries peripheral to the lesion and may undergo late apoptosis if no neuroprotective therapy is implemented [54]. Acute arterial occlusion leads to bioenergetic failure which occurs through oxygen and glucose deprivation (OGD), the loss of ionic homeostasis, excitotoxicity, mitochondrial dysfunction, and the generation of reactive oxygen species (ROS) in the neurons (Figure 6). This process may also lead to the activation of inflammatory cells that release cytokines, leading to an increase in inflammatory processes that may also be responsible for late apoptosis and the expansion of the area of the lesion [54,55]. Stroke triggers cell death by necrosis (acute) and apoptosis/neurodegeneration (late). More studies are therefore needed to better characterize signaling pathways where EO extract can act in the control and attenuation of apoptotic processes, as well as the antioxidant pathways that may be activated and result in neuronal survival. As observed in previous studies, the clarified EO extract is rich in these compounds [14,20,25,26]. Given this fact, one of the probable therapeutic targets of the EO extract would likely be the reduction in the formation of reactive oxygen species, helping to avoid DNA damage in addition to reducing the oxidative stress caused by glutamate excitotoxicity and mitochondrial damage. The process would improve the inflammatory status through the control of the reactive glia. This is one of the possible hypotheses that may explain the relief of stroke damage by the EO extract (Figure 6). From this perspective, studies involving reactive oxygen species generation, glial reactivity, and inflammatory biomarkers are needed to understand the mechanisms modulated by EO extract compounds on inflammatory and oxidative activity in a model of cerebral ischemia. ## 5. Conclusions Overall, the findings of the present study indicate clearly that treatment with EO extract in the acute phase following a stroke may trigger signaling pathways that culminate in neuronal survival and contribute to a partial recovery of the clinical scores of the animals. Even so, more systematic studies of the intracellular signaling pathways involved in this process will be needed to better understand its mechanisms. ## References 1. Benjamin E.J., Virani S.S., Callaway C.W., Chamberlain A.M., Chang A.R., Cheng S., Chiuve S.E., Cushman M., Delling F.N., Deo R.. Heart Disease and Stroke Statistics—2018 Update: A Report From the American Heart Association. Circulation (2018) 137 e67-e492. DOI: 10.1161/CIR.0000000000000558 2. Leon-Carrion J., Martin-Rodriguez J.F., Damas-Lopez J., Barroso y Martin J.M., Dominguez-Morales M.R.. Delta–alpha ratio correlates with level of recovery after neurorehabilitation in patients with acquired brain injury. Clin. Neurophysiol. 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--- title: 'Effectiveness of Apigenin, Resveratrol, and Curcumin as Adjuvant Nutraceuticals for Calvarial Bone Defect Healing: An In Vitro and Histological Study on Rats' authors: - Felice Lorusso - Antonio Scarano - Stefania Fulle - Luca Valbonetti - Rosa Mancinelli - Ester Sara Di Filippo journal: Nutrients year: 2023 pmcid: PMC10005597 doi: 10.3390/nu15051235 license: CC BY 4.0 --- # Effectiveness of Apigenin, Resveratrol, and Curcumin as Adjuvant Nutraceuticals for Calvarial Bone Defect Healing: An In Vitro and Histological Study on Rats ## Abstract Bone healing is a major clinical issue, especially in bone defects of critical dimensions. Some studies have reported in vivo positive effects on bone healing by some bioactive compounds, such as the phenolic derivatives found in vegetables and plants, such as resveratrol, curcumin, and apigenin. The aim of this work was [1] to analyze in vitro in human dental pulp stem cells the effects of these three natural compounds on the gene expression of related genes downstream to RUNX2 and SMAD5, key factor transcriptions associated with osteoblast differentiation, in order to better understand the positive effects that can occur in vivo in bone healing, and [2] to evaluate in vivo the effects on bone healing of critical-size defects in the calvaria in rats of these three nutraceuticals tested in parallel and for the first time administered by the gastric route. Upregulation of the RUNX2, SMAD5, COLL1, COLL4, and COLL5 genes in the presence of apigenin, curcumin, and resveratrol was detected. In vivo, apigenin induced more consistent significant bone healing in critical-size defects in rat calvaria compared to the other study groups. The study findings encourage a possible therapeutic supplementation with nutraceuticals during the bone regeneration process. ## 1. Introduction The treatment of critical-size bone defects in humans, severe maxillary atrophies and long-bone critical-size defects, often requires a multidisciplinary approach and extensive bone grafting [1,2]. The adoption of the appropriate animal study design gives reliable data and translational application to human bone defects [3,4]. The critical-size bone defect represents an orthotopic model where the hard tissue is not able to heal spontaneously with no intervention [5]. Experimental calvaria critical-size defects are histologically characterized by focal competition between inflammatory tissues and new bone formation [5]. This model has been validated for the evaluation of the biological effects of biomaterials to bridge nonunion defects. In addition, it is optimal to investigate the effects of adjuvant supplements on osteogenesis and bone maturation [6]. Human dental pulp stem cells (hDPSCs) are mesenchymal stem cells (MSCs) capable of both self-renewal and differentiation according to an osteogenic phenotype. In recent years, this capability has been proposed for tissue engineering and cell seeding on biomaterial to ameliorate new bone formation and graft osseointegration [7]. In addition, the osteogenic differentiation properties of MSCs are determinants to sustain the regenerative process [8]. Tissue engineering aims at the structural and functional restoration of damaged tissues through MSC differentiation protocols alone or complexed with biological scaffolds to produce a tissue neoformation immunologically, functionally, structurally, and mechanically identical to the native one [3]. Nutraceuticals represent bioactive compounds, products derived from food sources, characterized by medical or healthy benefits, including prevention and protection against several systemic diseases [9,10,11,12]. Indeed, bioactive compounds are involved in many physiological and pathophysiological processes as tissue damage repair or protection from chronic diseases and cellular oxidative stress [10,12,13,14]. Some in vivo studies have reported positive effects on bone healing by some bioactive compounds, such as the phenolic derivatives found in vegetables and plants, such as resveratrol, curcumin, and apigenin [11,12,13,14,15]. As reported by several studies, resveratrol is a polyphenol with antioxidant, anti-inflammatory, and antiaging properties [15]. In the literature, resveratrol has been evaluated in association with three-dimensional-cell–engineered scaffolds, showing the promotion of osteogenesis and the overexpression of the runt-related transcription factor 2 (RUNX2) and osteocalcin (OCN) genes [16,17,18]. In mice, resveratrol administered in combination with insulin produced a significant increase in new bone formation of critical-size defects in the calvaria in animals affected by diabetes. Furthermore, the combination of insulin and resveratrol induced the modulation of bone morphogenetic protein type 2 (BMP-2) gene expression [19,20]. Apigenin is a flavonoid commonly found in different plants (such as chamomile) and vegetables, and it is recognized for its antioxidant, anti-inflammatory, and protective properties in chronic diseases [21,22,23]. However, very little information is available about its effects on bone metabolism [24,25,26]. Zhang et al. reported that apigenin promotes osteogenic differentiation in MSCs via the JNK and p38 MAPK pathways, through increased expression of RUNX2 and osterix (OSX) proteins [23]. Furthermore, other studies have reported that apigenin inhibits osteoclastogenesis and osteoclast function [25,27,28]. Curcumin is a natural polyphenolic phytochemical that is characterized by a total of seven carbon linkers with three major functional groups, including α,β-unsaturated β-diketone with an aromatic O-methoxy-phenolic functional group [29,30,31]. Curcumin is able to modulate cytokines, growth factors, transcription factors, and inflammatory molecules through different pathways [32,33,34]. The principal way is associated with the inhibition of the transcription by nuclear factor-kappa B (NF-kB) [15]. As described in the literature, this molecule has a protective and preventive effect against oral cancer and several metabolic diseases [15]. In tissue engineering, it has been reported that curcumin elution nanopolymers produce in vitro an increased gene and protein expression of osteogenic markers RUNX2, ALP, and BMP2 [15,35,36]. Altogether, the cited studies have demonstrated that all three compounds induced an increase in gene and/or protein expression of RUNX2. To the best of our knowledge, however, no studies have been carried out on a simultaneous analysis of these three compounds and which pathways downstream of RUNX2 are modulated in the process of osteogenesis both in vivo and in vitro. Furthermore, even if these compounds exhibit a protective function of bone physiology [37], their role in bone fracture should be clarified [38]. The only study involving these three phenolic compounds concerns their effects in inducing cancer signaling pathway manipulation and possibly facilitating new treatment modalities for osteosarcoma [39]. Therefore, the aim of the present study was to [1] analyze in vitro in hDPSCs, in the presence of these three natural compounds, the gene expression downstream of RUNX2 and SMAD5, key factor transcriptions associated with osteoblast differentiation, in order to better understand the positive effects that can occur in vivo in bone healing; and [2] evaluate in vivo the effects of these three nutraceuticals used in parallel on the bone healing of critical-size defects in rat calvaria using an innovative method of administration by gastric gavage in a single dose daily repeated for 30 consecutive days. To this purpose, in vitro experiments were performed on hDPSCs in the presence of these three natural compounds added individually, and the gene expression of RUNX2, SMAD5, COLL1, COLL4, and COLL5 was analyzed. These substances can prove to be effective in the field of regenerative medicine as they modulate molecular mechanisms, which can therefore increase osteogenic differentiation and consequently improve bone regeneration. The investigated molecules could be considered promising supplements able to repair bone defect healing. The null hypothesis considered no differences in osteogenic properties and bone repair capability between the control and the different nutraceuticals groups. ## 2.1. Dental Pulp Stem Cells Cultures Human dental pulp stem cells (hDPSCs) were obtained by stem cell banks (#PT-5025, Lonza, Walkersville, MD, USA) and maintained at −80 °C. The hDPSCs were defrosted and cultured with a growth medium (GM) obtained by DPSC basal medium (#PT3927, Lonza, Walkersville, MD, USA), supplemented with the DPSC SingleQuots™ Kit (#PT4516, Lonza, Walkersville, MD, USA) and incubated at 37 °C and $5\%$ CO2. The GM was changed twice a week, just before cells became confluent (subconfluent). Once the cells reached an adequate number, they were washed twice with phosphate-buffered saline (PBS) (#ECB4004L, Euroclone, Milan, Italy) and detached using 1 mL Trypsin–EDTA 1× in PBS (#ECB3052D, Euroclone, Milan, Italy) for 5 min at 37 °C. The cells were collected in a sterile tube and centrifuged for 5 min at 900 rpm. Once resuspended, the cells were counted by a Bürker chamber and used for further experiments. The differentiation medium (DM) was obtained by supplementation of Human Mesenchymal Stem Cell Osteogenic Differentiation Basal Medium (#PT-3924, Lonza, Walkersville, MD, USA) with hMSC Osteogenic SingleQuots (#PT-4120, Lonza, Walkersville, MD, USA) and used to evaluate osteogenic differentiation. ## 2.2. In Vitro Study Design Human DPSCs were cultured as described above in GM for 24 and 72 h to perform the proliferation rate assay and in DM for 14 days to perform Alizarin Red and gene expression analysis in four experimental conditions: hDPSCs (control), hDPSCs + apigenin (#10798, Sigma-Aldrich, Saint Louis, MO, USA), hDPSCs + resveratrol (#R5010, Sigma-Aldrich, Saint Louis, MO, USA), hDPSCs + curcumin (#08511, Sigma-Aldrich, Saint Louis, MO, USA). The molecules were prepared in DMSO (#D5879, Sigma-Aldrich, Saint Louis, MO, USA) to avoid an immediate decomposition, according to the guidelines. ## 2.3. Cell Proliferation Assay Cells were seeded in 96-well plates at a density of 1.6 × 103 cells/well in 0.2 mL medium. After 2 h, the cells were stimulated with high or low concentrations of our compounds. High concentration: 1, 5, and 10 µM apigenin; 1, 10, and 50 µM resveratrol; and 1, 10, and 100 µM curcumin; low concentration: 100 and 500 nM and 1 µM apigenin; 100 and 500 nM and 1 µM resveratrol; 50, 100, and 500 nM curcumin. Cell proliferation in the presence of high concentrations was followed for 24 and 48 h and in the presence of low concentrations was followed for 24 and 72 h. At the end of each incubation interval, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-tetrazolium bromide (MTT, #M5655 Sigma-Aldrich, Saint Louis, MO, USA) was added to each well to a final concentration of 0.5 mg/mL. The plates were incubated for 3 h at 37 °C and then centrifuged at 500× g. The supernatants were removed and discarded, and 200 µL dimethyl sulfoxide (DMSO, #D5879, Sigma-Aldrich, Saint Louis, MO, USA) was added. After incubating for 30 min at 37 °C, the absorbance was determined by spectrophotometry (SpectraMAX 190) at a wavelength of 560 nm. ## 2.4. Alizarin Red Staining Cells were plated in 6-well plates at a density of about ~20,000 cells/well. After 24 h, GM was replaced with DM, and stimuli were added as follows: 1 µM apigenin, 100 nM resveratrol, and 50 nM curcumin. The Alizarin Red assay was performed to evaluate the mineralized nodule deposit in hDPSCs after 14 days. Cells were washed twice in PBS and then fixed with 1 mL/well of $4\%$ paraformaldehyde (#157–8, Electron Microscopy Sciences, Hatfield, PA, USA) for 15 min. Specimens were washed twice with deionized water, and 1 mL/well of $1\%$ Alizarin Red 40 nM (#A5533, Sigma-Aldrich, Saint Louis, MO, USA) was added and incubated for 20 min at room temperature. Specimens were then washed four times with deionized water for 5 min and viewed under a light microscope at a magnification of 10×. ## 2.5. Quantitative Real-Time PCR for Gene Expression Analysis Cells were plated at a density of 2 × 103 cells/cm2. After 24 h, GM was replaced with DM, and stimuli were added as follows: 1 µM apigenin, 100 nM resveratrol, and 50 nM curcumin. The cells were stimulated at these specific concentrations based on the results obtained with the cell proliferation assay. After 14 days of differentiation, hDPSCs were harvested for RNA extraction and real-time PCR analysis. The total RNA was isolated using Tri Reagent (#T9424, Sigma-Aldrich, Saint Louis, MO, USA), according to the manufacturer’s protocol. A quantity of 1 µg RNA was directly processed by High-Capacity cDNA Archive Kits (Applied Biosystems, Life Technologies, Monza-Italy) according to the manufacturer’s instructions. Singleplex real-time PCR was conducted to evaluate the relative quantification of gene expression of RUNX2, SMAD5, COLL4, COLL5, and COLL1 versus GAPDH by TaqMan technology on an ABI Prism 9700HT Sequence Detection System instrument, connected to Sequence Detector Software (SDS, version 2.0; Applied Biosystem, Life Technologies, Monza, Italy) for data collection and analysis. The primer pairs and TaqMan probes for all of the target genes and for the GAPDH reference gene were provided as 20× mixtures that were ready to use at a concentration of 1×. According to the manufacturer’s recommendations, 25 µL reactions were performed in a MicroAmp Optical 96-well reaction plate using the 12.5 µL 2× TaqMan Universal PCR Master Mix, with the 1.25 µL 20× Inventoried Gene Expression Product for the mouse Runx2 target gene, SMAD5, COLL1, COLL4, and COLL5 versus GAPDH (FAM-dye-labeled TaqMan MGB probe). PCR was performed at 50 °C for 2 min, and at 95 °C for 10 min, and then run for 45 cycles at 95 °C for 15 s and at 60 °C for 1 min. All of the reactions were run in triplicate, and each experiment was repeated three times. The relative quantification of target gene expression was evaluated with data from the SDS software, using the arithmetic formula 2−ΔΔCt, according to the comparative Ct method, which represents the amount of target, as normalized to the GAPDH endogenous control. Data derived from the 2−ΔΔCt formula are named relative quantification. ## 2.6.1. Surgical Procedure The study received the approval of the ethical committee of the local Ethics Committee of the University of Chieti-Pescara, Chieti, Italy (No. $\frac{84}{2020}$), and the Italian Ministry of Health. Twenty adult male Sprague Dawley rats were used for this study. Bone defects were produced in the calvaria bone (Figure 1A–D). Anesthesia was obtained by an intraperitoneal injection of sodium pentobarbital (Pentobarbital, Italy, 50 mg/kg). After shaving, the surgical field was prepared with $10\%$ iodine solution. A sagittal incision of the midline was made starting from the occipital region and proceeding with the periosteal dissection highlighting the parietal region. The unilateral cranial bone defect (diameter, 5 mm) (1 defect/rat) was produced by using a drill under abundant irrigation of sterile physiological solution. After removing the bone disc, the various planes were sutured. Pain relieving and antibiotic therapy were administered with the methods and dosages previously described [5,20]. Twenty experimental defects were created (Figure 1A–D):[1]Group A: Ctr—empty bone defect;[2]Group B: Resveratrol (resveratrol $98\%$, No. 3183, Galeno SRL, Comeana, Italy) 1 mL (10 mg/kg)—empty bone defect/administration of resveratrol by gastric gavage in a single dose daily [40];[3]Group C: Curcumin (curcumin $95\%$, No. 4507, Galeno SRL, Comeana, Italy) 1 mL (10 mg/kg)—empty bone defect/administration of curcumin by gastric gavage in a single dose daily [41];[4]Group D: Apigenin (apigenin $98\%$, Biorigins, Sandleheath, UK) 1 mL (10 mg/kg)—empty bone defect/administration of apigenin by gastric gavage in a single dose daily [42,43]. Concentration and solution preparation was performed following a previously described method by Correa et al. [ 40,41,44]. The resveratrol solution was prepared in 100 mL polysorbate 80 (Sigma-Aldrich, St. Louis, MI, USA), a surfactant and nonionic emulsifier common in pharmaceuticals and food preparation. The curcumin and apigenin solution was obtained in $9\%$ ethanol and diluted in order to obtain the concentrations considered for the present study [40,41,44]. Eventual complications and infections were treated by administering post-operative antibiotic therapy and painkiller therapy. The daily clinical evaluation of the post-operative surgery was performed through the rat grimace scale prior to surgery on Days 1, 3, 7, 14, and 30. The animals were sacrificed after 30 days, and the biopsies were retrieved for further analysis. The obtained samples were radiographically and evaluated by 3D CBCT scans (EZ3D, Vatech, Gyeonggi-do, Republic of Korea) to evaluate the level of bone healing and defect recorticalization. ## 2.6.2. Specimen Processing The biopsies were fixed into $4\%$ paraformaldehyde and $0.1\%$ glutaraldehyde in 0.15 M cacodylate buffer and pH 7.4 at room temperature for 1 week. The samples were dehydrated in ascending concentration rinses of ethyl alcohol from $60\%$ to $100\%$ and embedded in a hydrophilic acrylic resin of high viscosity (LR White Resin London Resin Company Ltd., UK). After polymerization, the specimens were sectioned, along their longitudinal axis, with a high-precision and -accuracy diamond disc at about 150 µm and ground down to about 30 µm with a specially designed grinding machine. Two slides were obtained for each specimen. The slides were stained with toluidine blue and acid fuchsin to evaluate the newly formed and mature bone. The samples were observed in normal transmitted light under a Nikon microscope ECLIPSE (Nikon, Tokyo, Japan). ## 2.7. Statistical Analysis The statistical software package GraphPad 8 (Prism, San Diego, CA, USA) was used for the data analysis. The parametric methods were applied considering the existence of the required assumptions. The study variables were the time elapsed, the molecule concentration, and the gene expression levels. The sample size of the in vivo experiments was calculated for a total of 4 different groups, according to an α error of 0.05 and a power of $80\%$. The minimum sample size for statistical significance was 5 defects for each group (total of 20 sites and animals). The statistical analysis of the in vitro experiments was conducted by applying the unpaired t-Student test. The level of significance was assessed considering a $p \leq 0.05.$ The descriptive statistic of bone defect healing in vivo was calculated by CBCT scans considering the means, standard deviation, and $95\%$ confidence intervals for conditions. ## 3.1.1. Cell Proliferation Assay The MTT assay dose–response experiment was assessed to identify the optimal concentration for hDPSC cultures (Figure 2). Apigenin at final concentrations of 1 µM, 500 and 100 nM; resveratrol 1 µM, 500 and 100 nM; and curcumin 50, 100, and 500 nM were tested. The observation was performed at 24 and 72 h to verify the effect after the cells had completed a replication period (Figure 2). To select the most appropriate substance concentrations, we opted for the best concentrations that at both 24 h and 72 h had no toxic effects or with values very similar to controls (CTRL) or even had a proliferative effect. Based on the results obtained, for apigenin, resveratrol, and curcumin, we selected and utilized for further experiments the following concentrations: 1 µM apigenin, 100 nM resveratrol, and 50 nM curcumin. ## 3.1.2. Alizarin Red Assay The formation of calcification nodules obtained in hDPSC cultures was shown by Alizarin Red staining. The cells cultured in the presence of 1 µM apigenin or 100 nM resveratrol or 50 nM curcumin were analyzed after 14 days. All samples cultured in the presence of the nutraceuticals showed more nodules positive for Alizarin Red in differentiation medium (DM) conditions with respect to the control (CTRL) (Figure 3). ## 3.1.3. Gene Expression RT-PCR analysis was performed for RUNX2, SMAD5, COLL1, COLL4, and COLL5 genes after 14 days of differentiation in DM, and the data were reported as relative quantification means ± standard error (Figure 4). The expression of genes SMAD5 and RUNX2 was statistically increased in the presence of apigenin, resveratrol, and curcumin compared to the control group ($p \leq 0.05$). Even COLL1, COLL4, and COLL5 were upregulated in the presence of apigenin, resveratrol, and curcumin compared to the control ($p \leq 0.05$). ## 3.1.4. In Vivo Procedure No dropouts were reported after the surgery and the treatment protocol. A good tolerance of the treatment was evidenced for all time points on Days 1, 3, 7, 14, and 30, with no difference between the treatment groups (rat grimace scale) ($p \leq 0.05$) (Table 1). No evidence of infection or inflammation was present in all groups. ## 3.1.5. Tomography Assessment All defects showed a bone repair at 30 days from the surgery visible by CBCT scans and optical microscopy. After 30 days of treatment, a significantly improved corticalization was visible for apigenin (Figure 5B) compared to the resveratrol (Figure 5C) and curcumin groups (Figure 5D) after the healing period, while this evidence was not evident in the control group (Figure 5A) ($p \leq 0.05$). No evidence of fibrous tissue was present in all specimens at the microscopic observation. ## 3.1.6. Histological Assay All the samples treated healed normally with no evidence of infection or inflammatory infiltrate. The histological evaluation with acid fuchsin and toluidine blue staining showed that after 30 days from the surgery, the control group reported evidence of marginal bone resorption with a few areas of new bone formation localized in the intracranial side of the cortical bone. Active multinucleated osteoclasts activity is evident at the level of the marginal walls of the defect. No evidence of inflammatory infiltrate was reported at the higher magnification (Figure 6) level of the margin of the defect. Focal regions of new bone formation were present at the level of the intracranial side of the defect. In the apigenin group, bone morphology presented differentiated cell lineages specific to the bone tissues, such as osteoblasts, osteocytes, and newly formed blood vessels. The histological images showed a wide number of newly formed bone trabeculae (Figure 6 and Figure 7), while osteoblasts actively secreted the osteoid matrix that, in some areas, was undergoing mineralization. At higher magnifications, an osteoclast rim was present at the level of the defect margins with evidence of an active remodeling of the bone tissue (Figure 8). In the resveratrol group no fibrous tissue was observed at the level of the newly formed bone surfaces compared to the control group (Figure 9 and Figure 10). Newly formed bone was found in close contact with the calvarial defect margins with wide bone trabeculae and large osteocyte lacunae (Figure 10). The osteoblasts were actively secreting the osteoid matrix that, in some areas, was undergoing mineralization. In the curcumin group, a noncomplete filling of the bone defect was evident after 30 days of curcumin administration, and a nonorganized osteoid matrix neo-apposition was reported (Figure 11 and Figure 12). ## 3.1.7. Bone Defect Repair The bone defect radiographically measured reported that the control group showed a significantly lower bone repair level compared to the experimental groups with a mean resorption of 18.5 ± $2.4\%$ ($p \leq 0.05$). The bone defect repair of the apigenin group was 69.6 ± $2\%$. The bone defect healing of the resveratrol group and curcumin groups was, respectively, 42.5 ± $3.6\%$ and 23.7 ± $4.1\%$ smaller than the baseline diameter defect, as reported in Figure 13. The defect area treated with apigenin gave the highest new bone formation with respect to the other treated groups. In the apigenin, resveratrol, and curcumin groups, a higher new bone formation was detected compared to the control group. The curcumin group showed a lower percentage of bone compared to apigenin and resveratrol after 30 days of treatment, but even higher than the control group (Figure 13). ## 4. Discussion The functional and aesthetic restoration of bone defects and atrophies represents a clinical condition that could require a regenerative approach and grafting procedures [3,45,46]. The identification of novel approaches and adjuvant therapies able to improve the healing of damaged tissues and bone defects represents one of the recent orientations of regenerative medicine [47,48,49]. Several studies have been performed to verify the regenerative effects of some plant-derived substances, such as phenolic compounds, both in vitro and in vivo in different experimental models [26,50,51]. However, no studies have been carried out that simultaneously analyze these three compounds in parallel and which pathways downstream of RUNX2 are modulated in the process of osteogenesis. Furthermore, even in vivo, there is no research investigating the parallel use of these natural compounds. The only study involving these three phenolic compounds concerns the effects of these nutraceuticals in inducing cancer signaling pathway manipulation and possibly facilitating new treatment modalities for osteosarcoma [39]. The present compound concentrations were adopted considering the evidence of the literature in this field. The dosages used demonstrated their effectiveness in very wide experimental models on rats, but the critical-size bone defect represents a novelty of the present paper. Correa et al. investigated the same concentration of nutraceutical administered by daily gastric gavage to evaluate in an experimental model of periodontitis, validating their effectiveness and safety with no adverse effect on rats [40]. In addition, apigenin and curcumin administered orally demonstrated in two different studies a consistent effectiveness for bone loss prevention and mineral density on ovariectomy-induced bone loss in rats [41,43]. Low-dose daily administration is a strategy that is able to avoid the risk of high-dosage compound administration that could produce significant systemic side effects. On the contrary, gastric absorption could represent a potential weak point due to the compound absorption levels and the peripheric balance levels necessary to produce a therapeutic level [40,41,43]. Among the most studied bioactive compounds is certainly resveratrol, as well as other molecules such as apigenin, a natural flavone, and curcumin with beneficial effects on cell differentiation [38,52]. A fairly recent study reports that apigenin, at µM concentration, stimulates myogenic differentiation of the murine cell line C2C12 and regulates the expression of total myosin heavy chain (MHC), MHC2A, and MHC2B [53]. In the present investigation, we evaluated the putative role of apigenin, resveratrol, and curcumin dietary supplements in improving the in vitro osteogenic differentiation of hDPSCs, as well as in affecting bone healing with an improvement of the clinical outcomes in vivo. The in vitro results of the study showed that apigenin, resveratrol, and curcumin could provide a promoting action for osteogenetic proliferation and differentiation of hDPSCs. The positivity for Alizarin Red staining showed mineral deposition obtained from hDPSCs cultured either in the presence or not of apigenin, resveratrol, and curcumin. The calcification nodules were more observable after 14 days of culture in DM in the presence of stimuli, demonstrating an earlier differentiation of hDPSCs in an osteogenic pattern. These results were in line with previous outcomes showing positive alizarin-red-stained calcification nodules in 21-day-differentiated hDPSCs [54]. However, the more pronounced calcification nodules In the presence of stimuli confirmed an earlier differentiation process related to their effect. We can speculate that the three substances were effective in boosting osteogenic differentiation both in in vitro and in vivo experiments. Indeed, we demonstrated that the hDPSCs cultured in the presence of the nutraceuticals showed more nodules positive for Alizarin Red in differentiation medium conditions with respect to the control. Moreover, in vivo, in the apigenin, resveratrol, and curcumin groups, a higher new bone formation was detected compared to the control group. Multipotent stem cells are induced to osteogenic differentiation into osteoblasts according to BMP signaling [55,56,57]. BMP2 signaling appears to permit the expression of osteo-specific RUNX2, which induces the expression of alkaline phosphatase and osteocalcin, and SMAD5, which activates the expression of early osteoblast differentiation markers [57,58,59]. Moreover, BMP2 and SMAD5 can bind RUNX2 with an increase in transcriptional activity [57]. In fact, RUNX2 activity with osteocalcin and β-catenin is known to be essential for osteoblast formation [60,61,62]. The activation of this signaling is confirmed by the increased upregulation of SMAD5 and RUNX2. The study data confirmed the literature showing the maximum upregulation at 14 days of culture [7]. RUNX2 is a determinant transcription factor in bone formation and upregulates osteocalcin, a key regulator of the development of the osteoblast phenotype by modulating bone extracellular matrix proteins and collagen types 1, 4, and 5. Indeed, its increased activity could suggest a more rapid evolution and a major degree of differentiation of hDPSCs on the osteogenic line [60,61,62]. In agreement with the study findings, resveratrol oral administration is able to decrease alveolar bone resorption in experimental defects with reduced levels of proinflammatory cytokines IL1b, IL6, and TNFa [33,43]. In addition, in the literature, treatment with curcumin in experimental bone defects has been shown by others to downregulate RANKL/RANK/osteoprotegerin, as well as to reduce bone loss [33]. These physiopathogenetic mechanisms seem to be correlated with the regulation of various molecular targets, including increasing ALP activity and osteoblast-specific mRNA expression of RUNX2 and osteocalcin [29,63,64]. Curcumin is able to decrease the release of inflammatory cytokines in articular chondrocytes and produce antagonist activity against proinflammatory molecules [33]. As reported in the literature, apigenin, curcumin, and resveratrol have potent antioxidant activity [20,30,33,43,49,65]. The cell cultures exhibit upregulation of all tested genes in the presence of nutraceutical administration during osteogenic differentiation, which may be associated with augmented cell activity and growth. In order to understand if the enhanced differentiative features demonstrated in vitro on hDPSC cultures could be related to the more effective healing of bone defects, we also evaluated in vivo in rats the effects of apigenin, resveratrol, and curcumin administration on experimental critical-size bone defects. Park et al. reported an ovariectomy-induced bone loss in rats, where the administration of apigenin with a dose of 10 mg/kg three times a week for 15 weeks induced an increase in the bone density of the trabecular bone of the rat femur, with an inhibition of bone resorption and osteoclast apoptosis [43,66]. In agreement with the current literature, we used 10 mg/kg of apigenin, curcumin, and resveratrol. Our data showed that apigenin seems to be more effective at enhancing the healing of bone defects and mineralization of the osteoid matrix in rat calvaria compared to the control group. Moreover, the apigenin group resulted in more effective and significantly higher bone defect repair compared to the other study groups. The control group revealed after 30 days a marginal bone resorption that is compatible with a local adaptation that produced a significant increase in the bone defect diameter. Conversely, mild new bone formation activity was localized in the intracranial portion of the defect. Another notable aspect is the histological findings of the initial active remodeling process of the osteoid matrix visible in all test groups compared to the control, which reported evidence of marginal bone resorption activity. These results are in agreement with previous studies reported in the literature [26,33,43]. In addition, the findings of the in vivo experiment confirmed the rejection of the null hypothesis, revealing a significant difference concerning the percentage of calvarial defect repair and the histological evidence of a more active nonmature bone synthesis of the test groups. ## 5. Conclusions Apigenin, curcumin, and resveratrol investigated in parallel in the present study showed a significant increase in bone repair in critical-size defects in rat calvaria. Among these, apigenin induced the best results. These in vivo results could be related to a differentiative boost due to all substances, as shown by the in vitro results. Altogether, the study evidence encourages a translational application for fracture defect repair as adjuvant supplement therapy. 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--- title: Hyptis obtusiflora C. Presl ex Benth Methanolic Extract Exhibits Anti-Inflammatory and Anti-Gastritis Activities via Suppressing AKT/NF-κB Pathway authors: - Jieun Oh - Jae Youl Cho - Daewon Kim journal: Plants year: 2023 pmcid: PMC10005599 doi: 10.3390/plants12051146 license: CC BY 4.0 --- # Hyptis obtusiflora C. Presl ex Benth Methanolic Extract Exhibits Anti-Inflammatory and Anti-Gastritis Activities via Suppressing AKT/NF-κB Pathway ## Abstract Inflammation is an indispensable part of the human body’s self-defense mechanism against external stimuli. The interactions between Toll-like receptors and microbial components trigger the innate immune system via NF-κB signaling, which regulates the overall cell signaling including inflammatory responses and immune modulations. The anti-inflammatory effects of Hyptis obtusiflora C. Presl ex Benth, which has been used as a home remedy for gastrointestinal disorders and skin disease in rural areas of Latin America, have not yet been studied. Here, we investigate the medicinal properties of Hyptis obtusiflora C. Presl ex Benth methanol extract (Ho-ME) for inflammatory response suppression. Nitric oxide secretion in RAW264.7 cells triggered by TLR2, 3, or 4 agonists was reduced by Ho-ME. Reduction of inducible nitric oxide synthase (iNOS), cyclooxygenase (COX)-2, and interleukin (IL)-1b mRNA expression was observed. Decreased transcriptional activity in TRIF- and MyD88-overexpressing HEK293T cells was detected with a luciferase assay. Additionally, serially downregulated phosphorylation of kinase in the NF-κB pathway by Ho-ME was discovered in lipopolysaccharide-treated RAW264.7 cells. Together with the overexpression of its constructs, AKT was identified as a target protein of Ho-ME, and its binding domains were reaffirmed. Moreover, Ho-ME exerted gastroprotective effects in an acute gastritis mouse model generated by the administration of HCl and EtOH. In conclusion, Ho-ME downregulates inflammation via AKT targeting in the NF-κB pathway, and the combined results support Hyptis obtusiflora as a new candidate anti-inflammatory drug. ## 1. Introduction Maintaining homeostasis is a lifetime objective for living organisms. Continuously interacting with the environment, organisms have evolved to protect themselves from extracellular materials such as bacteria, fungi, and viruses. Alien pathogens have unique glycan molecules [1], and target recognition contributes to the host’s immune system to distinguish “non-self” with pattern recognition receptors (PRRs). Immune receptors, including Toll-like receptors (TLRs) [2], C-type lectins [3], and Siglecs [4], are used to analyze pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs). Of these receptors, TLRs discriminate the types of PAMP and activate inflammatory responses. TLRs are comprised of extracellular ligand-binding domains, transmembrane domains, and cytosolic Toll-interleukin-1 receptor (TIR) domains. In the eukaryotic system, 13 subtypes of TLRs with unique ligand patterns have been discovered [1]. While TLR1 and 2 or TLR2 and 6 form heterodimers and recognize Gram-positive bacteria, TLR4 can recognize Gram-negative endotoxins. Bacterial flagella are detected as positivity for TLR5, and ssRNA, dsRNA, and CpG DNA bind with TLR3, 7, 8, and 9. With TLR pattern recognition, adaptor proteins containing TIR domains, such as MyD88 and TRIF, are recruited toward the TIR domain of TLR. Intracellular signals then descend toward the nucleus to react to external stimuli by serial phosphorylation. Through phosphorylation, kinases are activated and amplify powerful signal transduction toward transcription factors. Finally, nuclear factor-κB (NF-κB) becomes phosphorylated and moves into the nucleus for the transcription of proinflammatory genes [5]. NF-κB orchestrates homeostasis via the rearrangement of cytokine expression [6,7,8,9]. However, unexpected, uncontrollable inflammation induced by NF-κB can cause a catastrophic situation. For example, fortissimo activation of NF-κB leads to a so-called “cytokine storm” [10], which can cause a health emergency. Moreover, prolonged inflammation can develop into chronic diseases [11]. Fine-tuning of NF-κB is important to harmonize homeostasis in the immune system. According to previous reports, the Lamiaceae family is the sixth-largest plant family on earth. Approximately 258 genera and 7193 species are reported to belong to the Lamiaceae family [12]. Some of the most common species of Lamiaceae, such as classic oregano, sage, mint, basil, lemon balm, thyme, and rosemary, have been used as ornamental and medicinal plants [13]. Several studies have stated that the Lamiaceae family contains a variety of phytochemical compounds that have beneficial effects [14]. Among the hundreds of Lamiaceae genera, the Hyptis genus includes over 700 species of plants. Plants in the Hyptis genus contain several secondary metabolites including flavonoids [15], lactones [16], and steroids [17]. Plants from Hyptis have been commonly used to treat gastrointestinal problems, skin infections, and menstrual pain. According to ethnopharmacological literature, Hyptis plants are being researched for their pharmaceutical potential. Recently, Machado et al. have suggested that Hyptis suaveolens (L.) Poit has colon-protective effects [18], and Barbosa et al. have elucidated anti-inflammatory and antinociceptive effects of Hyptis martiusii Benth [19]. Additionally, anti-inflammatory bioactive metabolites in Hyptis plants have been identified [20]. Hyptis obtusiflora C. Presl ex Benth (Lamiaceae family and Hyptis genus) lives in Central America [21]. The ethnopharmacological literature reports oils infused with Hyptis obtusiflora as a folk medicine that treats gastrointestinal disorders and skin diseases [22]. Based on these reports, we hypothesized that Hyptis obtusiflora exerts anti-inflammatory effects, and we conducted a series of experiments to test the anti-inflammatory response efficacy and mechanism. ## 2.1. Ho-ME Suppresses Nitric Oxide Production in TLR-Activated Macrophages Nitric oxide (NO) assays were conducted to evaluate whether Ho-ME suppresses anti-inflammatory responses. RAW264.7 cells were inflamed by treatment with TLR4 ligand lipopolysaccharide (LPS), TLR2/TLR1 agonist Pam3CysSerLys4 (PAM3CSK), or TLR3 immunostimulant polyinosinic:polycytidylic acid (Poly I:C). TLR2-, TLR3-, and TLR4-mediated inflammation was effectively decreased when Ho-ME was co-administered with each pathogen. In Figure 1a, when 150 μg/mL of Ho-ME was administered for 24 h, NO production was inhibited to $14\%$ of the control value. Moreover, only 12 h of Ho-ME treatment suppressed NO production by more than half compared to the positive control (Figure 1b). L-NAME, a standard NO-inhibitory compound, exhibited a dose-dependent reduction of NO production as expected (Figure 1c). Similar NO-reducing patterns were observed in the Pam3CSK-treated conditions (Figure 1d) and poly(I:C)-treated conditions (Figure 1e). To determine whether cell viability affects NO production, we employed MTT assays. Ho-ME rescued over $80\%$ of cell viability compared to the non-treatment group at every concentration across both time points (Figure 1f,g). In addition, time-of-flight mass spectrometry (TOF-MS) was performed to identify the main actors in the Ho-ME-mediated anti-inflammatory effects. Trifolin, genistin, and 4′,5,7,8-tetramethoxy-flavone were identified in the spectra (Figure 1h). Since other peaks are seen in the profile, detailed approaches like purification of individual components, analysis of chemical structures, and HPLC analysis with authentic compounds to identify each peak will be continued. For confirming whether these compounds can suppress NO production, the available compound (genistin) was exposed to RAW264.7 cells to evaluate the NO inhibition ability of Ho-ME. Treatment with only genistin not only dose-dependently suppressed NO production, but also downregulated NO production by up to $43\%$ compared to that of the control group (Figure 1I). Currently, we are unable to check the NO inhibitory activity of other ingredients, due to no information about other peaks and the unavailability of these compounds. However, since it is important for us to know what compound can make synergistic or antagonistic activities between genistin and other compounds, we will continue additional work including the purification of ingredients and pharmacological tests with these compounds. ## 2.2. Ho-ME Altered Activities of Inflammation-Related Transcription Factors and mRNA Expression of Inflammatory Genes NO, a paracrine signal messenger, stimulates the immune system as a chemoattractant that can induce vasodilation [23]. Several authors have recognized that NO production is controlled by inducible, neuronal, and endothelial NO synthases [23]. The experimental design of this paper assumed that mRNA expression of inducible NO synthase (iNOS) participates in NO production from RAW264.7 cells activated by TLR stimulators. Therefore, we first tested the level of iNOS under such conditions, as reported previously [24,25,26]. Moreover, IL-1β is a representative pro-inflammatory cytokine that can be regulated by NF-κB-dependent transcriptional activation [27,28,29]. Macrophage-dependent inflammation requires the expression of cyclooxygenase-2 (COX-2) to produce prostaglandin E2 (PGE2) [30,31,32]. Therefore, we examined whether these genes were suppressed by Ho-ME using both semi-quantitative reverse transcription polymerase chain reaction (RT-PCR, Figure 2a) and real-time quantitative RT-PCR (q-RT PCR, Figure 2b–d). The relative intensity of mRNA bands was reduced according to real-time q-RT PCR results. More specifically, 150 μg/mL of Ho-ME downregulated mRNA expression levels of iNOS and IL-1β to one-quarter that of the control group. These results imply that Ho-ME treatment could affect transcriptional activity. HEK293T cell viability assays (Figure 2g) were performed, followed by a luciferase assay to identify transcriptional activities (Figure 2e,f). Transcription factors NF-κB and AP-1 and adaptor molecules MyD88 and TRIF were used with β-galactosidase. Exogenous NF-κB activities decreased under Ho-ME treatment in both MyD88- or TRIF-transfected cells in a dose-dependent manner (Figure 2e,f). Moreover, transcription factor activities were confirmed with Western blotting. NF-κB heterodimer subunits p50 and p65 and each phosphorylated form were analyzed, as shown in Figure 2h. With Ho-ME treatment, phosphorylation of p50 and p65 was downregulated after 15–30 min. ## 2.3. Ho-ME Interrupts AKT Phosphorylation during an Intracellular Signaling Cascade Considering the aim of the transduction of transcription factors in a signaling cascade, we investigated intracellular signals in the NF-κB pathway. We conducted a whole lysate assay to determine the proteins affected by Ho-ME. AKT was not affected by blocking IκBα and IKKα/β activation (Figure 3a), indicating AKT as a target of Ho-ME. To validate this, HA-tagged AKT1 and AKT2 constructs were introduced to HEK293T cells (Figure 3b,c). Both overexpressed constructs showed upregulated phosphorylation of the IKK complex, but Ho-ME changed the phosphorylation pattern by blocking the AKT series proteins (Figure 3b,c). A cellular thermal shift assay was adopted to elucidate the protein stability change caused by the interaction between Ho-ME and AKT. In this study, the thermo-dependent protein degradation environments were set to 44, 46, 48, 50, 52, 54, and 56 °C. Ho-ME treatment exhibited a considerable thermal stability shift at 46, 48, and 56 °C (Figure 3d). Additionally, to determine to which domain of AKT some of the active components of Ho-ME would bind, several AKT2 domain deletion mutants were transfected in HEK293T cells. As shown in previous research, the AKT structure is characterized by the kinase, pleckstrin homology (PH), and regulatory domains [33]. AKT2 truncated by the regulatory or PH domain exhibited similar downregulation of the level of p-IKKα/β to that of AKT wild-type when treated with Ho-ME, suggesting that Ho-ME physically interacts with the AKT kinase domain (Figure 3e). The effect of the AKT pathway inhibitor LY294002 was tested through an NO assay (Figure 3f). ## 2.4. Ho-ME Alleviated DAMP-Induced Acute Gastritis The ability of Ho-ME to suppress inflammation and its mechanism was revealed through in vitro experiments. To ensure that Ho-ME would be effective in vivo, we investigated a gastritis model induced by HCl and ethanol. Stomach wall wounds were induced by oral administration of $60\%$ EtOH/150 mM HCl (Figure 4a). Because the blood spot size is closely related to the severity of gastritis, the area of the blood spots was quantified with ImageJ software. As shown in Figure 4b, Ho-ME-administered groups showed smaller lesion areas than those of the ranitidine group, suggesting that Ho-ME attenuated DAMP-mediated inflammation. To confirm the molecular mechanism of Ho-ME in the mouse model, we analyzed the transcription levels of mRNA and proteins (Figure 4c–e). Ho-ME-mediated mRNA suppression was noted in patterns similar to the in vitro conditions, and p50 phosphorylation was downregulated as expected. ## 3. Discussion The Hyptis genus has been widely used as traditional medicine for a variety of illnesses in tropical America. Due to its strong aromatic components and various phytochemical constituents, essential oils derived from the Hyptis genus have been studied for their effectiveness. However, only a few such species have been examined. Hyptis obtusiflora from plants in Central America was used as a home remedy. Literature from Peru states that Hyptis obtusiflora was used against ringworms and head wounds. In Ecuador, the use of Hyptis obtusiflora differed from province to province: using juice to heal wounds, infusions or ashes for a hot bath, and cooking leaves for skin infections and flu [22]. This practical knowledge was nearly lost until the Ecuadorian government conducted ethnopharmacological research on their native plants. Our study can not only help understand the medicinal properties of native plants, but also provide a link between traditional knowledge and future applications of these plants. Inflammation involves a series of steps that protect the host from infection. Once patterns from pathogens are recognized via DAMP and PAMP, immune receptors activate the anti-inflammatory pathway. TLRs are responsible for mediating inflammatory responses. Regulating TLR responses is key to controlling inflammation because TLR acts like a toll gate that discriminates against pathogens [2]. TLRs sense changes in the extracellular environment and transduce signals to respond to external triggers. Dealing with environmental change is an urgent task for an organism, and signal transduction should be fast and accurate to support organism safety. Thus, sensitive control of early TLR response is very important. Careful modulation of TLR signaling is needed to maintain immune system equilibrium [2,5]. Rapid progression of inflammation, such as in sepsis, can threaten host safety [34]. Chronic inflammation can result in serious illnesses like cancer [35], inflammatory bowel disease [36], and rheumatoid arthritis [37]. In a series of NO assays, Ho-ME suppressed activation of not only TLR4, but also TLR2 and TLR3. Additionally, Ho-ME alleviated inflammation that was stimulated by DAMP. As seen in Figure 4, Ho-ME reduced mRNA expression of IL-1β and COX-2 and phosphorylation of p50 at the protein level. Taken together, these results show that Ho-ME might be an effective controller of front-line immune responses and could be a candidate for a universal anti-inflammatory drug. In efficacy tests of herbal extracts, fragment analysis is a valuable process for understanding the material characteristics. Flavonoids, which are the main constituents of plant-based extracts, are secondary metabolites that have polyphenolic structures, and more than 5000 types of natural flavonoids have been reported [38]. In the present study, TOF-MS was used to identify fingerprints of Ho-ME. With this approach, genistin, an isoflavone that provides beneficial effects for general health [39], was found to be an ingredient of Ho-ME. In previous studies, it was reported that genistin inhibited cancer cell invasion and migration through the PI3K-AKT-mTOR axis and had a regulatory role in cell proliferation [40,41,42,43]. Cardioprotective effects achieved by blocking NF-κB pathways and suppressing proinflammatory cytokines [44] were reported as another beneficial effect of genistin. In addition, a galactose-conjugated flavonol, trifolin (kaempferol-3-O-galactoside), was detected in Ho-ME. Based on immunopharmacological approaches toward kaempferol and kaempferide, kaempferol has antimicrobial, anticancer, antiallergic, antidiabetic, antioxidant, and anti-inflammatory effects [45,46]. Trifolin exhibits both antifungal [47] and anticancer activities [48]. Moreover, Hataichanok et al. demonstrated anti-inflammatory effects of another identified compound in Ho-ME, 4′,5,6,7-tetramethoxy-flavone (scutellarein tetramethyl ether) [49]. This compound was reported to not only inhibit COX-2 and iNOS mRNA expression levels, but also suppress translocation of p65 under LPS-stimulation conditions. Taken together, these results led to the conclusion that these flavonoids in Hyptis obtusiflora play vital roles in negatively controlling NF-κB pathway-mediated inflammatory responses. The ability to modulate IL-1β is necessary to maintain the consistency of the immune system. IL-1β is a precursor protein composed of 269 amino acids [50]. Once IL-1β is transcribed by NF-κB binding to the consensus binding sites of its promoter region, biologically active IL-1β is processed with caspase-1 and the inflammasome [51,52]. From a transcriptional point of view, Ho-ME effectively reduces NO production and proinflammatory gene expression. Especially, IL-1β mRNA expression is lower than that in the negative control group, as seen in Figure 2d and Figure 4d. However, excessive exposure to IL-1β can occur and exacerbate autoinflammatory diseases [53,54,55,56,57,58] and metabolic disorders [59,60,61,62]. High level of IL-1β promotes imbalanced immune states and increases in the Th17 cell population occur because of chronic exposure to IL-1β [63]. Moreover, at the hematopoietic cell level, IL-1β affects immune cell fate. Increased M1 polarizes macrophage and monocyte differentiation rates and activates B lymphocytes [64,65,66]. AKT, also known as protein kinase B, is a well-known key regulator within the NF-κB pathway. AKT exists as three subtypes, AKT1, AKT2, and AKT3. Even though the AKT isotypes have similar structures and biochemical characteristics, they have spatial differences in expression. AKT1 modulates cell survival and controls proliferation [67], AKT2 regulates insulin-mediated signaling [68], and AKT3 controls brain development [69]. AKT series proteins manage different aspects of cell biology, such as migration [70,71,72], ICAM-1 expression [73], respiratory burst [74], phagocytosis [75], and NF-κB signaling-related proteins [76]. In this study, an AKT2 domain mutant overexpression experiment was used to determine that Ho-ME binds with the AKT2 kinase domain (Figure 3). Despite the functional differences between the parts of the AKT series, they share similar sequences and structures. It can be deduced that Ho-ME can suppress AKT protein kinase activity by binding with the kinase domain. Therefore, Ho-ME has the potential as a candidate therapeutic for inflammatory and metabolic diseases by targeting AKT/NF-κB pathway, as summarized in Figure 5. ## 4.1. Materials and Reagents International Biological Material Extract Bank (Daejeon, Korea) provided Ho-ME. RAW 264.7 and HEK293T cell lines were provided by ATCC (Rockville, MD, USA). Cell culture media (Roswell Park Memorial Institute 1640 (RPMI 1640), Dulbecco’s Modified Eagle Medium (DMEM), Opti-MEM, and streptomycin/penicillin) were purchased from Cytiva (Malborough, MA, USA). Fetal bovine serum (FBS) was obtained from Gibco (Grand Island, NY, USA). 1-Bromo-3-chloropropane, Nω-nitro-L-arginine methyl ester (L-NAME), 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT), LPS (E. coli 0111:B4), and polyethylenimine (PEI) were purchased from Sigma Chemical Co. (St. Louis, MO, USA). TRI reagent® was obtained from Molecular Research Center Inc. (Cincinnati, OH, USA). Primers used for PCR experiments were synthesized by Macrogen (Seongnam, Korea), and qPCRBIO SyGreen Mix Lo-ROX and HS Taq PreMix Red were purchased from PCR Biosystems (London, United Kingdom). Antibodies specific for β-actin and p-p50 were purchased from Santa Cruz Biotechnology (Dallas, TX, USA), and both the total and phosphorylated forms of AKT series, IKKα/β, IκBα, HA-tag, p65, and p50 were acquired from Cell Signaling Technology (Danvers, MA, USA). Ethanol, methanol, isopropanol, and hydrochloric acid were manufactured by Daejung Chemicals and Metals (Seoul, Korea). ## 4.2. Plant Extract Processing The leaves of Hyptis obtusiflora (53 g) were extracted in 1 L of $99.9\%$ (v/v) methanol by repeated sonication (15 min) and rest (2 h) for 3 days at 45 °C. Filtration and concentration of Hyptis obtusiflora were conducted as reported previously [77]. A total of 1.3 g of Hyptis obtusiflora methanol extract powder was obtained by lyophilization. ## 4.3. Cell Culture RAW264.7 cells were maintained in a RPMI 1640 medium. HEK293T cells were cultured in DMEM. Both media contained $10\%$ heat-inactivated FBS, penicillin (100 U/mL), and streptomycin (100 μg/mL). For subculture, RAW264.7 cells were detached with a scraper, and HEK293T cells were treated with 1 mL of trypsin (Cytiva, Malborough, MA, USA) for detachment. All cells were subcultured every 3 days, and only cells younger than passage 40 were used for in vitro experiments. ## 4.4. NO and MTT Assays RAW264.7 cells and HEK293T cells were seeded in 96-well plates and used in experiments after overnight incubation. For the NO assay, Ho-ME was administered at the indicated final concentration (0, 25, 50, 75, 100, or 150 μg/mL), and LPS was added. After 12 or 24 h, supernatants and Griess reagent were mixed at a 1:1 ratio. Absorbance at 540 nm was detected via a spectrometer. Cell viability was measured after Ho-ME treatment and incubation for 24 h. After the addition of 10 μL of MTT solution and 4 h of incubation, 100 μL of MTT stop solution was added to each well. After overnight incubation to dissolve the purple-colored formazan, the absorbance at 570 nm was detected with a spectrometer. ## 4.5. RNA Extraction and Polymerase Chain Reaction RAW264.7 cells were seeded in 6-well plates and cultured overnight at 37 °C in a $5\%$ CO2 incubator. Ho-ME was added 30 min before LPS stimulus. After 6 h of incubation, all supernatants were aspirated, and the cells were harvested. RNA was isolated with the Trizol and bromochloropropane method, as previously reported. cDNA was synthesized according to the manufacturer’s instructions. Both RT-PCR and real-time PCR were conducted using 100 ng of cDNA. The used primer sequences are listed in Table 1. In real-time PCR, all mRNA levels were expressed relative to the level of GAPDH expression. ## 4.6. Luciferase Assay HEK293T cells were plated in 24-well plates at a density of 1 × 106 cells/mL. Cells were transfected with the following plasmids: one with a luciferase gene-included transcription factor (NF-κB-Luc) and one with an adaptor (FLAG-MyD88 or CFP-TRIF) and β-galactosidase. After 24 h of transfection, the HEK293T cells were treated with Ho-ME or vehicle and incubated for another 24 h. The supernatants were aspirated, and cells were lysed with 400 μL of luciferase lysis buffer. Luminescence detection was performed according to a previously reported luciferase assay system [78]. Each luminescence reading was normalized to that of β-galactosidase. ## 4.7. Western Blot Cells or tissues were lysed with RIPA buffer (20 mM Tris-HCl, pH 7.4, 2 mM ethylene glycol tetraacetic acid, 50 mM β-glycerol phosphate, 1 mM sodium orthovandate, 1 mM dithiothreitol, $1\%$ Triton X-100, $10\%$ glycerol, 10 μg/mL aprotinin, 10 μg/mL pepstatin, 1 mM benzamide, and 2 mM phenylmethylsulfonyl fluoride [PMSF]). After lysis, supernatants were collected via centrifugation. Proteins were quantified using a Bradford assay. Each experimental sample contained 20 μg/mL of total protein concentration. SDS-PAGE was conducted with 20 μL samples, and electrophoresis was performed at 100 V. Proteins in gels were transferred to a PVDF membrane at 100 V for 2 h. Membranes were soaked in $3\%$ bovine serum albumin (BSA) in 1 × TBST solution for 1 h to block non-specific binding. Primary antibodies were diluted with a $3\%$ BSA solution at a 1:2500 ratio and were incubated for 3–4 h at room temperature. The following specific primary antibodies were used in this study: AKT (#9272), p-AKT (Ser473) (#4058), IKKα (#2682), p-IKKα/β (#2697), IκBα (#9242), p-IκBα (#9246), p50 (#12540), p65 (#8242), p-p65 (#3039), HA-tag (#2367) (Cell Signaling Technology), p-p50 (SC271908), and β-actin (SC4778) (Santa Cruz Biotechnology). After three washes with 1 × TBST solution, secondary antibodies were incubated at a 1:2500 ratio for 2 h at room temperature. When the incubation ended, the membranes were washed with 1 × TBST three times to remove unintended secondary antibody binding. ECL-mediated chemiluminescence was detected with a ChemiDoc system (BIO-RAD, Hercules, CA, USA). ## 4.8. Overexpression HEK293T cells were seeded at a confluency of 1 × 106 cells/mL in six-well plates. For overexpression, 8 μg of each construct was transfected with PEI. After 24 h of transfection, the old media was aspirated, and Ho-ME or vehicle was administered for another 24 h. Harvested cells were lysed with RIPA buffer. After protein quantification and mixing with loading buffer, phosphorylation of signaling cascade proteins was analyzed with Western blotting. ## 4.9. Cellular Thermal Shift Assay HEK293T cells were transfected with HA-AKT2 plasmid for 24 h and then treated with Ho-ME or vehicle for another 24 h. Harvested cells were placed in PCR tubes and heated for 3 min at each intended temperature and then chilled at room temperature for 3 min. Cells were placed in a deep freezer and subjected to three thaw–freeze cycles with liquid nitrogen. Finally, lysates were centrifuged to collect the supernatant. After mixing with a loading buffer, the lysates were subjected to Western blot, as previously reported [79]. ## 4.10. Mice A collection of 25 male, five-week-old ICR mice was purchased from Orient Bio (Sungnam, Korea). The animals had free access to food and water ad libitum during a one-week acclimation. All mice were housed in autoclaved plastic cages. A maximum of five mice were housed in each cage. A 12 h:12 h light–dark cycle was applied to maintain the homeostasis of the circadian rhythm. Animal care and experimental procedures were performed in accordance with the guidelines for animal care (NIH Publication 80-23, revised in 1996) and were approved by the Institutional Animal Care and Use Committee of Sungkyunkwan University (approval number: SKKUIACUC2020-06-30-1). ## 4.11. Acute Gastritis Mouse Model Generated with HCl/EtOH The mice were randomly split into five groups: Normal, Control, Ho-ME-treated (100 and 150 mg/kg), and Ranitidine-treated (40 mg/kg). A $0.5\%$ solution of carboxy methyl cellulose (CMC) was used as the vehicle, and the normal and control groups received only $0.5\%$ of CMC solution. Every drug was delivered orally, and 100 μL of solution was administered for each injection. During the experiments, mice were not allowed to be fed but had access to water. Oral administrations were conducted thrice in total. On the first day of experiments, oral injections were performed twice in 8 h. The next day, 5 h after final drug administration, 300 μL of $60\%$ EtOH/150 mM HCl was delivered to each mouse except those in the normal group. After 1 h, mice were anaesthetized, and tissues were harvested. Obtained samples were photographed for blood spot analysis and then stored at -80 °C. Blood spot areas were quantified with ImageJ software, and the stomachs were analyzed via PCR and Western blot assays. Stomach grinding in liquid nitrogen was performed before RNA extraction or lysis with RIPA buffer. ## 4.12. Quadrupole Time-of-Flight LC/MS The phytochemical characteristics of Ho-ME were confirmed with a Xevo G2-XS quadrupole time-of-flight liquid chromatography/mass spectrometry (Q-TOF-LC/MS) system (Waters, Milford, MA, USA). Reverse-phase BEH C18, 2.1 × 100 mm, 1.7 mm (Waters) resin was packed for UPLC, with $0.1\%$ formic acid in water and acetonitrile as mobile phases (A) and (B), respectively. The gradient method was used as previously reported [77]. The designated column temperature was 45 ℃, and the mobile phase flow rate was 0.3 mL/min. A 2 μL sample of Ho-ME was injected for each experiment. The mass spectrometry conditions were set as described previously [80]. All data were acquired and analyzed with Waters LC-MS-QTOF MassLynx Software version 4.2 and Waters UNIFI Portal Software (Waters). ## 4.13. Statistical Analysis The data are presented as the mean and standard deviation of independent replicate experiments, six for NO assay and MTT assay, five for luciferase assay and in vivo acute gastritis model, and 4 technical replicates per group in real-time qRT-PCR tests. Statistical comparisons were performed with Student’s t-test, the Mann–Whitney U test, or one-way analysis of variance (ANOVA). A p-value < 0.05 was considered statistically significant. All statistical analyses were performed using GraphPad Prism 8 software (GraphPad, San Diego, CA, USA). ## 5. Conclusions This study suggests that Ho-ME has an inflammation-suppressive ability by targeting AKT kinase in pathogen-stimulated murine macrophages, as summarized in Figure 5. Ho-ME suppresses mRNA expression of proinflammatory cytokines and phosphorylation of inflammatory response-related proteins, and transcription factor activities are also down-regulated. 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--- title: Low-Income Families’ Direct Participation in Food-Systems Innovation to Promote Healthy Food Behaviors authors: - Aparna Katre - Brianna Raddatz journal: Nutrients year: 2023 pmcid: PMC10005603 doi: 10.3390/nu15051271 license: CC BY 4.0 --- # Low-Income Families’ Direct Participation in Food-Systems Innovation to Promote Healthy Food Behaviors ## Abstract Low-income families, especially those who reside in food deserts, face significant systemic barriers regarding their ability to access affordable and nutritious food. The food behaviors exhibited by low-income families are a reflection of the shortcomings of the built environment and conventional food system. Policy and public-health initiatives to improve food security have, thus far, failed to deliver interventions that simultaneously address multiple pillars of food security. Centering the voices of the marginalized and their place-based knowledge may result in the development of food-access solutions that are a much better fit for the population that they intend to serve. Community-based participatory research has emerged as a solution to better meet the needs of communities in food-systems innovation, but little is known about the extent to which direct participation improves nutritional outcomes. The purpose of this research is to answer the following question: how can food-access solutions authentically engage marginalized community members in food-system innovation, and if participation is related to changes in their food behaviors, how is it related? This action research project leveraged a mixed-methods approach to analyze nutritional outcomes and define the nature of participation for 25 low-income families who reside in a food desert. Our findings suggest that nutritional outcomes improve when major barriers to healthy food consumption are addressed, for example, time, education, and transportation. Furthermore, participation in social innovations can be characterized by the nature of involvement as either a producer or consumer, actively or inactively involved. We conclude that when marginalized communities are at the center of food-systems innovation, individuals self-select their level of participation, and when primary barriers are addressed, deeper participation in food-systems innovation is associated with positive changes in healthy food behaviors. ## 1. Introduction Achieving food security is a persistent and severe issue in the United States—especially for low-income populations residing in food deserts. The United States Department of Agriculture (USDA) reported that $33.2\%$ of low-income individuals in the United States lived in food deserts, and $10.2\%$ of households were food insecure for at least a portion of time during 2021. Furthermore, $3.8\%$ or 5.1 million households were highly food insecure [1]. Although historically in the United States it has been, food insecurity in food deserts should not be considered a solely geographical issue. Building supermarkets in low-income neighborhoods, connecting food-insecure residents to farmers’ markets, or other food-assistance programs may not improve dietary quality in the absence of other types of interventions [2]. Instead, food insecurity in food deserts should be addressed via multilevel approaches informed by an intersectional lens [3]. Interactions between social class, ethnicity, culture, economic status, and the food and built environment contribute to food behaviors displayed by low-income populations [2,4]. For example, family structure may influence how a household accesses and utilizes food. The challenges faced by Black, Indigenous, and People of Color (BIPOC) and single-parent families, who place a premium on time and convenience, are different from multigenerational and two-parent households, independent of socioeconomic status [2]. Ziso et al. [ 3] suggest that community-based participatory research may be a beneficial way to understand these interactions and center the voices of those most affected by food insecurity. However, even though knowledge about participatory research into food-systems innovation is expanding, as observed in the agroecology literature, studies about changes in food behaviors and nutritional outcomes, especially for low-income families, are lacking. In their systematic review evaluating community-based participatory research and interventions to improve food security, Doustmohammadian et al. [ 5] found just twelve studies, of which six were relevant for this research, i.e., they included a focus on nutrition and were conducted in poverty regions of developed countries. The interventions included store and shopping programs, fresh fruit and vegetable programs, nutritional education programs for mothers, and voucher/food-assistance programs. A deeper review of the six studies showed participation of marginalized voices in the intervention was limited to involvement in meetings and as research subjects. These studies did not explore the agency dimension of marginalized community members. Furthermore, the authors concluded that the study design in all cases was moderate/weak, and the nutritional interventions were ineffective for some food-access components. Therefore, we set out to answer the question: how can food-access solutions authentically engage marginalized community members in food-system innovation, and if participation relates to changes in food behaviors, how does it do so? We begin by reviewing the literature on food behaviors of low-income families, food-security interventions and their outcomes, and community participation in food-systems innovation. ## 2.1. Overview The USDA defines food-and-nutrition security as “access by all people at all times to enough food for an active and healthy life” [6]. Food security ranges from high to very low, where the latter is characterized by several indicators of disrupted eating patterns and reduced caloric intake, https://www.ers.usda.gov/topics/food-nutrition-assistance/food-security-in-the-u-s/definitions-of-food-security/#ranges (accessed on 22 December 2022). In the United States, single-mother households and households with incomes below the poverty line have been identified as those with the highest rates of low and very-low food security [1,7]. These families often reside in food deserts that have severely limited availability of affordable and nutritious foods [8]. Although historically the USDA has prioritized unequal geographical access to food, causes of food insecurity, especially among low-income households, are systemic or the result of structural inequalities [8,9,10]. Food deserts have low availability of healthy food; median household income is at or below $185\%$ of the federal poverty level (USD 51,338 for a family of four); a large share of the population ($30\%$ of households) lack access to a vehicle; and the distance to a supermarket is greater than a quarter of a mile. Fitzpatrick and Willis [4] found that the factors that characterize the residents of food deserts, for example, structural inequality, are correlated with diet-related chronic diseases. However, despite limited household income contributing to poorer diet quality, some individuals can eat better for less—a concept known as nutrition resilience [2,3]. It is an individual or group’s ability to construct an affordable, appealing, and health-promoting diet in the face of potential inequities in the built food environment. Given the interplay between physical, economic, and social factors impacting an individual’s food decisions, Drewnowski and Kawachi [2] suggest research is needed into the promotion of culturally acceptable, healthy, and inexpensive foods. Exploring the diverse food procurement strategies and behaviors exhibited by low-income residents of food deserts provides insight into how interventions might adopt a multidimensional approach. ## 2.2. Low-Income Families’ Food Behaviors The overemphasis of geographical access as a determinant of eating behavior has led to a new field of thought questioning whether physical distance is a true measure of access to healthy foods. The purchasing power of neighborhoods, in addition to household economic conditions, may be a much better indicator [2]. To come to these conclusions, Drewnowski and Kawachi [2] leveraged data from the Seattle Obesity Study (SOS II) and Kavli HUMAN Project (KHP) to paint a behavioral, economic, and cultural picture of diet quality and health in New York City. Belon et al. [ 11] conducted a mixed-methods study to establish how multiple environmental factors shape food decisions for the purpose of informing health policies and programs. Qualitative and quantitative analysis resulted in the emergence of four environmental themes: physical, sociocultural, economic, and political. The study found that, although people’s purchasing decisions are initially shaped by what is available within their built food environment, eating behaviors are nuanced by considerations of cost, sociocultural contexts, and policy [11]. A sense of belonging and understanding the complexities of perceptions of self and others are critical to low-income residents’ willingness to procure food at alternative venues such as mobile and farmers’ markets, food-giveaway events, and food shelves [12]. The Drewnowski and Kawachi [2] review portrays this concept using the phrase “build it and they may not come” (p. 194). For example, constructing a supermarket in a low-income area may not result in the declared manifest goal of improving the neighborhood’s nutritional status. Instead, sociocultural and economic interventions should be paired with transformations to the physical environment if improvements are to be made regarding the food-and-nutrition security of low-income households [2,11]. Low-income families appear to place a premium on time and convenience when it comes to food-purchasing decisions. Time scarcity, or limited time availability for cooking and shopping, is a significant barrier to the uptake of a health-promoting diet [11,12,13,14]. Increased time spent on food preparation and cooking has been linked to higher quality diets and health status [2,14]. These authors, however, suggest that low-income families experiencing time poverty due to unpredictable work schedules are more likely to eat out at fast-food restaurants and rely on packaged foods that are fast and easy to prepare. Monsivais et al. [ 14] hypothesized that an increase in time spent preparing, cooking, and cleaning up meals at home would be associated with healthier patterns of food consumption measured by an increase in fruit and vegetable consumption, decreased spending on food consumed outside of the home, and fewer visits to fast-food restaurants. The results of this study found a significant association between time and each of the named dependent variables, suggesting that cooking at home may be a prerequisite to consuming a healthy diet at the lowest possible cost. The exploration of individual-level and household factors may provide insight into low-income families’ food behaviors. For example, young working adults and single-parent families were found to be the most likely to eat away from home and the least likely to prepare home-cooked meals [2,13]. To support individuals who place a premium on time and convenience, quick-to-prepare, healthy, and affordable meals should be developed for, and advertised to, low-income families. Household family structure appears to impact food-and-nutrition security [2], but little is known about the extent to which the nature of the family unit affects food choices. Household and individual-level food-insecurity data suggest that parents often shield their children from experiencing food insecurity [1]. Furthermore, working and single-parent families face significant challenges regarding their ability to shop for, prepare, and cook food for themselves and their children. In a study that sought to determine the ways in which working mothers allotted time for food provisioning, Jabs et al. [ 13] found that the helping roles of older children and contributing partners resulted in reduced feelings of time scarcity and stress regarding food preparation. Similarly, multi-generational households have demonstrated a more equitable distribution of food-preparation responsibilities alleviating pressure on the families’ younger, working adults [2]. Likewise, larger families, who are more likely to prepare and eat food at home, often consume more healthy diets [14]. Cultural practices and ethnicity also play a role in the food behaviors of low-income families. Sweeney et al. [ 15] leveraged thematic analysis to evaluate the food behaviors of African American and Hispanic low-income families for the purpose of developing a culturally appropriate meal-kit intervention. The study determined that both African American and Hispanic families had not yet considered meal kits as a way to overcome barriers (cost and time) to eating at home, despite the fact that both groups prioritized cooking and eating together as a family [15]. The review concludes that meal kits must be semi-tailored to address the taste and cultural preferences of the target audience if they are to improve the diet quality of families with low income [15]. For example, a meal kit prepared for a Hispanic family might include fruits, vegetables, and grains that are traditionally used in Hispanic cuisine. One can conclude that for nutritional resilience, the availability of culturally appropriate and acceptable foods that reduce cooking time and encourage family participation is critical to the uptake of a healthy diet among low-income families. The promotion of community-based solutions—such as local, sustainable food systems—may be a viable means to reduce food insecurity while fostering solidarity and connectedness within disadvantaged communities [11,16]. Food sovereignty, i.e., people’s ability to define their food systems for the purpose of ensuring their own livelihoods and ability to access culturally appropriate foods, may be considered a prerequisite for food security [17]. Food systems are often dominated by market economics. In areas with a higher concentration of racial and ethnic minorities, the overabundance of fast-food restaurants and convenience stores selling obesogenic food severely limits the food choices of low-income families [18]. Despite these conditions, some minority groups exhibit nutritional resilience. For example, Drewnowski and Kawachi [2] detail an analysis of diet quality in relation to cost that suggests that Mexican Americans are able to eat better for less. This draws attention to the need for educational interventions that teach low-income shoppers how to maximize their budgets while making the best food-related decisions [2]. To increase the health status of food-insecure populations and reduce disparities that exist along racial and socioeconomic lines, it is critical that access to healthy and culturally appropriate food improves. Ultimately, interventions that aim to reduce food insecurity must take into consideration the nuanced needs and cultural preferences related to the food choices of low-income populations. ## 2.3. Food-Security Interventions and Outcomes A multitude of strategies and interventions have been employed by public health and governmental entities to combat food insecurity in the United States. Since a person’s food choices are directly affected by their food environment [3], interventions that look at food systems and facilitate multilevel interactions can result in favorable outcomes. Some examples include: multicomponent educational interventions, prolonged motivational campaigns, reduction of fruit and vegetable prices, discounted-produce markets, chef-run cooking demonstrations, shared recipes, community taste-testing events, and educational boxes [19]. Programs with educational components have been shown to reduce food insecurity among low-income populations. In one study, health-and-nutrition educational strategies, included providing healthy recipes, improving cooking skills, and informing individuals on the health risks associated with consuming processed and calorie-dense foods, were shown to improve diet-related outcomes [19]. In an effort to update the tools and technology available to the participants in a supplemental nutrition program for Women, Infants and Children (WIC) in the United States, a review was conducted to analyze the features of mobile phone apps available to the program’s participants. Apps that provided support while shopping and also featured nutrition-education modules were positively perceived by the program’s participants. These easy-to-use platforms were reported to be useful because of their ability to help save time spent shopping for WIC eligible items [20]. Personal nutrition education and programs culturally tailored to a specific group have also demonstrated effectiveness in improving the diet quality and food behaviors of low-income populations [21,22]. Finally, a longitudinal study [23] was conducted to evaluate the impact of school-based nutritional education on increasing children’s intake of fruits and vegetables. Strategies included increasing the frequency of cooking, the usage of nutritional labels in purchasing decisions, and the availability of fruits and vegetables. Changes to the home environment over a two-year period resulted in a significant decrease in total fat intake, in addition to an increased intake of fruits and vegetables for both children and parents [23]. However, educational interventions alone are insufficient, as cost perceptions and availability (related to transportation issues) may significantly impact consumption habits [3]. The Loopstra and Tarasuk [24] study examining the uptake and perceptions of community gardens, community kitchens, and food-box programs as a means to reduce food insecurity for low-income families in Canada showed low participation in any of the programs, even when the programs were in close proximity to the families’ residence. Primary causes included knowledge gaps of when, where, and how to participate, lack of information regarding its location or eligibility requirements, misalignment with family’s busy schedules, chronic health issues, and hesitancy in sharing communal spaces and having to work alongside strangers. Place-based solutions capable of overcoming inequities in the conventional food system are opportunities to improve food security and reduce health disparities [4,25]. Food hubs, which aggregate, distribute, and sell locally or regionally sourced foods, may help to address lack of access to healthy food in certain low-income communities [25]. In fact, low-income residents perceive food hubs to be potentially more inclusive and aligned with their preferences, especially compared to farmers’ markets which, despite efforts to incorporate electronic benefits transfer (EBT) as a means to purchase goods, are considered to racialize, stratify, and exclude other low-income individuals [25,26]. Similar to the recommendations by Clapp et al. [ 17], these considerations underscore the importance of incorporating the voices of adults facing food insecurity in the detection of issues and the conception and design of food-access solutions within communities [11]. The preceding sections suggest that interventions embracing as many of the following characteristics as possible present the potential for optimal food-security outcomes for low-income families in food deserts: culturally appropriate, reduced cooking time, encouraging family participation, providing education, and being affordable. ## 2.4. Community Participation in Food Systems Food security is widely understood to rest upon four pillars: availability, access, utilization, and stability. Clapp et al. [ 17] promote the incorporation of agency into policy and intervention frameworks developed to combat food insecurity. Sustainable food systems are respectful and empowering, where all people are able to make choices and exercise their voice in shaping the system. While measuring agency is challenging, Ziso et al. [ 3] emphasize the need to study it to understand the effectiveness of interventions. Increasingly, communities are recognizing the importance of food citizenship, wherein people move beyond simply being food consumers to holding and developing capacities to actively engage in shaping the food system. Agency, defined as the capacity of individuals or groups to make their own decisions about what foods they eat and produce, and how that food is produced, processed, and distributed within food systems [27], is needed to shape food-system policies and governance. The HLPE [27] report elevates the need to promote the ability of food-system participants to exercise their agency and their right to food, as these are linked with improved food security [28] and developmental outcomes in general [29]. The report further recommends upholding governance structures to facilitate the protection of agency and food-and-nutrition security for all (p. 28). Designing public participation, including participation in food citizenship, is often conceptualized as a linear process with designated steps and execution methods. However, as Clark [30] shows through a case study, the approach has to be iterative, with several participation opportunities, so that local knowledge can be elevated to expert input. The study also recommends adopting a flat decision-making structure and taking the time to build consensus. As people’s collective-efficacy perception influences their participation in development initiatives [31], investing in building collective efficacy is imperative. It is necessary to acknowledge the agency of the least advantaged in society in order to improve food security, but this topic is least understood. Marginalized communities may lack the agency to define their own place in, and relationships with, the food system, and approaches to strategically increase participation in low-income neighborhoods are not well understood. For example, Bornemann and Weiland [32] suggest that individuals can exercise agency by cooking at home and embracing values related to organic foods. However, prices, promotions, food types sold, and the proportion of heavily vs. less-processed foods are influenced by corporate concentration in the food-distribution and retail sectors, limiting low-income consumers’ ability to exercise agency [33]. At the community level, they can actively participate in local food initiatives such as food cooperatives, community-supported agriculture, and community gardens. However, such initiatives are often designed and deployed by nonprofit and public-sector organizations with their own agenda, but who act as trustees of the marginalized [34]. Marginalized communities who are most affected by food insecurity, when invited to participate, face information asymmetry and power differential affecting their ability to influence the solution. The level of beneficiary participation in developmental projects is associated with their perceptions of whether the projects will happen in reality, the success or failures of prior attempts, and their relationship with the agencies driving the change [34]. The author recommends a social contract that emphasizes the content of relationships between the various agencies and social groups, and rejects the idea of institutionalizing civil-society actors representing the marginalized. Clark [30] and Prost [35] suggest that involving marginalized voices requires a commitment to social equity, and they call for investing in relationship building to make community participation accessible. Recognizing that not all of what is conveyed by marginalized communities will be integrated into food-systems design, Clark [30] recommends that trust developed through relationship building can be used to handle these situations. Therefore, scholars emphasize investing in relationship building and moving away from interventions focusing on skills development for the marginalized. However, food-democracy scholars, for example, Booth and Coveney [36], argue that to radically transform the food system, interventions need to improve the capacity of individuals and groups to act independently and make free food choices. This includes knowledge and skills related to growing your own food, home cooking, and challenging the rules and existing structures causing food insecurity, among others. Residents are more likely to engage in individual and collective action when they experience a sense of community, where there is hope for change and there is collective efficacy [36]. Laying out practical tools for increasing the participation of marginalized communities, these authors suggest beginning with active listening and challenging the beliefs of other food-system actors about the lack of participation, followed by mediation and negotiation to search for a shared solution. Social innovations and social enterprises play a key role in enabling individual and collective agency to transform food systems [37], including the agency of the disenfranchised and bringing about social inclusion at the community level [38]. Several examples of social innovation demonstrate that they establish social cohesion and socialization, helping fight the social exclusion of the marginalized. In such cases, social innovation enables the most excluded members to improve their circumstances, thereby developing and exercising collective agency. Fitzpatrick and Willis [39] suggest that collective agency is critical for allowing local communities to leverage place-based knowledge to shape their own food environments. Recognizing limited access to affordable and healthy foods as a place-based issue is central to developing place-based solutions which center on the local knowledge of community members most affected by the issue. Fernandez-Wulff [37] interviewed 104 individuals across an equal number of social innovations in the food system. They identified four dimensions of collective agency: consciousness, individual voluntary action, cooperative agency, and agency feedback loop. The social innovations in the study aimed to stimulate consciousness by improving information sharing and establishing new producer–consumer relationships, to increase control over food-related daily decisions. However, the finding of a lower level of consciousness among low-income communities suggests the need to create an environment where participants can engage authentically at their individual level of consciousness. Consciousness leads to individual voluntary action when social-innovation projects allow the heterogeneous participation of community members (best suited for the individual). Special consideration is needed of the disconnect between expert knowledge [37] or universal awareness and the lived experiences of low-income communities. This finding identifies a need for authentic, compassionate discourse that amplifies the voices of food-insecure populations for the purpose of transitioning to engaged action [37]. These individual actions can work as a stepping stone towards deeper engagement, progressing towards cooperative actions and collective agency. However, the transition in their study required “developing personal relationships with and among participants, designing activities for an active involvement, engaging participants on an individual level, and acknowledging and managing conflict as a natural part of the life of a group” (p. 10). While their study is significant in its scope and size, it lacks a special focus on strategies for mobilizing marginalized communities’ agency and mobilization outcomes. In conclusion, there is a growing interest in including disenfranchised communities in co-designing and decision-making for local food systems. Including community participation and the agency of marginalized groups as core dimensions of food security, Clapp et al. [ 17] recognize the challenges in defining and measuring it. While studies of food systems for community participation primarily focus on food-policy changes, this research attempts to analyze how food-systems innovation enables the participation of marginalized community members in food deserts and the resulting changes to their food behaviors. ## 3.1. Research Setting This action research project studies an evolving social enterprise, Food Forward, to uncover how marginalized community members engage, build capacities, and exercise agency in the social innovation of food systems, and to identify early indicators of food-behavior changes. The findings are situated and analyzed in the context of the collective-agency framework proposed by Fernandez-Wulff [37]. Food Forward is a nascent social enterprise whose mission is to provide low-income residents of Duluth, Minnesota’s Central Hillside neighborhood, with more equitable access to nutritious foods. This neighborhood is a food desert. Its residents face significant health disparities and, on average, have a life expectancy of more than ten years less than those residing in adjacent neighborhoods. According to the Community Health Needs Assessment (CHNA) conducted by Bridging Health Duluth in 2015 [40], $41.6\%$ of Central Hillside adults reported feeling worried about running out of food. The root causes of food insecurity include limited income, lack of access to nutritious food, lack of transportation, and knowledge barriers [41]. Food Forward home delivers partially prepared meal kits to Central Hillside’s low-income residents once a week to help alleviate stress around food, including lack of knowledge of cooking and nutrition, financial stress, and transportation issues. The social enterprise leverages a participatory engagement framework that involves communities most impacted by food insecurity as consumers, and some, such as single moms receiving food stamps, in Food Forward’s design, development, and delivery of the service. For example, the participants made decisions about the choice of meal kits, striking a balance between familiarity, desirability, and nutritional value. Accessibility to healthy foods is a social determinant of health, but interventions targeted at this neighborhood in the past few decades are among those which have resulted in marginal improvements. Food Forward is supported by university and community partners. ## 3.2. Data Collection and Analysis Action research projects provide solutions to immediate problems and contribute to scientific knowledge and theory [42]. Exploratory studies such as this one benefit from qualitative methods [43,44]. Three kinds of data were collected. First, Food Forward provided the relevant documents: (a) social contracts established with various stakeholders, including its consumers, (b) its consumers’ demographics with no identifying information revealed, and (c) details about the meal kits and the delivery schedule. Second, Food Forward gathered anonymized survey-based meal-kit-specific consumer-feedback data. These data were used to review consumer satisfaction with the meal service (rating on a scale of 1 to 5) and to study open-ended consumer feedback. Third, all Food Forward consumers were invited to participate in two focus groups, lasting 60 to 90 min each, spaced over six months. The goals were to discuss their participation in the social enterprise and capture their food behaviors. Changes to their food behaviors were deciphered through new actions taken and support requested from the enterprise. An independent consultant captured graphic recordings of each focus group in real-time, enabling follow-through conversations with the consumers (see Figure 1 and Figure 2). Childcare, transportation, and snacks were provided to address barriers to participation in the focus group. An additional focus group was held with the First Ladies of the Hillside (see below for a description of this group) to capture their participation in production. For each meal kit delivered, Food Forward provided the meal name, date, recipe card, ingredients, and the number of servings that were compiled into a cumulative “meal repository” for the research project. The survey data for meal kits were aggregated to develop descriptive statistics. Food Forward’s consumers in 2022 included 25 distinct low-income families on food stamps from Duluth’s Central Hillside. A maximum of 19 families and 56 servings were involved in any given week. Seven participants (families) represented one of the most vulnerable groups in the community regarding food-and-nutrition security—single mothers living primarily on welfare and in the city’s supportive housing, who are victims of generational trauma and have mental health issues. By design, this group of seven, the First Ladies of the Hillside, was involved in the conception of Food Forward and weekly service delivery. They had the greatest opportunities for participation as producers and consumers in social innovation. Over the course of the study, four remained consistently involved, whereas the participation of three diminished, eventually dropping off. Another seven families were actively involved as consumers, providing regular feedback to help develop the service and consuming healthy meals; one of these dropped out, due to changed life circumstances. The remaining eleven families were profiled as passive consumers, of whom three dropped off at various stages, and eight received the meals, providing feedback occasionally. Table 1 summarizes the family data, and Table 2 lists the meals delivered. Data about satisfaction with the meal, familiarity with the meal, involvement of children in making the meal, ease of preparing the meal, ease of following the directions and time taken to prepare the meals are summarized as the percentage of responses in various categories (see Table 3). For qualitative data, we began by reviewing the social contracts with the First Ladies and with Food Forward’s consumers to identify participation opportunities and expectations. The facilitators at each table in the focus group made extensive notes of the discussions. These discussions and open-ended survey comments were compiled in a document. The coauthors separately studied the document, highlighting important fragments of texts as codes and marking them as actions demonstrating participation in the form of consciousness, voluntary actions, and barriers to participation. Through discussion and deliberations, the coauthors reached a consensus on the codes. Actions indicating consumers’ relationship with food and food-related behavioral changes conveyed as their needs, desires, and (dis)satisfaction with the meal-kit service were also highlighted. The codes were organized thematically into three groups representing different levels of participation, namely, passive consumers, active consumers not involved in the production, and active consumers involved in the production by design. Each group of codes provided a nuanced characterization of participation, demonstrating the consciousness of healthy foods and individual voluntary action. The food behaviors of typical consumers in the group were captured alongside. Preliminary propositions are developed by studying the differences in participation and food behaviors within and across the three groups. ## 4. Results From its inception, the social innovation studied in this action research centers voices of the food-insecure, taking both producer and consumer roles while providing several participation avenues for other marginalized community members. The findings describe community participation in three primary categories, i.e., passive consumers, active consumers, and active consumers deeply involved in participation. We begin by presenting the changes to their food behaviors across the three groups. ## 4.1. Changes to Food Behaviors Food Forward has a reasonably high retention rate of $72\%$ over a year (Table 1), and while many meals were either unfamiliar or only a little familiar ($43\%$), families reported high satisfaction with the meal ($74\%$) (Table 3). Food Forward is achieving its goal to design meal kits so that meals can be prepared in less than 30 min, as indicated by $82\%$ of the responses in this category (Table 3). Consumer feedback on early meal kits indicated dissatisfaction with the instructions to prepare meals. However, they took individual voluntary action, providing this feedback to help improve the instructions: Over time, the satisfaction with instructions has improved significantly. Regarding involvement of children in preparing the meals, the high percentage of responses with little to no involvement ($55\%$), indicates Food Forward has more work to do (Table 3). However, a majority of consumers ($88\%$) found the meals easy to make (Table 3). These data suggest that Food Forward’s meal-kit model is headed in the desired direction to eliminate basic barriers of transportation and lack of familiarity with healthy meals, and, among others, to promote ease of cooking, contributing to simultaneously improving food and nutrition security. A few open-ended comments in the surveys support the above conclusions from the quantitative data: When the alternative is consuming less healthy foods, in an effort to not go hungry, families are choosing to give the service a try. However, when taste preferences are not met, families have reported reverting to usual eating habits to, oftentimes, appease children, as exemplified by these quotes: The data also suggests that, at least for some children, receiving chopped vegetables and other ingredients in mason jars, and visually appealing recipe cards incite curiosity and the desire to participate in making the meal. ## 4.2. Passive Consumers These are consumers who received the meal kit but did not participate in the focus groups or rarely provided feedback on the meal service. Many consumers signed up for the program so that their children could be oriented to eating healthily. Those who dropped off from the service cited a lack of their children’s interest in the meals. For instance, one consumer said, “(her child) does not like the food and so I want to withdraw.” When asked if they would continue for just the adults, the response was, “it is too much work to make two meals when [their child] isn’t interested.” When the meals did not meet the children’s preferences, and parents could not convince them, the service was no longer of value, and they chose to drop off. Other reasons involved personal life changes, wherein healthy eating was no longer a priority. The remaining seven passive consumers continued to participate by receiving weekly meals, thanking Food Forward during delivery. The only observable change to their healthy-eating practices was consuming one healthy meal a week. Thus, for passive consumers with low levels of participation in social innovation, changes, if any, to their relationship with healthy foods, are minor. ## 4.3. Active Consumers This group consisted of consumers who took a more active role in providing feedback to help improve the service. Feedback was gathered during meal-kit deliveries, through weekly surveys specific to the meal for the week, and through bi-annual focus groups. These opportunities served as places where consumers took advantage of a direct line of communication with Food Forward. Only one consumer requested to stop receiving the meal kits. This consumer was involved in voluntary action for meal-kit deliveries on at least five occasions and referred other needy consumers to the program, but had to drop out, due to changes to their personal life circumstances. Many active consumers share social networks with the First Ladies, and participating in Food Forward provided opportunities to connect. One consumer described their desire for connection as, “humanize the project. Highlight the work of the First Ladies, put faces to the force behind Food Forward.” These consumers had provided feedback on several occasions to improve Food Forward’s operations. For example, The feedback resulted in improvements such as adding Food Forward labels to the mason jars, moving the delivery time to earlier in the day and providing single-serving sizes on the recipe cards. Active consumers shared information demonstrating changes to their relationship with food. For example, “my diabetic son is getting exposed to new foods,” “knowing that the vegetables are fresh coming from a local farm makes me comfortable, we are avoiding the pesticides,” “it changes up our dinner routine. It makes us more open to trying new foods and some healthy foods,” and “the meals are expanding our horizons because of the new meal each time.” These consumers demanded more support from Food Forward to transition into eating more healthily (see Figure 1 and Figure 2). For example, one consumer requested alternatives, tips, and instructions, so they could repeat the meal all by themselves; another asked for substitutes they could use in a meal and demanded recipes for sauces and curry paste prepared by Food Forward. ## 4.4. Active Consumers Deeply Involved in Production This group constitutes the First Ladies of the Hillside. As both producers and consumers of Food Forward’s service, First Ladies are at the core of this investigation. Engagement, curiosity, viewing the opportunity to support their families and community in promoting positive health outcomes, and consistently showing up, are some emerging themes in this research, despite several barriers to participation. Food Forward addressed basic barriers to their participation in production by providing free childcare and transportation to the production site, and by compensating them. The study revealed that their participation in production and as consumers are correlated. These findings are presented below in two groups, one for the group that stayed consistently engaged, and another with wavering participation. Inconsistent participation: Three of the seven First Ladies demonstrated inconsistent participation throughout the study. For the purpose of this study, they are referred to as participants A, B, and C. Their choice to receive meals mirrored their ability to stay involved in the production. Mental health issues attributed to past trauma were cited as the primary reason for inconsistent participation as a consumer and producer. Participant A first discontinued participation in production, lacking any communication with Food Forward. Shortly after, they were dropped from the consumer list. Participant B initially stayed engaged with production but wavered as a consumer, citing the reason, “I have a lot going on at home, do not have time to cook the meal.” They preferred to resort to unhealthy processed food, even though free healthy food was on the doorstep and could be cooked in less than 30 min. Eventually, participant B also dropped off from the production side. Participant C, like A, stopped being involved in the production first. Some of the First Ladies were able to leverage their social network to break through communication barriers and uncover the causes, which were once again related to mental health and trauma. These findings suggest that even when other barriers are eliminated, mental health and trauma continue to disrupt their ability to consume healthy foods. Consistent participation: The remaining four First Ladies received 27 meals over the study duration. Since they were core for production, deep insights were gained about factors influencing their food choices and changes in food behaviors. Most conveyed that they didn’t necessarily use all the contents of the meal and would drop some vegetables, for example. They reported trying new meals, such as beet hummus, squash risotto, and yellow curry, simply due to their involvement in preparing the meals and assembling the meal kits. They have even reported repeating the new meals mentioned above. Some have reported developing new cooking habits, such as cooking the meal using Food Forward’s meal kits after a full day of work, whereas, in the past, they would have resorted to processed, ready-to-eat meals. Unlike some passive consumers, the women in this group would continue to cook the meal and remain a consumer even if their children didn’t eat it. Upon probing, one of them said, “we can set an example, and eventually [the child] might be interested in eating it”. Clearly, initiative and individual voluntary actions were more abundant in this group, compared to other groups. ## 5. Discussion The findings reinforce the fact that low-income families face many barriers that impact their ability to consume a health-promoting diet. It is critically important that efforts to reduce these barriers are anchored in the knowledge possessed by marginalized groups. This study reinforces Vincent’s [34] suggestion of social contracts as a mechanism to enable the direct participation of marginalized groups instead of trustees representing their interests. As seen in this study, the contradiction in the literature of investing in relationship building [30,35] vs. skills development [36] can be resolved through a model that directly involves marginalized communities. Their shared identities and social groups provide the necessary relationships and trust, whereas codesigning the social innovation and involvement in production helps skill building. As this study demonstrates, there are many opportunities in which expert knowledge can be blended with traditional knowledge and the lived experiences of marginalized communities to raise their consciousness of healthy foods and stimulate individual voluntary action. The propositions introduced at the end of this discussion represent a culmination of the literature, theory, and findings from the social innovation studied. We begin by discussing the short-term outcomes of social innovation, followed by approaches to enabling multiple levels of authentic engagement and the specific outcomes observed. ## 5.1. Changes to Food Behaviors Interventions for low-income families in food deserts addressing only one dimension of the food-access issue help develop healthy-eating awareness, but rarely result in sustained changes to food behaviors [2,9,11,15], as other barriers to healthy eating persist. Meal kits are boxes of pre-portioned ingredients for a set number of meal servings, along with step-by-step instructions for cooking the meal at home. Home-delivered, partially prepared meal kits address several barriers to home cooking, which, as per Monsivais et al. [ 14], is a prerequisite to consuming a healthy diet. Meal kits can eliminate barriers of cost, time to shop for ingredients, food storage, cooking time, and food-preparation knowledge. Due to their multidimensional approach, home-delivered meal kits can create a favorable food environment and, as suggested by Ziso, Chun [3], can influence an individual’s dietary habits. Even though the meal-kit market is large (USD 7.64 billion for the U.S. in 2022) and growing, there are a few issues: they serve individuals with higher incomes, and of the $18\%$ estimated population who are buying meal kits, the frequency of usage is low, with the vast majority ordering only a few times a year [45]. Early studies exploring changes to food habits as a result of meal kits are promising [46,47,48]. To the best of our knowledge, Carman et al. [ 48] is the only study exploring the acceptability of meal kits among low-income families. The results of our study support their findings, wherein Food Forward’s consumers also demonstrated a high rate of satisfaction and retention in the program for an entire year, especially when the only incentive was free meals. We offer two explanations for a higher acceptance of Food Forward’s social innovation among food-insecure families, compared with those in the Loopstra and Tarasuk [24] study. First, as suggested by several scholars, for example [11,13,14], the innovation prioritized eliminating cooking-time barriers by providing chopped vegetables and premade sauces and home delivery, keeping the meal preparation time under 30 min. Second, providing authentic engagement avenues [37] where consumers could provide feedback and see prompt improvements could have motivated them to continue with the service. Consumers requesting redesigned recipe cards indicated their desire to learn to cook new meals, and requests for full recipes and substitute ingredients demonstrated their desire to make dietary changes toward healthy foods. The study confirms that partially prepared meal kits with the proper support (home delivery, chopped vegetables, and easy instructions to cook) can encourage healthy food behaviors. ## 5.2. Participation in Social Innovation and Changes to Food Behaviors Prost [35] suggests the need to make participation in food-system-innovation accessible, especially for marginalized voices. Food Forward is designed to accommodate varying levels of participation at the discretion of the consumers (primarily low-income families living on food stamps), thus providing authentic engagement opportunities, as Fernandez-Wulff [37] suggested. This section discusses the association between varying participation levels and food-behavior changes, to arrive at propositions. Individuals’ perceptions of their food environment may impact their food behaviors [3], but deeper participation in food-systems innovation is associated with increased awareness of healthy food behaviors. Contrary to the current literature, which largely focuses on participatory research methods (for example, Ziso, et al. [ 3]), this work centers on the voices of the marginalized via direct participation in developing the social enterprise model. This highly engaged, multi-level method of altering the food environment in Duluth’s Central Hillside has demonstrated that those most deeply involved have increased knowledge and awareness of healthy food behaviors. Loopstra and Tarasuk [24] conclude that the uptake of food-assistance programs by low-income families is directly related to their perceptions of the program. Specifically, is the program accessible, and does it fit into the family’s lifestyle and behaviors? Food Forward consumers’ self-determined participation is authentic, and creates an opportunity for low-income families to formulate perceptions of improving their food system based on first-hand experiences. Fernandez-Wulff [37] recognizes the nonlinearity of individual participation and collective expression in democratized food systems. This conclusion reinforces the need to validate the barriers that impact low-income individuals’ ability to engage in social innovations. Food Forward eliminated basic barriers by providing multimodal-feedback opportunities. For example, conversing with the delivery personnel, meal-specific paper-based and online feedback forms, and the invitation to participate in focus groups. Additional measures for focus groups included providing meals, childcare, and transportation services. The same measures were necessary to enable participation for consumers who were also involved in the day-to-day production and service delivery. The data suggest that as individuals deepen their level of participation in the food system innovation, they appear to take increasing levels of voluntary action toward improving their dietary behaviors. For example, passive consumer participation has been limited to receiving meals. Due to limited feedback and participation in focus groups, little is known about changes in healthy food behaviors other than weekly consumption of meals and retention as a consumer. Active consumers have taken voluntary action in providing feedback, deepening their social networks, and involving their children in the preparation of meals. Data suggests that active consumers experience social inclusion and a sense of community. Thus, meal kits foster solidarity and connection with and among disadvantaged communities, and, as suggested by Belon et al. [ 11] and Myers and Painter [16], these are important factors in fostering food security. These individuals are exercising agency (as suggested by Šimundža [38] and Booth and Coveney [36]) to improve their relationship with healthy foods. Finally, those involved in production have demonstrated an increase in their knowledge of preparing healthy meals and also their willingness to try new foods. The above discussion suggests that when marginalized communities are at the center of food-systems innovation: Proposition 1a: Marginalized voices self-select their level of participation. Further, when primary barriers to participation are addressed: Proposition 1b: Deeper participation in food-systems innovation is associated with positive changes in healthy food behaviors. However, some of Food Forward’s consumers involved in production demonstrated inconsistent participation, citing mental health and trauma as the primary reasons. Despite an option to stay enrolled in the service, these individuals chose to opt out of the program, both as consumers and producers. This finding highlights the need for additional support for low-income individuals who experience mental health issues and trauma, if they are to increase their participation in food-systems innovation and, therefore, their healthy eating behaviors. As a result, we propose that: Proposition 2: Opportunities for deep participation alone are insufficient to produce healthy food behaviors. An individual’s worsened mental health: Proposition 2a: is associated with decreased participation in food-systems innovation, and Proposition 2b: interrupts changes in healthy food behaviors ## 6. Conclusions The importance of involving marginalized communities in shaping their food systems to increase healthy eating outcomes cannot be overstated. This research suggests that limited access to affordable and healthy foods is a place-based issue that requires a solution informed by local knowledge. Marginalized and low-income groups face an array of systemic barriers but, when given the opportunity to participate in food-systems innovation, demonstrate positive changes in healthy food behaviors. Efforts to combat food insecurity in low-income populations would be well served by adopting an integrated approach to eliminating multiple barriers. More important, though, is adopting an agency-enabling perspective to developing intervention programming and policy framework. This research emphasizes the value of starting from the social groups of marginalized communities where strong relationships and trust exist, and facilitating the growth of their social networks and skills, in turn increasing their capacity to affect change. Preliminary propositions can guide future research on studying the effects of direct participation of food-insecure communities on their dietary outcomes. This research is limited to a single case which is still in its early stages of innovation, and involves a limited number of participants. The analyses used mixed qualitative and quantitative data to examine emerging themes and develop propositions. However, the research did not focus on theoretical saturation, and the statistical analyses were limited to descriptive and summary statistics characterizing retention rates and consumer satisfaction. Therefore, the propositions should be considered preliminary, and used as guidance for future research design. ## References 1. 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--- title: Evaluation of the Multifunctionality of Soybean Proteins and Peptides in Immune Cell Models authors: - Samuel Paterson - Samuel Fernández-Tomé - Alfredo Galvez - Blanca Hernández-Ledesma journal: Nutrients year: 2023 pmcid: PMC10005611 doi: 10.3390/nu15051220 license: CC BY 4.0 --- # Evaluation of the Multifunctionality of Soybean Proteins and Peptides in Immune Cell Models ## Abstract Inflammatory and oxidative processes are tightly regulated by innate and adaptive immune systems, which are involved in the pathology of a diversity of chronic diseases. Soybean peptides, such as lunasin, have emerged as one of the most hopeful food-derived peptides with a positive impact on health. The aim was to study the potential antioxidant and immunomodulatory activity of a lunasin-enriched soybean extract (LES). The protein profile of LES was characterized, and its behavior under simulated gastrointestinal digestion was evaluated. Besides its in vitro radical scavenging capacity, LES and lunasin’s effects on cell viability, phagocytic capacity, oxidative stress, and inflammation-associated biomarkers were investigated in both RAW264.7 macrophages and lymphocytes EL4. Lunasin and other soluble peptides enriched after aqueous solvent extraction partially resisted the action of digestive enzymes, being potentially responsible for the beneficial effects of LES. This extract scavenged radicals, reduced reactive oxygen species (ROS) and exerted immunostimulatory effects, increasing nitric oxide (NO) production, phagocytic activity, and cytokine release in macrophages. Lunasin and LES also exerted dose-dependent immunomodulatory effects on EL4 cell proliferation and cytokine production. The modulatory effects of soybean peptides on both immune cell models suggest their potential protective role against oxidative stress, inflammation, and immune response-associated disorders. ## 1. Introduction Chronic diseases increased their presence in our society in recent years as a serious disease burden recognized worldwide. With more than $50\%$ of all deaths being attributable to them, inflammation-related diseases such as stroke, cancer, ischemic heart disease, chronic kidney disease, and auto-immune and neurodegenerative conditions are now some of the biggest threats and challenges to human health. Novel studies suggest that the risk of developing chronic inflammation can be traced back to early development in the younger stages of life, and its effects are now known to persist throughout a person’s life to affect adulthood health and the risk of mortality. Moreover, lifestyle factors, including diet, are among the major risk factors contributing to these diseases [1]. Consequently, the development of potential and original preventive and therapeutic strategies against infection, inflammation, and oxidative stress is being done not only to fight these elements directly, but also to complement and reinforce existing therapeutic strategies. Along with diverse pharmaceutical options, bioactive peptides surfaced as one of the most promising strategies [2,3]. Bioactive peptides are defined as inactive amino acid sequences within the source protein that, once liberated after microbial fermentation and the chemical or enzymatic hydrolysis that occurs during food processing or gastrointestinal digestion, can perform different biological activities [4]. Regarding their sources, it should be noted that any food source of protein of animal or vegetal origin can generate bioactive peptides. In the case of vegetable proteins, most studies have focused on cereals and legumes, mainly soybeans [4]. Nowadays, different biological effects of bioactive peptides have been described, such as antioxidant, anticancer or antitumor, immunomodulatory, anti-inflammatory, antidiabetic, antihypertensive, and antimicrobial activity, among others; thus, they exert beneficial effects on the cardiovascular, nervous, and/or immune systems [4,5]. Lunasin, identified 24 years ago, is a soybean peptide encoded by the 2S albumin Gm2S-1 gene [6]. It is made up of the 43-amino acid sequence SKWQHQQDSCRKQLQGVNLTPCEKHIMEKIQGRGDDDDDDDDD (National Center for Biotechnology Information, NCBI, number AAP62458), which has four distinct regions with different functions. Although the soybean is the main source studied for the origin of this peptide, it was also identified in other cereals such as barley, rye, and wheat, although there is still controversy over the results described in the literature [6]. Regarding the bioavailability of lunasin, studies carried out in vitro have delved into its absorption mechanism, and the peptides released after its gastrointestinal digestion were also identified [7]. Thus, it was shown in a monolayer Caco-2 cell model that both lunasin and the 11RKQLQGVN18 fragment can cross the intestinal epithelium by means of a passive paracellular diffusion mechanism [8,9]. Currently, due to its involvement in the control of various pathologies, the immune system is considered a possible therapeutic target for new strategies against chronic and inflammatory disorders. Plentiful research groups over the past several years have focused their studies on the evaluation of diverse biological activities of the peptide lunasin, so far demonstrating the chemopreventive, antioxidant, anti-inflammatory, hypocholesterolemic, and modulating properties of the nervous and cardiovascular systems, among others [10]. Regarding the immune system, the ex vivo immunomodulatory activity of synthetic lunasin was even explored in primary human cells of the intestinal mucosa [11]. However, most of lunasin’s biological activities, such as its immunomodulatory action, have been carried out with chemically synthesized lunasin [12]. Similarly, lunasin’s potential therapeutic roles will directly depend on its bioavailability and digestibility, which both are related to the type of administration format or the food matrix containing lunasin. The bioactivity of lunasin, and specially its immunomodulatory effects as a part of a food-derived matrix, has not been extensively explored. Therefore, throughout this work, we aimed to study the potential antioxidant and immunomodulatory activities of a lunasin-enriched soybean extract (LES) and to elucidate, in different cell models, its mechanisms of action on biomarkers associated with oxidative stress, inflammation, and immune response. ## 2.1. Materials 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), 6-hydroxy-2,5,7,8-tetramethylchromium-2-carboxylic acid (Trolox), fluorescein disodium (FL), 2,2′-azobis(2-amidinopropane dihydrochloride) (AAPH), 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazol bromide (MTT), dimethyl sulfoxide (DMSO), 2′7′-dichlorofluorescein diacetate (DCFA-DA), phorbol 12-myristate 13-acetate (PMA), and ionomycin (Ion) were purchased from Sigma-Aldrich (St. Louis, MO, USA). All other reagents were of analytical grade. The albumin-enriched soybean extract (ES) was supplied by Reliv International Inc. (Chesterfield, MO, USA). Synthetic lunasin (purity of $95.2\%$) was obtained by DGpeptides Co., Ltd. (Hangzhou, China). ## 2.2. Preparation of Lunasin-Enriched Soybean Extract (LES) LES was obtained from ES through the elimination of the insoluble fraction of the sample. First, 500 mg of ES were dissolved in 50 mL of PBS and left stirring at 4 °C overnight. Subsequently, it was centrifuged at 1000× g for 5 min, and the supernatant was collected via decantation and then centrifuged two times more under the same conditions. The final supernatant was subsequently lyophilized (LES) and kept at −20 °C until further assays. The protein concentration of LES was measured via the bicinchoninic acid (BCA) method, which was performed using the Pierce BCA kit (Thermo Scientific, Waltham, MA, USA). The standard used was bovine serum albumin (BSA) at concentrations that ranged from 50 to 1000 µg/mL. ## 2.3. Simulated Gastrointestinal Digestion of Lunasin-Enriched Soybean Extract (LES) The in vitro “INFOGEST” protocol with some modifications was followed to simulate the gastrointestinal digestion of LES [13]. Saliva was collected from five healthy volunteers, pooled, and stored at −80 °C. The enzymes and reagents needed to prepare the simulated gastric fluid (SGF) and the simulated intestinal fluid (SIF) were provided by Merck (St. Louis, MO, USA). A volume of 300 mg of LES was dissolved in 5 mL of saliva, leaving it under mild agitation at 37 °C for 5 min. Next, the pH was adjusted to 3.0 with HCl, 4 mL of the pepsin (EC 3.4.23.1, Sigma-Aldrich, 3026 U/mg) was dissolved in the SGF (1.62 mg/mL), and 2.5 µL of CaCl2 was added, incubating the mixture by shaking it at 37 °C for 2 h. Pepsin was inactivated by increasing the pH to 7.0 with NaOH, heating at 85 °C for 15 min, and rapidly cooling the mixture on ice (gastric digest, GD). To carry out the gastrointestinal digestion once the gastric phase was finalized and the pH of the solution was adjusted to 7.0, the bile salts dissolved in Mili-Q water, the CaCl2, and the pancreatin (Sigma-Aldrich, 6205 U/mg) dissolved in SIF (76.91 mg/mL) were added, and the mixture was incubated by shaking it at 37 °C for 2 h. The pancreatin was inactivated by heating it to 85 °C for 15 min and rapidly cooling it (gastrointestinal digest, GID). A control of digestion (CD) was also obtained following the same simulated process but in the absence of the sample. ## 2.4.1. Gel Electrophoresis (SDS-PAGE) Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) was conducted to evaluate the protein profiles of LES and its digests (GD and GID). Samples dissolved in sample buffer (60 mM Tris-HCl pH 6.8, $25\%$ glycerol (v/v), $2\%$ SDS (p/v), 14.4 mM 2-mercaptoethanol, and $0.1\%$ 2-bromophenol (p/v)) were prepared by heating them at 100 °C for 4 min and then cooling them to room temperature. The samples (50 µg of protein) were loaded into $12\%$ Bis-Tris CriterionTM XT Precast Gel polyacrylamide gel and run through a Mini-Protean Tetra Cell Electrophoresis System (Bio-Rad, Hercules, CA, USA). For separation, the commercial buffer XT MES Running Buffer 20X (Bio-Rad) was used, and the electrophoretic migration and running of the gel were carried out, first with a voltage of 100 V for 5 min and with a voltage of 150 V for 1 h after that. Gel staining was performed with BlueSafe stain (Bio-Rad). Precision Plus Protein Standard Unstained (Bio-Rad) was used as a molecular weight marker. Finally, the gel image was taken using the Versadoc Imaging System (Bio-Rad) gel reader. ## 2.4.2. Western Blot The presence of lunasin in LES and its behavior during the simulated digestive process were studied by using a Western blot test. Synthetic lunasin (12.5–50 µM) was employed as the standard. SDS-PAGE was conducted with $16.5\%$ Bis-Tris Criterion™ XT Precast gels (Bio-Rad) and voltage conditions of 60 V for 2 min, which was followed by 100 V for 3 h. Polyvinylidene membranes (PVDF) (Bio-Rad) were used to transfer the proteins by using a voltage of 18 V for 30 min. The membrane was blocked, washed, and incubated overnight (4 °C) with the primary rabbit antibody against lunasin (Biomedal, Seville, Spain, 1:12,000). Then, the incubation of the membrane with the mouse IgG-horseradish peroxidase (HRP)-conjugated anti-rabbit IgG secondary antibody (Santa Cruz Biotechnology, Dallas, TX, USA; 1:5000, 1 h, room temperature) was performed. Finally, the development of the membrane was achieved with the Amersham reagent (Merck, Darmstadt, Germany) for 3 min, and images were taken on the Versadoc Imaging System reader while using an AF 50 mm f/1 4D photographic objective (Nikon, Tokyo, Japan). ## 2.5. Assessment of the Antioxidant Activity of the Lunasin-Enriched Soybean Extract (LES) with Biochemical Assays The ABTS•+ scavenging activity was determined according to the enhanced ABTS•+ protocol described by Re et al. [ 14]. Volumes of 7 mM ABTS and 2.45 mM potassium persulfate were mixed overnight at room temperature to form the ABTS•+ radical. Volumes of 180 μL of diluted radical and 20 μL of PBS (blank), Trolox (25–200 µM) (standard), or sample were mixed and incubated for 7 min at room temperature, and absorbance was measured at 734 nm in the Biotek SynergyTM HT plate reader (Winooski, VT, USA). TEAC values are expressed as µmol Trolox equivalents (TE)/mg of protein. The oxygen radical absorbance capacity (ORAC) was measured following the protocol reported by Hernández-Ledesma and coworkers [15]. Briefly, the mixture (200 µL) containing FL (117 nM), AAPH (14 mM), and either antioxidant (Trolox (0.2–1.6 nmol) or sample) was incubated at 37 °C in 75 mM phosphate buffer (pH 7.4). The fluorescence was recorded every 2 min for 120 min at 485 and 520 nm wavelengths of excitation and emission, respectively, with the Fluostar Optima BMG Labtech plate reader controlled by using FLUOstar Control ver. 1.32 R2 software (Ortenberg, Germany). ORAC values are expressed as µmol TE/mg of protein. ## 2.6.1. Cell Culture The mouse RAW264.7 macrophages and lymphoma EL4 cells were purchased from the American Type Culture Collection (ATCC, Rockville, MD, USA) and grown in modified Dulbecco Eagle medium (DMEM) supplemented with $10\%$ of fetal bovine serum (FBS) (v/v) and $1\%$ penicillin/streptomycin (v/v) (Biowest, Nuaillé, France). Cells were grown at 37 °C in a humidified incubator containing $5\%$ CO2 and $95\%$ air. ## 2.6.2. Effects on Cell Viability and Cell Proliferation The evaluation of the effect of LES on the viability of RAW264.7 macrophages was carried out using the MTT assay. Cells were seeded onto 96-well plates (Sarstedt AG & Co., Nümbrecht, Germany) at a density of 6 × 104 cells/well and incubated at 37 °C for 24 h. Then, the culture medium was removed, 120 μL of LES dissolved in DMEM without FBS at different concentrations (0.5–15 μg protein/mL) was added, and the plates were incubated for 8, 16, and 24 h at 37 °C. As negative and positive controls, DMEM medium without FBS and DMEM + lipopolysaccharide (LPS) 10 ng/mL (Merck, Kenilworth, NJ, USA) were used, respectively. After incubation, cells were washed with PBS and incubated with MTT solution (0.5 mg/mL) for 2 h at 37 °C. Once the supernatant was removed, the formazan crystals were dissolved in dimethyl sulfoxide (DMSO), and the absorbance was measured at 570 nm with the Multiskan FC plate reader (ThermoTM Scientific, Wilmington, DE, USA). The results are expressed as percentages of the control, which is considered to be $100\%$. The effect of LES on EL4 cell proliferation was evaluated using the WST-1 (Roche, Basilea, Sweden) assay. Cells were seeded onto 96-well suspension plates (Sarstedt AG & Co.) at a density of 5 × 103 cells/well and incubated at 37 °C for 24 h. Then, the cells were treated with LES (0.5–15 μg protein/mL) or synthetic lunasin (1–500 µM) dissolved in DMEM without FBS in the absence or presence of PMA (10 ng/mL) and Ion (100 ng/mL), and they were incubated for 24 h at 37 °C. After that time, the WST-1 reagent was added to the plates, and the cells were incubated for 3 h at 37 °C. The plates were shaken for 1 min at room temperature in the dark, and their absorbance was measured at 450 nm using a Multiskan FC plate reader (ThermoTM Scientific). The cell proliferation rate was calculated relative to that of the control, which was considered to be $100\%$. ## 2.6.3. Effects on Reactive Oxygen Species (ROS) Generation The effect of LES on the intracellular ROS levels was determined following the assay described by LeBel et al. using DCFH-DA as the fluorescent probe [16]. Cells were seeded onto 48-well plates (Corning Costar Corp., Corning, NY, USA) (4.75 × 104 cells/well) and incubated at 37 °C for 24 h. After that time, and once the medium had aspirated, cells were treated with 120 μL of LES dissolved in DMEM without FBS (0.5–15 μg protein/mL), and incubated for 8, 16, and 24 h at 37 °C. The negative and positive controls were DMEM without FBS and DMEM + LPS (100 ng/mL), respectively. After removing the treatment, 100 µL/well of the probe (0.4 mg/mL), which was dissolved in Hank’s Balanced Salt Solution (HBSS, Sigma-Aldrich), was added, and the plate was incubated for 1 h at 37 °C. The fluorescence was measured at λexcitation and λemission of 485 nm and 520 nm, respectively, by using the Fluostar Optima BMG Labtech plate reader (BMG Labtech). The results are expressed as ROS levels (% compared to the control, which is considered to be $100\%$). ## 2.6.4. Effects on Nitric Oxide (NO) Levels To determine the effect of LES on the release of nitric oxide (NO), the Griess assay was used. Cells were seeded onto 96-well plates (Corning Costar Corp.) at a density of 1 × 105 cells/well and incubated at 37 °C for 24 h. The medium was aspirated, and the cells were treated with 120 μL of LES (0.5–15 μg protein/mL) dissolved in DMEM without FBS for 8, 16, and 24 h at 37 °C. The negative and positive controls were DMEM without FBS and DMEM + LPS (100 ng/mL), respectively. Afterward, 100 µL of the supernatant was mixed with 100 µL of Griess reagent for 15 min at room temperature, and the absorbance was measured at 540 nm in the Biotek SynergyTM HT reader. The amount of NO was calculated using a NaNO2 standard curve (3.125–100 μM). ## 2.6.5. Effects on the Phagocytic Capacity of Macrophages To study the effect of LES on the phagocytic capacity of the cells, the neutral red assay was used. Macrophages were seeded onto 96-well plates (Sarstedt AG & Co.) at a density of 2 × 105 cells/well and incubated for 24 h at 37 °C. Then, cells were treated with LES (0.5–15 μg protein/mL) for 8, 16, and 24 h at 37 °C. Once the treatment was discarded, a neutral red solution (0.125 mg/mL) was added, and the plate was incubated at 37 °C for 30 min. Lysis buffer (acetic acid $1\%$:ethanol, 1:1) was added, and the cells were incubated overnight at 37 °C, measuring the absorbance at 540 nm with the Biotek SynergyTM HT plate reader. DMEM without FBS and DMEM + LPS 10 ng/mL were used as negative and positive controls, respectively. ## 2.6.6. Effects on the Cytokine Release To evaluate the effect of LES on the cellular production of cytokines (IL-6, IL-1β, IL-10, IL-5, IL-2, IL-4, IL-12 p70, and IFN-ɣ), the corresponding ELISA kits (Mouse IL-6 ELISA Ready-Set-Go!, Mouse IL-1β ELISA Ready-Set-Go!, Mouse IL-10 ELISA Ready-Set-Go, Mouse IL-12 p70 ELISA Ready-Set-Go!, and Mouse IL-5 ELISA Ready-Set-Go! from eBioscience, Affymetrix Company, Santa Clara, CA, USA, and Mouse IFN gamma Uncoated ELISA kit, Mouse IL-4 Uncoated ELISA kit, and Mouse IL-2 Uncoated ELISA kit from Thermo Scientific) were used. Cells were seeded onto 48-well plates (Corning Costar Corp., 1 × 106 cells/well) and incubated at 37 °C for 24 h. Then, cells were treated with LES (0.5–15 μg protein/mL) dissolved in DMEM without FBS. In the case of EL4 cells, PMA (10 ng/mL) and Ion (100 ng/mL) were also added. DMEM medium without FBS was used as a negative control. As positive controls, DMEM + LPS (100 and 1000 ng/mL) and DMEM + PMA (10 ng/mL) + Ion (100 ng/mL) were used for RAW264.7 and EL4 cells, respectively. After 8, 16, and 24 h of incubation at 37 °C of RAW264.7 cells, and after 24 h incubation for EL4 cells, cell supernatants were collected for the ELISA assays by following the manufacturer’s instructions. After being coated with capture antibodies overnight, the plates were blocked, washed, incubated with detection antibodies and horseradish peroxidase-conjugated streptavidin, and finally washed and incubated with the substrate solution. The absorbance was measured at 450 nm using the Multiskan FC plate reader (ThermoTM Scientific), and data were calculated based on the standard curve. ## 2.7. Statistical Analysis A one-way ANOVA analysis followed by a Bonferroni test was used to analyze the results, using the statistical analysis program GraphPad Prism 7.0 (GraphPad Software, San Diego, CA, USA). ## 3. Results and Discussion Part of the results shown in this article were presented at the 2nd International Electronic Conference on Nutrients—Nutrition Support for Immunity and Countermeasure Effects on Infection, Inflammation, and Oxidative Stress [17]. ## 3.1. Characterization of Lunasin-Enriched Soybean Extract (LES) and Behavior under Simulated Gastrointestinal Digestion The protein concentration of ES was $52.7\%$ and decreased up to $12.6\%$ when aqueous solvent extraction was carried out to remove insoluble proteins and recover LES. The protein profiles of both samples were analyzed via SDS-PAGE, as shown in Figure 1A. Both profiles were similar, with bands corresponding to proteins with molecular weights ranging from 4 to 138 kDa. However, the intensities of some bands in LES were higher than those shown by ES, indicating the enrichment of these proteins after extraction with the aqueous solvent. It was possible to observe the major soybean proteins, glycinin, β-conglycinin and its corresponding α and α’ subunits (82.03 kDa and 76.14 kDa, respectively), and the subunits glycinin A1a and A2 (36.11 kDa), which similarly correspond to the molecular weights previously described for these proteins by different authors [18]. The presence of peptide lunasin in LES was detected in the Western blot assay by using an antibody specific for this peptide (Figure 1B). The concentration, which was quantified by using a standard curve with synthetic lunasin ($y = 11$,099x; R2 = 0.9922), was 16.42 mg lunasin/g protein, or 2.07 mg lunasin/g of extract. This concentration was lower compared to that reported for a lunasin-enriched soy extract produced in a two-step pilot plant-based ultrafiltration process (58.2 mg lunasin/g protein) [19], although it was within the reported range for different lunasin-based commercial soybean products (9.2 ± 0.6 to 25.7 ± 1.1 mg lunasin/g protein) [20,21]. The behavior of the proteins and peptides contained in LES during gastrointestinal digestion was studied by using the SDS-PAGE and Western blot tests. The electrophoretic analysis revealed the susceptibility of high molecular weight proteins to the action of pepsin during the gastric phase of digestion, releasing proteins and polypeptides with lower molecular weights that were further digested by pancreatic enzymes. However, some proteins showed resistance to the action of both gastric and intestinal digestion, as their bands appeared at the end of the digestive process (Figure 1A). It was possible to identify the presence of lunasin after the gastric phase, and $60\%$ of the initial lunasin remained in the GD. However, pancreatic enzymes degraded lunasin to a greater extent, and only $2.9\%$ of the intact peptide could be visualized in the gastrointestinal digest (GID). Previous studies with synthetic lunasin described the susceptibility of the peptide to the action of digestive enzymes, which was partially reversed by the presence of protease inhibitors such as the Bowman–Birk inhibitor (BBI) or the Kunitz inhibitor [21,22]. The inhibitors potentially inherent in LES could, therefore, be those responsible for the resistance of lunasin to the digestive process. The residual lunasin at the end of the digestive process could exert its effects locally, or it could be absorbed and reach the target organs. In fact, lunasin was identified in human plasma samples after soy protein consumption [23]. ## 3.2. Antioxidant Activity of Lunasin-Enriched Soybean Extract (LES) and Its Digests The antioxidant activity of ES and LES was evaluated with the biochemical assays ORAC and ABTS, and higher effects for LES (1.67 μmol TE/mg protein and 0.23 μmol TE/mg protein, respectively) than for ES (0.50 μmol TE/mg protein and 0.11 μmol TE/mg protein, respectively) were observed. The enrichment in low-molecular weight peptides during LES preparation could favor the increase of antioxidant potential. Thus, peptides containing between 5 and 16 amino acids were identified and characterized as the major factors responsible for the antioxidant effects of soybean fractions [12,24,25,26]. The TEAC value of LES (28.97 μmol TE/g LES) was slightly lower than those observed in other soybean samples (44.9–50.0 μmol TE/g of extract) [27,28]. These differences could be due to its soybean origin as well as to the treatment and extraction conditions used in each study. In addition, the effect of simulated gastrointestinal digestion on the radical scavenging capacity of LES was evaluated. It was found that after the action of gastric and pancreatic enzymes on LES, its antioxidant activity increased up to 2.17 µmol TE/mg protein (ORAC) and 0.33 µmol TE/mg protein (TEAC) in GID. These findings indicate that fragments released from lunasin or from other proteins contained in LES could exert more potent antioxidant effects than their protein source. Similarly, the peroxyl radical scavenging capacity of soybean concentrates and of corolase PP hydrolyzates notably increased after being digested, simulating gastrointestinal conditions [29,30]. Peptides released during the digestion process could be responsible for this increase in the radical scavenging capacity of gastrointestinal digests. ## 3.3. Effects of Lunasin-Enriched Soybean Extract (LES) in RAW264.7 Macrophages and EL4 Cells To evaluate the action of LES on biomarkers associated with oxidative stress, inflammation, and immune response, two immune cell models, RAW264.7 macrophages and lymphocytes EL4, were used. First, the dose- and time-dependent effects of LES on the viability of RAW264.7 macrophages cells were evaluated with the MTT assay. Cells were treated with concentrations that ranged from 0.5 to 15 µg protein/mL for 8, 16, and 24 h. In Table 1, the percentages of viable cells after 8 and 24 h of treatment with LES or LPS are shown. After 8 h, LES significantly increased the viability of macrophages, whereas LPS (10 ng/mL) did not provoke any significant effect. This result indicates an immunostimulatory effect of protein and peptides contained in LES at short incubation times. However, when the cells were incubated for 16 h (data not shown) and 24 h (Table 1), LES inhibited cell viability at concentrations higher than 7.5 µg protein/mL and 2.5 µg protein/mL, respectively. After 24 h of treatment, the highest assayed dose of LES (15 µg protein/mL) inhibited cell viability up to $47.0\%$, whereas LPS reduced viability by $23.8\%$. This result indicates that both protein and peptides contained in LES exerted cytotoxic effects on macrophages, as previously reported for soybean protein-derived peptides [31]. To determine whether peptides contained in LES could scavenge the ROS, a DCFH-DA assay was carried out. As shown in Table 1, LPS treatment at 100 ng/mL induced greater ROS accumulation compared to untreated cells at the three times tested. The highest induction was observed after 8 h of stimulation, reaching ROS levels of 124.9 ± $12.6\%$ compared to non-induced cells (100.0 ± $10.0\%$). These ROS-inducing effects were previously reported in RAW264.7 cells treated with LPS at 2 µg/mL for 24 h [32]. In the case of LES, it exerted protective effects against oxidative stress at the three assayed times, reducing ROS levels at doses lower than 7.5 µg protein/mL. The radical scavenging activity demonstrated in this study with biochemical assays could contribute to the ROS-reducing effects of LES. A previous study carried out in our laboratory demonstrated that synthetic lunasin exerted potent protective effects on cell viability and oxidative status in RAW264.7 macrophages challenged with chemicals tert-butylhydroperoxide and hydrogen peroxide [22]. Recent findings show that liposomes loaded with amaranth unsaponifiable matter and soybean lunasin (0.5–2 mg/mL) have an antioxidant effect in RAW264.7 macrophages by significantly decreasing ROS production after 1 h of treatment [33]. The protective effects of lunasin against oxidative stress was also demonstrated in other cell lines. Thus, pepsin-pancreatin hydrolyzates from a lunasin-enriched preparation were shown to inhibit the production of intracellular ROS in THP-1 human macrophages [34]. In challenged Caco-2 cells, synthetic lunasin (0.5–10 µM) also exerted a protective role by reducing the ROS increase induced by chemical agents [35]. Thus, the lunasin present in LES could be responsible for its antioxidant effects, although other soybean derived peptides could also provide important contributions. At the highest assayed dose of LES, a significant pro-oxidant effect was observed when cells were treated for 24 h (Table 1). Even though the increase of intracellular ROS could favor a more efficient destruction of microorganisms during the immune response of the macrophages [36], the oxidizing effect of LES at the highest concentration should be explored more in detail to understand the balance between antioxidant and oxidant compounds present in LES and their influence in the redox metabolism of immunological cells such as macrophages. To further investigate the immunomodulatory effects of lunasin and other soybean peptides contained in LES on cells related to the adaptative immune system, a lymphocyte EL4 model was also explored. The effects of LES (0.5–25 μg protein/mL) and lunasin (1–500 μM) on cell proliferation after 24 h of treatment under basal conditions are shown in Figure 2. Previous findings show that synthetic lunasin stimulates EL4 proliferation irrespective of the presence or absence of obesity-inducing factors [37]. However, in our study, lunasin did not cause any effect on cell proliferation (Figure 2A). In the case of LES, a significant dose-dependent increase of EL4 cell proliferation was observed (Figure 2B), suggesting that other peptides could be responsible for the immunostimulant effects of LES. The effects of lunasin and LES on EL4 cells challenged by PMA and Ion were also evaluated. PMA and Ion are common reagents used in immune cell culture for their capacity to activate T cells, increasing their proliferation and stimulating the release of different cytokines [38]. In our study, the increase in EL4 cell proliferation caused by the combination of PMA and Ion was not reversed with LES or lunasin (data not shown). The effects of LES on NO production by RAW264.7 cells were assessed by using the Griess assay. NO is a molecule that acts as an inflammation biomarker, as it is harmful to the body at high concentrations [39]. However, macrophages produce this compound as an intermediary of their cytotoxic action against pathogens, favoring their phagocytic activity [40]. Although NO production could not be detected at basal conditions, after stimulating the cells with LPS, NO levels significantly increased in a time-dependent manner, reaching values of 4.96, 17.47, and 34.81 µM at 8, 16, and 24 h, respectively (Figure 3A,B). LES also caused a stimulating effect on NO production in a dose- and time-dependent manner. Hence, at the maximum LES concentration tested, the NO levels detected were 21.08, 43.19, and 63.40 μM at 8, 16, and 24 h of treatment, respectively. Soybean lunasin at 10 µM was reported to inhibit NO production induced by LPS in macrophages [41], as it had been demonstrated for quinoa-derived lunasin at a dose of 0.40 g/L [42]. This controversy could be due to the concentration of lunasin in LES or to the presence of other peptides able to induce NO release. Similarly, peptides from other food sources were also found to induce NO production in macrophages. For example, bioactive peptides from European eel Anguilla anguilla stimulated NO release in a dose-dependent manner when basal RAW264.7 cells were treated for 24 h [43]. The treatment of macrophages for 24 h with the peptide YGPSSYGYG from Pseudostellaria heterophylla protein hydrolyzates also resulted in cell activation and an increase of NO levels [44]. The results obtained in this work suggest that the immunostimulatory effect of LES in macrophages, by increasing their NO production, could favor their cytostatic and cytotoxic capacity and, on the other hand, exert a vasodilator effect that would help the other cells of the immune system reach the possible source of infection. To elucidate the effect of LES on the phagocytic capacity of RAW264.7 macrophages, the neutral red assay was carried out. The results obtained after 8 and 24 h of treatment are shown in Figure 3C,D. After 8 h, phagocytic capacity increased significantly at the four concentrations tested without differences among them. The stimulatory effect was similar to that observed for cells induced with LPS (Figure 3C). Previous findings reported the immune-enhancing effects of food-derived proteins and peptides by increasing the phagocytic capacity of macrophages [45]. After 16 h (data not shown), phagocytic capacity was affected by neither LPS nor LES. Finally, at 24 h, LPS did not modify the phagocytic capacity of the cells. In the case of LES, only the lower concentration caused an increase in phagocytic capacity, whereas the higher concentrations caused a significant decrease in phagocytosis, which could be due to the inhibitory effect of cell viability exerted by the high concentrations of the extract after 24 h of incubation (Figure 3D). Cytokines are soluble proteins secreted from a variety of cells (lymphocyte, macrophage, natural killer, mast, and stromal cells) that can act as mediators of inflammation and immune responses [46]. Given their key role in the immune system, the effects of LES on the secretion of IL-6, IL-1β, IL-10, IL-12 p70, and IFN-ɣ by RAW264.7 macrophages were evaluated. IL-12 p70 could not be detected in the cell supernatants obtained after treatment with LES. In the case of IFN-γ, only the highest concentration of LES was able to slightly stimulate this cytokine after 24 h of treatment (data not shown). IFN-ɣ primarily mediates antiviral and antibacterial immunity, enhancing antigen presentation and the innate immune system activation that results in a Th1 proinflammatory response [47]. Our previous study demonstrated the ability of synthetic lunasin to abrogate the ex vivo secretion of IFN-γ in human intestinal biopsies [11]. In Figure 4, the effects of LES on the secretion of IL-6, IL-1β, and IL-10 after 8 h of treatment are shown. As shown in Figure 4A, LES caused a dose-dependent stimulatory effect of IL-6 secretion until reaching a value of 1978 pg/mL at the highest dose assayed (15 µg protein/mL) after 8 h of treatment. The stimulation was more notable when cells were treated for 16 h (data not shown), and IL-6 levels were 5190 pg/mL, which was slightly lower than those measured in the supernatants obtained from LPS-challenged cells (6023 pg/mL). Although IL-6 is mostly considered as a proinflammatory cytokine, it also performs many regenerative or anti-inflammatory activities [48]. Thus, in models of chronic inflammatory diseases, such as collagen-induced arthritis, murine colitis, or experimental autoimmune encephalomyelitis, IL-6 acts as proinflammatory cytokine, whereas in models of acute inflammation, it also exhibits an anti-inflammatory profile [49]. LES also showed a dose-dependent stimulatory effect on IL-1β secretion at the three times assayed, reaching values for this cytokine up to 23.04, 12.54, and 3.81 pg/mL after 8, 16 (data not shown), and 24 h of treatment (Figure 4B), respectively, at the dose of 15 µg protein/mL. These induced effects were more potent than those resulting from LPS. IL-1β is a member of the IL-1 family of cytokines produced by activated macrophages and recognized as an important mediator of the inflammatory response, as it is involved in a variety of cellular activities, including cell proliferation, differentiation, and apoptosis [50]. Other proteins and peptides were also found to induce IL-1β secretion in immune cell models. For example, lactoferrin and zinc-supplemented lactoferrin exerted stimulating dose-dependent effects on IL-1β secretion in RAW264.7 macrophages after 24 h of incubation [51]. However, lunasin was reported to reduce IL-1β secretion in RAW264.7 cells cultured in both adipocyte-conditioned medium and leptin-containing medium [52]. Thus, the low concentration of lunasin in LES or the presence of other peptides with stimulatory properties on IL-1β release that surpass the downregulatory properties of lunasin could be determined by the observed effects for the soybean extract. LPS (1000 ng/mL) had a stimulatory effect on IL-10 levels that decreased over time, with values of 2366.8 pg/mL, 1966.1 pg/mL, and 335.7 pg/mL for cells treated for 8, 16, and 24 h (Figure 4C), respectively. When cells were treated with LES at 0.5 and 2.5 μg protein/mL, a slight but not significant stimulating effect was observed. However, this effect was more potent when LES was used at higher concentrations, reaching IL-10 values of 240.03 pg/mL and 577.24 pg/mL for 7.5 and 15 μg protein/mL doses, respectively, after 8 h of treatment (Figure 4C), and 642.2 pg/mL and 956.0 pg/mL for 7.5 and 15 μg protein/mL doses, respectively, after 16 h (data not shown). These values were diminished after 24 h of treatment. IL-10 is a cytokine with potent anti-inflammatory action produced by T cells, B cells, macrophages, and keratinocytes, and it can profoundly alter cell morphology and the production of cytokines by monocytes, which in turn can affect a variety of immunological responses [53]. Therefore, IL-10 plays a central role in limiting the host’s immune response to pathogens, thereby preventing damage to the host and maintaining normal tissue homeostasis [54]. Controversial results on the effects of soy and derived compounds can be found in the literature. For example, some studies have reported the IL-10-inducing effects of soy isoflavones in RAW264.7 macrophages [55], whereas others showed no significant effects [56,57,58]. In the case of soybean polypeptides, the BBI demonstrated that it can regulate inflammation by increasing anti-inflammatory IL-10 levels in LPS-stimulated RAW264.7 and other immune cells [59]. Therefore, the observed stimulatory effects of LES on IL-10 production cells could be the result of the action of multiple compounds. The combined action of LES on the cytokine secretion, NO release, and phagocytic capacity of macrophages suggests the potential action of this soybean extract to activate immune cells as antigen-presenting cells, pathogen detectors, and initiators of the inflammatory innate immune response. To study the immunomodulatory activity of lunasin and LES in the adaptive immune system, their effects on the production of cytokines IL-4, IL-5, IL-2, and IL-10 were evaluated in PMA-Ion challenged EL4 cells after 24 h of treatment. Doses of LES ranged from 0.5 to 25 μg protein/mL, and doses of lunasin ranged between 1 to 500 μM. The results are shown in Figure 5. Lunasin, at intermediate doses of 10 and 100 μM, significantly reverted the inducing effects of the PMA and Ion combination on IL-4 production, whereas the highest dose provoked a notable challenge to cells, increasing IL-4 values up to 50.60 pg/mL (Figure 5A). LES, at all tested doses, potentiated the inducing effects of PMA and Ion (Figure 5B). IL-4 is a cytokine produced primarily by mast cells, Th2 cells, eosinophils, and basophils that has many biological roles, including the stimulation of activated B cell and T cell proliferation, the differentiation of B cells into plasma cells, and the differentiation of naive helper T cells to Th2 cells [60]. Plant-derived bioactive compounds show different effects on IL-4 production. Thus, phytoestrogen formononetin and its metabolites, daidzein and equol, were found to significantly enhance IL-4 production in both CD4+ T cells and EL4 cells in a dose-dependent manner [61], whereas the pigment shikonin and its derivatives from the Chinese medicinal herb Lithospermum erythrorhizon showed inhibitory effects on mitogen-induced IL-4 and IL-5 production in EL4T cells [62]. All lunasin doses but 500 μM reduced increased IL-5 production caused by PMA and Ion (Figure 5C). LES also had a significant inhibitory effect for all doses assayed, reaching 73.8 pg/mL in supernatants collected from cells treated with 25 μg protein/mL of LES compared to the PMA + Ion group (131.8 pg/mL, Figure 5D). IL-5 is mainly produced by type-2 T helper cells and mast cells, and it acts by stimulating B cell growth and increasing immunoglobulin (Ig) secretion, primarily IgA. It is also a key mediator in eosinophil activation and is associated with the etiology of several allergic diseases, including allergic rhinitis and asthma [63,64]. Comparably to LES, previous studies with other plant-based bioactive compounds showed that they display a similar downregulatory effect [65,66]. IL-2 is a proinflammatory cytokine mainly secreted by activated CD4+ and CD8+ T cells that plays a pivotal role in both regulatory T cell biology and immune response homeostasis between tolerance and immunity [67,68]. In comparison with PMA + Ion treated cells (1963.7 pg/mL), both lunasin and LES presented a significant inhibitory effect on IL-2 secretion. In the case of synthetic lunasin, the effects were dose-dependent, reaching a value of 776.9 pg/mL in the supernatant collected from cells treated with 500 µM of peptide (Figure 5E). However, a recent study found that lunasin, at concentrations of 10 and 50 µM, was able to significantly increase IL-2 secretion in PMA + Ion-challenged EL4 cells under conditions simulating an obesity-related microenvironment [37]. In contrast, although LES also reverted the IL-2-inducing effects of the PMA and Ion combination, no dose-dependence was observed, with IL-2 values ranging from 1186.5 to 1425.04 pg/mL (Figure 5F). However, the protective effects against the increase of IL-10 induced by PMA + Ion were dose-dependent for both LES and synthetic lunasin. Individual peptides showed stronger inhibitory activity, as the measured value of this cytokine in the supernatant collected from the lunasin (500 µM)-treated group was 1586.5 pg/mL, almost five times lower than the value measured in the PMA + Ion group (7640.7 pg/mL) (Figure 5G). These results are different from those found by Hsieh et al. that did not report any effect on IL-10 secretion by PMA + Ion activated EL4 cells under simulated obese conditions [37]. These differences could be due to the absence of obesity-related conditions in our assay that modified the response of cells to lunasin treatment. In the case of the supernatant collected from the LES-treated group, the measured IL-10 level was 3586.8 pg/mL (Figure 5H). Similarly, other bioactive compounds from food sources were also found to suppress IL-2 production by EL4 cells [69]. ## 4. Conclusions The enrichment in soluble proteins and small peptides such as lunasin was achieved during LES preparation. Among them, the protease inhibitors could be responsible for the protection against the action of digestive enzymes on lunasin, favoring its beneficial effects at both local and systemic levels. The in vitro radical scavenging action of LES was confirmed in the RAW264.7 macrophages model, observing a protective role on oxidative stress at low doses. Moreover, LES was found to exert immunostimulatory effects in both innate (RAW264.7) and adaptive (EL4) immune cell models. A dose- and time-dependent immunostimulant effect was observed in RAW264.7 macrophages in terms of their phagocytosis activity as well as in terms of NO, IL-6, IL-1β, and IL-10 production. Likewise, lunasin and LES exerted a dose-dependent immunomodulatory effect on EL4 cell proliferation and cytokine production. The effects of lunasin and soybean protein-based samples on macrophage phagocytosis specifically are still a scattered topic; nevertheless, these findings in combination with the increase in NO levels and regulatory cytokines mentioned before suggest that LES immunomodulatory action could lean towards the stimulation of macrophages; thus, it would favor their cytotoxic action and ability to eliminate pathogens. EL4 activation and cytokine production could potentially indicate that soybean protein’s immunomodulatory effects do not end in the innate immune system but continue to modulate the adaptive immune cells’ activity. Further research is needed to confirm this hypothesis and to identify the contribution of each protein/peptide in order to confirm the potentiality of this extract as an ingredient of functional foods and supplements with beneficial effects on immune response. ## References 1. 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--- title: Phytochemical Composition and Biological Activities of Extracts from Early, Mature, and Germinated Somatic Embryos of Cotyledon orbiculata L. authors: - Gokhan Zengin - Zoltán Cziáky - József Jekő - Kyung Won Kang - José Manuel Lorenzo - Iyyakkannu Sivanesan journal: Plants year: 2023 pmcid: PMC10005620 doi: 10.3390/plants12051065 license: CC BY 4.0 --- # Phytochemical Composition and Biological Activities of Extracts from Early, Mature, and Germinated Somatic Embryos of Cotyledon orbiculata L. ## Abstract Cotyledon orbiculata L. (Crassulaceae)—round-leafed navelwort—is used worldwide as a potted ornamental plant, and it is also used in South African traditional medicine. The current work aims to assess the influence of plant growth regulators (PGR) on somatic embryogenesis (SE) in C. orbiculata; compare the metabolite profile in early, mature, and germinated somatic embryos (SoEs) by utilizing ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS); and determine the antioxidant and enzyme inhibitory potentials of SoEs. A maximum SoE induction rate of $97.2\%$ and a mean number of SoEs per C. orbiculata leaf explant of 35.8 were achieved on Murashige and Skoog (MS) medium with 25 µM 2,4-Dichlorophenoxyacetic acid and 2.2 µM 1-phenyl-3-(1,2,3,-thiadiazol-5-yl)urea. The globular SoEs were found to mature and germinate best on MS medium with gibberellic acid (4 µM). The germinated SoE extract had the highest amounts of both total phenolics (32.90 mg gallic acid equivalent/g extract) and flavonoids (1.45 mg rutin equivalent/g extract). Phytochemical evaluation of SoE extracts by UHPLC-MS/MS reveals the presence of three new compounds in mature and germinated SoEs. Among the SoE extracts tested, germinated SoE extract exhibited the most potent antioxidant activity, followed by early and mature somatic embryos. The mature SoE extract showed the best acetylcholinesterase inhibitory activity. The SE protocol established for C. orbiculata can be used for the production of biologically active compounds, mass multiplication, and conservation of this important species. ## 1. Introduction Cotyledon orbiculata L.—a member of the Crassulaceae—is commonly called round-leafed navelwort or pig’s ear, is native to South Africa, and is typically found in Southern Africa [1]. C. orbiculata is widely used as a potted plant worldwide due to its attractive bellflowers along with its leaves and low-care requirements. In South African traditional medicine, leaves collected from the wild populations of C. orbiculata are used to treat deworming, earache, inflammation, neurological problem, skin infection, and wounds [2,3]. The crude extracts obtained from the aerial parts of C. orbiculata have been shown to possess anticancer [4], anticonvulsant [1], anti-inflammatory [5,6], antimicrobial [2,7], antinociceptive [5], antioxidant [6], and anthelmintic [2,8] activities. Several bufadienolides, including cotyledosides [9], orbicusides, and tyledoside C [10], are found in the aerial parts of C. orbiculata. Phytochemical analysis of C. orbiculata leaf extract has also confirmed the presence of cardiac glycosides, flavonoids, phenolics, reducing sugars, saponins, condensed tannin, gallotannin, and triterpene steroids [1,2,3]. Due to its ornamental and medicinal values, wild populations of C. orbiculata are collected extensively; therefore, it has been designated as a near-threatened plant in parts of South Africa [7,11]. The traditional propagation methods are Inefficient in their ability to meet the current demand for C. orbiculata due to the shortage of planting materials. Hence, an efficient method for propagating C. orbiculata is needed to achieve its mass production and germplasm conservation. Micropropagation is an in vitro culture method that is widely used for the mass propagation of various plants [12,13]. Somatic embryogenesis (SE) is one of the most efficient micropropagation methods [14], and it is widely used for mass propagation [15,16], virus elimination [17], germplasm conservation [18], genetic transformation [19], synthetic seeds [20], and secondary metabolites [21] production. SE is the developmental process of somatic cell differentiation into a somatic embryo (SoE) [22]. Several factors, including culture medium composition [23], explant type [24], plant growth regulators [25] (PGR), and culture environment [26], affect the formation of somatic embryo. PGR play a vital role in the induction, development, and conversion of somatic embryos [25,26]. Research has shown that the addition of PGR is required for the induction of somatic embryos in vitro in Crassulaceae members such as Crassula ovata [27], Kalanchoe blossfeldiana [28], and Orostachys japonicus [29]. To date, there has been no report investigating the somatic embryogenesis of Cotyledon species. Kumari et al. [ 7] reported an in vitro method for C. orbiculata regeneration via organogenesis. The authors also showed that ethanolic extracts from calli, in vitro-raised shoots and plantlets, and leaves of ex vitro-grown C. orbiculata (2-month-old) had higher antimicrobial activity against Klebsiella pneumoniae than mother plants (10-year-old) leaves extract. However, they did not examine the bioactive metabolites in the tissues of C. orbiculata. Further, there has been no study examining the production of bioactive compounds from in vitro cultures of Cotyledon species. Several studies have confirmed the presence of diverse phytochemicals in C. orbiculata extracts [1,2,3]. Still, the phytochemical profile of C. orbiculata has not been documented, except for bufadienolides. Liquid chromatography with tandem mass spectrometry (LC-MS/MS) is the most effective method for the qualitative detection and identification of major and minor compounds in plant extracts [30,31]. This work aims to assess the impact of PGR on somatic embryogenesis in C. orbiculata; compare the metabolite profile in early, mature, and germinated somatic embryos by utilizing UHPLC-MS/MS; and determine the antioxidant and enzyme inhibitory potential of somatic embryos. ## 2.1. Somatic Embryogenesis (SE) Healthy, young shoots isolated from greenhouse-raised Cotyledon orbiculata (L.) plants were soaked in a mild detergent solution and kept under running tap water for 30 min. Shoots were disinfected in ethanol ($70\%$, 90 s), then mercuric chloride ($0.1\%$, 15 min), followed by three washes (60 s per wash) in sterilized distilled water and air-dried. Leaves were dissected, cut into 0.6–1.0 cm long segments, and placed in a sterilized culture bottle (500 mL) containing Murashige and Skoog [32] (MS) medium with 8 g/L agar, 30 g/L sucrose, and 0–30 µM of 2,4-Dichlorophenoxyacetic acid (2,4-D), along with indole-3-acetic-acid (IAA), indole-3-butyric acid (IBA), and α-naphthalene acetic acid (NAA) or 1.2–8.8 µM of N6-benzyladenine (6-BA), kinetin (KN) and 1-phenyl-3-(1,2,3,-thiadiazol-5-yl)urea (TDZ) plus 25 µM of 2,4-D for SoE induction. The pH of the SoE medium was adjusted to 5.7 and autoclaved for 22 min at 122 °C. The culture bottles were incubated in the darkness for three weeks, then kept under a 16-h photoperiod (40–45 µMol s−1 m−2) for nine weeks at a temperature of 23 to 26 °C. Fifty leaf segments were used per treatment, with three repetitions. The leaf segments were assessed for SoE induction after 12 weeks. The SoE induction percentage was calculated as the number of leaf segments with SoEs divided by the total number of leaf segments cultured × 100 [33]. Globular SoEs were subcultured into the MS medium with 0, 1, 2, 4, or 8 µM 6-BA or gibberellic acid (GA3), then proceeded to further development and germination. The cultures were kept under a 16-h photoperiod (20–25 µMol s−1 m−2) at temperatures from 23 to 26 °C. Fifty globular SoEs were used per treatment, with three repetitions. After eight weeks, the SoE conversion percentage was calculated as the number of germinated SoEs divided by the total number of SoEs cultured × 100 [34]. ## 2.2.1. Extract Preparation Early (globular), mature (torpedo), and germinated (cotyledonary) SoEs were lyophilized. The extracts were obtained using a homogenizer-assisted extraction. In the procedure, C. orbiculata samples (50 mg) were extracted with $80\%$ methanol using an Ultraturrax at 6000 g for 30 min. After filtration, the extracts were dried using a rotary vacuum evaporator before being stored at 4 °C until further analysis. ## 2.2.2. Estimation of Total Phenolics Content (TPC) and Flavonoids Content (TFC) The TPCs of C. orbiculata SoEs extracts were determined using the Folin–Ciocalteu reagent described by Slinkard and Singleton [35], and the results were expressed in terms of mg of gallic acid equivalent (GAE). The TFCs of C. orbiculata SoEs extracts were determined using the aluminum chloride (AlCl3) method described by Zengin et al. [ 36] and calculated in terms of mg of rutin equivalent (RE). ## 2.2.3. Chemical Characterization A previously optimized and described UHPLC/MS/MS technique was used to screen the chemical compositions of three extracts containing phenolic and flavonoid compounds. Mass spectrometry was conducted using an electrospray ionization source (ESI) operating in both negative and positive ion modes. Mass spectra were recorded as full MS between m/z 100 and 1500 atomic mass units and MS/MS mode using a Q-Exactive (Thermo Fisher Scientific) Orbitrap mass spectrometer. These data can be examined to detect and confirm analytes in complex matrices. The detected compounds were identified through comparison with authentic standards, their MS/MS spectra and fragmentation patterns, and their HRMS spectral information. All data were processed using the TraceFinder software and tentatively identified by comparing their retention time (Rt) and mass spectrum with the reported data and our spectral library. The difference between the mass of measured and calculated* exact protonated or deprotonated molecular ions was less than 5 ppm [37]. ## 2.3.1. Antioxidant Assay The antioxidant capacity of C. orbiculata SoEs extracts was estimated using the metal chelating ability (MCA), phosphomolybdenum (total antioxidant capacity, PBD), ferric reducing antioxidant power (FRAP), cupric reducing antioxidant capacity (CUPRAC), 2,2-diphenyl-1-picrylhydrazyl (DPPH), and 2,2-azino-bis (3-ethylbenzothiazoline-6-sulphonic acid) (ABTS) methods described by Uysal et al. [ 38]. Assays were performed in triplicate. The results are presented as IC50 values (mg/mL). ## 2.3.2. Enzyme Inhibition Assay The amylase, acetylcholinesterase (AChE), tyrosinase, and butyrylcholinesterase (BChE) inhibitory effects of C. orbiculata SoEs extracts were each conducted in triplicate according to the procedures described by Uysal et al. [ 38]. The results are given as IC50 values (mg/mL). ## 2.4. Statistical Analysis Data were subjected to analysis of variance (ANOVA), and significant differences ($p \leq 0.05$) among means were determined by Duncan’s multiple range test (DMRT) using SAS version 9.4 (SAS Institute, Cary, NC, USA). ## 3.1.1. Influence of Auxins on SE in C. orbiculata The surface sterilization of C. orbiculata shoots with ethanol and mercuric chloride resulted in $100\%$ sterile leaf culture. The explants cultivated on MS PGR-free medium (control) did not produce SoEs or callus. On the other hand, the explants of C. orbiculata developed callus, root, or SoE within 45 days of culture on an auxin-containing medium. However, the addition of auxin at 5 or 10 µM in the cultivation medium did not support SE. The SoE formation occurred at the cut edges of C. orbiculata leaf segments in the presence of 15–30 µM auxin (Table 1). After eight weeks of cultivation, pale green globular SoEs were detected (Figure 1a). The ANOVA showed that auxin type, auxin concentration, and the interaction of type and concentration of auxin all had significant ($p \leq 0.001$) effects on SoE induction and the number of SoE developed per explant (Table 1). Of the studied auxin types, a high rate of SoE induction was obtained on 2,4-D ($25.6\%$), followed in descending order by NAA ($16.9\%$), IBA ($13.9\%$), and IAA ($11.7\%$). Similarly, 2,4-D produced the highest mean number of SoEs (6.3), followed in descending order by NAA (4.1), IBA (2.6), and IAA (2.2). Of the studied auxin concentrations, a high incidence of SoE induction was obtained on 25 µM ($34.7\%$), followed in descending order by 20 µM ($31.3\%$), 30 µM ($23.9\%$), and 15 µM ($13.4\%$). Lastly, 25 µM produced the highest mean number of SoEs (8.0), followed in descending order by 20 µM (6.7), 30 µM (4.7), and 15 µM (3.4). The maximum SoE induction rate ($60.6\%$) and mean number of SoEs per C. orbiculata leaf explant (14.9) were achieved on an MS medium with 25 µM of 2,4-D (Table 1). Thus, 25 µM of 2,4-D was selected for the additional SE experiments. ## 3.1.2. Effect of Cytokinins Plus 25 µM 2,4-D on SE in C. orbiculata The addition of cytokinins to the 2,4-D (25 µM) containing MS medium significantly ($p \leq 0.05$) affected the rate of SoE induction and the mean number of SoEs. Different developmental stages (globular, heart, and cotyledonary) of SoEs were observed from C. orbiculata leaf explants within 12 weeks of culturing on MS medium with 25 µM 2,4-D and cytokinins (Figure 1b,c). The rate of SoE induction and the number of SoEs were both significantly ($p \leq 0.001$) affected by cytokinin type, cytokinin concentration, and their interaction (Table 2). Of the tested cytokinin types, a high rate of SoE induction was obtained on TDZ ($77.3\%$), followed in descending order by KN ($75.1\%$), and 6-BA ($70.6\%$). Similarly, TDZ produced the highest mean number of SoEs (24.9), followed in descending order by KN (18.4) and 6-BA (14.7). Among the studied cytokinin concentrations, a high incidence of SoE induction was obtained on 4.4 µM ($85.9\%$) followed in descending order by 2.2 µM ($83.8\%$), 1.2 µM ($76.4\%$), and 8.8 µM ($51.3\%$). By contrast, 2.2 µM produced the highest mean number of SoEs (25.5), followed in descending order by 4.4 µM (23.0), 1.2 µM (19.0), and 8.8 µM (9.7). The optimal SE medium (MS + 25 µM 2,4-D), with the addition of 4.4 µM 6-BA, led to the maximum rate of SoE induction ($88.7\%$) and number of SoEs (21.3). Increasing the 6-BA dose from 1.2 to 4.4 µM in the optimal SE medium led to an increase in the rate of SoE induction from $66.6\%$ to $88.7\%$ and an increase in the mean number of SoEs from 11.8 to 21.3. However, an increase in the 6-BA dose beyond 4.4 µM reduced the frequency of SoE induction ($52.6\%$) and the average number of SoEs (7.4). When the optimal SE medium was combined with KN (1.2–8.8 µM), 56.9–$92.3\%$ of C. orbiculata leaf explants produced a mean of 8.0–27.0 SoEs. The optimal SE medium supplemented with 4.4 µM KN was found to be the best in SoE production from C. orbiculata leaf explants (Table 2). Adding TDZ (1.2–4.4 µM) to optimal SE medium improved production SoEs. The greatest rate of SoE induction ($97.2\%$) and the highest mean number of SoEs (35.8) were obtained with optimal SE medium with 2.2 µM TDZ (Table 2). Raising the TDZ doses above 2.2 µM reduced the SE response of C. orbiculata leaf explants. ## 3.1.3. Effect of 6-BA and GA3 on Conversion of C. orbiculata SoEs Within eight weeks, globular SoEs matured and germinated. Only a few SoEs germinated on the control (MS) medium. The conversion of globular SoEs was boosted by supplementing MS medium with 6-BA and GA3 (1–8 µM). The frequency of SoE conversion ranged from $23.7\%$ to $100\%$. Among the two tested PGRs, GA3 proved to be the most effective for converting C. orbiculata SoEs. The highest rate of SoE conversion ($100\%$) was attained on a medium with 4 µM GA3 (Figure 2). ## 3.2. Phytochemical Analysis In the present study, we determined the total amounts of phenolic and flavonoids of C. orbiculata extracts. The results are presented in Table 3. Among the tested samples, the highest levels of total phenolics and flavonoids were determined in the extract of germinated somatic embryos (32.90 mg GAE/g extract and 1.45 mg RE/g extract, respectively). The extracts from early and mature somatic embryos contained almost the same contents of total phenolics and flavonoids. The characterized compounds are listed in Table 4. The chromatographic and mass spectrometric data (retention times, protonated or deprotonated molecular ions, fragment ions) and assigned identities for compounds were given Tables S1–S3. In total, 38 compounds were identified in the extracts. A total of 32 compounds were found in early somatic embryo extract, 33 were found in mature somatic embryo extract, and 32 were found in germinated somatic embryo extract (Figures S1–S3). The structures of some compounds are given in Figure 3. The samples showed similar chromatographic profiles, and a wide range of compounds—mainly derivatives of nicotinic acid and flavones—were characterized. The lowest molecular mass component was nicotinamide [3] (rt: 1.61–1.63 min, [M+H]+: 123.0559), and two compounds had the highest molecular mass. These were characterized as Hyperoside [15] (rt: 23.22 min, [M−H]−: 463.0877) and Isoquercitrin [16] (rt: 23.44 min, [M−H]−: 463.0877). The positive ion mode of ESI-MS/MS was a powerful complementary tool of the negative ion mode for the determination of the chemical structure of the compounds. In many cases, the sensitivity was higher and more fragment ions could be detected in positive mode; examples include Nicotinic acid and its derivatives, oxybutanedioic acid derivatives, hydroxy-, dihydroxy- and trihydroxy-methoxy/dimethyoxy/trimethoxy(iso)flavones. The major advantage of negative ion mode (ESI−) is the reduced background noise. The exact identification of constitutional isomers detected in extracts is not possible even when using high mass resolution MS measurements, for example, Luteolin-O-hexoside isomers, Trihydroxy-trimethoxy(iso)flavone, Dihydroxy-trimethoxy(iso)flavone isomers, Dihydroxy-dimethoxy(iso)flavone, Dimethoxy(iso)flavone, Trimethoxy(iso)flavone, Dihydroxy-methoxy(iso)flavone, Hydroxy-trimethoxy(iso)flavone, and Hydroxy-methoxy(iso)flavone. ## 3.3. Antioxidant Abilities We determined the antioxidant properties of C. orbiculata extracts, and the results are presented in Table 5. Among the antioxidant assays, DPPH and ABTS are the most popular for evaluating plant extracts’ radical scavenging ability. As can be seen in Table 5, the most active extract was germinated somatic embryos with an IC50 of 0.62 mg/mL, followed by early and mature somatic embryos. However, Trolox showed a stronger ability to scavenge free radicals compared to the tested extracts. The transformations of cupric to cuprous and ferric to ferrous reflect the electron-donating ability of antioxidant compounds, and the mechanism is known to be the reduction in power. For this purpose, we performed CUPRAC and FRAP assays. In both assays, the best reduction ability was provided by germinated somatic embryos (CUPRAC: 0.92 mg/mL; FRAP: 0.55 mg/mL). However, all extracts were less active than the standard antioxidant, Trolox. Phosphomolybdenum (PBD) assay is one of the total antioxidant assays, and all antioxidant compounds could play an effective role in the assay. As presented in Table 5, the tested samples were in descending order of germinated > early >mature. The chelation of transition metals can hinder the production of hydroxyl radicals via the Fenton reaction and, therefore, be considered an important antioxidant mechanism. In contrast to other assays, the extracts of early and germinated somatic embryos exhibited similar chelating abilities. However, the extract of mature somatic embryos showed the weakest chelating ability. Moreover, EDTA was shown to be an excellent chelator with the lowest IC50 value (0.02 mg/mL). ## 3.4. Enzyme Inhibition Effects The present study reported the enzyme inhibitory properties of C. orbiculata extracts against AChE, BChE, tyrosinase, and amylase. The results are listed in Table 6. In the AChE inhibition assay, the mature somatic embryo extract provided the best inhibition with the lowest IC50 value (0.75 mg/mL). The early and germinated somatic embryo extracts had almost the same inhibitory potency. Regarding the BChE inhibition assay, the best effect was found in the germinated somatic embryo extract, but the ability was close to that of the mature somatic embryo extract. The extract of early somatic embryos was found to have the weakest ability to inhibit BChE. Tyrosinase is a key enzyme in melanogenesis, and its inhibition is important for controlling hyperpigmentation problems. As listed in Table 6, the tested extracts showed similar tyrosinase inhibitory activities, and the most active one was from the germinated somatic embryos. However, kojic acid was the superior inhibitor with the lowest IC50 (0.08 mg/mL). Amylase is the main enzyme involved in the hydrolysis of carbohydrates, and its inhibition can control blood sugar levels in diabetics. The highest amylase inhibition was achieved by early somatic embryos, followed by germinated and mature somatic embryos. All extracts also showed weaker abilities compared to acarbose (IC50: 0.68 mg/mL). ## 4. Discussion The surface sterilization of explants (plant materials) is an essential aspect of establishing in vitro aseptic culture [39]. In this study, disinfection of C. orbiculata shoots resulted in a $100\%$ sterile in vitro culture. A similar disinfection method was also used to obtain sterile explants of C. orbiculata [7]. The control (MS) medium and MS medium supplemented with lower levels (5 and 10 µM) of auxin failed to promote SE in C. orbiculata. However, incorporating high levels (above 10 µM) of auxin resulted in SoE induction from leaf explants of C. orbiculata (Table 1). In many species, the presence of auxin—often at high concentrations—is required to induce SoEs [26,40,41]. In this study, 2,4-D (25 µM) proved to be significantly ($p \leq 0.001$) superior in inducing SE from leaf explants of C. orbiculata than NAA, IBA, and IAA, which is likely attributable to the fact that the degradation rate of 2,4-D is lower than those of other studied auxins. The effectiveness of 2,4-D for stimulating SE has already been disclosed in various species [24,26,33,40,41,42]. The 2,4-D and cytokinin combination was frequently used to enhance SoE induction in most species. The addition of cytokinin (6-BA, KN, or TDZ at 1.2–4.4 µM) to the SE-promoting level (25 µM) of 2,4-D significantly enhanced the formation of SoEs (Table 2). The combination of 2,4-D and 6-BA has been shown to be effective for the induction of SoEs in Ananas comosus* [15], Betula platyphalla [43], Campanula punctata [24], Crassula ovata [27], Orostachys japonicus [29], and Picea pungens [44]. Similarly, a combination of 2,4-D and KN was effective for the induction of SoEs in chrysanthemum ‘Hornbill Dark’ [45], Trachyspermum ammi [46], and Viola canescens [47]. Likewise, a combination of 2,4-D and TDZ was found to be the best for the induction of SoEs in Camellia oleifera [48], Hippeastrum [49], Prunus dulcis [50], and Tulipa gesneriana [51]. Among the texted cytokinins, the highest rate of SoE induction with the maximum number of SoEs per C. orbiculata leaf explant was achieved using the optimal SE medium with TDZ (Table 2). TDZ is a PGR that is often used for the induction of SoEs and callus, adventitious shoot regeneration, and multiple shoot induction in various plants [52]. It is often combined with other PGRs to achieve the best in vitro culture results. However, the ratio of auxin and cytokinin significantly affects SE. In this study, the best rate of SoE formation ($97.2\%$) with a maximum number of SoEs per C. orbiculata leaf explant (35.8) was obtained in the MS medium containing 2,4-D (25 µM) and TDZ (2.2 µM). Similarly, the presence of a high level of auxin (22.5 µM of 2,4-D) and a low level of cytokinin (2.2 µM of 6-BA) was found to be effective for SE in Orostachys japonicus [29]. By contrast, a low level of auxin (2.3 µM of 2,4-D) and a high level of cytokinin (4.4 µM of 6-BA) were found to be the best conditions for SE in Crassula ovata [27]. Therefore, the requirement of the PGRs ratio for SE in Crassulaceae varies according to genus. The globular-, heart-, and torpedo-stage SoEs were formed when the C. orbiculata leaf explants were cultured on optimal SE induction medium with TDZ (Figure 1a–c). However, only a few globular SoEs matured, and germination was not accomplished. Similar results have also been reported in another Crassulaceae member, Orostachys japonicus [29]. SoE maturation and subsequent plantlet conversion are often affected by the presence of PGRs in the SE medium. Globular SoE conversion (maturation and germination) has commonly been achieved on PGR-free medium [22,41]; however, in some species, the addition of cytokinins [21,29,49], abscisic acid [44,53], or GA3 [33,34] is needed for the development and germination of SoE. In this study, the highest conversion of C. orbiculata SoE was accomplished on a medium with 4 µM GA3. GA3 has been reported to have positive effects on SoE conversion in Haworthia retusa [33], Hosta minor [34], and Juglans regia [54]. Over the last decade, phenols have attracted more interest in nutraceutical and pharmaceutical applications due to their promising biological activities [55]. In this sense, when the content of phenols in an extract is detected, it is a significant indicator of its biological effects. In the current work, the extract of germinated somatic embryos was found to have the highest total phenolic and flavonoid content. In a previous study conducted by Ondua et al. [ 6], the total phenolic level of C. orbiculata extracts varied from 1.34 (in n-hexane extract) to 23.93 mg GAE/g (in methanol extract), which is lower than that of the extract from germinated somatic embryos tested in the study. Although the spectrophotometric methods could provide initial insight into the pharmacological value of plant extracts, certain concerns have recently arisen from the assays. Due to the complex nature of plant extracts, not only will certain compounds of interest react with the reagent used in the assays, but so will other phytochemicals. Therefore, the results of these assays could be suspect. Keeping this in mind, chromatographic techniques are needed to obtain more accurate chemical profiles of plant extracts. In the present study, the chemical composition of the tested extracts was characterized using the UHPLC/MS/MS technique, and the results are listed in Table 2. The extracts had a similar chemical composition, and, interestingly, new compounds were identified in mature SoE (33–35) and germinated SoE (36–38) (Figure 4). Over the last century, most people have come to be familiar with the term antioxidant. The term denotes protection against free radical attacks that affect the progression of serious health problems such as cancer, diabetes, or obesity. Several studies have reported that antioxidant intake is inversely associated with the development of these diseases [56,57]. With this in mind, we determined the antioxidant properties of C. orbiculata extracts, and the results are presented in Table 5. The germinated somatic embryo extract generally showed stronger antioxidant ability than other tested extracts. Ondua et al. [ 6] reported that the IC50 values of the methanol extract of C. orbiculata were 3.76 g/mL and 3.35 g/mL for DPPH and ABTS, respectively. Based on their results, our extracts showed weaker free radical scavenging ability than their tested extracts. From Table 5, when the combined scavenger and reduction performance results were obtained, we found an almost similar order. The obtained results almost agreed with the results of the total phenolic and flavonoid content of the tested extracts; therefore, we concluded that phenolics made the main contribution to the radical scavenging and reducing ability. Moreover, some compounds have only been detected in germinated SoE extract, and these are also known to be powerful antioxidants [58,59]. Enzymes are pharmaceutical targets for treating various health problems, including Alzheimer’s disease, obesity and diabetes. In particular, the inhibition of key enzyme abilities might alleviate the symptoms of the diseases mentioned above [60]. For this purpose, several compounds have been manufactured as enzyme inhibitors, and most of them are presented on pharmacy shelves. However, synthetic compounds exhibit unpleasant side effects, including gastrointestinal problems and toxicity [61,62,63]. Therefore, several studies have focused on replacing synthetic inhibitors with natural ones. The tested extracts showed remarkable inhibitory effects on AChE, BChE, tyrosinase, and amylase. The observed capabilities of the tested samples can be explained by the presence of some chemical compounds. As listed in Table 6, some compounds have been reported to serve as enzyme inhibitors [64,65,66]. The current work is the first report examining the enzyme-inhibitory effect of C. orbiculata. Thus, these results could establish future directions for studies using C. orbiculata to develop functional applications. ## 5. Conclusions In this work, direct SE from the leaf tissue was described for the first time. Among the studied auxin types, the highest rate of SoE induction was obtained on 2,4-D, followed in descending order by NAA, IBA, and IAA. The inclusion of cytokinin (6-BA, KN, or TDZ at 1.2–4.4 µM) in the optimal SE medium MS containing 25 µM of 2,4-D enhanced the formation of SoEs. In total, 38 metabolites were identified in C. orbiculata SoEs by UHPLC-MS/MS. Among them, quercetin-O-pentosylhexoside, dihydroxy-trimethoxy(iso)flavone isomer 1, dihydroxy-trimethoxy(iso)flavone isomer 2 (33–35), isorhamnetin (3′-Methoxy-3,4′,5,7-tetrahydroxyflavone), and rhamnetin (7-Methoxy-3,3′,4′,5-tetrahydroxyflavone, trihydroxy-trimethoxy(iso)flavone) isomer II (36–38) are only found in mature SoEs and germinated SoEs, respectively. ## References 1. Amabeoku G.J., Green I., Kabatende J.. Anticonvulsant activity of. J. Ethnopharmacol. (2007) 112 101-107. DOI: 10.1016/j.jep.2007.02.016 2. Aremu A.O., Ndhlala A.R., Fawole O.A., Light M.E., Finnie J.F., Van Staden J.. 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--- title: Investigating the Dietary Intake Using the CyFFQ Semi-Quantitative Food Frequency Questionnaire in Cypriot Huntington’s Disease Patients authors: - Christiana C. Christodoulou - Christiana A. Demetriou - Elena Philippou - Eleni Zamba Papanicolaou journal: Nutrients year: 2023 pmcid: PMC10005621 doi: 10.3390/nu15051136 license: CC BY 4.0 --- # Investigating the Dietary Intake Using the CyFFQ Semi-Quantitative Food Frequency Questionnaire in Cypriot Huntington’s Disease Patients ## Abstract Huntington’s disease (HD) is a rare progressive neurodegenerative disease characterised by autosomal dominant inheritance. The past decade saw a growing interest in the associations between the Mediterranean Diet (MD) and HD risk and outcomes. The aim of this case-control study was to assess the dietary intake and habits of Cypriot HD patients, comparing them to gender and age-matched controls, using the Cyprus Food Frequency Questionnaire (CyFFQ) and to assess adherence to the MD by disease outcomes. The method relied on the validated CyFFQ semi-quantitative questionnaire to assess energy, macro- and micronutrient intake over the past year in $$n = 36$$ cases and $$n = 37$$ controls. The MedDiet Score and the MEDAS score were used to assess adherence to the MD. Patients were grouped based on symptomatology such as movement and cognitive and behavioral impairment. The two-sample Wilcoxon rank-sum (Mann–Whitney) test was used to compare cases vs. controls. Statistically significant results were obtained for energy intake (kcal/day) (median (IQR): 4592 [3376] vs. 2488 [1917]; $$p \leq 0.002$$) from cases and controls. Energy intake (kcal/day) (median (IQR): 3751 [1894] vs. 2488 [1917]; $$p \leq 0.044$$) was also found to be significantly different between asymptomatic HD patients and controls. Symptomatic patients were also different from controls in terms of energy intake (kcal/day) (median (IQR): 5571 [2907] vs. 2488 [1917]; $$p \leq 0.001$$); % energy monounsaturated fatty acids (median (IQR): 13.4 (5.2) vs. 15.5 (5.7); $$p \leq 0.0261$$) and several micronutrients. A significant difference between asymptomatic and symptomatic HD patients was seen in the MedDiet score (median (IQR): 31.1 (6.1) vs. 33.1 (8.1); $$p \leq 0.024$$) and a significant difference was observed between asymptomatic HD patient and controls (median (IQR): 5.5 (3.0) vs. 8.2 (2.0); $$p \leq 0.014$$) in the MEDAS score. This study confirmed previous findings that HD cases have a significantly higher energy intake than controls, revealing differences in macro and micronutrients and adherence to the MD by both patients and controls and by HD symptom severity. These findings are important as they are an effort to guide nutritional education within this population group and further understand diet–disease associations. ## 1. Introduction Huntington’s Disease (HD) is a rare and progressive neurodegenerative disease characterised by autosomal dominant inheritance, affecting the medium spiny neurons of the basal ganglia [1,2]. The mean age of onset is approximately 40 years of age [1] and clinical features include movement (incoordination), cognitive (lapse in short-term memory) and behavioural (depression) impairments [3]. HD is caused by a mutation of the Huntington (HTT) gene, which is located on chromosome 4 of exon 1 and, more specifically, a CAG trinucleotide repeat expansion at the N-terminus of the HTT gene [4]. The number of CAG repeats is the main predictor for the age of onset, disease severity and occurrence of HD. While the CAG trinucleotide is repeated between 10–35 times in healthy individuals, HD individuals can have from 36–120 CAG repeats. Individuals that have between 36–39 CAG repeats may or may not develop signs and symptoms of the disease, meaning that there is a reduced penetrance [5]. Individuals, however, with 40 or more repeats will always develop signs and symptoms of HD [5]. The trinucleotide repeat varies in length among individuals and among generations [5]. Despite the number of CAG repeats being the major determinant of the age of onset, there is still variation in the age of disease onset among individuals with the same number of repeats. This is a finding that remains unexplained [5]. Furthermore, although HD is a monogenic disease, its molecular manifestations seem highly complex and involve multiple cellular processes [5]. In Cyprus, in 2015, the prevalence and incidence of symptomatic HD were 4.64 per 100,000 population ($95\%$ CI: 3.30–6.34) and 0.12 per 100,000 population ($95\%$ CI: 0.00–0.66), respectively [6]. The frequency of individuals with a pathogenic triplet expansion in the population at the end of 2014 was estimated to be 14.1 per 100,000 population [6]. In other words, 1 in 7097 individuals was expected to have one allele with a pathogenic CAG repeat range, which translates to roughly 120 heterozygotes in the population in the areas controlled by the Republic of Cyprus. These rates are comparable to other European Countries [6]. A nutritional assessment that considers the disease stage and feeding difficulties in HD patients is important due to a high prevalence of malnutrition, as evidenced by lower-than-average body weight in many of these patients [7,8]. Due to the variability in energy requirements and rapid weight loss, early assessment and regular reviewing of nutritional care plans are fundamental [8]. This calls for frequent monitoring of the patients’ body weight and adjustment of their energy intake to reach the ideal or target body weight. Furthermore, many HD patients have increased energy requirements either due to motor impairment or having a hypermetabolic state, defined as an elevated resting energy expenditure. Therefore, it is essential to provide adequate macro and micronutrients [8]. Nutritional education should be central to disease management both for the patients themselves and their families since this can guide them in choosing a healthier diet, address nutritional issues of concern depending on the disease stage and reduce the risk of malnutrition [9]. Previous studies have investigated dietary intake and the effect of adhering to the Mediterranean Diet (MD) adherence in delaying disease progression, improving the Unified Huntington’s Disease Rating Scale (UHDRS) score as well as improving motor function and cognition in HD patients, as recently published in our systematic review (SLR) [10]. In this SLR, a total of 18 studies, including randomized controlled trials and non-randomized intervention trials, case-control studies and cohort studies, were carried out. The studies investigated (i) dietary intake and patterns, (ii) MD adherence, (iii) nutritional supplementation, and (iv) caloric intake in individuals with HD. The findings suggested an improvement in the motor and cognitive scores and a better quality of life in people with HD with higher MD adherence [10]. Furthermore, a high energy intake was repeatedly observed in people with HD, likely due to the higher energy consumption [10]. Moreover, certain food groups, such as milk and dairy products and caffeine consumption, greater than 190 mg/day were found to be associated with an earlier age of disease onset [10], although these findings need further investigation. There is limited research on energy and macronutrient intake in HD patients, with only two studies being identified. Marder et al., 2009 [11] conducted a case-control study that investigated energy, macronutrient intake and body mass index (BMI) in 217 HD carriers with expanded CAG ≥ 37 and 435 non-expanded CAG < 37 HD carriers and controls [10,11]. Individuals with expanded CAG ≥ 37 had significantly higher UHDRS motor scores compared to non-expanded CAG < 37 HD. Energy intake was strongly associated with CAG repeat length and with the estimated 5-year probability for HD onset in the expanded CAG ≥ 37 group. Increased caloric intake may be necessary to maintain BMI in asymptomatic HD individuals with CAG ≥ 37. This may be related to increased energy expenditure as a result of subtle motor impairment or a hypermetabolic state [10,11]. Furthermore, carbohydrate intake was also significantly higher in the expanded CAG ≥ 37 group compared to the non-expanded CAG < 37 HD carriers and control group. A case-control study including 32 individuals with an abnormal CAG repeat length (>36) (15 asymptomatic and 17 HD patients) and 21 controls by Mochel et al., 2007 [10] performed a multi-parametric study to investigate body weight and mechanisms of body weight loss [10]. A semi-quantitative questionnaire inquiring about regular food and beverage consumption was used to observe energy intake for 24 h in HD patients [10]. Body weight change was determined by subtracting current weight from the recorded weight in the medical records of each participant 5 years prior to study inclusion. HD patients were found to have significant weight loss compared to controls. Furthermore, men with HD had lower BMI compared to controls, while total energy intake was inversely associated with weight and lean body mass, indicating that people with HD exhibit an early hypermetabolic state due to increased energy expenditure. Weight loss was also observed in asymptomatic carriers, even though they had higher energy intake compared to controls [10,12]. Over the past decade, there is growing scientific evidence indicating the beneficial effects of the MD in a number of diseases, including neurodegenerative disease (ND), cardiovascular, type II diabetes mellitus and obesity [13,14]. The MD is a dietary pattern that originated from the regions of the Mediterranean basin e.g., Italy, Spain, Portugal, Greece and Cyprus. It is characterized by: a high intake of whole grains, legumes, fruit, vegetables, nuts and seeds, fresh herbs and spices and the use of extra virgin olive oil as the main source of fat; a moderate consumption of dairy products, poultry, fish, seafood and eggs; a limited consumption of red meat and desserts; and moderate alcohol intake, mainly red wine with meals [15]. Plant-based foods, such as whole grains, legumes, fruits, vegetables and olive oil, are rich sources of phytochemicals, carotenoids, flavonoids, vitamins and minerals with anti-oxidative, neuro-protective and anti-inflammatory properties preventing reactive oxidative stress (ROS) and lipid and protein damage [15]. Interestingly, in recent years, the effects of the MD on the prevention of NDs such as Alzheimer’s and Parkinson’s disease have been studied [14]. Long-term consumption of the MD not only reduces ROS and neuroinflammation in ND, but leads to an increase of longevity via the maintenance of telomere length and prevention of brain atrophy [15]. As in other NDs, the MD may also be protective in HD patients. Indeed, some evidence suggests that higher adherence to the MD is associated with a better-quality diet and patient outcomes. A study by Rivadeneyra et al., 2016 [16] observed that HD patients with high or moderate MD adherence had increased micronutrient intake compared to those with low MD adherence [10,16]. Moderate/high MD adherence was characterized by a higher intake of monounsaturated fatty acids (MUFA), saturated fatty acids (SFA) and polyunsaturated (PUFA) + MUFA/SFA, which was associated with a slight improvement of Total Functional Capacity (TFC) and UHDRS cognitive scores compared to low MD adherence. Furthermore, moderate to high MD adherence was associated with a slight improvement in UHDRS motor and cognitive scores. However, HD severity was similar between subjects with low vs. moderate/high MD adherence [16]. Even though adherence to the MD has been shown to be beneficial in HD patients, there is a lack of research investigating dietary intake and MD adherence using validated food frequency questionnaires (FFQs), which assess macro- and micronutrient intake in HD patients with different symptomatology and disease stage. The present case-control study aimed to perform this by further categorizing patients by HD stages, namely: asymptomatic, symptomatic and symptomatic advanced. The inclusion of three HD stages is not easily observed among research studies in HD either, due to the small sample size or lack of participants in each disease stage. Including patients from all disease stages may lead to a better understanding of the association between their dietary intake, including MD adherence, and HD symptoms. Furthermore, to the best of our knowledge, there are no studies investigating dietary intake and MD adherence in Cypriot HD patients in comparison to matched controls. Additionally, our study used the Cyprus FFQ (CyFFQ), a semi-quantitative validated FFQ, for assessing the dietary intake of the Cypriot population, which provides a detailed assessment of culture-specific intake and Western foods [17]. The present investigation thus aimed to investigate the dietary intake and MD adherence scores of Cypriot HD patients who are either (i) asymptomatic, (ii) symptomatic and (iii) symptomatic advanced versus gender/age-matched controls using the CyFFQ to identify any associations between MD adherence and disease stage. It was hypothesized that high adherence to the MD would be associated with delayed disease symptoms and a better quality of life due to higher consumption of neuroprotective and anti-inflammatory foods. ## 2. Materials and Methods The workflow implemented in our study is illustrated in Figure 1 and details regarding each step of the study workflow are described below. ## 2.1. Study Design All HD patients being cared for by the Cyprus Institute of Neurology and Genetics (CING), a referral center for HD in Cyprus, were invited to participate in the study by their neurologist (E.Z.P). Participant recruitment occurred between November 2017 to March 2019. The age of recruitment for HD patients was between 18–75 years old. In addition, healthy gender/age-matched controls were recruited from HD families (members without the pathological CAG trinucleotide expansion), patients’ carers and CING staff members. Each HD patient was matched with a control based on age and gender. All participants were from the area under the control of the Republic of Cyprus. Demographic and lifestyle information, including diet and medical history, was obtained through an interviewer-administered questionnaire. The information collected is shown below: Demographics: sex, date of birth, birthplace and current city of residence, parents’ birthplace, family status (single, married, divorced or widowed) and occupation;Medical and family history of HD and their family as well as presence and age of HD symptoms family members diagnosed with HD and other chronic illnesses for patients and controls;Age of onset and symptoms, number of CAG repeat and current medical treatment;Anthropometrics: weight and height at least a year prior to HD diagnosis;Lifestyle, such as smoking, current smoking, physical exercise and hobbies;Medical history, CAG repeat counts, treatments and other co-morbidities were also obtained from the patients’ medical records, or via self-reporting for controls. The study was reviewed and ethically approved by the Cyprus National Bioethics Committee (EEBK/EP/$\frac{2017}{29}$) and conducted in accordance with the Declaration of Helsinki. Written, informed consent was obtained from all participants prior to the study participation. ## 2.2. Huntington’s Disease Assessment The Unified Huntington’s Disease Rating Scale (UHDRS) was used to assess four domains of clinical performance and capacity in HD, these being motor and cognitive function, behavioral abnormalities and functional capacity [18]. Asymptomatic and symptomatic HD patients were categorized based on their UHDRS scores. Patients were categorized as symptomatic based on their scores; higher scores indicate the inability to perform motor tasks and behavioral impairment while lower scores are an indication of cognitive impairment and a decrease in functional capacity [18]. ## 2.3. Food Frequency Questionnaire The validated semi-quantitative CyFFQ was developed by two experienced dietitians as described by Philippou et al., 2022 [17]. It consisted of 171 food items that reflected the dietary intake of commonly consumed foods within the Cypriot population over the previous year. The average energy, macro-and micronutrient intake is calculated as previously explained in detail [17]. In brief, the CyFFQ was interview-administered and the participant had to respond for each food item with reference to frequency of consumption during the previous year (nine possible responses ranging from never to every day) and the amount consumed, using either the Greek translation of the Block Portion Size Picture© (used with permission after the purchase of copyrights from NutritionQuest©) (https://www.nutritionquest.com/) (accessed on 1 September 2017), tablespoons/teaspoons or the number of items, depending on the food item. In addition to the items included in the CyFFQ, participants had the opportunity to report foods and beverages usually consumed, using open-ended questions. It is of note that items other than the ones in the main FFQ were rarely reported. The FFQ administration and completion varied from approximately 1–2 h, depending on the study participant. The analysis of the CyFFQ was performed using the Dietplan7 software (https://www.foresoft.co.uk/) (accessed on 5 May 2018) to which traditional food items were added, as described elsewhere [17]. ## 2.4. Presentation of FFQ Data The macronutrients were expressed as percentages of energy intake and grams, while the micronutrients were expressed as g or mg/1000 kcal. ## 2.5. Comparison of Intake with Dietary Reference Intakes The participants’ intake grouped by life stage and gender was compared against the Dietary Reference Intake (DRIs) [19]. ## 2.6. MD Adherence Scores MD adherence was evaluated based on the CyFFQ dietary intake reports of individuals using the following scores: the MedDiet score by Panagiotakos et al., 2007 [20] and the Mediterranean Diet Adherence Screener (MEDAS) score proposed by Martínez-González et al., 2004 [21], which was validated for the Cypriot population [17]. The MedDiet score includes 11 food groups and was developed to estimate the adherence to MD and its association with cardiovascular disease risk and biomarkers [20], while the MEDAS consists of a 14-food item questionnaire. For the MedDiet score, food items that are regularly consumed within the MD dietary pattern, such as non-refined cereals, fruits, vegetables, legumes, olive oil, fish and potatoes, were assigned a score ranging from 1 to 5, indicating rare or no consumption to daily consumption [20]. The reverse scores were assigned for the consumption of foods that deviated from the MD dietary pattern, such as meat and meat products. A score of 5 was assigned when individuals reported no consumption of the specified food group and 0 for daily consumption [20]. In the case of alcohol consumption, the following scoring method was assigned: a score of 5 for <300 mL of alcohol/day, a score of 0 for >700 mL per day or no consumption, scores of 4 to 1 for consumption of 600–700 mL/day, 500–600 mL/day, 400–500 mL/day and 300–400 mL/day respectively. In total, the lowest possible score for the MedDiet score is 0 and the maximum is 55, with the highest score indicating a higher adherence to the MD diet. For the MEDAS score [21], a score of 1 is given for the consumption of a beneficial food item above a certain frequency and a score of 0 is given if the consumption of a beneficial food item is below the required intake. The total MEDAS score ranges from 0 to 14, and a higher score indicates better MD adherence [21]. ( The criteria and scoring for each approach are presented in Tables S4 and S5 in the Supplementary Material). The MD adherence scores were derived from the FFQs by grouping foods based on the main categories assessed by each MD adherence score. As an example, the legume group included lima beans, chickpeas, lentils, green peas and black-eyed beans. Grouping was performed by trained researchers (C.C.C and C.A.D). ## 2.7. Statistical Analysis All statistical analyses were performed using the STATA statistical software, version SE16 (StatCorp. 2007. College Station, TX, USA) and R studio, using R statistical packages and scripts, version 3.6.1 (R Core Team, R Foundation for Statistical Computing, Vienna, Austria). Descriptive statistical analyses of mean and standard deviation and median and interquartile range (minimum and maximum) were applied to the energy-adjusted values obtained from the FFQ for cases and controls. Additionally, a statistical analysis comparing the energy-adjusted values for controls versus asymptomatic HD patients and for controls versus symptomatic HD patients was carried out using the two-sample Wilcoxon rank-sum (Mann–Whitney) test. Statistical analysis also compared macronutrient intakes in cases and controls against the recommended gender and age-specific macronutrient intakes. The Fisher’s exact test was used to calculate a p-value comparing the percentages of each group that were within or outside the recommendation range. Comparisons between the MedDiet and the MEDAS scores, asymptomatic patients and controls, symptomatic patients and controls and asymptomatic and symptomatic HD patients were performed using the two–sample Wilcoxon rank-sum (Mann–Whitney). For all analyses, a p-value of <0.05 was considered statistically significant. ## 3.1. Demographic and Anthropometric Characteristics of Participants This case-control study consisted of 36 HD patients, of which $$n = 18$$ were asymptomatic, $$n = 10$$ were symptomatic and $$n = 8$$ were symptomatic advanced-stage HD patients, as well as their gender/age-matched controls ($$n = 37$$). The demographic characteristics of participants are shown in the Supplementary Material as Supplementary Tables S1 and S2. Overall, the three study groups were similar in terms of their demographics, with a few exceptions. There was a statistically significant difference between the educational level of the asymptomatic HD patients versus controls, with a higher percentage of asymptomatic HD patients having completed higher levels of education [primary school ($1\%$ asymptomatic vs. $2\%$ controls), lower secondary school ($5\%$ asymptomatic vs. $5\%$ controls), high school ($4\%$ asymptomatic vs. $6\%$ controls) and higher education ($25\%$ asymptomatic vs. $5\%$ controls) ($$p \leq 0.014$$)]. A statistically significant difference in age was identified between asymptomatic HD patients versus controls ($$p \leq 0.026$$), symptomatic HD patients versus controls ($$p \leq 0.041$$) and asymptomatic HD patients versus symptomatic HD patients (p = <0.00001) Supplementary Tables S1–S3). There was a statistically significant difference between weight in controls versus symptomatic HD patients ($$p \leq 0.030$$) (Supplementary Table S2). However, no significant difference was identified for controls versus asymptomatic HD patients and between the asymptomatic and symptomatic HD patients. Furthermore, the body mass index (BMI) did not differ significantly between all three groups (Supplementary Tables S1–S3). A statistically significant difference was also identified for marital status between symptomatic HD patients versus controls, with more controls being married ($9\%$ symptomatic vs. $26\%$ controls), single ($4\%$ symptomatic vs. $8\%$ controls), divorced ($3\%$ symptomatic vs. $0\%$ controls) and widowed ($1\%$ symptomatic vs. $2\%$ controls) ($$p \leq 0.035$$)] (Supplementary Table S2). ## 3.2. Energy Intake, EA, Macronutrients and Micronutrients As seen in Table 1, energy intake (kcal/day) was higher in cases vs. controls (median (IQR): 4592 [3376] vs. 2488 [1917] kcal/day; $$p \leq 0.0002$$). Additionally, cases consumed a lower percentage of energy from non-starch polysaccharides (NSPs) compared to controls (median (IQR): 5.5 (3.2) vs. 8.1 (4.1)% energy NSPs; $$p \leq 0.034$$). A comparison of the EA macronutrients and micronutrients between asymptomatic HD ($$n = 18$$) and controls ($$n = 37$$) was also performed as shown in Table 2. Cases had a higher energy intake (median (IQR): 3751 [1894] kcal/day) compared to controls (2488 [1917] kcal/day; $$p \leq 0.028$$). Additionally, the following macronutrients and minerals were significantly different between asymptomatic HD patients vs. controls, respectively: % energy of polyunsaturated fatty acids (PUFAs) (median (IQR): 7.5 (2.6) vs. 6.4 (1.7)% energy; $$p \leq 0.032$$); cholesterol (median (IQR): 135.8 (55.4) vs. 106.8 (58.8) mg/1000 kcal; $$p \leq 0.023$$); sodium (median (IQR: 1028.6 (290.2) vs. 915.9 (344.0) mg/1000 kcal; $$p \leq 0.044$$); selenium (median (IQR: 19.7 (6.8) vs. 16.9 (6.4) ug/1000 kcal; $$p \leq 0.023$$); and tryptophan (median and (IQR): 6.8 (2.1) vs. 5.7 (1.7) mg/1000 kcal; $$p \leq 0.007$$). A comparison of the macronutrient and micronutrient intake between controls ($$n = 37$$) and symptomatic HD ($$n = 18$$) was also performed as shown in Table 3. The symptomatic and advanced symptomatic patient groups were grouped together for this analysis. Energy (kcal/day) was found to be statistically significantly different between symptomatic HD patients and controls (median and (IQR): 5517 [2907] vs. 2488 [1917] kcal/day; $$p \leq 0.001$$). The following dietary macronutrients and micronutrients were significantly different in symptomatic HD patients versus controls, respectively: % energy monounsaturated fatty acids (MUFA) (median and (IQR): 13.4 (5.2) vs. 15.5 (5.7) % energy; $$p \leq 0.026$$); % energy NSPs (median and (IQR): 1.09 (0.57) vs. 1.62 (0.81)% energy; $$p \leq 0.043$$); potassium (median and (IQR): 1155.7 (185.3) vs. 1377.8 (292.0) mg/1000 kcal; $$p \leq 0.017$$); calcium (median and (IQR): 348.2 (99.3) vs. 402.6 (159.4) mg/1000 kcal; $$p \leq 0.024$$); magnesium (median and (IQR): 115.8 (38.1) vs. 141.7 (33.7) mg/1000 kcal; $$p \leq 0.023$$); phosphorus (median and (IQR): 503.8 (84.2) vs. 619.8 (158.4) mg/1000 kcal; $$p \leq 0.002$$); and biotin (median and (IQR): 8.1 (2.2) vs. 9.4 (3.5) ug/1000 kcal; $$p \leq 0.021$$). A comparison of adherence to macro and micronutrient intake recommendations between cases ($$n = 36$$) versus controls ($$n = 37$$) was also performed, as shown in Table 4. Cases showed higher adherence to recommendations for magnesium, vitamin E, vitamin B6, folate and pantothenic acid. Controls demonstrated a higher percentage of adherence to the recommendations of macro and micronutrients compared to symptomatic HD patients regarding energy, cholesterol, sodium and chloride. With regards to a higher adherence in cases vs. controls, in particular, the percentage (lower $95\%$ CI-Upper $95\%$ CI) of cases that fell within the recommended range for magnesium was: 80.6 (63.8–90.7) vs. controls: 56.8 (40.1–72.0)%; $$p \leq 0.043$$); % within recommendation for vitamin E (cases: 61.1 (44.0–75.9) vs. controls: 21.6 (10.9–38.3)%; $$p \leq 0.010$$); % within recommendation for vitamin B6 cases: 94.4 (79.5–98.7) vs. controls: 75.7 (58.9–87.1)%; $$p \leq 0.046$$); % within recommendation for folate cases: 33.3 (19.6–50.6) vs. controls: 10.8 (4.0–26.2)%; $$p \leq 0.025$$); and % within recommendation for pantothenic acid cases: 86.1 (70.0–94.3) vs. controls: 51.4 (35.1–67.3)%; $$p \leq 0.002$$). All were significantly higher for the case compared to the control group. In contrast, adherence to other dietary recommendations was higher in controls compared to cases. In particular, % within recommendation for energy intake (kcal/day) of cases vs. controls: 8.3 (2.6–23.6) vs. 29.7 (16.9–46.7)%; $$p \leq 0.035$$); % within recommendation for cholesterol intake cases: 8.3 (2.6–23.6) vs. controls: 51.4 (35.1–67.3)%; p = <0.0001); % within recommendation for sodium cases: 8.3 (2.6–23.6) vs. controls: 45.9 (30.3–62.4)%; p = <0.0001); and % within recommendation for chloride intake cases: 13.9 (5.7–30.0) vs. controls: 51.4 (35.1–67.3)%; $$p \leq 0.001$$). ## 3.3. MD Adherence Scores Using the MedDiet Score and the MEDAS Score As seen in Table 5, no significant difference in MD adherence assessed using the MedDiet score was observed when comparing asymptomatic individuals and controls (median and (interquartile range (IQR)): 31.1 (6.1) vs. 31.1 (8.1); $$p \leq 0.363$$), nor between symptomatic individuals and controls (median and (IQR): 33.1 (6.1) vs. 31.1 (8.1); $$p \leq 0.061$$). However, a significant difference was identified for MD adherence for asymptomatic individuals and symptomatic individuals (median and (IQR): 31.1 (6.1) vs. 33.1 (8.1); $$p \leq 0.024$$). Symptomatic patients had, on average, higher adherence to the MD than asymptomatic patients (Table 5). As seen in Table 6, the assessment of MD adherence using the MEDAS score revealed a significant difference in MD adherence between asymptomatic individuals and controls (median and (IQR): 5.5 (3.0) vs. 8.2 (2.0); $$p \leq 0.014$$), with controls adhering more to the MD than asymptomatic individuals. However, no significant difference was observed between symptomatic individuals and controls (median and (IQR): 6.2 (1.0) vs. 8.2 (2.0); $$p \leq 0.066$$) or between asymptomatic individuals and symptomatic individuals (median and (IQR): 5.5 (3.0) vs. 6.2 (1.0); $$p \leq 0.216$$). ## 4. Discussion The present study investigated energy, macro and micronutrient intake and MD adherence in Cypriot HD patients who were either asymptomatic, symptomatic or symptomatic advanced versus age and gender-matched controls using the validated semi-quantitative CyFFQ and MD adherence scores. In brief, HD cases were found to have significantly higher energy intake compared to controls. This was observed for asymptomatic vs. controls and symptomatic vs. controls. The intake of asymptomatic HD was significantly higher regarding intakes of PUFA, cholesterol, sodium, selenium and tryptophan compared to controls, whereas intakes of MUFA, NSPs, potassium, calcium, magnesium, phosphorus and biotin were significantly higher in symptomatic HD versus controls. Symptomatic patients had higher MD adherence compared to asymptomatic patients based on the MedDiet score, while the MEDAS score controls demonstrated higher MD adherence compared to asymptomatic individuals. The BMI status was also investigated for controls versus asymptomatic HD patients, controls versus symptomatic HD patients and asymptomatic HD patients versus symptomatic HD patients. However, no statistically significant results were identified. Thus, although HD patients have more caloric intake, this does not translate to a higher BMI. In fact, HD patients have a significantly lower weight than controls. Previous studies have investigated different dietary supplementations, such as ethyl-EPA [22,23], L-acetyl-carnitne (LACC) [24], uric acid [25], nutritional and oral supplementation of vitamins and minerals [26,27], to assess whether these improve the motor function of individuals with HD. The studies by Puri et al. [ 22,23] investigated the effect of ethyl-EPA in terms of motor improvement between the intent to treat (ITT) and protocol violations (PP) in cohort patient groups. However, no motor improvement was observed in the ITT-treated-group. However, in the protocol violations (PP) cohort, the ethyl-EPA group showed motor improvement in comparison to the placebo group [22,23]. Another study with ethyl-EPA supplementation [22,23] showed that ethyl-EPA was effective in significantly reducing global cerebral atrophy during the first month of treatment. More precisely, a decrease of the caudate and thalamus regions was observed in the ethyl-EPA-treated group. Therefore, ethyl-EPA shows some beneficial effects on reducing brain atrophy and improving motor function [22,23]. The relationship between uric acid (UA) and the progression of HD was investigated by looking at the functional decline in people with HD [25]. An association was observed between baseline UA and total functional capacity (TFC) over a 30-month period, from the lowest to highest quintile. More precisely, increasing UA levels were associated with less decline in the total motor scores from the lowest to the highest quintile. This suggests an association between the baseline UA concentration and slower progression of HD [25]. A study by Auinger et al. [ 26,27] investigated the use of oral nutritional supplements in HD patients [26,27]. The dietary assessment of macronutrients and energy intake was assessed in HD patients along with their UHDRS scores to monitor improvement in their motor, cognitive or behavioral domains. No change was observed in HD patients’ UHDRS scores from day 0 to day 90, indicating no association between diet and UHDRS scores [26,27]. An additional study [26,27] investigated oral supplementation of vitamins and minerals. The study found that a higher intake of water-soluble vitamins and minerals was more common in advanced HD patients. However, no significant benefit was observed with supplementation intake in terms of motor, cognitive or functional states of patients [26,27]. However, there are studies that investigated nutrient supplementation in HD. Some nutrients have demonstrated the ability to improve motor function or slow disease progression. Further research is required to better understand the effect of these vitamins and minerals on HD and their effect on improving HD disease symptoms and progression. The higher energy intake in HD patients compared to controls was not surprising and increased intake was observed as follows: symptomatic → asymptomatic → controls. Our results agree with previous studies that have identified that HD patients have an increased energy intake [11]. A previous study investigated the energy intake, BMI and macronutrient intake between 435 HD patients with <37 CAG repeats and 217 HD patients with ≥37 CAG repeats [11]. HD patients with ≥37 CAG repeats had higher energy intake compared to <37 CAG repeat HD patients. Furthermore, the ≥37 CAG HD group had a decreased BMI compared to the <37 CAG repeat group. Higher energy intake was related to both the increased CAG repeat length and the 5-year probability of HD onset. Additionally, the study did not find any differences in macronutrient intake following adjustment for confounders in the ≥37 CAG group compared to the <37 CAG group [11]. Another study [27] investigated the relationship between nutritional status and HD severity in 224 Spanish HD patients versus controls. This study revealed that the energy intake of 48 HD patients was below the recommended dietary allowance, while, in 150 HD patients, energy intake was higher than the recommended dietary intake [27]. Our study’s findings thus agree with previous studies arguing that HD patients have a higher energy intake compared to controls. The hypermetabolic state seen in HD patients is not explained by common pathophysiological mechanisms such as inflammation and altered endocrine function, although both mechanisms have been implicated in HD and are likely part of the pathological process induced by mutated huntingtin (mHTT) [12]. As a result, weight loss starts during the early stage of the disease and it is evident in asymptomatic patients despite high caloric intake. A study by Mochel et al. [ 12] investigated early energy alternations in 32 asymptomatic and HD patients compared to 21 controls, revealing possible mechanisms which might explain the hypermetabolic state and weight loss observed in HD patients [12]. Nuclear magnetic resonance (NMR) conducted on the participants’ plasma identified low concentrations of the branched chain amino acids (BCAA), namely valine, leucine and isoleucine in HD patients compared to controls. BCAA levels were correlated with weight loss, disease progression and an abnormal CAG repeat expansion [12]. Therefore, the early weight loss seen in HD is associated with a systemic metabolic defect. BCAA concentrations may be indicative of disease onset and progression [12]. Further research can provide insight into the exact mechanisms contributing to weight loss observed in HD. In exploring energy dysfunction, brain, biochemical and cellular mechanisms have been identified. Brain energy metabolism revealed a decrease in glucose consumption in the basal ganglia and increases in lactate concentration in the basal ganglia and occipital cortex of HD patients [28]. Furthermore, ATP depletion was also seen in HD brain tissues. Various mechanisms have been proposed to explain the energy deficit in HD brains. These include impaired oxidative phosphorylation, oxidative distress, impaired mitochondrial calcium handling and a decrease in glycolysis, among others [28]. The biochemical evidence relating to energy deficit involves dysfunction in the respiratory chain complex II succinate dehydrogenase (SDH) [28]. Post-mortem studies of symptomatic HD patients showed a dysfunction in SDH (complex II) and Coenzyme Q-cytochrome c reductase (complex III) [28]. In a yeast model, it was observed that mHTT suppress mitochondrial respiration by suppressing succinate dehydrogenase and Coenzyme Q-cytochrome c reductase [28]. A possible cellular mechanism involved in energy deficit may be that mHTT impairs mitochondrial motility in mammalian neurons through a toxic gain of function from the CAG repeat expansion and loss of function of wild-type HTT [28]. Another possible explanation for energy dysfunction in HD is the downregulation of the peroxisome proliferator-activated receptor gamma coactivator (PGC-1a) in the striatum, which was shown to affect mitochondrial energy metabolism by impairing oxidative phosphorylation [28]. A few studies were conducted to investigate the nutritional status and severity in the Spanish HD population versus controls [27]. The study by Cubo et al. [ 27] identified possible dietary insufficiency in the intake of carbohydrates, PUFA, MUFA, fibre, Vitamin A, Vitamin E, pantothenic acid, biotin, folic acid, Vitamin D, iodine, potassium, copper and manganese, with the aforementioned macronutrients and micronutrients being below the recommended daily allowance [27]. Furthermore, symptomatic advanced HD patients demonstrated a higher intake of water-soluble vitamins such as Vitamin C, Vitamin B2, Vitamin B6, biotin and Vitamin B5. Our results found that MUFA, fibre, calcium, magnesium and biotin were significantly higher in controls compared to patients, suggesting a lower consumption of whole grains, legumes, eggs, meat, fruits and dark leafy vegetables by patients. Furthermore, PUFA intake was higher in asymptomatic HD patients and, since PUFA sources are usually rich in antioxidants, they may be providing neuronal protection by eliminating ROS, thus possibly delaying symptom onset in these patients. In contrast, intake of magnesium, zinc, selenium, Vitamin E, Vitamin B6, folate, biotin and Vitamin B2 was significantly higher in cases compared to controls. With regards to MD adherence, our study used two MD adherence scores, namely MedDiet and the MEDAS scores, to assess adherence. Using the MedDiet score, symptomatic HD patients demonstrated higher MD adherence compared to asymptomatic HD patients. The difference in MD adherence between the two HD stages may be the result of asymptomatic patients having less concern about their diet or using emotional eating to handle their diagnosis, as revealed by some patients and their families (unpublished data), while symptomatic patients may be considering healthy eating as a way to reduce symptomatology. A higher MD adherence was demonstrated in symptomatic HD patients compared to asymptomatic HD patients. A possible explanation is that asymptomatic HD patients consume more energy-dense foods, which may not be in accordance with the components of the MD. Indeed, since HD patients need to aim for a higher BMI, energy-dense foods such as full-fat dairy products, cream, red meat, fried foods and desserts may be preferred [24]. A previous study by Rivadeneyra et al. [ 16] investigated the factors associated with MD adherence in HD using the Trichopoulou score [16]. It revealed that HD patients with moderate to high MD adherence compared to those with low MD adherence had a higher intake of cereals, alcohol, fish, MUFA/SFA and dairy products [16]. Furthermore, moderate MD adherence was statistically significantly associated with old age, a decrease in comorbidities, UHDRS motor scores and lower abdominal obesity. In participants with high MD adherence, there was a decrease of UHDRS motor scores and psychiatric comorbidities and an improvement in the quality of life compared to those with lower MD adherence [16]. Our study results agree with the study by Rivadeneyra et al. [ 16] that the symptomatic HD patients have a higher MD adherence score compared to HD gene carriers and asymptomatic HD patients, although a different MD adherence score was used to assess this. With regard to adherence to the MD diet and dietary quality, a previous study investigated the dietary intake of HD patients based on their MD adherence [16]. Numerous vitamins and minerals, such as thiamin, biotin, folic acid, Vitamins A, C, D and E, phosphorus and potassium were studied in 98 HD patients who had low or moderate/high adherence to the MD. It revealed that biotin, folic acid, Vitamin C, Vitamin E, copper and selenium were significantly higher in the HD group with high/moderate compared to low MD adherence [16]. The results of the present study agree that the intake of some vitamins and minerals such as magnesium, zinc, Vitamin E and biotin were found to be significantly higher in HD patients compared to controls, suggesting that HD patients may be consuming foods higher in these micronutrients. Although HD patients have higher energy intake, they often have lower-than-average body weight, struggle with malnutrition and may have an imbalance in food intake. Despite having higher energy intake, they may not meet energy requirements, since they often have lower- quality diet [29]. Insufficient energy, macro- and/or micronutrient intake leads to vitamin and mineral deficiencies such as anaemia and associated fatigue [29]. The HD patients’ inability to meet energy and nutrient requirements may be on account of (i) their not preferring certain foods or food groups such as fruits and vegetables, which are known to have antioxidant properties and are high in vitamins and minerals, (ii) having reduced or high appetite and (iii) chronic obsessive-compulsive tendencies towards certain foods or food groups for a long period of time [7,29]. The present study has a number of strengths and limitations. With regards to strengths, to the best of our knowledge, this is the first study in Cypriot HD patients that comprehensively assessed the dietary intake of this population group, both in terms of energy intake and macro and micronutrients, relying on the validated CyFFQ and adherence to the MD using two validated tools. The study included a high percentage of known HD patients in Cyprus and a matched control group. Generally, all the FFQs were completed except for one, which was partly completed. The study is limited because the sample size is small, due to the rarity of HD. Due to the cross-sectional nature of the study, nutritional changes could not be assessed in parallel with body composition, missing demographic and lifestyle data, since questionnaire completion was time-consuming and sometimes tiresome for some patients and their next of kin. Although completion of the CyFFQ typically takes 1 h, as mentioned by Philippou et al. [ 17] the next of kin of symptomatic HD patients sometimes took 2 h to complete it as they delved into more detail regarding food consumption, or they would be side-tracked and discuss other issues. This was not the case for asymptomatic and control participants. It is also of note that controls had a higher energy intake than expected, which might have deviated from some of the comparisons. A number of reasons may explain this, such as: (i) consumption of energy-dense meals and snacks contributing to higher energy intake; (ii) various snacking behaviors (eating alone, outside home or work, in front of the computer, late in the day and stress eating); and (iii) it is not known if the controls were weight stable [30]. Future work can investigate the association between dietary intake using omics techniques on HD symptomatology and outcomes. Additionally, future work, including further prospective studies, would be of value to ascertain whether dietary intake is associated with (delayed) disease onset and symptoms and improvement in quality of life. Furthermore, it would be interesting to investigate patients who have phenoconverted to observe any associations with dietary changes from their baseline CyFFQ. Future longitudinal investigations are needed to better understand the nutritional and anthropometric status changes that occur as the disease progresses. ## 5. Conclusions In summary, this study confirmed previous findings arguing that HD cases have a significantly higher energy intake than controls and revealed differences in macro and micronutrients and adherence to the MD, both between patients and controls and by HD symptom severity. These findings are important, as they provide evidence to further understand diet–disease associations and may guide future nutritional education efforts within this population group. Insights from research on diet in HD patients may pave the way for future, tailored dietary interventions, including the MD, which helps delay disease and symptom onset, decreases disease severity and improves the patients’ quality of life. ## References 1. Walker F.O.. Huntington’s Disease. Lancet (2007) 369 218-228. DOI: 10.1016/S0140-6736(07)60111-1 2. 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--- title: 'Highly Processed Food Consumption and Its Association with Anthropometric, Sociodemographic, and Behavioral Characteristics in a Nationwide Sample of 2742 Japanese Adults: An Analysis Based on 8-Day Weighed Dietary Records' authors: - Nana Shinozaki - Kentaro Murakami - Shizuko Masayasu - Satoshi Sasaki journal: Nutrients year: 2023 pmcid: PMC10005625 doi: 10.3390/nu15051295 license: CC BY 4.0 --- # Highly Processed Food Consumption and Its Association with Anthropometric, Sociodemographic, and Behavioral Characteristics in a Nationwide Sample of 2742 Japanese Adults: An Analysis Based on 8-Day Weighed Dietary Records ## Abstract This cross-sectional study assessed highly processed food (HPF) consumption and its association with individual characteristics in a nationwide sample of Japanese adults. Eight-day weighed dietary records were obtained from 2742 free-living adults aged 18–79 years across Japan. HPFs were identified based on a classification method developed by researchers at the University of North Carolina at Chapel Hill. The basic characteristics of the participants were assessed using a questionnaire. On average, HPF contributed to $27.9\%$ of daily energy intake. The contribution of HPF to the daily intake of 31 nutrients ranged from $5.7\%$ for vitamin C to $99.8\%$ for alcohol (median, $19.9\%$). Cereals and starchy foods were the main food groups that contributed to the total energy intake of HPF. Multiple regression analysis showed that the older group (60–79 years) had a lower HPF energy contribution than the younger group (18–39 y) (regression coefficient (β) = −3.55, $p \leq 0.0001$). Compared to current smokers, past and never-smokers had lower HPF energy contributions (β = −1.41, $p \leq 0.02$; and −4.20, $p \leq 0.0001$, respectively). In conclusion, HPFs account for approximately one-third of energy intake in Japan. Younger age and current smoking status should be considered in future intervention strategies to reduce HPF consumption. ## 1. Introduction Highly processed foods (HPFs), defined as multi-ingredient industrially formulated mixtures [1], are increasingly contributing to diets worldwide [2]. Previous studies have reported that HPF consumption is associated with poor overall diet quality, characterized by increased intake of total fat, saturated fat, trans fat, and free sugars, and decreased intakes of dietary fiber, vitamins (e.g., vitamins A, C, and D), and minerals (e.g., potassium and iron) [2]. Furthermore, high HPF consumption may lead to adverse health outcomes, such as overweight and obesity, cardiovascular and cerebrovascular diseases, metabolic syndromes, depression, and mortality [3,4,5]. Therefore, the official dietary guidelines in several countries recommend reducing HPF consumption [6,7,8,9]. For effective nutrition policies tailored to each population, it is important to identify the factors associated with HPF consumption [10]. Previous studies have evaluated the anthropometric, sociodemographic, and behavioral characteristics associated with HPF consumption and reported several consistent findings. For example, body mass index (BMI) was positively associated with HPF consumption [11,12,13,14,15,16], while age was inversely associated [10,13,14,17,18,19,20,21,22,23,24,25]. However, conflicting results have been found regarding the association between HPF consumption and sex [13,14,17,18,19,20,21,22,24,25], income [13,20,21,22,24], education [10,13,14,17,18,19,20,21,22,24,26], smoking [10,13,17,26], and physical activity [10,17]. Because the human diet is influenced by many other factors, such as culture, vulnerable groups (i.e., those most likely to consume HPFs) may vary between countries [18] and should be investigated on a national or regional basis. The Japanese diet is considered to be based on dishes and meals made from various unprocessed or less processed foods [27]. However, the literature on HPF consumption and related individual characteristics in Japan is limited. To the best of our knowledge, only two studies have reported HPF consumption among Japanese adults [28,29], of which only one evaluated its association with the characteristics of participants (BMI) [28,29]. These studies were carried out in a single prefecture with small sample sizes [29]. Furthermore, despite the increase in the intake of alcoholic beverages and ready-made foods [30,31], these items were excluded from the previous studies, which may have led to an underestimation of HPF consumption. In addition, the Japanese diet is characterized by some unique features, including meal combinations consisting of a staple food, main dish, and side dish [32], with a high intake of vegetables, greens and beans, refined grains, and sodium [33]. Given the uniqueness of the Japanese diet, the distribution of HPF consumption among the Japanese population and its associated characteristics should be clarified to consider the direction of nutrition policies on HPF. This cross-sectional study aimed to assess HPF consumption and its association with anthropometric, sociodemographic, and behavioral characteristics in a nationwide sample of Japanese adults. ## 2.1. Study Procedure This study used data from the Ministry of Health, Labor and Welfare-sponsored Nationwide Study on Dietary Intake Evaluation (MINNADE) survey, designed to elucidate dietary characteristics and eating behaviors across Japan. The details of the survey are provided elsewhere [34]. Briefly, the survey consisted of two rounds of 1-year data collection (first round: from November 2016 to September 2017; second round: from October 2017 to September 2018). The target population was healthy, community-dwelling Japanese people aged 1–79 years in Japan. Initially, 32 of the 47 prefectures, which represent more than $85\%$ of the total population of Japan, were selected to reflect the proportion of the population number in each region of Japan [34,35] based on the geographical diversity and feasibility of the survey. A total of 441 research dietitians consented to participate in the survey and were responsible for recruitment and data collection. Considering the feasibility and human and financial resources, we decided to include 256 individuals (128 males and 128 females) in each of the nine age groups (1–6, 7–13, 14–19, 20–29, 30–39, 40–49, 50–59, 60–69, and 70–79 years) during the first round of data collection ($$n = 2304$$ in total). Based on the dropout rate in each sex–age category in the first round, 110–119 participants for each sex–age group were recruited in the second round ($$n = 2051$$ in total). Therefore, the total target number of individuals was 4355. The main inclusion criterion was free-living individuals willing to participate in the survey. Exclusion criteria were dietitians, those living with a dietitian, individuals working with a research dietitian, those who had received dietary counseling from a doctor or dietitian, those undergoing insulin or dialysis treatment, pregnant or lactating women (at the start of the survey), and infants habitually drinking human milk. Night workers were included, but they were asked not to complete a dietary record (DR) on overnight working days and on the days before and after. Participants in this survey were not randomly selected. Only one person per household participated in the study. ## 2.2. Analytic Sample A total of 4299 individuals aged 1–79 years participated in the MINNADE survey (first round: $$n = 2263$$; second round: $$n = 2036$$). Participants were asked to conduct two non-consecutive days of DRs in each of the four seasons (8 days in total). The number of adults aged 18–79 years with ≥1 day of DR data was 2969 (Figure 1). Among them, we excluded 84 participants with <8-day DR data, 65 participants with at least 2 days of consecutive DR data, and 3 participants who did not perform DRs in the appropriate months (i.e., October, November, and December in fall; January, February, and March in winter; April, May, and June in spring; and July, August, and September in summer). We also excluded 12 participants who lactated or became pregnant during data collection, one who lived in a different geographic area (identified after data collection began), and 62 with missing information on variables of interest. Consequently, the present analysis included 2742 adults aged 18–79 years. ## 2.3. Dietary Assessment Dietary data were obtained using DRs for a total of 8 days (2 non-consecutive days in each of the four seasons). Details of the DR are provided elsewhere [34]. Briefly, the research dietitians explained verbally and in writing to the participants how to maintain DR. Participants were asked to weigh and record all foods and beverages consumed using a digital scale (KS-812WT; Tanita, Tokyo, Japan) that can measure up to 2 kg in increments of 1 g. The set of two recording days in each season comprised two weekdays (Monday to Friday) for half of the participants and one weekday and one weekend day (Saturday or Sunday, as well as national holidays) for the other half. This approach was used to obtain approximate overall dietary data (3:1 ratio of weekdays to weekends [actually 5:2]) without compromising the feasibility and simplicity of the survey. The main items recorded were as follows: (i) dish names; (ii) location where the eating occasion occurred (home, restaurants, school or workplace [i.e., food services], or other location); (iii) food names (ingredients included in dishes); and (iv) measured weights or approximate amounts of food consumed. The recording sheets were retrieved by a research dietitian within a few days of each recording day (usually the next day). The research dietitian verified the completeness of the records and recorded additional information if necessary. The research dietitian assigned a food code from the Standard Tables of Food Composition in Japan (STFCJ) [36] to each food item. For packaged foods and home-prepared meals, each ingredient and its weight consumed were estimated as accurately as possible from approximate portion sizes, restaurant and manufacturer websites, ingredient labels, nutritional information on food packages, and cookbooks. Other research dietitians confirmed all food codes and weights in the central office of the study. Daily energy and nutrient intake were calculated for each participant using the STFCJ [36]. ## 2.4. Classification of Foods Based on the Degree of Food Processing A total of 21,936 DRs were obtained from 2742 participants. After excluding dietary supplements, 1,107,350 food items (1989 food codes) were identified. We classified all food items according to their level of processing using the framework developed by researchers at the University of North Carolina (UNC) at Chapel Hill [1]. The UNC system is based on the most widely used food classification system, NOVA [27], but with enhanced definitions of food categories [1,37]. A previous study showed that the UNC system had higher inter-rater reliability than the NOVA classification system [38]. The UNC system classifies food items into four groups: [1] unprocessed and minimally processed; [2] basic processed; [3] moderately processed; and [4] highly processed. Regarding food classification, the Food and Agricultural Organization recommends distinguishing food items processed in industrial settings and those prepared by hand at home or in artisan settings (e.g., street foods) and disaggregating homemade recipes into their ingredients when possible [39]. However, it is difficult to distinguish between artisanal and industrial foods [40], especially for mixed dishes prepared outside the home (e.g., in supermarkets and restaurants), as detailed information on brand names, ingredients, and the preparation process is not always available [26,41]. Therefore, no consensus has been reached on whether to classify food according to the individual ingredients contained or according to the dish as a whole without disaggregating into its ingredients [42]. Therefore, we decided to classify foods consumed outside the home as mixed dishes in two different scenarios: one considers all mixed dishes prepared outside the home as artisanal food and classifies each ingredient based on its food code (low-estimate scenario), while the other considers mixed dishes prepared outside the home as industrial foods and classifies all ingredients into HPFs (high-estimate scenario). A flowchart of food classification is shown in Figure 2. For foods eaten at home ($$n = 769$$,285), we classified each food ingredient according to its food code. Similarly, foods consumed outside the home as a single item (e.g., apples, black tea, and black coffee) ($$n = 33$$,555) were classified based on their food code. Meanwhile, foods consumed outside of the home as a mixed dish ($$n = 304$$,510) were classified according to their food code in the low-estimate scenario and classified into HPFs uniformly in the high-estimate scenario. Taking apple pie as an example, the low-estimate scenario classified individual ingredients, such as apple and butter, based on food codes, while the high-estimate scenario classified all ingredients as HPF. When foods were classified according to food codes, 421 food codes were identified as HPF. Each food code classified as HPF was further classified into ten food groups based on the STFCJ classification [36] and the similarity of the nutrient composition or culinary use of foods. Examples of HPFs classified according to food codes in each food group are shown in Table S1. ## 2.5. Assessment of Basic Characteristics Body weight (in 0.1 kg) and height (in 0.1 cm) were measured barefoot and in light clothing by a family member or research dietitian using standardized procedures. Self-reported height and weight were used for participants who could not be measured ($$n = 5$$). BMI was calculated as body weight (kg) divided by height squared (m2). Information on other characteristics was collected using a questionnaire. The age at the beginning of the study was calculated based on the date of birth. The annual household income was asked in 16 options and reclassified into three categories (<4, ≥4 to <7, and ≥7 million Japanese yen). Similarly, the education level was asked in five categories and reclassified into three categories: junior high school or high school, junior college or technical school, and university or higher. Smoking status was categorized into three categories: current, past, or never. The employment status was categorized as unemployed (including students), part-time, or full-time. For six activities (walking, cycling, standing, running, exercise that causes sweating, and sleeping), self-reported hours spent per day or week during the previous month were asked. Physical activity (as a total metabolic equivalent, h/day) was calculated by summing the product of the self-reported daily hours spent on each activity and the corresponding metabolic equivalent value [43,44,45,46]. ## 2.6. Data Analysis All analyses were performed using the statistical software package SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). Statistical significance was defined as a two-sided p-value of <0.05. First, the basic characteristics of the participants were expressed as mean and standard deviation (SD) for continuous variables and as number and percentage for categorical variables. The mean daily HPF consumption (g) and energy (kJ) was calculated in the low-estimate scenario (all food items were classified according to their food code) and the high-estimate scenario (food items consumed outside the home as a mixed dish were classified as HPF). The difference in estimates between the scenarios was examined using a paired t-test. Pearson’s correlation coefficients were computed to assess the association between HPF consumption and the consumption of foods in other processing categories (i.e., unprocessed/minimally processed, basic processed, and moderately processed foods). The means of HPF intake (g/day and kJ/day) and the weight and energy contribution (%) to total HPF consumption were calculated for each of the 10 food groups. In addition, we calculated the mean daily intake of energy and 31 nutrients from the HPFs and their contribution (%) to the total daily intake. Moreover, differences in HPF weight and energy contributions between categories of basic characteristics (age, sex, BMI, annual household income, educational level, smoking status, employment status, and physical activity) were examined using univariate analysis, followed by Tukey’s test when appropriate. Since parametric and non-parametric tests showed similar results, we show the parametric test results; an unpaired t-test was used for sex, and one-way analysis of variance was used for other variables. Finally, the differences in HPF weight and energy contributions across the categories of basic characteristics were analyzed using multivariable linear regression, with all variables entered into the model simultaneously. ## 3. Results The basic characteristics of the participants are listed in Table 1. The mean age was 48.4 years (SD, 17.6) and the mean BMI was 23.0 kg/m2 (SD, 3.5). The participants were almost equally distributed in the three categories for each annual household income and educational level. Never-smokers and full-time workers represented >$60\%$ of the participants. The mean HPF consumption in the low-estimate scenario (447 g/day) was significantly ($p \leq 0.0001$) lower than in the high-estimate scenario (845 g/day). Similarly, the mean energy intake from HPFs in the low-estimate scenario (2357 kJ/day) was significantly ($p \leq 0.0001$) lower than in the high-estimate scenario (3547 kJ/day). For both low- and high-estimate scenarios, the weight contribution of HPFs was significantly correlated with that of unprocessed/minimally processed foods (r = −0.41 and −0.66, respectively), basic processed foods (r = −0.45 and −0.60, respectively), and moderately processed foods ($r = 0.04$ and −0.37, respectively). Similarly, the energy contribution of HPFs in the low- and high-estimate scenarios was significantly correlated with that of unprocessed/minimally processed foods (r = −0.48 and −0.72, respectively), basic processed foods (r = −0.62 and −0.73, respectively), and moderately processed foods (r = −0.11 and −0.49, respectively). Table 2 shows the HPF consumption of each food group and its percentage contribution to the total HPF consumption. In the low-estimate scenario, the average HPF consumption in grams was the largest for alcoholic beverages among all food groups. In contrast, seasoning and spices (e.g., dashi [stock], soy sauce, and seasoning sauces) had the highest average weight contribution. Meanwhile, in the high-estimate scenario, non-alcoholic beverages were the largest both in weight and weight contribution. Furthermore, regardless of the classification method, cereals and starchy foods had the highest total energy intake from HPFs and contribution to the total energy intake from HPFs. The energy and nutrient intake from HPFs and their contribution to the total daily intake are shown in Table 3. The mean energy contribution of HPFs in the low-estimate scenario was significantly lower than in the high-estimate scenario ($27.9\%$ vs. $42.4\%$, respectively, $p \leq 0.0001$). The median HPF contribution to the total daily intake of 31 nutrients was $19.9\%$ in the low-estimate scenario and $35.9\%$ in the high-estimate scenario. In both scenarios, the contribution of HPFs to total daily intake was highest for alcohol ($99.8\%$ and $99.9\%$, respectively), followed by sodium ($58.9\%$ and $69.7\%$, respectively), and lowest for vitamin C ($5.7\%$ and $22.8\%$, respectively). The percentage contribution of HPFs to the total dietary intake according to the categories of basic characteristics is shown in Table 4. Regardless of the classification method or the unit of contribution (i.e., the percentages of grams or energy), some consistent associations were found between basic characteristics and HPF contribution. For example, the older group (60–79 years) had lower mean weight and energy contributions from HPFs than the younger and middle-aged groups (18–39 and 40–59 years, respectively) ($p \leq 0.005$). Similarly, females had lower weight and energy contributions from HPFs than males ($p \leq 0.005$). Moreover, the weight and energy contributions of HPFs were higher in current smokers than in past- or never-smokers, and in full-time workers than in part-time workers or non-workers ($p \leq 0.0001$ for all). Table 5 shows the associations between basic characteristics and HPF consumption in the low-estimate scenario. After adjustment for other variables of basic characteristics, the older group (60–79 years) had significantly lower weight and energy contributions of HPFs than the younger group (18–39 years) ($p \leq 0.0001$). Moreover, compared to current smokers, past and never-smokers had lower weight and energy contributions of HPFs ($p \leq 0.02$). Furthermore, females had a higher weight contribution of HPF than males ($p \leq 0.0001$), although no differences were observed between sexes for the energy contribution. All these results were observed similarly in the high-estimate scenario (Table S2). In addition, the results of the high-estimate scenario showed that the middle-aged group (40–59 years) had significantly lower weight and energy contributions than the younger group (18–39 years) ($p \leq 0.0001$). Moreover, both the weight and energy contributions of HPFs were higher in part-time and full-time workers than in non-workers. ## 4.1. Main Findings We cross-sectionally examined HPF consumption and its association with anthropometric, sociodemographic, and behavioral characteristics among Japanese adults. The energy contributions of HPFs were $27.9\%$ and $42.4\%$ in the low- and high-estimate scenarios, respectively. In addition, younger age (18–39 years) and current smoking status were associated with higher weight and energy contributions of HPFs. To the best of our knowledge, this is the first study to evaluate HPF consumption and its association with various individual characteristics in a large nationwide sample from a diverse geographic area in Japan. ## 4.2. Scenarios of HPF Consumption Previous studies have varied in deciding whether to disaggregate mixed dishes prepared outside the home before classifying foods according to the degree of processing [28,47,48]. Disaggregating recipes into ingredients is important to accurately estimate the consumption of culinary ingredients [26]. Meanwhile, this may lead to an underestimation of HPF consumption because some of these ingredients may have been industrially processed [26]. We observed that the average energy contribution of the HPFs in the high-estimate scenario was approximately 1.5 times higher than that in the low-estimate scenario. Similarly, a previous study classified food items on a food frequency questionnaire using upper- or lower-bound scenarios, in which some food items were classified into more and less processed categories, respectively, compared to normal classification [26]. The results showed that in the upper-bound scenario, the energy contribution of the HPFs was nearly double that of the lower-bound scenario. Furthermore, a very recent study using 24 h dietary recall data also observed that the energy contribution of HPFs ranged from $53.4\%$ to $60.1\%$ across the least and most conservative classifications [41]. Therefore, the estimated HPF consumption can differ considerably depending on the assumption of the processing level of each food item. Differences in food classification methods should be noted when comparing results between studies. Meanwhile, the difference in the scenarios did not significantly change most of the observations on the association between HPF consumption. ## 4.3. Contribution of HPFs The energy contribution of HPFs was $28\%$ or $42\%$, depending on the scenario. The percentages were comparable to Brazil ($24\%$ [17], $25\%$ [16]), South Korea ($26\%$) [18], Japan ($30\%$ [29], $38\%$ [28]), Chile ($29\%$) [20], Mexico ($30\%$) [22], France ($36\%$) [13], and Australia ($39\%$) [12,24], but lower than the United Kingdom ($51\%$ [15], $53\%$ [25]), Canada ($44\%$ [11], $54\%$ [23]), and the United States ($59\%$) [21], and higher than Italy ($17\%$) [19]. As in other countries [15,16,21,26,49], cereals and starchy foods, including bread, contributed the most to the total energy intake of HPFs. Different main food sources have been identified in other studies, such as pre-prepared/ready-to-eat and frozen dishes [11], processed meat [19], and soft drinks [20], probably due to differences in food culture and classification methods for food groups and HPFs. Furthermore, consistent with previous studies [49,50], HPFs greatly contributed to alcohol and sodium intake. One of the main concerns of the Japanese diet is its high sodium intake, which is a factor that reduces the quality of the diet [33]. In these dietary data, seasonings and spices represented $23\%$ ($\frac{95}{421}$) of all food codes in the HPF and $67\%$ of the sodium intake from the HPF. Therefore, reducing HPF consumption may lead to a decrease in sodium intake, ultimately improving the quality of the Japanese diet. ## 4.4. Participant Characteristics Related to HPF Consumption HPFs have high palatability, convenience, and affordability, as well as low satiety, all of which may promote overeating and weight gain [11]. Moreover, a recent study found that HPFs have a higher energy intake rate (kcal/min) than unprocessed foods, which may further promote excess energy intake [51]. Previous studies have consistently reported a positive association between HPF consumption and BMI [11,12,13,14,15,16]. However, no association was found between BMI and HPF consumption in the present study. The reasons for this are unclear but can be explained by the relatively narrow distribution of BMI. In addition, the nutritional characteristics of the major HPFs in Japan may differ from those of other countries. Meanwhile, we found an inverse association between age and HPF consumption, consistent with previous studies [10,13,14,17,18,19,20,21,22,23,24,25]. This relationship can be explained by several factors. For example, younger people tend to emphasize the convenience of food [52], which facilitates HPF consumption [53]. Moreover, younger people tend to be more exposed to packaged foods during the formation of eating habits than older generations [17]. In contrast to BMI and age, conflicting results have been reported regarding the association between HPF consumption and sex [13,14,17,18,19,20,21,22,24,25], income [13,20,21,22,24], education [10,13,14,17,18,19,20,21,22,24,26], smoking [10,13,17,26], and physical activity [10,17]. In this study, females had a lower HPF weight contribution than males. This has also been observed for the HPF energy contribution in previous studies [13,14,18,25]. The sex difference may be attributed to differences in eating behavior, food choice, and nutritional strategy between the sexes [54]. We did not observe an association between annual household income and HPF consumption, as in a previous study [18]. At the same time, negative associations between annual household income and HPF consumption have been reported in France [13], Australia [24], and the United States [21], and positive associations have been observed in Chile [20] and Mexico [22]. Purchases of HPFs have also been reported to be higher for households with low socioeconomic status in Australia [55] and those with high socioeconomic status in India [56]. Therefore, the relationship between income and HPF consumption may be opposite between high-and middle-income countries (except for Chile). One reason for this may be the cost of the HPFs. For example, the price of processed foods was lower than that of less processed foods in Belgium [57] and the United Kingdom [58] but higher in Brazil [58]. However, evidence of the cost of HPFs versus non-HPFs is not available in Japan. Therefore, further investigations are needed to understand the characteristics of HPF products in Japan. There is limited evidence on the association between HPF consumption and employment status or physical activity. We observed that HPF consumption was higher in part-time and full-time workers than non-workers, only in the high-estimate scenario. A previous study in Italy reported that retired people were less likely to consume HPF than manual workers [19]. Therefore, non-workers may have lower HPF consumption than workers. This association may be explained by the scarcity of time of workers. In Norway, time scarcity is associated with the consumption of HPF dinner products [14]. Moreover, in Ireland, time pressure has been positively associated with the use of ready-made foods, such as frozen pizza [14]. More research is needed to determine the association between employment status and HPF consumption. ## 4.5. Social Implications Our findings suggest that the Japanese consume much of their energy and nutrients from HPFs, similar to previous studies in Japan [28,29] and other countries [11,12,13,15,16,17,18,20,21,22,23,24,25]. Given the possible adverse health effects of HPFs [3,4,5], nutrition policies may be necessary to reduce HPF consumption. According to our results, reducing HPF may decrease sodium intake and, consequently, improve the quality of the diet. In fact, previous studies have reported that higher HPF consumption was associated with unfavorable profiles of nutrient intake among Japanese adults [28,29]. Our findings also suggest that young people and smokers may benefit from public policies and programs to reduce HPF intake. Nutrition education to reduce HPF consumption may be especially important for young people, as they are likely to maintain a lifelong diet in the future [10]. Other personal characteristics not examined in this study, such as food literacy and beliefs, require further investigation. ## 4.6. Strengths and Limitations The strength of this study is the use of 8-day DRs collected throughout the year from a large sample of males and females of a wide range of ages from different regions of Japan. This allowed us to represent Japanese dietary habits by considering day-to-day and seasonal variations [59]. However, this study has several limitations. First, although the sampling was carried out to reflect the percentage of population in each region, the study population was not a nationally representative sample of the general Japanese population, but volunteers. Given the burden of data collection, participants may be more attentive to their diets and have healthier eating patterns than the general population. Compared with the nationally representative sample, the distributions of household income, body height, weight, and BMI in this population were similar, although the level of education was somewhat higher [34]. Therefore, there is no strong evidence that the participants in this study differed significantly from the general Japanese population. Second, the UNC system was developed for food supply in the United States and may not be the most appropriate for classifying foods sold in Japan. However, the UNC system is based on the NOVA classification, the most widely used system in many countries, including Japan [28,29]. Furthermore, the UNC system provides detailed definitions and examples of foods in each processing category and has been applied in several countries other than the United States [60,61]. Third, the classification of foods by food codes was performed by a single author and may contain some misclassifications, although the UNC system has high inter-rater reliability [38]. Fourth, although physical activity was estimated according to the methods used in previous studies [45,46], the validity of the estimates has not been investigated. Therefore, the association between physical activity and HPF consumption should be examined more rigorously in future studies. Fifth, other participant characteristic variables were not available in this study and their association with HPF consumption was unknown. Other drivers of HPF consumption include low sociability, adverse life events, screen time, time scarcity, and poor sleep quality [14,19,62,63]. More research should be carried out to identify these factors. Sixth, owing to the cross-sectional nature of the present analysis, more research is warranted to determine the causal relationship between the degree of food processing and the anthropometric measurements. Finally, self-reported dietary data are affected by, for example, reactivity and social desirability bias [64], which may lead to a lower intake of unhealthy foods and underestimate the contribution of HPF. ## 5. Conclusions Our findings suggest that HPFs account for >$25\%$ of energy intake in Japanese adults. 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--- title: 'Food Insecurity and Dietary Deprivation: Migrant Households in Nairobi, Kenya' authors: - Elizabeth Opiyo Onyango - Jonathan S. Crush - Samuel Owuor journal: Nutrients year: 2023 pmcid: PMC10005626 doi: 10.3390/nu15051215 license: CC BY 4.0 --- # Food Insecurity and Dietary Deprivation: Migrant Households in Nairobi, Kenya ## Abstract The current study focuses on food consumption and dietary diversity among internal migrant households in Kenya using data from a city-wide household survey of Nairobi conducted in 2018. The paper examined whether migrant households are more likely to experience inferior diets, low dietary diversity, and increased dietary deprivation than their local counterparts. Second, it assesses whether some migrant households experience greater dietary deprivation than others. Third, it analyses whether rural-urban links play a role in boosting dietary diversity among migrant households. Length of stay in the city, the strength of rural-urban links, and food transfers do not show a significant relationship with greater dietary diversity. Better predictors of whether a household is able to escape dietary deprivation include education, employment, and household income. Food price increases also decrease dietary diversity as migrant households adjust their purchasing and consumption patterns. The analysis shows that food security and dietary diversity have a strong relationship with one another: food insecure households also experience the lowest levels of dietary diversity, and food secure households the highest. ## 1. Introduction Cities in Sub-Saharan Africa (SSA) are experiencing rapid growth with high rates of natural population increase and rural-to-urban migration [1]. UN-Habitat [2014] estimated that 200 million people in Africa or $62\%$ of the region’s urban population reside in city-centre slums and peripheral informal settlements. Many migrant households in the city live a precarious existence in these underserviced and overcrowded slums and settlements, unable to secure regular or any employment, and incapable of meeting many basic needs [2,3,4,5]. Poor neighbourhoods in most cities are not only deprived of livelihood opportunities and basic amenities, but disproportionally bear the burden of food insecurity [6,7,8]. As a result, slums and informal settlements in African cities have recently been labelled ‘urban food deserts’, reflecting the fact that many residents are both chronically food insecure, and vulnerable to political, economic, and environmental shocks that increase food insecurity [9,10,11,12,13]. The UN Food and Agriculture Organization [14] (p. 107) defines food security as existing when “all people, at all times, have physical, social and economic access to sufficient, safe and nutritious food that meets their dietary needs and food preferences for an active and healthy life.” This definition has four inter-related dimensions—food availability, food accessibility, food utilisation, and food stability. There is a large body of research in SSA on food availability [15,16,17,18] and a growing amount of literature on urban food accessibility; that is, the ability of households to ensure physical, social and economic access to enough nutritious food [6,10,19,20]. Somewhat less attention has been paid to food utilisation under conditions of hyper-urbanisation, although this is changing with the realisation that African cities are undergoing a major nutrition transition and shifts in the urban food environment and diet [21,22,23,24,25,26,27]. To capture the utilisation dimension of food security, dietary diversity is viewed as a robust proxy for the nutritional quality of the diet of individuals and households and the socio-economic status of households [28]. In Kenya, the study of dietary diversity is dominated by research on the relationship between smallholder agricultural production and dietary diversity in rural areas (for example, [29,30,31,32]). Less attention has been given to dietary diversity in urbanising Kenya, although that is beginning to change [33]. Kimani-Murage and colleagues in two separate papers [34,35] showed that slum households are not only highly vulnerable to food insecurity but also experience a double burden of malnutrition with high levels of chronic child malnutrition co-existing with high levels of maternal obesity. A recent household survey in the secondary city of Kisumu showed a clear link between poverty and dietary deprivation [32]. Another study of three small towns in central Kenya found significant negative shifts in dietary composition accompanying supermarket shopping with increased consumption of highly processed foods [33,36,37,38]. In this paper, we focus on the capital city of Kenya, Nairobi, the largest and most important city in the country (with a population of 4.4 million) for four main reasons. First, although there is a growing amount of literature on the levels and determinants of food insecurity in this city, little attention has been paid to the links between migration (which is driving the city’s rapid growth) and the quality of household diets. Second, there is evidence that migrant households in Nairobi retain strong multigenerational links with rural areas, and it is important to assess whether these links play any role in mitigating food insecurity and enhancing dietary diversity in the city [39]. Third, we conducted the first city-wide representative household food security survey in Nairobi in 2018 and are able to freely mine this large dataset for insights on the drivers of contemporary migration and dietary diversity in one of Africa’s most important cities. Finally, the city of Nairobi released an innovative and comprehensive Food System Strategy in March 2022, one of the first of its kind in Africa [40]. Monitoring and evaluation is a central component of this policy initiative, and this paper provides city policy makers with baseline information on dietary deprivation that can later be revisited to evaluate the impacts of the strategy on the quality of household diets in the city. The paper is a contribution to the growing amount of literature on urban food security in African cities and, in particular, to the emerging interest in the changing quality of food consumption among the newly urbanised. The paper has three main research objectives: First, it examines whether migrant households in Nairobi are more likely to experience inferior diets, low dietary diversity, and increased dietary deprivation. Second, it assesses whether and why some migrant households are more vulnerable to dietary deprivation than others. Third, the paper analyses whether rural-urban links play a role in boosting dietary diversity among migrant households in the city. ## 2. Literature Review Studies of low-income neighbourhoods in African cities have found consistently low levels of dietary diversity and identified a series of actual and potential variables that help to explain variations in household food consumption. A comparative analysis of dietary diversity in low-income communities in eleven Southern African cities, for example, found that households with young children consumed a limited diversity of food and experienced both short-term and long-term food and nutrition insecurity [8]. Studies of food consumption in South African urban informal settlements have identified low levels of dietary diversity, a heavy dependence on cereals, and a strong association between dietary diversity and household income, poverty, and unemployment [41,42,43,44,45]. Researchers in Accra, Ghana, found that household characteristics with statistically significant associations with dietary diversity included the sex and education level of the household head, income, and food source [46]. In Nigeria, as income increases, diets improve in quantity and quality [47]. In Tanzania, Cockx et al. [ 48] find that urbanisation is significantly associated with important changes in dietary patterns, including a shift away from traditional staples towards more processed and ready-to-eat foods and heightened consumption of sugar and fats. In Ghana and Cameroon, smaller, better-off, and more educated households with higher levels of dietary diversity are less likely to respond to rising food prices by reducing diets or shifting buying patterns [49]. In urban Africa, households rely on food purchase from the formal or informal sector for the majority of their food needs. This means that they are particularly attuned and sensitive to food price increases. The 2007–2008 global food price crisis, for example, led to a sudden and dramatic increase in urban food insecurity [50,51], as has the more recent COVID-19 pandemic [50,52,53]. While sudden or gradual food price increases led to various coping behaviours such as eating less and eating less often, more research is needed on how food price increases in particular food groups impact food consumption and dietary diversity. An innovative study in Burkina Faso did find that dietary diversity significantly decreased between 2007 and 2008 as food prices increased [12]. While households increased their expenditure on food, it was insufficient to compensate for the effects of the food price crisis earlier. In Nairobi, most research on food consumption and dietary diversity has targeted the city’s low-income informal slums, home to $60\%$ of the population. These studies have shown that poverty is deeply entwined with food insecurity and dietary deprivation. Poor dietary diversity and associated health outcomes have been attributed to the food environment in these low-income areas of the city [54,55,56]. However, Dominguez-Salas et al. [ 57] note that, even among low-income households in Nairobi, there can be a wide range of variability in the predictors of positive nutritional status, which include income, household head education, and female headship. Higher education is also likely to be associated with higher knowledge, higher income, and more positive health and nutrition practices. In a separate survey of low-income urban and peri-urban households, Nyakundi et al. [ 58] posit that households that are income or asset-poor have poor dietary diversity. In addition, they suggest that there is a strong overlap between food insecurity and dietary diversity, with food insecure households having poor dietary diversity. Several scholars have argued for a greater general focus on the connections between migration and dietary diversity under conditions of rapid urbanisation in the Global South [59,60,61,62]. However, very few studies of dietary diversity in urban Kenya or other African cities identify the migration status of a household as a potential factor influencing dietary diversity. There are even fewer examples of studies focused specifically on migrants as a target group, and who are potentially more vulnerable to dietary deprivation. One study of Zimbabwean migrants in South African cities found that migrants do have higher levels of food insecurity than locals, low levels of dietary diversity, and diets high in starch and excessive concentrations of sugar and oils [11]. Protein and vitamin-rich foods are consumed in only a minority of migrant households [11]. Migrants are more food insecure and have poorer diets than their local counterparts in part because remittances reduce their disposable income. In relation to internal migration, one study in Uganda found that remitting did not, in fact, reduce the food insecurity of remitters [63]. Pendleton et al. [ 64] further show that migrant households in Windhoek, Namibia, have a less diverse diet than other households and that a constant complaint is the lack of variety and the monotony of the diet. In Kampala, Uganda, new migrants to the city were found to be more vulnerable than established urban residents to low dietary diversity, and lower-income groups compensate by participating in food sharing networks [65]. Another study of a small urban centre in Zambia suggests that recent migrants have significantly better food access than non-migrant households and those that migrated earlier [66]. The nature and durability of rural-urban links and their role in improving dietary diversity have also received some attention, particularly in relation to informal food transfers from rural households to their urban counterparts [13,15,67]. The varied, and sometimes contradictory, findings of these case studies raise a number of important questions with significant policy implications. For example, are migrant households particularly vulnerable to food insecurity and poor dietary diversity under conditions of rapid urbanisation and in-migration? Does dietary diversity improve or decline with length of residence in the city? Are all migrant households in the city equally able to secure healthy diets and optimal dietary diversity? Is the quality of the diet of migrant households impacted by the strength of their rural-urban links? The inconclusive answers to these questions to date strongly suggest the need for more empirical studies using common methodological approaches before generalisations are possible about the broader connections between migration and dietary deprivation. ## 3.1. Study Design and Participants The primary objectives of the analysis were threefold: (a) to examine the relationship between household dietary diversity and household characteristics by determining which migrant household demographic and socio-economic variables had a significant relationship with dietary diversity; (b) to assess whether food price changes lead to a decrease in dietary diversity; and (c) to identify which migrant households are most likely to experience low dietary diversity and are therefore most vulnerable to its nutritional and health-related consequences. To address these questions, the paper draws on data from a household food security survey in Nairobi City in 2018 [68]. The survey involved a cross-sectional city-wide representative sample of 1414 households. The number of sampled households was determined using a multi-stage proportional-to-population size random sampling procedure. A three-stage cluster sampling strategy was used to identify 23 sublocations from 8 divisions in the 4 districts/sub-counties of Nairobi City. In the selected 23 sub-locations, systematic random sampling was used to identify the participating households, where every nth household was recruited and interviewed. The household head was the target interviewee in this survey. The data were collected in face-to-face interviews by trained enumerators using tablets for data collection. For the analysis in this paper, we drew a sub-sample of 941 ($67\%$) migrant households defined as households with a head who was an internal migrant who originated from another part of the country. ## 3.2. Data Collection The survey instrument collected information on household and individual demographic characteristics, the social and economic profile of the households (including employment, income, and expenditure), the health status of household members, household food consumption, and sources of food including formal, informal, and non-market sources. Data were also collected on household experiences of food price changes, change in the price of foods in specific food groups, and the effects of the food price change on household food consumption patterns. ## 3.2.1. Dependent Variables The most widely used validated household dietary diversity metric is the Household Dietary Diversity Score (HDDS) [69,70,71]. Data on household intake of foods from 12 food groups are collected for a 24-h recall. This study used the FAO standard classification of food groups (Table 1). Values for each food group were assigned “0” and “1”, where “0” equals not consumed and “1” equals consumed. An HDDS score was calculated for each household on a scale from 0 to 12, where higher scores indicate greater dietary diversity. For this analysis, the HDDS scale was binned to create four categorical variables. Household food security was measured using the Household Food Insecurity Access Score (HFIAS) [72]. An HFIAS score was first calculated for each household based on answers to nine frequency of occurrence questions in the previous four weeks (Table 2). Scores range between 0 and 27, with a score of 0 indicating that the household is completely food secure, and a maximum score of 27 indicating extreme food insecurity. The HFIAS responses are converted into a categorical variable, the HFIAP, using an algorithm to generate a four-part classification—food secure, mildly food insecure, moderately food insecure, and severely food insecure [72,73]. ## 3.2.2. Independent Variables The dependent and independent variables used in the analysis are summarised in Table 3. The independent variables include the sex, age, education, employment status, and health status of the household head at the time of the survey. The health status variable is a self-reported binary response to questions about whether the household head had any diagnosed health issues from a list including NCDs and communicable disease. Migration history is an ordinal variable designed to capture the time elapsed since the household head first migrated to Nairobi. Household variables in the analysis include household size, household type, house structure, main source of income, total monthly income, health status, and proportion of income spent on food. Households were grouped into four types: female-centred (a female head with no spouse/partner plus child dependents); male-centred (a male head with no spouse/partner plus child dependents); nuclear (two parents plus child dependents); and extended (two parents plus child dependents plus other relatives and non-relatives). Housing structure is a binary observational response (formal/informal), which was preferred as a formal/informal settlement binary, since the latter contains both formal and informal housing structures in Nairobi. The Lived Poverty Index (LPI) is a validated self-assessment tool for measuring the subjective experience of poverty based on the frequency with which households go without certain basic needs (food, water, medical care, cooking fuel, and cash income) [74]. Other binary household variables include whether or not a household remits cash to the rural areas, receives food transfers from the countryside, participates in urban agriculture, shops for food at supermarkets, and shares meals with neighbours, all of which have the potential to affect dietary diversity. Onyango et al. [ 39] show that sudden economic shocks including sharp food price increases have a significant impact on low-income Nairobi households. A key issue is whether food price volatility in different food groups is associated with reduced dietary diversity. The food price volatility variable in this analysis is based on questions about whether or not food price increases had negatively impacted the household in the six months prior to the survey. This measure was an ordinal variable with four options: never, about once a month, about once a week, or nearly every day of the week. Because food price volatility tends to affect some marketed foods more than others, an additional 11 binary variables were generated relating to which foods had become unaffordable. ## 3.3. Data Analysis The data were analysed using the software SPSS version 28 (IBM Statistics 28). The analysis included descriptive percentages, bar graphs, and crosstabulation to provide an overview of the distribution of response variables and the food security and dietary diversity frequency distributions. To investigate the relationship between the dependent and independent variables, we use logistic regression modelling in the form of ordinal logit analysis. A chi-square test was performed to determine within and between-group differences for the explanatory and response variables. Ordinal regression modelling was performed to ascertain which household characteristics and food price changes were associated with household dietary diversity and for the generation of the exponentials (odds ratios). The ordinal cumulative logit link was used given the ordered nature of the dependent HDDS variable. ## 4.1. Household Characteristics Table 4 summarises the socio-demographic and economic characteristics of the sampled migrant households. The majority of the household heads are male ($82.5\%$), with only $17.5\%$ headed by females. Most migrant heads ($80\%$) are of working age, between 25 and 55 years. The small proportion of migrant heads over the age of 55 is a reflection of the fact that retirees in Nairobi tend to return to their rural homes [75]. The household heads are relatively well-educated, with over $80\%$ having some secondary or tertiary education. Less than $1\%$ have no education. Almost all migrant household heads in Nairobi have some form of employment, although only $42\%$ are employed full-time in the formal sector. Nearly $40\%$ are self-employed, mainly in the informal sector, and only $4\%$ report that they are currently unemployed. Migrant households vary considerably in size and type. Only $17\%$ are single-person households, while $45\%$ have four or more household members. Female-centred households make up $17\%$ of the total, and male-centred another $20\%$. Over half of the households are nuclear families, and only a small number are extended families. Nine in ten of the households live in formal dwellings in informal and formal areas. The main source of income for nearly half of the households is formal employment, followed by informal employment ($30\%$) and self-employment ($23\%$). Household incomes also vary considerably, with half of the households making KShs 20,000 (USD 200 or less) per month and nearly $30\%$ with incomes of over KShs 40,000 (USD 400) per month. The Lived Poverty Index is more concentrated with two-thirds of households scoring between 0 and 0.5. The proportion of income that a household spends on food is a common poverty proxy. This is generally high among migrant households in Nairobi, with $65\%$ spending more than a third of their income on food, and $44\%$ spending more than half on food. Around half of all migrant households send cash remittances to rural areas. In relation to food sourcing, most households ($90\%$) do not engage in meal sharing with neighbouring households and only $2\%$ engage in urban agriculture, which is often held out by advocates as a key to greater food security and dietary diversity [76,77]. Meanwhile, more than half receive food remittances from the rural areas, which could mean greater dietary diversity. Finally, three-quarters of migrant households purchase at least some of their food from supermarkets in Nairobi. Around $60\%$ of migrant households said they had sacrificed eating some foods due to food price increases in the six months prior to the survey. Nearly $40\%$ of households experienced this at least once a week, or more frequently still. The food groups most severely impacted were cereals/grain (affecting $27\%$ of households), and red meat, poultry, and offal (affecting $43\%$ of households). ## 4.2. Household Food Security and Dietary Diversity Table 5 shows that only $26\%$ of the surveyed migrant households were completely food secure on the HFIAP scale. All of the other households experienced some degree of food insecurity, with $27\%$ experiencing severe food insecurity, $35\%$ moderate food insecurity, and $12\%$ mild food insecurity. On the HDDS, $11\%$ of migrant households consumed food from only 1–3 food groups, indicating an extremely deprived and monotonous diet. As many as half of the households had scores between 4 and 6, with milder dietary deprivation. The remaining $38\%$ had scores of 7 or more, indicative of a more balanced and diverse diet. Table 6 cross-tabulates the HFIAP and HDDS scores to assess whether there is a relationship between food insecurity and dietary diversity. Two-thirds of households with the lowest dietary diversity (HDDS 1–3) are also severely food insecure on the HFIAP. As dietary diversity increases, severe food insecurity declines to $30\%$ (for HDDS 4–6), $14\%$ (for HDDS 7–9), and $12\%$ (HDDS 10–12). The reverse pattern is true for food security, which increases from $6.5\%$ of households in HDDS 0–3, to $19\%$ (HDDS 4–6), $40\%$ (HDDS 7–9), and $64\%$ (HDDS 10–12). Figure 1 confirms that the majority of severely food-insecure migrant households fall into the two lowest dietary diversity categories (HDDS 1–3 and 4–6). ## 4.3. Household Characteristics and Dietary Diversity Table 7 cross-tabulates dietary diversity with household variables to identify any relationships of potential significance. The p-values for the variables in the table provide a first approximation of the relationships of statistical significance. While the HDDS distribution does vary with household head characteristics including sex, age, and health status, the significance of the relationship is very weak. Far more significant is the education level and employment status of the household head. As educational attainment increases, so does dietary diversity. Employment status is a good predictor of dietary diversity; households with heads in full-time wage employment have the highest dietary diversity, followed by those in self-employment. Households with unemployed heads have the lowest dietary diversity. In regards to household characteristics, the distribution of HDDS scores is weakly related to migrant household size, household type, and the health of household members. Dietary diversity is strongly related to household economic variables. Households in formal housing, with formal wages as the main source of income, and lowest lived poverty all have significantly greater dietary diversity. The relationship between dietary diversity and household income as well as the proportion of income spent on food is statistically significant. For example, $75\%$ of households in the lowest income sextile are in the two lowest HDDS categories, compared with only $34\%$ in the highest sextile. The only other variable with a statistically significant relationship with dietary diversity is supermarket patronage, which tends to be associated with increased diversity. Increased food prices are a key determinant of what lower-income households can afford and the kind of foods that end up on the dining table in urban centres. Of the surveyed migrant households, $62\%$ had gone without certain foods due to increases in food prices. The frequency with which they adjusted their food consumption was a significant determinant of dietary diversity. For example, households that experienced food price increases almost every day of the week represented over $50\%$ of those with lower food diversity (Figure 2). Foods most often sacrificed due to price increases included staple grains, meat, eggs, dairy, fish, and, to a lesser extent, vegetables. ## 4.4. Predictors of Dietary Diversity The results of the ordinal logistic regression analysis of predictors of HDDS are presented in Table 8. The table provides the predictive odds ratios (OR) for the explanatory variables together with the accompanying $95\%$ CI and the level of significance. The education level and employment status of the migrant household head are confirmed as significant predictors of HDDS. Each successively higher level of education is associated with increased odds of higher dietary diversity (OR:1.54 ($95\%$ CI of 1.212–1.944)). The employment status of the household head was associated with a lower odds ratio, meaning that households with an unemployed household head had reduced odds of dietary diversity compared with households with a head in full-time wage employment or self-employment (OR: 0.770 ($95\%$CI = 0.646–0.919)). Female heads had higher odds than male heads of belonging to households with greater dietary diversity (OR: 1.203 ($95\%$CI = 0.669–2.163)). Although the age of the household head is an insignificant predictor of dietary diversity, it does have a positive predictive relationship (OR: 1.085 ($95\%$CI = 0.923–1.274)), suggesting that households with older heads have slightly higher odds of having greater dietary diversity. Various household characteristics were also good predictors of whether a household would have higher dietary diversity. The most significant was housing type; households in formal dwellings had increased odds of better dietary diversity compared to those in informal dwellings (OR: 1.273 ($95\%$CI = 0.825–1.966)). A number of other household characteristics were not significant predictors of dietary diversity. These included household size (OR: 0.978 ($95\%$CI = 0.827–1.157)), household structure (OR: 0.942 ($95\%$CI = 0.706–1.257)), and household health status (OR: 0.884 ($95\%$CI = 0.538–1.450)). Households with more members, those experiencing some health issues, and those with male heads have slightly reduced odds of dietary diversity. However, household socioeconomic factors such as monthly income and lived poverty were significant predictors of migrant household diet diversity. Households with a higher monthly income were more likely to have higher dietary diversity (OR: 1.108 ($95\%$CI = 0.983–1.249)). Households that reported a higher LPI (i.e., greater poverty) had lower odds of dietary diversity (OR: 0.792 (0.647–0.970)). Sharing meals with neighbours was associated with lower odds of dietary diversity, as these were more likely to be poorer households as well (OR: 0.895 ($95\%$CI = 0.544–1.474)). On the other hand, purchase of food from supermarkets was associated with higher odds of dietary diversity (OR: 1.235 ($95\%$CI = 0.850–1.793)). Few households participate in urban agriculture, and those that did grow some of their own food did not have increased odds of dietary diversity (OR: 0.927 ($95\%$CI = 0.295–2.913)). Similarly, the proportion of income that a household spent on food was not a significant predictor of dietary diversity. Although statistically insignificant, going without certain foods due to food price increases was associated with reduced odds of dietary diversity (OR: 0.941 ($95\%$CI = 0.763–1.161)). However, going without both fruits and vegetables due to food price increases was actually associated with increased dietary diversity (OR: 2.098 ($95\%$CI = 1.238–3.555) and OR: 1.790 (1.001–3.340)). This might be because respondents who considered fruits and vegetable an important part of their diet were of higher socioeconomic status and increases in the price of these foods did not reduce their dietary diversity scores. Finally, the predictive relationship between two different indicators of household food security with different metrics and recall periods is of interest. In theory, we might expect the HFIAP and HDDS to move in tandem. As food security increases, so does dietary diversity, and vice-versa. This was confirmed by the negative predictive relationship between the two; that is, as food security declined, so did the odds of a household experiencing greater dietary diversity (OR: 0.760 ($95\%$CI = 0.630–0.917)). ## 5. Discussion In this paper, we set out to examine the food security experience of Nairobi households that trace their origins to the rural areas of Kenya and with which many retain strong connections [13,75]. To identify migrant households in a larger representative household survey dataset, we used the criterion of whether the household head had been born elsewhere in the country. Using this criterion, two-thirds of all households in the Nairobi dataset qualified for inclusion. The sub-sample of 941 households was sufficiently large and representative to draw conclusions about the migrant population of the city. The most obvious feature of the population is its demographic and socio-economic diversity. In part, this is a function of the long history of post-independence migration to the city. Nearly a quarter of the household heads had first migrated to Nairobi over 20 years previously and $45\%$ had been living in Nairobi for over 15 years. Only sixteen percent were relatively recent migrants, many in one-person households (that made up $17\%$ of the total). One sign of the ongoing strength of rural connections is that around half had sent cash remittances in the previous year and half had received food remittances from rural relatives, most on a monthly basis [39]. Most of the household heads (over $80\%$) were male in three types of households—male-centred, nuclear, and extended. The only households with female heads were also female-centred without a male spouse or partner ($17\%$ of the total). Only half of the households had a household head or member in full-time formal sector employment. The majority of other households obtained their main source of income from informal employment or self-employment ($42\%$ combined). As a result, there was significant income spread, with $50\%$ earning less than KES 20,000 per month and $30\%$ earning more than twice that amount. Very few migrant households in Nairobi participate in urban agriculture, with lack of access to land for cultivation in the city a primary constraint [68]. Almost all migrant households are therefore reliant on the market for most of their food supply, and well over half spend greater than $35\%$ of household income on food purchase. With regard to the first objective of the analysis, migrant households in Nairobi do have higher levels of food insecurity on average than non-migrant households, with only $25\%$ of migrant households completely food secure on the HFIAP scale, compared to $35\%$ of non-migrant households [39]. The primary focus of this paper was not on levels of food security per se, but on the quality of household diets as measured by the HDDS. However, as Figure 1 clearly shows, there is a strong association between food security and dietary diversity among migrant households in Nairobi: as food security increases, so does dietary diversity, and, as food insecurity increases, dietary diversity declines. As expected, increased food prices, which made food less affordable, had a strong positive association with dietary deprivation. Household dietary diversity varied significantly with price increases in most food groups. As the frequency of being affected increased, dietary diversity declined. Virtually every affected food group (with the exception of roots and tubers, fruit, and oils and fats) had a statistically significant relationship with household dietary diversity. Further insights into this relationship were provided by the cross-tabulation of HDDS scores with price increases in particular food groups. Staple foods essential to a balanced and nutritious diet were all affected. The second objective of the paper was to investigate whether all migrant households experience similar levels of food insecurity and dietary deprivation in Nairobi. Here, the analysis clearly revealed considerable variability. Table 5 shows that $27\%$ were severely food insecure, $47\%$ were mildly or moderately food insecure, and $26\%$ were food secure. Additionally, $11\%$ experienced severe dietary deprivation (HDDS 1–3), $51\%$ moderate deprivation (HDDS 4–6), and $38\%$ little or no dietary deprivation (HDDS 7–12). Table 6 shows that food insecurity and dietary deprivation are closely associated. For example, households with the highest levels of dietary deprivation are also the most food insecure. As dietary diversity increases, food insecurity declines. The two household head variables with a statistically significant relationship to dietary diversity were their amount of education and employment status. Both the age of the household head and their migration history had a weak relationship with dietary diversity. Dietary diversity, therefore, appears to be largely unrelated to the length of time since initial migration to the city. Economic variables that had the strongest statistical relationship with dietary diversity at household level included the main source of household income (formal or informal, full-time or part-time), household monthly income, lived poverty, the amount of household income spent on food, and whether the household lives in formal or informal housing. The ordinal logistic regression analysis of predictors of dietary diversity provided a more robust analysis of the odds of a household experiencing dietary deprivation. Migrant households with poorly educated heads without full-time employment had the highest odds of dietary deprivation. Male heads had slightly lower odds than female heads of belonging to households with low dietary diversity. Although migrant female-headed households were more likely to be food insecure overall than male-headed households, the reverse was true with regard to dietary diversity. This suggests that female heads are more likely to husband their resources to ensure a more balanced diet for household members [13,47]. Other household characteristics associated with increased odds of dietary deprivation included residence in informal housing, low monthly income, and increased lived poverty. The third objective of the paper was to analyse whether the strength of rural-urban links affects dietary diversity among migrant households in Nairobi. This analysis did not find that the degree of dietary diversity had a strong relationship with the duration of residence of the migrant household head in the city. Length of time since migration did not mean increased or decreased odds of dietary deprivation. Based on studies elsewhere, cash remitting to the rural areas was expected to decrease dietary diversity by reducing the migrant household’s disposable income [2]. On the other hand, informal food transfers were expected to increase dietary diversity by augmenting the household food supply with fresh produce from the countryside [19]. The analysis confirmed that migrant households that remit are no more or less likely to experience dietary deprivation. In addition, those that receive food transfers are no more likely than those that do not to have greater dietary diversity. This has potentially important policy implications in light of recent policy interventions focused on making rural-urban connections in Africa more robust. While these policies may offer some benefit to rural and urban dwellers, this case study suggests that improvement in dietary diversity is not one of them. ## 6. Conclusions This paper demonstrates that many migrant households across the city are vulnerable to food insecurity and dietary deprivation. Three-quarters of Nairobi’s migrant households experience some degree of food insecurity and almost two-thirds experience a degree of dietary deprivation. Further, there is a clear relationship between food insecurity and dietary deprivation; that is, food insecure households are also much more likely to experience low levels of dietary diversity. This reciprocal relationship means that measures to reduce food insecurity by increasing food accessibility are also likely to improve dietary diversity and that strategies to deal with dietary deprivation, such as food fortification, will impact positively on food insecurity. Despite the high levels of food insecurity and dietary deprivation amongst migrant households, not all are equally vulnerable. While the determinants of this distribution are complex and challenging to decipher given the cross-sectional nature of the data, the survey results identified that those households most likely to be associated with dietary deprivation have low incomes, high levels of lived poverty, and limited access to formal sector wage employment. Regarding the more general implications of the study, acculturation theory in the Global North asserts that the nutritional quality of migrant food consumption tends to decline over time [78,79,80,81,82]. Studies of this phenomenon in relation to migration in the Global South are less common and the limited evidence that exists is inconclusive [83,84,85]. One hypothesis in line with acculturation theory is that migrants to the city in the South experience a decline in dietary quality over time, as they consume less fresh produce and more processed food. An alternative position is that new migrants are more likely to experience immediate dietary deprivation, but that, over time, as they gain greater access to livelihood opportunities and build social and economic support networks in the city, dietary diversity improves. The evidence from Nairobi does not give strong support for either position. Both the descriptive statistics and the ordinal logistic regression indicate that dietary diversity is unrelated to the passage of time since the household head first migrated to Nairobi. The prevalence of dietary deprivation is very similar for short, medium, and long-term migrants in the city. This suggests that the theory from the Global North is inappropriate for the dietary experience of migrants within the Global South, and that alternative theory-building more appropriate to Southern realities is necessary. ## References 1. Migrants and cities: New partnerships to manage mobility. World Migration Report 2015 (2015) 2. 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--- title: Validating Healthy Eating Index, Glycemic Index, and Glycemic Load with Modern Diets for E-Health Era authors: - Zhao-Feng Chen - Joyce D. Kusuma - Shyang-Yun Pamela K. Shiao journal: Nutrients year: 2023 pmcid: PMC10005628 doi: 10.3390/nu15051263 license: CC BY 4.0 --- # Validating Healthy Eating Index, Glycemic Index, and Glycemic Load with Modern Diets for E-Health Era ## Abstract Predictors of healthy eating parameters, including the Healthy Eating Index (HEI), Glycemic Index (GI), and Glycemic Load (GL), were examined using various modern diets ($$n = 131$$) in preparation for personalized nutrition in the e-health era. Using Nutrition Data Systems for Research computerized software and artificial intelligence machine-learning-based predictive validation analyses, we included domains of HEI, caloric source, and various diets as the potentially modifiable factors. HEI predictors included whole fruits and whole grains, and empty calories. Carbohydrates were the common predictor for both GI and GL, with total fruits and Mexican diets being additional predictors for GI. The median amount of carbohydrates to reach an acceptable GL < 20 was predicted as 33.95 g per meal (median: 3.59 meals daily) with a regression coefficient of 37.33 across all daily diets. Diets with greater carbohydrates and more meals needed to reach acceptable GL < 20 included smoothies, convenient diets, and liquids. Mexican diets were the common predictor for GI and carbohydrates per meal to reach acceptable GL < 20; with smoothies (12.04), high-school (5.75), fast-food (4.48), Korean (4.30), Chinese (3.93), and liquid diets (3.71) presenting a higher median number of meals. These findings could be used to manage diets for various populations in the precision-based e-health era. ## 1. Introduction Healthy eating of essential nutrients is vital for nutrigenomics pathways to prevent chronic diseases in vulnerable populations of various social–ethnic contexts presenting inflammatory risks in the e-health era [1,2,3,4,5]. Healthy eating includes sufficient intakes of fruits, vegetables, grains, dairy, proteins, nuts and oils; while limiting saturated fats, salt, and empty calories [6,7,8,9]. The convenience of processed and pre-packaged foods loaded with saturated fats, empty calories (sugar and alcohol), and sodium in modern societies could be limited to improve diet quality [10]. Thus, the Healthy Eating Index (HEI), Glycemic Index (GI), and Glycemic Load (GL) could be used to assess diet quality in modern times [11,12,13,14]; with higher HEI, lower GI, and lower GL scores being associated with decreased inflammation, cancer risk, and chronic diseases [3,15,16]. The amount of carbohydrates in the diet contributes to the GI and GL, which have been used to evaluate healthy diets, by their potential to promote inflammation and the risk of cancer [17,18]. In recent years, related professions, including the American Diabetes Association (ADA), as well as medical and health professionals, further suggested that adults with diabetes could aim to eat 45 to 60 g of carbohydrates with each meal and 15–20 g per snack to control for acceptable GL of <20 for more stable blood glucose levels [19,20,21,22,23,24]. The elements of HEI, including fruits, grains, vegetables, and empty calories, could be major sources of carbohydrates. However, scientific data connecting the amount of carbohydrates with the sources of food categories per meal to reach an ideal GL of <20 per meal is lacking. Additionally, while mobile technologies evolved with accessibility to assess dietary nutrient intakes per meal and food items, the validations on the accuracy of essential nutrients with meals are lacking, limited to only the caloric nutrients but not other essential nutrients [2,25]. Furthermore, current dietary assessments have been validated with daily dietary diaries and longer durations (Food Frequency Questionnaires: per week and longer duration, with the need of adjusting for fat contents), but not per meal [2,25]. Therefore, in preparation for personalized nutrition in e-health era, further validations with the efforts to identify modifiable factors for health eating parameters per meal and daily diets are necessary. Healthy eating could prevent diseases for various ethnic populations, and the parameters of HEI could be used as modifiable factors for disease prevention [3,4,5]. Previously, using actual human diets across various ethnic groups, we validated the parameters of HEI, with predictors of total HEI score including whole fruits, milk, whole grains, saturated fats, oils and nuts (>80 as good score) [3]. Additionally, some parameters of HEI with the content of carbohydrates such as milk, empty calories, and vegetables were also predictors of GI scores (≤55 as good GI score included milk; and median GI score of <53.8 included milk, empty calories, and dark greens) in cancer patients having diabetes and chronic inflammatory diseases [3,4,5]. There are various diets in modern societies across ethnic populations including liquids and smoothies, convenient diets (canned food, high-school, and fast-food), Western diets (American, Mexican, Italian, and Mediterranean), and Eastern diets (Japanese, Chinese, and Korea) [2,25]. We validated essential nutrients with these domains of model diets [2,25], and healthy eating parameters with human diets across ethnic groups [3]. Our previous findings indicated that various modern diets might affect the accuracy in assessment of nutrients intakes, and heathy eating parameters [2,3,25]. Updated artificial intelligence (AI) machine-learning-based multivariate analytics, can be used to identify significant factors with improved accuracy [3,4,5]. The AI-based analytics with partition in iterations employs resampling with machine-learning operations for more accurate validations [5]. Thus, updated AI-based analytics with validation could be used to identify modifiable factors contributing to accurate prediction of healthy eating parameters for novel findings [2,25]. Therefore, the purpose of this study was to validate the predictors of HEI, GI, and GL across modern diets, in preparation for the precision-based e-health era. ## 2.1. Dietary Parameters and Indexes We entered daily dietary data and assessed the nutrients of 131 diets in four domain groups (liquids, convenience, ethnic, and smoothie diets) consumed by diverse populations across modern social contexts [2,3,4,5,25]. Based on previous studies [2,3,25], validation with model diets in addition to diets taken by various ethnic groups, is needed for accurate estimate of nutrients. We grouped possible modern diets into [1] liquid diets; [2] convenient diets (canned food, high-school, and fast foods); [3] ethnic diets of Western (American, Mexican, Italian, and Mediterranean) and Eastern (Japanese, Chinese, and Korean), and [4] smoothies added to these diets [2,25]. All diets were processed using the Nutrition Data Systems for Research (NDSR) software [26,27,28]. Data entry and analyses were verified for accuracy by team members. Healthy eating parameters were examined across various diets using HEI (HEI-2015) [29,30], GI [31], and GL [32]. HEI scored from 0 to 100 (0–50: poor, 51–80: moderate, >80: good [33]), which included food components of fruits, vegetables, grains, dairy, proteins, oils and nuts; with limiting saturated fats, sodium, and empty calories. Fruits (total and whole), vegetables (total and dark greens), and grains (total and whole) were each scored on a scale of 0–5. Dairy, proteins, oils and nuts, sodium, and saturated fats were scored on a scale of 0–10. Empty calories were scored on a scale of 0–20. For diet quality, the amount of carbohydrates in the diets contributes to the glycemic Index (GI), calculated using the NDSR software based on daily diets. GI accounts carbohydrates in foods and the affected levels of blood sugar [3]. GI scored from 0 to 100, with ≤55 being good (with carbohydrates that were digested and metabolized slower for blood glucose and insulin); 56–69 being moderate; and ≥70 being poor [19,34,35]. GL was calculated using the NDSR software based on daily diets by multiplying GI by the amount of carbohydrates in grams (g) with servings of foods divided by 100. Daily GL could be divided by the number of meals needed to yield an acceptable GL per meal. A GL score of <10 was good, 11–19 being moderate, and ≥20 being poor [36]; thus, <20 being acceptable per meal. Therefore, we calculated daily GL values with 20 as a denominator to obtain the number of meals needed per day for each diet; and calculated normalized and standardized carbohydrates per meal by dividing the daily carbohydrates per diet with the median number of meals from all diets. ## 2.2. Data Analysis Data analysis was performed using JMP® Pro version 16.0.0 software ([37,38,39], SAS Institute, Cary City, NC, USA). The analytics and rationales were reported before [2,3,25] and the strengths are summarized in the following. For predictive analyses, JMP software presented logistic regression (LR) as a baseline default exploratory model to predict dependent variables in categorical values. Following LR, other AI machine-learning-based GR validation models (the Leave-One-Out model where least significant factors might be eliminated to avoid over-fitting, or Elastic Net models) might be chosen with validations for further confirmatory analysis. Conventional statistical procedures, including the baseline LR models, are restricted by the sample size in the datasets [5]. If the number of factor parameters to be estimated exceeds the degrees of freedom, based on the sample size, the conventional models could be highly unstable; whereas machine-learning based GR models minimizes the number of predictors in the model to avoid over-fitting. This machine-learning-based approach is superior to conventional statistics, including the LR models, that tend to yield an overfitted model [4,5]. The AI-based analytics employs partitions in iteration by resampling within the datasets with machine-learning operations [5]. By resampling, observed biases would be corrected by repeated analyses on random subsets of datasets [5]. We incorporated Elastic Net models for their capacity to operate complex datasets with multiple domains and many factor variables, balancing possible interactions from domain factors [37]. We utilized AI-based GR analytics to validate the predictions, with an $\frac{80}{20}$ randomized split for training and validation sets for predictive modeling [38,39]; the final fittest model presented a lowest prediction error and minimized over-fitting [40]. For predictive modeling, we progressively examined significant factors per domains of HEI, caloric source, and various diets. With the Elastic Net validation models, we selected the fittest model based on precision criteria (Akaike Information Criterion with correction (AICc) the lower score, the fitter and better model; Misclassification Rate (MR): the lower the errors, the better; area under curve (AUC): the higher score, the more coverage and better) [41,42]. We further examined the graphical presentations on the prediction and interaction profilers to visualize potential interactions among the factors. To extend and continue a previous study [3], to identify the factors contributing to the health eating indices, we examined the prediction of GL in addition to HEI and GI. We further examined these indices with both measures of defined good quality scores (HEI > 80 and GI ≤ 55) and median scores. ## 3.1. Healthy Eating Parameters The HEI, GI, and GL parameters are summarized for daily diets per four diet groups in Table 1 and all diets in Supplementary Table S1. Among the four diet groups, mean HEI scores ranked highest from 78.61 for smoothie diets, 65.28 for ethnic diets, 57.86 for convenient diets, to 48.78 for liquid diets ($p \leq 0.0001$, Table 1). All HEI parameters, including total fruits, whole fruits, vegetables, dark greens, total grains, whole grains, dairy, proteins, oils and nuts, saturated fats, sodium, and empty calories, were significantly different among the four diet groups ($p \leq 0.0001$). None of the liquid and convenient diets presented a good HEI score of >80, whereas $1.4\%$ ($\frac{1}{71}$) of the ethnic diets and $50\%$ ($\frac{11}{22}$) of the smoothie diets illustrated a good HEI score. Mean GI scores ranked highest from 59.86 for convenient diets, 58.88 for ethnic diets, 56.38 for liquid diets, to 54.52 for smoothie diets ($p \leq 0.0001$). For a good GI score of ≤55, $3.33\%$ ($\frac{1}{30}$) of the convenient diets, $12.9\%$ ($\frac{9}{71}$) of the ethnic diets, $25\%$ ($\frac{2}{8}$) of the liquid diets, and $63.6\%$ ($\frac{14}{22}$) of the smoothie diets met the criteria. The mean daily GL (GI x carbohydrates/100) ranked highest from 240.8 for smoothie diets, 89.20 for convenient diets, 74.30 for liquid diets, to 68.69 for ethnic diets ($p \leq 0.0001$). To reach a good GL of <20 per meal, the daily number of meals with smoothie diets would need to be an average of 12.04 times, 4.46 for convenient diets, 3.71 for liquid diets, and 3.43 for ethnic diets ($p \leq 0.0001$, median = 3.59 meals daily). Contributing to GL, mean carbohydrates ranked highest from 442.2 g for smoothie diets, 148.7 g for convenient diets, 135.6 g for liquid diets, to 117.4 g for ethnic diets ($p \leq 0.0001$). Standardized mean carbohydrates (divided by the median number of meals 3.59 to reach a good GL of <20 per meal) ranked highest from 36.83 g for smoothies, 35.73 g for liquids, 34.23 g for ethnic, and 33.50 g for convenient diets ($$p \leq 0.0003$$, median = 33.95 g). To further validate the prediction for a good GL of <20 per meal, a regression coefficient of 37.33 g carbohydrates per meal was derived across all diets (Figure 1). Additional details for all diets per groups of diets on HEI, GI, GL, number of meals to reach acceptable GL, carbohydrates, and standardized carbohydrates/meals (divided by the median number of meals 3.59 to reach a good GL of <20 per meal) are listed in Supplementary Table S1. ## 3.2. Predictive Modeling for Healthy Eating Parameters In testing predictive modeling of HEI, GI, and GL, we progressively included related factors per domains of HEI, caloric sources, and various diets. Final predictive models of HEL, GI, and GL were determined based on the fittest models with the lowest AICc, least misclassification, and highest AUC for coverage (Table S2: progression for HEI, Table S3: GI, Table S4: GL, Table S5: carbohydrates, and Table S6: standardized carbohydrates per median number of meals needed for GL < 20). For HEI (>80 as a good score), three factors of HEI, including whole fruits, whole grains, and empty calories, were most significant for the fittest model ($p \leq 0.0001$; AICc: 9.78, misclassification: 0; Table 2: baseline LR model on the left panel and GR validation model on the right panel; AUC: 1.00: Figure S1). The inclusion of factors from the other two domains (caloric and diets) did not yield a better or fitter model on HEI (>80) prediction (Table S2). Additionally, prediction on HEI median score (≥64.4; AICc: 29.87, misclassification: 0.2, AUC: 0.94) did not reach a better model, while total fruits, dark greens, and canned foods were significant factors (Table S2). For predictive modeling of GI, significant predictors for GI (≤55 as good quality score) included total fruits, carbohydrates, and Mexican diets, which presented one factor from each of the HEI, caloric, and diet domains (Table 3). Additionally, significant predictors for GI median score (≤59) included total fruits from the HEI domain and two diets of Mexican and Chinese (Table 3, Table S3). Thus, both final models of GI presented very similar AICc; for precise fitness; compared to GI median score (≤59), GI (≤55) had slightly (0.03) lower and better AICc (33.63 vs. 33.66) but higher misclassification (0.23 versus 0.13), and lower AUC (0.84 versus 0.89, Figure S2). For predictive modeling of GL (≤71.8 median score), the unique significant predictor for the fittest model was carbohydrates ($p \leq 0.0001$; Table 4, Table S4, Figure S3). As carbohydrates was the common predictor for GI and GL, we further examined the factors from other domains that contributed to the source of carbohydrates (Table S5) and standardized carbohydrates per median number of meals across all diets (Table S6). To reach an acceptable GL < 20 per meal, we calculated and derived standardized carbohydrates (≤33.95 g) per median number of meals across all diets (3.59 meals needed daily). Significant predictors included three diets: canned food, Mexican, and smoothie diets (Table 5, $p \leq 0.01$). Additionally, total fruits and whole grains from the HEI domain and Mexican diets could be considered in looking for the sources of carbohydrates (Table 5, $p \leq 0.01$; Table S6, Figure S4). Model including two factors from the HEI domain (total fruits, whole grains) with Mexican diets presented better AUC:(0.88 versus 0.8125); however, slightly higher AICc (36.33 versus 35.09) and higher misclassification rate (0.3 versus 0.2). ## 4. Discussion We presented a novel study, using AI machine-learning-based analytics with added validation criteria for enhanced accuracy, to illustrate the predictors of healthy eating parameters measured by HEI, GI, and GL by including various modern diets. Consistent with a previous study [3], this study confirmed predictors of HEI 80 included whole fruits and whole grains [3], with empty calories as an additional predictor. Predictors of GI included total fruits, carbohydrates, and Mexican diets. The predictor of GL included carbohydrates. Predictors to reach an acceptable GL < 20 with carbohydrates per meal included three diet types (canned food, Mexican, and smoothies), considering total fruits and whole grains in the diets being the source of carbohydrates. To summarize, the common predictors included total fruits for HEI and GI; carbohydrates for GI and GL; and Mexican diets for GI and carbohydrates per meal to reach an acceptable GL < 20. For additional novel findings as strengths of this study, we demonstrated GI, GL, and related carbohydrates per day and per meal. To reach a good GL of <20 per meal, the daily number of meals ranged between 3.43 for ethnic diets and 12.04 for smoothie diets (median = 3.59). By accounting for the number of meals (median = 3.59) to reach a good GL of <20 per meal, standardized mean carbohydrates ranked between 33.5 g for convenient diets to 36.83 g for smoothies (median = 33.95), with a regression coefficient of 37.33 g (Figure 1). However, there are limitations to the accuracy of GI and GL due to factors such as food ripeness, processing, nutrient interactions, and cooking method [43]. Current nutritional assessment tools including diaries do not contain adjustments based on food ripeness, processing, nutrient interactions, and cooking method, except adjustment on fat content using the Food Frequency Questionnaire that we have integrated in this study [25]. Including modern diets as a factor (convenient and ethnic diets) in this study was an attempt to consider different cooking methods. These limitations may have implications for our findings. Further studies are needed to better understand the impact of these factors on the accuracy of GI, GL, and carbohydrates related factors. Based on the benefits of low GI diets, professional organizations, including ADA, medical, and health professionals, currently suggest limiting the amount of carbohydrates per meal to 45–60 g [19,20,21,22,23,24]. However, scientific data is lacking to solidify the amount of carbohydrates needed to reach for acceptable GL of <20 per meal. As a beginning effort, we calculated the amount of carbohydrates to reach an acceptable GL < 20 as 33.95 g per median number of meals (3.59 meals daily) across all diets. The source of carbohydrates in the HEI domain included total fruits, vegetables, whole grains, and empty calories in relation to GI, GL, and GL < 20. Adding smoothies into the diet can supplement essential nutrients; however, carbohydrates could be increased in the diet to increase GL. To reach an acceptable GL < 20 per meal, the number of meals per day would need to be increased. Other diets that presented a higher median number of meals (median average: 3.59 meals across all diets) included high-school (5.75), fast-food (4.48), Korean (4.30), Chinese (3.93), and liquids (3.71). Additionally, Mexican diets was the common predictor for GI and carbohydrates per meal to reach acceptable GL < 20. Specifically, the number of meals might need to be increased daily for diets that contain a higher amount of carbohydrates to reach an acceptable GL < 20 per meal. Professional organizations recommended counting carbohydrates (45–60 g per meal) to manage GL by monitoring carbohydrate sources [19,20,21]. To reach an acceptable GL < 20 per meal, we calculated the associated median amount of carbohydrates per meal as 33.95 g and a regression coefficient as 37.33 (Figure 1), which are lower than the 45 g recommended for people with diabetes [19,20,21,44,45]. Further studies are needed to account for the source and the amount of carbohydrates per meal to manage GL for disease prevention. 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--- title: TM6SF2-rs58542926 Genetic Variant Modifies the Protective Effect of a “Prudent” Dietary Pattern on Serum Triglyceride Levels authors: - Ioanna Panagiota Kalafati - Maria Dimitriou - Konstantinos Revenas - Alexander Kokkinos - Panos Deloukas - George V. Dedoussis journal: Nutrients year: 2023 pmcid: PMC10005630 doi: 10.3390/nu15051112 license: CC BY 4.0 --- # TM6SF2-rs58542926 Genetic Variant Modifies the Protective Effect of a “Prudent” Dietary Pattern on Serum Triglyceride Levels ## Abstract The epidemic prevalence of non-alcoholic fatty liver disease (NAFLD), despite extensive research in the field, underlines the importance of focusing on personalized therapeutic approaches. However, nutrigenetic effects on NAFLD are poorly investigated. To this end, we aimed to explore potential gene-dietary pattern interactions in a NAFLD case–control study. The disease was diagnosed with liver ultrasound and blood collection was performed after an overnight fast. Adherence to four a posteriori, data-driven, dietary patterns was used to investigate interactions with PNPLA3-rs738409, TM6SF2-rs58542926, MBOAT7-rs641738, and GCKR-rs738409 in disease and related traits. IBM SPSS Statistics/v21.0 and Plink/v1.07 were used for statistical analyses. The sample consisted of 351 Caucasian individuals. PNPLA3-rs738409 was positively associated with disease odds (OR = 1.575, $$p \leq 0.012$$) and GCKR-rs738409 with lnC-reactive protein (CRP) (beta = 0.098, $$p \leq 0.003$$) and Fatty Liver Index (FLI) levels (beta = 5.011, $$p \leq 0.007$$). The protective effect of a “Prudent” dietary pattern on serum triglyceride (TG) levels in this sample was significantly modified by TM6SF2-rs58542926 (pinteraction = 0.007). TM6SF2-rs58542926 carriers may not benefit from a diet rich in unsaturated fatty acids and carbohydrates in regard to TG levels, a commonly elevated feature in NAFLD patients. ## 1. Introduction The wide spectrum of non-alcoholic fatty liver disease (NAFLD) constitutes the most common cause of liver disease worldwide, estimated to currently affect $24\%$ of the global population [1]. Histologically, it ranges from simple steatosis to non-alcoholic steatohepatitis (NASH), and through to advanced fibrosis and cirrhosis [2]. The development of NAFLD is complex and involves the interplay of multiple genetic and environmental factors, including diet [3]. Its epidemic prevalence, coupled with the fact that no drugs have been licensed yet for its treatment, underline the importance of deepening our understanding on NAFLD development to such an extent that an early disease diagnosis, as well as the application of personalized and effective therapeutic approaches, become attainable. The role of genetics in the pathogenesis of NAFLD is strongly supported by twin and family studies, but also by the diverse prevalence of the disease among different ethnic backgrounds [4]. Its heritability is estimated at 22–$50\%$ [5]. To date, large genome-wide association studies (GWAS) have highlighted several single nucleotide polymorphisms (SNPs), which are involved in NAFLD onset and progression through metabolic pathways that mainly involve lipid and glucose metabolism, as well as inflammation [6,7,8,9]. One of the most robustly observed effects, both in lean and obese patients with NAFLD, is located in the PNPLA3 (patatin-like phospholipase domain-containing 3) gene, in which the rs738409 C > G variant is strongly associated with the disease. Other well-replicated NAFLD-associated genetic variants are TM6SF2 (transmembrane 6 superfamily member 2) rs58542926, GCKR (glucokinase regulator) rs780094, and MBOAT7 (membrane-bound O-acyltransferase domain-containing 7) rs641738. Diet is a major determinant of NAFLD onset, and nutritional epidemiologists support the study of dietary patterns instead of single nutrients or food groups in studies of chronic diseases [10]. Adherence to dietary patterns, such as Mediterranean diet (MD) and Western diet, has been strongly linked to hepatic steatosis and liver stiffness [11,12,13]. On the other hand, a posteriori assessment of dietary patterns of a population is often used to obtain a better understanding of their habitual dietary habits. Current data on dietary patterns of NAFLD patients are limited [14,15,16,17,18,19]; however, identified patterns are homogenous among different populations. There is growing evidence that gene-diet interactions play a role in the pathogenesis of chronic metabolic diseases, shedding light on the missing heritability mystery. Hitherto, only a few studies have explored these interactions in NAFLD [20,21,22,23,24,25,26,27]. Since the genetic background of NAFLD patients cannot be modified, it is important to identify favorable dietary factors that could minimize the genetic susceptibility of an individual. Providing patients with personalized dietary and lifestyle advice could reduce NAFLD incidence and improve its management [22]. To this end, the present work attempted to assess potential interactions between known genetic variants and a posteriori, data-driven, dietary patterns in a sample of Greek NAFLD patients and controls. ## 2.1. Study Population This is a secondary data analysis of a Greek case–control study for NAFLD [20]. Volunteers were consecutively enrolled during the period 2012–2015 from Laiko General Hospital of Athens. Individuals aged 18–65 years old with no self-declared concomitant liver injury at the time of recruitment were screened for NAFLD. Individuals with any congenital or acquired liver disease, chronic viral hepatitis, hepatotoxic drugs exposure, excessive alcohol consumption (more than 20 g of ethanol per day for women and more than 30 g for men), a life-threatening disease or psychiatric disorders impairing the patient’s ability to provide written informed consent, and pregnant or lactating women were excluded from enrolment. The final sample consisted of 351 non-related Caucasian individuals. All study subjects were informed about the aims of the study and signed a written consent. This study was approved by the Ethics Committee of Harokopio University of Athens ($\frac{38074}{13}$-07-2012), based on the Helsinki Declaration. ## 2.2. NAFLD Staging and Classification Participants underwent a liver ultrasound (U/S) in the Radiology Department of the hospital, and all U/S were performed by the same operator to reduce heterogeneity of the results. Staging of NAFLD was diagnosed based on Saadeh S. et al. [ 28] and were classified into two groups due to similar metabolic and clinical profiles: the control group included individuals with no and mild hepatic steatosis, and cases included moderate and severe hepatic steatosis. This classification allows the depicting of the major contributors to NAFLD development. ## 2.3. Demographic, Clinical, and Anthropometric Data All participants were interviewed regarding their demographics, family, and individual medical history. Blood collection was performed after a 12 h overnight fast, and blood tests included lipid and glycemic profile, liver enzymes, and uric acid. Low-density lipoprotein cholesterol (LDL-C) was calculated using the Friedewald equation, and the degree of insulin resistance was determined by the homeostatic model assessment (HOMA-IR) [29,30]. Two predictive indices were also calculated in our sample. ( i) NFS (NAFLD Fibrosis Score), which constitutes a scoring system validated to separate NAFLD patients with and without advanced fibrosis, and which is calculated based on the following formula: NFS = [−1.675 + 0.037 × age (years) + 0.094 × BMI (kg/m2) + 1.13 × IFG/diabetes (yes = 1, no = 0) + 0.99 × AST/ALT ratio − 0.013 × platelet (×109/L) − 0.66 × albumin (g/dL)] [31]. ( ii) FLI (Fatty Liver Index), a surrogate index to diagnose fatty liver, which is calculated based on the following formula: FLI = (e0.953×loge (triglycerides)+0.139×BMI+0.718×loge (gamma-GT)+0.053×waist circumference−15.745)/(1 + e0.953×loge (triglycerides)+0.139×BMI+0.718×loge (gamma-GT)+0.053×waist circumference−15.745) × 100 [32]. Participants underwent anthropometric measurements, and the mean value of two repeated measurements of anthropometric characteristics was reported. Body composition was assessed with an electronic scale (TANITA Segmental Body Composition Analyzer BC-418). Smoking habits were collected and assessed using the following formula: number of pack-years = (number of cigarettes smoked per day/20) × number of years smoked. Physical activity was assessed with the validated short self-reported questionnaire Athens Physical Activity Questionnaire (APAQ) [33]. ## 2.4. Dietary Assessment and Dietary Patterns Extraction A 172-food item, semi-quantitative Food Frequency Questionnaire (FFQ) was used to assess the dietary habits of the sample [17]. Factor analysis [Principal components (PCA)] was applied to extract the main dietary patterns of this sample. Four dietary patterns were derived as follows: 1. A “Fast-food-type” pattern, which included fast food, sweetened soft drinks, fried potatoes, and savory and puff pastry snacks. 2. A “Prudent” pattern, which consisted of oil-based cooked vegetables, legumes, potatoes, fruits, vegetables, and fatty fish. 3. A “High-protein” pattern, which included red meat, poultry, and eggs. 4. An “Unsaturated FA” pattern, which included nuts, chocolate, and other foods rich in unsaturated fatty acids. These four factors explained $46\%$ of the sample variability, with a KMO = 0.660 and Bartlett’s test of sphericity <0.001. Adherence of each individual to each of the dietary patterns was classified into four quartiles, where quartile 4 represents the highest adherence to the dietary pattern. Detailed methodology of dietary assessment and dietary patterns generation has been mentioned elsewhere [17]. Misreporting of dietary information was estimated using Goldberg’s method, updated by Black et al. [ 34,35]. ## 2.5. DNA Extraction and Genotyping Buffy coat samples were used to extract DNA, and DNA samples were genotyped using a genome-wide SNP assay (Infinium CoreExome-24 BeadChip, Illumina, San Diego, CA, USA). Genetic information for four SNPs were extracted from the initial database: PNPLA3-rs738409 (C > G), TM6SF2-rs58542926 (G > A), MBOAT7-rs641738 (G > A) and GCKR-rs780094 (G > A). All SNPs satisfied the Hardy–Weinberg equilibrium (HWE), had a minor allele frequency > $5\%$, and were successfully genotyped in our sample (SNP call rate ≥ $98\%$). Nine individuals were removed from genetic analyses due to low genotyping rate (sample call rate ≥ $95\%$). Genetic variants’ information and genotype distribution in NAFLD/control groups and lean/non-lean NAFLD groups are presented in the Supplementary File (Tables S1–S3). ## 2.6. Statistical Analysis Normality of variables was tested using Kolmogorov–Smirnov test. Mean ± standard deviation (SD) values were used to describe normally distributed quantitative variables, while non-parametric variables were described as median [interquartile range (IQR)]. Relative frequencies (%) were used to describe qualitative variables. To compare differences between the groups, Independent Samples t-test (parametric continuous variables) and Mann–Whitney test (non-parametric continuous variables) were performed. Chi-square test was applied to assess dependency of categorical variables. Binary multiple logistic regressions were performed to test the hypothesis of association between various risk factors with the presence of NAFLD. Linear regression models were applied in order to identify associations between risk factors and NAFLD-associated biochemical parameters and indices. Non-parametric continuous variables were log-transformed. Gene–diet interactions were investigated, assuming an additive model for PNPLA3, GCKR, MBOAT7, and a dominant model for TM6SF2. All tests were two-sided and the cut-off level of significance was defined at 0.05. Level of significance for genetic associations was defined as $a = 0.0125$ after Bonferroni correction. Statistical analyses were performed using IBM SPSS Statistics v21.0 and Plink v1.07. ## 3. Results Demographic, anthropometric, and clinical characteristics of the study population are displayed in Table 1. NAFLD patients ($38\%$ of the sample) had a higher mean age, a lower PAL, and reported less years of education and a higher rate of smoking compared to controls ($p \leq 0.05$). However, no significant differences were found regarding gender distribution in the sample. As expected, all anthropometric measurements and biochemical parameters were higher in cases compared to controls ($p \leq 0.01$). Misreporting of dietary intake was not different between the groups (data not shown). The association of each of the four SNPs with disease odds and disease-related traits was firstly investigated and is presented in Table 2. After adjusting for age, gender, and BMI, PNPLA3-rs738409/G allele was significantly associated with $57.5\%$ higher NAFLD odds compared to C allele ($$p \leq 0.012$$). GCKR-rs780094/A was positively associated with lnCRP levels after adjusting for age, gender, BMI, and NAFLD diagnosis ($B = 0.098$, $$p \leq 0.003$$), as well as with higher FLI values after adjusting for age, gender, and NAFLD diagnosis ($B = 5.011$, $$p \leq 0.007$$). The aforementioned associations were not changed after further adjustment for adherence to each dietary pattern (data not shown). No other association reached the corrected p-value threshold. Gene-dietary patterns interaction analyses in our sample indicated four associations with a p-value ≤ 0.0125 (Table 3). TM6SF2-rs58542926 was found to interact with adherence to the “Prudent” dietary pattern. Carriers of the A allele presented with 20.170 mg/dL elevated triglyceride (TG) levels compared to non-carriers as adherence to this dietary pattern increased, after adjusting for age, gender, energy intake, NAFLD diagnosis, adherence to “Prudent” dietary pattern, TM6SF2-rs58542926, and antilipidemic drug therapy ($$p \leq 0.007$$) (Figure 1). In the same context, A carriers had higher FLI levels than non-carriers as adherence increased, after adjusting for age, gender, energy intake, NAFLD diagnosis, adherence to “Prudent” dietary pattern, and TM6SF2-rs58542926 ($B = 9.351$, $$p \leq 0.009$$). On the other hand, carriers of the MBOAT7-rs641738/A allele benefited from a greater adherence to the “Prudent” dietary pattern regarding TG levels (B = −10.06, $$p \leq 0.003$$). All interaction results remained significant after further adjustment for pack-years, years of education, and PAL. ## 4. Discussion Using data from a Greek NAFLD case–control study, the effect of a posteriori dietary patterns on genetic susceptibility to NAFLD and selected NAFLD-related anthropometric and clinical traits was assessed. Results for the sole effect of adherence to four dietary patterns derived with PCA on disease and related traits have been reported in detail elsewhere [17]. In summary, adherence to the “Fast-food-type” pattern was independently associated with higher NAFLD odds, CRP, and uric acid levels (p ≤ 0.05), whereas a greater adherence to the “Unsaturated fatty acids” pattern was protective towards NAFLD, insulin, and HOMA-IR levels. The “Fast-food-type” pattern was further associated with higher CRP and uric acid levels and the “Unsaturated fatty acids” pattern with reduced levels of insulin and HOMA-IR (p ≤ 0.05). The “Prudent” dietary pattern was associated with decreased TG and uric acid levels ($$p \leq 0.037$$ and $$p \leq 0.035$$, respectively). As expected, NAFLD patients were older, less educated, less active, and presented with worse anthropometric and biochemical background than controls. NFS and FLI scores were higher in the patients group; however, mean NFS values are <−1.455, which indicates that this is a group of NAFLD patients without fibrosis, on average. Herein, the well-known positive association of rs738409-G with NAFLD odds was replicated. This genetic variant is known to be robustly associated with hepatic fat accumulation and elevated liver enzymes [36]. However, no significant associations were observed as regards liver enzymes’ levels, possibly due to the fact that this sample most probably consists of simple fatty liver patients rather than patients with advanced NASH and fibrosis (as assessed through NFS results and mean liver enzymes which are within normal range). Carrying the GCKR-rs780094/A allele was associated with higher lnCRP and FLI levels. The effect of the glucose-lowering allele A on CRP levels has been previously reported in various populations [37,38,39,40]. CRP is the prototype acute-phase reactant and a hallmark of low-grade systemic inflammation, thus it has been assumed that chronic low-grade systemic inflammation mediates the effect of the GCKR variant on complex metabolic diseases, including NAFLD. No association results reached the Bonferroni-corrected p-value threshold for TM6SF2 and MBOAT7 variants. The lack of evidence of association of TM6SF2 could be explained by the low effect allele frequency (EAF) of the SNP in our sample (EAF = $5.6\%$), highlighting the need for a larger sample size. A dominant genetic model was assumed for this SNP. Moreover, the fact that this is mainly a non-fibrotic NAFLD group of patients could explain the lack of association of r641738; the latter has been mainly linked to advanced disease stages through altering the remodeling of phosphatidylinositol [41,42] In this present study, two significant rs58542926-“Prudent” dietary pattern interactions were reported. Carriers of the A allele who adhered better to the pattern had higher TG values than non-carriers, independent of age, gender, energy intake, NAFLD diagnosis, adherence to “Prudent” dietary pattern, TM6SF2-rs58542926 and antilipidemic drug therapy. In line with this, carrying at least one copy of the A allele significantly interacted with adherence to the pattern to affect FLI levels, meaning that these individuals had greater odds of having NAFLD. However, since in this study no significant effects of rs58542926 or the dietary pattern on FLI levels were found, this interaction could potentially be mediated by the aforementioned interaction, since TG levels constitute a component of the FLI. Carriers of rs58542926, a missense polymorphism, have been found to experience higher levels of intrahepatic fat content and, thus, higher odds of developing NAFLD. The altered protein is believed to modify the excretion of TG and lipid droplets content from the liver, implying a lowering effect of the risk allele of rs58542926 on serum TG levels [43,44,45]. Hypertriglyceridemia constitutes a common feature and a major risk factor for NAFLD onset and progression [46]. In this study no significant effect of the polymorphism on TG levels was found. Nevertheless, given the fact that the association of the dietary pattern with TG levels and the interaction association had opposite directions, this is a valuable result that should be further investigated. Moreover, as Wang et al. suggested, this gene–environment interaction may be a “pure interaction”, meaning that the effect of one exposure is present only in the presence of the other [47]. Notably, previously published results from the same sample had indicated a significant interaction between the TM6SF2 variant and fish/fatty fish intake on TG levels [20]. Fish, and especially fatty fish, constitute a rich in polyunsaturated fatty acids (PUFA) food group [20]. At the same time, TM6SF2 protein is believed to constitute a novel key regulator of postprandial lipemia [45,48,49]. Malfunction of this protein may result in impaired lipids integration into TG, leading to increased lipid accumulation and decreased lipid export, which as a result leads to lower serum TGs levels. Indeed, in their study Musso et al. indicated that TM6SF2-A carriers experienced lower postprandial TG levels, along with lower non-esterified fatty acids and oxLDL responses and higher HDL-C levels, after an oral fat tolerance test [50]. However, in their RCT, Scorletti et al. reported no significant influence of the TM6SF2 genotype on the effect of DHA + EPA supplementation of NAFLD patients [51]. Taken together, it was hypothesized that in response to high PUFA intake, carriers of the TM6SF2 variant experience increased intrahepatic accumulation of TGs and thus hepatic steatosis. In line with the above, we could assume a similar effect of rs58542926 on individuals with greater adherence to the “Prudent” dietary pattern. The latter constitutes (a) a combination of two rich sources of fatty acids, namely the olive oil (included in oil-based cooked vegetables) and the fatty fish, along with (b) carbohydrate- and fructose-rich food groups, namely fruits, potatoes, legumes, and vegetables. High sugars and especially fructose intake have been associated with greater odds for NAFLD [52]. There is also evidence that TM6SF2 may be implicated in glucose metabolism. Lei et al. reported that TM6SF2 protein up-regulation is mediated by carbohydrate-responsive element-binding protein (ChREBP) [53], which in turn is stimulated by fructose intake. Moreover, it was recently shown that rs58542926 is associated with glucose intolerance both in humans and mice [54]. It could thus be hypothesized that G-allele carriers are susceptible to a diet combining high unsaturated fat and high carbohydrate foods as regards TG levels. However, the current scientific literature lacks data regarding interactions of TM6SF2 with distinct dietary patterns or food groups. More research is required to validate the nutritional regulation of TM6SF2, and large-scale RCTs as well are needed in order to clarify the potential interaction of PUFAs/total fat/carbohydrate intake and TM6SF2 polymorphism in NAFLD and its related traits. Unlike TM6SF2, carriers of the MBOAT7-rs641738/A allele benefited from a greater adherence to the “Prudent” dietary pattern regarding TG levels ($$p \leq 0.003$$). Due to the lack of direct association of the genetic variant with TG levels, along with the same direction of the interaction effect, it could be assumed that this interaction reflects the effect of the dietary pattern on serum TG levels. This study has some limitations that should be considered. The size of our sample is modestly small, which limits the probability of detecting genetic and nutrigenetic associations. Moreover, retrospective case–control studies, especially those conducted in a clinical setting, are potentially prone to analysis and sampling bias. Herein, NAFLD diagnosis was based on liver U/S, a method greatly dependent on the operator with 60–$94\%$ sensitivity and 84–$95\%$ specificity for detecting fatty liver. Last but not least, despite all actions taken to eliminate it, self-report of dietary intake is susceptible to recall bias. ## 5. Conclusions Despite its limitations, this is a novel study investigating for the first time the interplay between four genetic variants and four dietary patterns in NAFLD. We showed that rs58542926 in the TM6SF2 locus is a potential modifier of the protective effect of a “Prudent” dietary pattern on serum TG levels. The area of gene–diet interactions constitutes a large and diverse puzzle, and although it is not easy to solve, its gradual deciphering may take NAFLD management to the next level. 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--- title: 'Low Dietary Variety Is Associated with Incident Frailty in Older Adults during the Coronavirus Disease 2019 Pandemic: A Prospective Cohort Study in Japan' authors: - Miyuki Yokoro - Naoto Otaki - Megumu Yano - Tomomi Imamura - Norikazu Tanino - Keisuke Fukuo journal: Nutrients year: 2023 pmcid: PMC10005648 doi: 10.3390/nu15051145 license: CC BY 4.0 --- # Low Dietary Variety Is Associated with Incident Frailty in Older Adults during the Coronavirus Disease 2019 Pandemic: A Prospective Cohort Study in Japan ## Abstract Background: Stagnation of social activity due to the COVID-19 pandemic probably reduces motivation to maintain a healthy diet. It is important to report on the dietary changes observed in older adults during a period of restriction on outings and to clarify the relationship between dietary variety and frailty. This one-year follow-up study examined the association between frailty and dietary variety during the COVID-19 pandemic. Methods: Baseline and follow-up surveys were conducted in August 2020 and August 2021, respectively. The follow-up survey was distributed by mail to 1635 community-dwelling older adults aged ≥65 years. Of the 1235 respondents, 1008 respondents who were non-frail at baseline are included in this study. Dietary variety was examined using a dietary variety score developed for older adults. Frailty was assessed using a five-item frailty screening tool. The outcome was frailty incidence. Results: In our sample, 108 subjects developed frailty. A linear regression analysis revealed a significant association between dietary variety score and frailty score (β, −0.032; $95\%$ CI, −0.064 to −0.001; $$p \leq 0.046$$). This association was also significant in Model 1, adjusted for sex and age, (β, −0.051; $95\%$ CI, −0.083 to −0.019; $$p \leq 0.002$$) and in a multivariate analysis that added adjustments for living alone, smoking, alcohol use, BMI, and existing conditions to Model 1 (β, −0.045; $95\%$ CI, −0.078 to −0.012; $$p \leq 0.015$$). Conclusions: A low dietary variety score was associated with an increased frailty score during the COVID-19 pandemic. The restricted daily routine caused by the COVID-19 pandemic will probably continue to have a long-term effect in terms of reduced dietary variety. Thus, vulnerable populations, such as older adults, might require dietary support. ## 1. Introduction Since first detected in 2019, coronavirus disease (COVID-19) has rapidly impacted the global population, with over 700 million people becoming infected and suffering from severe acute respiratory syndrome [1]. Japan declared a state of emergency in April 2020 when no treatment or vaccine for COVID-19 was available to prevent the spread of infection. During this state of emergency, people were requested to work from home and use online services. Measures were also taken to reduce the opening hours of grocery stores and other businesses [2]. Japan launched its vaccination campaign and began vaccinating against COVID-19 on 17 February 2021. At present, approximately $80\%$ of the population aged ≥12 years have received the required number of vaccinations; the vaccination rate is reported to be over $90\%$ among older adults aged ≥65 years [3,4]. As the campaign proceeded and vaccination rates increased, social restrictions gradually eased. For example, recreation facilities that attract large numbers of people reopened with shorter hours of operation and more restrictions for admission [5]. Older adults and those with a history of diseases are at high risk of COVID-19; therefore, few community activities intended for older adults exist because sufficient space cannot be provided [6,7,8,9,10]. Increased life expectancy is accelerating the aging population proportion worldwide. People aged ≥65 years constituted $9.3\%$ of the world’s population in 2020, and this proportion is predicted to be $17.8\%$ by 2060 [11]. Frailty is significantly common among the elderly, and is characterized by pronounced fragility due to declining physical function. Adverse outcomes such as death, falls, institutionalization, and disability are associated with frailty [12,13,14,15]. An estimated $12\%$ of people aged ≥50 years live with frailty worldwide, and an estimated $8\%$ of people aged ≥65 years live with frailty in Japan [16,17]. Additionally, frailty is associated with an increased risk of serious COVID-19 [18,19,20,21]. For these reasons, identifying factors that prevent frailty is of considerable interest to many countries with aging societies. Many published studies describe changes in dietary behavior caused by the COVID-19 pandemic, and there are concerns that prolonged deterioration of dietary behavior due to the COVID-19 pandemic reduces disability-adjusted life years [22,23,24,25,26,27,28]. However, we are not aware of any studies that have examined long-term effects of COVID-19 on dietary behavior. Maintaining a healthy diet prevents frailty. Healthy dietary patterns such as the Mediterranean diet and the Dietary Approaches to Stop Hypertension diet, promote the consumption of a variety of foods that are beneficial to overall health, including the prevention of frailty [29,30]. Dietary variety, which is an important element of a healthy diet, refers to the intake of various food groups during a specific period, not to the amount of food consumed [31]. Healthy dietary behavior improves nutrient adequacy [32]. A higher dietary variety score is associated with faster walking speed and the prevention of a decline in grip strength [33,34,35]. Thus, it is important to document dietary changes observed in older adults whose mobility is restricted, and to clarify the relationship between a diverse diet and frailty. This study was a one-year follow-up survey in community-dwelling older adults that examined the association between frailty and dietary variety during the COVID-19 pandemic. ## 2.1. Study Subjects and Study Period This prospective cohort study was conducted in Japan in August 2020. We randomly selected 4996 community-dwelling adults from the elderly population aged ≥65 years as prospective study subjects using addresses recorded in the Health and Welfare Department office. Individuals who were hospitalized or who resided in a nursing home were excluded. The baseline survey forms were distributed by mail in August 2020, at which time subjects were also asked to cooperate in a follow-up survey. The follow-up survey forms were mailed in August 2021, and 1635 subjects responded. Figure 1 shows the flow chart of this study. ## 2.2. Ethical Approval Study details were explained in writing to the subjects, and the return of a completed survey form was considered as informed consent for participation. The Ethics Committee of Mukogawa Women’s University approved this study (Approval Number: 20-53). ## 2.3. Survey Content The survey included demographic questions such as sex, height, weight, age, smoking habits (smoking or non-smoking), drinking habits (drinking alcohol once per week or more or not drinking), and living arrangements (living alone or living with others). Body mass index (BMI) was calculated from the subjects’ self-reported weights and heights. Chronic conditions such as hypertension, diabetes, hyperlipidemia, stroke, and cardiac disease were also self-reported. Social activity was determined by the frequency of interactions with family and friends, as well as the frequency of participation in community activities. The negative impact of COVID-19 on social interactions was assessed by a modified version of the following question from the SF-36: “During the past four weeks, to what extent has your physical health or emotional problems interfered with your normal social activities with family, friends, neighbors, or groups?” [ 36] Potential responses to this question were “has not hindered at all”, “has hindered very little”, “has hindered somewhat”, “has hindered quite a bit”, “Extremely”, “could not do social activities”, and “No participation in social activities or No separated family, relatives or friends”. ## 2.4. Dietary Variety Score Subjects completed a food-group-based dietary questionnaire to determine their dietary variety score [37]. This score was calculated by quantifying how frequently an individual consumed foods from across 10 categories: meats, fish and shellfish, eggs and egg products, soybeans and soybean products, milk and milk products, seaweeds, vegetables, fruits, potatoes, and oils. The total score (food) ranged from 0 to 10 points, with the intake of each food group assigned 1 point for a response of “eat almost every day” and 0 for “eat once every two days/eat once or twice a week/eat hardly ever.” A more varied diet can reduce the risk of high-level functional decline, and can also help maintain physical performance as measured by grip strength and usual gait speed [34,37]. To best represent a long-term diet during a 1-year follow-up period and to account for changes in food consumption, we determined the cumulative mean dietary variety score from two food-group-based dietary questionnaires conducted at baseline and at the 1-year follow-up [38]. A total of 36 Scores of ≤3 points, ≥3 and <6 points, and ≥6 points indicated low, mid, and high dietary variety, respectively. ## 2.5. Frailty Score The frailty score was calculated from a “yes” or “no” response to the following five questions: “Have you lost 2 kg or more in the past 6 months?”, “ Do you think you walk slower than before?”, “ Do you go for a walk for your health at least once a week?”, “ Can you recall what happened 5 min ago?” and “In the past 2 weeks, have you felt tired without reason?”. The three questions, “Have you lost 2 kg or more in the past 6 months?”, “ Do you think you walk slower than before?” and “In the past 2 weeks, have you felt tired without reason?” were assigned a score of 1 point for “yes” and 0 points for “no.” The questions, “Do you go for a walk for your health at least once a week?” and “Can you recall what happened 5 min ago?” were scored as 1 point for “no” and 0 points for “yes.” The frailty score was the total score for all five questions, and could range from 0 to 5 points [37]. Scores of ≤2 and ≥3 points indicated non-frail and frail status, respectively. Based on the frailty score, frail older adults had significant risk of care insurance use after two years. The self-report questionnaire for frailty has predictive validity for disability in older Japanese adults [39]. ## 2.6. Statistical Analysis Statistical analyses were performed using IBM SPSS 25.0. Categorical data are displayed as number of respondents and percentages, while continuous variables are displayed as mean and standard deviation. To compare subjects exhibiting frailty with those exhibiting non-frailty, the Mann–Whitney U and chi-squared tests assessed the quantitative and categorical variables. Logistic regression analysis assessed associations between dietary variety score and frailty; Model A was adjusted for sex and age, while Model B included adjustments for BMI, alcohol use, smoking, living alone, self-reported hypertension, diabetes, hyperlipidemia, stroke, and cardiac disease. Additionally, linear regression was used to examine associations between the dietary variety and frailty scores; Model 1 was adjusted for sex and age, while Model 2 included adjustments for BMI, alcohol use, smoking, living alone, self-reported hypertension, diabetes, hyperlipidemia, stroke, and cardiac disease. Statistical significance was defined as a two-tailed p value of <0.05. ## 3. Results Of the 2764 original subjects, 1235 responded to the one-year follow-up survey; 170 subjects who were frail at baseline were excluded from the analysis. Finally, 1008 subjects were eligible after exclusion of those with missing data related to sex ($$n = 2$$), age ($$n = 7$$), living alone ($$n = 2$$), alcohol use ($$n = 9$$), smoking ($$n = 5$$), and frailty score ($$n = 32$$). A flow chart describing the selection process of this study sample is shown in Figure 1. Of the 1008 subjects, $11.2\%$ (113 subjects) were determined to be frail after one year. The incidence was 112.1 cases per 1000 person-years. Table 1 shows the potential confounders according to baseline characteristics such as age, sex, body mass index, alcohol intake, smoking status, history of disorders [40,41]. and compares the basic characteristics of subjects with frailty and non-frailty. The mean of the two dietary variety scores was significantly lower in subjects with frailty than in subjects with non-frailty. No significant difference was found between the proportion of male subjects with frailty and those with non-frailty. The mean age of subjects with frailty (75.7 years) was significantly higher than that those with non-frailty (73.8 years) ($$p \leq 0.002$$). DVS was significantly higher ($$p \leq 0.048$$) in subjects with non-frailty than in subjects with frailty. The change in consumption of various food groups over time is listed in Table 2. The frequency of milk and dairy product intake in subjects with non-frailty showed a decreasing trend ($$p \leq 0.076$$), as did the intake of seaweed ($$p \leq 0.054$$). The frequency of intake of meats decreased significantly in subjects with frailty ($$p \leq 0.031$$), and the frequency of intake of soybeans and soybean products also showed a decreasing trend ($$p \leq 0.054$$). The frequency of intake of eggs showed an increasing trend ($$p \leq 0.078$$). A significant decrease was observed in the frequency of intake of milk and dairy products ($$p \leq 0.032$$) in all subjects. Linear regression analysis revealed a significant association between dietary variety score and frailty score (β, −0.032; $95\%$ CI, −0.064 to −0.001; $$p \leq 0.046$$; Table 3). This association was also significant in Model 1 adjusted for sex and age (β, −0.051; $95\%$ CI, −0.083 to −0.019; $$p \leq 0.002$$) and in a multivariate analysis that added adjustments for living alone, smoking, alcohol use, BMI, and existing conditions to Model 1 (β, −0.045; $95\%$ CI, −0.078 to −0.012; $$p \leq 0.015$$). In a sensitivity analysis excluding subjects with low dietary variety scores less than 1 ($$n = 21$$), the association between frailty score and dietary variety score remained significant in the multivariate models (β,−0.047; $95\%$ CI, −0.081 to −0.013; $$p \leq 0.008$$) Table 4 shows the subjects characteristics based on food variety score. The variety score level were significantly associated with gender, BMI, alcohol intake, smoking status and higher prevalence of hypertension. Table 5 shows the association between dietary variety score and incident frailty among subjects with non-frailty at baseline. Compared to that among subjects with a high dietary variety score, the odds ratio (OR) for the onset of frailty among subjects with a low dietary variety score was 1.648 ($95\%$ confidence interval [CI], 0.941–2.887; $$p \leq 0.081$$). This association was significant in Model A, which adjusted for sex and age, (OR, 1.911; $95\%$ CI, 1.066–3.426; $$p \leq 0.030$$) and in a multivariate analysis that added adjustments for living alone, smoking, alcohol use, BMI, and existing conditions to Model A (OR, 1.877; $95\%$ CI, 1.034–3.409; $$p \leq 0.039$$). The effect of the COVID-19 pandemic on social activity during the surveyed period is noted in Table 6. The COVID-19 pandemic hindered participation in social activities and meeting with family and friends among at least half the community-dwelling older adults. No significant difference was seen in the frequency of interaction with family and friends during the pandemic. However, several subjects had less frequent interaction and contact with their friends and family. ## 4. Discussion This study investigated the change in dietary variety and frailty score among community-dwelling older adults over a one-year period during the COVID-19 pandemic. The dietary variety score during the surveyed period was significantly lower in subjects with frailty than in subjects with non-frailty. This study revealed the change in dietary variety during the survey period. Furthermore, a lower dietary variety score during the one-year period was positively associated with frailty score. This study did not include an assessment of DVS scores before the COVID-19 pandemic. Therefore, it is unclear whether the pandemic worsened DVS scores. However, at a minimum, the study shows that low DVS scores over one year of the COVID-19 pandemic are associated with an increased risk of frailty. The incidence rate of frailty in Japan is reported to be $8.7\%$ [17]. A meta-analysis reported a $13.6\%$ incidence rate of frailty, or 43.4 cases/1000 person-years, among older adults with non-frailty during a median 3-year follow-up period [40]. In this study, the incidence rate of new frailty cases during the one-year follow-up period was $11.2\%$ or 112.1 cases/1000 person-years, which is higher than the incidence rates reported in previous studies [17,40]. In this study, frailty was assessed in a self-administered format. Furthermore, the COVID-19 pandemic may cause subjects to be overly negative when evaluating their health. These factors may have caused the higher incidence rate of frailty in this study compared to those in previous reports. Several reports have shown changes in dietary behavior during the COVID-19 pandemic; however, almost all have examined the beginning of the pandemic [22,23,24,25,26,27]. Some of these reports have noted that a deterioration in diet due to the pandemic is negatively associated with frailty, functional limitations, and undernutrition [42,43,44,45,46]. In the early part of the pandemic, the restricted access to food caused by measures that reduced the opening hours of groceries and other businesses may have reduced the quality of people’s meals [43,44]. This decline in the quality of food caused by restricted access to food is probably a short-term effect. Restricted daily activities have continued for approximately 18 months due to the pandemic. This study did not assess dietary variety before the pandemic; thus, it cannot identify the changes in dietary variety at the beginning of the pandemic. However, the restricted daily routine caused by the COVID-19 pandemic will probably have a long-term effect in terms of reduced dietary variety. This study revealed that social interaction between community-dwelling older adults and others is greatly limited by the COVID-19 pandemic. Due to the pandemic, restrictions on movement were in place in Japan for almost the entire period from February 2021 to August 2022. The campaign for vaccination is also significantly active in Japan, where the proportion of older adults aged 65 years or older with two or more vaccinations is over $90\%$ [3,4]. Nonetheless, organizing community activities for older adults is difficult. This is because sufficient indoor space cannot be provided, and older adults and those with a history of diseases are at high risk of severe symptoms and death [7,8,9,10]. Social activity is a key factor in maintaining not a healthy diet but healthy life-style [47,48,49,50,51,52,53]. Stagnation of social activity due to the pandemic probably reduces motivation to maintain a healthy diet. In a study by Conklin et al., a lower level of contact with friends was associated with the reduced consumption of a wide variety of fruits and vegetables [53] The stagnation of social activities among older adults due to the pandemic will probably make it difficult for older adults to maintain a healthy diet. This might be one of the factors that explain the acceleration of frailty due to the COVID-19 pandemic. This large-scale follow-up survey conducted during the pandemic has some limitations. First, the follow-up survey was completed by a low percentage of subjects. This might have led to a nonresponse bias. The generalizability of findings may be limited. Second, no weighting methods were used in the dietary surveys. A dietary variety score does not evaluate the intake of specific nutrients, and an accurate evaluation of the association between frailty and diet requires an evaluation of the intake of specific nutrients, such as protein. Third, social desirability bias might have been present in responses. The pandemic may cause people to be overly negative when evaluating their own health and dietary situation. This study also evaluated frailty and dietary variety based on self-reporting by subjects. This suggests that associations may be overestimated in this study. Fourth, the surveys were conducted one-year apart, which is a short period of observation. Both surveys in this study fell in the middle of the COVID-19 pandemic. Although the number of new daily COVID-19 infections in Japan fell below 100 in October 2021, the pandemic subsequently spread again and reached over 100,000 daily infections for the first time, in February 2022. The COVID-19 pandemic is expected to persist long-term; thus, further follow-up surveys will be required. Finally, the food variety was not assessed before the pandemic; hence, dietary variety scores cannot be compared before and after the onset of the pandemic. Nevertheless, this study revealed the change in dietary variety during the surveyed one-year period. ## 5. Conclusions In conclusion, this study involved a one-year follow-up survey of older adults during the COVID-19 pandemic and examined the association between dietary variety and frailty. 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--- title: The Metabolites and Mechanism Analysis of Genistin against Hyperlipidemia via the UHPLC-Q-Exactive Orbitrap Mass Spectrometer and Metabolomics authors: - Zhe Li - Weichao Dong - Yanan Li - Xin Liu - Hong Wang - Long Dai - Jiayu Zhang - Shaoping Wang journal: Molecules year: 2023 pmcid: PMC10005657 doi: 10.3390/molecules28052242 license: CC BY 4.0 --- # The Metabolites and Mechanism Analysis of Genistin against Hyperlipidemia via the UHPLC-Q-Exactive Orbitrap Mass Spectrometer and Metabolomics ## Abstract Genistin, an isoflavone, has been reported to have multiple activities. However, its improvement of hyperlipidemia is still unclear, and the same is true with regard to its mechanism. In this study, a high-fat diet (HFD) was used to induce a hyperlipidemic rat model. The metabolites of genistin in normal and hyperlipidemic rats were first identified to cause metabolic differences with Ultra-High-Performance Liquid Chromatography Quadrupole Exactive Orbitrap Mass Spectrometry (UHPLC-Q-Exactive Orbitrap MS). The relevant factors were determined via ELISA, and the pathological changes of liver tissue were examined via H&E staining and Oil red O staining, which evaluated the functions of genistin. The related mechanism was elucidated through metabolomics and Spearman correlation analysis. The results showed that 13 metabolites of genistin were identified in plasma from normal and hyperlipidemic rats. Of those metabolites, seven were found in normal rat, and three existed in two models, with those metabolites being involved in the reactions of decarbonylation, arabinosylation, hydroxylation, and methylation. Three metabolites, including the product of dehydroxymethylation, decarbonylation, and carbonyl hydrogenation, were identified in hyperlipidemic rats for the first time. Accordingly, the pharmacodynamic results first revealed that genistin could significantly reduce the level of lipid factors ($p \leq 0.05$), inhibited lipid accumulation in the liver, and reversed the liver function abnormalities caused by lipid peroxidation. For metabolomics results, HFD could significantly alter the levels of 15 endogenous metabolites, and genistin could reverse them. Creatine might be a beneficial biomarker for the activity of genistin against hyperlipidemia, as revealed via multivariate correlation analysis. These results, which have not been reported in the previous literature, may provide the foundation for genistin as a new lipid-lowering agent. ## 1. Introduction Genistin (4′,5,7-Trihydroxyiso-flavone 7-glucoside) is one of the isoflavones found in soybeans, membranous milkvetch root, and botanical herbs from East Asia, Southeast Asia, and some Pacific islands [1]. In recent years, genistin has been repeatedly reported to have anti-oxidant, anti-inflammatory, anti-bacterial, and anti-viral activities, as well as inhibiting blood lipids [2,3,4]. Studies have shown that flavonoids also have the function of reducing blood glucose, regulating glucose metabolism, blood lipids, liver enzyme activity, and blood lipids, but their function in reducing blood lipids does not seem to be so significant [5]. Glycosides are difficult to absorb into the blood circulation due to their complicated structures [6]. Some studies have shown that the concentration of the aglycone (< 0.4μm) in plasma is lower than the IC50 values (10–50 μM) reported for its anti-cancer effect in vitro, even after ingestion of large amounts of genistin-containing soy products (16 mg/kg) [7]. Thus, the metabolites of genistin in vivo should be associated with its activities. However, genistin metabolites in blood are rarely found and identified using unscientific instruments with reasonable analysis strategies. Recently, Ultra-High-Performance Liquid Chromatography Quadrupole Exactive Orbitrap Mass Spectrometry (UHPLC-Q-Exactive Orbitrap MS) has been used in the discovery of trace substances in vivo based on its high resolution and high throughput [8]. In addition, a scientific analysis strategy is also one of the critical factors restricting the identification of metabolites. Among them, high-resolution extracted ion chromatography (HREIC), multiple mass defect filtering (MMDF), neutral loss filtering (NLF), and diagnostic product ions (DPIs) can be applied to the detection and identification of metabolites [9,10,11]. Hyperlipidemia, as a common metabolic disease, is accompanied by an increase in lipids in vivo, including total triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and total cholesterol (TC) levels and an decrease in high-density lipoprotein cholesterol (HDL-C) [12,13]. Excessive lipids are easily accumulated in the liver and lead to lipid peroxidation with the participation of reactive oxygen species (ROS). This will lead to a decrease in superoxide dismutase (SOD) with an increase in malondialdehyde (MDA) [14]. Lipid peroxidation is associated with liver damage via elevated alanine transaminase (ALT) [15]. Isoflavones seem to have better effects in treating hyperlipidemia. Puerarin, sophoricoside, and others have been reported to reduce blood lipids by regulating lipid metabolism and lipid homeostasis [16,17]. Genistin may also have a therapeutic effect on hyperlipidemia. However, the application of genistin in the treatment of hyperlipidemia has been rarely reported, and the same is true for its relevant mechanism. Metabolomics based on UHPLC-Q-Exactive Orbitrap MS has become a useful method to reveal the mechanism of natural products or agents. Meanwhile, the metabolic differences of natural products or agents in normal and pathological conditions are related to their activities. For genistin, the beneficial function and mechanism for treating hyperlipidemia still remains mysterious, and metabolic behaviors in normal and hyperlipidemia states are fuzzy. Hence, in this study, a high-fat diet (HFD) was used to induce a hyperlipidemic rat model. The metabolic pathways of genistin in normal and hyperlipidemic rats were deeply explored via UHPLC-Q-Exactive Orbitrap MS. The effect of genistin against hyperlipidemia was determined; its mechanism was analyzed via metabolomics results and cytokines with Spearman correlation analysis. The expected results, which were never reported in the previous literature, may reveal the beneficial effect of genistein against hyperlipidemia and may also inevitably elucidate the metabolic difference between genistein in normal rats and that in hyperlipidemic rats. ## 2.1. The Establishment of Analytical Strategy In this study, we established a comprehensive and effective strategy to discover and identify genistin metabolites by using UHPLC-Q-Exactive Orbitrap MS. Firstly, a high-quality full scan was performed with a resolution of 70,000 FWHM. Secondly, high-resolution extracted-ion chromatography was applied to withdraw the candidate data from positive and negative ion modes. Then, the candidate ions were systematically mined based on the common biological reactions and the reported metabolites in the literature. Those screened ions which we considered useful were added into the parent ion list (PIL) to obtain more accurate MS2 information for structure identification. Finally, the exact structures of these metabolites were resolved based on the exact molecular weight, fragmentation mode, DPIs, and information in the literature. Furthermore, the MMDF metabolic templates were of significance in identifying those metabolites present in low levels. In this study, four templates were set in parallel to encircle the metabolites: [1] genistin (m/z 431) and its conjugation templates (m/z 269 for deglycosylation, m/z 461 for hydroxylation and methylation, m/z 429 for glucuronidation); [2] genistein (m/z 271) and its conjugation templates (m/z 243 for decarbonylation, m/z 253 for dehydration, m/z 401 for arabino glycosylation); [3] daidzin (m/z 415) and its conjugation templates (m/z 433 for hydration, m/z 445 for hydroxylation and methylation); [4] daidzein (m/z 253) and its conjugation templates (m/z 223 for hydroxymethyl loss, m/z 257 for carbonyl hydrogenation reaction, m/z 225 for decarbonylation). In addition, some metabolites were also set as new templates when these metabolites were found during the subsequent identification or when the current templates did not cover the metabolic profiles of genistin. The mass spectrum information for four prototype drugs (genistin, genistein, daidzin and daidzein) were collected and resolved via the established analysis strategy. The metabolic profile of genistin is shown in Figure 1, and other compounds are shown in the Supplementary Figures S1–S3. ## 2.2. The Identification Results of Genistin Metabolites in Normal and Hyperlipidemic Rats The metabolites were screened and identified in plasma via UHPLC-Q-Exactive Orbitrap MS. Thirteen metabolites were detected in both positive and negative ion modes (Table 1). Among them, five metabolites were found in a positive ion mode, and eight metabolites were identified in a negative ion mode. Among them, the products of deglycosylation, decarbonylation, hydrogenation, and carbonyl hydrogenation were identified in the positive ion mode. The products of arabinosylation, glucuronidation, hydration, hydroxylation and methylation, decarbonylation, and dehydroxymethylation were identified in a negative ion mode. ## 2.2.1. The Identification of Genistin Metabolites in Normal Rats Daidzein (M0) was eluted at 6.97 min with [M+H]+ ion at m/z 255.06518; its formula was calculated as C15H11O4. In its MS/MS spectrum, the DPIs at m/z 137 ([M+H-C8H6O]+) and m/z 119 ([M+H-C7H4O3]+) were generated due to RDA fragmentation [18]. Genistin (M2) exhibited the theoretical [M-H]− ion at m/z 431.09837 (C21H20O10, −0.023 ppm) with a retention time of 1.24 min. Meanwhile, the fragment ion at m/z 92 ([M-H-C15H15O9]−) indicated the loss of B-ring, and the DPI at m/z 162 ([M-H-C11H12O7]−) indicated that the reaction of RDA cleavage occurred at positions 1 and 4 of M2. The two fragment ions were found in genistin in the MS/MS spectrum [19]. The glucuronidation product of daidzein (M3) was eluted at 0.98 min with the theoretical [M-H]− ion at m/z 429.08273 (C21H17O10, 4.452 ppm). It was 176 Da higher than that of daidzein. In the MS2 spectra, the DPIs at m/z 162 ([M-H-C12H14O7]−) and m/z 160 ([M-H-C12H16O7]−) indicated the occurrence of an RDA reaction. The characteristic fragment ion at m/z 92 ([M-H-C15H13O9]−) indicated that the glucose group should be not introduced into the B-ring. The fragment ion at m/z 187 ([M-H-C10H10O7]−) was produced by the rupture of the A-ring [20]. The hydration product of daidzin (M4) possessed the theoretical ion [M-H]− at m/z 433.11405 (C21H21O10, −0.716 ppm) with a retention time of 0.91 min. It was 18 Da higher than that of daidzin. In the MS2 spectra, the DPIs at m/z 92 ([M-H-C15H17O9]−) and m/z 197 ([M-H-C6H10O5-C2H2O3]−) indicated that the hydration reaction occurred in the C-ring [21]. The hydroxylation and methylation product of daidzin (M5) was 30 Da higher than that of daidzin with a retention time of 9.57 min. It exhibited the theoretical ion [M-H]− at m/z 445.11395 (C22H21O10, 1.573 ppm). In the MS2 spectrum, the methoxy group might be added to the A-ring due to the DPI at m/z 407 ([M-H-C3H2]−), which was produced by the loss of the B-ring. The fragment ion at m/z 148 ([M-H-C11H16O7]−) also confirmed the above conclusion. The decarbonylation product of equol (M7) was eluted at 12.53 min and produced the theoretical [M+H]+ ion at m/z 215.10667 (C14H15O2, 2.714 ppm) in the positive ion mode. It was 28 Da lower than that of equol. In its MS2 spectrum, the fragment ion at m/z 149 ([M+H-C5H6]+) was produced due to the removal of the B-ring, and the fragment ion at m/z 146 ([M+H-C4H5O]+) was generated by the loss of positions 5 and 8 on the A-ring. The hydrogenation product of equol (M8) was eluted at 9.04 min and possessed the theoretical [M+H]+ ion at m/z 245.08077 (C14H13O4, 0.100 ppm). It was 2 Da higher than that of equol. In its MS2 spectrum, the simultaneous removal of the A-ring and the B-ring produced the DPI at m/z 83 ([M+H-C10H10O2]+). The fragment ions at m/z 93 ([M+H-C8H7O3]+), m/z 226 ([M+H-H2O]+), and m/z 69 ([M+H-C10H8O3]+) indicated that the possible reaction could only occur at position 3 of the C-ring. ## 2.2.2. The Identification of Genistin Metabolites in Hyperlipidemic Rats The dehydroxymethylation product of daidzein (M10) was eluted at 9.97 min, at which point it produced the theoretical [M-H]− ion at m/z 223.04003 (C14H7O3, −1.02 ppm). In the MS2 spectrum, the characteristic fragment ion at m/z 111 ([M-H-C5H4O3]−) was produced via RDA rearrangement, which occurred on the 3’ and 6’ positions of the B-ring. The DPIs at m/z 183 ([M-H-C2O]−) and m/z 92 ([M-H-C8H3O2]−) were observed, suggesting that the hydroxyl and methyl groups were lost in the A-ring. The cleavage behavior was similar to that of the standard. The carbonyl hydrogenation product of daidzein (M11), which was eluted at 9.37 min, contributed the theoretical [M+H]+ ion at m/z 257.08088 (C15H13O4, 1.691 ppm). It was 2 Da higher than that of daidzein. The DPIs at m/z 69 ([M+H-C11H8O3]+) and m/z 189 ([M+H-C4H4O]+) were produced due to the RDA reaction on the B-ring in its MS2 spectrum. The decarbonylation product of daidzein (M12) exhibited the theoretical [M-H]− ion at m/z 225.05573 (C14H9O3, −0.566 ppm), which was 28 Da lower than that of daidzein. In its MS2 spectrum, the fragment ion at m/z 183 ([M-H-C2H2O]−) was produced by the B-ring cleavage, and the fragment ion at m/z 165 ([M-H-C2H2O-H2O]−) confirmed our conclusion. ## 2.2.3. Identification of Genistin Metabolites in Hyperlipidemia and Normal Rats The arabinylation product of genistein (M1) was eluted at 9.39 min and produced the theoretical [M-H]− ion at m/z 401.08777 (C20H17O9, 1.134 ppm), which was 132 Da higher than that of genistein. In the MS2 spectra, the DPIs at m/z 225 ([M-H-C5H8O4-CO2]−), m/z 357 ([M-H-CO2]−), and m/z 313 ([M-H-CO2-CO2]−) confirmed the theory above. The decarbonylation product of genistein (M6), the retention time of which was 9.87 min, was 28 Da lower than that of genistein in the positive ion mode. In the MS2 spectra, the DPIs at m/z 107 ([M+H-C7H4O3]+), m/z 226 ([M+H-O]+), m/z 69 ([M+H-C10H6O3]+), and m/z 93 ([M+H-C8H6O3]+) suggested that a decarbonylation reaction should not occur on the B-ring. However, the fragment ion at m/z 121 ([M+H-C7H6O2]+) indicated that the decarbonylation reaction could occur on the C-ring. The hydroxylation and methylation product of genistin (M9) was eluted at 10.09 min; it possessed the theoretical [M-H]− ion at m/z 461.10887 (C22H21O11, 2.289 ppm). It was 30 Da higher than that of genistin. In the MS2 spectrum, the characteristic fragment ion at m/z 137 ([M-H-C15H16O8]−) was produced due to the RDA reaction on the C-ring. Meanwhile, the DPIs at m/z 407 ([M-H-C3H2O]−), m/z 92 ([M-H-C6H10O5-C10H7O5]−), m/z 425 ([M-H-2H2O]−), and m/z 84 ([M-H-C6H10O5-C12H7O4]-) indicated the methoxyl group should occurr on the A-ring. ## 2.3. Proposed Metabolic Pathways of Genistin Thirteen metabolites (parent drug included) were found in normal and hyperlipidemic rats after oral administration of genistin. The proposed metabolic pathways of genistin are illustrated in Figure 2. Genistin (M2) served as a metabolic center to gradually produce secondary metabolites. For the common metabolic pathways of normal and hyperlipidemic rats, genistin was metabolized to M6, M1, and M9 mainly by decarbonylation, arabinylation, hydroxylation, and methylation. The reaction of three metabolites should be the conventional pathway of genistin metabolism in vivo. In normal rats, genistin was metabolized to daidzein (M0), and M0 was further metabolized to M3, M5, M4, M7, and M8 underwent glucuronidation, hydroxylation and methylation, hydration, decarbonylation, and hydrogenation, respectively. These metabolic reactions might be stress patterns of genistin being cleared in a normal organism. In hyperlipidemic rats, genistin was metabolized mainly to M10, M12, and M11 by dehydroxymethylation, decarbonylation, and carbonyl hydrogenation. It has been suggested that genistin may be metabolized into these three metabolites which are involved in the pathogenesis of hyperlipidemia. ## 2.4. Genistin Reduced Lipid Factor Levels and Hepatic Lipid Accumulation in Rats with Hyperlipidemia Induced by HFD During the experiment, the body weights of rats in all groups were measured per week. As shown in Figure 3, HFD could rapidly increase the body weight of rats. However, when we gave genistin, it significantly slowed down the weight gain, and the effect was consistent with simvastatin. At the end of the experiment (18 weeks), we found that the levels of plasma TC, TG, LDL-C in Mod rats were significantly increased ($p \leq 0.01$), whereas HDL-C was decreased compared with that in rats in Con ($p \leq 0.01$), which indicated that HFD could indeed cause hyperlipidemia in rats. After treatment, simvastatin and genistin at different doses significantly reduced the levels of plasma TC, TG, and LDL-C ($p \leq 0.01$), and elevated the standard of HDL-C ($p \leq 0.01$). This beneficial function was also observed in the liver color of rats in all groups. All results indicated that genistin has the remarkable function of regulating blood lipids. For Oil red O staining, more lipids accumulated in the liver after being raised with HFD. As we know, the excessive lipids could cause a lipid peroxidation reaction with the participation of reactive oxygen species, and lipid peroxidation was associated with cell permeability, DNA damage, and protein synthesis disorders. In Mod rats, HFD significantly increased the content of MDA ($p \leq 0.05$) while decreasing the level of SOD compared with that in Con rats ($p \leq 0.05$). The result indicated that excessive HFD could accelerate the process of lipid peroxidation in vivo. Moreover, lipid peroxidation emerged as having a negative relationship with liver function. For H&E staining, the livers of rats in Mod indeed showed increased symptoms of cavitation and fibrosis, which also corroborated the above statement. Moreover, these elevated levels of ALT in Mod rats also corroborated the abnormal liver function compared with Con ($p \leq 0.05$). Surprisingly, genistin obviously reversed the above situation; this means that genistin could reduce lipid accumulation in the liver. Concurrently, genistin also significantly reduced the level of MDA ($p \leq 0.05$) and increased the content of SOD ($p \leq 0.05$). It was shown that genistin has an inhibitory effect on lipid peroxidation. Genistin also had a beneficial effect on liver function by reducing fibrosis and vacuolation of liver cells. This result was also reflected in the decreased ALT due to genistin. In brief, genistin could reduce HFD-induced lipid factor levels and hepatic lipid accumulation in rats. ## 2.5. The Mechanism Analysis of Genistin against Hyperlipidemia via Plasma Metabolomics To determine the underlying mechanism of genistin in the treatment of hyperlipidemia, multivariant PCA and OPLS-DA were applied with SIMCA-P 14.0 Demo software (Figure 4A,B,D,E). PCA is an unsupervised cluster analysis model without preprocessed datasets. It can be used to evaluate differences in metabolic profiles of Con, Mod, and Gen. Above all, the QC sample could be gathered together in PCA score plots of positive and negative ion modes, illustrating the stability of the analysis system during data collection. Furthermore, the separation trend between Con and Mod were highly obvious. This consequence was reflected in the destructive effect of HFD on metabolic profiling in rats. As expected, Mod was also significantly different from Gen, the metabolic profiling of which was more inclined to that of the Con. Next, PLS-DA was adopted to verify the rationality of the data model. This supervisory analysis model revealed the difference between Mod, Con, and Gen. This simulation coefficient of R2Y (0.989, 0.984) and Q2 (0.869, 0.875) in positive and negative ion modes revealed the reliability of the model, and the 200 permutation tests also confirmed the above results. OPLS-DA was utilized to identify significantly changed metabolites after genistin treatment. On the score plots of OPLS-DA in positive and negative ion modes, there were obvious separation trends between Con and Mod and between Mod and Gen. Subsequently, the S-plot with the threshold of VIP > 1.0 and $p \leq 0.05$ could be used to screen and identify differential metabolites in the OPLS-DA model (Figure 4C,F). Finally, a total of 15 endogenous metabolites were significantly altered between the Con and Mod (Figure 4G, Table 2). Among them, six metabolites were obviously up-regulated ($p \leq 0.05$), and nine metabolites were significantly decreased ($p \leq 0.05$) with HFD intervention. Nevertheless, genistin could still obviously down-regulate six metabolites ($p \leq 0.05$), including L-Tryptophan, L-Proline, Octadecanoic acid, 4-Amino-4-cyanobutanoic acid, sn-Glycero-3-phosphoethanolamine, and 5-Aminolevulinate. Genistin also up-regulated 9 of 15 metabolites, involving L-Leucine, Creatine, L-Carnitine, O-Ureido-L-serine, anserine, N-Formimino-L-aspartate, L-Ornithine, 2,3-Dihydroxycarbamazepine, and 5-Hydroxyisourate. Approximate changes in multiples of metabolites are shown in Additional Table 1. These results demonstrated that genistin indeed reverses metabolic disorders caused by HFD. The cluster analysis with heat map was used to verify the above hypothesis (Figure 4H). We found that the metabolic profiling of genistin in the treatment of hyperlipidemia was remarkably similar to that of the Con, whereas the contrary was true for that of the Mod. The results showed that the levels of metabolites in the plasma were significantly different between the Con and Mod rats, and that the levels in the Gen tended to recover to those of the Con, suggesting that genistin could correct the abnormal levels of plasma metabolites in hyperlipidemic rats. ## 2.6. The Enrichment Analysis of Metabolic Pathways Fifteen differential metabolites were imported to the MetaboAnalyst 5.0 dataset, and their results revealed the metabolic pathway of genistin against hyperlipidemia (Figure 5A). The results showed that these metabolites in the plasma were responsible mainly for arginine and proline metabolism and arginine biosynthesis, and so on. Therefore, these metabolic pathways should be classified as target pathways, which were associated with the beneficial activity of genistin. Moreover, the Spearman correlation analysis was used to determine the significant correlations between metabolites and cytokines. As shown in Figure 5C, six metabolites, including 4-Amino-4-cyanobutanoic acid, octadecanoic acid, L-Proline, L-Tryptophan, sn-Glycero-3-phosphoethanolamine, and 5-Aminolevulinate, showed positive correlation with TC, TG, LDL-C, ALT, and MDA and were negatively related to SOD and HDL-C. Interestingly, six metabolites seemed to be selectively related to cytokines. Among them, 4-Amino-4-cyanobutanoic acid and octadecanoic acid indeed showed positive correlation with TG, TC, and LDL-C ($p \leq 0.05$), illustrating that two metabolites might aggravate the damage of hyperlipidemia to the body. The insignificant relationship between two metabolites and HDL-C also proved that they focused only on the transport of total cholesterol from the liver to the plasma. In addition, 4-Amino-4-cyanobutanoic acid and octadecanoic acid were all negative correlated with SOD ($p \leq 0.001$); the result suggested that they were associated with lipid peroxidation in the liver. 5-Aminolevulinate, sn-Glycero-3-phosphoethanolamine, and L-Tryptophan all showed negative correlations with SOD and HDL-C ($p \leq 0.05$). 5-Aminolevulinate, sn-Glycero-3-phosphoethanolamine, and L-Tryptophan could not only induce hyperlipidemia but also cause adverse effects on liver function based on the positive relationship between these compounds and TG, MDA, ALT, and LDL-C ($p \leq 0.05$). The relationships between nine other metabolites and cytokines were contrary to the aforementioned situation. For indicators, all metabolites were negatively correlated with ALT ($p \leq 0.05$), illustrating the function of liver protection. However, three of nine metabolites, namely anserine, creatine, and O-Ureido-L-serine, were negatively correlated with TG, TC, and LDL-C ($p \leq 0.05$). Among those metabolites, only creatine could show the negative correlation with the above three factors ($p \leq 0.01$) and was positively correlated with HDL-C ($p \leq 0.05$). The result suggested that creatine seemed to show a unique function in alleviating hyperlipidemia. Moreover, the correlations between creatine and SOD and MDA also confirmed that creatine could inhibit lipid peroxidation in the liver. Other metabolites that could demonstrate this function were anserine, 2,3-Dihydroxycarbamazepine, and N-Formimino-L-aspartate ($p \leq 0.05$). Distance-based redundancy analysis (db-RDA) was applied to determine the differential biomarkers of genistin against hyperlipidemia. 4-Amino-4-cyanobutanoic acid and octadecanoic acid were negatively associated with SOD while showing a positive correlation with TG, TC, and LDL-C. In addition, creatine was more closely associated with cytokines than other metabolites. Thus, creatine should be considered a beneficial biomarker for genistin in the treatment of hyperlipidemia. ## 3. Discussion The metabolic behaviors of drug or natural products in vivo have always been the focus of study for their continued development. The reactions involved in their metabolic pathways may be associated with the involved targets or endogenous pathways. In addition, the metabolic differences of drugs or natural products under normal and pathological conditions have been reported in the previous literature [22,23,24], which may provide some information about their mechanism. In this study, a high dose of genistin (150 mg/kg) was given orally to normal SD rats and hyperlipidemic SD rats [25]. Thirteen metabolites in plasma were found via UHPLC-Q-Exactive Orbitrap MS with an efficient analysis strategy. Among them, three metabolites were detected in both normal and pathological rats, involving the reactions of decarbonylation (M6), L-arabinylation (M1), and hydroxylation and methylation (M9). Meanwhile, seven metabolites were found only in rats administered normally, such as deglycosylation (M0), glucuronidation (M3), hydroxylation and methylation (M5), hydration (M4), decarbonylation (M7), and hydrogenation (M8) products of genistin. Based on the above results, we speculated that the reactions of three metabolites detected in both normal and pathological rats should be the routine pathways for genistin metabolism in vivo, and metabolic reactions of seven metabolites in normal rats might be stress patterns of genistin being cleared in a normal organism. Three other metabolites could be detected only in hyperlipidemic rats, including the reactions of dehydroxymethylation (M10), decarbonylation (M12), and carbonyl hydrogenation (M11). This fact demonstrates that genistin may be metabolized into three metabolites to participate in the pathogenesis of hyperlipidemia, or three metabolites have some advantages in the treatment of hyperlipidemia with genistin. It is worthwhile for this latent problem to be studied in the future. Isoflavones, also known as estrogen-like substances, have been proven to have remarkable abilities in regulating hormone homeostasis, cell proliferation, and metabolic regulation in women, such as soy isoflavone and Pueraria isoflavones [26,27,28]. In the next experiment, the special function of genistin was evaluated in HFD-induced hyperlipidemic rats. At first, HFD could disturb the lipid homeostasis in rats and accelerate the process of lipid peroxidation in liver [29]. The levels of ALT corroborated the above conclusions compared with normal rats. Secondly, genistin obviously reversed the disorder in vivo caused by HFD ($p \leq 0.05$); this effective activity was also confirmed by pathological results. Thirdly, hyperlipidemia was characterized by abnormally elevated levels of TC, TG, and LDL-C and abnormally reduced HDL-C levels. Genistin significantly improved this pathological change. At present, the research of genistin in the treatment of hyperlipidemia has never been mentioned, such that these results can construct the foundation for genistin as a new lipid-lowering agent. Finally, the mechanism behind genistin in the treatment of hyperlipidemia was preliminarily elucidated using metabolomics. In PCA and OPLS-DA score plots, the metabolic profile of genistin in rats, which was similar to that of normal rats, was verifiably separated in rats with hyperlipidemia induced by HFD. The levels of 15 metabolites in Mod rats were significantly adjusted compared with Con rats ($p \leq 0.05$), and these metabolites were significantly reversed by genistin ($p \leq 0.05$), which belonged to the metabolic pathways of arginine and proline metabolism and arginine biosynthesis. Spearman correlation analysis and db-RDA revealed that creatine should be considered a beneficial biomarker of genistin against hyperlipidemia. Creatine in mammal body could be synthesized from arginine, methionine, and proline found in the kidney, liver, and pancreas [30,31,32]. As an energy source that can be endogenously synthesized or obtained through diet and supplement, creatine is involved in cell metabolism through adenosine triphosphate (ATP) supplementation, which provides energy for skeletal muscles, organs, and tissues [33]. The deposition of lipids leads to the loss of ATP in the body, and creatine could promote the process of lipid β oxidation to release more ATP in the participation of multiple targets, such as adenosine 5’-monophosphate (AMP)-activated protein kinase (AMPK), peroxisome-proliferator-activated receptors (PPARs), sterol-regulatory element-binding proteins (SREBPs) [34]. Certainly, the relationship between creatine and lipids also reduces lipid peroxidation in the body [35]. In this study, genistin could significantly promote creatine production through the arginine and proline metabolic pathways. However, the relationship behind genistin, creatine, and the metabolic pathway remains mysterious, and the role of three genistin metabolites in hyperlipidemic rats should not be ignored. ## 4.1. Chemicals and Reagents The reference substances of genistin, genistein, daidzin, and daidzein were commercially provided by Chengdu Must Biotechnology Co., Ltd. (Chengdu, China) with a purity ≥$98\%$ via UV-UHPLC. Their structures were fully elucidated by comparing the spectral data (ESI-MS and 1H, 13C NMR). The kits of total cholesterol (TC), total triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), alanine aminotransferase (ALT), malondialdehyde (MDA), superoxide dismutase (SOD), and low-density lipoprotein cholesterol (LDL-C) were purchased from Nanjing Jiancheng Bioengineering Institute (Nanjing, China). In addition, the other reagents and solvents, which met the requirements of analytical experiments, were purchased from Beijing Chemical Works (Beijing, China). Simvastatin was supplied by Merck Pharmaceuticals Ltd (Hangzhou, China). Ultrapure water was derived from Milli-Q Gradient Å 10 water purification system (Millipore, Billerica, MA, USA). ## 4.2.1. Establishment of Animal Model A total of 12 male Sprague–Dawley (SD) rats (SPF level, weighing 180–200 g) were purchased from Jinan Pengyue Experimental Animal Breeding Co., Ltd. (Shandong, China, SYXK (RU)2019-0003). Before the experiment, all animals had to be maintained under standard animal room conditions (temperature 24 ± 2 °C, humidity 55–$60\%$, $\frac{12}{12}$ h light/dark cycles) with standard feed and water ad libitum for 1.0 week. Afterward, all rats were randomly divided into the control group (6 rats) and the hyperlipidemic group (6 rats) according to body weight. The rats in the normal group were fed normal rodent chow (Pengyue, Shandong, China), and those in the hyperlipidemic group were fed with a high-fat diet, containing a standard chow diet ($65\%$), sucrose ($20\%$), lard ($15\%$), cholesterol ($5\%$), sodium cholate ($5\%$), and $5\%$ yolk powder (Huafukang, Beijing, China) for 15 weeks. After 15 weeks, compared with the control group, the levels of TC, TG, and LDL-C in the hyperlipidemic group were significantly increased, and the level of HDL-C was significantly decreased, indicating that the animal model was successfully established. ## 4.2.2. Collection and Preparation of Plasma Samples All rats in both groups were given genistin (150 mg/kg) via oral administration. After oral administration, the blood samples (about 0.5 mL) were taken from the rats in the normal and hyperlipidemic groups at different times of 0.5, 1, 1.5, 2, 4, and 6 h. The obtained samples were placed in the anticoagulant EP tubes of heparin sodium. After resting for 10.0 min, each blood sample was centrifuged for 15.0 min (3500 rpm, 4 °C). An amount of 100.0 μL of upper plasma was taken from the rats in the normal administration group at each time point to obtain mixed plasma. Cold methanol (3.0 mL) was added to mixed plasma samples (1.0 mL) for precipitation, and the supernatant was obtained via centrifugation for 10.0 min (4000 rpm, 4 °C). Plasma of the two groups were blown dry with a nitrogen blow dryer and stored in a refrigerator at −80 °C until use. Before analysis, samples of these two groups were redissolved in 300 μL methanol and centrifuged at 20,000 rpm. ## 4.2.3. Collection of UHPLC-Q-Exactive Orbitrap MS Data The unsearchable metabolites were determined via UHPLC-Q-Exactive Orbitrap MS. Firstly, LC analysis was performed on a DIONEX Ultimate 300 UHPLC system (Thermo Fisher Scientific, MA, USA) with a binary pump, an autosampler, and a column oven. The chromatographic separation was performed with an ACQUITY UPLC BEH C18 column (2.1mm × 100 mm, 17 μm, Waters, Milford, MA, USA). The column temperature was maintained at 35 °C, and a 3.0 μL sample was injected at a flow rate of 0.3 mL/min. The mobile phases consisted of $0.1\%$ formic acid aqueous solution (A) and acetonitrile (B). The elution gradient was set as follows: 0–5 min, 5–$30\%$ (B); 5–10 min, 30–$50\%$ (B); 10–27 min, 50–$90\%$ (B); 27–27.1 min, 90–$5\%$ (B); 27.1–30 min, $5\%$ (B). Subsequently, the UHPLC-MS analysis was performed on a Q Exactive MS (Thermo Fisher Scientific, MA, USA). The data acquisition parameters in the positive and negative ion modes were set as follows: spray voltage of 3000 V (positive)/3500 V (negative); capillary voltage of −35 V; sheath gas flow rate of 30 arb; auxiliary gas flow rate of 10 arb; capillary temperature of 325 °C (positive)/350 °C (negative); tube lens of +110 V (positive)/−110 V (negative). All metabolites were detected using full-scan MS analysis (m/z 70–1050) at a resolving power of 70,000 FWHM. The resolution of dd-MS2 was set to 17,500 FWHM. The collision energy of collision-induced dissociation (CID) was 30 eV. The collected datasets were recorded and processed via a Thermo Xcalibur 2.1 workstation (Thermo Scientific, Bremen, Germany). ## 4.3.1. Sectionalization and Administration A total of 30 male Sprague–Dawley (SD) rats (SPF level, weighing 180–200 g) were purchased from Jinan Pengyue Experimental Animal Breeding Co., Ltd. (Shandong, China, SYXK (RU)2019-0003). They were used to evaluate the anti-hyperlipidemic activity of genistin. The modeling method was based on the above method “4.2.1.”. Then, the rats in the hyperlipidemic group were again allocated into the four following groups: the model group (Mod, $$n = 6$$); simvastatin group (Sim, $$n = 6$$) at the dose of 5 mg/kg/d (drug weight/body weight/day); genistin high-dose group (Hig, $$n = 6$$) at the dose of 5 mg/kg/d; and genistin low-dose group (Low, $$n = 6$$) at the dose of 2.5 mg/kg/d [36]. All drugs were administered orally to the respective rats. Except for the Con group, other rats were still fed a high-fat diet for the 3 weeks of treatment. ## 4.3.2. Collection and Preparation of Biological Samples At the end of the experiment, all rats were fasted for 12 h with only deionized water. Then, the rats from five groups were killed in parallel using $10\%$ chloral hydrate. All abdominal aortic blood samples were collected from each rat in each group and placed in EP tubes coated with heparin sodium and were centrifuged (3500 rpm) for 15.0 min at 4 °C, and the supernatants were taken for testing. The livers of all rats were taken out and rinsed with normal saline. Some livers were immersed in $4\%$ paraformaldehyde for histopathological analysis. The remaining livers were rapidly quenched in liquid nitrogen and stored at −80 °C until use. The levels of TG, TC, LDL-C, HDL-C, ALT, SOD, and MDA in plasma samples from all rats were measured using a microplate reader (SpectraMax iD5, Pleasanton, CA, USA) [37,38,39]. The hepatic tissues fixed in $4\%$ PFA were dehydrated and embedded in paraffin, cross-sectioned into 4 µm-thick slices, and stained with hematoxylin–eosin (H&E). The sections of the remaining liver tissues were cleaned with PBS and cultured with $60\%$ isopropanol for 5.0 min and then dyed in $0.5\%$ Oil Red O staining liquid (Sigma, St Saint Louis, MO, USA) for 20.0 min. After being cleaned by PBS, all sections were stained with hematoxylin stain (Solarbio Science and Technology, Beijing, China) for 2.0 min [40]. The abovementioned indicators were all used to evaluate the anti-hyperlipidemia function of genistin. ## 4.4.1. Preparation of Biological Samples In total, 200.0 μL plasma from each rat in each group was taken and added into an 800.0 μL mixture of cold methanol and acetonitrile (1:4). After 5.0 min, the miscible liquids were centrifuged at 4000 rpm for 10.0 min to obtain the supernatants [41]. All supernatants were rapidly dried with nitrogen and stored in a refrigerator at −80 °C until use. In addition, a 10 µL solution from each plasma sample was mixed and marked as quality control (QC) samples. The stability of the instrument needed to be calibrated using QC samples after every 5 plasma samples. ## 4.4.2. Collection of UHPLC-Q-Exactive Orbitrap MS Data LC analysis was performed on a DIONEX Ultimate 3000 UHPLC system (Thermo Fisher Scientific, MA, USA) with a binary pump, an autosampler, and a column oven. A 2.0 μL sample was injected into an Acquity UPLC BEH C18 column (100 × 2.1 mm, 1.7 μm), and the flow rate was set to 0.3 mL/min. The column temperature was 40 °C, and the mobile phases consisted of $0.1\%$ formic acid aqueous solution (A) and acetonitrile (B). The gradient elution condition was set as follows: 0–1min, $5\%$ (B); 1–3 min, 5–$40\%$ (B); 3–6 min, $40\%$ (B); 6–6.5 min, 40–$60\%$ (B); 6.5–7.5 min, $60\%$ (B); 7.5–10 min, 60–$90\%$ (B); 10–15 min, 90–$5\%$ (B); 15–20 min, $5\%$ (B). The UHPLC-MS analysis was performed on a Q-Exactive MS/MS (Thermo Fisher Scientific, MA, USA). An electrospray ionization (ESI) ion source was used. The samples were collected via Full MS/dd-MS2 scanning mode. The first-order scanning resolution was 70,000; the second-order scanning resolution was 35,000; the Fourier high-resolution scanning range was m/z 50–1050; and the ion chamber collision energy was $40\%$. The capillary temperature was 320 °C.The sheath gas flow rate was 30 arb; the auxiliary gas flow rate was 10 arb; and the spray voltage was 3.0 kV. ## 4.4.3. Multivariate Analysis of UHPLC-Q-Exactive Orbitrap MS Data The UHPLC-Q-Exactive Orbitrap MS data were processed with Compound Discoverer 3.0 software (Thermo Fisher Scientific, Waltham, MA, USA) for noise cancellation, baseline correction, and normalization to obtain reliable datasets with some information, including m/z, peak intensities, and retention times. The relevant parameters were set to C [0–30], H [0–60], O [0–10], N [0–5], ring unsaturated double bond (RDB) [0–15], and the mass accuracy error was within 5–10−6. Afterward, the processed datasets were added to the SIMCA-P 14.0 software (Umetrics, Sweden) to perform the principal component analysis (PCA) and orthogonal to partial least-squares-discriminant analysis (OPLS-DA). Among them, PCA was applied to discriminate the separation trends of all groups. OPLS-DA was used to characterize metabolic perturbation of hyperlipidemia. In addition, the S-plot scores were used to screen the differential metabolites of hyperlipidemia treated by genistin combined with other judgment methods, such as variable importance in projection (VIP) (generated in the OPLS-DA mode) and p-value (formed from relative intensity). Subsequently, the S-plot with the threshold of VIP > 1.0 and $p \leq 0.05$ could be used to screen and identify differential metabolites in the OPLS-DA model. The structures, molecular weights, and codes of differential metabolites were assigned according to the human metabolome database (What Is Dementia. Available online: http://www.alz.org/what-is-dementia.asp (accessed on 1 February 2023)). Finally, the Spearman correlation analysis and db-RDA analysis were used to determine the relationship between lipid factors and differential metabolites (https://www.bioincloud.tech/ (accessed on 1 February 2023)). ## 4.5. Statistical analysis The statistical analysis of the data was performed using SPSS 22.0 software (Chicago, IL, USA). An unpaired Student’s t-test was performed for a two-group comparison. For multiple comparisons, ANOVA was used. $p \leq 0.05$ was defined as statistically significant. The statistical analyses and figures were performed using GraphPad Prism 8.0 software (Santiago, MN, USA). Fasting body weight data were analyzed using one-way ANOVA. ## 5. Conclusions In this study, the metabolic differences and similarities of genistin in normal rats and hyperlipidemic rats were compared. These results are able to provide the foundation for the metabolic mechanism of genistin in pathological and normal rats. The efficacy results revealed the explicit function of genistin against hyperlipidemia, which has been rarely reported; thus, the results demonstrate the possibility of genistin as a new lipid-lowering agent. The mechanism on genistin in the treatment of hyperlipidemia was preliminarily elucidated using metabolomics. Genistin may treat hyperlipidemia by regulating the level of creatine, which can be produced by the arginine and proline metabolic pathway in vivo. However, there are some limitations of the present study. Firstly, the certain relationship between the differences in metabolic behavior and metabolic pathways of genistin in normal and hyperlipidemic rats and the results of pharmacodynamics is still unclear. Secondly, it is also ambiguous whether there are differences in the levels of this metabolite. Finally, the character of genistin metabolites in the treatment of hyperlipidemia by mediating the level of creatine in vivo should also not be ignored. In summary, the relationship between genistin and hyperlipidemia may be further revealed in subsequent studies. ## References 1. Islam A., Islam M.S., Uddin M.N., Hasan M.I., Akanda M.R.. 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--- title: 'Gamma-Muricholic Acid Inhibits Nonalcoholic Steatohepatitis: Abolishment of Steatosis-Dependent Peroxidative Impairment by FXR/SHP/LXRα/FASN Signaling' authors: - Yang Xie - Feng Shen - Yafang He - Canjie Guo - Ruixu Yang - Haixia Cao - Qin Pan - Jiangao Fan journal: Nutrients year: 2023 pmcid: PMC10005659 doi: 10.3390/nu15051255 license: CC BY 4.0 --- # Gamma-Muricholic Acid Inhibits Nonalcoholic Steatohepatitis: Abolishment of Steatosis-Dependent Peroxidative Impairment by FXR/SHP/LXRα/FASN Signaling ## Abstract Nonalcoholic steatohepatitis (NASH) reflects the outcome of steatosis-based peroxidative impairment. Here, the effect and mechanism of γ-muricholic acid (γ-MCA) on NASH were investigated on the basis of its actions in hepatic steatosis, lipid peroxidation, peroxidative injury, hepatocyte apoptosis, and its NAFLD activity score (NAS). The agonist action of γ-MCA on farnesoid X receptor (FXR) upregulated the small heterodimer partner (SHP) expression of hepatocytes. An increase in SHP attenuated the triglyceride-dominated hepatic steatosis which was induced in vivo by a high-fat high-cholesterol (HFHC) diet and in vitro by free fatty acids depending on the inhibition of liver X receptor α (LXRα) and fatty acid synthase (FASN). In contrast, FXR knockdown abrogated the γ-MCA-dependent lipogenic inactivation. When compared to their excessive production in HFHC diet-induced rodent NASH, products of lipid peroxidation (MDA and 4-HNE) exhibited significant reductions upon γ-MCA treatment. Moreover, the decreased levels of serum alanine aminotransferases and aspartate aminotransferases demonstrated an improvement in the peroxidative injury of hepatocytes. By TUNEL assay, injurious amelioration protected the γ-MCA-treated mice against hepatic apoptosis. The abolishment of apoptosis prevented lobular inflammation, which downregulated the incidence of NASH by lowering NAS. Collectively, γ-MCA inhibits steatosis-induced peroxidative injury to ameliorate NASH by targeting FXR/SHP/LXRα/FASN signaling. ## 1. Introduction Nonalcoholic fatty liver disease (NAFLD) is liver-specific metabolic stress characterized by hepatic steatosis, with a spectrum encompassing nonalcoholic fatty liver (NAFL), nonalcoholic steatohepatitis (NASH), liver fibrosis/cirrhosis, and hepatocellular carcinoma [1]. The prevalence of NAFLD has risen to around $25\%$ worldwide, reflecting major chronic liver disease [2]. NASH, instead of NAFL, takes the critical step toward NAFLD-related disability and mortality [3]. The onset of NASH undergoes a lot of interactive mechanisms, such as obesity, genetic predisposition, epigenetic regulation, and gut dysbiosis [4]. Microbial metabolites underlie, to a large extent, the effect of gut dysbiosis on NASH [5]. By their role in intestinal lipid absorption and their stimulatory impact on bile acid receptors (e.g., FXR, Takeda G protein-coupled receptor 5 (TGR5), pregnane X receptor), bile acids have been demonstrated to regulate host lipid metabolism [6]. Moreover, abnormalities in the bile acid pool and its composition (e.g., conjugated cholic acid) exhibit a close association with mammalian NASH, mainly based on lipid accumulation, hepatocyte injury, and related inflammation [7,8]. Being derived from α-muricholic acid (α-MCA) or β-muricholic acid (β-MCA), γ-muricholic acid (γ-MCA) reflects one kind of low-level secondary bile acid in the bile acid pool of mice [9]. It shares the molecular formula, 3,6,7-trihydroxy-5β-cholan-24-oic acid, with α-MCA and β-MCA that dominate rodent MCAs. When compared to 3α, 6β, 7α-trihydrol in α-MCA, and 3α, 6β, 7β-trihydrol in β-MCA, the 3α, 6α, 7α-trihydrol in γ-MCA confers a hydrophobicity-dependent affinity to farnesoid X receptor (FXR) [10,11]. Furthermore, the poor binding capacity of α-MCA/β-MCA and the hydrophobic pocket of FXR qualifies them as FXR-specific antagonists [12]. In contrast, the stereostructure of the 6α hydroxyl group in γ-MCA may restore the activity of FXR against α-MCA/β-MCA by its pocket-occupying capability [13]. Given the inhibitory roles of FXR in hepatic lipogenesis and the lobular inflammation upon steatosis-related lipid peroxidation, γ-MCA is suggested to serve as a therapeutic agent of NASH. Therefore, we established experimental NASH in mice by a high-fat high-cholesterol (HFHC) diet and performed 10 mg/kg or 100 mg/kg γ-MCA daily administration for 16 weeks. Both oil red O staining and triglyceride (TG) analysis revealed the effect of γ-MCA on hepatic steatosis. Then, liver concentrations of malondialdehyde (MDA) and 4-hydroxynonenal (4-HNE) exhibited steatosis-dependent lipid peroxidation. The lipid peroxidative injury and related hepatocyte apoptosis were further investigated by transferase levels and a terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling (TUNEL) assay, respectively. Pathological evaluation and the NAFLD activity score (NAS) finally demonstrated the outcome of γ-MCA treatment. Mechanically, γ-MCA-based activation of FXR and downstream signaling of small heterodimer partner (SHP)/liver X receptor α (LXRα)/fatty acid synthase (FASN) was analyzed in vivo and in vitro. ## 2.1. Establishment of Hepatocellular Steatosis and γ-MCA Administration Alpha mouse liver 12 (AML12) cells (Cell Bank of Chinese Academy of Sciences, Shanghai, China) were cultured with DMEM/F12, $1\%$ ITS Liquid Media Supplement, 40 ng/mL dexamethasone, $10\%$ fetal bovine serum (FBS), and $1\%$ Penicillin-Streptomycin Solution. They were divided into groups of normal control (NC), free fatty acid (FFA), FFA + 10 μM γ-MCA, and FFA + 100 μM γ-MCA (3 wells per group) at random. With an exception of the NC group, AML12 cells in each group were subjected to the induction of hepatocellular steatosis using 1.5 mM oleate/palmitate (oleate:palmitate = 2:1) for 24 h [14]. The other 2 groups were simultaneously administrated by 10 μM and 100 μM γ-MCA, respectively, during the same period. ## 2.2. Induction of Rodent NASH and γ-MCA Treatment A total of 40 adult male specific pathogen-free (SPF) c57BL/6 mice (Jihui Laboratory Animal Care Co., Shanghai, China) were randomized into the normal control (NC), NASH, NASH + vehicle, NASH + 10 mg/kg γ-MCA, and NASH + 100 mg/kg γ-MCA groups (8 mice per group), respectively. Except for those in the NC group with a normal diet, all the mice were exposed to the HFHC diet ($2\%$ cholesterol, $10\%$ lard, and $88\%$ normal diet) for 16 weeks [15]. Simultaneously, mice in the NASH + 10 mg/kg γ-MCA and NASH + 100 mg/kg γ-MCA groups were daily intragastrically administrated with 10 mg/kg and 100 mg/kg γ-MCA (H915264, Macklin, Shanghai, China), respectively, in 200 μL water for 16 weeks. The liver index of the mice was calculated as follow: liver index = liver weight / body weight. The sample size was estimated by the prospective difference in the steatotic incidence between the NASH and NASH + 100 mg/kg γ-MCA groups using Gpower v3.1.9.7 software [16]. The animal experiments were conducted according to the Guide for the Care and Use of Laboratory Animals [1996], with the approval of the ethical committee of Xinhua hospital. ## 2.3. Pathological Assessment Mice from each group were sacrificed to collect liver samples at the end of the 16th week. H and E staining was performed on each sample to evaluate the pathological indexes associated with NASH, including hepatocyte steatosis, ballooning degeneration, and lobular inflammation. A NAFLD activity score (NAS) was finally employed to diagnose NAFL, borderline-nonalcoholic steatohepatitis (NASH), and NASH [17]. Pathological diagnosis of each sample was performed in a blinded manner by a pathologist with diagnostic experience (Q.P.). ## 2.4. Assay for Serum Transferases Serum samples of each group were obtained from the blood by centrifugation at 3000 rpm and 4 °C for 30 min. The serum levels of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) were assessed using an ALT Assay Kit (C009-2-1, Jiancheng, Nanjing, China) and an AST Assay Kit (C010-2-1, Jiancheng), respectively, according to the same protocol as follows. The reagent 1 was warmed using water bath at 37 °C, incubated with 20 μL serum samples at 37 °C for 30 min, and mixed with 20 μL 2,4-dinitrophenylhydrazine at 37 °C for 20 min, subsequently. Then, the mixture was subjected to chromogenic reaction with 200 μL 0.4 mol/L NaOH at room temperature for 15 min, and OD analysis at 510 nm using the microplate spectrophotometer (Epoch, Agilent, Santa Clara, CA, USA). The serum levels of ALT and AST were finally calculated according to the multinomial standard curves, which were established using sodium pyruvate via gradient concentration. ## 2.5. Oil Red O (ORO) Staining To test the existence and localization of neutral lipids, ORO staining was carried out in both AML12 cells and frozen sections of liver samples. The ORO working solution was prepared using saturated ORO solution diluted in water to $60\%$ of the volume fraction and heated at 65 °C for 30 min. The AML12 cells were washed using phosphate buffer saline (PBS), fixed using $4\%$ paraformaldehyde for 10 min, washed again, and treated with $60\%$ isopropanol for 15 s. The frozen sections were warmed at room temperature for 10 min. Next, the cells and frozen sections were stained using ORO working solution in the dark for 30 min and 10 min, respectively, treated with $60\%$ isopropanol for 5 s, and washed in water for 15 min. Finally, the cells and frozen sections were counterstained using hematoxylin for 3 min, differentiated using $1\%$ hydrochloric acid alcohol for 20 s, and neutralized using $3\%$ ammonia water for 5 s. ## 2.6. Triglyceride (TG) Analysis The liver tissue of each mouse was ground into homogenate and centrifuged to obtain the supernatant. The total protein concentration of the supernatant was determined using a BCA protein concentration determination kit G2026 (Servicebio, Wuhan, China) according to the manufacturer’s instructions. The TG content in liver samples was then measured using the TG Determination Kit (GPO-PAP method) (C061, Huili) against the total protein concentration [18]. In detail, a 250 μL GPO-PAP working solution was mixed with 2.5 μL PBS, standard glycerol (1.7 mmol/L), or supernatant. The mixture was subjected to OD analysis at 510 nm using the automatic biochemistry analyzer. The TG concentration was resultantly determined using the colorimetric method with standard glycerol normalized by PBS. ## 2.7. Assays for Lipid Peroxidation To evaluate the indexes of lipid peroxidation, the concentration of MDA was determined in the supernatant of liver samples by the thiobarbituric acid method using the MDA Assay Kit (A003-1, Jiancheng, Nanjing, China). The working solution of the MDA Assay Kit was mixed with 200 μL supernatant, 10 nmol/mL standard, and ethanol. The mixture was heated at 95 °C for 40 min and centrifuged for supernatant at 4000 rpm for 10 min. Being normalized by that of ethanol, the OD of the supernatant at 532 nm colorimetrically reflected the MDA concentration. The hepatic 4-HNE concentration was also investigated by the enzyme-linked immunosorbent assay (ELISA) method using a Mouse 4-HNE Assay Kit (F15653, Westang, Shanghai, China). In succession, 5 μL of the supernatant of liver samples was mixed with 100 μL 4-aminoantipyrine solution in the coated wells, incubated with 20 μL enzyme-conjugated antibody at 37 °C for 15 min, and subjected to the DENLEY DRAGON Wellscan MK 3 (Thermo Fisher Scientific, Waltham, MA, USA) at 550 nm. The 4-HNE concentration was finally calculated according to the standard curve. Both MDA and 4-HNE contents were calculated using MDA and 4-HNE concentrations against the total protein concentration, respectively. ## 2.8. TUNEL Assay Sections of liver samples were exposed to deparaffinization, incubation with 20 μL/mL proteinase K at 37 °C for 20 min, permeabilization at room temperature for 20 min, and blockage with $3\%$ hydrogen peroxide buffer at room temperature for 20 min. Afterwards, the TUNEL Cell Apoptosis Detection Kit (G1507, Servicebio, China) was employed to highlight the apoptotic cells in the liver. Briefly, the TdT incubation buffer was prepared using recombinant TdT enzyme, biotin-dUTP labeling mix, and equilibration buffer in 1:5:50 ratios of volume. Liver sections were then incubated with 50 μL equilibrium buffer at room temperature for 10 min, and 56 μL TdT incubation buffer in a wet box at 37 °C for 1 h. After reaction with 100 μL streptavidin-HRP buffer at 37 °C for 30 min, sections were incubated with 100 μL 3,3′-diaminobenzidine (DAB) (G1212, Servicebio) working solution, microscopically observed for positive staining, and washed using water to terminate the chromogenic reaction. Pictures from 20× scopes of sections were collected and counted using ImageJ software (https://imagej.nih.gov/ij/, accessed on 8 November 2022) for the number of positive cells in every 20× scope. The positive cells were identified using the “Color Threshold” function, with no color pass of blue and an appropriate brightness threshold that excluded normal cells and covered positive cells. After the transformation of the image type into 8-bit, the “Fill Holes” and “Watershed” functions were applied to fill the holes within every positive cell and separate the overlapping positive cells. The numbers of positive cells were counted using the “Analyze Particles” function automatically, with the unit of numbers per 20× scope. ## 2.9. RNA Interference Small interfering RNA (siRNA) targeting mouse FXR (siRNA-FXR) and its negative control (siRNA-NC) were synthesized by GenePharma (Shanghai, China). The sequences of siRNA-FXR were 5′-CAGGUUUGUUAACUGAAAUTT-3′ (sense) and 5′-AUUUCAGUUAACAAACCUGTT-3′ (antisense). Being randomized into siRNA-FXR and siRNA-NC groups, the AML12 cells were transfected with siRNA-FXR or siRNA-NC using LIPOFECTAMINE 3000 (L3000015, Thermo Fisher Scientific) according to the instructions of the manufacturer. The effect of RNA interference on FXR was tested by reverse transcription-quantitative polymerase chain reaction (RT-QPCR) and Western blot. ## 2.10. RT-QPCR The total RNA of the liver samples was extracted using RNAiso Plus (9109, Takara Bio, Shiga, Japan) and subjected to reverse transcription using the PrimeScript™ RT Reagent Kit (RR036, Takara Bio) according to the manufacturer’s instructions. Thereafter, quantitative PCR was performed using the Hieff UNICON® qPCR SYBR® Green Master Mix (Low Rox) (11199ES, Yeasen Biotechnology, Shanghai, China) on QuantStudio 3 (Thermo Fisher Scientific, Waltham, MA, USA) with three replications. Relative expression levels were normalized to the glyceraldehyde-3-phosphate dehydrogenase (GAPDH) housekeeping gene using the 2-ΔΔct method. The primers used in the experiments are shown in Table 1. ## 2.11. Western Blot Protein samples were extracted from mouse liver using RIPA lysis buffer and quantified by the BCA Protein Assay Kit (Beyotime, Shanghai, China). Total protein extracts were incubated with loading buffer at 100 °C for 10 min and subjected to electrophoresis on $8\%$ or $10\%$ sodium dodecyl sulfate-polyacrylamide gel. Afterward, proteins were transferred onto polyvinylidene difluoride membranes. The membranes were incubated with protein-free rapid blocking buffer and subsequently with primary antibodies against α-tubulin (1:1000, 66031, Proteintech, Rosemont, IL, USA), FXR (1:1000, 25055, Proteintech), SHP (1:500, sc-271511, Santa Cruz, Dallas, TX, USA), LXR (1:2000, 14351, Proteintech), and FASN (1:1000, 10624, Proteintech) overnight at 4 °C, and finally with horseradish peroxidase (HRP)-conjugated secondary antibodies at room temperature for 1 h. Protein bands were visualized by chemiluminescence using Amersham Imager 680 (AI680, Cytiva, Tokyo, Japan) and assessed using ImageJ software. ## 2.12. Immunohistochemistry Sections of liver samples were subjected to antigen retrieval using citrate buffer at 95 °C for 3 min. The blockage was performed using $3\%$ hydrogen peroxide buffer in the dark for 25 min and $3\%$ bovine serum albumin for 30 min at room temperature. Then, the liver sections were reacted with primary antibodies specific to SHP (1:50, sc-271511, Santa Cruz), LXR (1:100, 14351, Proteintech) and FASN (1:100, 10624, Proteintech), respectively, in a wet box overnight at 4 °C. After labeling with HRP-conjugated secondary antibodies (A0208 and A0216, Beyotime, Shanghai, China), immunohistochemistry signals were visualized using the DAB method. The semiquantitative analyses were performed using ImageJ software. After the exclusion of normal cell nuclei using the “Color Threshold” function and the transformation of the image type into 8-bit, the positive part of each picture was identified using the auto “Threshold” function and subjected to integral calculus for the average optical density (OD) according to the following formula (OD(x,y) ds, OD of each pixel in the positive staining area, and S, total positive staining area):Average OD=∫SODx,y ds/ S ## 2.13. Statistical Analysis The numerical variables of this study were presented as the mean ± standard error (SEM). The Kolmogorov–Smirnov test and the Brown–Forsythe test were applied for assessing the normality and homogeneity of the variance of the data, respectively. One-way ANOVA was performed to investigate the difference between multiple groups, while the difference between the two groups was analyzed using Tukey’s multiple comparisons test for the data with a normal distribution and Dunn’s multiple comparisons test for the ranked data. Correlation analyses were conducted using the Pearson correlation test between two data with a normal distribution or the Spearman correlation test between normal-distributed data and ranked data. In addition, a chi-square test was performed to investigate the difference in pathological classification and was adjusted using Bonferroni correction for multiple comparisons between two groups. SPSS version 23.0 (SPSS Inc., Chicago, IL, USA) was used for the statistical analyses. A two-side significance threshold was set at $p \leq 0.05.$ ## 3.1. γ-MCA Targeted FXR/SHP/LXRα/FASN Signaling to Inhibit High Fat-Stimulated Hepatocellular Steatosis When compared to those in the NC group, AML12 cells without γ-MCA administration (FFA group) exhibited a significant increase in LXRα and FASN levels after the FFA stimuli (Figure 1A,B). In the groups of FFA + 10 μM γ-MCA and FFA + 100 μM γ-MCA, an agonist effect of γ-MCA on FXR upregulated SHP expression at both the transcriptional and translational levels in a dose-dependent manner (Figure 1B–D). The SHP-induced LXRα inhibition subsequently abolished its transcription-activating role of FASN (Figure 1B–D). In contrast to their upregulation in the FFA group, lipid droplets and TG content were resultantly decreased by γ-MCA administration, especially in the FFA + 100 μM γ-MCA group (Figure 1E,F). Furthermore, FXR-specific RNA interference was carried out to confirm the target effect of γ-MCA (Figure 2A). FXR knockdown fundamentally abrogated the γ-MCA-dependent SHP upregulation and then rescued the SHP-based inhibition of LXRα and FASN, with a significant loss of SHP expression and gain of LXRα and FASN expression compared to the negative control of RNA interference (Figure 2B–D). As a result, γ-MCA failed to ameliorate the high fat-stimulated hepatocellular steatosis at concentrations from 10–100 μM, with a higher TG level in the FXR knockdown than the negative control (Figure 2E,F). γ-MCA, therefore, prevented hepatic lipogenesis by targeting FXR/SHP/LXRα/FASN signaling. ## 3.2. γ-MCA Attenuated Rodent Liver Steatosis Induced by the HFHC Diet After administration of the HFHC-diet for 16 weeks (Figure 3A), liver steatosis was established in the NASH group with characteristics of a beige appearance (Figure 3B), hepatocyte steatosis (Figure 3C,D), and TG accumulation (NC vs. NASH: 0.2344 ± 0.0265 mmol/g vs. 0.8556 ± 0.0630 mmol/g, $p \leq 0.0001$) (Figure 3E,F). Contrastively, mice in both NASH + 10 mg/kg γ-MCA and NASH + 100 mg/kg γ-MCA displayed improvement in their gross appearance (Figure 3B) and in their steatotic degeneration (Figure 3D), as well as a prominent decrease in the number of hepatic lipid droplets positive to oil red O staining (Figure 3F). When compared to those of the NASH group, the NASH + 100 mg/kg γ-MCA group exhibited significant reduction in liver index and non-significant difference in body weight (Figure S1). The significant downregulation of the steatosis score (NASH vs. NASH + 100 mg/kg γ-MCA: 2.7500 ± 0.1637 vs. 0.6250 ± 0.1830, $$p \leq 0.012$$) (Figure 3C) and hepatic TG content (NASH vs. NASH + 100 mg/kg γ-MCA: 0.8556 ± 0.0630 mmol/g vs. 0.4186 ± 0.0765 mmol/g, $$p \leq 0.0013$$) (Figure 3E) indicated an anti-steatosis role of γ-MCA in the rodent NASH. Being consistent with its pharmacological activity in vitro, γ-MCA showed the best therapeutic effect at a dosage of 100 mg/kg rather than 10 mg/kg (Figure 3B–F). ## 3.3. Steatotic Amelioration Reduced Hepatic Lipid Peroxidation The products of lipid peroxidation, MDA and 4-HNE, were analyzed to highlight the impact of γ-MCA-based steatotic attenuation. In the NASH group, hepatic levels of both peroxides underwent a significant increase in comparison to those of the NC group (Figure 4A,C). On the contrary, γ-MCA treatment dose-dependently reduced the production of lipid peroxides. As compared to their ascending levels in the NASH group, MDA (NASH vs. NASH + 100 mg/kg γ-MCA: 0.7255 ± 0.0645 μmol/g vs. 0.4419 ± 0.0412 μmol/g, $$p \leq 0.0141$$) and 4-HNE (NASH vs. NASH + 100 mg/kg γ-MCA: 1.0780 ± 0.1249 μg/g vs. 0.6499 ± 0.0405 μg/g, $$p \leq 0.0104$$) were downregulated with statistical significance after 100 mg/kg γ-MCA gavage (Figure 4A,C). The positive correlation of TG content and lipid peroxides, with related coefficients 0.7282 and 0.6723, both of which are $p \leq 0.0001$ (Figure 4B,D) convinced the causality between steatotic attenuation and peroxidative inhibition. ## 3.4. Mitigation of Peroxidative Injury and Apoptosis Assisted Prevention of NASH In our study, mice with liver steatosis and related lipid peroxidation (NASH group) suffered from a significant increase in serum transferases (ALT, AST) (Figure 4E,H). These indexes of hepatocyte injury experienced prominent normalization in the NASH + 10 mg/kg γ-MCA and/or NASH + 100 mg/kg γ-MCA groups (Figure 4E,H), mimicking those of the NC group. The elevation of ALT and AST levels also took place upon the excessive production of MDA (Figure 4F,I) and 4-HNE (Figure 4G,J). By a TUNEL assay, peroxidative injury in the NASH group led to multiple hepatocyte apoptosis near the inflammatory foci, which was rarely detected in the NC group (NC vs. NASH: 7.6250 ± 1.4873 vs. 107.2500 ± 13.0750, $p \leq 0.0001$) (Figure 5A,B). However, γ-MCA treatment dose-dependently prevented hepatocytes from the apoptotic process (NASH vs. NASH + 100 mg/kg γ-MCA: 107.2500 ± 13.0750 vs. 30.6250 ± 7.6763, $$p \leq 0.0002$$; NASH + 10 mg/kg γ-MCA vs. NASH + 100 mg/kg γ-MCA: 81.3750 ± 9.9641 vs. 30.6250 ± 7.6763, $$p \leq 0.0198$$) (Figure 5A,B). The apparent association between peroxides and the count of apoptotic hepatocytes presented a key part of lipid peroxidation in injury-based apoptosis (Figure 5C,D). Hepatocyte apoptosis serves as an important trigger of lobular inflammation and, together with hepatocyte steatosis and ballooning, ultimately introduces NASH. Thus, a high incidence of NASH ($\frac{6}{8}$) and borderline-NASH ($\frac{2}{8}$) occurred in the NASH group (Figure 5E,F). With the mitigation of hepatocyte apoptosis, 100 mg/kg γ-MCA effectively protected mice from NASH (NASH, $\frac{0}{8}$; borderline-NASH, $\frac{2}{8}$) by the decrease in the NAS score (NASH vs. NASH + 100 mg/kg γ-MCA: 5.7500 ± 0.5900 vs. 1.3750 ± 0.4199, $$p \leq 0.0424$$) (Figure 5E,F). The close correlation of apoptotic cell count and NAS score provided further evidence for mechanisms of γ-MCA treatment (Figure 5G), mainly on a basis of improvement in peroxidative injury and apoptosis. ## 3.5. FXR-Based Inactivation of Lipogenesis Characterized γ-MCA Administration In Vivo In the NASH group without γ-MCA exposure, there was hepatic LXRα and FASN expression much higher than those of the NC group (Figure 6A–D). The levels of FXR and SHP were kept constant (Figure 6A–D). Contrastively, the agonist effect of γ-MCA on FXR-activated SHP expression at both transcriptional (NASH vs. NASH + 100 mg/kg γ-MCA, $p \leq 0.0001$) and translational levels (NASH vs. NASH + 100 mg/kg γ-MCA, $$p \leq 0.0002$$) (Figure 6A–C). The upregulated SHP exerted an inhibitory impact on LXRα and, successively, on FASN expression (NASH vs. NASH + 100 mg/kg γ-MCA, $$p \leq 0.0213$$ in the transcriptional level, $$p \leq 0.0003$$ in the translational level; NASH + 10 mg/kg γ-MCA vs. NASH + 100 mg/kg γ-MCA, $$p \leq 0.0410$$ in the transcriptional level, $$p \leq 0.0168$$ in the translational level) (Figure 6A–C). Immunohistochemical analysis showed that at high level of SHP and low levels of LXRα, FASN were more represented among the hepatocytes in the NASH + 100 mg/kg γ-MCA group than in the NASH group (Figure 6D–F). The semiquantitative analyses were performed to precisely describe the expressive differences of SHP, LXRα, and FASN between various groups (Figure S2). Finally, evident downregulation of FASN underlays lipogenic inactivation and steatotic attenuation in hepatocytes of γ-MCA-treated mice. ## 4. Discussion FXR, an important member of the nuclear receptor superfamily, has been well described as having a crucial role in hepatic lipid metabolism [6]. Although the FXR signaling may not change significantly between NASH patients and healthy controls, FXR has been considered as a potential therapeutic target of NASH. Upon activation, FXR induces the expression of SHP by targeting the inverted repeat separated by one nucleotide (IR-1) FXR response element [19]. Then, SHP dimerizes with LXRα to abrogate its effect on metabolic genes (e.g., FASN) [20,21,22,23]. In contrast to the LXRα-dependent transcriptional activation of FASN, through the direct repeats separated by four nucleotides (DR-4) of the LXR response element it predominantly downregulates the condition of SHP-based LXRα inactivation [24,25]. The major action of FASN, namely de novo lipogenesis, is ultimately inhibited in hepatocytes. In our study, high-dose γ-MCA treatment significantly increased the hepatic SHP levels in vivo and in vitro. An antagonist effect of SHP on LXRα repressed the FASN expression, which resulted in the descendent concentration of hepatocellular TG. On the contrary, FXR knockdown abolished the pharmacological effect of γ-MCA on SHP and successively abolished LXRα at both transcriptional and translational levels. γ-MCA-induced TG reduction was also absent in these FXR-lacking hepatocytes. Therefore, γ-MCA functions to inhibit lipogenesis and related hepatic steatosis via the FXR/SHP/LXRα/FASN signaling. Hepatic steatosis represents the hallmark of NASH, mainly by means of peroxidative impairment. Mechanically, lipid overload disturbs the electron transport chain (ETC) (e.g., complex I and III) and enhances the fatty acid oxidation (FAO) [26], while a large generation of reactive oxygen species (ROS) is attributed to the FAO-related enzymes (e.g., long chain acyl-CoA dehydrogenases and very long-chain acyl-CoA dehydrogenases) [27,28]. As a result, increased production and outflow of ROS take place in mitochondria. The reaction of ROS and fatty acid produces toxic peroxides, such as MDA and 4-HNE [29]. Contrastively, γ-MCA-induced steatotic attenuation demonstrated a correlation with reduced hepatic peroxides (MDA and 4-HNE) in the present experiments. Among these lipid peroxides, MDA has been shown to modify multiple proteins in an MDA-acetaldehyde (MAA) modification manner and injure hepatocytes by disrupting the membrane integrity. Similar impairments occur in hepatocytes after their exposure to 4-HNE [30,31,32]. When compared to the peroxide-related transferase (ALT and AST) upregulation in the NASH group, these indexes of hepatocyte injury underwent improvement in both NASH + 10 mg/kg γ-MCA and/or NASH + 100 mg/kg γ-MCA groups. Taking into account its inhibitory action in lipid peroxidation, γ-MCA was convinced to exert a protective impact against peroxidative injury. Injurious stimuli, especially steatosis-based lipid peroxidation, bring about apoptosis of hepatocytes in NASH [33,34]. First, peroxidative abrogation of lysosomal integrity initiates caspase-3-dependent apoptosis [35]. Second, the MAA-modified proteins exhibit proapoptotic characteristics upon MDA exposure [36,37]. Moreover, 4-HNE serves as a universal activator of apoptosis by multiple methods, including the activation of calpain, caspase-3 [38], p53 [39,40,41], and Fas-mediated ASK1/JNK/Caspase-3 signaling [41,42]. By the γ-MCA-dependent alleviation of peroxidative impairment, hepatic apoptosis was greatly reduced in the NASH + 100 mg/kg γ-MCA group as compared to that of the NASH group. Apoptosis of hepatocytes results in chemokine-related inflammation through the activation of caspase-3 and nuclear translocation of activator protein-1 [43]. The activated Fas also promotes an inflammatory response by caspase-8-dependent activation of NLRP3 inflammasome and maturation of IL-1β [44]. BAX/BAK signaling for apoptosis triggers inflammation by both caspase-8 and NLRP3-induced IL-1β secretion [45]. In addition, hepatocytes in mice with a high-fat diet release high-mobility group box1 (HMGB1), which activates Kupffer cells to stimulate inflammation [46]. Consistently, exacerbated hepatic apoptosis features NASH patients, with a positive correlation with inflammatory activity [47]. By its anti-apoptotic property, pan-caspase inhibitor (VX-166) contrastively reduces hepatic inflammation, serum ALT levels, and liver fibrosis [48,49]. Taken together, apoptosis-based inflammation underlies NASH. Dramatically, high-dose γ-MCA treatment (100 mg/kg) in our experiments prevented hepatic apoptosis to mitigate lobular inflammation in mice. The resultant reduction of the NAS score confirmed the therapeutic effect of γ-MCA against NASH. Strong agonists of FXR (e.g., obeticholic acid (OCA)) were synthesized for the NASH treatment. However, intensive activation of FXR gives rise to upregulated low-density lipoprotein (LDL)-cholesterol and downregulated high-density lipoprotein (HDL)- cholesterol. This dysmetabolism of lipoproteins is attributed to the generation of LDL by apoC II and apoC III imbalance [50,51,52] and the increased clearance of HDL by the hepatocellular expression of CETP and the scavenger receptor-B1 [53,54]. Moreover, The FXR-based upregulation of lithocholic acid (LCA), the strongest agonist of TGR5, may contribute to TGR5-induced pruritus, which was a concern in the OCA clinical trial [55,56,57,58,59]. Thus, a moderate but not strong agonist of FXR is suggested for the strategy of NASH therapy. γ-MCA, with its natural, moderate activity as an FXR agonist indeed exerts therapeutic action against NASH. There are some limitations in this study. First, we focus our experiments on NAFLD with hepatic TG accumulation, though investigation of serum TG and TC could highlight the effect of γ-MCA on other metabolic disorders associated with NAFLD. Mice exposed to γ-MCA did not exhibit abnormality in dietary intake, behavior, appearance, and faeces, but further analyses will be valuable to take deep insight into the possibility of adverse effects and the long-term outcomes. ## 5. Conclusions NASH serves in the key step from NAFL to hepatic fibrosis/cirrhosis and HCC depending on steatosis-based peroxidative impairment. The present study uncovers that γ-MCA agonizes FXR to inhibit the lipogenesis of hepatocytes via SHP/LXRα/FASN signaling. The lipogenic inactivation attenuates liver steatosis induced by an HFHC diet. An amelioration of hepatic steatosis protects hepatocytes from lipid peroxidation and related injury. The abolishment of injurious apoptosis finally prevents NASH in the rodent model (Figure 7). These findings highlight an inhibitory effect of γ-MCA against steatosis-induced peroxidative injury, and the mitigation of NASH by targeting FXR/SHP/LXRα/FASN signaling. It suggests a potential strategy for NASH treatment by γ-MCA. ## References 1. Powell E.E., Wong V.W., Rinella M.. Non-alcoholic fatty liver disease. Lancet (2021) 397 2212-2224. DOI: 10.1016/S0140-6736(20)32511-3 2. Younossi Z.M., Koenig A.B., Abdelatif D., Fazel Y., Henry L., Wymer M.. 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--- title: Effects of Intermittent Fasting on Hypothalamus–Pituitary–Thyroid Axis, Palatable Food Intake, and Body Weight in Stressed Rats authors: - Cinthia García-Luna - Ixchel Prieto - Paulina Soberanes-Chávez - Elena Alvarez-Salas - Iván Torre-Villalvazo - Gilberto Matamoros-Trejo - Patricia de Gortari journal: Nutrients year: 2023 pmcid: PMC10005667 doi: 10.3390/nu15051164 license: CC BY 4.0 --- # Effects of Intermittent Fasting on Hypothalamus–Pituitary–Thyroid Axis, Palatable Food Intake, and Body Weight in Stressed Rats ## Abstract Dietary regimens that are focused on diminishing total caloric intake and restricting palatable food ingestion are the most common strategies for weight control. However, restrictive diet therapies have low adherence rates in obese patients, particularly in stressed individuals. Moreover, food restriction downregulates the hypothalamic–pituitary–thyroid axis (HPT) function, hindering weight loss. Intermittent fasting (IF) has emerged as an option to treat obesity. We compared the effects of IF to an all-day feeding schedule on palatable diet (PD)-stress (S)-induced hyperphagia, HPT axis function, accumbal thyrotropin-releasing hormone (TRH), and dopamine D2 receptor expression in association with adipocyte size and PPARƔ coactivator 1α (PGC1α) and uncoupling protein 1 (UCP1) expression in stressed vs. non-stressed rats. After 5 weeks, S-PD rats showed an increased energy intake and adipocyte size, fewer beige cells, and HPT axis deceleration-associated low PGC1α and UCP1 expression, as well as decreased accumbal TRH and D2 expression. Interestingly, IF reversed those parameters to control values and increased the number of beige adipocytes, UCP1, and PGC1α mRNAs, which may favor a greater energy expenditure and a reduced body weight, even in stressed rats. Our results showed that IF modulated the limbic dopaminergic and TRHergic systems that regulate feeding and HPT axis function, which controls the metabolic rate, supporting this regimen as a suitable non-pharmacologic strategy to treat obesity, even in stressed individuals. ## 1. Introduction Chronic stress exposure increases the consumption of palatable food in humans and rodents [1,2], leading them to become overweight [3]. A sustained hyperactivation of the adrenal axis maintains an elevated serum concentration of glucocorticoids (GC), which impinges on the hypothalamic arcuate (ARC) neurons, increasing the synthesis of orexigenic peptides, such as neuropeptide Y (NPY), favoring appetite and food-seeking behavior [4]. A high percentage of the obese and hyperphagic population is afflicted with chronic stress [5,6], and, as a common therapeutic strategy, health practitioners routinely recommend a daily food restriction dietary plan that induces a negative energy balance (NEB). Adaptive mechanisms arise in response to NEB, including decreased circulating leptin and insulin levels and a high GC concentration [7]. These peripheral signals downregulate the hypothalamic–pituitary–thyroid (HPT) axis function by decreasing the synthesis of thyrotropin-releasing hormone (TRH) in the hypophysiotropic neurons of the hypothalamic paraventricular nucleus (PVN), which results in low serum levels of thyroid hormones (TH) and a slow degradation of the adipose tissue energy storage [8,9,10]. This adaptive mechanism interferes with the body weight (b.w.) loss of obese patients, which dampens the success rate of this dietary regimen, elevating patient dropout rates from therapeutic programs. As an alternative to continuous energy restrictive strategies, intermittent fasting (IF) dietary regimens are gaining popularity among patients that need to lose weight [11]. IF, which involves periods of feeding during an animal’s activity phase and periods of voluntary abstinence from food intake, has been successful in improving glycemic control and lipid profile in obese and diabetic patients [12]. Some of the beneficial effects of IF are not due to the loss of weight, but to the metabolic responses to fasting itself [13,14]. The previous findings from our laboratory study show that prepubertal rats with two weeks of IF during the resting phase increase their food intake, their b.w., and present an inhibition of HPT axis activity, which is not observed in rats that are subjected to IF during their activity phase [15]. However, the advantage of IF in reducing hyperphagia in the obese population after being exposed to chronic stress has not been evaluated. Therefore, we aimed to analyze the changes in some parameters of the HPT axis function, such as serum T3 levels and the expression of PVN TRH mRNA, in stressed rodents that were subjected to an IF schedule: the time-restricted feeding model, which consists in a daily fixed feeding window of 8 h during the active phase of the rats, with no food restriction [16,17]. The stress and hyperphagia of palatable food are associated with alterations in the accumbal dopaminergic pathway and, specifically, with a low expression of dopamine D2 receptors in the nucleus accumbens (NAc); moreover, the TRH expression in that nucleus is also related to the intake of palatable food in stressed rats [2], however, the effect of an IF schedule on this alteration has not been described. Furthermore, as accumbal TRH participates in the regulation of palatable food intake in chronically stressed rats, we also evaluated its expression levels, as well as those of the D2 receptors in NAc. We hypothesized that the IF regimen would allow the stressed and the non-stressed animals to lose b.w. when compared to the groups with an all-day schedule, which would be associated with an enhanced HPT axis function and thermogenesis as a result of a high expression of the adipocytes’ mitochondrial proteins. In addition, IF would help the animals to reduce their food intake in association with increased accumbal TRH mRNA levels and D2 expression. ## 2.1. Animals Male Wistar adult rats ($$n = 52$$) with a b.w. of 300 ± 10 g were obtained from the animal facility of the Instituto Nacional de Psiquiatría Ramón de la Fuente Muñiz (INPRFM) and maintained under controlled standard conditions of temperature (24 ± 1 °C) in an inverted light cycle, with lights on from 19:00 to 7:00. The animals were housed in pairs with food and water ad libitum for one week of acclimation. All procedures were conducted with the approval of the local Ethics Committee of Animal Experimentation of the INPRFM and following the guidelines outlined in the Mexican Official Norm NOM-062-ZOO-1999. The rats were randomly assigned to the different experimental groups with regard to stress exposure (housing conditions), type of diet, and feeding schedule. Stress: The control rats were pair-housed (non-stressed) (C, $$n = 26$$) or single-housed (isolation-stressed, S, $$n = 26$$). Isolation housing is a non-invasive model of chronic stress that induces GCs serum chronic elevation and promotes the hyperphagia of standard chow and palatable diet, as well as an increase in body weight [18,19]. Diet: The rats from each housing group were divided into the following two subgroups: a control diet (CD) group that was fed regular chow (5001, PMI Nutrition International, Brentwood, CA, USA, energy = 3.35 Kcal/g, protein = $28.8\%$, fat = $13.4\%$, carbohydrates = $56.7\%$) and a palatable diet (PD) group that was fed regular chow and an energy-dense beverage that consisted of chocolate milk prepared with lactose-free whole cow milk (Alpura, Ecatepec, Mexico) and chocolate powder (Nesquik, Nestlé, Tlalnepantla, Mexico; 5.75 g/100 mL; energy = 0.76 Kcal/mL, protein = $16.1\%$, fat = $37.6\%$, carbohydrates = $46\%$) for 2 weeks: C-CD-2w ($$n = 6$$), S-CD-2w ($$n = 6$$), C-PD-2w = ($$n = 20$$), and S-PD-2w ($$n = 20$$). Pair-housed rats were offered two bottles of PD and single-housed animals were offered one bottle of PD. The chow pellets were pre-weighed and offered freely on a daily basis and the chocolate milk was prepared daily and was available ad libitum; both foods were offered 2 h after the onset of the dark phase (9:00). All groups had unlimited access to tap water. The b.w. and food intake (cumulative over 24 h) were quantified daily between 8:00 and 10:00 over a 2-week period. Chow food and chocolate milk intakes were calculated by subtracting the amount of food offered minus the amount weighed on the next day from the initial amount. After 2 weeks, the C-CD-2w and S-CD-2w animals ($$n = 6$$/group) and the C-PD-2w and S-PD-2w rats ($$n = 4$$/group) were euthanized (Figure 1). Trunk blood was collected and centrifuged at 3000 rpm for 30 min at 4 °C to separate serum; brain and mesenteric adipose tissue were rapidly dissected, frozen, and maintained at −70 °C for mRNA and protein abundance analyses. Another adipose tissue sample from each rat was fixed in formalin for histological analysis. Feeding schedule: The remaining C and S rats that were fed with a PD for 2 weeks ($$n = 16$$/group) were divided into 2 groups subjected to 2 different feeding schedules. The rats were maintained either with food available all day (PD) (C-PD, S-PD; $$n = 8$$/group) or with an intermittent fasting regimen (IF) (C-IF, S-IF; $$n = 8$$/group) for 3 additional weeks. Rats fed all day with a PD had a cumulative intake time of 5 weeks (C-PD-5w, S-PD-5w). IF consisted of a feeding window of 8 h (9:00–17:00) during the activity phase. B.w. and regular chow and chocolate milk intake were registered daily, as described above. At the end of the 5th week, all animals were euthanized as described below (Figure 1). Trunk blood and brain and mesenteric adipose tissue were collected. ## 2.2. Brain and Adipose Tissue mRNA Quantification by Real-Time PCR Analysis Coronal slices from frozen NAc (3.00 mm to 0.6 mm from bregma), PVN (−1.08 mm to −2.16 mm from bregma), and ARC (−2.16 mm to −3.36 mm from bregma) [20] were punch-dissected employing a 1 mm diameter sample corer. The total RNA of each region, and from adipose tissue, was isolated using the guanidinium thiocyanate–phenol–chloroform isoamyl alcohol technique, as described [21]. Briefly, guanidinium thiocyanate reagent was added to frozen tissue samples, followed by homogenization with a sonicator. Phenol–chloroform isoamyl alcohol ($\frac{3}{10}$ of guanidinium thiocyanate reagent volume) was added to the homogenate, incubated for 15 min, and centrifuged at 10,000 rpm for 20 min at 4 °C. The aqueous phase was transferred to another tube and mixed with isopropanol (1:1 of aqueous phase volume). After overnight incubation at −20 °C, the samples were centrifuged at 13,800 rpm for 30 min at 4 °C, followed by two washes with cold $75\%$ ethanol. The pellet was vacuum-dried and dissolved in DEPC-treated water. The RNA concentration was determined using a Biophotometer (Eppendorf, Hamburg, Germany) and the integrity was assessed with $1\%$ agarose gel electrophoresis using ethidium bromide as a marker and visualized with a UV transilluminator. The optical density of the bands was analyzed with ImageJ software (NIH, Washington, MA, USA). RNA (1.5 μg) was used for complementary DNA (cDNA) synthesis with M-MLV reverse transcriptase (Invitrogen, ThermoFisher Scientific, Waltham, MA, USA). Quantitative PCR was performed with TaqMan Gene Expression probes and TaqMan Universal PCR Master Mix (ThermoFisher Scientific, Waltham, MA, USA) in a StepOnePlus Real-Time PCR System (ThermoFisher Scientific, Waltham, MA, USA). The TaqMan gene expression probes were as follows: Actb, Rn00667869_m1; Trh, Rn00564880_m1; Npy, Rn01410145_m1; Ucp1, Rn00562126_m1; and Ppargc1a, Rn00580241_m1. The mRNA levels of all genes were normalized using actin b as a housekeeping gene. Each sample was run in duplicate. The amplification program was 40 cycles of 95 °C for 15 s, then 60 °C for 1 min. The mRNA levels were quantified via the ΔΔCt method and the percentage of change in the gene of interest for each group was obtained using the following equation: ΔΔCt = 2 − e(ΔCt = Ct Control − ΔCt experimental group) × 100. ## 2.3. Determination of Serum Hormones Content The serum CORT concentration was measured with a competitive ELISA kit (Enzo Life Sciences Inc., New York, NY, USA), following the manufacturer’s instructions (sensitivity: 27 pg/mL, and $8.4\%$ and $8.2\%$ of inter- and intra-assay variation, respectively). The total T3 serum content was determined using a commercial ELISA kit (Alpco, Salem, NH, USA), according to manufacturer’s instructions. This product has a coefficient of variation for inter-assay of $3.2\%$, an intra-assay of $4.1\%$, and a sensitivity of 0.2 ng/mL. ## 2.4. Immunoblotting The right NAc was used to determine D2 receptor expression. A total of 100 µL of RIPA lysis buffer per tissue sample (Abcam, Cambridge, UK) with protease inhibitor (1:100, Thermo Fisher Scientific, Waltham, MA, USA) was used to extract proteins using a sonicator. Tissue lysates were incubated for 20 min on ice and then centrifuged at 13,800 rpm at 4 °C for 10 min. Supernatants were collected, the same volume of 2X Laemmli buffer (Sigma-Aldrich, St. Louis, MO, USA) was added, and the samples were denatured at 95 °C for 5 min. A total of 1 µL aliquot was used for protein determination by the micro-Lowry method. Protein samples (30 µg) were loaded in $10\%$ SDS-PAGE gels for electrophoresis and, after 1 h, were transferred to Hybond-C extra nitrocellulose membranes (Amersham, LifeScience, Buckinghamshire, UK). The membranes were then incubated in a blocking solution of $5\%$ BSA in 1X PBS/Tween $0.05\%$ for 1 h and for 72 h with the primary antibody for D2 receptors (1:1000, rabbit anti-D2, AB5084P, Merck Millipore, Darmstadt, Germany) in the blocking solution. After washing with PBS/$0.1\%$ Tween, the membranes were incubated for 1 h with the secondary antibody (1:10,000 goat anti-rabbit HRP conjugate; 6721 Abcam, Cambridge, UK) in $3\%$ BSA. The membranes were then incubated with the polyclonal goat anti-actin antibody (1:1000, sc-1616, Santa Cruz Biotechnology, Inc., Dallas, TX, USA) as a loading control. Protein bands were revealed using luminol and visualized with a densitometer iBright CL1000 imaging system (Invitrogen, Waltham, MA, USA). ImageJ software was used for the analysis and quantification of luminescent signals and the values are the intensity of the D2/actin signals in arbitrary units. ## 2.5. Adipose Tissue Histologic Analysis and Immunohistochemistry (IHC) Mesenteric adipose tissue samples were fixed in PBS-buffered $4\%$ paraformaldehyde, dehydrated, embedded in paraffin, and cut into pairs of 4 µm sections. For adipocyte morphology analysis, one section was stained with hematoxylin and eosin (H&E) and digitized in a Leica DM750 microscope (Leica, Wetzlar, Germany) using a 20X lens. Analysis of the adipocyte areas was performed using Adiposoft software, as previously described [22]. The adaptive thermogenesis marker UCP1 was determined in the other obtained tissue section, as described previously [23]. Briefly, endogenous peroxidase was blocked with $3\%$ H2O2 solution. Then, non-specific background staining was avoided by using the immunohistochemistry background blocker (Enzo Life Sciences, New York, NY, USA). The adipose tissues were incubated with rabbit monoclonal anti-mouse UCP1 antibody (Abcam, Cambridge, UK) diluted at 10 µg/mL for 40 min at room temperature. Binding was identified with Universal Dako-labeled streptavidin–biotin secondary antibody (Dako, Glostrup, Denmark). The slides were incubated with streptavidin peroxidase for 15 min, followed by incubation with the peroxidase substrate 3,3′-diaminobenzidine (DAB; Sigma-Aldrich, St. Louis, MO, USA) for 10 min. The sections were counterstained with hematoxylin, dehydrated with alcohol and xylene, and mounted in resin. The negative controls were incubated with an IHC universal negative control reagent (IHC universal negative control reagent, Enzo Life Sciences, Inc., New York, NY, USA) instead of a primary antibody. Digital images were taken of each section at 40X magnification and the staining areas were quantified with ImageJ software. ## 2.6. Statistical Analysis In order to have independent observations and to compare data collected under two different housing conditions (grouped, no stress vs. isolation, stress) within the same analysis, we used the amount of food eaten per cage of the pair-housed animals as the experimental unit. Thus, for the single-housed animals, we added the amount of food eaten by two rats by randomly selecting the pair of rat’s food intake to be added with an algorithm. B.w. gain and energy intake were analyzed by two-way repeated measures ANOVA (stress × diet along time; stress × feeding schedule along time). The adipose tissue parameters’ comparisons between C-PD-2w and S-PD-2w were analyzed using Student’s t-tests. The gene (TRH, NPY, PPARƔ coactivator 1α (PGC1α), and uncoupling protein 1 (UCP1)) expression, hormone levels (CORT and T3), adipocyte size, number of beige adipocytes, and accumbal D2 receptor content were analyzed using a two-way ANOVA (stress × type of diet; stress × feeding schedule). When p was <0.05, Bonferroni’s post hoc test was performed. ## 3.1.1. Body Weight Gain On both of the experimental weeks, the b.w. gain between the non-stressed C-CD-2w and the isolation-stressed S-CD-2w rats, as well as between the C-PD-2w and S-PD-2w groups, was similar. In contrast, after the 1st week of providing the animals with a PD containing chocolate milk, the C-PD-2w and S-PD-2w animals had a 3-fold increase in b.w. gain when compared to their respective chow-fed groups (C-CD-2w = $100\%$, 10.4 ± 1.5 g of b.w. gain; S-CD-2w = 6.6 ± 1.4 g of b.w. gain). In the 2nd week, the b.w. gain of both the C-PD-2w and the S-PD-2w groups was 2-fold higher than that of their respective chow-fed controls (C-CD-2w = $100\%$, 31.2 ± 2.6 g of b.w. gain; S-CD-2w = 27 ± 3.4 g of b.w. gain). The two-way repeated measures ANOVA of b.w. gain showed an effect of diet (palatable vs. chow) (F[1,147] = 334.38, $p \leq 0.001$) and time (F[2,147] = 195.80, $p \leq 0.001$), but there were no differences between the stress conditions (paired vs. single-grouped) (Figure 2A). Those results revealed that consuming the palatable diet increased b.w. gain, but that stress did not affect this parameter. ## 3.1.2. Energy Intake The amount of energy ingested from chow pellets between the stressed and the control groups was similar throughout the experiment, revealing that stress alone did not impact the consumption of the regular food (Figure 2B). However, when the animals had access to a PD, in the first week C-PD-2w showed a reduction of $84\%$ in their energy intake from chow in comparison to that of the animals that were consuming only chow (C-CD-2w) ($100\%$; 712 ± 35 Kcal/Kg); also, a reduction of $81\%$ in the S-PD-2w rats vs. the S-CD-2w group (647 ± 28 Kcal/Kg) was found (Figure 2B). On the 2nd week, the reduction was $87\%$ in the C-PD-2w rats and $84\%$ in the S-PD-2w rats vs. their chow-fed controls (C-CD-2w = $100\%$: 899 ± 66 Kcal/Kg; S-CD-2w = 899 ± 45 Kcal/Kg) (Figure 2B). Regarding the intake of a PD, isolation stress had an effect on the amount of chocolate milk consumed, given that in the 1st week S-PD-2w increased its PD energy intake by $129\%$, and on the 2nd week it raised to $148\%$ in comparison to that of the C-PD-2w animals ($100\%$, week 1, 1587 ± 66 Kcal/Kg, week two 1906 ± 75 Kcal/Kg) (Figure 2C). Additionally, regarding the combined intake of chow and PD, both the control C-PD-2w and the stressed S-PD-2w animals in the 1st week increased their total energy intake to $239\%$ and $336\%$ vs. that of C-CD-2w ($100\%$; 712 ± 35 Kcal/Kg) and of S-CD-2w (647 ± 28 Kcal/Kg), respectively, due to the intake of chocolate milk (Figure 2D). This trend was maintained during the 2nd week, suggesting that the animals preferred to consume a PD independently of being exposed or not to stressful conditions and despite the availability of regular chow food. Two-way repeated measures ANOVA showed a significant effect of diet (PD vs. CD): F[1,66] = 268.54, $p \leq 0.001$, time: F[2,66] = 7.2, $p \leq 0.05$, and stress conditions: F[1,66] = 8.89, $p \leq 0.01$, and an interaction between those variables: F[4,66] = 2.86, $p \leq 0.05.$ This observation corroborated that stress is a factor that exacerbates the consumption of a highly palatable diet, resulting in an increase in total energy intake. ## 3.1.3. Adipose Tissue Morphology and Thermogenesis Table 1 shows that, after 2 weeks, stress increased the adipocytes’ size in the mesenteric adipose tissue of the PD-fed animals by $37\%$ vs. that of the C-PD-2w group. The Student’s t-test showed a significant difference between the groups (t[1243] = 7.51, $p \leq 0.001$). It was also observed that the number of beige adipocytes, as well as the mRNA levels of UCP1 and PGC1α, decreased after 2 weeks to $36\%$, $47\%$, and $23\%$, respectively, vs. C-PD-2w. The effect was significant for the beige adipocytes (t[16] = 1.923, $p \leq 0.05$; UCP1 t[8] = 2.165, $p \leq 0.05$, and PGC1α t[8] = 4.045, $p \leq 0.01$). ## 3.1.4. Neuroendocrine Changes This study investigated whether stress alone or combined with palatable food intake changed PVN TRH mRNA expression after 2 weeks. Here, we observed only a tendency to decrease the TRH mRNA levels in S-CD-2w vs. C-CD-2w ($100\%$, 1.1 ± 0.2 a.u.; Figure 3A), which would support the described effect of high CORT levels (Figure 3B) on down regulating the peptide’s expression [24]. Figure 3B shows that, as expected, the CORT serum levels of the S-CD-2w group increased to $185\%$ vs. C-CD-2w ($100\%$, 267 ± 43 ng/mL). Importantly, when the stressed rats (S-PD-2w) ingested chocolate milk, their CORT levels decreased to $38\%$ vs. S-CD-2w (495 ± 58 ng/mL). Two-way ANOVA showed an effect of the dietary regimen of F[1,13] = 7.072, $p \leq 0.05$, stress of F[1,13] = 14, $p \leq 0.05$, and a significant interaction between the variables of F[1,13] = 14.65, $p \leq 0.05.$ ## 3.1.5. Limbic Changes Stress exposure reduced the accumbal TRH peptide expression to undetectable levels in the S-CD-2w animals vs. the controls, whereas the TRH mRNA levels in the NAc of S-PD-2w increased vs. those of S-CD-2w but did not reach the levels of the C-PD-2w group (Figure 3C). Two-way ANOVA showed an effect of stress of F[1,17] = 34.22, $p \leq 0.001$, feeding schedule of F[1,17] = 13.77, $p \leq 0.05$, and a significant interaction between the variables of F[1, 17] = 6.33, $p \leq 0.05.$ After two weeks, the stressed groups, either chow- or PD-fed, showed a similar reduction to $65\%$ and $58\%$, respectively, in D2 content in NAc vs. C-CD-2w ($100\%$, 2.5 ± 0.2 a.u.); additionally, the PD intake by itself in the non-stressed animals (C-PD-2w) induced the same decrease (to $69\%$) as that of S-CD-2w and of S-PD-2w. No additive effect of the PD and stress was observed (Figure 3D). Two-way ANOVA showed a significant effect of stress of F[1,12] = 8.11, $p \leq 0.05$ and feeding schedule of F[1,12] = 9.84, $p \leq 0.01.$ ## 3.2.1. Body Weight After two weeks of isolation stress and eating a PD, the C and S animals were subdivided into two groups and maintained for three more weeks with all-day feeding or IF schedules (with a feeding window of 8 h during the activity phase). We observed that the stressed rats with continuous access to palatable food (S-PD-5w) did not have a different b.w. gain compared to the C-PD-5w group at any week of the experiment. However, both of the groups with intermittent fasting (C-IF, S-IF) showed a lower b.w. gain than their respective all-day-fed controls (C-PD-5w and S-PD-5w), but with no difference between them (Figure 4A). Regarding the effect of IF, in the 2nd week the C-IF group reduced their b.w. gain to $35\%$, and in the 3rd to $36\%$ of that of C-PD-5w ($100\%$, 27 ± 4 g in week two; and 34 ± 4 g in week three). S-IF reduced $105\%$ of their b.w. gain on week two and decreased to $90\%$ on week three vs. S-PD-5w ($100\%$, week two: 23 ± 5 g b.w. gain; week three: 37 ± 6 g). Two-way repeated measures ANOVA showed an effect of stress of F[1,83] = 4.05, $p \leq 0.05$, time of F[2,83] = 7.04, $p \leq 0.001$, and feeding schedule of F[1,83] = 52.96, $p \leq 0.001$ (Figure 4A). ## 3.2.2. Energy Intake When the animals were fed all day, stress exposure (S-PD-5w) did not induce a greater ingestion of calories from the chow, from the PD, or from the total energy intake when compared to that of C-PD-5w at any week that was analyzed (Figure 4B–D). C-IF increased the energy intake from the chow pellets to $159\%$ on week two and to $160\%$ on week three, when compared to C-PD-5w ($100\%$, week two: 162 ± 21; week three: 131 ± 25 Kcal/Kg), and S-IF increased the chow energy intake until week three to $229\%$ vs. S-PD-5w ($100\%$, 88 ± 21 Kcal/Kg) (Figure 4B). Interestingly, the C-IF animals also decreased their PD ingestion to $54\%$ after two weeks, and to $52\%$ on week three vs. C-PD-5w ($100\%$, week two: 2330 ± 96; week three: 2243 ± 154 Kcal/Kg) (Figure 4C), and S-IF reduced their palatable food intake to $60\%$ after two weeks and to $63\%$ on week two vs. that of S-PD-5w ($100\%$, week two: 2997 ± 556; week three: 2518 ± 427 Kcal/Kg) (Figure 4C), but there was no difference between C-IF and S-IF (Figure 4C). Considering the total energy intake, C-IF showed a significant reduction to $60\%$ on week two and to $58\%$ on week three in comparison to C-PD-5w ($100\%$, week two: 2492 ± 90; week three: 2374 ± 140 Kcal/kg). When compared to S-PD-5w ($100\%$, week three: 2606 ± 449 Kcal/kg), S-IF also reduced their total energy intake to $69\%$ after three weeks (Figure 4D). Two-way repeated measures ANOVA showed an effect of stress of F[1,51] = 9.36, $p \leq 0.01$; time of F[3,51] = 5.91, $p \leq 0.05$, feeding schedule of F[1,51] = 37.35, $p \leq 0.0001$, and an interaction between time and feeding schedule of F[3,51] = 5.07, $p \leq 0.01.$ ## 3.2.3. Adipose Tissue Morphology and Thermogenesis Hematoxylin and eosin staining of the mesenteric adipose tissue sections (Figure 5A) revealed that isolation stress increased the mean size of the adipocytes in S-PD-5w to $155\%$ when compared to that of C-PD-5w ($100\%$, 973.5 ± 37 µm2), indicating impaired adipogenesis leading to hypertrophic growth (Figure 5B). Interestingly, the IF regimen was able to decrease the adipocyte size in both C-IF and S-IF to $68\%$ and $57\%$ of that of C-PD-5w (fed all day) or S-PD-5w, respectively (Figure 5B). For the adipocyte sizes, two-way ANOVA showed an effect of stress exposure of F[1,842] = 47.459, $p \leq 0.001$, feeding schedule of F[1,842] = 79.25, $p \leq 0.001$, and an interaction between the variables of F[1,842] = 9.894, $p \leq 0.01.$ IHC UCP1 staining (Figure 5A) showed that IF increased the number of beige adipocytes to $431\%$ in C-IF and to $400\%$ in S-IF vs. their respective all-day-fed groups (C-PD-5w = 2 ± 0.5 cells/field; S-PD-5w = 1 ± 0.3 cells/field) (Figure 5C). To confirm the IHC findings, we evaluated the mRNA expression of UCP1 and of the PGC1α in the adipose tissue. We observed that IF increased the UCP1 mRNA levels to $279\%$ in C-IF and to $220\%$ in S-IF groups vs. C-PD-5w and S-PD-5w, respectively, (C-PD-5w = $100\%$, 1.76 ± 0.2 a.u.; S-PD-5w = $100\%$ 1.32 ± 0.3 a.u.) ( Figure 5D), and the PGC1α mRNA content to $266\%$ and $256\%$ in C-IF and S-IF rats vs. their respective all-day-fed groups (C-PD-5w = $100\%$, 1.03 ± 0.2 a.u.; S-PD-5w = $100\%$, 0.54 ± 0.1) (Figure 5E). Two-way ANOVA showed an effect of stress of F[1,32] = 8.311, $p \leq 0.01$ and feeding schedule of F[1,32] = 48.46, $p \leq 0.001$ for brite adipocytes; an effect of stress of F[1,16] = 9.695, $p \leq 0.01$ and feeding schedule of F[1,16] = 36.766, $p \leq 0.001$ for the UCP1 mRNA levels; and an effect of stress of F[1,16] = 16.344, $p \leq 0.01$ and feeding schedule of F[1,16] = 31.167, $p \leq 0.001$ for the PGC1α mRNA content. ## 3.2.4. Neuroendocrine Changes The S-IF rats showed a significant upregulation of PVN TRH mRNA expression by $119\%$ when compared to C-IF ($100\%$, 2 ± 0.9 a.u.) ( Figure 6A), but it was similar to S-PD-5w. Two-way ANOVA revealed an effect of feeding schedule of F[1,12] = 9.235, $p \leq 0.01$ and stress exposure of F[1,12] = 3.683, $p \leq 0.05.$ The ARC NPY mRNA content decreased only in the S-IF group to $59\%$ when compared to C-IF ($100\%$, 1.02 ± 0.15 a.u.) ( Figure 6B). Two-way ANOVA showed an effect of stress and feeding schedule of F[1,11] = 3.969, $p \leq 0.05.$ The CORT values in S-PD-5w were similar to those of C-PD-5w. Knowing that low food intake is a stressor, it was interesting to observe that the C-IF group did not show an increase in CORT levels, even when that group ate less energy than C-PD-5w; more importantly, the combination of reduced food intake and chronic stress exposure in the S-IF rats was unable to increase the CORT levels (Figure 6C). When analyzing the T3 levels, we observed that the rats under an IF schedule, either with or without stress exposure (S-IF, C-IF), showed $16\%$ more hormone serum concentration than that of the groups with access to food all day (C-PD-5w: 5 ± 0.03, S-PD-5w: 5.4 ± 0.2 ng/mL) (Figure 6D), which could have facilitated their reduced b.w. gain. Two-way ANOVA showed differences in the feeding schedule of F[1,27] = 26.336, $p \leq 0.001.$ ## 3.2.5. Limbic Changes When analyzing the NAc TRH mRNA expression in both the C-IF and the S-IF animals, we observed an increment when compared to their respective all-day-fed controls (C-PD-5w and S-PD-5w), (Figure 7A). Two-way ANOVA showed that the feeding schedule had a significant effect of F[1,14] = 5.80, $p \leq 0.05.$ When comparing the accumbal D2 expression, we observed that only the S-PD-5w group showed a decrease in their D2 density vs. C-PD-5w, which was reversed by the IF schedule (Figure 7B). Two-way ANOVA showed a significant effect of stress of F[1,9] = 5.36, $p \leq 0.05$ and of the interaction between stress and feeding schedule of F[1,9] = 6.5, $p \leq 0.05.$ We also found that the duration of eating palatable food was a relevant factor for D2 expression changes. After 5 weeks of PD intake, the D2 density in NAc decreased to $39\%$ and $14\%$ vs. 2 weeks in the stressed or non-stressed animals, respectively, (C-PD-2w: $100\%$; 2.5 ± 0.2 a.u.). Furthermore, after 5 weeks, S-PD-5w showed a reduction of $64\%$ in D2 density vs. C-PD-5w (1 ± 0.2 a.u.), supporting an additive effect of stress and palatable food only in the long term (Figure 7B). There was a significant effect of stress of F[1,19] = 11.19, $p \leq 0.01$, feeding schedule of F[2,19] = 19.58, $p \leq 0.001$, and of the interaction between stress and feeding schedule of F[2,19] = 6.58, $p \leq 0.01.$ ## 4. Discussion In both rodents and humans, chronic stress is characterized by a sustained elevation of serum CORT levels, which is a steroid hormone that is implicated in different metabolic, neurologic, and behavioral changes as adaptive processes that allow individuals to cope with a persistent challenge. High serum CORT levels are responsible for the negative emotions that are experienced by individuals, including anxiety, irritability, and impulsivity, among others, which trigger some compensatory behaviors that ameliorate the adverse perception of stressful situations, such as a high intake of palatable food. ## 4.1. Two-Week Stressed and Non-Stressed Rats Eating Chow or Palatable Food The increased intake of palatable food in the stress-exposed rats allowed the animals to restore the energy stores that were depleted by CORT actions, which were elevated by the isolation. In contrast, when the stressed animals were fed with chow pellets, they did not show any change in their food intake, which supported the specificity of the high-fat-high-sugar palatable food to activate the rewarding processes in the brain and to motivate the animals to maintain hyperphagia of the PD. Our results are in agreement with the stress-induced high intake of palatable food after 2 weeks of isolation [2,18,19]. The higher intake of the PD, and of fat in particular, has been described as being able to stimulate the activity of the tyrosine hydroxylase enzyme, thus increasing the synthesis of dopamine in the ventral tegmental area (VTA), as well as its release in the NAc. Given that a high-fat-high-sugar diet modifies D1 and D2 dopamine receptors’ density in the NAc, it might enhance the rewarding effects of palatable foods [25]. As previously mentioned, the high palatable food consumption of the S-PD-2w group is known to be a compensatory behavior in response to the stress exposure and elevation of CORT serum levels, which our laboratory study [2] and others [18,19] have observed in single-housed rats. Stressful conditions can impair whole-body energy homeostasis by interfering with the central and peripheral circuits that regulate food intake and energy expenditure, leading to b.w. gain [26]. Adipose tissue is a pivotal organ regulating short- and long-term energy homeostasis in the body. In periods of increased food intake, adipose tissue expands in order to allocate energy excess as triacylglycerides in the lipid vacuole of adipocytes. Healthy adipose tissue expansion results from the coordinated process of hyperplasia (an increase in adipocyte number) and hypertrophy (an increase in adipocyte size) [27]. Hyperplastic adipose tissue growth also favors the differentiation and the recruitment of beige adipocytes, which are abundant in mitochondria as well as in the expression of the electron-transport uncoupler UCP1. These adipocytes dissipate the stored energy as heat, counteracting fat mass accretion [28]. However, a sustained exposure to elevated GC levels may impair adipose tissue hyperplasia and beige adipocyte recruitment [29,30]. Accordingly, the S-PD-2w rats with an early enhancement of CORT serum levels exhibited a significant increase in adipocyte size, denoting hypertrophic adipose tissue and visceral fat accumulation [31]. Thus, 2 weeks of isolation stress was able to impair healthy adipose tissue expansion, despite not being reflected in a significant b.w. gain. S-PD-2w could be experiencing greater muscle wasting than the controls due to an increase in the local T3 concentration by a higher activity of the muscular deiodinase enzyme, which has an inverse relation with CORT levels [32], however, this awaits confirmation. Stress also reduced the number of thermogenic beige adipocytes in S-PD-2w, hampering energy expenditure and b.w. regulation. This was associated with a decrease in the expression of the coactivator PGC1α, which is a critical component of the transcriptional machinery that activates thermogenic gene expression in beige adipocytes, including that of UCP1 [33,34]. The initial increase in GCs in this group might impair PGC1α expression and activity, preventing the transdifferentiation of white to beige adipocytes, as has been described [35]. Overall, the detrimental effects of CORT on adipose tissue differentiation and thermogenesis might affect the energy metabolism and ultimately promote obesity. ## 4.1.1. Neuroendocrine Changes The TRH mRNA levels decreased in the PVN of the stressed chow-fed rats (S-CD), a congruent change with the repressive effect of CORT on TRH transcription, since the pro-TRH gene promoter contains a consensus site for the glucocorticoid receptor (GR) [24,36]. This observation supported the idea that PVN TRH expression is downstream of the CORT endocrine effects in the isolation-stress group, which might induce a hypothyroideal condition, as has been previously described [7]. Interestingly, in the S-PD-2w rats, the TRH mRNA levels did not decrease, supporting the idea that the ingestion of palatable food decreased CORT levels and, in consequence, maintained TRH expression in basal levels. A high-fat diet intake reduces the stress-induced high serum levels of CORT [37,38], as well as animals’ negative emotions that are associated with the exposure to a threatful challenge. This is well described in chronically isolated stressed male rats during 2 weeks with access to chocolate milk, which showed basal levels of serum GCs, in contrast to the high CORT levels that were exhibited by the stressed rats that only eat chow food [2]. Our present findings support those observations, given that after 15 days of isolation the CORT levels decreased in the S-PD-2w rats to basal values, but those of the chow-fed rats (S-CD-2w) remained elevated. Our results also have suggested that GCs participated in the induction of palatable food hyperphagia, since once the GCs had triggered the activation of the PD intake, the motivation for the high-fat-high-sugar food was sustained, even after the reduction in GC serum content. ## 4.1.2. Limbic Changes Our results regarding TRH expression in the NAc are important to support the putative role of the peptide in regulating the stress-induced high intake of palatable food. The NAc is involved in controlling the motivated behaviors that are reinforced and repeated by individuals as a response to the pleasurable characteristics of the reward, which, in this case, is represented by the high-fat and high-sugar content of the chocolate milk. A decreased TRH expression in the NAc has been associated with a higher intake of palatable food in chronically stressed rats [2]. Moreover, elevated CORT levels modulate the accumbal TRHergic pathway’s involvement in palatable food hyperphagia, since the blockade of GR in the NAc avoids both the decrease in the TRH mRNA levels in this region and the high palatable food ingestion of the stressed rats. In this study we corroborated the participation of the accumbal TRHergic system in the regulation of stress-induced hedonic feeding, since the peptide’s expression diminished in the NAc of the stressed animals when consuming a high volume of palatable food (S-PD-2w). The effect of stress in inducing hyperphagia was specific for the PD, since the stressed animals that were fed with a chow diet (S-CD-2w) also displayed a reduction in TRH mRNA levels in the NAc, but did not ingest a higher amount of chow than C-CD-2w. Overall, it appears that the GC levels downregulated TRH synthesis in the NAc, and the low expression of TRH in this brain region might be involved only in the stress-induced hyperphagia of palatable food in animals, but not of chow food. As expected, we found that stress and palatable food intake for 2 weeks induced D2 receptor density reduction in the NAc. It is proposed that a previous high dopamine release induces the desensitization of the D2 receptor in NAc neurons as a compensatory mechanism for the hyperactivity of the dopaminergic pathway after animals are presented with a rewarding stimulus, such as palatable food [39]. A decreased density of D2 is found in the striatal brain region of obese patients [40] and of stressed patients [41], as well as in obese rodents [42]. Since we did not find an additive effect of stress and a PD, it is likely that both of these factors affected the same pathway to modify D2 density in the NAc. Furthermore, after 2 weeks of isolation, the PD intake, or a combination of both conditions, induced a decrease in the accumbal D2 content to similar levels; however, it was inversely correlated only with the hyperphagia of palatable food but not with chow food intake. ## 4.2. Effects of 3 Weeks of Intermittent Fasting Schedule in Stressed and Non-Stressed Rats Fed with Palatable Food Stress-exposed rats with access to palatable food (S-PD-5w) did not show a higher chocolate milk intake than the controls, as observed after 2 weeks. This might result from the CORT serum levels that were found in basal values in both of the groups, which might have been due to the palatable food ingestion [37,38]. As a consequence, their b.w. gain was also similar. However, the adipocyte size was higher, and the number of thermogenic beige cells decreased in S-PD-5w, as well as their expression of UCP1 and PGC1α, suggesting that the early elevation of CORT in the stress-exposed animals triggered alterations in the adipose tissue metabolism that persisted in the long term. Interestingly, an IF regimen was able to reduce the stress-induced high intake of chocolate milk and to increase chow consumption in both of the IF groups vs. the all-day-fed animals. As the IF groups were allowed to eat only during 8 h of their activity phase, our results showed that subjecting the rats to IF was sufficient to reduce the intake of the PD, even if it was offered ad libitum. The b.w. of the IF animals also decreased as a result of the lower food intake, along with an increased number of beige adipose cells. These results uphold this feeding schedule as a successful non-pharmacologic strategy for the treatment of overweight individuals and obesity. To explain the effect of IF on palatable food intake, we deemed it possible that the circadian-regulated expression of the brain feeding signals in the IF groups was switched-on and efficiently coordinated with the light-induced peripheral anabolic hormones’ release, in such a way that the appetite-stimulating system was turned off for a longer period (16 h of fasting per day). It is also plausible that, as the NAc is now considered as a region where the expression of the dopaminergic system’s genes are synchronized with circadian rhythms [43], offering food to animals only at the fixed schedule during their active phase might be working as a zeitgeber that entrained the accumbal reward’s signals to motivate the animals to eat only during that period, and to inhibit feeding during their resting phase. The effect of the activation of the reward system only during the activity phase, as induced by our IF schedule, has also proven to be beneficial in ameliorating the consumption of drugs of abuse induced by chronodisruption of the reward system [44]. The entraining of the circadian regulation of the reward system’s genes to the fixed feeding schedule seemed to avoid the dopaminergic system regulation of the rewarding process of the palatable food in the stressed animals (S-IF). The chronic intake of saturated fats reduces the density of D2 in the NAc, which impedes the released dopamine to stimulate its receptors and to induce the satisfactory experience of the rewarding effects of fat [39]. Here, IF seemed to avoid the decrease in the accumbal D2 density, even when a high-fat-high-sugar food was consumed by the stressed animals (S-IF). A reduced D2 density in the NAc of obese people supports a low perception of food-rewarding properties that is related to their hyperphagia [40]. The rats that were subjected to the IF schedule reduced their b.w. compared to the groups with constant food availability, independently of stress exposure. This phenomenon could be partially due to increased adipose tissue thermogenesis, as evidenced by the high expression levels of PGC1α and UCP-1 proteins and beige cell abundance in the adipose tissue of the rats that were subjected to IF. These changes, along with the reduction in adipocyte size, suggested that IF can stimulate thermogenic beige adipocyte proliferation and activity, increasing energy expenditure and leading to a NEB that favors body weight reduction. This is similar to that observed in rats that were fed with a chow or high-fat diet and subjected to an IF schedule, which exhibited an increased content of UCP1 and PGC1α, as well as more browning of visceral adipose tissue [45,46,47]. Moreover, the higher T3 serum concentration that is shown in IF rats correlated with their increased adipocytes’ UCP1 and PGC1α mRNAs, given that their gene promoters have a site for TH binding (thyroid hormones’ response element, TRE) [48,49]. The smaller size of the adipocytes exhibited by C-IF and S-IF groups could also be related to a higher activity of the lipoprotein lipase enzyme, as it has been described in mesenteric, perirenal, and subcutaneous adipose tissue of rats that were subjected to time-restricted feeding [50]; however, this needs further analysis. Furthermore, even if the IF rats were not food restricted or chow fed, they voluntarily reduced their chocolate milk drinking and increased the ingestion of chow food vs. the all-day-fed groups. Thus, the reduction in b.w. gain of the IF animals seemed to be in part due to their lower energy intake. ## 4.2.1. Neuroendocrine Changes Given the anorexic effects of TRH administration [51,52,53,54], we expected to observe high mRNA levels in the PVN of both of the IF groups, which ingested less palatable food than those that were maintained in an all-day-fed schedule; however, this was only true for the stressed-IF animals. The TRHergic neurons of the PVN receive different inputs, such as ARC NPY-expressing afferents, which send their projections to the PVN and are able to decrease the TRH mRNA levels and the HPT axis function [55,56]. Our results support the idea that the high TRH expression in the PVN might be a consequence of the reduced NPY levels that impaired the NPY orexigenic effects in IF-stressed animals, even when they were offered palatable food. The factor that was involved in inducing those changes in S-IF and not in C-IF could be a greater sensitivity to leptin and the return to basal levels of GCs (after being elevated before subjection to a PD and IF schedule), which allowed the NPY expression to decrease in S-IF only. The S-PD-5w rats exhibited CORT basal levels, likely due to the ingestion of a PD. In addition, even when C-IF and S-IF ate a lower amount of food than their all-day-eating controls, they did not show any elevation in their CORT levels either, as seen in the NEB conditions [7]. This suggests that IF is a feeding schedule that avoids the activation of the adrenal axis in stressed rats even when it decreases their food intake. The increased T3 serum levels that were observed in C-IF and S-IF animals might be responsible for their low b.w. gain, which was associated with their increased HPT axis activity that stimulated their metabolic rate. Only S-IF showed high levels of T3 along with increased PVN TRH mRNA expression, which revealed that the negative feedback of the TH to the hypothalamic cells was blocked, allowing the animals to metabolize the lipids and the carbohydrates of the chocolate milk efficiently. In contrast, although T3 was also elevated in the C-IF group, the PVN TRH mRNA levels did not decrease, but were maintained at control levels. This might slow the lipid degradation and b.w. loss of that group in the long term. The decrease in total energy intake is known to induce a negative energy balance, which is represented by a decreased expression of TRH in the PVN and low T3 serum levels in order to preserve the energy reserves. However, we found an increase in the T3 serum concentration in the C-IF and S-IF groups that was not able to reduce PVN TRH transcription. Therefore, IF allowed a reduction in food intake with no adaptations of the HPT axis, due to the NEB. ## 4.2.2. Limbic Changes The IF schedule was able to increase the TRH mRNA in the NAc of the stressed and non-stressed animals (C-IF and S-IF), which was associated with their reduced ingestion of palatable food. In contrast, the animals with all-day access to food showed no changes in TRH expression and greater food intake and b.w. As mentioned previously, the NAc expression of the TRH decreases in chronically stressed animals, which correlates with their higher intake of palatable food. Furthermore, the injection of the GR antagonist into the NAc reduces the hyperphagic behavior of rats along with enhancing the TRH mRNA levels in this region [2]. Therefore, our results here supported the anorexigenic role of the accumbal TRH pathway, but also that IF was able to dampen both the effects of the PD and of stress for inducing hyperphagia through activating the TRHergic system of the NAc. After 5 weeks of PD intake, both the stressed and the non-stressed groups showed a greater decrease in accumbal D2 receptor density than those of their 2-week respective groups, which supports the idea that D2 is sensitive to a sustained ingestion of energy-rich food. This is in agreement with the findings showing that the consumption of a PD stimulates the dopaminergic pathway [57,58,59] and decreases D2 density [39]. Furthermore, after 5 weeks, the stressed group with a PD (S-PD-5w) showed decreased accumbal D2 content vs. the non-stressed group (C-PD-5w); these results suggest that there was an additive effect of stress and PD in changing the D2 protein expression, but only at longer periods of ingesting chocolate milk. The IF schedule reversed the reduction in D2 in the stressed animals. As both of the groups that were subjected to IF decreased their palatable food intake in comparison to the all-day-eating rats, the reduced D2 content in the NAc could not be associated with the low PD intake that was observed. In contrast, this might be related to the low b.w. gain that was observed in C-IF and S-IF vs. their respective controls (C-PD-5w and S-PD-5w). This association between high b.w. and low D2 density in the NAc has been proposed previously, since rodents eating an equal amount of Kcal from a high-fat or a control diet showed decreased D2 density in the NAc only in heavier HFD-fed rats [39]. Thus, the b.w. gain seems to be more related to the D2 changes than to PD intake [60], which is likely due to an altered concentration of peripheral adipokines in lighter animals, which have receptors in the NAc or VTA and are able to modify the dopaminergic pathway [61,62]. ## 5. Conclusions Our results have supported the benefits of an IF schedule as a non-pharmacologic strategy to prevent and treat stress-associated obesity, by reducing the stress-induced hyperphagia of palatable food. The beneficial effects of IF involve the upregulation of the HPT axis function and the downregulation of the adrenal axis, favoring energy expenditure by beige adipocytes thermogenesis, as well as the modulation of the reward system through attenuating the accumbal D2 density decrease and that of TRHergic accumbal function. ## References 1. Falcón E., Valdés-Moreno M., Rodríguez C., Sanabrais-Jiménez M., Hernández-Muñoz S., Camarena B., de Gortari P.. 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--- title: Update of a Genetic Risk Score Predictive of the Plasma Triglyceride Response to an Omega-3 Fatty Acid Supplementation in the FAS Study authors: - Ellie Gauthier - Juan de Toro-Martín - Bastien Vallée-Marcotte - Simone Lemieux - Iwona Rudkowska - Patrick Couture - Marie-Claude Vohl journal: Nutrients year: 2023 pmcid: PMC10005670 doi: 10.3390/nu15051156 license: CC BY 4.0 --- # Update of a Genetic Risk Score Predictive of the Plasma Triglyceride Response to an Omega-3 Fatty Acid Supplementation in the FAS Study ## Abstract A genetic risk score (GRS) predictive of the plasma triglyceride (TG) response to an omega-3 fatty acid (n-3 FA) supplementation has been previously developed in the Fatty Acid Sensor (FAS) Study. Recently, novel single nucleotide polymorphisms (SNPs) interacting with a fish oil supplementation and associated with plasma lipid levels have been identified in the UK Biobank. The aim of this study was to verify whether the addition of SNPs identified in the UK Biobank to the GRS built in the FAS Study improves its capacity to predict the plasma TG response to an n-3 FA supplementation. SNPs interacting with fish oil supplementation in the modulation of plasma lipid levels in the UK Biobank and associated with plasma TG levels have been genotyped in participants of the FAS Study ($$n = 141$$). Participants have been supplemented with 5 g fish oil/day for six weeks. Plasma TG concentrations were measured before and after the supplementation. Based on the initial GRS of 31 SNPs (GRS31), we computed three new GRSs by adding new SNPs identified in the UK Biobank: GRS32 (rs55707100), GRS38 (seven new SNPs specifically associated with plasma TG levels), and GRS46 (all 15 new SNPs associated with plasma lipid levels). The initial GRS31 explained $50.1\%$ of the variance in plasma TG levels during the intervention, whereas GRS32, GRS38, and GRS46 explained $49.1\%$, $45.9\%$, and $45\%$, respectively. A significant impact on the probability of being classified as a responder or a nonresponder was found for each of the GRSs analyzed, but none of them outperformed the predictive capacity of GRS31 in any of the metrics analyzed, i.e., accuracy, area under the response operating curve (AUC-ROC), sensitivity, specificity and McFadden’s pseudo R2. The addition of SNPs identified in the UK Biobank to the initial GRS31 did not significantly improve its capacity to predict the plasma TG response to an n-3 FA supplementation. Thus, GRS31 still remains the most precise tool so far by which to discriminate the individual responsiveness to n-3 FAs. Further studies are needed in the field to increase our knowledge of factors underlying the heterogeneity observed in the metabolic response to an n-3 FA supplementation. ## 1. Introduction Numerous studies suggest that the consumption of marine omega-3 fatty acids (n-3 FAs), i.e., eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) may reduce the risk of cardiovascular events, by decreasing plasma triglyceride (TG) levels [1,2]. However, there is a large interindividual variability in the metabolic response to n-3 FAs [3]. In the Fatty Acid Sensor (FAS) Study conducted by our group, it was reported that $29\%$ of the participants did not lower their plasma TG levels after a six-week supplementation of 5 g of fish oil providing 1.9–2.2 g EPA and 1.1 g DHA daily. It is well demonstrated in the literature that this interindividual variability is partly due to genetic factors [4]. A genome-wide association study (GWAS) revealed genetic markers associated with the plasma TG response in participants of the FAS Study. Significant GWAS signals were found in 6 loci: IQCJ-SCHIP1, NXPH1, PHF17, MYB, NELL1, and SLIT2 [4]. After fine mapping, 31 single nucleotide polymorphisms (SNPs) were used to calculate a genetic risk score (GRS), which explained $49.7\%$ of the variance in the plasma TG levels during the supplementation protocol [5]. According to this GRS31, carriers of more risk alleles are more likely to be nonresponders and not to lower their plasma TG levels following the n-3 FA supplementation [5]. Similarly, a recent GWAS on 73,962 individuals of the UK Biobank identified new loci interacting with fish oil supplementation to modulate plasma lipid levels [6]. More precisely, they identified gene–diet interactions with fish oil supplementation and influencing either plasma TG, low-density cholesterol (LDL-C), high-density cholesterol (HDL-C), or total cholesterol (TC) levels by using longitudinal data. These SNPs were all individually located in different genes, with those associated with plasma TG levels being in GJB6, BAZ1B, LOC107986921, MAP1A, and HAPLN4 genes. These newly identified SNPs were of particular interest because of significant gene–diet interactions with n-3 FA supplementation, and they were located in different genes than those previously identified in the FAS Study. We then hypothesized that their addition to the previously developed GRS31 may improve its capacity to discriminate between different phenotypes of plasma TG response to an n-3 FA supplementation. The objective of the present study was then to test whether the addition of SNPs identified in the UK Biobank may improve the predictive capacity of the GRS31 built in the FAS Study, which will, ultimately, help predict an individual’s plasma TG response to an n-3 FA supplementation. ## 2.1. FAS Study Population A total of 254 healthy subjects from the Quebec City metropolitan area were recruited into the FAS Study from 2009 to 2011. Participants had a body mass index (BMI) between 25 kg/m2 and 40 kg/m2, were aged between 18 and 50 years old, were nonsmokers, and were free from metabolic or thyroid disorder requiring a pharmacological treatment. Subjects were excluded if they had taken n-3 FA supplements for a minimum of six months prior to the intervention. A total of 210 participants completed the intervention protocol. The experimental protocol was approved by the ethics committees of Laval University Hospital Research Center and Laval University. This nontrial was registered at www.clinicaltrials.gov as NCT01343342. Plasma TG levels of two participants were unavailable for further analyses, which excluded them from the final sample. The 208 remaining subjects were subsequently separated into two subgroups: responders and nonresponders. Responders were defined as subjects whose plasma TG levels decreased after the n-3 FA supplementation (change in TG levels ≤ 0.01 mM) whereas nonresponders had increased or stable plasma TG levels (change in TG of ≥ 0.00 mM), as previously described [4]. From these groups, GWAS was done on a total of 141 participants showing the most extreme plasma TG response to an n-3 FA supplementation, of which 81 were responders (showing at least a $10\%$ decrease) and 60 were nonresponders. These 141 participants with available GWAS data were included in the present study. ## 2.2. Study Design Study design and diets have been previously reported [7,8]. Briefly, all participants from the FAS Study followed a run-in period of two weeks, during which they were given dietary instructions by a trained registered dietitian in order to achieve dietary recommendations from Canada’s Food Guide [9]. The aims of these instructions were to maintain a stable body weight and control participants’ n-3 FA intake throughout the intervention protocol. After the run-in period, subjects were asked to take five capsules per day of fish oil for six weeks. Capsules provided a daily intake of 1.9–2.2 g EPA and 1.1 g DHA. Blood samples were collected before and after the supplementation. Prior to the collection, participants had to fast for 12 h and abstain from alcohol consumption for 48 h. Lipid concentrations were measured by enzymatic assays as previously described [4]. ## 2.3. SNP Genotyping All 14 SNPs identified in the UK Biobank were genotyped in the 141 participants of the FAS Study [5,6]. Genomic data from the blood samples were first extracted by using the GenElute Gel Extraction Kit (Sigma-Aldrich Co., St. Louis, MO, USA). Genotyping was performed by Agena MassARRAY platform with iPLEX gold chemistry at the Centre d’expertise et de services Génome Québec [10]. Three extra SNPs (rs11983997, rs80189144 and rs147166404) in linkage disequilibrium (r2 > 0.5) with rare SNPs (MAF < 0.01) were also genotyped by using TaqMan technology at the Institute of Nutrition and Functional Foods at Université Laval, which formed an initial sample of 17 SNPs. Genotyping failed for one participant, leading a final cohort of 140 subjects. ## 2.4. GRS Construction PLINK software [11] was used to compare SNP allele frequency between responders and nonresponders, calculate the SNPs’ minor allele frequency (MAF) and conduct a Hardy–Weinberg equilibrium (HWE) test for each SNP. HWE’s significance threshold was set at $$p \leq 0.05.$$ These analyses revealed two monomorphic SNPs (rs147166404 and rs530804537) in the FAS Study, i.e., none of the participants carried the rare allele. Both SNPs were excluded from further analysis, yielding a final total of 15 SNPs, of which seven were specifically associated with plasma TG levels in the UK Biobank. The odds ratios (OR) of the proportion of nonresponders over responders carrying the minor allele with a $95\%$ confidence interval ($95\%$ CI) were used to define a risk score for each SNP. A score of 1 was attributed to SNPs associated with an increase of plasma lipid levels after the n-3 FA supplementation (OR < 1), whereas a score of −1 was attributed to SNPs associated with a decrease of plasma lipid levels (OR > 1). SNPs with MAF = 0 in the responder group were excluded from the construction of the GRS. In total, three new GRSs were calculated for each participant by summing up the score of risk alleles of the 31 SNPs (GRS31) from our initial GRS with: 1- GRS32 (addition of rs55707100); 2- GRS38 (addition of seven SNPs specifically associated with plasma TG levels in the UK Biobank); 3- and GRS46 (addition of all 15 SNPs from the UK Biobank). ## 2.5. Statistical Analyses Statistical analyses were performed on SAS software version 9.4. Differences between responders and nonresponders before and after the n-3 FA supplementation were assessed with a two-tailed unpaired t-test. Independent associations between SNPs and changes in lipid traits in the FAS Study were assessed with linear mixed models (MIXED procedure in SAS) adjusted for age, sex, and BMI as fixed effects, and individuals as random effects to account for within-subject variability. Statistical significance was set at $p \leq 0.05.$ The GLM procedure in SAS was used to generate a general linear model adjusted for age, sex, and BMI, which evaluated the contribution of the GRSs to plasma TG levels after the n-3 FA supplementation. ## 2.6. Evaluation of the Predictive Performance The predictive performance of the different GRSs was assessed by calculating the area under the receiver operating characteristic curve (AUC-ROC) by using the logistic procedure. Individual prediction models were created for each of the clinical predictors (age, sex, BMI, and baseline plasma TG levels), as well as for each of the GRSs analyzed. Final prediction models were created by the sequential addition of clinical predictors and the given GRS. By using the R software (v4.2.1), the population study was then randomly split into training and testing datasets at $50\%$, with a balanced ratio of responders and nonresponders in each dataset, where models were trained and tested. The R caret package was used to perform a tenfold cross-validation in the training dataset. The cross-validated model obtained in the training dataset was further assessed in the testing dataset. The AUC-ROC and the accuracy were used to evaluate the predictive performance of each GRS in training and testing datasets. With the accuracy standing for the proportion of true responders and nonresponders out of the total number of subjects. The McFadden’s pseudo R2 was also used to determine the proportion of response variation accounted for by each GRS, which is an estimation of their predictive power. ## 3.1. Characteristics of Participants Characteristics of participants in the FAS Study have already been reported [4]. Both responders and nonresponders were overweight or presented obesity at baseline, with a mean BMI of 28.9 ± 3.6 and 27.9 ± 3.9 kg/m2, respectively. More precisely, among responders, $65.4\%$ were overweight and $34.6\%$ presented obesity, whereas among nonresponders, $79.7\%$ were overweight and $20.3\%$ presented obesity. Responders had higher plasma TG levels at baseline than nonresponders ($p \leq 0.001$). After the n-3 FA supplementation, responders significantly decreased their plasma TG levels by 0.50 ± 0.36 mmol/L ($p \leq 0.001$), whereas nonresponders increased them by 0.18 ± 0.17 mmol/L ($p \leq 0.001$). Changes in BMI ($p \leq 0.01$), plasma TG ($p \leq 0.0001$), HDL-C ($$p \leq 0.0002$$), and TC ($$p \leq 0.01$$) levels after the n-3 FA supplementation were significantly different between the two subgroups, whereas changes in LDL-C levels were similar ($$p \leq 0.6$$). ## 3.2. Association between SNPs and Plasma Lipid Traits Main characteristics of the 15 SNPs interacting with fish oil supplementation and associated with plasma lipid levels in the UK Biobank cohort and genotyped in participants of the FAS Study are presented in Table 1. All SNPs were in HWE. Allele frequencies of each SNP in both responders and nonresponders are shown in Table 2. Associations of all 15 SNPs with plasma TG, LDL-C, HDL-C, and TC levels in individuals from the FAS Study are presented in Table 3. None of the SNPs interacted with n-3 FA to modulate plasma TG levels in the FAS Study. ## 3.3. Evaluation of Genetic Risk Scores We first tested the effect of adding one SNP of interest (rs55707100) to GRS31. The SNP rs55707100, included in GRS32 was of particular interest because it is a missense variant with a MAF of $4.6\%$ in the FAS Study and was associated with plasma TG levels in the UK Biobank. Originally, GRS31 explained $49.7\%$ of the variance in the change of plasma TG levels in the sample of 141 participants. Herein, after the exclusion of one participant due to genotyping failure, GRS31 accounted for $50.1\%$ of the variance in plasma TG levels following the n-3 FA supplementation. The addition of rs55707100 in GRS32 did not show an incremental effect on the explained variance of GRS31 and slightly reduced it to $49.1\%$. Although the predictive capacity of the GRS31, measured as the AUC-ROC, was 0.954, the addition of rs55707100 in GRS32 set it to 0.950. The inclusion of additional SNPs did not lead to a further rise in the percentage of explained variance with GRS38 ($45.9\%$) or GRS46 ($45.0\%$) (Figure 1). Similarly, an incremental effect on the predictive capacity was not observed with GRS38 (AUC-ROC = 0.945) or GRS46 (AUC-ROC = 0.947), as compared to GRS31 (Figure 1). Individual prediction models for each of the clinical predictors, as well as for each GRS are shown in Figure S1. The increasing risk of nonresponse to n-3 FA supplementation, illustrated here by increasing GRS values, indicates that a subject carries a greater number of risk alleles, as shown in Figure 2. A statistically significant impact on the probability of being classified as a responder or a nonresponder was found for each of the GRS analyzed (Figure 3). Weighted versions of GRS32, GRS38, and GRS46 were also developed by using interaction effect sizes. Indeed, in the UK Biobank study, large effect sizes (greater than 0.8) were observed for most of SNPs showing significant interaction effects [6]. However, the weighted GRSs did not improve the prediction capacity of unweighted versions tested in the FAS Study, with explained variances of $35.3\%$, $36.4\%$, and $35.5\%$ for weighted versions of GRS32, GRS38, and GRS46, respectively. ## 3.4. Comparison of Genetic Risk Scores The evaluation of the performance of each GRS to predict the plasma TG response was assessed in both training and testing datasets after applying tenfold cross-validation. GRS31 showed an accuracy of 0.93 [$95\%$ CI 0.84–0.98] in the training dataset and 0.77 [$95\%$ CI 0.66–0.86] in the testing dataset (Table 4; Figure S2). GRS31 also showed a high AUC-ROC in both training (AUC-ROC = 0.97 [$95\%$ CI 0.93–0.99]) and testing datasets (AUC-ROC = 0.87 [$95\%$ CI 0.79–0.95]) (Table 4; Figure S3). More precisely, the sensitivity i.e., the proportion of responders correctly identified as such, decreased from 0.93 in the training dataset to 0.73 in the testing dataset. Similarly, the specificity, i.e., the proportion of nonresponders correctly identified as such, decreased from 0.92 during training to 0.80 in the testing dataset. As expected, the proportion of explained variance in the plasma TG response decreased from 0.66 in the training dataset to 0.48 in the testing dataset. None of the GRSs with newly added SNPs outperformed the predictive capacity of GRS31 in any of the metrics analyzed, i.e., accuracy, area under the response-operating curve (AUC-ROC), sensitivity, specificity, and McFadden’s pseudo R2 (Table 4). ## 3.5. Genetic Risk Score Threshold Selection The GRSs built in this study are aimed at aiding in clinical decision-making regarding treatment choice. Thus, a tradeoff between specificity and sensitivity has to be taken into account when considering the GRS threshold selection to increase n-3 FA treatment efficacy. Moreover, in order to maximize the potential number of patients to be treated, the correct identification of responders to the n-3 FA supplementation (sensitivity) need to be favored over the accurate classification of nonresponders (specificity). By using the median score value of GRS31 (GRS31 = 3), $78\%$ of actual responders would have an equal or lower score, whereas $97\%$ of nonresponders would have equal or higher scores (Figure 4). This means that, by using the testing prediction outcomes of the GRS31 model, this cutoff would lead to a sensitivity of $97\%$. At bottom and top tertiles of GRS31, the predicted probability of being identified as responder (GRS31 < 2) or nonresponder (GRS31 > 5) would be higher than $80\%$ for both, and the accuracy of the model would increase from $77\%$ to $89\%$. According to this, setting the GRS cutoff to 2 would increase the sensitivity of the model (the probability for an actual responder of being correctly classified as responder) to $100\%$. However, the percentage of actual responders having a GRS equal or lower than 2 would be reduced to only $66\%$ (Figure 4), which means that $44\%$ of potential responders would not be considered for n-3 FA treatment. Given that the goal of a decision aid for n-3 FA plasma lipid responsiveness would be to maximize the number of subjects to be treated, increasing the GRS cutoff would be preferred. Setting the threshold to 5, the top tertile of GRS31, would lead to a decrease in sensitivity to $87\%$, but the tool would be able to capture the majority of potential responders (concretely $93\%$ of them (Figure 4)). We may then consider a GRS31 equal or less than 5 as an optimal cutoff to identify potential responders to a n-3 FA supplementation in terms of plasma TG response. At this threshold, only 1 out of 10 actual nonresponders would be identified as a responder, while only 1 out of 8 actual responders would not be accurately identified as such. Based on these findings, we illustrated a decision aid tool prototype to identify individuals that would be more likely to benefit from the plasma TG lowering effect of n-3 FAs (Figure 5). ## 4. Discussion The aim of the present study was to test whether the addition of new SNPs identified in a recent UK Biobank study [6] to the GRS built in the FAS Study improved its capacity to predict the plasma TG response to an n-3 FA supplementation. A total of three GRSs was built from the addition of these new SNPs. On the whole, none of these GRSs managed to marginally increase the prediction capacity of the original GRS31. Moreover, it is important to point out that after quality control, a total of 7,954,107 autosomal variants from Affymetrix UK Biobank Axiom and Affymetrix UK BiLEVE Axiom arrays were included in the analyses of UK Biobank. From them, only 5 out of the 31 SNPs of our GRS31 were present in the arrays used in the UK Biobank. These SNPs are rs12702829 (NXPH1), rs72974149 (MYB), rs114348423 (PHF17), rs117114492, and rs78786240 (NELL1). None of these SNPs significantly interacted with fish oil consumption in the modulation of plasma lipid levels in the UK Biobank. The most plausible explanation to these results is the divergence between study designs. Francis et al. conducted a GWAS on participants of the UK Biobank being a longitudinal study [6], whereas the FAS Study was a clinical intervention trial. More precisely, UK Biobank participants’ supplementation status was assessed with a medical questionnaire, making it impossible to ascertain the frequency at which fish oil supplements were taken as well as the quantity of n-3 FA consumed daily. Participants of the FAS Study, however, followed a controlled protocol in which frequency and adherence to the n-3 FA supplementation where monitored. In addition, it is highly plausible that the daily consumption of EPA and DHA in the UK Biobank study was much lower than the daily intake of 1.9–2.2 g EPA and 1.1 g DHA in the FAS Study, given that many n-3 FA capsules sold on the market do not contain as much EPA and DHA. These differences could therefore explain why SNPs identified in the UK Biobank study did not improve the prediction capacity of GRS31. Finally, even though Francis et al. ’s study design has a good power to detect gene–diet interactions in a statistical model, there is no possibility to directly assess or predict the plasma TG response to an n-3 FA supplementation. These SNPs interact with n-3 FA supplementation and are associated with plasma TG levels but not necessarily with the plasma TG responsiveness to an n-3 FA supplementation as tested in the FAS Study. Another factor that could have contributed to the absence of stronger results is the large size difference between the two study samples. The novel genetic variants interacting with an n-3 FA supplementation to modulate plasma TG levels in the study of Francis et al. were identified after performing analyses on 73,962 individuals [6]. The genotyping of these new variants was conducted on 140 participants in the FAS Study. Correspondingly, most genotyped SNPs showed a weak MAF, which substantially decreased our ability to construct more qualified GRSs. Moreover, ancestry background may, at least partly, explain why genetic variants identified in the UK Biobank did not improve the prediction capacity of the GRS built in the FAS Study. UK Biobank subjects included in Francis et al. ’s study are British Caucasian [6], whereas FAS participants are French Canadians of European descent, which form a more homogenous founder population [12]. Given the ethnical differences between these two studies and the founder effect in the French Canadian population, it is plausible that the potential SNPs that modulate the association between n-3 FA supplements and the plasma TG response have different frequencies in each study. This hypothesis could explain why the SNPs identified in the UK Biobank study did not improve our GRS31 from the FAS Study. We initially built GRS32 by including the SNP rs55707100, a missense variant located within the MAP1A gene. The inclusion of this SNP was based on its significant association with plasma TG levels in the UK Biobank, as well as on its relatively high MAF of $4.6\%$ compared to most other SNPs, which would increase our ability to obtain meaningful results in the much shorter FAS Study. Missense variants are alterations of the nucleotide coding sequence that result in a substitution of the initial amino acid. They are of particular interest because the amino acid substitution can alter the function of the protein and lead to disease development, or in this case, interact with the plasma TG response following an n-3 FA supplementation [13]. The SNP rs55707100 was also associated with plasma TG levels in past studies conducted on large samples of individuals [14,15]. In view of these previous findings, we hypothesized that the addition of rs55707100 to GRS31 would improve the prediction capacity of the initial GRS31 more than what was actually observed. Our results then make it difficult to ascertain the degree of association of rs55707100 with plasma TG responsiveness to an n-3 FA supplementation. As for GRS38, only SNPs interacting with plasma TG levels in Francis et al. ’s study were added to the initial GRS31. In GRS46, we included all 15 new SNPs in order to verify whether SNPs associated with other lipid parameters and interacting with n-3 FA supplementation in the UK Biobank cohort also modulate plasma TG responsiveness in the FAS Study. This hypothesis comes from the fact that plasma HDL-C and TC levels were also diminished alongside plasma TG levels in the FAS Study. Moreover, most of the SNPs interacting with n-3 FA supplementation to modulate plasma TG levels in the UK Biobank study are located in genes previously associated with other lipid traits, which made these SNPs even more attractive for our study. GBJ6 encodes one of the connexin proteins in cell’s gap junction in multiple tissues [16]. BAZ1B encodes a protein involved in chromatin-dependent regulation of transcription [17], and it has been associated with plasma lipid profiles in Chinese patients with type 2 diabetes [18]. MAP1A is mostly expressed in the brain and is involved in microtubule assembly, an important step in neurogenesis [19], and it has also been linked to lipid metabolism [13]. As for HAPLN4, it has been predicted to be involved in the central nervous system, skeletal and hyaluronic acid binding activity [20], and it has been also identified as a common variant associated with plasma levels of different lipid species and with coronary artery diseases in a GWAS performed by Cadby et al. on individuals from the Busselton Health Study and the UK Biobank [21]. The LPL gene encodes for the lipoprotein lipase [22], an enzyme mostly expressed in the heart, skeletal muscle, and brown and white adipose tissue, which plays a role in fatty acid oxidation and storage. Mutations located in the LPL gene can cause hyperlipoproteinemia and lipid metabolism disorders [23,24]. As for MLXIPL, it encodes a transcription factor that activates the carbohydrate response element motifs in the plasma TG synthesis genes [25]. Moreover, a recent study from the Global Lipids Genetics Consortium provided a list of 32 loci modulating plasma TG levels, which included MLXIPL [26]. Similarly, SNPs in SLC12A3, a gene that encodes for a sodium-chloride cotransporter important for electrolyte homeostasis [27], were associated with lipid profiles in Mongolian and Chinese populations [28,29,30]. Finally, the gene ABCA6 encodes for the ATP binding-cassette transporter [31] and has been associated with cholesterol levels in a GWAS performed in nine Dutch biobanks [32]. LOC107985305 also interacted with LDL-C levels in multiomics analysis performed by Michelle et al. [ 33]. These previous findings led us to test whether the SNPs identified in Francis et al. ’s study were associated with plasma TG responsiveness to an n-3 FA supplementation. With most of these genes being associated with plasma lipid levels in past studies, it was relatively unexpected not to find stronger associations with plasma TG levels in the present study. Moreover, neither of these two approaches, either the seven SNPs specifically interacting with plasma TG levels (GRS38), or all the 15 SNPs identified in the UK Biobank (GRS46) improved the prediction capacity of GRS31. Thus, the initial GRS31 remains the best tool so far by which to discriminate responders from nonresponders to an n-3 FA supplementation [5]. As already mentioned, the fact that the UK Biobank results were based on a longitudinal study and the FAS Study was a nutritional intervention and then seek for genes associated with plasma TG responsiveness seems to be on the basis of the lack of expected results. Nevertheless, it is worth highlighting that GRS construction is a dynamic and continuous task that requires a continuous feeding with novel and promising variants. The evaluation used to compare the predictive capacity of the different GRSs was appropriately assessed herein by using cross-validation and by splitting the entire cohort into training and testing datasets. However, the sample size of the cohort on which the study relies, as well as the modest level of association between the plasma TG response and newly added SNPs, may constitute limitations. Moreover, these GRSs are based on French Canadians and European descent populations, which potentially misestimates the risk prediction if applied to other populations, especially of non-European descent. The ultimate goal of a GRS for the plasma TG response is the development of a precision tool for the identification of subjects more likely to benefit from the plasma TG lowering effects of n-3 FA. Thus, a genetic-informed estimation of n-3 FA responsiveness was drafted herein as a previous step in the development of a decision aid prototype, illustrated in Figure 5. The rationale behind GRS threshold selection is explained from the fact that the incorrect identification of a nonresponder to n-3 FA as a responder would not have severe consequences for the patient. In contrast, the incorrect identification of an actual responder as a nonresponder would prevent potential patients to be treated. Thus, giving n-3 FAs to all the potential responders, at the expense of decreasing sensitivity, was preferred here. As previously discussed, this type of genetic-informed decision tools is increasingly being used to guide clinical practice for healthcare professionals [34]. However, further research including larger, heterogeneous and comprehensive cohorts, are still needed to develop accurate decision aids related to n-3 FA responsiveness regarding plasma TGs, in order to support its potential widespread use. ## 5. Conclusions In conclusion, the addition of the novel SNPs identified in UK Biobank to the GRS initially built and refined in the FAS Study did not significantly improve its capacity to predict the plasma TG response to an n-3 FA supplementation. 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--- title: Higher Muscle Mass and Higher Serum Prealbumin Levels Are Associated with Better Survival in Hemodialysis Patients during a Five-Year Observation Period authors: - Anna Jeznach-Steinhagen - Iwona Boniecka - Aleksandra Rymarz - Monika Staszków - Jerzy Romaszko - Aneta Czerwonogrodzka-Senczyna journal: Nutrients year: 2023 pmcid: PMC10005672 doi: 10.3390/nu15051237 license: CC BY 4.0 --- # Higher Muscle Mass and Higher Serum Prealbumin Levels Are Associated with Better Survival in Hemodialysis Patients during a Five-Year Observation Period ## Abstract Background: Dialysis is the most commonly used renal replacement therapy in patients with end-stage renal disease. The mortality rate of hemodialysis patients is 15–$20\%$, with cardiovascular complications being the most common. There is an association between the severity of atherosclerosis and both the development of protein-calorie malnutrition and inflammatory mediators. The aim of this study was to assess the relationship between biochemical markers of nutritional status, body composition and survival in hemodialysis patients. Methods: Fifty-three hemodialysis patients were included in the study. Serum albumin, prealbumin, and IL-6 levels were measured, as well as body weight, body mass index, fat content and muscle mass. The five-year survival of patients was calculated using Kaplan–Meier estimators. The long-rank test was used for univariate comparison of survival curves, and the Cox proportional hazards model was used for multivariate analysis of survival predictors. Results: There were 47 deaths, 34 of which were due to cardiovascular disease. The hazard ratio (HR) for age in the middle-aged group (55–65 years) was 1.28 (confidence interval [CI] 0.58, 2.79) and 5.43 (CI 2.1, 14.07; statistically significant) for the oldest age group (over 65 years). A prealbumin level above 30 mg/dl was associated with an HR of 0.45 (CI 0.24, 0.84). Serum prealbumin (odds ratio [OR] = 5.23; CI 1.41, 19.43; $$p \leq 0.013$$) and muscle mass (OR = 7.5; CI 1.31, 43.03; $$p \leq 0.024$$) were significant predictors of all-cause mortality. Conclusions: Prealbumin level and muscle mass were associated with increased mortality risk. Identification of these factors may improve the survival of hemodialysis patients. ## 1. Introduction Worldwide, the total number of individuals with acute kidney injury, CKD and Renal Replacement Therapy (RRT) exceeds 850 million, a figure that is double the estimated number of people with diabetes worldwide [1]. Data from the 2017 Annual Report of the European Renal Association—European Dialysis and Transplant Association (ERA-EDTA) [2] report that 83,311 individuals from all countries of Europe started RRT in 2016. At the end of the same year, the total number of individuals requiring RRT was 564,638, with more than $80\%$ being on hemodialysis (HD). Dialysis is the most common renal replacement therapy for patients with end-stage renal disease. This paper considers the survival of dialysis patients and aims to assess both anthropometric and biochemical nutritional parameters. Currently, there are over 21,000 hemodialysis patients in Poland. The mortality rate for hemodialysis patients is 15–$20\%$, which is four to seven times higher than for the general population. The most common complications in this group of patients are cardiovascular complications, which cause more than $50\%$ of deaths [3]. A relationship exists between the severity of atherosclerosis and both the development of cardiovascular complications and mediators of inflammation, such as the concentration of proinflammatory cytokines (MIA syndrome: malnutrition, inflammation, atherosclerosis). The presence of MIA in dialysis patients is the cause of the reverse epidemiology of cardiovascular disease (CVD). Factors that reduce the risk of CVD in the general population increase the likelihood of the occurrence and development of these diseases, and of death, in patients who are dialyzed. Hemodialysis (HD) majorly represents the global treatment option for patients with chronic kidney disease at stage 5, and, despite advances in dialysis technology, these patients face a high risk of morbidity and mortality from malnutrition. Sahathevan et al. [ 4] aimed to provide a novel view that malnutrition susceptibility in the global HD community is either or both of iatrogenic and non-iatrogenic origins. This categorization of malnutrition origin clearly describes the role of each factor in contributing to malnutrition. Low dialysis adequacy resulting in uremia and metabolic acidosis and dialysis membranes and techniques, which incur greater amino-acid losses, are identified as modifiable iatrogenic factors of malnutrition. Dietary inadequacy as per suboptimal energy and protein intakes due to poor appetite status, low diet quality, high diet monotony index, and/or psychosocial and financial barriers are modifiable non-iatrogenic factors implicated in malnutrition in these patients. These factors should be included in a comprehensive nutritional assessment for malnutrition risk. Leveraging the point of origin of malnutrition in dialysis patients is crucial for healthcare practitioners to enable personalized patient care, as well as determine country-specific malnutrition treatment strategies. A meta-analysis of sub-Saharan African HD patients shows insufficient infrastructure and catastrophic out-of-pocket costs. Most patients remain undiagnosed, untreated, and die. In the pooled analysis, 390 ($96\%$) of 406 adults and 133 ($95\%$) of 140 children who could not access dialysis died or were presumed to have died. Among those dialyzed, 2747 ($88\%$) of 3122 adults in incident ESKD cohorts, 496 ($16\%$) of 3197 adults in prevalent ESKD cohorts, and 107 ($36\%$) of 294 children with ESKD died or were presumed to have died. Further to this, 2508 ($84\%$) of 2990 adults in incident ESKD cohorts discontinued dialysis compared with 64 ($5\%$) of 1364 adults in prevalent ESKD cohorts, and 41 ($1\%$) of 4483 adults in incident ESKD cohorts, 2280 ($19\%$) of 12 125 adults in prevalent ESKD cohorts, and 71 ($19\%$) of 381 children with ESKD received transplants. Sixteen studies reported on the management of anemia, 17 on dialysis frequency, eight on dialysis accuracy, and 22 on vascular access for dialysis. Most patients with ESKD starting dialysis in sub-Saharan Africa discontinue treatment and die. Further work is needed to develop equitable and sustainable strategies to manage individuals with ESKD in sub-Saharan Africa [5]. Malnutrition is a common issue among hospitalized patients [6]. Malnutrition in the chronic dialyzed patient has been a serious clinical problem for a long time and still occurs in about one-third of these patients, ranging from 20–$76\%$, according to various sources [6,7,8]. Two types of malnutrition are observed in patients with kidney disease, both of which may lead to accelerated development of the MIA syndrome. The first is associated with an insufficient supply of protein and with energy and protein absorption disorders. The second is associated with chronic inflammation [9]. Inflammation, manifested by an increase in C-reactive protein (CRP) and proinflammatory cytokine levels in the serum, is an important cause of malnutrition. Interleukin (IL)-6 plays a significant role in the development of malnutrition through the catabolism of muscle proteins and the anorectic effect of cytokines (increased leptin production and lipolysis). An increased level of cytokines is characteristic of hypoalbuminemic patients and is associated with a shorter survival [10,11]. Sarcopenia in end-stage kidney disease patients requiring dialysis is a frequent complication but remains an under-recognized problem. The Wathanavasin et al. [ 2022] meta-analysis was conducted to determine the prevalence of sarcopenia and explore its impacts on clinical outcomes, especially cardiovascular events and mortality in dialysis patients. The eligible studies were searched from PubMed, Scopus, and Cochrane Central Register of Controlled trials up to 31 March 2022. The result showed that forty-one studies with 7576 patients were included. The pooled prevalence of sarcopenia in dialysis patients was $25.6\%$ ($95\%$ CI 22.1 to $29.4\%$). Sarcopenia was significantly associated with higher mortality risk (adjusted OR 1.83 ($95\%$ CI 1.40 to 2.39)) and cardiovascular events (adjusted OR 3.80 ($95\%$ CI 1.79 to 8.09)). Additionally, both low muscle mass and low muscle strength were independently related to increased mortality risk in dialysis patients (OR 1.71; $95\%$ CI (1.20 to 2.44), OR 2.15 ($95\%$ CI 1.51 to 3.07)), respectively. This meta-analysis revealed that sarcopenia was highly prevalent among dialysis patients and has been shown to be an important predictor of cardiovascular events and mortality [12]. Progressive nutritional impairment has been recently reported during conventional hemodialysis (HD) treatment. The Chasot [2006] study showed that the nutritional parameters during a five-year follow-up in HD patients were stable during the five-year period. Thirty-three patients (15F/18M; 65 years old at the study start) filled out a three-day food questionnaire once a year between 1995 and 1999 (study group). Twenty patients who did not fill out the food records during this period served as a control group (control group). The food record was run by the renal dietician using dedicated software, providing daily energy and protein intakes. Serum albumin, normalized protein equivalent of nitrogen appearance (nPNA), and post-dialysis body weight (BW) at the time of food record were collected in the study group and from the patient chart in the control group. The energy intake in the study group and the protein intake in both groups were close to the recommended intakes in ESRD patients. Protein intake assessed from food questionnaires or from urea kinetics was not statistically different. Using ANOVA for repeated measures, no difference along the five years was found for daily energy intake, daily protein intake, nPNA, and BW in the study group. The BW and nPNA remained stable in the control group. Hence, this study does not confirm the progressive nutritional impairment reported in the HEMO study, whereas the patients’ age and vintage are largely higher in the present study [13]. The inflammatory process in hemodialysis patients plays a key role in the pathogenesis of atherosclerosis and CVD. Causes of inflammation in patients with kidney disease include increased oxidative stress, hypertension, subclinical infections, accumulation of metabolism products, endotoxin effects, hypercatabolism, and genetic factors. Around 35–$65\%$ of hemodialyzed patients experience chronic inflammation [14]. Already in the early stages of chronic renal failure, patients with no heart disease have an increased level of proinflammatory cytokines (IL-1, TNF-α, and IL-6), acute phase proteins (CRP and fibrinogen), adhesion molecules (selectins), and some blood coagulation factors, as well as reduced expression of anti-inflammatory cytokines. The level of IL-6 increases as the globular filtration rate decreases and is a strong predictor of poor prognosis. Increased serum levels of IL-6 and CRP predispose people without kidney disease to the development of CVD and increased mortality. A high concentration of IL-6 in patients initiating renal replacement therapy is associated with a worse distant prognosis [15]. Identifying risk factors for mortality may help in early intervention approaches to improve the survival of patients on chronic hemodialysis who have a substantially reduced life expectancy. ## 2. Materials and Methods We conducted a single-center study in the Dialysis and Internal Diseases Clinic of the Medical University of Warsaw. The study followed 53 patients (20 women and 33 men). The mean (SD) age of patients was 58.6 (5.6) years. They had chronic renal failure and received hemodialysis regularly since December 2005. ## 2.1. Blood and Anthropometric Parameters The levels of plasma albumin, prealbumin, and IL-6, as well as the weight, body mass index (BMI, calculated as weight/height2), fat content, and muscle mass of all patients, were measured at the beginning of the study. To be included in the study, patients had to have undergone hemodialysis regularly and been clinically stable, without any nutrition support, for at least three months before being enrolled in the study. The anthropometric parameters of nutritional status, including body weight, BMI, skinfold thickness over the triceps muscles, mid-upper arm circumference, and waist and hips circumference, were evaluated after dialysis. Body weight and fat content (fat %) were measured with high-quality electronic calibrated scales Tanita TBF 300P (to the nearest 0.1 kg), and height was measured with a wall-mounted stadiometer SECA 216 (to the nearest 0.5 cm). Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters. Waist circumference was measured at the midpoint between the lower margin of the least palpable rib and the top of the iliac crest using a stretch-resistant tape. Hip circumference was measured around the widest portion of the buttocks, with the tape parallel to the floor. The waist-hip ratio (WHR) was calculated by dividing waist circumference by hip circumference [16]. Mid-upper arm circumference was measured (to the nearest 0.1 cm) using a flexible nonstretch tape laid at the midpoint between the acromion and olecranon processes on the shoulder blade and the ulna, respectively, of the arm [17]. The triceps skin fold thickness was measured using a Harpenden skinfold caliper. The results were compared to the standards in Table 1. Blood was collected immediately before dialysis. The biochemical determinations were performed on a Cobas Integra 800 analyzer (Roche). The IL-6 cytokine assay was performed using the Fluorokine MAP kit cytokine multiplex kit (R&D Systems) and the Luminex 100 ™ instrument. Patients were under the constant care of the center. The attending physician had access to the patients’ full documentation, on the basis of which the date and cause of death were established. The study was approved by the Bioethics Committee of the Medical University of Warsaw. All patients signed an informed consent form before the beginning of the study. ## 2.2. Statistical Analysis The five-year patient survival was computed using Kaplan–Meier estimates. Univariate comparison of survival curves was performed using the log-rank test. A multivariate analysis of survival predictors was done using Cox proportional hazards model. The multivariate model was built using stepwise variable selection based on Akaike Information Criterion. Results are expressed as hazard ratios (HR) with $95\%$ confidence intervals (CI). A logistic regression analysis for the endpoint, defined as survival of more than five years, was performed. Results are expressed as the odds ratio (OR) with its $95\%$ CI. T-tests were used to compare patients living shorter and longer than five years. The dependence between selected descriptive variables was described using a linear correlation coefficient matrix. The statistical analysis was carried out using R 3.5.1 statistical software (R Core Team 2018). Statistical significance was considered at p values < 0.05. ## 3. Results The characteristics of the study group, including the mean age, length of follow-up, and hemodialysis vintage, are presented in Table 2. Patients who were switched over to other forms of renal replacement therapy, like renal transplantation, after inclusion in the study were excluded ($$n = 3$$). A total of 50 patients fulfilled the inclusion criteria and were included in the analysis. The mean follow-up time was 13 years, during which there were 47 deaths: 34 due to CVD and 13 due to sepsis. The most common causes of death were ischemic heart disease and pulmonary embolism, both accounting for nearly $72\%$ of the overall mortality. A statistically significant relationship was observed between the five-year survival of patients and the prealbumin concentration and muscle mass (Table 3, Figure 1 and Figure 2). In univariate analysis, the HR for age in the middle-aged group (55–65 years) was 1.28 (0.58, 2.79). For the oldest age group (over 65 years), the HR for age was 5.43 (2.1, 14.07; statistically significant). A prealbumin level over 30 mg/dL was associated with an HR of 0.45 (0.24, 0.84) (Table 4). A crude analysis showed that serum prealbumin (SPA; OR = 5.23 [1.41, 19.43], $$p \leq 0.013$$) and muscle mass (OR = 7.5 [1.31, 43.03], $$p \leq 0.024$$) were significant predictors of all-cause mortality (Table 5). ## 4. Discussion In this study, the amount of muscle mass rather than BMI was associated with survival in hemodialysis patients. Previous studies considered BMI as one of the markers of nutrition status capable of influencing survival rate. Unlike that of the general population, the survival rate of hemodialysis patients decreases as the BMI increases. A similar trend was described in patients with chronic obstructive pulmonary disease and heart failure [18,19]. This phenomenon is called reverse epidemiology or survival paradox. One of the explanations is that the BMI does not reflect body composition. In other words, the same BMI can be associated with different amounts of muscle and fat mass. The loss of muscle mass resulting from unfavorable metabolic disorders seems to be a sensitive predictor of mortality in the short term. Sarcopenia, the loss of muscle mass, can be masked by an increase in fat mass, which would not be reflected in the BMI. Sarcopenic obesity is reached when muscle mass decreases but fat mass increases, elevating the BMI to the level of obesity. This phenotype is associated with greater cardiovascular and all-cause mortality [20,21]. Ebrahimi et al. ’s study [22] evaluated the effect of different factors on the survival time of these patients. In this study, parametric survival models were used to find the factors affecting survival and discover their effect on survival time. Of 428 HD patients eligible for the analysis, 221 ($52\%$) experienced death. With the mean ± SD age of 60 ± 16 years and BMI of 23 ± 4.6 kg/m, they comprised 250 men ($58\%$). The median survival time ($95\%$ CI) was 624 days (550 to 716). The overall 1, 2, 3, and 4-year survival rates for the patients undergoing HD were 74, 42, 25, and $17\%$, respectively. By using AIC, AFT log-normal model was recognized as the best functional form of survival time. Cox-adjusted PH results showed that the amount of ultrafiltration volume (UF) (HR = 1.146, $$p \leq 0.049$$), WBC count (HR = 1.039, $$p \leq 0.001$$), RBC count (HR = 0.817, $$p \leq 0.044$$), MCHC (HR = 0.887, $$p \leq 0.001$$), and serum albumin (HR = 0.616, $p \leq 0.001$) had significant effects on mortality. AFT log-normal model indicated that WBC (ETR = 0.982, $$p \leq 0.018$$), RBC (ETR = 1.131, $$p \leq 0.023$$), MCHC (ETR = 1.067, $$p \leq 0.001$$), and serum albumin (ETR = 1.232, 0.002) had a significant influence on the survival time. The results of our study correspond with these observations. A higher amount of muscle mass was associated with a better survival rate, whereas the BMI did not influence survival ($$p \leq 0.1$$). The likelihood of surviving more than five years increased in each higher muscle mass quartile and was the highest for patients with the greatest muscle mass. The OR for the fourth quartile of muscle mass was 7.5 in comparison with the first quartile. Fukasawa et al. noted that lower tight muscle mass measured by computer tomography was associated with an increased all-cause and cardiovascular mortality in hemodialysis patients [23]. A lower lean tissue index, an equivalent of muscle mass measured by bioimpedance spectroscopy, also correlated with a worse survival rate in dialysis patients in recent studies [21,22,23,24,25,26,27]. A reduced muscle mass was also a predictor of mortality in individuals without kidney failure. Srikanthan et al. observed the lowest mortality rate in the highest muscle mass quartile in patients with cardiovascular disease [28]. In chronic kidney disease (CKD) patients, muscle mass loss can be caused by many factors, including inflammation. CKD is a low-grade inflammatory state, and elevated levels of cytokines such as IL-6, Il-1β, and TNF-α are observed in CKD patients [29,30]. Inflammation activates catabolic processes, which result in muscle degradation through the activation of the ubiquitin-proteasome system and insulin resistance. Consequently, a higher mortality rate is observed in patients with elevated levels of these cytokines and concomitant CKD [31,32]. Nonetheless, in our study, the serum level of IL-6 was not a statistically significant predictor of mortality ($$p \leq 0.9$$). Among the biochemical markers of nutrition that can define protein-energy wasting are serum albumin, prealbumin, and cholesterol levels [17,18]. The cut-off point for protein-energy wasting for serum albumin is 3.8 g/dL and 30 mg/dL for serum prealbumin (SPA). Our results revealed a significant association between the SPA level and mortality. Patients with a SPA level higher than 30 mg/dL had a higher survival rate compared with patients with a lower SPA concentration (OR = 5.23, $$p \leq 0.013$$). This result is related to the previously described association between muscle mass and mortality. Lean tissue mass mostly made up of muscle, is an important somatic protein store. If a decrease in muscle mass is associated with a higher mortality rate, a reduction in the SPA level can also affect survival. The same trend was described by Rambod et al. [ 32]. They observed an increased mortality rate in hemodialysis patients that had a SPA level lower than 20 mg/dL and in whom SPA concentrations decreased over 6 months [29]. Furthermore, the SPA level correlated with the amount of muscle mass. Others have also observed this relationship. Kamijo et al. observed a positive correlation between the SPA level and the amount of muscle mass in peritoneal dialysis patients [33]. Ferreira et al. [ 34] suggest that the optimal management of end-stage renal disease (ESRD) in hemodialysis (HD) patients should be studied more because it is a serious risk factor for mortality, being considered an unquestionable global priority. They performed a retrospective cohort study from the Nephrology Service in Brazil evaluating the survival of patients with ESRD in HD for 20 years to explore the association between survival time and demographic factors, quality of treatment and laboratory values. Data from 422 patients were included. The mean survival time was 6.79 ± 0.37. The overall survival rate in the first year was $82.3\%$. The survival time correlated significantly with clinical prognostic factors. Prognostic analyses with the Cox proportional hazards regression model and Kaplan–Meier survival curves further identified that leukocyte count (HR = 2.665, $95\%$ CI: 1.39–5.12), serum iron (HR = 8.396, $95\%$ CI: 2.02–34.96), serum calcium (HR = 4.102, $95\%$ CI: 1.35–12.46) and serum protein (HR = 4.630, $95\%$ CI: 2.07–10.34) as an independent risk factor for the prognosis of survival time, while patients with chronic obstructive pyelonephritis (HR = 0.085, $95\%$ CI: 0.01–0.74), high ferritin values (HR = 0.392, $95\%$ CI: 0.19–0.80), serum phosphorus (HR = 0.290, $95\%$ CI: 0.19–0.61) and serum albumin (HR = 0.230, $95\%$ CI: 0.10–0.54) were less risk to die. Survival remains low in the early years of ESRD treatment. The present study identified elevated values of ferritin, serum calcium, phosphorus, albumin, leukocyte, serum protein and serum iron values as useful prognostic factors for survival time. In our study, the most important parameters were: prealbumin and anthropometric data. The SPA level is also a predictor of mortality in patients with diseases other than CKD. A low prealbumin level was also associated with a higher short-term mortality rate in patients with acute heart failure [35]. A limitation of this study is the small sample size, and therefore, the results should be interpreted with caution. Anthropometric measurements are simple and inexpensive methods of assessing nutritional status, but their limitation is that they can be influenced by external factors (hydration). If they are not performed on the same equipment and at the same time of day, and by the same person, they are less reproducible. Biochemical markers of malnutrition in our study were analyzed separately. Due to the fact that a correlation between SPA and muscle mass content was shown, it was analyzed only in relation to this parameter. In addition, SPA was found to be a significant predictor of all-cause mortality. We also wanted to provide evidence that even if a determination of prealbumin levels is impossible, the survival prognosis can be estimated based only on anthropometric measurements. Identification of risk factors for mortality can assist in early intervention to improve the survival of patients on chronic hemodialysis who have a significantly shorter life expectancy. ## 5. Conclusions In our study, a decreased serum albumin level did not influence survival. Muscle mass, rather than BMI, was a statistically significant predictor of survival in hemodialysis patients. The highest muscle mass quartiles were associated with better survival. Among the biochemical markers of nutrition, the SPA level can be a sensitive predictor of mortality. A SPA higher than 30 mg/dL seems to be beneficial. ## References 1. Jager K.J., Kovesdy C., Langham R., Rosenberg M., Jha V., Zoccali C.. 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--- title: 'Eating Vegetables First Regardless of Eating Speed Has a Significant Reducing Effect on Postprandial Blood Glucose and Insulin in Young Healthy Women: Randomized Controlled Cross-Over Study' authors: - Saeko Imai - Shizuo Kajiyama - Kaoru Kitta - Takashi Miyawaki - Shinya Matsumoto - Neiko Ozasa - Shintaro Kajiyama - Yoshitaka Hashimoto - Michiaki Fukui journal: Nutrients year: 2023 pmcid: PMC10005673 doi: 10.3390/nu15051174 license: CC BY 4.0 --- # Eating Vegetables First Regardless of Eating Speed Has a Significant Reducing Effect on Postprandial Blood Glucose and Insulin in Young Healthy Women: Randomized Controlled Cross-Over Study ## Abstract People with fast eating habits have been reported to have an increased risk of diabetes and obesity. To explore whether the speed of eating a test meal (tomato, broccoli, fried fish, and boiled white rice) influences postprandial blood glucose, insulin, triglyceride, and free fatty acid levels, 18 young, healthy women consumed a 671 kcal breakfast at fast speed (10 min) and slow speed (20 min) with vegetables first and slow speed (20 min) with carbohydrate first on three separate days. This study was conducted using a within-participants cross-over design in which all participants consumed identical meals of three different eating speeds and food orders. Significant ameliorations of both fast and slow eating with vegetables first regimen on postprandial blood glucose and insulin levels at 30 and 60 min were observed compared with those of slow eating with carbohydrates first. In addition, the standard deviation, large amplitude of excursion, and incremental area under the curve for blood glucose and insulin in both fast and slow eating with vegetables first were all significantly lower than those of slow eating with carbohydrate first. Interestingly, there was no significant difference between fast and slow eating on postprandial blood glucose and insulin levels as long as vegetables were consumed first, although postprandial blood glucose at 30 min was significantly lower in slow eating with vegetables first than that of fast eating with the same food order. These results suggest that food order with vegetables first and carbohydrate last ameliorates postprandial blood glucose and insulin concentrations even if the meal was consumed at fast speed. ## 1. Introduction The number of people with diabetes is currently estimated to be as many as 537 million, and it is predicted to increase up to 783 million by 2045, with more than $90\%$ of them estimated to be type 2 diabetes (T2DM) [1]. Diabetes is known to be responsible for the development of various complications. For example, diabetes is the leading cause of renal failure, new onset blindness, and lower-extremity amputation in the United States [2]. The goal of treatment for T2DM is centered on preventing or delaying complications and maintaining quality of life for the patient, and those goals are achieved by the appropriate management of hyperglycemia according to the American Diabetes Association (ADA) and European Association for the Study of Diabetes (EASD) [3]. Postprandial blood glucose elevation and mean amplitude of glycemic excursions (MAGE) are associated with the pathogenesis of micro- and macrovascular complications in individuals with diabetes and the incidence of T2DM [4,5,6]. Therefore, reducing postprandial blood glucose and glycemic excursions are the key strategies of pharmacological and medical nutrition therapy for preventing diabetic complications and reducing the risk of T2DM. Above all, diet is the leading contributor in the management and prevention of T2DM [7]. Many dietary approaches are proven effective and available, such as the Mediterranean diet [8], low-calorie diet [9], low-carbohydrate diet [10], low-glycemic-index diet [11], vegetarian diet [12], and intermittent fasting diet [13]. The effectiveness of these diets has been demonstrated but some difficulties remain. Despite that medical nutrition therapy should continue as a lifelong remedy, long-term interventional studies are limited. For example, the Dietary Intervention Randomized Controlled Trial (DIRECT) study was a well-known trial that compared the long-term effect of three dietary methods, the Mediterranean diet, low-fat diet, and low-carbohydrate diet, on obese individuals for 6 years including a 4-year follow-up. The results demonstrated that the Mediterranean and low-carbohydrate diet were more effective than the low-fat diet on weight loss and lipid profiles after 2 years, although, after 6 years, $11\%$ of the participants had changed to another diet, and surprisingly, $22\%$ of the participants quit dieting [14]. This research reveals that it is difficult to continue any dietary methods for a long term of more than 2 years. Recent studies including our research indicate that the food order of preloading vegetables, protein, or fat with slow eating can ameliorate postprandial blood glucose excursions and decrease insulin secretion in both individuals with and without T2DM [15,16,17,18]. Nowadays, the food order, as an innovative medical nutrition therapy, is widely recognized as being effective for individuals with and without T2DM in Japan, and significant effects on acute and chronic glycemic control have been demonstrated in individuals with T2DM [19,20]. On the other hand, epidemiological and cohort studies demonstrated that fast eating speed resulted in weight gain [21,22,23] and increased incidence of T2DM and metabolic syndrome [24,25,26]. These studies demonstrated that the modification of eating speed could be an efficient and cost-effective method for promoting weight management in obese and healthy individuals to prevent the incidence of T2DM. However, people who were instructed to eat slowly had difficulty in accomplishing the task [27]. Therefore, instead of a slowing eating pace, strategies of different aspects are needed to improve postprandial blood glucose and insulin responses for preventing obesity and T2DM. We previously reported that eating fast (10 min) with a mixed eating of vegetables, protein, and carbohydrate demonstrated significant higher glycemic excursions compared with eating slow (20 min) with vegetables first in young healthy women [28]. However, the effect of eating fast with vegetables first on postprandial blood glucose and insulin concentrations is still uncertain. The purpose of this study was to examine the acute effect of different eating speeds with different food orders on postprandial blood glucose, insulin, triglyceride (TG), and free fatty acid (FFA) concentrations in young healthy women. ## 2.1. Study Design Participants were enrolled from volunteers from the Kyoto Women’s University, Kyoto, Japan. All volunteers were informed of the purpose, protocol, and risks of the research before the study began. Twenty-one participants were registered in the study. Written informed consents were obtained from all participants. The study was conducted between April 2022 and July 2022. None of the participants were pregnant or smokers. The participants were also free from eating disorders, metabolic diseases, other diets, and any medications and supplements identified to affect their blood glucose, insulin, and lipid levels. This study was conducted using an unblind randomized within-participants cross-over design in which all participants consumed identical meals on three separate days. The study protocol relating to human subjects was approved by the Ethics Committee of Kyoto Women’s University [2021-21] according to the guidelines of the Declaration of Helsinki. The study protocol was registered in the UMIN Clinical Trials Registry (UMIN000050266). Each participant consumed identical test meals on three separate days, and each study day was 1 week apart with three different eating patterns of fast speed and slow speed with vegetables first and slow speed with carbohydrate first, as shown in Figure 1. Carbohydrate first with slow eating speed: carbohydrate (boiled white rice) first for 6 min, and then protein (fried fish) for 7 min, and then vegetables (tomato and broccoli with sesame oil) for 7 min, for a total eating time of 20 min. Vegetables first with slow eating speed: vegetables (tomato and broccoli with sesame oil) first for 7 min, and then protein (fried fish) for 7 min, and then carbohydrate (boiled white rice) for 6 min, for a total eating time of 20 min. Vegetables first with fast eating speed: vegetables (tomato and broccoli with sesame oil) first for 4 min, and then protein (fried fish) for 3 min, and then carbohydrate (boiled white rice) for 3 min, for a total eating time of 10 min. The study flow is shown in Figure 2. Twenty-one participants were divided into 3 groups with 7 participants each. All participants consumed identical test meals for three days according to the study protocol. The allocation sequence of Group A, B, and C was assigned by the research members in order of the research ID number of each participant. Group A: Participants consumed the identical test meal in slow eating with carbohydrate first on the first week, then consumed in slow eating with vegetables first on the second week, and consumed in fast eating with vegetables first on the third week. Group B: Participants consumed the identical test meal in fast eating with vegetables first on the first week, then consumed in slow eating with vegetables first on the second week, and consumed in slow eating with carbohydrate first on the third week. Group C: Participants consumed the identical test meal in slow eating with vegetables first on the first week, then consumed in fast eating with vegetables first on the second week, and consumed in slow eating with carbohydrate first on the third week. On the study day, the participants arrived at 8:30 at the Kyoto Women’s University after a 12 h overnight fast, and each meal was consumed at 9:00 under the experimental conditions that were randomly assigned to the participants (Figure 2). Blood samples were collected by the nurse at Kyoto Women’s University at 0, 30, 60, and 120 min after test meal consumption (Figure 1). Postprandial blood glucose, insulin, TG, and FFA concentrations were examined. The area under the curve (IAUC) measurements for glucose and insulin concentrations were calculated by the trapezoidal method above the baseline concentration at 9:00 and 120 min after consuming the test meals. The concentrations of postprandial blood glucose, insulin, TG, and FFA were compared within the participants among the three study days. ## 2.2. Meals for the Study The macronutrient amounts of the test meal ($63\%$ from carbohydrate, $15\%$ from protein, $22\%$ from fat) are shown in Table 1. The frozen bento of fried fish (Tokatsu Foods, Yokohama, Japan), vegetables (150 g of tomato and 70 g of broccoli), and 200 g of boiled white rice were purchased and served to the participants as breakfast by the research members. The frozen bento boxes were kept in the freezer until consumption and heated with a microwave by researchers on the test days before consumption. The participants consumed test meals with 200 g of water. The researchers measured and recorded eating speed and food order and assessed for compliance of the study protocol. The participants who did not follow the protocol were excluded. ## 2.3. Primary and Secondary Measurements and Statistical Analysis On the first day of the study, anthropometric measurements of height and body weight, and the hemoglobin A1c (HbA1c) of the participants were measured at Kyoto Women’s University in the morning after an overnight fast. All blood samples were examined by Nihon Rinsho, Inc, Kyoto, Japan. Plasma glucose concentration was measured by HK-G6PDH methods (KANTO CHEMICAL CO., INC. Tokyo, Japan). HbA1c levels were determined by NGSP method (Kyowa Medix CO., INC, Tokyo, Japan). Serum insulin levels were determined by CLEIA method (FUJIREBIO. CO., INC. Tokyo, Japan). Serum TG concentrations were determined by GK-GPO method (SEKISUI MEDICAL CO., LTD. Tokyo, Japan). Serum FFA concentrations were determined by ACS-ACOD method (FUJIFILM Wako Pure Chemical Corporation, Osaka, Japan). In the present study, a total of fourteen participants was calculated as the sample size to provide $95\%$ power to detect $5\%$ difference in postprandial blood glucose levels (GPower 3.1, Heinrich-Heine-Universität Düsseldorf, Germany), matched to our study of consuming test meals of different food order in young, healthy women [29]. The primary outcome was the postprandial blood glucose concentration and the secondary outcome was the postprandial blood insulin concentration. Since we could not statistically confirm homogeneity of variance and normal distribution for all blood parameters by Shapiro–Wilk and Levene’s tests, we used a paired comparison by the Wilcoxon matched-pairs signed-rank test followed by post hoc Bonferroni’s inequality when Friedman’s test revealed significant effects for parameters ($p \leq 0.05$). The results are expressed as the mean ± standard error of the mean (SEM) unless otherwise stated. All analyses were performed with SPSS Statistics ver. 24 software (IBM Corp., Armonk, NY, USA). ## 3. Results Among the 21 participants, 3 participants could not complete the study defined by the study protocol, so the results were analyzed based on 18 young, healthy women [age 21.3 ± 0.4 years, BMI 19.6 ± 1.6 kg/m2, FPG 4.7 ± 0.3 mmol/L, HbA1c 33 ± 2 mmol/mol (5.2 ± $0.3\%$), mean ± standard deviation (SD), Figure 2]. None of the participants had a family history of T2DM. The postprandial blood glucose, insulin, TG, and FFA profiles for three different days with different food orders and eating speeds are shown in Figure 3. The significant amelioration of both fast and slow eating with vegetables first on postprandial blood glucose at 30 min (slow eating with carb. first vs. fast eating with veg. first; 7.09 ± 0.34 vs. 5.94 ± 0.24 mmol/L, $p \leq 0.05.$ vs. slow eating with veg. first; 5.53 ± 0.25 mmol/L, $p \leq 0.01$, mean ± SEM) and 60 min (slow eating with carb. first vs. fast eating with veg. first; 5.88 ± 0.34 vs. 4.95 ± 0.18 mmol/L, vs. slow eating with veg. first; 4.97 ± 0.16 mmol/L, both $p \leq 0.05$) was demonstrated compared with those in slow eating with carbohydrate first (Figure 3A). Additionally, postprandial blood insulin concentrations at 30 min (slow eating with carb. first vs. fast eating with veg. first; 83.6 ± 7.5 vs. 63.5 ± 7.5 μIU/mL, vs. slow eating with veg. first; 55.8 ± 6.5μIU/mL, both $p \leq 0.01$) and 60 min (slow eating with carb. first vs. slow eating with veg. first; 58.2 ± 6.4 vs. 39.4 ± 5.2 μIU/mL, $p \leq 0.01$) were significantly lower in slow and/or fast eating with vegetables first than those of slow eating with carbohydrate first (Figure 3B). In contrast, postprandial blood FFA concentrations decreased after consuming the test meal in the three days. Postprandial blood TG and FFA concentrations at 60 min and 120 min in slow eating with carbohydrate first showed lower values than those of slow and/or fast eating with vegetables first; yet, pre- and postprandial blood TG and FFA concentrations were transit within the normal range in all three study days (Figure 3C,D). Blood glucose and insulin parameters of different speeds with different food orders were shown in Table 2. The significant reductions of both fast and slow eating with vegetable first on SD, large amplitue of glucemic excursion (LAGE), and IAUC 120 min for blood glucose were observed compared with those in slow eating with carbohydrate first. Additionally, SD, MAX, large amplitude of insulin excursion, and IAUC 120 min for insulin in both fast and slow eating with vegetable first were significantly lower than those of slow eating with carbohydrate first. However, there was no significant difference between fast and slow eating with vegetable first on postprandial blood glucose and insulin, except postprandial blood glucose at 30 min in slow eating with vegetable first was significantly lower than that of fast eating with vegetable first (Table 2 and Figure 3A). ## 4. Discussion This is the first interventional study to demonstrate that fast eating with vegetables first ameliorates postprandial blood glucose elevation and insulin secretion in young, healthy women. Our present study indicates that as long as vegetables are consumed first, eating speed, whether slow (20 min) or fast (10 min), does not affect postprandial blood glucose and insulin levels, except slow eating with vegetables first showed lower postprandial blood glucose concentration at 30 min than that of fast eating with the same food order. It is important to “eat vegetables first and carbohydrate last” to ameliorate postprandial blood glucose and insulin even in fast eating, as supported by our previous study showing that eating fast with a mixed eating with vegetable, protein, and carbohydrate elevated postprandial blood glucose compared with slow eating with vegetables first [28]. As the test meals were provided in the morning after 12 h fasting, postprandial blood FFA concentrations decreased after consumption of the meal in all three days, while postprandial blood glucose and insulin secretion increased after consuming the meal. This seems reasonable because the postprandial blood FFA concentrations were inversely correlated to the blood glucose concentration. Thus, the postprandial blood FFA concentrations in slow eating with carbohydrate first were lower than those of slow and fast eating with vegetables first. The reason of higher postprandial blood TG concentrations shown in slow and fast eating with vegetables first compared with those in slow eating with carbohydrate first might be explained by the sesame oil consumed first with vegetables. Although postprandial parameters of blood TG and FFA in fast and slow eating with vegetables first showed statistically higher than the values in slow eating with carbohydrate first, all of the values were within the normal range of a healthy population without dyslipidemia, suggesting these variations were normal, rather than any pathological phenomena. Shukla et al. reported that a food order with vegetables for 10 min, followed by a 10 min interval, and then eating protein and carbohydrate for 10 min was effective to suppress postprandial blood glucose elevation and insulin secretion [16]. However, in the present study, we demonstrated the effect of eating fast for 10 min with a food order of vegetables, protein, and carbohydrate without any interval time. Kuwata et al. reported that eating protein first ameliorated postprandial blood glucose, although it did not suppress insulin secretion [30]. This fact is particularly important for Japanese individuals with and without T2DM because the secretion of insulin in East Asian people including Japanese is* often delayed, and the ability for insulin secretion is weak, about half that of Caucasian’s [31]. Thus, for East Asians, it is essential to suppress excessive insulin secretion to maintain β cell function and potentially lower the risk of obesity, T2DM, cancer [32], and Alzheimer’s disease [33]. The food order and eating speed in the present study, i.e., “eating vegetables first, then the main dish (protein), and then carbohydrate last for 10 min”, is easier to maintain in real life than other methods of medical nutrition therapy for T2DM. For instance, we have reported previously that the dietitian-led medical nutrition therapy of food order with vegetables first has been reported to be effective in the long term, up to 5 years, on glycemic control and the prevention of diabetic complications in individuals with T2DM [19,34]. One of the possible reasons for the amelioration of postprandial blood glucose and insulin concentration observed in fast eating with vegetables first may be explained by the preloading of dietary fiber contained in vegetables (7.1 g of dietary fiber in the test meal). The dietary fiber in the test meal was digested slowly to ameliorate postprandial blood glucose elevation and reduce insulin secretion [35,36]. Another possibility in the present study is that the secretion of incretin hormones, such as glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP), might have been induced by the sesame oil consumed with the vegetables. Then, consuming the main dish which contains protein and fat may have induced incretin hormones. Incretin hormones induce insulin secretion for subsequent metabolic responses and delayed gastric emptying. The delay of gastric emptying by incretin hormones was reported to ameliorate postprandial blood glucose elevation and minimize the enhancement of glucose-dependent insulin secretion [17,18,20,30]. Various cohort and epidemiological studies revealed that a faster eating speed showed a positive correlation with body weight, blood glucose concentration, insulin resistance, and the risk of metabolic syndrome and T2DM [21,22,23,24,25,26]. However, the slowing of eating pace was not an easy task to achieve [27]. Therefore, instead of changing eating speed, strategies from different aspects are needed to improve postprandial blood glucose and insulin responses. In addition, these epidemiological studies were not interventional studies; they were analyzed through surveys reported by the subjects themselves, which might cause bias in assessing the effect of eating speed on metabolic parameters. On the other hand, some research has reported that eating speed does not affect postprandial blood glucose, insulin, and incretin hormone levels in individuals with and without T2DM [37,38]. Moreover, not only eating speed but also specific dietary patterns are associated with an increased risk of T2DM, obesity, and metabolic syndrome. The dietary patterns in individuals who eat fast tended to be Western-style eating patterns high in red meat, processed meat, snacks, sweetened beverages, fried food, and fast-food, and low in vegetables [39]. In addition, eating quickly often resulted in higher energy intake by increasing appetite and hunger [21,22,23,24,25,26,40]. Thus, eating quickly may increase the risk of obesity and insulin resistance, and subsequently worsen the metabolic responses. Various evidence-based studies suggest that a diet rich in vegetables and low GI grains is the substantial dietary pattern predicting low risk in T2DM, obesity, and cardiovascular diseases [11]. We assume that individuals with a dietary pattern of fast eating are likely to avoid fiber-rich food which requires chewing thoroughly and takes a certain time for ingesting [41]. In the present study, boiled white rice with high GI was used because low GI grains such as brown rice and whole-grain bread are unfavorable for Japanese people. Therefore, dietary fiber should be taken from vegetables, seaweeds, mushrooms, soy beans, and soy products in Japan. However, the average vegetable intake was less than the 350 g recommended by the Ministry of Health, Labor and Welfare in Japan [42]. The U.S. report reveals that $70\%$ of individuals in the U.S. do not consume the amount of vegetables recommended by the U.S. Department of Agriculture, and shockingly, $25\%$ of individuals do not consume vegetables at all. [ 43]. The reasons of low vegetable intake are described to be such as economic problems, absence of knowledge of the benefits of vegetables, low accessibility to fresh products, taste preference, and lack of cooking time and skills. Therefore, we previously demonstrated that the preloading of tomato juice [44] or vegetable juice [45] was effective to reduce postprandial blood glucose concentrations. Tomato and vegetable juice are easy to consume and cost less than consuming fresh vegetables; thus, their preloading is one of the simple and economical methods to ameliorate postprandial blood glucose concentrations. Furthermore, cost–benefit analysis suggests that dietary education would produce a significant potential cost-saving effect in national healthcare budgets [46]. International treatment costs for diabetes may reach as high as $10\%$ of national health expenditures, and the losses of national income can be equally costly; treatment costs are escalating along with the increasing prevalence of diabetes. Above all, there is concern that drugs alone may not prevent the progression of diabetic complications [47]. Nevertheless, patients with T2DM treated by medical nutrition therapy may be less managed than patients on medication with a list of prescriptions. Therefore, the eating behavior shown in this study should be one of the effective medical nutrition therapies not only to improve metabolic responses but also national healthcare budgets. Our results indicate that in addition to the amount of energy, carbohydrate, fat, protein, dietary fiber, and other nutrient contents, food order, rather than eating speed, is the most important factor for postprandial blood glucose and insulin responses. It is essential to avoid only eating carbohydrates, such as boiled rice, noodles, and bread, for preventing postprandial blood glucose elevation. Obviously, medical nutrition therapy with food order should require counselling patients to support the individual’s dietary habit, socio-economic situation, and lifestyle, as well as their medical condition. The current study has limitations to be mentioned. First, the present experiment was designed to examine the acute effects of eating speed with different food order in a single meal, requiring additional investigations to clarify the long-term effect on glycemic control and the improvement of diabetic complications in T2DM management, and the chronic effect of diabetes prevention in individuals without T2DM. Second, the study participants were Japanese young women without T2DM and dyslipidemia; therefore, it is uncertain whether the present results could be applied to individuals with T2DM and dyslipidemia, and of other gender, age groups, or racial groups. Third, incretin hormones may be involved in the mechanisms for amelioration of blood glucose, insulin, and the gastric emptying rate, because enhanced GLP-1 secretion is reported to delay gastric emptying in both individuals with and without T2DM [16,17,19,29]. Although GLP-1, GIP, and gastric emptying rates were not examined in this study, the role of eating fast and food order on the postprandial blood glucose–incretin hormone interaction remains unclear. Therefore, the results of this research should be used carefully. Further investigations are required to determine the overall mechanisms of eating rate and food order on glycemic and hormone responses in individuals with and without T2DM. ## 5. 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--- title: 'Effect of Vitamin E Supplementation on Chronic Insomnia Disorder in Postmenopausal Women: A Prospective, Double-Blinded Randomized Controlled Trial' authors: - Wirun Thongchumnum - Sakda Arj-Ong Vallibhakara - Areepan Sophonsritsuk - Orawin Vallibhakara journal: Nutrients year: 2023 pmcid: PMC10005674 doi: 10.3390/nu15051187 license: CC BY 4.0 --- # Effect of Vitamin E Supplementation on Chronic Insomnia Disorder in Postmenopausal Women: A Prospective, Double-Blinded Randomized Controlled Trial ## Abstract Chronic insomnia disorder is one of the most common problems in postmenopausal women, exacerbated by underdiagnosis and improper treatment. This double-blinded, randomized, placebo-controlled trial was conducted to evaluate the potential of vitamin E to treat chronic insomnia as an alternative to sedative drugs and hormonal therapy. The study enrolled 160 postmenopausal women with chronic insomnia disorder, divided randomly into two groups. The vitamin E group received 400 units of mixed tocopherol daily, while the placebo group received an identical oral capsule. The primary outcome of this study was sleep quality assessed by the Pittsburgh Sleep Quality Index (PSQI), a self-evaluated and standardized questionnaire. The secondary outcome was the percentage of participants using sedative drugs. There were no significant differences in baseline characteristics between the study groups. However, the median PSQI score at baseline was slightly higher in the vitamin E group compared with the placebo (13 [6, 20] vs. 11 [6, 20]; p-value 0.019). After one month of intervention, the PSQI score was significantly lower (indicating better sleep quality) in the vitamin E group compared with the placebo (6 [1, 18] vs. 9 [1, 19]; p-value 0.012). Moreover, the improvement score was significantly higher in the vitamin E group compared with the placebo (5 (−6, 14) vs. 1 (−5,13); p-value < 0.001). In addition, there was a significant reduction in the percentage of patients using sedative drugs in the vitamin E group ($15\%$; p-value 0.009), while this reduction was not statistically significant in the placebo group ($7.5\%$; p-value 0.077). This study demonstrates vitamin E’s potential as an excellent alternative treatment for chronic insomnia disorder that improves sleep quality and reduces sedative drug use. ## 1. Introduction Postmenopausal women are defined as women with amenorrhea for at least 12 consecutive months due to the cessation of ovarian function. The average age of natural menopause is marginally different across ethnicity and is 49.5 years old for Thai women [1]. Having an average life expectancy at birth of 80.5 years [2], an increased number of aging Thai women are experiencing menopausal symptoms such as vasomotor symptoms, vulvovaginal atrophy, and long-term consequences including dementia, increased risk of cardiovascular disease, and osteoporosis [3]. Another common health problem among them is insomnia, which is defined as difficulties initiating or maintaining sleep or early morning awakening associated with impaired daytime cognitive performance, fatigue, or mood disturbances [4]. The etiologies of insomnia are based on the “3Ps” model: predisposing, precipitation, and perpetuating factors [5]. With an age older than 45 years and being in the menopause transition period, women are more likely to have predisposing factors for insomnia compared with men [6]. The menopausal symptoms, such as hot flushes, night sweats, and nocturia, are the precipitating factors. The perpetuating factor becomes operative when insomnia transitions from acute to chronic. Insomnia involves hyperarousal stages during sleep and wakefulness with increased arousal levels in the cognitive, emotional, and physiologic domains. This manifests as an elevated, whole-body metabolic rate during sleep and wakefulness, elevated cortisol and adrenocorticotropic hormone during the early sleep period, and reduced parasympathetic tone in heart rate variability. Moreover, there is evidence that insomniacs have increased power in fast electroencephalographic (EEG) frequencies during non-rapid eye movement (NREM) sleep and an increased frequency of microarousals during rapid eye movement (REM) sleep, leading them to perceive parts of REM sleep to be wakefulness [5]. The prevalence of insomnia among the general population is about $10\%$, with a female and increasing-age preponderance. This places menopausal women at high risk. Sleep complaints increased dramatically during the menopausal transition, with the prevalence increasing from 12 to $40\%$ from late reproductive age (around the late 40s) into perimenopause (in the early 50s) [7]. Moreover, the prevalence of insomnia among menopausal women was twice that of reproductive-age women ($26\%$ versus $13\%$). The changes in the levels of various sex hormones—including decreases in estradiol level and increases in the follicle-stimulating hormone, progesterone, and testosterone across the menopausal transition—are associated with perceived poor sleep quality, sleep fragmentation, and increased awakenings. Medical conditions that increase during midlife, including obesity, cancer, gastroesophageal reflux, urinary incontinence, nocturnal micturition, thyroid dysfunction, chronic pain syndromes, and fibromyalgia, add to the impact of age on sleep. The available modalities to evaluate sleep disturbance are subjective insomnia rating scales or questionnaires and objective devices, such as wrist actigraphy and polysomnography [5]. However, the mixed results yielded by objective sleep monitoring and polysomnography (PSG) have inconsistently evidenced improvement or worsening of sleep patterns in the menopausal transition [8]. Young T et al. ’s report of sleep patterns from PSG among premenopausal, perimenopausal, and postmenopausal women showed no significant difference across all groups [9]. In contrast, Xu M et al. revealed a worsening sleep pattern in menopausal women, defined by longer total wake time, and lower sleep efficiency compared to premenopausal women [10]. Due to these inconsistencies, PSG is not recommended for the evaluation of insomnia, despite its merits as an evaluation tool for sleep apnea or parasomnias. The effect of insomnia on health outcomes has been established. Not only does insomnia significantly increase the risk for many chronic diseases including arterial hypertension, myocardial infarction, heart failure, type 2 diabetes, cognitive impairment, neurodegenerative disease, major depression, sick leave, and accidents, both at work and in motor vehicles [4], but it also has the effect of increasing mortality. A recent meta-analysis by Ge L et al. aimed to assess the association between insomnia disorder and mortality risk. Insomnia is linked to an increased risk of all-cause mortality (hazard ratio (HR) 1.23, $95\%$ CI 1.07–1.42, $$p \leq 0.003$$) and cardiovascular disease mortality (HR 1.48, $95\%$ CI 1.07–1.42, $$p \leq 0.003$$) but not cancer-related mortality [11]. Chronic insomnia disorder is diagnosed based on the American Academy of Sleep Medicine’s International Classification of Sleep Disorders, Third Edition, which defines diagnostic criteria as a disturbance of nocturnal sleep at least three times a week for at least three months. Disturbances include difficulty in the initiation or maintaining of sleep or waking up earlier than desired and related daytime impairment such as fatigue, sleepiness, mood disturbance, and impaired cognitive and work performance [12]. Chronic insomnia is associated with a greater risk of mortality, morbidity, and accidents compared to acute insomnia [13,14]. The incidence of chronic insomnia in community-dwelling adults from cohort studies in the U.S., U.K., and Taiwan was 2–$7\%$ per year [15]. Incidence was much higher among postmenopausal women and more prevalent in the late than early stage of menopause, as reported by The Study of Women Across the Nation (SWAN), which found around one-third of women reporting chronic insomnia by the end of their menopausal transition [16]. In line with that cross-sectional, Punyahotra S. et al. revealed a $40\%$ prevalence of insomnia among mid-aged Thai women [17]. Insomnia symptoms are often neglected by both patients and doctors, especially in gynecological practice. As a result, patients are not receiving effective treatment, which leads to persistent insomnia. Not only does chronic insomnia produce numerous ill-health outcomes, but it also affects the quality of life of menopausal women. Yazdi and colleagues evaluated the effect of insomnia on the quality of life among menopausal women through the Menopause-Specific Quality of Life (MENQOL) Questionnaire, a 29-item questionnaire that assesses the effects of menopausal symptoms on four categories of quality of life: vasomotor, psychosocial, physical, and sexual. The study showed that postmenopausal women with chronic insomnia disorder had significantly worse quality of life across all domains of their score [18]. The roles of oxidative stress and chronic sleep deprivation have been evaluated in many studies. This complex relationship works both ways; while oxidative stress may cause sleep disturbance by breaking the sleep–wake cycles, poor sleep quality also increases oxidative stress and lowers antioxidant levels [19]. The central nervous system (CNS), which contains high levels of polyunsaturated fatty acids (PUFAs) and lipids, is a vulnerable and well-known target of reactive oxygen species (ROS) [20]. Furthermore, the association between oxidative stress and various neurodegenerative diseases, such as Alzheimer’s disease, Amyotrophic lateral sclerosis, Friedreich’s ataxia, Huntington’s disease, Multiple sclerosis, and Parkinson’s diseases, was also well described [21]. Therefore, El-Helaly M. and a colleague investigated participants’ exposure to shallow frequency electromagnetic fields (ELF-EMF) and the relationship between oxidative stress and sleep quality in the clinical setting, which suggested that ELF-EMF exposure generates ROS, reflected by the high level of serum malondialdehyde level, and low melatonin, a scavenger of ROS. Undoubtedly, participants in the exposure group had higher ROS, lower levels of melatonin, and a more frequent prevalence of sleep insufficiency when compared to controls [22]. The study by Gulec and colleagues showed that the serum level of malondialdehyde (oxidative stress marker) in patients with primary insomnia was significantly higher than in controls. In the same way, insomnia was detrimental to antioxidant levels, where glutathione peroxidase was much lower in the poor sleep group [23]. Feng and colleagues discovered the same result in animal model research [24]. Vitamin E, which can be found in beans, vegetable oil, or supplement drugs, is an antioxidant that acts to reduce the destruction of cell membranes in the body by eradicating free radicals and reducing inflammation. Two main isoforms of vitamin E are tocopherol and tocotrienol. Each isoform is divided into four distinct analogs, including alpha (α), beta (β), gamma (γ), and delta (δ), which all have antioxidating properties and act as free radical scavengers [25]. Currently, the Recommended Dietary Allowance (RDA) is only given for alpha-tocopherol supplementation. The RDA for both male and female adults is 33.1 IU per day, and the maximum dose is 1000 IU per day [26]. The use of vitamin E is now more prevalent among postmenopausal women as a dietary supplement or an anti-aging agent. Large numbers of studies have shown that vitamin E may help with hot flushes caused by estrogen deficiency compared to placebo [27] or have a beneficial effect on bone formation and destruction in postmenopausal women with no effect on all-cause mortality [28,29,30,31]. Although there was no clinical trial performed to determine the potential of vitamin E for treating chronic insomnia disorder in humans, some studies found that vitamin E consumption significantly restored normal blood levels of glutathione peroxidase while suppressing malondialdehyde [32,33,34], which leads us to propose that a reduction in oxidative stress may be the mechanism by which vitamin E improves sleep quality. There are several different approaches to treating chronic insomnia disorder including menopause hormone therapy, Cognitive Behavioral Therapy for Insomnia (CBT-I), and alternative herbal medicines such as Valerian and isoflavones, according to a 2016 publication by the North American Society of Menopause [12]. To build on their recommendations, the researchers conducted this study of the potential of vitamin E to treat chronic insomnia as an alternative to drugs with harmful side effects, especially hormone replacement therapy and sedatives. ## 2.1. Study Design A double-blinded, randomized, placebo-controlled trial was conducted at the Department of Obstetrics and Gynaecology, Faculty of Medicine, Ramathibodi Hospital, Bangkok, Thailand, between November 2021 and May 2022. The study aimed to evaluate the effects of oral mixed tocopherol on postmenopausal chronic insomnia disorder. The results were measured by a subjective, self-evaluated questionnaire, the Pittsburgh Sleep Quality Index (PSQI), after one month of intervention. The PSQI was first developed in 1988 by Dr. Daniel J. Buysse and colleagues at the University of Pittsburgh. The questionnaire is intended to be a highly effective, standardized tool for assessing a patient’s sleep quality with a test reliability coefficient (Cronbach’s alpha) of 0.83, a sensitivity of $89.6\%$, and a specificity of $86.5\%$ for sleep disorders [35]. The questionnaire registers a score from 0 (no sleep disturbance) to 3 (severe sleep disturbance) for each of the 7 components: subjective sleep quality, sleep latency, duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction. Possible scores range from 0 to 21, and a value of more than 5 indicates poor sleep quality. The PSQI questionnaire has been translated into more than 56 languages, demonstrating its international recognition. Furthermore, the questionnaire was already translated and validated in Thai by Sitasuwan and colleagues in 2014 [36]. The primary outcome of this study was post-intervention sleep quality as assessed by the PSQI, a self-evaluated and standardized questionnaire. The secondary outcome was the reduction of sedative drugs used. ## 2.2. Participants Postmenopausal women, defined as women who have had amenorrhea for a consecutive 12 months or who have undergone previous bilateral oophorectomy, who presented with chronic insomnia disorder meeting the diagnostic criteria of the American Academy of Sleep Medicine’s International Classification of Sleep Disorders Third Edition [12] and who had a PSQI score > 5 were included. Informed consent was provided by all participants. Women with hot flushes, with a contraindication to vitamin E intake (for example, taking an antiplatelet or anticoagulant or severe hepatic or renal disease), taking vitamin E or any other herbal medicine that may affect sleep, with an underlying psychiatric disease with prescribing medication, consuming caffeine more than two shots per day, working night shifts, undergoing cancer therapy, and with uncontrolled diabetes or hypertension were all excluded. ## 2.3. Sample Size Calculation The sample size calculation was based on the difference in sleep efficiency score in the exploratory study by Lichstein et al. [ 37] with the n4Studies application for randomized-controlled trials (version 1.4.1) [38]. Finally, with a power of 0.8, there were 72 participants in each study group. A total of 160 postmenopausal women were included in this study (accommodating for $10\%$ data loss). ## 2.4. Randomization, Blinding, and the Study Protocol All participants were assigned using a computer-generated randomization sequence into each study group (1:1; blocks of four). The participants and investigators were blinded to the group allocation. The mixed tocopherol used for the intervention (Nat E® (Mega Lifesciences Public Company Limited, Samutprakarn, Thailand), 400 units per tablet) contained $20\%$ delta-tocopherol, $1\%$ beta-tocopherol, $62\%$ gamma-tocopherol, and $10\%$ alpha-tocopherol. Participants in the vitamin E group received one tablet daily for one month. In contrast, those in the placebo group received identical placebo capsules once daily for one month. Participants were encouraged to perform normal daily activities and to avoid other drugs besides the assigned intervention. However, participants could continue taking medication for underlying diseases or previous sedative drugs. ## 2.5. Data Collection and Measurements The participants’ baseline characteristics were collected at the time of enrollment, including age, body mass index (BMI), age at menopause, years since menopause, type of menopause, marital status, number of children, education, socioeconomic status, underlying disease, history of sedative drugs used, and caffeine consumption. In the first and third weeks, the investigators contacted all participants by phone or social media application to monitor drug compliance and side effects. This study defined good drug compliance as all participants taking more than $80\%$ of the assigned drugs. The participants completed the PSQI questionnaire at the time of enrollment and at the end of one month of intervention. ## 2.6. Statistical Analysis Baseline characteristics are reported as descriptive data. The normal distribution was tested for continuous variables with the Shapiro-Wilk normality test. When the data were normal or non-normally distributed, the mean (standard deviation, S.D.) or median (range) were used, respectively. The discrete variables were reported in counts (percentages). Statistical analysis was performed by STATA Version 15.0 (College Station, TX, USA). The student t-test was used to compare the continuous variables in parametric data. The Mann–Whitney U test was used for nonparametric data to compare continuous variables. The categorical data were tested by Pearson’s chi-squared test or Fisher exact test. A p-value of 0.05 defined statistical significance. Intention-to-treat analysis was used in this clinical trial. ## 2.7. Ethical Approval Human Research Ethics Committee, Faculty of Medicine Ramathibodi Hospital, Mahidol University approved this study (MURA$\frac{2021}{819}$). In addition, the study protocol was submitted to the Thai Clinical Trials Registry; TCTR (www.thaiclinicaltrials.org access on 21 April 2022); clinical trial registration number: TCTR20220405002. ## 3.1. Protocol Flow Diagram As shown in the protocol flow diagram (Figure 1), one hundred eighty-two postmenopausal women met the eligibility criteria, and twenty-two participants lost contact. A total of 160 participants were included in our study, and all participants were randomly and equally separated into the vitamin E and placebo groups. Fortunately, there were no losses to follow up in our study. The reason could be our design of a short study period of one month and the follow-up strategy, in which the authors contacted participants at least twice (in the first and third weeks) to monitor compliance and side effects. Furthermore, participants had the willingness to overcome chronic insomnia. As a result, all the participants cooperated well with the study’s design and reported no serious adverse effects. ## 3.2. Main Results As shown in Table 1, baseline characteristics between the two study groups showed no statistical difference, including age, body mass index (BMI), age at menopause, years since menopause, type of menopause, marital status, number of children, education, socioeconomic status, underlying diseases, and caffeine consumption. There was a higher rate of sedative drug use in the vitamin E group compared to the placebo group, but there was no statistical significance. All of the prescribed sedative drugs in both study groups were benzodiazepines (either lorazepam or alprazolam). Most participants had well-controlled underlying diseases such as diabetes, dyslipidemia, and hypertension. No diseases could affect sleep, as described in our exclusion criteria. The primary outcome of this study was to evaluate the improvement in PSQI scores after one month of intervention. The pre-intervention PSQI scores of the two groups were marginally different (11 [6, 20] in the placebo group and 13 [6, 20] in the vitamin E group; p-value 0.019). This event was also observed in the previous study [36]. After the intervention, participants in the vitamin E group had significantly better sleep quality compared to the placebo group, with a score of 6 [1, 18] vs. 9 [1, 19] (p-value 0.012). Furthermore, the improvement of sleep quality was significantly greater in the vitamin E group compared with the placebo group (5 (−6, 14) vs. 1 (−5, 13); p-value 0.001), as shown in Table 2 and Figure 2. With regard to the secondary outcome, the prevalence of sedative drug use in the vitamin E group decreased from $30\%$ to $15\%$ (p-value 0.004). In comparison, there was no statistically significant reduction in the placebo group from $17.5\%$ to $10\%$ (p-value 0.077), as shown in Table 3 and Figure 3. ## 4. Discussion The main findings of our study indicate that a one-month vitamin E prescription can improve sleep quality and reduce sedative drug use in postmenopausal women with chronic insomnia disorder. There is much evidence to back up the benefits of vitamin E in menopause, whether it is for postmenopausal symptoms or bone health [27,28,29,30]. However, as described previously, no clinical trial has been conducted to evaluate the potential effect of vitamin E on chronic insomnia disorder, especially in postmenopausal women. Currently, apart from CBT-I, menopausal hormone therapy, or sedative drugs, two alternative agents (Valerian and isoflavones) can be used for treating chronic insomnia in menopausal women [12,39]. In 2011, Taavoni and colleagues conducted a triple-blinded, randomized controlled trial in a population comprising 100 postmenopausal women with self-reported insomnia. All the participants were randomized and divided equally into two groups. The first group took a pill containing 530 mg of valerian extract, taken once daily, versus a placebo. The investigators assessed sleep quality before and after four weeks using the Pittsburgh Sleep Quality Index questionnaire and found that the valerian extract supplementation group had a statistically significant reduction in PSQI score compared to the placebo group. Moreover, sleep quality increased by $30\%$ in the valerian group compared to just $4\%$ in the placebo group [40]. Hachul et al. conducted a double-blinded, randomized controlled trial in 2011. The study divided thirty-eight participants into two groups: women who were prescribed 80 mg/day of isoflavones, a naturally occurring substance found in soy, and those who were given a placebo. The authors compared the measured values from polysomnography after four months of intervention. They found that in the isoflavone group, the sleep efficiency from polysomnography showed a statistically significant improvement compared to the placebo group [41]. Despite these discoveries, the current knowledge about the relationship between vitamin E and sleep quality improvement is based only on descriptive data or studies in animal models. An exploratory study in 2008 by Lichstein et al. surveyed a total population of 519 men and women aged 20–98 in Shelby County, Tennessee. The study aimed to assess changes in sleep quality associated with vitamins. When separating the subgroups, the participants who were only prescribed vitamin E had better sleep quality and scores than those with no vitamin use [37]. However, the study had many limitations, such as the study design, the method for data collection, and the small number of participants who were only prescribed vitamin E. Furthermore, the population in the study had a wide range of demographic characteristics, whereas our study is more specific. The study with the most potential to evaluate the molecular mechanism for the effect of vitamin E on sleep deprivation was conducted in 2011 by Alzoubi and colleagues on mice divided into five cages, some of which were induced into a state of sleep deprivation. Vitamin E was fed to the chronically sleep-deprived mice for six weeks, and behavioral changes, spatial learning, and memory were assessed periodically. In addition, the oxidative stress markers were assessed by a calorimetric immunoassay method after the dissection of the hippocampus. In terms of oxidative stress, learning ability, and both short- and long-term memory, those prescribed vitamin E performed better, experienced less oxidative stress, and had better antioxidant level markers [42]. The PSQI questionnaire comprises seven domains: sleep quality, sleep latency, sleep duration, sleep efficiency, sleep disturbance, sleep medication use, and daytime dysfunction, in all of which the chronic stress stage is involved. Vitamin E supplementation may lower the stage of stress or resolve its chronicity through its ability to improve biomarkers of oxidative stress, described earlier in the proposed mechanism. The improvement score of the vitamin E group in this study was much greater than that of the placebo group, indicating that vitamin E improved sleep quality. When exploring micronutrient inadequacy and sleep in a large general population, Ikonte and colleagues discovered that women with adequate intakes of vitamin E below $60\%$ were more likely to suffer from short sleep than the population as a whole [43]. Although the study was cross-sectional, its large sample size may justify using it to support the findings of our clinical trial. Our secondary objective was the reduction in sedative drugs, and we found the reduction in the percentage of women using them to be significantly greater in the vitamin E group versus the placebo. In Thailand, the most commonly used sedative drugs in outpatient clinics were in the benzodiazepine group (lorazepam, alprazolam, and diazepam) [44], which cause side effects such as drowsiness, sedated state, or dizziness. These side effects were disliked, especially in postmenopausal women, because they increased the risk of falling or decreased self-response to perturbations [45,46,47], which vitamin E could potentially reduce. To our knowledge, the strength of this study is that it was the first randomized placebo-controlled trial to evaluate vitamin E’s effects on chronic insomnia disorder. Another strength is that our study chose the PSQI questionnaire, which is simple, internationally standardized, and validated in its Thai version for identifying poor sleepers and following up on sleep quality. The main limitation is that we did not evaluate serum vitamin E or oxidative stress markers before and after the intervention. Second, the PSQI questionnaire was designed only to evaluate sleep quality within one month, so the long-term result of a vitamin E prescription for chronic insomnia disorder is still unknown and requires further study. Third, the baseline PSQI scores in the vitamin E group were higher than the placebo group. Although we used a randomization design intending to have similar sleep quality between the two participant groups, this situation could happen by chance. While worse sleep quality in the intervention group was observed, there was more room for improvement which might raise some concern that the actual effect of vitamin E on relieving insomnia would be obscured. Nevertheless, the difference between the baseline PSQI scores of both participant groups was only two, which may not be clinically significant. Furthermore, the improvement score and rate of sedative drugs used were much better in the vitamin E group, reflecting some benefits of vitamin E supplements on sleep. Chronic insomnia disorder is a complex disease with multiple unidentified causes and various treatments available. Management options include non-pharmacological and pharmacological treatments, as mentioned in the introduction. At the same time, oxidative stress plays a role in chronic insomnia, which is improved by potent fat-soluble antioxidant supplementation, or vitamin E, as shown in our results. As a result, future research should correlate the oxidative stress level, the anti-oxidative level, the sleep disturbance, and changes in pre- and post-antioxidant, or vitamin E, supplements to further investigate this relationship. ## 5. Conclusions Chronic insomnia disorder is one of the most common problems among postmenopausal women, with various treatment strategies available. We have examined vitamin E’s potential as an alternative treatment for chronic insomnia disorder that improves sleep quality and reduces sedative drug use. ## References 1. Gold E.B.. The Timing of the Age at Which Natural Menopause Occurs. Obstet. Gynecol. Clin. N. Am. (2011.0) 38 425-440. DOI: 10.1016/j.ogc.2011.05.002 2. Population Gazette 3. Santoro N., Epperson C.N., Mathews S.B.. Menopausal Symptoms and Their Management. 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--- title: Associations of Food Insecurity with Dietary Inflammatory Potential and Risk of Low Muscle Strength authors: - Su Min Kim - Yoon Jung Park - Hyesook Kim - Oran Kwon - Kwang Suk Ko - Yuri Kim - Yangha Kim - Hyesook Park - Seungyoun Jung journal: Nutrients year: 2023 pmcid: PMC10005676 doi: 10.3390/nu15051120 license: CC BY 4.0 --- # Associations of Food Insecurity with Dietary Inflammatory Potential and Risk of Low Muscle Strength ## Abstract Food insecurity refers to the uncertain availability of or limited access to nutritious food. Poor diets prevalent among food insecure populations may incite an inflammatory state and subsequently negatively affect skeletal muscle metabolism. To examine the inflammatory mechanistic potential of the association between food insecurity and the risk of low muscle strength, we analyzed cross-sectional data from 8624 adults aged ≥20 years from the Korean National Health and Nutrition Examination Survey 2014–2015. Household food security status was assessed using an 18-item food security survey module. The inflammatory potential of diets was estimated by the dietary inflammation index (DII). Low muscle strength was ascertained using hand grip strength. In the multivariable-adjusted model, greater food insecurity was significantly associated with a higher DII score and risk of low muscle strength. The multivariable-adjusted mean difference ($95\%$ confidence interval) on the DII, comparing the “moderate-to-severe” food insecurity group with the “food secure” group, was 0.43 (0.06–0.80) (P-trend: <0.001) and the odds ratio ($95\%$ confidence intervals) of low muscle strength for the same comparison groups was 2.06 (1.07–3.96) (P-trend: 0.005). Our results suggest that individuals with greater food insecurity may be susceptible to diets with greater inflammatory potential, which may contribute to a loss of muscle strength. ## 1. Introduction Skeletal muscle is an integral body tissue that plays a pivotal role in physical strength, physical performance, and metabolic regulation [1]. Muscle strength is an indicator of muscle function and is increasingly seen as a robust predictor of a range of health outcomes [2,3,4,5]. For instance, hand grip strength (HGS), a simple and reliable measurement of muscle strength [6,7], has been inversely associated with the risk of physical disability, respiratory disease, cancers, cardiovascular diseases, and all-cause and cardiovascular mortality [2,8]. The onset of loss of muscle mass and muscle strength begins in the early 30s and accelerates with aging, being observed to disproportionally affect individuals of low socioeconomic status [9,10]. Food insecurity refers to a lack of consistent access to sufficient, safe, and nutritious food for an active, healthy life and is associated with poor nutritional status [11] and unfavorable health outcomes, including obesity, metabolic syndrome, and diabetes [12]. According to the United Nations, nearly 2.3 billion people, accounting for $29.3\%$ of the global population, were reported to be moderately or severely food insecure in 2021, which is an increase of 350 million since the onset of the COVID-19 pandemic [13]. Diet is well known to be important for preserving healthy muscle [14,15], and individuals at risk of food insecurity are thus more likely to be susceptible to muscle deterioration. Indeed, the positive association of food insecurity with the risk of low muscle strength has been reported in two previous cross-sectional studies [16,17], but evidence is still limited [16,17] and the underlying mechanisms of the associations have not been elucidated. Nevertheless, substantial evidence suggests that food insecurity may affect muscle strength by altering the inflammatory state of the body. Diet is thought to modulate systemic inflammation, whose association with inflammatory biomarkers is well-documented in the literature [18], and inflammation is increasingly recognized as having a negative effect on muscle maintenance, altering cellular protein metabolisms to favor proteolysis over synthesis [19,20,21,22]. Previously reported associations of a pro-inflammatory diet [23,24,25] or inflammatory markers [22,26,27] with the risk of low muscle strength further support this speculation. However, there is a paucity of evidence that the poor diets prevalent among food insecure groups indeed incite diets with high inflammatory potential [28,29]. Therefore, we hypothesized that the greater possibility of inflammation via poor diets associated with food insecurity may have a significant implication for muscle strength. To examine our hypothesis, we investigated [1] the association between food insecurity levels and the dietary inflammatory index (DII), a well-known composite measure for estimating the inflammatory capacity of the entire diet [18], and [2] the association between food insecurity and the risk of low muscle strength in the Korea Health and Nutrition Survey (KNHANES), a nationally representative cross-sectional survey of Koreans [30]. In a secondary analysis, we also explored whether the association of low muscle strength with levels of food insecurity varies according to population characteristics. ## 2.1. Study Population This study used data from the 6th KNHANES (2104–2015). In brief, the KNHANES is an ongoing national survey that has been conducted by the Korea Centers for Disease Control (KCDC) since 1998 [30]. As a nationwide survey, the KNHANES enrolls nationally representative non-institutional civilians following a multi-level clustered probabilistic sampling design. The KNHANES consists of a household screening survey, health interview, health examination, and nutritional survey and collects details of several variables pertaining to the demographic, social, health, and nutritional status of the participants. All surveys are conducted with the consent of the participants. The institutional review boards of the KCDC have approved all KNHANES protocols. The present study was exempt from review by the institutional review board of Ewha Womans University because it used de-identified and publicly available data (IRB no. 202209-0020-01). Of the 14,930 individuals who participated in the KNHANES (2014–2015), we excluded those who: [1] were under 19 years of age ($$n = 3178$$), [2] were pregnant ($$n = 658$$), [3] provided no response to the food security survey ($$n = 1043$$), [4] had no measurements for HGS ($$n = 971$$), and [5] had implausible energy intake (≤500 kcal, ≥5000 kcal [31]) ($$n = 456$$) (Figure 1). Consequently, the final sample size analyzed in our study was 8624 adults. ## 2.2. Assessment of Food Insecurity The primary exposure of interest was the household food security level. Household food insecurity was measured using the revised 18-item US Household Food Security Survey Module (USHFSSM) [32], the use of which has previously been validated in the Korean population [33]. In brief, the USHFSSM asks questions related to household- and individual-level experiences or behaviors relating to food purchasing, food availability, and diet over the previous 12 months. Of the 18 questions, eight pertain to children’s experiences and thus were omitted if no child was present in the household. The overall level of food insecurity was quantified by scoring the responses to the USHFSSM. Applying the USDA standard coding procedure of giving a point to affirmative responses indicative of insufficient resources for food, 0 or 1 points were allocated to each question [32]. For example, 1 point was given to those who answered “yes” to questions with two responses (“yes” or “no”) or those who answered “frequently” or “occasionally” to questions with three responses (“frequently”, “occasionally”, or “never”). The points from all 18 questions were summed to create a food insecurity score within the range of 0–18 for households with children and 0–10 for households without children. Higher scores indicate greater food insecurity levels. The reliability coefficient α of the USHFSSM is ≥0.89 for households both with and without children, which suggests a high level of confidence [31]. The USDA guidelines define food insecurity levels into four categories: “food secure” (score of 0–2 for households with/without children), “food insecure without hunger” (3–7 for households with children; 3–5 for households without children), “moderately food insecure with hunger” (8–12 for households with children; 6–8 for households without children), and “severely food insecure with hunger” (13–18 for households with children; 9–10 for households without children) [32]. However, the number of individuals within the “moderately food insecure with hunger” and “severely food insecure with hunger” categories was small in our study. Thus, we combined the latter two USDA categories and analyzed our study population by re-defining the food insecurity level into three groups, as follows [34]: “food secure” (USDA food secure group), “mildly food insecure” (USDA food insecure without hunger group), and “moderate-to-severe food insecurity” (USDA moderately and severely food insecure with hunger groups). ## 2.3. Measurement of Dietary Inflammatory Potential The inflammatory potential of diets was assessed using the DII, which was developed by Shivappa et al. [ 18]; the details of its development [18] and validation [35,36] have been given previously. In brief, the DII is a composite score of 45 food parameters, including micronutrients, macronutrients, and individual food items, weighted by their dietary inflammatory effects. Each food parameter’s inflammatory effect was quantified as a score on the basis of its associations with inflammatory markers obtained from nearly 2000 articles [18]. To calculate the DII, the present study used DII food parameter intake data obtained from 24 h recall. The following 23 of the 45 DII food parameters in the original DII scoring algorithm were available and used for the DII calculation: energy, vitamin A, vitamin C, carbohydrates, proteins, total fat, saturated fatty acids, single unsaturated fatty acids, polyunsaturated fatty acids, omega-3 fatty acids, omega-6 fatty acids, cholesterol, dietary fiber, iron, carotene, thiamine, riboflavin, niacin, garlic, onions, ginger, pepper, and tea (black tea, green tea). For each DII food parameter, the actual intake was first standardized to a z-score using the world standard mean and deviation derived from the world composite database from 11 nations [18]. The z-score was then converted into a percentile and centered on zero by doubling the value and subtracting 1. The centered percentile intake value was multiplied by the respective inflammatory effect score to create a food-parameter-specific DII score. Finally, the DII score for each food parameter was summed across all the available food parameters to create an overall DII. A higher DII score suggests a higher pro-inflammatory potential for the diet. ## 2.4. Ascertainment of Low Muscle Strength Muscle strength was estimated by measuring HGS, a well-known and reliable measure of muscle strength [6,7], using a digital grip strength dynamometer (Model T.K.K.5401, Takei Co., Ltd., Ishioka, Japan). The HGS testing was performed according to the standardized manual recommended by the Centers for Disease Control and Prevention, and the dynamometer was calibrated and checked by trained staffs to ensure the accuracy of the data [37]. HGS was assessed in a standing position with the arm and wrist in the anatomical position, and participants were asked not to wield the grip dynamometer or hold their breath during testing [38]. HGS was measured three times on each hand with a 1 min interval between each trial, alternating the left and right hands. To determine low muscle strength, the maximally measured HGS value out of the three measurements of the dominant hand was used, and low muscle strength was defined as <28 kg for men and <18 kg for women, using the recently revised diagnostic criteria recommended by the Asian Working Group for Sarcopenia [39,40]. ## 2.5. Assessment of Covariates The KNHANES collected data on sociodemographics (age, sex, marital status, area of residence, education, income), behavioral factors (body mass index (BMI), smoking, alcohol drinking, activity), and health status using standard self-reported questionnaires [30]. Using the information from these questionnaires, residential areas were defined as either urban or rural, according to the addresses of the participants. Smoking and alcohol drinking status were defined as never, past, or current. Physical activity was assessed through the International Physical Activity Questionnaire (IPAQ) [41,42]; participants were defined as having low, medium, or high levels of physical activity if their activity level was <600 metabolic equivalent (MET) minutes per week (min/week), 600–3000 MET (min/week), or ≥3000 MET (min/week) [41]. Height and weight were measured by trained staff, and BMI was calculated as the square (kg/m2) of weight (kg) over height (m). Comorbidities were defined by counting the number of existing chronic disease conditions, including diabetes mellitus, hypertension, dyslipidemia, stroke, myocardial infarction, angina pectoris, pulmonary tuberculosis, tuberculosis, and asthma [24,43]. Dietary data were collected via 24 h recall with the assistance of trained interviewers and converted to individual nutrient intakes based on the national standard food composition table [44]. ## 3. Statistical Analyses Sampling weights were applied to all analyses to yield unbiased national estimates of the KNHANES. The characteristics of the study population according to the food security level were averaged ± standard deviation using the SURVEYMEANS procedure for continuous variables, and the categorical variables were measured as numbers and percentages using the SURVEYFREQ procedure. The analysis evaluated the association of food security with the DII score using the SURVEYREG procedure, while the association between food security and low muscle strength was evaluated using the SURVEYLOGISTIC procedure. To address the potential for confounding, well-known risk factors of muscle strength and food security were selected a priori from previous studies [16,17] and adjusted in the multivariable model as follows: age (continuous, year), sex (male, female), marital status (married, never married), residence (urban, rural), education level (less than elementary school, middle school graduate, high school graduate, college graduate or higher, missing), income (quartiles, missing), BMI (underweight, normal, overweight, obese, missing), smoking status (never, past, current, missing), alcohol drinking status (never, past, current), physical activity (high, medium, low), and number of chronic disease (0, 1, ≥2, missing). An indicator for missing responses in the covariates was created, if applicable. The P-trend was tested by modeling the median value for each food insecurity category as a continuous term. We also conducted several sensitivity analyses, defining low muscle strength by using age-specific criteria to take into account the variability of muscle strength with aging [45] and including protein intake, which was primarily not adjusted for because it could mediate the association between food insecurity and muscle strength. To examine whether associations varied by population characteristics, stratified analyses were conducted by sex, age, physical activity level, BMI, and smoking status. P for interaction was tested using the cross-product term between food security levels and stratification factors. All analyses were performed using SAS version 9.4 (SAS Institute, Inc., Cary, NC, USA). All statistical tests were two-sided at a significance level <0.05. ## 4. Results The characteristics of the study population are described in Table 1. Of the 8624 Korean adults in our study, 7995 ($92.4\%$) were food secure, 529 ($6.4\%$) were mildly food insecure, and 100 ($1.2\%$) were moderate-to-severe food insecure. The mean age of our study population was 46.6 ± 0.3 years, and $50.8\%$ were male. The majority were married ($78.5\%$), never smokers ($54.1\%$), and current alcoholic drinkers ($73.0\%$) and lived in rural regions ($69.7\%$), engaged in a low level of physical activity ($63.8\%$), and had no chronic disease condition ($75.3\%$). Participants with a greater level of food insecurity were more likely to be older, have less than college or higher education, earn a lower income, be overweight, and currently smoke, while also drinking less alcohol, being less physically active, and having a greater number of chronic disease conditions. They were also more likely to have greater DII and lower HGS levels (Supplementary Table S1) Sex, marital status, and residence were similar regardless of food security levels. Table 2 shows the association between food insecurity and the DII score. In our analyses estimating the mean difference in DII score when comparing groups with increasing levels of food insecurity, we observed a significant positive dose-response trend for the association between food insecurity and DII score in both the age-adjusted and multivariable-adjusted models. Compared to the food secure group, the mean DII ($95\%$ CI) was higher by 0.36 (0.21–0.51) for the mildly food insecure group and by 0.69 (0.28–1.10) for the moderate-to-severe food insecure group in an age-adjusted model (P-trend 0.01). Similarly, the mean DII ($95\%$ CI) was higher by 0.15 (0.01–0.30) for the mildly food insecure group and 0.43 (0.06–0.80) for the moderate-to-severe food insecure group compared to the food secure group in the multivariable-adjusted model (P-trend < 0.001). Table 3 shows the results for the association between food insecurity and the risk of low muscle strength. We observed a statistically significant positive dose-response trend for the association between food insecurity and the risk of low muscle strength in both age-adjusted and multivariable-adjusted models. The age-adjusted OR ($95\%$ CI) compared to the food secure group was 1.38 (0.98–1.95) for the mildly food insecure group and 2.76 (1.55–4.92) for the moderate-to-severe food insecure group (P-trend: <0.001). The multivariable-adjusted OR ($95\%$ CI) compared with the food secure group was 1.18 (0.83–1.70) for the mildly food insecure group and 2.06 (1.07–3.96) for the moderate-to-severe food insecure group (P-trend: 0.005). In our sensitivity analyses to test the robustness of our results, the inverse associations did not change substantially when protein intakes were further adjusted in the model (Supplementary Table S2) or when an age-specific cut-off was used to define low muscle strength [45] (Supplementary Table S3). We further explored possible effect modification by population characteristics for the association between food insecurity and the risk of low muscle strength (Table 4). However, the positive association observed between food insecurity and the risk of low muscle strength was not significantly modified by sex, age, physical activity, BMI, or smoking status (all P-interaction ≥ 0.43). ## 5. Discussion In this nationally representative cross-sectional study of Korean adults, greater food insecurity was associated with diets with greater inflammatory potential, as measured by the DII, and an elevated risk of low muscle strength. The positive association of food insecurity with risk of low muscle strength did not significantly vary by sex, age, BMI, smoking status, or physical activity. Given the increased risk of morbidity and mortality associated with weak muscle strength [2,3,4,5], the identification of individuals at risk of muscle loss and its underlying mechanisms is important. Our results reinforce findings on the adverse health impact of food insecurity [12] and further expand the literature suggesting greater susceptibility to pro-inflammatory processes and muscle decline among individuals with lower food security. Food insecurity refers to limited or uncertain access to or availability of nutritionally adequate and safe foods [12], which may lead to suboptimal, inadequate diets and compensatory reliance on affordable low-cost energy-dense foods. The increased risk of poor diet among food insecure groups is well established [11,34,46]. However, there are few studies on the inflammatory potential of the diet consumed by food insecure groups [28,29], which may underlie the poor health outcomes associated with food insecurity [12]. To date, two cross-sectional studies have examined the association between food insecurity and the DII [28,29]. A previous study of 10,630 low-income adults in the US reported a greater DII score, by 0.31, among the very low food secure group compared to the high food secure group ($$p \leq 0.0033$$) [28]. Similarly, results from 525 high school girls in Iran showed a greater DII score, by 0.025, for every one unit increase in the food insecurity score [29]. A recent Hungarian study also observed a greater DII level among the Hungarian Rome region population at risk of greater food insecurity [47] than the general population [48]. Our positive association of food insecurity with the DII adds to the consistent, though limited, literature. Previous studies that examined the intake of individual nutrients [11,49] or diet patterns [11,34] in relation to food insecurity further support our positive association of food insecurity with the DII. A systematic review of 26 previous studies [11] and subsequent large national representative studies in the US [50], Canada [50], Brazil [51], and Korea [34,49] consistently reported that food insecure adults adhere less to a healthy diet and consume fewer vegetables and fruits and less fiber and polyunsaturated fat, but have a greater intake of calories, sugars, and saturated fat, which are associated with pro-inflammation [18,52]. This evidence thus indicates that a lack of food availability and disturbed access to food may induce an adverse change in diet quality, which may translate into a diet with greater inflammatory potential, thereby explaining our results, and predispose individuals to greater risk of chronic inflammation. An altered inflammatory state has been implicated in many chronic diseases, including metabolic syndrome, type 2 diabetes, cardiovascular diseases, and various cancers [53], but relatively little attention has been paid to muscle strength. However, proinflammatory cytokines have been speculated to contribute to skeletal muscle loss [21] by impairing insulin signaling associated with protein synthesis [54] through the transcriptional activation of NF-kB or the induction of JK that contributes to phosphorylation of serine residue in IRS-1 [55]. Indeed, substantial evidence from animal and human studies shows that greater inflammatory milieu negatively affects muscle protein metabolism and skeletal muscle maintenance [19,21,22]. For example, in experimental studies, rats administered IL-6 or TNF-α were shown to have elevated skeletal muscle protein breakdown, decreased protein synthesis, and reduced total skeletal muscle amino acid concentration [56,57]. Similarly, a systematic review and meta-analysis of 149 cross-sectional studies and 19 longitudinal studies reported associations between higher levels of C-reactive protein (CRP), IL-6, and TNF-α and decreasing muscle strength [46]. These results were further confirmed in subsequent large, national, cross-sectional studies of 8232 adults [22], 2127 postmenopausal women [27], and 917 adolescents [26], which found significantly lower HGS with increasing inflammatory biomarker profiles, such as white blood cell counts [26], CRP [27], and systemic immune-inflammation index scores, which combine platelets, neutrophils, and lymphocytes [22]. Thus, food insecurity associated with a poor diet with greater inflammatory potential may accelerate degradation of muscle strength partly through the inflammatory pathway. Indeed, the majority of the previous four cross-sectional studies that evaluated the associations of DII with the risk of low muscle strength showed lower HGS among those with greater DII [23,24,25], although not all [58]. However, few studies have precisely evaluated the association between food insecurity and the risk of low muscle strength [16,17]. Our results, which indicate a positive association between food insecurity and the risk of low HGS, contribute to this limited literature, aligning with three previous studies’ results [16,17,59]. In the first, a National Health and Nutrition Examination Surveys study of the elderly ($$n = 3632$$) revealed a significantly increased association between food insecurity and risk of low HGS (OR:1.51, $95\%$ CI: 1.03–2.22) [16]. In the second, a cross-sectional analysis of 664 Iranian adults, the odds ratios for food insecurity in men and women with low HGS were 5.46 and 3.12, respectively ($p \leq 0.001$) [17]. In the third, the Global Aging and Adult Health study of 14,585 adults also showed a significant 2.05 times increased risk of sarcopenia, defined as low muscle mass and either low muscle strength or slow gait [59]. Our study has several strengths. It is the first study to evaluate the plausibility of an inflammatory mechanism possibly mediated by diet through the association between food insecurity and muscle strength. Household food insecurity levels were measured using the most accurate instrument, that is, the full 18 items of the USHFSSM [32], the reliability and validity of which have previously been demonstrated in Korea [33]. Across the wide range of the USHFSSM’s food insecurity scale, we were able to evaluate the dose-response associations of food insecurity with diet and the risk of low muscle strength for various levels of food insecurity. Moreover, the quality of our HGS data was high given the multiple measurements of HGS performed by trained staff following the KCDC’s standardized HGS test protocols. We also used the most up-to-date diagnostic criteria to define low muscle strength in Asians, which account for the relatively small body size and adiposity compared to Caucasians [39]. Using the DII, we examined the possible impact of food insecurity across the overall inflammatory potential of the diet, accounting for complex interactions of nutrients beyond individual nutrients or food items [60]. Finally, utilizing data on food security, grip strength, diet, demographics, medical conditions, and lifestyle from the KNHANES, we comprehensively adjusted for the known possible confounding factors of low muscle strength and food insecurity and tested the robustness of our results by conducting several subgroup and sensitivity analyses. There are several limitations to our study. First, the cross-sectional nature of our study design makes it difficult to determine the temporality between food insecurity and muscle strength data, which limits the causal interpretations of our results. Our inverse association between food insecurity and the risk of muscle strength can be alternatively interpreted as the consequence of low muscle strength limiting individuals’ behaviors and worsening their food insecurity. Second, although the use of 24 h recall in national nutritional surveys is common and has proven validity, precision, and high response rates [61], our 24 h recall dietary data are susceptible to random measurement error when estimating usual diets due to the inability to capture the day-to-day variations in food consumption. We calculated the DII using only 23 out of the 45 food parameters that comprise the DII due to the lack of dietary data in the KNHANES. Nonetheless, missing food parameters such as turmeric and saffron are likely to make up only a small proportion of total nutrients consumed by the general population. Furthermore, the significant association of the DII with circulating levels of inflammatory markers observed in previous studies with a limited number of food parameters, as in the present study, validates the use of the DII without the full 45 food parameters [25]. Finally, despite our comprehensive adjustment of potential confounding factors, we cannot rule out the possibility of unknown or residual confounding variables. ## 6. Conclusions In conclusion, food insecurity was associated with diets with a greater DII score and an increased risk of low muscle strength in this nationally representative adult population in Korea. These findings support the notion that inflammation is likely to be elevated among those at risk of food insecurity, in part due to their nutritiously poor, pro-inflammatory diets, which may negatively affect muscle maintenance and muscle strength. 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