The Interplay Between Premenstrual Syndrome, Eating Disorder Risk, and Adiposity Indicators: A Cross Sectional Study on Women

The Interplay Between Premenstrual Syndrome, Eating Disorder Risk, and Adiposity Indicators: A Cross Sectional Study on Women | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Interplay Between Premenstrual Syndrome, Eating Disorder Risk, and Adiposity Indicators: A Cross Sectional Study on Women Esengul OZKAN, Neslihan KOCATEPE, Hande SEVEN AVUK This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8246542/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Apr, 2026 Read the published version in BMC Women's Health → Version 1 posted 11 You are reading this latest preprint version Abstract Background This study aimed to examine the relationship between the presence of Premenstrual Syndrome (PMS), Visceral Adiposity Index (VAI), eating attitudes, and daily energy and nutrient intake levels in women of reproductive age. Methods This cross-sectional study was conducted with 252 volunteer women aged 18–49 who admitted to the Healthy Nutrition Unit in Istanbul. Data were collected using a Personal Information Form, the Premenstrual Syndrome Scale (PMSS), the Eating Attitudes Test (EAT-26), and a retrospective 24-hour dietary recall. Anthropometric measurements (weight, height, waist circumference) were taken, body composition was determined via bioelectrical impedance analysis (BIA), and VAI was calculated. Mann-Whitney U, Chi-square, and Logistic Regression tests were used for statistical analyses. Results PMS was detected in 57.9% of participants. The PMS group exhibited significantly higher body fat percentage (38.1% vs 37.1%, p = 0.039) and smoking rates (28.8% vs 16.0%, p = 0.018) compared to controls. While VAI levels did not differ, regression analysis revealed that high BMI, rather than PMS status, was the primary independent risk factor for eating disorders. Conclusion The findings reveal that increased body fat percentage and smoking are more determinant factors in PMS etiology than VAI, which is an indicator of visceral adiposity. The lack of significant difference in nutrient intake emphasizes the necessity of holistic lifestyle changes targeting smoking cessation and body fat reduction, rather than solely diet-focused approaches in PMS management. Eating attitudes Mediterranean diet Obesity Premenstrual syndrome Visceral adiposity Figures Figure 1 Introduction Premenstrual syndrome (PMS) is a cluster of physical, emotional, and behavioral symptoms that affect a significant proportion of women of reproductive age, emerging during the luteal phase of the menstrual cycle and diminishing with the onset of menstruation [ 1 ]. Although its global prevalence is estimated to be approximately 47.8% [ 2 ], this rate varies widely between 20% and 90% depending on diagnostic criteria and cultural factors [ 3 – 6 ]. According to a meta-analysis, the overall prevalence of PMS in Türkiye has been reported as 52.2% [ 7 ]. Beyond reducing individual quality of life, PMS is considered a significant public health issue due to its associated impact on work productivity and disruptions in social functioning. Although its etiology has not been fully elucidated, current evidence suggests that cyclic fluctuations in progesterone and estrogen levels have a significant influence, particularly through their effects on neurotransmitters such as serotonin and GABA [ 3 , 8 ]. The most common symptoms of premenstrual syndrome (PMS) include breast tenderness, bloating, headaches, mood swings, depression, anxiety, anger, and irritability [ 9 ]. Additionally, increased appetite, intense cravings for sweet or salty foods, and binge-eating episodes are frequently reported [ 10 ]. Recent studies have demonstrated a strong association between PMS symptom severity and emotional eating as well as the risk of disordered eating attitudes, further supporting the potential link between these conditions [ 11 – 13 ]. Literature indicates that reduced serotonin levels during the luteal phase of the menstrual cycle trigger carbohydrate intake to induce temporary relief; however, this cycle may predispose individuals to weight gain, depressive symptoms, and disordered eating behaviors over time [ 11 ]. In young adult populations—particularly university students—the combination of body image concerns and PMS symptoms emerges as a significant factor that may increase the risk of developing eating disorders [ 12 ]. It is well recognized that dietary habits, eating behaviors, body composition, and overall adiposity constitute essential determinants in the etiology of PMS [ 14 , 15 ]. Accumulation of visceral adipose tissue has been proposed to induce chronic low-grade inflammation, thereby disrupting endocrine homeostasis and potentially exacerbating the severity of PMS symptoms [ 14 , 16 – 18 ]. In contrast to the inherent limitations of the conventional Body Mass Index (BMI), emerging anthropometric indices such as the Visceral Adiposity Index (VAI) are considered to provide a more sensitive representation of visceral fat distribution and have demonstrated strong associations with metabolic risk profiles and inflammatory processes [ 19 ]. Nevertheless, current evidence addressing the interrelationship between PMS, disordered eating risk, and visceral adiposity—particularly through the simultaneous assessment of anthropometric and biochemical parameters—remains markedly limited. Accordingly, the present study seeks to elucidate the influence of PMS on disordered eating attitudes and visceral adiposity in women of reproductive age. Materials and Methods Study Design and Participants This descriptive, cross-sectional study was conducted between June 2024 and April 2025. The study population consisted of 252 volunteer women aged 18–49 years who applied for nutritional counseling at the Healthy Nutrition and Active Life Unit affiliated with the Republic of Türkiye Ministry of Health, Istanbul Sariyer District Health Directorate. The sample size was calculated using G*Power (v3.1.9.7) based on an independent samples t-test design. Assuming a medium effect size (d = 0.5) as suggested by Cohen [ 20 ], a type I error rate of 0.05, and a power of 95%, the minimum required sample size was determined to be 210. To compensate for potential data loss, the target sample size was increased by 20%, resulting in a total of 252 participants. Women were excluded from the study if they were under 18 or over 49 years of age, postmenopausal, pregnant, or lactating. Additionally, those who had previously been diagnosed with polycystic ovary syndrome (PCOS) and irregular menstruation by a physician, or those currently using hormonal contraceptives or psychiatric medications, were not included in the study. All procedures involving human participants were conducted in accordance with the ethical standards outlined in the 1964 Declaration of Helsinki and its subsequent amendments. Ethical approval was obtained from the Istanbul Bilgi University Human Research Ethics Committee (Date: November 28, 2023, Approvel No: 2023-20160-146), and written institutional permission was secured from the relevant health directorate. Written informed consent was obtained from all individual participants included in the study. Data Collection Data were collected via face-to-face interviews using a questionnaire developed by the researcher based on a review of the relevant literature. During these interviews, participants' sociodemographic characteristics, nutritional habits, and physical activity levels were queried and recorded. Anthropometric measurements were taken directly by the researcher. Additionally, biochemical parameters from the preceding three months were retrieved from laboratory results registered in the medical database. The STROBE flow diagram of the study is presented in Fig. 1 . Survey Form The questionnaire was structured to systematically assess key demographic and health-related characteristics of the participants, including age, educational level, socioeconomic status, occupational factors, history of chronic diseases, regular medication use, and smoking and alcohol consumption habits. Premenstrual Syndrome Scale (PMSS) The PMSS, developed by Gençdoğan [ 21 ], was employed to evaluate the severity of premenstrual symptoms. This 44-item scale utilizes a 5-point Likert-type scoring system and assesses symptoms experienced during the week preceding menstruation. Total scores range from 44 to 220, with higher scores indicating greater symptom severity. A cut-off score of 110 (representing more than 50% of the maximum possible score) is used to classify the presence of PMS. The scale comprises nine sub-dimensions: depressive affect, anxiety, fatigue, irritability, depressive thoughts, pain, appetite changes, sleep changes, and bloating. While the Cronbach’s alpha coefficient was reported as 0.75 in the original study, it was calculated as 0.94 in the present study. Eating Attitudes Test-26 (EAT-26) The Eating Attitudes Test-26 (EAT-26) was utilized to assess the risk and symptoms of eating disorders. Developed initially as EAT-40 by Garner and Garfinkel [ 22 ] and revised by Garner et al. [ 23 ], the Turkish validity and reliability of the scale were established by Ergüney-Okumuş and Sertel-Berk [ 24 ]. This 6-point Likert-type scale consists of three sub-dimensions: Dieting, Bulimia/Food Preoccupation, and Oral Control. Total scores range from 0 to 78, with a clinical cut-off point of 20. Participants scoring 20 or higher were classified as “High Risk for Eating Disorders” while those scoring below 20 were considered to have "Low Risk for Eating Disorders”. The Cronbach’s alpha coefficient, reported as 0.84 in the Turkish adaptation, was calculated as 0.72 in the present study. Dietary Assessment Dietary intake was assessed using the retrospective 24-hour dietary recall method, and energy and nutrient intake levels were analyzed using the BeBIS (Nutrition Information System) software, version 7.2. The inclusion of individuals with and without PMS who had similar energy and macronutrient intakes enhanced the comparability between groups (data are presented as supplementary material). Anthropometric Measurements Body weight, fat mass, lean body mass, body fat percentage, and total body water were analyzed using a calibrated TANITA BC-418 MA Bioelectrical Impedance Analysis (BIA) device, following standard measurement protocols [ 25 ]. Height was measured using a calibrated stadiometer with the participant standing barefoot, upright, and with the head positioned in the Frankfurt horizontal plane [ 26 ]. The Body Mass Index (BMI) was calculated as weight in kilograms divided by the square of height in meters (kg/m²). Participants were categorized according to the World Health Organization (WHO) classification: underweight (< 18.5 kg/m²), normal weight (18.5–24.9 kg/m²), overweight (25.0–29.9 kg/m²), and obese (≥ 30 kg/m²) [ 27 ]. Waist, hip, and neck circumferences were measured using a non-stretchable tape measure in accordance with standard guidelines. Additionally, Waist-to-Hip Ratio (WHR) and Waist-to-Height Ratio (WHtR) were calculated and evaluated. Biochemical Parameters Biochemical data were obtained retrospectively from the medical database. The analysis encompassed lipid profiles (Total Cholesterol, HDL-C, LDL-C, Triglycerides), inflammation markers (CRP, ferritin), minerals (iron, magnesium), and vitamins (B 12 , folic acid, Vitamin D). All recorded values were based on 12-hour fasting blood samples ordered by a physician and analyzed within the three months preceding the study. Since these biochemical data reflect real-world clinical screening rather than a controlled trial setting, standardization of the menstrual cycle phase was not feasible. However, the selected parameters (e.g., Vitamin D, B 12 , Ferritin, Lipid Profile) serve as relatively stable markers of long-term nutritional and metabolic status and are significantly less susceptible to acute daily hormonal fluctuations compared to sex steroids. Visceral Adiposity Index (VAI) The Visceral Adiposity Index (VAI) is a composite marker that reflects visceral fat accumulation and dysfunction, integrating both anthropometric (BMI, waist circumference) and metabolic (triglycerides, HDL-C) parameters. For female participants, VAI scores were determined using the sex-specific equation: $$\:VAI=\left(\frac{WC}{36.58\:+\:\left(1.89\:X\:BMI\right)}\right)\:\text{X}\:\left(\frac{TG}{0.81}\right)\:X\:\left(\frac{1.52}{HDL\:-\:C}\right)$$ Where WC is expressed in cm, and TG and HDL-C levels are in mmol/L [ 28 ]. Statistical Analysis Statistical analyses were performed using the SPSS software (Statistical Package for the Social Sciences, version 30.0; IBM Corp., Armonk, NY, USA). Appropriate descriptive and inferential methods were applied. The normality of the data distribution was assessed using the Shapiro–Wilk test. Continuous variables were presented as mean ± standard deviation or median [25th–75th percentile], depending on the data's normality. Categorical variables were expressed as frequencies and percentages. Between-group comparisons were conducted using the Independent Samples t-test for normally distributed variables and the Mann–Whitney U test for non-normally distributed variables. The Pearson Chi-square test was used to analyze associations between categorical variables, and the Kruskal–Wallis H test was applied for comparisons across more than two independent groups. To identify factors associated with the risk of eating disorders, a multivariate binomial logistic regression analysis was performed, including PMS status, age, BMI, and chronic disease as independent variables. Model fit was evaluated using Cox and Snell R², Nagelkerke R², and model chi-square statistics. VAI was excluded from the logistic regression model to avoid multicollinearity, as BMI and waist circumference are already integral components of the VAI formula. A p-value of < 0.05 was considered statistically significant. Results Among the 252 women included in the study, 57.9% were identified as having PMS, while 42.1% did not meet the PMS criteria. No significant differences were observed between the PMS and non-PMS groups in terms of age, menstrual duration, marital status, educational status, employment status, or perceived income level (p > 0.05). Examination of lifestyle habits revealed a significant association between smoking and PMS status, with a higher proportion of smokers in the PMS group (28.8%) compared with the non-PMS group (16.0%) (p = 0.018). Alcohol consumption was also associated with higher PMSS scores (p = 0.022). Although the prevalence of eating disorder risk did not significantly differ between PMS and non-PMS groups (p = 0.392), participants at high risk for eating disorders had substantially higher PMSS scores (p < 0.001) (Table 1 ). Table 1 Comparison of sociodemographic characteristics and nutritional habits according to the presence of PMS PMS Group (n = 146) Non-PMS Group (n = 106) p-Value 1 PMSS Scores p-Value 2 Age (years) 34.47 ± 8.99 35.75 ± 8.14 0.246 * NA NA Menstruation duration (d) 6,36 ± 1,84 6,12 ± 1,60 0.358 ** Marital status 0.068 + 0.017 ** Married 87 (59.6) 75 (70.8) 114 [92.75–137.5] Single 59 (40.4) 31 (29.2) 126.5 [100–148.5] Educational status 0.145 + 0.177 *** Primary education 25 (17.1) 27 (25.5) 107.5 [92.25–135.75] High school 51 (34.9) 27 (25.5) 122.5 [98–144.75] University 70 (47.9) 52 (49.0) 119 [96.75–142] Employment status 0.090 + 0.036 ** Unemployed 56 (38.4) 52 (49.1) 113 [88–138.5] Employed 90 (61.6) 54 (50.9) 122 [99.25–144] Income status 0.258 + 0.413 *** Income less than expenses 39 (26.7) 19 (17.9) 122.5 [101–142.5] Income equal to expenses 80 (54.8) 66 (62.3) 118 [96–142] Income higher than expenses 27 (18.5) 21 (19.8) 118.5 [86.25–141.75] Presence of chronic disease 0.261 + 0.258 ** No 92 (63.0) 74 (69.8) 122 [98–144] Yes 54 (37.0) 32 (30.2) 116.5 [95–140.5] Smoking status 0.018 + 0.010 ** Smoker 42 (28.8) 17 (16.0) 129 [104–148] Non-smoker 104 (71.2) 89 (84.0) 114 [94–141] Alcohol consumption 0.052 + 0.022 ** Yes 30 (20.5) 12 (11.3) 132 [103–159] No 116 (79.5) 94 (88.7) 115.5 [95–140] Daily snack consumption 0.649 + 0.766 *** None 22 (15.1) 17 (16.0) 122 [93–145] 1 48 (32.9) 35 (33.0) 119 [98–137] 2 53 (36.3) 43 (40.6) 116.5 [92.25–142] 3 or more 23 (15.8) 11 (10.4) 122.5 [98.75–151] Number of main meals 0.821 + 0.705 *** 1 1 (0.7) 1 (0.9) 110 [97–110] 2 80 (54.8) 54 (50.9) 120 [96.75–144.25] 3 65 (44.5) 51(48.1) 118 [95–140.75] Skipping main meals 0.214 + 0.212 *** Yes 78 (53.4) 46 (43.4) 123 [97.25–144.75] No 26 (17.8) 27 (25.5) 107 [83–140.5] Sometimes 42 (28.8) 33 (31.1) 117 [95–140] EAT-26 Classification 0.392 + < 0.001 ** High Risk for ED 40 (27.4) 24 (22.6) 125 [100.75–158.75] Low Risk for ED 106 (72.6) 82 (77.4) 94 [0–126] Data are presented as Mean ± Standard Deviation, Median [25th − 75th percentile], and number (percentage). Abbreviations: BMI: Body Mass Index, EAT: Eating Attitude Disorder, PMS: Premenstrual Syndrome, PMSS: Premenstrual Syndrome Scale. Statistical Analysis: * Independent Samples t-test, ** Mann-Whitney U test, *** Kruskal-Wallis H, + Pearson Chi-square test was used. p 1 : Within-group comparison p-value (PMS Group vs. Non-PMS Group). p 2 : Comparison of PMSS Scores by Variables. p < 0.05 was considered statistically significant. Comparison of anthropometric measurements revealed that the median body fat percentage was significantly higher in the PMS group (38.1%) compared with the non-PMS group (37.1%) (p = 0.039). Although body weight, BMI, and waist circumference were numerically higher in the PMS group, these differences were not statistically significant (p > 0.05). Biochemical parameters, including lipid profile, ferritin, CRP, vitamins, and minerals, showed no significant differences between groups (p > 0.05). When each PMS subgroup was compared by eating disorder risk, women without PMS but at high risk for eating disorders had significantly higher waist circumferences (p = 0.014), waist-to-hip ratios (p = 0.006), and waist-to-height ratios (p = 0.015) than those at low risk. In the PMS group, neck circumference was significantly higher in participants at high risk for eating disorders compared to those at low risk (p = 0.049). No other anthropometric or biochemical variables showed significant differences according to eating disorder risk (Table 2 ). Table 2 Comparison of anthropometric measurements and biochemical parameters of participants according to the presence of PMS status and eating attitude disorder risk Variables n PMS Group (n = 146) p 1 Non-PMS Group (n = 106) p 1 p 2 Low Risk for ED (n = 106) High Risk for ED (n = 40) Total Low Risk for ED (n = 82) High Risk for ED (n = 24) Total Body weight (kg) * 252 75.9 [68.9–85.2] 79.9 [72.2–90.3] 76.7 [69.5–87.2] 0.089 75.2 [65.6–85.1] 79.0 [69.6–87.1] 76.0 [66.3–85.9] 0.359 0.179 BMI (kg/m 2 ) * 252 28.8 [25.6–34.9] 30.7 [27.8–36.2] 29.6 [26.3–35.0] 0.078 29.0 [25.6–32.1] 30.1 [27.0-33.6] 29.3 [26.1–32.5] 0.230 0.287 Fat (%) * 252 37.8 [32.1–43.0] 39.0 [37.0-42.9] 38.1 [33.3–43.0] 0.117 37.1 [31.2–39.6] 37.2 [34.5–40.9] 37.1 [31.9–40.1] 0.284 0.039 Fat mass (kg) * 252 27.8 [22.5–36.4] 30.1 [26.9–38.7] 29.2 [23.7–37.1] 0.063 28.2 [21.4–34.5] 29.3 [23.6–35.1] 28.4 [21.4–35.0] 0.485 0.177 Fat free mass (kg) * 252 47.4 [43.3–51.5] 48.6 [45.7–51.2] 47.8 [44.7–51.5] 0.244 46.4 [42.8–50.3] 48.2 [44.0-52.3] 47.0 [43.0–51.0] 0.251 0.167 Total body water (%) * 252 45.3 [41.5–48.8] 44.7 [41.8–46.2] 45.2 [41.6–48.2] 0.269 45.7 [43.7–49.9] 45.9 [43.2–48.0] 45.8 [43.7–49.5] 0.561 0.062 Waist circumference (cm) ** 252 91.0 ± 15.2 95.5 ± 16.2 92.21 ± 15.51 0.114 87.8 ± 14.0 95.8 ± 13.3 89.61 ± 14.23 0.014 0.229 Neck Circumference (cm) ** 252 34.3 ± 3.1 35.4 ± 3.0 34.0 [32.0–37.0] 0.049 33.9 ± 2.6 34.8 ± 2.4 34.0 [32.1–36.0] 0.115 0.348 Waist/Hip Ratio * 252 0.8 [0.7–0.9] 0.8 [0.8–0.9] 0.82 [0.8–0.9] 0.224 0.8 [0.7–0.8] 0.8 [0.8–0.9] 0.81 [0.8–0.9] 0.006 0.285 Waist/Height Ratio * 252 0.6 [0.5–0.6] 0.6 [0.5–0.7] 0.57 ± 0.10 0.129 0.6 [0.5–0.6] 0.6 [0.5–0.7] 0.55 ± 0.09 0.015 0.202 VAİ * 252 3.0 [2.0-4.8] 3.5 [2.5–4.7] 3.09 [2.1–4.7] 0.281 2.8 [2.0-4.8] 3.7 [2.8–5.4] 3.06 [2.0–4.9] 0.057 0.864 TC (mg/dL) * 252 190.5 [166.5-222.8] 195.5 [178.0-219.2] 193.0 [171.0–221.8] 0.479 190.0 [170.0-219.8] 183.0 [163.5-202.2] 189.0 [167.2–214.5] 0.207 0.347 HDL (mg/dL) * 252 55.0 [45.0-63.8] 55.0 [44.8–65.0] 55.0 [45.0–64.0] 0.673 53.4 [47.0-60.8] 50.0 [45.0–57.0] 53.0 [47.0–59.8] 0.082 0.476 LDL (mg/dL) * 241 114.0 [95.2-141.5] 120.0 [101.0-137.0] 116.0 [96.5–140.5] 0.592 113.0 [95.0-137.5] 102.0 [92.5–131.0] 110.5 [95.0–135.5] 0.166 0.404 TG (mg/dL) * 252 86.0 [65.0-127.8] 101.0 [75.8–129.0] 90.5 [65.1–127.8] 0.222 82.0 [65.2-118.8] 94.0 [71.5–138.0] 87.0 [66.2–131.0] 0.151 0.678 Ferritin(ng/ml) * 215 15.9 [9.4–26.7] 11.6 [8.7–17.8] 14.0 [9.2–25.2] 0.072 12.3 [8.1–21.6] 16.5 [9.4–19.8] 14.0 [8.2–21.5] 0.539 0.387 CRP (mg/L) * 157 2.9 [1.1–6.8] 2.5 [1.4-5.0] 2.8 [1.3–5.7] 1.000 2.0 [1.0-7.2] 4.2 [2.0-6.3] 2.3 [1.1–6.5] 0.420 0.755 Serum Iron (µg/L) * 167 64.0 [45.2–96.8] 54.0 [43.0-81.5] 60.0 [45.0–92.0] 0.530 68.0 [47.0–88.0] 71.0 [52.0–97.0] 68.0 [47.2–91.0] 0.603 0.576 Mg (mg/dL) * 171 2.0 [1.9-2.0] 1.9 [1.8-2.0] 1.9 [1.9–2.0] 0.313 2.0 [1.9–2.1] 2.0 [1.8-2.0] 2.0 [1.9–2.1] 0.533 0.446 Vitamine B 12 (pg/ml) * 161 224.0 [166.5-340.5] 182.5 [149.0-272.2] 214.0 [160.0–313.0] 0.139 202.0 [151.0-249.0] 217.0 [184.5–318.0] 205.0 [153.2–271.5] 0.235 0.298 Serum Folate (ng/ml) * 114 6.7 [5.4–8.9] 7.0 [6.2–9.3] 6.8 [5.7–9.1] 0.346 7.4 [5.5–9.8] 7.2 [4.4–12.3] 7.4 [5.2–11.5] 0.938 0.574 Vitamine D (ng/ml) * 79 17.1 [12.1–24.6] 20.1 [14.0-23.4] 17.5 [12.4–24.0] 0.720 19.4 [11.4–23.4] 22.9 [19.3–29.2] 20.6 [15.1–23.5] 0.129 0.415 Data are presented as Mean ± Standard Deviation, Median [25th − 75th percentile]. Abbreviations: n: Number of participants, BMI: Body Mass Index, CRP: C-Reactive Protein, ED: Eating Disorders, HDL: High Density Lipoprotein, LDL: Low Density Lipoprotein, PMS: Premenstrual Syndrome, VAI: Visceral Adiposity Index. Statistical Analysis: * Mann-Whitney U test, and ** Independent Samples t-test were used. p 1 : Within-group comparison p-value (Low vs. High Eating Disorder Risk). p 2 : Between-group comparison p-value (PMS Group vs. Non-PMS Group). p < 0.05 was considered statistically significant. PMSS total scores were significantly higher in the high-risk eating disorder group (median = 146.5) compared with the low-risk group (median = 136.5) (p = 0.010). Analysis of PMSS subdimensions revealed that depressive affect (p = 0.038), anxiety (p = 0.012), depressive thoughts (p = 0.027), and appetite changes (p = 0.016) were significantly higher among participants at high eating disorder risk. No significant differences were observed for fatigue, irritability, pain, sleep disturbances, or bloating (p > 0.05). As expected, all PMSS subdimension scores were significantly higher in the PMS group compared with the non-PMS group (p < 0.001) (Table 3 ). Table 3 Comparison of PMSS scores according to participants' PMS status and eating attitude disorder risk Variables PMS Group (n = 146) p 1 Non-PMS Group (n = 106) p 1 p 2 Low Risk for ED (n = 106) High Risk for ED (n = 40) Total Low Risk for ED (n = 82) High Risk for ED (n = 24) Total PMS Total 136,5 [123–149] 146,5 [131–167] 139,0 [124,0–155,8] 0,010 91,0 [75–100] 96,5 [79–102] 91,5 [75,2–100,0] 0,543 < 0,001 Depressive affect 23,0 [ 19 – 28 ] 25,5 [ 21 – 32 ] 23,0 [20,0–28,0] 0,038 11,0 [ 7 – 16 ] 9,5 [ 7 – 12 ] 11,0 [7,0–15,0] 0,311 < 0,001 Anxiety 15,0 [ 11 – 18 ] 17,5 [ 13 – 27 ] 15,0 [12,0–20,0] 0,012 9,0 [ 7 – 10 ] 9,0 [ 7 – 10 ] 9,0 [7,0–10,0] 0,848 < 0,001 Fatigue 22,0 [ 19 – 24 ] 24,0 [ 17 – 28 ] 23,0 [19,0–25,0] 0,206 14,0 [ 10 – 17 ] 14,0 [ 11 – 19 ] 14,0 [10,0–17,8] 0,607 < 0,001 Irritation 17,0 [ 14 – 21 ] 19,0 [ 14 – 22 ] 17,5 [14,0–21,0] 0,182 10,0 [ 6 – 13 ] 11,0 [ 7 – 13 ] 10,0 [6,2–13,0] 0,347 < 0,001 Depressive thoughts 18,0 [ 13 – 22 ] 21,5 [ 14 – 26 ] 19,0 [14,0–23,0] 0,027 9,0 [ 7 – 13 ] 7,0 [ 7 – 9 ] 9,0 [7,0–13,0] 0,168 < 0,001 Pain 10,0 [ 7 – 12 ] 10,0 [ 7 – 12 ] 10,0 [7,0–12,0] 0,972 6,0 [ 5 – 8 ] 7,0 [ 7 – 9 ] 7,0 [5,0–9,0] 0,032 < 0,001 Appetite changes 13,0 [ 11 – 15 ] 15,0 [ 11 – 15 ] 13,0 [11,0–15,0] 0,016 10,0 [ 8 – 13 ] 11,5 [ 10 – 13 ] 11,0 [8,0–13,0] 0,448 < 0,001 Sleep changes 9,0 [ 6 – 11 ] 10,0 [ 7 – 12 ] 9,0 [7,0–11,0] 0,124 5,0 [ 3 – 7 ] 6,5 [ 3 – 7 ] 5,0 [3,0–7,0] 0,587 < 0,001 Bloating 13,0 [ 10 – 15 ] 15,0 [ 9 – 15 ] 14,0 [10,0–15,0] 0,342 11,0 [ 6 – 15 ] 8,0 [ 7 – 13 ] 11,0 [7,0–15,0] 0,836 < 0,001 Data are presented as Median [25th − 75th percentile]. Abbreviations: PMS: Premenstrual Syndrome, PMSS: Premenstrual Syndrome Scale, ED: Eating Disorders, n: Number of participants. Statistical Analysis: The Mann-Whitney U test was used. p 1 : Within-group comparison p-value (Low vs. High Eating Disorder Risk). p 2 : Between-group comparison p-value (PMS Group vs. Non-PMS Group). p < 0.05 was considered statistically significant. The multivariate binomial logistic regression model examining predictors of eating disorder risk was statistically significant (χ² = 9.93, p < 0.05). After adjusting for age and chronic disease, PMS status was not a significant predictor of eating disorder risk (p = 0.625). BMI emerged as the only significant independent predictor in the model, indicating that each 1-unit increase in BMI increased the likelihood of being at risk for an eating disorder by 1.05 times (aOR = 1.049, 95% CI: 1.002–1.098; p = 0.041) (Table 4 ). Table 4 Multivariate Binomial Logistic Regression analysis identifying risk factors for eating disorders Variables β S.E. Wald p aOR 95% C.I. PMS Presence (Ref: Absent) 0.149 0.305 0.239 0.625 1.161 0.639–2.109 Age (years) -0.019 0.019 1.022 0.312 0.981 0.946–1.018 BMI (kg/m²) 0.048 0.023 4.186 0.041* 1.049 1.002–1.098 Chronic Disease (Ref: Absent) 0.350 0.317 1.219 0.270 1.419 0.762–2.643 Constant -2.094 0.831 6.345 0.012 0.123 Dependent Variable: Risk of Eating Disorder. Independent Variables: Presence of PMS, Age, BMI, Chronic Disease. Abbreviations: β: Regression Coefficient, S.E.: Standard Error, aOR: Adjusted Odds Ratio, 95% C.I.: Confidence Interval. Model Fit Statistics: Cox & Snell R 2 = 0.026, Nagelkerke R 2 = 0.038, Model X 2 = 9.93, p < 0.05. p < 0.05 was considered statistically significant. Discussion In this study, we investigated the impact of premenstrual syndrome (PMS) on the risk of eating disorders, the visceral adiposity index (VAI), and biochemical parameters in women of reproductive age. PMS was identified in 57.9% of the participants. Although this rate is slightly higher than the global prevalence of 47.8% reported in the literature [ 3 , 29 , 30 ], it is consistent with findings from other studies conducted in Türkiye [ 7 ]. Variations in PMS prevalence across studies are thought to reflect differences in diagnostic criteria, cultural factors, and regional variations in lifestyle habits. One of the notable findings of our study was the significant association observed between smoking and PMS. The prevalence of smoking was significantly higher among women with PMS compared with the control group. Smoking may exacerbate PMS through multiple physiological and neuroendocrine pathways. Nicotine disrupts estrogen and progesterone levels, contributing to hormonal imbalance [ 15 ], and also interferes with the hypothalamic–pituitary–adrenal (HPA) axis, increasing vulnerability to stress [ 31 ]. In addition, the anxiogenic effects of nicotine may intensify the emotional symptoms experienced during PMS [ 32 ]. Body composition and obesity have become increasingly important factors in the etiology of PMS. In our study, the body fat percentage of women with PMS was significantly higher than that of women without PMS, supporting hypotheses suggesting that increased adiposity may play a role in PMS pathophysiology. Adipose tissue is not merely an energy reservoir but also an active endocrine organ that influences estrogen metabolism and secretes inflammatory cytokines [ 33 ]. Previous research has demonstrated that obesity contributes to chronic low-grade inflammation, disrupts neurotransmitter balance, and may trigger PMS symptoms [ 34 , 35 ]. Although body weight and Body Mass Index (BMI) were numerically higher in the PMS group, these differences did not reach statistical significance, suggesting that body fat percentage may be a more sensitive indicator than overall body weight. One of the primary objectives of our study was to investigate the relationship between PMS and the Visceral Adiposity Index (VAI). This composite indicator differs from traditional anthropometric measurements by incorporating both anatomical (waist circumference and BMI) and physiological (triglycerides and HDL) parameters. The literature suggests that visceral adipose tissue may contribute to the etiology of PMS through chronic low-grade inflammation driven by the secretion of pro-inflammatory cytokines [ 4 ]. However, despite the higher overall body fat percentage observed in the PMS group, no significant difference in VAI levels was found between the groups. The lack of significant difference in VAI, despite higher body fat percentage in the PMS group, can be explained by the 'Metabolically Healthy Obese' (MHO) phenotype often seen in young women. In this age group, excess fat is preferentially stored subcutaneously rather than viscerally. Since VAI is specifically sensitive to visceral adipose dysfunction and triglyceride levels, it may not fully capture the subcutaneous adiposity load that drives PMS-related inflammation in this younger, non-diabetic cohort [ 36 ]. This finding may be attributable to the characteristics and fat distribution patterns of the study population. Because the majority of participants were young and metabolically healthy, triglyceride and HDL levels — key components of the VAI formula — may have remained within normal ranges. Consistent with this interpretation, our biochemical analyses showed no significant differences in lipid profiles between groups. This suggests that, in young women, PMS-related increases in adiposity may not yet have progressed to metabolically adverse visceral fat accumulation resembling metabolic syndrome but may instead reflect subcutaneous or general adiposity. Additionally, waist circumference — a component of VAI — may be influenced by abdominal bloating and fluid retention frequently observed during the luteal phase of the menstrual cycle, potentially masking actual differences in visceral adiposity between groups. In our study, there were no statistically significant differences observed between the PMS and control groups in terms of biochemical parameters. The literature, however, provides strong evidence that deficiencies in micronutrients such as magnesium, calcium, vitamin D, and B-group vitamins may influence PMS etiology by altering neurotransmitter synthesis and exacerbating symptom severity [ 37 ]. For example, the regulatory effects of magnesium on serotonin receptors and the role of vitamin D in calcium homeostasis underscore the importance of these micronutrients in managing PMS [ 38 ]. Several potential explanations exist for the absence of significant biochemical differences in our findings. First, the biochemical data were obtained retrospectively from laboratory records ordered by physicians within the last three months, rather than through standardized blood sampling for research purposes. This limited our ability to obtain uniform biochemical measurements for the entire sample and may have resulted in missing data, thereby reducing statistical power. Second, the timing of blood collection is a critical factor. Hormonal and biochemical fluctuations related to PMS are particularly pronounced during the luteal phase of the menstrual cycle. Because the retrospective records did not indicate the cycle day on which samples were collected, measurements taken during the follicular phase may have masked potential luteal-phase reductions in micronutrient levels. Third, homeostatic mechanisms likely played a role. The body tightly regulates serum concentrations to maintain physiological stability, meaning that blood levels may remain within normal ranges even when intracellular stores are depleted. The young and generally healthy nature of our sample likely contributed to the effectiveness of these compensatory mechanisms. In conclusion, clarifying the relationship between PMS and micronutrient status will require prospective studies in which biochemical samples are collected during the luteal phase, and intracellular levels are also assessed. The multifaceted relationship between nutrition and PMS becomes even more complex when the prevalence of eating disorders is taken into account [ 12 ]. In our study, we observed that as eating disorder scores increased, the severity of PMS symptoms also rose proportionally. This finding is consistent with the study by Yi et al. [ 39 ], which reported a positive correlation between eating disorder severity and PMS. Similarly, an Iranian study found that women with a high risk of eating disorders experienced more severe PMS symptoms, even though the statistical significance was borderline [ 13 ]. The underlying mechanism of this association may relate to hormonal fluctuations throughout the menstrual cycle that disrupt appetite regulation. In particular, decreased serotonin levels during the luteal phase may trigger carbohydrate cravings, making it more challenging to regulate eating behavior and potentially predisposing individuals to disordered eating patterns [ 11 ]. However, our multivariate logistic regression results suggest that PMS does not solely drive this relationship. After adjusting for age and chronic diseases, PMS was not identified as an independent predictor of eating disorder risk. Although a symptomatic correlation exists between these two conditions, our findings weaken the hypothesis that PMS directly causes eating disorders. While our results do not establish a definitive causal relationship, they highlight the need for future large-scale studies to elucidate further the underlying mechanisms involved in this association. In our study, we found that while PMS symptom severity was associated with disordered eating behaviors, the primary determinant of clinical eating disorder risk was not the presence of PMS itself but rather a higher BMI. Similarly, Pearce et al. [ 19 ] emphasized that increased BMI is one of the strongest predictors of eating disorder development. Another study demonstrated that women with a BMI above 27.5 kg/m² were more likely to develop severe PMS ten years later compared with those with a BMI below 20 kg/m² [ 40 ]. Although there is strong evidence in the literature suggesting that obesity increases PMS risk, some studies have reported a negative or U-shaped relationship between BMI and PMS symptom severity, particularly in young and normal-weight populations [ 41 , 42 ]. This pattern suggests that both low body weight and inadequate nutritional intake may induce physiological stress that disrupts hormonal balance, potentially triggering PMS symptoms. In our subgroup analyses, we found that among women without PMS, those at risk for eating disorders had significantly higher waist circumference, waist-to-hip ratio, and waist-to-height ratio. However, this relationship disappeared within the PMS group, where only neck circumference remained significantly associated with eating disorder risk. Neck circumference is a stable anthropometric measure that is minimally affected by abdominal bloating and cycle-related fluid retention, yet it correlates strongly with visceral adiposity [ 43 ]. The lack of significance in waist-related variables within the PMS group may be attributed to abdominal edema caused by fluctuations in aldosterone and progesterone during the luteal phase, which could mask actual differences in abdominal fat accumulation between groups. Therefore, the finding that neck circumference—unaffected by edema—remained significant in the PMS group suggests that eating disorder risk in these women is still linked to adiposity. However, this relationship becomes difficult to detect using standard waist measurements. In our study, participants were grouped according to the presence of PMS, and the impact of eating disorder risk on PMS symptom severity was examined. The analyses revealed that among women diagnosed with PMS, those at risk for eating disorders had significantly higher total PMSS scores, as well as higher scores in the subdimensions of depressive affect, anxiety, depressive thoughts, and appetite changes compared with those without such risk. In contrast, among women without PMS, eating disorder risk did not produce a significant difference in total PMSS scores or in psychological subdimensions. This pattern suggests that a tendency toward disordered eating may act as an “exacerbating factor” that intensifies an already existing PMS profile. The relationship between PMS and eating disorders is frequently interpreted through the serotonergic dysregulation hypothesis [ 11 ]. In individuals at risk for eating disorders, biological vulnerability may be compounded by cognitive factors such as fear of weight gain and restrictive eating, which can elevate stress levels and exacerbate anxiety and depressive symptoms [ 44 ]. The finding in our study that anxiety and depressive affect scores were particularly high among PMS-diagnosed individuals with eating disorder risk supports this psycho-biological burden hypothesis. Additionally, the significantly higher appetite change scores in the risk group suggest that these women may experience the physiological increase in appetite during the luteal phase more chaotically (i.e., cycles of binge eating or excessive restriction). Supporting this, Mighani et al. [ 13 ] reported that premenstrual appetite increases in women with disordered eating behaviors may intensify symptom perception when combined with emotional eating tendencies. Interestingly, among women without PMS, eating disorder risk was not associated with heightened psychological symptoms; instead, it was linked only to higher pain scores. This may indicate that women at risk for eating disorders, but without PMS, may experience lowered pain thresholds or increased somatization due to inadequate or irregular eating patterns. Overall, however, the findings suggest that eating disorder risk specifically intensifies PMS-related symptomatology, whereas it does not generate a comparable psychological profile in women without PMS. Therefore, in clinical practice, we recommend that women presenting with PMS, particularly those reporting pronounced depressive or anxious symptoms, should also be assessed for eating disorder risk. Limitations The findings of this study should be interpreted considering several limitations. First, the cross-sectional design allows for the identification of associations and their direction but does not permit the establishment of definitive causal relationships between variables. Second, the data collection relied on self-reported scales. Although this approach carries the potential risk of recall bias, a standard limitation in nutrition and psychological research, we attempted to minimize this limitation by employing standardized instruments with established validity and reliability. Third, the biochemical parameters were obtained retrospectively from hospital records covering the previous three months. While this method provides valuable real-world data from a large sample, it limits our ability to standardize blood collection according to menstrual cycle phases (follicular/luteal). However, the biochemical markers assessed in this study—such as vitamin B12, vitamin D, ferritin, and lipid profile are relatively stable indicators reflecting long-term nutritional and metabolic status rather than acute hormonal fluctuations, which may have mitigated the impact of the lack of phase-specific sampling. Finally, although the single-center nature of the study may limit the generalizability of the findings, the achieved sample size was consistent with the calculated power analysis, thereby strengthening the statistical validity of the results. Conclusion This study is significant in that it reveals the multidimensional relationship between premenstrual syndrome (PMS), eating behavior, body composition, and biochemical parameters in women of reproductive age. Our findings demonstrated that women diagnosed with PMS had a significantly higher body fat percentage compared with those without PMS, and that smoking was more prevalent among women with PMS, supporting the role of increased adiposity and lifestyle factors in the etiology of PMS. One of the most noteworthy results of this study concerns the nature of the relationship between eating disorder risk and PMS. Although our findings showed that PMS symptom severity increased in parallel with higher eating disorder scores, the multivariate regression analysis revealed that the primary determinant of eating disorder risk was not the presence of PMS itself, but rather an elevated Body Mass Index (BMI). Furthermore, even among healthy women without PMS, eating disorder risk was associated with abdominal obesity indicators such as waist circumference and waist-to-hip ratio, confirming the strong link between disordered eating tendencies and central adiposity. In conclusion, PMS, eating disorders, and obesity are complex conditions that can mutually reinforce one another and share overlapping physiological mechanisms. Given that BMI, rather than PMS itself, is the primary driver of eating disorder risk, clinicians should prioritize weight management strategies over solely symptom-based treatments. While lifestyle interventions targeting smoking cessation and body fat reduction are essential, precise assessment is equally critical. Therefore, neck circumference should be considered a practical and edema-independent alternative to waist measurements for assessing adiposity risks in women experiencing severe PMS bloating. Future research employing prospective designs with biochemical monitoring across different phases of the menstrual cycle will help clarify the underlying mechanisms of these interconnected relationships. Declarations Acknowledgements The authors would like to thank the volunteer women participants and the Sarıyer District Health Directorate for their support of the study. Authorship Contributions E.O. and H.S.A. designed the study. E.O. contributed to sample collection. E.O., NK and HSA conducted the research, analyzed and interpreted the data. E.O., N.K., and H.S.A. wrote the draft. E.O., N.K., and H.S.A. had primary responsibility for the final content, and all authors carefully reviewed the manuscript and approved the final version submitted for publication. Funding The author(s) received no financial support for the research, authorship, and/or publication of this article. Data Availability The data presented in this study are available on request from the corresponding author due to privacy or ethical restrictions. Ethics approval and consent to participate This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving research study participants were approved by the Human Research Ethics Committee of Istanbul Bilgi University (Date: November 28, 2023, Approvel No: 2023-20160-146). The current study was in compliance with the Declaration of Helsinki. Written informed consent was obtained from all participants prior to data collection. Consent for publication The written consent was taken from the participants. Competing interests The authors declare no competing interests. Permission to Reproduce Material From Other Sources No material reproduced from other sources. Author details 1 Esengul OZKAN, Istanbul Bilgi University Instutite of Graduate Programs Department of Nutrition and Dietetics, Istanbul, Turkey 2 Neslihan KOCATEPE, Istanbul Bilgi University Faculty of Health Sciences Department of Nutrition and Dietetics, Istanbul, Turkey 3 Hande SEVEN AVUK, İstanbul Bilgi University Faculty of Health Sciences Department of Nutrition and Dietetics, Istanbul, Turkey References Modzelewski S, Oracz A, Żukow X, Iłendo K, Śledzikowka Z, Waszkiewicz N. Premenstrual syndrome: new insights into etiology and review of treatment methods. Front Psychiatry. 2024;15:1363875. https://doi.org/10.3389/fpsyt.2024.1363875 . Direkvand-Moghadam A, Sayehmiri K, Delpisheh A, Kaikhavandi S. 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1","display":"","copyAsset":false,"role":"figure","size":89272,"visible":true,"origin":"","legend":"\u003cp\u003eSTROBE flow diagram of the study\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8246542/v1/1d617215e42c6181f980a735.png"},{"id":106344788,"identity":"9a752fa5-caf0-4d39-855d-c046152b1df5","added_by":"auto","created_at":"2026-04-07 16:16:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1581102,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8246542/v1/4537dc3e-48fc-4717-a4b6-836d8937a676.pdf"},{"id":98754863,"identity":"6ba9eba1-a80b-4c4e-ae7b-cd8cc9b29a23","added_by":"auto","created_at":"2025-12-22 09:25:27","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17649,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMetarial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8246542/v1/38ca44a490193a399f105e71.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Interplay Between Premenstrual Syndrome, Eating Disorder Risk, and Adiposity Indicators: A Cross Sectional Study on Women","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePremenstrual syndrome (PMS) is a cluster of physical, emotional, and behavioral symptoms that affect a significant proportion of women of reproductive age, emerging during the luteal phase of the menstrual cycle and diminishing with the onset of menstruation [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Although its global prevalence is estimated to be approximately 47.8% [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], this rate varies widely between 20% and 90% depending on diagnostic criteria and cultural factors [\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. According to a meta-analysis, the overall prevalence of PMS in T\u0026uuml;rkiye has been reported as 52.2% [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Beyond reducing individual quality of life, PMS is considered a significant public health issue due to its associated impact on work productivity and disruptions in social functioning. Although its etiology has not been fully elucidated, current evidence suggests that cyclic fluctuations in progesterone and estrogen levels have a significant influence, particularly through their effects on neurotransmitters such as serotonin and GABA [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe most common symptoms of premenstrual syndrome (PMS) include breast tenderness, bloating, headaches, mood swings, depression, anxiety, anger, and irritability [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Additionally, increased appetite, intense cravings for sweet or salty foods, and binge-eating episodes are frequently reported [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Recent studies have demonstrated a strong association between PMS symptom severity and emotional eating as well as the risk of disordered eating attitudes, further supporting the potential link between these conditions [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Literature indicates that reduced serotonin levels during the luteal phase of the menstrual cycle trigger carbohydrate intake to induce temporary relief; however, this cycle may predispose individuals to weight gain, depressive symptoms, and disordered eating behaviors over time [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In young adult populations\u0026mdash;particularly university students\u0026mdash;the combination of body image concerns and PMS symptoms emerges as a significant factor that may increase the risk of developing eating disorders [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. It is well recognized that dietary habits, eating behaviors, body composition, and overall adiposity constitute essential determinants in the etiology of PMS [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Accumulation of visceral adipose tissue has been proposed to induce chronic low-grade inflammation, thereby disrupting endocrine homeostasis and potentially exacerbating the severity of PMS symptoms [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn contrast to the inherent limitations of the conventional Body Mass Index (BMI), emerging anthropometric indices such as the Visceral Adiposity Index (VAI) are considered to provide a more sensitive representation of visceral fat distribution and have demonstrated strong associations with metabolic risk profiles and inflammatory processes [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Nevertheless, current evidence addressing the interrelationship between PMS, disordered eating risk, and visceral adiposity\u0026mdash;particularly through the simultaneous assessment of anthropometric and biochemical parameters\u0026mdash;remains markedly limited. Accordingly, the present study seeks to elucidate the influence of PMS on disordered eating attitudes and visceral adiposity in women of reproductive age.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Participants\u003c/h2\u003e \u003cp\u003eThis descriptive, cross-sectional study was conducted between June 2024 and April 2025. The study population consisted of 252 volunteer women aged 18\u0026ndash;49 years who applied for nutritional counseling at the Healthy Nutrition and Active Life Unit affiliated with the Republic of T\u0026uuml;rkiye Ministry of Health, Istanbul Sariyer District Health Directorate. The sample size was calculated using G*Power (v3.1.9.7) based on an independent samples t-test design. Assuming a medium effect size (d\u0026thinsp;=\u0026thinsp;0.5) as suggested by Cohen [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], a type I error rate of 0.05, and a power of 95%, the minimum required sample size was determined to be 210. To compensate for potential data loss, the target sample size was increased by 20%, resulting in a total of 252 participants. Women were excluded from the study if they were under 18 or over 49 years of age, postmenopausal, pregnant, or lactating.\u003c/p\u003e \u003cp\u003eAdditionally, those who had previously been diagnosed with polycystic ovary syndrome (PCOS) and irregular menstruation by a physician, or those currently using hormonal contraceptives or psychiatric medications, were not included in the study. All procedures involving human participants were conducted in accordance with the ethical standards outlined in the 1964 Declaration of Helsinki and its subsequent amendments. Ethical approval was obtained from the Istanbul Bilgi University Human Research Ethics Committee (Date: November 28, 2023, Approvel No: 2023-20160-146), and written institutional permission was secured from the relevant health directorate. Written informed consent was obtained from all individual participants included in the study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eData were collected via face-to-face interviews using a questionnaire developed by the researcher based on a review of the relevant literature. During these interviews, participants' sociodemographic characteristics, nutritional habits, and physical activity levels were queried and recorded. Anthropometric measurements were taken directly by the researcher. Additionally, biochemical parameters from the preceding three months were retrieved from laboratory results registered in the medical database. The STROBE flow diagram of the study is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eSurvey Form\u003c/h3\u003e\n\u003cp\u003eThe questionnaire was structured to systematically assess key demographic and health-related characteristics of the participants, including age, educational level, socioeconomic status, occupational factors, history of chronic diseases, regular medication use, and smoking and alcohol consumption habits.\u003c/p\u003e\n\u003ch3\u003ePremenstrual Syndrome Scale (PMSS)\u003c/h3\u003e\n\u003cp\u003eThe PMSS, developed by Gen\u0026ccedil;doğan [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], was employed to evaluate the severity of premenstrual symptoms. This 44-item scale utilizes a 5-point Likert-type scoring system and assesses symptoms experienced during the week preceding menstruation. Total scores range from 44 to 220, with higher scores indicating greater symptom severity. A cut-off score of 110 (representing more than 50% of the maximum possible score) is used to classify the presence of PMS. The scale comprises nine sub-dimensions: depressive affect, anxiety, fatigue, irritability, depressive thoughts, pain, appetite changes, sleep changes, and bloating. While the Cronbach\u0026rsquo;s alpha coefficient was reported as 0.75 in the original study, it was calculated as 0.94 in the present study.\u003c/p\u003e\n\u003ch3\u003eEating Attitudes Test-26 (EAT-26)\u003c/h3\u003e\n\u003cp\u003eThe Eating Attitudes Test-26 (EAT-26) was utilized to assess the risk and symptoms of eating disorders. Developed initially as EAT-40 by Garner and Garfinkel [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and revised by Garner et al. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], the Turkish validity and reliability of the scale were established by Erg\u0026uuml;ney-Okumuş and Sertel-Berk [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This 6-point Likert-type scale consists of three sub-dimensions: Dieting, Bulimia/Food Preoccupation, and Oral Control. Total scores range from 0 to 78, with a clinical cut-off point of 20. Participants scoring 20 or higher were classified as \u0026ldquo;High Risk for Eating Disorders\u0026rdquo; while those scoring below 20 were considered to have \"Low Risk for Eating Disorders\u0026rdquo;. The Cronbach\u0026rsquo;s alpha coefficient, reported as 0.84 in the Turkish adaptation, was calculated as 0.72 in the present study.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDietary Assessment\u003c/h2\u003e \u003cp\u003eDietary intake was assessed using the retrospective 24-hour dietary recall method, and energy and nutrient intake levels were analyzed using the BeBIS (Nutrition Information System) software, version 7.2. The inclusion of individuals with and without PMS who had similar energy and macronutrient intakes enhanced the comparability between groups (data are presented as supplementary material).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAnthropometric Measurements\u003c/h3\u003e\n\u003cp\u003eBody weight, fat mass, lean body mass, body fat percentage, and total body water were analyzed using a calibrated TANITA BC-418 MA Bioelectrical Impedance Analysis (BIA) device, following standard measurement protocols [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Height was measured using a calibrated stadiometer with the participant standing barefoot, upright, and with the head positioned in the Frankfurt horizontal plane [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The Body Mass Index (BMI) was calculated as weight in kilograms divided by the square of height in meters (kg/m\u0026sup2;). Participants were categorized according to the World Health Organization (WHO) classification: underweight (\u0026lt;\u0026thinsp;18.5 kg/m\u0026sup2;), normal weight (18.5\u0026ndash;24.9 kg/m\u0026sup2;), overweight (25.0\u0026ndash;29.9 kg/m\u0026sup2;), and obese (\u0026ge;\u0026thinsp;30 kg/m\u0026sup2;) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Waist, hip, and neck circumferences were measured using a non-stretchable tape measure in accordance with standard guidelines. Additionally, Waist-to-Hip Ratio (WHR) and Waist-to-Height Ratio (WHtR) were calculated and evaluated.\u003c/p\u003e\n\u003ch3\u003eBiochemical Parameters\u003c/h3\u003e\n\u003cp\u003eBiochemical data were obtained retrospectively from the medical database. The analysis encompassed lipid profiles (Total Cholesterol, HDL-C, LDL-C, Triglycerides), inflammation markers (CRP, ferritin), minerals (iron, magnesium), and vitamins (B\u003csub\u003e12\u003c/sub\u003e, folic acid, Vitamin D). All recorded values were based on 12-hour fasting blood samples ordered by a physician and analyzed within the three months preceding the study. Since these biochemical data reflect real-world clinical screening rather than a controlled trial setting, standardization of the menstrual cycle phase was not feasible. However, the selected parameters (e.g., Vitamin D, B\u003csub\u003e12\u003c/sub\u003e, Ferritin, Lipid Profile) serve as relatively stable markers of long-term nutritional and metabolic status and are significantly less susceptible to acute daily hormonal fluctuations compared to sex steroids.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eVisceral Adiposity Index (VAI)\u003c/h2\u003e \u003cp\u003eThe Visceral Adiposity Index (VAI) is a composite marker that reflects visceral fat accumulation and dysfunction, integrating both anthropometric (BMI, waist circumference) and metabolic (triglycerides, HDL-C) parameters. For female participants, VAI scores were determined using the sex-specific equation:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:VAI=\\left(\\frac{WC}{36.58\\:+\\:\\left(1.89\\:X\\:BMI\\right)}\\right)\\:\\text{X}\\:\\left(\\frac{TG}{0.81}\\right)\\:X\\:\\left(\\frac{1.52}{HDL\\:-\\:C}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere WC is expressed in cm, and TG and HDL-C levels are in mmol/L [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using the SPSS software (Statistical Package for the Social Sciences, version 30.0; IBM Corp., Armonk, NY, USA). Appropriate descriptive and inferential methods were applied. The normality of the data distribution was assessed using the Shapiro\u0026ndash;Wilk test. Continuous variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median [25th\u0026ndash;75th percentile], depending on the data's normality. Categorical variables were expressed as frequencies and percentages. Between-group comparisons were conducted using the Independent Samples t-test for normally distributed variables and the Mann\u0026ndash;Whitney U test for non-normally distributed variables. The Pearson Chi-square test was used to analyze associations between categorical variables, and the Kruskal\u0026ndash;Wallis H test was applied for comparisons across more than two independent groups. To identify factors associated with the risk of eating disorders, a multivariate binomial logistic regression analysis was performed, including PMS status, age, BMI, and chronic disease as independent variables. Model fit was evaluated using Cox and Snell R\u0026sup2;, Nagelkerke R\u0026sup2;, and model chi-square statistics. VAI was excluded from the logistic regression model to avoid multicollinearity, as BMI and waist circumference are already integral components of the VAI formula. A p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eAmong the 252 women included in the study, 57.9% were identified as having PMS, while 42.1% did not meet the PMS criteria. No significant differences were observed between the PMS and non-PMS groups in terms of age, menstrual duration, marital status, educational status, employment status, or perceived income level (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Examination of lifestyle habits revealed a significant association between smoking and PMS status, with a higher proportion of smokers in the PMS group (28.8%) compared with the non-PMS group (16.0%) (p\u0026thinsp;=\u0026thinsp;0.018). Alcohol consumption was also associated with higher PMSS scores (p\u0026thinsp;=\u0026thinsp;0.022). Although the prevalence of eating disorder risk did not significantly differ between PMS and non-PMS groups (p\u0026thinsp;=\u0026thinsp;0.392), participants at high risk for eating disorders had substantially higher PMSS scores (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of sociodemographic characteristics and nutritional habits according to the presence of PMS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePMS Group (n\u0026thinsp;=\u0026thinsp;146)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-PMS Group (n\u0026thinsp;=\u0026thinsp;106)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-Value\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePMSS Scores\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-Value\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.47\u0026thinsp;\u0026plusmn;\u0026thinsp;8.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.75\u0026thinsp;\u0026plusmn;\u0026thinsp;8.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.246\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eNA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMenstruation duration (d)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,36\u0026thinsp;\u0026plusmn;\u0026thinsp;1,84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,12\u0026thinsp;\u0026plusmn;\u0026thinsp;1,60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.358\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.068\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.017\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87 (59.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75 (70.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e114 [92.75\u0026ndash;137.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59 (40.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e126.5 [100\u0026ndash;148.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.145\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.177\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (25.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e107.5 [92.25\u0026ndash;135.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (34.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (25.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e122.5 [98\u0026ndash;144.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (47.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (49.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e119 [96.75\u0026ndash;142]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEmployment status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.090\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.036\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56 (38.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (49.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e113 [88\u0026ndash;138.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90 (61.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (50.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e122 [99.25\u0026ndash;144]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIncome status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.258\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.413\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome less than expenses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e122.5 [101\u0026ndash;142.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome equal to expenses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80 (54.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (62.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e118 [96\u0026ndash;142]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome higher than expenses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (19.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e118.5 [86.25\u0026ndash;141.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePresence of chronic disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.261\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.258\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92 (63.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74 (69.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e122 [98\u0026ndash;144]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (30.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e116.5 [95\u0026ndash;140.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.010\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (28.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e129 [104\u0026ndash;148]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104 (71.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89 (84.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e114 [94\u0026ndash;141]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol consumption\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.052\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.022\u003c/b\u003e\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e132 [103\u0026ndash;159]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116 (79.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94 (88.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e115.5 [95\u0026ndash;140]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDaily snack consumption\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.649\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.766\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (15.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e122 [93\u0026ndash;145]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (32.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (33.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e119 [98\u0026ndash;137]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (36.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (40.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e116.5 [92.25\u0026ndash;142]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (10.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e122.5 [98.75\u0026ndash;151]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of main meals\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.821\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.705\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e110 [97\u0026ndash;110]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80 (54.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (50.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120 [96.75\u0026ndash;144.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65 (44.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51(48.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e118 [95\u0026ndash;140.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSkipping main meals\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.214\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.212\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78 (53.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (43.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e123 [97.25\u0026ndash;144.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (17.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (25.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e107 [83\u0026ndash;140.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSometimes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (28.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (31.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e117 [95\u0026ndash;140]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEAT-26 Classification\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.392\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh Risk for ED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (27.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e125 [100.75\u0026ndash;158.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow Risk for ED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106 (72.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82 (77.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94 [0\u0026ndash;126]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eData are presented as Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;Standard Deviation, Median [25th \u0026minus;\u0026thinsp;75th percentile], and number (percentage).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: BMI: Body Mass Index, EAT: Eating Attitude Disorder, PMS: Premenstrual Syndrome, PMSS: Premenstrual Syndrome Scale.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eStatistical Analysis: \u003csup\u003e*\u003c/sup\u003eIndependent Samples t-test, \u003csup\u003e**\u003c/sup\u003eMann-Whitney U test, \u003csup\u003e***\u003c/sup\u003eKruskal-Wallis H, \u003csup\u003e+\u003c/sup\u003ePearson Chi-square test was used.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003ep\u003csup\u003e1\u003c/sup\u003e: Within-group comparison p-value (PMS Group vs. Non-PMS Group).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003ep\u003csup\u003e2\u003c/sup\u003e: Comparison of PMSS Scores by Variables.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eComparison of anthropometric measurements revealed that the median body fat percentage was significantly higher in the PMS group (38.1%) compared with the non-PMS group (37.1%) (p\u0026thinsp;=\u0026thinsp;0.039). Although body weight, BMI, and waist circumference were numerically higher in the PMS group, these differences were not statistically significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Biochemical parameters, including lipid profile, ferritin, CRP, vitamins, and minerals, showed no significant differences between groups (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). When each PMS subgroup was compared by eating disorder risk, women without PMS but at high risk for eating disorders had significantly higher waist circumferences (p\u0026thinsp;=\u0026thinsp;0.014), waist-to-hip ratios (p\u0026thinsp;=\u0026thinsp;0.006), and waist-to-height ratios (p\u0026thinsp;=\u0026thinsp;0.015) than those at low risk. In the PMS group, neck circumference was significantly higher in participants at high risk for eating disorders compared to those at low risk (p\u0026thinsp;=\u0026thinsp;0.049). No other anthropometric or biochemical variables showed significant differences according to eating disorder risk (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of anthropometric measurements and biochemical parameters of participants according to the presence of PMS status and eating attitude disorder risk\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003ePMS Group (n\u0026thinsp;=\u0026thinsp;146)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eNon-PMS Group (n\u0026thinsp;=\u0026thinsp;106)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow Risk for ED (n\u0026thinsp;=\u0026thinsp;106)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh Risk for ED (n\u0026thinsp;=\u0026thinsp;40)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLow Risk for ED (n\u0026thinsp;=\u0026thinsp;82)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHigh Risk for ED (n\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody weight (kg)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75.9 [68.9\u0026ndash;85.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e79.9 [72.2\u0026ndash;90.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76.7 [69.5\u0026ndash;87.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e75.2 [65.6\u0026ndash;85.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e79.0 [69.6\u0026ndash;87.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e76.0 [66.3\u0026ndash;85.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.8 [25.6\u0026ndash;34.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.7 [27.8\u0026ndash;36.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.6 [26.3\u0026ndash;35.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e29.0 [25.6\u0026ndash;32.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e30.1 [27.0-33.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e29.3 [26.1\u0026ndash;32.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.287\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFat (%)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.8 [32.1\u0026ndash;43.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.0 [37.0-42.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38.1 [33.3\u0026ndash;43.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e37.1 [31.2\u0026ndash;39.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e37.2 [34.5\u0026ndash;40.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e37.1 [31.9\u0026ndash;40.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFat mass (kg)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.8 [22.5\u0026ndash;36.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.1 [26.9\u0026ndash;38.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.2 [23.7\u0026ndash;37.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28.2 [21.4\u0026ndash;34.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e29.3 [23.6\u0026ndash;35.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e28.4 [21.4\u0026ndash;35.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFat free mass (kg)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.4 [43.3\u0026ndash;51.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.6 [45.7\u0026ndash;51.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47.8 [44.7\u0026ndash;51.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e46.4 [42.8\u0026ndash;50.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e48.2 [44.0-52.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e47.0 [43.0\u0026ndash;51.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal body water (%)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.3 [41.5\u0026ndash;48.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.7 [41.8\u0026ndash;46.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45.2 [41.6\u0026ndash;48.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e45.7 [43.7\u0026ndash;49.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e45.9 [43.2\u0026ndash;48.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e45.8 [43.7\u0026ndash;49.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist circumference (cm)\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91.0\u0026thinsp;\u0026plusmn;\u0026thinsp;15.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95.5\u0026thinsp;\u0026plusmn;\u0026thinsp;16.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e92.21\u0026thinsp;\u0026plusmn;\u0026thinsp;15.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e87.8\u0026thinsp;\u0026plusmn;\u0026thinsp;14.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e95.8\u0026thinsp;\u0026plusmn;\u0026thinsp;13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e89.61\u0026thinsp;\u0026plusmn;\u0026thinsp;14.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeck Circumference (cm)\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34.0 [32.0\u0026ndash;37.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e33.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e34.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e34.0 [32.1\u0026ndash;36.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.348\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist/Hip Ratio\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8 [0.7\u0026ndash;0.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8 [0.8\u0026ndash;0.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.82 [0.8\u0026ndash;0.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.8 [0.7\u0026ndash;0.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8 [0.8\u0026ndash;0.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.81 [0.8\u0026ndash;0.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist/Height Ratio\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6 [0.5\u0026ndash;0.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6 [0.5\u0026ndash;0.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.6 [0.5\u0026ndash;0.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.6 [0.5\u0026ndash;0.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVAİ\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.0 [2.0-4.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.5 [2.5\u0026ndash;4.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.09 [2.1\u0026ndash;4.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.8 [2.0-4.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.7 [2.8\u0026ndash;5.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.06 [2.0\u0026ndash;4.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.864\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mg/dL)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e190.5 [166.5-222.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e195.5 [178.0-219.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e193.0 [171.0\u0026ndash;221.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e190.0 [170.0-219.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e183.0 [163.5-202.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e189.0 [167.2\u0026ndash;214.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL (mg/dL)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55.0 [45.0-63.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55.0 [44.8\u0026ndash;65.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55.0 [45.0\u0026ndash;64.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e53.4 [47.0-60.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e50.0 [45.0\u0026ndash;57.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e53.0 [47.0\u0026ndash;59.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.476\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL (mg/dL)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e114.0 [95.2-141.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e120.0 [101.0-137.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e116.0 [96.5\u0026ndash;140.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e113.0 [95.0-137.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e102.0 [92.5\u0026ndash;131.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e110.5 [95.0\u0026ndash;135.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mg/dL)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86.0 [65.0-127.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e101.0 [75.8\u0026ndash;129.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e90.5 [65.1\u0026ndash;127.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e82.0 [65.2-118.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e94.0 [71.5\u0026ndash;138.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e87.0 [66.2\u0026ndash;131.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.678\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFerritin(ng/ml)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.9 [9.4\u0026ndash;26.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.6 [8.7\u0026ndash;17.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.0 [9.2\u0026ndash;25.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.3 [8.1\u0026ndash;21.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e16.5 [9.4\u0026ndash;19.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e14.0 [8.2\u0026ndash;21.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.387\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.9 [1.1\u0026ndash;6.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.5 [1.4-5.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.8 [1.3\u0026ndash;5.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.0 [1.0-7.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.2 [2.0-6.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.3 [1.1\u0026ndash;6.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.755\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum Iron (\u0026micro;g/L)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64.0 [45.2\u0026ndash;96.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.0 [43.0-81.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60.0 [45.0\u0026ndash;92.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e68.0 [47.0\u0026ndash;88.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e71.0 [52.0\u0026ndash;97.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e68.0 [47.2\u0026ndash;91.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.576\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMg (mg/dL)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.0 [1.9-2.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.9 [1.8-2.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.9 [1.9\u0026ndash;2.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.0 [1.9\u0026ndash;2.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.0 [1.8-2.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.0 [1.9\u0026ndash;2.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.446\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamine B\u003csub\u003e12\u003c/sub\u003e (pg/ml)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e224.0 [166.5-340.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e182.5 [149.0-272.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e214.0 [160.0\u0026ndash;313.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e202.0 [151.0-249.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e217.0 [184.5\u0026ndash;318.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e205.0 [153.2\u0026ndash;271.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum Folate (ng/ml)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.7 [5.4\u0026ndash;8.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.0 [6.2\u0026ndash;9.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.8 [5.7\u0026ndash;9.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.4 [5.5\u0026ndash;9.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.2 [4.4\u0026ndash;12.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.4 [5.2\u0026ndash;11.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.574\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamine D (ng/ml)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.1 [12.1\u0026ndash;24.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.1 [14.0-23.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.5 [12.4\u0026ndash;24.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19.4 [11.4\u0026ndash;23.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e22.9 [19.3\u0026ndash;29.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e20.6 [15.1\u0026ndash;23.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.415\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eData are presented as Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;Standard Deviation, Median [25th \u0026minus;\u0026thinsp;75th percentile].\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eAbbreviations: n: Number of participants, BMI: Body Mass Index, CRP: C-Reactive Protein, ED: Eating Disorders, HDL: High Density Lipoprotein, LDL: Low Density Lipoprotein, PMS: Premenstrual Syndrome, VAI: Visceral Adiposity Index.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eStatistical Analysis: \u003csup\u003e*\u003c/sup\u003eMann-Whitney U test, and \u003csup\u003e**\u003c/sup\u003eIndependent Samples t-test were used.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003ep\u003csup\u003e1\u003c/sup\u003e: Within-group comparison p-value (Low vs. High Eating Disorder Risk).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003ep\u003csup\u003e2\u003c/sup\u003e: Between-group comparison p-value (PMS Group vs. Non-PMS Group).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePMSS total scores were significantly higher in the high-risk eating disorder group (median\u0026thinsp;=\u0026thinsp;146.5) compared with the low-risk group (median\u0026thinsp;=\u0026thinsp;136.5) (p\u0026thinsp;=\u0026thinsp;0.010). Analysis of PMSS subdimensions revealed that depressive affect (p\u0026thinsp;=\u0026thinsp;0.038), anxiety (p\u0026thinsp;=\u0026thinsp;0.012), depressive thoughts (p\u0026thinsp;=\u0026thinsp;0.027), and appetite changes (p\u0026thinsp;=\u0026thinsp;0.016) were significantly higher among participants at high eating disorder risk. No significant differences were observed for fatigue, irritability, pain, sleep disturbances, or bloating (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). As expected, all PMSS subdimension scores were significantly higher in the PMS group compared with the non-PMS group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of PMSS scores according to participants' PMS status and eating attitude disorder risk\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePMS Group (n\u0026thinsp;=\u0026thinsp;146)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eNon-PMS Group (n\u0026thinsp;=\u0026thinsp;106)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow Risk for ED (n\u0026thinsp;=\u0026thinsp;106)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh Risk for ED (n\u0026thinsp;=\u0026thinsp;40)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLow Risk for ED (n\u0026thinsp;=\u0026thinsp;82)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHigh Risk for ED (n\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePMS Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136,5 [123\u0026ndash;149]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146,5 [131\u0026ndash;167]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e139,0 [124,0\u0026ndash;155,8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e91,0 [75\u0026ndash;100]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e96,5 [79\u0026ndash;102]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e91,5 [75,2\u0026ndash;100,0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0,001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepressive affect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23,0 [\u003cspan additionalcitationids=\"CR20 CR21 CR22 CR23 CR24 CR25 CR26 CR27\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25,5 [\u003cspan additionalcitationids=\"CR22 CR23 CR24 CR25 CR26 CR27 CR28 CR29 CR30 CR31\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23,0 [20,0\u0026ndash;28,0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11,0 [\u003cspan additionalcitationids=\"CR8 CR9 CR10 CR11 CR12 CR13 CR14 CR15\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9,5 [\u003cspan additionalcitationids=\"CR8 CR9 CR10 CR11\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11,0 [7,0\u0026ndash;15,0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0,001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15,0 [\u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15 CR16 CR17\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17,5 [\u003cspan additionalcitationids=\"CR14 CR15 CR16 CR17 CR18 CR19 CR20 CR21 CR22 CR23 CR24 CR25 CR26\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15,0 [12,0\u0026ndash;20,0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9,0 [\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9,0 [\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9,0 [7,0\u0026ndash;10,0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0,001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22,0 [\u003cspan additionalcitationids=\"CR20 CR21 CR22 CR23\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24,0 [\u003cspan additionalcitationids=\"CR18 CR19 CR20 CR21 CR22 CR23 CR24 CR25 CR26 CR27\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23,0 [19,0\u0026ndash;25,0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14,0 [\u003cspan additionalcitationids=\"CR11 CR12 CR13 CR14 CR15 CR16\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14,0 [\u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15 CR16 CR17 CR18\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14,0 [10,0\u0026ndash;17,8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0,001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIrritation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17,0 [\u003cspan additionalcitationids=\"CR15 CR16 CR17 CR18 CR19 CR20\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19,0 [\u003cspan additionalcitationids=\"CR15 CR16 CR17 CR18 CR19 CR20 CR21\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17,5 [14,0\u0026ndash;21,0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10,0 [\u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10 CR11 CR12\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11,0 [\u003cspan additionalcitationids=\"CR8 CR9 CR10 CR11 CR12\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10,0 [6,2\u0026ndash;13,0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0,001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepressive thoughts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18,0 [\u003cspan additionalcitationids=\"CR14 CR15 CR16 CR17 CR18 CR19 CR20 CR21\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21,5 [\u003cspan additionalcitationids=\"CR15 CR16 CR17 CR18 CR19 CR20 CR21 CR22 CR23 CR24 CR25\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19,0 [14,0\u0026ndash;23,0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9,0 [\u003cspan additionalcitationids=\"CR8 CR9 CR10 CR11 CR12\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7,0 [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9,0 [7,0\u0026ndash;13,0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0,001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10,0 [\u003cspan additionalcitationids=\"CR8 CR9 CR10 CR11\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,0 [\u003cspan additionalcitationids=\"CR8 CR9 CR10 CR11\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10,0 [7,0\u0026ndash;12,0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6,0 [\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7,0 [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7,0 [5,0\u0026ndash;9,0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0,001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAppetite changes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13,0 [\u003cspan additionalcitationids=\"CR12 CR13 CR14\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15,0 [\u003cspan additionalcitationids=\"CR12 CR13 CR14\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13,0 [11,0\u0026ndash;15,0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10,0 [\u003cspan additionalcitationids=\"CR9 CR10 CR11 CR12\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11,5 [\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11,0 [8,0\u0026ndash;13,0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0,001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep changes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9,0 [\u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,0 [\u003cspan additionalcitationids=\"CR8 CR9 CR10 CR11\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,0 [7,0\u0026ndash;11,0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5,0 [\u003cspan additionalcitationids=\"CR4 CR5 CR6\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6,5 [\u003cspan additionalcitationids=\"CR4 CR5 CR6\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5,0 [3,0\u0026ndash;7,0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0,001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBloating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13,0 [\u003cspan additionalcitationids=\"CR11 CR12 CR13 CR14\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15,0 [\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13 CR14\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14,0 [10,0\u0026ndash;15,0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11,0 [\u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10 CR11 CR12 CR13 CR14\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8,0 [\u003cspan additionalcitationids=\"CR8 CR9 CR10 CR11 CR12\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11,0 [7,0\u0026ndash;15,0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0,001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eData are presented as Median [25th \u0026minus;\u0026thinsp;75th percentile].\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eAbbreviations: PMS: Premenstrual Syndrome, PMSS: Premenstrual Syndrome Scale, ED: Eating Disorders, n: Number of participants.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eStatistical Analysis: The Mann-Whitney U test was used.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003ep\u003csup\u003e1\u003c/sup\u003e: Within-group comparison p-value (Low vs. High Eating Disorder Risk).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003ep\u003csup\u003e2\u003c/sup\u003e: Between-group comparison p-value (PMS Group vs. Non-PMS Group).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe multivariate binomial logistic regression model examining predictors of eating disorder risk was statistically significant (χ\u0026sup2; = 9.93, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). After adjusting for age and chronic disease, PMS status was not a significant predictor of eating disorder risk (p\u0026thinsp;=\u0026thinsp;0.625). BMI emerged as the only significant independent predictor in the model, indicating that each 1-unit increase in BMI increased the likelihood of being at risk for an eating disorder by 1.05 times (aOR\u0026thinsp;=\u0026thinsp;1.049, 95% CI: 1.002\u0026ndash;1.098; p\u0026thinsp;=\u0026thinsp;0.041) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate Binomial Logistic Regression analysis identifying risk factors for eating disorders\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWald\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eaOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% C.I.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePMS Presence (Ref: Absent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.639\u0026ndash;2.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.946\u0026ndash;1.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.041*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.002\u0026ndash;1.098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic Disease (Ref: Absent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.762\u0026ndash;2.643\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eDependent Variable: Risk of Eating Disorder. Independent Variables: Presence of PMS, Age, BMI, Chronic Disease.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAbbreviations: β: Regression Coefficient, S.E.: Standard Error, aOR: Adjusted Odds Ratio, 95% C.I.: Confidence Interval.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel Fit Statistics: Cox \u0026amp; Snell R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.026, Nagelkerke R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.038, Model X\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;9.93, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we investigated the impact of premenstrual syndrome (PMS) on the risk of eating disorders, the visceral adiposity index (VAI), and biochemical parameters in women of reproductive age. PMS was identified in 57.9% of the participants. Although this rate is slightly higher than the global prevalence of 47.8% reported in the literature [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], it is consistent with findings from other studies conducted in T\u0026uuml;rkiye [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Variations in PMS prevalence across studies are thought to reflect differences in diagnostic criteria, cultural factors, and regional variations in lifestyle habits.\u003c/p\u003e \u003cp\u003eOne of the notable findings of our study was the significant association observed between smoking and PMS. The prevalence of smoking was significantly higher among women with PMS compared with the control group. Smoking may exacerbate PMS through multiple physiological and neuroendocrine pathways. Nicotine disrupts estrogen and progesterone levels, contributing to hormonal imbalance [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and also interferes with the hypothalamic\u0026ndash;pituitary\u0026ndash;adrenal (HPA) axis, increasing vulnerability to stress [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In addition, the anxiogenic effects of nicotine may intensify the emotional symptoms experienced during PMS [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBody composition and obesity have become increasingly important factors in the etiology of PMS. In our study, the body fat percentage of women with PMS was significantly higher than that of women without PMS, supporting hypotheses suggesting that increased adiposity may play a role in PMS pathophysiology. Adipose tissue is not merely an energy reservoir but also an active endocrine organ that influences estrogen metabolism and secretes inflammatory cytokines [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Previous research has demonstrated that obesity contributes to chronic low-grade inflammation, disrupts neurotransmitter balance, and may trigger PMS symptoms [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Although body weight and Body Mass Index (BMI) were numerically higher in the PMS group, these differences did not reach statistical significance, suggesting that body fat percentage may be a more sensitive indicator than overall body weight.\u003c/p\u003e \u003cp\u003eOne of the primary objectives of our study was to investigate the relationship between PMS and the Visceral Adiposity Index (VAI). This composite indicator differs from traditional anthropometric measurements by incorporating both anatomical (waist circumference and BMI) and physiological (triglycerides and HDL) parameters. The literature suggests that visceral adipose tissue may contribute to the etiology of PMS through chronic low-grade inflammation driven by the secretion of pro-inflammatory cytokines [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, despite the higher overall body fat percentage observed in the PMS group, no significant difference in VAI levels was found between the groups. The lack of significant difference in VAI, despite higher body fat percentage in the PMS group, can be explained by the 'Metabolically Healthy Obese' (MHO) phenotype often seen in young women. In this age group, excess fat is preferentially stored subcutaneously rather than viscerally. Since VAI is specifically sensitive to visceral adipose dysfunction and triglyceride levels, it may not fully capture the subcutaneous adiposity load that drives PMS-related inflammation in this younger, non-diabetic cohort [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. This finding may be attributable to the characteristics and fat distribution patterns of the study population. Because the majority of participants were young and metabolically healthy, triglyceride and HDL levels \u0026mdash; key components of the VAI formula \u0026mdash; may have remained within normal ranges. Consistent with this interpretation, our biochemical analyses showed no significant differences in lipid profiles between groups. This suggests that, in young women, PMS-related increases in adiposity may not yet have progressed to metabolically adverse visceral fat accumulation resembling metabolic syndrome but may instead reflect subcutaneous or general adiposity. Additionally, waist circumference \u0026mdash; a component of VAI \u0026mdash; may be influenced by abdominal bloating and fluid retention frequently observed during the luteal phase of the menstrual cycle, potentially masking actual differences in visceral adiposity between groups.\u003c/p\u003e \u003cp\u003eIn our study, there were no statistically significant differences observed between the PMS and control groups in terms of biochemical parameters. The literature, however, provides strong evidence that deficiencies in micronutrients such as magnesium, calcium, vitamin D, and B-group vitamins may influence PMS etiology by altering neurotransmitter synthesis and exacerbating symptom severity [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. For example, the regulatory effects of magnesium on serotonin receptors and the role of vitamin D in calcium homeostasis underscore the importance of these micronutrients in managing PMS [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Several potential explanations exist for the absence of significant biochemical differences in our findings. First, the biochemical data were obtained retrospectively from laboratory records ordered by physicians within the last three months, rather than through standardized blood sampling for research purposes. This limited our ability to obtain uniform biochemical measurements for the entire sample and may have resulted in missing data, thereby reducing statistical power. Second, the timing of blood collection is a critical factor. Hormonal and biochemical fluctuations related to PMS are particularly pronounced during the luteal phase of the menstrual cycle. Because the retrospective records did not indicate the cycle day on which samples were collected, measurements taken during the follicular phase may have masked potential luteal-phase reductions in micronutrient levels. Third, homeostatic mechanisms likely played a role. The body tightly regulates serum concentrations to maintain physiological stability, meaning that blood levels may remain within normal ranges even when intracellular stores are depleted. The young and generally healthy nature of our sample likely contributed to the effectiveness of these compensatory mechanisms. In conclusion, clarifying the relationship between PMS and micronutrient status will require prospective studies in which biochemical samples are collected during the luteal phase, and intracellular levels are also assessed.\u003c/p\u003e \u003cp\u003eThe multifaceted relationship between nutrition and PMS becomes even more complex when the prevalence of eating disorders is taken into account [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In our study, we observed that as eating disorder scores increased, the severity of PMS symptoms also rose proportionally. This finding is consistent with the study by Yi et al. [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], which reported a positive correlation between eating disorder severity and PMS. Similarly, an Iranian study found that women with a high risk of eating disorders experienced more severe PMS symptoms, even though the statistical significance was borderline [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The underlying mechanism of this association may relate to hormonal fluctuations throughout the menstrual cycle that disrupt appetite regulation. In particular, decreased serotonin levels during the luteal phase may trigger carbohydrate cravings, making it more challenging to regulate eating behavior and potentially predisposing individuals to disordered eating patterns [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, our multivariate logistic regression results suggest that PMS does not solely drive this relationship. After adjusting for age and chronic diseases, PMS was not identified as an independent predictor of eating disorder risk. Although a symptomatic correlation exists between these two conditions, our findings weaken the hypothesis that PMS directly causes eating disorders. While our results do not establish a definitive causal relationship, they highlight the need for future large-scale studies to elucidate further the underlying mechanisms involved in this association.\u003c/p\u003e \u003cp\u003eIn our study, we found that while PMS symptom severity was associated with disordered eating behaviors, the primary determinant of clinical eating disorder risk was not the presence of PMS itself but rather a higher BMI. Similarly, Pearce et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] emphasized that increased BMI is one of the strongest predictors of eating disorder development. Another study demonstrated that women with a BMI above 27.5 kg/m\u0026sup2; were more likely to develop severe PMS ten years later compared with those with a BMI below 20 kg/m\u0026sup2; [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Although there is strong evidence in the literature suggesting that obesity increases PMS risk, some studies have reported a negative or U-shaped relationship between BMI and PMS symptom severity, particularly in young and normal-weight populations [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. This pattern suggests that both low body weight and inadequate nutritional intake may induce physiological stress that disrupts hormonal balance, potentially triggering PMS symptoms.\u003c/p\u003e \u003cp\u003eIn our subgroup analyses, we found that among women without PMS, those at risk for eating disorders had significantly higher waist circumference, waist-to-hip ratio, and waist-to-height ratio. However, this relationship disappeared within the PMS group, where only neck circumference remained significantly associated with eating disorder risk. Neck circumference is a stable anthropometric measure that is minimally affected by abdominal bloating and cycle-related fluid retention, yet it correlates strongly with visceral adiposity [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The lack of significance in waist-related variables within the PMS group may be attributed to abdominal edema caused by fluctuations in aldosterone and progesterone during the luteal phase, which could mask actual differences in abdominal fat accumulation between groups. Therefore, the finding that neck circumference\u0026mdash;unaffected by edema\u0026mdash;remained significant in the PMS group suggests that eating disorder risk in these women is still linked to adiposity. However, this relationship becomes difficult to detect using standard waist measurements.\u003c/p\u003e \u003cp\u003eIn our study, participants were grouped according to the presence of PMS, and the impact of eating disorder risk on PMS symptom severity was examined. The analyses revealed that among women diagnosed with PMS, those at risk for eating disorders had significantly higher total PMSS scores, as well as higher scores in the subdimensions of depressive affect, anxiety, depressive thoughts, and appetite changes compared with those without such risk. In contrast, among women without PMS, eating disorder risk did not produce a significant difference in total PMSS scores or in psychological subdimensions. This pattern suggests that a tendency toward disordered eating may act as an \u0026ldquo;exacerbating factor\u0026rdquo; that intensifies an already existing PMS profile. The relationship between PMS and eating disorders is frequently interpreted through the serotonergic dysregulation hypothesis [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In individuals at risk for eating disorders, biological vulnerability may be compounded by cognitive factors such as fear of weight gain and restrictive eating, which can elevate stress levels and exacerbate anxiety and depressive symptoms [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The finding in our study that anxiety and depressive affect scores were particularly high among PMS-diagnosed individuals with eating disorder risk supports this psycho-biological burden hypothesis. Additionally, the significantly higher appetite change scores in the risk group suggest that these women may experience the physiological increase in appetite during the luteal phase more chaotically (i.e., cycles of binge eating or excessive restriction). Supporting this, Mighani et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] reported that premenstrual appetite increases in women with disordered eating behaviors may intensify symptom perception when combined with emotional eating tendencies.\u003c/p\u003e \u003cp\u003eInterestingly, among women without PMS, eating disorder risk was not associated with heightened psychological symptoms; instead, it was linked only to higher pain scores. This may indicate that women at risk for eating disorders, but without PMS, may experience lowered pain thresholds or increased somatization due to inadequate or irregular eating patterns. Overall, however, the findings suggest that eating disorder risk specifically intensifies PMS-related symptomatology, whereas it does not generate a comparable psychological profile in women without PMS. Therefore, in clinical practice, we recommend that women presenting with PMS, particularly those reporting pronounced depressive or anxious symptoms, should also be assessed for eating disorder risk.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThe findings of this study should be interpreted considering several limitations. First, the cross-sectional design allows for the identification of associations and their direction but does not permit the establishment of definitive causal relationships between variables. Second, the data collection relied on self-reported scales. Although this approach carries the potential risk of recall bias, a standard limitation in nutrition and psychological research, we attempted to minimize this limitation by employing standardized instruments with established validity and reliability. Third, the biochemical parameters were obtained retrospectively from hospital records covering the previous three months. While this method provides valuable real-world data from a large sample, it limits our ability to standardize blood collection according to menstrual cycle phases (follicular/luteal). However, the biochemical markers assessed in this study\u0026mdash;such as vitamin B12, vitamin D, ferritin, and lipid profile are relatively stable indicators reflecting long-term nutritional and metabolic status rather than acute hormonal fluctuations, which may have mitigated the impact of the lack of phase-specific sampling.\u003c/p\u003e \u003cp\u003eFinally, although the single-center nature of the study may limit the generalizability of the findings, the achieved sample size was consistent with the calculated power analysis, thereby strengthening the statistical validity of the results.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study is significant in that it reveals the multidimensional relationship between premenstrual syndrome (PMS), eating behavior, body composition, and biochemical parameters in women of reproductive age. Our findings demonstrated that women diagnosed with PMS had a significantly higher body fat percentage compared with those without PMS, and that smoking was more prevalent among women with PMS, supporting the role of increased adiposity and lifestyle factors in the etiology of PMS. One of the most noteworthy results of this study concerns the nature of the relationship between eating disorder risk and PMS. Although our findings showed that PMS symptom severity increased in parallel with higher eating disorder scores, the multivariate regression analysis revealed that the primary determinant of eating disorder risk was not the presence of PMS itself, but rather an elevated Body Mass Index (BMI). Furthermore, even among healthy women without PMS, eating disorder risk was associated with abdominal obesity indicators such as waist circumference and waist-to-hip ratio, confirming the strong link between disordered eating tendencies and central adiposity. In conclusion, PMS, eating disorders, and obesity are complex conditions that can mutually reinforce one another and share overlapping physiological mechanisms. Given that BMI, rather than PMS itself, is the primary driver of eating disorder risk, clinicians should prioritize weight management strategies over solely symptom-based treatments. While lifestyle interventions targeting smoking cessation and body fat reduction are essential, precise assessment is equally critical. Therefore, neck circumference should be considered a practical and edema-independent alternative to waist measurements for assessing adiposity risks in women experiencing severe PMS bloating. Future research employing prospective designs with biochemical monitoring across different phases of the menstrual cycle will help clarify the underlying mechanisms of these interconnected relationships.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the volunteer women participants and the Sarıyer District Health Directorate for their support of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eE.O. and H.S.A. designed the study. E.O. contributed to sample collection. E.O., NK and HSA conducted the research, analyzed and interpreted the data. E.O., N.K., and H.S.A. wrote the draft. E.O., N.K., and H.S.A. had primary responsibility for the final content, and all authors carefully reviewed the manuscript and approved the final version submitted for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) received no financial support for the research, authorship, and/or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data presented in this study are available on request from the corresponding author due to privacy or ethical restrictions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving research study participants were approved by the Human Research Ethics Committee of Istanbul Bilgi University (Date: November 28, 2023, Approvel No: 2023-20160-146). The current study was in compliance with the Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all participants prior to data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe written consent was taken from the participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePermission to Reproduce Material From Other Sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo material reproduced from other sources.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eEsengul OZKAN, Istanbul Bilgi University Instutite of Graduate Programs Department of Nutrition and Dietetics, Istanbul, Turkey\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eNeslihan KOCATEPE, Istanbul Bilgi University Faculty of Health Sciences Department of Nutrition and Dietetics, Istanbul, Turkey\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003eHande SEVEN AVUK, İstanbul Bilgi University Faculty of Health Sciences Department of Nutrition and Dietetics, Istanbul, Turkey\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eModzelewski S, Oracz A, Żukow X, Iłendo K, Śledzikowka Z, Waszkiewicz N. 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Int J Psychiatry Med. 2024;59(1):101\u0026ndash;11. https://doi.org/10.1177/00912174231189936. \u003c/li\u003e\n\u003cli\u003eYesildemir O, Arslan N, Dubus EN, Guner H. Relationship between premenstrual syndrome and compliance with the Mediterranean diet among women of reproductive age. Med Science. 2025;14(3):686-92. https://doi.org/10.5455/medscience.2025.03.072. \u003c/li\u003e\n\u003cli\u003eChoi SH, Hamidovic A. Association between smoking and premenstrual syndrome: a meta-analysis. Front Psychiatry. 2020;11:575526. https://doi.org/10.3389/fpsyt.2020.575526. \u003c/li\u003e\n\u003cli\u003eWu AD, Gao M, Aveyard P, Taylor G. Smoking cessation and changes in anxiety and depression in adults with and without psychiatric disorders. JAMA Netw Open. 2023;6(5):e2316111. https://doi.org/10.1001/jamanetworkopen.2023.16111.\u003c/li\u003e\n\u003cli\u003eKuryłowicz A. Estrogens in adipose tissue physiology and obesity-related dysfunction. Biomedicines. 2023;11(3):690. https://doi.org/10.3390/biomedicines11030690. \u003c/li\u003e\n\u003cli\u003eDeng H, Chen Y, Xing J, Zhang N, Xu L. Systematic low-grade chronic inflammation and intrinsic mechanisms in polycystic ovary syndrome. Front Immunol. 2024;15:1470283. https://doi.org/10.3389/fimmu.2024.1470283.\u003c/li\u003e\n\u003cli\u003eHestiantoro A, Kapnosa Hasani RD, Shadrina A, Situmorang H, Ilma N, Muharam R, et al. Body fat percentage is a better marker than body mass index for determining inflammation status in polycystic ovary syndrome. Int J Reprod Biomed. 2018;16(10):623\u0026ndash;8. PMID: 30643854; PMCID: PMC6314644. \u003c/li\u003e\n\u003cli\u003eBl\u0026uuml;her M. Metabolically healthy obesity. Endocr Rev. 2020;41(3):bnaa004. https://doi.org/10.1210/endrev/bnaa004. \u003c/li\u003e\n\u003cli\u003eSiminiuc R, Ţurcanu D. Impact of nutritional diet therapy on premenstrual syndrome. Front Nutr. 2023;10:1079417. https://doi.org/10.3389/fnut.2023.1079417. \u003c/li\u003e\n\u003cli\u003eRobinson J, Ferreira A, Iacovou M, Kellow NJ. Effect of nutritional interventions on the psychological symptoms of premenstrual syndrome in women of reproductive age: a systematic review of randomized controlled trials. Nutr Rev. 2025;83(2):280\u0026ndash;306. https://doi.org/10.1093/nutrit/nuae043. \u003c/li\u003e\n\u003cli\u003eYi SJ, Kim M, Park I. Factors influencing PMS among female college students. BMC Womens Health. 2023;23:592. https://doi.org/10.1186/s12905-023-02752-y. \u003c/li\u003e\n\u003cli\u003eBertone-Johnson ER, Hankinson SE, Willett WC, Johnson SR, Manson JE. Adiposity and PMS. J Womens Health (Larchmt). 2010;19(11):1955\u0026ndash;62. https://doi.org/10.1089/jwh.2010.2128. \u003c/li\u003e\n\u003cli\u003eCheng SH, Shih CC, Yang YK, Chen KT, Chang YH, Yang YC. Factors associated with PMS in new university students. \u003c/li\u003e\n\u003cli\u003eJu H, Jones M, Mishra GD. U-shaped relationship between BMI and dysmenorrhea. PLoS One. 2015;10(7):e0134187. https://doi.org/10.1371/journal.pone.0134187.\u003c/li\u003e\n\u003cli\u003eScovronec A, Provencher A, Iceta S, Pelletier M, Leblanc V, Nadeau M, et al. Neck circumference vs insulin resistance: a systematic review and meta-analysis of the association between neck circumference and markers of insulin resistance. Obes Res Clin Pract. 2022;16(4):307\u0026ndash;13. https://doi.org/10.1016/j.orcp.2022.07.005.\u003c/li\u003e\n\u003cli\u003eFinch JE, Xu Z, Baker JH. Understanding comorbidity between eating disorder and premenstrual symptoms using network analysis. Appetite. 2023;181:106410. https://doi.org/10.1016/j.appet.2022.106410. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Eating attitudes, Mediterranean diet, Obesity, Premenstrual syndrome, Visceral adiposity","lastPublishedDoi":"10.21203/rs.3.rs-8246542/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8246542/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis study aimed to examine the relationship between the presence of Premenstrual Syndrome (PMS), Visceral Adiposity Index (VAI), eating attitudes, and daily energy and nutrient intake levels in women of reproductive age.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis cross-sectional study was conducted with 252 volunteer women aged 18\u0026ndash;49 who admitted to the Healthy Nutrition Unit in Istanbul. Data were collected using a Personal Information Form, the Premenstrual Syndrome Scale (PMSS), the Eating Attitudes Test (EAT-26), and a retrospective 24-hour dietary recall. Anthropometric measurements (weight, height, waist circumference) were taken, body composition was determined via bioelectrical impedance analysis (BIA), and VAI was calculated. Mann-Whitney U, Chi-square, and Logistic Regression tests were used for statistical analyses.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePMS was detected in 57.9% of participants. The PMS group exhibited significantly higher body fat percentage (38.1% vs 37.1%, p\u0026thinsp;=\u0026thinsp;0.039) and smoking rates (28.8% vs 16.0%, p\u0026thinsp;=\u0026thinsp;0.018) compared to controls. While VAI levels did not differ, regression analysis revealed that high BMI, rather than PMS status, was the primary independent risk factor for eating disorders.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe findings reveal that increased body fat percentage and smoking are more determinant factors in PMS etiology than VAI, which is an indicator of visceral adiposity. The lack of significant difference in nutrient intake emphasizes the necessity of holistic lifestyle changes targeting smoking cessation and body fat reduction, rather than solely diet-focused approaches in PMS management.\u003c/p\u003e","manuscriptTitle":"The Interplay Between Premenstrual Syndrome, Eating Disorder Risk, and Adiposity Indicators: A Cross Sectional Study on Women","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 09:25:22","doi":"10.21203/rs.3.rs-8246542/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-27T13:50:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-22T19:41:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-20T11:21:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"114837746286478121243470512445755530458","date":"2026-01-20T07:46:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"114235851796383768735132229242905715481","date":"2026-01-17T09:10:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"174473740135349010230132385075378110617","date":"2025-12-15T05:30:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-12T20:00:22+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-07T18:05:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-02T23:40:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-02T23:39:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Women's Health","date":"2025-12-01T05:52:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fd620c77-f5aa-4729-8d26-c0d4e632986c","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T16:13:25+00:00","versionOfRecord":{"articleIdentity":"rs-8246542","link":"https://doi.org/10.1186/s12905-026-04412-3","journal":{"identity":"bmc-womens-health","isVorOnly":false,"title":"BMC Women's Health"},"publishedOn":"2026-04-02 16:00:01","publishedOnDateReadable":"April 2nd, 2026"},"versionCreatedAt":"2025-12-22 09:25:22","video":"","vorDoi":"10.1186/s12905-026-04412-3","vorDoiUrl":"https://doi.org/10.1186/s12905-026-04412-3","workflowStages":[]},"version":"v1","identity":"rs-8246542","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8246542","identity":"rs-8246542","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}