Associations of Classical and Novel Anthropometric Indices with Vitamin D Status in Thai Adults

Associations of Classical and Novel Anthropometric Indices with Vitamin D Status in Thai Adults | 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 Associations of Classical and Novel Anthropometric Indices with Vitamin D Status in Thai Adults Suphawadee Phababpha, Gulladawan Jan-on, Chalirmporn Atasilp, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8400364/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Vitamin D deficiency remains prevalent in tropical regions despite abundant sunlight and is linked to adverse skeletal and cardiometabolic outcomes. The relationship between vitamin D status and adiposity is inconsistent, partly due to the use of classical anthropometric indices that do not account for visceral fat. This study explores whether both classical and novel anthropometric indices reflecting visceral adiposity provide better insight into vitamin D variation in Thai adults. Methods: A cross-sectional study of 403 adults categorized participants into deficient (20 ng/mL) serum 25-hydroxyvitamin D [25(OH)D] groups. Classical anthropometry indices (waist circumference [WC], waist–hip ratio [WHR], body fat percentage [BF%]) and novel indices (body roundness index [BRI], conicity index [CI], weight-adjusted waist index [WWI], abdominal volume index [AVI]) were assessed. Multivariable linear and logistic regression models evaluated independent associations between adiposity markers and serum 25(OH)D after adjustment for age, sex, lifestyle, supplement use, and sun exposure time. Results: Although the crude comparisons mainly reflected differences in age and sex distribution across groups, adjusted analyses showed a different pattern of associations. Participants with normal vitamin D levels showed higher central adiposity indices (WHR, BRI, CI, and WWI) than those with deficiency. After adjustment, WC demonstrated a positive association with 25(OH)D (β = 3.53, p = 0.01), whereas AVI showed an inverse association (β = −3.90, p = 0.01), suggesting that indices capturing visceral fat volume rather than simple girth better reflect the adiposity–vitamin D relationship. BF% consistently predicted vitamin D deficiency across all logistic regression models (OR range = 2.18–2.28, all p < 0.05). Conclusions: AVI appeared to be the most informative indicator of visceral adiposity associated with lower vitamin D levels. Measures that more accurately capture fat distribution, rather than overall body size, may better identify individuals at risk of vitamin D deficiency, especially in tropical settings where sun-exposure behavior may exceed adiposity alone. vitamin D anthropometry visceral adiposity abdominal volume index body composition INTRODUCTION Vitamin D deficiency remains a notable public health concern worldwide, affecting populations in both developed and developing regions, including areas with abundant sunlight [ 1 ]. Vitamin D insufficiency has been defined by the Institute of Medicine as a serum 25-hydroxyvitamin D [25(OH)D] concentration below 20 ng/mL. Serum 25(OH)D levels below this threshold have been associated with increased fracture risk, accelerated bone loss, higher mortality, immune dysfunction, and a greater prevalence of multiple chronic diseases [ 2 , 3 ]. Adipose tissue dysfunction in obesity further contributes to metabolic and inflammatory disturbances, and vitamin D appears to play regulatory roles across these pathways. The metabolically active fat depot is most strongly associated with vitamin D deficiency. Vitamin D influences adipokine secretion, lipid handling, adipogenesis, thermogenesis, oxidative stress balance, and inflammatory signaling through gene-level mechanisms [ 4 ]. Several mechanisms have been proposed to explain lower vitamin D levels in individuals with higher adiposity, including sequestration within adipose stores, impaired mobilization, inflammation-induced alterations in vitamin D metabolism, increased metabolic requirements, and disrupted adipokine signaling such as leptin-mediated energy regulation [ 5 , 6 ]. Classical anthropometric indices such as body mass index (BMI), waist circumference (WC), and waist-to-hip ratio (WHR) are widely used but provide limited insight into visceral adiposity [ 7 , 8 ]. In recent years, novel anthropometric indices have been developed to capture various dimensions of adiposity that classical measures fail to identify. These indices integrate multiple body measurements to provide a more comprehensive assessment of body composition and fat distribution. These include a body shape index (ABSI), which reflects visceral adiposity; the body adiposity index (BAI), which estimates total adiposity; the body roundness index (BRI), which predicts total and regional fat; and the abdominal volume index (AVI), which more sensitively captures abdominal and visceral fat. The weight-adjusted waist index (WWI) has been associated with cardiometabolic morbidity and mortality, while the conicity index (CI) estimates central obesity and fat distribution [ 9 ]. A more detailed evaluation of which anthropometric aspects are most helpful for explaining variance in vitamin D status is possible when many indices are evaluated within a single group. Although anthropometric indices have shown varying predictive ability for vitamin D adequacy across different populations, several studies have shown inverse associations between vitamin D status and adiposity, with obesity consistently linked to a higher prevalence of vitamin D deficiency [ 10 – 13 ]. The prevalence of vitamin D deficiency increases with greater adiposity across most anthropometric indices, with notable exceptions such as ABSI in women and BMI in men [ 10 ]. In addition, sex-specific analyses have reported inverse associations between serum 25(OH)D and WC, waist-to-height ratio (WHtR), CI, fat mass index (FMI), and body fat percentage (BF%) among women [ 14 ]. Moreover, novel anthropometric indices such as BRI and ABSI exhibit stronger inverse associations with serum 25(OH)D than classical measures, particularly among men [ 15 ]. However, not all studies report consistent findings. Some studies have found null relationships between vitamin D status and BMI, WC, or BF%. While central adiposity indices, including WC and WHR, are correlated with vitamin D status, other measures—such as body weight, hip circumference, BMI, BF%, and FMI—may not be [ 7 , 16 ]. It consequently remains unclear whether these variations represent true biological heterogeneity or methodological limitations related to adiposity assessment, behavioral confounders such as sun exposure, or the inability of classical indices to adequately capture visceral fat distribution. In tropical countries, vitamin D deficiency remains common despite abundant year-round sunlight. This apparent paradox underscores the influence of behavioral and environmental factors, including predominantly indoor urban lifestyles, occupational patterns that limit daytime sun exposure, and widespread sun-avoidance practices for cosmetic and thermal comfort reasons. This gap limits understanding of whether variation in 25(OH)D among tropical adults is driven primarily by lifestyle-related UVB exposure or by differences in fat distribution. Accordingly, this study examined associations between serum 25(OH)D and both classical and novel adiposity indices in Thai adults, while also considering age, sex, sunlight exposure, and supplement use. The findings may clarify which adiposity indicators are most informative for identifying individuals at risk for vitamin D insufficiency in tropical environments. Understanding these relationships is important for developing targeted prevention strategies, especially in settings where low-cost, practical screening tools are needed. Materials and Methods 1. Study Population Thai adults aged ≥ 20 years who attended health check-ups at the Health Care Service Center, Faculty of Allied Health Sciences, Thammasat University, Thailand, between March and December 2024 were invited to participate. Participants were excluded if they (1) were missing covariate data required for adjustment (e.g., lifestyle factors, supplement intake); (2) had a body-weight change exceeding ± 3% within the 3 months before enrollment; or (3) had medical conditions known to affect vitamin D synthesis, absorption, or metabolism (e.g., chronic liver disease, chronic kidney disease, malabsorption syndromes, inflammatory bowel disease, pancreatitis, or cholestatic disorders). Participants were then classified into three vitamin D status categories (deficient, insufficient, and normal 25(OH)D level). All participants completed self-administered questionnaires collecting demographic information (age, sex, and chronic diseases), lifestyle factors (smoking status [never, former, current], alcohol consumption [never, former, current], physical activity [yes/no], and daily sunlight exposure ≥ 30 min/day [yes/no]), and supplement use (milk and dairy products [yes/no], vitamin D and calcium supplements [yes/no], and fish oil or omega-3 supplements [yes/no]). The study protocol was reviewed and approved by the Human Research Ethics Committee of Thammasat University (Medicine) (project number MTU-EC-00-0-073/67). Written informed consent was obtained from all participants. 2. Anthropometric and Body Composition Measurements Anthropometric measurements were obtained with participants wearing light clothing and no shoes. Height and body weight were assessed using a calibrated digital standing scale (accuracy: ±0.1 cm and ± 0.1 kg). BMI was calculated as body weight (kg) divided by height squared (m²), and obesity was defined as BMI ≥ 25.0 kg/m² following the Asia–Pacific criteria [ 17 ]. WC was measured at the midpoint between the lower rib and the iliac crest using a non-stretchable tape, with participants standing upright and breathing normally. Hip circumference (HC) was measured at the widest part of the hips and buttocks. WHR was calculated as WC (cm) divided by HC (cm), and WHtR as WC (cm) divided by height (cm). Abdominal obesity was defined as WC ≥ 90 cm for men and ≥ 80 cm for women, and WHR ≥ 0.90 for men and ≥ 0.80 for women, according to Asian cutoffs. A WHtR ≥ 0.5 indicated central adiposity [ 18 – 19 ]. Body composition was assessed using a bioelectrical impedance analyzer (Tanita BC-541, Tokyo, Japan). Participants stood barefoot on the device and removed metal accessories (e.g., belts, watches, jewelry) to avoid interference with the electrical signal. The analyzer transmits a low-level electrical current through the body and estimates body compartments based on the resistance detected. This method provided %BF. High body fat was defined as %BF ≥ 25% in men and ≥ 35% in women. A fat mass index (FMI) was calculated as fat mass (kg) divided by height squared (m²), and high FMI as ≥ 6.6 kg/m² [ 20 ]. Novel anthropometric indices were also calculated using established formulas, including ABSI, AVI, BAI, BRI, CI, and WWI. Cutoff values for elevated adiposity were based on published thresholds: ABSI ≥ 0.0866, AVI ≥ 23.89, BAI ≥ 36.40, BRI ≥ 6.96, CI ≥ 1.25 (for men), CI ≥ 1.18 (for women), and WWI ≥ 11.92 [ 9 , 18 , 21 ]. Novel anthropometric index formulas $$\:\text{A}\text{B}\text{S}\text{I}=\frac{\text{W}\text{C}\:\left(\text{m}\right)}{\text{B}\text{M}\text{I}{\:}^{2∕3}\cdot\:{\:\text{H}\text{e}\text{i}\text{g}\text{h}\text{t}\:\left(\text{m}\right)}^{1∕2}}$$ $$\:\text{A}\text{V}\text{I}=\frac{{2\text{W}\text{C}}^{2\:\:\:\:}\left(\text{c}\text{m}\right)+\:{0.7\times\:\left(\text{W}\text{C}-\text{H}\text{C}\right)}^{2\:}\left(\text{c}\text{m}\right)}{\text{1,000}}$$ $$\:\text{B}\text{A}\text{I}\:=\left(\frac{\text{H}\text{C}\:\left(\text{c}\text{m}\right)}{{\text{H}\text{e}\text{i}\text{g}\text{h}\text{t}\:\left(\text{m}\right)}^{1.5\:}}\right)-18$$ $$\:\text{B}\text{R}\text{I}\:=364.2\:-365.5\:\times\:\sqrt{1-{\left(\frac{\text{W}\text{C}\:\left(\text{m}\right)}{2{\pi\:}\:\times\:\:\text{H}\text{e}\text{i}\text{g}\text{h}\text{t}\:\left(\text{m}\right)}\right)}^{2}}$$ $$\:\text{C}\text{I}=\frac{\text{W}\text{C}\:\left(\text{m}\right)}{0.109\sqrt[\:]{\frac{\text{B}\text{W}\:\left(\text{k}\text{g}\right)}{\text{H}\text{e}\text{i}\text{g}\text{h}\text{t}\:\left(\text{m}\right)}}}$$ $$\:\text{W}\text{W}\text{I}=\frac{\text{W}\text{C}\:\left(\text{c}\text{m}\right)}{\sqrt{\text{B}\text{W}\:\left(\text{k}\text{g}\right)}}$$ 2. Blood sample collection and measurements Blood samples were collected in the morning (between 8:00 and 10:00 AM) after an overnight fast of at least 8 hours at the Health Care Service Center, Faculty of Allied Health Sciences, Thammasat University. Fasting blood sugar and lipid profiles were processed using routine automated analyzers. Serum 25(OH)D was measured with a chemiluminescent immunoassay (Liaison XL, DiaSorin Inc., Stillwater, MN, USA). For interpretation, 25(OH)D levels were classified as > 20 ng/mL (normal), 12–20 ng/mL (insufficient), and < 12 ng/mL (deficient), following thresholds commonly applied in previous research [ 22 , 23 ]. 3. Statistical analysis Data were summarized as mean ± standard deviation (SD) for approximately normally distributed continuous variables and as median with interquartile range (IQR) for skewed variables. Categorical variables were presented as counts and percentages. The distribution of continuous variables was assessed using the Kolmogorov–Smirnov and Shapiro–Wilk tests. The differences in baseline characteristics and anthropometric variables among the serum 25(OH)D status categories (deficient, insufficient, and normal) were assessed using one-way analysis of variance (ANOVA) for normally distributed variables or the Kruskal–Wallis test for those that were not normally distributed. Categorical variables were compared using chi-square tests or Fisher’s exact tests, as appropriate. Pearson correlation coefficients were used to evaluate the relationships between serum 25(OH)D levels and anthropometric or body composition indices. Multiple linear regression analyses were performed to assess the independent associations between each anthropometric index and serum levels of 25(OH)D. In these models, serum 25(OH)D level (continuous) was specified as the dependent variable, and individual anthropometric indices were entered as predictors. Age, sex, smoking status, alcohol intake, supplement use, and physical activity were included as covariates. Binary logistic regression models were fitted with low vitamin D status (< 20 ng/mL) as the dependent variable and higher anthropometric and body composition measures as independent variables. Model 1 was unadjusted. Model 2 was adjusted for lifestyle factors (smoking status, alcohol consumption, and physical activity) and supplement intake (milk and dairy products, vitamin D and calcium supplements, and fish oil/omega-3 supplements). Model 3 was additionally adjusted for sunlight exposure time. Results were expressed as odds ratio (OR) with 95% confidence interval (CI). All statistical analyses were performed using SPSS, and a p-value < 0.05 was considered statistically significant. Results This study included 403 participants who were categorized into three groups based on serum 25(OH)D levels: deficient (n = 53), insufficient (n = 208), and normal (n = 142). Participants with normal 25(OH)D levels were significantly older (median: 50.00 years, IQR: 39.00–58.00) than those with insufficient (37.00 years, IQR: 28.00–47.00) and deficient levels (30.00 years, IQR: 24.00–41.00) (p < 0.05). A greater proportion of participants aged ≥ 65 years (11.3%) and males (37.3%) were observed in the normal group compared to the other two groups (p < 0.05). No significant differences were found in body weight, height, systolic BP, or diastolic BP across the groups (Table 1 ). Table 1 Baseline characteristic of the study participants according to 25(OH)D status Characteristics 25(OH)D status Deficient ( 20 ng/mL) n = 53 n = 208 n = 142 Age (years) 30.00 (24.00–41.00) 37.00 (28.00–47.00) 50.00 (39.00–58.00) #,$ Age ≥ 65, n (%) 0 (0) 5 (2.4) 16 (11.3) #,$ Sex (Male/Female) 9/44 (17.0/83.0) 47/161 (22.6/77.4) 53/89 (37.3/62.7) #,$ Body weight (kg) 65.10 (55.60–80.90) 66.70 (56.40–77.00) 64.80 (56.88–73.83) Height (m) 1.60 (1.56–1.67) 1.60 (1.56–1.67) 1.59 (1.53–1.67) Systolic BP (mmHg) 116.00 (105.00-121.00) 114.00 (105.00-127.00) 117.00 (109.75–125.00) Diastolic BP (mmHg) 69.00 (61.00–75.00) 67.00 (61.00–74.00) 67.00 (60.00–74.00) Blood chemistry FBS (mg/dL) 88.00 (84.00–94.00) 88.00 (83.00–96.00) 90.00 (85.00–98.00) $ TC (mg/dL) 212.00 (180.00-232.00) 203.00 (181.00-228.00) 194.50 (167.50–221.00) #,$ TG (mg/dL) 83.00 (64.00-119.00) 92.00 (65.00-145.00) 94.00 (71.00-148.25) HDL-C (mg/dL) 60.00 (49.00–72.00) 58.00 (50.00–67.00) 59.00 (49.00-70.25) LDL-C (mg/dL) 128.17 ± 35.51 124.97 ± 32.72 114.27 ± 33.54 #,$ 25 (OH)D (ng/mL) 10.00 (9.00–11.00) 16.00 (14.00–17.00) 23.00 (21.75-27.00) Chronic diseases Diabetes, n (%) 1 (1.9) 8 (3.8) 8 (5.6) Hypertension, n (%) 4 (7.5) 19 (9.1) 33 (23.2) #,$ Dyslipidemia, n (%) 5 (9.4) 11 (5.3) 18 (12.7) $ CVD, n (%) 0 (0) 0 (0) 3 (2.1) $ Lifestyles Current smoking, n (%) 1 (1.9) 10 (4.8) 12 (8.5) Alcohol drinking, n (%) 23 (44.2) 82 (39.6) 70 (49.6) Regular leisure-time physical activity, n (%) 8 (15.4) 37 (17.9) 17 (12.1) Sunlight exposure time ≥ 30 min, n (%) 5 (9.6) 38 (18.4) 32 (22.5) # Milk and dairy product > 300 g/day, n (%) 1 (1.9) 12 (5.8) 12 (8.6) Vitamin D and calcium supplement, n (%) 44 (84.6) 155 (74.9) 97 (68.3) # Fish oil/omega-3 supplement, n (%) 41 (78.8) 152 (73.4) 97 (68.3) Notes: Data are presented as mean ± standard deviation (SD) or median [interquartile range (IQR)] for continuous variables and as count (percentage) for categorical variables. Between-group comparisons were performed using one-way ANOVA for normally distributed variables or the Kruskal–Wallis test for non-normally distributed variables. Chi-square tests were used for categorical variables. *: p < 0.05 for the deficient group vs. the insufficient group #: p < 0.05 for the deficient group vs. the normal group $: p < 0.05 for the insufficient group vs. the normal group Abbreviations: BP, blood pressure; FBS, fasting blood sugar; TC, total cholesterol; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; CVD, cardiovascular diseases . In terms of blood chemistry, FBS was significantly higher in the normal group (median: 90.00 mg/dL, IQR: 85.00–98.00) than in the deficient and insufficient groups (both 88.00 mg/dL; p < 0.05). On the other hand, TC (median: 194.50 mg/dL, IQR: 167.50–221.00) and LDL-C (114.27 ± 33.54 mg/dL) were significantly lower in the normal group compared to the deficient and insufficient groups (p < 0.05). No significant differences were observed in TG and HDL-C levels (Table 1 ). The prevalence of hypertension (23.2%) and dyslipidemia (12.7%) was highest in the normal 25(OH)D group (p < 0.05). Regarding lifestyle characteristics, daily sunlight exposure for more than 30 minutes was most prevalent in the normal group (22.5%), followed by the insufficient (18.4%) and deficient groups (9.6%) (p < 0.05). The use of vitamin D and calcium supplements was highest in the deficient group (84.6%) and declined with increasing 25(OH)D levels (p < 0.05). A similar decreasing trend in usage was observed for fish oil or omega-3 supplements (Table 1 ). In terms of anthropometric measures, participants with normal 25(OH)D levels had significantly higher WHR (0.87 ± 0.07), ABSI (0.08 [0.08–0.08]), BRI (4.17 [3.25–5.36]), CI (1.25 [1.20–1.30]), and WWI (10.76 [10.29–11.21]) compared to those in the deficient and/or insufficient groups (all p < 0.05). Additionally, the proportion of individuals with high WHR ( ≥ 0.90 for men, ≥ 0.80 for women) was significantly greater in the normal 25(OH)D group (56.3%) than in the deficient (39.6%) and insufficient groups (46.6%) (p < 0.05). No significant differences were found in BF%, FMI, or FFMI across the groups (Table 2 ). Table 2 Classical and novel anthropometric and body composition measures according to 25(OH)D status Variables 25(OH)D status Deficient ( 20 ng/mL) n = 53 n = 208 n = 142 Classical anthropometric measures BMI (kg/m 2 ) 25.04 (22.44–27.70) 25.55 (22.24–29.44) 25.17 (22.45–28.63) High BMI ( ≥ 25), n (%) 26 (49.1) 118 (56.7) 71 (50.0) WC (cm) 83.84 ± 13.33 86.00 ± 12.35 86.78 ± 11.56 High WC ≥ 0.90 (M), ≥ 0.80 (W), n (%) 27 (50.9) 129 (62.0) 85 (59.9) HC (cm) 100.00 (92.50-108.50) 101.00 (94.00-107.50) 98.75 (94.50-105.50) WHR 0.83 ± 0.06 0.85 ± 0.07 0.87 ± 0.07 #,$ High WHR ≥ 0.90 (M), ≥ 0.80 (W), n (%) 21 (39.6) 97 (46.6) 80 (56.3) # WHtR 0.52 (0.47–0.57) 0.53 (0.48–0.59) 0.54 (0.49–0.60) High WHtR ≥ 0.5, n (%) 31 (58.5) 140 (67.3) 102 (71.8) Novel anthropometric measures ABSI 0.08 (0.07–0.08) 0.08 (0.07–0.08) 0.08 (0.08–0.08) #,$ High ABSI ≥ 0.0866, n (%) 2 (3.8) 7 (3.4) 7 (4.9) AVI 13.82 (11.29–17.79) 15.24 (12.06–18.15) 15.54 (12.92–17.88) High AVI ≥ 23.89, n (%) 3 (5.7) 7 (3.4) 2 (1.4) BAI 30.45 (27.66–34.10) 31.01 (27.96–34.62) 31.48 (27.89–34.85) High BAI ≥ 36.40, n (%) 11 (20.8) 40 (19.2) 27 (19.0) BRI 3.74 (2.90–4.71) 3.97 (2.95–5.16) 4.17 (3.25–5.36) # High BRI ≥ 6.96, n (%) 4 (7.5) 12 (5.8) 7 (4.9) CI 1.20 (1.14–1.26) 1.23 (1.16–1.29) 1.25 (1.20–1.30) # High CI ≥ 1.25 (M)¸ ≥ 1.18 (W), n (%) 30 (56.6) 136 (65.4) 100 (70.4) WWI 10.28 (9.87–10.77) 10.55 (9.95–10.99) 10.76 (10.29–11.21) #,$ High WWI ≥ 11.92, n (%) 0 (0) 8 (3.8) 8 (5.6) Body composition measures BF (%) 31.10 (27.40–37.20) 32.30 (28.10–36.60) 31.20 (26.88–36.73) High BF ≥ 25% (M), ≥ 35% (W), n (%) 22 (41.5) 94 (45.2) 76 (53.5) FMI (kg/m 2 ) 7.93 (5.98–10.92) 8.29 (6.26–10.07) 7.98 (6.18–10.03) High FMI ≥ 6.6 kg/m 2 , n (%) 36 (67.9) 146 (70.2) 95 (66.9) Notes: Data are presented as mean ± standard deviation (SD) or median [interquartile range (IQR)] for continuous variables and as count (percentage) for categorical variables. Between-group comparisons were performed using one-way ANOVA for normally distributed variables or the Kruskal–Wallis test for non-normally distributed variables. Chi-square tests were used for categorical variables. *: p < 0.05 for the deficient group vs. the insufficient group #: p < 0.05 for the deficient group vs. the normal group $: p < 0.05 for the insufficient group vs. the normal group Abbreviations: M, men; W, women; BMI, body mass index; WC, waist circumference; HC, hip circumference; WHR, waist-to-hip ratio; WHtR, waist-to-height ratio; ABSI , a body shape index ; AVI, abdominal volume index; BAI, body adiposity index; BRI, body roundness index; CI, conicity index; WWI, weight-adjusted waist index; BF, body fat; FMI, fat mass index. Correlation analysis showed that several anthropometric measures, including WC, WHR, WHtR, ABSI, AVI, BRI, CI, and WWI, were significantly associated with serum 25(OH)D levels (p < 0.05). However, in multiple linear regression analysis adjusted for potential confounders, only WC (B = 3.53, p = 0.01) and AVI (B = − 3.90, p = 0.01) remained significantly associated (Table 3 ). Table 3 The associations between anthropometric and body composition measures and serum 25(OH)D levels. Correlation analysis Multiple linear regression analysis R p-value B (Unstandardized coefficient) β (Standardized coefficient) p-value Classical anthropometric measures BMI (kg/m 2 ) 0.02 0.68 0.80 0.63 0.41 WC (cm) 0.11 0.02* 3.53 6.82 0.01* HC (cm) -0.03 0.56 -0.95 -1.48 0.23 WHR 0.21 < 0.01* -84.01 -0.90 0.24 WHtR 0.13 0.01* -134.20 -1.61 0.20 Novel anthropometric measures ABSI 0.20 < 0.01* 61.86 0.06 0.58 AVI 0.11 0.03* -3.90 -2.59 0.01* BAI 0.02 0.75 0.34 0.31 0.74 BRI 0.13 0.01* 4.06 1.01 0.33 CI 0.20 < 0.01* -152.37 -2.20 0.07 WWI 0.21 < 0.01* 10.45 1.33 0.28 Body composition measures BF (%) -0.03 0.56 -0.18 -0.18 0.60 FMI -0.01 0.91 -2.02 -0.96 0.15 Notes: Pearson correlation (r) assessed the relationships between anthropometric parameters and serum 25(OH)D levels. Multiple regression analysis estimated the independent effects (β) of each anthropometric parameter on 25(OH)D, adjusting for age, sex, smoking status, alcohol consumption, supplement intake, sunlight exposure time and physical activity. Statistical significance is indicated as follows: *p < 0.05. Values are presented as coefficients with significance marks where applicable. Abbreviations: BMI, body mass index; WC, waist circumference; HC, hip circumference; WHR, waist-to-hip ratio; WHtR, waist-to-height ratio; ABSI , a body shape index ; AVI, abdominal volume index; BAI, body adiposity index; BRI, body roundness index; CI, conicity index; WWI, weight-adjusted waist index; BF, body fat; FMI, fat mass index. In multivariable-adjusted binary logistic regression models, high BF% was significantly associated with increased odds of having deficient or insufficient 25(OH)D levels (model 1: OR = 2.20, 95% CI: 1.16–4.19, p = 0.02; model 2: OR = 2.28, 95% CI: 1.19–4.36, p = 0.01; model 3: OR = 2.18, 95% CI: 1.13–4.18, p = 0.02). No other anthropometric or body composition indices showed consistent associations across all models (Table 4 ). Table 4 Binary logistic regression analysis of anthropometric and body composition measures with obesity and abnormal 25(OH)D status. Variables Model 1 Model 2 Model 3 OR (CI%95) p-value OR (CI%95) p-value OR (CI%95) p-value High BMI ( ≥ 25), n (%) 0.85 (0.54–1.34) 0.49 0.95 (0.60–1.52) 0.84 0.95 (0.60–1.50) 0.82 High WC ≥ 0.90 (M), ≥ 0.80 (W), n (%) 0.64 (0.30–1.35) 0.24 0.68 (0.32–1.47) 0.33 0.68 (0.31–1.45) 0.31 High WHR ≥ 0.90 (M), ≥ 0.85 (W), n (%) 1.61 (0.90–2.88) 0.11 1.57 (0.86–2.85) 0.14 1.53 (0.84–2.78) 0.17 High WHtR ≥ 0.5, n (%) 1.82 (0.75–4.39) 0.18 1.33 (0.53–3.33) 0.54 1.28 (0.51–3.21) 0.60 High ABSI ≥ 0.0866, n (%) 1.04 (0.32–3.34) 0.95 1.28 (0.39–4.15) 0.68 1.25 (0.38–4.10) 0.71 High AVI ≥ 23.89, n (%) 0.23 (0.03–1.50) 0.12 0.25 (0.04–1.74) 0.16 0.22 (0.03–1.55) 0.13 High BAI ≥ 36.40, n (%) 0.93 (0.47–1.85) 0.83 0.84 (0.42–1.70) 0.63 0.88 (0.43–1.78) 0.71 High BRI ≥ 6.96, n (%) 0.73 (0.19–2.84) 0.65 0.80 (0.20–3.25) 0.75 0.87 (0.21–3.55) 0.84 High CI ≥ 1.25 (M)¸ ≥ 1.18 (W), n (%) 1.16 (0.62–2.18) 0.65 1.19 (0.63–2.25) 0.60 1.26 (0.66–2.40) 0.48 High WWI ≥ 11.92, n (%) 2.95 (0.78–11.14) 0.11 2.52 (0.65–9.84) 0.18 2.25 (0.57–8.93) 0.25 High BF ≥ 25% (M), ≥ 35% (W), n (%) 2.20 (1.16–4.19) 0.02* 2.28 (1.19–4.36) 0.01* 2.18 (1.13–4.18) 0.02* High FMI ≥ 6.6 kg/m 2 , n (%) 0.57 (0.28–1.16) 0.12 0.58 (0.28–1.21) 0.15 0.63 (0.30–1.32) 0.22 Note: Data are presented as odds ratios (OR) and 95% confidence intervals (CI) A low serum 25(OH)D concentration was defined as < 20 ng/mL. Data were obtained using binary logistic regression analysis. Statistical significance is indicated as follows: *p < 0.05. Values are presented as coefficients with significance marks where applicable. Model 1: Unadjusted Model 2: Adjusted for lifestyle factors (smoking status, alcohol consumption, physical activity), and supplement intake (milk and dairy products, vitamin D and calcium supplements, fish oil/omega 3 supplements) Model 3: Adjusted for variables in model 2 plus sunlight exposure time. Abbreviations: BMI, body mass index; WC, waist circumference; WHR, waist-to-hip ratio; WHtR, waist-to-height ratio; ABSI , a body shape index ; AVI, abdominal volume index; BAI, body adiposity index; BRI, body roundness index; CI, conicity index; WWI, weight-adjusted waist index; BF, body fat; FMI, fat mass index. Discussion This study examined how vitamin D status relates to anthropometry using classical and novel anthropometric and body composition indices in Thai adults. The overall pattern suggests that adiposity—particularly visceral fat—may play an important role, although lifestyle factors remain influential in this tropical setting. Aging is associated with reduced calcium absorption and a decline in epidermal 7-dehydrocholesterol concentration, leading to a diminished capacity for cutaneous vitamin D₃ synthesis in response to UVB exposure [ 24 , 25 ]. Despite these age-related physiological changes, older adults in this study exhibited higher or adequate serum 25(OH)D levels. This apparent paradox may be explained by behavioral factors, as older adults may spend more time outdoors during periods of peak UVB availability, whereas younger adults often experience lower effective UVB exposure due to long indoor working hours, clothing coverage, sunscreen use, reliance on air-conditioned environments, and habitual sun avoidance motivated by thermal discomfort or cosmetic concerns. These findings suggest that lifestyle-related sun exposure may partially offset age-related declines in cutaneous vitamin D synthesis, contributing to higher vitamin D levels among older adults in this tropical population. Sex differences also contributed to variation in vitamin D status, with a higher proportion of males observed in the normal 25(OH)D group. Women often exhibit lower vitamin D levels, partly because greater total fat mass may increase sequestration of fat-soluble vitamins [ 26 ]. In contrast, men may show different patterns of vitamin D distribution and metabolism related to sex-specific body composition and age-related hormonal changes. Declining testosterone levels with aging are associated with shifts toward increased visceral adiposity, which may influence vitamin D storage and mobilization [ 27 ]. However, previous studies have reported minimal sex differences or even higher vitamin D levels among older women, suggesting that lifestyle and behavioral factors may modify biological sex effects [ 28 ]. In this study, participants with normal serum 25(OH)D levels exhibited lower total cholesterol and LDL-C concentrations but a higher prevalence of diagnosed hypertension and dyslipidemia. Conversely, prior studies have reported that low vitamin D status is associated with less favorable lipid profiles, including higher LDL-C and triglyceride levels and lower HDL-C concentrations [ 29 ]. Therefore, the interpretation of lipid differences in the present cohort requires caution. One possible explanation is the influence of clinical management. Individuals diagnosed with hypertension or dyslipidemia are more likely to undergo regular health evaluations and to receive pharmacological treatments, which may lower circulating LDL-C and total cholesterol independently of vitamin D status. In addition, chronic conditions such as hypertension intersect mechanistically with vitamin D pathways through effects on the renin–angiotensin–aldosterone system and endothelial function [ 30 ]. Because information on lipid-lowering and antihypertensive medication use was not available in this study, the extent to which treatment effects contributed to the observed lipid patterns cannot be determined. Supplement use was highest for vitamin D and calcium products among participants with deficiency, likely reflecting a compensatory response rather than effective correction. Dietary sources contribute minimally to circulating vitamin D, and excess adiposity may further reduce bioavailability through sequestration, impaired mobilization, inflammation, and increased physiological demand [ 31 , 32 ], thereby limiting the effectiveness of supplementation alone. Fish oil and omega-3 supplements contain negligible vitamin D unless fortified, consistent with their lack of association with serum 25(OH)D levels. In addition, milk and dairy intake did not differ across vitamin D status groups, likely reflecting generally low consumption in this population, as commonly reported in many Asian populations due to the high prevalence of lactose intolerance, which may limit dietary vitamin D and calcium intake overall. Participants with normal 25(OH)D levels were more likely to report sunlight exposure exceeding 30 minutes per day; because UVB-mediated cutaneous synthesis accounts for most circulating vitamin D and varies with skin pigmentation, body surface area exposed, geography, and season [ 33 ], behavioral and seasonal differences in sun exposure likely contribute to the persistence of vitamin D deficiency even in tropical regions. Numerous studies have reported inverse associations between vitamin D status and adiposity, with obesity consistently linked to a higher prevalence of vitamin D deficiency [ 10 – 13 ]. However, other investigations have reported null associations with classical indices such as BMI, WC, or BF% [ 34 , 35 ]. Novel anthropometric indices may therefore offer additional insight into central and visceral adiposity. ABSI has been shown to be a better predictor of premature mortality than BMI or WC alone, while the BRI reflects body shape and visceral adipose tissue [ 8 ]. Several studies have demonstrated that BRI and ABSI exhibit stronger inverse associations with serum 25(OH)D than classical anthropometric measures, particularly among men [ 15 ]. Vitamin D deficiency has been shown to increase with greater adiposity across most anthropometric indices, with notable exceptions such as ABSI in women and BMI in men [ 10 ]. While central adiposity indices, including WC and WHR, are often associated with vitamin D status, other measures—such as body weight, HC, BMI, BF%, and FMI—may not be [ 7 ]. Among women, additional inverse associations have been reported between serum 25(OH)D and WC, WHtR, CI, FMI, and BF% [ 14 ]. In the regression models, WC and AVI remained independent predictors of serum 25(OH)D. The positive association between WC and vitamin D is unlikely to reflect a direct biological relationship in isolation and may instead indicate context-specific patterns of occupational sun exposure. In some Thai settings, individuals with larger waist circumferences may be more likely to engage in outdoor work, resulting in greater UVB exposure; however, this interpretation remains hypothetical and may not be generalizable beyond this population. In contrast, AVI—a more sensitive indicator of visceral adiposity—was inversely associated with serum 25(OH)D. Previous studies have reported that AVI estimates overall abdominal volume and shows good correlation with abdominal fat measured by CT scans [ 8 ]. Visceral adiposity may reduce circulating vitamin D by limiting its release during lipolysis and by influencing inflammatory and adipokine pathways [ 10 , 36 ]. Notably, participants with normal serum 25(OH)D levels exhibited higher values of several central and body shape–related indices, including WHR, ABSI, BRI, CI, and WWI, whereas no significant differences were observed in body fat percentage, fat mass index, or fat-free mass index. These findings suggest that, in this population, body shape and fat distribution indices may not uniformly reflect metabolically active adiposity and may be influenced by age, sex, or lifestyle-related factors such as sunlight exposure. Previous studies have reported sex-specific associations between vitamin D status and adiposity indices, with inverse relationships observed for ABSI and BMI in women and for body weight and body fat percentage in men [ 36 ]. Although body fat percentage did not differ significantly across vitamin D status groups, it emerged as an independent predictor of deficient or insufficient 25(OH)D levels in multivariable models, suggesting that total adiposity may influence vitamin D risk even in the absence of between-group differences. High body fat percentage also emerged as a strong predictor of deficiency, consistent with evidence that greater total adiposity dilutes circulating vitamin D and promotes storage within fat depots [ 32 ]. Together, these results suggest that indices capturing visceral adiposity—particularly AVI—may offer more physiologically meaningful insight into vitamin D risk than traditional anthropometric measures. The strengths of this research include its large sample size, the evaluation of several anthropometric indices, and the investigation of vitamin D status in a tropical setting where environmental and behavioral factors have substantial influence. However, several limitations should be noted. The cross-sectional design limits causal inference and may affect the generalizability of the findings. The absence of imaging-based adiposity measures may have reduced the precision of adiposity assessment. Additionally, the lack of medication and dietary information, as well as unmeasured seasonal variation in UVB exposure, could have influenced the results. Conclusion Vitamin D status reflects a combined influence of adiposity, behavioral exposure, and demographic characteristics. Visceral adiposity showed a physiologically consistent inverse association with serum 25(OH)D. This pattern suggests that novel anthropometric indices, particularly AVI, may be useful for identifying individuals at heightened risk for vitamin D deficiency. Integrating visceral adiposity profiling with behavioral assessment may strengthen population screening approaches and inform more targeted strategies for vitamin D optimization in primary care and public health practice where sunlight is abundant but deficiency remains prevalent. Declarations Acknowledgements The authors would like to thank all study participants. Authors’ contributions SP, GJO, CA and TR conceived the study. SP, GJO, and CA managed data collection. TR analyzed and interpreted the data. SP and TR wrote the manuscript. SP, GJO, CA and TR critically edited the manuscript. All authors read and approved of the final manuscript. Funding This research was supported by Thammasat University Research Fund (TUFT 051/2568) Data availability The dataset generated during the current study is available from the corresponding author upon reasonable request. Ethics approval and consent to participate. The study was approved by the ethics committees of Thammasat University (Medicine) MTU-EC-00-0-073/67. The study was conducted in accordance with the ethical principles stated in the Declaration of Helsinki. All parents and guardians received printed information regarding the study and signed a letter of consent. All students were informed that they could decline survey completion on their own accord at any time. Competing for publication No applicable. Competing interests The authors declare no competing interests. References Palacios C, Gonzalez L. Is vitamin D deficiency a major global public health problem? J Steroid Biochem Mol Biol. 2014;144:138–145. Ross AC, Taylor CL, Yaktine AL, Del Valle HB, editors. Dietary Reference Intakes for Calcium and Vitamin D. Washington (DC): National Academies Press; 2011. Autier P, Boniol M, Pizot C, Mullie P. Vitamin D status and ill health: a systematic review. Lancet Diabetes Endocrinol. 2014;2:76–89. Abbas MA. Physiological functions of vitamin D in adipose tissue. J Steroid Biochem Mol Biol. 2017;165:369–381. Bouillon R, Carmeliet G, Lieben L, Watanabe M, Perino A, Auwerx J, et al. Vitamin D and energy homeostasis: of mice and men. Nat Rev Endocrinol. 2014;10:79–87. Vimaleswaran KS, Berry DJ, Lu C, Tikkanen E, Pilz S, Hiraki LT, et al. Causal relationship between obesity and vitamin D status: bi-directional Mendelian randomization analysis. PLoS Med. 2013;10:e1001383. Andreozzi F, Muscogiuri G, Gnessi L, et al. Vitamin D and visceral obesity: role of adipose tissue inflammation. Nutrients. 2016;8:371. Krakauer NY, Krakauer JC. A new body shape index predicts mortality hazard independently of body mass index. PLoS One. 2012;7:e39504. Thomas DM, Bredlau C, Bosy-Westphal A, Mueller M, Shen W, Gallagher D, et al. Relationships between body roundness index and body fat distribution. Sci Rep. 2025;15:86489. Pereira-Santos M, Costa PRF, Assis AMO, Santos CAST, Santos DB. Obesity and vitamin D deficiency: a systematic review and meta-analysis. Obes Rev. 2015;16:341–349. Al-Daghri NM, Al-Saleh Y, Aljohani N, Sulimani R, Al-Othman AM, Alfawaz H, et al. Vitamin D status correction and obesity: a randomized controlled trial. Nutrients. 2017;9:552. Pourshahidi LK. Vitamin D and obesity: current perspectives and future directions. Nutr Res Rev. 2015;28:1–14. Wortsman J, Matsuoka LY, Chen TC, Lu Z, Holick MF. Decreased bioavailability of vitamin D in obesity. Am J Clin Nutr. 2000;72:690–693. Oliveira LF, Santos JLF, Gigante DP. Association between vitamin D status and adiposity indicators in women. Nutrients. 2024;16:1267. Liu X, Baylin A, Levy PD. Anthropometric indices and serum vitamin D levels in adults. Clin Nutr. 2019;38:2596–2603. Jungert A, Neuhäuser-Berthold M. Associations of body composition with vitamin D status. Nutrients. 2018;10:1778. WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications. Lancet. 2004;363:157–163. Zaki ME, El-Bassyouni HT, El-Gammal M, Kamal S. Cut-off values of anthropometric indices in Asian populations. BMC Public Health. 2024;24:17846. Hsieh SD, Yoshinaga H, Muto T. Waist-to-height ratio as a simple screening tool for obesity. Int J Obes Relat Metab Disord. 2003;27:610–616. Kyle UG, Bosaeus I, De Lorenzo AD, Deurenberg P, Elia M, Gómez JM, et al. Bioelectrical impedance analysis—part II. Clin Nutr. 2004;23:1226–1243. Amato MC, Giordano C, Galia M, Criscimanna A, Vitabile S, Midiri M, et al. Visceral adiposity index. Nutr Metab Cardiovasc Dis. 2013;23:476–482. NIH Office of Dietary Supplements. Vitamin D fact sheet for health professionals. Bethesda (MD): NIH; 2022. Sempos CT, Heijboer AC, Bikle DD, Bollerslev J, Bouillon R, Brannon PM, et al. Vitamin D assays and vitamin D status. Am J Clin Nutr. 2021;113:695–706. Heaney RP. Aging, calcium absorption, and vitamin D requirements. J Nutr. 2004;134:3177S–3180S. Bikle DD. Vitamin D metabolism, mechanism of action, and clinical applications. Int J Mol Sci. 2021;22:8894. Maugeri A, Barchitta M, Agodi A. Sex differences in vitamin D metabolism. Br J Nutr. 2019;122:1313–1324. Kelly DM, Jones TH. Testosterone and obesity. Obes Rev. 2015;16:581–606. Hilger J, Friedel A, Herr R, Rausch T, Roos F, Wahl DA, et al. A systematic review of vitamin D status by sex and age. Nutr Metab Cardiovasc Dis. 2014;24:865–874. Wang H, Chen W, Li D, Yin X, Zhang X, Olsen N, et al. Vitamin D and lipid metabolism. Curr Med Res Opin. 2018;34:1657–1664. Forman JP, Williams JS, Fisher NDL. Plasma 25-hydroxyvitamin D and risk of hypertension. Hypertension. 2010;55:1283–1290. Holick MF. Vitamin D deficiency. N Engl J Med. 2007;357:266–281. Drincic AT, Armas LAG, Van Diest EE, Heaney RP. Volumetric dilution of vitamin D in obesity. Am J Clin Nutr. 2012;95:1311–1316. Webb AR, Kline L, Holick MF. Influence of season and latitude on vitamin D synthesis. J Clin Endocrinol Metab. 2010;95:213–218. Mai XM, Chen Y, Camargo CA Jr, Langhammer A. BMI, physical activity, and vitamin D status. Eur J Clin Nutr. 2012;66:120–125. Zhou M, Li Y, Li J, Liu H, Wang L. Vitamin D status and body composition. Nutrients. 2018;10:1093. Jungert A, Roth HJ, Neuhäuser-Berthold M. Sex-specific determinants of vitamin D status. Nutrients. 2018;10:1778. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Vitamin D insufficiency has been defined by the Institute of Medicine as a serum 25-hydroxyvitamin D [25(OH)D] concentration below 20 ng/mL. Serum 25(OH)D levels below this threshold have been associated with increased fracture risk, accelerated bone loss, higher mortality, immune dysfunction, and a greater prevalence of multiple chronic diseases [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdipose tissue dysfunction in obesity further contributes to metabolic and inflammatory disturbances, and vitamin D appears to play regulatory roles across these pathways. The metabolically active fat depot is most strongly associated with vitamin D deficiency. Vitamin D influences adipokine secretion, lipid handling, adipogenesis, thermogenesis, oxidative stress balance, and inflammatory signaling through gene-level mechanisms [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Several mechanisms have been proposed to explain lower vitamin D levels in individuals with higher adiposity, including sequestration within adipose stores, impaired mobilization, inflammation-induced alterations in vitamin D metabolism, increased metabolic requirements, and disrupted adipokine signaling such as leptin-mediated energy regulation [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eClassical anthropometric indices such as body mass index (BMI), waist circumference (WC), and waist-to-hip ratio (WHR) are widely used but provide limited insight into visceral adiposity [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In recent years, novel anthropometric indices have been developed to capture various dimensions of adiposity that classical measures fail to identify. These indices integrate multiple body measurements to provide a more comprehensive assessment of body composition and fat distribution. These include a body shape index (ABSI), which reflects visceral adiposity; the body adiposity index (BAI), which estimates total adiposity; the body roundness index (BRI), which predicts total and regional fat; and the abdominal volume index (AVI), which more sensitively captures abdominal and visceral fat. The weight-adjusted waist index (WWI) has been associated with cardiometabolic morbidity and mortality, while the conicity index (CI) estimates central obesity and fat distribution [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. A more detailed evaluation of which anthropometric aspects are most helpful for explaining variance in vitamin D status is possible when many indices are evaluated within a single group.\u003c/p\u003e \u003cp\u003eAlthough anthropometric indices have shown varying predictive ability for vitamin D adequacy across different populations, several studies have shown inverse associations between vitamin D status and adiposity, with obesity consistently linked to a higher prevalence of vitamin D deficiency [\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The prevalence of vitamin D deficiency increases with greater adiposity across most anthropometric indices, with notable exceptions such as ABSI in women and BMI in men [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In addition, sex-specific analyses have reported inverse associations between serum 25(OH)D and WC, waist-to-height ratio (WHtR), CI, fat mass index (FMI), and body fat percentage (BF%) among women [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Moreover, novel anthropometric indices such as BRI and ABSI exhibit stronger inverse associations with serum 25(OH)D than classical measures, particularly among men [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, not all studies report consistent findings. Some studies have found null relationships between vitamin D status and BMI, WC, or BF%. While central adiposity indices, including WC and WHR, are correlated with vitamin D status, other measures\u0026mdash;such as body weight, hip circumference, BMI, BF%, and FMI\u0026mdash;may not be [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. It consequently remains unclear whether these variations represent true biological heterogeneity or methodological limitations related to adiposity assessment, behavioral confounders such as sun exposure, or the inability of classical indices to adequately capture visceral fat distribution.\u003c/p\u003e \u003cp\u003eIn tropical countries, vitamin D deficiency remains common despite abundant year-round sunlight. This apparent paradox underscores the influence of behavioral and environmental factors, including predominantly indoor urban lifestyles, occupational patterns that limit daytime sun exposure, and widespread sun-avoidance practices for cosmetic and thermal comfort reasons. This gap limits understanding of whether variation in 25(OH)D among tropical adults is driven primarily by lifestyle-related UVB exposure or by differences in fat distribution.\u003c/p\u003e \u003cp\u003eAccordingly, this study examined associations between serum 25(OH)D and both classical and novel adiposity indices in Thai adults, while also considering age, sex, sunlight exposure, and supplement use. The findings may clarify which adiposity indicators are most informative for identifying individuals at risk for vitamin D insufficiency in tropical environments. Understanding these relationships is important for developing targeted prevention strategies, especially in settings where low-cost, practical screening tools are needed.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e \u003cb\u003e1. Study Population\u003c/b\u003e \u003c/p\u003e \u003cp\u003e Thai adults aged\u0026thinsp;\u0026ge;\u0026thinsp;20 years who attended health check-ups at the Health Care Service Center, Faculty of Allied Health Sciences, Thammasat University, Thailand, between March and December 2024 were invited to participate. Participants were excluded if they (1) were missing covariate data required for adjustment (e.g., lifestyle factors, supplement intake); (2) had a body-weight change exceeding\u0026thinsp;\u0026plusmn;\u0026thinsp;3% within the 3 months before enrollment; or (3) had medical conditions known to affect vitamin D synthesis, absorption, or metabolism (e.g., chronic liver disease, chronic kidney disease, malabsorption syndromes, inflammatory bowel disease, pancreatitis, or cholestatic disorders). Participants were then classified into three vitamin D status categories (deficient, insufficient, and normal 25(OH)D level).\u003c/p\u003e \u003cp\u003eAll participants completed self-administered questionnaires collecting demographic information (age, sex, and chronic diseases), lifestyle factors (smoking status [never, former, current], alcohol consumption [never, former, current], physical activity [yes/no], and daily sunlight exposure\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;30 min/day [yes/no]), and supplement use (milk and dairy products [yes/no], vitamin D and calcium supplements [yes/no], and fish oil or omega-3 supplements [yes/no]).\u003c/p\u003e \u003cp\u003e The study protocol was reviewed and approved by the Human Research Ethics Committee of Thammasat University (Medicine) (project number MTU-EC-00-0-073/67). Written informed consent was obtained from all participants.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2. Anthropometric and Body Composition Measurements\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAnthropometric measurements were obtained with participants wearing light clothing and no shoes. Height and body weight were assessed using a calibrated digital standing scale (accuracy: \u0026plusmn;0.1 cm and \u0026plusmn;\u0026thinsp;0.1 kg). BMI was calculated as body weight (kg) divided by height squared (m\u0026sup2;), and obesity was defined as BMI\u0026thinsp;\u0026ge;\u0026thinsp;25.0 kg/m\u0026sup2; following the Asia\u0026ndash;Pacific criteria [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. WC was measured at the midpoint between the lower rib and the iliac crest using a non-stretchable tape, with participants standing upright and breathing normally. Hip circumference (HC) was measured at the widest part of the hips and buttocks. WHR was calculated as WC (cm) divided by HC (cm), and WHtR as WC (cm) divided by height (cm). Abdominal obesity was defined as WC\u0026thinsp;\u0026ge;\u0026thinsp;90 cm for men and \u0026ge;\u0026thinsp;80 cm for women, and WHR\u0026thinsp;\u0026ge;\u0026thinsp;0.90 for men and \u0026ge;\u0026thinsp;0.80 for women, according to Asian cutoffs. A WHtR\u0026thinsp;\u0026ge;\u0026thinsp;0.5 indicated central adiposity [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBody composition was assessed using a bioelectrical impedance analyzer (Tanita BC-541, Tokyo, Japan). Participants stood barefoot on the device and removed metal accessories (e.g., belts, watches, jewelry) to avoid interference with the electrical signal. The analyzer transmits a low-level electrical current through the body and estimates body compartments based on the resistance detected. This method provided %BF. High body fat was defined as %BF\u0026thinsp;\u0026ge;\u0026thinsp;25% in men and \u0026ge;\u0026thinsp;35% in women. A fat mass index (FMI) was calculated as fat mass (kg) divided by height squared (m\u0026sup2;), and high FMI as \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;6.6 kg/m\u0026sup2; [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNovel anthropometric indices were also calculated using established formulas, including ABSI, AVI, BAI, BRI, CI, and WWI. Cutoff values for elevated adiposity were based on published thresholds: ABSI\u0026thinsp;\u0026ge;\u0026thinsp;0.0866, AVI\u0026thinsp;\u0026ge;\u0026thinsp;23.89, BAI\u0026thinsp;\u0026ge;\u0026thinsp;36.40, BRI\u0026thinsp;\u0026ge;\u0026thinsp;6.96, CI\u0026thinsp;\u0026ge;\u0026thinsp;1.25 (for men), CI\u0026thinsp;\u0026ge;\u0026thinsp;1.18 (for women), and WWI\u0026thinsp;\u0026ge;\u0026thinsp;11.92 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNovel anthropometric index formulas\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{A}\\text{B}\\text{S}\\text{I}=\\frac{\\text{W}\\text{C}\\:\\left(\\text{m}\\right)}{\\text{B}\\text{M}\\text{I}{\\:}^{2∕3}\\cdot\\:{\\:\\text{H}\\text{e}\\text{i}\\text{g}\\text{h}\\text{t}\\:\\left(\\text{m}\\right)}^{1∕2}}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\text{A}\\text{V}\\text{I}=\\frac{{2\\text{W}\\text{C}}^{2\\:\\:\\:\\:}\\left(\\text{c}\\text{m}\\right)+\\:{0.7\\times\\:\\left(\\text{W}\\text{C}-\\text{H}\\text{C}\\right)}^{2\\:}\\left(\\text{c}\\text{m}\\right)}{\\text{1,000}}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\text{B}\\text{A}\\text{I}\\:=\\left(\\frac{\\text{H}\\text{C}\\:\\left(\\text{c}\\text{m}\\right)}{{\\text{H}\\text{e}\\text{i}\\text{g}\\text{h}\\text{t}\\:\\left(\\text{m}\\right)}^{1.5\\:}}\\right)-18$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:\\text{B}\\text{R}\\text{I}\\:=364.2\\:-365.5\\:\\times\\:\\sqrt{1-{\\left(\\frac{\\text{W}\\text{C}\\:\\left(\\text{m}\\right)}{2{\\pi\\:}\\:\\times\\:\\:\\text{H}\\text{e}\\text{i}\\text{g}\\text{h}\\text{t}\\:\\left(\\text{m}\\right)}\\right)}^{2}}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:\\text{C}\\text{I}=\\frac{\\text{W}\\text{C}\\:\\left(\\text{m}\\right)}{0.109\\sqrt[\\:]{\\frac{\\text{B}\\text{W}\\:\\left(\\text{k}\\text{g}\\right)}{\\text{H}\\text{e}\\text{i}\\text{g}\\text{h}\\text{t}\\:\\left(\\text{m}\\right)}}}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$$\\:\\text{W}\\text{W}\\text{I}=\\frac{\\text{W}\\text{C}\\:\\left(\\text{c}\\text{m}\\right)}{\\sqrt{\\text{B}\\text{W}\\:\\left(\\text{k}\\text{g}\\right)}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003e2. Blood sample collection and measurements\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBlood samples were collected in the morning (between 8:00 and 10:00 AM) after an overnight fast of at least 8 hours at the Health Care Service Center, Faculty of Allied Health Sciences, Thammasat University. Fasting blood sugar and lipid profiles were processed using routine automated analyzers. Serum 25(OH)D was measured with a chemiluminescent immunoassay (Liaison XL, DiaSorin Inc., Stillwater, MN, USA). For interpretation, 25(OH)D levels were classified as \u0026gt;\u0026thinsp;20 ng/mL (normal), 12\u0026ndash;20 ng/mL (insufficient), and \u0026lt;\u0026thinsp;12 ng/mL (deficient), following thresholds commonly applied in previous research [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003e3. Statistical analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eData were summarized as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) for approximately normally distributed continuous variables and as median with interquartile range (IQR) for skewed variables. Categorical variables were presented as counts and percentages. The distribution of continuous variables was assessed using the Kolmogorov\u0026ndash;Smirnov and Shapiro\u0026ndash;Wilk tests.\u003c/p\u003e \u003cp\u003eThe differences in baseline characteristics and anthropometric variables among the serum 25(OH)D status categories (deficient, insufficient, and normal) were assessed using one-way analysis of variance (ANOVA) for normally distributed variables or the Kruskal\u0026ndash;Wallis test for those that were not normally distributed. Categorical variables were compared using chi-square tests or Fisher\u0026rsquo;s exact tests, as appropriate.\u003c/p\u003e \u003cp\u003ePearson correlation coefficients were used to evaluate the relationships between serum 25(OH)D levels and anthropometric or body composition indices. Multiple linear regression analyses were performed to assess the independent associations between each anthropometric index and serum levels of 25(OH)D. In these models, serum 25(OH)D level (continuous) was specified as the dependent variable, and individual anthropometric indices were entered as predictors. Age, sex, smoking status, alcohol intake, supplement use, and physical activity were included as covariates.\u003c/p\u003e \u003cp\u003eBinary logistic regression models were fitted with low vitamin D status (\u0026lt;\u0026thinsp;20 ng/mL) as the dependent variable and higher anthropometric and body composition measures as independent variables. Model 1 was unadjusted. Model 2 was adjusted for lifestyle factors (smoking status, alcohol consumption, and physical activity) and supplement intake (milk and dairy products, vitamin D and calcium supplements, and fish oil/omega-3 supplements). Model 3 was additionally adjusted for sunlight exposure time. Results were expressed as odds ratio (OR) with 95% confidence interval (CI).\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using SPSS, and a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThis study included 403 participants who were categorized into three groups based on serum 25(OH)D levels: deficient (n\u0026thinsp;=\u0026thinsp;53), insufficient (n\u0026thinsp;=\u0026thinsp;208), and normal (n\u0026thinsp;=\u0026thinsp;142). Participants with normal 25(OH)D levels were significantly older (median: 50.00 years, IQR: 39.00\u0026ndash;58.00) than those with insufficient (37.00 years, IQR: 28.00\u0026ndash;47.00) and deficient levels (30.00 years, IQR: 24.00\u0026ndash;41.00) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). A greater proportion of participants aged\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;65 years (11.3%) and males (37.3%) were observed in the normal group compared to the other two groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No significant differences were found in body weight, height, systolic BP, or diastolic BP across the groups (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline characteristic of the study participants according to 25(OH)D status\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e25(OH)D status\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDeficient\u003c/p\u003e\n \u003cp\u003e(\u0026lt;\u0026thinsp;12 ng/mL)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInsufficient\u003c/p\u003e\n \u003cp\u003e(12\u0026ndash;20 ng/mL)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003cp\u003e(\u0026gt;\u0026thinsp;20 ng/mL)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;53\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;208\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;142\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.00 (24.00\u0026ndash;41.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.00 (28.00\u0026ndash;47.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.00 (39.00\u0026ndash;58.00) \u003csup\u003e#,$\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;65, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (11.3) \u003csup\u003e#,$\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex (Male/Female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9/44 (17.0/83.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47/161 (22.6/77.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53/89 (37.3/62.7) \u003csup\u003e#,$\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBody weight (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.10 (55.60\u0026ndash;80.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.70 (56.40\u0026ndash;77.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.80 (56.88\u0026ndash;73.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeight (m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.60 (1.56\u0026ndash;1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.60 (1.56\u0026ndash;1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.59 (1.53\u0026ndash;1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSystolic BP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116.00 (105.00-121.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114.00 (105.00-127.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e117.00 (109.75\u0026ndash;125.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiastolic BP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.00 (61.00\u0026ndash;75.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.00 (61.00\u0026ndash;74.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.00 (60.00\u0026ndash;74.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlood chemistry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFBS (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.00 (84.00\u0026ndash;94.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.00 (83.00\u0026ndash;96.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.00 (85.00\u0026ndash;98.00) \u003csup\u003e$\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e212.00 (180.00-232.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e203.00 (181.00-228.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e194.50 (167.50\u0026ndash;221.00) \u003csup\u003e#,$\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTG (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.00 (64.00-119.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.00 (65.00-145.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.00 (71.00-148.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDL-C (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.00 (49.00\u0026ndash;72.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.00 (50.00\u0026ndash;67.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.00 (49.00-70.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDL-C (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e128.17\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;35.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124.97\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;32.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114.27\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;33.54\u003csup\u003e#,$\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (OH)D (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.00 (9.00\u0026ndash;11.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.00 (14.00\u0026ndash;17.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.00 (21.75-27.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eChronic diseases\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33 (23.2) \u003csup\u003e#,$\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDyslipidemia, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (12.7) \u003csup\u003e$\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCVD, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (2.1) \u003csup\u003e$\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eLifestyles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent smoking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlcohol drinking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (44.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82 (39.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70 (49.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegular leisure-time physical activity, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37 (17.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSunlight exposure time\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;30 min, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38 (18.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (22.5) \u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMilk and dairy product\u0026thinsp;\u0026gt;\u0026thinsp;300 g/day, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVitamin D and calcium supplement, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44 (84.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e155 (74.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97 (68.3) \u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFish oil/omega-3 supplement, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41 (78.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e152 (73.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97 (68.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNotes:\u003c/strong\u003e Data are presented as mean \u0026plusmn; standard deviation (SD) or median [interquartile range (IQR)] for continuous variables and as count (percentage) for categorical variables.\u003cbr\u003e\u0026nbsp;Between-group comparisons were performed using one-way ANOVA for normally distributed variables or the Kruskal\u0026ndash;Wallis test for non-normally distributed variables. Chi-square tests were used for categorical variables.\u003c/p\u003e\n\u003cp\u003e*:\u0026nbsp;\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 for the deficient group vs. the insufficient group\u0026nbsp;\u003cbr\u003e#:\u0026nbsp;\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 for the deficient group vs. the normal group\u0026nbsp;\u003cbr\u003e$: \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 for the insufficient group vs. the normal group\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e BP, blood pressure; FBS, fasting blood sugar; TC, total cholesterol; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; CVD, cardiovascular diseases\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn terms of blood chemistry, FBS was significantly higher in the normal group (median: 90.00 mg/dL, IQR: 85.00\u0026ndash;98.00) than in the deficient and insufficient groups (both 88.00 mg/dL; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). On the other hand, TC (median: 194.50 mg/dL, IQR: 167.50\u0026ndash;221.00) and LDL-C (114.27\u0026thinsp;\u0026plusmn;\u0026thinsp;33.54 mg/dL) were significantly lower in the normal group compared to the deficient and insufficient groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No significant differences were observed in TG and HDL-C levels (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe prevalence of hypertension (23.2%) and dyslipidemia (12.7%) was highest in the normal 25(OH)D group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Regarding lifestyle characteristics, daily sunlight exposure for more than 30 minutes was most prevalent in the normal group (22.5%), followed by the insufficient (18.4%) and deficient groups (9.6%) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The use of vitamin D and calcium supplements was highest in the deficient group (84.6%) and declined with increasing 25(OH)D levels (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). A similar decreasing trend in usage was observed for fish oil or omega-3 supplements (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn terms of anthropometric measures, participants with normal 25(OH)D levels had significantly higher WHR (0.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07), ABSI (0.08 [0.08\u0026ndash;0.08]), BRI (4.17 [3.25\u0026ndash;5.36]), CI (1.25 [1.20\u0026ndash;1.30]), and WWI (10.76 [10.29\u0026ndash;11.21]) compared to those in the deficient and/or insufficient groups (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, the proportion of individuals with high WHR (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;0.90 for men, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e 0.80 for women) was significantly greater in the normal 25(OH)D group (56.3%) than in the deficient (39.6%) and insufficient groups (46.6%) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No significant differences were found in BF%, FMI, or FFMI across the groups (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eClassical and novel anthropometric and body composition measures according to 25(OH)D status\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e25(OH)D status\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDeficient\u003c/p\u003e\n \u003cp\u003e(\u0026lt;\u0026thinsp;12 ng/mL)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInsufficient\u003c/p\u003e\n \u003cp\u003e(12\u0026ndash;20 ng/mL)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003cp\u003e(\u0026gt;\u0026thinsp;20 ng/mL)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;53\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;208\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;142\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eClassical anthropometric measures\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.04 (22.44\u0026ndash;27.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.55 (22.24\u0026ndash;29.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.17 (22.45\u0026ndash;28.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh BMI (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;25), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26 (49.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118 (56.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWC (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.84\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;13.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.00\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;12.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.78\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;11.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh WC\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;0.90 (M), \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e 0.80 (W), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (50.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129 (62.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85 (59.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHC (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00 (92.50-108.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101.00 (94.00-107.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.75 (94.50-105.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.07\u003csup\u003e#,$\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh WHR\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;0.90 (M), \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e 0.80 (W), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (39.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97 (46.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80 (56.3) \u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWHtR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52 (0.47\u0026ndash;0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53 (0.48\u0026ndash;0.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54 (0.49\u0026ndash;0.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh WHtR\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;0.5, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (58.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140 (67.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102 (71.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eNovel anthropometric measures\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eABSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08 (0.07\u0026ndash;0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08 (0.07\u0026ndash;0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08 (0.08\u0026ndash;0.08) \u003csup\u003e#,$\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh ABSI\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;0.0866, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAVI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.82 (11.29\u0026ndash;17.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.24 (12.06\u0026ndash;18.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.54 (12.92\u0026ndash;17.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh AVI\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;23.89, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.45 (27.66\u0026ndash;34.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.01 (27.96\u0026ndash;34.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.48 (27.89\u0026ndash;34.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh BAI\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;36.40, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (20.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 (19.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (19.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.74 (2.90\u0026ndash;4.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.97 (2.95\u0026ndash;5.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.17 (3.25\u0026ndash;5.36) \u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh BRI\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;6.96, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.20 (1.14\u0026ndash;1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23 (1.16\u0026ndash;1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.25 (1.20\u0026ndash;1.30) \u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh CI\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;1.25 (M)\u0026cedil; \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e 1.18 (W), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (56.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e136 (65.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100 (70.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWWI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.28 (9.87\u0026ndash;10.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.55 (9.95\u0026ndash;10.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.76 (10.29\u0026ndash;11.21) \u003csup\u003e#,$\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh WWI\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;11.92, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eBody composition measures\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBF (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.10 (27.40\u0026ndash;37.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.30 (28.10\u0026ndash;36.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.20 (26.88\u0026ndash;36.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh BF\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;25% (M), \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e 35% (W), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 (41.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94 (45.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76 (53.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.93 (5.98\u0026ndash;10.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.29 (6.26\u0026ndash;10.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.98 (6.18\u0026ndash;10.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh FMI\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;6.6 kg/m\u003csup\u003e2\u003c/sup\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 (67.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e146 (70.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95 (66.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNotes:\u003c/strong\u003e Data are presented as mean \u0026plusmn; standard deviation (SD) or median [interquartile range (IQR)] for continuous variables and as count (percentage) for categorical variables.\u003cbr\u003e\u0026nbsp;Between-group comparisons were performed using one-way ANOVA for normally distributed variables or the Kruskal\u0026ndash;Wallis test for non-normally distributed variables. Chi-square tests were used for categorical variables.\u003c/p\u003e\n\u003cp\u003e*:\u0026nbsp;\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 for the deficient group vs. the insufficient group\u0026nbsp;\u003cbr\u003e#:\u0026nbsp;\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 for the deficient group vs. the normal group\u0026nbsp;\u003cbr\u003e$: \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 for the insufficient group vs. the normal group\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e\u0026nbsp; M, men; W, women; BMI, body mass index; WC, waist circumference; HC, hip circumference; WHR, waist-to-hip ratio; WHtR, waist-to-height ratio; ABSI\u003cem\u003e,\u0026nbsp;\u003c/em\u003ea\u003cem\u003e\u0026nbsp;\u003c/em\u003ebody shape index\u003cem\u003e;\u003c/em\u003e AVI, abdominal volume index; BAI, body adiposity index; BRI, body roundness index; CI, conicity index; WWI, weight-adjusted waist index; BF, body fat; FMI, fat mass index.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCorrelation analysis showed that several anthropometric measures, including WC, WHR, WHtR, ABSI, AVI, BRI, CI, and WWI, were significantly associated with serum 25(OH)D levels (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, in multiple linear regression analysis adjusted for potential confounders, only WC (B\u0026thinsp;=\u0026thinsp;3.53, p\u0026thinsp;=\u0026thinsp;0.01) and AVI (B = \u0026minus;\u0026thinsp;3.90, p\u0026thinsp;=\u0026thinsp;0.01) remained significantly associated (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe associations between anthropometric and body composition measures and serum 25(OH)D levels.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCorrelation analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eMultiple linear regression analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eB (Unstandardized coefficient)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003cp\u003e(Standardized coefficient)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eClassical anthropometric measures\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWC (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHC (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-84.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWHtR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-134.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eNovel anthropometric measures\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eABSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAVI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-152.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWWI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eBody composition measures\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBF (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNotes:\u003c/strong\u003e Pearson correlation (r) assessed the relationships between anthropometric parameters and serum 25(OH)D levels. Multiple regression analysis estimated the independent effects (\u0026beta;) of each anthropometric parameter on 25(OH)D, adjusting for age, sex, smoking status, alcohol consumption, supplement intake, sunlight exposure time and physical activity.\u003c/p\u003e\n\u003cp\u003eStatistical significance is indicated as follows: *p \u0026lt; 0.05.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eValues are presented as coefficients with significance marks where applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e\u0026nbsp; BMI, body mass index; WC, waist circumference; HC, hip circumference; WHR, waist-to-hip ratio; WHtR, waist-to-height ratio; ABSI\u003cem\u003e,\u0026nbsp;\u003c/em\u003ea\u003cem\u003e\u0026nbsp;\u003c/em\u003ebody shape index\u003cem\u003e;\u003c/em\u003e AVI, abdominal volume index; BAI, body adiposity index; BRI, body roundness index; CI, conicity index; WWI, weight-adjusted waist index; BF, body fat; FMI, fat mass index.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn multivariable-adjusted binary logistic regression models, high BF% was significantly associated with increased odds of having deficient or insufficient 25(OH)D levels (model 1: OR\u0026thinsp;=\u0026thinsp;2.20, 95% CI: 1.16\u0026ndash;4.19, p\u0026thinsp;=\u0026thinsp;0.02; model 2: OR\u0026thinsp;=\u0026thinsp;2.28, 95% CI: 1.19\u0026ndash;4.36, p\u0026thinsp;=\u0026thinsp;0.01; model 3: OR\u0026thinsp;=\u0026thinsp;2.18, 95% CI: 1.13\u0026ndash;4.18, p\u0026thinsp;=\u0026thinsp;0.02). No other anthropometric or body composition indices showed consistent associations across all models (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBinary logistic regression analysis of anthropometric and body composition measures with obesity and abnormal 25(OH)D status.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (CI%95)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (CI%95)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (CI%95)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh BMI (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;25), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003cp\u003e(0.54\u0026ndash;1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003cp\u003e(0.60\u0026ndash;1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003cp\u003e(0.60\u0026ndash;1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh WC\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;0.90 (M), \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e 0.80 (W), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003cp\u003e(0.30\u0026ndash;1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003cp\u003e(0.32\u0026ndash;1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003cp\u003e(0.31\u0026ndash;1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh WHR\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;0.90 (M), \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e 0.85 (W), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003cp\u003e(0.90\u0026ndash;2.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003cp\u003e(0.86\u0026ndash;2.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.53\u003c/p\u003e\n \u003cp\u003e(0.84\u0026ndash;2.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh WHtR\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;0.5, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003cp\u003e(0.75\u0026ndash;4.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003cp\u003e(0.53\u0026ndash;3.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003cp\u003e(0.51\u0026ndash;3.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh ABSI\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;0.0866, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003cp\u003e(0.32\u0026ndash;3.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003cp\u003e(0.39\u0026ndash;4.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003cp\u003e(0.38\u0026ndash;4.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh AVI\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;23.89, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003cp\u003e(0.03\u0026ndash;1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003cp\u003e(0.04\u0026ndash;1.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003cp\u003e(0.03\u0026ndash;1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh BAI\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;36.40, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003cp\u003e(0.47\u0026ndash;1.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003cp\u003e(0.42\u0026ndash;1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003cp\u003e(0.43\u0026ndash;1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh BRI\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;6.96, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003cp\u003e(0.19\u0026ndash;2.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003cp\u003e(0.20\u0026ndash;3.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003cp\u003e(0.21\u0026ndash;3.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh CI\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;1.25 (M)\u0026cedil; \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e 1.18 (W), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003cp\u003e(0.62\u0026ndash;2.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003cp\u003e(0.63\u0026ndash;2.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003cp\u003e(0.66\u0026ndash;2.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh WWI\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;11.92, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.95\u003c/p\u003e\n \u003cp\u003e(0.78\u0026ndash;11.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.52\u003c/p\u003e\n \u003cp\u003e(0.65\u0026ndash;9.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003cp\u003e(0.57\u0026ndash;8.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh BF\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;25% (M), \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;35% (W), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.20\u003c/p\u003e\n \u003cp\u003e(1.16\u0026ndash;4.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.28\u003c/p\u003e\n \u003cp\u003e(1.19\u0026ndash;4.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.18\u003c/p\u003e\n \u003cp\u003e(1.13\u0026ndash;4.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh FMI\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;6.6 kg/m\u003csup\u003e2\u003c/sup\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003cp\u003e(0.28\u0026ndash;1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003cp\u003e(0.28\u0026ndash;1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003cp\u003e(0.30\u0026ndash;1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e Data are presented as odds ratios (OR) and 95% confidence intervals (CI)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA low serum 25(OH)D concentration was defined as \u0026lt; 20 ng/mL.\u003c/p\u003e\n\u003cp\u003eData were obtained using binary logistic regression analysis.\u003c/p\u003e\n\u003cp\u003eStatistical significance is indicated as follows: *p \u0026lt; 0.05.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eValues are presented as coefficients with significance marks where applicable.\u003c/p\u003e\n\u003cp\u003eModel 1: Unadjusted\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eModel 2: Adjusted for lifestyle factors (smoking status, alcohol consumption, physical activity), and supplement intake (milk and dairy products, vitamin D and calcium supplements, fish oil/omega 3 supplements)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eModel 3: Adjusted for variables in model 2 plus sunlight exposure time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e BMI, body mass index; WC, waist circumference; WHR, waist-to-hip ratio; WHtR, waist-to-height ratio; ABSI\u003cem\u003e,\u0026nbsp;\u003c/em\u003ea\u003cem\u003e\u0026nbsp;\u003c/em\u003ebody shape index\u003cem\u003e;\u003c/em\u003e AVI, abdominal volume index; BAI, body adiposity index; BRI, body roundness index; CI, conicity index; WWI, weight-adjusted waist index; BF, body fat; FMI, fat mass index.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined how vitamin D status relates to anthropometry using classical and novel anthropometric and body composition indices in Thai adults. The overall pattern suggests that adiposity\u0026mdash;particularly visceral fat\u0026mdash;may play an important role, although lifestyle factors remain influential in this tropical setting.\u003c/p\u003e \u003cp\u003eAging is associated with reduced calcium absorption and a decline in epidermal 7-dehydrocholesterol concentration, leading to a diminished capacity for cutaneous vitamin D₃ synthesis in response to UVB exposure [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Despite these age-related physiological changes, older adults in this study exhibited higher or adequate serum 25(OH)D levels. This apparent paradox may be explained by behavioral factors, as older adults may spend more time outdoors during periods of peak UVB availability, whereas younger adults often experience lower effective UVB exposure due to long indoor working hours, clothing coverage, sunscreen use, reliance on air-conditioned environments, and habitual sun avoidance motivated by thermal discomfort or cosmetic concerns. These findings suggest that lifestyle-related sun exposure may partially offset age-related declines in cutaneous vitamin D synthesis, contributing to higher vitamin D levels among older adults in this tropical population.\u003c/p\u003e \u003cp\u003eSex differences also contributed to variation in vitamin D status, with a higher proportion of males observed in the normal 25(OH)D group. Women often exhibit lower vitamin D levels, partly because greater total fat mass may increase sequestration of fat-soluble vitamins [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In contrast, men may show different patterns of vitamin D distribution and metabolism related to sex-specific body composition and age-related hormonal changes. Declining testosterone levels with aging are associated with shifts toward increased visceral adiposity, which may influence vitamin D storage and mobilization [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. However, previous studies have reported minimal sex differences or even higher vitamin D levels among older women, suggesting that lifestyle and behavioral factors may modify biological sex effects [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, participants with normal serum 25(OH)D levels exhibited lower total cholesterol and LDL-C concentrations but a higher prevalence of diagnosed hypertension and dyslipidemia. Conversely, prior studies have reported that low vitamin D status is associated with less favorable lipid profiles, including higher LDL-C and triglyceride levels and lower HDL-C concentrations [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Therefore, the interpretation of lipid differences in the present cohort requires caution. One possible explanation is the influence of clinical management. Individuals diagnosed with hypertension or dyslipidemia are more likely to undergo regular health evaluations and to receive pharmacological treatments, which may lower circulating LDL-C and total cholesterol independently of vitamin D status. In addition, chronic conditions such as hypertension intersect mechanistically with vitamin D pathways through effects on the renin\u0026ndash;angiotensin\u0026ndash;aldosterone system and endothelial function [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Because information on lipid-lowering and antihypertensive medication use was not available in this study, the extent to which treatment effects contributed to the observed lipid patterns cannot be determined.\u003c/p\u003e \u003cp\u003eSupplement use was highest for vitamin D and calcium products among participants with deficiency, likely reflecting a compensatory response rather than effective correction. Dietary sources contribute minimally to circulating vitamin D, and excess adiposity may further reduce bioavailability through sequestration, impaired mobilization, inflammation, and increased physiological demand [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], thereby limiting the effectiveness of supplementation alone. Fish oil and omega-3 supplements contain negligible vitamin D unless fortified, consistent with their lack of association with serum 25(OH)D levels. In addition, milk and dairy intake did not differ across vitamin D status groups, likely reflecting generally low consumption in this population, as commonly reported in many Asian populations due to the high prevalence of lactose intolerance, which may limit dietary vitamin D and calcium intake overall. Participants with normal 25(OH)D levels were more likely to report sunlight exposure exceeding 30 minutes per day; because UVB-mediated cutaneous synthesis accounts for most circulating vitamin D and varies with skin pigmentation, body surface area exposed, geography, and season [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], behavioral and seasonal differences in sun exposure likely contribute to the persistence of vitamin D deficiency even in tropical regions.\u003c/p\u003e \u003cp\u003eNumerous studies have reported inverse associations between vitamin D status and adiposity, with obesity consistently linked to a higher prevalence of vitamin D deficiency [\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, other investigations have reported null associations with classical indices such as BMI, WC, or BF% [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Novel anthropometric indices may therefore offer additional insight into central and visceral adiposity. ABSI has been shown to be a better predictor of premature mortality than BMI or WC alone, while the BRI reflects body shape and visceral adipose tissue [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Several studies have demonstrated that BRI and ABSI exhibit stronger inverse associations with serum 25(OH)D than classical anthropometric measures, particularly among men [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Vitamin D deficiency has been shown to increase with greater adiposity across most anthropometric indices, with notable exceptions such as ABSI in women and BMI in men [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. While central adiposity indices, including WC and WHR, are often associated with vitamin D status, other measures\u0026mdash;such as body weight, HC, BMI, BF%, and FMI\u0026mdash;may not be [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Among women, additional inverse associations have been reported between serum 25(OH)D and WC, WHtR, CI, FMI, and BF% [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the regression models, WC and AVI remained independent predictors of serum 25(OH)D. The positive association between WC and vitamin D is unlikely to reflect a direct biological relationship in isolation and may instead indicate context-specific patterns of occupational sun exposure. In some Thai settings, individuals with larger waist circumferences may be more likely to engage in outdoor work, resulting in greater UVB exposure; however, this interpretation remains hypothetical and may not be generalizable beyond this population. In contrast, AVI\u0026mdash;a more sensitive indicator of visceral adiposity\u0026mdash;was inversely associated with serum 25(OH)D. Previous studies have reported that AVI estimates overall abdominal volume and shows good correlation with abdominal fat measured by CT scans [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Visceral adiposity may reduce circulating vitamin D by limiting its release during lipolysis and by influencing inflammatory and adipokine pathways [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNotably, participants with normal serum 25(OH)D levels exhibited higher values of several central and body shape\u0026ndash;related indices, including WHR, ABSI, BRI, CI, and WWI, whereas no significant differences were observed in body fat percentage, fat mass index, or fat-free mass index. These findings suggest that, in this population, body shape and fat distribution indices may not uniformly reflect metabolically active adiposity and may be influenced by age, sex, or lifestyle-related factors such as sunlight exposure. Previous studies have reported sex-specific associations between vitamin D status and adiposity indices, with inverse relationships observed for ABSI and BMI in women and for body weight and body fat percentage in men [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough body fat percentage did not differ significantly across vitamin D status groups, it emerged as an independent predictor of deficient or insufficient 25(OH)D levels in multivariable models, suggesting that total adiposity may influence vitamin D risk even in the absence of between-group differences. High body fat percentage also emerged as a strong predictor of deficiency, consistent with evidence that greater total adiposity dilutes circulating vitamin D and promotes storage within fat depots [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Together, these results suggest that indices capturing visceral adiposity\u0026mdash;particularly AVI\u0026mdash;may offer more physiologically meaningful insight into vitamin D risk than traditional anthropometric measures.\u003c/p\u003e \u003cp\u003eThe strengths of this research include its large sample size, the evaluation of several anthropometric indices, and the investigation of vitamin D status in a tropical setting where environmental and behavioral factors have substantial influence. However, several limitations should be noted. The cross-sectional design limits causal inference and may affect the generalizability of the findings. The absence of imaging-based adiposity measures may have reduced the precision of adiposity assessment. Additionally, the lack of medication and dietary information, as well as unmeasured seasonal variation in UVB exposure, could have influenced the results.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eVitamin D status reflects a combined influence of adiposity, behavioral exposure, and demographic characteristics. Visceral adiposity showed a physiologically consistent inverse association with serum 25(OH)D. This pattern suggests that novel anthropometric indices, particularly AVI, may be useful for identifying individuals at heightened risk for vitamin D deficiency. Integrating visceral adiposity profiling with behavioral assessment may strengthen population screening approaches and inform more targeted strategies for vitamin D optimization in primary care and public health practice where sunlight is abundant but deficiency remains prevalent.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all study participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSP, GJO, CA and TR conceived the study.\u0026nbsp;SP, GJO, and CA managed\u003c/p\u003e\n\u003cp\u003edata collection.\u0026nbsp;TR analyzed and interpreted the data.\u0026nbsp;SP and TR wrote the manuscript.\u0026nbsp;SP, GJO, CA and TR critically edited the manuscript.\u0026nbsp;All authors read and approved of the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by Thammasat University Research Fund (TUFT 051/2568)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset generated during the current study is available from the corresponding\u003c/p\u003e\n\u003cp\u003eauthor upon reasonable request.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate.\u003c/p\u003e\n\u003cp\u003eThe study was approved by the ethics committees of Thammasat University (Medicine)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMTU-EC-00-0-073/67. The study was conducted in accordance with the ethical\u003c/p\u003e\n\u003cp\u003eprinciples stated in the Declaration of Helsinki.\u0026nbsp;All parents and guardians\u003c/p\u003e\n\u003cp\u003ereceived printed information regarding the study and signed a letter of consent.\u003c/p\u003e\n\u003cp\u003eAll students were informed that they could decline survey completion\u003c/p\u003e\n\u003cp\u003eon their own accord at any time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003ePalacios C, Gonzalez L. Is vitamin D deficiency a major global public health problem? J Steroid Biochem Mol Biol. 2014;144:138\u0026ndash;145.\u003c/li\u003e\n \u003cli\u003eRoss AC, Taylor CL, Yaktine AL, Del Valle HB, editors. Dietary Reference Intakes for Calcium and Vitamin D. Washington (DC): National Academies Press; 2011.\u003c/li\u003e\n \u003cli\u003eAutier P, Boniol M, Pizot C, Mullie P. Vitamin D status and ill health: a systematic review. Lancet Diabetes Endocrinol. 2014;2:76\u0026ndash;89.\u003c/li\u003e\n \u003cli\u003eAbbas MA. Physiological functions of vitamin D in adipose tissue. J Steroid Biochem Mol Biol. 2017;165:369\u0026ndash;381.\u003c/li\u003e\n \u003cli\u003eBouillon R, Carmeliet G, Lieben L, Watanabe M, Perino A, Auwerx J, et al. Vitamin D and energy homeostasis: of mice and men. Nat Rev Endocrinol. 2014;10:79\u0026ndash;87.\u003c/li\u003e\n \u003cli\u003eVimaleswaran KS, Berry DJ, Lu C, Tikkanen E, Pilz S, Hiraki LT, et al. Causal relationship between obesity and vitamin D status: bi-directional Mendelian randomization analysis. PLoS Med. 2013;10:e1001383.\u003c/li\u003e\n \u003cli\u003eAndreozzi F, Muscogiuri G, Gnessi L, et al. Vitamin D and visceral obesity: role of adipose tissue inflammation. Nutrients. 2016;8:371.\u003c/li\u003e\n \u003cli\u003eKrakauer NY, Krakauer JC. A new body shape index predicts mortality hazard independently of body mass index. PLoS One. 2012;7:e39504.\u003c/li\u003e\n \u003cli\u003eThomas DM, Bredlau C, Bosy-Westphal A, Mueller M, Shen W, Gallagher D, et al. Relationships between body roundness index and body fat distribution. Sci Rep. 2025;15:86489.\u003c/li\u003e\n \u003cli\u003ePereira-Santos M, Costa PRF, Assis AMO, Santos CAST, Santos DB. Obesity and vitamin D deficiency: a systematic review and meta-analysis. Obes Rev. 2015;16:341\u0026ndash;349.\u003c/li\u003e\n \u003cli\u003eAl-Daghri NM, Al-Saleh Y, Aljohani N, Sulimani R, Al-Othman AM, Alfawaz H, et al. Vitamin D status correction and obesity: a randomized controlled trial. Nutrients. 2017;9:552.\u003c/li\u003e\n \u003cli\u003ePourshahidi LK. Vitamin D and obesity: current perspectives and future directions. Nutr Res Rev. 2015;28:1\u0026ndash;14.\u003c/li\u003e\n \u003cli\u003eWortsman J, Matsuoka LY, Chen TC, Lu Z, Holick MF. Decreased bioavailability of vitamin D in obesity. Am J Clin Nutr. 2000;72:690\u0026ndash;693.\u003c/li\u003e\n \u003cli\u003eOliveira LF, Santos JLF, Gigante DP. Association between vitamin D status and adiposity indicators in women. Nutrients. 2024;16:1267.\u003c/li\u003e\n \u003cli\u003eLiu X, Baylin A, Levy PD. Anthropometric indices and serum vitamin D levels in adults. Clin Nutr. 2019;38:2596\u0026ndash;2603.\u003c/li\u003e\n \u003cli\u003eJungert A, Neuh\u0026auml;user-Berthold M. Associations of body composition with vitamin D status. Nutrients. 2018;10:1778.\u003c/li\u003e\n \u003cli\u003eWHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications. Lancet. 2004;363:157\u0026ndash;163.\u003c/li\u003e\n \u003cli\u003eZaki ME, El-Bassyouni HT, El-Gammal M, Kamal S. Cut-off values of anthropometric indices in Asian populations. BMC Public Health. 2024;24:17846.\u003c/li\u003e\n \u003cli\u003eHsieh SD, Yoshinaga H, Muto T. Waist-to-height ratio as a simple screening tool for obesity. Int J Obes Relat Metab Disord. 2003;27:610\u0026ndash;616.\u003c/li\u003e\n \u003cli\u003eKyle UG, Bosaeus I, De Lorenzo AD, Deurenberg P, Elia M, G\u0026oacute;mez JM, et al. Bioelectrical impedance analysis\u0026mdash;part II. Clin Nutr. 2004;23:1226\u0026ndash;1243.\u003c/li\u003e\n \u003cli\u003eAmato MC, Giordano C, Galia M, Criscimanna A, Vitabile S, Midiri M, et al. Visceral adiposity index. Nutr Metab Cardiovasc Dis. 2013;23:476\u0026ndash;482.\u003c/li\u003e\n \u003cli\u003eNIH Office of Dietary Supplements. Vitamin D fact sheet for health professionals. Bethesda (MD): NIH; 2022.\u003c/li\u003e\n \u003cli\u003eSempos CT, Heijboer AC, Bikle DD, Bollerslev J, Bouillon R, Brannon PM, et al. Vitamin D assays and vitamin D status. Am J Clin Nutr. 2021;113:695\u0026ndash;706.\u003c/li\u003e\n \u003cli\u003eHeaney RP. Aging, calcium absorption, and vitamin D requirements. J Nutr. 2004;134:3177S\u0026ndash;3180S.\u003c/li\u003e\n \u003cli\u003eBikle DD. Vitamin D metabolism, mechanism of action, and clinical applications. Int J Mol Sci. 2021;22:8894.\u003c/li\u003e\n \u003cli\u003eMaugeri A, Barchitta M, Agodi A. Sex differences in vitamin D metabolism. Br J Nutr. 2019;122:1313\u0026ndash;1324.\u003c/li\u003e\n \u003cli\u003eKelly DM, Jones TH. Testosterone and obesity. Obes Rev. 2015;16:581\u0026ndash;606.\u003c/li\u003e\n \u003cli\u003eHilger J, Friedel A, Herr R, Rausch T, Roos F, Wahl DA, et al. A systematic review of vitamin D status by sex and age. Nutr Metab Cardiovasc Dis. 2014;24:865\u0026ndash;874.\u003c/li\u003e\n \u003cli\u003eWang H, Chen W, Li D, Yin X, Zhang X, Olsen N, et al. Vitamin D and lipid metabolism. Curr Med Res Opin. 2018;34:1657\u0026ndash;1664.\u003c/li\u003e\n \u003cli\u003eForman JP, Williams JS, Fisher NDL. Plasma 25-hydroxyvitamin D and risk of hypertension. Hypertension. 2010;55:1283\u0026ndash;1290.\u003c/li\u003e\n \u003cli\u003eHolick MF. Vitamin D deficiency. N Engl J Med. 2007;357:266\u0026ndash;281.\u003c/li\u003e\n \u003cli\u003eDrincic AT, Armas LAG, Van Diest EE, Heaney RP. Volumetric dilution of vitamin D in obesity. Am J Clin Nutr. 2012;95:1311\u0026ndash;1316.\u003c/li\u003e\n \u003cli\u003eWebb AR, Kline L, Holick MF. Influence of season and latitude on vitamin D synthesis. J Clin Endocrinol Metab. 2010;95:213\u0026ndash;218.\u003c/li\u003e\n \u003cli\u003eMai XM, Chen Y, Camargo CA Jr, Langhammer A. BMI, physical activity, and vitamin D status. Eur J Clin Nutr. 2012;66:120\u0026ndash;125.\u003c/li\u003e\n \u003cli\u003eZhou M, Li Y, Li J, Liu H, Wang L. Vitamin D status and body composition. Nutrients. 2018;10:1093.\u003c/li\u003e\n \u003cli\u003eJungert A, Roth HJ, Neuh\u0026auml;user-Berthold M. Sex-specific determinants of vitamin D status. Nutrients. 2018;10:1778.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"vitamin D, anthropometry, visceral adiposity, abdominal volume index, body composition","lastPublishedDoi":"10.21203/rs.3.rs-8400364/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8400364/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eVitamin D deficiency remains prevalent in tropical regions despite abundant sunlight and is linked to adverse skeletal and cardiometabolic outcomes. The relationship between vitamin D status and adiposity is inconsistent, partly due to the use of classical anthropometric indices that do not account for visceral fat. This study explores whether both classical and novel anthropometric indices reflecting visceral adiposity provide better insight into vitamin D variation in Thai adults.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A cross-sectional study of 403 adults categorized participants into deficient (\u0026lt;12 ng/mL), insufficient (12–20 ng/mL), and normal (\u0026gt;20 ng/mL) serum 25-hydroxyvitamin D [25(OH)D] groups. Classical anthropometry indices (waist circumference [WC], waist–hip ratio [WHR], body fat percentage [BF%]) and novel indices (body roundness index [BRI], conicity index [CI], weight-adjusted waist index [WWI], abdominal volume index [AVI]) were assessed. Multivariable linear and logistic regression models evaluated independent associations between adiposity markers and serum 25(OH)D after adjustment for age, sex, lifestyle, supplement use, and sun exposure time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eAlthough the crude comparisons mainly reflected differences in age and sex distribution across groups, adjusted analyses showed a different pattern of associations. Participants with normal vitamin D levels showed higher central adiposity indices (WHR, BRI, CI, and WWI) than those with deficiency. After adjustment, WC demonstrated a positive association with 25(OH)D (β = 3.53, p = 0.01), whereas AVI showed an inverse association (β = −3.90, p = 0.01), suggesting that indices capturing visceral fat volume rather than simple girth better reflect the adiposity–vitamin D relationship. BF% consistently predicted vitamin D deficiency across all logistic regression models (OR range = 2.18–2.28, all p \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eAVI appeared to be the most informative indicator of visceral adiposity associated with lower vitamin D levels. Measures that more accurately capture fat distribution, rather than overall body size, may better identify individuals at risk of vitamin D deficiency, especially in tropical settings where sun-exposure behavior may exceed adiposity alone.\u003c/p\u003e","manuscriptTitle":"Associations of Classical and Novel Anthropometric Indices with Vitamin D Status in Thai Adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-09 17:19:46","doi":"10.21203/rs.3.rs-8400364/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"95b19b26-e910-4fce-b916-17e3f5e57b71","owner":[],"postedDate":"January 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-26T10:10:30+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-09 17:19:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8400364","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8400364","identity":"rs-8400364","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}