Visceral Adiposity Index and Public Health Strategies for Metabolic Syndrome Prevention in Postmenopausal Women

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The Visceral Adiposity Index (VAI), an indirect marker of visceral fat dysfunction, may serve as a valuable predictor of MetS. Objective This study aims to investigate the association between VAI and MetS in postmenopausal women. Methods Using data from the 1999–2020 National Health and Nutrition Examination Survey (NHANES), we analyzed 5,159 postmenopausal women. MetS was defined according to the NCEP-ATP III criteria. Weighted multivariable logistic regression, subgroup analyses, and restricted cubic spline models were employed to assess the relationship between VAI and MetS. Results A significant positive association was observed between VAI and MetS (OR: 3.72, 95% CI: 3.26–4.25, P < 0.01), persisting after multivariable adjustments. In subgroup analyses, the association was stronger in non-hypertensive individuals (OR: 4.21, 95% CI: 3.48–5.09) compared to those with hypertension (OR: 2.97, 95% CI: 2.45–3.60, P-interaction < 0.01). Restricted cubic spline models suggested a nonlinear relationship, indicating that a significant positive linear relationship between MetS and VAI. Conclusion VAI is strongly associated with MetS risk in postmenopausal women and may serve as a practical tool for early screening and risk stratification. These findings highlight the need for targeted metabolic interventions in this high-risk population. VAI MetS Postmenopausal Women NHANES Women's Health Figures Figure 1 Figure 2 Figure 3 1. Introduction With the ongoing global trend of population aging, the prevalence of metabolic syndrome (MetS) continues to rise worldwide. MetS affects approximately 25% of the global adult population, with an estimated one-third of adults in the United States diagnosed with this condition [ 1 – 2 ] . Metabolic syndrome is a cluster of interrelated metabolic abnormalities, typically encompassing insulin resistance, hypertension, dyslipidemia, and obesity. Its defining features include central obesity, hyperglycemia, elevated blood pressure, hypertriglyceridemia, and reduced high-density lipoprotein cholesterol (HDL-C) levels [ 3 ] . The adverse consequences of MetS extend beyond individual health, imposing a significant burden on healthcare systems while contributing to productivity loss and a decline in overall quality of life [ 4 ] . Most notably, MetS substantially elevates the risk of cardiovascular disease (CVD), a leading cause of morbidity and mortality worldwide [ 4 ] . Furthermore, it is strongly associated with the onset of type 2 diabetes, particularly among individuals with obesity. Additionally, MetS has been linked to an increased risk of chronic kidney disease, non-alcoholic fatty liver disease (NAFLD), and certain malignancies [ 5 – 8 ] In postmenopausal women, the prevalence of MetS rises significantly, primarily driven by abnormal visceral fat accumulation and estrogen depletion-induced alterations in fat distribution [ 9 – 10 ] . The pathophysiological mechanisms underlying visceral adiposity are closely associated with insulin resistance, glucose metabolism dysfunction, and hypertension. Compared to subcutaneous fat, visceral adipose tissue (VAT) exhibits higher metabolic activity and releases elevated levels of free fatty acids and pro-inflammatory cytokines (e.g., TNF-α and IL-6) into the portal circulation. This contributes to hepatic insulin resistance and systemic chronic inflammation, thereby exacerbating metabolic disturbances [ 11 ] . Notably, studies using visceral fat area (VFA) cut-off values have demonstrated that excessive VAT accumulation in postmenopausal women serves as a potent predictor of MetS risk, revealing a dose-response relationship between VAT deposition and metabolic dysregulation [ 9 ] . Moreover, estrogen deficiency is known to regulate adipose tissue distribution through gene expression modulation, leading to a shift from subcutaneous to visceral fat accumulation. Clinical evidence further supports this, as postmenopausal women with MetS exhibit significantly elevated serum leptin levels, which strongly correlate with VAT volume, reinforcing the pivotal role of visceral adiposity in metabolic regulatio n [ 12 ] . Additionally, vitamin D deficiency and VAT accumulation may act synergistically to amplify insulin resistance via inflammatory pathway activation [ 11 ] . The Visceral Adiposity Index (VAI) is a sex-specific metric incorporating waist circumference, body mass index (BMI), triglycerides (TG), and HDL-C, designed to indirectly assess visceral fat distribution and functionality. Compared to traditional obesity indices such as BMI and waist circumference, VAI demonstrates superior sensitivity and specificity in evaluating visceral adiposity and metabolic risk [ 13 – 14 ] . Extensive research has validated the reliability of VAI in predicting and assessing conditions such as diabetes, insulin resistance, MetS, and cardiovascular disease [ 15 – 16 ] . Despite its established predictive utility, existing research on VAI has predominantly focused on the general population or male cohorts, leaving a critical knowledge gap regarding its applicability in postmenopausal women—a population particularly vulnerable to MetS. To address this gap, the present study utilizes multi-cycle data from the National Health and Nutrition Examination Survey (NHANES) spanning 1999–2020 to evaluate the role of VAI as a potential biomarker for MetS. The findings aim to provide scientific evidence supporting early screening, risk stratification, and tailored intervention strategies for postmenopausal women, contributing to the precision prevention and management of MetS. 2. Materials and methods 2..1 Study design and sample This study was based on data from the National Health and Nutrition Examination Survey (NHANES), which received approval from the Institutional Review Board (IRB), with all participants providing written informed consent. A total of 14,072 postmenopausal women were initially included from the NHANES cycles spanning 1999–2020. The sample was refined through a sequential exclusion process: first, individuals with incomplete laboratory data (n = 4,935) and missing physical examination records (n = 2,899) were excluded, leaving 6,238 participants. Subsequently, those with missing demographic information (n = 653) were further excluded, resulting in 5,585 individuals. Finally, participants with incomplete dietary data (n = 396) and those with extreme values for menopausal age or menarche onset (n = 30) were removed. Ultimately, a total of 5,159 postmenopausal women were included in the final analysis. The detailed flowchart of the participant selection process is presented in Fig. 1 . 2.2 Definition and Sources of Core Variables In this study, metabolic syndrome (MetS) was defined according to the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III) criteria, wherein individuals meeting three or more of the following conditions were classified as having MetS: (1) Fasting blood glucose (FBG) > 100 mg/dL or use of antidiabetic medication. (2) High-density lipoprotein cholesterol (HDL-C) < 50 mg/dL in women and 150 mg/dL or treatment for hypertriglyceridemia. (4) Waist circumference > 88 cm in women and > 102 cm in men. (5) Blood pressure > 130/85 mmHg or the use of antihypertensive medication [ 17 ] . The VAI was calculated using the following formula: VAI = (Waist circumference (cm) / 36.58 + (1.89 × Waist circumference (cm))) × (Triglycerides (mmol/L) / 0.81) × (1.52 / HDL cholesterol (mmol/L)) [ 18 – 19 ] A VAI value > 1.5 was considered indicative of visceral adiposity accumulation [ 20 – 21 ] . Laboratory data were obtained from serum samples collected from NHANES participants, which were processed and stored at -30°C in mobile examination centers (MECs) before being transported to the Advanced Research and Diagnostic Laboratory (ARDL) at the University of Minnesota, Minneapolis, for biochemical analysis. Detailed laboratory procedures and standard operating protocols can be accessed at: https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2017/DataFiles/P_HDL.htm . 2.3 Covariate The covariates included in this study were as follows: age, race (Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, and other races), educational attainment (less than high school, high school graduate, and more than high school), body mass index (BMI, < 30 vs. ≥30), waist circumference (< 88 cm vs. ≥88 cm), smoking status (never smoked, former smoker, and current smoker), alcohol consumption (never, former, light, moderate, and heavy drinking), the poverty-to-income ratio (PIR; ≤1.30, 1.30–3.50, and > 3.50), history of hypertension, and age at menarche. 2.4 Statistical analyses To enhance analytical robustness and minimize potential bias, multiple imputation was applied to address missing data assumed to be missing at random (MAR). For systematically missing data associated with specific sample characteristics, case wise exclusion was performed to maintain representativeness. Data analysis was conducted using R (v.4.4.3). Continuous variables were expressed as means with standard deviations (SD) and compared via t-tests, while categorical variables were reported as frequencies and percentages (n, %) and analyzed using chi-square tests. For sensitivity analyses, multivariable logistic regression models were constructed to assess the associations of VAI and visceral adiposity accumulation with MetS. To ensure robustness, three models with progressive adjustments were developed: Model 1 adjusted for age and race; Model 2 further included educational level, BMI, PIR, and waist circumference; Model 3 incorporated smoking status, alcohol consumption, age at menarche, and hypertension status. Subgroup analyses and cross-validation were performed to refine risk stratification. A restricted cubic spline (RCS) model with three knots was applied to explore the nonlinear association between VAI and MetS. Stratified analyses based on hypertension status were further conducted to assess potential effect modification, with adjustments for all 10 covariates. All statistical procedures accounted for NHANES' complex, multistage sampling design, incorporating appropriate weighting to ensure population representativeness. Weighted multivariable logistic regression was employed, with statistical significance set at P < 0.05. 3. Results 3.1 Characteristics of the Study Population A total of 5,159 participants from 11 NHANES survey cycles (1999–2020) were included in this study, with an overall weighted prevalence of MetS of 47.57%. Table 1 summarizes the associations between MetS and demographic, lifestyle, and health-related variables. Participants in the MetS group were significantly older (P < 0.01) and exhibited higher BMI, waist circumference, VAI, and hypertension prevalence compared to the non-MetS group (P < 0.01). Additionally, significant differences were observed between the two groups regarding race/ethnicity, education level, household income, smoking status, and alcohol consumption (P < 0.01), whereas age at menarche did not differ significantly (P = 0.69). Notably, the proportion of individuals with visceral adiposity accumulation was markedly higher in the MetS group than in the non-MetS group (81.62% vs. 32.39%, P < 0.01). Table.1. Table of baseline characteristics of the population. Characteristics Metabolic Syndrome Total No Yes P-value Total 5159(100.00) 2481(52.43) 2678(47.57) Age ~ years 60.61(0.25) 59.23(0.34) 62.12(0.31) < 0.01 Race~% < 0.01 Mexican American 758(4.63) 308(3.85) 450(5.50) Non-Hispanic Black 1010(9.29) 459(8.53) 551(10.13) Non-Hispanic White 2547(76.61) 1281(78.33) 1266(74.73) Other Hispanic 447(4.17) 206(3.81) 241(4.57) Other Race 397(5.29) 227(5.49) 170(5.08) Education levels~% High school < 0.01 Less than high school 1306(27.17) 578(23.52) 728(31.18) More than high school 1342(16.67) 500(12.33) 842(21.46) Family PIR 2511(56.16) 1403(64.15) 1108(47.36) BMI ~ kg/m2 29.37(0.14) 26.72(0.16) 32.30(0.19) < 0.01 Waist ~ cm 98.75(0.33) 91.69(0.37) 106.52(0.40) < 0.01 Smoking behavior~% < 0.01 Former 1322(27.71) 603(25.97) 719(29.63) Never 3015(54.51) 1498(57.61) 1517(51.10) Now 822(17.78) 380(16.42) 442(19.27) Alcohol consumption~% < 0.01 Never 1135(16.25) 498(14.36) 637(18.34) Former 1011(17.45) 421(14.05) 590(21.19) Mild 1690(37.43) 878(40.34) 812(34.22) Moderate 811(18.96) 423(20.03) 388(17.78) Heavy 512(9.91) 261(11.22) 251(8.47) Hypertension~% < 0.01 No 1922(43.75) 1351(61.49) 571(24.19) Yes 3237(56.25) 1130(38.51) 2107(75.81) Age at menarche ~ years 4.18(0.05) 4.16(0.07) 4.20(0.07) 0.69 VAI 2.28(0.05) 1.35(0.02) 3.30(0.08) < 0.01 Visceral adiposity accumulation ~% < 0.01 No 2199(44.19) 1652(67.61) 547(18.38) Yes 2960(55.81) 829(32.39) 2131(81.62) 3.2 Association Between Visceral Adiposity Levels and Metabolic Syndrome The relationship between visceral adiposity levels and MetS risk is presented in Table 2 . When VAI was treated as an independent variable, a significant positive association was observed between VAI and MetS (P < 0.01, OR: 3.72, 95% CI: 3.26–4.25), which remained robust after adjusting for multiple covariates (P < 0.01). Similarly, when visceral adiposity accumulation was considered the outcome variable, a significant positive association with MetS was also identified (P < 0.01, OR: 7.64, 95% CI: 6.18–9.45), and this association persisted after adjusting for potential confounders (P < 0.01). Table.2. Association Between Visceral Adiposity Levels and Metabolic Syndrome Variable model OR (95%CI) P-value Visceral adiposity accumulation Crude 9.27(7.78,11.05) < 0.01 model1 10.14(8.43,12.20) < 0.01 model2 6.78(5.46,8.43) < 0.01 model3 7.64(6.18,9.45) < 0.01 VAI Crude 3.55(3.16,3.99) < 0.01 model1 3.86(3.41,4.37) < 0.01 model2 3.33(2.92,3.79) < 0.01 model3 3.72(3.26,4.25) < 0.01 3.3 Subgroup Analysis Subgroup analyses examining the associations of VAI and visceral adiposity accumulation with MetS are shown in Tables 3 and Table 4 . While the strength of these associations varied across subgroups, no significant interactions were detected in any subgroup except for hypertension. A significant interaction between hypertension status and visceral adiposity accumulation was observed (P for interaction = 0.01). However, strong associations between visceral adiposity accumulation and MetS were evident in both hypertensive and non-hypertensive subgroups, with a stronger association observed in the non-hypertensive group (OR = 6.86, 95% CI: 4.38–10.75). Table.3. Results of subgroup analyses of VAI and MetS Subgroup Variable Metabolic Syndrome OR (95%CI) P value P for interaction Age 0.23 <65 4.05(3.38,4.86) < 0.01 ≥65 3.26(2.69,3.96) < 0.01 Race 0.25 Mexican American 4.68(3.30,6.63) < 0.01 Non-Hispanic Black 3.07(2.42,3.89) < 0.01 Non-Hispanic White 3.76(3.19,4.43) < 0.01 Other Hispanic 5.53(3.68,8.30) < 0.01 Other Race 4.84(2.62,8.94) < 0.01 Education levels 0.35 High school 4.12(3.11,5.46) < 0.01 Less than high school 3.31(2.74,4.01) < 0.01 More than high school 3.80(3.14,4.59) < 0.01 Family PIR 0.74 ≤1.30 4.24(3.45,5.21) < 0.01 1.30–3.50 3.84(3.16,4.66) 3.50 3.55(2.80,4.50) < 0.01 BMI 0.25 <30 3.95(3.27,4.76) < 0.01 ≥30 3.72(3.03,4.56) < 0.01 Waist 0.71 <88 4.30(3.08,6.00) < 0.01 ≥88 4.04(3.50,4.66) < 0.01 Smoking behavior 0.24 Former 3.35(2.66,4.22) < 0.01 Never 4.18(3.54,4.94) < 0.01 Now 3.73(2.86,4.86) < 0.01 Alcohol consumption 0.80 Never 4.35(3.20,5.92) < 0.01 Former 3.49(2.83,4.30) < 0.01 Mild 3.86(3.15,4.74) < 0.01 Moderate 3.88(2.57,5.85) < 0.01 Heavy 3.99(2.88,5.54) < 0.01 Hypertension 0.17 No 4.31(3.52,5.28) < 0.01 Yes 3.44(2.95,4.02) < 0.01 Table.4. Results of subgroup analyses of Visceral adiposity accumulation and MetS Subgroup Variable Metabolic Syndrome OR (95%CI) P value P for interaction Age 0.26 <65 4.90(3.51,6.85) < 0.01 ≥65 3.17(2.25,4.46) < 0.01 Race 0.23 Mexican American 7.66(4.59,12.77) < 0.01 Non-Hispanic Black 3.23(2.24,4.66) < 0.01 Non-Hispanic White 4.12(3.01,5.63) < 0.01 Other Hispanic 5.52(2.90,10.48) < 0.01 Other Race 4.19(2.08,8.45) < 0.01 Education levels 0.38 High school 4.53(2.79,7.36) < 0.01 Less than high school 4.36(2.91,6.53) < 0.01 More than high school 3.67(2.66,5.07) < 0.01 Family PIR 0.89 ≤1.30 4.85(3.50,6.74) < 0.01 1.30–3.50 4.01(2.87,5.62) 3.50 3.84(2.57,5.76) < 0.01 BMI 0.26 <30 4.05(2.98,5.51) < 0.01 ≥30 4.01(2.86,5.62) < 0.01 Waist 0.43 <88 9.62(7.34,12.61) < 0.01 ≥88 3.89(3.08,4.91) < 0.01 Smoking behavior 0.45 Former 3.53(2.22,5.61) < 0.01 Never 4.31(3.26,5.71) < 0.01 Now 4.64(2.67,8.09) < 0.01 Alcohol consumption 0.77 Never 3.87(2.45,6.11) < 0.01 Former 4.35(2.66,7.11) < 0.01 Mild 4.86(3.35,7.07) < 0.01 Moderate 3.74(2.00,6.99) < 0.01 Heavy 3.02(1.45,6.28) < 0.01 Hypertension 0.01 No 6.86(4.38,10.75) < 0.01 Yes 3.26(2.49,4.25) < 0.01 3.4 Nonlinear Association Between VAI and Metabolic Syndrome Risk The nonlinear relationship between VAI and MetS risk is depicted in Fig. 2 . After adjusting for all covariates, a significant positive linear association was observed between VAI levels and MetS risk (P for overall < 0.01, P for non-linear = 0.78). Given the significant interaction effect of hypertension in the subgroup analysis, we further stratified participants by hypertension status to examine the nonlinear association between VAI and MetS. As illustrated in Fig. 3 , a significant nonlinear relationship was identified in both hypertensive and non-hypertensive groups (P for overall < 0.01, P for nonlinearity < 0.01). 4. Discussion This study utilized a large, representative sample from the 1999–2020 NHANES database to systematically evaluate the association between VAI and MetS in postmenopausal women. The findings revealed a significant positive correlation between VAI and MetS, with a markedly increased risk of MetS in individuals with higher VAI. This association remained robust after multivariable adjustments and was further validated through subgroup analyses. Additionally, non-linear analyses suggested a potential threshold effect in the relationship between VAI and MetS. These results not only reinforce the clinical utility of VAI as a surrogate marker for visceral adiposity but also provide novel insights into metabolic health management in postmenopausal women. Previous studies have demonstrated that VAI outperforms traditional obesity indices, such as BMI and waist circumference, in predicting metabolic disturbances. Our findings further support this notion, particularly highlighting the predictive value of VAI in postmenopausal women. The decline in estrogen levels during menopause shifts fat distribution from predominantly subcutaneous to visceral adiposity [ 22 – 23 ] . Estrogen plays a crucial role in lipid metabolism and insulin sensitivity, and its reduction exacerbates chronic low-grade inflammation in adipose tissue, leading to heightened insulin resistance [ 24 ] . Furthermore, menopausal fat redistribution is closely linked to sympathetic nervous system activation and alterations in the hypothalamic–pituitary–adrenal (HPA) axis [ 25 ] , both of which contribute to MetS development. Our study suggests that VAI may serve as more than a simple adiposity marker; rather, it reflects a cluster of metabolic dysfunctions. In individuals with elevated VAI, heightened insulin resistance may drive increased lipolysis, leading to excessive free fatty acid (FFA) release, which further disrupts insulin signaling and accelerates MetS progression [ 26 ] . Subgroup analyses revealed a potential interaction effect of hypertension on the VAI–MetS relationship. The association between VAI and MetS was stronger in individuals without hypertension compared to those with hypertension. This could be attributed to several factors. First, hypertension itself represents a significant metabolic disturbance, strongly associated with insulin resistance, chronic inflammation, and autonomic dysfunction [ 27 – 28 ] . In hypertensive individuals, metabolic dysregulation may already be severe, thereby diminishing the incremental predictive value of VAI. Second, there exists a bidirectional relationship between hypertension and adipose tissue function. Excess visceral fat promotes the secretion of pro-inflammatory cytokines, such as IL-6 and TNF-α, contributing to endothelial dysfunction and arterial stiffness, ultimately leading to elevated blood pressure [ 29 – 31 ] . Conversely, chronic sympathetic activation in hypertensive individuals may further promote visceral adiposity, perpetuating a vicious cycle [ 32 ] . These findings suggest that VAI alone may be insufficient for assessing metabolic risk in hypertensive individuals, and additional metabolic indices, such as the homeostasis model assessment of insulin resistance (HOMA-IR) or fatty liver index (FLI), may be required for a more comprehensive evaluation. Interestingly, the RCS analysis indicated a potential threshold effect in the VAI–MetS relationship, wherein VAI exhibited a weaker predictive role at lower levels but was strongly associated with MetS risk beyond a certain threshold. This may reflect metabolic adaptability, where compensatory mechanisms maintain homeostasis at low VAI levels, whereas a loss of compensatory capacity at high VAI levels exacerbates metabolic disturbances [ 33 – 35 ] . Additionally, alterations in adipose tissue secretory function may play a role. At lower VAI levels, insulin-sensitizing adipokines, such as adiponectin, may counteract the adverse effects of visceral adiposity, whereas at higher VAI levels, an increase in pro-inflammatory cytokines exacerbates insulin resistance [ 36 – 37 ] . Furthermore, genetic predisposition and lifestyle factors could influence the impact of VAI on MetS, as individuals with metabolically protective genotypes may not necessarily develop MetS despite elevated VAI [ 38 ] . These findings underscore the need for individualized VAI cutoffs for MetS screening, warranting further research to optimize its predictive threshold. The implications of our findings extend beyond individual clinical management to public health strategies. Given the high prevalence of MetS and its associated cardiovascular and metabolic complications in postmenopausal women, early identification and intervention are paramount. As a simple and accessible index, VAI may serve as a valuable tool for metabolic risk assessment in population health management [ 39 ] . Incorporating VAI into routine health screenings, particularly among postmenopausal women, could aid in identifying high-risk individuals for early intervention. Implementing VAI calculation tools in primary care settings, alongside traditional indices such as BMI and waist circumference, could enhance personalized metabolic risk stratification. Additionally, given the strong association between VAI and lifestyle factors, targeted interventions—such as dietary modifications and increased physical activity—may effectively reduce VAI and, in turn, mitigate MetS risk. Public health initiatives should promote healthy lifestyle interventions, particularly in hypertensive individuals, where elevated VAI may indicate advanced metabolic dysregulation, necessitating early monitoring of glucose and lipid profiles and timely pharmacological intervention. This study leveraged the NHANES database, encompassing over two decades of multi-cycle data, ensuring robust representativeness and reliability of the findings. The use of multivariable regression, subgroup analyses, and non-linear modeling further strengthens the study’s conclusions. Notably, by focusing on postmenopausal women—a high-risk population for MetS—this study fills a critical research gap and provides new insights for clinical metabolic health management. However, several limitations should be acknowledged. First, the cross-sectional design precludes causal inference regarding the VAI–MetS relationship, necessitating longitudinal studies to establish temporal causality. Second, despite adjusting for multiple confounders, unmeasured variables such as hormonal status and genetic predisposition may have influenced the association. Additionally, while VAI is widely utilized as a metabolic risk marker, it remains an indirect measure of visceral fat. Future studies incorporating imaging modalities, such as MRI or CT, could validate the accuracy of VAI and refine its predictive model to enhance MetS risk assessment. 5. Conclusion Based on a large-scale population dataset, this study confirms a significant positive association between VAI and MetS risk in postmenopausal women, while also elucidating the interactive effect of hypertension and the potential non-linear characteristics of this relationship. These findings suggest that VAI may serve as a potential predictive marker for MetS, with applications extending beyond individualized clinical management to early screening and health promotion strategies in public health. Declarations Acknowledgments The author thanks the staff and the participants of the NHANES study for their valuable contributions. Ethics approval and consent to participate This study was conducted in accordance with the principles of the Declaration of Helsinki. The data used in this study were obtained from the NHANES, which is publicly available and de-identified. NHANES is approved by the National Center for Health Statistics (NCHS) Ethics Review Board, and all participants provided written informed consent. No additional ethical approval was required for this secondary analysis. Author Contributions HS: Methodology, Data Analysis; JL, XF managed and cleaned the data. BZ criticized and revised the manuscript. All authors contributed to the article and approved the submitted version. The authors declare no conflict of interest. Funding Beijing Natural Science Foundation (J230038) Ethics approval and consent to participate All the data were from NHANES and ethics were exempt in this study. Consent for publication We confirm that this manuscript has not been published elsewhere and is not under consideration for publication elsewhere, in whole or in part. All authors have approved the manuscript and agree with the submission. Competing interests The authors declare that they have no competing interests. Availability of data and materials Publicly available datasets were analyzed in this study. These data are available at https://wwwn.cdc.gov/nchs/nhanes/default.aspx. Clinical trial number Not applicable References Abdullozoda SM. Some aspects of epidemiology and etiopathogenesis of metabolic syndrome. Avicenna Bull. 2020;22(4):580–94. 10.25005/2074-0581-2020-22-4-580-594 . Mittal S. The Metabolic Syndrome in Clinical Practice. Springer Lond. 2008. 10.1007/978-1-84628-911-8 . Pigeot I, Ahrens W. Epidemiology of metabolic syndrome. Pflügers Archiv - Eur J Physiol. 2025. 10.1007/s00424-024-03051-7 . Wali M, Ram CVS. Metabolic Syndrome in South Asians. In Metabolic Syndrome . Springer Int Publishing. 2015;1–14. 10.1007/978-3-319-12125-3_7-1 . Mitsinikos T, Aw MM, Bandsma R, et al. FISPGHAN statement on the global public health impact of metabolic dysfunction-associated steatotic liver disease. 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Public Health Nutr. 2019;22(9):1545–54. 10.1017/S136898001800335X . Štěpánek L, Horáková D, Cibičková Ľ, et al. Can Visceral Adiposity Index Serve as a Simple Tool for Identifying Individuals with Insulin Resistance in Daily Clinical Practice? Med (Kaunas). 2019;55(9):545. 10.3390/medicina55090545 . Published 2019 Aug 29. Bermúdez VJ, Salazar J, et al. Optimal cutoff for visceral adiposity index in a Venezuelan population: Results from the Maracaibo City Metabolic Syndrome Prevalence Study. Revista Argentina de Endocrinología y Metabolismo. 2017;54(4):176–83. 10.1016/j.raem.2017.07.004 . Visceral adiposity index in kidney stone patients who have undergone surgery. Cent Eur J Urol. 2022. 10.5173/ceju.2022.0175 . Lv Y, Wang F, Sheng Y, et al. Estrogen supplementation deteriorates visceral adipose function in aged postmenopausal subjects via Gas5 targeting IGF2BP1. Exp Gerontol. 2022;163:111796. 10.1016/j.exger.2022.111796 . Hetemäki N, Savolainen-Peltonen H, Tikkanen MJ, et al. Estrogen Metabolism in Abdominal Subcutaneous and Visceral Adipose Tissue in Postmenopausal Women. J Clin Endocrinol Metabolism. 2017;102(12):4588–95. 10.1210/jc.2017-01474 . Kawakami M, Yokota-Nakagi N, Uji M, et al. Estrogen replacement enhances insulin-induced AS160 activation and improves insulin sensitivity in ovariectomized rats. Am J Physiology-Endocrinology Metabolism. 2018;315(6):E1296–304. 10.1152/ajpendo.00131.2018 . Stenyaeva NN, Khritinin DF, Stenyaev, et al. Menopause and sleep disturbances. Meditsinskiy Sovet = Med Council. 2023;15:119–24. 10.21518/ms2023-333 . Pu X, Chen D. (2021). Targeting Adipokines in Obesity-Related Tumors. Frontiers in Oncology , 11 . 10.3389/fonc.2021.685923 Rostorotskaya VV, Elgardt IA, et al. Arterial hypertension and obstructive sleep apnoea syndrome: treatment resistance and the role of autonomic dysfunction. Cardiovasc Therapy Prev. 2012;11(5):11–7. 10.15829/1728-8800-2012-5-11-17 . Nishida K, Otsu K. Inflammation and metabolic cardiomyopathy. Cardiovascular Res. 2017;113(4):389–98. 10.1093/cvr/cvx012 . Dongre UJ. Obesity, adipose tissue dysfunction and atherosclerosis. J Adv Sci Res. 2021;12(01):1–8. 10.55218/jasr.202112101 . Penna C, Pagliaro P. Endothelial Dysfunction: Redox Imbalance, NLRP3 Inflammasome, and Inflammatory Responses in Cardiovascular Diseases. Antioxidants. 2025;14(3):256. 10.3390/antiox14030256 . Balakumar P, Orayj KM, et al. Impact of the local renin–angiotensin system in perivascular adipose tissue on vascular health and disease. Cell Signal. 2024;124:111461. 10.1016/j.cellsig.2024.111461 . Lambert EA, Straznicky, et al. Should the sympathetic nervous system be a target to improve cardiometabolic risk in obesity? Am J Physiol Heart Circ Physiol. 2015;309(2):H244–58. 10.1152/ajpheart.00096.2015 . Socea B, Radu L, Clenciu D, et al. The Utility of Visceral Adiposity Index in Prediction of Metabolic Syndrome and Hypercholesterolemia. Rev Chim. 2018;69(11):3112–4. 10.37358/rc.18.11.6693 . Tabassum M, Mozaffor M, Muna, et al. Visceral Adiposity Index: An Effective Tool for Predicting Metabolic Syndrome in Bangladeshi Adult Population. Mugda Med Coll J. 2023;5(2):88–92. 10.3329/mumcj.v5i2.68807 . Barrea L, Muscogiuri G, Modica R et al. (2021). Cardio-Metabolic Indices and Metabolic Syndrome as Predictors of Clinical Severity of Gastroenteropancreatic Neuroendocrine Tumors. Frontiers in Endocrinology , 12 . 10.3389/fendo.2021.649496 Al-Mansoori L, Al-Jaber H, Prince MS, et al. Role of Inflammatory Cytokines, Growth Factors and Adipokines in Adipogenesis and Insulin Resistance. Inflammation. 2021;45(1):31–44. 10.1007/s10753-021-01559-z . Strand K, Stiglund N, Haugstøyl ME, Kamyab Z, Langhelle V, et al. Subtype-Specific Surface Proteins on Adipose Tissue Macrophages and Their Association to Obesity-Induced Insulin Resistance. Front Endocrinol. 2022;13. 10.3389/fendo.2022.856530 . Rocha ALL, Baêta T, Nazareth IR, Costa JM, et al. The role of the visceral adiposity index in the assessment of metabolic syndrome of polycystic ovary syndrome patients: a new anthropometric index. Arch Gynecol Obstet. 2024;309(4):1643–9. 10.1007/s00404-023-07328-7 . Thamilovia SA, S.Uma Mageshwari. Deriving the Cut-Off of Visceral Adiposity Index as a Promising Tool to Assess Abdominal Obesity. Sustain Agri Food Environ Research-DISCONTINUED. 2024;12(2). 10.7770/safer-v13n1-art734 . 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7061664","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":501353666,"identity":"e25915f7-602c-4647-8982-8f49912669ab","order_by":0,"name":"Sun Hao","email":"","orcid":"","institution":"Qingdao Hospital， University of Health and Rehabilitation Sciences（ Qingdao Municipal Hospital）","correspondingAuthor":false,"prefix":"","firstName":"Sun","middleName":"","lastName":"Hao","suffix":""},{"id":501353667,"identity":"d8370bde-02fa-4ca9-b596-622ba16b1e26","order_by":1,"name":"Jingwen Li","email":"","orcid":"","institution":"Ophthalmology， Qingdao Municipal Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jingwen","middleName":"","lastName":"Li","suffix":""},{"id":501353668,"identity":"ede875ea-c9ae-45d7-8a26-4c7e13f8c0bb","order_by":2,"name":"Xingyu Fu","email":"","orcid":"","institution":"Ocean University of China","correspondingAuthor":false,"prefix":"","firstName":"Xingyu","middleName":"","lastName":"Fu","suffix":""},{"id":501353669,"identity":"fcce7c36-be02-49e7-981f-80a4ea4e1958","order_by":3,"name":"Bingli Zuo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBACA2Yg8cAASLA3Nj74QLSWBJAWnsPNhjOI0gIiEkCERHqbNAcxWszZeQ+/SCioS+yXfNggzcBgJ6fbQECLZTNfmkWCAVvizNmJDcYFDMnGZgcIOewwj5lBggFP4obbiQ3JMxgOJG4jUotE4oabBxsO8xCpxfhBgoFB4oYbjI3NRGmxbOYxAwZygvHMnsRmxhkGRPjFnP+M8YcPf+pk+9mPP//xocJOjqAWIGCTQHInYeUgwExUMhkFo2AUjIIRDAAtokH79BJINgAAAABJRU5ErkJggg==","orcid":"","institution":"Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Bingli","middleName":"","lastName":"Zuo","suffix":""}],"badges":[],"createdAt":"2025-07-07 05:53:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7061664/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7061664/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89654236,"identity":"aac57e45-bbf3-46fd-a436-f685406ab61c","added_by":"auto","created_at":"2025-08-22 10:16:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":66652,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart for screening the study population.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7061664/v1/f87aefe7c201453d51475498.png"},{"id":89657106,"identity":"748a63bd-0763-4560-abb9-db0f1117ccb7","added_by":"auto","created_at":"2025-08-22 10:32:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":21804,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline curve (RCS) showing the relationship between VAI score and risk of MetS.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7061664/v1/ca74801da5ab0a1e69fa5612.png"},{"id":89654241,"identity":"9ca9c5b7-b274-4819-84bc-413def19ff12","added_by":"auto","created_at":"2025-08-22 10:16:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":28872,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline curve (RCS) showing the relationship between VAI and MetS, grouped by Hypertension.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7061664/v1/cc4ed160413f738788820a31.png"},{"id":92939510,"identity":"5da44396-bd1e-4b38-a802-12b491c7e522","added_by":"auto","created_at":"2025-10-07 11:02:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1351150,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7061664/v1/5aa4c29c-cffb-479c-8411-17dc15b6a6d0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Visceral Adiposity Index and Public Health Strategies for Metabolic Syndrome Prevention in Postmenopausal Women","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWith the ongoing global trend of population aging, the prevalence of metabolic syndrome (MetS) continues to rise worldwide. MetS affects approximately 25% of the global adult population, with an estimated one-third of adults in the United States diagnosed with this condition \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Metabolic syndrome is a cluster of interrelated metabolic abnormalities, typically encompassing insulin resistance, hypertension, dyslipidemia, and obesity. Its defining features include central obesity, hyperglycemia, elevated blood pressure, hypertriglyceridemia, and reduced high-density lipoprotein cholesterol (HDL-C) levels \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. The adverse consequences of MetS extend beyond individual health, imposing a significant burden on healthcare systems while contributing to productivity loss and a decline in overall quality of life \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Most notably, MetS substantially elevates the risk of cardiovascular disease (CVD), a leading cause of morbidity and mortality worldwide \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Furthermore, it is strongly associated with the onset of type 2 diabetes, particularly among individuals with obesity. Additionally, MetS has been linked to an increased risk of chronic kidney disease, non-alcoholic fatty liver disease (NAFLD), and certain malignancies \u003csup\u003e[\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eIn postmenopausal women, the prevalence of MetS rises significantly, primarily driven by abnormal visceral fat accumulation and estrogen depletion-induced alterations in fat distribution \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. The pathophysiological mechanisms underlying visceral adiposity are closely associated with insulin resistance, glucose metabolism dysfunction, and hypertension. Compared to subcutaneous fat, visceral adipose tissue (VAT) exhibits higher metabolic activity and releases elevated levels of free fatty acids and pro-inflammatory cytokines (e.g., TNF-α and IL-6) into the portal circulation. This contributes to hepatic insulin resistance and systemic chronic inflammation, thereby exacerbating metabolic disturbances \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Notably, studies using visceral fat area (VFA) cut-off values have demonstrated that excessive VAT accumulation in postmenopausal women serves as a potent predictor of MetS risk, revealing a dose-response relationship between VAT deposition and metabolic dysregulation \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Moreover, estrogen deficiency is known to regulate adipose tissue distribution through gene expression modulation, leading to a shift from subcutaneous to visceral fat accumulation. Clinical evidence further supports this, as postmenopausal women with MetS exhibit significantly elevated serum leptin levels, which strongly correlate with VAT volume, reinforcing the pivotal role of visceral adiposity in metabolic regulatio\u003csup\u003en [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Additionally, vitamin D deficiency and VAT accumulation may act synergistically to amplify insulin resistance via inflammatory pathway activation \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe Visceral Adiposity Index (VAI) is a sex-specific metric incorporating waist circumference, body mass index (BMI), triglycerides (TG), and HDL-C, designed to indirectly assess visceral fat distribution and functionality. Compared to traditional obesity indices such as BMI and waist circumference, VAI demonstrates superior sensitivity and specificity in evaluating visceral adiposity and metabolic risk \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Extensive research has validated the reliability of VAI in predicting and assessing conditions such as diabetes, insulin resistance, MetS, and cardiovascular disease \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDespite its established predictive utility, existing research on VAI has predominantly focused on the general population or male cohorts, leaving a critical knowledge gap regarding its applicability in postmenopausal women\u0026mdash;a population particularly vulnerable to MetS. To address this gap, the present study utilizes multi-cycle data from the National Health and Nutrition Examination Survey (NHANES) spanning 1999\u0026ndash;2020 to evaluate the role of VAI as a potential biomarker for MetS. The findings aim to provide scientific evidence supporting early screening, risk stratification, and tailored intervention strategies for postmenopausal women, contributing to the precision prevention and management of MetS.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cp\u003e\u003cb\u003e2..1 Study design and sample\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study was based on data from the National Health and Nutrition Examination Survey (NHANES), which received approval from the Institutional Review Board (IRB), with all participants providing written informed consent. A total of 14,072 postmenopausal women were initially included from the NHANES cycles spanning 1999\u0026ndash;2020. The sample was refined through a sequential exclusion process: first, individuals with incomplete laboratory data (n\u0026thinsp;=\u0026thinsp;4,935) and missing physical examination records (n\u0026thinsp;=\u0026thinsp;2,899) were excluded, leaving 6,238 participants. Subsequently, those with missing demographic information (n\u0026thinsp;=\u0026thinsp;653) were further excluded, resulting in 5,585 individuals. Finally, participants with incomplete dietary data (n\u0026thinsp;=\u0026thinsp;396) and those with extreme values for menopausal age or menarche onset (n\u0026thinsp;=\u0026thinsp;30) were removed. Ultimately, a total of 5,159 postmenopausal women were included in the final analysis. The detailed flowchart of the participant selection process is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Definition and Sources of Core Variables\u003c/h2\u003e\u003cp\u003eIn this study, metabolic syndrome (MetS) was defined according to the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III) criteria, wherein individuals meeting three or more of the following conditions were classified as having MetS: (1) Fasting blood glucose (FBG)\u0026thinsp;\u0026gt;\u0026thinsp;100 mg/dL or use of antidiabetic medication. (2) High-density lipoprotein cholesterol (HDL-C)\u0026thinsp;\u0026lt;\u0026thinsp;50 mg/dL in women and \u0026lt;\u0026thinsp;40 mg/dL in men, or the use of lipid-lowering therapy targeting HDL-C. (3) Plasma triglycerides (TG)\u0026thinsp;\u0026gt;\u0026thinsp;150 mg/dL or treatment for hypertriglyceridemia. (4) Waist circumference\u0026thinsp;\u0026gt;\u0026thinsp;88 cm in women and \u0026gt;\u0026thinsp;102 cm in men. (5) Blood pressure\u0026thinsp;\u0026gt;\u0026thinsp;130/85 mmHg or the use of antihypertensive medication \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe VAI was calculated using the following formula:\u003c/p\u003e\u003cp\u003eVAI = (Waist circumference (cm) / 36.58 + (1.89 \u0026times; Waist circumference (cm))) \u0026times; (Triglycerides (mmol/L) / 0.81) \u0026times; (1.52 / HDL cholesterol (mmol/L)) \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eA VAI value\u0026thinsp;\u0026gt;\u0026thinsp;1.5 was considered indicative of visceral adiposity accumulation \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Laboratory data were obtained from serum samples collected from NHANES participants, which were processed and stored at -30\u0026deg;C in mobile examination centers (MECs) before being transported to the Advanced Research and Diagnostic Laboratory (ARDL) at the University of Minnesota, Minneapolis, for biochemical analysis. Detailed laboratory procedures and standard operating protocols can be accessed at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2017/DataFiles/P_HDL.htm\u003c/span\u003e\u003cspan address=\"https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2017/DataFiles/P_HDL.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Covariate\u003c/h2\u003e\u003cp\u003eThe covariates included in this study were as follows: age, race (Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, and other races), educational attainment (less than high school, high school graduate, and more than high school), body mass index (BMI, \u0026lt;\u0026thinsp;30 vs. \u0026ge;30), waist circumference (\u0026lt;\u0026thinsp;88 cm vs. \u0026ge;88 cm), smoking status (never smoked, former smoker, and current smoker), alcohol consumption (never, former, light, moderate, and heavy drinking), the poverty-to-income ratio (PIR; \u0026le;1.30, 1.30\u0026ndash;3.50, and \u0026gt;\u0026thinsp;3.50), history of hypertension, and age at menarche.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Statistical analyses\u003c/h2\u003e\u003cp\u003eTo enhance analytical robustness and minimize potential bias, multiple imputation was applied to address missing data assumed to be missing at random (MAR). For systematically missing data associated with specific sample characteristics, case wise exclusion was performed to maintain representativeness. Data analysis was conducted using R (v.4.4.3). Continuous variables were expressed as means with standard deviations (SD) and compared via t-tests, while categorical variables were reported as frequencies and percentages (n, %) and analyzed using chi-square tests.\u003c/p\u003e\u003cp\u003eFor sensitivity analyses, multivariable logistic regression models were constructed to assess the associations of VAI and visceral adiposity accumulation with MetS. To ensure robustness, three models with progressive adjustments were developed: Model 1 adjusted for age and race; Model 2 further included educational level, BMI, PIR, and waist circumference; Model 3 incorporated smoking status, alcohol consumption, age at menarche, and hypertension status.\u003c/p\u003e\u003cp\u003eSubgroup analyses and cross-validation were performed to refine risk stratification. A restricted cubic spline (RCS) model with three knots was applied to explore the nonlinear association between VAI and MetS. Stratified analyses based on hypertension status were further conducted to assess potential effect modification, with adjustments for all 10 covariates.\u003c/p\u003e\u003cp\u003eAll statistical procedures accounted for NHANES' complex, multistage sampling design, incorporating appropriate weighting to ensure population representativeness. Weighted multivariable logistic regression was employed, with statistical significance set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Characteristics of the Study Population\u003c/h2\u003e\u003cp\u003eA total of 5,159 participants from 11 NHANES survey cycles (1999\u0026ndash;2020) were included in this study, with an overall weighted prevalence of MetS of 47.57%. Table\u0026nbsp;1 summarizes the associations between MetS and demographic, lifestyle, and health-related variables. Participants in the MetS group were significantly older (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and exhibited higher BMI, waist circumference, VAI, and hypertension prevalence compared to the non-MetS group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Additionally, significant differences were observed between the two groups regarding race/ethnicity, education level, household income, smoking status, and alcohol consumption (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), whereas age at menarche did not differ significantly (P\u0026thinsp;=\u0026thinsp;0.69). Notably, the proportion of individuals with visceral adiposity accumulation was markedly higher in the MetS group than in the non-MetS group (81.62% vs. 32.39%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\u003cp\u003e\u003cb\u003eTable.1.\u003c/b\u003e Table of baseline characteristics of the population.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u003cp\u003eMetabolic Syndrome\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5159(100.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2481(52.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2678(47.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u0026thinsp;~\u0026thinsp;years\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60.61(0.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59.23(0.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e62.12(0.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRace~%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMexican American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e758(4.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e308(3.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e450(5.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eNon-Hispanic Black\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1010(9.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e459(8.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e551(10.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eNon-Hispanic White\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2547(76.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1281(78.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1266(74.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eOther Hispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e447(4.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e206(3.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e241(4.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eOther Race\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e397(5.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e227(5.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e170(5.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation levels~%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHigh school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eLess than high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1306(27.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e578(23.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e728(31.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMore than high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1342(16.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e500(12.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e842(21.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFamily PIR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2511(56.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1403(64.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1108(47.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI\u0026thinsp;~\u0026thinsp;kg/m2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.37(0.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.72(0.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32.30(0.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWaist\u0026thinsp;~\u0026thinsp;cm\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e98.75(0.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e91.69(0.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e106.52(0.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmoking behavior~%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eFormer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1322(27.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e603(25.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e719(29.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3015(54.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1498(57.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1517(51.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eNow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e822(17.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e380(16.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e442(19.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAlcohol consumption~%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1135(16.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e498(14.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e637(18.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eFormer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1011(17.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e421(14.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e590(21.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMild\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1690(37.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e878(40.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e812(34.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e811(18.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e423(20.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e388(17.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHeavy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e512(9.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e261(11.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e251(8.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypertension~%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1922(43.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1351(61.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e571(24.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3237(56.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1130(38.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2107(75.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge at menarche\u0026thinsp;~\u0026thinsp;years\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.18(0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.16(0.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.20(0.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVAI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.28(0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.35(0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.30(0.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVisceral adiposity accumulation ~%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2199(44.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1652(67.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e547(18.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2960(55.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e829(32.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2131(81.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Association Between Visceral Adiposity Levels and Metabolic Syndrome\u003c/h2\u003e\u003cp\u003eThe relationship between visceral adiposity levels and MetS risk is presented in \u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e. When VAI was treated as an independent variable, a significant positive association was observed between VAI and MetS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, OR: 3.72, 95% CI: 3.26\u0026ndash;4.25), which remained robust after adjusting for multiple covariates (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Similarly, when visceral adiposity accumulation was considered the outcome variable, a significant positive association with MetS was also identified (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, OR: 7.64, 95% CI: 6.18\u0026ndash;9.45), and this association persisted after adjusting for potential confounders (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\u003cp\u003e\u003cb\u003eTable.2.\u003c/b\u003e Association Between Visceral Adiposity Levels and Metabolic Syndrome\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003emodel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003eVisceral adiposity accumulation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCrude\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.27(7.78,11.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emodel1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.14(8.43,12.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emodel2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.78(5.46,8.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emodel3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.64(6.18,9.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003eVAI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCrude\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.55(3.16,3.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emodel1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.86(3.41,4.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emodel2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.33(2.92,3.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emodel3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.72(3.26,4.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Subgroup Analysis\u003c/h2\u003e\u003cp\u003eSubgroup analyses examining the associations of VAI and visceral adiposity accumulation with MetS are shown in \u003cb\u003eTables\u0026nbsp;3\u003c/b\u003e and \u003cb\u003eTable\u0026nbsp;4\u003c/b\u003e. While the strength of these associations varied across subgroups, no significant interactions were detected in any subgroup except for hypertension. A significant interaction between hypertension status and visceral adiposity accumulation was observed (P for interaction\u0026thinsp;=\u0026thinsp;0.01). However, strong associations between visceral adiposity accumulation and MetS were evident in both hypertensive and non-hypertensive subgroups, with a stronger association observed in the non-hypertensive group (OR\u0026thinsp;=\u0026thinsp;6.86, 95% CI: 4.38\u0026ndash;10.75).\u003c/p\u003e\u003cp\u003e\u003cb\u003eTable.3.\u003c/b\u003e Results of subgroup analyses of VAI and MetS\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSubgroup Variable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eMetabolic Syndrome\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP for interaction\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.05(3.38,4.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.26(2.69,3.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRace\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMexican American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.68(3.30,6.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic Black\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.07(2.42,3.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic White\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.76(3.19,4.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Hispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.53(3.68,8.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Race\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.84(2.62,8.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation levels\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.12(3.11,5.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLess than high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.31(2.74,4.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMore than high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.80(3.14,4.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFamily PIR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.24(3.45,5.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.30\u0026ndash;3.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.84(3.16,4.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;3.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.55(2.80,4.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.95(3.27,4.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.72(3.03,4.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWaist\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.30(3.08,6.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.04(3.50,4.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmoking behavior\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFormer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.35(2.66,4.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.18(3.54,4.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.73(2.86,4.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAlcohol consumption\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.35(3.20,5.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFormer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.49(2.83,4.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMild\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.86(3.15,4.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.88(2.57,5.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeavy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.99(2.88,5.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.31(3.52,5.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.44(2.95,4.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eTable.4.\u003c/b\u003e Results of subgroup analyses of Visceral adiposity accumulation and MetS\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabd\" border=\"1\"\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSubgroup Variable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eMetabolic Syndrome\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP for interaction\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.90(3.51,6.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.17(2.25,4.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRace\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMexican American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.66(4.59,12.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic Black\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.23(2.24,4.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic White\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.12(3.01,5.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Hispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.52(2.90,10.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Race\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.19(2.08,8.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation levels\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.53(2.79,7.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLess than high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.36(2.91,6.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMore than high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.67(2.66,5.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFamily PIR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.85(3.50,6.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.30\u0026ndash;3.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.01(2.87,5.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;3.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.84(2.57,5.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.05(2.98,5.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.01(2.86,5.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWaist\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.62(7.34,12.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.89(3.08,4.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmoking behavior\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFormer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.53(2.22,5.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.31(3.26,5.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.64(2.67,8.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAlcohol consumption\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.87(2.45,6.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFormer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.35(2.66,7.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMild\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.86(3.35,7.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.74(2.00,6.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeavy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.02(1.45,6.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.86(4.38,10.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.26(2.49,4.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Nonlinear Association Between VAI and Metabolic Syndrome Risk\u003c/h2\u003e\u003cp\u003eThe nonlinear relationship between VAI and MetS risk is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. After adjusting for all covariates, a significant positive linear association was observed between VAI levels and MetS risk (P for overall\u0026thinsp;\u0026lt;\u0026thinsp;0.01, P for non-linear\u0026thinsp;=\u0026thinsp;0.78). Given the significant interaction effect of hypertension in the subgroup analysis, we further stratified participants by hypertension status to examine the nonlinear association between VAI and MetS. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, a significant nonlinear relationship was identified in both hypertensive and non-hypertensive groups (P for overall\u0026thinsp;\u0026lt;\u0026thinsp;0.01, P for nonlinearity\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study utilized a large, representative sample from the 1999\u0026ndash;2020 NHANES database to systematically evaluate the association between VAI and MetS in postmenopausal women. The findings revealed a significant positive correlation between VAI and MetS, with a markedly increased risk of MetS in individuals with higher VAI. This association remained robust after multivariable adjustments and was further validated through subgroup analyses. Additionally, non-linear analyses suggested a potential threshold effect in the relationship between VAI and MetS. These results not only reinforce the clinical utility of VAI as a surrogate marker for visceral adiposity but also provide novel insights into metabolic health management in postmenopausal women.\u003c/p\u003e\u003cp\u003ePrevious studies have demonstrated that VAI outperforms traditional obesity indices, such as BMI and waist circumference, in predicting metabolic disturbances. Our findings further support this notion, particularly highlighting the predictive value of VAI in postmenopausal women. The decline in estrogen levels during menopause shifts fat distribution from predominantly subcutaneous to visceral adiposity \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Estrogen plays a crucial role in lipid metabolism and insulin sensitivity, and its reduction exacerbates chronic low-grade inflammation in adipose tissue, leading to heightened insulin resistance \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Furthermore, menopausal fat redistribution is closely linked to sympathetic nervous system activation and alterations in the hypothalamic\u0026ndash;pituitary\u0026ndash;adrenal (HPA) axis \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e, both of which contribute to MetS development. Our study suggests that VAI may serve as more than a simple adiposity marker; rather, it reflects a cluster of metabolic dysfunctions. In individuals with elevated VAI, heightened insulin resistance may drive increased lipolysis, leading to excessive free fatty acid (FFA) release, which further disrupts insulin signaling and accelerates MetS progression \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eSubgroup analyses revealed a potential interaction effect of hypertension on the VAI\u0026ndash;MetS relationship. The association between VAI and MetS was stronger in individuals without hypertension compared to those with hypertension. This could be attributed to several factors. First, hypertension itself represents a significant metabolic disturbance, strongly associated with insulin resistance, chronic inflammation, and autonomic dysfunction \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. In hypertensive individuals, metabolic dysregulation may already be severe, thereby diminishing the incremental predictive value of VAI. Second, there exists a bidirectional relationship between hypertension and adipose tissue function. Excess visceral fat promotes the secretion of pro-inflammatory cytokines, such as IL-6 and TNF-α, contributing to endothelial dysfunction and arterial stiffness, ultimately leading to elevated blood pressure \u003csup\u003e[\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Conversely, chronic sympathetic activation in hypertensive individuals may further promote visceral adiposity, perpetuating a vicious cycle \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. These findings suggest that VAI alone may be insufficient for assessing metabolic risk in hypertensive individuals, and additional metabolic indices, such as the homeostasis model assessment of insulin resistance (HOMA-IR) or fatty liver index (FLI), may be required for a more comprehensive evaluation.\u003c/p\u003e\u003cp\u003eInterestingly, the RCS analysis indicated a potential threshold effect in the VAI\u0026ndash;MetS relationship, wherein VAI exhibited a weaker predictive role at lower levels but was strongly associated with MetS risk beyond a certain threshold. This may reflect metabolic adaptability, where compensatory mechanisms maintain homeostasis at low VAI levels, whereas a loss of compensatory capacity at high VAI levels exacerbates metabolic disturbances \u003csup\u003e[\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Additionally, alterations in adipose tissue secretory function may play a role. At lower VAI levels, insulin-sensitizing adipokines, such as adiponectin, may counteract the adverse effects of visceral adiposity, whereas at higher VAI levels, an increase in pro-inflammatory cytokines exacerbates insulin resistance \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. Furthermore, genetic predisposition and lifestyle factors could influence the impact of VAI on MetS, as individuals with metabolically protective genotypes may not necessarily develop MetS despite elevated VAI \u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. These findings underscore the need for individualized VAI cutoffs for MetS screening, warranting further research to optimize its predictive threshold.\u003c/p\u003e\u003cp\u003eThe implications of our findings extend beyond individual clinical management to public health strategies. Given the high prevalence of MetS and its associated cardiovascular and metabolic complications in postmenopausal women, early identification and intervention are paramount. As a simple and accessible index, VAI may serve as a valuable tool for metabolic risk assessment in population health management \u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. Incorporating VAI into routine health screenings, particularly among postmenopausal women, could aid in identifying high-risk individuals for early intervention. Implementing VAI calculation tools in primary care settings, alongside traditional indices such as BMI and waist circumference, could enhance personalized metabolic risk stratification. Additionally, given the strong association between VAI and lifestyle factors, targeted interventions\u0026mdash;such as dietary modifications and increased physical activity\u0026mdash;may effectively reduce VAI and, in turn, mitigate MetS risk. Public health initiatives should promote healthy lifestyle interventions, particularly in hypertensive individuals, where elevated VAI may indicate advanced metabolic dysregulation, necessitating early monitoring of glucose and lipid profiles and timely pharmacological intervention.\u003c/p\u003e\u003cp\u003eThis study leveraged the NHANES database, encompassing over two decades of multi-cycle data, ensuring robust representativeness and reliability of the findings. The use of multivariable regression, subgroup analyses, and non-linear modeling further strengthens the study\u0026rsquo;s conclusions. Notably, by focusing on postmenopausal women\u0026mdash;a high-risk population for MetS\u0026mdash;this study fills a critical research gap and provides new insights for clinical metabolic health management. However, several limitations should be acknowledged. First, the cross-sectional design precludes causal inference regarding the VAI\u0026ndash;MetS relationship, necessitating longitudinal studies to establish temporal causality. Second, despite adjusting for multiple confounders, unmeasured variables such as hormonal status and genetic predisposition may have influenced the association. Additionally, while VAI is widely utilized as a metabolic risk marker, it remains an indirect measure of visceral fat. Future studies incorporating imaging modalities, such as MRI or CT, could validate the accuracy of VAI and refine its predictive model to enhance MetS risk assessment.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eBased on a large-scale population dataset, this study confirms a significant positive association between VAI and MetS risk in postmenopausal women, while also elucidating the interactive effect of hypertension and the potential non-linear characteristics of this relationship. These findings suggest that VAI may serve as a potential predictive marker for MetS, with applications extending beyond individualized clinical management to early screening and health promotion strategies in public health.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author thanks the staff and the participants of the NHANES study for their valuable contributions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the principles of the Declaration of Helsinki. The data used in this study were obtained from the NHANES, which is publicly available and de-identified. NHANES is approved by the National Center for Health Statistics (NCHS) Ethics Review Board, and all participants provided written informed consent. No additional ethical approval was required for this secondary analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHS:\u0026nbsp;Methodology,\u0026nbsp;Data Analysis;\u0026nbsp;JL, XF managed and cleaned the data. BZ criticized and revised the manuscript.\u0026nbsp;All authors contributed to the article and approved the submitted version. The authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBeijing Natural Science Foundation (J230038)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data were from NHANES and ethics were exempt in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe confirm that this manuscript has not been published elsewhere and is not under consideration for publication elsewhere, in whole or in part. All authors have approved the manuscript and agree with the submission.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublicly available datasets were analyzed in this study. These data are available at https://wwwn.cdc.gov/nchs/nhanes/default.aspx.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdullozoda SM. Some aspects of epidemiology and etiopathogenesis of metabolic syndrome. 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Sustain Agri Food Environ Research-DISCONTINUED. 2024;12(2). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7770/safer-v13n1-art734\u003c/span\u003e\u003cspan address=\"10.7770/safer-v13n1-art734\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\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":"VAI, MetS, Postmenopausal Women, NHANES, Women's Health","lastPublishedDoi":"10.21203/rs.3.rs-7061664/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7061664/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003ePostmenopausal women are at increased risk for metabolic syndrome (MetS), largely due to visceral adiposity accumulation. The Visceral Adiposity Index (VAI), an indirect marker of visceral fat dysfunction, may serve as a valuable predictor of MetS.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eThis study aims to investigate the association between VAI and MetS in postmenopausal women.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eUsing data from the 1999\u0026ndash;2020 National Health and Nutrition Examination Survey (NHANES), we analyzed 5,159 postmenopausal women. MetS was defined according to the NCEP-ATP III criteria. Weighted multivariable logistic regression, subgroup analyses, and restricted cubic spline models were employed to assess the relationship between VAI and MetS.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eA significant positive association was observed between VAI and MetS (OR: 3.72, 95% CI: 3.26\u0026ndash;4.25, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), persisting after multivariable adjustments. In subgroup analyses, the association was stronger in non-hypertensive individuals (OR: 4.21, 95% CI: 3.48\u0026ndash;5.09) compared to those with hypertension (OR: 2.97, 95% CI: 2.45\u0026ndash;3.60, P-interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Restricted cubic spline models suggested a nonlinear relationship, indicating that a significant positive linear relationship between MetS and VAI.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eVAI is strongly associated with MetS risk in postmenopausal women and may serve as a practical tool for early screening and risk stratification. These findings highlight the need for targeted metabolic interventions in this high-risk population.\u003c/p\u003e","manuscriptTitle":"Visceral Adiposity Index and Public Health Strategies for Metabolic Syndrome Prevention in Postmenopausal Women","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-22 10:16:21","doi":"10.21203/rs.3.rs-7061664/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":"bef6ee5c-2194-41bb-9652-5fe3eb45a2fe","owner":[],"postedDate":"August 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-07T10:54:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-22 10:16:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7061664","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7061664","identity":"rs-7061664","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}