Utility of Six Novel Anthropometric Indicators for Assessing Metabolic Dysfunction-Associated Steatotic Liver Disease in US Reproductive-Aged Women: An NHANES Cross-Sectional Study

Utility of Six Novel Anthropometric Indicators for Assessing Metabolic Dysfunction-Associated Steatotic Liver Disease in US Reproductive-Aged Women: An NHANES Cross-Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Utility of Six Novel Anthropometric Indicators for Assessing Metabolic Dysfunction-Associated Steatotic Liver Disease in US Reproductive-Aged Women: An NHANES Cross-Sectional Study Huiya Huang, Tong Luo, Yangni Lu, Huabei Wu, Jinfeng Li, Tingting Tang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7188234/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In recent years, novel anthropometric indices have been shown to improve the accuracy of body fat percentage estimation and are regarded as more effective in predicting their association with metabolic dysfunction-associated fatty liver disease (MAFLD); this study aimed to evaluate their effectiveness in risk assessment for metabolic dysfunction-associated steatotic liver disease (MASLD) among U.S. women of reproductive age. Utilizing data from the U.S. NHANES database (2017–2020 cycle), 1,060 women aged 20–44 years (of reproductive age) were enrolled, with a MASLD prevalence of 40.8% (433/1,060). Multivariate logistic regression, generalized additive models with smoothing curve fitting (for dose-response relationships), and receiver operating characteristic (ROC) curve analysis (to measure predictive ability) were employed;The fully adjusted multivariable logistic regression identified significant positive associations between MASLD and all six anthropometric indices: lnLAP (OR = 7.06, 95%CI = 5.12–9.73), WTI (OR = 6.84, 95%CI = 4.65–10.06), WWI (OR = 2.95, 95%CI = 2.34–3.72), ABSI (OR = 1.89, 95%CI = 1.34–2.67), WHTR (OR = 1.13, 95%CI = 1.11–1.16), BRI (OR = 1.69, 95%CI = 1.54–1.84). Dose-response analysis revealed a 48.91-fold increased MASLD risk (95%CI = 24.68–96.93) in the highest vs. lowest lnLAP quartile, with a significant risk threshold at the lnLAP = 2.34. ROC analysis demonstrated superior predictive performance for lnLAP (AUC = 0.85, 95%CI = 0.82–0.87), followed by WHTR and BRI (AUC = 0.84, 95%CI = 0.82–0.87), and moderate accuracy for WTI (AUC = 0.80, 95%CI = 0.77–0.82), Subgroup analysis indicated race significantly modified associations for WTI and ABSI.The novel anthropometric indices—particularly lnLAP—represent effective screening tools for MASLD in women of reproductive age. These findings provide a clinically applicable and cost-effective strategy for early risk stratification in this population, with notable implications for preventive care in primary health settings. metabolic dysfunction-associated steatotic liver disease (MASLD) women of reproductive age lipid accumulation product (LAP) waist-to-height ratio (WHTR) anthropometric indices early screening Figures Figure 1 Figure 2 Figure 3 1. Introduction The liver acts as the central regulator of lipid homeostasis and orchestrates crucial physiological processes, including blood volume regulation, immune system maintenance, endocrine-modulated growth signaling, cholesterol metabolism, and xenobiotic compound detoxification – encompassing the metabolism of numerous pharmacological agents. Metabolic dysfunction-associated fatty liver disease (MASLD), designated by the World Health Organization as a priority non-communicable disease ( 1 ), poses a mounting threat to global health. Recent epidemiological data reveal that the worldwide prevalence of MASLD reached 1.2679 billion cases in 2021, marking a 24.3% increase since 1990 ( 2 , 3 ) . Women, particularly those of reproductive age, constitute a vulnerable population whose health remains a priority focus in global public health initiatives. Study demonstrates that women with metabolic syndrome exhibit a disproportionately higher susceptibility to MASLD compared to affected men ( 4 ). Women, particularly those of reproductive age, constitute a vulnerable population whose health remains a priority focus in global public health initiatives. Study demonstrates that women with metabolic syndrome exhibit a disproportionately higher susceptibility to MASLD compared to affected men ( 5 , 6 ). With lifestyle changes, the prevalence of MASLD among US women of reproductive age has shown a persistent upward trend, as consistently documented across multiple NHANES survey cycles ( 7 , 8 ). As the core demographic for social productivity and family nurturing, the health status of reproductive-aged women not only impacts individual quality of life but also influences offspring development and family-social functioning. Consequently, in-depth investigation into the epidemiological features and early warning indicators of MASLD in this population carries substantial clinical value and societal importance. The current diagnostic framework for MASLD faces unique challenges in clinical practice. While liver biopsy remains the gold standard, its invasive nature poses inherent risks. Body mass index (BMI) is routinely employed to classify obesity grades. It was commonly believed that only individuals with elevated BMI are at a bigger risk of developing MASLD. This perspective is fundamentally imprecise, as BMI neither reflects body fat percentage nor accurately evaluates associated health risks( 9 ) - evidenced by the significant prevalence of MASLD among normal-weight individuals. To address these limitations, researchers have developed a series of novel composite indices ( 10 – 12 ) : the logarithmic lipid accumulation product (lnLAP), which integrates waist circumference and triglyceride levels, has demonstrated outstanding performance in assessing metabolic abnormalities ( 13 ) ; the waist-to-height ratio (WHTR) effectively reflects visceral fat accumulation, while the body roundness index (BRI) demonstrates unique value in cardiovascular risk assessment ( 14 ) ; a new generation of anthropometric indices has emerged, including the A Body Shape Index (ABSI), Weight-to-Height Index (WTI), and Weight-Adjusted-Waist Index (WWI) ( 15 ), WWI circumvents BMI's interference from muscle mass, while ABSI significantly enhances mortality risk prediction accuracy by integrating waist circumference, BMI, and height ( 16 ). These novel indices provide promising approaches for non-invasive MASLD screening in women of reproductive age. However, current evidence remains insufficient to comprehensively validate the correlation between these emerging anthropometric parameters and MASLD risk specifically in this population. This study aimed to investigate associations between novel anthropometric indices and MASLD in U.S. reproductive-aged women, assess their predictive value for MASLD risk, and identify evidence-based prevention strategies. 2. Materials and Methods 2.1 Data Source All data in this study were derived from the 2017–2020 cycle of the National Health and Nutrition Examination Survey (NHANES), comprising comprehensive variables including: demographic characteristics, physical examination metrics, laboratory biomarkers (e.g., fasting glucose, lipid profiles), lifestyle factors, as well as liver health parameters - controlled attenuation parameter (CAP) and liver stiffness measurement (LSM) ( 17 ). This study strictly adhered to ethical guidelines, with all participants providing written informed consent and obtaining approval from the NCHS Ethics Review Board (Protocol Numbers: 2011-17 and 2018-01). Both study implementation and reporting complied with STROBE guidelines ( 18 ) . 2.2 Diagnostic Criteria and Study Population Based on established guidelines and prior research ( 19 – 21 ), we defined significant hepatic steatosis as a controlled attenuation parameter (CAP) ≥ 248 dB/m. The diagnosis of MASLD required ( 22 ) : ( 1 ) significant hepatic steatosis; ( 2 ) the presence of at least one cardiometabolic risk factor (CMRF); ( 3 ) exclusion of excessive alcohol consumption (> 140 g/week for women or > 210 g/week for men) and other known causes of hepatic steatosis. As shown in Fig. 1 , a multi-stage process was used to exclude ineligible subjects. The exclusion criteria were as follows: ( 1 ) participants who did not complete elastography examination (n = 5,862); ( 2 ) male participants (n = 4,852); ( 3 ) age 44 years (n = 3,297); ( 4 ) missing key data (anthropometric measurements n = 43, serum biomarkers n = 80, alcohol consumption and blood pressure data n = 203); ( 5 ) excessive alcohol consumption (n = 3) or viral hepatitis (n = 7); ( 6 ) missing sleep data (n = 3) or extreme obesity/underweight (n = 156). Finally, the eligible subjects were divided into the MASLD group and the non-MASLD group. The following formulas were used to calculate lnLAP, WHTR, BRI, WTI, WWI, and ABSI ( 23 – 27 ) : $$\:\:\:\text{W}\text{H}\text{T}\text{R}\:=\:\text{W}\text{C}\:\left(\text{c}\text{m}\right)\:/\text{H}\text{e}\text{i}\text{g}\text{h}\text{t}\:\left(\text{m}\right)$$ ; $$\:\text{B}\text{R}\text{I}=364.2-365.5\times\:\sqrt{1-{\left(\frac{WC\left(cm\right)}{2\pi\:}\right)}^{2}/{(0.5\times\:\text{H}\text{e}\text{i}\text{g}\text{h}\text{t}(\text{m}\left)\right)}^{2}}$$ ; $$\:\text{l}\text{n}\text{L}\text{A}\text{P}=\text{ln}\left[\right(\text{W}\text{C}\left(\text{c}\text{m}\right)58)\times\:\text{T}\text{G}（\text{m}\text{m}\text{o}\text{l}/\text{L})]$$ ; $$\:\text{W}\text{T}\text{I}\:=\:\text{ln}\left[\text{T}\text{G}\right(\text{m}\text{g}/\text{d}\text{L})\:\times\:\:\text{W}\text{C}(\text{c}\text{m})/2]$$ ; $$\:\text{W}\text{W}\text{I}=WC\left(cm\right)/\sqrt{Weigℎt\left(kg\right)}$$ ; $$\:ABSI=WC\left(cm\right)/\sqrt{Heigℎt\left(m\right)}\times\:\sqrt[3]{{BMI}^{2}}$$ 2.3. Statistical Analysis This study employed R 4.4.3, SPSS 27, and Zstats software for data analysis. All statistical tests were two-tailed, with P < 0.05 considered statistically significant. Following NHANES analytical guidelines, 2-year sample weights were applied to enhance national representativeness ( 28 ). Continuous variables were presented as mean ± standard deviation (x̄±s) and compared using linear regression, while categorical variables were expressed as counts (percentages) and compared via χ² tests. Multivariable regression analysis utilized a stepwise adjustment approach with three models: Model 1 (unadjusted); Model 2 (adjusted for age, race, diabetes, and hypertension); Model 3 (further adjusted for sleep duration, LSM, alkaline phosphatase (ALP), γ-glutamyl transferase (GGT), total bilirubin (TBIL), glucose (GLU), platelets (PLT), and high-density lipoprotein cholesterol (HDL-C)). Variance inflation factors (VIFs) < 5 in logistic regression models confirmed no significant multicollinearity. The analysis further evaluated MASLD risk differences across quartile groups (Q1-Q4) of lnLAP, WHTR, BRI, WTI, WWI, and ABSI. Stratified subgroup analyses by age, race, and hypertension status with interaction tests (Pinteraction) assessed potential effect modifications ( 29 ). Smoothing curve fittings detected nonlinear relationships, with threshold effects determined by two-piece linear regression models and verified by log-likelihood ratio tests (LRTs). Receiver operating characteristic (ROC) curve analysis quantified predictive performance through area under the curve (AUC) values ( 30 ) ,comparing all six novel anthropometric indices. 3. Results 3.1 Baseline Characteristics of Study Participants As shown in Table 1 , a total of 1,060 women aged 20–44 years of reproductive age were included. Of these 1,060 female participants, 433 (40.85%) were diagnosed with MASLD, with a mean age of 33.43 ± 7.05 years. Compared to the non-MASLD group, the MASLD group had a higher proportion of Hispanic individuals, an older mean age, shorter daily sleep duration, and significantly higher prevalence rates of diabetes and hypertension (all p < 0.05). In terms of metabolic parameters, the MASLD group eshibited higher levels of BMI, waist circumference (WC), triglycerides (TG), total cholesterol (TC), aspartate aminotransferase (AST), CAP, LSM, GLU, PLT, and ALP (all p < 0.05), along with significantly decreased HDL-C levels. Additionally, all metabolic assessment indices - including lnLAP, WHTR, BRI, WTI, WWI, ABSI, and HIS - demonstrated significantly higher values in the MASLD group (all p < 0.05). Table 1 presents weighted baseline characteristics of participants with and without MASLD as assessed by VCTE in the NHANES 2017–2020 database Variables Total (n = 1060) Non-MASLD(n = 627) MASLD (n = 433) p Demographic parameters AGE(years), Mean ± SD 32.06 ± 7.16 31.11 ± 7.09 33.43 ± 7.05 < 0.001 RACE, n(%) < 0.001 Mexican American 146 (13.77) 65 (10.37) 81 (18.71) Non-Hispanic White 335 (31.60) 219 (34.93) 116 (26.79) Non-Hispanic Black 271 (25.57) 166 (26.48) 105 (24.25) Other 308 (29.06) 177 (28.23) 131 (30.25) EDUCATION 0.521 Less than high school 26 (2.45) 18 (2.87) 8 (1.85) High school 95 (8.96) 54 (8.61) 41 (9.47) More than high school 939 (88.58) 555 (88.52) 384 (88.68) MARITAL 0.162 Married 559 (52.74) 332 (52.95) 227 (52.42) Unmarried 393 (37.08) 240 (38.28) 153 (35.33) Others 108 (10.19) 55 (8.77) 53 (12.24) Anthropometric parameters BMI (kg/m2) 29.35 ± 7.87 25.79 ± 5.98 34.51 ± 7.41 < 0.001 WC (cm) 94.56 ± 17.96 85.96 ± 13.64 107.02 ± 16.07 < 0.001 VCTE parameters LSM(kPa) 4.80 ± 1.96 4.46 ± 1.32 5.28 ± 2.55 < 0.001 CAP (dB/m) 238.40 ± 59.03 198.45 ± 31.76 296.25 ± 37.55 < 0.001 Serum test ALT(U/L) 15.95 ± 9.94 13.92 ± 7.32 18.90 ± 12.24 < 0.001 AST(U/L) 17.92 ± 8.77 17.14 ± 4.86 19.03 ± 12.33 < 0.001 ALP(IU/L) 68.18 ± 21.66 63.02 ± 18.62 75.66 ± 23.51 < 0.001 GGT(IU/L) 20.07 ± 27.10 17.10 ± 26.08 24.38 ± 27.98 < 0.001 TBIL (mg/dL) 0.39 ± 0.24 0.41 ± 0.26 0.36 ± 0.21 < 0.001 GRE(mg/dL) 0.72 ± 0.12 0.72 ± 0.12 0.71 ± 0.13 0.202 GLU(mg/dL) 90.07 ± 18.15 87.71 ± 13.86 93.49 ± 22.57 < 0.001 PLT(10 9 /L) 274.77 ± 68.48 264.52 ± 62.69 289.62 ± 73.67 < 0.001 HDL-C(mg/dL) 57.02 ± 15.16 60.70 ± 14.73 51.68 ± 14.17 < 0.001 TC(mg/dL) 174.86 ± 31.99 171.62 ± 30.25 179.55 ± 33.84 < 0.001 TG(mg/dL) 96.58 ± 44.02 83.92 ± 37.67 114.90 ± 46.11 < 0.001 Noninvasive indices and models WHTR 58.42 ± 10.99 53.18 ± 8.53 65.99 ± 9.66 < 0.001 AIP 0.49 ± 0.12 0.46 ± 0.11 0.55 ± 0.12 < 0.001 BRI 5.31 ± 2.56 4.12 ± 1.85 7.04 ± 2.46 < 0.001 lnLAP 3.45 ± 0.81 3.07 ± 0.72 4.01 ± 0.58 < 0.001 WTI 3.83 ± 0.53 3.61 ± 0.47 4.15 ± 0.44 < 0.001 WWI 10.81 ± 0.80 10.50 ± 0.70 11.27 ± 0.72 < 0.001 ABSI 7.87 ± 0.43 7.79 ± 0.41 7.97 ± 0.43 < 0.001 HIS 38.49 ± 9.05 34.29 ± 6.81 44.59 ± 8.41 < 0.001 Lifestyle Alcohol consumption (g/day) 7.61 ± 1.59 7.73 ± 1.60 7.44 ± 1.55 0.983 Sleep hours(h/day) 1.28 ± 14.33 1.29 ± 14.08 1.27 ± 14.71 < 0.001 Metabolic diseases Hypertension (%) 74 (6.98) 30 (4.78) 44 (10.16) < 0.001 Diabetes (%) 39 (3.68) 10 (1.59) 29 (6.70) < 0.001 3.2. The associations between six novel anthropometric indices and MASLD risk This study employed binary logistic regression models to examine the associations between six novel anthropometric indices and MASLD risk among U.S. women of reproductive age. In continuous variable analyses, each standard deviation increase in all indices demonstrated significant positive correlations with MASLD prevalence (all trend test p-values < 0.05). The associations were most pronounced for lnLAP (OR = 7.06, 95%CI:5.12–9.73) and WTI (OR = 6.84, 95%CI:4.65–10.06), while WWI (OR = 2.95), ABSI (OR = 1.89), WHTR (OR = 1.13), and BRI (OR = 1.69) showed relatively lower but still statistically significant effect sizes.Quartile analyses revealed significant dose-response relationships for all indices (trend test p < 0.001), with risk ratios ranging from 2.08 (95%CI:1.37–3.16) for ABSI to 48.91 (95%CI:24.68–96.93) for lnLAP when comparing the highest (Q4) versus lowest (Q1) quartiles. Notably, lnLAP, WTI, and WHTR/BRI exhibited exceptionally strong predictive capacity in Q4 (all ORs > 30). After full adjustment, while effect sizes were attenuated, all indices maintained high significance - lnLAP showed the most stable association (OR decreased from 8.47 to 7.06), whereas WTI demonstrated the greatest adjustment impact (OR reduced from 11.45 to 6.84). (Table 2 ) Table 2 Binary logistic regression analysis of the associations between six novel anthropometric indices and MASLD Variables Crude model 1 Minimally adjusted model 2 Fully adjusted model 3 OR (95%CI) p OR (95%CI) p OR (95%CI) p lnLAP 8.47 (6.48 ~ 11.06) < 0.001 8.14 (6.18 ~ 10.72) < 0.001 7.06 (5.12 ~ 9.73) < 0.001 lnLAP group Q1 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) Q2 4.46 (2.49 ~ 7.98) < 0.001 4.28 (2.38 ~ 7.70) < 0.001 4.25 (2.32 ~ 7.81) < 0.001 Q3 17.70 (10.11 ~ 30.98) < 0.001 16.84 (9.52 ~ 29.78) < 0.001 15.47 (8.32 ~ 28.77) < 0.001 Q4 70.36 (38.83 ~ 127.47) < 0.001 63.19 (34.49 ~ 115.79) < 0.001 48.91 (24.68 ~ 96.93) < 0.001 WTI 11.45 (8.24 ~ 15.93) < 0.001 10.39 (7.40 ~ 14.58) < 0.001 6.84 (4.65 ~ 10.06) < 0.001 WTI group Q1 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) Q2 3.89 (2.36 ~ 6.40) < 0.001 3.73 (2.26 ~ 6.18) < 0.001 3.25 (1.93 ~ 5.50) < 0.001 Q3 11.42 (7.04 ~ 18.52) < 0.001 10.64 (6.52 ~ 17.37) < 0.001 7.75 (4.59 ~ 13.07) < 0.001 Q4 27.44 (16.64 ~ 45.23) < 0.001 23.44 (14.07 ~ 39.06) < 0.001 12.93 (7.32 ~ 22.81) < 0.001 WWI 4.42 (3.58 ~ 5.46) < 0.001 4.04 (3.25 ~ 5.01) < 0.001 2.95 (2.34 ~ 3.72) < 0.001 WWI group Q1 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) Q2 2.58 (1.65 ~ 4.05) < 0.001 2.38 (1.51 ~ 3.75) < 0.001 2.05 (1.28 ~ 3.29) < 0.001 Q3 6.85 (4.44 ~ 10.56) < 0.001 5.87 (3.77 ~ 9.13) < 0.001 3.94 (2.47 ~ 6.28) < 0.001 Q4 18.21 (11.61 ~ 28.57) < 0.001 15.05 (9.49 ~ 23.86) < 0.001 7.93 (4.83 ~ 13.01) < 0.001 ABSI 2.78 (2.05 ~ 3.76) < 0.001 2.29 (1.67 ~ 3.13) < 0.001 1.89 (1.34 ~ 2.67) < 0.001 ABSI group Q1 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) Q2 1.37 (0.95 ~ 1.98) 0.093 1.28 (0.88 ~ 1.87) 0.190 1.22 (0.81 ~ 1.84) 0.349 Q3 2.00 (1.40 ~ 2.87) < 0.001 1.76 (1.22 ~ 2.55) < 0.001 1.55 (1.03 ~ 2.32) 0.035 Q4 3.20 (2.23 ~ 4.60) < 0.001 2.52 (1.73 ~ 3.67) < 0.001 2.08 (1.37 ~ 3.16) < 0.001 WHTR 1.15 (1.13 ~ 1.18) < 0.001 1.15 (1.13 ~ 1.17) < 0.001 1.13 (1.11 ~ 1.16) < 0.001 WHTR group Q1 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) Q2 5.16 (2.90 ~ 9.19) < 0.001 4.59 (2.56 ~ 8.24) < 0.001 3.97 (2.19 ~ 7.22) < 0.001 Q3 17.97 (10.26 ~ 31.46) < 0.001 15.79 (8.92 ~ 27.95) < 0.001 11.62 (6.39 ~ 21.13) < 0.001 Q4 58.08 (32.35 ~ 104.28) < 0.001 51.66 (28.52 ~ 93.56) < 0.001 31.34 (16.42 ~ 59.82) < 0.001 BRI 1.85 (1.71 ~ 2.00) < 0.001 1.82 (1.68 ~ 1.97) < 0.001 1.69 (1.54 ~ 1.84) < 0.001 BRI group Q1 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) Q2 5.16 (2.90 ~ 9.19) < 0.001 4.59 (2.56 ~ 8.24) < 0.001 3.97 (2.19 ~ 7.22) < 0.001 Q3 17.97 (10.26 ~ 31.46) < 0.001 15.79 (8.92 ~ 27.95) < 0.001 11.62 (6.39 ~ 21.13) < 0.001 Q4 58.08 (32.35 ~ 104.28) < 0.001 51.66 (28.52 ~ 93.56) < 0.001 31.34 (16.42 ~ 59.82) < 0.001 Further stratified analyses (Table 3 ) demonstrated that all six anthropometric indices maintained significant positive associations with MASLD risk (p < 0.05), though the effect sizes exhibited substantial heterogeneity across subgroups. Notably, significant interaction effects were observed between race and the predictive value of WTI (p < 0.001), ABSI (p < 0.001), and WHTR/BRI (p = 0.025/0.026) for MASLD risk. Table 3 Stratified associations between six novel anthropometric indices and MASLD by age, race, and hypertension status Variable Subgroup Total(N) MASLD(N) OR (95% CI) p for interaction lnLAP 1060 433 7.21 (5.30 ~ 9.82) AGE (years) 0.629 20–30 470 156 6.62 (4.09 ~ 10.70) 31–44 590 277 7.53 (4.86 ~ 11.67) RACE 0.099 Mexican American 146 81 3.98 (1.70 ~ 9.33) Non-HispanicWhite 335 116 2.52 (1.44 ~ 4.42) Non-Hispanic Black 271 105 11.14 (5.69 ~ 21.83) Other 308 131 16.61 (8.20 ~ 33.67) HYPERTENSION 0.123 No 986 389 6.88 (5.00 ~ 9.47) Yes 74 44 10.64 (2.41 ~ 47.09) WTI 1060 433 7.50 (5.18 ~ 10.87) AGE (years) 0.523 20–30 470 156 6.43 (3.57 ~ 11.57) 31–44 590 277 7.16 (4.29 ~ 11.96) RACE < 0.001 Mexican American 146 81 3.49 (1.20 ~ 10.10) Non-HispanicWhite 335 116 1.07 (0.50 ~ 2.27) Non-Hispanic Black 271 105 33.74 (13.52 ~ 84.18) Other 308 131 14.03 (6.45 ~ 30.52) HYPERTENSION 0.698 No 986 389 7.06 (4.79 ~ 10.39) Yes 74 44 9.15 (1.87 ~ 44.90) WWI 1060 433 3.18 (2.53 ~ 4.00) AGE (years) 0.518 20–30 470 156 2.73 (1.93 ~ 3.85) 31–44 590 277 3.28 (2.39 ~ 4.51) RACE 0.469 Mexican American 146 81 1.66 (0.84 ~ 3.28) Non-HispanicWhite 335 116 2.39 (1.51 ~ 3.79) Non-Hispanic Black 271 105 3.47 (2.25 ~ 5.37) Other 308 131 3.76 (2.41 ~ 5.87) HYPERTENSION 0.529 No 986 389 3.16 (2.49 ~ 4.00) Yes 74 44 3.21 (1.18 ~ 8.74) ABSI 1060 433 2.20 (1.57–3.08) AGE (years) 0.283 20–30 470 156 2.17 (1.56 ~ 3.04) 31–44 590 277 RACE 1.71 (0.99 ~ 2.96) < 0.001 Mexican American 146 81 2.24 (1.45 ~ 3.45) Non-HispanicWhite 335 116 Non-Hispanic Black 271 105 0.79 (0.27 ~ 2.27) Other 308 131 0.62 (0.30 ~ 1.27) HYPERTENSION 3.44 (1.80 ~ 6.60) 0.981 No 986 389 5.44 (2.68 ~ 11.05) Yes 74 44 WHTR 1060 433 1.14 (1.11 ~ 1.16) AGE (years) 0.836 20–30 470 156 1.13 (1.09 ~ 1.16) 31–44 590 277 1.14 (1.11 ~ 1.17) RACE 0.025 Mexican American 146 81 1.11 (1.04 ~ 1.18) Non-HispanicWhite 335 116 1.15 (1.11 ~ 1.20) Non-Hispanic Black 271 105 1.09 (1.05 ~ 1.13) Other 308 131 1.17 (1.12 ~ 1.22) HYPERTENSION 0.169 No 986 389 1.14 (1.11 ~ 1.16) Yes 74 44 1.13 (1.03 ~ 1.23) BRI 1060 433 1.70 (1.56 ~ 1.86) AGE (years) 0.871 20–30 470 156 1.65 (1.44 ~ 1.89) 31–44 590 277 1.73 (1.53 ~ 1.96) RACE 0.026 Mexican American 146 81 1.55 (1.18 ~ 2.03) Non-HispanicWhite 335 116 1.83 (1.52 ~ 2.21) Non-Hispanic Black 271 105 1.44 (1.24 ~ 1.69) Other 308 131 1.92 (1.59 ~ 2.31) HYPERTENSION 0.116 No 986 389 1.72 (1.57 ~ 1.88) Yes 74 44 1.57 (1.10 ~ 2.25) Additionally, Fig. 2 presents smoothed curve fitting analyses examining nonlinear correlations between lnLAP, WTI, WHTR, BRI, WWI, ABSI and MASLD. The smoothing curves demonstrate robust associations between these indices and MASLD (all p < 0.001), with adjustment strategies mirroring the fully adjusted model. Figures 2 e and 2 f illustrate continuous, approximately linear relationships of WWI and ABSI with MASLD risk, without evident saturation or threshold effects. Notably, Fig. 2 a reveals a distinct pattern for lnLAP: while initial MASLD risk changes were minimal, a marked risk escalation occurred at the specific threshold of lnLAP = 2.34, indicating a piecewise linear association. Log-likelihood ratio tests (LRT) confirmed statistical significance at this inflection point (p < 0.001), supporting the superiority of a two-piece regression model. Similar threshold effects were identified for WTI (inflection point = 3.63), WHTR (52.84), and BRI (4.15). 3.3 Comparative predictive performance of lnLAP versus other anthropometric indices for MASLD: A receiver operating characteristic (ROC) curve analysis The hepatic steatosis index (HSI) is a commonly used noninvasive model for MASLD diagnosis. To evaluate the effectiveness of lnLAP versus other anthropometric indices in identifying MASLD, we conducted comparative analyses of specificity (SPE) and sensitivity (SEN) among lnLAP, WHTR, BRI, WTI, WWI, ABSI, and HSI for MASLD prediction.As illustrated in Fig. 3 , we employed receiver operating characteristic (ROC) curve analysis to determine the discriminative performance of six novel anthropometric indices—lnLAP, WHTR, BRI, WTI, WWI, and ABSI—for MASLD detection. As shown in Table 4 , lnLAP demonstrated superior predictive performance with the highest area under the curve (AUC) of 0.85 (95%CI: 0.82–0.87), followed by WHTR/BRI (AUC = 0.84, 95%CI: 0.82–0.87), both significantly outperforming other indices (all p < 0.001) including HIS (0.83, 95%CI: 0.81–0.86), WTI (0.80, 95%CI: 0.77–0.82), WWI (0.78, 95%CI: 0.75–0.81), and ABSI (0.62, 95%CI: 0.59–0.66). Further analysis of lnLAP's diagnostic characteristics revealed at its optimal cutoff (3.48): sensitivity (SEN) of 82%, specificity (SPE) of 87%, positive predictive value (PPV) of 85%, and negative predictive value (NPV) of 82%. Table 4 Performance evaluation of lnLAP, WHTR, BRI, WTI, WWI and ABSI for predicting MASLD Variables AUC (95% CI) SEN (95% CI) SPE (95% CI) PPV (95% CI) NPV (95% CI) Cut of value lnLAP 0.85(0.82–0.87) 0.82(0.87 − 0.85) 0.87(0.85 − 0.82) 0.85(0.82–0.88) 0.82(0.88 − 0.71) 3.48 BRI 0.84(0.82–0.87) 0.82(0.87 − 0.76) 0.87(0.76 − 0.71) 0.76(0.71–0.8) 0.71(0.8 − 0.78) 5.2 WHTR 0.84(0.82–0.87) 0.82(0.87 − 0.76) 0.87(0.76 − 0.71) 0.76(0.71–0.8) 0.71(0.8 − 0.78) 58.97 WWI 0.78(0.75–0.81) 0.75(0.81 − 0.72) 0.81(0.72 − 0.67) 0.72(0.67–0.76) 0.67(0.76 − 0.72) 10.85 WTI 0.80(0.77–0.82) 0.77(0.82 − 0.77) 0.82(0.77 − 0.73) 0.77(0.73–0.81) 0.73(0.81 − 0.7) 3.84 ABSI 0.62(0.59–0.66) 0.59(0.66–0.7) 0.66(0.7 − 0.65) 0.7(0.65–0.74) 0.65(0.74 − 0.51) 7.78 HIS 0.83(0.81–0.86) 0.81(0.86 − 0.75) 0.86(0.75 − 0.71) 0.75(0.71–0.79) 0.71(0.79 − 0.78) 39.06 4. Discussion This large-scale cross-sectional study, based on data from the NHANES, systematically evaluated six novel anthropometric indicators (lnLAP, WHTR, BRI, WTI, WWI, ABSI) for screening MASLD in women of reproductive age. The findings revealed that nearly half of U.S. women of reproductive age have MASLD, indicating this condition has become a prevalent chronic disease among this demographic. Our results confirmed independent associations between all six new anthropometric indicators and MASLD risk after fully adjusting for confounding factors. Notably, lnLAP and WTI demonstrated the strongest predictive power, laying the foundation for developing non-invasive screening strategies. MASLD is a chronic condition that develops over years, making regular screening and timely diagnosis crucial for preventing severe complications such as hepatocellular carcinoma and cirrhosis ( 31 ). However, diagnosis and screening remain challenging due to high costs and limited accessibility. Multiple international studies have identified obesity and advanced age as risk factors for MASLD. Thus, current research primarily uses BMI and weight as key indicators for predicting MASLD. Notably, in young, muscular individuals, what appears as obesity may actually result from increased muscle mass ( 32 ). Furthermore, reduced muscle mass may impair BMI's accuracy in quantifying obesity within specific populations ( 33 ). Body weight and BMI do not accurately reflect body fat distribution ( 34 ). To more precisely examine various obesity patterns, several novel anthropometric indicators have recently been introduced. For instance, lnLAP and WTI have garnered significant attention due to their exceptional predictive value for visceral fat accumulation and insulin resistance ( 35 , 36 ) . Based on our findings, individuals in the highest quartile of lnLAP exhibited a 48.91-fold increased risk of MASLD compared to those in the lowest quartile (95% CI: 24.68–96.93). Furthermore, lnLAP demonstrated optimal predictive capacity (AUC = 0.85, 95% CI: 0.82–0.87), with significantly superior sensitivity (82% vs 79%) and specificity (87% vs 81%) over the conventional HIS index. As composite indicators integrating waist circumference and triglycerides, lnLAP simultaneously capture two major pathophysiological features of MASLD: visceral obesity and insulin resistance ( 37 ). Visceral adipose tissue promotes hepatic steatosis through two pathways: direct action (release of free fatty acids) and indirect action (through inflammatory cytokines like TNF-α and IL-6 that exacerbate insulin resistance) ( 38 – 41 ).Notably, a significant threshold effect was observed in the relationship between MASLD and lnLAP. The smoothing curve fitting determined a critical point of lnLAP = 2.34, suggesting that when this threshold is exceeded, the positive correlation between lnLAP and MASLD in women of reproductive age becomes significantly enhanced, indicating its potential as an important clinical early-warning indicator. The predictive performance of BRI and WTI was comparable (AUC = 0.84). The predictive power of WWI (AUC = 0.78) and ABSI (AUC = 0.62) is relatively limited, especially ABSI shows poor sensitivity and specificity in the independent screening scenario. Our findings indicate that the WTI indicator demonstrates the strongest predictive power among women of reproductive age who are non-Hispanic Black, while the ABSI indicator shows optimal performance in non-Hispanic White populations. A multicenter study ( 42 ) (n = 51,452) revealed that Hispanic and non-Hispanic American patients with MASLD exhibited significantly higher rates of cirrhosis, diabetes, and mortality compared to European Americans across different racial groups. Another cohort study based on the NHANES ( 43 ) (n = 40,166) highlighted a notable increase in MASLD prevalence and mortality risk (11.12%) among non-Hispanic White patients compared to other ethnic groups. These racial disparities suggest that primary care institutions should select appropriate assessment indicators according to target population demographics to enhance MASLD prediction accuracy. Notably, the study found limited influence of age and hypertension status on these metrics' predictive efficacy. This finding enhances their generalizability across reproductive-age women, demonstrating consistent applicability across age groups and health conditions within specific demographic subgroups. Notably, these new indicators exhibit broader clinical value beyond MASLD risk assessment. Growing evidence suggests that (44–47) , lnLAP, and WTI can serve as comprehensive biomarkers for metabolic abnormalities, showing significant associations with metabolic syndrome, type 2 diabetes, and cardiovascular disease (CVD) risks. This finding is particularly noteworthy given that CVD is the leading cause of mortality in MASLD patients ( 48 ). The correlation between WWI/ABSI and MASLD primarily stems from their ability to characterize central obesity. A prospective study by Chen et al. confirmed that central obesity in MASLD patients is independently associated with all-cause mortality rate ( 49 ). Notably, while ABSI and WWI demonstrated relatively limited overall predictive power in this study, they showed significant correlations with imaging-derived visceral fat parameters (e.g., CT-quantified abdominal fat area) ( 50 , 51 ). A cross-sectional study involving 4,286 middle-aged adults ( 52 ) further revealed that the prevalence of hepatic steatosis was significantly higher in individuals with abdominal obesity than in non-obese individuals. Therefore, ABSI and WWI may serve as good predictors of MASLD in non-obese individuals, although this possibility remains to be demonstrated.These findings provide crucial references for understanding the clinical value of different indicators in specific populations. Furthermore, studies have demonstrated correlations between WHTR and BRI with ALT levels, which effectively explains their positive correlation with (MASLD scores. Collectively, these findings indicate that in primary care settings, selecting appropriate physical measurement combinations based on demographic characteristics (e.g., race, age) not only enables more precise assessment of MASLD risks and early intervention, but also achieves the "one check, multiple screenings" capability. In conclusion, the findings of this study offer significant implications for MASLD screening practices in primary care institutions. First, within resource-constrained primary healthcare settings, lnLAP stands out as the preferred initial screening tool due to its operational simplicity (requiring only routine physical examination data) and cost-effectiveness. Its balanced sensitivity and specificity effectively meet both accuracy requirements and practical feasibility demands in primary care, making it particularly suitable for large-scale population screenings. Second, indicator selection should be tailored to different ethnic groups: WTI screening should prioritize non-Hispanic Black populations, ABSI screening should target non-Hispanic White populations, while other ethnic groups may consider WHTR or BRI. Finally, we recommend setting lnLAP ≥ 3.48 (with 82% sensitivity and 87% specificity) as the screening threshold for primary care. This threshold ensures screening efficiency while minimizing missed diagnoses. Notably, although these novel indicators cannot fully replace basic clinical evaluations, they provide reliable alternatives for special populations without routine imaging access and for primary care facilities. However, we are aware that this particular study possesses certain restrictions. ( 1 ) Cross-sectional design fails to establish causal relationships; ( 2 ) Self-administered questionnaire data may introduce recall bias; ( 3 ) Residual confounding factors may persist even after adjusting for multiple variables; ( 4 ) Limited sample size in high LAP value ranges could compromise the reliability of threshold effects. Future research should conduct large-scale prospective cohort studies to validate these findings and explore their underlying mechanisms. 5. Conclusion In conclusion, this study provides the first evidence in reproductive-aged women demonstrating significant associations between novel metabolic indices (particularly lnLAP and WTI) and MASLD, with lnLAP exhibiting optimal predictive performance. These noninvasive indicators—characterized by operational simplicity, cost-effectiveness, and excellent reproducibility—hold strong potential as practical tools for primary care settings to screen high-risk MASLD populations, offering a novel approach for early prevention and management of metabolic liver diseases. Declarations Ethics approval and consent to participate The portions of this study involving human participants, human materials, or human data were conducted by the Declaration of Helsinki and were approved by the NCHS Ethics Review Board. The patients/participants provided their written informed consent to participate in this study. Conflict of Interest The authors declare no conflicts of interest. Fundings This work was supported by Self-funded Research Project of Guangxi Zhuang Autonomous Region Health Commission (Contract No.: Z20211281) and Guangxi Traditional Chinese Medicine Appropriate Technology Development and Promotion Project (Contract No.: GZSY2024060). Author Contribution C.M. and W.J. conceived and designed the study.H.H.,L.T. and L.Y. analyzed the data and wrote the manuscript.L.J., T.T and X.X. collated the data .W.H. directed the data analysis.All authors contributed to the revision of the manuscript before submission and approved the final version. All authors revised the manuscripts critically and approved the final version for publication.H.H.,L.T. and L.Y. contributed equally to this work and are listed as co-first authors. Data Availability Statement The datasets supporting the findings of this study are publicly available in the NHANES database at https://wwwn.cdc.gov/nchs/nhanes/Default.aspx . References Younossi ZM, Wong G, Anstee QM, Henry L. The Global Burden of Liver Disease. Clin Gastroenterol Hepatol. 2023;21(8):1978–91. Fouad Y, Alboraie M, Shiha G. Epidemiology and diagnosis of metabolic dysfunction-associated fatty liver disease. Hepatol Int. 2024;18(Suppl 2):827–33. Huang M, Chen H, Wang H, Zhang Y, Li L, Lan Y, et al. Global burden and risk factors of MASLD: trends from 1990 to 2021 and predictions to 2030. Intern Emerg Med. 2025;20(4):1013–24. Hamaguchi M, Kojima T, Takeda N, Nakagawa T, Taniguchi H, Fujii K, et al. The metabolic syndrome as a predictor of nonalcoholic fatty liver disease. Ann Intern Med. 2005;143(10):722–8. Xu Q, Zhang J, Lu Y, Wu L. Association of metabolic-dysfunction associated steatotic liver disease with polycystic ovary syndrome. iScience. 2024;27(2):108783. Jung YM, Lee SM, Wi W, Oh MJ, Park JS, Cho GJ, et al. Adverse pregnancy outcomes as a risk factor for new-onset metabolic dysfunction-associated steatotic liver disease in postpartum women: A nationwide study. JHEP Rep. 2024;6(4):101033. Koliaki C, Dalamaga M, Kakounis K, Liatis S. Metabolically Healthy Obesity and Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD): Navigating the Controversies in Disease Development and Progression. Curr Obes Rep. 2025;14(1):46. Yang Z, Feng G, Gao X, Yan X, Li Y, Wang Y, et al. Maternal adiposity and perinatal and offspring outcomes: an umbrella review. Nat Hum Behav. 2024;8(12):2406–22. Hughes V. The big fat truth. Nature. 2013;497(7450):428–30. Xia C, Li R, Zhang S, Gong L, Ren W, Wang Z, et al. Lipid accumulation product is a powerful index for recognizing insulin resistance in non-diabetic individuals. Eur J Clin Nutr. 2012;66(9):1035–8. Sun JY, Hua Y, Zou HY, Qu Q, Yuan Y, Sun GZ, et al. Association Between Waist Circumference and the Prevalence of (Pre) Hypertension Among 27,894 US Adults. Front Cardiovasc Med. 2021;8:717257. Bouchi R, Asakawa M, Ohara N, Nakano Y, Takeuchi T, Murakami M, et al. Indirect measure of visceral adiposity 'A Body Shape Index' (ABSI) is associated with arterial stiffness in patients with type 2 diabetes. BMJ Open Diabetes Res Care. 2016;4(1):e000188. Rico-Martín S, Calderón-García JF, Sánchez-Rey P, Franco-Antonio C, Martínez Alvarez M, Sánchez Muñoz-Torrero JF. Effectiveness of body roundness index in predicting metabolic syndrome: A systematic review and meta-analysis. Obes Rev. 2020;21(7):e13023. Nkwana MR, Monyeki KD, Lebelo SL, Body Roundness Index A, Body Shape, Index. Conicity Index, and Their Association with Nutritional Status and Cardiovascular Risk Factors in South African Rural Young Adults. Int J Environ Res Public Health. 2021;18(1). Thomas DM, Bredlau C, Bosy-Westphal A, Mueller M, Shen W, Gallagher D, et al. Relationships between body roundness with body fat and visceral adipose tissue emerging from a new geometrical model. Obes (Silver Spring). 2013;21(11):2264–71. Krakauer NY, Krakauer JC. A new body shape index predicts mortality hazard independently of body mass index. PLoS ONE. 2012;7(7):e39504. Patel CJ, Pho N, McDuffie M, Easton-Marks J, Kothari C, Kohane IS, et al. A database of human exposomes and phenomes from the US National Health and Nutrition Examination Survey. Sci Data. 2016;3:160096. Cuschieri S. The STROBE guidelines. Saudi J Anaesth. 2019;13(Suppl 1):S31–4. Gangireddy VGR, Pilkerton C, Xiang J, Tinajero R, Ashcraft AM. Hepatic Fibrosis and Steatosis in Metabolic Syndrome. J Obes Metab Syndr. 2022;31(1):61–9. Suwała S, Junik R. Assessment of the Liver Steatosis and Fibrosis Risk in Metabolic Syndrome and Its Individual Components, Considering the Varying Definitions Used in Clinical Practice throughout Time: A Retrospective Cross-Sectional Study. Biomedicines. 2024;12(8). Eddowes PJ, Sasso M, Allison M, Tsochatzis E, Anstee QM, Sheridan D, et al. Accuracy of FibroScan Controlled Attenuation Parameter and Liver Stiffness Measurement in Assessing Steatosis and Fibrosis in Patients With Nonalcoholic Fatty Liver Disease. Gastroenterology. 2019;156(6):1717–30. Eslam M, Newsome PN, Sarin SK, Anstee QM, Targher G, Romero-Gomez M, et al. A new definition for metabolic dysfunction-associated fatty liver disease: An international expert consensus statement. J Hepatol. 2020;73(1):202–9. Lin W, Lin ME. Novel anthropometric measures are positively associated with erectile dysfunction: a cross-sectional study. Int Urol Nephrol. 2024;56(3):855–65. Zwierzchowska A, Kantyka J, Rosołek B, Nawrat-Szołtysik A, Małecki A. Sensitivity and Specificity of Anthropometric Indices in Identifying Obesity in Women over 40 Years of Age and Their Variability in Subsequent Decades of Life. Biology (Basel). 2022;11(12). Zhang J, Liang D, Xu L, Liu Y, Jiang S, Han X, et al. Associations between novel anthropometric indices and the prevalence of gallstones among 6,848 adults: a cross-sectional study. Front Nutr. 2024;11:1428488. Li Y, Zheng R, Li S, Cai R, Ni F, Zheng H, et al. Association Between Four Anthropometric Indexes and Metabolic Syndrome in US Adults. Front Endocrinol (Lausanne). 2022;13:889785. Li H, Jiang Y, Su X, Meng Z. The triglyceride glucose index was U-shape associated with all-cause mortality in population with cardiovascular diseases. Diabetol Metab Syndr. 2023;15(1):181. Akinbami LJ, Chen TC, Davy O, Ogden CL, Fink S, Clark J, et al. National Health and Nutrition Examination Survey, 2017-March 2020 Prepandemic File: Sample Design, Estimation, and Analytic Guidelines. Vital Health Stat. 2022;1(190):1–36. Brankovic M, Kardys I, Steyerberg EW, Lemeshow S, Markovic M, Rizopoulos D, et al. Understanding of interaction (subgroup) analysis in clinical trials. Eur J Clin Invest. 2019;49(8):e13145. Nahm FS. Receiver operating characteristic curve: overview and practical use for clinicians. Korean J Anesthesiol. 2022;75(1):25–36. Hagström H, Shang Y, Hegmar H, Nasr P. Natural history and progression of metabolic dysfunction-associated steatotic liver disease. Lancet Gastroenterol Hepatol. 2024;9(10):944–56. Grier T, Canham-Chervak M, Sharp M, Jones BH. Does body mass index misclassify physically active young men. Prev Med Rep. 2015;2:483–7. Batsis JA, Villareal DT. Sarcopenic obesity in older adults: aetiology, epidemiology and treatment strategies. Nat Rev Endocrinol. 2018;14(9):513–37. van der Kolk BW, Saari S, Lovric A, Arif M, Alvarez M, Ko A, et al. Molecular pathways behind acquired obesity: Adipose tissue and skeletal muscle multiomics in monozygotic twin pairs discordant for BMI. Cell Rep Med. 2021;2(4):100226. Shi J, Chen J, Zhang Z, Qian G. Multi-dimensional comparison of abdominal obesity indices and insulin resistance indicators for assessing NAFLD. BMC Public Health. 2024;24(1):2161. Ahn N, Baumeister SE, Amann U, Rathmann W, Peters A, Huth C, et al. Visceral adiposity index (VAI), lipid accumulation product (LAP), and product of triglycerides and glucose (TyG) to discriminate prediabetes and diabetes. Sci Rep. 2019;9(1):9693. Chan WK, Chuah KH, Rajaram RB, Lim LL, Ratnasingam J, Vethakkan SR. Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD): A State-of-the-Art Review. J Obes Metab Syndr. 2023;32(3):197–213. Hajer GR, van Haeften TW, Visseren FL. Adipose tissue dysfunction in obesity, diabetes, and vascular diseases. Eur Heart J. 2008;29(24):2959–71. Duan Y, Pan X, Luo J, Xiao X, Li J, Bestman PL, et al. Association of Inflammatory Cytokines With Non-Alcoholic Fatty Liver Disease. Front Immunol. 2022;13:880298. Choudhury J, Sanyal AJ. Insulin resistance and the pathogenesis of nonalcoholic fatty liver disease. Clin Liver Dis. 2004;8(3):575–94. ix. Milić S, Lulić D, Štimac D. Non-alcoholic fatty liver disease and obesity: biochemical, metabolic and clinical presentations. World J Gastroenterol. 2014;20(28):9330–7. Aboona MB, Faulkner C, Rangan P, Ng CH, Huang DQ, Muthiah M, et al. Disparities among ethnic groups in mortality and outcomes among adults with MASLD: A multicenter study. Liver Int. 2024;44(6):1316–28. Zhang X, Linden S, Levesley CR, He X, Yang Z, Barnet SD, et al. Projected Trends in Metabolic Dysfunction-Associated Steatotic Liver Disease Mortality Through 2040. JAMA Netw Open. 2025;8(6):e2516367. Kahn HS. The lipid accumulation product performs better than the body mass index for recognizing cardiovascular risk: a population-based comparison. BMC Cardiovasc Disord. 2005;5:26. Tellechea ML, Aranguren F, Martínez-Larrad MT, Serrano-Ríos M, Taverna MJ, Frechtel GD. Ability of lipid accumulation product to identify metabolic syndrome in healthy men from Buenos Aires. Diabetes Care. 2009;32(7):e85. Kahn HS. The lipid accumulation product is better than BMI for identifying diabetes: a population-based comparison. Diabetes Care. 2006;29(1):151–3. Hosseinpanah F, Barzin M, Mirbolouk M, Abtahi H, Cheraghi L, Azizi F. Lipid accumulation product and incident cardiovascular events in a normal weight population: Tehran Lipid and Glucose Study. Eur J Prev Cardiol. 2016;23(2):187–93. Chew NWS, Mehta A, Goh RSJ, Zhang A, Chen Y, Chong B, et al. Cardiovascular-Liver-Metabolic Health: Recommendations in Screening, Diagnosis, and Management of Metabolic Dysfunction-Associated Steatotic Liver Disease in Cardiovascular Disease via Modified Delphi Approach. Circulation. 2025;151(1):98–119. Chen J, Zhou H, Liu K. Normal weight and central obesity as predictors of increased all-cause mortality in metabolic dysfunction associated steatotic liver disease. J Hepatol. 2024;80(4):e143–5. Bertoli S, Leone A, Krakauer NY, Bedogni G, Vanzulli A, Redaelli VI, et al. Association of Body Shape Index (ABSI) with cardio-metabolic risk factors: A cross-sectional study of 6081 Caucasian adults. PLoS ONE. 2017;12(9):e0185013. Kim JY, Choi J, Vella CA, Criqui MH, Allison MA, Kim NH. Associations between Weight-Adjusted Waist Index and Abdominal Fat and Muscle Mass: Multi-Ethnic Study of Atherosclerosis. Diabetes Metab J. 2022;46(5):747–55. Song K, Seol EG, Yang H, Jeon S, Shin HJ, Chae HW, et al. Bioelectrical impedance parameters add incremental value to waist-to-hip ratio for prediction of metabolic dysfunction associated steatotic liver disease in youth with overweight and obesity. Front Endocrinol (Lausanne). 2024;15:1385002. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-7188234","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":508485895,"identity":"657d88c1-8d30-4369-b60d-5f830040e802","order_by":0,"name":"Huiya Huang","email":"","orcid":"","institution":"Wuming Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Huiya","middleName":"","lastName":"Huang","suffix":""},{"id":508485896,"identity":"0879c95e-2f1a-4a75-9be6-a445ab31db3a","order_by":1,"name":"Tong Luo","email":"","orcid":"","institution":"Youjiang Medical University For Nationalities","correspondingAuthor":false,"prefix":"","firstName":"Tong","middleName":"","lastName":"Luo","suffix":""},{"id":508485897,"identity":"35c7956f-f0d4-4fc2-a50a-554cd21d4032","order_by":2,"name":"Yangni Lu","email":"","orcid":"","institution":"Wuming Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yangni","middleName":"","lastName":"Lu","suffix":""},{"id":508485898,"identity":"5929038c-a397-4610-a1e1-50bde5a8797a","order_by":3,"name":"Huabei Wu","email":"","orcid":"","institution":"Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Huabei","middleName":"","lastName":"Wu","suffix":""},{"id":508485899,"identity":"cd9d45ac-af18-4408-9b51-7ad3f5185d71","order_by":4,"name":"Jinfeng Li","email":"","orcid":"","institution":"Wuming Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jinfeng","middleName":"","lastName":"Li","suffix":""},{"id":508485900,"identity":"2e1e8067-e10d-427e-b42e-050222b38844","order_by":5,"name":"Tingting Tang","email":"","orcid":"","institution":"Wuming Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Tingting","middleName":"","lastName":"Tang","suffix":""},{"id":508485901,"identity":"e9fac025-4618-4d5b-b048-c291ba2fb8ce","order_by":6,"name":"Xianli Xv","email":"","orcid":"","institution":"Wuming Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xianli","middleName":"","lastName":"Xv","suffix":""},{"id":508485902,"identity":"1bd7edac-3973-4a79-8b5c-bec9d8e8c13d","order_by":7,"name":"Jianlin Wu","email":"","orcid":"","institution":"Wuming Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jianlin","middleName":"","lastName":"Wu","suffix":""},{"id":508485903,"identity":"869e8a18-8eed-4c43-8140-ec0d59865d38","order_by":8,"name":"Maowei Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYDACCRiDmfnAgYQKGx5+/gaitbAlPvhwJk1GcsYBYrUw8Bgbzmw7bGPQkIBfB//s5mOPbtTYJPazM5hJ85w5z2PAcIDxw8ccPJbcOZZunHMsLXFmM0OaNE/FbR5z5gZmyZnbcGsxkMgxk85hO5y74TDDMaAtt3ksGw6wMfPi1ZL/TTrn3+Hc/YcZ26R5287xGBxIIKQlh006tw1oCzMzM9D7BwhrkbiRZiad25dWP+MwGyMwkJN5JGccbMbrF/4Zyc+kc77ZGPP3n/8AjEo7e37+5oMfPuLRgg0wNpCmfhSMglEwCkYBBgAA7lxTTP7/HfAAAAAASUVORK5CYII=","orcid":"","institution":"Wuming Hospital of Guangxi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Maowei","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-07-22 14:38:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7188234/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7188234/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90415604,"identity":"89f9a7fb-9af0-4112-9e9f-90c63e3e4e9e","added_by":"auto","created_at":"2025-09-02 13:09:24","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":63305,"visible":true,"origin":"","legend":"\u003cp\u003eTechnical Roadmap.\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7188234/v1/ae1e847c2666ba11e41c05b6.jpg"},{"id":90416283,"identity":"48adbf4f-f7a4-4144-9c89-12daadbcb0f2","added_by":"auto","created_at":"2025-09-02 13:17:24","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":666368,"visible":true,"origin":"","legend":"\u003cp\u003eThe associations between MASLD risk and (a) lnLAP, (b) WTI, (c) BRI, (d) WHTR, (e) WWI, and (f) ABSI, represented by smoothed fitting curves (red bands) with 95% confidence intervals (blue bands). The shaded areas on the x-axis indicate the central distribution ranges of each respective index.\u003c/p\u003e","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7188234/v1/b63a6d0207f047532fa8cdca.jpg"},{"id":90415607,"identity":"ef5a8748-42f6-44a4-9edf-f76a8d4ad964","added_by":"auto","created_at":"2025-09-02 13:09:24","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":69677,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curves of all indices were significantly above the reference line (diagonal, AUC=0.5), demonstrating their predictive value for MASLD.\u003c/p\u003e","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7188234/v1/2c1937df589662308be5568e.jpg"},{"id":93146972,"identity":"49e23070-d3df-4714-9240-419bf6b8694a","added_by":"auto","created_at":"2025-10-09 14:01:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2354992,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7188234/v1/87234000-40a8-4469-85e8-17d8964c9365.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Utility of Six Novel Anthropometric Indicators for Assessing Metabolic Dysfunction-Associated Steatotic Liver Disease in US Reproductive-Aged Women: An NHANES Cross-Sectional Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe liver acts as the central regulator of lipid homeostasis and orchestrates crucial physiological processes, including blood volume regulation, immune system maintenance, endocrine-modulated growth signaling, cholesterol metabolism, and xenobiotic compound detoxification \u0026ndash; encompassing the metabolism of numerous pharmacological agents. Metabolic dysfunction-associated fatty liver disease (MASLD), designated by the World Health Organization as a priority non-communicable disease (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), poses a mounting threat to global health. Recent epidemiological data reveal that the worldwide prevalence of MASLD reached 1.2679\u0026nbsp;billion cases in 2021, marking a 24.3% increase since 1990 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) .\u003c/p\u003e\u003cp\u003eWomen, particularly those of reproductive age, constitute a vulnerable population whose health remains a priority focus in global public health initiatives. Study demonstrates that women with metabolic syndrome exhibit a disproportionately higher susceptibility to MASLD compared to affected men (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Women, particularly those of reproductive age, constitute a vulnerable population whose health remains a priority focus in global public health initiatives. Study demonstrates that women with metabolic syndrome exhibit a disproportionately higher susceptibility to MASLD compared to affected men (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). With lifestyle changes, the prevalence of MASLD among US women of reproductive age has shown a persistent upward trend, as consistently documented across multiple NHANES survey cycles (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). As the core demographic for social productivity and family nurturing, the health status of reproductive-aged women not only impacts individual quality of life but also influences offspring development and family-social functioning. Consequently, in-depth investigation into the epidemiological features and early warning indicators of MASLD in this population carries substantial clinical value and societal importance.\u003c/p\u003e\u003cp\u003eThe current diagnostic framework for MASLD faces unique challenges in clinical practice. While liver biopsy remains the gold standard, its invasive nature poses inherent risks. Body mass index (BMI) is routinely employed to classify obesity grades. It was commonly believed that only individuals with elevated BMI are at a bigger risk of developing MASLD. This perspective is fundamentally imprecise, as BMI neither reflects body fat percentage nor accurately evaluates associated health risks(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) - evidenced by the significant prevalence of MASLD among normal-weight individuals. To address these limitations, researchers have developed a series of novel composite indices (\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) : the logarithmic lipid accumulation product (lnLAP), which integrates waist circumference and triglyceride levels, has demonstrated outstanding performance in assessing metabolic abnormalities (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) ; the waist-to-height ratio (WHTR) effectively reflects visceral fat accumulation, while the body roundness index (BRI) demonstrates unique value in cardiovascular risk assessment (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) ; a new generation of anthropometric indices has emerged, including the A Body Shape Index (ABSI), Weight-to-Height Index (WTI), and Weight-Adjusted-Waist Index (WWI) (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), WWI circumvents BMI's interference from muscle mass, while ABSI significantly enhances mortality risk prediction accuracy by integrating waist circumference, BMI, and height (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). These novel indices provide promising approaches for non-invasive MASLD screening in women of reproductive age. However, current evidence remains insufficient to comprehensively validate the correlation between these emerging anthropometric parameters and MASLD risk specifically in this population.\u003c/p\u003e\u003cp\u003eThis study aimed to investigate associations between novel anthropometric indices and MASLD in U.S. reproductive-aged women, assess their predictive value for MASLD risk, and identify evidence-based prevention strategies.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data Source\u003c/h2\u003e\u003cp\u003eAll data in this study were derived from the 2017\u0026ndash;2020 cycle of the National Health and Nutrition Examination Survey (NHANES), comprising comprehensive variables including: demographic characteristics, physical examination metrics, laboratory biomarkers (e.g., fasting glucose, lipid profiles), lifestyle factors, as well as liver health parameters - controlled attenuation parameter (CAP) and liver stiffness measurement (LSM) (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). This study strictly adhered to ethical guidelines, with all participants providing written informed consent and obtaining approval from the NCHS Ethics Review Board (Protocol Numbers: 2011-17 and 2018-01). Both study implementation and reporting complied with STROBE guidelines (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) .\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Diagnostic Criteria and Study Population\u003c/h2\u003e\u003cp\u003eBased on established guidelines and prior research (\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), we defined significant hepatic steatosis as a controlled attenuation parameter (CAP)\u0026thinsp;\u0026ge;\u0026thinsp;248 dB/m. The diagnosis of MASLD required (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) : (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) significant hepatic steatosis; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) the presence of at least one cardiometabolic risk factor (CMRF); (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) exclusion of excessive alcohol consumption (\u0026gt;\u0026thinsp;140 g/week for women or \u0026gt;\u0026thinsp;210 g/week for men) and other known causes of hepatic steatosis.\u003c/p\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, a multi-stage process was used to exclude ineligible subjects. The exclusion criteria were as follows: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) participants who did not complete elastography examination (n\u0026thinsp;=\u0026thinsp;5,862); (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) male participants (n\u0026thinsp;=\u0026thinsp;4,852); (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) age\u0026thinsp;\u0026lt;\u0026thinsp;20 or \u0026gt;\u0026thinsp;44 years (n\u0026thinsp;=\u0026thinsp;3,297); (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) missing key data (anthropometric measurements n\u0026thinsp;=\u0026thinsp;43, serum biomarkers n\u0026thinsp;=\u0026thinsp;80, alcohol consumption and blood pressure data n\u0026thinsp;=\u0026thinsp;203); (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) excessive alcohol consumption (n\u0026thinsp;=\u0026thinsp;3) or viral hepatitis (n\u0026thinsp;=\u0026thinsp;7); (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) missing sleep data (n\u0026thinsp;=\u0026thinsp;3) or extreme obesity/underweight (n\u0026thinsp;=\u0026thinsp;156). Finally, the eligible subjects were divided into the MASLD group and the non-MASLD group.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe following formulas were used to calculate lnLAP, WHTR, BRI, WTI, WWI, and ABSI (\u003cspan additionalcitationids=\"CR24 CR25 CR26\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) :\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\:\\:\\text{W}\\text{H}\\text{T}\\text{R}\\:=\\:\\text{W}\\text{C}\\:\\left(\\text{c}\\text{m}\\right)\\:/\\text{H}\\text{e}\\text{i}\\text{g}\\text{h}\\text{t}\\:\\left(\\text{m}\\right)$$\u003c/div\u003e\u003c/div\u003e;\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\text{B}\\text{R}\\text{I}=364.2-365.5\\times\\:\\sqrt{1-{\\left(\\frac{WC\\left(cm\\right)}{2\\pi\\:}\\right)}^{2}/{(0.5\\times\\:\\text{H}\\text{e}\\text{i}\\text{g}\\text{h}\\text{t}(\\text{m}\\left)\\right)}^{2}}$$\u003c/div\u003e\u003c/div\u003e;\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\text{l}\\text{n}\\text{L}\\text{A}\\text{P}=\\text{ln}\\left[\\right(\\text{W}\\text{C}\\left(\\text{c}\\text{m}\\right)58)\\times\\:\\text{T}\\text{G}（\\text{m}\\text{m}\\text{o}\\text{l}/\\text{L})]$$\u003c/div\u003e\u003c/div\u003e;\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:\\text{W}\\text{T}\\text{I}\\:=\\:\\text{ln}\\left[\\text{T}\\text{G}\\right(\\text{m}\\text{g}/\\text{d}\\text{L})\\:\\times\\:\\:\\text{W}\\text{C}(\\text{c}\\text{m})/2]$$\u003c/div\u003e\u003c/div\u003e;\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:\\text{W}\\text{W}\\text{I}=WC\\left(cm\\right)/\\sqrt{Weigℎt\\left(kg\\right)}$$\u003c/div\u003e\u003c/div\u003e;\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$$\\:ABSI=WC\\left(cm\\right)/\\sqrt{Heigℎt\\left(m\\right)}\\times\\:\\sqrt[3]{{BMI}^{2}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Statistical Analysis\u003c/h2\u003e\u003cp\u003eThis study employed R 4.4.3, SPSS 27, and Zstats software for data analysis. All statistical tests were two-tailed, with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant. Following NHANES analytical guidelines, 2-year sample weights were applied to enhance national representativeness (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Continuous variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (x̄\u0026plusmn;s) and compared using linear regression, while categorical variables were expressed as counts (percentages) and compared via χ\u0026sup2; tests. Multivariable regression analysis utilized a stepwise adjustment approach with three models: Model 1 (unadjusted); Model 2 (adjusted for age, race, diabetes, and hypertension); Model 3 (further adjusted for sleep duration, LSM, alkaline phosphatase (ALP), γ-glutamyl transferase (GGT), total bilirubin (TBIL), glucose (GLU), platelets (PLT), and high-density lipoprotein cholesterol (HDL-C)). Variance inflation factors (VIFs)\u0026thinsp;\u0026lt;\u0026thinsp;5 in logistic regression models confirmed no significant multicollinearity.\u003c/p\u003e\u003cp\u003eThe analysis further evaluated MASLD risk differences across quartile groups (Q1-Q4) of lnLAP, WHTR, BRI, WTI, WWI, and ABSI. Stratified subgroup analyses by age, race, and hypertension status with interaction tests (Pinteraction) assessed potential effect modifications (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Smoothing curve fittings detected nonlinear relationships, with threshold effects determined by two-piece linear regression models and verified by log-likelihood ratio tests (LRTs). Receiver operating characteristic (ROC) curve analysis quantified predictive performance through area under the curve (AUC) values (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) ,comparing all six novel anthropometric indices.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Baseline Characteristics of Study Participants\u003c/h2\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, a total of 1,060 women aged 20\u0026ndash;44 years of reproductive age were included. Of these 1,060 female participants, 433 (40.85%) were diagnosed with MASLD, with a mean age of 33.43\u0026thinsp;\u0026plusmn;\u0026thinsp;7.05 years. Compared to the non-MASLD group, the MASLD group had a higher proportion of Hispanic individuals, an older mean age, shorter daily sleep duration, and significantly higher prevalence rates of diabetes and hypertension (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In terms of metabolic parameters, the MASLD group eshibited higher levels of BMI, waist circumference (WC), triglycerides (TG), total cholesterol (TC), aspartate aminotransferase (AST), CAP, LSM, GLU, PLT, and ALP (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), along with significantly decreased HDL-C levels. Additionally, all metabolic assessment indices - including lnLAP, WHTR, BRI, WTI, WWI, ABSI, and HIS - demonstrated significantly higher values in the MASLD group (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003epresents weighted baseline characteristics of participants with and without MASLD as assessed by VCTE in the NHANES 2017\u0026ndash;2020 database\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;1060)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-MASLD(n\u0026thinsp;=\u0026thinsp;627)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMASLD (n\u0026thinsp;=\u0026thinsp;433)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDemographic parameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGE(years), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.06\u0026thinsp;\u0026plusmn;\u0026thinsp;7.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.11\u0026thinsp;\u0026plusmn;\u0026thinsp;7.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.43\u0026thinsp;\u0026plusmn;\u0026thinsp;7.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRACE, n(%)\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMexican American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e146 (13.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65 (10.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e81 (18.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\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\u003e335 (31.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e219 (34.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e116 (26.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\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\u003e271 (25.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e166 (26.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e105 (24.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e308 (29.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e177 (28.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e131 (30.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEDUCATION\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.521\u003c/p\u003e\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\u003e26 (2.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (2.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8 (1.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95 (8.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54 (8.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41 (9.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\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\u003e939 (88.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e555 (88.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e384 (88.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMARITAL\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.162\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e559 (52.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e332 (52.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e227 (52.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnmarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e393 (37.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e240 (38.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e153 (35.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e108 (10.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55 (8.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53 (12.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAnthropometric parameters\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29.35\u0026thinsp;\u0026plusmn;\u0026thinsp;7.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.79\u0026thinsp;\u0026plusmn;\u0026thinsp;5.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34.51\u0026thinsp;\u0026plusmn;\u0026thinsp;7.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWC\u0026nbsp;(cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e94.56\u0026thinsp;\u0026plusmn;\u0026thinsp;17.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85.96\u0026thinsp;\u0026plusmn;\u0026thinsp;13.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e107.02\u0026thinsp;\u0026plusmn;\u0026thinsp;16.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVCTE parameters\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLSM(kPa)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.80\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.46\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.28\u0026thinsp;\u0026plusmn;\u0026thinsp;2.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCAP\u0026nbsp;(dB/m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e238.40\u0026thinsp;\u0026plusmn;\u0026thinsp;59.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e198.45\u0026thinsp;\u0026plusmn;\u0026thinsp;31.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e296.25\u0026thinsp;\u0026plusmn;\u0026thinsp;37.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSerum test\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT(U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.95\u0026thinsp;\u0026plusmn;\u0026thinsp;9.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.92\u0026thinsp;\u0026plusmn;\u0026thinsp;7.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.90\u0026thinsp;\u0026plusmn;\u0026thinsp;12.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAST(U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.92\u0026thinsp;\u0026plusmn;\u0026thinsp;8.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.14\u0026thinsp;\u0026plusmn;\u0026thinsp;4.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.03\u0026thinsp;\u0026plusmn;\u0026thinsp;12.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALP(IU/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68.18\u0026thinsp;\u0026plusmn;\u0026thinsp;21.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63.02\u0026thinsp;\u0026plusmn;\u0026thinsp;18.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e75.66\u0026thinsp;\u0026plusmn;\u0026thinsp;23.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGGT(IU/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.07\u0026thinsp;\u0026plusmn;\u0026thinsp;27.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.10\u0026thinsp;\u0026plusmn;\u0026thinsp;26.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.38\u0026thinsp;\u0026plusmn;\u0026thinsp;27.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTBIL (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGRE(mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.202\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGLU(mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90.07\u0026thinsp;\u0026plusmn;\u0026thinsp;18.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e87.71\u0026thinsp;\u0026plusmn;\u0026thinsp;13.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e93.49\u0026thinsp;\u0026plusmn;\u0026thinsp;22.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLT(10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e274.77\u0026thinsp;\u0026plusmn;\u0026thinsp;68.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e264.52\u0026thinsp;\u0026plusmn;\u0026thinsp;62.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e289.62\u0026thinsp;\u0026plusmn;\u0026thinsp;73.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C(mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57.02\u0026thinsp;\u0026plusmn;\u0026thinsp;15.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60.70\u0026thinsp;\u0026plusmn;\u0026thinsp;14.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51.68\u0026thinsp;\u0026plusmn;\u0026thinsp;14.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTC(mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e174.86\u0026thinsp;\u0026plusmn;\u0026thinsp;31.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e171.62\u0026thinsp;\u0026plusmn;\u0026thinsp;30.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e179.55\u0026thinsp;\u0026plusmn;\u0026thinsp;33.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG(mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e96.58\u0026thinsp;\u0026plusmn;\u0026thinsp;44.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83.92\u0026thinsp;\u0026plusmn;\u0026thinsp;37.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e114.90\u0026thinsp;\u0026plusmn;\u0026thinsp;46.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNoninvasive indices and models\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWHTR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58.42\u0026thinsp;\u0026plusmn;\u0026thinsp;10.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53.18\u0026thinsp;\u0026plusmn;\u0026thinsp;8.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65.99\u0026thinsp;\u0026plusmn;\u0026thinsp;9.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAIP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.31\u0026thinsp;\u0026plusmn;\u0026thinsp;2.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.12\u0026thinsp;\u0026plusmn;\u0026thinsp;1.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.04\u0026thinsp;\u0026plusmn;\u0026thinsp;2.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elnLAP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWTI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWWI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eABSI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38.49\u0026thinsp;\u0026plusmn;\u0026thinsp;9.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.29\u0026thinsp;\u0026plusmn;\u0026thinsp;6.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44.59\u0026thinsp;\u0026plusmn;\u0026thinsp;8.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLifestyle\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol consumption (g/day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.61\u0026thinsp;\u0026plusmn;\u0026thinsp;1.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.44\u0026thinsp;\u0026plusmn;\u0026thinsp;1.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.983\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep hours(h/day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.28\u0026thinsp;\u0026plusmn;\u0026thinsp;14.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;14.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.27\u0026thinsp;\u0026plusmn;\u0026thinsp;14.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMetabolic diseases\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74 (6.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (4.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44 (10.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39 (3.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (1.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29 (6.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\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=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.2. The associations between six novel anthropometric indices and MASLD risk\u003c/h2\u003e\u003cp\u003eThis study employed binary logistic regression models to examine the associations between six novel anthropometric indices and MASLD risk among U.S. women of reproductive age. In continuous variable analyses, each standard deviation increase in all indices demonstrated significant positive correlations with MASLD prevalence (all trend test p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The associations were most pronounced for lnLAP (OR\u0026thinsp;=\u0026thinsp;7.06, 95%CI:5.12\u0026ndash;9.73) and WTI (OR\u0026thinsp;=\u0026thinsp;6.84, 95%CI:4.65\u0026ndash;10.06), while WWI (OR\u0026thinsp;=\u0026thinsp;2.95), ABSI (OR\u0026thinsp;=\u0026thinsp;1.89), WHTR (OR\u0026thinsp;=\u0026thinsp;1.13), and BRI (OR\u0026thinsp;=\u0026thinsp;1.69) showed relatively lower but still statistically significant effect sizes.Quartile analyses revealed significant dose-response relationships for all indices (trend test p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with risk ratios ranging from 2.08 (95%CI:1.37\u0026ndash;3.16) for ABSI to 48.91 (95%CI:24.68\u0026ndash;96.93) for lnLAP when comparing the highest (Q4) versus lowest (Q1) quartiles. Notably, lnLAP, WTI, and WHTR/BRI exhibited exceptionally strong predictive capacity in Q4 (all ORs\u0026thinsp;\u0026gt;\u0026thinsp;30). After full adjustment, while effect sizes were attenuated, all indices maintained high significance - lnLAP showed the most stable association (OR decreased from 8.47 to 7.06), whereas WTI demonstrated the greatest adjustment impact (OR reduced from 11.45 to 6.84). (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBinary logistic regression analysis of the associations between six novel anthropometric indices and MASLD\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eCrude model 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eMinimally adjusted model 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eFully adjusted model 3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003elnLAP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.47 (6.48\u0026thinsp;~\u0026thinsp;11.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.14 (6.18\u0026thinsp;~\u0026thinsp;10.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.06 (5.12\u0026thinsp;~\u0026thinsp;9.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elnLAP group\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.46 (2.49\u0026thinsp;~\u0026thinsp;7.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.28 (2.38\u0026thinsp;~\u0026thinsp;7.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.25 (2.32\u0026thinsp;~\u0026thinsp;7.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.70 (10.11\u0026thinsp;~\u0026thinsp;30.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.84 (9.52\u0026thinsp;~\u0026thinsp;29.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15.47 (8.32\u0026thinsp;~\u0026thinsp;28.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70.36 (38.83\u0026thinsp;~\u0026thinsp;127.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63.19 (34.49\u0026thinsp;~\u0026thinsp;115.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e48.91 (24.68\u0026thinsp;~\u0026thinsp;96.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWTI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.45 (8.24\u0026thinsp;~\u0026thinsp;15.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.39 (7.40\u0026thinsp;~\u0026thinsp;14.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.84 (4.65\u0026thinsp;~\u0026thinsp;10.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWTI group\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.89 (2.36\u0026thinsp;~\u0026thinsp;6.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.73 (2.26\u0026thinsp;~\u0026thinsp;6.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.25 (1.93\u0026thinsp;~\u0026thinsp;5.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.42 (7.04\u0026thinsp;~\u0026thinsp;18.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.64 (6.52\u0026thinsp;~\u0026thinsp;17.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.75 (4.59\u0026thinsp;~\u0026thinsp;13.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27.44 (16.64\u0026thinsp;~\u0026thinsp;45.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.44 (14.07\u0026thinsp;~\u0026thinsp;39.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.93 (7.32\u0026thinsp;~\u0026thinsp;22.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWWI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.42 (3.58\u0026thinsp;~\u0026thinsp;5.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.04 (3.25\u0026thinsp;~\u0026thinsp;5.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.95 (2.34\u0026thinsp;~\u0026thinsp;3.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWWI group\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.58 (1.65\u0026thinsp;~\u0026thinsp;4.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.38 (1.51\u0026thinsp;~\u0026thinsp;3.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.05 (1.28\u0026thinsp;~\u0026thinsp;3.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.85 (4.44\u0026thinsp;~\u0026thinsp;10.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.87 (3.77\u0026thinsp;~\u0026thinsp;9.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.94 (2.47\u0026thinsp;~\u0026thinsp;6.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.21 (11.61\u0026thinsp;~\u0026thinsp;28.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.05 (9.49\u0026thinsp;~\u0026thinsp;23.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.93 (4.83\u0026thinsp;~\u0026thinsp;13.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eABSI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.78 (2.05\u0026thinsp;~\u0026thinsp;3.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.29 (1.67\u0026thinsp;~\u0026thinsp;3.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.89 (1.34\u0026thinsp;~\u0026thinsp;2.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eABSI group\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.37 (0.95\u0026thinsp;~\u0026thinsp;1.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.28 (0.88\u0026thinsp;~\u0026thinsp;1.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.22 (0.81\u0026thinsp;~\u0026thinsp;1.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.349\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.00 (1.40\u0026thinsp;~\u0026thinsp;2.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.76 (1.22\u0026thinsp;~\u0026thinsp;2.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.55 (1.03\u0026thinsp;~\u0026thinsp;2.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.20 (2.23\u0026thinsp;~\u0026thinsp;4.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.52 (1.73\u0026thinsp;~\u0026thinsp;3.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.08 (1.37\u0026thinsp;~\u0026thinsp;3.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWHTR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.15 (1.13\u0026thinsp;~\u0026thinsp;1.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.15 (1.13\u0026thinsp;~\u0026thinsp;1.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.13 (1.11\u0026thinsp;~\u0026thinsp;1.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWHTR group\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.16 (2.90\u0026thinsp;~\u0026thinsp;9.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.59 (2.56\u0026thinsp;~\u0026thinsp;8.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.97 (2.19\u0026thinsp;~\u0026thinsp;7.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.97 (10.26\u0026thinsp;~\u0026thinsp;31.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.79 (8.92\u0026thinsp;~\u0026thinsp;27.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.62 (6.39\u0026thinsp;~\u0026thinsp;21.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58.08 (32.35\u0026thinsp;~\u0026thinsp;104.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51.66 (28.52\u0026thinsp;~\u0026thinsp;93.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e31.34 (16.42\u0026thinsp;~\u0026thinsp;59.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBRI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.85 (1.71\u0026thinsp;~\u0026thinsp;2.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.82 (1.68\u0026thinsp;~\u0026thinsp;1.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.69 (1.54\u0026thinsp;~\u0026thinsp;1.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBRI group\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.16 (2.90\u0026thinsp;~\u0026thinsp;9.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.59 (2.56\u0026thinsp;~\u0026thinsp;8.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.97 (2.19\u0026thinsp;~\u0026thinsp;7.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.97 (10.26\u0026thinsp;~\u0026thinsp;31.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.79 (8.92\u0026thinsp;~\u0026thinsp;27.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.62 (6.39\u0026thinsp;~\u0026thinsp;21.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58.08 (32.35\u0026thinsp;~\u0026thinsp;104.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51.66 (28.52\u0026thinsp;~\u0026thinsp;93.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e31.34 (16.42\u0026thinsp;~\u0026thinsp;59.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFurther stratified analyses (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) demonstrated that all six anthropometric indices maintained significant positive associations with MASLD risk (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), though the effect sizes exhibited substantial heterogeneity across subgroups. Notably, significant interaction effects were observed between race and the predictive value of WTI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), ABSI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and WHTR/BRI (p\u0026thinsp;=\u0026thinsp;0.025/0.026) for MASLD risk.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStratified associations between six novel anthropometric indices and MASLD by age, race, and hypertension status\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\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\u003eSubgroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal(N)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMASLD(N)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e for interaction\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e\u003cp\u003elnLAP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e433\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.21 (5.30\u0026thinsp;~\u0026thinsp;9.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAGE (years)\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=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.629\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u0026ndash;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e470\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.62 (4.09\u0026thinsp;~\u0026thinsp;10.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31\u0026ndash;44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e277\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.53 (4.86\u0026thinsp;~\u0026thinsp;11.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRACE\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=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.099\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMexican American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.98 (1.70\u0026thinsp;~\u0026thinsp;9.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-HispanicWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e335\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.52 (1.44\u0026thinsp;~\u0026thinsp;4.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-Hispanic Black\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11.14 (5.69\u0026thinsp;~\u0026thinsp;21.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e308\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e16.61 (8.20\u0026thinsp;~\u0026thinsp;33.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHYPERTENSION\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=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.123\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e986\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e389\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.88 (5.00\u0026thinsp;~\u0026thinsp;9.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.64 (2.41\u0026thinsp;~\u0026thinsp;47.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e\u003cp\u003eWTI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e433\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.50 (5.18\u0026thinsp;~\u0026thinsp;10.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAGE (years)\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=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.523\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u0026ndash;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e470\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.43 (3.57\u0026thinsp;~\u0026thinsp;11.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31\u0026ndash;44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e277\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.16 (4.29\u0026thinsp;~\u0026thinsp;11.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRACE\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=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMexican American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.49 (1.20\u0026thinsp;~\u0026thinsp;10.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-HispanicWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e335\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.07 (0.50\u0026thinsp;~\u0026thinsp;2.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-Hispanic Black\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e33.74 (13.52\u0026thinsp;~\u0026thinsp;84.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e308\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14.03 (6.45\u0026thinsp;~\u0026thinsp;30.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHYPERTENSION\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=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.698\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e986\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e389\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.06 (4.79\u0026thinsp;~\u0026thinsp;10.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.15 (1.87\u0026thinsp;~\u0026thinsp;44.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e\u003cp\u003eWWI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e433\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.18 (2.53\u0026thinsp;~\u0026thinsp;4.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAGE (years)\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=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.518\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u0026ndash;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e470\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.73 (1.93\u0026thinsp;~\u0026thinsp;3.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31\u0026ndash;44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e277\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.28 (2.39\u0026thinsp;~\u0026thinsp;4.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRACE\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=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.469\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMexican American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.66 (0.84\u0026thinsp;~\u0026thinsp;3.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-HispanicWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e335\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.39 (1.51\u0026thinsp;~\u0026thinsp;3.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-Hispanic Black\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.47 (2.25\u0026thinsp;~\u0026thinsp;5.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e308\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.76 (2.41\u0026thinsp;~\u0026thinsp;5.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHYPERTENSION\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=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.529\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e986\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e389\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.16 (2.49\u0026thinsp;~\u0026thinsp;4.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.21 (1.18\u0026thinsp;~\u0026thinsp;8.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e\u003cp\u003eABSI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e433\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.20 (1.57\u0026ndash;3.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAGE (years)\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=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.283\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u0026ndash;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e470\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.17 (1.56\u0026thinsp;~\u0026thinsp;3.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31\u0026ndash;44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e277\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRACE\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=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.71 (0.99\u0026thinsp;~\u0026thinsp;2.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMexican American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.24 (1.45\u0026thinsp;~\u0026thinsp;3.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-HispanicWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e335\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-Hispanic Black\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.79 (0.27\u0026thinsp;~\u0026thinsp;2.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e308\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.62 (0.30\u0026thinsp;~\u0026thinsp;1.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHYPERTENSION\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=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.44 (1.80\u0026thinsp;~\u0026thinsp;6.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.981\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e986\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e389\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.44 (2.68\u0026thinsp;~\u0026thinsp;11.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e\u003cp\u003eWHTR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e433\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.14 (1.11\u0026thinsp;~\u0026thinsp;1.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAGE (years)\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=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.836\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u0026ndash;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e470\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.13 (1.09\u0026thinsp;~\u0026thinsp;1.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31\u0026ndash;44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e277\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.14 (1.11\u0026thinsp;~\u0026thinsp;1.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRACE\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=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMexican American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.11 (1.04\u0026thinsp;~\u0026thinsp;1.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-HispanicWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e335\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.15 (1.11\u0026thinsp;~\u0026thinsp;1.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-Hispanic Black\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.09 (1.05\u0026thinsp;~\u0026thinsp;1.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e308\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.17 (1.12\u0026thinsp;~\u0026thinsp;1.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHYPERTENSION\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=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.169\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e986\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e389\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.14 (1.11\u0026thinsp;~\u0026thinsp;1.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.13 (1.03\u0026thinsp;~\u0026thinsp;1.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e433\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.70 (1.56\u0026thinsp;~\u0026thinsp;1.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAGE (years)\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=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.871\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u0026ndash;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e470\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.65 (1.44\u0026thinsp;~\u0026thinsp;1.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31\u0026ndash;44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e277\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.73 (1.53\u0026thinsp;~\u0026thinsp;1.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRACE\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=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMexican American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.55 (1.18\u0026thinsp;~\u0026thinsp;2.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-HispanicWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e335\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.83 (1.52\u0026thinsp;~\u0026thinsp;2.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-Hispanic Black\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.44 (1.24\u0026thinsp;~\u0026thinsp;1.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e308\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.92 (1.59\u0026thinsp;~\u0026thinsp;2.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHYPERTENSION\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=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.116\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e986\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e389\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.72 (1.57\u0026thinsp;~\u0026thinsp;1.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.57 (1.10\u0026thinsp;~\u0026thinsp;2.25)\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\u003cp\u003eAdditionally, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents smoothed curve fitting analyses examining nonlinear correlations between lnLAP, WTI, WHTR, BRI, WWI, ABSI and MASLD. The smoothing curves demonstrate robust associations between these indices and MASLD (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with adjustment strategies mirroring the fully adjusted model. Figures\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef illustrate continuous, approximately linear relationships of WWI and ABSI with MASLD risk, without evident saturation or threshold effects.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eNotably, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea reveals a distinct pattern for lnLAP: while initial MASLD risk changes were minimal, a marked risk escalation occurred at the specific threshold of lnLAP\u0026thinsp;=\u0026thinsp;2.34, indicating a piecewise linear association. Log-likelihood ratio tests (LRT) confirmed statistical significance at this inflection point (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), supporting the superiority of a two-piece regression model. Similar threshold effects were identified for WTI (inflection point\u0026thinsp;=\u0026thinsp;3.63), WHTR (52.84), and BRI (4.15).\u003c/p\u003e\u003cp\u003e3.3 Comparative predictive performance of lnLAP versus other anthropometric indices for MASLD: A receiver operating characteristic (ROC) curve analysis\u003c/p\u003e\u003cp\u003eThe hepatic steatosis index (HSI) is a commonly used noninvasive model for MASLD diagnosis. To evaluate the effectiveness of lnLAP versus other anthropometric indices in identifying MASLD, we conducted comparative analyses of specificity (SPE) and sensitivity (SEN) among lnLAP, WHTR, BRI, WTI, WWI, ABSI, and HSI for MASLD prediction.As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, we employed receiver operating characteristic (ROC) curve analysis to determine the discriminative performance of six novel anthropometric indices\u0026mdash;lnLAP, WHTR, BRI, WTI, WWI, and ABSI\u0026mdash;for MASLD detection.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, lnLAP demonstrated superior predictive performance with the highest area under the curve (AUC) of 0.85 (95%CI: 0.82\u0026ndash;0.87), followed by WHTR/BRI (AUC\u0026thinsp;=\u0026thinsp;0.84, 95%CI: 0.82\u0026ndash;0.87), both significantly outperforming other indices (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) including HIS (0.83, 95%CI: 0.81\u0026ndash;0.86), WTI (0.80, 95%CI: 0.77\u0026ndash;0.82), WWI (0.78, 95%CI: 0.75\u0026ndash;0.81), and ABSI (0.62, 95%CI: 0.59\u0026ndash;0.66). Further analysis of lnLAP's diagnostic characteristics revealed at its optimal cutoff (3.48): sensitivity (SEN) of 82%, specificity (SPE) of 87%, positive predictive value (PPV) of 85%, and negative predictive value (NPV) of 82%.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePerformance evaluation of lnLAP, WHTR, BRI, WTI, WWI and ABSI for predicting MASLD\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAUC (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSEN (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSPE (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePPV (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNPV (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCut of value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elnLAP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.85(0.82\u0026ndash;0.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.82(0.87\u0026thinsp;\u0026minus;\u0026thinsp;0.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e0.87(0.85\u0026thinsp;\u0026minus;\u0026thinsp;0.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.85(0.82\u0026ndash;0.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e\u003cp\u003e0.82(0.88\u0026thinsp;\u0026minus;\u0026thinsp;0.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.84(0.82\u0026ndash;0.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.82(0.87\u0026thinsp;\u0026minus;\u0026thinsp;0.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e0.87(0.76\u0026thinsp;\u0026minus;\u0026thinsp;0.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.76(0.71\u0026ndash;0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e\u003cp\u003e0.71(0.8\u0026thinsp;\u0026minus;\u0026thinsp;0.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWHTR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.84(0.82\u0026ndash;0.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.82(0.87\u0026thinsp;\u0026minus;\u0026thinsp;0.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e0.87(0.76\u0026thinsp;\u0026minus;\u0026thinsp;0.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.76(0.71\u0026ndash;0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e\u003cp\u003e0.71(0.8\u0026thinsp;\u0026minus;\u0026thinsp;0.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e58.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWWI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.78(0.75\u0026ndash;0.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.75(0.81\u0026thinsp;\u0026minus;\u0026thinsp;0.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e0.81(0.72\u0026thinsp;\u0026minus;\u0026thinsp;0.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.72(0.67\u0026ndash;0.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e\u003cp\u003e0.67(0.76\u0026thinsp;\u0026minus;\u0026thinsp;0.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e10.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWTI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.80(0.77\u0026ndash;0.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.77(0.82\u0026thinsp;\u0026minus;\u0026thinsp;0.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e0.82(0.77\u0026thinsp;\u0026minus;\u0026thinsp;0.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.77(0.73\u0026ndash;0.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e\u003cp\u003e0.73(0.81\u0026thinsp;\u0026minus;\u0026thinsp;0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eABSI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.62(0.59\u0026ndash;0.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.59(0.66\u0026ndash;0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e0.66(0.7\u0026thinsp;\u0026minus;\u0026thinsp;0.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.7(0.65\u0026ndash;0.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e\u003cp\u003e0.65(0.74\u0026thinsp;\u0026minus;\u0026thinsp;0.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e7.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.83(0.81\u0026ndash;0.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.81(0.86\u0026thinsp;\u0026minus;\u0026thinsp;0.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e0.86(0.75\u0026thinsp;\u0026minus;\u0026thinsp;0.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.75(0.71\u0026ndash;0.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e\u003cp\u003e0.71(0.79\u0026thinsp;\u0026minus;\u0026thinsp;0.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e39.06\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"},{"header":"4. Discussion","content":"\u003cp\u003eThis large-scale cross-sectional study, based on data from the NHANES, systematically evaluated six novel anthropometric indicators (lnLAP, WHTR, BRI, WTI, WWI, ABSI) for screening MASLD in women of reproductive age. The findings revealed that nearly half of U.S. women of reproductive age have MASLD, indicating this condition has become a prevalent chronic disease among this demographic. Our results confirmed independent associations between all six new anthropometric indicators and MASLD risk after fully adjusting for confounding factors. Notably, lnLAP and WTI demonstrated the strongest predictive power, laying the foundation for developing non-invasive screening strategies.\u003c/p\u003e\u003cp\u003eMASLD is a chronic condition that develops over years, making regular screening and timely diagnosis crucial for preventing severe complications such as hepatocellular carcinoma and cirrhosis (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). However, diagnosis and screening remain challenging due to high costs and limited accessibility. Multiple international studies have identified obesity and advanced age as risk factors for MASLD. Thus, current research primarily uses BMI and weight as key indicators for predicting MASLD. Notably, in young, muscular individuals, what appears as obesity may actually result from increased muscle mass (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Furthermore, reduced muscle mass may impair BMI's accuracy in quantifying obesity within specific populations (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Body weight and BMI do not accurately reflect body fat distribution (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). To more precisely examine various obesity patterns, several novel anthropometric indicators have recently been introduced. For instance, lnLAP and WTI have garnered significant attention due to their exceptional predictive value for visceral fat accumulation and insulin resistance (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) .\u003c/p\u003e\u003cp\u003eBased on our findings, individuals in the highest quartile of lnLAP exhibited a 48.91-fold increased risk of MASLD compared to those in the lowest quartile (95% CI: 24.68\u0026ndash;96.93). Furthermore, lnLAP demonstrated optimal predictive capacity (AUC\u0026thinsp;=\u0026thinsp;0.85, 95% CI: 0.82\u0026ndash;0.87), with significantly superior sensitivity (82% vs 79%) and specificity (87% vs 81%) over the conventional HIS index. As composite indicators integrating waist circumference and triglycerides, lnLAP simultaneously capture two major pathophysiological features of MASLD: visceral obesity and insulin resistance (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Visceral adipose tissue promotes hepatic steatosis through two pathways: direct action (release of free fatty acids) and indirect action (through inflammatory cytokines like TNF-α and IL-6 that exacerbate insulin resistance) (\u003cspan additionalcitationids=\"CR39 CR40\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e).Notably, a significant threshold effect was observed in the relationship between MASLD and lnLAP. The smoothing curve fitting determined a critical point of lnLAP\u0026thinsp;=\u0026thinsp;2.34, suggesting that when this threshold is exceeded, the positive correlation between lnLAP and MASLD in women of reproductive age becomes significantly enhanced, indicating its potential as an important clinical early-warning indicator. The predictive performance of BRI and WTI was comparable (AUC\u0026thinsp;=\u0026thinsp;0.84). The predictive power of WWI (AUC\u0026thinsp;=\u0026thinsp;0.78) and ABSI (AUC\u0026thinsp;=\u0026thinsp;0.62) is relatively limited, especially ABSI shows poor sensitivity and specificity in the independent screening scenario.\u003c/p\u003e\u003cp\u003eOur findings indicate that the WTI indicator demonstrates the strongest predictive power among women of reproductive age who are non-Hispanic Black, while the ABSI indicator shows optimal performance in non-Hispanic White populations. A multicenter study (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e) (n\u0026thinsp;=\u0026thinsp;51,452) revealed that Hispanic and non-Hispanic American patients with MASLD exhibited significantly higher rates of cirrhosis, diabetes, and mortality compared to European Americans across different racial groups. Another cohort study based on the NHANES (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e) (n\u0026thinsp;=\u0026thinsp;40,166) highlighted a notable increase in MASLD prevalence and mortality risk (11.12%) among non-Hispanic White patients compared to other ethnic groups. These racial disparities suggest that primary care institutions should select appropriate assessment indicators according to target population demographics to enhance MASLD prediction accuracy. Notably, the study found limited influence of age and hypertension status on these metrics' predictive efficacy. This finding enhances their generalizability across reproductive-age women, demonstrating consistent applicability across age groups and health conditions within specific demographic subgroups.\u003c/p\u003e\u003cp\u003eNotably, these new indicators exhibit broader clinical value beyond MASLD risk assessment. Growing evidence suggests that \u003csup\u003e(44\u0026ndash;47)\u003c/sup\u003e, lnLAP, and WTI can serve as comprehensive biomarkers for metabolic abnormalities, showing significant associations with metabolic syndrome, type 2 diabetes, and cardiovascular disease (CVD) risks. This finding is particularly noteworthy given that CVD is the leading cause of mortality in MASLD patients (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). The correlation between WWI/ABSI and MASLD primarily stems from their ability to characterize central obesity. A prospective study by Chen et al. confirmed that central obesity in MASLD patients is independently associated with all-cause mortality rate (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Notably, while ABSI and WWI demonstrated relatively limited overall predictive power in this study, they showed significant correlations with imaging-derived visceral fat parameters (e.g., CT-quantified abdominal fat area) (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). A cross-sectional study involving 4,286 middle-aged adults (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e) further revealed that the prevalence of hepatic steatosis was significantly higher in individuals with abdominal obesity than in non-obese individuals. Therefore, ABSI and WWI may serve as good predictors of MASLD in non-obese individuals, although this possibility remains to be demonstrated.These findings provide crucial references for understanding the clinical value of different indicators in specific populations. Furthermore, studies have demonstrated correlations between WHTR and BRI with ALT levels, which effectively explains their positive correlation with (MASLD scores. Collectively, these findings indicate that in primary care settings, selecting appropriate physical measurement combinations based on demographic characteristics (e.g., race, age) not only enables more precise assessment of MASLD risks and early intervention, but also achieves the \"one check, multiple screenings\" capability.\u003c/p\u003e\u003cp\u003eIn conclusion, the findings of this study offer significant implications for MASLD screening practices in primary care institutions. First, within resource-constrained primary healthcare settings, lnLAP stands out as the preferred initial screening tool due to its operational simplicity (requiring only routine physical examination data) and cost-effectiveness. Its balanced sensitivity and specificity effectively meet both accuracy requirements and practical feasibility demands in primary care, making it particularly suitable for large-scale population screenings. Second, indicator selection should be tailored to different ethnic groups: WTI screening should prioritize non-Hispanic Black populations, ABSI screening should target non-Hispanic White populations, while other ethnic groups may consider WHTR or BRI. Finally, we recommend setting lnLAP\u0026thinsp;\u0026ge;\u0026thinsp;3.48 (with 82% sensitivity and 87% specificity) as the screening threshold for primary care. This threshold ensures screening efficiency while minimizing missed diagnoses. Notably, although these novel indicators cannot fully replace basic clinical evaluations, they provide reliable alternatives for special populations without routine imaging access and for primary care facilities.\u003c/p\u003e\u003cp\u003eHowever, we are aware that this particular study possesses certain restrictions. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Cross-sectional design fails to establish causal relationships; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Self-administered questionnaire data may introduce recall bias; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Residual confounding factors may persist even after adjusting for multiple variables; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Limited sample size in high LAP value ranges could compromise the reliability of threshold effects. Future research should conduct large-scale prospective cohort studies to validate these findings and explore their underlying mechanisms.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, this study provides the first evidence in reproductive-aged women demonstrating significant associations between novel metabolic indices (particularly lnLAP and WTI) and MASLD, with lnLAP exhibiting optimal predictive performance. These noninvasive indicators\u0026mdash;characterized by operational simplicity, cost-effectiveness, and excellent reproducibility\u0026mdash;hold strong potential as practical tools for primary care settings to screen high-risk MASLD populations, offering a novel approach for early prevention and management of metabolic liver diseases.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\u003cp\u003eThe portions of this study involving human participants, human materials, or human data were conducted by the Declaration of Helsinki and were approved by the NCHS Ethics Review Board. The patients/participants provided their written informed consent to participate in this study.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\u003ch2\u003eFundings\u003c/h2\u003e\u003cp\u003eThis work was supported by Self-funded Research Project of Guangxi Zhuang Autonomous Region Health Commission (Contract No.: Z20211281) and Guangxi Traditional Chinese Medicine Appropriate Technology Development and Promotion Project (Contract No.: GZSY2024060).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eC.M. and W.J. conceived and designed the study.H.H.,L.T. and L.Y. analyzed the data and wrote the manuscript.L.J., T.T and X.X. collated the data .W.H. directed the data analysis.All authors contributed to the revision of the manuscript before submission and approved the final version. All authors revised the manuscripts critically and approved the final version for publication.H.H.,L.T. and L.Y. contributed equally to this work and are listed as co-first authors.\u003c/p\u003e\u003ch2\u003eData Availability Statement\u003c/h2\u003e\u003cp\u003eThe datasets supporting the findings of this study are publicly available in the NHANES database at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wwwn.cdc.gov/nchs/nhanes/Default.aspx\u003c/span\u003e\u003cspan address=\"https://wwwn.cdc.gov/nchs/nhanes/Default.aspx\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYounossi ZM, Wong G, Anstee QM, Henry L. The Global Burden of Liver Disease. Clin Gastroenterol Hepatol. 2023;21(8):1978\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFouad Y, Alboraie M, Shiha G. Epidemiology and diagnosis of metabolic dysfunction-associated fatty liver disease. Hepatol Int. 2024;18(Suppl 2):827\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang M, Chen H, Wang H, Zhang Y, Li L, Lan Y, et al. Global burden and risk factors of MASLD: trends from 1990 to 2021 and predictions to 2030. Intern Emerg Med. 2025;20(4):1013\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHamaguchi M, Kojima T, Takeda N, Nakagawa T, Taniguchi H, Fujii K, et al. The metabolic syndrome as a predictor of nonalcoholic fatty liver disease. Ann Intern Med. 2005;143(10):722\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXu Q, Zhang J, Lu Y, Wu L. Association of metabolic-dysfunction associated steatotic liver disease with polycystic ovary syndrome. iScience. 2024;27(2):108783.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJung YM, Lee SM, Wi W, Oh MJ, Park JS, Cho GJ, et al. Adverse pregnancy outcomes as a risk factor for new-onset metabolic dysfunction-associated steatotic liver disease in postpartum women: A nationwide study. JHEP Rep. 2024;6(4):101033.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKoliaki C, Dalamaga M, Kakounis K, Liatis S. Metabolically Healthy Obesity and Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD): Navigating the Controversies in Disease Development and Progression. Curr Obes Rep. 2025;14(1):46.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang Z, Feng G, Gao X, Yan X, Li Y, Wang Y, et al. Maternal adiposity and perinatal and offspring outcomes: an umbrella review. Nat Hum Behav. 2024;8(12):2406\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHughes V. The big fat truth. Nature. 2013;497(7450):428\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXia C, Li R, Zhang S, Gong L, Ren W, Wang Z, et al. Lipid accumulation product is a powerful index for recognizing insulin resistance in non-diabetic individuals. Eur J Clin Nutr. 2012;66(9):1035\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSun JY, Hua Y, Zou HY, Qu Q, Yuan Y, Sun GZ, et al. Association Between Waist Circumference and the Prevalence of (Pre) Hypertension Among 27,894 US Adults. Front Cardiovasc Med. 2021;8:717257.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBouchi R, Asakawa M, Ohara N, Nakano Y, Takeuchi T, Murakami M, et al. Indirect measure of visceral adiposity 'A Body Shape Index' (ABSI) is associated with arterial stiffness in patients with type 2 diabetes. BMJ Open Diabetes Res Care. 2016;4(1):e000188.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRico-Mart\u0026iacute;n S, Calder\u0026oacute;n-Garc\u0026iacute;a JF, S\u0026aacute;nchez-Rey P, Franco-Antonio C, Mart\u0026iacute;nez Alvarez M, S\u0026aacute;nchez Mu\u0026ntilde;oz-Torrero JF. Effectiveness of body roundness index in predicting metabolic syndrome: A systematic review and meta-analysis. Obes Rev. 2020;21(7):e13023.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNkwana MR, Monyeki KD, Lebelo SL, Body Roundness Index A, Body Shape, Index. Conicity Index, and Their Association with Nutritional Status and Cardiovascular Risk Factors in South African Rural Young Adults. Int J Environ Res Public Health. 2021;18(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThomas DM, Bredlau C, Bosy-Westphal A, Mueller M, Shen W, Gallagher D, et al. Relationships between body roundness with body fat and visceral adipose tissue emerging from a new geometrical model. Obes (Silver Spring). 2013;21(11):2264\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKrakauer NY, Krakauer JC. A new body shape index predicts mortality hazard independently of body mass index. PLoS ONE. 2012;7(7):e39504.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePatel CJ, Pho N, McDuffie M, Easton-Marks J, Kothari C, Kohane IS, et al. A database of human exposomes and phenomes from the US National Health and Nutrition Examination Survey. Sci Data. 2016;3:160096.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCuschieri S. The STROBE guidelines. Saudi J Anaesth. 2019;13(Suppl 1):S31\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGangireddy VGR, Pilkerton C, Xiang J, Tinajero R, Ashcraft AM. Hepatic Fibrosis and Steatosis in Metabolic Syndrome. J Obes Metab Syndr. 2022;31(1):61\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSuwała S, Junik R. Assessment of the Liver Steatosis and Fibrosis Risk in Metabolic Syndrome and Its Individual Components, Considering the Varying Definitions Used in Clinical Practice throughout Time: A Retrospective Cross-Sectional Study. Biomedicines. 2024;12(8).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEddowes PJ, Sasso M, Allison M, Tsochatzis E, Anstee QM, Sheridan D, et al. Accuracy of FibroScan Controlled Attenuation Parameter and Liver Stiffness Measurement in Assessing Steatosis and Fibrosis in Patients With Nonalcoholic Fatty Liver Disease. Gastroenterology. 2019;156(6):1717\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEslam M, Newsome PN, Sarin SK, Anstee QM, Targher G, Romero-Gomez M, et al. A new definition for metabolic dysfunction-associated fatty liver disease: An international expert consensus statement. J Hepatol. 2020;73(1):202\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLin W, Lin ME. Novel anthropometric measures are positively associated with erectile dysfunction: a cross-sectional study. Int Urol Nephrol. 2024;56(3):855\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZwierzchowska A, Kantyka J, Rosołek B, Nawrat-Szołtysik A, Małecki A. Sensitivity and Specificity of Anthropometric Indices in Identifying Obesity in Women over 40 Years of Age and Their Variability in Subsequent Decades of Life. Biology (Basel). 2022;11(12).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang J, Liang D, Xu L, Liu Y, Jiang S, Han X, et al. Associations between novel anthropometric indices and the prevalence of gallstones among 6,848 adults: a cross-sectional study. Front Nutr. 2024;11:1428488.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi Y, Zheng R, Li S, Cai R, Ni F, Zheng H, et al. Association Between Four Anthropometric Indexes and Metabolic Syndrome in US Adults. Front Endocrinol (Lausanne). 2022;13:889785.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi H, Jiang Y, Su X, Meng Z. The triglyceride glucose index was U-shape associated with all-cause mortality in population with cardiovascular diseases. Diabetol Metab Syndr. 2023;15(1):181.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAkinbami LJ, Chen TC, Davy O, Ogden CL, Fink S, Clark J, et al. National Health and Nutrition Examination Survey, 2017-March 2020 Prepandemic File: Sample Design, Estimation, and Analytic Guidelines. Vital Health Stat. 2022;1(190):1\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrankovic M, Kardys I, Steyerberg EW, Lemeshow S, Markovic M, Rizopoulos D, et al. Understanding of interaction (subgroup) analysis in clinical trials. Eur J Clin Invest. 2019;49(8):e13145.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNahm FS. Receiver operating characteristic curve: overview and practical use for clinicians. Korean J Anesthesiol. 2022;75(1):25\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHagstr\u0026ouml;m H, Shang Y, Hegmar H, Nasr P. Natural history and progression of metabolic dysfunction-associated steatotic liver disease. Lancet Gastroenterol Hepatol. 2024;9(10):944\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGrier T, Canham-Chervak M, Sharp M, Jones BH. Does body mass index misclassify physically active young men. Prev Med Rep. 2015;2:483\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBatsis JA, Villareal DT. Sarcopenic obesity in older adults: aetiology, epidemiology and treatment strategies. Nat Rev Endocrinol. 2018;14(9):513\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003evan der Kolk BW, Saari S, Lovric A, Arif M, Alvarez M, Ko A, et al. Molecular pathways behind acquired obesity: Adipose tissue and skeletal muscle multiomics in monozygotic twin pairs discordant for BMI. Cell Rep Med. 2021;2(4):100226.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShi J, Chen J, Zhang Z, Qian G. Multi-dimensional comparison of abdominal obesity indices and insulin resistance indicators for assessing NAFLD. BMC Public Health. 2024;24(1):2161.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAhn N, Baumeister SE, Amann U, Rathmann W, Peters A, Huth C, et al. Visceral adiposity index (VAI), lipid accumulation product (LAP), and product of triglycerides and glucose (TyG) to discriminate prediabetes and diabetes. Sci Rep. 2019;9(1):9693.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChan WK, Chuah KH, Rajaram RB, Lim LL, Ratnasingam J, Vethakkan SR. Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD): A State-of-the-Art Review. J Obes Metab Syndr. 2023;32(3):197\u0026ndash;213.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHajer GR, van Haeften TW, Visseren FL. Adipose tissue dysfunction in obesity, diabetes, and vascular diseases. Eur Heart J. 2008;29(24):2959\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDuan Y, Pan X, Luo J, Xiao X, Li J, Bestman PL, et al. Association of Inflammatory Cytokines With Non-Alcoholic Fatty Liver Disease. Front Immunol. 2022;13:880298.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChoudhury J, Sanyal AJ. Insulin resistance and the pathogenesis of nonalcoholic fatty liver disease. Clin Liver Dis. 2004;8(3):575\u0026ndash;94. ix.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMilić S, Lulić D, Štimac D. Non-alcoholic fatty liver disease and obesity: biochemical, metabolic and clinical presentations. World J Gastroenterol. 2014;20(28):9330\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAboona MB, Faulkner C, Rangan P, Ng CH, Huang DQ, Muthiah M, et al. Disparities among ethnic groups in mortality and outcomes among adults with MASLD: A multicenter study. Liver Int. 2024;44(6):1316\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang X, Linden S, Levesley CR, He X, Yang Z, Barnet SD, et al. Projected Trends in Metabolic Dysfunction-Associated Steatotic Liver Disease Mortality Through 2040. JAMA Netw Open. 2025;8(6):e2516367.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKahn HS. The lipid accumulation product performs better than the body mass index for recognizing cardiovascular risk: a population-based comparison. BMC Cardiovasc Disord. 2005;5:26.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTellechea ML, Aranguren F, Mart\u0026iacute;nez-Larrad MT, Serrano-R\u0026iacute;os M, Taverna MJ, Frechtel GD. Ability of lipid accumulation product to identify metabolic syndrome in healthy men from Buenos Aires. Diabetes Care. 2009;32(7):e85.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKahn HS. The lipid accumulation product is better than BMI for identifying diabetes: a population-based comparison. Diabetes Care. 2006;29(1):151\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHosseinpanah F, Barzin M, Mirbolouk M, Abtahi H, Cheraghi L, Azizi F. Lipid accumulation product and incident cardiovascular events in a normal weight population: Tehran Lipid and Glucose Study. Eur J Prev Cardiol. 2016;23(2):187\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChew NWS, Mehta A, Goh RSJ, Zhang A, Chen Y, Chong B, et al. Cardiovascular-Liver-Metabolic Health: Recommendations in Screening, Diagnosis, and Management of Metabolic Dysfunction-Associated Steatotic Liver Disease in Cardiovascular Disease via Modified Delphi Approach. Circulation. 2025;151(1):98\u0026ndash;119.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen J, Zhou H, Liu K. Normal weight and central obesity as predictors of increased all-cause mortality in metabolic dysfunction associated steatotic liver disease. J Hepatol. 2024;80(4):e143\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBertoli S, Leone A, Krakauer NY, Bedogni G, Vanzulli A, Redaelli VI, et al. Association of Body Shape Index (ABSI) with cardio-metabolic risk factors: A cross-sectional study of 6081 Caucasian adults. PLoS ONE. 2017;12(9):e0185013.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim JY, Choi J, Vella CA, Criqui MH, Allison MA, Kim NH. Associations between Weight-Adjusted Waist Index and Abdominal Fat and Muscle Mass: Multi-Ethnic Study of Atherosclerosis. Diabetes Metab J. 2022;46(5):747\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSong K, Seol EG, Yang H, Jeon S, Shin HJ, Chae HW, et al. Bioelectrical impedance parameters add incremental value to waist-to-hip ratio for prediction of metabolic dysfunction associated steatotic liver disease in youth with overweight and obesity. Front Endocrinol (Lausanne). 2024;15:1385002.\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":"metabolic dysfunction-associated steatotic liver disease (MASLD), women of reproductive age, lipid accumulation product (LAP), waist-to-height ratio (WHTR), anthropometric indices, early screening","lastPublishedDoi":"10.21203/rs.3.rs-7188234/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7188234/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn recent years, novel anthropometric indices have been shown to improve the accuracy of body fat percentage estimation and are regarded as more effective in predicting their association with metabolic dysfunction-associated fatty liver disease (MAFLD); this study aimed to evaluate their effectiveness in risk assessment for metabolic dysfunction-associated steatotic liver disease (MASLD) among U.S. women of reproductive age. Utilizing data from the U.S. NHANES database (2017\u0026ndash;2020 cycle), 1,060 women aged 20\u0026ndash;44 years (of reproductive age) were enrolled, with a MASLD prevalence of 40.8% (433/1,060). Multivariate logistic regression, generalized additive models with smoothing curve fitting (for dose-response relationships), and receiver operating characteristic (ROC) curve analysis (to measure predictive ability) were employed;The fully adjusted multivariable logistic regression identified significant positive associations between MASLD and all six anthropometric indices: lnLAP (OR\u0026thinsp;=\u0026thinsp;7.06, 95%CI\u0026thinsp;=\u0026thinsp;5.12\u0026ndash;9.73), WTI (OR\u0026thinsp;=\u0026thinsp;6.84, 95%CI\u0026thinsp;=\u0026thinsp;4.65\u0026ndash;10.06), WWI (OR\u0026thinsp;=\u0026thinsp;2.95, 95%CI\u0026thinsp;=\u0026thinsp;2.34\u0026ndash;3.72), ABSI (OR\u0026thinsp;=\u0026thinsp;1.89, 95%CI\u0026thinsp;=\u0026thinsp;1.34\u0026ndash;2.67), WHTR (OR\u0026thinsp;=\u0026thinsp;1.13, 95%CI\u0026thinsp;=\u0026thinsp;1.11\u0026ndash;1.16), BRI (OR\u0026thinsp;=\u0026thinsp;1.69, 95%CI\u0026thinsp;=\u0026thinsp;1.54\u0026ndash;1.84). Dose-response analysis revealed a 48.91-fold increased MASLD risk (95%CI\u0026thinsp;=\u0026thinsp;24.68\u0026ndash;96.93) in the highest vs. lowest lnLAP quartile, with a significant risk threshold at the lnLAP\u0026thinsp;=\u0026thinsp;2.34. ROC analysis demonstrated superior predictive performance for lnLAP (AUC\u0026thinsp;=\u0026thinsp;0.85, 95%CI\u0026thinsp;=\u0026thinsp;0.82\u0026ndash;0.87), followed by WHTR and BRI (AUC\u0026thinsp;=\u0026thinsp;0.84, 95%CI\u0026thinsp;=\u0026thinsp;0.82\u0026ndash;0.87), and moderate accuracy for WTI (AUC\u0026thinsp;=\u0026thinsp;0.80, 95%CI\u0026thinsp;=\u0026thinsp;0.77\u0026ndash;0.82), Subgroup analysis indicated race significantly modified associations for WTI and ABSI.The novel anthropometric indices\u0026mdash;particularly lnLAP\u0026mdash;represent effective screening tools for MASLD in women of reproductive age. These findings provide a clinically applicable and cost-effective strategy for early risk stratification in this population, with notable implications for preventive care in primary health settings.\u003c/p\u003e","manuscriptTitle":"Utility of Six Novel Anthropometric Indicators for Assessing Metabolic Dysfunction-Associated Steatotic Liver Disease in US Reproductive-Aged Women: An NHANES Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-02 13:09:19","doi":"10.21203/rs.3.rs-7188234/v1","editorialEvents":[{"type":"communityComments","content":1}],"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":"e76ddcd0-7cea-4823-b735-aa58f7da1746","owner":[],"postedDate":"September 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-09T13:53:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-02 13:09:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7188234","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7188234","identity":"rs-7188234","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}