Association Between Metabolic Score for Visceral Fat and Liver Fibrosis Risk in Non-Viral Hepatitis Populations: NHANES 2017–2020

Association Between Metabolic Score for Visceral Fat and Liver Fibrosis Risk in Non-Viral Hepatitis Populations: NHANES 2017–2020 | 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 Association Between Metabolic Score for Visceral Fat and Liver Fibrosis Risk in Non-Viral Hepatitis Populations: NHANES 2017–2020 Qi Shang, Xiao Liang, Gaixia He, Keping Feng, Shuai Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6832417/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Visceral adiposity dysfunction has been recognized as an independent risk factor for liver fibrosis, and early identification may improve prognosis. This study aimed to investigate the association of metabolic score for visceral fat (METS-VF) and other visceral adiposity metabolic indices (METS-IR, VAI, CMI, LAP) with liver fibrosis in U.S. non-viral hepatitis populations. Methods Using data from the National Health and Nutrition Examination Survey (NHANES 2017–March 2020), we conducted weighted multivariable logistic regression and trend analyses to evaluate the associations of visceral adiposity metabolic indices with overall, significant and advanced fibrosis. In addition, restricted cubic spline (RCS) model was used to examine potential nonlinear relationships. Receiver operating characteristic (ROC) curves were further used to assess the diagnostic performance of these indices. Finally, subgroup analyses were performed to evaluate potential variations of association between METS-VF and fibrosis across different population strata. Results A total of 3,490 participants were included in this study. Among all visceral adiposity metabolic indices, METS-VF showed the strongest association with liver fibrosis, demonstrating superior predictive performance. Participants in the highest METS-VF quartile had 7.81-fold greater odds of fibrosis (adjusted OR 7.812, 95% CI: 2.421–25.207), with an AUC of 0.748 (95% CI: 0.696–0.796). This association exhibited a severity-dependent pattern, with the odds ratio increasing to 23.44 for advanced fibrosis. Subgroup analyses revealed higher association between METS-VF and fibrosis among individuals who were married or living with partner or had hypertension or diabetes. Conclusions METS-VF demonstrated significant association with liver fibrosis and superior predictive performance compared to FIB-4 and other visceral adiposity metabolic indices, suggesting its clinical utility for liver fibrosis screening in non-viral hepatitis populations Liver fibrosis METS-VF NHANES Visceral adiposity metabolism Cross-sectional study Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Liver fibrosis represents a critical pathological stage in the progression of chronic liver disease toward cirrhosis and hepatocellular carcinoma, characterized by excessive deposition of extracellular matrix components in the liver[1–3]. This pathological process not only leads to progressive structural and functional deterioration of the liver, but also significantly increases the risk of end-stage liver diseases, including liver failure, and HCC[4, 5]. Notably, liver fibrosis is a dynamic and potentially reversible process[3], making early identification and intervention crucial for improving patient prognosis. Currently, liver biopsy remains the gold standard for fibrosis diagnosis. However, its invasiveness, high cost, and sampling variability limit its widespread clinical application. In this context, noninvasive diagnostic methods have rapidly evolved, primarily falling into two categories: (1) serological markers and composite models[6–8], including simple clinical indices (e.g., APRI, FIB-4 index, NAFLD Fibrosis Score, BARD) and specialized biomarker panels (e.g., ELF, FibroTest, NIS4); and (2) imaging-based techniques[9–13], further subdivided into transient elastography (TE), magnetic resonance elastography (MRE), and hybrid serum-imaging algorithms (e.g., FAST, MAST, MEFIB). Among these, the FIB-4 index—which incorporates age, AST, ALT, and platelet count (with cutoffs of 2.67 indicating high risk)—has gained international guideline endorsement as a first-line screening tool due to its simplicity[14]. However, current evidence demonstrates that these indices exhibit limited diagnostic accuracy in the general population, particularly showing significant age-dependent variability among patients with metabolic dysfunction-associated steatotic liver disease (MASLD), with notable risks of both false-positive and false-negative results[15–21]. This underscores the urgent need to develop more reliable screening tools. Against the backdrop of the global metabolic disease epidemic, dysfunctional lipid metabolism has emerged as a central driver of fibrogenesis[22]. Metabolic disturbances—including insulin resistance, visceral adiposity, and dyslipidemia—synergistically promote fibrosis through multiple pathways, such as hepatic stellate cell activation, oxidative stress, and chronic inflammation[23]. Current indices for assessing visceral adiposity and insulin resistance include the Metabolic Score for Insulin Resistance (METS-IR)[24], Lipid Accumulation Product (LAP)[25], Visceral Adiposity Index (VAI)[26], and Cardiometabolic Index (CMI)[25]. However, these metrics are often limited to evaluating single metabolic dimensions. The novel METS-VF, which integrates the insulin-independent METS-IR index, waist-to-height ratio (WHtR), age, and sex, offers a theoretically more comprehensive risk assessment[27]. Yet, systematic comparisons of these visceral adiposity metabolic indices (METS-VF, METS-IR, LAP, VAI, and CMI) in relation to liver fibrosis—particularly in non-viral hepatitis populations—remain lacking. Therefore, this study employs a multidimensional analytical approach to comprehensively evaluate the predictive value of these indices for liver fibrosis in non-viral hepatitis populations, aiming to provide evidence-based guidance for selecting optimal risk assessment tools in clinical practice. Materials and Methods Data Source and Study Population NHANES is a biennial, cross-sectional survey conducted by the Centers for Disease Control and Prevention (CDC) to assess the health and nutritional status of the non-institutionalized U.S. population. The survey incorporates structured interviews, physical examinations, questionnaires, and laboratory tests, with data collected through a complex, multistage probability sampling design. All participants provided written informed consent before interviews and examinations. The study protocol was approved by the National Center for Health Statistics Research Ethics Review Board in compliance with the U.S. Department of Health and Human Services Policy for Protection of Human Research Subjects. Detailed survey methodology and data documentation are publicly available ( https://www.cdc.gov/nchs/nhanes/ ) This cross-sectional study utilized data from the NHANES 2017-March 2020 pre-pandemic surveys. A total of 15,560 individuals participated in the survey, from which the following participants were excluded: (1) age < 18 years (n = 5,867); (2) missing METS-VF calculation data (n = 5,791); (3) missing LSM data (n = 192); (4) fasting weight (WTSAFPRP) recorded as 0 (n = 159); and (5) hepatitis B or C (n = 61). Ultimately, 3,490 participants were included in the final analysis (Fig. 1 ). Diagnosis of Hepatic Fibrosis The diagnosis of liver fibrosis was determined by liver stiffness measurement (LSM) using FibroScan® with vibration-controlled transient elastography (VCTE™), where LSM values ≥ 8 kPa were defined as indicative of liver fibrosis. Specifically, significant fibrosis was classified as 8 kPa ≤ LSM < 11.6 kPa, while advanced fibrosis was defined as LSM ≥ 11.6 kPa[28]. Measurement of Visceral Adiposity Metabolic Indicators Visceral adiposity metabolic indicators were designated as exposure variables in this investigation. These parameters were computed using standardized formulas[25]. Laboratory assays provided quantitative measurements of triglycerides (TG), fasting blood glucose (FBG), and cholesterol-high-density lipoprotein (HDL-C). Anthropometric data including sex, age, body mass index (BMI),waist circumference (WC), and height (HT) were collected at mobile examination centers. For METS-VF calculation, sex was numerically coded with males as 1 and females as 0. In addition, waist-to-height ratio (WHtR) was calculated by WHtR = WC/HT. $$\:METS-IR=\text{l}\text{n}(2\times\:FBG+TG)\times\:\frac{BMI}{ln(HDL-C)}$$ $$\:METS-VF=4.466+0.011\times\:{\left(\text{l}\text{n}\right(METS-IR\left)\right)}^{3}+3.329\times\:{\left(\text{l}\text{n}\right(WHtR\left)\right)}^{3}+0.319\times\:gender+0.594\times\:\text{l}\text{n}\left(age\right)$$ $$\:VAI\left(men\right)=\left(\frac{WC}{39.68+1.88\times\:BMI}\right)\times\:\left(\frac{TG}{1.03}\right)\times\:\left(\frac{1.31}{HDL-C}\right)$$ $$\:VAI\left(women\right)=\left(\frac{WC}{36.58+1.89\times\:\text{B}\text{M}\text{I}}\right)\times\:\left(\frac{TG}{0.81}\right)\times\:\left(\frac{1.52}{HDL-C}\right)$$ $$\:LAP\left(men\right)=(WC－65)\times\:TG$$ $$\:LAP\left(women\right)=(WC－58)\times\:TG$$ $$\:CMI\:=\:\left(\frac{TG}{HDL-C}\right)\times\:WHtR$$ Covariates This study incorporated comprehensive covariate data extracted from the NHANES database, encompassing both demographic characteristics and clinical risk factors. The demographic variables included age, sex, race, and family poverty-income ratio (PIR). Behavioral and clinical variables were derived from integrated questionnaire responses, laboratory tests, and physical examination data, specifically focusing on alcohol consumption patterns, smoking status, and histories of diabetes, hypertension, and hyperlipidemia. Standardized diagnostic criteria were applied for all clinical assessments: heavy drinking was defined as > 196 g/week for males or > 98 g/week for females[29]; smoking status was determined by lifetime cigarette consumption (> 100 cigarettes); hypertension was diagnosed based on blood pressure thresholds (SBP ≥ 140 mmHg or DBP ≥ 90 mmHg) or current antihypertensive medication use; diabetes was confirmed through either pharmacological treatment records or laboratory-confirmed diagnosis; and hyperlipidemia was identified by specific lipid profile cutoffs (LDL ≥ 3.4 mmol/L, HDL-C < 1.0 mmol/L (men) or < 1.3 mmol/L (women), triglycerides ≥ 1.7 mmol/L, or total cholesterol ≥ 5.2 mmol/L)[30] or current lipid-lowering therapy. All covariate definitions followed established clinical guidelines to ensure methodological consistency across the study population. Statistical Analysis Statistical analyses were performed using R (version 4.4.2). Missing values were imputed using random forest, and a two-sided p-value < 0.05 was considered statistically significant. Given the complex, stratified and multistage probability cluster sampling design of NHANES, all analyses followed CDC-recommended guidelines ( https://wwwn.cdc.gov/nchs/nhanes/tutorials/default.aspx ) and incorporated appropriate sample weights. For this study, the fasting subsample weight was applied in the final analysis. Continuous variables were presented as weighted survey means ± standard deviations and analyzed using survey-weighted linear regression. Categorical variables were expressed as counts and weighted survey proportions (%) and analyzed using survey-weighted chi-square tests. Weighted multivariable logistic regression models were employed to examine the association between liver fibrosis and quartiles of the five visceral adiposity metabolic indicators. Trends in fibrosis risk across quartiles were assessed using median values of each metabolic indicator. The same approach was applied to evaluate associations between the visceral adiposity metabolic indicators and significant fibrosis and advanced fibrosis. Additionally, RCS with three knots were constructed to assess the dose-response relationship between the visceral adiposity metabolic indicators and liver fibrosis. ROC curves were generated for each metabolic indicator, with area under the curve (AUC) values quantifying their predictive accuracy for liver fibrosis. The DeLong test was employed to compare the predictive performance (AUC) across metabolic indicators. Subgroup analyses were further conducted to explore potential variations in the association between METS-VF and liver fibrosis across different populations. Interaction effects were assessed using the Wald test. Results Baseline Characteristics The study population comprised 3,490 eligible participants, including 3,132 (89.7%) without liver fibrosis and 358 (10.3%) with liver fibrosis. As shown in Table 1 , participants with liver fibrosis exhibited distinct characteristics compared to those without fibrosis. They were generally older, had a lower PIR, and attained lower education levels. A higher proportion were widowed/divorced/separated. Clinically, the fibrosis group showed significantly higher prevalence rates of diabetes and hypertension, along with a greater burden of steatotic liver disease (SLD) —predominantly metabolic dysfunction-associated steatotic liver disease (MASLD). Laboratory profiles revealed elevated lipid parameters and liver enzyme levels. Additionally, all five metabolic indices—METS-VF, METS-IR, LAP, VAI, and CMI—were markedly higher in the fibrosis group (all p < 0.05). Table 1 Weighted baseline characteristics by liver fibrosis Characteristics Non-Fibrosis Fibrosis P -value 3132 358 Gender,n(%) 0.123 Female 1630(51.2) 155(44.3) Male 1502(48.8) 203(55.7) Age(mean(SD)) 46.49 (17.46) 51.19 (16.69) 0.001 Race,n(%) 0.359 Mexican American 419(9.5) 58(10.3) Non-Hispanic Black 766(11.3) 81(9.3) Non-Hispanic White 1050(62.1) 139(65.7) Other Hispanic 322(6.9) 36(7.6) Other Race 575(10.2) 44(7.1) PIR(mean(SD)) 3.13 (1.58) 2.74 (1.46) 0.001 PIR_new,n(%) 3.5 935(44.0) 84(28.6) Marital status,n(%) 0.472 Married/Living with Partner 1787(60.2) 211(60.3) Never married 722(22.0) 59(19.3) Widowed/Divorced/Separated 623(17.9) 88(20.4) Education level,n(%) 0.043 College graduate or above 796(33.2) 66(26.5) Some college or AA degree 1043(30.6) 131(31.0) High school graduate/GED or equivalent 737(25.9) 95(30.4) 9-11th grade 333(7.1) 33(6.5) Less than 9th grade 223(3.1) 33(5.6) Alcohol consumption(mean(SD)) 48.58 (105.01) 42.35 (117.16) 0.493 Smoking,n(%) 0.608 No 1890(58.4) 193(56.5) Yes 1242(41.6) 165(43.5) Hyptersion,n(%) < 0.001 No 1847(64.3) 124(42.8) Yes 1285(35.7) 234(57.2) Diabetes Mellitus,n(%) < 0.001 No 2565(87.3) 177(57.4) Yes 567(12.7) 181(42.6) Hyperlipidemia,n(%) 0.058 No 1287(41.7) 115(34.7) Yes 1845(58.3) 243(65.3) WC (mean (SD)) 98.19 (15.67) 115.59 (21.42) < 0.001 BMI (mean (SD)) 28.83 (6.46) 35.89 (10.11) < 0.001 CAP (mean (SD)) 259.31 (59.41) 315.24 (65.88) < 0.001 TG (mean (SD)) 1.22 (1.00) 1.48 (1.29) < 0.001 TC (mean (SD)) 4.82 (1.04) 4.56 (1.09) 0.001 LDL-C (mean (SD)) 2.83 (0.93) 2.63 (1.00) 0.009 HDL-C (mean (SD)) 1.41 (0.41) 1.26 (0.36) < 0.001 FBG(mean (SD)) 5.95 (1.55) 7.18 (2.79) < 0.001 Hba1c (mean (SD)) 5.59 (0.84) 6.22 (1.73) < 0.001 GGT (mean (SD)) 25.60 (23.77) 48.29 (59.15) < 0.001 ALP (mean (SD)) 73.88 (22.35) 83.16 (34.38) 0.013 ALT (mean (SD)) 21.32 (16.03) 29.73 (21.67) < 0.001 AST (mean (SD)) 20.79 (10.01) 26.30 (16.44) < 0.001 ALB (mean (SD)) 40.83 (3.15) 40.19 (3.90) 0.075 TBIL (mean (SD)) 8.66 (5.41) 9.11 (4.75) 0.218 SLD(CAP ≥ 285) < 0.001 No 2081(67.1) 101(29.3) Yes 1051(32.9) 257(70.7) ALD 18 ( 0.6) 7 (1.4) MASLD 978(30.5) 240 (66.9) MetALD 51 (1.7) 10 (2.3) other SLD 4 (0.1) 0 (0.0) SLD(CAP ≥ 263) < 0.001 No 1664(53.8) 73(23.1) Yes 1468(46.2) 285(76.9) ALD 29 ( 1.1) 7 (1.4) MASLD 1358 (42.0) 267 (72.9) MetALD 70 (2.8) 11 (2.6) other SLD 11 (0.4) 0 (0.0) METS-VF(mean(SD)) 6.73 (0.77) 7.28 (0.73) < 0.001 METS-IR(mean(SD)) 42.38 (11.75) 55.91 (18.38) < 0.001 LAP(mean(SD)) 48.01 (48.32) 82.87 (82.17) < 0.001 VAI(mean(SD)) 1.68 (1.87) 2.27 (2.69) < 0.001 CMI(mean(SD)) 0.63 (0.79) 0.98 (1.27) < 0.001 PIR Poverty income ratio, WC Waist circumference, BMI Body mass index, TG Triglycerides, TC Total cholesterol, LDL-C Low-density lipoprotein cholesterol, HDL-C High-density lipoprotein cholesterol, FPG Fasting plasma glucose, ALT Alanine aminotransferase, AST Aspartate aminotransferase, GGT Gamma-glutamyl transferase, ALP Alkaline phosphatase, ALB Albumin, TBIL Total bilirubin, CAP Controlled attenuation parameter, SLD Steatotic liver disease, METS-VF Visceral adiposity metabolic score, METS-IR Metabolic syndrome insulin resistance index, LAP Lipid accumulation product, VAI Visceral adiposity index, CMI Cardiometabolic index. [Insert Table 1 here] Association of METS-VF, METS-IR, LAP, VAI, and CMI with Liver Fibrosis This study examined the relationship between five visceral adiposity metabolic indices (METS-VF, METS-IR, LAP, VAI, and CMI) and the risk of liver fibrosis in non-viral hepatitis populations. Three regression models were constructed: Model 1 (unadjusted); Model 2 (adjusted for sex, age, race, marital status, education level, PIR, alcohol use, and smoking); and Model 3 (further adjusted for hypertension, diabetes mellitus, and hyperlipidemia in addition to Model 2 variables). Each indicator was categorized into quartiles for association and trend analyses. Weighted multivariable logistic regression and trend tests (Table 2 ) revealed that METS-VF exhibited a significant dose-dependent increase in fibrosis risk with higher quartiles (P for trend < 0.01). Specifically, the highest quartile (Q4) showed markedly elevated odds of fibrosis in both the unadjusted model (OR = 7.521, 95% CI: 4.089–13.834) and the fully adjusted model (OR = 7.812, 95% CI: 2.421–25.207), whereas Q2 and Q3 demonstrated non-significant associations. This suggests a potential threshold effect, where only the uppermost METS-VF quartile confers significant risk. Table 2 Association of METS-VF, METS-IR, LAP, VAI, and CMI with Liver Fibrosis Model1 OR(95%CI) Model2 OR(95%CI) Model3 OR(95%CI) METS-VF Q1 Reference Reference Reference Q2 1.007(0.422, 2.404) 1.160(0.473, 2.843) 1.198(0.397,3.616) Q3 1.847(0.943, 3.617) 2.257(0.935, 5.453) 2.134(0.785,5.806) Q4 7.521(4.089, 13.834) 10.412(4.124, 26.288) 7.812(2.421,25.207) P for trend <0.001 <0.001 <0.01 METS-IR Q1 Reference Reference Reference Q2 0.342(0.136,0.859) 0.271(0.085,0.860)* 0.270(0.067,1.082) Q3 1.308(0.602,2.843) 1.022(0.389,2.689) 0.904(0.286,2.861) Q4 5.192(2.461,10.954) 4.628(1.920,11.159)** 3.463(1.166,10.288) P for trend <0.01 0.05 LAP Q1 Reference Reference Reference Q2 0.640(0.293,1.398) 0.640(0.223,1.361) 0.596(0.206,1.724) Q3 2.463(1.059,4.420) 2.163(0.730,4.688) 1.821(0.645,5.140) Q4 5.510(2.607,8.687) 4.759(1.822,8.491) 3.469(1.244,9.672) P for trend <0.001 <0.001 <0.01(0.0017) VAI Q1 Reference Reference Reference Q2 1.742(0.866,3.505) 1.560(0.678,3.590) 1.503(0.583,3.878) Q3 2.121(1.341,3.354) 1.876(1.143,3.080) 1.473(0.819,2.649) Q4 3.251(2.042,5.176) 2.831(1.611,4.976) 2.169(0.963,4.884) P for trend <0.001 0.05 CMI Q1 Reference Reference Reference Q2 1.265(0.465,3.443) 1.137(0.348,3.721) 1.103(0.273,4.458) Q3 1.861(1.035,3.345) 1.624(0.793,3.324) 1.392(0.617,3.147) Q4 4.076(2.148,7.734) 3.438(1.583,7.470) 2.735(0.950,7.878) P for trend <0.001 <0.001 <0.05 Model 1: no covariates were adjusted; Model 2: Gender, Age, Race, Marital status, Education level, PIR, Alcohol use and smoking were adjusted; Model 3: Gender, Age, Race, Marital, Education level, PIR, Alcohol use and smoking, Hyptersion, Diabetes Mellitus and Hyperlipidemia were adjusted. For METS-IR, Q4 was associated with an OR of 5.192 (95% CI: 2.461–10.954) in the unadjusted model, which attenuated to 3.463 (95% CI: 1.166–10.288) after full adjustment, with loss of trend significance (P > 0.05). LAP showed moderate association strength, with Q4 OR of 3.469 (1.244–9.672) post-adjustment and retained trend significance (P < 0.01). In contrast, VAI and CMI demonstrated weaker associations: VAI lost both significance and trend after adjustment, while CMI maintained only marginal trend significance (P < 0.05). [Insert Table 2 here] This study conducted stratified analyses to investigate the associations between five visceral adiposity metabolic indices (METS-VF, METS-IR, LAP, VAI, and CMI) and different stages of liver fibrosis. The results demonstrated that the strength of these associations significantly increased with disease progression. In the analysis of significant fibrosis (Table S1 ), METS-VF showed an OR of 4.529 (95% CI: 2.000-10.055) for the highest quartile (Q4) in the unadjusted model (Model 1), which decreased to 4.552 (95% CI: 0.993–20.864) after full adjustment (Model 3), with both the association and trend losing statistical significance. Similarly, LAP, VAI, and CMI all became non-significant after full adjustment. Notably, METS-IR demonstrated a negative association with liver fibrosis that reached statistical significance in both unadjusted and fully adjusted models, with an OR of 0.160 (95% CI: 0.040–0.644). For advanced fibrosis (Table 3 ), all indicators exhibited stronger associations: METS-VF in Q4 showed a markedly elevated OR of 23.438 (95% CI: 4.634-118.539), while METS-IR, LAP, and CMI in Q4 had ORs of 13.159 (95% CI: 2.598–66.654), 8.764 (1.550-49.543), and 11.356 (1.325–97.290) respectively. These values were consistently higher than those observed for significant fibrosis, with both the associations and trends demonstrating statistically significant differences. Table 3 Association of METS-VF, METS-IR, LAP, VAI, and CMI with Liver Advance Fibrosis Model1 OR(95%CI) Model2 OR(95%CI) Model3 OR(95%CI) METS-VF Q1 Reference Reference Reference Q2 1.323(0.359, 4.877) 1.366(0.276, 5.679) 1.414(0.207, 9.683) Q3 4.326(0.687, 27.244) 5.195(0.581, 30.675) 5.057(0.383, 66.809) Q4 23.774(7.359, 76.800) 31.855(8.644, 117.383) 23.438(4.634, 118.539) P for trend <0.001 <0.001 <0.01 METS-IR Q1 Reference Q2 1.401(0.248,7.905) 1.078(0.169,6.860) 1.084(0.130,9.017) Q3 6.414(1.451,28.355) 5.075(0.991,25.998) 4.282(0.664,27.615) Q4 22.116(6.869,71.207) 19.926(5.014,79.184) 13.159(2.598,66.654) P for trend <0.001 <0.01 <0.05 LAP Q1 Reference Reference Reference Q2 1.020(0.309,3.374) 1.020(0.214,3.056) 0.901(0.187,4.331) Q3 5.278(1.892,14.725) 5.278(1.262,13.666) 3.994(0.948,16.838) Q4 13.989(4.403,44.445) 13.989(2.911,37.678) 8.764(1.550,49.543) P for trend <0.001 <0.001 <0.01 VAI Q1 Reference Reference Reference Q2 5.193(1.605,16.797) 4.513(1.161,17.549) 4.203(0.825,21.410) Q3 9.452(2.386,37.438) 8.040(1.762,36.693) 5.839(0.834,40.902) Q4 10.807(3.181,36.715) 8.936(2.237, 35.692) 6.065(0.881,41.772) P for trend <0.001 0.05 CMI Q1 Reference Reference Reference Q2 4.477(1.605,18.905) 4.026(0.793,20.435) 3.889(0.560,26.999) Q3 9.504(3.049,29.627) 8.085(2.307,28.343) 6.645(1.323,33.374) Q4 18.454(4.546,74.912) 15.556(3.415,70.856) 11.356(1.325,97.290) P for trend <0.001 <0.001 <0.05 Model 1: no covariates were adjusted; Model 2: Gender, Age, Race, Marital status, Education level, PIR, Alcohol use and smoking were adjusted; Model 3: Gender, Age, Race, Marital, Education level, PIR, Alcohol use and smoking, Hyptersion, Diabetes Mellitus and Hyperlipidemia were adjusted. [Insert Table 3 here] FCS analysis of the association between METS-VF, METS-IR, LAP, VAI, CMI and liver fibrosis. To better visualize these associations, we performed FCS analyses (Fig. 2 ). The results revealed nonlinear relationships between all indicators and liver fibrosis. Specifically, using the median METS-VF value of 7.04 as reference, the fibrosis risk exhibited an accelerated upward trend when METS-VF exceeded 7.04. relation to liver fibrosis. The solid orange line and orange-shaded area represent the estimated odds ratio (OR) and its 95% confidence interval, respectively. ROC Analysis of METS-VF, METS-IR, LAP, VAI and CMI in Identifying Liver Fibrosis We subsequently performed ROC analysis to evaluate the predictive capacity of METS-VF, METS-IR, LAP, VAI, and CMI for liver fibrosis. AUC, optimal cutoff values, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for each indicator (Table 4 ), and ROC curves were plotted (Fig. 3 ). The results showed that METS-VF had the highest AUC (0.748, 95% CI: 0.696–0.796) for predicting liver fibrosis. Further DeLong’s test revealed that METS-VF had a significantly better discriminative ability for liver fibrosis than LAP, VAI, and CMI (P ≤ 0.001), but no statistically significant difference was observed compared with METS-IR (P = 0.128). Table 4 Performance of METS-VF, METS-IR, LAP, VAI, and CMI in Predicting Liver Fibrosis AUC 95%CI Best Threshold Sensitivity Specificity PPV NPV METS-VF 0.748 0.696–0.796 7.2 0.788 0.655 0.207 0.964 METS-IR 0.732 0.677–0.785 51.359 0.628 0.793 0.258 0.949 LAP*** 0.696 0.643–0.745 55.665 0.640 0.713 0.203 0.945 CMI*** 0.658 0.612–0.705 0.553 0.659 0.617 0.164 0.941 VAI*** 0.616 0.568–0.662 1.613 0.559 0.650 0.154 0.928 PPV, positive predictive value; NPV, negative predictive value; CI, confidence interval; ***, P < 0.001 compared with METS-VF. Additionally, we assessed the predictive performance of FIB-4 for liver fibrosis in the non-viral hepatitis population and plotted the corresponding ROC curve ( Fig. S1 ). The results showed that FIB-4 had an AUC of 0.592, which was significantly lower than that of METS-VF (P ≤ 0.001). Subgroup Analysis of the Association Between METS-VF and Liver Fibrosis To further explore potential differences in the association between METS-VF and liver fibrosis across different populations and identify high-risk subgroups, we conducted subgroup analyses and interaction tests based on age, gender, education level, marital, poverty of family income, smoking, alcohol consumption, diabetes mellitus, hyperlipidemia and hypertension. The final results are presented in a forest plot (Fig. 4 ). Subsequently, our stratified analysis and interaction tests revealed that the association of METS-VF and liver fibrosis risk varied significantly by hypertension status, diabetes mellitus status, and marital status. In the diabetes mellitus subgroup, those with diabetes showed a notably increased METS-VF-associated liver fibrosis risk (OR: 20.10 vs. 2.56; P-interaction = 0.039). In the hypertension subgroup, individuals with hypertension exhibited a significantly higher METS-VF-associated liver fibrosis risk (OR: 12.76 vs. 2.27; P-interaction = 0.017). Additionally, compared to widowed/divorced/separated or never married individuals, those who were married/living with a partner had a higher METS-VF-associated liver fibrosis risk (OR: 10.51 vs. 4.46 vs. 1.43; P-interaction = 0.046). Discussion This study represents the first systematic comparison of five Visceral Adiposity Metabolic Indicators (METS-VF, METS-IR, LAP, VAI, and CMI) in relation to liver fibrosis among non-viral hepatitis populations, utilizing large-scale data from the NHANES database. Our key findings demonstrate that METS-VF exhibited the strongest association with liver fibrosis. In the fully adjusted model, the highest quartile (Q4) of METS-VF showed the most robust association strength (OR = 7.812), with a significant non-linear dose-response relationship (P for trend < 0.001). Notably, the association between METS-VF and advanced liver fibrosis (OR = 23.438) was substantially stronger than with significant fibrosis, suggesting its enhanced predictive value for progressive disease stages. Regarding predictive performance, METS-VF achieved a significantly higher area under the receiver operating characteristic curve (AUC = 0.748) compared to the FIB-4 index (AUC = 0.592), the internationally recommended first-line screening tool for liver fibrosis, as well as other Visceral Adiposity Metabolic Indicators (all P < 0.001 by DeLong's test). This underscores METS-VF's superiority as a multidimensional composite indicator integrating age, waist circumference, BMI, and glycolipid metabolic parameters, making it particularly suitable for screening high-risk populations with liver fibrosis in non-viral hepatitis populations. However, no statistically significant difference was observed between METS-VF and the metabolic insulin resistance index (METS-IR) in predictive capability (P = 0.128), implying that insulin resistance may play a pivotal role in the pathogenesis of liver fibrosis. Liver fibrosis, a pathological consequence characterized by excessive extracellular matrix deposition during tissue repair following chronic liver injury, represents the critical transitional stage from chronic liver disease to cirrhosis. The progression of fibrosis severity significantly increases the risk of hepatic decompensation, portal hypertension, and hepatocellular carcinoma. Current evidence demonstrates that multiple pathogenic factors - including insulin resistance, oxidative stress, chronic inflammation, and lipotoxicity - collectively drive the development and progression of fibrosis. Early identification of high-risk populations is essential for preventing disease advancement. The FIB-4 index has been widely recommended as a first-line screening tool in international guidelines due to its clinical practicality. This composite index incorporates age, aspartate aminotransferase (AST), alanine aminotransferase (ALT), and platelet count, with established cutoff values ( 2.67 suggesting high risk). Its application in targeted at-risk populations enables effective assessment of liver fibrosis. Notably, The EASL-EASD-EASO guidelines have recommend a non-invasive staging pathway for liver fibrosis management based on FIB-4 scoring[14]. However, FIB-4 demonstrates significant limitations in general populations and certain at-risk subgroups. Multiple studies have demonstrated that FIB-4 has suboptimal accuracy for liver fibrosis screening in general populations, carrying risks of both overdiagnosis and false-negative results[19, 20]. Notably, this index may fail to detect over 50% of cirrhosis cases during both pre-diagnostic evaluation and clinical diagnosis[17]. In the context of MASLD-related fibrosis risk stratification, FIB-4 has been shown to cause substantial misclassification[18], with approximately 20% of patients at referral centers exhibiting false-negative results despite clinically significant fibrosis[21]. Furthermore, FIB-4 demonstrates reduced specificity in elderly populations, where the currently recommended cutoff values may lead to excessive/unnecessary referrals[16].Our findings align with these international observations - in non-viral hepatitis general populations, FIB-4 showed inferior predictive performance (AUC 0.592) that was significantly lower than METS-VF (P ≤ 0.001). A U.S. adolescent population study demonstrated significant associations between METS-VF and both non-alcoholic fatty liver disease (NAFLD) (OR = 15.74, 95%CI:10.44–23.72) and liver fibrosis (OR = 1.85, 95%CI:1.27–2.70), with particularly strong correlations observed among Mexican-American female adolescents[31]. While our study similarly confirmed METS-VF's robust association with liver fibrosis, we observed higher OR values compared to the adolescent cohort. This divergence likely stems from distinct population characteristics: our research focused on adults with non-viral hepatitis (typically an older demographic with higher comorbidity prevalence and longer disease duration). These factors may collectively intensify the association between METS-VF and fibrosis progression.These comparative results suggest that the predictive value of METS-VF may vary across population subgroups, underscoring the need for individualized clinical decision-making tailored to specific patient demographics and metabolic profiles. Multiple clinical studies have demonstrated a significant positive correlation between the severity of insulin resistance and degree of hepatic steatosis[32, 33] confirming its critical role in the initiation and progression of liver fibrosis. Notably, in patients with type 2 diabetes mellitus (T2DM), insulin resistance exhibits superior predictive value for fibrosis progression compared to the extent of hepatic lipid deposition[34, 35]. Our analytical data indicated that while METS-VF (AUC = 0.748) showed marginally better predictive performance for liver fibrosis than METS-IR (AUC = 0.732), this difference did not achieve statistical significance (P = 0.128). The comparable predictive efficacy between these indices not only validates METS-VF's capability for comprehensive metabolic assessment, but more importantly, provides clinical evidence that insulin resistance serves as the fundamental pathological mechanism driving fibrotic progression. METS-VF outperforms other adiposity indices in both association strength (OR = 7.812) and predictive accuracy (AUC = 0.748) by comprehensively integrating multiple pathogenic pathways: (1) visceral fat-derived NEFA overflow induces hepatic steatosis through enhanced β-oxidation and DNL, triggering mitochondrial dysfunction and oxidative stress; (2) insulin resistance amplifies this damage via adipose-liver axis dysregulation - impaired lipolysis suppression causes excessive NEFA flux [32], while hyperinsulinemia activates SREBP1c/ChREBP-mediated DNL[36–39], collectively driving fibrotic progression. This study utilized the nationally representative NHANES database with a large sample size to ensure result reliability. It represents the first systematic comparison of multiple adiposity indices with liver fibrosis in a general non-viral liver disease population, and innovatively identified a non-linear dose-response relationship between METS-VF and liver fibrosis. However, several limitations should be noted: the cross-sectional design precludes causal inferences; transient elastography may underestimate early-stage fibrosis; limited sample size resulted in wide confidence intervals for some results; and dynamic changes in metabolic indices were not assessed. Future validation through larger-scale, multicenter studies incorporating more accurate fibrosis assessment and long-term follow-up is warranted. Conclusions This study establishes METS-VF as a superior predictor of liver fibrosis in non-viral hepatitis populations compared to FIB-4 and other metabolic indices, with scores below 7.04 potentially mitigating risk, suggesting its clinical utility for early screening. Abbreviations AUC Area under the ROC curve BMI Body mass index CDC The Centers for Disease Control and Prevention CMI Cardiometabolic Index FBG Fasting blood glucose HDL-C Cholesterol HighDensity Lipoprotein HT Height LAP Lipid Accumulation Product LSM Liver stiffness measurement MASLD Metabolic dysfunction-associated steatotic liver disease METS-IR Metabolic Score for Insulin Resistance NHANES National Health and Nutrition Examination Survey NPV Negative predictive value PPV Positive predictive value PIR Poverty-income ratio RCS Restricted cubic spline ROC Receiver operating characteristic SLD Steatotic liver disease TG Triglycerides VAI Visceral Adiposity Index WC Waist circumference WHtR Waist-to-height ratio Declarations Ethics approval and consent to participate The survey protocol was approved by the NCHS Ethics Review Board. The approved protocol can be found at this link: https://www.cdc.gov/nchs/ nhanes/irba98.htm. Additionally, every participant in the survey supplied written informed permission. Consent for publication Not applicable Availability of data and materials No datasets were generated or analysed during the current study. Competing interests The authors declare no competing interests. Funding National Natural Science Foundation of China (No. 81802455) Natural Science Basic Research Project of Shaanxi Province (No. 2019JQ-962) Authors' contributions QS conceived and wrote the main manuscript. QS and GXH collected and validated the data. XL and KPF performed data analysis. SZ supervised the research, critically reviewed and edited the manuscript, and acquired funding. All authors read and approved the final manuscript. Acknowledgements We are grateful to the participants and staff of NHANES. References Friedman SL. Mechanisms of hepatic fibrogenesis. Gastroenterology. 2008;134(6):1655-69. Hernandez-Gea V, Friedman SL. Pathogenesis of liver fibrosis. Annu Rev Pathol. 2011;6:425 − 56. Kisseleva T, Brenner D. Molecular and cellular mechanisms of liver fibrosis and its regression. Nat Rev Gastroenterol Hepatol. 2021;18(3):151 − 66. Bataller R, Brenner DA. Liver fibrosis. J Clin Invest. 2005;115(2):209 − 18. Schuppan D, Afdhal NH. Liver cirrhosis. Lancet. 2008;371(9615):838 − 51. Kjaergaard M, Lindvig KP, Thorhauge KH, Andersen P, Hansen JK, Kastrup N, et al. Using the ELF test, FIB-4 and NAFLD fibrosis score to screen the population for liver disease. J Hepatol. 2023;79(2):277 − 86. van Kleef LA, Francque SM, Prieto-Ortiz JE, Sonneveld MJ, Sanchez-Luque CB, Prieto-Ortiz RG, et al. Metabolic Dysfunction-Associated Fibrosis 5 (MAF-5) Score Predicts Liver Fibrosis Risk and Outcome in the General Population With Metabolic Dysfunction. Gastroenterology. 2024;167(2):357 − 67.e9. Han JW, Kim HY, Yu JH, Kim MN, Chon YE, An JH, et al. Diagnostic accuracy of the Fibrosis-4 index for advanced liver fibrosis in nonalcoholic fatty liver disease with type 2 diabetes: A systematic review and meta-analysis. Clin Mol Hepatol. 2024;30(Suppl):S147-s58. Zhang L, Long X, Chen L, Han X, Zhang X, Sun P, et al. Detecting Cellular Microstructural Changes of Liver Fibrosis with Time-Dependent Diffusion MRI. Radiology. 2024;313(1):e240343. Duarte-Rojo A, Taouli B, Leung DH, Levine D, Nayfeh T, Hasan B, et al. Imaging-based noninvasive liver disease assessment for staging liver fibrosis in chronic liver disease: A systematic review supporting the AASLD Practice Guideline. Hepatology. 2025;81(2):725 − 48. Chon YE, Jin YJ, An J, Kim HY, Choi M, Jun DW, et al. Optimal cut-offs of vibration-controlled transient elastography and magnetic resonance elastography in diagnosing advanced liver fibrosis in patients with nonalcoholic fatty liver disease: A systematic review and meta-analysis. Clin Mol Hepatol. 2024;30(Suppl):S117-s33. An J, Chon YE, Kim G, Kim MN, Kim HY, Lee HA, et al. Diagnostic accuracy of vibration-controlled transient elastography for staging liver fibrosis in autoimmune liver diseases: A systematic review and meta-analysis. Clin Mol Hepatol. 2024;30(Suppl):S134-s46. Abdelhameed F, Kite C, Lagojda L, Dallaway A, Chatha KK, Chaggar SS, et al. Non-invasive Scores and Serum Biomarkers for Fatty Liver in the Era of Metabolic Dysfunction-associated Steatotic Liver Disease (MASLD): A Comprehensive Review From NAFLD to MAFLD and MASLD. Curr Obes Rep. 2024;13(3):510 − 31. EASL-EASD-EASO Clinical Practice Guidelines on the management of metabolic dysfunction-associated steatotic liver disease (MASLD). J Hepatol. 2024;81(3):492–542. Kim HY, Yu JH, Chon YE, Kim SU, Kim MN, Han JW, et al. Prevalence of clinically significant liver fibrosis in the general population: A systematic review and meta-analysis. Clin Mol Hepatol. 2024;30(Suppl):S199-s213. Sugiyama A, Kurisu A, E B, Ouoba S, Ko K, Rakhimov A, et al. Distribution of FIB-4 index in the general population: analysis of 75,666 residents who underwent health checkups. BMC Gastroenterol. 2022;22(1):241. Parikh ND, Mehta M, Tapper EB. FIB-4 and APRI for cirrhosis detection in a privately insured national cohort. JHEP Rep. 2024;6(1):100925. Hazzan R, Abu Ahmad N, Habib AS, Saleh I, Ziv N. Suboptimal reliability of FIB-4 and NAFLD-fibrosis scores for staging of liver fibrosis in general population. JGH Open. 2024;8(2):e13034. Graupera I, Thiele M, Serra-Burriel M, Caballeria L, Roulot D, Wong GL, et al. Low Accuracy of FIB-4 and NAFLD Fibrosis Scores for Screening for Liver Fibrosis in the Population. Clin Gastroenterol Hepatol. 2022;20(11):2567-76.e6. Chang M, Chang D, Kodali S, Harrison SA, Ghobrial M, Alkhouri N, et al. Degree of Discordance Between FIB-4 and Transient Elastography: An Application of Current Guidelines on General Population Cohort. Clin Gastroenterol Hepatol. 2024;22(7):1453-61.e2. Viganò M, Pugliese N, Cerini F, Turati F, Cimino V, Ridolfo S, et al. Accuracy of FIB-4 to Detect Elevated Liver Stiffness Measurements in Patients with Non-Alcoholic Fatty Liver Disease: A Cross-Sectional Study in Referral Centers. Int J Mol Sci. 2022;23(20). Younossi ZM, de Avila L, Racila A, Nader F, Paik J, Henry L, et al. Prevalence and predictors of cirrhosis and portal hypertension in the United States. Hepatology. 2025. Steinberg GR, Valvano CM, De Nardo W, Watt MJ. Integrative metabolism in MASLD and MASH: Pathophysiology and emerging mechanisms. J Hepatol. 2025. Bello-Chavolla OY, Almeda-Valdes P, Gomez-Velasco D, Viveros-Ruiz T, Cruz-Bautista I, Romo-Romo A, et al. METS-IR, a novel score to evaluate insulin sensitivity, is predictive of visceral adiposity and incident type 2 diabetes. Eur J Endocrinol. 2018;178(5):533 − 44. Sun Q, Ren Q, Du L, Chen S, Wu S, Zhang B, et al. Cardiometabolic Index (CMI), Lipid Accumulation Products (LAP), Waist Triglyceride Index (WTI) and the risk of acute pancreatitis: a prospective study in adults of North China. Lipids Health Dis. 2023;22(1):190. Amato MC, Giordano C, Galia M, Criscimanna A, Vitabile S, Midiri M, et al. Visceral Adiposity Index: a reliable indicator of visceral fat function associated with cardiometabolic risk. Diabetes Care. 2010;33(4):920-2. Bello-Chavolla OY, Antonio-Villa NE, Vargas-Vázquez A, Viveros-Ruiz TL, Almeda-Valdes P, Gomez-Velasco D, et al. Metabolic Score for Visceral Fat (METS-VF), a novel estimator of intra-abdominal fat content and cardio-metabolic health. Clin Nutr. 2020;39(5):1613-21. Kim D, Danpanichkul P, Wijarnpreecha K, Cholankeril G, Loomba R, Ahmed A. Current burden of steatotic liver disease and fibrosis among adults in the United States, 2017–2023. Clin Mol Hepatol. 2025;31(2):382 − 93. US Department of Agriculture and US Department of Health and Human Services. Dietary Guidelines for Americans, 2020–2025. 9th ed. Washington, DC: US Government Publishing Office; 2020. Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation. 2002;106(25):3143 − 421. Xu X, Yang J, Li Y, Li Y, Zeng X, Chen B. Association of metabolic evaluation of visceral fat score with nonalcoholic fatty liver disease and liver fibrosis: A cross-sectional study based on NHANES. Medicine (Baltimore). 2025;104(17):e42213. Lomonaco R, Ortiz-Lopez C, Orsak B, Webb A, Hardies J, Darland C, et al. Effect of adipose tissue insulin resistance on metabolic parameters and liver histology in obese patients with nonalcoholic fatty liver disease. Hepatology. 2012;55(5):1389-97. Bril F, Barb D, Portillo-Sanchez P, Biernacki D, Lomonaco R, Suman A, et al. Metabolic and histological implications of intrahepatic triglyceride content in nonalcoholic fatty liver disease. Hepatology. 2017;65(4):1132-44. Kalavalapalli S, Leiva EG, Lomonaco R, Chi X, Shrestha S, Dillard R, et al. Adipose Tissue Insulin Resistance Predicts the Severity of Liver Fibrosis in Patients With Type 2 Diabetes and NAFLD. J Clin Endocrinol Metab. 2023;108(5):1192 − 201. Sakaguchi M, Fujisaka S, Cai W, Winnay JN, Konishi M, O'Neill BT, et al. Adipocyte Dynamics and Reversible Metabolic Syndrome in Mice with an Inducible Adipocyte-Specific Deletion of the Insulin Receptor. Cell Metab. 2017;25(2):448 − 62. Perry RJ, Camporez JG, Kursawe R, Titchenell PM, Zhang D, Perry CJ, et al. Hepatic acetyl CoA links adipose tissue inflammation to hepatic insulin resistance and type 2 diabetes. Cell. 2015;160(4):745 − 58. Petersen MC, Smith GI, Palacios HH, Farabi SS, Yoshino M, Yoshino J, et al. Cardiometabolic characteristics of people with metabolically healthy and unhealthy obesity. Cell Metab. 2024;36(4):745 − 61.e5. Shaikh SR, Beck MA, Alwarawrah Y, MacIver NJ. Emerging mechanisms of obesity-associated immune dysfunction. Nat Rev Endocrinol. 2024;20(3):136 − 48. Xu S, Lu F, Gao J, Yuan Y. Inflammation-mediated metabolic regulation in adipose tissue. Obes Rev. 2024;25(6):e13724. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-6832417","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":486271268,"identity":"25cdda9a-3bb5-4b1a-9b38-01936f48e244","order_by":0,"name":"Qi Shang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Shang","suffix":""},{"id":486271269,"identity":"70cfe14d-46c0-42b2-9e95-529129780fa3","order_by":1,"name":"Xiao Liang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Xi'an 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00:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6832417/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6832417/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87036783,"identity":"232ace01-f33f-447c-8fc1-ba71a0a1c809","added_by":"auto","created_at":"2025-07-18 13:22:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":329349,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA flowchart showing the selection of study participants\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6832417/v1/450c7bf7f93d8cb086e6f95e.png"},{"id":87035544,"identity":"f0820293-cb33-4813-bacf-3d628d349744","added_by":"auto","created_at":"2025-07-18 13:14:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":600623,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe RCS plots of METS-VF, METS-IR, CMI, LAP, and VAI in relation to liver fibrosis. \u003c/strong\u003eThe solid orange line and orange-shaded area represent the estimated odds ratio (OR) and its 95% confidence interval, respectively.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6832417/v1/fdbc3afbb15e8a1270c95b8c.png"},{"id":87038557,"identity":"b3d5e636-4bf2-47bc-8cb1-da0f6a28c108","added_by":"auto","created_at":"2025-07-18 13:30:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":140538,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curves of METS-VF, METS-IR, LAP, VAI and CMI\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6832417/v1/b2feb768daa023cd4f290b38.png"},{"id":87035546,"identity":"e3c28993-cc92-4370-bacd-792fb7d1a2d8","added_by":"auto","created_at":"2025-07-18 13:14:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":555575,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubgroup analysis of the association between METS-VF and liver fibrosis\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6832417/v1/d25913e2a868a1100143e904.png"},{"id":93137925,"identity":"c635d031-3d58-46d1-905c-8b54f61039a6","added_by":"auto","created_at":"2025-10-09 12:32:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3138080,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6832417/v1/dbe8d08b-1101-4e9d-a4d6-360b5b9d027e.pdf"},{"id":87036784,"identity":"3daee69f-f65e-4c8d-a53e-99e7e87960ad","added_by":"auto","created_at":"2025-07-18 13:22:02","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":252431,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6832417/v1/e14502b0dc4f461f6bc1ecbc.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association Between Metabolic Score for Visceral Fat and Liver Fibrosis Risk in Non-Viral Hepatitis Populations: NHANES 2017–2020","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLiver fibrosis represents a critical pathological stage in the progression of chronic liver disease toward cirrhosis and hepatocellular carcinoma, characterized by excessive deposition of extracellular matrix components in the liver[1\u0026ndash;3]. This pathological process not only leads to progressive structural and functional deterioration of the liver, but also significantly increases the risk of end-stage liver diseases, including liver failure, and HCC[4, 5]. Notably, liver fibrosis is a dynamic and potentially reversible process[3], making early identification and intervention crucial for improving patient prognosis.\u003c/p\u003e\u003cp\u003eCurrently, liver biopsy remains the gold standard for fibrosis diagnosis. However, its invasiveness, high cost, and sampling variability limit its widespread clinical application. In this context, noninvasive diagnostic methods have rapidly evolved, primarily falling into two categories: (1) serological markers and composite models[6\u0026ndash;8], including simple clinical indices (e.g., APRI, FIB-4 index, NAFLD Fibrosis Score, BARD) and specialized biomarker panels (e.g., ELF, FibroTest, NIS4); and (2) imaging-based techniques[9\u0026ndash;13], further subdivided into transient elastography (TE), magnetic resonance elastography (MRE), and hybrid serum-imaging algorithms (e.g., FAST, MAST, MEFIB). Among these, the FIB-4 index\u0026mdash;which incorporates age, AST, ALT, and platelet count (with cutoffs of \u0026lt;\u0026thinsp;1.3 indicating low risk and \u0026gt;\u0026thinsp;2.67 indicating high risk)\u0026mdash;has gained international guideline endorsement as a first-line screening tool due to its simplicity[14]. However, current evidence demonstrates that these indices exhibit limited diagnostic accuracy in the general population, particularly showing significant age-dependent variability among patients with metabolic dysfunction-associated steatotic liver disease (MASLD), with notable risks of both false-positive and false-negative results[15\u0026ndash;21]. This underscores the urgent need to develop more reliable screening tools.\u003c/p\u003e\u003cp\u003eAgainst the backdrop of the global metabolic disease epidemic, dysfunctional lipid metabolism has emerged as a central driver of fibrogenesis[22]. Metabolic disturbances\u0026mdash;including insulin resistance, visceral adiposity, and dyslipidemia\u0026mdash;synergistically promote fibrosis through multiple pathways, such as hepatic stellate cell activation, oxidative stress, and chronic inflammation[23]. Current indices for assessing visceral adiposity and insulin resistance include the Metabolic Score for Insulin Resistance (METS-IR)[24], Lipid Accumulation Product (LAP)[25], Visceral Adiposity Index (VAI)[26], and Cardiometabolic Index (CMI)[25]. However, these metrics are often limited to evaluating single metabolic dimensions. The novel METS-VF, which integrates the insulin-independent METS-IR index, waist-to-height ratio (WHtR), age, and sex, offers a theoretically more comprehensive risk assessment[27]. Yet, systematic comparisons of these visceral adiposity metabolic indices (METS-VF, METS-IR, LAP, VAI, and CMI) in relation to liver fibrosis\u0026mdash;particularly in non-viral hepatitis populations\u0026mdash;remain lacking.\u003c/p\u003e\u003cp\u003eTherefore, this study employs a multidimensional analytical approach to comprehensively evaluate the predictive value of these indices for liver fibrosis in non-viral hepatitis populations, aiming to provide evidence-based guidance for selecting optimal risk assessment tools in clinical practice.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData Source and Study Population\u003c/h2\u003e\u003cp\u003eNHANES is a biennial, cross-sectional survey conducted by the Centers for Disease Control and Prevention (CDC) to assess the health and nutritional status of the non-institutionalized U.S. population. The survey incorporates structured interviews, physical examinations, questionnaires, and laboratory tests, with data collected through a complex, multistage probability sampling design. All participants provided written informed consent before interviews and examinations. The study protocol was approved by the National Center for Health Statistics Research Ethics Review Board in compliance with the U.S. Department of Health and Human Services Policy for Protection of Human Research Subjects. Detailed survey methodology and data documentation are publicly available (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eThis cross-sectional study utilized data from the NHANES 2017-March 2020 pre-pandemic surveys. A total of 15,560 individuals participated in the survey, from which the following participants were excluded: (1) age\u0026thinsp;\u0026lt;\u0026thinsp;18 years (n\u0026thinsp;=\u0026thinsp;5,867); (2) missing METS-VF calculation data (n\u0026thinsp;=\u0026thinsp;5,791); (3) missing LSM data (n\u0026thinsp;=\u0026thinsp;192); (4) fasting weight (WTSAFPRP) recorded as 0 (n\u0026thinsp;=\u0026thinsp;159); and (5) hepatitis B or C (n\u0026thinsp;=\u0026thinsp;61). Ultimately, 3,490 participants were included in the final analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDiagnosis of Hepatic Fibrosis\u003c/h3\u003e\n\u003cp\u003eThe diagnosis of liver fibrosis was determined by liver stiffness measurement (LSM) using FibroScan\u0026reg; with vibration-controlled transient elastography (VCTE\u0026trade;), where LSM values\u0026thinsp;\u0026ge;\u0026thinsp;8 kPa were defined as indicative of liver fibrosis. Specifically, significant fibrosis was classified as 8 kPa\u0026thinsp;\u0026le;\u0026thinsp;LSM\u0026thinsp;\u0026lt;\u0026thinsp;11.6 kPa, while advanced fibrosis was defined as LSM\u0026thinsp;\u0026ge;\u0026thinsp;11.6 kPa[28].\u003c/p\u003e\n\u003ch3\u003eMeasurement of Visceral Adiposity Metabolic Indicators\u003c/h3\u003e\n\u003cp\u003eVisceral adiposity metabolic indicators were designated as exposure variables in this investigation. These parameters were computed using standardized formulas[25]. Laboratory assays provided quantitative measurements of triglycerides (TG), fasting blood glucose (FBG), and cholesterol-high-density lipoprotein (HDL-C). Anthropometric data including sex, age, body mass index (BMI),waist circumference (WC), and height (HT) were collected at mobile examination centers. For METS-VF calculation, sex was numerically coded with males as 1 and females as 0. In addition, waist-to-height ratio (WHtR) was calculated by WHtR\u0026thinsp;=\u0026thinsp;WC/HT.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:METS-IR=\\text{l}\\text{n}(2\\times\\:FBG+TG)\\times\\:\\frac{BMI}{ln(HDL-C)}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:METS-VF=4.466+0.011\\times\\:{\\left(\\text{l}\\text{n}\\right(METS-IR\\left)\\right)}^{3}+3.329\\times\\:{\\left(\\text{l}\\text{n}\\right(WHtR\\left)\\right)}^{3}+0.319\\times\\:gender+0.594\\times\\:\\text{l}\\text{n}\\left(age\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:VAI\\left(men\\right)=\\left(\\frac{WC}{39.68+1.88\\times\\:BMI}\\right)\\times\\:\\left(\\frac{TG}{1.03}\\right)\\times\\:\\left(\\frac{1.31}{HDL-C}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:VAI\\left(women\\right)=\\left(\\frac{WC}{36.58+1.89\\times\\:\\text{B}\\text{M}\\text{I}}\\right)\\times\\:\\left(\\frac{TG}{0.81}\\right)\\times\\:\\left(\\frac{1.52}{HDL-C}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:LAP\\left(men\\right)=(WC－65)\\times\\:TG$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$$\\:LAP\\left(women\\right)=(WC－58)\\times\\:TG$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equg\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equg\" name=\"EquationSource\"\u003e\n$$\\:CMI\\:=\\:\\left(\\frac{TG}{HDL-C}\\right)\\times\\:WHtR$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eThis study incorporated comprehensive covariate data extracted from the NHANES database, encompassing both demographic characteristics and clinical risk factors. The demographic variables included age, sex, race, and family poverty-income ratio (PIR). Behavioral and clinical variables were derived from integrated questionnaire responses, laboratory tests, and physical examination data, specifically focusing on alcohol consumption patterns, smoking status, and histories of diabetes, hypertension, and hyperlipidemia. Standardized diagnostic criteria were applied for all clinical assessments: heavy drinking was defined as \u0026gt;\u0026thinsp;196 g/week for males or \u0026gt;\u0026thinsp;98 g/week for females[29]; smoking status was determined by lifetime cigarette consumption (\u0026gt;\u0026thinsp;100 cigarettes); hypertension was diagnosed based on blood pressure thresholds (SBP\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg or DBP\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg) or current antihypertensive medication use; diabetes was confirmed through either pharmacological treatment records or laboratory-confirmed diagnosis; and hyperlipidemia was identified by specific lipid profile cutoffs (LDL\u0026thinsp;\u0026ge;\u0026thinsp;3.4 mmol/L, HDL-C\u0026thinsp;\u0026lt;\u0026thinsp;1.0 mmol/L (men) or \u0026lt;\u0026thinsp;1.3 mmol/L (women), triglycerides\u0026thinsp;\u0026ge;\u0026thinsp;1.7 mmol/L, or total cholesterol\u0026thinsp;\u0026ge;\u0026thinsp;5.2 mmol/L)[30] or current lipid-lowering therapy. All covariate definitions followed established clinical guidelines to ensure methodological consistency across the study population.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed using R (version 4.4.2). Missing values were imputed using random forest, and a two-sided p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Given the complex, stratified and multistage probability cluster sampling design of NHANES, all analyses followed CDC-recommended guidelines (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wwwn.cdc.gov/nchs/nhanes/tutorials/default.aspx\u003c/span\u003e\u003cspan address=\"https://wwwn.cdc.gov/nchs/nhanes/tutorials/default.aspx\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and incorporated appropriate sample weights. For this study, the fasting subsample weight was applied in the final analysis.\u003c/p\u003e\u003cp\u003eContinuous variables were presented as weighted survey means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations and analyzed using survey-weighted linear regression. Categorical variables were expressed as counts and weighted survey proportions (%) and analyzed using survey-weighted chi-square tests. Weighted multivariable logistic regression models were employed to examine the association between liver fibrosis and quartiles of the five visceral adiposity metabolic indicators. Trends in fibrosis risk across quartiles were assessed using median values of each metabolic indicator. The same approach was applied to evaluate associations between the visceral adiposity metabolic indicators and significant fibrosis and advanced fibrosis.\u003c/p\u003e\u003cp\u003eAdditionally, RCS with three knots were constructed to assess the dose-response relationship between the visceral adiposity metabolic indicators and liver fibrosis. ROC curves were generated for each metabolic indicator, with area under the curve (AUC) values quantifying their predictive accuracy for liver fibrosis. The DeLong test was employed to compare the predictive performance (AUC) across metabolic indicators.\u003c/p\u003e\u003cp\u003eSubgroup analyses were further conducted to explore potential variations in the association between METS-VF and liver fibrosis across different populations. Interaction effects were assessed using the Wald test.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eBaseline Characteristics\u003c/h2\u003e\u003cp\u003eThe study population comprised 3,490 eligible participants, including 3,132 (89.7%) without liver fibrosis and 358 (10.3%) with liver fibrosis.\u003c/p\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, participants with liver fibrosis exhibited distinct characteristics compared to those without fibrosis. They were generally older, had a lower PIR, and attained lower education levels. A higher proportion were widowed/divorced/separated. Clinically, the fibrosis group showed significantly higher prevalence rates of diabetes and hypertension, along with a greater burden of steatotic liver disease (SLD) \u0026mdash;predominantly metabolic dysfunction-associated steatotic liver disease (MASLD). Laboratory profiles revealed elevated lipid parameters and liver enzyme levels. Additionally, all five metabolic indices\u0026mdash;METS-VF, METS-IR, LAP, VAI, and CMI\u0026mdash;were markedly higher in the fibrosis 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\u003eWeighted baseline characteristics by liver fibrosis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-Fibrosis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFibrosis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e358\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender,n(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.123\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1630(51.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e155(44.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1502(48.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e203(55.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge(mean(SD))\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46.49 (17.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51.19 (16.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRace,n(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.359\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\u003e419(9.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58(10.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic Black\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e766(11.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e81(9.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic White\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1050(62.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e139(65.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Hispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e322(6.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36(7.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Race\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e575(10.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44(7.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePIR(mean(SD))\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.13 (1.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.74 (1.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePIR_new,n(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e730(16.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84(18.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.3\u0026ndash;3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1467(39.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e190(53.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e935(44.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84(28.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital status,n(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.472\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried/Living with Partner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1787(60.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e211(60.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e722(22.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59(19.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWidowed/Divorced/Separated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e623(17.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e88(20.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation level,n(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCollege graduate or above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e796(33.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66(26.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSome college or AA degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1043(30.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e131(31.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school graduate/GED or equivalent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e737(25.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95(30.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9-11th grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e333(7.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33(6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLess than 9th grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e223(3.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33(5.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAlcohol consumption(mean(SD))\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48.58 (105.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42.35 (117.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.493\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmoking,n(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.608\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1890(58.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e193(56.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1242(41.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e165(43.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHyptersion,n(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1847(64.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e124(42.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1285(35.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e234(57.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiabetes Mellitus,n(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2565(87.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e177(57.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e567(12.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e181(42.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHyperlipidemia,n(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1287(41.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e115(34.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1845(58.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e243(65.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWC (mean (SD))\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e98.19 (15.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e115.59 (21.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003eBMI (mean (SD))\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.83 (6.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35.89 (10.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003eCAP (mean (SD))\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e259.31 (59.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e315.24 (65.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003eTG (mean (SD))\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.22 (1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.48 (1.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003eTC (mean (SD))\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.82 (1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.56 (1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLDL-C (mean (SD))\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.83 (0.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.63 (1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHDL-C (mean (SD))\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.41 (0.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.26 (0.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003eFBG(mean (SD))\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.95 (1.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.18 (2.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003eHba1c (mean (SD))\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.59 (0.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.22 (1.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003eGGT (mean (SD))\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.60 (23.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48.29 (59.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003eALP (mean (SD))\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73.88 (22.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83.16 (34.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eALT (mean (SD))\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21.32 (16.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.73 (21.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003eAST (mean (SD))\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.79 (10.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.30 (16.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003eALB (mean (SD))\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40.83 (3.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40.19 (3.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTBIL (mean (SD))\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.66 (5.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.11 (4.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.218\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSLD(CAP\u0026thinsp;\u0026ge;\u0026thinsp;285)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2081(67.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e101(29.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1051(32.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e257(70.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18 ( 0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMASLD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e978(30.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e240 (66.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetALD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51 (1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eother SLD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (0.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSLD(CAP\u0026thinsp;\u0026ge;\u0026thinsp;263)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1664(53.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73(23.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1468(46.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e285(76.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29 ( 1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMASLD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1358 (42.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e267 (72.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetALD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70 (2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (2.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eother SLD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11 (0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMETS-VF(mean(SD))\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.73 (0.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.28 (0.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003eMETS-IR(mean(SD))\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42.38 (11.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55.91 (18.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003eLAP(mean(SD))\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48.01 (48.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82.87 (82.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003eVAI(mean(SD))\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.68 (1.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.27 (2.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003eCMI(mean(SD))\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.63 (0.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.98 (1.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003ePIR Poverty income ratio, WC Waist circumference, BMI Body mass index, TG Triglycerides, TC Total cholesterol, LDL-C Low-density lipoprotein cholesterol, HDL-C High-density lipoprotein cholesterol, FPG Fasting plasma glucose, ALT Alanine aminotransferase, AST Aspartate aminotransferase, GGT Gamma-glutamyl transferase, ALP Alkaline phosphatase, ALB Albumin, TBIL Total bilirubin, CAP Controlled attenuation parameter, SLD Steatotic liver disease, METS-VF Visceral adiposity metabolic score, METS-IR Metabolic syndrome insulin resistance index, LAP Lipid accumulation product, VAI Visceral adiposity index, CMI Cardiometabolic index.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e[Insert\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003ehere]\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAssociation of METS-VF, METS-IR, LAP, VAI, and CMI with Liver Fibrosis\u003c/h3\u003e\n\u003cp\u003eThis study examined the relationship between five visceral adiposity metabolic indices (METS-VF, METS-IR, LAP, VAI, and CMI) and the risk of liver fibrosis in non-viral hepatitis populations. Three regression models were constructed: Model 1 (unadjusted); Model 2 (adjusted for sex, age, race, marital status, education level, PIR, alcohol use, and smoking); and Model 3 (further adjusted for hypertension, diabetes mellitus, and hyperlipidemia in addition to Model 2 variables). Each indicator was categorized into quartiles for association and trend analyses.\u003c/p\u003e\u003cp\u003eWeighted multivariable logistic regression and trend tests (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) revealed that METS-VF exhibited a significant dose-dependent increase in fibrosis risk with higher quartiles (P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Specifically, the highest quartile (Q4) showed markedly elevated odds of fibrosis in both the unadjusted model (OR\u0026thinsp;=\u0026thinsp;7.521, 95% CI: 4.089\u0026ndash;13.834) and the fully adjusted model (OR\u0026thinsp;=\u0026thinsp;7.812, 95% CI: 2.421\u0026ndash;25.207), whereas Q2 and Q3 demonstrated non-significant associations. This suggests a potential threshold effect, where only the uppermost METS-VF quartile confers significant risk.\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\u003eAssociation of METS-VF, METS-IR, LAP, VAI, and CMI with Liver Fibrosis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel1 OR(95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel2 OR(95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel3 OR(95%CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMETS-VF\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\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\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\u003e1.007(0.422, 2.404)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.160(0.473, 2.843)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.198(0.397,3.616)\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\u003e1.847(0.943, 3.617)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.257(0.935, 5.453)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.134(0.785,5.806)\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\u003e7.521(4.089, 13.834)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.412(4.124, 26.288)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.812(2.421,25.207)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMETS-IR\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\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\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\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\u003e0.342(0.136,0.859)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.271(0.085,0.860)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.270(0.067,1.082)\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\u003e1.308(0.602,2.843)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.022(0.389,2.689)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.904(0.286,2.861)\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\u003e5.192(2.461,10.954)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.628(1.920,11.159)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.463(1.166,10.288)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026gt;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLAP\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\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\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\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\u003e0.640(0.293,1.398)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.640(0.223,1.361)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.596(0.206,1.724)\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\u003e2.463(1.059,4.420)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.163(0.730,4.688)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.821(0.645,5.140)\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\u003e5.510(2.607,8.687)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.759(1.822,8.491)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.469(1.244,9.672)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.01(0.0017)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVAI\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\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\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\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\u003e1.742(0.866,3.505)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.560(0.678,3.590)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.503(0.583,3.878)\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\u003e2.121(1.341,3.354)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.876(1.143,3.080)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.473(0.819,2.649)\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\u003e3.251(2.042,5.176)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.831(1.611,4.976)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.169(0.963,4.884)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026gt;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCMI\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\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\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\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\u003e1.265(0.465,3.443)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.137(0.348,3.721)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.103(0.273,4.458)\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\u003e1.861(1.035,3.345)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.624(0.793,3.324)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.392(0.617,3.147)\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\u003e4.076(2.148,7.734)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.438(1.583,7.470)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.735(0.950,7.878)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel 1: no covariates were adjusted;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel 2: Gender, Age, Race, Marital status, Education level, PIR, Alcohol use and smoking were adjusted;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel 3: Gender, Age, Race, Marital, Education level, PIR, Alcohol use and smoking, Hyptersion, Diabetes Mellitus and Hyperlipidemia were adjusted.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFor METS-IR, Q4 was associated with an OR of 5.192 (95% CI: 2.461\u0026ndash;10.954) in the unadjusted model, which attenuated to 3.463 (95% CI: 1.166\u0026ndash;10.288) after full adjustment, with loss of trend significance (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). LAP showed moderate association strength, with Q4 OR of 3.469 (1.244\u0026ndash;9.672) post-adjustment and retained trend significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In contrast, VAI and CMI demonstrated weaker associations: VAI lost both significance and trend after adjustment, while CMI maintained only marginal trend significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cb\u003e[Insert\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003ehere]\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study conducted stratified analyses to investigate the associations between five visceral adiposity metabolic indices (METS-VF, METS-IR, LAP, VAI, and CMI) and different stages of liver fibrosis. The results demonstrated that the strength of these associations significantly increased with disease progression.\u003c/p\u003e\u003cp\u003eIn the analysis of significant fibrosis (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), METS-VF showed an OR of 4.529 (95% CI: 2.000-10.055) for the highest quartile (Q4) in the unadjusted model (Model 1), which decreased to 4.552 (95% CI: 0.993\u0026ndash;20.864) after full adjustment (Model 3), with both the association and trend losing statistical significance. Similarly, LAP, VAI, and CMI all became non-significant after full adjustment. Notably, METS-IR demonstrated a negative association with liver fibrosis that reached statistical significance in both unadjusted and fully adjusted models, with an OR of 0.160 (95% CI: 0.040\u0026ndash;0.644).\u003c/p\u003e\u003cp\u003eFor advanced fibrosis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), all indicators exhibited stronger associations: METS-VF in Q4 showed a markedly elevated OR of 23.438 (95% CI: 4.634-118.539), while METS-IR, LAP, and CMI in Q4 had ORs of 13.159 (95% CI: 2.598\u0026ndash;66.654), 8.764 (1.550-49.543), and 11.356 (1.325\u0026ndash;97.290) respectively. These values were consistently higher than those observed for significant fibrosis, with both the associations and trends demonstrating statistically significant differences.\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\u003eAssociation of METS-VF, METS-IR, LAP, VAI, and CMI with Liver Advance Fibrosis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel1 OR(95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel2 OR(95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel3 OR(95%CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMETS-VF\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\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\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\u003e1.323(0.359, 4.877)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.366(0.276, 5.679)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.414(0.207, 9.683)\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\u003e4.326(0.687, 27.244)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.195(0.581, 30.675)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.057(0.383, 66.809)\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\u003e23.774(7.359, 76.800)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.855(8.644, 117.383)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.438(4.634, 118.539)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMETS-IR\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\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\u003eReference\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\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\u003e1.401(0.248,7.905)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.078(0.169,6.860)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.084(0.130,9.017)\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\u003e6.414(1.451,28.355)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.075(0.991,25.998)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.282(0.664,27.615)\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\u003e22.116(6.869,71.207)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.926(5.014,79.184)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.159(2.598,66.654)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLAP\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\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\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\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\u003e1.020(0.309,3.374)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.020(0.214,3.056)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.901(0.187,4.331)\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\u003e5.278(1.892,14.725)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.278(1.262,13.666)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.994(0.948,16.838)\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\u003e13.989(4.403,44.445)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.989(2.911,37.678)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.764(1.550,49.543)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVAI\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\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\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\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\u003e5.193(1.605,16.797)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.513(1.161,17.549)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.203(0.825,21.410)\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\u003e9.452(2.386,37.438)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.040(1.762,36.693)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.839(0.834,40.902)\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\u003e10.807(3.181,36.715)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.936(2.237, 35.692)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.065(0.881,41.772)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026gt;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCMI\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\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\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\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\u003e4.477(1.605,18.905)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.026(0.793,20.435)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.889(0.560,26.999)\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\u003e9.504(3.049,29.627)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.085(2.307,28.343)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.645(1.323,33.374)\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\u003e18.454(4.546,74.912)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.556(3.415,70.856)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.356(1.325,97.290)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel 1: no covariates were adjusted;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel 2: Gender, Age, Race, Marital status, Education level, PIR, Alcohol use and smoking were adjusted;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel 3: Gender, Age, Race, Marital, Education level, PIR, Alcohol use and smoking, Hyptersion, Diabetes Mellitus and Hyperlipidemia were adjusted.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e[Insert\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cb\u003ehere]\u003c/b\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eFCS analysis of the association between METS-VF, METS-IR, LAP, VAI, CMI\u003c/h2\u003e\u003cp\u003e\u003cb\u003eand liver fibrosis.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo better visualize these associations, we performed FCS analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The results revealed nonlinear relationships between all indicators and liver fibrosis.\u003c/p\u003e\u003cp\u003eSpecifically, using the median METS-VF value of 7.04 as reference, the fibrosis risk exhibited an accelerated upward trend when METS-VF exceeded 7.04.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003erelation to liver fibrosis.\u003c/b\u003e The solid orange line and orange-shaded area represent the estimated odds ratio (OR) and its 95% confidence interval, respectively.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eROC Analysis of METS-VF, METS-IR, LAP, VAI and CMI in Identifying Liver Fibrosis\u003c/h2\u003e\u003cp\u003eWe subsequently performed ROC analysis to evaluate the predictive capacity of METS-VF, METS-IR, LAP, VAI, and CMI for liver fibrosis. AUC, optimal cutoff values, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for each indicator (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), and ROC curves were plotted (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The results showed that METS-VF had the highest AUC (0.748, 95% CI: 0.696\u0026ndash;0.796) for predicting liver fibrosis. Further DeLong\u0026rsquo;s test revealed that METS-VF had a significantly better discriminative ability for liver fibrosis than LAP, VAI, and CMI (P\u0026thinsp;\u0026le;\u0026thinsp;0.001), but no statistically significant difference was observed compared with METS-IR (P\u0026thinsp;=\u0026thinsp;0.128).\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 of METS-VF, METS-IR, LAP, VAI, and CMI in Predicting Liver Fibrosis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAUC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBest Threshold\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSensitivity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSpecificity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePPV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNPV\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\u003eMETS-VF\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.748\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.696\u0026ndash;0.796\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.788\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.655\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.964\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMETS-IR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.732\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.677\u0026ndash;0.785\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51.359\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.628\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.793\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.949\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLAP***\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.696\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.643\u0026ndash;0.745\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e55.665\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.640\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.713\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.945\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCMI***\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.658\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.612\u0026ndash;0.705\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.553\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.659\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.617\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.941\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVAI***\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.616\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.568\u0026ndash;0.662\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.613\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.559\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.650\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.928\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\u003ePPV, positive predictive value; NPV, negative predictive value; CI, confidence interval; ***, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 compared with METS-VF.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAdditionally, we assessed the predictive performance of FIB-4 for liver fibrosis in the non-viral hepatitis population and plotted the corresponding ROC curve ( Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The results showed that FIB-4 had an AUC of 0.592, which was significantly lower than that of METS-VF (P\u0026thinsp;\u0026le;\u0026thinsp;0.001).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eSubgroup Analysis of the Association Between METS-VF and Liver Fibrosis\u003c/h2\u003e\u003cp\u003eTo further explore potential differences in the association between METS-VF and liver fibrosis across different populations and identify high-risk subgroups, we conducted subgroup analyses and interaction tests based on age, gender, education level, marital, poverty of family income, smoking, alcohol consumption, diabetes mellitus, hyperlipidemia and hypertension. The final results are presented in a forest plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSubsequently, our stratified analysis and interaction tests revealed that the association of METS-VF and liver fibrosis risk varied significantly by hypertension status, diabetes mellitus status, and marital status. In the diabetes mellitus subgroup, those with diabetes showed a notably increased METS-VF-associated liver fibrosis risk (OR: 20.10 vs. 2.56; P-interaction\u0026thinsp;=\u0026thinsp;0.039). In the hypertension subgroup, individuals with hypertension exhibited a significantly higher METS-VF-associated liver fibrosis risk (OR: 12.76 vs. 2.27; P-interaction\u0026thinsp;=\u0026thinsp;0.017). Additionally, compared to widowed/divorced/separated or never married individuals, those who were married/living with a partner had a higher METS-VF-associated liver fibrosis risk (OR: 10.51 vs. 4.46 vs. 1.43; P-interaction\u0026thinsp;=\u0026thinsp;0.046).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study represents the first systematic comparison of five Visceral Adiposity Metabolic Indicators (METS-VF, METS-IR, LAP, VAI, and CMI) in relation to liver fibrosis among non-viral hepatitis populations, utilizing large-scale data from the NHANES database. Our key findings demonstrate that METS-VF exhibited the strongest association with liver fibrosis. In the fully adjusted model, the highest quartile (Q4) of METS-VF showed the most robust association strength (OR\u0026thinsp;=\u0026thinsp;7.812), with a significant non-linear dose-response relationship (P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, the association between METS-VF and advanced liver fibrosis (OR\u0026thinsp;=\u0026thinsp;23.438) was substantially stronger than with significant fibrosis, suggesting its enhanced predictive value for progressive disease stages. Regarding predictive performance, METS-VF achieved a significantly higher area under the receiver operating characteristic curve (AUC\u0026thinsp;=\u0026thinsp;0.748) compared to the FIB-4 index (AUC\u0026thinsp;=\u0026thinsp;0.592), the internationally recommended first-line screening tool for liver fibrosis, as well as other Visceral Adiposity Metabolic Indicators (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 by DeLong's test). This underscores METS-VF's superiority as a multidimensional composite indicator integrating age, waist circumference, BMI, and glycolipid metabolic parameters, making it particularly suitable for screening high-risk populations with liver fibrosis in non-viral hepatitis populations. However, no statistically significant difference was observed between METS-VF and the metabolic insulin resistance index (METS-IR) in predictive capability (P\u0026thinsp;=\u0026thinsp;0.128), implying that insulin resistance may play a pivotal role in the pathogenesis of liver fibrosis.\u003c/p\u003e\u003cp\u003eLiver fibrosis, a pathological consequence characterized by excessive extracellular matrix deposition during tissue repair following chronic liver injury, represents the critical transitional stage from chronic liver disease to cirrhosis. The progression of fibrosis severity significantly increases the risk of hepatic decompensation, portal hypertension, and hepatocellular carcinoma. Current evidence demonstrates that multiple pathogenic factors - including insulin resistance, oxidative stress, chronic inflammation, and lipotoxicity - collectively drive the development and progression of fibrosis. Early identification of high-risk populations is essential for preventing disease advancement. The FIB-4 index has been widely recommended as a first-line screening tool in international guidelines due to its clinical practicality. This composite index incorporates age, aspartate aminotransferase (AST), alanine aminotransferase (ALT), and platelet count, with established cutoff values (\u0026lt;\u0026thinsp;1.3 indicating low risk and \u0026gt;\u0026thinsp;2.67 suggesting high risk). Its application in targeted at-risk populations enables effective assessment of liver fibrosis. Notably, The EASL-EASD-EASO guidelines have recommend a non-invasive staging pathway for liver fibrosis management based on FIB-4 scoring[14].\u003c/p\u003e\u003cp\u003eHowever, FIB-4 demonstrates significant limitations in general populations and certain at-risk subgroups. Multiple studies have demonstrated that FIB-4 has suboptimal accuracy for liver fibrosis screening in general populations, carrying risks of both overdiagnosis and false-negative results[19, 20]. Notably, this index may fail to detect over 50% of cirrhosis cases during both pre-diagnostic evaluation and clinical diagnosis[17]. In the context of MASLD-related fibrosis risk stratification, FIB-4 has been shown to cause substantial misclassification[18], with approximately 20% of patients at referral centers exhibiting false-negative results despite clinically significant fibrosis[21]. Furthermore, FIB-4 demonstrates reduced specificity in elderly populations, where the currently recommended cutoff values may lead to excessive/unnecessary referrals[16].Our findings align with these international observations - in non-viral hepatitis general populations, FIB-4 showed inferior predictive performance (AUC 0.592) that was significantly lower than METS-VF (P\u0026thinsp;\u0026le;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eA U.S. adolescent population study demonstrated significant associations between METS-VF and both non-alcoholic fatty liver disease (NAFLD) (OR\u0026thinsp;=\u0026thinsp;15.74, 95%CI:10.44\u0026ndash;23.72) and liver fibrosis (OR\u0026thinsp;=\u0026thinsp;1.85, 95%CI:1.27\u0026ndash;2.70), with particularly strong correlations observed among Mexican-American female adolescents[31]. While our study similarly confirmed METS-VF's robust association with liver fibrosis, we observed higher OR values compared to the adolescent cohort. This divergence likely stems from distinct population characteristics: our research focused on adults with non-viral hepatitis (typically an older demographic with higher comorbidity prevalence and longer disease duration). These factors may collectively intensify the association between METS-VF and fibrosis progression.These comparative results suggest that the predictive value of METS-VF may vary across population subgroups, underscoring the need for individualized clinical decision-making tailored to specific patient demographics and metabolic profiles.\u003c/p\u003e\u003cp\u003eMultiple clinical studies have demonstrated a significant positive correlation between the severity of insulin resistance and degree of hepatic steatosis[32, 33] confirming its critical role in the initiation and progression of liver fibrosis. Notably, in patients with type 2 diabetes mellitus (T2DM), insulin resistance exhibits superior predictive value for fibrosis progression compared to the extent of hepatic lipid deposition[34, 35]. Our analytical data indicated that while METS-VF (AUC\u0026thinsp;=\u0026thinsp;0.748) showed marginally better predictive performance for liver fibrosis than METS-IR (AUC\u0026thinsp;=\u0026thinsp;0.732), this difference did not achieve statistical significance (P\u0026thinsp;=\u0026thinsp;0.128). The comparable predictive efficacy between these indices not only validates METS-VF's capability for comprehensive metabolic assessment, but more importantly, provides clinical evidence that insulin resistance serves as the fundamental pathological mechanism driving fibrotic progression.\u003c/p\u003e\u003cp\u003eMETS-VF outperforms other adiposity indices in both association strength (OR\u0026thinsp;=\u0026thinsp;7.812) and predictive accuracy (AUC\u0026thinsp;=\u0026thinsp;0.748) by comprehensively integrating multiple pathogenic pathways: (1) visceral fat-derived NEFA overflow induces hepatic steatosis through enhanced β-oxidation and DNL, triggering mitochondrial dysfunction and oxidative stress; (2) insulin resistance amplifies this damage via adipose-liver axis dysregulation - impaired lipolysis suppression causes excessive NEFA flux [32], while hyperinsulinemia activates SREBP1c/ChREBP-mediated DNL[36\u0026ndash;39], collectively driving fibrotic progression.\u003c/p\u003e\u003cp\u003eThis study utilized the nationally representative NHANES database with a large sample size to ensure result reliability. It represents the first systematic comparison of multiple adiposity indices with liver fibrosis in a general non-viral liver disease population, and innovatively identified a non-linear dose-response relationship between METS-VF and liver fibrosis. However, several limitations should be noted: the cross-sectional design precludes causal inferences; transient elastography may underestimate early-stage fibrosis; limited sample size resulted in wide confidence intervals for some results; and dynamic changes in metabolic indices were not assessed. Future validation through larger-scale, multicenter studies incorporating more accurate fibrosis assessment and long-term follow-up is warranted.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study establishes METS-VF as a superior predictor of liver fibrosis in non-viral hepatitis populations compared to FIB-4 and other metabolic indices, with scores below 7.04 potentially mitigating risk, suggesting its clinical utility for early screening.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAUC Area under the ROC curve\u003c/p\u003e\n\u003cp\u003eBMI Body mass index\u003c/p\u003e\n\u003cp\u003eCDC The Centers for Disease Control and Prevention\u003c/p\u003e\n\u003cp\u003eCMI Cardiometabolic Index\u003c/p\u003e\n\u003cp\u003eFBG Fasting blood glucose\u003c/p\u003e\n\u003cp\u003eHDL-C Cholesterol HighDensity Lipoprotein\u003c/p\u003e\n\u003cp\u003eHT Height\u003c/p\u003e\n\u003cp\u003eLAP Lipid Accumulation Product\u003c/p\u003e\n\u003cp\u003eLSM Liver stiffness measurement\u003c/p\u003e\n\u003cp\u003eMASLD Metabolic dysfunction-associated steatotic liver disease\u003c/p\u003e\n\u003cp\u003eMETS-IR Metabolic Score for Insulin Resistance\u003c/p\u003e\n\u003cp\u003eNHANES National Health and Nutrition Examination Survey\u003c/p\u003e\n\u003cp\u003eNPV Negative predictive value\u003c/p\u003e\n\u003cp\u003ePPV Positive predictive value\u003c/p\u003e\n\u003cp\u003ePIR Poverty-income ratio\u003c/p\u003e\n\u003cp\u003eRCS Restricted cubic spline\u003c/p\u003e\n\u003cp\u003eROC Receiver operating characteristic\u003c/p\u003e\n\u003cp\u003eSLD Steatotic liver disease\u003c/p\u003e\n\u003cp\u003eTG Triglycerides\u003c/p\u003e\n\u003cp\u003eVAI Visceral Adiposity Index\u003c/p\u003e\n\u003cp\u003eWC Waist circumference\u003c/p\u003e\n\u003cp\u003eWHtR Waist-to-height ratio\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe survey protocol was approved by the NCHS Ethics Review Board. The approved protocol can be found at this link: https://www.cdc.gov/nchs/ nhanes/irba98.htm. Additionally, every participant in the survey supplied written informed permission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;No datasets were generated or analysed during the current study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNational Natural Science Foundation of China (No. 81802455)\u003c/p\u003e\n\u003cp\u003eNatural Science Basic Research Project of Shaanxi Province (No. 2019JQ-962)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQS conceived and wrote the main manuscript. QS and GXH collected and validated the data. XL and KPF performed data analysis. SZ supervised the research, critically reviewed and edited the manuscript, and acquired funding. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to the participants and staff of NHANES.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFriedman SL. Mechanisms of hepatic fibrogenesis. Gastroenterology. 2008;134(6):1655-69.\u003c/li\u003e\n\u003cli\u003eHernandez-Gea V, Friedman SL. Pathogenesis of liver fibrosis. Annu Rev Pathol. 2011;6:425 − 56.\u003c/li\u003e\n\u003cli\u003eKisseleva T, Brenner D. Molecular and cellular mechanisms of liver fibrosis and its regression. Nat Rev Gastroenterol Hepatol. 2021;18(3):151 − 66.\u003c/li\u003e\n\u003cli\u003eBataller R, Brenner DA. Liver fibrosis. 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Clin Mol Hepatol. 2024;30(Suppl):S199-s213.\u003c/li\u003e\n\u003cli\u003eSugiyama A, Kurisu A, E B, Ouoba S, Ko K, Rakhimov A, et al. Distribution of FIB-4 index in the general population: analysis of 75,666 residents who underwent health checkups. BMC Gastroenterol. 2022;22(1):241.\u003c/li\u003e\n\u003cli\u003eParikh ND, Mehta M, Tapper EB. FIB-4 and APRI for cirrhosis detection in a privately insured national cohort. JHEP Rep. 2024;6(1):100925.\u003c/li\u003e\n\u003cli\u003eHazzan R, Abu Ahmad N, Habib AS, Saleh I, Ziv N. Suboptimal reliability of FIB-4 and NAFLD-fibrosis scores for staging of liver fibrosis in general population. JGH Open. 2024;8(2):e13034.\u003c/li\u003e\n\u003cli\u003eGraupera I, Thiele M, Serra-Burriel M, Caballeria L, Roulot D, Wong GL, et al. Low Accuracy of FIB-4 and NAFLD Fibrosis Scores for Screening for Liver Fibrosis in the Population. 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J Hepatol. 2025.\u003c/li\u003e\n\u003cli\u003eBello-Chavolla OY, Almeda-Valdes P, Gomez-Velasco D, Viveros-Ruiz T, Cruz-Bautista I, Romo-Romo A, et al. METS-IR, a novel score to evaluate insulin sensitivity, is predictive of visceral adiposity and incident type 2 diabetes. Eur J Endocrinol. 2018;178(5):533 − 44.\u003c/li\u003e\n\u003cli\u003eSun Q, Ren Q, Du L, Chen S, Wu S, Zhang B, et al. Cardiometabolic Index (CMI), Lipid Accumulation Products (LAP), Waist Triglyceride Index (WTI) and the risk of acute pancreatitis: a prospective study in adults of North China. Lipids Health Dis. 2023;22(1):190.\u003c/li\u003e\n\u003cli\u003eAmato MC, Giordano C, Galia M, Criscimanna A, Vitabile S, Midiri M, et al. Visceral Adiposity Index: a reliable indicator of visceral fat function associated with cardiometabolic risk. Diabetes Care. 2010;33(4):920-2.\u003c/li\u003e\n\u003cli\u003eBello-Chavolla OY, Antonio-Villa NE, Vargas-Vázquez A, Viveros-Ruiz TL, Almeda-Valdes P, Gomez-Velasco D, et al. Metabolic Score for Visceral Fat (METS-VF), a novel estimator of intra-abdominal fat content and cardio-metabolic health. Clin Nutr. 2020;39(5):1613-21.\u003c/li\u003e\n\u003cli\u003eKim D, Danpanichkul P, Wijarnpreecha K, Cholankeril G, Loomba R, Ahmed A. Current burden of steatotic liver disease and fibrosis among adults in the United States, 2017–2023. Clin Mol Hepatol. 2025;31(2):382 − 93.\u003c/li\u003e\n\u003cli\u003eUS Department of Agriculture and US Department of Health and Human Services. Dietary Guidelines for Americans, 2020–2025. 9th ed. Washington, DC: US Government Publishing Office; 2020.\u003c/li\u003e\n\u003cli\u003eThird Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation. 2002;106(25):3143 − 421.\u003c/li\u003e\n\u003cli\u003eXu X, Yang J, Li Y, Li Y, Zeng X, Chen B. 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J Clin Endocrinol Metab. 2023;108(5):1192 − 201.\u003c/li\u003e\n\u003cli\u003eSakaguchi M, Fujisaka S, Cai W, Winnay JN, Konishi M, O'Neill BT, et al. Adipocyte Dynamics and Reversible Metabolic Syndrome in Mice with an Inducible Adipocyte-Specific Deletion of the Insulin Receptor. Cell Metab. 2017;25(2):448 − 62.\u003c/li\u003e\n\u003cli\u003ePerry RJ, Camporez JG, Kursawe R, Titchenell PM, Zhang D, Perry CJ, et al. Hepatic acetyl CoA links adipose tissue inflammation to hepatic insulin resistance and type 2 diabetes. Cell. 2015;160(4):745 − 58.\u003c/li\u003e\n\u003cli\u003ePetersen MC, Smith GI, Palacios HH, Farabi SS, Yoshino M, Yoshino J, et al. Cardiometabolic characteristics of people with metabolically healthy and unhealthy obesity. Cell Metab. 2024;36(4):745 − 61.e5.\u003c/li\u003e\n\u003cli\u003eShaikh SR, Beck MA, Alwarawrah Y, MacIver NJ. Emerging mechanisms of obesity-associated immune dysfunction. 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Obes Rev. 2024;25(6):e13724.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Liver fibrosis, METS-VF, NHANES, Visceral adiposity metabolism, Cross-sectional study","lastPublishedDoi":"10.21203/rs.3.rs-6832417/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6832417/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eVisceral adiposity dysfunction has been recognized as an independent risk factor for liver fibrosis, and early identification may improve prognosis. This study aimed to investigate the association of metabolic score for visceral fat (METS-VF) and other visceral adiposity metabolic indices (METS-IR, VAI, CMI, LAP) with liver fibrosis in U.S. non-viral hepatitis populations.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eUsing data from the National Health and Nutrition Examination Survey (NHANES 2017\u0026ndash;March 2020), we conducted weighted multivariable logistic regression and trend analyses to evaluate the associations of visceral adiposity metabolic indices with overall, significant and advanced fibrosis. In addition, restricted cubic spline (RCS) model was used to examine potential nonlinear relationships. Receiver operating characteristic (ROC) curves were further used to assess the diagnostic performance of these indices. Finally, subgroup analyses were performed to evaluate potential variations of association between METS-VF and fibrosis across different population strata.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eA total of 3,490 participants were included in this study. Among all visceral adiposity metabolic indices, METS-VF showed the strongest association with liver fibrosis, demonstrating superior predictive performance. Participants in the highest METS-VF quartile had 7.81-fold greater odds of fibrosis (adjusted OR 7.812, 95% CI: 2.421\u0026ndash;25.207), with an AUC of 0.748 (95% CI: 0.696\u0026ndash;0.796). This association exhibited a severity-dependent pattern, with the odds ratio increasing to 23.44 for advanced fibrosis. Subgroup analyses revealed higher association between METS-VF and fibrosis among individuals who were married or living with partner or had hypertension or diabetes.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eMETS-VF demonstrated significant association with liver fibrosis and superior predictive performance compared to FIB-4 and other visceral adiposity metabolic indices, suggesting its clinical utility for liver fibrosis screening in non-viral hepatitis populations\u003c/p\u003e","manuscriptTitle":"Association Between Metabolic Score for Visceral Fat and Liver Fibrosis Risk in Non-Viral Hepatitis Populations: NHANES 2017–2020","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-18 13:13:57","doi":"10.21203/rs.3.rs-6832417/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"36c66f44-4e9e-42d3-aa74-654f050319a4","owner":[],"postedDate":"July 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-09T12:24:03+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-18 13:13:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6832417","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6832417","identity":"rs-6832417","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}