Association of Fat-Muscle-Body Mass Index (FMBI) with Non-Alcoholic Fatty Liver Disease

Association of Fat-Muscle-Body Mass Index (FMBI) with Non-Alcoholic Fatty Liver Disease | 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 of Fat-Muscle-Body Mass Index (FMBI) with Non-Alcoholic Fatty Liver Disease Xiaokang Wu, Jiafeng Yin, Yue Li, Chaoliang Xiong, Hailong Liu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9304928/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Background Non-alcoholic fatty liver disease (NAFLD) is a global burden with unmet non-invasive biomarker needs; BMI and FMRs have limitations, and their combined index FMBI is unstudied. We aim to explore FMBI’s association with NAFLD, associated liver fibrosis and atherosclerotic cardiovascular disease risk(ASCVD) risk. Methods This retrospective study enrolled 1592 eligible participants aged 40–79 years from a Chinese hospital. The Fat-Muscle-BMI Index (FMBI) was calculated as Fat-to-muscle ratio (FMR) × Body mass index (BMI). Multivariable logistic/linear regression with three hierarchical models analyzed FMBI’s associations with NAFLD, hepatic fibrosis and ASCVD; restricted cubic splines༈RCS, subgroup and sensitivity/E-value analyses validated the findings. Results Higher FMBI quartiles exhibited a progressive increase in NAFLD prevalence (46.0% to 72.4%, P < 0.001). FMBI demonstrated a significant linear dose-response relationship with NAFLD (OR = 1.20 per unit increment, P < 0.001), with risk rising markedly as FMBI increased—particularly when FMBI ≥ 10. A significant additive interaction between FMR and smoking was observed (AP = 0.37, P = 0.010). Additionally, FMBI independently predicted ASCVD risk (β = 0.32, P = 0.001) and hepatic fibrosis (OR = 1.79 for Q4 vs. Q1, P = 0.008) in NAFLD patients. E-value analysis confirmed the robustness of these findings to unmeasured confounding. Conclusion This study identifies FMBI as a robust NAFLD biomarker exhibiting linear dose-response, additive synergy with smoking, and independent prediction of hepatic fibrosis and ASCVD risk, supporting its use in targeted NAFLD management. Fat-Muscle-BMI Index (FMBI) Non-alcoholic fatty liver disease (NAFLD) Hepatic fibrosis Atherosclerotic cardiovascular disease Figures Figure 1 Figure 2 Figure 3 1. Introduction Non-alcoholic fatty liver disease (NAFLD), defined as hepatic steatosis of ≥ 5% in the absence of excessive alcohol consumption and secondary liver injury causes 1 , affects approximately 25% of the global population, with its prevalence projected to rise further by 2030 2 . As a leading aetiology of cirrhosis and hepatocellular carcinoma, NAFLD is also strongly comorbid with cardiovascular disease, extrahepatic malignancies, metabolic syndrome, type 2 diabetes mellitus (T2DM), and hypertension, imposing a substantial global disease burden 3 . Despite this, validated non-invasive biomarkers for the accurate diagnosis, risk stratification, progression monitoring, and treatment response assessment of NAFLD remain lacking. Liver biopsy, the gold standard for evaluating hepatic fibrosis, is limited in clinical practice by its invasiveness, sampling variability, and poor patient acceptance 4 . Body mass index (BMI) is widely used for NAFLD screening due to its robust association with disease risk, yet it has critical limitations in clinical management 5 . First, BMI alone fails to identify 10–20% of "lean" NAFLD cases (BMI < 25 kg/m² in the general population, < 23 kg/m² in Asians), leading to underdiagnosis; notably, lean NAFLD patients may face a higher risk of all-cause mortality 6 , 7 . Second, BMI does not capture key pathological features driving non-alcoholic steatohepatitis (NASH) progression and disease severity, including myosteatosis, visceral adiposity, and metabolic dysfunction 8 – 11 . While BMI remains valuable for initial screening and weight management, its standalone diagnostic and predictive value is insufficient. 12 , 13 A multidimensional risk stratification approach integrating waist circumference, visceral fat assessment, metabolic markers, and muscle metrics (e.g., grip strength, skeletal muscle index) is therefore essential to improve diagnostic accuracy and guide clinical decision-making 8 , 14 , 15 . Previous studies have demonstrated the multidimensional value of fat-muscle ratios (FMRs),including the subcutaneous-to-visceral fat ratio (SVR), visceral-to-abdominal fat ratio (VAR), and fat mass-to-fat-free mass ratio (FM/FFM), in NAFLD research. Elevated FMR is positively correlated with NAFLD risk and exhibits superior diagnostic performance in T2DM patients 16 – 20 . Pathologically, low SVR or high VAR is associated with hepatic steatosis and fibrosis progression, while FM/FFM identifies body composition imbalance as a core mechanism underlying lean NAFLD; a low liver fat-to-total fat ratio independently predicts hepatic fibrosis risk 16 , 21 . However, FMRs have inherent limitations: device-dependent measurements (bioelectrical impedance analysis [BIA], dual-energy X-ray absorptiometry [DXA], computed tomography [CT]) raise comparability concerns, ultrasound-based NAFLD diagnosis leads to methodological heterogeneity, associations are population-specific (e.g., a stronger SVR-NAFLD link in males), and optimal cut-off values remain undefined 19 , 22 , 23 . Standalone FMRs show limited predictive efficacy, necessitating combination with traditional indices such as BMI for optimized NAFLD risk stratification 21 , 24 . Combining FMR with BMI addresses the limitations of each individual metric: BMI reflects overall obesity but neglects body composition distribution, while FMR captures the antagonistic muscle-fat relationship. Their integration enables precise metabolic risk assessment, avoids obesity-centric bias, and facilitates multidimensional evaluation of obesity phenotypes, metabolic risk, muscle function, and clinical prognosis 25 – 27 . To our knowledge, few study has investigated the association between the product of FMR and BMI—a novel composite index—and NAFLD. This study therefore aimed to explore the association between the Fat-Muscle-BMI Index (FMBI, FMR×BMI) and NAFLD, and further investigate the relationships of FMR and FMBI with hepatic fibrosis and atherosclerotic cardiovascular disease (ASCVD) risk in NAFLD patients. We hypothesized that FMBI would serve as a novel and superior biomarker for NAFLD risk stratification and comprehensive clinical management. 2. Methods 2.1 Study Population This retrospective cross-sectional study utilized data from the Dryad digital repository ( https://doi.org/10.5061/dryad.7d7wm3809 ), mainly including individuals who underwent routine health check-ups at Wuhan Union Hospital from January 2020 to November 2021. The study protocol was approved by the Ethics Committee of Wuhan Union Hospital. The detailed methodology and primary findings have been previously reported by Yan et al 28 . The original study consecutively enrolled 1830 participants aged 40–79 years who voluntarily completed body composition analysis and liver ultrasonography. After applying exclusion criteria (excessive alcohol intake, history of chronic liver disease, acute illness, renal insufficiency, active malignancy, use of corticosteroids, or incomplete data), 1592 participants were included in the final analysis. NAFLD was diagnosed by abdominal ultrasonography based on the presence of hepatic steatosis in the absence of other liver diseases. 2.2 Definition of NAFLD, Hepatic Fibrosis, and ASCVD Risk NAFLD was diagnosed by conventional abdominal B-mode ultrasonography (Philips IU22, Philips Healthcare, N.V.) performed by certified sonographers; participants with hepatic steatosis and no excluded hepatic comorbidities were classified as having NAFLD. Hepatic fibrosis burden was assessed using the Fibrosis-4 (FIB-4) index, a well-validated non-invasive scoring system. Hepatic fibrosis risk was categorized using FIB-4 index thresholds: low ( 2.67), with moderate and high categories combined for analysis due to limited sample size in the high-risk group. Due to the small sample size of the high-risk group (FIB-4 > 2.67), which would compromise statistical power, moderate and high fibrosis risk groups were combined for subsequent analyses. The 10-year ASCVD risk was calculated using the prediction algorithm recommended by the 2013 American College of Cardiology/American Heart Association (ACC/AHA) guidelines. An ASCVD risk score > 10% was defined as high cardiovascular risk, and a score ≤ 10% as low risk. 2.3 Anthropometric and Laboratory Measurements Anthropometric assessments were conducted by a professionally trained technician in a dedicated body composition analysis room. Body composition (body weight, fat mass, and muscle mass) was measured using a multi-frequency bioelectrical impedance analyzer (Tsinghua Tongfan, BCA-2A, China), following the manufacturer’s standard protocols. Height was measured separately, and BMI was calculated as weight (kg) divided by height squared (m²). Blood pressure (BP) was measured with an electronic sphygmomanometer (Panasonic, EW3106, China) after participants rested in a seated position for at least 10 minutes; two readings were taken at 5-minute intervals, and the mean value was used for analysis. Venous blood samples were collected after an overnight fast of ≥ 8 hours, and biochemical analyses were performed in the hospital’s central clinical laboratory. The following parameters were measured: platelet count (PLT), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), alanine aminotransferase (ALT), aspartate aminotransferase (AST), uric acid (UA), and fasting blood glucose (FBG). PLT was quantified using a Beckman Coulter hematology analyzer; biochemical parameters were assayed on a Beckman AU5800 fully automated biochemical analyzer, with the following detection methods: glycerol phosphate oxidase method for TG, cholesterol oxidase method for TC, selective solubilization method for LDL-C, and chemically modified enzyme method for HDL-C. FMR was calculated as total body fat mass divided by total body muscle mass. T2DM was defined as a self-reported history of diabetes or current use of glucose-lowering medications. Hypertension was defined as a self-reported history of hypertension or current use of oral antihypertensive agents. 2.4 Calculation of FMBI A novel composite metric, the Fat-Muscle-BMI Index (FMBI), was constructed as the product of FMR and BMI, with the formula: FMBI = Fat-to-muscle ratio (FMR) × Body mass index (BMI) 2.5 Statistical Analysis Participant baseline characteristics were stratified by FMBI quartiles (Q1–Q4). Continuous variables were presented as mean ± standard deviation (normally distributed) or median (interquartile range) (non-normally distributed), with normality assessed by the Shapiro-Wilk test. Categorical variables were expressed as frequencies and proportions. One-way ANOVA or the Kruskal-Wallis test was used for comparisons of continuous variables across groups, and the χ² test for categorical variables. The Boruta algorithm (a random forest-based method) was used for covariate selection: variable importance was compared with permuted "shadow" features, and features that consistently outperformed shadow variables across 500 iterations were retained as confirmed predictors. Multivariable logistic regression analyses were used to assess the association between FMBI and risks of NAFLD and NAFLD-associated hepatic fibrosis. Three hierarchical models were constructed for each outcome: Model 1 (unadjusted); Model 2 (adjusted for sex and tobacco use); Model 3 (fully adjusted for sex, tobacco use, ALT, AST, UA, FBG, TG, HDL-C, systolic blood pressure [SBP], hypertension, and T2DM). Continuous FMBI was analysed per unit increment; categorical FMBI was analysed by quartiles (Q1 as reference) for NAFLD and fibrosis, and by tertiles (T1 as reference) for additional NAFLD validation, with trend significance evaluated by treating quartiles/tertiles as ordinal variables. Odds ratios (ORs) and 95% confidence intervals (CIs) were reported. Multivariable linear regression models were used to assess the association between FMBI (continuous and quartiles) and ASCVD risk score, with the same three hierarchical adjustment models as above; trend tests treated FMBI quartiles as ordinal variables, and regression coefficients (β) and 95% CIs were reported. Restricted cubic splines (RCS) with four knots (placed at the 5th, 35th, 65th, and 95th percentiles of FMBI) were used to model the dose-response relationship between FMBI and NAFLD risk, with adjustment for Model 3 covariates. A reference point of FMBI = 9.832 was set to calculate ORs, and nonlinearity was tested by likelihood ratio tests comparing linear and spline models. Subgroup analyses were performed using multivariable logistic regression stratified by sex, age, BMI, tobacco use, hypertension, and T2DM status. Interaction terms (FMBI × subgroup variable) were included to test for effect modification, with continuous FMBI analysed per unit increment; results were visualized by forest plots, and interaction was considered significant at P < 0.05. All subgroup models retained Model 3 covariates. The interaction between smoking status and FMBI on NAFLD risk was assessed on both multiplicative and additive scales. Multiplicative interaction was tested by including product terms in logistic regression models and using likelihood ratio tests. Additive interaction was quantified using three established indices: Relative Excess Risk due to Interaction (RERI), Attributable Proportion (AP), and Synergy Index (SI), with their 95% CIs. E-value analysis was performed to evaluate the robustness of the FMBI-NAFLD association to unmeasured confounding. Sensitivity analyses were conducted to verify the consistency of the association between FMBI and NAFLD, hepatic fibrosis, and ASCVD risk across different analytical approaches. All statistical analyses were performed using R Statistical Software (Version 4.2.2, The R Foundation) and the Free Statistics Analysis Platform (Version 2.2, Beijing, China), with a two-tailed P < 0.05 considered statistically significant. 3. Results 3.1 Baseline Characteristics of the Study Population Baseline characteristics of the 1592 participants stratified by FMBI quartiles are presented in Table 1 , with significant differences observed for most variables across quartiles (P < 0.05). Sex distribution varied markedly, with the highest proportion of females in Q4 (59.0%) versus Q1 (11.6%). Age showed a progressive increase across FMBI quartiles. Metabolic parameters including BMI (Q1: 23.1 ± 1.8 kg/m²; Q4: 27.8 ± 3.3 kg/m²), FMR (Q1: 0.3 ± 0.0; Q4: 0.5 ± 0.1), FBG, TG, and HDL-C exhibited significant ascending trends across quartiles (all P < 0.001). The prevalence of hypertension (Q1: 44.5%; Q4: 72.9%), T2DM (Q1: 25.1%; Q4: 37.2%), and NAFLD (Q1: 46.0%; Q4: 72.4%) increased significantly with higher FMBI quartiles. No significant interquartile differences were observed for PLT and AST, indicating substantial metabolic heterogeneity across FMBI strata. 3.2 Covariate Selection Univariate logistic regression identified multiple variables with significant unadjusted associations with NAFLD (Table S1 ), most notably FMBI (OR = 1.15 per unit increment, 95% CI: 1.11–1.18, P < 0.001), age categories, and key metabolic parameters (all P < 0.001). In contrast, PLT (OR = 1.00, P = 0.152) and AST (OR = 1.01, P = 0.190) showed no significant associations and were not included in adjusted models. Boruta analysis (Fig. 1 ) confirmed the following NAFLD-associated predictors: TG, ALT, UA, FBG, T2DM, hypertension, HDL-C, SBP, tobacco use, AST, and sex. Alcohol use, TC, and age were deemed statistically insignificant, while diastolic blood pressure (DBP) showed tentative importance. This machine learning approach validated the manually selected covariates and identified TG as the highest-impact metabolic predictor. Based on these results, sex, tobacco use, ALT, AST, UA, FBG, TG, HDL-C, SBP, hypertension, and T2DM were included in all fully adjusted models (Model 3). The Boruta algorithm, a random forest-based method, was used to identify important predictors of NAFLD. Variable importance (Z-score) is plotted for each candidate variable. TG, triglycerides; ALT, alanine aminotransferase; UA, uric acid; FBG, fasting blood glucose; T2DM, type 2 diabetes mellitus; HDL-C, high-density lipoprotein cholesterol; SBP, systolic blood pressure; AST, aspartate aminotransferase; DBP, diastolic blood pressure; TC, total cholesterol. Table 1 Baseline characteristics of participant Variables Total (n = 1592) Q1 (n = 398) Q2 (n = 398) Q3 (n = 398) Q4 (n = 398) p Sex, n (%) < 0.001 Female 444 (27.9) 46 (11.6) 57 (14.3) 106 (26.6) 235 (59) Male 1148 (72.1) 352 (88.4) 341 (85.7) 292 (73.4) 163 (41) Age(year) [n (%)] < 0.001 40–49 354 (22.2) 113 (28.4) 110 (27.6) 75 (18.8) 56 (14.1) 50–59 709 (44.5) 200 (50.3) 185 (46.5) 180 (45.2) 144 (36.2) 60–69 360 (22.6) 64 (16.1) 59 (14.8) 103 (25.9) 134 (33.7) 70–79 169 (10.6) 21 (5.3) 44 (11.1) 40 (10.1) 64 (16.1) BMI (kg/m 2 ) 25.4 ± 2.9 23.1 ± 1.8 24.8 ± 1.7 26.0 ± 2.2 27.8 ± 3.3 < 0.001 FMR 0.4 ± 0.1 0.3 ± 0.0 0.3 ± 0.0 0.4 ± 0.0 0.5 ± 0.1 < 0.001 PLT(10 9 /L) 211.1 ± 53.3 209.1 ± 54.3 208.7 ± 53.0 210.4 ± 51.5 216.3 ± 54.3 0.152 ALT(U/L) 21.0 (15.0, 30.0) 20.0 (14.0, 26.0) 21.0 (15.0, 30.0) 23.5 (16.0, 33.8) 22.0 (16.0, 32.0) < 0.001 AST(U/L) 23.1 ± 11.0 22.6 ± 14.0 22.4 ± 9.1 23.6 ± 10.0 23.9 ± 10.1 0.19 UA(umol/L) 366.8 ± 95.8 361.7 ± 89.2 372.2 ± 91.7 376.8 ± 98.7 356.3 ± 102.0 0.009 FBG(mmol/L) 5.5 ± 1.7 5.3 ± 1.7 5.6 ± 1.7 5.4 ± 1.4 5.9 ± 1.8 < 0.001 TC(mmol/L) 4.5 ± 1.1 4.4 ± 1.0 4.5 ± 1.1 4.5 ± 1.1 4.6 ± 1.2 0.08 TG(mmol/L) 1.4 (1.0, 2.2) 1.2 (0.9, 1.8) 1.5 (1.0, 2.4) 1.6 (1.1, 2.3) 1.5 (1.0, 2.2) < 0.001 HDL.C(mmol/L) 1.1 ± 0.3 1.2 ± 0.3 1.1 ± 0.3 1.1 ± 0.3 1.2 ± 0.3 < 0.001 LDL.C(mmol/L) 2.7 ± 0.9 2.6 ± 0.8 2.7 ± 0.9 2.7 ± 0.9 2.7 ± 0.9 0.767 SBP(mm Hg) 130.9 ± 15.8 127.3 ± 14.5 131.3 ± 16.3 130.2 ± 16.1 134.9 ± 15.4 < 0.001 DBP(mm Hg) 81.6 ± 11.2 80.6 ± 10.9 82.6 ± 11.6 81.1 ± 11.5 82.1 ± 10.6 0.054 Tobacco use, n (%) 538 (33.8) 158 (39.7) 153 (38.4) 145 (36.4) 82 (20.6) < 0.001 Alcohol use, n (%) 520 (32.7) 143 (35.9) 163 (41) 137 (34.4) 77 (19.3) < 0.001 FMBI 10.1 ± 3.7 6.3 ± 1.0 8.5 ± 0.6 10.6 ± 0.7 15.1 ± 3.4 < 0.001 Hypertension, n (%) 943 (59.2) 177 (44.5) 227 (57) 249 (62.6) 290 (72.9) < 0.001 Diabetes, n (%) 498 (31.3) 100 (25.1) 127 (31.9) 123 (30.9) 148 (37.2) 0.004 NAFLD, n (%) 973 (61.1) 183 (46) 238 (59.8) 264 (66.3) 288 (72.4) < 0.001 ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; DBP, diastolic blood pressure; FBG, fasting blood glucose; FMR, fat-to-muscle ratio; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; NAFLD, non-alcoholic fatty liver disease; PLT, platelets; SBP, systolic blood pressure; TC, total cholesterol; TG, triglycerides; UA, uric acid. 3.3 Association between FMBI and NAFLD A significant positive association between FMBI and NAFLD was observed across all analytical approaches (Table 2 ). Per unit increment in continuous FMBI was associated with a significantly increased NAFLD risk in the fully adjusted model (OR = 1.20, 95% CI: 1.14–1.25, P < 0.001). Quartile analysis revealed a clear dose-dependent relationship: after full adjustment, Q4 had a 3.95-fold higher odds of NAFLD compared with Q1 (95% CI: 2.64–5.90, P < 0.001), with a significant trend persisting in the fully adjusted model (OR for trend = 1.54, 95% CI: 1.36–1.74, P < 0.001). The association remained robust across sequentially adjusted models, confirming FMBI’s independent relationship with NAFLD after accounting for demographic, metabolic, and comorbidity confounders. Table 2 Multivariate logistic regression analysis between FMBI and NAFLD Variable Model1 Model2 Model3 OR (95%CI) P value OR (95%CI) P value OR (95%CI) P value FMBI Continuous 1.15 (1.11 ~ 1.18) < 0.001 1.28 (1.22 ~ 1.33) < 0.001 1.2 0(1.14 ~ 1.25) < 0.001 FMBI Categories Q1 1(Ref) 1(Ref) 1(Ref) Q2 1.75 (1.32 ~ 2.31) < 0.001 1.88 (1.41 ~ 2.51) < 0.001 1.46 (1.07 ~ 2.00) 0.019 Q3 2.31 (1.74 ~ 3.08) < 0.001 3.09 (2.28 ~ 4.20) < 0.001 2.02 (1.45 ~ 2.81) < 0.001 Q4 3.08 (2.29 ~ 4.13) < 0.001 7.12 (4.96 ~ 10.22) < 0.001 3.95 (2.64 ~ 5.90) < 0.001 Trend test 1.45 (1.32 ~ 1.59) < 0.001 1.88 (1.68 ~ 2.10) < 0.001 1.54 (1.36 ~ 1.74) < 0.001 Model1: no adjusted Model2: adjusted sex,tobacco Model3: adjusted sex,tobacco use,ALT,AST,UA,FBG,TG,HDL.C,SBP,Hypertension,Diabetes. Q:Quartiles FMR, fat-to-muscle ratio,NAFLD, non-alcoholic fatty liver disease,ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; FBG, fasting blood glucose;HDL-C, high-density lipoprotein cholesterol,SBP, systolic blood pressure;TG, triglycerides; UA, uric acid. RCS analysis (Fig S1 ) demonstrated a significant linear dose-response relationship between FMBI and NAFLD risk (overall P < 0.01; nonlinearity P = 0.245). Per unit increment in FMBI above the reference point (9.832) was associated with a monotonic increase in NAFLD odds (e.g., OR = 2.0 at FMBI = 15.0), and this linear relationship persisted across the clinically relevant range of FMBI values, further validating FMBI as a robust NAFLD predictor. 3.4 Subgroup and Interaction Analysis Forest plot analysis (Fig. 2 ) showed a consistent positive association between FMBI and NAFLD across most pre-defined subgroups. A significant interaction was observed for tobacco use (P = 0.032): the effect of FMBI on NAFLD risk was weaker in tobacco users (OR = 1.15, 95% CI: 1.06–1.25) compared with non-users (OR = 1.22, 95% CI: 1.16–1.29). Both FMBI and smoking status were independently associated with increased NAFLD risk (ORs > 1.9, P < 0.001). The multiplicative interaction term between FMBI and smoking was not statistically significant (interaction OR = 1.20, 95% CI: 0.69–2.10, P = 0.520). However, additive interaction analysis yielded significant results (Table 3 , Fig. 3 ): AP due to interaction was 0.37 (95% CI: 0.05–0.68, P = 0.010), indicating that 37% of the excess NAFLD risk in individuals with both high FMBI and smoking exposure was attributable to their interaction; SI was 1.88 (95% CI: 0.97–3.63, P < 0.050), suggesting a synergistic additive effect; RERI was 1.68 (95% CI: -0.47–3.83, P = 0.060), showing a marginally significant trend towards additive interaction. Forest plot showing the odds ratios (ORs) and 95% confidence intervals (CIs) for NAFLD per unit increment in continuous FMBI across predefined subgroups. The four groups represent combinations of FMBI (B: 0 and 1) and smoking status (A: 0, non-smoker; 1, smoker). The odds ratios (ORs) for NAFLD are presented for each group. Table 3 Assessment of Additive and Multiplicative Interactions Between FMBI and Tobacco Use in Relation to the Outcome. Measures Estimates CI.95.low CI.95.up P.value OR00 1 NA NA NA OR01 1.98 1.53 2.57 0 OR10 1.93 1.5 2.5 0 OR11 4.6 2.86 7.4 0 OR(FMBI on outcome [Tobacco.use = = 0] 1.98 1.53 2.57 0 OR(FMBI on outcome [Tobacco.use = = 1] 2.38 1.45 3.9 0 OR(Tobacco.use on outcome [FMBI = = 0] 1.93 1.5 2.5 0 OR(Tobacco.use on outcome [FMBI = = 1] 2.32 1.41 3.81 0 Multiplicative scale 1.2 0.69 2.1 0.52 RERI 1.68 -0.47 3.83 0.06 AP 0.37 0.05 0.68 0.01 SI 1.88 0.97 3.63 0 3.5 Sensitivity Analyses 3.5.1 FMBI and ASCVD Risk in NAFLD In the fully adjusted model (Model 3), per unit increment in FMBI was significantly associated with higher ASCVD risk scores in NAFLD patients (β = 0.32, 95% CI: 0.13–0.50, P = 0.001). Quartile analysis revealed a significant dose-response relationship (P for trend < 0.001), with Q4 having a 3.24-point higher ASCVD score than Q1 (95% CI: 1.32–5.16, P = 0.001) (Table S2 ). The strength of the association increased after adjustment for metabolic confounders (Model 2 vs. Model 1), confirming FMBI’s independent contribution to ASCVD risk in NAFLD patients. 3.5.2 FMBI and NAFLD-Associated Hepatic Fibrosis Fully adjusted models demonstrated a significant dose-dependent relationship between FMBI quartiles and NAFLD-associated fibrosis risk (P for trend = 0.023), with Q4 having a 1.79-fold higher odds of fibrosis compared with Q1 (95% CI: 1.16–2.77, P = 0.008). Continuous FMBI showed a modest but significant association with fibrosis risk (OR = 1.05 per unit increment, 95% CI: 1.01–1.10, P = 0.015) (Table S3 ). 3.5.3 FMBI Tertile Analysis for NAFLD Tertile analysis of FMBI confirmed a robust dose-dependent relationship with NAFLD risk (Table S4 ): after full adjustment, T3 had a 3.01-fold higher odds of NAFLD than T1 (95% CI: 2.14–4.24, P < 0.001), with a significant trend across all models (P for trend < 0.001). Although adjustment for metabolic factors reduced the magnitude of ORs (e.g., T2: OR = 1.63, 95% CI: 1.23–2.15, P < 0.001 in Model 3), statistical significance was preserved, further validating FMBI’s independent association with NAFLD. 3.5.4 E-value Analysis for Unmeasured Confounding E-value analysis confirmed the robustness of the FMBI-NAFLD association to unmeasured confounding (Table S5, Fig S2 ). For Q4 versus Q1, an unmeasured confounder would need to have an OR ≥ 3.39 with both FMBI and NAFLD to explain away the observed association (lower 95% CI limit: 2.63). Continuous FMBI showed moderate robustness (E-value = 1.42, 95% CI: 1.34). These magnitude-dependent E-values reinforce that the FMBI-NAFLD association is unlikely to be explained by unmeasured confounding. 4. Discussion This study identifies FMBI, a novel composite index of FMR and BMI, as a robust biomarker for NAFLD: FMBI exhibits a significant linear dose-response relationship with NAFLD risk, interacts additively with smoking to exacerbate NAFLD risk, and independently predicts NAFLD-associated hepatic fibrosis and ASCVD risk beyond traditional metabolic factors. These findings support FMBI as a novel tool for identifying high-risk NAFLD populations and guiding targeted clinical management. Sarcopenic obesity (SO), a phenotype characterized by concurrent muscle mass loss and adiposity, is a well-established predictor of severe hepatic pathological outcomes in NAFLD and an independent risk factor for significant hepatic fibrosis 29 . Sarcopenia-induced muscle mass loss exacerbates insulin resistance, promotes hepatic fat accumulation and chronic inflammation, and accelerates the progression from simple steatosis to NASH and fibrosis 30 – 32 ; transient elastography studies have further confirmed a positive correlation between SO and the severity of NAFLD-related fibrosis 33 . While high BMI is a major driver of NAFLD, pure obese phenotypes (without sarcopenia) are associated with milder hepatic histological injury compared with SO phenotypes 34 . Notably, lean NAFLD (normal BMI with hepatic steatosis) may present with milder metabolic abnormalities at diagnosis but has a long-term prognosis (e.g., cirrhosis, hepatocellular carcinoma risk) that is not superior to obese NAFLD 35 ; pure obesity is also associated with a lower risk of fibrosis progression than SO 29 . These observations align with our findings of a dose-dependent association between FMBI and NAFLD-related fibrosis risk, as FMBI integrates both adiposity and muscle mass to capture the SO phenotype that is missed by BMI alone. In pure obese NAFLD patients, the association between obesity and NAFLD becomes non-significant after adjustment for BMI and visceral fat, suggesting masking by fat distribution-related confounders 34 . Patients with concurrent SO and NAFLD have a significantly higher risk of significant fibrosis and ASCVD events than those with pure obesity 36 , 37 . Lean NAFLD is highly prevalent in Asian populations, and despite mild initial metabolic parameters, it may progress insidiously and is closely linked to SO (with lean NAFLD potentially acting as a risk factor for SO development)—further highlighting the limitations of BMI-only risk assessment for obesity-related NAFLD 34 , 38 . Consistent with previous studies that emphasize the need for body composition analysis to evaluate obesity’s modifying effect on the NAFLD-ASCVD association (as BMI alone underestimates actual risk), our study demonstrates that per unit increment in FMBI is associated with a 20% higher risk of NAFLD and an approximately 33% higher risk of NAFLD-related ASCVD, validating FMBI’s robust predictive value for both NAFLD and its cardiovascular comorbidities. The insidious progression of lean NAFLD and its link to SO further reinforce the inadequacy of BMI-based risk stratification. SO is particularly prevalent in the elderly, and its coexistence with NAFLD predicts a poor prognosis 39 , 40 ; however, it is often overlooked in clinical practice due to the lack of a unified diagnostic consensus, leading to delayed intervention 41 . Compared with a sole focus on general obesity, integrated assessment of muscle mass and fat distribution -FBMI is more valuable for accurately identifying high-risk NAFLD individuals and optimizing intervention strategies. Supported by rigorous covariate screening and unmeasured confounding evaluation, our results confirm a strong linear association between FMBI and NAFLD, with no apparent safe threshold for elevated FMBI: FMBI ≥ 10 is associated with increased NAFLD risk, which rises continuously and nearly linearly with further increases in FMBI. By integrating muscle and adiposity indicators, FMBI effectively captures both muscular and non-muscular NAFLD subtypes, underscoring the clinical importance and necessity of incorporating muscle parameters into NAFLD risk assessment. Sarcopenia promotes NAFLD development and progression through multi-system interactive mechanisms: (1) Insulin resistance: Reduced muscle mass impairs peripheral glucose disposal, exacerbating systemic insulin resistance and hepatic lipid accumulation 31 , 42 – 44 ; (2) Chronic inflammation: Sarcopenia is associated with increased pro-inflammatory cytokines (TNF-α, IL-6) and decreased adiponectin, which activate hepatic stellate cells and drive fibrogenesis 32 , 44 – 46 ; (3) Myokine dysregulation: Decreased irisin and elevated myostatin directly enhance hepatic steatosis by regulating lipid metabolism 32 , 45 ; (4) Gut-liver axis dysfunction: Sarcopenia impairs intestinal barrier integrity, leading to endotoxin translocation and Kupffer cell activation, which exacerbates hepatic inflammation 45 ; (5) Nutritional and behavioural factors: Protein deficiency and sedentary lifestyles worsen metabolic disturbances, creating a vicious cycle between sarcopenia and NAFLD 47 . Notably, this relationship is bidirectional, as NAFLD itself accelerates muscle mass loss through systemic inflammation and malabsorption. Emerging evidence confirms smoking as an independent NAFLD risk factor, despite historical debate 48 – 50 . Cigarette smoke contains over 4000 bioactive compounds that induce cellular damage; nicotine directly promotes NAFLD development and exacerbates obesity-induced hepatic steatosis, suggesting a biological synergy between smoking and obesity in metabolic liver injury 51 . The "obesity plus smoking" phenotype represents a high-risk combination with significantly elevated complication risks 48 . Our study is the first to report a significant additive interaction between smoking and FMBI in NAFLD: FMBI exerts a stronger effect in non-smokers, and FMR and smoking exhibit a synergistic additive effect, with over one-third of the excess NAFLD risk in co-exposed individuals attributable to their interaction. These findings provide novel mechanistic insights into NAFLD pathogenesis and highlight the need for targeted interventions in high-FMBI smokers to reduce NAFLD and its comorbidity risks. This study possesses several key strengths, including the development of a novel composite index (FMBI) integrating body composition and overall obesity, along with the application of rigorous covariate selection methods (univariate regression and Boruta algorithm) to minimize confounding. Furthermore, the linear dose-response relationship between FMBI and NAFLD was validated using RCS analysis, and interaction effects were assessed on both multiplicative and additive scales. Finally, the robustness of our findings to potential unmeasured confounding was evaluated using E-value analysis.However, the study has inevitable limitations that should be acknowledged: (1) the retrospective cross-sectional design only confirms a correlation, not a causal relationship, between FMBI and NAFLD and its complications; (2) objective biases in indicator detection (e.g., BIA-based body composition analysis, ultrasound-based NAFLD diagnosis) may affect the accuracy of results; (3) the single-center study design introduces potential selection bias; (4) the study population is exclusively Chinese, limiting the generalizability of findings to other ethnic groups. 5. Conclusion This study demonstrates a significant linear association between FMBI and NAFLD, with NAFLD risk increasing markedly as FMBI rises—particularly when FMBI ≥ 10—and smoking exacerbates this association through additive synergy. As a simple and easy-to-calculate screening tool, FMBI enables large-scale NAFLD screening and accurately identifies sarcopenic NAFLD subtypes that are easily missed by BMI alone. Additionally, our findings provide a solid evidence base for targeted interventions in high-FMBI individuals with smoking exposure to reduce the risks of NAFLD and its associated hepatic fibrosis and ASCVD. Future multicenter, multi-ethnic, large-sample prospective studies are warranted to validate the causal relationship between FMBI and NAFLD, establish ethnic-specific optimal cut-off values, and explore the value of FMBI in guiding NAFLD treatment and monitoring treatment response. Declarations Ethics statement and consent to participate declarations The study involved the Publicly open-data from the Dryad digital repository including raw available clinical information data collected by other research teams from various countries and hospitals, has been used and analyzed by some studies. This data was from individuals who underwent routine health check-ups at Wuhan Union Hospital from January 2020 to November 2021, and the study protocol was approved by the Ethics Committee of Wuhan Tongji Medical College (S155). The committee waived the need for individual informed consent because all medical data were retrospectively reviewed and analysed anonymously. Supplementary Materials The following supporting information can be downloaded at: Supplementary Figures and Tables. Conflicts of Interest The authors declare no conflict of interest. Funding This project was funded by Key Research and Development Plan Projects in Shaanxi Province(2021SF-133). Author Contribution Xiaokang Wu: Conceptualization, Methodology, Investigation, Writing - Original Draft, Writing - Review & Editing. Jiafeng Yin and Yue Li: Software, Formal Analysis and Data Curation. Chaoliang Xiong: Investigation, Resources. Hailong Liu and Hao Meng: Investigation. Xinjiang Hou: Visualization. All authors have read and agreed to the published version of the manuscript. Acknowledgments The authors would like to thank the investigators at Tongji Medical College of Huazhong University of Science and Technology for sharing their data. Data Availability Data are available in a public, open access repository. The datasets analyzed in this study are openly available in the Dryad Digital Repository ( [https://doi.org/10.5061/dryad.7d7wm3809](https:/doi.org/10.5061/dryad.7d7wm3809) ) 28 . The following supporting information can be downloaded at: Supplementary Figures and Tables. References Han SK, Baik SK, Kim MY. Non-alcoholic fatty liver disease: Definition and subtypes. Clin Mol Hepatol. 2022;29:S5–16. Powell EE, Wong VW-S, Rinella M. Non-alcoholic fatty liver disease. Lancet. 2021;397:2212–24. Lazarus JV, Mark HE, Villota-Rivas M, et al. The global NAFLD policy review and preparedness index: Are countries ready to address this silent public health challenge? 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The association between muscle-to-fat ratio and cardiometabolic risks: The China National Health Survey. Exp Gerontol. 2023;175:112155. Yan F, Nie G, Zhou N, et al. Association of fat-to-muscle ratio with non-alcoholic fatty liver disease: a single-centre retrospective study. BMJ Open. 2023;13:e072489–072489. Chun HS, Lee M, Lee HA, et al. Risk Stratification for Sarcopenic Obesity in Subjects With Nonalcoholic Fatty Liver Disease. Clin Gastroenterol Hepatol. 2022;21:2298–e230718. Chung GE, Kim MJ, Yim JY, et al. Sarcopenia Is Significantly Associated with Presence and Severity of Nonalcoholic Fatty Liver Disease. J Obes Metab Syndr. 2019;28:129–38. Kim JA, Choi KM. Sarcopenia and fatty liver disease. Hepatol Int. 2019;13:674–87. Polyzos SA, Vachliotis ID, Mantzoros CS. Sarcopenia, sarcopenic obesity and nonalcoholic fatty liver disease. Metabolism. 2023;147:155676. Wijarnpreecha K, Aby ES, Ahmed A, et al. Association between Sarcopenic Obesity and Nonalcoholic Fatty Liver Disease and Fibrosis detected by Fibroscan. J Gastrointestin Liver Dis. 2021;30:227–32. Kashiwagi K, Takayama M, Fukuhara K, et al. A significant association of non-obese non-alcoholic fatty liver disease with sarcopenic obesity. Clin Nutr ESPEN. 2020;38:86–93. Shao C, Ye J, Li F, et al. Different predictors of steatosis and fibrosis severity among lean, overweight and obese patients with nonalcoholic fatty liver disease. Dig Liver Dis. 2019;51:1392–9. Jin H, Oh H-J, Lee B-Y. GABA Prevents Age-Related Sarcopenic Obesity in Mice with High-Fat-Diet-Induced Obesity. Cells 2023;12. Maliszewska K, Adamska-Patruno E, Kretowski A. The interplay between muscle mass decline, obesity, and type 2 diabetes. Pol Arch Intern Med. 2019;129:809–16. Dey P. The emerging phenotype of nonalcoholic fatty liver disease in lean individuals: what’s different? Front Endocrinol (Lausanne). 2025;16:1693123. Merli M, Lattanzi B, Aprile F. Sarcopenic obesity in fatty liver. Curr Opin Clin Nutr Metab Care. 2019;22:185–90. Hanna DJ, Jamieson ST, Lee CS, et al. Bioelectrical impedance analysis in managing sarcopenic obesity in NAFLD. Obes Sci Pract. 2021;7:629–45. Wijarnpreecha K, Panjawatanan P, Aby E, et al. Nonalcoholic fatty liver disease in the over-60s: Impact of sarcopenia and obesity. Maturitas. 2019;124:48–54. Iwaki M, Kobayashi T, Nogami A et al. Impact of Sarcopenia on Non-Alcoholic Fatty Liver Disease. Nutrients 2023;15. Joo SK, Kim W. Interaction between sarcopenia and nonalcoholic fatty liver disease. Clin Mol Hepatol. 2022;29:S68–78. Zambon Azevedo V, Silaghi CA, Maurel T, et al. Impact of Sarcopenia on the Severity of the Liver Damage in Patients With Non-alcoholic Fatty Liver Disease. Front Nutr. 2022;8:774030. Ferenc K, Jarmakiewicz-Czaja S, Filip R. What Does Sarcopenia Have to Do with Nonalcoholic Fatty Liver Disease? Life (Basel) 2024;14. Mikolasevic I, Pavic T, Kanizaj TF, et al. Nonalcoholic Fatty Liver Disease and Sarcopenia: Where Do We Stand? Can J Gastroenterol Hepatol. 2020;2020:8859719. Hsu C-S, Kao J-H. Management of non-alcoholic fatty liver disease in patients with sarcopenia. Expert Opin Pharmacother. 2021;23:221–33. Munsterman ID, Smits MM, Andriessen R, et al. Smoking is associated with severity of liver fibrosis but not with histological severity in nonalcoholic fatty liver disease. Results from a cross-sectional study. Scand J Gastroenterol. 2017;52:881–5. Jang YS, Joo HJ, Park YS, et al. Association between smoking cessation and non-alcoholic fatty liver disease using NAFLD liver fat score. Front Public Health. 2023;11:1015919. Joe H, Oh J-E, Cho Y-J, et al. Association between smoking status and non-alcoholic fatty liver disease. PLoS ONE. 2025;20:e0325305. Xu J, Li Y, Feng Z et al. Cigarette Smoke Contributes to the Progression of MASLD: From the Molecular Mechanisms to Therapy. Cells 2025;14. Additional Declarations No competing interests reported. Supplementary Files TableS1.docx TableS3.SenesitiveAnalysisMultivariate.docx Fig.S1.pdf Fig.S2.jpg Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 16 Apr, 2026 Reviews received at journal 15 Apr, 2026 Reviews received at journal 14 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviews received at journal 13 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers invited by journal 13 Apr, 2026 Editor invited by journal 10 Apr, 2026 Editor assigned by journal 10 Apr, 2026 Submission checks completed at journal 10 Apr, 2026 First submitted to journal 02 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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15:23:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9304928/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9304928/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107258470,"identity":"f41bee19-92e0-4e79-86ed-68bf413b783f","added_by":"auto","created_at":"2026-04-19 12:39:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":52724,"visible":true,"origin":"","legend":"\u003cp\u003eBoruta algorithm-based feature selection for NAFLD-associated predictors\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-9304928/v1/a67271478e9a90a810153f7c.png"},{"id":107258472,"identity":"874a3f9b-76ac-489d-8332-cc0893f05d62","added_by":"auto","created_at":"2026-04-19 12:39:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":84501,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubgroup analysis of the association between FMBI and NAFLD risk\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-9304928/v1/e87428145d0524da4495a311.png"},{"id":107258498,"identity":"9a7bd35c-2df6-401b-b12a-f567053d263a","added_by":"auto","created_at":"2026-04-19 12:39:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":38612,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditive interaction between FMBI and smoking on NAFLD risk\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-9304928/v1/131df4579ab0858b4428fd9e.png"},{"id":107705112,"identity":"1aea1fb5-50fb-4a60-82f2-8f9cbee478f5","added_by":"auto","created_at":"2026-04-24 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12:39:35","extension":"jpg","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":391021,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9304928/v1/e4f1ec4ee6cfb14942803d82.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of Fat-Muscle-Body Mass Index (FMBI) with Non-Alcoholic Fatty Liver Disease","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eNon-alcoholic fatty liver disease (NAFLD), defined as hepatic steatosis of \u0026ge;\u0026thinsp;5% in the absence of excessive alcohol consumption and secondary liver injury causes\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, affects approximately 25% of the global population, with its prevalence projected to rise further by 2030\u003csup\u003e2\u003c/sup\u003e. As a leading aetiology of cirrhosis and hepatocellular carcinoma, NAFLD is also strongly comorbid with cardiovascular disease, extrahepatic malignancies, metabolic syndrome, type 2 diabetes mellitus (T2DM), and hypertension, imposing a substantial global disease burden\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Despite this, validated non-invasive biomarkers for the accurate diagnosis, risk stratification, progression monitoring, and treatment response assessment of NAFLD remain lacking. Liver biopsy, the gold standard for evaluating hepatic fibrosis, is limited in clinical practice by its invasiveness, sampling variability, and poor patient acceptance\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBody mass index (BMI) is widely used for NAFLD screening due to its robust association with disease risk, yet it has critical limitations in clinical management\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. First, BMI alone fails to identify 10\u0026ndash;20% of \"lean\" NAFLD cases (BMI\u0026thinsp;\u0026lt;\u0026thinsp;25 kg/m\u0026sup2; in the general population, \u0026lt;\u0026thinsp;23 kg/m\u0026sup2; in Asians), leading to underdiagnosis; notably, lean NAFLD patients may face a higher risk of all-cause mortality\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Second, BMI does not capture key pathological features driving non-alcoholic steatohepatitis (NASH) progression and disease severity, including myosteatosis, visceral adiposity, and metabolic dysfunction\u003csup\u003e\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. While BMI remains valuable for initial screening and weight management, its standalone diagnostic and predictive value is insufficient.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e A multidimensional risk stratification approach integrating waist circumference, visceral fat assessment, metabolic markers, and muscle metrics (e.g., grip strength, skeletal muscle index) is therefore essential to improve diagnostic accuracy and guide clinical decision-making\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePrevious studies have demonstrated the multidimensional value of fat-muscle ratios (FMRs),including the subcutaneous-to-visceral fat ratio (SVR), visceral-to-abdominal fat ratio (VAR), and fat mass-to-fat-free mass ratio (FM/FFM), in NAFLD research. Elevated FMR is positively correlated with NAFLD risk and exhibits superior diagnostic performance in T2DM patients\u003csup\u003e\u003cspan additionalcitationids=\"CR17 CR18 CR19\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Pathologically, low SVR or high VAR is associated with hepatic steatosis and fibrosis progression, while FM/FFM identifies body composition imbalance as a core mechanism underlying lean NAFLD; a low liver fat-to-total fat ratio independently predicts hepatic fibrosis risk\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. However, FMRs have inherent limitations: device-dependent measurements (bioelectrical impedance analysis [BIA], dual-energy X-ray absorptiometry [DXA], computed tomography [CT]) raise comparability concerns, ultrasound-based NAFLD diagnosis leads to methodological heterogeneity, associations are population-specific (e.g., a stronger SVR-NAFLD link in males), and optimal cut-off values remain undefined\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Standalone FMRs show limited predictive efficacy, necessitating combination with traditional indices such as BMI for optimized NAFLD risk stratification\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCombining FMR with BMI addresses the limitations of each individual metric: BMI reflects overall obesity but neglects body composition distribution, while FMR captures the antagonistic muscle-fat relationship. Their integration enables precise metabolic risk assessment, avoids obesity-centric bias, and facilitates multidimensional evaluation of obesity phenotypes, metabolic risk, muscle function, and clinical prognosis\u003csup\u003e\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. To our knowledge, few study has investigated the association between the product of FMR and BMI\u0026mdash;a novel composite index\u0026mdash;and NAFLD.\u003c/p\u003e \u003cp\u003eThis study therefore aimed to explore the association between the Fat-Muscle-BMI Index (FMBI, FMR\u0026times;BMI) and NAFLD, and further investigate the relationships of FMR and FMBI with hepatic fibrosis and atherosclerotic cardiovascular disease (ASCVD) risk in NAFLD patients. We hypothesized that FMBI would serve as a novel and superior biomarker for NAFLD risk stratification and comprehensive clinical management.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Population\u003c/h2\u003e \u003cp\u003eThis retrospective cross-sectional study utilized data from the Dryad digital repository (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5061/dryad.7d7wm3809\u003c/span\u003e\u003cspan address=\"10.5061/dryad.7d7wm3809\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), mainly including individuals who underwent routine health check-ups at Wuhan Union Hospital from January 2020 to November 2021. The study protocol was approved by the Ethics Committee of Wuhan Union Hospital. The detailed methodology and primary findings have been previously reported by Yan et al\u003csup\u003e28\u003c/sup\u003e. The original study consecutively enrolled 1830 participants aged 40\u0026ndash;79 years who voluntarily completed body composition analysis and liver ultrasonography. After applying exclusion criteria (excessive alcohol intake, history of chronic liver disease, acute illness, renal insufficiency, active malignancy, use of corticosteroids, or incomplete data), 1592 participants were included in the final analysis. NAFLD was diagnosed by abdominal ultrasonography based on the presence of hepatic steatosis in the absence of other liver diseases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Definition of NAFLD, Hepatic Fibrosis, and ASCVD Risk\u003c/h2\u003e \u003cp\u003eNAFLD was diagnosed by conventional abdominal B-mode ultrasonography (Philips IU22, Philips Healthcare, N.V.) performed by certified sonographers; participants with hepatic steatosis and no excluded hepatic comorbidities were classified as having NAFLD.\u003c/p\u003e \u003cp\u003eHepatic fibrosis burden was assessed using the Fibrosis-4 (FIB-4) index, a well-validated non-invasive scoring system. Hepatic fibrosis risk was categorized using FIB-4 index thresholds: low (\u0026lt;\u0026thinsp;1.30), moderate (1.30\u0026ndash;2.67), and high (\u0026gt;\u0026thinsp;2.67), with moderate and high categories combined for analysis due to limited sample size in the high-risk group. Due to the small sample size of the high-risk group (FIB-4\u0026thinsp;\u0026gt;\u0026thinsp;2.67), which would compromise statistical power, moderate and high fibrosis risk groups were combined for subsequent analyses.\u003c/p\u003e \u003cp\u003eThe 10-year ASCVD risk was calculated using the prediction algorithm recommended by the 2013 American College of Cardiology/American Heart Association (ACC/AHA) guidelines. An ASCVD risk score\u0026thinsp;\u0026gt;\u0026thinsp;10% was defined as high cardiovascular risk, and a score\u0026thinsp;\u0026le;\u0026thinsp;10% as low risk.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Anthropometric and Laboratory Measurements\u003c/h2\u003e \u003cp\u003eAnthropometric assessments were conducted by a professionally trained technician in a dedicated body composition analysis room. Body composition (body weight, fat mass, and muscle mass) was measured using a multi-frequency bioelectrical impedance analyzer (Tsinghua Tongfan, BCA-2A, China), following the manufacturer\u0026rsquo;s standard protocols. Height was measured separately, and BMI was calculated as weight (kg) divided by height squared (m\u0026sup2;). Blood pressure (BP) was measured with an electronic sphygmomanometer (Panasonic, EW3106, China) after participants rested in a seated position for at least 10 minutes; two readings were taken at 5-minute intervals, and the mean value was used for analysis.\u003c/p\u003e \u003cp\u003eVenous blood samples were collected after an overnight fast of \u0026ge;\u0026thinsp;8 hours, and biochemical analyses were performed in the hospital\u0026rsquo;s central clinical laboratory. The following parameters were measured: platelet count (PLT), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), alanine aminotransferase (ALT), aspartate aminotransferase (AST), uric acid (UA), and fasting blood glucose (FBG). PLT was quantified using a Beckman Coulter hematology analyzer; biochemical parameters were assayed on a Beckman AU5800 fully automated biochemical analyzer, with the following detection methods: glycerol phosphate oxidase method for TG, cholesterol oxidase method for TC, selective solubilization method for LDL-C, and chemically modified enzyme method for HDL-C. FMR was calculated as total body fat mass divided by total body muscle mass.\u003c/p\u003e \u003cp\u003eT2DM was defined as a self-reported history of diabetes or current use of glucose-lowering medications. Hypertension was defined as a self-reported history of hypertension or current use of oral antihypertensive agents.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Calculation of FMBI\u003c/h2\u003e \u003cp\u003eA novel composite metric, the Fat-Muscle-BMI Index (FMBI), was constructed as the product of FMR and BMI, with the formula: FMBI\u0026thinsp;=\u0026thinsp;Fat-to-muscle ratio (FMR) \u0026times; Body mass index (BMI)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e \u003cp\u003eParticipant baseline characteristics were stratified by FMBI quartiles (Q1\u0026ndash;Q4). Continuous variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (normally distributed) or median (interquartile range) (non-normally distributed), with normality assessed by the Shapiro-Wilk test. Categorical variables were expressed as frequencies and proportions. One-way ANOVA or the Kruskal-Wallis test was used for comparisons of continuous variables across groups, and the χ\u0026sup2; test for categorical variables.\u003c/p\u003e \u003cp\u003eThe Boruta algorithm (a random forest-based method) was used for covariate selection: variable importance was compared with permuted \"shadow\" features, and features that consistently outperformed shadow variables across 500 iterations were retained as confirmed predictors.\u003c/p\u003e \u003cp\u003eMultivariable logistic regression analyses were used to assess the association between FMBI and risks of NAFLD and NAFLD-associated hepatic fibrosis. Three hierarchical models were constructed for each outcome: Model 1 (unadjusted); Model 2 (adjusted for sex and tobacco use); Model 3 (fully adjusted for sex, tobacco use, ALT, AST, UA, FBG, TG, HDL-C, systolic blood pressure [SBP], hypertension, and T2DM). Continuous FMBI was analysed per unit increment; categorical FMBI was analysed by quartiles (Q1 as reference) for NAFLD and fibrosis, and by tertiles (T1 as reference) for additional NAFLD validation, with trend significance evaluated by treating quartiles/tertiles as ordinal variables. Odds ratios (ORs) and 95% confidence intervals (CIs) were reported.\u003c/p\u003e \u003cp\u003eMultivariable linear regression models were used to assess the association between FMBI (continuous and quartiles) and ASCVD risk score, with the same three hierarchical adjustment models as above; trend tests treated FMBI quartiles as ordinal variables, and regression coefficients (β) and 95% CIs were reported.\u003c/p\u003e \u003cp\u003eRestricted cubic splines (RCS) with four knots (placed at the 5th, 35th, 65th, and 95th percentiles of FMBI) were used to model the dose-response relationship between FMBI and NAFLD risk, with adjustment for Model 3 covariates. A reference point of FMBI\u0026thinsp;=\u0026thinsp;9.832 was set to calculate ORs, and nonlinearity was tested by likelihood ratio tests comparing linear and spline models.\u003c/p\u003e \u003cp\u003eSubgroup analyses were performed using multivariable logistic regression stratified by sex, age, BMI, tobacco use, hypertension, and T2DM status. Interaction terms (FMBI \u0026times; subgroup variable) were included to test for effect modification, with continuous FMBI analysed per unit increment; results were visualized by forest plots, and interaction was considered significant at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All subgroup models retained Model 3 covariates.\u003c/p\u003e \u003cp\u003eThe interaction between smoking status and FMBI on NAFLD risk was assessed on both multiplicative and additive scales. Multiplicative interaction was tested by including product terms in logistic regression models and using likelihood ratio tests. Additive interaction was quantified using three established indices: Relative Excess Risk due to Interaction (RERI), Attributable Proportion (AP), and Synergy Index (SI), with their 95% CIs.\u003c/p\u003e \u003cp\u003eE-value analysis was performed to evaluate the robustness of the FMBI-NAFLD association to unmeasured confounding. Sensitivity analyses were conducted to verify the consistency of the association between FMBI and NAFLD, hepatic fibrosis, and ASCVD risk across different analytical approaches.\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using R Statistical Software (Version 4.2.2, The R Foundation) and the Free Statistics Analysis Platform (Version 2.2, Beijing, China), with a two-tailed P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline Characteristics of the Study Population\u003c/h2\u003e \u003cp\u003eBaseline characteristics of the 1592 participants stratified by FMBI quartiles are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, with significant differences observed for most variables across quartiles (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Sex distribution varied markedly, with the highest proportion of females in Q4 (59.0%) versus Q1 (11.6%). Age showed a progressive increase across FMBI quartiles. Metabolic parameters including BMI (Q1: 23.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8 kg/m\u0026sup2;; Q4: 27.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3 kg/m\u0026sup2;), FMR (Q1: 0.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0; Q4: 0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1), FBG, TG, and HDL-C exhibited significant ascending trends across quartiles (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The prevalence of hypertension (Q1: 44.5%; Q4: 72.9%), T2DM (Q1: 25.1%; Q4: 37.2%), and NAFLD (Q1: 46.0%; Q4: 72.4%) increased significantly with higher FMBI quartiles. No significant interquartile differences were observed for PLT and AST, indicating substantial metabolic heterogeneity across FMBI strata.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Covariate Selection\u003c/h2\u003e \u003cp\u003eUnivariate logistic regression identified multiple variables with significant unadjusted associations with NAFLD (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), most notably FMBI (OR\u0026thinsp;=\u0026thinsp;1.15 per unit increment, 95% CI: 1.11\u0026ndash;1.18, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), age categories, and key metabolic parameters (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, PLT (OR\u0026thinsp;=\u0026thinsp;1.00, P\u0026thinsp;=\u0026thinsp;0.152) and AST (OR\u0026thinsp;=\u0026thinsp;1.01, P\u0026thinsp;=\u0026thinsp;0.190) showed no significant associations and were not included in adjusted models.\u003c/p\u003e \u003cp\u003eBoruta analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) confirmed the following NAFLD-associated predictors: TG, ALT, UA, FBG, T2DM, hypertension, HDL-C, SBP, tobacco use, AST, and sex. Alcohol use, TC, and age were deemed statistically insignificant, while diastolic blood pressure (DBP) showed tentative importance. This machine learning approach validated the manually selected covariates and identified TG as the highest-impact metabolic predictor. Based on these results, sex, tobacco use, ALT, AST, UA, FBG, TG, HDL-C, SBP, hypertension, and T2DM were included in all fully adjusted models (Model 3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Boruta algorithm, a random forest-based method, was used to identify important predictors of NAFLD. Variable importance (Z-score) is plotted for each candidate variable. TG, triglycerides; ALT, alanine aminotransferase; UA, uric acid; FBG, fasting blood glucose; T2DM, type 2 diabetes mellitus; HDL-C, high-density lipoprotein cholesterol; SBP, systolic blood pressure; AST, aspartate aminotransferase; DBP, diastolic blood pressure; TC, total cholesterol.\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\u003eBaseline characteristics of participant\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1592)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;398)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;398)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;398)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;398)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e444 (27.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e106 (26.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e235 (59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1148 (72.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e352 (88.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e341 (85.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e292 (73.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e163 (41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(year)\u003c/p\u003e \u003cp\u003e[n (%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e354 (22.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113 (28.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110 (27.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e75 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e56 (14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e709 (44.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e200 (50.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e185 (46.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e180 (45.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e144 (36.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e360 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64 (16.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59 (14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e103 (25.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e134 (33.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e169 (10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64 (16.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT(10\u003csup\u003e9\u003c/sup\u003e /L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e211.1\u0026thinsp;\u0026plusmn;\u0026thinsp;53.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e209.1\u0026thinsp;\u0026plusmn;\u0026thinsp;54.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e208.7\u0026thinsp;\u0026plusmn;\u0026thinsp;53.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e210.4\u0026thinsp;\u0026plusmn;\u0026thinsp;51.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e216.3\u0026thinsp;\u0026plusmn;\u0026thinsp;54.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.0 (15.0, 30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.0 (14.0, 26.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.0 (15.0, 30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.5 (16.0, 33.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.0 (16.0, 32.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.1\u0026thinsp;\u0026plusmn;\u0026thinsp;11.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.6\u0026thinsp;\u0026plusmn;\u0026thinsp;14.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.4\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.9\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUA(umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e366.8\u0026thinsp;\u0026plusmn;\u0026thinsp;95.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e361.7\u0026thinsp;\u0026plusmn;\u0026thinsp;89.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e372.2\u0026thinsp;\u0026plusmn;\u0026thinsp;91.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e376.8\u0026thinsp;\u0026plusmn;\u0026thinsp;98.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e356.3\u0026thinsp;\u0026plusmn;\u0026thinsp;102.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBG(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.4 (1.0, 2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2 (0.9, 1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5 (1.0, 2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.6 (1.1, 2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.5 (1.0, 2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL.C(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL.C(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP(mm Hg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e130.9\u0026thinsp;\u0026plusmn;\u0026thinsp;15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127.3\u0026thinsp;\u0026plusmn;\u0026thinsp;14.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131.3\u0026thinsp;\u0026plusmn;\u0026thinsp;16.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e130.2\u0026thinsp;\u0026plusmn;\u0026thinsp;16.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e134.9\u0026thinsp;\u0026plusmn;\u0026thinsp;15.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP(mm Hg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e81.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e81.1\u0026thinsp;\u0026plusmn;\u0026thinsp;11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e82.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTobacco use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e538 (33.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158 (39.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e153 (38.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e145 (36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e82 (20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e520 (32.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143 (35.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e163 (41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e137 (34.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e77 (19.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFMBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e943 (59.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e177 (44.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e227 (57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e249 (62.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e290 (72.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e498 (31.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 (25.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e127 (31.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e123 (30.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e148 (37.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNAFLD, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e973 (61.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e183 (46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e238 (59.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e264 (66.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e288 (72.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; DBP, diastolic blood pressure; FBG, fasting blood glucose; FMR, fat-to-muscle ratio; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; NAFLD, non-alcoholic fatty liver disease; PLT, platelets; SBP, systolic blood pressure; TC, total cholesterol; TG, triglycerides; UA, uric acid.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Association between FMBI and NAFLD\u003c/h2\u003e \u003cp\u003eA significant positive association between FMBI and NAFLD was observed across all analytical approaches (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Per unit increment in continuous FMBI was associated with a significantly increased NAFLD risk in the fully adjusted model (OR\u0026thinsp;=\u0026thinsp;1.20, 95% CI: 1.14\u0026ndash;1.25, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Quartile analysis revealed a clear dose-dependent relationship: after full adjustment, Q4 had a 3.95-fold higher odds of NAFLD compared with Q1 (95% CI: 2.64\u0026ndash;5.90, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with a significant trend persisting in the fully adjusted model (OR for trend\u0026thinsp;=\u0026thinsp;1.54, 95% CI: 1.36\u0026ndash;1.74, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The association remained robust across sequentially adjusted models, confirming FMBI\u0026rsquo;s independent relationship with NAFLD after accounting for demographic, metabolic, and comorbidity confounders.\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\u003eMultivariate logistic regression analysis between FMBI and NAFLD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFMBI\u003c/p\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.15 (1.11\u0026thinsp;~\u0026thinsp;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.28 (1.22\u0026thinsp;~\u0026thinsp;1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.2 0(1.14\u0026thinsp;~\u0026thinsp;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFMBI\u003c/p\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.75 (1.32\u0026thinsp;~\u0026thinsp;2.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.88 (1.41\u0026thinsp;~\u0026thinsp;2.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.46 (1.07\u0026thinsp;~\u0026thinsp;2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.019\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\u003e2.31 (1.74\u0026thinsp;~\u0026thinsp;3.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.09 (2.28\u0026thinsp;~\u0026thinsp;4.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.02 (1.45\u0026thinsp;~\u0026thinsp;2.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.08 (2.29\u0026thinsp;~\u0026thinsp;4.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.12 (4.96\u0026thinsp;~\u0026thinsp;10.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.95 (2.64\u0026thinsp;~\u0026thinsp;5.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrend test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.45 (1.32\u0026thinsp;~\u0026thinsp;1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.88 (1.68\u0026thinsp;~\u0026thinsp;2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.54 (1.36\u0026thinsp;~\u0026thinsp;1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel1: no adjusted\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel2: adjusted sex,tobacco\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eModel3: adjusted sex,tobacco use,ALT,AST,UA,FBG,TG,HDL.C,SBP,Hypertension,Diabetes.\u003c/p\u003e \u003cp\u003eQ:Quartiles FMR, fat-to-muscle ratio,NAFLD, non-alcoholic fatty liver disease,ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; FBG, fasting blood glucose;HDL-C, high-density lipoprotein cholesterol,SBP, systolic blood pressure;TG, triglycerides; UA, uric acid.\u003c/p\u003e \u003cp\u003eRCS analysis (Fig \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) demonstrated a significant linear dose-response relationship between FMBI and NAFLD risk (overall P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; nonlinearity P\u0026thinsp;=\u0026thinsp;0.245). Per unit increment in FMBI above the reference point (9.832) was associated with a monotonic increase in NAFLD odds (e.g., OR\u0026thinsp;=\u0026thinsp;2.0 at FMBI\u0026thinsp;=\u0026thinsp;15.0), and this linear relationship persisted across the clinically relevant range of FMBI values, further validating FMBI as a robust NAFLD predictor.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Subgroup and Interaction Analysis\u003c/h2\u003e \u003cp\u003eForest plot analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) showed a consistent positive association between FMBI and NAFLD across most pre-defined subgroups. A significant interaction was observed for tobacco use (P\u0026thinsp;=\u0026thinsp;0.032): the effect of FMBI on NAFLD risk was weaker in tobacco users (OR\u0026thinsp;=\u0026thinsp;1.15, 95% CI: 1.06\u0026ndash;1.25) compared with non-users (OR\u0026thinsp;=\u0026thinsp;1.22, 95% CI: 1.16\u0026ndash;1.29).\u003c/p\u003e \u003cp\u003eBoth FMBI and smoking status were independently associated with increased NAFLD risk (ORs\u0026thinsp;\u0026gt;\u0026thinsp;1.9, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The multiplicative interaction term between FMBI and smoking was not statistically significant (interaction OR\u0026thinsp;=\u0026thinsp;1.20, 95% CI: 0.69\u0026ndash;2.10, P\u0026thinsp;=\u0026thinsp;0.520). However, additive interaction analysis yielded significant results (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e): AP due to interaction was 0.37 (95% CI: 0.05\u0026ndash;0.68, P\u0026thinsp;=\u0026thinsp;0.010), indicating that 37% of the excess NAFLD risk in individuals with both high FMBI and smoking exposure was attributable to their interaction; SI was 1.88 (95% CI: 0.97\u0026ndash;3.63, P\u0026thinsp;\u0026lt;\u0026thinsp;0.050), suggesting a synergistic additive effect; RERI was 1.68 (95% CI: -0.47\u0026ndash;3.83, P\u0026thinsp;=\u0026thinsp;0.060), showing a marginally significant trend towards additive interaction.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eForest plot showing the odds ratios (ORs) and 95% confidence intervals (CIs) for NAFLD per unit increment in continuous FMBI across predefined subgroups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe four groups represent combinations of FMBI (B: 0 and 1) and smoking status (A: 0, non-smoker; 1, smoker). The odds ratios (ORs) for NAFLD are presented for each group.\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\u003eAssessment of Additive and Multiplicative Interactions Between FMBI and Tobacco Use in Relation to the Outcome.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasures\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCI.95.low\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCI.95.up\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP.value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOR00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOR01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOR10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOR11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOR(FMBI on outcome [Tobacco.use\u0026thinsp;=\u0026thinsp;=\u0026thinsp;0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOR(FMBI on outcome [Tobacco.use\u0026thinsp;=\u0026thinsp;=\u0026thinsp;1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOR(Tobacco.use on outcome [FMBI\u0026thinsp;=\u0026thinsp;=\u0026thinsp;0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOR(Tobacco.use on outcome [FMBI\u0026thinsp;=\u0026thinsp;=\u0026thinsp;1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiplicative scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRERI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Sensitivity Analyses\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.5.1 FMBI and ASCVD Risk in NAFLD\u003c/h2\u003e \u003cp\u003eIn the fully adjusted model (Model 3), per unit increment in FMBI was significantly associated with higher ASCVD risk scores in NAFLD patients (β\u0026thinsp;=\u0026thinsp;0.32, 95% CI: 0.13\u0026ndash;0.50, P\u0026thinsp;=\u0026thinsp;0.001). Quartile analysis revealed a significant dose-response relationship (P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with Q4 having a 3.24-point higher ASCVD score than Q1 (95% CI: 1.32\u0026ndash;5.16, P\u0026thinsp;=\u0026thinsp;0.001) (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). The strength of the association increased after adjustment for metabolic confounders (Model 2 vs. Model 1), confirming FMBI\u0026rsquo;s independent contribution to ASCVD risk in NAFLD patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.5.2 FMBI and NAFLD-Associated Hepatic Fibrosis\u003c/h2\u003e \u003cp\u003eFully adjusted models demonstrated a significant dose-dependent relationship between FMBI quartiles and NAFLD-associated fibrosis risk (P for trend\u0026thinsp;=\u0026thinsp;0.023), with Q4 having a 1.79-fold higher odds of fibrosis compared with Q1 (95% CI: 1.16\u0026ndash;2.77, P\u0026thinsp;=\u0026thinsp;0.008). Continuous FMBI showed a modest but significant association with fibrosis risk (OR\u0026thinsp;=\u0026thinsp;1.05 per unit increment, 95% CI: 1.01\u0026ndash;1.10, P\u0026thinsp;=\u0026thinsp;0.015) (Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.5.3 FMBI Tertile Analysis for NAFLD\u003c/h2\u003e \u003cp\u003eTertile analysis of FMBI confirmed a robust dose-dependent relationship with NAFLD risk (Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e): after full adjustment, T3 had a 3.01-fold higher odds of NAFLD than T1 (95% CI: 2.14\u0026ndash;4.24, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with a significant trend across all models (P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Although adjustment for metabolic factors reduced the magnitude of ORs (e.g., T2: OR\u0026thinsp;=\u0026thinsp;1.63, 95% CI: 1.23\u0026ndash;2.15, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 in Model 3), statistical significance was preserved, further validating FMBI\u0026rsquo;s independent association with NAFLD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.5.4 E-value Analysis for Unmeasured Confounding\u003c/h2\u003e \u003cp\u003eE-value analysis confirmed the robustness of the FMBI-NAFLD association to unmeasured confounding (Table S5, Fig \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). For Q4 versus Q1, an unmeasured confounder would need to have an OR\u0026thinsp;\u0026ge;\u0026thinsp;3.39 with both FMBI and NAFLD to explain away the observed association (lower 95% CI limit: 2.63). Continuous FMBI showed moderate robustness (E-value\u0026thinsp;=\u0026thinsp;1.42, 95% CI: 1.34). These magnitude-dependent E-values reinforce that the FMBI-NAFLD association is unlikely to be explained by unmeasured confounding.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study identifies FMBI, a novel composite index of FMR and BMI, as a robust biomarker for NAFLD: FMBI exhibits a significant linear dose-response relationship with NAFLD risk, interacts additively with smoking to exacerbate NAFLD risk, and independently predicts NAFLD-associated hepatic fibrosis and ASCVD risk beyond traditional metabolic factors. These findings support FMBI as a novel tool for identifying high-risk NAFLD populations and guiding targeted clinical management.\u003c/p\u003e \u003cp\u003eSarcopenic obesity (SO), a phenotype characterized by concurrent muscle mass loss and adiposity, is a well-established predictor of severe hepatic pathological outcomes in NAFLD and an independent risk factor for significant hepatic fibrosis\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Sarcopenia-induced muscle mass loss exacerbates insulin resistance, promotes hepatic fat accumulation and chronic inflammation, and accelerates the progression from simple steatosis to NASH and fibrosis\u003csup\u003e\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e; transient elastography studies have further confirmed a positive correlation between SO and the severity of NAFLD-related fibrosis\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. While high BMI is a major driver of NAFLD, pure obese phenotypes (without sarcopenia) are associated with milder hepatic histological injury compared with SO phenotypes\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Notably, lean NAFLD (normal BMI with hepatic steatosis) may present with milder metabolic abnormalities at diagnosis but has a long-term prognosis (e.g., cirrhosis, hepatocellular carcinoma risk) that is not superior to obese NAFLD\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e; pure obesity is also associated with a lower risk of fibrosis progression than SO\u003csup\u003e29\u003c/sup\u003e. These observations align with our findings of a dose-dependent association between FMBI and NAFLD-related fibrosis risk, as FMBI integrates both adiposity and muscle mass to capture the SO phenotype that is missed by BMI alone.\u003c/p\u003e \u003cp\u003eIn pure obese NAFLD patients, the association between obesity and NAFLD becomes non-significant after adjustment for BMI and visceral fat, suggesting masking by fat distribution-related confounders\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Patients with concurrent SO and NAFLD have a significantly higher risk of significant fibrosis and ASCVD events than those with pure obesity\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Lean NAFLD is highly prevalent in Asian populations, and despite mild initial metabolic parameters, it may progress insidiously and is closely linked to SO (with lean NAFLD potentially acting as a risk factor for SO development)\u0026mdash;further highlighting the limitations of BMI-only risk assessment for obesity-related NAFLD\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Consistent with previous studies that emphasize the need for body composition analysis to evaluate obesity\u0026rsquo;s modifying effect on the NAFLD-ASCVD association (as BMI alone underestimates actual risk), our study demonstrates that per unit increment in FMBI is associated with a 20% higher risk of NAFLD and an approximately 33% higher risk of NAFLD-related ASCVD, validating FMBI\u0026rsquo;s robust predictive value for both NAFLD and its cardiovascular comorbidities.\u003c/p\u003e \u003cp\u003eThe insidious progression of lean NAFLD and its link to SO further reinforce the inadequacy of BMI-based risk stratification. SO is particularly prevalent in the elderly, and its coexistence with NAFLD predicts a poor prognosis\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e; however, it is often overlooked in clinical practice due to the lack of a unified diagnostic consensus, leading to delayed intervention\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Compared with a sole focus on general obesity, integrated assessment of muscle mass and fat distribution -FBMI is more valuable for accurately identifying high-risk NAFLD individuals and optimizing intervention strategies. Supported by rigorous covariate screening and unmeasured confounding evaluation, our results confirm a strong linear association between FMBI and NAFLD, with no apparent safe threshold for elevated FMBI: FMBI\u0026thinsp;\u0026ge;\u0026thinsp;10 is associated with increased NAFLD risk, which rises continuously and nearly linearly with further increases in FMBI. By integrating muscle and adiposity indicators, FMBI effectively captures both muscular and non-muscular NAFLD subtypes, underscoring the clinical importance and necessity of incorporating muscle parameters into NAFLD risk assessment.\u003c/p\u003e \u003cp\u003eSarcopenia promotes NAFLD development and progression through multi-system interactive mechanisms: (1) Insulin resistance: Reduced muscle mass impairs peripheral glucose disposal, exacerbating systemic insulin resistance and hepatic lipid accumulation\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e; (2) Chronic inflammation: Sarcopenia is associated with increased pro-inflammatory cytokines (TNF-α, IL-6) and decreased adiponectin, which activate hepatic stellate cells and drive fibrogenesis\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e; (3) Myokine dysregulation: Decreased irisin and elevated myostatin directly enhance hepatic steatosis by regulating lipid metabolism\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e; (4) Gut-liver axis dysfunction: Sarcopenia impairs intestinal barrier integrity, leading to endotoxin translocation and Kupffer cell activation, which exacerbates hepatic inflammation\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e; (5) Nutritional and behavioural factors: Protein deficiency and sedentary lifestyles worsen metabolic disturbances, creating a vicious cycle between sarcopenia and NAFLD\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Notably, this relationship is bidirectional, as NAFLD itself accelerates muscle mass loss through systemic inflammation and malabsorption.\u003c/p\u003e \u003cp\u003eEmerging evidence confirms smoking as an independent NAFLD risk factor, despite historical debate\u003csup\u003e\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Cigarette smoke contains over 4000 bioactive compounds that induce cellular damage; nicotine directly promotes NAFLD development and exacerbates obesity-induced hepatic steatosis, suggesting a biological synergy between smoking and obesity in metabolic liver injury\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. The \"obesity plus smoking\" phenotype represents a high-risk combination with significantly elevated complication risks\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Our study is the first to report a significant additive interaction between smoking and FMBI in NAFLD: FMBI exerts a stronger effect in non-smokers, and FMR and smoking exhibit a synergistic additive effect, with over one-third of the excess NAFLD risk in co-exposed individuals attributable to their interaction. These findings provide novel mechanistic insights into NAFLD pathogenesis and highlight the need for targeted interventions in high-FMBI smokers to reduce NAFLD and its comorbidity risks.\u003c/p\u003e \u003cp\u003eThis study possesses several key strengths, including the development of a novel composite index (FMBI) integrating body composition and overall obesity, along with the application of rigorous covariate selection methods (univariate regression and Boruta algorithm) to minimize confounding. Furthermore, the linear dose-response relationship between FMBI and NAFLD was validated using RCS analysis, and interaction effects were assessed on both multiplicative and additive scales. Finally, the robustness of our findings to potential unmeasured confounding was evaluated using E-value analysis.However, the study has inevitable limitations that should be acknowledged: (1) the retrospective cross-sectional design only confirms a correlation, not a causal relationship, between FMBI and NAFLD and its complications; (2) objective biases in indicator detection (e.g., BIA-based body composition analysis, ultrasound-based NAFLD diagnosis) may affect the accuracy of results; (3) the single-center study design introduces potential selection bias; (4) the study population is exclusively Chinese, limiting the generalizability of findings to other ethnic groups.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study demonstrates a significant linear association between FMBI and NAFLD, with NAFLD risk increasing markedly as FMBI rises\u0026mdash;particularly when FMBI\u0026thinsp;\u0026ge;\u0026thinsp;10\u0026mdash;and smoking exacerbates this association through additive synergy. As a simple and easy-to-calculate screening tool, FMBI enables large-scale NAFLD screening and accurately identifies sarcopenic NAFLD subtypes that are easily missed by BMI alone. Additionally, our findings provide a solid evidence base for targeted interventions in high-FMBI individuals with smoking exposure to reduce the risks of NAFLD and its associated hepatic fibrosis and ASCVD. Future multicenter, multi-ethnic, large-sample prospective studies are warranted to validate the causal relationship between FMBI and NAFLD, establish ethnic-specific optimal cut-off values, and explore the value of FMBI in guiding NAFLD treatment and monitoring treatment response.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics statement and consent to participate declarations\u003c/h2\u003e\n\u003cp\u003eThe study involved the Publicly open-data from the Dryad digital repository including raw available clinical information data collected by other research teams from various countries and hospitals, has been used and analyzed by some studies. This data was from individuals who underwent routine health check-ups at Wuhan Union Hospital from January 2020 to November 2021, and the study protocol was approved by the Ethics Committee of Wuhan Tongji Medical College (S155). The committee waived the need for individual informed consent because all medical data were retrospectively reviewed and analysed anonymously.\u003c/p\u003e\n\u003ch2\u003eSupplementary Materials\u003c/h2\u003e\n\u003cp\u003eThe following supporting information can be downloaded at: Supplementary Figures and Tables.\u003c/p\u003e\n\u003ch2\u003eConflicts of Interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis project was funded by Key Research and Development Plan Projects in Shaanxi Province(2021SF-133).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eXiaokang Wu: Conceptualization, Methodology, Investigation, Writing - Original Draft, Writing - Review \u0026amp; Editing. Jiafeng Yin and Yue Li: Software, Formal Analysis and Data Curation. Chaoliang Xiong: Investigation, Resources. Hailong Liu and Hao Meng: Investigation. Xinjiang Hou: Visualization. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eThe authors would like to thank the investigators at Tongji Medical College of Huazhong University of Science and Technology for sharing their data.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eData are available in a public, open access repository. The datasets analyzed in this study are openly available in the Dryad Digital Repository ( [https://doi.org/10.5061/dryad.7d7wm3809](https:/doi.org/10.5061/dryad.7d7wm3809) ) 28 . The following supporting information can be downloaded at: Supplementary Figures and Tables.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHan SK, Baik SK, Kim MY. Non-alcoholic fatty liver disease: Definition and subtypes. Clin Mol Hepatol. 2022;29:S5\u0026ndash;16.\u003c/li\u003e\n\u003cli\u003ePowell EE, Wong VW-S, Rinella M. 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Cells 2023;12.\u003c/li\u003e\n\u003cli\u003eMaliszewska K, Adamska-Patruno E, Kretowski A. The interplay between muscle mass decline, obesity, and type 2 diabetes. Pol Arch Intern Med. 2019;129:809\u0026ndash;16.\u003c/li\u003e\n\u003cli\u003eDey P. The emerging phenotype of nonalcoholic fatty liver disease in lean individuals: what\u0026rsquo;s different? Front Endocrinol (Lausanne). 2025;16:1693123.\u003c/li\u003e\n\u003cli\u003eMerli M, Lattanzi B, Aprile F. Sarcopenic obesity in fatty liver. Curr Opin Clin Nutr Metab Care. 2019;22:185\u0026ndash;90.\u003c/li\u003e\n\u003cli\u003eHanna DJ, Jamieson ST, Lee CS, et al. Bioelectrical impedance analysis in managing sarcopenic obesity in NAFLD. Obes Sci Pract. 2021;7:629\u0026ndash;45.\u003c/li\u003e\n\u003cli\u003eWijarnpreecha K, Panjawatanan P, Aby E, et al. Nonalcoholic fatty liver disease in the over-60s: Impact of sarcopenia and obesity. Maturitas. 2019;124:48\u0026ndash;54.\u003c/li\u003e\n\u003cli\u003eIwaki M, Kobayashi T, Nogami A et al. Impact of Sarcopenia on Non-Alcoholic Fatty Liver Disease. Nutrients 2023;15.\u003c/li\u003e\n\u003cli\u003eJoo SK, Kim W. Interaction between sarcopenia and nonalcoholic fatty liver disease. Clin Mol Hepatol. 2022;29:S68\u0026ndash;78.\u003c/li\u003e\n\u003cli\u003eZambon Azevedo V, Silaghi CA, Maurel T, et al. Impact of Sarcopenia on the Severity of the Liver Damage in Patients With Non-alcoholic Fatty Liver Disease. Front Nutr. 2022;8:774030.\u003c/li\u003e\n\u003cli\u003eFerenc K, Jarmakiewicz-Czaja S, Filip R. What Does Sarcopenia Have to Do with Nonalcoholic Fatty Liver Disease? Life (Basel) 2024;14.\u003c/li\u003e\n\u003cli\u003eMikolasevic I, Pavic T, Kanizaj TF, et al. Nonalcoholic Fatty Liver Disease and Sarcopenia: Where Do We Stand? Can J Gastroenterol Hepatol. 2020;2020:8859719.\u003c/li\u003e\n\u003cli\u003eHsu C-S, Kao J-H. Management of non-alcoholic fatty liver disease in patients with sarcopenia. Expert Opin Pharmacother. 2021;23:221\u0026ndash;33.\u003c/li\u003e\n\u003cli\u003eMunsterman ID, Smits MM, Andriessen R, et al. Smoking is associated with severity of liver fibrosis but not with histological severity in nonalcoholic fatty liver disease. Results from a cross-sectional study. Scand J Gastroenterol. 2017;52:881\u0026ndash;5.\u003c/li\u003e\n\u003cli\u003eJang YS, Joo HJ, Park YS, et al. Association between smoking cessation and non-alcoholic fatty liver disease using NAFLD liver fat score. Front Public Health. 2023;11:1015919.\u003c/li\u003e\n\u003cli\u003eJoe H, Oh J-E, Cho Y-J, et al. Association between smoking status and non-alcoholic fatty liver disease. PLoS ONE. 2025;20:e0325305.\u003c/li\u003e\n\u003cli\u003eXu J, Li Y, Feng Z et al. Cigarette Smoke Contributes to the Progression of MASLD: From the Molecular Mechanisms to Therapy. Cells 2025;14.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-gastroenterology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmge","sideBox":"Learn more about [BMC Gastroenterology](http://bmcgastroenterol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmge/default.aspx","title":"BMC Gastroenterology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Fat-Muscle-BMI Index (FMBI), Non-alcoholic fatty liver disease (NAFLD), Hepatic fibrosis, Atherosclerotic cardiovascular disease","lastPublishedDoi":"10.21203/rs.3.rs-9304928/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9304928/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eNon-alcoholic fatty liver disease (NAFLD) is a global burden with unmet non-invasive biomarker needs; BMI and FMRs have limitations, and their combined index FMBI is unstudied. We aim to explore FMBI\u0026rsquo;s association with NAFLD, associated liver fibrosis and atherosclerotic cardiovascular disease risk(ASCVD) risk.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective study enrolled 1592 eligible participants aged 40\u0026ndash;79 years from a Chinese hospital. The Fat-Muscle-BMI Index (FMBI) was calculated as Fat-to-muscle ratio (FMR) \u0026times; Body mass index (BMI). Multivariable logistic/linear regression with three hierarchical models analyzed FMBI\u0026rsquo;s associations with NAFLD, hepatic fibrosis and ASCVD; restricted cubic splines༈RCS, subgroup and sensitivity/E-value analyses validated the findings.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eHigher FMBI quartiles exhibited a progressive increase in NAFLD prevalence (46.0% to 72.4%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). FMBI demonstrated a significant linear dose-response relationship with NAFLD (OR\u0026thinsp;=\u0026thinsp;1.20 per unit increment, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with risk rising markedly as FMBI increased\u0026mdash;particularly when FMBI\u0026thinsp;\u0026ge;\u0026thinsp;10. A significant additive interaction between FMR and smoking was observed (AP\u0026thinsp;=\u0026thinsp;0.37, P\u0026thinsp;=\u0026thinsp;0.010). Additionally, FMBI independently predicted ASCVD risk (β\u0026thinsp;=\u0026thinsp;0.32, P\u0026thinsp;=\u0026thinsp;0.001) and hepatic fibrosis (OR\u0026thinsp;=\u0026thinsp;1.79 for Q4 vs. Q1, P\u0026thinsp;=\u0026thinsp;0.008) in NAFLD patients. E-value analysis confirmed the robustness of these findings to unmeasured confounding.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study identifies FMBI as a robust NAFLD biomarker exhibiting linear dose-response, additive synergy with smoking, and independent prediction of hepatic fibrosis and ASCVD risk, supporting its use in targeted NAFLD management.\u003c/p\u003e","manuscriptTitle":"Association of Fat-Muscle-Body Mass Index (FMBI) with Non-Alcoholic Fatty Liver Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-19 12:38:22","doi":"10.21203/rs.3.rs-9304928/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-16T05:12:02+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-15T14:35:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-15T03:10:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"189774445946207400894199644170089082170","date":"2026-04-14T18:34:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"332866121221532895041082822042845358506","date":"2026-04-13T13:45:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"93614482985160636091174650758756021639","date":"2026-04-13T13:36:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-13T13:21:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"232529770480468636881166748284914541639","date":"2026-04-13T06:11:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-13T06:04:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-10T05:45:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-10T05:16:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-10T05:15:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Gastroenterology","date":"2026-04-02T15:10:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-gastroenterology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmge","sideBox":"Learn more about [BMC Gastroenterology](http://bmcgastroenterol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmge/default.aspx","title":"BMC Gastroenterology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"15fc233f-9135-4190-936a-7c09235c29c4","owner":[],"postedDate":"April 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-19T08:53:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-19 12:38:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9304928","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9304928","identity":"rs-9304928","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}