Clínica Universidad de Navarra Body Adiposity Estimator and Risk of Incident Metabolic Dysfunction–Associated Steatotic Liver Disease in Non‑Obese Chinese Adults: A Prospective Cohort Study with the Triglyceride–Glucose Index as a Partial Mediator

Clínica Universidad de Navarra Body Adiposity Estimator and Risk of Incident Metabolic Dysfunction–Associated Steatotic Liver Disease in Non‑Obese Chinese Adults: A Prospective Cohort Study with the Triglyceride–Glucose Index as a Partial Mediator | 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 Article Clínica Universidad de Navarra Body Adiposity Estimator and Risk of Incident Metabolic Dysfunction–Associated Steatotic Liver Disease in Non‑Obese Chinese Adults: A Prospective Cohort Study with the Triglyceride–Glucose Index as a Partial Mediator Xueyan Wu, Rong Zhang, Shenglian Gan, Haifeng Zhou, Fang Yu, Jian Luo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7751064/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Background While metabolic dysfunction-associated steatotic liver disease (MASLD) increasingly affects non-obese individuals, current screening approaches show poor performance in this population. We investigated whether the Clínica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE) could better identify MASLD risk than traditional measures in non-obese adults, and examined how the triglyceride-glucose (TyG) index might mediate this relationship. Methods Using data from the Dryad public database, we followed 16,173 Chinese non-obese adults (BMI < 25 kg/m²) without baseline MASLD for 5 years. MASLD diagnosis relied on abdominal ultrasonography. We applied multivariable logistic regression to assess cross-sectional associations and Cox models for incident disease risk. Restricted cubic splines revealed dose-response patterns in sex-stratified analyses, while structural equation modeling quantified TyG index mediation effects. Results Our cohort included 8,483 men and 7,690 women. After full adjustment, each standard deviation increased in CUN-BAE linked to 35% higher MASLD risk (HR = 1.35, 95% CI: 1.29–1.41, P < 0.001). Comparing top versus bottom tertiles showed 95% increased risk (HR = 1.95, 95% CI: 1.74–2.18, P < 0.001). Five-year cumulative incidence rose from 8.4% (lowest tertile) to 15.8% (middle) to 18.9% (highest tertile, Log-rank P < 0.0001). Cubic spline analysis uncovered sex differences: women showed a sharp risk increase above CUN-BAE 31.2, while men displayed more gradual, linear patterns. The TyG index accounted for 24.7% of the CUN-BAE-MASLD association (P < 0.001). Conclusions CUN-BAE effectively predicts MASLD development in Chinese non-obese adults through clear dose-response relationships that differ by sex. Since TyG index only partially explains this association, insulin resistance appears important but insufficient to account for the full relationship. CUN-BAE could serve as a practical screening tool to identify high-risk individuals missed by conventional BMI-based approaches, enabling more precise risk stratification in non-obese populations. Health sciences/Diseases Health sciences/Endocrinology Health sciences/Gastroenterology Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors Metabolic dysfunction-associated steatotic liver disease(MASLD) Clínica Universidad de Navarra-Body Adiposity Estimator(CUN-BAE) Non-obese Triglyceride-glucose index(TyG) Cohort study Figures Figure 1 Figure 2 Figure 3 1. Introduction Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly called non-alcoholic fatty liver disease (NAFLD), has emerged as the leading cause of chronic liver disease worldwide, affecting roughly one-quarter to one-third of the global population and posing substantial challenges for healthcare systems[ 1 , 2 ]. Beyond its direct hepatic consequences including cirrhosis and hepatocellular carcinoma, MASLD intertwines with cardiovascular disease, type 2 diabetes, and chronic kidney disease, creating a web of comorbidities that elevate patient mortality while straining healthcare resources[ 3 , 4 ]. While MASLD was historically viewed as a disease of obesity, growing evidence reveals that many patients maintain normal body mass index (BMI) values, with this phenomenon particularly pronounced in Asian populations where 7–20% of individuals develop MASLD despite normal weight status[ 5 – 7 ]. These non-obese patients present a clinical paradox: they typically show fewer traditional metabolic risk factors—including lower rates of diabetes, hypertension, and dyslipidemia—yet often develop more severe liver fibrosis and face higher cardiovascular mortality compared to their obese counterparts[ 8 , 9 ]. This counterintuitive pattern creates diagnostic challenges, as clinicians may dismiss MASLD risk in normal-weight individuals, potentially delaying critical interventions and missing early treatment windows[ 10 , 11 ]. MASLD diagnosis still depends heavily on imaging studies and liver biopsy, yet these approaches face practical barriers including high costs, limited availability, and variable reproducibility across different settings[ 12 , 13 ]. Simple, cost-effective screening tools would therefore fill a critical gap in early MASLD detection, especially among non-obese individuals where the need for accessible risk assessment has become increasingly urgent [ 14 ]. The Clínica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE) calculates body fat content using age, sex, and BMI parameters, with validation studies confirming its accuracy in Asian populations[ 15 ]. Unlike conventional BMI measurements, CUN-BAE captures individual variations in fat distribution and shows superior ability to detect "hidden obesity"—cases where normal BMI masks elevated body fat percentage[ 16 , 17 ]. These features make CUN-BAE particularly valuable for metabolic risk assessment in non-obese individuals, where traditional anthropometric measures often fall short. Growing research has established the triglyceride-glucose index (TyG) as a robust surrogate for insulin resistance, making it increasingly valuable in metabolic disease risk assessment[ 18 , 19 ]. Evidence suggests that TyG index correlates strongly with both MASLD development and progression, potentially bridging abnormal body fat distribution with hepatic steatosis[ 20 , 21 ]. Yet among non-obese individuals, the relationship between CUN-BAE and MASLD remains poorly understood, and TyG index mediating role in this population requires investigation. Although studies have explored relationships between body fat assessment indicators and MASLD, existing research has significant limitations including predominantly cross-sectional designs that cannot establish causal relationships and focus mainly on obese populations with insufficient attention to non-obese individuals[ 22 , 23 ]. Moreover, there is a lack of large-sample, long-term longitudinal cohort studies to validate predictive efficacy in clinical practice. Prospective cohort designs offer the temporal framework needed to establish exposure-outcome sequences, strengthening causal inference beyond cross-sectional observations. Non-obese populations warrant dedicated investigation, as their MASLD risk profiles and progression trajectories may diverge substantially from patterns seen in obese cohorts. Using longitudinal cohort data from non-obese populations, we sought to evaluate CUN-BAE's association with MASLD prevalence and incidence, examine dose-response patterns and sex-specific differences, and determine whether TyG index mediates this relationship. We anticipated that CUN-BAE would demonstrate predictive capacity for MASLD risk in non-obese individuals, with dose-dependent associations that vary by sex and partial mediation through insulin resistance pathways. These findings could inform risk stratification approaches and targeted screening strategies in populations traditionally considered metabolically low-risk. 2. Methods 2.1 Study Design and Data Sources This study is a secondary analysis based on a public database, employing a longitudinal cohort design. The original data were sourced from the Dryad public database (repository at http://datadryad.org/ with the doi: 10.5061/dryad.1n6c4[24] in compliance with Dryad's terms of service, with data freely available for researchers. The study population comprised 16,173 non-obese participants without MASLD at baseline, with a prospective 5-year follow-up period. The original study protocol was approved by the Ethics Committee of Wenzhou People's Hospital, and all participants provided informed consent. As a secondary analysis of a publicly available dataset, this study did not require additional ethical review. 2.2 Study Population and Inclusion/Exclusion Criteria The study population consisted of non-obese Chinese adults. Baseline exclusion criteria were as follows: (1) incomplete clinical data or loss to follow-up; (2) current use of oral antihypertensive, lipid-lowering, or antidiabetic medications; (3) excessive alcohol consumption (> 140 g/week for men, > 70 g/week for women); (4) presence of MASLD, autoimmune hepatitis, viral hepatitis, or chronic liver disease of known etiology; (5) low-density lipoprotein cholesterol > 3.12 mmol/L; (6) BMI ≥ 25 kg/m². 2.3 Data Collection Medical history and lifestyle habits were collected through questionnaire surveys conducted by trained physicians. Blood pressure measurements were obtained using automated sphygmomanometers in a quiet environment. Height and weight were measured with participants wearing light clothing and no shoes. Fasting blood samples were collected in the early morning after overnight fasting to assess biochemical parameters, including alanine aminotransferase (ALT), aspartate aminotransferase (AST), total protein (TP), albumin (ALB), globulin (GLB), creatinine (Cr), blood urea nitrogen (BUN), fasting plasma glucose (FPG), uric acid (UA), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) levels. 2.4 Definitions and Variable Calculations 2.4.1 CUN-BAE Index = (0.00021 × BMI 2 × age) - (0.005 × BMI 2 × sex) - (0.02 × BMI × age) + (0.181 × BMI × sex) − (0.026 × BMI 2 ) + (3.172 × BMI) + (10.689 × sex) + (0.503 × age) − 44.988; where sex is defined as male = 0, female = 1[ 25 ]; 2.4.2 BMI (kg/m²) = weight (kg)/height² (m²); 2.4.3 TyG = Ln[FBG (mg/dL) ×TG (mg/dL)/2]; 2.4.4 MASLD Diagnostic Criteria MASLD was diagnosed by abdominal ultrasonography according to the standards recommended by the Chinese Society of Hepatology. MASLD was defined as the presence of at least 2 of the following 3 abnormal ultrasound findings: (1) diffuse enhancement of liver near-field echoes ("bright liver"); (2) liver echogenicity stronger than kidney echogenicity; (3) vascular blurring and gradual attenuation of far-field echoes. 2.5 Statistical Analysis Missing data were handled by excluding variables with > 25% missing values and imputing those with < 25% missing values using random forest methodology. Sensitivity analyses were conducted before and after imputation to ensure data stability (Supplementary Table 1). Participants were categorized into tertiles based on CUN-BAE values for descriptive analysis, with CUN-BAE standardized using Z-scores for regression analyses. Continuous variables are presented as mean ± standard deviation. Between-group comparisons were performed using one-way ANOVA or non-parametric tests for continuous variables. Cumulative incidence of MASLD was estimated using the Kaplan-Meier method with log-rank tests comparing survival distributions between CUN-BAE tertiles. Cox proportional hazards regression models assessed associations between standardized CUN-BAE and incident MASLD using progressive adjustment: Model 1 adjusted for liver function markers (ALT, AST, TP, ALB, GLB); Model 2 additionally adjusted for renal function markers (BUN, CR, UA) based on Model 1; Model 3 further added lipid profiles (TC, TG, HDL-C, LDL-C) to Model 2; and Model 4 additionally incorporated metabolic parameters and blood pressure (FBG, SBP, DBP) to Model 3. Restricted cubic spline regression examined non-linear dose-response relationships with optimal knot placement determined using Akaike Information Criterion, with sex-stratified analyses to explore interaction patterns. Stratified analyses across demographic and clinical subgroups tested effect modification using likelihood ratio tests. Mediation analysis evaluated TyG index as a potential mediator using structural equation modeling, with bootstrap resampling (n = 1,000 iterations) to assess statistical significance of mediation effects and calculate mediation proportions. Sensitivity analyses included comparison of complete case versus imputed data analyses, evaluation of association stability across adjustment models, and alternative CUN-BAE categorization approaches to assess result robustness. All analyses were performed using R software version 4.2.0 and EmpowerStats version 4.0, with two-sided P-values < 0.05 considered statistically significant. 3. Results 3.1 Baseline Characteristics of Study Population This study included 16,173 participants, comprising 8,483 males and 7,690 females. Based on the presence of MASLD, participants were categorized into MASLD and non-MASLD groups. During the 5-year follow-up period, 1,294 males (15.25%) and 1,028 females (13.37%) were diagnosed with MASLD.As shown in Table 1 , for both males and females, the MASLD group demonstrated significantly higher age, weight, height, BMI, systolic blood pressure (SBP), and diastolic blood pressure (DBP) compared to the non-MASLD group (all P < 0.001). Regarding biochemical parameters, the MASLD group exhibited significantly elevated levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), creatinine (CR), uric acid (UA), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and fasting plasma glucose (FPG), while high-density lipoprotein cholesterol (HDL-C) levels were significantly decreased (all P < 0.001). In males, CUN-BAE was significantly higher in the MASLD group (21.82 ± 3.14) compared to the non-MASLD group (18.36 ± 4.35, P < 0.001). In females, CUN-BAE was similarly elevated in the MASLD group (32.93 ± 2.82) versus the non-MASLD group (29.36 ± 3.97, P < 0.001). Additionally, the triglyceride-glucose index (TyG) was significantly higher in the MASLD group (P < 0.001). For both sexes, no statistically significant differences were observed between MASLD and non-MASLD groups for total protein (TP), albumin (ALB), globulin (GLB), and blood urea nitrogen (BUN). Table 1 Characteristics of the subjects. Male Female MASLD No Yes P- value No Yes P -value* N 7189 1294 6662 1028 Age, years 45.55 ± 16.31 47.29 ± 16.26 < 0.001 40.20 ± 12.69 41.48 ± 12.85 < 0.001 Height, cm 164.79 ± 7.85 167.66 ± 7.63 < 0.001 163.27 ± 7.51 166.49 ± 7.80 < 0.001 Weight, kg 57.73 ± 8.39 65.48 ± 7.36 < 0.001 56.09 ± 7.87 64.23 ± 7.38 < 0.001 BMI, kg/m2 21.18 ± 2.02 23.23 ± 1.32 < 0.001 20.97 ± 1.96 23.11 ± 1.35 < 0.001 SBP, mmHg 120.47 ± 16.53 128.74 ± 16.32 < 0.001 118.43 ± 16.41 127.27 ± 15.63 < 0.001 DBP, mmHg 72.54 ± 10.26 78.31 ± 10.27 < 0.001 71.35 ± 9.94 77.21 ± 10.26 < 0.001 ALT, U/L 16.00 (13.00–19.00) 21.00 (16.00–30.00) < 0.001 16.00 (13.00–17.00) 21.00 (16.00–29.00) < 0.001 AST, U/L 21.00 (19.00–23.00) 23.00 (20.00–27.00) < 0.001 21.00 (19.00–22.00) 23.00 (20.00–27.00) < 0.001 TP, g/L 73.94 ± 3.92 73.97 ± 4.07 0.967 73.80 ± 4.07 74.12 ± 4.12 0.023 ALB, g/L 44.51 ± 2.66 44.49 ± 2.56 0.891 44.27 ± 2.51 44.48 ± 2.66 0.008 GLB, g/L 29.42 ± 3.65 29.46 ± 3.94 0.725 29.53 ± 3.68 29.63 ± 3.82 0.321 BUN, mmol/L 4.61 ± 1.38 4.64 ± 1.26 0.251 4.51 ± 1.38 4.56 ± 1.29 0.239 CR, µmo/L 79.08 ± 26.73 86.66 ± 18.40 < 0.001 75.27 ± 25.72 84.77 ± 21.83 < 0.001 UA, mmol/L 279.38 ± 84.80 330.27 ± 83.94 < 0.001 263.77 ± 80.95 323.29 ± 88.22 < 0.001 TC, mmol/L 4.59 ± 0.73 4.81 ± 0.82 < 0.001 4.60 ± 0.73 4.79 ± 0.73 < 0.001 TG, mmol/L 1.23 ± 0.82 2.04 ± 1.56 < 0.001 1.14 ± 0.62 1.92 ± 1.29 < 0.001 HDL-C, mmol/L 1.48 ± 0.36 1.29 ± 0.32 < 0.001 1.50 ± 0.36 1.31 ± 0.32 < 0.001 LDL-C, mmol/L 2.25 ± 0.47 2.40 ± 0.46 < 0.001 2.24 ± 0.46 2.40 ± 0.44 < 0.001 FPG, mmol/L 5.11 ± 0.71 5.46 ± 0.97 < 0.001 5.07 ± 0.74 5.44 ± 1.05 < 0.001 CUN-BAE 18.36 ± 4.35 21.82 ± 3.14 < 0.001 29.36 ± 3.97 32.93 ± 2.82 < 0.001 TYG 8.39 ± 0.49 8.93 ± 0.54 < 0.001 8.33 ± 0.48 8.87 ± 0.56 < 0.001 Values were expressed as mean (SD) or medians (Q1-Q3); ALB, Albumin; ALP, Alkaline phosphatase; ALT, Alanine aminotransferase; AST, Aspartate aminotransferase; BMI, Body mass index; BUN, Blood urea nitrogen; Cr, Creatinine; CUN-BAE, Clínica Universidad de Navarra-Body Adiposity Estimator; DBIL, Direct bilirubin; DBP, Diastolic blood pressure; FPG, Fasting plasma glucose; GGT, Gamma-glutamyl transferase; GLB, Globulin; HDL-C, High-density lipoprotein cholesterol; LDL-C, Low-density lipoprotein cholesterol; MASLD, Metabolic Dysfunction-Associated Steatotic Liver Disease; SBP, Systolic blood pressure; TB, Total bilirubin; TC, Total cholesterol; TG, Triglyceride; TP, Total Protein; TYG, Triglyceride-Glucose index; UA, Uric acid. 3.2 Association Analysis Between CUN-BAE and MASLD Prevalence Risk To evaluate the association between CUN-BAE z-score and MASLD prevalence risk, we employed multivariable logistic regression analysis. The analysis was based on the entire study population and constructed four progressively adjusted regression models to examine result robustness: Model 1 adjusted for liver function markers (ALT, AST, TP, ALB, GLB); Model 2 added renal function markers (BUN, CR, UA); Model 3 added lipid profiles (TC, TG, HDL-C, LDL-C); and Model 4 added metabolic parameters and blood pressure (FPG, SBP, DBP). Results are presented in Table 2 . When CUN-BAE was included as a continuous variable, it demonstrated significant positive associations with MASLD risk across all models (all P < 0.001). In the unadjusted model, each one-unit increase in CUN-BAE z-score was associated with a 51% increased risk of MASLD (HR = 1.51, 95% CI: 1.45–1.57, P < 0.001). In the fully adjusted Model 4, this strong association persisted, with each one-unit increase in CUN-BAE z-score associated with a 35% increased risk of MASLD (HR = 1.35, 95% CI: 1.29–1.41, P < 0.001). Subsequently, we conducted sensitivity analysis based on CUN-BAE z-score tertiles (Q1-Q3). In the fully adjusted Model 4, using the lowest CUN-BAE level (Q1) as reference, both Q2 and Q3 groups showed significantly elevated MASLD prevalence risk. The highest CUN-BAE group (Q3) demonstrated a 1.9-fold increased risk compared to Q1 (HR = 1.95, 95% CI: 1.74–2.18, P < 0.001). The trend test (P for trend) yielded P < 0.001, indicating a highly significant dose-response relationship between increasing CUN-BAE levels and MASLD prevalence risk. Table 2 Association between CUN-BAE and MASLD in different models Exposure MASLD, HR (95%CI) Crude model model 1 model 2 Model 3 Model 4 CUN-BAE z-score 1.5 1(1.45, 1.57) < 0.001 1.5 0(1.43, 1.56) < 0.001 1.4 4(1.38, 1.50) < 0.001 1.41 (1.35, 1.47) < 0.0001 1.35 (1.29, 1.41) < 0.0001 CUN-BAE z-score quartile Low 1.0 1.0 1.0 1.0 1.0 Middle 1.94 (1.73, 2.17) < 0.0001 1.96 (1.75, 2.20) < 0.0001 1.96 (1.75, 2.20) < 0.0001 1.95 (1.74, 2.19) < 0.0001 1.77 (1.58, 1.99) < 0.0001 High 2.30 (2.06, 2.57) < 0.0001 2.28 (2.04, 2.54) < 0.0001 2.18 (1.95, 2.43) < 0.0001 2.14 (1.91, 2.40) < 0.0001 1.95 (1.74, 2.18) < 0.0001 P for trend < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 Crude model adjusted for none; model 1 adjusted for: ALT; AST; TP; ALB; GLB model 2 adjusted for: ALT; AST; TP; ALB; GLB; BUN; CR; UA model 3 adjusted for: ALT; AST; TP; ALB; GLB; BUN; CR; UA; TC; TG; HDL-C; LDL-C model 4 adjusted for: ALT; AST; TP; ALB; GLB; BUN; CR; UA; TC; TG; HDL; LDL-C; FBG; SBP; DBP 3.3 Dose-Response Relationship Analysis Between CUN-BAE and MASLD Risk To further explore the dose-response relationship between CUN-BAE and MASLD prevalence risk, we employed restricted cubic spline regression analysis, which can identify potential non-linear associations and provide more precise risk assessment. Overall population analysis (Fig. 1 .A): Restricted cubic spline analysis revealed a significant non-linear dose-response relationship between CUN-BAE and MASLD risk, with increasing MASLD risk as CUN-BAE increased. Sex-stratified analysis (Fig. 1 .B) revealed distinctly different association patterns between CUN-BAE and MASLD risk across gender groups (threshold effect analysis detailed in Supplementary Table 2): In the female population (red solid line), the risk curve demonstrated rapid escalation at lower CUN-BAE values (< 31.2), followed by a relatively moderate upward trend beyond the 31.2 threshold, creating a pronounced "inflection point" effect. In contrast, the male population (blue dashed line) exhibited a steeper and more consistently linear upward trend across the entire CUN-BAE range compared to their female counterparts. 3.4 Cohort Analysis of CUN-BAE Predicting MASLD Incidence Risk To validate the predictive value of CUN-BAE for incident MASLD cases, we conducted prospective cohort analysis among participants without MASLD at baseline. Study subjects were categorized into low, middle, and high groups based on CUN-BAE tertiles, with 5,391 participants in each group. Cumulative incidence was analyzed using the Kaplan-Meier method over 60 months of follow-up (Fig. 2 ). Results demonstrated a significant dose-response relationship between CUN-BAE levels and incident MASLD risk (Log-rank test, P < 0.0001). At study completion, cumulative incidence rates for low, middle, and high CUN-BAE groups were approximately 8.4% (451 cases), 15.8% (853 cases), and 18.9% (1,018 cases), respectively. 3.5 Stratified Analysis of CUN-BAE-MASLD Association To evaluate the consistency of the CUN-BAE-MASLD association across different subgroups, we performed stratified analyses using standardized CUN-BAE z-scores (Table 3 ). All stratified groups showed CUN-BAE as a risk factor for MASLD (HR > 1). Age stratification revealed HRs of 1.45, 1.35, 1.29, and 1.21 for groups < 30, 30–45, 45–60, and ≥ 60 years, respectively, with no significant interaction (P interaction = 0.5852). In blood pressure stratification, diastolic blood pressure showed significant interaction (P interaction = 0.0340). Among metabolic parameters, uric acid (P interaction = 0.0007) and triglycerides (P interaction < 0.0001) demonstrated significant interactions. Table 3 The effect size of CUN-BAE on MASLD in prespecified and exploratory subgroups in each subgroup. CUN-BAE z-score N HR (95%CI) P-value P interaction Age, years 0.5852 < 30 3548 1.45 (1.31, 1.61) < 0.001 ≥ 30, < 45 6480 1.35 (1.26, 1.45) < 0.001 ≥ 45, < 60 3777 1.29 (1.19, 1.41) < 0.001 ≥ 60 2368 1.21 (1.07, 1.36) 0.002 SBP, mmHg 0.6367 < 140 14090 1.39 (1.32, 1.45) < 0.001 ≥ 140 2083 1.22 (1.11, 1.34) < 0.001 DBP, mmHg 0.0340 < 90 15024 1.37 (1.31, 1.44) < 0.001 ≥ 90 1149 1.17 (1.05, 1.31) 0.005 ALT, U/L 0.0790 < 40 15438 1.38 (1.32, 1.45) < 0.001 ≥ 40 735 1.19 (1.05, 1.35) 0.005 AST, U/L 0.1332 < 40 15804 1.34 (1.28, 1.40) < 0.001 ≥ 40 369 1.37 (1.12, 1.67) 0.002 UA, mmol/L 0.0007 < 360 13329 1.45 (1.37, 1.53) < 0.001 ≥ 360 2843 1.23 (1.14, 1.32) < 0.001 CR, µmo/L 0.7752 < 104 14759 1.35 (1.29, 1.41) < 0.001 ≥ 104 1413 1.31 (1.17, 1.47) < 0.001 TG, mmol/L < 0.0001 < 1.7 13090 1.48 (1.39, 1.57) < 0.001 ≥ 1.7 3083 1.18 (1.11, 1.26) < 0.001 HDL-C, mmol/L 0.0991 < 1.03 1647 1.24 (1.12, 1.37) < 0.001 ≥ 1.03 14526 1.38 (1.32, 1.45) < 0.001 TC, mmol/L 0.7185 < 5.2 12563 1.34 (1.28, 1.41) < 0.001 ≥ 5.2 3610 1.28 (1.18, 1.38) < 0.001 FPG, mmol/L 0.7257 < 6.1 15219 1.36 (1.30, 1.43) < 0.001 ≥ 6.1 953 1.26 (1.10, 1.43) < 0.001 3.6 Mediating Effect of TyG Index in the CUN-BAE-MASLD Association To investigate whether the TyG index mediates the association between CUN-BAE and MASLD, we performed mediation analysis (Fig. 3 ). Results demonstrated that CUN-BAE had a significant positive predictive effect on the TyG index (β = 0.022, P < 0.001). The TyG index also showed significant association with MASLD (β = 0.022, P < 0.001). After controlling for the TyG index, CUN-BAE maintained a significant direct effect on MASLD (β = 0.068, P < 0.001). Mediation analysis revealed that the TyG index served as a partial mediator in the CUN-BAE-MASLD association, with a mediation proportion of 24.7%. Bootstrap testing confirmed the statistical significance of this mediation effect (P < 0.001). 4. Discussion This large prospective cohort study of 16,173 Chinese non-obese adults provides compelling evidence that CUN-BAE serves as an independent predictor of MASLD risk, with TyG index partially mediating this association. Our key findings demonstrate that higher CUN-BAE levels are associated with increased MASLD prevalence and incidence, exhibiting clear dose-response relationships and distinct sex-specific patterns. Specifically, each one-unit increase in standardized CUN-BAE was associated with a 35% increased risk of prevalent MASLD (OR = 1.35, 95% CI: 1.29–1.41), with the high CUN-BAE group showing a cumulative incidence of 18.9% over 5 years of follow-up. These findings challenge the traditional paradigm that normal BMI equates to metabolic health, revealing the existence of subclinical metabolic dysfunction among apparently healthy individuals[ 26 , 27 ]. CUN-BAE, by integrating age, sex, and BMI information, provides a more refined assessment of body fat distribution, effectively identifying individuals who have normal BMI but relatively high fat content[ 28 ]. The observed dose-response relationship confirms the continuous nature of adiposity-related risk, suggesting that even modest increases in body fat within the non-obese range can trigger pathological processes leading to hepatic steatosis. The most striking finding was the significant sex-specific patterns revealed by restricted cubic spline analysis. Women showed a distinct risk inflection point at CUN-BAE 31.2, while men exhibited a more linear dose-response relationship. This sex-specific pattern reflects complex biological mechanisms involving hormonal regulation, fat distribution patterns, and metabolic adaptation differences. The protective effects of estrogen on fat metabolism may effectively operate at lower adiposity levels by promoting subcutaneous fat distribution and improving insulin sensitivity[ 29 – 31 ]. However, once total adiposity reaches a critical threshold, this protective mechanism may become compromised, potentially contributing to ectopic fat deposition. The "threshold effect" observed in women may represent a critical tipping point where metabolic buffering capacity is exceeded, resulting in accelerated metabolic deterioration[ 32 , 33 ]. Our mediation analysis revealed that TyG index mediated 24.7% of the CUN-BAE-MASLD association, indicating that insulin resistance represents an important but non-exclusive mechanistic pathway. The TyG index, as a well-validated surrogate marker of insulin resistance, provides crucial mechanistic insights into the CUN-BAE-MASLD relationship[ 19 , 34 ]. Insulin resistance represents a central pathophysiological mechanism linking excessive adiposity to hepatic steatosis through multiple pathways, including enhanced hepatic de novo lipogenesis, increased lipolysis from peripheral adipose tissue, and impaired hepatic fatty acid oxidation[ 35 ]. Therefore, our finding that TyG index partially mediates 24.7% of the CUN-BAE-MASLD association suggests that the increased MASLD risk observed in non-obese individuals with higher CUN-BAE values is, in part, attributable to insulin resistance-mediated metabolic dysfunction. This mechanistic understanding is particularly important in non-obese populations, where traditional obesity markers may fail to capture underlying metabolic abnormalities that predispose to hepatic steatosis. This partial mediation suggests that while insulin resistance plays a central role in pathophysiology, other mechanisms also contribute significantly to this relationship [ 36 , 37 ]. The remaining 75.3% non-mediated effect may involve direct lipotoxic effects, gut-liver axis disruption, chronic low-grade inflammation, dysregulated adipokine secretion, and epigenetic modifications related to metabolic stress[ 38 , 39 ]. This mechanistic complexity emphasizes the multifactorial nature of MASLD pathogenesis in non-obese individuals. Comparison with existing literature reveals both important consistencies and novel findings. The MASLD incidence rates observed in our study (8.4%-18.9%) are generally consistent with the 7–20% range reported in Western studies among lean populations[ 8 , 10 ]; however, our study demonstrated more definitive dose-response gradients and sex-specific patterns. This consistency supports the potential utility of CUN-BAE as a cross-ethnic risk assessment tool, while the refined risk stratification observed may reflect the application of more sensitive identification methods. Notably, Asian populations tend to exhibit higher body fat percentages and visceral fat proportions at equivalent BMI levels, along with relatively lower insulin secretory capacity, which may explain why significant risk differences can be identified in non-obese populations using the refined CUN-BAE indicator[ 17 , 40 ]. Our study extends previous research by specifically examining non-obese individuals using refined body composition indicators, revealing risk patterns and sex-specific characteristics that traditional anthropometric indices might overlook. The clinical implications of these findings are substantial. Current clinical practice often underestimates MASLD risk in BMI-normal individuals, potentially leading to delayed diagnosis and missed prevention opportunities[ 41 , 42 ]. CUN-BAE, calculated simply from readily available clinical parameters, can serve as a practical screening tool for identifying high-risk non-obese individuals. The identified sex-specific risk patterns and dose-response relationships provide crucial guidance for individualized risk assessment strategies. The critical inflection point observed in women suggests that targeted screening for females approaching this threshold may be particularly beneficial. Furthermore, the partial mediating role of TyG index indicates that interventions targeting insulin resistance may effectively reduce MASLD risk, although multi-target approaches addressing non-mediated pathways may be necessary for optimal prevention [ 43 , 44 ]. From a public health perspective, the simplicity of CUN-BAE calculation makes it potentially applicable for widespread implementation in primary healthcare settings, facilitating early identification and stratified management of MASLD. Several limitations warrant acknowledgment. First, MASLD diagnosis was based on ultrasonographic rather than histological assessment, which may result in some degree of misclassification and inability to distinguish simple steatosis from steatohepatitis. Second, our study population consisted entirely of Chinese adults from a single geographic region, which may limit generalizability to other ethnic groups and populations with different genetic backgrounds, environmental exposures, and lifestyle patterns. Third, despite comprehensive adjustment for potential confounders, residual confounding from unmeasured factors such as genetic susceptibility, gut microbiome composition, sleep quality, and psychological stress cannot be completely excluded. Fourth, while the 5-year follow-up period is adequate for epidemiological research, it may not capture long-term disease progression patterns or account for dynamic changes in body composition and lifestyle factors over extended periods. Finally, the observational nature of our study, despite its prospective design, limits definitive causal inference due to potential reverse causality, unmeasured confounding, and selection bias. Future research should prioritize several key areas. Validation studies in diverse ethnic populations are crucial for establishing the generalizability of CUN-BAE as a MASLD predictor across different demographic groups. Multi-omics integration studies combining genomics, transcriptomics, metabolomics, and microbiomics data could provide deeper insights into the biological networks underlying the CUN-BAE-MASLD association[ 45 , 46 ]. Intervention studies examining whether CUN-BAE-guided lifestyle modifications can effectively reduce MASLD risk will provide critical evidence for clinical implementation. Additionally, development of artificial intelligence-driven risk prediction models integrating multiple risk factors could enhance precision medicine approaches. Longitudinal studies with extended follow-up periods and repeated measurements will better characterize disease trajectories and identify optimal intervention windows. Of particular interest, given our findings of sex-specific differences, future intervention studies should incorporate sex-specific prevention strategies, exploring intervention effect heterogeneity at different CUN-BAE thresholds. These findings have important implications for clinical practice and public health policy. The paradigm shift from simple BMI-based classification toward sophisticated body composition assessment represents a critical advancement in precision medicine approaches for metabolic disease prevention. The identification of sex-specific risk thresholds provides actionable guidance for clinicians to implement targeted screening strategies, particularly in populations traditionally considered low-risk. Moreover, the partial mediating role of insulin resistance suggests that interventions targeting glucose-lipid metabolism may be effective, while the substantial non-mediated component indicates that comprehensive multi-pathway approaches will likely be necessary for optimal prevention outcomes. 5. Conclusions This prospective cohort study establishes CUN-BAE as a robust predictor of MASLD risk in Chinese non-obese adults, demonstrating clear dose-response relationships and distinct sex-specific patterns. With TyG index serving as a partial mediator of this association, our findings reveal that insulin resistance represents an important but non-exclusive pathological mechanism. CUN-BAE offers a practical, clinically accessible tool for identifying high-risk individuals who may be overlooked by conventional BMI screening, supporting precision-based early detection and risk stratification in non-obese populations. Abbreviations ALB - Albumin HDL-C - High-density lipoprotein cholesterol ALT - Alanine aminotransferase HR - Hazard ratio AST - Aspartate aminotransferase LDL-C - Low-density lipoprotein cholesterol BMI - Body mass index NAFLD - Non-alcoholic fatty liver disease BUN - Blood urea nitrogen SBP - Systolic blood pressure CI - Confidence interval TC - Total cholesterol CR - Creatinine TG - Triglycerides DBP - Diastolic blood pressure TP - Total protein FBG - Fasting blood glucose TyG - Triglyceride-glucose index FPG - Fasting plasma glucose UA - Uric acid GLB - Globulin CUN-BAE - Clínica Universidad de Navarra-Body Adiposity Estimator MASLD - Metabolic dysfunction-associated steatotic liver disease Declarations Data Availability Statement The datasets analyzed during the current study were originally obtained from the Dryad Digital Repository (previously available at doi:10.5061/dryad.1n6c4). Due to technical issues with the original repository link, the complete dataset and analysis code are available from the corresponding author upon reasonable request and with appropriate data use agreements. All data handling and analysis procedures comply with relevant ethical guidelines and institutional policies, and data access will be provided in accordance with BMC's data sharing policies. Ethical Considerations and Participant Consent This study was conducted in accordance with the Declaration of Helsinki and its later amendments. The original research protocol received ethical approval from the Ethics committee of Wenzhou People's Hospital. All study participants provided written informed consent according to ethical standards for human subject research. As this investigation represents a secondary analysis of publicly available, anonymized data, no additional ethical approval was required for the current study. Author Contributions The study's conceptual framework and design were developed by Jian Luo. Data analysis and result interpretation were undertaken by Xueyan Wu and Shenglian Gan. The initial manuscript draft was collaboratively prepared by Xueyan Wu, Fang Yu and Rong Zhang. Data verification procedures were conducted by Haifeng Zhou. The manuscript underwent comprehensive review and revision by all authors, who collectively approved the final submitted version. Jian Luo takes overall responsibility for the manuscript. Acknowledgements We acknowledge the contributions of all investigators involved in the original study. Funding Declaration This research was financially supported by the Science and Technology Department of Hunan Province under Grant No. 2023SK4090. The funding agency played no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Declaration of Interests The authors collectively declare no conflicts of interest or competing financial relationships that could potentially influence the research findings or manuscript preparation. AI Assistance Disclosure: The authors acknowledge the use of artificial intelligence translation tools to assist in translating this manuscript from Chinese to English. All content was subsequently reviewed, edited, and verified by the authors to ensure scientific accuracy and linguistic quality. References Riazi K, Azhari H, Charette JH, Underwood FE, King JA, Afshar EE, Swain MG, Congly SE, Kaplan GG, Shaheen AA: The prevalence and incidence of NAFLD worldwide: a systematic review and meta-analysis . Lancet Gastroenterol Hepatol 2022, 7 (9):851-861. Younossi ZM, Golabi P, Paik JM, Henry A, Van Dongen C, Henry L: The global epidemiology of nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH): a systematic review . Hepatology 2023, 77 (4):1335-1347. 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Supplementary Files SupplementaryTable1.Sensitivityanalysisbeforeinterpolation.docx SupplementaryTable2.Thresholdeffectanalysis.docx rawdata.sav Dataprocessingandanalysiscode.zip Cite Share Download PDF Status: Published Journal Publication published 02 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 06 Nov, 2025 Reviews received at journal 04 Nov, 2025 Reviews received at journal 04 Nov, 2025 Reviewers agreed at journal 07 Oct, 2025 Reviewers agreed at journal 07 Oct, 2025 Reviewers invited by journal 07 Oct, 2025 Editor invited by journal 07 Oct, 2025 Editor assigned by journal 04 Oct, 2025 Submission checks completed at journal 03 Oct, 2025 First submitted to journal 30 Sep, 2025 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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11:42:30","extension":"xml","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":149415,"visible":true,"origin":"","legend":"","description":"","filename":"41b6ac27d3e54d708aee62d7f1d1cccb1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7751064/v1/7c7cdad2ec191aab9b3d2d82.xml"},{"id":93770367,"identity":"e7755eab-0f8b-411b-a730-aa4e08958bde","added_by":"auto","created_at":"2025-10-17 11:50:30","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":166644,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7751064/v1/4628cc0754c26c26258aad46.html"},{"id":93770140,"identity":"ac603972-7f87-4bf4-a087-fcd6f8179ca5","added_by":"auto","created_at":"2025-10-17 11:42:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":103785,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDose-response relationship between CUN-BAE and MASLD risk. (A) Non-linear association between CUN-BAE and log relative risk for MASLD in the overall population. (B) Sex-stratified analysis of the CUN-BAE-MASLD association (red line: female; cyan dashed line: male).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7751064/v1/9ed7ebe88bf51e0969b25941.png"},{"id":93770144,"identity":"357c58b4-59c8-4309-a047-37efbbe046ff","added_by":"auto","created_at":"2025-10-17 11:42:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":186455,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier survival curves for MASLD incidence by CUN-BAE tertiles.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7751064/v1/6a7a3510009f07bcd7335c26.png"},{"id":93771233,"identity":"f51a88b0-9371-477f-8b13-643eee80e62b","added_by":"auto","created_at":"2025-10-17 11:58:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":33458,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMediation analysis of the association between CUN-BAE and MASLD through TYG.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7751064/v1/8fc1e516e781f83d35b82bd4.png"},{"id":97723922,"identity":"c47853ce-a2b6-436b-afc5-5ebe0fbc1cff","added_by":"auto","created_at":"2025-12-08 16:09:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5208374,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7751064/v1/d75b78a0-e5d4-4fd6-b244-9a84602c9751.pdf"},{"id":93770364,"identity":"120bf9d4-36db-474e-a750-0491b081ac86","added_by":"auto","created_at":"2025-10-17 11:50:30","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":17910,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.Sensitivityanalysisbeforeinterpolation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7751064/v1/a223c4c7d00f434ada3e4139.docx"},{"id":93770361,"identity":"33608d43-7a4f-4720-96cf-a20b8374987c","added_by":"auto","created_at":"2025-10-17 11:50:30","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17938,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.Thresholdeffectanalysis.docx","url":"https://assets-eu.researchsquare.com/files/rs-7751064/v1/d7a3f883cff30b291e6731d8.docx"},{"id":93770160,"identity":"6e57e91b-cae0-48d3-8096-248f411563ac","added_by":"auto","created_at":"2025-10-17 11:42:31","extension":"sav","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3457211,"visible":true,"origin":"","legend":"","description":"","filename":"rawdata.sav","url":"https://assets-eu.researchsquare.com/files/rs-7751064/v1/cc3d9dd065b88e54546bf8f7.sav"},{"id":93770161,"identity":"e462b099-5ddb-4f21-b3f2-fbeff4dec499","added_by":"auto","created_at":"2025-10-17 11:42:31","extension":"zip","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":30176219,"visible":true,"origin":"","legend":"","description":"","filename":"Dataprocessingandanalysiscode.zip","url":"https://assets-eu.researchsquare.com/files/rs-7751064/v1/4593c0b9c4041a890e005995.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clínica Universidad de Navarra Body Adiposity Estimator and Risk of Incident Metabolic Dysfunction–Associated Steatotic Liver Disease in Non‑Obese Chinese Adults: A Prospective Cohort Study with the Triglyceride–Glucose Index as a Partial Mediator","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMetabolic dysfunction-associated steatotic liver disease (MASLD), formerly called non-alcoholic fatty liver disease (NAFLD), has emerged as the leading cause of chronic liver disease worldwide, affecting roughly one-quarter to one-third of the global population and posing substantial challenges for healthcare systems[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Beyond its direct hepatic consequences including cirrhosis and hepatocellular carcinoma, MASLD intertwines with cardiovascular disease, type 2 diabetes, and chronic kidney disease, creating a web of comorbidities that elevate patient mortality while straining healthcare resources[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhile MASLD was historically viewed as a disease of obesity, growing evidence reveals that many patients maintain normal body mass index (BMI) values, with this phenomenon particularly pronounced in Asian populations where 7\u0026ndash;20% of individuals develop MASLD despite normal weight status[\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. These non-obese patients present a clinical paradox: they typically show fewer traditional metabolic risk factors\u0026mdash;including lower rates of diabetes, hypertension, and dyslipidemia\u0026mdash;yet often develop more severe liver fibrosis and face higher cardiovascular mortality compared to their obese counterparts[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This counterintuitive pattern creates diagnostic challenges, as clinicians may dismiss MASLD risk in normal-weight individuals, potentially delaying critical interventions and missing early treatment windows[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. MASLD diagnosis still depends heavily on imaging studies and liver biopsy, yet these approaches face practical barriers including high costs, limited availability, and variable reproducibility across different settings[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Simple, cost-effective screening tools would therefore fill a critical gap in early MASLD detection, especially among non-obese individuals where the need for accessible risk assessment has become increasingly urgent [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe Cl\u0026iacute;nica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE) calculates body fat content using age, sex, and BMI parameters, with validation studies confirming its accuracy in Asian populations[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Unlike conventional BMI measurements, CUN-BAE captures individual variations in fat distribution and shows superior ability to detect \"hidden obesity\"\u0026mdash;cases where normal BMI masks elevated body fat percentage[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. These features make CUN-BAE particularly valuable for metabolic risk assessment in non-obese individuals, where traditional anthropometric measures often fall short. Growing research has established the triglyceride-glucose index (TyG) as a robust surrogate for insulin resistance, making it increasingly valuable in metabolic disease risk assessment[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Evidence suggests that TyG index correlates strongly with both MASLD development and progression, potentially bridging abnormal body fat distribution with hepatic steatosis[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Yet among non-obese individuals, the relationship between CUN-BAE and MASLD remains poorly understood, and TyG index mediating role in this population requires investigation.\u003c/p\u003e\u003cp\u003eAlthough studies have explored relationships between body fat assessment indicators and MASLD, existing research has significant limitations including predominantly cross-sectional designs that cannot establish causal relationships and focus mainly on obese populations with insufficient attention to non-obese individuals[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Moreover, there is a lack of large-sample, long-term longitudinal cohort studies to validate predictive efficacy in clinical practice. Prospective cohort designs offer the temporal framework needed to establish exposure-outcome sequences, strengthening causal inference beyond cross-sectional observations. Non-obese populations warrant dedicated investigation, as their MASLD risk profiles and progression trajectories may diverge substantially from patterns seen in obese cohorts.\u003c/p\u003e\u003cp\u003eUsing longitudinal cohort data from non-obese populations, we sought to evaluate CUN-BAE's association with MASLD prevalence and incidence, examine dose-response patterns and sex-specific differences, and determine whether TyG index mediates this relationship. We anticipated that CUN-BAE would demonstrate predictive capacity for MASLD risk in non-obese individuals, with dose-dependent associations that vary by sex and partial mediation through insulin resistance pathways. These findings could inform risk stratification approaches and targeted screening strategies in populations traditionally considered metabolically low-risk.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Design and Data Sources\u003c/h2\u003e\u003cp\u003eThis study is a secondary analysis based on a public database, employing a longitudinal cohort design. The original data were sourced from the Dryad public database (repository at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://datadryad.org/\u003c/span\u003e\u003cspan address=\"http://datadryad.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e with the doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5061/dryad.1n6c4[24]\u003c/span\u003e\u003cspan address=\"10.5061/dryad.1n6c4[24]\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e in compliance with Dryad's terms of service, with data freely available for researchers. The study population comprised 16,173 non-obese participants without MASLD at baseline, with a prospective 5-year follow-up period.\u003c/p\u003e\u003cp\u003e The original study protocol was approved by the Ethics Committee of Wenzhou People's Hospital, and all participants provided informed consent. As a secondary analysis of a publicly available dataset, this study did not require additional ethical review.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Study Population and Inclusion/Exclusion Criteria\u003c/h2\u003e\u003cp\u003eThe study population consisted of non-obese Chinese adults. Baseline exclusion criteria were as follows: (1) incomplete clinical data or loss to follow-up; (2) current use of oral antihypertensive, lipid-lowering, or antidiabetic medications; (3) excessive alcohol consumption (\u0026gt;\u0026thinsp;140 g/week for men, \u0026gt;\u0026thinsp;70 g/week for women); (4) presence of MASLD, autoimmune hepatitis, viral hepatitis, or chronic liver disease of known etiology; (5) low-density lipoprotein cholesterol\u0026thinsp;\u0026gt;\u0026thinsp;3.12 mmol/L; (6) BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u0026sup2;.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Data Collection\u003c/h2\u003e\u003cp\u003eMedical history and lifestyle habits were collected through questionnaire surveys conducted by trained physicians. Blood pressure measurements were obtained using automated sphygmomanometers in a quiet environment. Height and weight were measured with participants wearing light clothing and no shoes.\u003c/p\u003e\u003cp\u003eFasting blood samples were collected in the early morning after overnight fasting to assess biochemical parameters, including alanine aminotransferase (ALT), aspartate aminotransferase (AST), total protein (TP), albumin (ALB), globulin (GLB), creatinine (Cr), blood urea nitrogen (BUN), fasting plasma glucose (FPG), uric acid (UA), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) levels.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Definitions and Variable Calculations\u003c/h2\u003e\u003cp\u003e\u003cb\u003e2.4.1 CUN-BAE Index\u003c/b\u003e = (0.00021 \u0026times; BMI\u003csup\u003e2\u003c/sup\u003e \u0026times; age) - (0.005 \u0026times; BMI\u003csup\u003e2\u003c/sup\u003e \u0026times; sex) - (0.02 \u0026times; BMI \u0026times; age) + (0.181 \u0026times; BMI \u0026times; sex) \u0026minus; (0.026 \u0026times; BMI\u003csup\u003e2\u003c/sup\u003e) + (3.172 \u0026times; BMI) + (10.689 \u0026times; sex) + (0.503 \u0026times; age)\u0026thinsp;\u0026minus;\u0026thinsp;44.988; where sex is defined as male\u0026thinsp;=\u0026thinsp;0, female\u0026thinsp;=\u0026thinsp;1[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e];\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e\u003cb\u003e2.4.2 BMI\u003c/b\u003e (kg/m\u0026sup2;)\u0026thinsp;=\u0026thinsp;weight (kg)/height\u0026sup2; (m\u0026sup2;);\u003c/h2\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e\u003cb\u003e2.4.3 TyG\u003c/b\u003e\u0026thinsp;=\u0026thinsp;Ln[FBG (mg/dL) \u0026times;TG (mg/dL)/2];\u003c/h2\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.4.4 MASLD Diagnostic Criteria\u003c/h2\u003e\u003cp\u003eMASLD was diagnosed by abdominal ultrasonography according to the standards recommended by the Chinese Society of Hepatology. MASLD was defined as the presence of at least 2 of the following 3 abnormal ultrasound findings: (1) diffuse enhancement of liver near-field echoes (\"bright liver\"); (2) liver echogenicity stronger than kidney echogenicity; (3) vascular blurring and gradual attenuation of far-field echoes.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e\u003cp\u003eMissing data were handled by excluding variables with \u0026gt;\u0026thinsp;25% missing values and imputing those with \u0026lt;\u0026thinsp;25% missing values using random forest methodology. Sensitivity analyses were conducted before and after imputation to ensure data stability (Supplementary Table\u0026nbsp;1). Participants were categorized into tertiles based on CUN-BAE values for descriptive analysis, with CUN-BAE standardized using Z-scores for regression analyses. Continuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. Between-group comparisons were performed using one-way ANOVA or non-parametric tests for continuous variables.\u003c/p\u003e\u003cp\u003eCumulative incidence of MASLD was estimated using the Kaplan-Meier method with log-rank tests comparing survival distributions between CUN-BAE tertiles. Cox proportional hazards regression models assessed associations between standardized CUN-BAE and incident MASLD using progressive adjustment: Model 1 adjusted for liver function markers (ALT, AST, TP, ALB, GLB); Model 2 additionally adjusted for renal function markers (BUN, CR, UA) based on Model 1; Model 3 further added lipid profiles (TC, TG, HDL-C, LDL-C) to Model 2; and Model 4 additionally incorporated metabolic parameters and blood pressure (FBG, SBP, DBP) to Model 3.\u003c/p\u003e\u003cp\u003eRestricted cubic spline regression examined non-linear dose-response relationships with optimal knot placement determined using Akaike Information Criterion, with sex-stratified analyses to explore interaction patterns. Stratified analyses across demographic and clinical subgroups tested effect modification using likelihood ratio tests. Mediation analysis evaluated TyG index as a potential mediator using structural equation modeling, with bootstrap resampling (n\u0026thinsp;=\u0026thinsp;1,000 iterations) to assess statistical significance of mediation effects and calculate mediation proportions.\u003c/p\u003e\u003cp\u003eSensitivity analyses included comparison of complete case versus imputed data analyses, evaluation of association stability across adjustment models, and alternative CUN-BAE categorization approaches to assess result robustness. All analyses were performed using R software version 4.2.0 and EmpowerStats version 4.0, with two-sided P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Baseline Characteristics of Study Population\u003c/h2\u003e\u003cp\u003eThis study included 16,173 participants, comprising 8,483 males and 7,690 females. Based on the presence of MASLD, participants were categorized into MASLD and non-MASLD groups. During the 5-year follow-up period, 1,294 males (15.25%) and 1,028 females (13.37%) were diagnosed with MASLD.As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, for both males and females, the MASLD group demonstrated significantly higher age, weight, height, BMI, systolic blood pressure (SBP), and diastolic blood pressure (DBP) compared to the non-MASLD group (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eRegarding biochemical parameters, the MASLD group exhibited significantly elevated levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), creatinine (CR), uric acid (UA), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and fasting plasma glucose (FPG), while high-density lipoprotein cholesterol (HDL-C) levels were significantly decreased (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In males, CUN-BAE was significantly higher in the MASLD group (21.82\u0026thinsp;\u0026plusmn;\u0026thinsp;3.14) compared to the non-MASLD group (18.36\u0026thinsp;\u0026plusmn;\u0026thinsp;4.35, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In females, CUN-BAE was similarly elevated in the MASLD group (32.93\u0026thinsp;\u0026plusmn;\u0026thinsp;2.82) versus the non-MASLD group (29.36\u0026thinsp;\u0026plusmn;\u0026thinsp;3.97, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, the triglyceride-glucose index (TyG) was significantly higher in the MASLD group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eFor both sexes, no statistically significant differences were observed between MASLD and non-MASLD groups for total protein (TP), albumin (ALB), globulin (GLB), and blood urea nitrogen (BUN).\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\u003eCharacteristics of the subjects.\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMASLD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value*\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7189\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1294\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6662\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1028\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge, years\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e45.55\u0026thinsp;\u0026plusmn;\u0026thinsp;16.31\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e47.29\u0026thinsp;\u0026plusmn;\u0026thinsp;16.26\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e40.20\u0026thinsp;\u0026plusmn;\u0026thinsp;12.69\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e41.48\u0026thinsp;\u0026plusmn;\u0026thinsp;12.85\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHeight, cm\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e164.79\u0026thinsp;\u0026plusmn;\u0026thinsp;7.85\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e167.66\u0026thinsp;\u0026plusmn;\u0026thinsp;7.63\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e163.27\u0026thinsp;\u0026plusmn;\u0026thinsp;7.51\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e166.49\u0026thinsp;\u0026plusmn;\u0026thinsp;7.80\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWeight, kg\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e57.73\u0026thinsp;\u0026plusmn;\u0026thinsp;8.39\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e65.48\u0026thinsp;\u0026plusmn;\u0026thinsp;7.36\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e56.09\u0026thinsp;\u0026plusmn;\u0026thinsp;7.87\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e64.23\u0026thinsp;\u0026plusmn;\u0026thinsp;7.38\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI, kg/m2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e21.18\u0026thinsp;\u0026plusmn;\u0026thinsp;2.02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e23.23\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e20.97\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e23.11\u0026thinsp;\u0026plusmn;\u0026thinsp;1.35\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSBP, mmHg\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e120.47\u0026thinsp;\u0026plusmn;\u0026thinsp;16.53\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e128.74\u0026thinsp;\u0026plusmn;\u0026thinsp;16.32\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e118.43\u0026thinsp;\u0026plusmn;\u0026thinsp;16.41\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e127.27\u0026thinsp;\u0026plusmn;\u0026thinsp;15.63\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDBP, mmHg\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e72.54\u0026thinsp;\u0026plusmn;\u0026thinsp;10.26\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e78.31\u0026thinsp;\u0026plusmn;\u0026thinsp;10.27\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e71.35\u0026thinsp;\u0026plusmn;\u0026thinsp;9.94\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e77.21\u0026thinsp;\u0026plusmn;\u0026thinsp;10.26\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eALT, U/L\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e16.00\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(13.00\u0026ndash;19.00)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e21.00\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(16.00\u0026ndash;30.00)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e16.00\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(13.00\u0026ndash;17.00)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e21.00\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(16.00\u0026ndash;29.00)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAST, U/L\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e21.00\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(19.00\u0026ndash;23.00)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e23.00\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(20.00\u0026ndash;27.00)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e21.00\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(19.00\u0026ndash;22.00)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e23.00\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(20.00\u0026ndash;27.00)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTP, g/L\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e73.94\u0026thinsp;\u0026plusmn;\u0026thinsp;3.92\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e73.97\u0026thinsp;\u0026plusmn;\u0026thinsp;4.07\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.967\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e73.80\u0026thinsp;\u0026plusmn;\u0026thinsp;4.07\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e74.12\u0026thinsp;\u0026plusmn;\u0026thinsp;4.12\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.023\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eALB, g/L\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e44.51\u0026thinsp;\u0026plusmn;\u0026thinsp;2.66\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e44.49\u0026thinsp;\u0026plusmn;\u0026thinsp;2.56\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.891\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e44.27\u0026thinsp;\u0026plusmn;\u0026thinsp;2.51\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e44.48\u0026thinsp;\u0026plusmn;\u0026thinsp;2.66\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGLB, g/L\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e29.42\u0026thinsp;\u0026plusmn;\u0026thinsp;3.65\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e29.46\u0026thinsp;\u0026plusmn;\u0026thinsp;3.94\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.725\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e29.53\u0026thinsp;\u0026plusmn;\u0026thinsp;3.68\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e29.63\u0026thinsp;\u0026plusmn;\u0026thinsp;3.82\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.321\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBUN, mmol/L\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e4.61\u0026thinsp;\u0026plusmn;\u0026thinsp;1.38\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e4.64\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.251\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e4.51\u0026thinsp;\u0026plusmn;\u0026thinsp;1.38\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e4.56\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.239\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCR, \u0026micro;mo/L\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e79.08\u0026thinsp;\u0026plusmn;\u0026thinsp;26.73\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e86.66\u0026thinsp;\u0026plusmn;\u0026thinsp;18.40\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e75.27\u0026thinsp;\u0026plusmn;\u0026thinsp;25.72\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e84.77\u0026thinsp;\u0026plusmn;\u0026thinsp;21.83\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUA, mmol/L\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e279.38\u0026thinsp;\u0026plusmn;\u0026thinsp;84.80\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e330.27\u0026thinsp;\u0026plusmn;\u0026thinsp;83.94\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e263.77\u0026thinsp;\u0026plusmn;\u0026thinsp;80.95\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e323.29\u0026thinsp;\u0026plusmn;\u0026thinsp;88.22\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTC, mmol/L\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e4.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e4.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e4.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e4.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTG, mmol/L\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e2.04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.56\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHDL-C, mmol/L\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLDL-C, mmol/L\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e2.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e2.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e2.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e2.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFPG, mmol/L\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e5.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e5.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e5.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e5.44\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCUN-BAE\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e18.36\u0026thinsp;\u0026plusmn;\u0026thinsp;4.35\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e21.82\u0026thinsp;\u0026plusmn;\u0026thinsp;3.14\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e29.36\u0026thinsp;\u0026plusmn;\u0026thinsp;3.97\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e32.93\u0026thinsp;\u0026plusmn;\u0026thinsp;2.82\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTYG\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e8.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e8.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e8.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e8.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\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\u003e\u003cb\u003eValues were expressed as mean (SD) or medians (Q1-Q3);\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eALB, Albumin; ALP, Alkaline phosphatase; ALT, Alanine aminotransferase; AST, Aspartate aminotransferase; BMI, Body mass index; BUN, Blood urea nitrogen; Cr, Creatinine; CUN-BAE, Cl\u0026iacute;nica Universidad de Navarra-Body Adiposity Estimator; DBIL, Direct bilirubin; DBP, Diastolic blood pressure; FPG, Fasting plasma glucose; GGT, Gamma-glutamyl transferase; GLB, Globulin; HDL-C, High-density lipoprotein cholesterol; LDL-C, Low-density lipoprotein cholesterol; MASLD, Metabolic Dysfunction-Associated Steatotic Liver Disease; SBP, Systolic blood pressure; TB, Total bilirubin; TC, Total cholesterol; TG, Triglyceride; TP, Total Protein; TYG, Triglyceride-Glucose index; UA, Uric acid.\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Association Analysis Between CUN-BAE and MASLD Prevalence Risk\u003c/h2\u003e\u003cp\u003eTo evaluate the association between CUN-BAE z-score and MASLD prevalence risk, we employed multivariable logistic regression analysis. The analysis was based on the entire study population and constructed four progressively adjusted regression models to examine result robustness: Model 1 adjusted for liver function markers (ALT, AST, TP, ALB, GLB); Model 2 added renal function markers (BUN, CR, UA); Model 3 added lipid profiles (TC, TG, HDL-C, LDL-C); and Model 4 added metabolic parameters and blood pressure (FPG, SBP, DBP). Results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eWhen CUN-BAE was included as a continuous variable, it demonstrated significant positive associations with MASLD risk across all models (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the unadjusted model, each one-unit increase in CUN-BAE z-score was associated with a 51% increased risk of MASLD (HR\u0026thinsp;=\u0026thinsp;1.51, 95% CI: 1.45\u0026ndash;1.57, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the fully adjusted Model 4, this strong association persisted, with each one-unit increase in CUN-BAE z-score associated with a 35% increased risk of MASLD (HR\u0026thinsp;=\u0026thinsp;1.35, 95% CI: 1.29\u0026ndash;1.41, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eSubsequently, we conducted sensitivity analysis based on CUN-BAE z-score tertiles (Q1-Q3). In the fully adjusted Model 4, using the lowest CUN-BAE level (Q1) as reference, both Q2 and Q3 groups showed significantly elevated MASLD prevalence risk. The highest CUN-BAE group (Q3) demonstrated a 1.9-fold increased risk compared to Q1 (HR\u0026thinsp;=\u0026thinsp;1.95, 95% CI: 1.74\u0026ndash;2.18, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The trend test (P for trend) yielded P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, indicating a highly significant dose-response relationship between increasing CUN-BAE levels and MASLD prevalence risk.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation between CUN-BAE and MASLD in different models\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eExposure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eMASLD, HR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCrude model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003emodel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003emodel 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModel 4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCUN-BAE z-score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.5 1(1.45, 1.57)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.5 0(1.43, 1.56)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.4 4(1.38, 1.50)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.41 (1.35, 1.47)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.35 (1.29, 1.41)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCUN-BAE z-score quartile\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLow\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMiddle\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.94 (1.73, 2.17)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.96 (1.75, 2.20)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.96 (1.75, 2.20)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.95 (1.74, 2.19)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.77 (1.58, 1.99)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHigh\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e2.30 (2.06, 2.57)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e2.28 (2.04, 2.54)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e2.18 (1.95, 2.43)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e2.14 (1.91, 2.40)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.95 (1.74, 2.18)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eP\u003c/b\u003e \u003cb\u003efor\u0026nbsp;trend\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCrude model adjusted for none;\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003emodel 1 adjusted for: ALT; AST; TP; ALB; GLB\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003emodel 2 adjusted for: ALT; AST; TP; ALB; GLB; BUN; CR; UA\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003emodel 3 adjusted for: ALT; AST; TP; ALB; GLB; BUN; CR; UA; TC; TG; HDL-C; LDL-C\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003emodel 4 adjusted for: ALT; AST; TP; ALB; GLB; BUN; CR; UA; TC; TG; HDL; LDL-C; FBG; SBP; DBP\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Dose-Response Relationship Analysis Between CUN-BAE and MASLD Risk\u003c/h2\u003e\u003cp\u003eTo further explore the dose-response relationship between CUN-BAE and MASLD prevalence risk, we employed restricted cubic spline regression analysis, which can identify potential non-linear associations and provide more precise risk assessment. Overall population analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.A): Restricted cubic spline analysis revealed a significant non-linear dose-response relationship between CUN-BAE and MASLD risk, with increasing MASLD risk as CUN-BAE increased.\u003c/p\u003e\u003cp\u003eSex-stratified analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.B) revealed distinctly different association patterns between CUN-BAE and MASLD risk across gender groups (threshold effect analysis detailed in Supplementary Table\u0026nbsp;2): In the female population (red solid line), the risk curve demonstrated rapid escalation at lower CUN-BAE values (\u0026lt;\u0026thinsp;31.2), followed by a relatively moderate upward trend beyond the 31.2 threshold, creating a pronounced \"inflection point\" effect. In contrast, the male population (blue dashed line) exhibited a steeper and more consistently linear upward trend across the entire CUN-BAE range compared to their female counterparts.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Cohort Analysis of CUN-BAE Predicting MASLD Incidence Risk\u003c/h2\u003e\u003cp\u003eTo validate the predictive value of CUN-BAE for incident MASLD cases, we conducted prospective cohort analysis among participants without MASLD at baseline. Study subjects were categorized into low, middle, and high groups based on CUN-BAE tertiles, with 5,391 participants in each group. Cumulative incidence was analyzed using the Kaplan-Meier method over 60 months of follow-up (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eResults demonstrated a significant dose-response relationship between CUN-BAE levels and incident MASLD risk (Log-rank test, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). At study completion, cumulative incidence rates for low, middle, and high CUN-BAE groups were approximately 8.4% (451 cases), 15.8% (853 cases), and 18.9% (1,018 cases), respectively.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Stratified Analysis of CUN-BAE-MASLD Association\u003c/h2\u003e\u003cp\u003eTo evaluate the consistency of the CUN-BAE-MASLD association across different subgroups, we performed stratified analyses using standardized CUN-BAE z-scores (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). All stratified groups showed CUN-BAE as a risk factor for MASLD (HR\u0026thinsp;\u0026gt;\u0026thinsp;1). Age stratification revealed HRs of 1.45, 1.35, 1.29, and 1.21 for groups\u0026thinsp;\u0026lt;\u0026thinsp;30, 30\u0026ndash;45, 45\u0026ndash;60, and \u0026ge;\u0026thinsp;60 years, respectively, with no significant interaction (P interaction\u0026thinsp;=\u0026thinsp;0.5852). In blood pressure stratification, diastolic blood pressure showed significant interaction (P interaction\u0026thinsp;=\u0026thinsp;0.0340). Among metabolic parameters, uric acid (P interaction\u0026thinsp;=\u0026thinsp;0.0007) and triglycerides (P interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) demonstrated significant interactions.\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\u003eThe effect size of CUN-BAE on MASLD in prespecified and exploratory subgroups in each subgroup.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCUN-BAE z-score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHR (95%CI) P-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP interaction\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, years\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.5852\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;30\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3548\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.45 (1.31, 1.61)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;30, \u0026lt;\u0026thinsp;45\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e6480\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.35 (1.26, 1.45)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;45, \u0026lt;\u0026thinsp;60\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e3777\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.29 (1.19, 1.41)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;60\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e2368\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.21 (1.07, 1.36) 0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSBP, mmHg\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.6367\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;140\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e14090\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.39 (1.32, 1.45)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;140\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e2083\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.22 (1.11, 1.34)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDBP, mmHg\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.0340\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;90\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e15024\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.37 (1.31, 1.44)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;90\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1149\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.17 (1.05, 1.31) 0.005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eALT, U/L\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.0790\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;40\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e15438\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.38 (1.32, 1.45)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;40\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e735\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.19 (1.05, 1.35) 0.005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAST, U/L\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.1332\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;40\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e15804\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.34 (1.28, 1.40)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;40\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e369\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.37 (1.12, 1.67) 0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUA, mmol/L\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.0007\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;360\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e13329\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.45 (1.37, 1.53)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;360\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e2843\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.23 (1.14, 1.32)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCR, \u0026micro;mo/L\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.7752\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;104\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e14759\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.35 (1.29, 1.41)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;104\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1413\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.31 (1.17, 1.47)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTG, mmol/L\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;1.7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e13090\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.48 (1.39, 1.57)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;1.7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e3083\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.18 (1.11, 1.26)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHDL-C, mmol/L\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.0991\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;1.03\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1647\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.24 (1.12, 1.37)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;1.03\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e14526\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.38 (1.32, 1.45)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTC, mmol/L\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.7185\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;5.2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e12563\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.34 (1.28, 1.41)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;5.2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e3610\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.28 (1.18, 1.38)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFPG, mmol/L\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.7257\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;6.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e15219\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.36 (1.30, 1.43)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;6.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e953\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.26 (1.10, 1.43)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Mediating Effect of TyG Index in the CUN-BAE-MASLD Association\u003c/h2\u003e\u003cp\u003eTo investigate whether the TyG index mediates the association between CUN-BAE and MASLD, we performed mediation analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Results demonstrated that CUN-BAE had a significant positive predictive effect on the TyG index (β\u0026thinsp;=\u0026thinsp;0.022, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The TyG index also showed significant association with MASLD (β\u0026thinsp;=\u0026thinsp;0.022, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After controlling for the TyG index, CUN-BAE maintained a significant direct effect on MASLD (β\u0026thinsp;=\u0026thinsp;0.068, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Mediation analysis revealed that the TyG index served as a partial mediator in the CUN-BAE-MASLD association, with a mediation proportion of 24.7%. Bootstrap testing confirmed the statistical significance of this mediation effect (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis large prospective cohort study of 16,173 Chinese non-obese adults provides compelling evidence that CUN-BAE serves as an independent predictor of MASLD risk, with TyG index partially mediating this association. Our key findings demonstrate that higher CUN-BAE levels are associated with increased MASLD prevalence and incidence, exhibiting clear dose-response relationships and distinct sex-specific patterns. Specifically, each one-unit increase in standardized CUN-BAE was associated with a 35% increased risk of prevalent MASLD (OR\u0026thinsp;=\u0026thinsp;1.35, 95% CI: 1.29\u0026ndash;1.41), with the high CUN-BAE group showing a cumulative incidence of 18.9% over 5 years of follow-up.\u003c/p\u003e\u003cp\u003eThese findings challenge the traditional paradigm that normal BMI equates to metabolic health, revealing the existence of subclinical metabolic dysfunction among apparently healthy individuals[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. CUN-BAE, by integrating age, sex, and BMI information, provides a more refined assessment of body fat distribution, effectively identifying individuals who have normal BMI but relatively high fat content[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The observed dose-response relationship confirms the continuous nature of adiposity-related risk, suggesting that even modest increases in body fat within the non-obese range can trigger pathological processes leading to hepatic steatosis.\u003c/p\u003e\u003cp\u003eThe most striking finding was the significant sex-specific patterns revealed by restricted cubic spline analysis. Women showed a distinct risk inflection point at CUN-BAE 31.2, while men exhibited a more linear dose-response relationship. This sex-specific pattern reflects complex biological mechanisms involving hormonal regulation, fat distribution patterns, and metabolic adaptation differences. The protective effects of estrogen on fat metabolism may effectively operate at lower adiposity levels by promoting subcutaneous fat distribution and improving insulin sensitivity[\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. However, once total adiposity reaches a critical threshold, this protective mechanism may become compromised, potentially contributing to ectopic fat deposition. The \"threshold effect\" observed in women may represent a critical tipping point where metabolic buffering capacity is exceeded, resulting in accelerated metabolic deterioration[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur mediation analysis revealed that TyG index mediated 24.7% of the CUN-BAE-MASLD association, indicating that insulin resistance represents an important but non-exclusive mechanistic pathway. The TyG index, as a well-validated surrogate marker of insulin resistance, provides crucial mechanistic insights into the CUN-BAE-MASLD relationship[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Insulin resistance represents a central pathophysiological mechanism linking excessive adiposity to hepatic steatosis through multiple pathways, including enhanced hepatic de novo lipogenesis, increased lipolysis from peripheral adipose tissue, and impaired hepatic fatty acid oxidation[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Therefore, our finding that TyG index partially mediates 24.7% of the CUN-BAE-MASLD association suggests that the increased MASLD risk observed in non-obese individuals with higher CUN-BAE values is, in part, attributable to insulin resistance-mediated metabolic dysfunction. This mechanistic understanding is particularly important in non-obese populations, where traditional obesity markers may fail to capture underlying metabolic abnormalities that predispose to hepatic steatosis.\u003c/p\u003e\u003cp\u003eThis partial mediation suggests that while insulin resistance plays a central role in pathophysiology, other mechanisms also contribute significantly to this relationship [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The remaining 75.3% non-mediated effect may involve direct lipotoxic effects, gut-liver axis disruption, chronic low-grade inflammation, dysregulated adipokine secretion, and epigenetic modifications related to metabolic stress[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. This mechanistic complexity emphasizes the multifactorial nature of MASLD pathogenesis in non-obese individuals.\u003c/p\u003e\u003cp\u003eComparison with existing literature reveals both important consistencies and novel findings. The MASLD incidence rates observed in our study (8.4%-18.9%) are generally consistent with the 7\u0026ndash;20% range reported in Western studies among lean populations[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]; however, our study demonstrated more definitive dose-response gradients and sex-specific patterns. This consistency supports the potential utility of CUN-BAE as a cross-ethnic risk assessment tool, while the refined risk stratification observed may reflect the application of more sensitive identification methods. Notably, Asian populations tend to exhibit higher body fat percentages and visceral fat proportions at equivalent BMI levels, along with relatively lower insulin secretory capacity, which may explain why significant risk differences can be identified in non-obese populations using the refined CUN-BAE indicator[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Our study extends previous research by specifically examining non-obese individuals using refined body composition indicators, revealing risk patterns and sex-specific characteristics that traditional anthropometric indices might overlook.\u003c/p\u003e\u003cp\u003eThe clinical implications of these findings are substantial. Current clinical practice often underestimates MASLD risk in BMI-normal individuals, potentially leading to delayed diagnosis and missed prevention opportunities[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. CUN-BAE, calculated simply from readily available clinical parameters, can serve as a practical screening tool for identifying high-risk non-obese individuals. The identified sex-specific risk patterns and dose-response relationships provide crucial guidance for individualized risk assessment strategies. The critical inflection point observed in women suggests that targeted screening for females approaching this threshold may be particularly beneficial. Furthermore, the partial mediating role of TyG index indicates that interventions targeting insulin resistance may effectively reduce MASLD risk, although multi-target approaches addressing non-mediated pathways may be necessary for optimal prevention [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. From a public health perspective, the simplicity of CUN-BAE calculation makes it potentially applicable for widespread implementation in primary healthcare settings, facilitating early identification and stratified management of MASLD.\u003c/p\u003e\u003cp\u003eSeveral limitations warrant acknowledgment. First, MASLD diagnosis was based on ultrasonographic rather than histological assessment, which may result in some degree of misclassification and inability to distinguish simple steatosis from steatohepatitis. Second, our study population consisted entirely of Chinese adults from a single geographic region, which may limit generalizability to other ethnic groups and populations with different genetic backgrounds, environmental exposures, and lifestyle patterns. Third, despite comprehensive adjustment for potential confounders, residual confounding from unmeasured factors such as genetic susceptibility, gut microbiome composition, sleep quality, and psychological stress cannot be completely excluded. Fourth, while the 5-year follow-up period is adequate for epidemiological research, it may not capture long-term disease progression patterns or account for dynamic changes in body composition and lifestyle factors over extended periods. Finally, the observational nature of our study, despite its prospective design, limits definitive causal inference due to potential reverse causality, unmeasured confounding, and selection bias.\u003c/p\u003e\u003cp\u003eFuture research should prioritize several key areas. Validation studies in diverse ethnic populations are crucial for establishing the generalizability of CUN-BAE as a MASLD predictor across different demographic groups. Multi-omics integration studies combining genomics, transcriptomics, metabolomics, and microbiomics data could provide deeper insights into the biological networks underlying the CUN-BAE-MASLD association[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Intervention studies examining whether CUN-BAE-guided lifestyle modifications can effectively reduce MASLD risk will provide critical evidence for clinical implementation. Additionally, development of artificial intelligence-driven risk prediction models integrating multiple risk factors could enhance precision medicine approaches. Longitudinal studies with extended follow-up periods and repeated measurements will better characterize disease trajectories and identify optimal intervention windows. Of particular interest, given our findings of sex-specific differences, future intervention studies should incorporate sex-specific prevention strategies, exploring intervention effect heterogeneity at different CUN-BAE thresholds.\u003c/p\u003e\u003cp\u003eThese findings have important implications for clinical practice and public health policy. The paradigm shift from simple BMI-based classification toward sophisticated body composition assessment represents a critical advancement in precision medicine approaches for metabolic disease prevention. The identification of sex-specific risk thresholds provides actionable guidance for clinicians to implement targeted screening strategies, particularly in populations traditionally considered low-risk. Moreover, the partial mediating role of insulin resistance suggests that interventions targeting glucose-lipid metabolism may be effective, while the substantial non-mediated component indicates that comprehensive multi-pathway approaches will likely be necessary for optimal prevention outcomes.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis prospective cohort study establishes CUN-BAE as a robust predictor of MASLD risk in Chinese non-obese adults, demonstrating clear dose-response relationships and distinct sex-specific patterns. With TyG index serving as a partial mediator of this association, our findings reveal that insulin resistance represents an important but non-exclusive pathological mechanism. CUN-BAE offers a practical, clinically accessible tool for identifying high-risk individuals who may be overlooked by conventional BMI screening, supporting precision-based early detection and risk stratification in non-obese populations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eALB - Albumin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHDL-C - High-density lipoprotein cholesterol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eALT - Alanine aminotransferase\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR - Hazard ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAST - Aspartate aminotransferase\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLDL-C - Low-density lipoprotein cholesterol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI - Body mass index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNAFLD - Non-alcoholic fatty liver disease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBUN - Blood urea nitrogen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSBP - Systolic blood pressure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCI - Confidence interval\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTC - Total cholesterol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCR - Creatinine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTG - Triglycerides\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDBP - Diastolic blood pressure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTP - Total protein\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFBG - Fasting blood glucose\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTyG - Triglyceride-glucose index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFPG - Fasting plasma glucose\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUA - Uric acid\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGLB - Globulin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 649px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCUN-BAE - Cl\u0026iacute;nica Universidad de Navarra-Body Adiposity Estimator\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 649px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMASLD - Metabolic dysfunction-associated steatotic liver disease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study were originally obtained from the Dryad Digital Repository (previously available at doi:10.5061/dryad.1n6c4). Due to technical issues with the original repository link, the complete dataset and analysis code are available from the corresponding author upon reasonable request and with appropriate data use agreements. All data handling and analysis procedures comply with relevant ethical guidelines and institutional policies, and data access will be provided in accordance with BMC\u0026apos;s data sharing policies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Considerations and Participant Consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki and its later amendments. The original research protocol received ethical approval from the Ethics committee of Wenzhou People\u0026apos;s Hospital. All study participants provided written informed consent according to ethical standards for human subject research. As this investigation represents a secondary analysis of publicly available, anonymized data, no additional ethical approval was required for the current study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study\u0026apos;s conceptual framework and design were developed by Jian Luo. Data analysis and result interpretation were undertaken by Xueyan Wu and Shenglian Gan. The initial manuscript draft was collaboratively prepared by Xueyan Wu, Fang Yu and Rong Zhang. Data verification procedures were conducted by Haifeng Zhou. The manuscript underwent comprehensive review and revision by all authors, who collectively approved the final submitted version. Jian Luo takes overall responsibility for the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the contributions of all investigators involved in the original study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was financially supported by the Science and Technology Department of Hunan Province under Grant No. 2023SK4090. The funding agency played no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors collectively declare no conflicts of interest or competing financial relationships that could potentially influence the research findings or manuscript preparation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAI Assistance Disclosure:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the use of artificial intelligence translation tools to assist in translating this manuscript from Chinese to English. All content was subsequently reviewed, edited, and verified by the authors to ensure scientific accuracy and linguistic quality.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRiazi K, Azhari H, Charette JH, Underwood FE, King JA, Afshar EE, Swain MG, Congly SE, Kaplan GG, Shaheen AA: \u003cstrong\u003eThe prevalence and incidence of NAFLD worldwide: a systematic review and meta-analysis\u003c/strong\u003e. \u003cem\u003eLancet Gastroenterol Hepatol \u003c/em\u003e2022, \u003cstrong\u003e7\u003c/strong\u003e(9):851-861.\u003c/li\u003e\n\u003cli\u003eYounossi ZM, Golabi P, Paik JM, Henry A, Van Dongen C, Henry L: \u003cstrong\u003eThe global epidemiology of nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH): a systematic review\u003c/strong\u003e. \u003cem\u003eHepatology \u003c/em\u003e2023, \u003cstrong\u003e77\u003c/strong\u003e(4):1335-1347.\u003c/li\u003e\n\u003cli\u003ePowell EE, Wong VW, Rinella M: \u003cstrong\u003eNon-alcoholic fatty liver disease\u003c/strong\u003e. \u003cem\u003eLancet \u003c/em\u003e2021, \u003cstrong\u003e397\u003c/strong\u003e(10290):2212-2224.\u003c/li\u003e\n\u003cli\u003eMantovani A, Csermely A, Petracca G, Beatrice G, Corey KE, Simon TG, Byrne CD, Targher G: \u003cstrong\u003eNon-alcoholic fatty liver disease and risk of fatal and non-fatal cardiovascular events: an updated systematic review and meta-analysis\u003c/strong\u003e. \u003cem\u003eLancet Gastroenterol Hepatol \u003c/em\u003e2021, \u003cstrong\u003e6\u003c/strong\u003e(11):903-913.\u003c/li\u003e\n\u003cli\u003eEslam M, Newsome PN, Sarin SK, Anstee QM, Targher G, Romero-Gomez M, Zelber-Sagi S, Wai-Sun Wong V, Dufour JF, Schattenberg JM\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eA new definition for metabolic dysfunction-associated fatty liver disease: An international expert consensus statement\u003c/strong\u003e. \u003cem\u003eJ Hepatol \u003c/em\u003e2020, \u003cstrong\u003e73\u003c/strong\u003e(1):202-209.\u003c/li\u003e\n\u003cli\u003eRinella ME, Lazarus JV, Ratziu V, Francque SM, Sanyal AJ, Kanwal F, Romero D, Abdelmalek MF, Anstee QM, Arab JP\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eA multisociety Delphi consensus statement on new fatty liver disease nomenclature\u003c/strong\u003e. \u003cem\u003eAnn Hepatol \u003c/em\u003e2024, \u003cstrong\u003e29\u003c/strong\u003e(1):101133.\u003c/li\u003e\n\u003cli\u003eDao AD, Nguyen VH, Ito T, Cheung R, Nguyen MH: \u003cstrong\u003ePrevalence, characteristics, and mortality outcomes of obese and nonobese MAFLD in the United States\u003c/strong\u003e. \u003cem\u003eHepatol Int \u003c/em\u003e2023, \u003cstrong\u003e17\u003c/strong\u003e(1):225-236.\u003c/li\u003e\n\u003cli\u003eYe Q, Zou B, Yeo YH, Li J, Huang DQ, Wu Y, Yang H, Liu C, Kam LY, Tan XXE\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eGlobal prevalence, incidence, and outcomes of non-obese or lean non-alcoholic fatty liver disease: a systematic review and meta-analysis\u003c/strong\u003e. \u003cem\u003eLancet Gastroenterol Hepatol \u003c/em\u003e2020, \u003cstrong\u003e5\u003c/strong\u003e(8):739-752.\u003c/li\u003e\n\u003cli\u003eXu R, Pan J, Zhou W, Ji G, Dang Y: \u003cstrong\u003eRecent advances in lean NAFLD\u003c/strong\u003e. \u003cem\u003eBiomed Pharmacother \u003c/em\u003e2022, \u003cstrong\u003e153\u003c/strong\u003e:113331.\u003c/li\u003e\n\u003cli\u003eYoung S, Tariq R, Provenza J, Satapathy SK, Faisal K, Choudhry A, Friedman SL, Singal AK: \u003cstrong\u003ePrevalence and Profile of Nonalcoholic Fatty Liver Disease in Lean Adults: Systematic Review and Meta-Analysis\u003c/strong\u003e. \u003cem\u003eHepatol Commun \u003c/em\u003e2020, \u003cstrong\u003e4\u003c/strong\u003e(7):953-972.\u003c/li\u003e\n\u003cli\u003eHagstrom H, Nasr P, Ekstedt M, Hammar U, Stal P, Hultcrantz R, Kechagias S: \u003cstrong\u003eRisk for development of severe liver disease in lean patients with nonalcoholic fatty liver disease: A long-term follow-up study\u003c/strong\u003e. \u003cem\u003eHepatol Commun \u003c/em\u003e2018, \u003cstrong\u003e2\u003c/strong\u003e(1):48-57.\u003c/li\u003e\n\u003cli\u003eSanyal AJ, Castera L, Wong VW: \u003cstrong\u003eNoninvasive Assessment of Liver Fibrosis in NAFLD\u003c/strong\u003e. \u003cem\u003eClin Gastroenterol Hepatol \u003c/em\u003e2023, \u003cstrong\u003e21\u003c/strong\u003e(8):2026-2039.\u003c/li\u003e\n\u003cli\u003eRinella ME, Neuschwander-Tetri BA, Siddiqui MS, Abdelmalek MF, Caldwell S, Barb D, Kleiner DE, Loomba R: \u003cstrong\u003eAASLD Practice Guidance on the clinical assessment and management of nonalcoholic fatty liver disease\u003c/strong\u003e. \u003cem\u003eHepatology \u003c/em\u003e2023, \u003cstrong\u003e77\u003c/strong\u003e(5):1797-1835.\u003c/li\u003e\n\u003cli\u003eEuropean Association for the Study of the L: \u003cstrong\u003eEASL Clinical Practice Guidelines on non-invasive tests for evaluation of liver disease severity and prognosis - 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\u003cstrong\u003e78\u003c/strong\u003e(2):637-648.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Metabolic dysfunction-associated steatotic liver disease(MASLD), Clínica Universidad de Navarra-Body Adiposity Estimator(CUN-BAE), Non-obese, Triglyceride-glucose index(TyG), Cohort study","lastPublishedDoi":"10.21203/rs.3.rs-7751064/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7751064/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eWhile metabolic dysfunction-associated steatotic liver disease (MASLD) increasingly affects non-obese individuals, current screening approaches show poor performance in this population. We investigated whether the Cl\u0026iacute;nica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE) could better identify MASLD risk than traditional measures in non-obese adults, and examined how the triglyceride-glucose (TyG) index might mediate this relationship.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eUsing data from the Dryad public database, we followed 16,173 Chinese non-obese adults (BMI\u0026thinsp;\u0026lt;\u0026thinsp;25 kg/m\u0026sup2;) without baseline MASLD for 5 years. MASLD diagnosis relied on abdominal ultrasonography. We applied multivariable logistic regression to assess cross-sectional associations and Cox models for incident disease risk. Restricted cubic splines revealed dose-response patterns in sex-stratified analyses, while structural equation modeling quantified TyG index mediation effects.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOur cohort included 8,483 men and 7,690 women. After full adjustment, each standard deviation increased in CUN-BAE linked to 35% higher MASLD risk (HR\u0026thinsp;=\u0026thinsp;1.35, 95% CI: 1.29\u0026ndash;1.41, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Comparing top versus bottom tertiles showed 95% increased risk (HR\u0026thinsp;=\u0026thinsp;1.95, 95% CI: 1.74\u0026ndash;2.18, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Five-year cumulative incidence rose from 8.4% (lowest tertile) to 15.8% (middle) to 18.9% (highest tertile, Log-rank P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Cubic spline analysis uncovered sex differences: women showed a sharp risk increase above CUN-BAE 31.2, while men displayed more gradual, linear patterns. The TyG index accounted for 24.7% of the CUN-BAE-MASLD association (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eCUN-BAE effectively predicts MASLD development in Chinese non-obese adults through clear dose-response relationships that differ by sex. Since TyG index only partially explains this association, insulin resistance appears important but insufficient to account for the full relationship. CUN-BAE could serve as a practical screening tool to identify high-risk individuals missed by conventional BMI-based approaches, enabling more precise risk stratification in non-obese populations.\u003c/p\u003e","manuscriptTitle":"Clínica Universidad de Navarra Body Adiposity Estimator and Risk of Incident Metabolic Dysfunction–Associated Steatotic Liver Disease in Non‑Obese Chinese Adults: A Prospective Cohort Study with the Triglyceride–Glucose Index as a Partial Mediator","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-17 11:42:25","doi":"10.21203/rs.3.rs-7751064/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-06T05:42:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-04T17:28:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-04T16:43:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"337903356284940408098556806962115655961","date":"2025-10-07T12:04:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"203882341016166011013623847656350910580","date":"2025-10-07T07:09:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-07T07:07:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-07T06:14:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-04T06:16:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-03T06:46:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-30T11:33:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ed2d1c79-bb99-45b0-9d41-25fc3ce0e976","owner":[],"postedDate":"October 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":56186045,"name":"Health sciences/Diseases"},{"id":56186046,"name":"Health sciences/Endocrinology"},{"id":56186047,"name":"Health sciences/Gastroenterology"},{"id":56186048,"name":"Health sciences/Health care"},{"id":56186049,"name":"Health sciences/Medical research"},{"id":56186050,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-12-08T16:03:16+00:00","versionOfRecord":{"articleIdentity":"rs-7751064","link":"https://doi.org/10.1038/s41598-025-30646-6","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-12-02 15:57:18","publishedOnDateReadable":"December 2nd, 2025"},"versionCreatedAt":"2025-10-17 11:42:25","video":"","vorDoi":"10.1038/s41598-025-30646-6","vorDoiUrl":"https://doi.org/10.1038/s41598-025-30646-6","workflowStages":[]},"version":"v1","identity":"rs-7751064","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7751064","identity":"rs-7751064","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}