Association of residual cholesterol-inflammation index with MAFLD and related mortality risk: a population-based study integrating mediation and machine learning analyses

Association of residual cholesterol-inflammation index with MAFLD and related mortality risk: a population-based study integrating mediation and machine learning analyses | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association of residual cholesterol-inflammation index with MAFLD and related mortality risk: a population-based study integrating mediation and machine learning analyses Zhongqiao Lu, Yingxia Hu, Desan Zong, Bin Yue This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7620270/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The residual cholesterol-inflammation index (RCII), a composite indicator integrating lipid metabolism and systemic inflammation, may serve as a novel predictor for metabolic dysfunction-associated fatty liver disease (MAFLD) and its related adverse outcomes. This study aimed to investigate the association between RCII and the risks of MAFLD and related mortality, assess its predictive value in clinical settings, and explore the mediating role of fasting plasma glucose (FPG) in these relationships. Methods A total of 13,254 participants from the NHANES 1999–2010 cycles were included. RC, CRP, and RCII were evaluated as exposures, with their distributions compared between MAFLD and non-MAFLD populations. Multivariable logistic and Cox regression models were used to assess the associations of RCII with MAFLD prevalence and three types of mortality (all-cause, cardiovascular, and premature). Nonlinear relationships were examined using restricted cubic splines (RCS). Mediation analysis was conducted to quantify the contribution of FPG to RCII-related risks, complemented by Mendelian randomization to infer causal effects of TC, HDL-C, LDL-C, and CRP on MAFLD. Multiple machine learning models were constructed to evaluate the predictive utility of RCII, with SHapley Additive exPlanations (SHAP) used for model interpretation. Results Compared to non-MAFLD individuals, participants with MAFLD exhibited pronounced metabolic dysregulation and inflammation, with significantly elevated RCII levels. RCII showed the strongest predictive power for MAFLD (Q4 vs Q1: OR = 17.79, P < 0.001). Higher RCII levels were independently associated with increased risks of MAFLD-related all-cause, cardiovascular, and premature death in both Kaplan–Meier and Cox models, with a clear dose-response pattern. These associations remained consistent across subgroups, with evidence of interaction effects. Mediation analysis revealed that FPG partially mediated the relationship between RCII and adverse outcomes, accounting for 2.02%–8.06% of the total effect. Among all models, the random forest algorithm achieved the highest predictive performance (accuracy = 89.70%, AUC = 0.960), with SHAP analysis confirming RCII as a top-ranking feature. Conclusions: RCII is independently and positively associated with both MAFLD risk and related mortality outcomes, demonstrating robust predictive capability. Its effects may be partially mediated by FPG. These findings underscore the potential of RCII as a clinically valuable biomarker for early identification and stratified management of individuals with high metabolic-inflammatory burdens. Residual cholesterol-inflammation index (RCII) Metabolic dysfunction-associated fatty liver disease (MAFLD) Mendelian randomization (MR) Fasting plasma glucose (FPG) NHANES mediation analysis mortality risk machine learning Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Metabolic dysfunction-associated fatty liver disease (MAFLD) has emerged as the most prevalent chronic liver condition globally, driven by the escalating burden of metabolic dysregulation, chronic inflammation, and insulin resistance [ 1 , 2 ]. With an estimated prevalence exceeding 30% among adults worldwide, MAFLD is now a leading contributor to cirrhosis, hepatocellular carcinoma, and all-cause mortality, posing a significant global public health challenge [ 3 ]. In China, the prevalence of MAFLD is rising at an alarming rate, placing increasing strain on healthcare systems and national resources [ 4 ]. Early identification and precise risk stratification of high-risk individuals are crucial for effective prevention and intervention. However, current predictive tools lack robust composite indices that simultaneously capture metabolic overload and chronic inflammatory status, thereby limiting the development of efficient screening and targeted management strategies. Against this backdrop, remnant cholesterol (RC)—a triglyceride-rich component of atherogenic lipoproteins primarily comprising very-low-density lipoproteins (VLDL), intermediate-density lipoproteins (IDL), and chylomicron remnants—has garnered increasing attention in the field of metabolic disease research [ 5 ]. Robust evidence from large-scale cohort studies has established RC as an independent predictor of cardiovascular events, beyond the traditional lipid markers LDL-C and HDL-C [ 6 ], and has implicated it as a key contributor to the pathogenesis of atherosclerosis. Recently, attention has turned to the potential mechanistic role of RC in metabolic liver diseases. Studies based on the NHANES population have demonstrated a strong association between serum RC levels and both hepatic steatosis and fibrosis in individuals with nonalcoholic fatty liver disease (NAFLD), with RC outperforming conventional cholesterol measures in predicting liver stiffness, underscoring its potential clinical relevance in hepatic risk stratification [ 7 ]. C-reactive protein (CRP), one of the most widely used biomarkers of chronic low-grade inflammation, has long been recognized as a critical factor in the progression of NAFLD/MAFLD [ 8 – 10 ]. Chronic systemic inflammation, moreover, has been shown to exacerbate adipose tissue dysfunction, disrupt insulin signaling pathways, and amplify hepatic steatosis and hepatocyte apoptosis via the adipose-liver axis [ 11 , 12 ]. Single biomarkers often fall short in capturing the complex interplay between metabolic dysfunction and chronic inflammation in multifactorial diseases. To address this limitation, the RCII—a composite metric integrating RC and CRP—has recently been proposed as a surrogate for the coupled metabolic–inflammatory axis. Emerging evidence supports the predictive utility of RCII across a spectrum of chronic conditions. For instance, studies based on NHANES and CHARLS cohorts identified RCII as an independent predictor of incident stroke, with a graded increase in 7-year stroke risk across RCII quartiles [ 13 ]. Similarly, another NHANES-based analysis demonstrated that RCII outperforms RC or CRP alone in predicting all-cause, cardiovascular, and cancer-related mortality, exhibiting robust dose–response associations [ 14 ]. Although the RCII has shown preliminary promise in predicting several chronic diseases, its prognostic value, mechanistic relevance, and generalizability across populations in the context of MAFLD and associated mortality remain poorly characterized. In particular, it is unclear whether RCII exhibits a nonlinear association with MAFLD risk, whether it serves as an independent predictor, and whether its effects are mediated through specific metabolic pathways such as fasting plasma glucose. To date, no comprehensive epidemiological evidence has systematically addressed these questions. Furthermore, conventional statistical models are inherently limited in capturing complex feature interactions and high-dimensional data structures, potentially obscuring key risk patterns in chronic disease prediction. In contrast, machine learning approaches have emerged as powerful tools in medical risk stratification, offering enhanced accuracy, robustness, and the ability to uncover latent risk determinants in large-scale, multi-variable datasets. To address these gaps, we leveraged cross-sectional and longitudinal data from the 1999–2010 National Health and Nutrition Examination Survey (NHANES) to systematically evaluate the association between the RCII and both MAFLD risk and related mortality outcomes. We further investigated potential nonlinear exposure–response relationships and mediating metabolic pathways. In parallel, we employed Boruta-based feature selection and a suite of machine learning algorithms to develop predictive models for MAFLD, aiming to establish RCII as a novel integrative biomarker and to advance intelligent modeling strategies for the early detection of metabolic diseases. Materials and Methods Data Sources This study was based on data from the National Health and Nutrition Examination Survey (NHANES; https://www.cdc.gov/nchs/nhanes ), a nationally representative survey conducted in the United States that comprehensively evaluates health status, nutritional intake, and socioeconomic factors among the civilian non-institutionalized population. The study adhered to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines for observational research. We included data from adult participants enrolled between 1999 and 2010, during which measurements of total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and CRP were consistently available to calculate the RCII. Subsequent cycles (2011–2014) were excluded due to missing CRP data, and although high-sensitivity CRP (hsCRP) data were available from 2015–2018, differences in assay methodology and the absence of mortality follow-up data post-2018 precluded their inclusion [ 13 ]. Participants were excluded if they met any of the following criteria: (1) age < 18 years; (2) missing data on TC, HDL-C, LDL-C, or CRP; or (3) lack of follow-up information. After applying these criteria, a total of 13,254 NHANES participants were included in the final analysis. A detailed flowchart of the inclusion and exclusion process is presented in Fig. 1 . Definition of MAFLD and RCII MAFLD was diagnosed according to the latest international consensus, requiring evidence of hepatic steatosis (via imaging or biochemical indicators) in addition to at least one of the following three metabolic conditions: (1) overweight or obesity; (2) diagnosed type 2 diabetes mellitus (T2DM); or (3) evidence of metabolic dysregulation. Metabolic dysregulation was defined as meeting at least two of the following six criteria: (1) central obesity (waist circumference > 102 cm in men or > 88 cm in women); (2) elevated blood pressure (systolic ≥ 130 mmHg or diastolic ≥ 85 mmHg, or current use of antihypertensive medication); (3) hypertriglyceridemia (triglycerides > 1.70 mmol/L or on lipid-lowering therapy); (4) reduced high-density lipoprotein cholesterol (HDL-C < 1.0 mmol/L in men or < 1.3 mmol/L in women); (5) prediabetes, defined as fasting plasma glucose of 5.6–6.9 mmol/L, 2-hour postprandial glucose of 7.8–11.0 mmol/L, or HbA1c of 5.7%–6.4%; (6) elevated high-sensitivity C-reactive protein (hsCRP > 2 mg/L) [ 15 ]. In the absence of abdominal imaging and liver biopsy data, this study employed the fatty liver index (FLI) to determine the presence of hepatic steatosis. The FLI was calculated according to the following formula: $$\:FLI=\frac{{e}^{0.953\times\:ln\left(TG\right)+0.139\times\:BMI+0.718\times\:ln\left(GGT\right)+0.053\times\:waist\:circumference-15.745}}{1+{e}^{0.953\times\:ln\left(TG\right)+0.139\times\:BMI+0.718\times\:ln\left(GGT\right)+0.053\times\:waist\:circumference-15.745}}\times\:100$$ where TG is serum triglycerides (mg/dL), BMI is body mass index (kg/m²), GGT is γ-glutamyl transferase (U/L), and waist circumference is measured in centimeters. Participants were classified as having hepatic steatosis if the FLI was ≥ 60, as previously validated in epidemiological studies [ 16 ]. The RCII, serving as the primary exposure variable in this study, is a composite marker integrating metabolic and inflammatory burden. It was calculated as: RCII = RC × CRP, where residual cholesterol (RC) was estimated using the formula RC = TC − (HDL-C + LDL-C), with all values expressed in mg/dL [ 17 ]. Outcome Assessment Mortality outcomes were ascertained through linkage of the NHANES cohort with the National Death Index (NDI), with follow-up through December 31, 2019. The primary endpoints included all-cause mortality (death from any cause), cardiovascular mortality (defined using ICD-10 codes I00–I09, I11, I13, I20–I51, and I60–I69), and premature death (defined as death occurring before the age of 75). Cause-of-death classifications were based on the "ucode_leading" variable provided in the publicly available mortality files curated by the National Center for Health Statistics (NCHS), ensuring standardized and consistent endpoint determination [ 18 ]. Covariates In multivariable analyses, a comprehensive set of covariates was included to account for potential confounding factors, encompassing demographic characteristics, lifestyle behaviors, and comorbid conditions. Demographic variables comprised age, sex, educational attainment, marital status, and race/ethnicity. Behavioral factors included smoking status and alcohol consumption. Clinical comorbidities—namely diabetes, hypertension, and dyslipidemia—were identified based on self-reported physician diagnoses or corresponding biochemical criteria, as previously defined [ 13 ]. Restricted Cubic Spline Analysis To characterize the shape of the association between the RCII and the risk of MAFLD and mortality, restricted cubic spline (RCS) functions were applied. RCII was modeled as a continuous variable within logistic regression frameworks for MAFLD prevalence and Cox proportional hazards models for mortality outcomes, allowing for the identification of potential non-linear dose–response relationships. Knot placement was determined based on the empirical distribution of RCII, and all models were adjusted for key covariates [ 18 ]. Mediation Analysis To evaluate the mediating role of fasting plasma glucose (FPG) in the associations between the RCII and both MAFLD risk and mortality outcomes, mediation analyses were conducted using the "mediation" package in R. FPG was selected as the mediator due to its central role in glucose metabolism dysregulation and its potential to mechanistically link RCII with metabolic liver disease and adverse outcomes. Two regression models were specified: one to predict the mediator (FPG) and another to model the outcome—logistic regression for MAFLD and an accelerated failure time (AFT) model (via survreg) for mortality endpoints. Inference was based on nonparametric bootstrapping. Estimates included the average causal mediation effect (ACME), total effect, and the proportion mediated. All models were adjusted for key covariates, including age, sex, education level, marital status, BMI, waist circumference, and smoking status [ 19 ]. Subgroup Analyses Subgroup analyses were conducted to assess the robustness of the associations between RCII and study outcomes across strata of key demographic and health-related variables. Participants were stratified by age (< 60 vs. ≥60 years), sex (male vs. female), body mass index (BMI < 30 vs. ≥30 kg/m²), marital status (unmarried/widowed vs. married/cohabiting), educational attainment (high school or above vs. below high school), smoking status (non-smoker vs. smoker), alcohol consumption (no vs. yes), and history of diabetes, coronary heart disease, stroke, angina, and cancer—resulting in 12 predefined subgroups. Within each stratum, three progressively adjusted multivariable models (Model 1–3) were constructed to evaluate the association between RCII and outcome variables, with hazard ratios (HRs) and corresponding 95% confidence intervals reported. Statistical interactions were tested by incorporating multiplicative interaction terms into the fully adjusted models, and P-values for interaction were calculated to assess effect modification [ 20 ]. Survival Analysis Kaplan–Meier survival curves were generated to estimate all-cause mortality, cardiovascular mortality, and premature death across quartiles of RCII. Group differences were assessed using the log-rank test. To further quantify the association between RCII and mortality outcomes, Cox proportional hazards models were constructed with three levels of covariate adjustment. Model 1 was unadjusted. Model 2 adjusted for age, sex, race, and educational attainment. Model 3 was additionally adjusted for body mass index (BMI), smoking status, alcohol consumption, and diabetes status. Results were reported as hazard ratios (HRs) with corresponding 95% confidence intervals (CIs). All analyses were performed using R software, and statistical significance was defined as a two-sided P-value < 0.05 [ 21 ]. Mendelian Randomization A two-sample Mendelian randomization (MR) approach was employed to investigate the potential causal relationships between blood lipid traits (TC, HDL-C, LDL-C), CRP, FPG, and the risk of MAFLD [ 22 ]. Genetic instruments for both exposures and outcomes were derived from publicly available genome-wide association study (GWAS) summary statistics, including datasets for MAFLD (finngen_R12_NAFLD), TC (met-d-Total_C), HDL-C (GCST008035), LDL-C (GCST008037), CRP (GCST90029070), and FPG (GCST008032). Single nucleotide polymorphisms (SNPs) were selected as instrumental variables based on genome-wide significance (P < 5 × 10⁻⁸) and pruned for linkage disequilibrium using a threshold of r² < 0.001 and a clumping window of < 10,000 kb to ensure independence. If the number of eligible SNPs was insufficient, a relaxed significance threshold (P < 1 × 10⁻⁵) was applied, in accordance with prior studies [ 23 ]. Causal effects were estimated using multiple MR methods to ensure robustness and consistency of results, including inverse variance weighted (IVW), MR-Egger regression, weighted median, simple mode, and weighted mode approaches. For each method, effect estimates, 95% confidence intervals, and P-values were reported. Heterogeneity tests and sensitivity analyses were also conducted to evaluate the validity of instrumental variables and the underlying MR assumptions. All analyses were performed using R software. Statistical Analysis For the NHANES dataset, all analyses accounted for the complex multistage, stratified, and weighted sampling design by incorporating appropriate survey weights to yield nationally representative estimates. Continuous variables were summarized as means with standard deviations if normally distributed and compared using independent samples t-tests; otherwise, medians with interquartile ranges were reported, and group differences were assessed using the Wilcoxon rank-sum test. Categorical variables were compared using the chi-square test [ 24 ] [23] . In the MR analysis, five complementary methods were used to estimate the genetic associations between exposures and outcomes: IVW, weighted median, MR-Egger regression, simple mode, and weighted mode approaches. Heterogeneity across genetic instruments in the IVW model was assessed using Cochran’s Q statistic, while directional horizontal pleiotropy was evaluated via the MR-Egger intercept. To ensure robustness, leave-one-out sensitivity analysis was performed to evaluate the influence of individual single-nucleotide polymorphisms (SNPs) on the overall causal estimates. All hypothesis tests were two-sided, with a significance threshold of P < 0.05 [ 22 ]. A total of 13,254 participants were randomly split into training and test sets at a 7:3 ratio. Feature selection was performed using the Boruta algorithm to identify variables most predictive of MAFLD. Five classification models were subsequently constructed: random forest (RF), k-nearest neighbors (KNN), naïve Bayes (NB), light gradient boosting machine (LightGBM), and decision tree (rpart). Model performance was evaluated based on accuracy, Kappa statistic, sensitivity, specificity, precision, area under the receiver operating characteristic curve (AUROC), and area under the precision-recall curve (AUPR) [ 25 ]. To enhance model interpretability, SHAP was applied to quantify the relative contribution of each predictor to model outputs. SHAP values provide individualized estimates of feature importance, indicating both the direction and magnitude of each variable’s effect while accounting for feature interactions [ 26 ]. Machine learning analyses were conducted in Python (v3.10) using core libraries including xgboost, sklearn, shap, pandas, and matplotlib. All other statistical analyses were performed in R (v4.2.1) using packages such as survey, ggplot2, dplyr, mgcv, and rms. All statistical tests were two-sided, with P-values < 0.05 considered statistically significant. Results Baseline Characteristics of the Study Population A total of 13,254 participants from the NHANES cohort were included in the analysis, comprising 5,693 individuals with MAFLD and 7,561 without (Table 1 ). Compared to the non-MAFLD group, participants with MAFLD were older (median age: 52.00 vs. 45.00 years) and exhibited significantly higher levels of key metabolic and inflammatory markers, including BMI (32.39 vs. 24.75 kg/m²), waist circumference (108.80 vs. 89.00 cm), FPG (102.70 vs. 95.00 mg/dL), and C-reactive protein (0.35 vs. 0.15 mg/dL). In terms of lipid profiles, the MAFLD group had elevated triglycerides (150.00 vs. 94.00 mg/dL) and LDL-C (119.00 vs. 112.00 mg/dL), along with reduced HDL-C (46.00 vs. 56.00 mg/dL). Notably, both residual cholesterol (RC; 30.00 vs. 19.00 mg/dL) and the residual cholesterol–inflammation index (RCII; 10.56 vs. 2.85) were markedly higher in the MAFLD group (P < 0.001 for both comparisons). When stratified by RCII quartiles, 40.21% of individuals in the MAFLD group fell within the highest quartile (Q4), compared to only 13.64% in the non-MAFLD group. Conversely, the proportion of individuals in the lowest RCII quartile (Q1) was significantly lower among those with MAFLD (7.22% vs. 38.32%). Comorbidities were also more prevalent in the MAFLD group, including diabetes (15.30% vs. 5.69%), CHD (5.38% vs. 3.15%), stroke (4.39% vs. 2.96%), and angina (4.46% vs. 1.92%). These findings collectively suggest that individuals with MAFLD are characterized by pronounced metabolic dysregulation and systemic inflammation, and that RCII may serve as a robust marker for identifying high-risk individuals. Table 1 Baseline characteristics of clinical information Features Overall (n = 13254) non-MAFLD (n = 7561) MAFLD (n = 5693) P-Value Age (median, IQR) 48.00 (31.00) 45.00 (33.00) 52.00 (28.00) < 0.001 Gender (n, %) < 0.001 Male 6305 (47.57) 3294 (43.57) 3011 (52.89) Female 6949 (52.43) 4267 (56.43) 2682 (47.11) Race (n, %) < 0.001 Mexican American 2733 (20.62) 1396 (18.46) 1337 (23.48) Non-Hispanic White 6583 (49.67) 3888 (51.42) 2695 (47.34) Non-Hispanic Black 2482 (18.73) 1371 (18.13) 1111 (19.52) Other Hispanic 913 (6.89) 526 (6.96) 387 (6.80) Other Race 543 (4.10) 380 (5.03) 163 (2.86) Education (n, %) < 0.001 Less than High School 4014 (30.29) 2109 (27.89) 1905 (33.46) High School and above 9240 (69.71) 5452 (72.11) 3788 (66.54) Marital_Status (n, %) < 0.001 Never married/Widowed 5125 (38.67) 3065 (40.54) 2060 (36.18) Married/Living with partner 8129 (61.33) 4496 (59.46) 3633 (63.82) BMI (median, IQR) 27.57 (7.63) 24.75 (4.62) 32.39 (7.03) < 0.001 Waist_Circumference (median, IQR) 97.00 (20.00) 89.00 (13.80) 108.80 (15.00) < 0.001 SBP (median, IQR) 122.00 (24.00) 118.00 (24.00) 126.00 (24.00) < 0.001 DBP (median, IQR) 70.00 (16.00) 68.00 (14.00) 72.00 (16.00) < 0.001 Smoking (n, %) < 0.001 No 7181 (54.18) 3787 (50.09) 3394 (59.62) Yes 6073 (45.82) 3774 (49.91) 2299 (40.38) Drinking (n, %) < 0.001 No 3366 (25.40) 1759 (23.26) 1607 (28.23) Yes 9888 (74.60) 5802 (76.74) 4086 (71.77) Diabetes (n, %) < 0.001 No 11953 (90.18) 7131 (94.31) 4822 (84.70) Yes 1301 (9.82) 430 (5.69) 871 (15.30) CHD (n, %) < 0.001 No 12710 (95.90) 7323 (96.85) 5387 (94.62) Yes 544 (4.10) 238 (3.15) 306 (5.38) Stroke (n, %) < 0.001 No 12780 (96.42) 7337 (97.04) 5443 (95.61) Yes 474 (3.58) 224 (2.96) 250 (4.39) Angina (n, %) < 0.001 No 12855 (96.99) 7416 (98.08) 5439 (95.54) Yes 399 (3.01) 145 (1.92) 254 (4.46) Cancer (n, %) 0.083 No 12066 (91.04) 6912 (91.42) 5154 (90.53) Yes 1188 (8.96) 649 (8.58) 539 (9.47) FPG (median, IQR) 98.00 (16.70) 95.00 (14.30) 102.70 (20.00) < 0.001 CRP (median, IQR) 0.22 (0.42) 0.15 (0.28) 0.35 (0.58) < 0.001 TC (median, IQR) 195.00 (54.00) 192.00 (53.00) 199.00 (55.00) < 0.001 HDL-C (median, IQR) 51.00 (21.00) 56.00 (22.00) 46.00 (17.00) < 0.001 LDL-C (median, IQR) 115.00 (47.00) 112.00 (46.00) 119.00 (47.00) < 0.001 TG (median, IQR) 114.00 (85.00) 94.00 (60.00) 150.00 (99.00) < 0.001 GGT (median, IQR) 20.00 (17.00) 17.00 (12.00) 27.00 (23.00) < 0.001 FLI (median, IQR) 52.19 (58.26) 26.20 (31.30) 84.03 (20.45) < 0.001 RC (median, IQR) 23.00 (17.00) 19.00 (12.00) 30.00 (20.00) < 0.001 RCII (median, IQR) 5.22 (12.22) 2.85 (6.36) 10.56 (18.48) < 0.001 RCII_Type (n, %) < 0.001 Q1 3308 (24.96) 2897 (38.32) 411 (7.22) Q2 3316 (25.02) 2117 (28.00) 1199 (21.06) Q3 3310 (24.97) 1516 (20.05) 1794 (31.51) Q4 3320 (25.05) 1031 (13.64) 2289 (40.21) MAFLD, metabolic dysfunction-associated fatty liver disease; IQR, inter-quartile range; SBP, systolic blood pressure; DBP, diastolic blood pressure; CHD, coronary heart disease; FPG, fasting plasma glucose; CRP, C-reactive protein; TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; TG, triglyceride; GGT, glutamyl transferase; FLI, fatty liver index; RC, remnant cholesterol; RCII, residual cholesterol-inflammation index Association Between RCII and Risk of MAFLD and related Mortality In the study cohort, higher levels of RC, CRP, and the RCII were all significantly associated with increased odds of MAFLD, exhibiting robust dose–response relationships (Fig. 2 ). Compared to the lowest RCII quartile (Q1), adjusted odds ratios (ORs) for MAFLD were progressively elevated across quartiles: 4.16 (95% CI: 3.67–4.72) for Q2, 8.88 (95% CI: 7.77–10.14) for Q3, and 17.79 (95% CI: 15.69–20.17) for Q4 (all P < 0.001). Although both RC and CRP alone were also independently associated with MAFLD risk, the magnitude of their associations was comparatively lower. The adjusted OR for the highest RC quartile was 17.05 (95% CI: 14.46–20.11), and for CRP was 7.46 (95% CI: 6.54–8.52). Collectively, these findings suggest that RCII—by integrating lipid dysregulation and systemic inflammation—offers superior predictive performance for identifying individuals at high risk of MAFLD. Kaplan–Meier survival analysis revealed a significant association between RCII quartiles and all three mortality outcomes: all-cause mortality, cardiovascular mortality, and premature death (Fig. 3 ). Survival probability declined progressively across increasing RCII quartiles (Q1 to Q4), with the lowest survival observed in the highest RCII group (Q4). All differences were statistically significant (P < 0.001), indicating that individuals with elevated RCII levels are at substantially higher risk of mortality. To further evaluate the relationship between RCII and mortality, we constructed multivariable Cox proportional hazards models using all-cause, cardiovascular, and premature death as outcomes (Fig. 4 A–C). In the analysis of all-cause mortality (Fig. 4 A), using the lowest RCII quartile (Q1) as the reference, a stepwise increase in mortality risk was observed across higher quartiles. In the fully adjusted model (Model 3), hazard ratios (HRs) for Q2, Q3, and Q4 were 1.14 (95% CI: 0.96–1.34, P = 0.127), 1.28 (95% CI: 1.12–1.48, P < 0.001), and 1.83 (95% CI: 1.60–2.10, P < 0.001), respectively, indicating a clear dose–response trend. A similar pattern was found for cardiovascular mortality (Fig. 4 B), with adjusted HRs of 1.17 (95% CI: 0.89–1.54, P = 0.262) for Q2, 1.22 (95% CI: 0.98–1.53, P = 0.071) for Q3, and 1.79 (95% CI: 1.40–2.29, P < 0.001) for Q4, mirroring the all-cause mortality results. For premature death (Fig. 4 C), the associations were even more pronounced. Compared to Q1, the adjusted HRs were 1.37 (95% CI: 1.10–1.69, P = 0.004) for Q2, 1.61 (95% CI: 1.33–1.94, P < 0.001) for Q3, and 2.36 (95% CI: 1.97–2.82, P < 0.001) for Q4, again demonstrating a robust dose-dependent relationship. Collectively, elevated RCII levels were independently associated with significantly increased risks of all-cause, cardiovascular, and premature death, with consistent dose–response gradients across quartiles. These findings suggest that RCII may serve as a reliable prognostic biomarker for long-term adverse outcomes. Nonlinear Associations Between RCII and Risk of MAFLD and related mortality RCS regression analyses revealed nonlinear relationships between the RCII and multiple adverse health outcomes (Fig. 5 A–D). As shown in Fig. 5 A, RCII exhibited a pronounced nonlinear association with MAFLD risk: the risk sharply increased when RCII was below approximately 5.83, plateaued thereafter, and showed a slight decline at higher levels. In contrast, Fig. 5 B demonstrated a significant linear relationship between RCII and all-cause mortality (Overall P = 0.006; Nonlinear P = 0.822), with increasing RCII levels corresponding to a steadily rising mortality risk, indicating a robust dose–response association. Figure 5 C indicated a marginally significant overall association between RCII and cardiovascular mortality (overall P = 0.055; nonlinear P = 0.274), with modest risk elevation observed at higher RCII levels despite the absence of clear nonlinearity. Figure 5 D showed a strong positive association between RCII and premature death (overall P < 0.001; nonlinear P = 0.277), with risk increasing consistently across the full range of RCII values. Collectively, these findings underscore that elevated RCII is independently and positively associated with MAFLD, all-cause mortality, CVD mortality, and premature death, supporting its potential utility as a predictive biomarker for long-term adverse outcomes. Subgroup Analyses of the Stability and Heterogeneity of RCII in Predicting MAFLD and related Mortality Risk To further assess the robustness and subgroup-specific variations in the association between the RCII and the risk of MAFLD and adverse outcomes, stratified analyses were conducted across multiple key covariates, including age, sex, BMI, marital status, education level, smoking and alcohol consumption, and history of chronic diseases (Figures S1 –S4). The results demonstrated that elevated RCII was consistently and significantly associated with increased risk of MAFLD (Figure S1 ), all-cause mortality (Figure S2), cardiovascular mortality (Figure S3), and premature death (Figure S4) across most subpopulations, even after adjusting for potential confounders. Notably, significant interactions were observed between RCII and several subgroup variables (e.g., age, sex, BMI, diabetes, and cancer), suggesting that the predictive strength of RCII may be more pronounced in certain high-risk groups. Causal Effects of HDL-C and CRP on MAFLD Risk Using five Mendelian randomization (MR) methods—including MR-Egger, weighted median, IVW, simple mode, and weighted mode—we systematically evaluated the causal effects of key lipid and inflammatory biomarkers on the risk of MAFLD. For TC, all MR methods yielded odds ratios (ORs) close to 1.00 with non-significant P values (all P > 0.2), suggesting no evidence of a causal relationship between total cholesterol levels and MAFLD (Fig. 6 A). Similarly, for LDL-C, results were highly consistent across methods (OR = 1.00 for all), with narrow 95% confidence intervals and P values > 0.4, indicating no significant causal association with MAFLD (Fig. 6 B). In contrast, for HDL-C, the IVW method estimated an odds ratio (OR) of 0.78 (95% CI: 0.58–0.98, P = 0.018), the MR-Egger method yielded an OR of 0.75 (P = 0.024), and the weighted mode method also reported an OR of 0.75 (P = 0.018) (Fig. 6 C). CRP demonstrated a positive, potentially pathogenic association with MAFLD across all MR approaches except the simple mode. The weighted median method showed a statistically significant effect estimate (OR = 1.39, 95% CI: 1.11–1.74, P = 0.004) (Fig. 6 D). To enhance the robustness and credibility of these causal inferences, we have provided comprehensive sensitivity analyses—including scatter plots, funnel plots, single SNP effect plots, and leave-one-out analyses for TC, LDL-C, HDL-C, and CRP—in the supplementary materials (Figure S5-8). These findings suggest that TC and LDL-C appear unrelated to MAFLD risk in causal inference. In contrast, elevated HDL-C levels may causally reduce the risk of MAFLD, whereas higher CRP levels are likely to increase it. Mediation Analysis Mediation analysis revealed that FPG played a statistically significant mediating role in the relationship between the RCII and MAFLD risk, accounting for 2.02% of the total effect (Average Causal Mediation Effect [ACME]: β = 2.34×10⁻⁵, P < 0.001) (Fig. 7 A, Table S1 ). For survival outcomes, the proportion of mediation by FPG was 6.76% for all-cause mortality (ACME: β = − 0.21, P < 0.001), 8.06% for cardiovascular mortality (ACME: β = − 1.32, P < 0.001), and 7.33% for premature death (ACME: β = − 0.18, P < 0.001) (Fig. 7 B–D, Table S1 ). Given that survival analyses were conducted using an accelerated failure time (AFT) model, the estimated effects reflect the influence of RCII on log-transformed survival time via FPG. In other words, RCII may contribute to increased mortality risk in part by elevating FPG levels. Additionally, a two-step Mendelian randomization mediation framework was applied to assess the mediating role of FPG in the causal pathways linking HDL-C and CRP with MAFLD risk. As shown in Table 2 , the total effect of HDL-C on MAFLD was β = − 0.132, with a direct effect of β = − 0.125 and a mediated effect of β = − 0.007 (95% CI: − 0.010 to − 0.002), indicating a significant mediation proportion of 5.3%. For CRP, the total effect was β = 0.166, with a direct effect of β = 0.158 and a mediated effect of β = 0.008 (95% CI: 0.001 to 0.028), accounting for 4.8% of the total effect and also reaching statistical significance. Collectively, these findings suggest that FPG partially mediates the effects of RCII, HDL-C, and CRP on both MAFLD risk and adverse mortality outcomes. Table 2 Two-step Mendelian randomization mediation analysis results: estimates of the total, direct, and FPG-mediated effects of HDL-C and CRP on MAFLD risk Exposure Mediator Outcome Total_beta Direct_beta Mediation_beta HDL-C FPG MAFLD -0.132 -0.125 -0.007 (-0.010 to -0.002) CRP FPG MAFLD 0.166 0.158 0.008 (0.001 to 0.028) MAFLD, metabolic dysfunction-associated fatty liver disease; FPG, fasting plasma glucose; HDL-C, high-density lipoprotein cholesterol; CRP, C-reactive protein Performance Evaluation of MAFLD Prediction Models and Identification of Key Predictors To identify the most informative features associated with MAFLD, we applied the Boruta algorithm to a broad set of candidate variables. Waist circumference, BMI, and age emerged as the top-ranking predictors with the highest importance scores. Additional contributors included systolic blood pressure (SBP), sex, diabetes status, and diastolic blood pressure (DBP). These variables, together with RCII, were subsequently incorporated into machine learning–based prediction models for MAFLD identification. To compare the predictive performance of different modeling strategies, we constructed and evaluated five machine learning classifiers: RF, KNN, NB, LightGBM, and decision tree (rpart). Model performance was assessed separately in both the training and testing datasets (Table 3 ). In the training cohort, the RF model demonstrated the best performance, achieving an accuracy of 98.0%, an area under the receiver operating characteristic curve (AUC-ROC) of 0.999, and an area under the precision-recall curve (PR-AUC) of 0.988, indicating a near-perfect fit (Fig. 8 A–B). LightGBM and KNN also showed strong predictive capacity with accuracies of 94.4% and 93.4%, respectively, and AUCs exceeding 0.98. In contrast, the NB and rpart models underperformed in the training dataset. Table 3 Performance comparison of different machine learning models for MAFLD classification on training and testing datasets Models Train datasets Testing datasets Accuracy Kappa AUC-ROC Sensitivity Specificity Precision Accuracy Kappa AUC-ROC Sensitivity Specificity Precision RF 0.98 0.958 0.999 0.964 0.991 0.988 0.897 0.788 0.96 0.864 0.921 0.892 KNN 0.934 0.864 0.987 0.911 0.951 0.933 0.845 0.681 0.921 0.793 0.884 0.837 NB 0.874 0.74 0.949 0.81 0.922 0.887 0.865 0.72 0.943 0.786 0.924 0.886 LGB 0.944 0.887 0.991 0.934 0.952 0.936 0.891 0.776 0.956 0.868 0.907 0.876 rpart 0.865 0.728 0.868 0.884 0.851 0.817 0.859 0.715 0.861 0.875 0.847 0.811 MAFLD, metabolic dysfunction-associated fatty liver disease; RF, random forest; KNN, k-nearest neighbors; NB, naïve Bayes; LGB, light gradient boosting machine; AUC-ROC, area under the receiver operating characteristic curve Figure lengths In the testing set, the RF model maintained superior generalizability, achieving an accuracy of 89.7%, an AUC-ROC of 0.960, and a PR-AUC of 0.949 (Fig. 8 C–D). LightGBM and KNN models also demonstrated strong external validity, with AUCs of 0.956 and 0.921, respectively. NB and rpart showed comparatively weaker performance in the validation phase. Model interpretability was examined using SHAP (Fig. 8 E) and feature importance ranking (Fig. 8 F). Waist circumference was identified as the most influential predictor, followed by RCII. Other notable contributors included age, blood pressure (SBP and DBP), sex, and history of angina. Taken together, the RF model exhibited the highest predictive accuracy and interpretability across all tested algorithms. Notably, RCII—representing an integrated measure of systemic inflammation and residual cholesterol—demonstrated robust and independent predictive value in identifying individuals at high risk for MAFLD. Discussion Leveraging data from the NHANES cohort, this study systematically evaluated the association between the RCII and multiple adverse health outcomes, including MAFLD, all-cause mortality, cardiovascular mortality, and premature death. RCII demonstrated a robust and independent positive association with each endpoint, even after multivariable adjustment. Mendelian randomization and mediation analyses further supported the mechanistic roles of HDL-C, CRP, and FPG in this relationship. Among several machine learning algorithms, the RF model achieved superior predictive performance. SHAP analysis corroborated RCII as a key predictor of MAFLD risk, underscoring its potential utility in precision risk stratification and early identification of high-risk individuals. In recent years, multiple research groups have employed machine learning techniques to develop predictive models for MAFLD, yielding promising results. However, variations in model performance, feature selection, and target populations have been noted across studies. A study based on NHANES 2017–2020 data developed a model centered on the non-HDL to HDL cholesterol ratio (NHHR), where the XGBoost algorithm achieved an AUC of 0.828. While demonstrating reasonable predictive power, the model relied predominantly on conventional lipid parameters and featured limited variable diversity [ 27 ]. In a large-scale investigation involving over five million individuals in Northwestern China, LASSO regression was used for feature selection, and the CatBoost model attained an AUC of 0.862, highlighting the predictive relevance of age, BMI, triglycerides, and fasting glucose; however, the absence of inflammatory markers limited its comprehensiveness [ 28 ]. Another study integrated vibration-controlled transient elastography (VCTE) parameters to stratify MAFLD risk, with an RF model achieving an AUC of 0.80 in the validation set, mainly for delineating low-, intermediate-, and high-risk groups [ 29 ]. Additionally, leveraging long-term follow-up data from NHANES III, a recent study employed multiple machine learning models to predict all-cause mortality among MAFLD patients, with the Coxnet model reaching an AUC of 0.88 at the 25-year mark—underscoring the clinical potential for long-term prognostic assessment [ 30 ]. We constructed and compared five machine learning models based on routine clinical and laboratory parameters. The RF model demonstrated superior performance in the test set, with an AUC of 0.960 and an accuracy of 89.7%, comparable to or exceeding previously reported models. Notably, the inclusion of RCII—a novel composite biomarker reflecting both metabolic dysfunction and systemic inflammation—substantially enhanced model interpretability and adaptability. These findings support the utility of RCII-enhanced machine learning models as robust tools for early MAFLD detection and individualized risk stratification. Previous studies have established that RC is closely associated with hepatic lipid dysregulation and serves as a predictor for MAFLD and its cardiovascular outcomes [ 31 ]. Likewise, CRP, including hs-CRP, has been widely used as a convenient marker of systemic inflammation and is strongly linked to increased MAFLD risk [ 8 ] However, reliance on single biomarkers often yields inconsistent predictive performance across different populations, limiting their clinical robustness. In contrast, RCII, by integrating metabolic and inflammatory dimensions, demonstrates superior consistency and stability in predicting both MAFLD and mortality outcomes. Compared with other metabolic or inflammatory indicators—such as the systemic immune-inflammation index (SII) [ 32 ], homeostatic model assessment of insulin resistance (HOMA-IR) [ 33 ], and low thyroid function status [ 34 ] RCII exhibits greater external validity and translational potential in diverse clinical settings. Multiple large-scale prospective cohort studies have confirmed that MAFLD is independently associated with elevated risks of all-cause and cardiovascular mortality [ 35 – 37 ], particularly among individuals with coexisting diabetes or the "lean MAFLD" phenotype [ 38 , 39 ]. Extending this evidence, our study further reveals a positive and independent association between elevated RCII levels and premature death. Notably, this relationship persists even after comprehensive adjustment for potential confounders. These findings suggest that RCII may serve not only as a general prognostic marker for mortality, but also as an early-warning indicator for premature death—offering critical value for optimizing the timing of interventions and informing public health resource allocation. The predictive power of the RCII for MAFLD, all-cause mortality, and cardiovascular mortality likely stems from its integration of two central pathological axes: metabolic dysregulation and chronic inflammation. RCII combines RC, a marker of lipid accumulation, with C-reactive protein (CRP or hs-CRP), a canonical indicator of systemic inflammation—each representing distinct yet interrelated biological pathways implicated in the pathogenesis and progression of metabolic diseases [ 40 , 41 ]. Their synergistic interaction may potentiate vascular injury and organ dysfunction across multiple systems. Elevated RC promotes lipid deposition within arterial walls, contributes to endothelial dysfunction, and exacerbates lipid derangements via impaired reverse cholesterol transport mediated by HDL-C [ 42 , 43 ]. In parallel, CRP not only suppresses pancreatic β-cell function but also stimulates hepatic glucose production, thereby aggravating FBG levels [ 44 ]. In our mediation analysis, FBG emerged as a significant intermediary linking RCII to increased mortality risk. Notably, elevated FBG serves as both a surrogate for insulin resistance and a pathogenic factor in its own right. Through activation of the AGE–RAGE axis, hyperglycemia accelerates vascular stiffening and myocardial remodeling, impairs HDL function, and promotes LDL oxidation—collectively compounding the metabolic and inflammatory disturbances driven by RCII [ 45 , 46 ]. This tripartite interaction among dyslipidemia, inflammation, and glucose imbalance constitutes a tightly coupled risk network. Thus, RCII may be conceptualized as an integrated biomarker of metabolic–inflammatory stress, with its close association with FBG highlighting the pivotal role of glucose dysregulation in mediating the adverse outcomes linked to RCII. This study has several limitations. First, the construction of RCII relies on the availability of CRP and lipid measurements, which may restrict its applicability in populations lacking inflammatory biomarker data. Second, although both traditional regression models and multiple machine learning algorithms consistently supported the robustness of RCII in assessing MAFLD risk, the cross-sectional nature of the NHANES dataset precludes definitive causal inference. Potential reverse causality and residual confounding cannot be fully ruled out. While Mendelian randomization provided suggestive evidence for a causal role, its validity is inherently constrained by the choice of instrumental variables and the characteristics of the study population. Therefore, longitudinal cohort studies and interventional trials are needed to further validate the prognostic utility of RCII. In summary, RCII—a novel composite biomarker integrating metabolic and inflammatory signals—demonstrates strong predictive capacity and cross-model stability for MAFLD risk assessment, underscoring its potential clinical relevance. Future investigations should prioritize validating the accuracy and clinical effectiveness of RCII through animal models, randomized trials, and prospective cohort studies. Moreover, integrating multi-omics approaches, such as transcriptomics and metabolomics, may help elucidate the underlying metabolic–inflammatory pathways captured by RCII and provide mechanistic insight to support its translation into clinical practice. Conclusion As a composite biomarker integrating lipid dysregulation and systemic inflammation, the RCII is independently and significantly associated with increased risk of MAFLD and adverse mortality outcomes, demonstrating strong predictive utility. Its underlying mechanism may be partially mediated by elevated FPG levels. Abbreviations RC Residual cholesterol RCII Residual cholesterol-inflammation index SII Systemic immune-inflammation index MAFLD Metabolic dysfunction-associated fatty liver disease NAFLD Non-alcoholic fatty liver disease NHANES National health and nutrition examination survey NDI National Death Index BMI Body mass index FLI Fatty liver index FPG Fasting plasma glucose GGT Glutamyl transferase HDL-C High-density lipoprotein cholesterol LDL-C Low-density lipoprotein cholesterol TC Total cholesterol IDL Intermediate-density lipoproteins VLDL Very-low-density lipoproteins CRP C-reactive protein NHHR Non-HDL to HDL cholesterol ratio ROC Receiver operating characteristic AUC Area under the curve AUPR Area under the precision-recall curve HRs Hazard ratios ORs Odds ratios CIs Confidence intervals RCS Restricted cubic splines CVD Cardiovascular disease T2DM Type 2 diabetes mellitus SBP Systolic blood pressure DBP Diastolic blood pressure HOMA-IR Homeostatic model assessment of insulin resistance hsCRP High-sensitivity CRP ICD International Classification of Diseases ACME Average causal mediation effect AFT Accelerated failure time ADE Average direct effect GWAS Genome-wide association study MR Mendelian randomization IVW Inverse variance weighted LASSO Least absolute shrinkage and selection operator KNN K-nearest neighbors LightGBM Light gradient boosting machine NB Naïve Bayes PR Precision–recall RF Random forest SHAP SHapley Additive exPlanations SNPs Single nucleotide polymorphisms VCTE Vibration-controlled transient elastography Declarations Ethics approval and consent to participate The NHANES program was approved by the National Center for Health Statistics (NCHS) Ethics Review Board and all participants signed an informed consent form. Consent for publication Written informed consent for publication was obtained from all participants. Competing interest The authors declare no competing interests. Funding This study has no funding. Author Contribution Zhongqiao Lu: Writing– original draft, data curation and formal analysis. Yingxia Hu: Writing– original draft, data curation and formal analysis. Deshan Zong: Writing– review, data curation and formal analysis. Bin Yue: Writing– review and editing, & Methodology. Acknowledgements We extend our gratitude to the participants of the NHANES database in the United States for their invaluable contribution to this study. Data Availability All data used in this study is available through the National Health and Nutrition Examination Survey repository, which is publicly accessible at: [https://wwwn.cdc.gov/nchs/nhanes/default.aspx] (https:/wwwn.cdc.gov/nchs/nhanes/default.aspx) . References Sakurai Y et al. Role of Insulin Resistance in MAFLD. 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Int J Angiol. 2016;25:110–6. https://doi.org/10.1055/s-0035-1570754 . Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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3","display":"","copyAsset":false,"role":"figure","size":982789,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan–Meier survival curves for all-cause mortality, cardiovascular disease mortality, and premature death across RCII quartiles. (A) all-cause mortality, (B) cardiovascular disease mortality, (C) Premature death.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7620270/v1/c54ca68f3f7fe7e4a68e52bb.jpg"},{"id":92736272,"identity":"bdb2f1cc-e375-4705-a68b-3b3f8711a28d","added_by":"auto","created_at":"2025-10-03 16:35:45","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":521875,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations between RCII quartiles and the risks of all-cause mortality, cardiovascular mortality, and premature death. (A) all-cause mortality. (B) cardiovascular disease mortality. (C) premature death.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7620270/v1/c428704ecf2c30c311aad523.jpg"},{"id":92736283,"identity":"a890382b-031c-470d-9fac-3e98e5992dfd","added_by":"auto","created_at":"2025-10-03 16:35:45","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2507191,"visible":true,"origin":"","legend":"\u003cp\u003eDose–response relationships between RCII and the risk of MAFLD and related mortality outcomes. (A) RCS models illustrate the associations between the RCII and the risk of MAFLD. (B) all-cause mortality (C) cardiovascular disease mortality. (D) premature death.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7620270/v1/5f73e98e83380083c277cda6.jpg"},{"id":92736284,"identity":"9949571f-ae6f-4efa-a544-e166f4f79b56","added_by":"auto","created_at":"2025-10-03 16:35:45","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":421518,"visible":true,"origin":"","legend":"\u003cp\u003eMendelian Randomization Estimates of key lipid and inflammatory biomarkers on MAFLD Risk\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7620270/v1/d969d59b9edbbc18ba597413.jpg"},{"id":92736271,"identity":"aa905f27-95fd-4356-9009-8384cbc1288f","added_by":"auto","created_at":"2025-10-03 16:35:45","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1769526,"visible":true,"origin":"","legend":"\u003cp\u003eMediation pathways illustrating the role of FPG in the association between RCII and MAFLD or adverse outcomes.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7620270/v1/f24c4562b169ff1cca3751f9.jpg"},{"id":92737259,"identity":"6d8975bd-da10-4720-aaef-ade1c2365ffe","added_by":"auto","created_at":"2025-10-03 16:43:46","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2603373,"visible":true,"origin":"","legend":"\u003cp\u003ePerformance comparison and feature importance analysis of machine learning models for MAFLD prediction. (A-D) Performance metrics of five machine learning classifiers evaluated on the training and testing datasets, including precision–recall (PR) curves and receiver operating characteristic (ROC) curves. (E) SHAP plot illustrating the contribution of individual features to MAFLD prediction in the RF model. (F) Ranked feature importance based on the RF algorithm.\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7620270/v1/9208d317785830591671bf2d.jpg"},{"id":95524049,"identity":"e388700e-a0c7-447d-86b3-28f3e8d327fb","added_by":"auto","created_at":"2025-11-10 10:02:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10719643,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7620270/v1/2876bf19-f3e1-457f-a79b-1fc4f30adc4e.pdf"},{"id":92736274,"identity":"9d21b03d-1aba-4ed2-8bfa-0c4c65fcb610","added_by":"auto","created_at":"2025-10-03 16:35:45","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1190866,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-7620270/v1/3ffe2f006a241232b451edf0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of residual cholesterol-inflammation index with MAFLD and related mortality risk: a population-based study integrating mediation and machine learning analyses","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMetabolic dysfunction-associated fatty liver disease (MAFLD) has emerged as the most prevalent chronic liver condition globally, driven by the escalating burden of metabolic dysregulation, chronic inflammation, and insulin resistance [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. With an estimated prevalence exceeding 30% among adults worldwide, MAFLD is now a leading contributor to cirrhosis, hepatocellular carcinoma, and all-cause mortality, posing a significant global public health challenge [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In China, the prevalence of MAFLD is rising at an alarming rate, placing increasing strain on healthcare systems and national resources [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Early identification and precise risk stratification of high-risk individuals are crucial for effective prevention and intervention. However, current predictive tools lack robust composite indices that simultaneously capture metabolic overload and chronic inflammatory status, thereby limiting the development of efficient screening and targeted management strategies.\u003c/p\u003e\u003cp\u003eAgainst this backdrop, remnant cholesterol (RC)\u0026mdash;a triglyceride-rich component of atherogenic lipoproteins primarily comprising very-low-density lipoproteins (VLDL), intermediate-density lipoproteins (IDL), and chylomicron remnants\u0026mdash;has garnered increasing attention in the field of metabolic disease research [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Robust evidence from large-scale cohort studies has established RC as an independent predictor of cardiovascular events, beyond the traditional lipid markers LDL-C and HDL-C [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], and has implicated it as a key contributor to the pathogenesis of atherosclerosis. Recently, attention has turned to the potential mechanistic role of RC in metabolic liver diseases. Studies based on the NHANES population have demonstrated a strong association between serum RC levels and both hepatic steatosis and fibrosis in individuals with nonalcoholic fatty liver disease (NAFLD), with RC outperforming conventional cholesterol measures in predicting liver stiffness, underscoring its potential clinical relevance in hepatic risk stratification [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. C-reactive protein (CRP), one of the most widely used biomarkers of chronic low-grade inflammation, has long been recognized as a critical factor in the progression of NAFLD/MAFLD [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Chronic systemic inflammation, moreover, has been shown to exacerbate adipose tissue dysfunction, disrupt insulin signaling pathways, and amplify hepatic steatosis and hepatocyte apoptosis via the adipose-liver axis [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSingle biomarkers often fall short in capturing the complex interplay between metabolic dysfunction and chronic inflammation in multifactorial diseases. To address this limitation, the RCII\u0026mdash;a composite metric integrating RC and CRP\u0026mdash;has recently been proposed as a surrogate for the coupled metabolic\u0026ndash;inflammatory axis. Emerging evidence supports the predictive utility of RCII across a spectrum of chronic conditions. For instance, studies based on NHANES and CHARLS cohorts identified RCII as an independent predictor of incident stroke, with a graded increase in 7-year stroke risk across RCII quartiles [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Similarly, another NHANES-based analysis demonstrated that RCII outperforms RC or CRP alone in predicting all-cause, cardiovascular, and cancer-related mortality, exhibiting robust dose\u0026ndash;response associations [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough the RCII has shown preliminary promise in predicting several chronic diseases, its prognostic value, mechanistic relevance, and generalizability across populations in the context of MAFLD and associated mortality remain poorly characterized. In particular, it is unclear whether RCII exhibits a nonlinear association with MAFLD risk, whether it serves as an independent predictor, and whether its effects are mediated through specific metabolic pathways such as fasting plasma glucose. To date, no comprehensive epidemiological evidence has systematically addressed these questions. Furthermore, conventional statistical models are inherently limited in capturing complex feature interactions and high-dimensional data structures, potentially obscuring key risk patterns in chronic disease prediction. In contrast, machine learning approaches have emerged as powerful tools in medical risk stratification, offering enhanced accuracy, robustness, and the ability to uncover latent risk determinants in large-scale, multi-variable datasets.\u003c/p\u003e\u003cp\u003eTo address these gaps, we leveraged cross-sectional and longitudinal data from the 1999\u0026ndash;2010 National Health and Nutrition Examination Survey (NHANES) to systematically evaluate the association between the RCII and both MAFLD risk and related mortality outcomes. We further investigated potential nonlinear exposure\u0026ndash;response relationships and mediating metabolic pathways. In parallel, we employed Boruta-based feature selection and a suite of machine learning algorithms to develop predictive models for MAFLD, aiming to establish RCII as a novel integrative biomarker and to advance intelligent modeling strategies for the early detection of metabolic diseases.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData Sources\u003c/h2\u003e\u003cp\u003eThis study was based on data from the National Health and Nutrition Examination Survey (NHANES; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a nationally representative survey conducted in the United States that comprehensively evaluates health status, nutritional intake, and socioeconomic factors among the civilian non-institutionalized population. The study adhered to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines for observational research.\u003c/p\u003e\u003cp\u003eWe included data from adult participants enrolled between 1999 and 2010, during which measurements of total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and CRP were consistently available to calculate the RCII. Subsequent cycles (2011\u0026ndash;2014) were excluded due to missing CRP data, and although high-sensitivity CRP (hsCRP) data were available from 2015\u0026ndash;2018, differences in assay methodology and the absence of mortality follow-up data post-2018 precluded their inclusion [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eParticipants were excluded if they met any of the following criteria: (1) age\u0026thinsp;\u0026lt;\u0026thinsp;18 years; (2) missing data on TC, HDL-C, LDL-C, or CRP; or (3) lack of follow-up information. After applying these criteria, a total of 13,254 NHANES participants were included in the final analysis. A detailed flowchart of the inclusion and exclusion process is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDefinition of MAFLD and RCII\u003c/h3\u003e\n\u003cp\u003eMAFLD was diagnosed according to the latest international consensus, requiring evidence of hepatic steatosis (via imaging or biochemical indicators) in addition to at least one of the following three metabolic conditions: (1) overweight or obesity; (2) diagnosed type 2 diabetes mellitus (T2DM); or (3) evidence of metabolic dysregulation. Metabolic dysregulation was defined as meeting at least two of the following six criteria: (1) central obesity (waist circumference\u0026thinsp;\u0026gt;\u0026thinsp;102 cm in men or \u0026gt;\u0026thinsp;88 cm in women); (2) elevated blood pressure (systolic\u0026thinsp;\u0026ge;\u0026thinsp;130 mmHg or diastolic\u0026thinsp;\u0026ge;\u0026thinsp;85 mmHg, or current use of antihypertensive medication); (3) hypertriglyceridemia (triglycerides\u0026thinsp;\u0026gt;\u0026thinsp;1.70 mmol/L or on lipid-lowering therapy); (4) reduced high-density lipoprotein cholesterol (HDL-C\u0026thinsp;\u0026lt;\u0026thinsp;1.0 mmol/L in men or \u0026lt;\u0026thinsp;1.3 mmol/L in women); (5) prediabetes, defined as fasting plasma glucose of 5.6\u0026ndash;6.9 mmol/L, 2-hour postprandial glucose of 7.8\u0026ndash;11.0 mmol/L, or HbA1c of 5.7%\u0026ndash;6.4%; (6) elevated high-sensitivity C-reactive protein (hsCRP\u0026thinsp;\u0026gt;\u0026thinsp;2 mg/L) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn the absence of abdominal imaging and liver biopsy data, this study employed the fatty liver index (FLI) to determine the presence of hepatic steatosis. The FLI was calculated according to the following formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:FLI=\\frac{{e}^{0.953\\times\\:ln\\left(TG\\right)+0.139\\times\\:BMI+0.718\\times\\:ln\\left(GGT\\right)+0.053\\times\\:waist\\:circumference-15.745}}{1+{e}^{0.953\\times\\:ln\\left(TG\\right)+0.139\\times\\:BMI+0.718\\times\\:ln\\left(GGT\\right)+0.053\\times\\:waist\\:circumference-15.745}}\\times\\:100$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere TG is serum triglycerides (mg/dL), BMI is body mass index (kg/m\u0026sup2;), GGT is γ-glutamyl transferase (U/L), and waist circumference is measured in centimeters. Participants were classified as having hepatic steatosis if the FLI was \u0026ge;\u0026thinsp;60, as previously validated in epidemiological studies [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe RCII, serving as the primary exposure variable in this study, is a composite marker integrating metabolic and inflammatory burden. It was calculated as:\u003c/p\u003e\u003cp\u003eRCII\u0026thinsp;=\u0026thinsp;RC \u0026times; CRP,\u003c/p\u003e\u003cp\u003ewhere residual cholesterol (RC) was estimated using the formula RC\u0026thinsp;=\u0026thinsp;TC \u0026minus; (HDL-C\u0026thinsp;+\u0026thinsp;LDL-C), with all values expressed in mg/dL [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eOutcome Assessment\u003c/h3\u003e\n\u003cp\u003eMortality outcomes were ascertained through linkage of the NHANES cohort with the National Death Index (NDI), with follow-up through December 31, 2019. The primary endpoints included all-cause mortality (death from any cause), cardiovascular mortality (defined using ICD-10 codes I00\u0026ndash;I09, I11, I13, I20\u0026ndash;I51, and I60\u0026ndash;I69), and premature death (defined as death occurring before the age of 75). Cause-of-death classifications were based on the \"ucode_leading\" variable provided in the publicly available mortality files curated by the National Center for Health Statistics (NCHS), ensuring standardized and consistent endpoint determination [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eIn multivariable analyses, a comprehensive set of covariates was included to account for potential confounding factors, encompassing demographic characteristics, lifestyle behaviors, and comorbid conditions. Demographic variables comprised age, sex, educational attainment, marital status, and race/ethnicity. Behavioral factors included smoking status and alcohol consumption. Clinical comorbidities\u0026mdash;namely diabetes, hypertension, and dyslipidemia\u0026mdash;were identified based on self-reported physician diagnoses or corresponding biochemical criteria, as previously defined [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eRestricted Cubic Spline Analysis\u003c/h3\u003e\n\u003cp\u003eTo characterize the shape of the association between the RCII and the risk of MAFLD and mortality, restricted cubic spline (RCS) functions were applied. RCII was modeled as a continuous variable within logistic regression frameworks for MAFLD prevalence and Cox proportional hazards models for mortality outcomes, allowing for the identification of potential non-linear dose\u0026ndash;response relationships. Knot placement was determined based on the empirical distribution of RCII, and all models were adjusted for key covariates [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eMediation Analysis\u003c/h2\u003e\u003cp\u003eTo evaluate the mediating role of fasting plasma glucose (FPG) in the associations between the RCII and both MAFLD risk and mortality outcomes, mediation analyses were conducted using the \"mediation\" package in R. FPG was selected as the mediator due to its central role in glucose metabolism dysregulation and its potential to mechanistically link RCII with metabolic liver disease and adverse outcomes. Two regression models were specified: one to predict the mediator (FPG) and another to model the outcome\u0026mdash;logistic regression for MAFLD and an accelerated failure time (AFT) model (via survreg) for mortality endpoints. Inference was based on nonparametric bootstrapping. Estimates included the average causal mediation effect (ACME), total effect, and the proportion mediated. All models were adjusted for key covariates, including age, sex, education level, marital status, BMI, waist circumference, and smoking status [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSubgroup Analyses\u003c/h3\u003e\n\u003cp\u003eSubgroup analyses were conducted to assess the robustness of the associations between RCII and study outcomes across strata of key demographic and health-related variables. Participants were stratified by age (\u0026lt;\u0026thinsp;60 vs. \u0026ge;60 years), sex (male vs. female), body mass index (BMI\u0026thinsp;\u0026lt;\u0026thinsp;30 vs. \u0026ge;30 kg/m\u0026sup2;), marital status (unmarried/widowed vs. married/cohabiting), educational attainment (high school or above vs. below high school), smoking status (non-smoker vs. smoker), alcohol consumption (no vs. yes), and history of diabetes, coronary heart disease, stroke, angina, and cancer\u0026mdash;resulting in 12 predefined subgroups. Within each stratum, three progressively adjusted multivariable models (Model 1\u0026ndash;3) were constructed to evaluate the association between RCII and outcome variables, with hazard ratios (HRs) and corresponding 95% confidence intervals reported. Statistical interactions were tested by incorporating multiplicative interaction terms into the fully adjusted models, and P-values for interaction were calculated to assess effect modification [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eSurvival Analysis\u003c/h3\u003e\n\u003cp\u003eKaplan\u0026ndash;Meier survival curves were generated to estimate all-cause mortality, cardiovascular mortality, and premature death across quartiles of RCII. Group differences were assessed using the log-rank test. To further quantify the association between RCII and mortality outcomes, Cox proportional hazards models were constructed with three levels of covariate adjustment. Model 1 was unadjusted. Model 2 adjusted for age, sex, race, and educational attainment. Model 3 was additionally adjusted for body mass index (BMI), smoking status, alcohol consumption, and diabetes status. Results were reported as hazard ratios (HRs) with corresponding 95% confidence intervals (CIs). All analyses were performed using R software, and statistical significance was defined as a two-sided P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eMendelian Randomization\u003c/h2\u003e\u003cp\u003eA two-sample Mendelian randomization (MR) approach was employed to investigate the potential causal relationships between blood lipid traits (TC, HDL-C, LDL-C), CRP, FPG, and the risk of MAFLD [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Genetic instruments for both exposures and outcomes were derived from publicly available genome-wide association study (GWAS) summary statistics, including datasets for MAFLD (finngen_R12_NAFLD), TC (met-d-Total_C), HDL-C (GCST008035), LDL-C (GCST008037), CRP (GCST90029070), and FPG (GCST008032). Single nucleotide polymorphisms (SNPs) were selected as instrumental variables based on genome-wide significance (P\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10⁻⁸) and pruned for linkage disequilibrium using a threshold of r\u0026sup2; \u0026lt; 0.001 and a clumping window of \u0026lt;\u0026thinsp;10,000 kb to ensure independence. If the number of eligible SNPs was insufficient, a relaxed significance threshold (P\u0026thinsp;\u0026lt;\u0026thinsp;1 \u0026times; 10⁻⁵) was applied, in accordance with prior studies [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCausal effects were estimated using multiple MR methods to ensure robustness and consistency of results, including inverse variance weighted (IVW), MR-Egger regression, weighted median, simple mode, and weighted mode approaches. For each method, effect estimates, 95% confidence intervals, and P-values were reported. Heterogeneity tests and sensitivity analyses were also conducted to evaluate the validity of instrumental variables and the underlying MR assumptions. All analyses were performed using R software.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eFor the NHANES dataset, all analyses accounted for the complex multistage, stratified, and weighted sampling design by incorporating appropriate survey weights to yield nationally representative estimates. Continuous variables were summarized as means with standard deviations if normally distributed and compared using independent samples t-tests; otherwise, medians with interquartile ranges were reported, and group differences were assessed using the Wilcoxon rank-sum test. Categorical variables were compared using the chi-square test [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003csup\u003e[23]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn the MR analysis, five complementary methods were used to estimate the genetic associations between exposures and outcomes: IVW, weighted median, MR-Egger regression, simple mode, and weighted mode approaches. Heterogeneity across genetic instruments in the IVW model was assessed using Cochran\u0026rsquo;s Q statistic, while directional horizontal pleiotropy was evaluated via the MR-Egger intercept. To ensure robustness, leave-one-out sensitivity analysis was performed to evaluate the influence of individual single-nucleotide polymorphisms (SNPs) on the overall causal estimates. All hypothesis tests were two-sided, with a significance threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA total of 13,254 participants were randomly split into training and test sets at a 7:3 ratio. Feature selection was performed using the Boruta algorithm to identify variables most predictive of MAFLD. Five classification models were subsequently constructed: random forest (RF), k-nearest neighbors (KNN), na\u0026iuml;ve Bayes (NB), light gradient boosting machine (LightGBM), and decision tree (rpart). Model performance was evaluated based on accuracy, Kappa statistic, sensitivity, specificity, precision, area under the receiver operating characteristic curve (AUROC), and area under the precision-recall curve (AUPR) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo enhance model interpretability, SHAP was applied to quantify the relative contribution of each predictor to model outputs. SHAP values provide individualized estimates of feature importance, indicating both the direction and magnitude of each variable\u0026rsquo;s effect while accounting for feature interactions [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMachine learning analyses were conducted in Python (v3.10) using core libraries including xgboost, sklearn, shap, pandas, and matplotlib. All other statistical analyses were performed in R (v4.2.1) using packages such as survey, ggplot2, dplyr, mgcv, and rms. All statistical tests were two-sided, with P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eBaseline Characteristics of the Study Population\u003c/h2\u003e\u003cp\u003eA total of 13,254 participants from the NHANES cohort were included in the analysis, comprising 5,693 individuals with MAFLD and 7,561 without (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Compared to the non-MAFLD group, participants with MAFLD were older (median age: 52.00 vs. 45.00 years) and exhibited significantly higher levels of key metabolic and inflammatory markers, including BMI (32.39 vs. 24.75 kg/m\u0026sup2;), waist circumference (108.80 vs. 89.00 cm), FPG (102.70 vs. 95.00 mg/dL), and C-reactive protein (0.35 vs. 0.15 mg/dL). In terms of lipid profiles, the MAFLD group had elevated triglycerides (150.00 vs. 94.00 mg/dL) and LDL-C (119.00 vs. 112.00 mg/dL), along with reduced HDL-C (46.00 vs. 56.00 mg/dL). Notably, both residual cholesterol (RC; 30.00 vs. 19.00 mg/dL) and the residual cholesterol\u0026ndash;inflammation index (RCII; 10.56 vs. 2.85) were markedly higher in the MAFLD group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for both comparisons). When stratified by RCII quartiles, 40.21% of individuals in the MAFLD group fell within the highest quartile (Q4), compared to only 13.64% in the non-MAFLD group. Conversely, the proportion of individuals in the lowest RCII quartile (Q1) was significantly lower among those with MAFLD (7.22% vs. 38.32%). Comorbidities were also more prevalent in the MAFLD group, including diabetes (15.30% vs. 5.69%), CHD (5.38% vs. 3.15%), stroke (4.39% vs. 2.96%), and angina (4.46% vs. 1.92%). These findings collectively suggest that individuals with MAFLD are characterized by pronounced metabolic dysregulation and systemic inflammation, and that RCII may serve as a robust marker for identifying high-risk individuals.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of clinical information\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFeatures\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;13254)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003enon-MAFLD\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;7561)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMAFLD\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;5693)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-Value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e48.00 (31.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45.00 (33.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e52.00 (28.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6305 (47.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3294 (43.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3011 (52.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6949 (52.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4267 (56.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2682 (47.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMexican American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2733 (20.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1396 (18.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1337 (23.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic White\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6583 (49.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3888 (51.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2695 (47.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic Black\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2482 (18.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1371 (18.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1111 (19.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Hispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e913 (6.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e526 (6.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e387 (6.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Race\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e543 (4.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e380 (5.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e163 (2.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLess than High School\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4014 (30.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2109 (27.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1905 (33.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh School and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9240 (69.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5452 (72.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3788 (66.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital_Status (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever married/Widowed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5125 (38.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3065 (40.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2060 (36.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried/Living with partner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8129 (61.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4496 (59.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3633 (63.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27.57 (7.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24.75 (4.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32.39 (7.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWaist_Circumference (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e97.00 (20.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e89.00 (13.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e108.80 (15.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e122.00 (24.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e118.00 (24.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e126.00 (24.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e70.00 (16.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e68.00 (14.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e72.00 (16.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7181 (54.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3787 (50.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3394 (59.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6073 (45.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3774 (49.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2299 (40.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrinking (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3366 (25.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1759 (23.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1607 (28.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9888 (74.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5802 (76.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4086 (71.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11953 (90.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7131 (94.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4822 (84.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1301 (9.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e430 (5.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e871 (15.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCHD (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12710 (95.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7323 (96.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5387 (94.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e544 (4.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e238 (3.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e306 (5.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStroke (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12780 (96.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7337 (97.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5443 (95.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e474 (3.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e224 (2.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e250 (4.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAngina (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12855 (96.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7416 (98.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5439 (95.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e399 (3.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e145 (1.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e254 (4.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCancer (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12066 (91.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6912 (91.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5154 (90.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1188 (8.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e649 (8.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e539 (9.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFPG (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e98.00 (16.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e95.00 (14.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e102.70 (20.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRP (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.22 (0.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.15 (0.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.35 (0.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTC (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e195.00 (54.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e192.00 (53.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e199.00 (55.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e51.00 (21.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e56.00 (22.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46.00 (17.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL-C (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e115.00 (47.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e112.00 (46.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e119.00 (47.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e114.00 (85.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e94.00 (60.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e150.00 (99.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGGT (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.00 (17.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17.00 (12.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27.00 (23.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFLI (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e52.19 (58.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26.20 (31.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e84.03 (20.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23.00 (17.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19.00 (12.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.00 (20.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRCII (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.22 (12.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.85 (6.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.56 (18.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRCII_Type (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3308 (24.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2897 (38.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e411 (7.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3316 (25.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2117 (28.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1199 (21.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3310 (24.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1516 (20.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1794 (31.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3320 (25.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1031 (13.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2289 (40.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eMAFLD, metabolic dysfunction-associated fatty liver disease; IQR, inter-quartile range; SBP, systolic blood pressure; DBP, diastolic blood pressure; CHD, coronary heart disease; FPG, fasting plasma glucose; CRP, C-reactive protein; TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; TG, triglyceride; GGT, glutamyl transferase; FLI, fatty liver index; RC, remnant cholesterol; RCII, residual cholesterol-inflammation index\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eAssociation Between RCII and Risk of MAFLD and related Mortality\u003c/h2\u003e\u003cp\u003eIn the study cohort, higher levels of RC, CRP, and the RCII were all significantly associated with increased odds of MAFLD, exhibiting robust dose\u0026ndash;response relationships (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Compared to the lowest RCII quartile (Q1), adjusted odds ratios (ORs) for MAFLD were progressively elevated across quartiles: 4.16 (95% CI: 3.67\u0026ndash;4.72) for Q2, 8.88 (95% CI: 7.77\u0026ndash;10.14) for Q3, and 17.79 (95% CI: 15.69\u0026ndash;20.17) for Q4 (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Although both RC and CRP alone were also independently associated with MAFLD risk, the magnitude of their associations was comparatively lower. The adjusted OR for the highest RC quartile was 17.05 (95% CI: 14.46\u0026ndash;20.11), and for CRP was 7.46 (95% CI: 6.54\u0026ndash;8.52). Collectively, these findings suggest that RCII\u0026mdash;by integrating lipid dysregulation and systemic inflammation\u0026mdash;offers superior predictive performance for identifying individuals at high risk of MAFLD.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eKaplan\u0026ndash;Meier survival analysis revealed a significant association between RCII quartiles and all three mortality outcomes: all-cause mortality, cardiovascular mortality, and premature death (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Survival probability declined progressively across increasing RCII quartiles (Q1 to Q4), with the lowest survival observed in the highest RCII group (Q4). All differences were statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that individuals with elevated RCII levels are at substantially higher risk of mortality.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo further evaluate the relationship between RCII and mortality, we constructed multivariable Cox proportional hazards models using all-cause, cardiovascular, and premature death as outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA\u0026ndash;C). In the analysis of all-cause mortality (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), using the lowest RCII quartile (Q1) as the reference, a stepwise increase in mortality risk was observed across higher quartiles. In the fully adjusted model (Model 3), hazard ratios (HRs) for Q2, Q3, and Q4 were 1.14 (95% CI: 0.96\u0026ndash;1.34, P\u0026thinsp;=\u0026thinsp;0.127), 1.28 (95% CI: 1.12\u0026ndash;1.48, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and 1.83 (95% CI: 1.60\u0026ndash;2.10, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively, indicating a clear dose\u0026ndash;response trend. A similar pattern was found for cardiovascular mortality (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), with adjusted HRs of 1.17 (95% CI: 0.89\u0026ndash;1.54, P\u0026thinsp;=\u0026thinsp;0.262) for Q2, 1.22 (95% CI: 0.98\u0026ndash;1.53, P\u0026thinsp;=\u0026thinsp;0.071) for Q3, and 1.79 (95% CI: 1.40\u0026ndash;2.29, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for Q4, mirroring the all-cause mortality results. For premature death (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), the associations were even more pronounced. Compared to Q1, the adjusted HRs were 1.37 (95% CI: 1.10\u0026ndash;1.69, P\u0026thinsp;=\u0026thinsp;0.004) for Q2, 1.61 (95% CI: 1.33\u0026ndash;1.94, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for Q3, and 2.36 (95% CI: 1.97\u0026ndash;2.82, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for Q4, again demonstrating a robust dose-dependent relationship. Collectively, elevated RCII levels were independently associated with significantly increased risks of all-cause, cardiovascular, and premature death, with consistent dose\u0026ndash;response gradients across quartiles. These findings suggest that RCII may serve as a reliable prognostic biomarker for long-term adverse outcomes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eNonlinear Associations Between RCII and Risk of MAFLD and related mortality\u003c/h2\u003e\u003cp\u003eRCS regression analyses revealed nonlinear relationships between the RCII and multiple adverse health outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA\u0026ndash;D). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, RCII exhibited a pronounced nonlinear association with MAFLD risk: the risk sharply increased when RCII was below approximately 5.83, plateaued thereafter, and showed a slight decline at higher levels. In contrast, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB demonstrated a significant linear relationship between RCII and all-cause mortality (Overall P\u0026thinsp;=\u0026thinsp;0.006; Nonlinear P\u0026thinsp;=\u0026thinsp;0.822), with increasing RCII levels corresponding to a steadily rising mortality risk, indicating a robust dose\u0026ndash;response association. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC indicated a marginally significant overall association between RCII and cardiovascular mortality (overall P\u0026thinsp;=\u0026thinsp;0.055; nonlinear P\u0026thinsp;=\u0026thinsp;0.274), with modest risk elevation observed at higher RCII levels despite the absence of clear nonlinearity. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD showed a strong positive association between RCII and premature death (overall P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; nonlinear P\u0026thinsp;=\u0026thinsp;0.277), with risk increasing consistently across the full range of RCII values. Collectively, these findings underscore that elevated RCII is independently and positively associated with MAFLD, all-cause mortality, CVD mortality, and premature death, supporting its potential utility as a predictive biomarker for long-term adverse outcomes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSubgroup Analyses of the Stability and Heterogeneity of RCII in Predicting MAFLD and related Mortality Risk\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo further assess the robustness and subgroup-specific variations in the association between the RCII and the risk of MAFLD and adverse outcomes, stratified analyses were conducted across multiple key covariates, including age, sex, BMI, marital status, education level, smoking and alcohol consumption, and history of chronic diseases (Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u0026ndash;S4). The results demonstrated that elevated RCII was consistently and significantly associated with increased risk of MAFLD (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), all-cause mortality (Figure S2), cardiovascular mortality (Figure S3), and premature death (Figure S4) across most subpopulations, even after adjusting for potential confounders. Notably, significant interactions were observed between RCII and several subgroup variables (e.g., age, sex, BMI, diabetes, and cancer), suggesting that the predictive strength of RCII may be more pronounced in certain high-risk groups.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eCausal Effects of HDL-C and CRP on MAFLD Risk\u003c/h2\u003e\u003cp\u003eUsing five Mendelian randomization (MR) methods\u0026mdash;including MR-Egger, weighted median, IVW, simple mode, and weighted mode\u0026mdash;we systematically evaluated the causal effects of key lipid and inflammatory biomarkers on the risk of MAFLD. For TC, all MR methods yielded odds ratios (ORs) close to 1.00 with non-significant P values (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.2), suggesting no evidence of a causal relationship between total cholesterol levels and MAFLD (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Similarly, for LDL-C, results were highly consistent across methods (OR\u0026thinsp;=\u0026thinsp;1.00 for all), with narrow 95% confidence intervals and P values\u0026thinsp;\u0026gt;\u0026thinsp;0.4, indicating no significant causal association with MAFLD (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). In contrast, for HDL-C, the IVW method estimated an odds ratio (OR) of 0.78 (95% CI: 0.58\u0026ndash;0.98, P\u0026thinsp;=\u0026thinsp;0.018), the MR-Egger method yielded an OR of 0.75 (P\u0026thinsp;=\u0026thinsp;0.024), and the weighted mode method also reported an OR of 0.75 (P\u0026thinsp;=\u0026thinsp;0.018) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). CRP demonstrated a positive, potentially pathogenic association with MAFLD across all MR approaches except the simple mode. The weighted median method showed a statistically significant effect estimate (OR\u0026thinsp;=\u0026thinsp;1.39, 95% CI: 1.11\u0026ndash;1.74, P\u0026thinsp;=\u0026thinsp;0.004) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). To enhance the robustness and credibility of these causal inferences, we have provided comprehensive sensitivity analyses\u0026mdash;including scatter plots, funnel plots, single SNP effect plots, and leave-one-out analyses for TC, LDL-C, HDL-C, and CRP\u0026mdash;in the supplementary materials (Figure S5-8). These findings suggest that TC and LDL-C appear unrelated to MAFLD risk in causal inference. In contrast, elevated HDL-C levels may causally reduce the risk of MAFLD, whereas higher CRP levels are likely to increase it.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eMediation Analysis\u003c/h2\u003e\u003cp\u003eMediation analysis revealed that FPG played a statistically significant mediating role in the relationship between the RCII and MAFLD risk, accounting for 2.02% of the total effect (Average Causal Mediation Effect [ACME]: β\u0026thinsp;=\u0026thinsp;2.34\u0026times;10⁻⁵, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). For survival outcomes, the proportion of mediation by FPG was 6.76% for all-cause mortality (ACME: β = \u0026minus;\u0026thinsp;0.21, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 8.06% for cardiovascular mortality (ACME: β = \u0026minus;\u0026thinsp;1.32, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and 7.33% for premature death (ACME: β = \u0026minus;\u0026thinsp;0.18, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003eB\u0026ndash;D, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Given that survival analyses were conducted using an accelerated failure time (AFT) model, the estimated effects reflect the influence of RCII on log-transformed survival time via FPG. In other words, RCII may contribute to increased mortality risk in part by elevating FPG levels.\u003c/p\u003e\u003cp\u003eAdditionally, a two-step Mendelian randomization mediation framework was applied to assess the mediating role of FPG in the causal pathways linking HDL-C and CRP with MAFLD risk. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the total effect of HDL-C on MAFLD was β = \u0026minus;\u0026thinsp;0.132, with a direct effect of β = \u0026minus;\u0026thinsp;0.125 and a mediated effect of β = \u0026minus;\u0026thinsp;0.007 (95% CI: \u0026minus;\u0026thinsp;0.010 to \u0026minus;\u0026thinsp;0.002), indicating a significant mediation proportion of 5.3%. For CRP, the total effect was β\u0026thinsp;=\u0026thinsp;0.166, with a direct effect of β\u0026thinsp;=\u0026thinsp;0.158 and a mediated effect of β\u0026thinsp;=\u0026thinsp;0.008 (95% CI: 0.001 to 0.028), accounting for 4.8% of the total effect and also reaching statistical significance. Collectively, these findings suggest that FPG partially mediates the effects of RCII, HDL-C, and CRP on both MAFLD risk and adverse mortality outcomes.\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\u003eTwo-step Mendelian randomization mediation analysis results: estimates of the total, direct, and FPG-mediated effects of HDL-C and CRP on MAFLD risk\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExposure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMediator\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOutcome\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal_beta\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDirect_beta\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMediation_beta\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFPG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMAFLD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.007 (-0.010 to -0.002)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFPG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMAFLD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.008 (0.001 to 0.028)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eMAFLD, metabolic dysfunction-associated fatty liver disease; FPG, fasting plasma glucose; HDL-C, high-density lipoprotein cholesterol; CRP, C-reactive protein\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003ePerformance Evaluation of MAFLD Prediction Models and Identification of Key Predictors\u003c/h2\u003e\u003cp\u003eTo identify the most informative features associated with MAFLD, we applied the Boruta algorithm to a broad set of candidate variables. Waist circumference, BMI, and age emerged as the top-ranking predictors with the highest importance scores. Additional contributors included systolic blood pressure (SBP), sex, diabetes status, and diastolic blood pressure (DBP). These variables, together with RCII, were subsequently incorporated into machine learning\u0026ndash;based prediction models for MAFLD identification.\u003c/p\u003e\u003cp\u003eTo compare the predictive performance of different modeling strategies, we constructed and evaluated five machine learning classifiers: RF, KNN, NB, LightGBM, and decision tree (rpart). Model performance was assessed separately in both the training and testing datasets (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In the training cohort, the RF model demonstrated the best performance, achieving an accuracy of 98.0%, an area under the receiver operating characteristic curve (AUC-ROC) of 0.999, and an area under the precision-recall curve (PR-AUC) of 0.988, indicating a near-perfect fit (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e8\u003c/span\u003eA\u0026ndash;B). LightGBM and KNN also showed strong predictive capacity with accuracies of 94.4% and 93.4%, respectively, and AUCs exceeding 0.98. In contrast, the NB and rpart models underperformed in the training dataset.\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\u003ePerformance comparison of different machine learning models for MAFLD classification on training and testing datasets\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eModels\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u003cp\u003eTrain datasets\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u003cp\u003eTesting datasets\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAccuracy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eKappa\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAUC-ROC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSensitivity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSpecificity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePrecision\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAccuracy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eKappa\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eAUC-ROC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eSensitivity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eSpecificity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003ePrecision\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.958\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.964\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.991\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.897\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.788\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.864\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.921\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.892\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKNN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.934\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.864\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.911\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.951\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.933\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.845\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.681\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.921\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.793\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.884\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.837\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.874\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.949\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.922\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.865\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.943\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.924\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.886\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLGB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.944\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.991\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.934\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.952\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.936\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.891\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.776\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.956\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.868\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.907\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.876\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003erpart\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.865\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.728\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.868\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.884\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.851\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.817\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.715\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.875\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.847\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.811\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"13\"\u003eMAFLD, metabolic dysfunction-associated fatty liver disease; RF, random forest; KNN, k-nearest neighbors; NB, na\u0026iuml;ve Bayes; LGB, light gradient boosting machine; AUC-ROC, area under the receiver operating characteristic curve\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"13\"\u003e\u003cb\u003eFigure lengths\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn the testing set, the RF model maintained superior generalizability, achieving an accuracy of 89.7%, an AUC-ROC of 0.960, and a PR-AUC of 0.949 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e8\u003c/span\u003eC\u0026ndash;D). LightGBM and KNN models also demonstrated strong external validity, with AUCs of 0.956 and 0.921, respectively. NB and rpart showed comparatively weaker performance in the validation phase.\u003c/p\u003e\u003cp\u003eModel interpretability was examined using SHAP (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e8\u003c/span\u003eE) and feature importance ranking (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e8\u003c/span\u003eF). Waist circumference was identified as the most influential predictor, followed by RCII. Other notable contributors included age, blood pressure (SBP and DBP), sex, and history of angina.\u003c/p\u003e\u003cp\u003eTaken together, the RF model exhibited the highest predictive accuracy and interpretability across all tested algorithms. Notably, RCII\u0026mdash;representing an integrated measure of systemic inflammation and residual cholesterol\u0026mdash;demonstrated robust and independent predictive value in identifying individuals at high risk for MAFLD.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eLeveraging data from the NHANES cohort, this study systematically evaluated the association between the RCII and multiple adverse health outcomes, including MAFLD, all-cause mortality, cardiovascular mortality, and premature death. RCII demonstrated a robust and independent positive association with each endpoint, even after multivariable adjustment. Mendelian randomization and mediation analyses further supported the mechanistic roles of HDL-C, CRP, and FPG in this relationship. Among several machine learning algorithms, the RF model achieved superior predictive performance. SHAP analysis corroborated RCII as a key predictor of MAFLD risk, underscoring its potential utility in precision risk stratification and early identification of high-risk individuals.\u003c/p\u003e\u003cp\u003eIn recent years, multiple research groups have employed machine learning techniques to develop predictive models for MAFLD, yielding promising results. However, variations in model performance, feature selection, and target populations have been noted across studies. A study based on NHANES 2017\u0026ndash;2020 data developed a model centered on the non-HDL to HDL cholesterol ratio (NHHR), where the XGBoost algorithm achieved an AUC of 0.828. While demonstrating reasonable predictive power, the model relied predominantly on conventional lipid parameters and featured limited variable diversity [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In a large-scale investigation involving over five million individuals in Northwestern China, LASSO regression was used for feature selection, and the CatBoost model attained an AUC of 0.862, highlighting the predictive relevance of age, BMI, triglycerides, and fasting glucose; however, the absence of inflammatory markers limited its comprehensiveness [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Another study integrated vibration-controlled transient elastography (VCTE) parameters to stratify MAFLD risk, with an RF model achieving an AUC of 0.80 in the validation set, mainly for delineating low-, intermediate-, and high-risk groups [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Additionally, leveraging long-term follow-up data from NHANES III, a recent study employed multiple machine learning models to predict all-cause mortality among MAFLD patients, with the Coxnet model reaching an AUC of 0.88 at the 25-year mark\u0026mdash;underscoring the clinical potential for long-term prognostic assessment [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. We constructed and compared five machine learning models based on routine clinical and laboratory parameters. The RF model demonstrated superior performance in the test set, with an AUC of 0.960 and an accuracy of 89.7%, comparable to or exceeding previously reported models. Notably, the inclusion of RCII\u0026mdash;a novel composite biomarker reflecting both metabolic dysfunction and systemic inflammation\u0026mdash;substantially enhanced model interpretability and adaptability. These findings support the utility of RCII-enhanced machine learning models as robust tools for early MAFLD detection and individualized risk stratification.\u003c/p\u003e\u003cp\u003ePrevious studies have established that RC is closely associated with hepatic lipid dysregulation and serves as a predictor for MAFLD and its cardiovascular outcomes [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Likewise, CRP, including hs-CRP, has been widely used as a convenient marker of systemic inflammation and is strongly linked to increased MAFLD risk [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] However, reliance on single biomarkers often yields inconsistent predictive performance across different populations, limiting their clinical robustness. In contrast, RCII, by integrating metabolic and inflammatory dimensions, demonstrates superior consistency and stability in predicting both MAFLD and mortality outcomes. Compared with other metabolic or inflammatory indicators\u0026mdash;such as the systemic immune-inflammation index (SII) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], homeostatic model assessment of insulin resistance (HOMA-IR) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], and low thyroid function status [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] RCII exhibits greater external validity and translational potential in diverse clinical settings.\u003c/p\u003e\u003cp\u003eMultiple large-scale prospective cohort studies have confirmed that MAFLD is independently associated with elevated risks of all-cause and cardiovascular mortality [\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], particularly among individuals with coexisting diabetes or the \"lean MAFLD\" phenotype [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Extending this evidence, our study further reveals a positive and independent association between elevated RCII levels and premature death. Notably, this relationship persists even after comprehensive adjustment for potential confounders. These findings suggest that RCII may serve not only as a general prognostic marker for mortality, but also as an early-warning indicator for premature death\u0026mdash;offering critical value for optimizing the timing of interventions and informing public health resource allocation.\u003c/p\u003e\u003cp\u003eThe predictive power of the RCII for MAFLD, all-cause mortality, and cardiovascular mortality likely stems from its integration of two central pathological axes: metabolic dysregulation and chronic inflammation. RCII combines RC, a marker of lipid accumulation, with C-reactive protein (CRP or hs-CRP), a canonical indicator of systemic inflammation\u0026mdash;each representing distinct yet interrelated biological pathways implicated in the pathogenesis and progression of metabolic diseases [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Their synergistic interaction may potentiate vascular injury and organ dysfunction across multiple systems. Elevated RC promotes lipid deposition within arterial walls, contributes to endothelial dysfunction, and exacerbates lipid derangements via impaired reverse cholesterol transport mediated by HDL-C [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. In parallel, CRP not only suppresses pancreatic β-cell function but also stimulates hepatic glucose production, thereby aggravating FBG levels [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In our mediation analysis, FBG emerged as a significant intermediary linking RCII to increased mortality risk. Notably, elevated FBG serves as both a surrogate for insulin resistance and a pathogenic factor in its own right. Through activation of the AGE\u0026ndash;RAGE axis, hyperglycemia accelerates vascular stiffening and myocardial remodeling, impairs HDL function, and promotes LDL oxidation\u0026mdash;collectively compounding the metabolic and inflammatory disturbances driven by RCII [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. This tripartite interaction among dyslipidemia, inflammation, and glucose imbalance constitutes a tightly coupled risk network. Thus, RCII may be conceptualized as an integrated biomarker of metabolic\u0026ndash;inflammatory stress, with its close association with FBG highlighting the pivotal role of glucose dysregulation in mediating the adverse outcomes linked to RCII.\u003c/p\u003e\u003cp\u003eThis study has several limitations. First, the construction of RCII relies on the availability of CRP and lipid measurements, which may restrict its applicability in populations lacking inflammatory biomarker data. Second, although both traditional regression models and multiple machine learning algorithms consistently supported the robustness of RCII in assessing MAFLD risk, the cross-sectional nature of the NHANES dataset precludes definitive causal inference. Potential reverse causality and residual confounding cannot be fully ruled out. While Mendelian randomization provided suggestive evidence for a causal role, its validity is inherently constrained by the choice of instrumental variables and the characteristics of the study population. Therefore, longitudinal cohort studies and interventional trials are needed to further validate the prognostic utility of RCII.\u003c/p\u003e\u003cp\u003eIn summary, RCII\u0026mdash;a novel composite biomarker integrating metabolic and inflammatory signals\u0026mdash;demonstrates strong predictive capacity and cross-model stability for MAFLD risk assessment, underscoring its potential clinical relevance. Future investigations should prioritize validating the accuracy and clinical effectiveness of RCII through animal models, randomized trials, and prospective cohort studies. Moreover, integrating multi-omics approaches, such as transcriptomics and metabolomics, may help elucidate the underlying metabolic\u0026ndash;inflammatory pathways captured by RCII and provide mechanistic insight to support its translation into clinical practice.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAs a composite biomarker integrating lipid dysregulation and systemic inflammation, the RCII is independently and significantly associated with increased risk of MAFLD and adverse mortality outcomes, demonstrating strong predictive utility. Its underlying mechanism may be partially mediated by elevated FPG levels.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eResidual cholesterol\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRCII\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eResidual cholesterol-inflammation index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSII\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSystemic immune-inflammation index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMAFLD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMetabolic dysfunction-associated fatty liver disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNAFLD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNon-alcoholic fatty liver disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNHANES\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNational health and nutrition examination survey\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNDI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNational Death Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBody mass index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFLI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFatty liver index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFPG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFasting plasma glucose\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGGT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGlutamyl transferase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHDL-C\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHigh-density lipoprotein cholesterol\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLDL-C\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLow-density lipoprotein cholesterol\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTotal cholesterol\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIDL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eIntermediate-density lipoproteins\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eVLDL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eVery-low-density lipoproteins\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCRP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eC-reactive protein\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNHHR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNon-HDL to HDL cholesterol ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eReceiver operating characteristic\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eArea under the curve\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAUPR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eArea under the precision-recall curve\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHRs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHazard ratios\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eORs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOdds ratios\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCIs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eConfidence intervals\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRCS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRestricted cubic splines\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCVD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCardiovascular disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eT2DM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eType 2 diabetes mellitus\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSBP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSystolic blood pressure\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDBP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDiastolic blood pressure\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHOMA-IR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHomeostatic model assessment of insulin resistance\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ehsCRP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHigh-sensitivity CRP\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eICD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInternational Classification of Diseases\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eACME\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAverage causal mediation effect\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAFT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAccelerated failure time\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eADE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAverage direct effect\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGWAS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGenome-wide association study\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMendelian randomization\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIVW\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInverse variance weighted\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLASSO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLeast absolute shrinkage and selection operator\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eKNN\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eK-nearest neighbors\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLightGBM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLight gradient boosting machine\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNa\u0026iuml;ve Bayes\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePrecision\u0026ndash;recall\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRandom forest\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSHAP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSHapley Additive exPlanations\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSNPs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSingle nucleotide polymorphisms\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eVCTE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eVibration-controlled transient elastography\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003e The NHANES program was approved by the National Center for Health Statistics (NCHS) Ethics Review Board and all participants signed an informed consent form.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003e Written informed consent for publication was obtained from all participants.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study has no funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZhongqiao Lu: Writing\u0026ndash; original draft, data curation and formal analysis. Yingxia Hu: Writing\u0026ndash; original draft, data curation and formal analysis. Deshan Zong: Writing\u0026ndash; review, data curation and formal analysis. Bin Yue: Writing\u0026ndash; review and editing, \u0026amp; Methodology.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eWe extend our gratitude to the participants of the NHANES database in the United States for their invaluable contribution to this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data used in this study is available through the National Health and Nutrition Examination Survey repository, which is publicly accessible at: [https://wwwn.cdc.gov/nchs/nhanes/default.aspx] (https:/wwwn.cdc.gov/nchs/nhanes/default.aspx) .\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSakurai Y et al. Role of Insulin Resistance in MAFLD. 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Int J Angiol. 2016;25:110\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1055/s-0035-1570754\u003c/span\u003e\u003cspan address=\"10.1055/s-0035-1570754\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Residual cholesterol-inflammation index (RCII), Metabolic dysfunction-associated fatty liver disease (MAFLD), Mendelian randomization (MR), Fasting plasma glucose (FPG), NHANES, mediation analysis, mortality risk, machine learning","lastPublishedDoi":"10.21203/rs.3.rs-7620270/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7620270/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u003c/strong\u003eThe residual cholesterol-inflammation index (RCII), a composite indicator integrating lipid metabolism and systemic inflammation, may serve as a novel predictor for metabolic dysfunction-associated fatty liver disease (MAFLD) and its related adverse outcomes. This study aimed to investigate the association between RCII and the risks of MAFLD and related mortality, assess its predictive value in clinical settings, and explore the mediating role of fasting plasma glucose (FPG) in these relationships.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eA total of 13,254 participants from the NHANES 1999–2010 cycles were included. RC, CRP, and RCII were evaluated as exposures, with their distributions compared between MAFLD and non-MAFLD populations. Multivariable logistic and Cox regression models were used to assess the associations of RCII with MAFLD prevalence and three types of mortality (all-cause, cardiovascular, and premature). Nonlinear relationships were examined using restricted cubic splines (RCS). Mediation analysis was conducted to quantify the contribution of FPG to RCII-related risks, complemented by Mendelian randomization to infer causal effects of TC, HDL-C, LDL-C, and CRP on MAFLD. Multiple machine learning models were constructed to evaluate the predictive utility of RCII, with SHapley Additive exPlanations (SHAP) used for model interpretation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e Compared to non-MAFLD individuals, participants with MAFLD exhibited pronounced metabolic dysregulation and inflammation, with significantly elevated RCII levels. RCII showed the strongest predictive power for MAFLD (Q4 vs Q1: OR = 17.79, P \u0026lt; 0.001). Higher RCII levels were independently associated with increased risks of MAFLD-related all-cause, cardiovascular, and premature death in both Kaplan–Meier and Cox models, with a clear dose-response pattern. These associations remained consistent across subgroups, with evidence of interaction effects. Mediation analysis revealed that FPG partially mediated the relationship between RCII and adverse outcomes, accounting for 2.02%–8.06% of the total effect. Among all models, the random forest algorithm achieved the highest predictive performance (accuracy = 89.70%, AUC = 0.960), with SHAP analysis confirming RCII as a top-ranking feature.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eRCII is independently and positively associated with both MAFLD risk and related mortality outcomes, demonstrating robust predictive capability. Its effects may be partially mediated by FPG. These findings underscore the potential of RCII as a clinically valuable biomarker for early identification and stratified management of individuals with high metabolic-inflammatory burdens.\u003c/p\u003e","manuscriptTitle":"Association of residual cholesterol-inflammation index with MAFLD and related mortality risk: a population-based study integrating mediation and machine learning analyses","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-03 16:35:40","doi":"10.21203/rs.3.rs-7620270/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a638d5d1-60b2-445d-a231-7d6f912d4f9b","owner":[],"postedDate":"October 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-04T11:53:55+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-03 16:35:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7620270","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7620270","identity":"rs-7620270","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}