Metabolic-Inflammatory Signatures and MASLD Risk: Insights from Composite Biomarkers and Predictive Modelling

Objective Metabolic dysfunction-associated fatty liver disease (MASLD) is the most prevalent chronic liver disease worldwide. This study, based on data from the National Health and Nutrition Examination Survey (NHANES), examined associations between composite inflammatory biomarkers—the inflammatory burden index (IBI), systemic inflammation response index (SIRI), aggregate index of systemic inflammation (AISI), and remnant cholesterol inflammatory index (RCII)—and MASLD and developed a predictive model using machine learning. Methods Data from 5,112 NHANES participants (1999–2010) were analyzed. Associations of the IBI, SIRI, AISI, and RCII with MASLD were assessed using multivariate logistic regression, restricted cubic splines, and subgroup and sensitivity analyses. Eight machine learning models were constructed: AdaBoost, Decision Tree, Elastic-Net, K-Nearest Neighbors, Multilayer Perceptron, Ridge Regression, Support Vector Machine, and extreme gradient boosting (XGBoost), with a 7:3 ratio for training and validation sets. Model performance was evaluated by receiver operating characteristic (ROC) curves and the area under curve (AUC), and Shapley additive explanation (SHAP) values were used to enhance interpretability. Results The prevalence of MASLD was 20.0%. All four inflammatory biomarkers showed significant dose–response and nonlinear positive associations with MASLD (p < 0.001). RCII had the strongest effect (odds ratio [OR] = 5.76, 95% confidence interval [CI]: 4.08–8.14). Subgroup analyses revealed heterogeneity across populations. All biomarkers achieved an area under the curve (AUC) > 0.70, with XGBoost showing the best performance (training AUC = 0.872, test AUC = 0.802). SHAP analysis identified race, gamma-glutamyl transferase, albumin, high-density lipoprotein cholesterol, age, and RCII as major predictors of MASLD. Conclusion RCII is a powerful biomarker for predicting MASLD. The XGBoost-based model demonstrated excellent diagnostic value, which may provide a reliable tool for early screening and precision prevention.