Nomogram Integrating Serological Markers and Clinical Parameters for Predicting Severe Hepatic Steatosis in Patients with Abnormal Glucose Metabolism

Objective To develop a nomogram integrating serological markers and clinical parameters for predicting severe hepatic steatosis in patients with abnormal glucose metabolism. Methods This prospective study included 186 patients with abnormal glucose metabolism who underwent controlled attenuation parameter (CAP) measurement 和serological examination between February 2023 and May 2024. Patients were classified as severe (n = 56) or non-severe (n = 130) steatosis and randomly assigned to training and validation cohorts (7:3). least absolute shrinkage and selection operator (Lasso) and multivariate logistic regression identified independent predictors. Restricted cubic splines (RCS) assessed nonlinear associations, and correlations with CAP were examined. A predictive nomogram was constructed and evaluated using receiver operating characteristic (ROC) analysis, calibration, and decision curve analysis (DCA). Results Body mass index (BMI), triglycerides (TG), adiponectin (ADPN), and chemerin were independent predictors. RCS indicated nonlinear relationships for BMI, ADPN, and chemerin, and a positive linear trend for TG (all P < 0.01). CAP correlated positively with BMI, TG, and chemerin, and negatively with ADPN. The nomogram demonstrated strong discriminatory power, with an area under the curve (AUC) of 0.862 (95% CI: 0.797–0.927) in the training cohort and 0.889 (95% CI: 0.800–0.978) in the validation cohort. Calibration and DCA confirmed its good performance and clinical utility. Conclusion The nomogram based on BMI, TG, ADPN, and chemerin accurately predicts severe hepatic steatosis in patients with abnormal glucose metabolism, providing a practical tool for individualized clinical decision-making.