HDL-3 Subfractions and IL-6 Dynamics: A Novel Biomarker Panel for Precision Prediction of Prediabetes in High-Risk Populations of Hunan, China

Objective Develop and evaluate a nomogram to predict prediabetes risk in Hunan province, China. Methods A retrospective cohort study was conducted with 124 prediabetic and 112 healthy individuals recruited from Xiangya Hospital between June and December 2023. Sample size was calculated based on an expected odds ratio of 2.0 for key predictors with 80% power (α = 0.05).. Analyzed baseline data using T test, Wilcoxon rank sum test or Chi-square test. Selected independent risk factors via univariate or multivariate logistic regression. Used 5-fold cross-validation and random forest algorithm. Results Insulin, HOMA-IR score, and levels of blood glucose, TG, TC, LDL-C, ALT, TBA, HbA1c, Hb, RBC and IL-6 were higher in prediabetes; HDL-3, HOMA-β, HOMA-IS and Scr were lower. HDL-3, IL-6 and LDL-C identified as independent factors. The nomogram demonstrated perfect discrimination in the training set (C-index 1.00, 95% CI 1.00–1.00), likely due to model overfitting, and acceptable performance in the validation set (C-index 0.78, 95% CI 0.73–0.84). AUC was 1.00 and 0.78 respectively. Well-calibrated and DCA verified clinical value. Conclusion HDL-3, IL-6 and LDL-C are strong predictors. Nomogram is a reliable tool for prediabetes prediction, beneficial for clinicians in prevention and individualized treatment.