A novel glycolipid composite index predicting cardiovascular disease in Chinese adults with abnormal glucose metabolism: a nationwide cohort study

Background Cardiovascular disease (CVD) is the leading cause of mortality among individuals with abnormal glucose metabolism. Existing insulin resistance (IR) surrogate indexes show limited predictive capacity in Chinese populations and fail to capture comprehensive glycolipid metabolic dysregulation. We developed and validated TyG-GLM6, a composite index integrating six metabolic parameters (fasting glucose, triglycerides, HDL-C, LDL-C, Age, and BMI), and compared its predictive performance against nine conventional IR indexes. Methods This prospective cohort study analyzed 3,684 participants aged ≥45 years with abnormal glucose metabolism from the China Health and Retirement Longitudinal Study (2011–2020). Associations between TyG-GLM6 and incident CVD were evaluated using multivariate logistic regression and restricted cubic splines. Seven machine learning algorithms were implemented, with performance assessed via ROC curves and SHAP analysis. External validation was conducted in 2,105 participants from a tertiary hospital. Results During 9-year follow-up, 824 (22.4%) participants developed CVD. After full adjustment including biochemical markers, TyG-GLM6 was the only index retaining independent predictive significance (OR: 1.04, 95% CI: 1.01–1.08, P = 0.028), while eGDR, TyG-WC, and CVAI were attenuated to non-significance. TyG-GLM6 exhibited a linear dose-response relationship with CVD risk ( P for nonlinea r = 0.768, P for overall 0.001) and consistent performance across sex and age subgroups. Logistic regression achieved optimal performance (AUC: 0.587), with TyG-GLM6 among top predictors. External validation confirmed independent prediction (adjusted OR: 2.02, 95% CI: 1.84–2.20, P 0.001). Conclusions TyG-GLM6 demonstrates superior independent predictive value for CVD in Chinese adults with abnormal glucose metabolism, outperforming conventional IR indexes in fully adjusted models. Its linear dose-response relationship, demographic robustness, and external validation support its utility for early risk stratification and personalized prevention strategies. Validation in ethnically diverse populations is warranted.