Objective To compare the predictive performance of the novel Cholesterol, High-Density Lipoprotein, and Glucose (CHG) index for the incident type 2 diabetes mellitus (T2DM) versus the established indices—triglyceride-glucose (TyG), atherogenic index of plasma (AIP), and metabolic score for insulin resistance (METS-IR). Methods A longitudinal cohort study was performed on 15, 453 participants from the NAGALA cohort (2004–2015) without baseline T2DM. The CHG index was calculated from total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and fasting blood glucose (FBG). Several additional indices were established, including the TyG, AIP, and METS-IR. Cox regression, Kaplan-Meier analysis, restricted cubic spline (RCS) analysis, and time-dependent receiver operating characteristic (ROC) curves were performed to determine the predictive performance, after adjusting for various confounders (e.g., obesity, liver function). Robustness was assessed through sensitivity analyses. Results During a 6.13-year follow-up, 373 T2DM cases were recorded. In the fully adjusted models, we found that the CHG index had the highest hazard ratio (HR): 6.18 per unit (95% CI: 3.38–11.27), outperforming the TyG index (HR: 1.39), AIP (HR: 1.80), and METS-IR (HR: 1.07). Quartile analysis revealed a dose-response relationship for CHG (Q4 HR: 4.36 vs. Q1, P 0.001). Further, the RCS analysis revealed a linear association between CHG and T2DM risk ( P for overall trend 0.001; P for nonlinearity = 0.712). Moreover, the CHG index achieved a satisfactory discriminative accuracy (based on the area under the curve AUC: 0.783 vs. TyG: 0.751, AIP: 0.745, METS-IR: 0.780) and its predictive power was consisted over the 12 years (peak AUC: 0.798 at 7 years). Sensitivity analyses for various subgroups (e.g., non-obese, non-fatty liver) confirmed that the obtained results were robust. Conclusions The CHG index that integrates cholesterol, HDL-C, and glucose metabolism, showed superior predictive performance for the incidence of T2DM. Suggesting that it effectively captures the multifactorial pathophysiology of the disease. It is based on routine biomarkers making it easily applicable in risk stratification, offering a cost-effective tool for early intervention and precise prevention in clinical and public health settings.
Chen et al. (2026) studied this question.