BACKGROUND: The American Heart Association (AHA) recently proposed the concept of Cardiovascular-Kidney-Metabolic (CKM) syndrome, highlighting the strong pathophysiological links among metabolic disorders, chronic kidney disease, and cardiovascular disease (CVD). Insulin resistance (IR) is regarded as a central mechanism underlying CKM syndrome. However, studies comparing the predictive value of different IR surrogate markers for incident CVD are still limited. This study aimed to evaluate the associations between multiple IR surrogate markers and incident cardiovascular disease and to further assess their predictive performance using machine learning approaches. METHODS: Using data from the China Health and Retirement Longitudinal Study (CHARLS), this prospective cohort study included 5,528 participants. Twelve IR surrogate indices were assessed, including TyG-related indices, TG/HDL-C, METS-IR, CTI, CHG, and eGDR. Incident CVD was defined as self-reported physician-diagnosed heart disease or stroke during follow-up. Cox proportional hazards models were used to estimate associations between standardized IR indices and incident CVD. Restricted cubic splines, weighted quantile sum regression, and quantile g-computation were used to examine dose-response patterns and the relative contribution of correlated IR indices. Predictive performance was evaluated using ROC analysis, calibration, Brier score, decision curve analysis, NRI, IDI, and machine-learning models. RESULTS: During a median follow-up of 7.0 years, 741 participants developed incident CVD. In fully adjusted Cox models, several IR surrogate indices were associated with incident CVD. TyG-related composite indices incorporating adiposity-related information, particularly TyG-WC, TyG-CVAI, TyG-WHtR, and TyG-BMI, showed stronger positive associations with CVD risk, whereas eGDR showed an inverse association. Restricted cubic spline analyses showed significant overall associations for most indices, with nonlinear patterns observed for METS-IR, CTI, and eGDR. Mixture-based analyses suggested relatively larger contributions of CTI, TyG-BMI, and TyG-WC. Among individual indices, eGDR showed the highest discrimination for incident CVD, followed by TyG-CVAI and TyG-WC. Adding selected IR indices, particularly eGDR, to the covariate-based model modestly improved discrimination and reclassification. CONCLUSIONS: Among adults with CKM syndrome stages 0-3, several IR surrogate indices were prospectively associated with incident CVD, with stronger and more consistent associations observed for TyG-based indices incorporating adiposity-related measures and for eGDR. These results suggest that the combined assessment of metabolic dysfunction, adiposity, and insulin sensitivity may provide useful information for identifying individuals at higher cardiovascular risk in early-stage CKM syndrome.
Xue et al. (Tue,) studied this question.