Integration of triglyceride-glucose index and biological aging to predict cardiovascular disease risk in cardiovascular–kidney–metabolic syndrome stages 0–3: a multiple prospective cohort study

BACKGROUND: The triglyceride-glucose (TyG) index serves as a surrogate marker of insulin resistance and reflects underlying metabolic dysfunction, while biological aging (KDM-BA) independently contributes to cardiovascular vulnerability. However, no study has attempted to integrate metabolic dysfunction and biological aging to improve cardiovascular disease (CVD) risk prediction in individuals with cardiovascular-kidney-metabolic (CKM) syndrome stages 0-3. METHODS: Multiple prospective cohorts were used, including 286,864 participants from the UK Biobank (UKB), 7237 from the China Health and Retirement Longitudinal Study (CHARLS), and 517 from Sun Yat-sen Memorial Hospital, Sun Yat-sen University, which were further included for external validation of the primary outcome. TyG-KDM-BA was calculated by multiplying the TyG index by KDM-BA. Cox proportional hazards models were used to evaluate the association. Time-dependent receiver operating characteristic (ROC) curves, as well as analyses of incremental predictive value, sensitivity analyses, and external validation were conducted to evaluate predictive performance. RESULTS: During a median follow-up of 15.27 years in UKB, 57,896 incident CVD events occurred (cumulative incidence: 20.18%). During a median follow-up of 7.84 years in CHARLS cohort, 1,610 incident CVD events were observed (cumulative incidence: 22.25%). CVD risk increased progressively across TyG-KDM-BA quartiles in both cohorts. In the fully adjusted model, each 1-SD increase in TyG-KDM-BA was associated with higher CVD risk in both cohorts (UKB: HR = 1.65, 95% CI 1.64-1.67; CHARLS: HR = 1.25, 95% CI 1.18-1.32). Participants in the highest quartile had significantly higher CVD risk than those in the lowest (UKB: HR = 3.97, 95% CI 3.86-4.09; CHARLS: HR = 1.92, 95% CI 1.63-2.25). TyG-KDM-BA was positively associated with CHD and stroke risk in both cohorts. Compared with conventional TyG-derived indices, TyG-KDM-BA achieved higher discrimination for CVD risk prediction, with an area under the curve of 0.657-0.659 over 5-15 years in UKB and moderate, consistent performance in CHARLS. Incremental analyses demonstrated that TyG-KDM-BA provided additional predictive value beyond conventional models and its individual components. External validation analyses supported the robustness of results. CONCLUSION: TyG-KDM-BA integrates metabolic and aging dimensions, demonstrating superior predictive ability for CVD risk among individuals with CKM stages 0-3 compared to traditional TyG-derived indicators, and may serve as a more effective tool for the early screening and intervention.