Longitudinal associations of cumulative and repeated-measure patterns of insulin resistance surrogate indices with biological age acceleration and incident cardiovascular disease across cardiovascular-kidney-metabolic syndrome stages 0–3: evidence from CHARLS 2011–2020

BACKGROUND: Cardiovascular-kidney-metabolic (CKM) syndrome stages 0-3 represent a critical window for preventing progression to overt cardiovascular disease (CVD). Insulin resistance (IR) is central to CKM pathophysiology, yet the comparative utility of longitudinal IR patterns (cumulative burden and longitudinal pattern groups) across multiple surrogates, and the mediating role of biological ageing, remain unexamined in this preclinical population. METHODS: We included 3948 participants with CKM stages 0-3 from the CHARLS 2011-2020. Twelve IR surrogates (TyG and derivatives, METS-IR, CTI, eGDR, TG/HDL-C) were assessed via cumulative exposure and K-means-derived pattern groups. Associations with incident CVD were evaluated using Fine-Gray competing risk models, spline regression, receiver operating characteristic analyses, and quantile-based models. Mediation analyses quantified the contribution of biological age acceleration. RESULTS: 756 (19.1%) of 3948 participants with CKM stages 0-3 developed incident CVD. Higher cumulative levels and the least favorable pattern groups of TyG-based indices, METS-IR, CTI, and TG/HDL-C were consistently associated with increased CVD risk, whereas elevated cumulative eGDR was protective. Cumulative eGDR demonstrated superior predictive performance for CVD risk (AUC: 0.613; all DeLong P < 0.05 vs. other indices) and was consistently identified as the top contributor by both WQS and Qgcomp analyses. Both KDM- and Light-BioAgeAccel partially mediated several IR-CVD associations (up to 45.8% and 25.9%, respectively). CONCLUSIONS: Sustained IR burden and unfavorable longitudinal IR patterns are linked to higher CVD risk in CKM stages 0-3, partly through accelerated biological aging. Integrating longitudinal IR profiling with aging metrics may sharpen early risk stratification and support scalable prevention targeting upstream metabolic drivers.