Cardiovascular–kidney–metabolic (CKM) syndrome, newly introduced by the American Heart Association, aims to promote the integrated management of metabolic dysfunction, chronic kidney disease, and cardiovascular disease (CVD).
To date, no studies have systematically examined the association between CTI and CVD risk, particularly across CKM syndrome stages 0 to 3.
In the fully adjusted model, a significant positive association was observed between CTI and CVD risk, with each one–standard deviation increases in CTI corresponding to a 7% higher risk of CVD.
Moreover, analyses stratified across most CKM syndrome stages consistently revealed a positive correlation between CTI and CVD risk.
Our findings suggest a positive, S-shaped association between CTI and CVD risk in individuals with CKM syndrome stages 0–3.
Cardiovascular–kidney–metabolic (CKM) syndrome, newly introduced by the American Heart Association, aims to promote the integrated management of metabolic dysfunction, chronic kidney disease, and cardiovascular disease (CVD). The C-reactive protein–triglyceride–glucose index (CTI), a novel composite biomarker that captures both systemic inflammation and insulin resistance, has emerged as a promising metric in cardiometabolic research. However, current evidence regarding its clinical utility remains limited. To date, no studies have systematically examined the association between CTI and CVD risk, particularly across CKM syndrome stages 0 to 3. The China Health and Retirement Longitudinal Study (CHARLS), initiated in 2011, is a large-scale, nationally representative, multicenter prospective cohort study. In accordance with predefined inclusion and exclusion criteria, a total of 6,859 participants were included in the final analysis. To assess the association between CTI and CVD risk, Cox proportional hazards models, receiver operating characteristic (ROC) curve analysis, restricted cubic spline modeling, and stratified subgroup analyses were conducted. During the 10-year follow-up period, 1391 incident CVD events were recorded. In the fully adjusted model, a significant positive association was observed between CTI and CVD risk, with each one–standard deviation increases in CTI corresponding to a 7% higher risk of CVD. Receiver operating characteristic (ROC) curve analysis demonstrated that the CTI improved the discriminatory power of the baseline model for predicting CVD. Restricted cubic spline modeling revealed a nonlinear, S-shaped relationship between CTI and CVD risk. Moreover, analyses stratified across most CKM syndrome stages consistently revealed a positive correlation between CTI and CVD risk. Our findings suggest a positive, S-shaped association between CTI and CVD risk in individuals with CKM syndrome stages 0–3. Enhancing the assessment of CTI could provide a more accessible and efficient screening tool for the prevention and management of CVD in this population.
