The aging population and medical advancements have led to a rise in chronic conditions such as metabolic syndrome, cardiovascular disease (CVD), and chronic kidney disease, giving rise to the concept of cardiovascular–kidney–metabolic (CKM) syndrome. The estimated glucose disposal rate (eGDR) is an important surrogate marker of insulin sensitivity; however, its longitudinal impact on CVD risk across CKM syndrome stages 0 to 3 remains unclear. Data were derived from the China Health and Retirement Longitudinal Study (CHARLS). After excluding participants lacking CKM stage 0–3 diagnostic indicators and those with incomplete data, a total of 3,503 eligible individuals were included. eGDR was calculated based on waist circumference, hypertension status, and HbA1c level. K-means clustering was applied to classify participants into five distinct trajectories based on eGDR dynamics. Cox proportional hazards models with increasing levels of adjustment were constructed to examine the association between eGDR and incident CVD. Additionally, restricted cubic spline (RCS) and weighted quantile sum (WQS) regression analyses were conducted for further evaluation. During a three-year follow-up, 504 participants (14.39%) developed CVD. Compared with participants in the persistently high eGDR group (Class 1), the fully adjusted Cox models revealed significantly increased CVD risk in Class 4 (HR = 2.01; 95% CI 1.46–2.77; P 0.05). WQS regression further identified waist circumference as the most influential component of eGDR in relation to CVD risk. Lower cumulative eGDR and unfavorable eGDR trajectories are significantly associated with increased CVD risk among individuals in CKM stages 0–3. These findings suggest that monitoring eGDR may enhance early CVD risk prediction and guide prevention strategies in this population.
Zhang et al. (2025) studied this question.