Joint Association of Cholesterol, High‐Density Lipoprotein and Glucose Index, and Circadian Syndrome With Incidence of Cardiovascular Disease: Results From National Longitudinal Prospective Studies

Abstract

Background The cholesterol, high‐density lipoprotein, and glucose (CHG) index and circadian syndrome (CircS) are potential contributors for cardiovascular disease (CVD). This study evaluated their independent, combined associations, and interactions with CVD risk. Methods The study included participants aged ≥ 45 years without baseline CVD. Participants were classified according to the median or quartile levels of the CHG index and the binary status of CircS. Univariate and multivariate Cox regression models, along with Fine–Gray competing risk models assessed individual and joint effects. Both additive and multiplicative interactions were evaluated. Restricted cubic spline (RCS) analyses were conducted to visualize the dose–response relationship between the CHG index and CVD risk. Receiver operating characteristic (ROC) curve analyses assessed the predictive performance with the SCORE2 Asia‐Pacific model for CVD at multiple time points. Results Among 6739 eligible participants, 1420 (21.1%) participants developed CVD during follow‐up. Both higher CHG index and CircS were independently associated with increased CVD risk. Compared with low CHG index and no CircS, participants with high CHG index and CircS had higher CVD risk (hazard ratio (HR)): 1.67, 95% confidence interval (CI): 1.46–1.90). No significant additive or multiplicative interactions were observed, but the significant ones were identified in external validation ELSA cohort. Integrating CHG index and CircS modestly improved SCORE2 Asia‐Pacific model predictions, especially for 5‐, 7‐, and 9‐year long‐term CVD risk. Conclusions Elevated CHG index and CircS independently associated with a higher risk of CVD. Significant interactions were identified, incorporating them into the SCORE2 Asia‐Pacific model modestly enhances long‐term CVD risk prediction. These readily measurable markers may therefore be applied to earlier identify high‐risk individuals, enabling more targeted CVD prevention strategies and optimize risk management in clinical practice.