Abstract
BACKGROUND: Cardiometabolic multimorbidity (CMM) is a growing public health challenge, with limited practical biomarkers available for early detection. Given the established relationship between impaired lipid as well as glucose homeostasis and CMM, this study aims to examine the association between the cholesterol, high-density lipoprotein, and glucose (CHG) index and CMM risk. METHODS: A nationwide prospective cohort (CHARLS, 2011-2018; n = 8,425) and a multi-community cross-sectional cohort (Beijing, 2021; n = 1,734) were analyzed. In the CHARLS cohort, Cox proportional hazards models were used to assess the association between the CHG index and incident CMM. In both cohorts, logistic regression and restricted cubic splines (RCS) were employed to examine the cross-sectional association between CHG and CMM. Time-dependent receiver operating characteristic (ROC) curves evaluated predictive performance in CHARLS cohort, while conventional ROC analysis was used in the multi-community cohort. Subgroup analyses, interaction tests, and sensitivity analyses further evaluated result reliability. RESULTS: In the CHARLS cohort, 491 (5.8%) individuals developed incident CMM over 7 years of follow-up. CHG levels were significantly elevated in patients with CMM (p < 0.001). Overall, the CHG index was associated with incident CMM (OR = 1.97, 95% CI: 1.53-2.53; HR = 1.82, 95% CI: 1.45-2.27). A nonlinear positive association was found in both cohorts. The CHG index demonstrated stable predictive accuracy over time (AUCs: 0.67 at 3 years, 0.64 at 5 years, 0.63 at 7 years). These findings were robustly validated in the multi-community cohort, where the CHG index was also significantly associated with prevalent CMM (OR = 2.82, 95% CI: 2.04-3.90). Subgroup analyses confirmed robustness across populations, especially among those without baseline cardiometabolic disease. CONCLUSION: An elevated CHG index is independently associated with an increased risk of CMM across both prospective and cross-sectional studies. It serves as a robust and reproducible predictor for the early identification of CMM in diverse Chinese populations.