Association of triglyceride glucose and hsCRP/HDL-C index with cardiometabolic multimorbidity in middle-aged and older adults：a national prospective cohort study

Background Cardiometabolic multimorbidity (CMM) is defined as the coexistence of two or more cardiometabolic diseases (CMD), including heart disease, stroke, and diabetes. The triglyceride-glucose (TyG) index and the high-sensitivity C-reactive protein (hsCRP) to high-density lipoprotein cholesterol (HDL-C) ratio (CHR) are established indicators of insulin resistance and inflammation, respectively. This study therefore aims to investigate the relationship between TyG-CHR and CMM. Methods This prospective cohort of 4941 participants was derived from the China Health and Retirement Longitudinal Study (CHARLS), and categorized into four groups based on the quartile of TyG-CHR. The association between TyG-CHR and CMM was explored through Kaplan-Meier curves, multivariable Cox proportional hazards regression, logistic regression, and restricted cubic splines (RCS) analysis, and the predictive performance was assessed using time-dependent receiver operating characteristic (ROC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) analyses. In addition, subgroup analyses stratified by age, gender, BMI, smoking status, drinking status, and hypertension were conducted to examine associations among different participants, and sensitivity tests were conducted to assess the robustness of the study findings. Results The mean age of participants included in the study was 58.58 ± 8.46 years, with 55.37% being female. During a median follow-up period of 9 years, a total of 381 (6.76%) participants were diagnosed with CMM. Multivariable Cox regression analysis indicated higher quartiles of TyG-CHR were associated with increased risk of CMM. Compared with the lowest quartile (Q1), participants in Q2, Q3, and Q4 of TyG-CHR exhibited progressively elevated hazard ratios of 1.57 (1.07–2.30), 1.91 (1.32–2.76), and 2.00 (1.38–2.90), respectively. RCS analysis showed a nonlinear relationship between TyG-CHR and the prevalence of CMM ( P for overall trend < 0.001; P for nonlinear trend < 0.001). At the same time, subgroup analyses and sensitivity tests demonstrated the robustness of the findings. Conclusion This study found a significant positive association between TyG-CHR and the prevalence of CMM. Considering alongside other risk factors, TyG-CHR could serve as a valuable biomarker for identifying individuals at higher risk of developing CMM.