The relationship between C-reactive protein-triglyceride-glucose index and incident cardiometabolic multimorbidity in postmenopausal women: insights from the China Health and Retirement Longitudinal Study (CHARLS)

Cardiometabolic multimorbidity (CMM), defined as the coexistence of at least two cardiometabolic diseases, is an increasing public health burden. Postmenopausal women may be particularly vulnerable because menopause-related metabolic and inflammatory changes can intensify cardiometabolic risk. However, simple tools for early CMM risk prediction in this population remain limited. The C-reactive protein-triglyceride-glucose index (CTI) integrates inflammatory status and insulin resistance, but its association with incident CMM in postmenopausal women has not been fully evaluated. We therefore investigated the relationship between CTI and new-onset CMM and assessed its predictive performance and potential clinical utility when combined with other clinical variables. Data were obtained from the China Health and Retirement Longitudinal Study (CHARLS). The study included 2972 postmenopausal women (aged ≥45 years) without CMM at baseline, with a mean follow-up duration of 103.53 months. The association between CTI and the risk of CMM was assessed using a multivariate Cox regression model. The dose-response relationship was examined through restricted cubic splines (RCS), and the cumulative incidence of CMM across different CTI level groups was compared using the Kaplan-Meier curve. Subgroup and sensitivity analyses, which included repeated validation with the third wave as the baseline, reanalysis after excluding missing values, and stratification by age and menopausal age, were conducted to confirm the robustness of the findings. The predictive performance and incremental value of CTI were evaluated using the receiver operating characteristic (ROC) curve, net reclassification improvement index (NRI), and integrated discrimination improvement index (IDI). Additionally, a nomogram was constructed based on variables selected through LASSO regression. A total of 395 participants (13.3%) developed new-onset CMM during follow-up. Multivariate Cox regression analysis indicated that for each 1-unit increase in CTI, the risk of CMM rose by 95% (HR=1.95, 95%CI: 1.66-2.30). In comparison to the lowest quartile group of CTI, the highest quartile group had a higher risk of CMM (HR=3.91, 95%CI: 2.62-5.82, P for trend <0.001). RCS analysis suggested a positive linear association between CTI and the risk of CMM (P-overall <0.001, P-nonlinear = 0.577). The Kaplan-Meier curve showed a higher cumulative incidence of CMM in the high CTI group (log-rank P <0.001). Subgroup analysis indicated that the association between CTI and CMM was more pronounced in postmenopausal women under 60 years of age (interaction P=0.003). The results of the sensitivity analysis were consistent with those of the primary analysis. The AUC of CTI for predicting CMM was 0.649 (95%CI: 0.621-0.678), higher than that of TyG (0.616), CRP (0.612), FBG (0.638), and TG (0.582). Following the inclusion of CTI, the NRI was 0.230 (P <0.001) and the IDI was 0.225 (P <0.001). The nomogram constructed from the six variables selected by LASSO (self-rated health, hypertension, dyslipidemia, CTI, FBG, and HbA1c) showed acceptable discrimination in the training set (AUC=0.742) and the validation set (AUC=0.703). Elevated CTI levels were positively associated with the risk of new-onset CMM in postmenopausal women, with a clear dose-response pattern. CTI showed moderate standalone discrimination and added incremental predictive value to traditional risk models. The nomogram incorporating CTI showed acceptable discrimination and calibration. As a simple inflammation-metabolic biomarker, CTI may help identify postmenopausal women who warrant closer cardiometabolic risk assessment when interpreted together with other clinical variables.