Modified cardiometabolic index and the risk of new-onset chronic diseases: a nationwide prospective cohort study

BACKGROUND: Metabolic syndrome, characterized by a clustering of cardiometabolic risk factors, is a key driver of chronic diseases. The Cardiometabolic Index (CMI) is a useful metric, but its exclusion of hyperglycemia—a cornerstone of metabolic dysregulation—limits its scope. A Modified Cardiometabolic Index (MCMI) incorporating glucose has been proposed, but its long-term predictive value for incident type 2 diabetes, cardiovascular disease, and subsequent multimorbidity has not been prospectively validated. We aimed to evaluate the MCMI as a predictor for these outcomes and compare its performance against the original CMI. METHODS: This study included 8,251 participants aged ≥ 45 years from the China Health and Retirement Longitudinal Study. The MCMI was calculated at baseline (2011) using waist-to-height ratio, the triglyceride to HDL-C ratio, and fasting blood glucose. Multivariable Cox proportional hazards models were used to assess the associations between the MCMI (as both a continuous variable and in quartiles) and the incidence of 13 chronic diseases over a median follow-up of 7.0 years. The predictive accuracy of the MCMI and CMI was compared using the area under the receiver operating characteristic curve (AUC) and the DeLong test. RESULTS: A higher baseline MCMI was significantly associated with an increased risk of developing type 2 diabetes (HR 1.15, 95% CI 1.12–1.18), hypertension (HR 1.12, 95% CI 1.09–1.15), dyslipidemia (HR 1.11, 95% CI 1.08–1.14), heart disease (HR 1.09, 95% CI 1.03–1.16), and stroke (HR 1.13, 95% CI 1.07–1.19), with significant dose-response relationships observed across quartiles. The MCMI was also associated with progression to multimorbidity, showing a stronger association with the development of a second chronic disease (HR 1.22, 95% CI 1.13–1.31) than a first (HR 1.09, 95% CI 1.03–1.16). Compared to the CMI, the MCMI demonstrated significantly superior predictive accuracy for type 2 diabetes (AUC 0.663 vs. 0.647), hypertension (AUC 0.747 vs. 0.742), stroke (AUC 0.692 vs. 0.686), and dyslipidemia (AUC 0.657 vs. 0.650) (all P < 0.001). CONCLUSIONS: In this large, nationally representative cohort, the MCMI was a robust and independent predictor of incident cardiometabolic diseases and the development of multimorbidity. Its superior predictive accuracy over the original CMI supports its utility as a simple, low-cost, and more effective tool for stratifying cardiometabolic risk and guiding early prevention, particularly for type 2 diabetes and related cardiovascular events, in clinical and public health settings.