Association of Cardiometabolic Index With Cardiometabolic Disease Prevalence and Mortality: Evidence From Two National Surveys.

Background

The cardiometabolic index (CMI), a composite marker derived from the product of waist-to-height ratio and triglyceride-to- high-density lipoprotein cholesterol ratio, has emerged as a potential indicator of metabolic risk. However, the prognostic value of the CMI across diverse populations and the continuum from cardiometabolic disease prevalence to mortality remains poorly characterised.

Method

This study integrated data from two nationally representative datasets: 17,922 adults from the US National Health and Nutrition Examination Survey (NHANES; 1999-2018; longitudinal) and 9,313 adults from the China Health and Retirement Longitudinal Study (CHARLS; 2011; cross-sectional). Multivariate Cox regression models estimated hazard ratios (HRs) for mortality, whereas logistic regression models estimated odds ratios (ORs) for disease prevalence. Restricted cubic splines (RCSs) were used to evaluate dose-response relationships.

Results

In the NHANES cohort, elevated CMI was significantly associated with increased risks of all-cause mortality (HR 1.35; 95% confidence interval [CI] 1.26-1.44), cardiovascular disease (CVD) mortality (HR 1.30; 95% CI 1.17-1.45), and diabetes mortality (HR 1.91; 95% CI 1.56-2.34). Age significantly modified these associations (p for interaction <0.001), with stronger predictive power observed in adults aged <60 years. In the CHARLS cohort, CMI was strongly associated with prevalent CVD (OR 1.06; 95% CI 1.01-1.11) and diabetes (OR 1.66; 95% CI 1.56-1.77). RCS analyses revealed a nonlinear threshold effect for all-cause mortality in the US population and a steep linear increase in diabetes prevalence in the Chinese population.

Conclusions

CMI is a robust, low-cost indicator for predicting mortality in US adults and cardiometabolic disease burden in Chinese adults. Its greater predictive utility in younger populations supports its clinical implementation for early-onset risk stratification and targeted prevention globally.