The Impact of Cumulative Exposure to Cardiac Metabolic Index on the Risk of Cardiovascular Disease

BACKGROUND To investigate the association between cumulative cardiac metabolic index (cumCMI) exposure and cardiovascular disease (CVD) risk. METHODS The study was based on 87 967 participants of the Kailuan study who underwent at least two health examinations between 2006 and 2010. Time-weighted average cumCMI was calculated, and participants were categorized into four groups based on cumCMI quartiles: Q1 (cumCMI<0.327), Q2 (cumCMI 0.327–<0.483), Q3 (cumCMI 0.483–<0.755), and Q4 (cumCMI ≥ 0.755). Cox proportional hazard models were used to assess the relationship between cumCMI and the risk of CVD and its subtypes, with stratification by age and gender. Based on the Prediction for Atherosclerotic Cardiovascular Disease Risk in China (China-PAR) model, the values of cumCMI and other related indicators in predicting the risk of CVD were evaluated using C-statistic, integrated discrimination improvement, and net reclassification index. In the sensitivity analysis, the Cox proportional hazards analyses were repeated after excluding individuals who experienced endpoint events within 2 years of follow-up, and those who took antihypertensive, antidiabetic, and lipid-lowering medications at baseline, as well as replacing low-density lipoprotein cholesterol (LDL-C) with cumulative LDL-C (cumLDL-C) as a covariate. Fine-Gray competing risk models were used, with non-CVD mortality considered a competing event, to assess the potential risk of disease mortality from competing events. RESULTS During a mean follow-up of (10.41 ± 2.51) years, 6337 individuals experienced CVD events. With the increase of cumCMI quartile, the cumulative incidence of CVD increased (the cumulative incidence in the Q1 to Q4 group was 5.20%, 6.54%, 7.89%, and 9.18%, respectively, χ2 = 289.789, P<0.001). Multivariate Cox regression analysis showed that after adjusting for other confounding factors, compared with Q1 group, Q2, Q3, and Q4 groups exhibited 21% (HR = 1.21, 95% CI 1.12–1.30), 38% (HR = 1.38, 95% CI: 1.28–1.49), and 58% (HR = 1.58, 95% CI: 1.47–1.70) higher risks, respectively. Stratified analyses indicated a stronger association between elevated cumCMI and CVD risk in populations aged <60 years and females, with the Q4 group showing 91% (HR = 1.91, 95% CI: 1.69–2.16) and 64% (HR = 1.64, 95% CI: 1.33–2.03) higher risk relative to Q1 group, respectively. Sensitivity analysis yielded consistent results. Prediction analysis based on the China-PAR model indicated that cumCMI outperforms other single indicators in predicting CVD risk. CONCLUSIONS High cumCMI levels are significantly associated with the risk of cardiovascular disease and can effectively predict the risk of CVD.