Associations of cumulative exposure and dynamic trajectories of the modified cardiometabolic index with incident cardiovascular disease in middle-aged and elderly adults: a nationwide prospective cohort study

Background The modified cardiometabolic index (MCMI) has been proven to be closely associated with cardiovascular disease (CVD). However, most previous studies relied on single-point MCMI measurements, lacking comprehensive evaluations of long-term cumulative exposure and dynamic trajectories. This study aimed to investigate the associations of cumulative MCMI (cuMCMI) and MCMI dynamic trajectories with incident CVD in middle-aged and elderly adults, and to evaluate their predictive performance. Methods Based on data from the China Health and Retirement Longitudinal Study (CHARLS) 2011–2020, a total of 3390 participants free of CVD at both the 2011 and 2015 surveys were included. Cox proportional hazards regression models, restricted cubic splines (RCS), Kaplan–Meier survival curves, subgroup analyses and sensitivity analyses were applied to assess the associations of cuMCMI and MCMI trajectories with incident CVD. C-index, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to evaluate the predictive performance. Results During the follow-up period of 5 years, 647 (19.09%) participants developed CVD. In the fully adjusted model, per 1-standard deviation (SD) increase in cuMCMI was associated with a 20% higher risk of CVD (HR = 1.20, 95% CI: 1.10–1.31), a 13% higher risk of heart disease (HR = 1.13, 95% CI: 1.02–1.26), and a 38% higher risk of stroke (HR = 1.38, 95% CI: 1.20–1.58). Compared with the lowest tertile of cuMCMI, the highest tertile was associated with a 60% increased risk of CVD (HR = 1.60, 95% CI: 1.28–2.00). Compared with the low-stable trajectory group, the high-stable trajectory group had a 58% higher risk of CVD (HR = 1.58, 95% CI: 1.24–2.02). RCS analysis indicated a linear dose-response relationship between cuMCMI and CVD risk ( P for nonlinearity > 0.05). When added to the basic model, cuMCMI increased the C-index from 0.571 to 0.606, with an NRI of 0.1909 and an IDI of 0.0085 (both P < 0.001), outperforming single-point MCMI and other cumulative indicators. Findings from subgroup and sensitivity analyses were consistent with the primary results. Conclusion Elevated cumulative MCMI exposure and the persistently high-level MCMI trajectory are significantly associated with an increased risk of incident CVD in middle-aged and elderly adults, with the strongest association observed for stroke. cuMCMI provides incremental predictive value beyond single-point MCMI, making it a promising indicator for CVD risk stratification.