Frontiers in cardiovascular medicine · 2026
Nationwide prospective cohort study in China: the impact of cumulative modified cardiometabolic index on cardiovascular disease incidence.
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Pu De-Heng, Zhang Li-Lin, Yang Jing, Wang Xian-Bin, Luo Hao
Аннотация
BACKGROUND: The global burden of cardiovascular disease (CVD) is rising. The modified cardiometabolic index (MCMI), which includes dyslipidemia, impaired glucose tolerance, and central obesity, effectively predicts metabolic diseases. There are no studies examining whether cumulative MCMI (cumMCMI) exposure is associated with the onset and progression of CVD. This research aims to determine if increased cumMCMI correlates with higher CVD rates.
METHODS: The first three waves of the "China Health and Retirement Longitudinal Study" (CHARLS) excluded individuals with CVD, missing MCMI data, or not meeting study criteria, resulting in 3,466 eligible participants. We assessed the impact of high cumMCMI on CVD risk over a median 5-year follow-up using multivariate Cox regression and Kaplan-Meier curves, considering exposure duration and analyzing both continuous variables and quartiles. The dose-response relationship of cumMCMI was evaluated using restricted cubic splines (RCS), while subgroup analyses examined population characteristics. Researchers assessed model robustness by varying exposure thresholds and employing Poisson and logistic regression models.
RESULTS: In all models, the Q4 group showed the highest hazard ratio (HR) [HR 1.51, 95% confidence interval (95%CI) 1.13-2.00, P = 0.005], indicating that cumMCMI is a CVD risk factor. RCS analysis indicates the presence of a linear positive correlation in CHARLS (P for overall = 0.015, P for nonlinearity = 0.737). Subgroup analyses found higher risks for those with lower education and married individuals (P for interaction < 0.05). Sensitivity analyses yielded materially similar results.
CONCLUSION: In short, cumMCMI is independently linked to CVD and could aid in CVD stratification and early warning. Although education and marital status might influence this connection, further validation is needed.
METHODS: The first three waves of the "China Health and Retirement Longitudinal Study" (CHARLS) excluded individuals with CVD, missing MCMI data, or not meeting study criteria, resulting in 3,466 eligible participants. We assessed the impact of high cumMCMI on CVD risk over a median 5-year follow-up using multivariate Cox regression and Kaplan-Meier curves, considering exposure duration and analyzing both continuous variables and quartiles. The dose-response relationship of cumMCMI was evaluated using restricted cubic splines (RCS), while subgroup analyses examined population characteristics. Researchers assessed model robustness by varying exposure thresholds and employing Poisson and logistic regression models.
RESULTS: In all models, the Q4 group showed the highest hazard ratio (HR) [HR 1.51, 95% confidence interval (95%CI) 1.13-2.00, P = 0.005], indicating that cumMCMI is a CVD risk factor. RCS analysis indicates the presence of a linear positive correlation in CHARLS (P for overall = 0.015, P for nonlinearity = 0.737). Subgroup analyses found higher risks for those with lower education and married individuals (P for interaction < 0.05). Sensitivity analyses yielded materially similar results.
CONCLUSION: In short, cumMCMI is independently linked to CVD and could aid in CVD stratification and early warning. Although education and marital status might influence this connection, further validation is needed.