Abstract
BACKGROUND: The Cardiovascular Metabolic Index (CMI) has emerged as a robust indicator for predicting metabolic diseases. However, the extent to which CMI can forecast the future risk of cardiovascular disease among individuals with impaired glucose metabolism—specifically those with diabetes and prediabetes—remains uncertain. This uncertainty is particularly concerning given the substantial burden of cardiovascular complications that these populations face. To address this critical gap in knowledge and to provide actionable insights for targeted prevention and management strategies, we undertook this study. By doing so, we aimed to elucidate whether CMI can serve as a reliable predictor in this high-risk group of individuals with impaired glucose metabolism, thereby informing more effective and personalized approaches to managing cardiovascular risk. METHOD: We included patients with impaired glucose metabolism in our baseline cohort and utilized K-means clustering to categorize changes in CMI into three distinct groups. We then applied a multivariate logistic regression model to assess the association between CMI, cumulative CMI, and changes in CMI with the incidence of hypertension and stroke within this population. This statistical approach enabled us to control for multiple confounding variables, thereby providing a more accurate assessment of the predictive power of CMI-related metrics. Furthermore, to explore potential nonlinear relationships between CMI and cumulative CMI with the occurrence of hypertension and stroke events, we employed restricted cubic spline (RCS) regression models. Finally, we utilized ROC curve analysis to evaluate the predictive ability of CMI and cumulative CMI for future risks of hypertension and stroke in individuals with impaired glucose metabolism. RESULTS: During the follow-up period from 2015 to 2018, we monitored 4,207 participants with impaired glucose metabolism and observed 746 new cases of hypertension and 144 stroke events. After meticulously adjusting for multiple potential confounding factors, our analysis revealed that the CMI significantly increases the risk of hypertension (OR = 1.27, 95% CI: 1.15–1.40) and stroke (OR = 1.31, 95% CI: 1.05–1.67) in this population. Compared with the first quartile of CMI, both the third and fourth quartiles were associated with heightened risks of hypertension and stroke. While cumulative CMI and its quartiles were linked to hypertension, no significant association was found with stroke. A nonlinear relationship was observed between CMI and hypertension risk (p = 0.017). Participants with moderate CMI levels (OR: 1.44, 95% CI: 1.21–1.71) and high CMI levels (OR: 2.69, 95% CI: 1.66–4.31) had significantly higher risks of hypertension than those with stable CMI levels. ROC curves showed AUC values of 0.73 and 0.67 for CMI in predicting hypertension and stroke, respectively. CONCLUSION: Elevated levels of the CMI are associated with an increased risk of hypertension and stroke among individuals with impaired glucose metabolism. Maintaining low CMI levels may help mitigate the risk of hypertension in these populations. Given that CMI can be easily measured in community settings, clinicians can utilize it for risk stratification and develop personalized prevention strategies. These strategies may help reduce the burden of future complications such as hypertension and stroke in patients with impaired glucose metabolism.