| Sumario: | Aim: Although previous studies have reported the predictive role of heavy metals in diseases, only a few studies have focused on the relationship between exposure to metal mixtures and cardiovascular diseases (CVDs). This study aimed to explore the impact of exposure to metal mixtures (lead, cadmium, manganese, and mercury) on CVDs. Patients and methods: Overall, we included 16,143 adults from the National Health and Nutrition Examination Survey 2011–2018. The K-medoids algorithm was used to stratify the study population after clustering of blood metals. Based on metal concentration subgroups, we constructed a generalized linear model and adjusted for multiple variables to further analyze the association between exposure to metal mixtures and CVDs. Results: Participants were divided into three strata by k-medoids. High-exposure, moderate-exposure, and low-exposure groups were identified according to the level of blood concentrations. After adjustment of the model, high exposure to metal mixtures significantly increased the risk of heart attack (odds ratio [OR] 1.63, 95% confidence interval [CI] 1.11–2.38) and stroke (OR 1.78, 95% CI 1.25–2.54). Conclusions: Exposure to high levels of metal mixtures may be a potential predictor of CVDs. Unsupervised clustering method-based stratification of the population using metal mixtures provides a reliable and promising method for the analysis of the relationship between metal mixtures and health endpoints.
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