Abstract
BACKGROUND: Epidemiological research on the association between heavy metals and congestive heart failure (CHF) in individuals with abnormal glucose metabolism is scarce. The study addresses this research gap by examining the link between exposure to heavy metals and the odds of CHF in a population with dysregulated glucose metabolism. METHOD: This cross-sectional study includes 7326 patients with diabetes and prediabetes from the National Health and Nutrition Examination Survey from 2011 to 2018. The exposure variables are five environmental heavy metals-cadmium (Cd), lead (Pb), mercury (Hg), selenium (Se), and manganese (Mn)-and the endpoint is CHF, determined via face-to-face interviews. Logistic regression, weighted quantile sum (WQS), and Bayesian kernel machine learning (BKMR) models were employed to investigate the association between exposure to mixtures of five heavy metals and the odds of having CHF in individuals with diabetes and prediabetes. RESULT: Multivariate logistic regression analysis Shows that only blood Cd exhibited a significant linear positive correlation with CHF odds (OR: 1.26, 95%CI 1.07-1.47, p = 0.005), there was a significant 14% decrease in the odds rate of CHF for each additional standard deviation of log10 Se (OR: 0.86,95%CI 0.76-0.96, P = 0.009). The WQS index for the metal mixture only marginally increased the odds of CHF by 1% (OR = 1.01, 95% CI 1.00-1.02, P = 0.032). BKMR analysis demonstrated a positive association between Cd levels and the odds of CHF, an inverse relationship with Se levels in patients with diabetes and prediabetes. However, no significant association was observed between the metal mixture and CHF. CONCLUSION: This cross-sectional study demonstrates that increased Cd levels are associated with a higher odds of CHF in patients with diabetes and pre-diabetes, whereas elevated blood Se levels significantly mitigate this odds.