Assessing the Risk of Heart Attack: A Bayesian Kernel Machine Regression Analysis of Heavy Metal Mixtures.

Area of Science:

• Environmental Health
• Cardiovascular Epidemiology
• Toxicology

Background:

• Human health impact assessments often focus on single heavy metals, neglecting the effects of complex mixtures.
• The association between heavy metal mixtures and heart attack risk remains largely unknown.

Purpose of the Study:

• To investigate the association between heavy metal mixture exposure and heart attack risk.
• To apply the Bayesian kernel machine regression model (BKMR) for analyzing complex environmental exposures.

Main Methods:

• Utilized the 2011-2016 National Health and Nutrition Examination Survey (NHANES) data.
• Included 2972 participants over the age of 20.
• Employed the Bayesian kernel machine regression model (BKMR) to assess heavy metal mixtures.

Main Results:

• Elevated blood cadmium and lead, and urinary cadmium, cobalt, and tin were associated with heart attack.
• Lower levels of blood mercury, manganese, and selenium, and urinary manganese, barium, tungsten, and strontium were observed in heart attack patients.
• Increased risk of heart attack was linked to higher percentiles of heavy metal exposure, with a positive association of 0.0285 units at the 75th percentile compared to the 50th.

Conclusions:

• Heavy metal exposure, especially cadmium and lead, is potentially associated with an increased risk of heart attack.
• The BKMR model is a valuable tool for exploring associations between complex exposure mixtures and health outcomes.