Mixture effects of trace element levels on cardiovascular diseases and type 2 diabetes risk in adults using G-computation analysis

Abstract There is an increasing concern about the health effects of exposure to a mixture of pollutants. This study aimed to evaluate the associations between serum levels of heavy/essential metals ([Arsenic (As), Cadmium (Cd), Mercury (Hg), Lead (Pb), Nickel (Ni), Chromium (Cr), Copper (Cu), Iron (Fe), and Zinc (Zn)]) and the risk of developing cardiovascular diseases (CVDs) and type 2 diabetes mellitus (T2D). Data were collected from 450 participants (150 with CVDs, 150 with T2D, and 150 healthy subjects) randomly selected from the Ravansar Non-Communicable Disease (RaNCD) cohort in Western Iran, covering the years 2018–2023. Trace element levels in the serum samples were assayed using ICP-MS. Logistic regression was performed to estimate the adjusted risk of exposure to single and multi-metals and CVD/T2D. Odds ratios were adjusted for age, sex, education, residential areas, hypertension, and BMI. The mixture effect of exposure to multi-metals and CVD/T2D was obtained using Quantile G-computation (QGC). In the logistic regression model, chromium, nickel, and zinc levels were associated with CVD, and significant trends were observed for these chemical quartiles (P