Abstract Body: Introduction
Toxic metal exposures have been linked to cardiovascular diseases (CVD), yet most studies are cross-sectional and single-exposure focused. We investigated the independent and joint effects of arsenic (As), cadmium (Cd), lead (Pb), and mercury (Hg) on incident CVD in a large prospective cohort, using advanced mixture modeling and multi-media biomarker (MMB) assessments.

Methods
We conducted a nested case-control study within the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) cohort, including 176 incident CVD cases and 7,810 controls. Baseline serum and urine samples were analyzed for toxic metals. We used conditional logistic regression to investigate associations between toxic metal levels and incident CVD risk. Mixture effects were assessed using quantile g-computation (QGC), weighted quantile sum (WQS) regression, and MMB modeling.

Results
Higher urinary levels of Hg (OR: 1.82; 95% CI: 1.19-2.80) was associated with increased CVD risk. No consistent associations were found for serum metals. Urinary metal mixtures showed robust positive associations with CVD in both QGC and WQS models. MMB analyses identified urinary Hg as the strongest contributor to CVD risk across all modeling techniques. Nonlinear relationships were observed for urinary Cd and Hg.

Conclusions
Urinary levels of Hg, and heavy metals as a mixture were associated with higher CVD risk. These findings support the use of advanced exposure modeling and call for greater attention to environmental metal exposures, particularly in low- and middle-income countries.