Abstract
Certain heavy metal(loid)s are established nephrotoxicants; however, the associations between metal(loid) exposure and renal function remain complex and may be confounded in observational studies. This study investigated the relationships between urinary concentrations of total arsenic (As), cadmium (Cd), mercury (Hg), and lead (Pb) and estimated glomerular filtration rate (eGFR) in two distinct Dutch populations: individuals with kidney disease (the disease group) and healthy kidney donors prior to nephrectomy (the donor group). We applied a Bayesian Kernel Machine Regression (BKMR) model with multiple imputation to assess both individual and combined associations between urinary metal(loid) concentrations and eGFR using cross-sectional data. In the disease group, urinary Cd, Hg, and Pb concentrations showed weak positive associations with eGFR, whereas urinary total As concentrations were weakly negatively associated with eGFR. Further mechanistic studies are warranted to explain these findings. No statistically significant associations were found in the donor group, possibly due to compensatory renal mechanisms. No strong interactions were detected among heavy metal(loid)s in relation to renal function. Model diagnostics confirmed the robustness of the results.
| Original language | English |
|---|---|
| Article number | 14 |
| Number of pages | 21 |
| Journal | Journal of Environmental Exposure Assessment |
| Volume | 5 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - Jun-2026 |
Keywords
- Bayesian Kernel Machine Regression model
- cross-sectional study
- heavy metal(loid)s
- Human biomonitoring
- renal function
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