Background: Both occupational and environmental exposure to heavy metals are associated with various neurodegenerative diseases. However, limited evidence is available on the potential effects of exposure to metallic mixtures and neural damage.
Objectives: This study aimed to evaluate the association between metal mixtures in urine and neural damage biomarkers in welders.
Methods: In this cross-sectional study, a total of 186 workers were recruited from steel mills. Twenty-three metals in urine were measured by inductively coupled plasma mass spectrometry. Serum neural damage biomarkers, including neurofilament light chain (NfL), sphingosine-1-phosphate (S1P), prolactin (PRL), and dopamine (DA) were detected using enzyme-linked immunosorbent assay kits. Multivariable linear regression, Bayesian kernel machine regression (BKMR), and Quantile g-computation (QG-C) were employed to estimate the association between metals exposure and neural damage biomarkers.
Results: Inverted u-shaped associations of nickel with NfL, S1P, and DA were observed in the BKMR model. A non-linear relationship was also found between Fe and PRL. Urinary cobalt was positively associated with serum PRL and had the strongest positive weights in the QG-C model. Urinary lead was associated with higher serum S1P levels. We also found the interaction among nickel, zinc, arsenic, strontium, iron, and lead with the neural damage biomarkers.
Conclusion: This study provides new evidence of a direct association between metal mixture exposure and the serum biomarkers of neural damage. Several metals Ni, Co, Pb, Sr, As and Fe, may have adverse effects on the nervous system, while Zn may have neuroprotective effects.
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