Association of single and combined metal burdens with systemic immune inflammation in smelter workers: A...

Occupational exposure to mixed heavy metals, including manganese (Mn), copper (Cu), cadmium (Cd), and lead (Pb), is a major health hazard in non-ferrous metal smelting industries. Such exposure may trigger systemic inflammatory responses, which play a key role in the development of various chronic diseases. The neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII) are widely recognized as sensitive biomarkers of inflammation. However, their associations with mixed heavy metal exposure remain insufficiently understood among smelting workers.

To investigate the association between combined exposure to multiple heavy metals (including Mn, Cu, Cd, Pb, and others) and systemic immune-inflammatory markers (NLR, PLR, and SII) among smelting workers.

A cross-sectional study was conducted involving 1087 non-ferrous metal smelting workers. Demographic, lifestyle, and occupational data were collected via questionnaires and physical examinations. Urinary concentrations of 12 metals were quantified using inductively coupled plasma mass spectrometry (ICP-MS) and adjusted for urine dilution using specific gravity (SG). Peripheral blood samples were analyzed to determine neutrophil, lymphocyte, and platelet counts, and inflammatory markers (NLR, PLR, and SII) were subsequently calculated. To evaluate associations between mixed metal exposure and inflammatory markers, generalized linear models (GLM), weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR) models were applied.

After adjustment for urine specific gravity, 11 of the 12 urinary metals exhibited detection rates exceeding 90%, and most metals were positively correlated with one another. The strongest correlations were observed between antimony and lead (r = 0.64), arsenic and lead (r = 0.61), arsenic and antimony (r = 0.59), indium and bismuth (r = 0.58), zinc and copper (r = 0.57), tin and antimony (r = 0.55), and copper and Cd (r = 0.53). WQS regression showed that mixed metal exposure was significantly and positively associated with the (NLR; β = 0.100, 95% CI: 0.006-0.194, P = 0.036), (PLR; β = 6.644, 95% CI: 1.820-11.468, P = 0.007), and (SII; β = 46.402, 95% CI: 14.603-78.200, P = 0.004) in the overall population. In sex-stratified analyses, these associations remained statistically significant among males (NLR: β = 0.141, 95% CI: 0.034-0.248, P = 0.010; PLR: β = 6.392, 95% CI: 1.487-11.297, P = 0.011; SII: β = 37.623, 95% CI: 5.430-69.816, P = 0.022), but not among females. Among male workers, the metals with the highest WQS weights were In (0.335), Cu (0.226), and Zn (0.146) for NLR; In (0.445), Mn (0.202), and Sb (0.086) for PLR; and In (0.424), Cu (0.214), and Zn (0.110) for SII. BKMR analysis indicated an increasing trend in overall effect estimates for NLR and SII with higher quantiles of mixed metal exposure in the overall population, although these associations were not statistically significant. In male workers, overall effect estimates for NLR and SII increased significantly with rising mixture quantiles, whereas PLR showed an upward trend at higher exposure quantiles without reaching statistical significance. Formal interaction analysis identified a significant sex interaction in the association between urinary copper (Cu) and SII (P for interaction = 0.039), whereas interactions for Cu with NLR and Mn with PLR were not statistically significant. Copper demonstrated nonlinear exposure-response relationships with both NLR and SII, and its single-metal effect estimates remained significantly positive across different background exposure levels. Mn also exhibited a nonlinear exposure-response relationship with PLR.

After adjustment for urine specific gravity, mixed metal exposure was positively associated with elevated systemic immune-inflammatory markers among smelting workers, with stronger and more consistent associations observed in males. Copper (Cu) showed the most stable and consistent association across all analytical models. These findings suggest that combined monitoring of urinary metal exposure and inflammatory markers may have important implications for occupational health surveillance.