Association between exposure to multiple metals and lung function in welders by multi-pollutant statistical models

Background Welders' exposure to welding fumes with multiple metals leads to decreased pulmonary function. Previous studies have focused on single metal exposure, while giving little attention to the impact of metal mixtures.

Objective To assess the association between metal levels in urine and blood of welders and pulmonary function indicators, and to identify key metals for occupational health risk assessment.

Methods Questionnaire surveys, lung function tests, urine and blood sampling were conducted among welders and control workers in a shipyard in Shanghai. Inductively coupled plasma mass spectrometry (ICP-MS) was used to detect the concentrations of 12 metals such as vanadium, chromium, and manganese in urine and blood. Spearman correlation was applied to analyze the correlations between the metals in urine and blood. Multiple linear regression, weighted quantile sum (WQS) and Bayesian kernel machine regression (BKMR) were used to analyze the relationships between mixed metal exposure and pulmonary function parameters, such as forced vital capacity (FVC), forced vital capacity as a percentage of predicted value (FVC%), forced expiratory volume in the first second (FEV₁), forced expiratory volume in the first second as a percentage of predicted value (FEV₁%), and forced expiratory volume in the first second/forced vital capacity (FEV₁/FVC).

Results This study enrolled 445 subjects, including 322 welders (72.36%) and 123 controls (27.64%). The mean age of the 445 participants was (37.64±8.80) years, and 87.19% participants were male. The welders had significantly higher levels of urinary cadmium (0.88 vs 0.58 μg·L⁻¹), blood chromium (5.86 vs 5.06 μg·L⁻¹), and blood manganese (24.24 vs 21.38 μg·L⁻¹) than the controls (P<0.05). The Spearman correlation coefficients between the metals in urine and blood ranged from −0.46 to 0.68. After adjustment for confounders, the multiple linear regression indicted that the urine molybdenum of the welders was negatively correlated with FVC and FEV₁. There were also negative correlations between the molybdenum in blood and FVC, FVC%, FEV₁, and FEV₁%, and between the copper in blood and FEV₁/FVC. The WQS model showed that FEV₁ and FVC decreased by 0.112 L and 0.353 L with each quartile increase of metal mixture concentrations in urine and blood among the welders respectively, and the leading contributors were copper, zinc, vanadium, and antimony. The BKMR model showed a negative overall effect of metal mixtures in urine and blood among the welders on FVC, FVC%, FEV₁, and FEV₁%, and the univariate exposure response-relationship between the molybdenum concentration in urine or blood and FVC, FVC%, FEV₁, or FEV₁% had an approximately linear decreasing trend. Meanwhile, there may be an interaction of cadmium with manganese, nickel, or vanadium, and an interaction of vanadium with iron, molybdenum, zinc, or copper, when different metals in urine among the welders interacted with FEV₁%.

Conclusion Exposure to multiple metals in welders leads to a decline in lung function, with molybdenum, antimony, copper, and zinc as the leading contributors.