Association between combined exposure of heavy metals and biomarkers of early renal damage in occupational population / 中国职业医学

ABSTRACT

Objective: To investigate the effect of combined exposure to four heavy metals (lead, cadmium, arsenic, mercury) on early kidney injury in occupational population. Methods: A total of 384 workers exposed to combined heavy metals in a non-ferrous metal smelting plant in Guangdong Province were selected as the research subjects using judgment sampling method. The levels of blood lead, urinary cadmium and urinary arsenic were detacted by inductively coupled plasma mass spectrometry, while urinary mercury levels were measured using cold atomic absorption spectroscopy (acidic tin chloride reduction method). The levels of biomarkers such as urinary β2-microglobulin (β2-MG), kidney injury molecule-1 (Kim-1) and neutrophil gelatinase-associated lipocalin (NGAL) were detected by enzyme-linked immunosorbent assay. Spearman correlation analysis, linear regression, weighted quantile sum (WQS) regression and Bayesian kernel machine regression (BKMR) models were used to analyze the association between the exposure to the four heavy metals and early kidney injury biomarkers. Results: The median of blood lead, urinary cadmium, urinary arsenic and urinary mercury were 0.47 μmol/L and 4.450, 27.790 and 0.520 μg/gCr, respectively. The median of urinary β2-MG, Kim-1 and NGAL were 62.960, 1.130 and 18.150 μg/gCr, respectively. Spearman correlation analysis showed that urinary levels of β2-MG, Kim-1, and NGAL were weakly correlated with blood lead and urinary mercury levels (all P<0.01), but not correlated with urinary cadmium and urinary arsenic (all P>0.05). The results of multiple linear regression analysis showed that urinary mercury was positively correlated with urinary β2-MG, Kim-1 and NGAL (all P<0.01), urinary arsenic was positively correlated with urinary β2-MG level (P<0.01), and blood lead was negatively correlated with urinary β2-MG and Kim-1 (all P<0.05). The WQS regression analysis showed that the combined effect of the four heavy metals was positively correlated with urinary β2-MG, Kim-1 and NGAL (all P<0.01), with mercury having the highest impact and lead the lowest. BKMR model analysis showed the increasing trend in urinary β2-MG, Kim-1 and NGAL with the increasing levels of the combined exposure to the four heavy metals. Urinary β2-MG, Kim-1 and NGAL decreased when urinary mercury level increased from the 25th percentile to the 75th percentile and the other metals were correspondingly fixed at a certain level. When the blood exposure levels of other metals remained at the corresponding median levels, urinary β2-MG, Kim-1 and NGAL levels were positively correlated with urinary arsenic level, but no significant linear dose-response relationship was observed with the other three heavy metals. Conclusion: sLead, arsenic, and mercury are independently associated with early kidney injury biomarkers in occupational population from non-ferrous metal smelting. The four heavy metals had positive combined effects on urinary β2-MG, Kim-1 and NGAL, with mercury having the greatest impact.