Impact of multi-heavy metal exposure on renal damage indicators in Korea: An analysis using Bayesian Kernel Machine Regression

Abstract

Exposure to cadmium (Cd), arsenic (As), and mercury (Hg) is associated with renal tubular damage. People living near refineries are often exposed to multiple heavy metals at high concentrations. This cross-sectional study investigated the association between combined urinary Cd, As, and Hg levels and renal damage markers in 871 residents living near the Janghang refinery plant and in a control area. Urinary Cd, As, Hg, N-acetyl-β-D-glucosaminidase (NAG), and β2-microglobulin (β2-MG) levels were measured. The combined effects of Cd, As, and Hg on renal tubular damage markers were assessed using linear regression and a Bayesian Kernel Machine Regression (BKMR) model. The results of the BKMR model were compared using a stratified analysis of the exposure and control groups. While the linear regression showed that only Cd concentration was significantly associated with urinary NAG levels (β = 0.447, P value < .05), the BKMR model showed that Cd and Hg levels were also significantly associated with urinary NAG levels. The combined effect of the 3 heavy metals on urinary NAG levels was significant and stronger in the exposure group than in the control group. However, no relationship was observed between the exposure concentrations of the 3 heavy metals and urinary β2-MG levels. The results suggest that the BKMR model can be used to assess the health effects of heavy-metal exposure on vulnerable residents.