Background: Congenital heart defects (CHDs) are the most common congenital malformations with a complex etiology, and environmental factors play an important role. Large epidemiology studies on prenatal exposure to selected heavy metals and their association with risk for CHDs are scarce and joint effects are not well understood.
Objectives: To examine the association between prenatal exposure to selected heavy metals and risk for CHDs.
Methods: Inductively coupled plasma mass spectrometry (ICP-MS) was used to determine the maternal plasma concentrations of arsenic, cadmium, mercury, lead, and manganese were in 303 CHD cases and 303 healthy controls that were recruited in eight hospitals in China. Generalized linear mixed model (GLMM) and Bayesian kernel machine regression (BKMR) were fitted to evaluate the individual and joint effects of metal concentrations on CHDs.
Results: In GLMM, two metals were each significantly associated with an increased risk for CHDs [adjusted odds ratio (95% confidence interval): mercury, 2.88 (1.22-6.77); lead, 2.74 (1.00-7.57)]. In BKMR, CHD risk increased with mixture levels of the five metals when their concentrations were at the 40th percentile or higher, compared to when all metals were below their 35th percentile, and mercury was the major metal that contributed to the mixture effect. The interaction between mercury and lead was observed in BKMR.
Conclusions: Using metal concentrations in maternal plasma obtained during the second or third trimester as exposure markers, we found that the risk of CHDs increased with the levels of the mixtures of As, Cd, Hg, Pb, and Mn, with Hg being the most important contributor to the mixture effect.
Keywords: Bayesian kernel machine regression; Congenital heart defects; Heavy metals; Lead; Mercury.
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