Multiple exposures to heavy metals and changes in steroid hormones production in 4-year-old children

Abstract

Background: Prenatal exposure to multiple heavy metals can interfere with early neurodevelopment, lead to changes in sex hormone concentrations in children, and affect female reproductive health. To date, the influence of prenatal exposure to heavy metals on the endocrine system of children in Chinese electronic waste (e-waste) recycling areas has not been elucidated.

Methods: Four weeks after delivery, 10 mL of human milk was collected for analysis of three heavy metals (lead (Pb), cadmium (Cd), and mercury (Hg)) via inductively coupled plasma mass spectrometry (ICP-MS). Four serum steroid hormones, including progesterone, testosterone, androstenedione (A-dione), and dehydroepiandrosterone (DHEA), were analyzed in 4-year-old children (25 boys and 17 girls). A multiple linear regression (MLR) model was implemented to investigate the association between each individual metal and serum steroid hormone. The exposure-response relationships were explored by generalized additive models (GAMs). Additionally, a Bayesian kernel machine regression (BKMR) model was used to assess the effects of multiple heavy metal exposures on each steroid hormone.

Results: The MLR results show a significant positive association between a natural log unit increase in Hg and DHEA levels after adjusting for confounders (β = 65.50, 95% confidence interval (CI) = 4.37, 126.62). According to the GAM, the univariate exposure-response relationship of Hg on DHEA was almost linear. However, this association was attenuated based on the multiple metal MLR and BKMR results after accounting for multiple heavy metal exposures.

Significance: Prenatal Hg exposure may affect sex hormones in children by affecting DHEA levels.

Impact statement: Prenatal maternal exposure to Hg may have long-term effects on the next generation. Hence, regulatory measures to reduce Hg exposure and long-term monitoring of children's health in e-waste areas are needed.

Keywords: Bayesian kernel machine regression; DHEA; E-waste; Mercury; Multiple heavy metals; Prenatal exposure.