Prenatal and postnatal exposures to heavy metal mixtures and IQ in 6-year-old children: a prospective cohort study in South Korea

Abstract

BACKGROUND AND AIM: Humans are exposed to a mixture of heavy metals during their lifetime; however, evidence of neurotoxicity of such mixtures against critical time windows is still insufficient. We aimed to elucidate the associations of heavy metal mixtures across multiple time points with children’s intelligence quotient (IQ) in a prospective cohort study. METHODS: Prenatal exposure and exposure at age 4 and 6 to four types of heavy metals were quantified in pregnant women and their children who participated in the Environment and Development Cohort study. Children’ s IQ scores were assessed using the Wechsler Intelligence Scale at age 6. Linear regression models, Bayesian kernel machine regression (BKMR), weighted quantile sum (WQS) regression models, and elastic net (ENET) models were used to assess the associations of each heavy metal and their mixtures with IQ scores. RESULTS:Linear regression models indicated that postnatal blood lead level at the age of 6 years and manganese levels at the ages of 4 and 6 were significantly negatively associated with total IQ at 6 years of age. In the multi-chemical BKMR and WQS models, statistically significant inverse associations were found between the mixture of prenatal and postnatal heavy metal exposures and total IQ scores. Higher quantiles of metal mixtures were associated with lower children’s IQ. Interestingly, we found that manganese level at the age of 6 years was the most contributing factor to children’s IQ at 6 years of age in the mixture models of BKMR, WQS, and ENET. CONCLUSIONS:Multi-pollutant mixtures of prenatal and postnatal exposures to heavy metals affected child IQ at 6 years of age. We found a strong relationship between postnatal lead exposure and children’s IQ at the age of 6 years. Additional studies are warranted to confirm these associations and to control the exposure to different metals during pregnancy and preschool childhood. KEYWORDS: Bayesian kernel machine regression, elastic net, heavy metals, intelligence quotient, weighted quantile sum