Background: Humans are exposed to a mixture of metals during their lifetime; however, evidence of neurotoxicity of such mixtures in critical time windows is still insufficient. We aimed to elucidate the associations of four metals mixture 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 years to four types of blood metals, namely lead, mercury, cadmium, and manganese were quantified in 502 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. Bayesian kernel machine regression (BKMR), quantile g-computation models, and elastic net (ENET) models were used to assess the associations of their blood metals mixture with IQ scores.
Results: Multivariate linear regression models indicated that postnatal blood manganese exposure at the age of 4 years was significantly negatively associated with children's IQ [β = - 5.99, 95% confidence interval (CI): -11.37 to - 0.61]. In the multi-chemical BKMR and quantile g-computation model, statistically significant inverse associations were found between the mixture of prenatal and postnatal metals and children's IQ score (Difference in children' IQ per quartile increase: -2.83; 95% CI: -5.28, -0.38). Interestingly, we found that manganese levels at both age of 4 and 6 years were contributing factors to children's IQ in the mixture models, namely, BKMR, quantile g-computation, and ENET models.
Conclusions: Multi-pollutant mixtures of prenatal and postnatal exposures to four metals affected child IQ at 6 years of age. We found a relationship between manganese exposure at both age 4, and 6 years and children's IQ. 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; Intelligence quotient; Metals; Quantile g-computation.
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