The Joint Effect of Prenatal Exposure to Metal Mixtures on Neurodevelopmental Outcomes at 20–40 Months of Age: Evidence from Rural Bangladesh

Abstract

BACKGROUND: Exposure to chemical mixtures is recognized as the real-life scenario in all populations, needing new statistical methods that can assess their complex effects. OBJECTIVES: We aimed to assess the joint effect of in utero exposure to arsenic, manganese, and lead on children's neurodevelopment. METHODS: We employed a novel statistical approach, Bayesian kernel machine regression (BKMR), to study the joint effect of coexposure to arsenic, manganese, and lead on neurodevelopment using an adapted Bayley Scale of Infant and Toddler Development™. Third Edition, in 825 mother-child pairs recruited into a prospective birth cohort from two clinics in the Pabna and Sirajdikhan districts of Bangladesh. Metals were measured in cord blood using inductively coupled plasma-mass spectrometry. RESULTS: Analyses were stratified by clinic due to differences in exposure profiles. In the Pabna district, which displayed high manganese levels [interquartile range (IQR): 4.8, 18 μg/dl], we found a statistically significant negative effect of the mixture of arsenic, lead, and manganese on cognitive score when cord blood metals concentrations were all above the 60th percentile (As≥0.7 μg/dl, Mn≥6.6 μg/dl, Pb≥4.2 μg/dl) compared to the median (As=0.5 μg/dl, Mn=5.8 μg/dl, Pb=3.1 μg/dl). Evidence of a nonlinear effect of manganese was found. A change in log manganese from the 25th to the 75th percentile when arsenic and manganese were at the median was associated with a decrease in cognitive score of −0.3 (−0.5, −0.1) standard deviations. Our study suggests that arsenic might be a potentiator of manganese toxicity. CONCLUSIONS: Employing a novel statistical method for the study of the health effects of chemical mixtures, we found evidence of neurotoxicity of the mixture, as well as potential synergism between arsenic and manganese. https://doi.org/10.1289/EHP614.

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• Statistical software for analyzing the health effects of multiple concurrent exposures via Bayesian kernel machine regression · 2018
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• Associations of cumulative exposure to heavy metal mixtures with obesity and its comorbidities among U.S. adults in NHANES 2003–2014 · 2018
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