Because humans are invariably exposed to complex chemical mixtures, estimating the health effects of multi-pollutant exposures is of critical concern in environmental epidemiology, and to regulatory agencies such as the U.S. Environmental Protection Agency. However, most health effects studies focus on single agents or consider simple two-way interaction models, in part because we lack the statistical methodology to more realistically capture the complexity of mixed exposures. We introduce Bayesian kernel machine regression (BKMR) as a new approach to study mixtures, in which the health outcome is regressed on a flexible function of the mixture (e.g. air pollution or toxic waste) components that is specified using a kernel function. In high-dimensional settings, a novel hierarchical variable selection approach is incorporated to identify important mixture components and account for the correlated structure of the mixture. Simulation studies demonstrate the success of BKMR in estimating the exposure-response function and in identifying the individual components of the mixture responsible for health effects. We demonstrate the features of the method through epidemiology and toxicology applications.

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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, 18μg/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≥0.7μg/dl, Mn≥6.6μg/dl, Pb≥4.2μg/dl) compared to the median (As=0.5μg/dl, Mn=5.8μg/dl, Pb=3.1μg/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

BackgroundThe people of Bangladesh are currently exposed to high concentrations of arsenic and manganese in drinking water, as well as elevated lead in many regions. The objective of this study was to investigate associations between environmental exposure to these contaminants and neurodevelopmental outcomes among Bangladeshi children.MethodsWe evaluated data from 524 children, members of an ongoing prospective birth cohort established to study the effects of prenatal and early childhood arsenic exposure in the Sirajdikhan and Pabna Districts of Bangladesh. Water was collected from the family’s primary drinking source during the first trimester of pregnancy and at ages 1, 12 and 20–40 months. At age 20–40 months, blood lead was measured and neurodevelopmental outcomes were assessed using a translated, culturally-adapted version of the Bayley Scales of Infant and Toddler Development, Third Edition (BSID-III).ResultsMedian blood lead concentrations were higher in Sirajdikhan than Pabna (7.6 vs.