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.

Estimating the health effects of multi-pollutant mixtures is of increasing interest in environmental epidemiology. Recently, a new approach for estimating the health effects of mixtures, Bayesian kernel machine regression (BKMR), has been developed. This method estimates the multivariable exposure-response function in a flexible and parsimonious way, conducts variable selection on the (potentially high-dimensional) vector of exposures, and allows for a grouped variable selection approach that can accommodate highly correlated exposures. However, the application of this novel method has been limited by a lack of available software, the need to derive interpretable output in a computationally efficient manner, and the inability to apply the method to non-continuous outcome variables. This paper addresses these limitations by (i) introducing an open-source software package in the R programming language, the bkmr R package, (ii) demonstrating methods for visualizing high-dimensional exposure-response functions, and for estimating scientifically relevant summaries, (iii) illustrating a probit regression implementation of BKMR for binary outcomes, and (iv) describing a fast version of BKMR that utilizes a Gaussian predictive process approach. All of the methods are illustrated using fully reproducible examples with the provided R code. Applying the methods to a continuous outcome example illustrated the ability of the BKMR implementation to estimate the health effects of multi-pollutant mixtures in the context of a highly nonlinear, biologically-based dose-response function, and to estimate overall, single-exposure, and interactive health effects. The Gaussian predictive process method led to a substantial reduction in the runtime, without a major decrease in accuracy. In the setting of a larger number of exposures and a dichotomous outcome, the probit BKMR implementation was able to correctly identify the variables included in the exposure-response function and yielded interpretable quantities on the scale of a latent continuous outcome or on the scale of the outcome probability. This newly developed software, integrated suite of tools, and extended methodology makes BKMR accessible for use across a broad range of epidemiological applications in which multiple risk factors have complex effects on health.

Lead (Pb) and mercury (Hg) neurotoxicity is well established. In recent years, a growing body of evidence suggests that environmental exposure to other metals including arsenic (As), cadmium (Cd), and manganese (Mn) and their mixtures also poses public health threats. In this paper, we summarize the recent literature examining the relationship of prenatal and childhood environmental metal exposures with cognitive and behavioral outcomes in children. We conducted a literature search to identify epidemiologic studies that examined the relationship of Cd, Mn, and metal mixtures with children’s neurodevelopmental/cognitive and behavioral outcomes. We restricted the search to peer-reviewed studies published in English between January 2009 and March 2015. We identified a total of 31 articles of which 16, 17, and 16 studies examined the effects of Cd, Mn, or metal mixtures, respectively. Based on our review, there is suggestive evidence that prenatal/childhood Cd exposure may be associated with poorer cognition, but additional research is clearly needed. We found little evidence of behavioral effects of early life Cd exposure, and no studies found a significant relationship with attention deficit hyperactivity disorder. Studies of early life Mn exposure consistently reported negative impacts on both cognition and behavior. There is also growing evidence that co-exposure to multiple metals can result in increased neurotoxicity compared to single-metal exposure, in particular during early life. Few studies have evaluated behavioral effects related to metal co-exposure.

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Background: Recent evidence suggests that low-level environmental exposure to manganese adversely affects child growth and neurodevelopment. Previous studies have addressed the effects of prenatal exposure, but little is known about developmental effects of early postnatal exposure. Methods: We studied 448 children born in Mexico City from 1997 through 2000, using a longitudinal study to investigate neurotoxic effects of early-life manganese exposure. Archived blood samples, collected from children at 12 and 24 months of age, were analyzed for manganese levels using inductively coupled plasma mass spectrometry. Mental and psychomotor development were scored using Bayley Scales of Infant Development at 6-month intervals between 12 and 36 months of age. Results: At 12 months of age, the mean (SD) blood manganese level was 24.3 (4.5) μg/L and the median was 23.7 μg/L; at 24 months, these values were 21.1 (6.2) μg/L and 20.3 μg/L, respectively. Twelve- and 24-month manganese concentrations were correlated (Spearman correlation = 0.55) and levels declined over time (β = −5.7 [95% CI = −6.2 to −5.1]). We observed an inverted U-shaped association between 12-month blood manganese and concurrent mental development scores (compared with the middle 3 manganese quintiles, for the lowest manganese quintile, β = −3.3 [−6.0 to −0.7] and for the highest manganese quintile, β = −2.8 [−5.5 to −0.2]). This 12-month manganese effect was apparent but diminished with mental development scores at later ages. The 24-month manganese levels were not associated with neurodevelopment. Conclusions: These results suggest a possible biphasic dose-response relationship between early-life manganese exposure at lower exposure levels and infant neurodevelopment. The data are consistent with manganese as both an essential nutrient and a toxicant.

Environmental Health Perspectives is an Open Access journal published by the National Institute of Environmental Health Sciences. Environmental Health Perspectives is an Open Access journal published by the National Institute of Environmental Health Sciences.