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      Associations of a Metal Mixture Measured in Multiple Biomarkers with IQ: Evidence from Italian Adolescents Living near Ferroalloy Industry

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          Abstract

          Background:

          Research on the health effects of chemical mixtures has focused mainly on early life rather than adolescence, a potentially important developmental life stage.

          Objectives:

          We examined associations of a metal mixture with general cognition in a cross-sectional study of adolescents residing near ferromanganese industry, a source of airborne metals emissions.

          Methods:

          We measured manganese (Mn), lead (Pb), copper (Cu), and chromium (Cr) in hair, blood, urine, nails, and saliva from 635 Italian adolescents 10–14 years of age. Full-scale, verbal, and performance intelligence quotient (FSIQ, VIQ, PIQ) scores were assessed using the Wechsler Intelligence Scale for Children-III. Multivariable linear regression and Bayesian kernel machine regression (BKMR) were used to estimate associations of the metal mixture with IQ. In secondary analyses, we used BKMR’s hierarchical variable selection option to inform biomarker selection for Mn, Cu, and Cr.

          Results:

          Median metal concentrations were as follows: hair Mn, 0.08 μ g / g ; hair Cu, 9.6 μ g / g ; hair Cr, 0.05 μ g / g ; and blood Pb, 1.3 μ g / dL . Adjusted models revealed an inverted U-shaped association between hair Cu and VIQ, consistent with Cu as an essential nutrient that is neurotoxic in excess. At low levels of hair Cu (10th percentile, 5.4 μ g / g ), higher concentrations (90th percentiles) of the mixture of Mn, Pb, and Cr ( 0.3 μ g / g , 2.6 μ g / dL , and 0.1 μ g / g , respectively) were associated with a 2.9 (95% CI: 5.2 , 0.5 )–point decrease in VIQ score, compared with median concentrations of the mixture. There was suggestive evidence of interaction between Mn and Cu. In secondary analyses, saliva Mn, hair Cu, and saliva Cr were selected as the biomarkers most strongly associated with VIQ score.

          Discussion:

          Higher adolescent levels of Mn, Pb, and Cr were associated with lower IQ scores, especially at low Cu levels. Findings also support further investigation into Cu as both beneficial and toxic for neurobehavioral outcomes. https://doi.org/10.1289/EHP6803

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          Bayesian kernel machine regression for estimating the health effects of multi-pollutant mixtures.

          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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            Is Open Access

            Statistical software for analyzing the health effects of multiple concurrent exposures via Bayesian kernel machine regression

            Background 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. Methods 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. Results 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. Conclusions 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.
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              Wilson's disease.

              Progressive hepatolenticular degeneration, or Wilson's disease, is a genetic disorder of copper metabolism. Knowledge of the clinical presentations and treatment of the disease are important both to the generalist and to specialists in gastroenterology and hepatology, neurology, psychiatry, and paediatrics. Wilson's disease invariably results in severe disability and death if untreated. The diagnosis is easily overlooked but if discovered early, effective treatments are available that will prevent or reverse many manifestations of this disorder. Studies have identified the role of copper in disease pathogenesis and clinical, biochemical, and genetic markers that can be useful in diagnosis. There are several chelating agents and zinc salts for medical therapy. Liver transplantation corrects the underlying pathophysiology and can be lifesaving. The discovery of the Wilson's disease gene has opened up a new molecular diagnostic approach, and could form the basis of future gene therapy.
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                Author and article information

                Journal
                Environ Health Perspect
                Environ. Health Perspect
                EHP
                Environmental Health Perspectives
                Environmental Health Perspectives
                0091-6765
                1552-9924
                8 September 2020
                September 2020
                : 128
                : 9
                : 097002
                Affiliations
                [ 1 ]Department of Environmental Health, Boston University School of Public Health , Boston, Massachusetts, USA
                [ 2 ]Division of Biomedical Statistics and Informatics, Mayo Clinic Arizona , Scottsdale, Arizona, USA
                [ 3 ]Biostatistics Unit, Kaiser Permanente Washington Health Research Institute , Seattle, Washington, USA
                [ 4 ]Department of Biostatistics, Harvard T.H. Chan School of Public Health , Boston, Massachusetts, USA
                [ 5 ]Department of Environmental Health, Harvard T.H. Chan School of Public Health , Boston, Massachusetts, USA
                [ 6 ]Departments of Neurology and Psychiatry, Boston Children’s Hospital , Boston, Massachusetts, USA
                [ 7 ]Departments of Neurology and Psychiatry, Harvard Medical School , Boston, Massachusetts, USA
                [ 8 ]Department of Medical-Surgical Specialties, Radiological Science and Public Health, University of Brescia , Brescia, Italy
                [ 9 ]Azienda Unità Sanitaria Locale Reggio Emilia , Reggio Emilia, Italy
                [ 10 ]Department of Neurology, Boston University Medical School , Boston, Massachusetts, USA
                [ 11 ]Department of Biostatistics, Boston University School of Public Health , Boston, Massachusetts, USA
                [ 12 ]Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai , New York, New York, USA
                [ 13 ]Department of Microbiology and Environmental Toxicology, University of California, Santa Cruz , Santa Cruz, California, USA
                Author notes
                Address correspondence to Julia A. Bauer, Boston University School of Public Health, Department of Environmental Health, 715 Albany St., Boston, MA 02118 USA. Telephone: (907) 250-3864. Email: jnab@ 123456bu.edu
                Article
                EHP6803
                10.1289/EHP6803
                7478128
                32897104
                1e94a5f7-7dab-4acb-a985-25c82c7e5b25

                EHP is an open-access journal published with support from the National Institute of Environmental Health Sciences, National Institutes of Health. All content is public domain unless otherwise noted.

                History
                : 16 January 2020
                : 03 June 2020
                : 04 August 2020
                Categories
                Research

                Public health
                Public health

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