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Advancing Dose-Response Assessment Methods for Environmental Regulatory Impact Analysis: A Bayesian Belief Network Approach Applied to Inorganic Arsenic.

ABSTRACT: Dose-response functions used in regulatory risk assessment are based on studies of whole organisms and fail to incorporate genetic and metabolomic data. Bayesian belief networks (BBNs) could provide a powerful framework for incorporating such data, but no prior research has examined this possibility. To address this gap, we develop a BBN-based model predicting birthweight at gestational age from arsenic exposure via drinking water and maternal metabolic indicators using a cohort of 200 pregnant women from an arsenic-endemic region of Mexico. We compare BBN predictions to those of prevailing slope-factor and reference-dose approaches. The BBN outperforms prevailing approaches in balancing false-positive and false-negative rates. Whereas the slope-factor approach had 2% sensitivity and 99% specificity and the reference-dose approach had 100% sensitivity and 0% specificity, the BBN's sensitivity and specificity were 71% and 30%, respectively. BBNs offer a promising opportunity to advance health risk assessment by incorporating modern genetic and metabolomic data.

SUBMITTER: Zabinski JW

PROVIDER: S-EPMC5063306 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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Advancing Dose-Response Assessment Methods for Environmental Regulatory Impact Analysis: A Bayesian Belief Network Approach Applied to Inorganic Arsenic.

Zabinski Joseph W JW Garcia-Vargas Gonzalo G Rubio-Andrade Marisela M Fry Rebecca C RC Gibson Jacqueline MacDonald JM

Environmental science & technology letters 20160420 5

Dose-response functions used in regulatory risk assessment are based on studies of whole organisms and fail to incorporate genetic and metabolomic data. Bayesian belief networks (BBNs) could provide a powerful framework for incorporating such data, but no prior research has examined this possibility. To address this gap, we develop a BBN-based model predicting birthweight at gestational age from arsenic exposure via drinking water and maternal metabolic indicators using a cohort of 200 pregnant ...[more]

PMID: 27747248

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Project description:Arsenic occurs naturally in the environment, and exists predominantly as inorganic arsenite (As (III) and arsenate As (V)). Arsenic contamination of drinking water has long been recognized as a major global health concern. Arsenic exposure causes changes in skin color and lesions, and more severe health conditions such as black foot disease as well as various cancers originating in the lungs, skin, and bladder. In order to efficiently metabolize and excrete arsenic, it is methylated to monomethylarsonic and dimethylarsinic acid. One single enzyme, arsenic methyltransferase (AS3MT) is responsible for generating both metabolites. AS3MT has been purified from several mammalian and nonmammalian species, and its mRNA sequences were determined from amino acid sequences. With the advent of genome technology, mRNA sequences of AS3MT have been predicted from many species throughout the animal kingdom. Horizontal gene transfer had been postulated for this gene through phylogenetic studies, which suggests the importance of this gene in appropriately handling arsenic exposures in various organisms. An altered ability to methylate arsenic is dependent on specific single nucleotide polymorphisms (SNPs) in AS3MT. Reduced AS3MT activity resulting in poor metabolism of iAs has been shown to reduce expression of the tumor suppressor gene, p16, which is a potential pathway in arsenic carcinogenesis. Arsenic is also known to induce oxidative stress in cells. However, the presence of antioxidant response elements (AREs) in the promoter sequences of AS3MT in several species does not correlate with the ability to methylate arsenic. ARE elements are known to bind NRF2 and induce antioxidant enzymes to combat oxidative stress. NRF2 may be partly responsible for the biotransformation of iAs and the generation of methylated arsenic species via AS3MT. In this article, arsenic metabolism, excretion, and toxicity, a discussion of the AS3MT gene and its evolutionary history, and DNA methylation resulting from arsenic exposure have been reviewed.

Project description:Following an official request to EFSA from the European Commission, EFSA assessed the chronic dietary exposure to inorganic arsenic (iAs) in the European population. A total of 13,608 analytical results on iAs were considered in the current assessment (7,623 corresponding to drinking water and 5,985 to different types of food). Samples were collected across Europe between 2013 and 2018. The highest mean dietary exposure estimates at the lower bound (LB) were in toddlers (0.30 ?g/kg body weight (bw) per day), and in both infants and toddlers (0.61 ?g/kg bw per day) at the upper bound (UB). At the 95th percentile, the highest exposure estimates (LB-UB) were 0.58 and 1.20 ?g/kg bw per day in toddlers and infants, respectively. In general, UB estimates were two to three times higher than LB estimates. The mean dietary exposure estimates (LB) were overall below the range of benchmark dose lower confidence limit (BMDL 01) values of 0.3-8 ?g/kg bw per day established by the EFSA Panel on Contaminants in the Food Chain in 2009. However, for the 95th percentile dietary exposure (LB), the maximum estimates for infants, toddlers and other children were within this range of BMDL 01 values. Across the different age classes, the main contributors to the dietary exposure to iAs (LB) were 'Rice', 'Rice-based products', 'Grains and grain-based products (no rice)' and 'Drinking water'. Different ad hoc exposure scenarios (e.g. consumption of rice-based formulae) showed dietary exposure estimates in average and for high consumers close to or within the range of BMDL 01 values. The main uncertainties associated with the dietary exposure estimations refer to the impact of using the substitution method to treat the left-censored data (LB-UB differences), to the lack of information (consumption and occurrence) on some iAs-containing ingredients in specific food groups, and to the effect of food preparation on the iAs levels. Recommendations were addressed to improve future dietary exposure assessments to iAs.

Dataset Information

Advancing Dose-Response Assessment Methods for Environmental Regulatory Impact Analysis: A Bayesian Belief Network Approach Applied to Inorganic Arsenic.

Publications

Advancing Dose-Response Assessment Methods for Environmental Regulatory Impact Analysis: A Bayesian Belief Network Approach Applied to Inorganic Arsenic.

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