Evaluation of the Association between Arsenic and Diabetes: A National Toxicology Program Workshop Review

Background: Diabetes affects an estimated 346 million persons globally, and total deaths from diabetes are projected to increase > 50% in the next decade. Understanding the role of environmental chemicals in the development or progression of diabetes is an emerging issue in environmental health. In 2011, the National Toxicology Program (NTP) organized a workshop to assess the literature for evidence of associations between certain chemicals, including inorganic arsenic, and diabetes and/or obesity to help develop a focused research agenda. This review is derived from discussions at that workshop

Evaluation of the Association between Arsenic and Diabetes: A National Toxicology Program Workshop Review

BACKGROUND: Diabetes affects an estimated 346 million persons globally, and total deaths from diabetes are projected to increase > 50% in the next decade. Understanding the role of environmental chemicals in the development or progression of diabetes is an emerging issue in environmental health. In 2011, the National Toxicology Program (NTP) organized a workshop to assess the literature for evidence of associations between certain chemicals, including inorganic arsenic, and diabetes and/or obesity to help develop a focused research agenda. This review is derived from discussions at that workshop. OBJECTIVES: Our objectives were to assess the consistency, strength/weaknesses, and biological plausibility of findings in the scientific literature regarding arsenic and diabetes and to identify data gaps and areas for future evaluation or research. The extent of the existing literature was insufficient to consider obesity as an outcome. DATA SOURCES, EXTRACTION, AND SYNTHESIS: Studies related to arsenic and diabetes or obesity were identified through PubMed and supplemented with relevant studies identified by reviewing the reference lists in the primary literature or review articles. CONCLUSIONS: Existing human data provide limited to sufficient support for an association between arsenic and diabetes in populations with relatively high exposure levels (>= 150 mu g arsenic/L in drinking water). The evidence is insufficient to conclude that arsenic is associated with diabetes in lower exposure (< 150 mu g arsenic/L drinking water), although recent studies with better measures of outcome and exposure support an association. The animal literature as a whole was inconclusive; however, studies using better measures of diabetes-relevant end points support a link between arsenic and diabetes

Evaluation of the Association between Arsenic and Diabetes: A National Toxicology Program Workshop Review

Background: Diabetes affects an estimated 346 million persons globally, and total deaths from diabetes are projected to increase > 50% in the next decade. Understanding the role of environmental chemicals in the development or progression of diabetes is an emerging issue in environmental health. In 2011, the National Toxicology Program (NTP) organized a workshop to assess the literature for evidence of associations between certain chemicals, including inorganic arsenic, and diabetes and/or obesity to help develop a focused research agenda. This review is derived from discussions at that workshop. Objectives: Our objectives were to assess the consistency, strength/weaknesses, and biological plausibility of findings in the scientific literature regarding arsenic and diabetes and to identify data gaps and areas for future evaluation or research. The extent of the existing literature was insufficient to consider obesity as an outcome. Data Sources, Extraction, and Synthesis: Studies related to arsenic and diabetes or obesity were identified through PubMed and supplemented with relevant studies identified by reviewing the reference lists in the primary literature or review articles. Conclusions: Existing human data provide limited to sufficient support for an association between arsenic and diabetes in populations with relatively high exposure levels (≥ 150 µg arsenic/L in drinking water). The evidence is insufficient to conclude that arsenic is associated with diabetes in lower exposure (< 150 µg arsenic/L drinking water), although recent studies with better measures of outcome and exposure support an association. The animal literature as a whole was inconclusive; however, studies using better measures of diabetes-relevant end points support a link between arsenic and diabetes

A hybrid gene selection approach to create the S1500+ targeted gene sets for use in high-throughput transcriptomics

<div><p>Changes in gene expression can help reveal the mechanisms of disease processes and the mode of action for toxicities and adverse effects on cellular responses induced by exposures to chemicals, drugs and environment agents. The U.S. Tox21 Federal collaboration, which currently quantifies the biological effects of nearly 10,000 chemicals via quantitative high-throughput screening(qHTS) in <i>in vitro</i> model systems, is now making an effort to incorporate gene expression profiling into the existing battery of assays. Whole transcriptome analyses performed on large numbers of samples using microarrays or RNA-Seq is currently cost-prohibitive. Accordingly, the Tox21 Program is pursuing a high-throughput transcriptomics (HTT) method that focuses on the targeted detection of gene expression for a carefully selected subset of the transcriptome that potentially can reduce the cost by a factor of 10-fold, allowing for the analysis of larger numbers of samples. To identify the optimal transcriptome subset, genes were sought that are (1) representative of the highly diverse biological space, (2) capable of serving as a proxy for expression changes in unmeasured genes, and (3) sufficient to provide coverage of well described biological pathways. A hybrid method for gene selection is presented herein that combines data-driven and knowledge-driven concepts into one cohesive method. Our approach is modular, applicable to any species, and facilitates a robust, quantitative evaluation of performance. In particular, we were able to perform gene selection such that the resulting set of “sentinel genes” adequately represents all known canonical pathways from Molecular Signature Database (MSigDB v4.0) and can be used to infer expression changes for the remainder of the transcriptome. The resulting computational model allowed us to choose a purely data-driven subset of 1500 sentinel genes, referred to as the S1500 set, which was then augmented using a knowledge-driven selection of additional genes to create the final S1500+ gene set. Our results indicate that the sentinel genes selected can be used to accurately predict pathway perturbations and biological relationships for samples under study.</p></div

S1500+ gene selection workflow.

<p>To compile the S1500+ gene set, a combination of modular data-driven algorithms as well as manual crowd-sourced knowledge-based gene nominations was used to optimize for pathway coverage and the ability to extrapolate to the whole transcriptome.</p

Pathway coverage.

<p>Pathway coverage.</p

Dimension reduction plot.

<p>X-axis shows the percentage of the total principal components (eigengenes) and the Y-axis shows percentage of variability captured. The red line represents the expected relationship given statistically independent gene expression, whereas the blue curve shows the observed relationship.</p

Summary of gene and pathway level extrapolation performance of the S1500 and S1500+ gene sets using independent test set.

<p>Summary of gene and pathway level extrapolation performance of the S1500 and S1500+ gene sets using independent test set.</p