Can Data Science Inform Environmental Justice and Community Risk Screening for Type 2 Diabetes?

<div><p>Background</p><p>Having the ability to scan the entire country for potential “hotspots” with increased risk of developing chronic diseases due to various environmental, demographic, and genetic susceptibility factors may inform risk management decisions and enable better environmental public health policies.</p><p>Objectives</p><p>Develop an approach for community-level risk screening focused on identifying potential genetic susceptibility hotpots.</p><p>Methods</p><p>Our approach combines analyses of phenotype-genotype data, genetic prevalence of single nucleotide polymorphisms, and census/geographic information to estimate census tract-level population attributable risks among various ethnicities and total population for the state of California.</p><p>Results</p><p>We estimate that the rs13266634 single nucleotide polymorphism, a type 2 diabetes susceptibility genotype, has a genetic prevalence of 56.3%, 47.4% and 37.0% in Mexican Mestizo, Caucasian, and Asian populations. Looking at the top quintile for total population attributable risk, 16 California counties have greater than 25% of their population living in hotspots of genetic susceptibility for developing type 2 diabetes due to this single genotypic susceptibility factor.</p><p>Conclusions</p><p>This study identified counties in California where large portions of the population may bear additional type 2 diabetes risk due to increased genetic prevalence of a susceptibility genotype. This type of screening can easily be extended to include information on environmental contaminants of interest and other related diseases, and potentially enables the rapid identification of potential environmental justice communities. Other potential uses of this approach include problem formulation in support of risk assessments, land use planning, and prioritization of site cleanup and remediation actions.</p></div

Geographic distribution of low and high PAR Census tracts across California.

<p>Census tracts in the green and red are those in the lowest and highest quintiles for Total PAR, respectively.</p

Percent of total population at increased risk of developing T2DM.

<p>Geographic distribution across the state of California for percent of population at increased risk of developing T2DM due to the rs13266634 single nucleotide polymorphism.</p

Counties with at least one Census Tract in the highest quintile of Total PAR.

<p>^a Census tracts in the highest quintile of total PAR as identified in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0121855#pone.0121855.g002" target="_blank">Fig 2</a>.</p><p>^b Total population in county calculated as the sum of all census tracts in that county</p><p>Counties with at least one Census Tract in the highest quintile of Total PAR.</p

CC genotype frequencies for T2DM cases and controls, with calculated population attributable risks.

<p>^a PAR calculated using ORs of 1.19, 1.21, and 1.28 for Asian, Caucasian, and Mexican cohorts, respectively;</p><p>^b risk allele frequency calculated from provided genotype incidences assuming Hardy-Weinberg equilibrium;</p><p>^c calculated assuming Hardy-Weinberg equilibrium: numbers with CC Genotype = p²n, where p is the risk allele frequency and n is the number of cases or controls</p><p>CC genotype frequencies for T2DM cases and controls, with calculated population attributable risks.</p

Overview of Chronic Oral Toxicity Values for Chemicals Present in Hydraulic Fracturing Fluids, Flowback, and Produced Waters

Concerns have been raised about potential public health effects that may arise if hydraulic fracturing-related chemicals were to impact drinking water resources. This study presents an overview of the chronic oral toxicity valuesspecifically, chronic oral reference values (RfVs) for noncancer effects, and oral slope factors (OSFs) for cancerthat are available for a list of 1173 chemicals that the United States (U.S.) Environmental Protection Agency (EPA) identified as being associated with hydraulic fracturing, including 1076 chemicals used in hydraulic fracturing fluids and 134 chemicals detected in flowback or produced waters from hydraulically fractured wells. The EPA compiled RfVs and OSFs using six governmental and intergovernmental data sources. Ninety (8%) of the 1076 chemicals reported in hydraulic fracturing fluids and 83 (62%) of the 134 chemicals reported in flowback/produced water had a chronic oral RfV or OSF available from one or more of the six sources. Furthermore, of the 36 chemicals reported in hydraulic fracturing fluids in at least 10% of wells nationwide (identified from EPA’s analysis of the FracFocus Chemical Disclosure Registry 1.0), 8 chemicals (22%) had an available chronic oral RfV. The lack of chronic oral RfVs and OSFs for the majority of these chemicals highlights the significant knowledge gap that exists to assess the potential human health hazards associated with hydraulic fracturing

Estimating the Potential Toxicity of Chemicals Associated with Hydraulic Fracturing Operations Using Quantitative Structure–Activity Relationship Modeling

The United States Environmental Protection Agency (EPA) identified 1173 chemicals associated with hydraulic fracturing fluids, flowback, or produced water, of which 1026 (87%) lack chronic oral toxicity values for human health assessments. To facilitate the ranking and prioritization of chemicals that lack toxicity values, it may be useful to employ toxicity estimates from quantitative structure–activity relationship (QSAR) models. Here we describe an approach for applying the results of a QSAR model from the TOPKAT program suite, which provides estimates of the rat chronic oral lowest-observed-adverse-effect level (LOAEL). Of the 1173 chemicals, TOPKAT was able to generate LOAEL estimates for 515 (44%). To address the uncertainty associated with these estimates, we assigned qualitative confidence scores (high, medium, or low) to each TOPKAT LOAEL estimate, and found 481 to be high-confidence. For 48 chemicals that had both a high-confidence TOPKAT LOAEL estimate and a chronic oral reference dose from EPA’s Integrated Risk Information System (IRIS) database, Spearman rank correlation identified 68% agreement between the two values (permutation p-value =1 × 10^–11). These results provide support for the use of TOPKAT LOAEL estimates in identifying and prioritizing potentially hazardous chemicals. High-confidence TOPKAT LOAEL estimates were available for 389 of 1026 hydraulic fracturing-related chemicals that lack chronic oral RfVs and OSFs from EPA-identified sources, including a subset of chemicals that are frequently used in hydraulic fracturing fluids

Developmental Ontology: Computational modeling of Retinoic Acid in Development of Spina Bifida

Presented at the Annual Society of Toxicology meetin

The full questions included in the SOAR manual and the source of the question if it was taken from an existing publication.

<p>Not every question will be answered for every manuscript, given variation in the methods (in vivo, in vitro, etc). See <a href="https://docs.google.com/spreadsheet/ccc?key=0AgWXniu3KhthdEhCcXdUMFVTeF9LVnZ1TFpJNkxZdEE=sharing" target="_blank">https://docs.google.com/spreadsheet/ccc?key=0AgWXniu3KhthdEhCcXdUMFVTeF9LVnZ1TFpJNkxZdEE=sharing</a> for a link to a publicly available version that includes weights applied to the questions, possible answers, and comments that provide more detail for each question.</p><p>The full questions included in the SOAR manual and the source of the question if it was taken from an existing publication.</p

The papers used to develop and test the SOAR tool. The first four were used only during internal development of the questions.

<p>Papers 1–8 were used by seven experts (internal and external) for 2 rounds of revising the questions. The last 11 were used by the same group to validate the tool and determine inter-rater reliability. Papers were chosen by performing a broad literature search and removing any that were affiliated with the expert in this study.</p><p>The papers used to develop and test the SOAR tool. The first four were used only during internal development of the questions.</p