Machine Learning Models of Arsenic in Private Wells Throughout the Conterminous United States As a Tool for Exposure Assessment in Human Health Studies.

Arsenic from geologic sources is widespread in groundwater within the United States (U.S.). In several areas, groundwater arsenic concentrations exceed the U.S. Environmental Protection Agency maximum contaminant level of 10 µg per liter (µg/L). However, this standard applies only to public-supply drinking water and not to private-supply, which is not federally regulated and is rarely monitored. As a result, arsenic exposure from private wells is a potentially substantial, but largely hidden, public health concern. Machine learning models using boosted regression trees (BRT) and random forest classification (RFC) techniques were developed to estimate probabilities and concentration ranges of arsenic in private wells throughout the conterminous U.S. Three BRT models were fit separately to estimate the probability of private well arsenic concentrations exceeding 1, 5, or 10 µg/L whereas the RFC model estimates the most probable category (≤5, >5 to ≤10, or >10 µg/L). Overall, the models perform best at identifying areas with low concentrations of arsenic in private wells. The BRT 10 µg/L model estimates for testing data have an overall accuracy of 91.2%, sensitivity of 33.9%, and specificity of 98.2%. Influential variables identified across all models included average annual precipitation and soil geochemistry. Models were developed in collaboration with public health experts to support U.S.-based studies focused on health effects from arsenic exposure.

Cross-Sectional Analysis of Baseline Visual Parameters in Subjects Recruited Into the RESCUE and REVERSE ND4-LHON Gene Therapy Studies.

OBJECTIVE: This report presents a cross-sectional analysis of the baseline characteristics of subjects with Leber hereditary optic neuropathy enrolled in the gene therapy trials RESCUE and REVERSE, to illustrate the evolution of visual parameters over the first year after vision loss. METHODS: RESCUE and REVERSE were 2 phase III clinical trials designed to assess the efficacy of rAAV2/2-ND4 gene therapy in ND4-LHON subjects. At enrollment, subjects had vision loss for ≤6 months in RESCUE, and between 6 and 12 months in REVERSE. Functional visual parameters (best-corrected visual acuity [BCVA], contrast sensitivity [CS], and Humphrey Visual Field [HVF]) and structural parameters assessed by spectral-domain optical coherence tomography were analyzed in both cohorts before treatment. The cross-sectional analysis of functional and anatomic parameters included the baseline values collected in all eyes at 2 different visits (Screening and Inclusion). RESULTS: Seventy-six subjects were included in total, 39 in RESCUE and 37 in REVERSE. Mean BCVA was significantly worse in RESCUE subjects compared with REVERSE subjects (1.29 and 1.61 LogMAR respectively, P = 0.0029). Similarly, mean CS and HVF were significantly more impaired in REVERSE vs RESCUE subjects (P < 0.005). The cross-sectional analysis showed that the monthly decrease in BCVA, ganglion cell layer macular volume, and retinal nerve fiber layer thickness was much more pronounced in the first 6 months after onset (+0.24 LogMAR, -0.06 mm3, and -6.00 µm respectively) than between 6 and 12 months after onset (+0.02 LogMAR, -0.01 mm3, and -0.43 µm respectively). CONCLUSION: LHON progresses rapidly in the first months following onset during the subacute phase, followed by relative stabilization during the dynamic phase.

Relationships Between Urinary Metals and Diabetes Traits Among Mexican Americans in Starr County, Texas, USA.

Hispanics/Latinos have higher rates of type 2 diabetes (T2D), and the origins of these disparities are poorly understood. Environmental endocrine-disrupting chemicals (EDCs), including some metals and metalloids, are implicated as diabetes risk factors. Data indicate that Hispanics/Latinos may be disproportionately exposed to EDCs, yet they remain understudied with respect to environmental exposures and diabetes. The objective of this study is to determine how metal exposures contribute to T2D progression by evaluating the associations between 8 urinary metals and measures of glycemic status in 414 normoglycemic or prediabetic adults living in Starr County, Texas, a Hispanic/Latino community with high rates of diabetes and diabetes-associated mortality. We used multivariable linear regression to quantify the differences in homeostatic model assessments for pancreatic ß-cell function, insulin resistance, and insulin sensitivity (HOMA-ß, HOMA-IR, HOMA-S, respectively), plasma insulin, plasma glucose, and hemoglobin A1c (HbA1c) associated with increasing urinary metal concentrations. Quantile-based g-computation was utilized to assess mixture effects. After multivariable adjustment, urinary arsenic and molybdenum were associated with lower HOMA-ß, HOMA-IR, and plasma insulin levels and higher HOMA-S. Additionally, higher urinary copper levels were associated with a reduced HOMA-ß. Lastly, a higher concentration of the 8 metal mixtures was associated with lower HOMA-ß, HOMA-IR, and plasma insulin levels as well as higher HOMA-S. Our data indicate that arsenic, molybdenum, copper, and this metal mixture are associated with alterations in measures of glucose homeostasis among non-diabetics in Starr County. This study is one of the first to comprehensively evaluate associations of urinary metals with glycemic measures in a high-risk Mexican American population.

Arsenic metabolism, diabetes prevalence, and insulin resistance among Mexican Americans: A mendelian randomization approach.

Background: Differences in arsenic metabolism capacity may influence risk for type 2 diabetes, but the mechanistic drivers are unclear. We evaluated the associations between arsenic metabolism with overall diabetes prevalence and with static and dynamic measures of insulin resistance among Mexican Americans living in Starr County, Texas. Methods: We utilized data from cross-sectional studies conducted in Starr County, Texas, from 2010-2014. A Mendelian randomization approach was utilized to evaluate the associations between arsenic metabolism and type 2 diabetes prevalence using the intronic variant in the arsenic methylating gene, rs9527, as the instrumental variable for arsenic metabolism. To further assess mechanisms for diabetes pathogenesis, proportions of the urinary arsenic metabolites were employed to assess the association between arsenic metabolism and insulin resistance among participants without diabetes. Urinary biomarkers of arsenic metabolites were modeled as individual proportions of the total. Arsenic metabolism was evaluated both with a static outcome of insulin resistance, homeostatic measure of assessment (HOMA-IR), and a dynamic measure of insulin sensitivity, Matsuda Index. Results: Among 475 Mexican American participants from Starr County, higher metabolism capacity for arsenic is associated with higher diabetes prevalence driven by worse insulin resistance. Presence of the minor T allele of rs9527 is independently associated with an increase in the proportion of monomethylated arsenic (MMA%) and is associated with an odds ratio of 0.50 (95% CI: 0.24, 0.90) for type 2 diabetes. This association was conserved after potential covariate adjustment. Furthermore, among participants without type 2 diabetes, the highest quartile of MMA% was associated with 22% (95% CI: -33.5%, -9.07%) lower HOMA-IR and 56% (95% CI: 28.3%, 91.3%) higher Matsuda Index for insulin sensitivity. Conclusions: Arsenic metabolism capacity, indicated by a lower proportion of monomethylated arsenic, is associated with increased diabetes prevalence driven by an insulin resistant phenotype among Mexican Americans living in Starr County, Texas.