Low-level arsenic exposure, AS3MT gene polymorphism and cardiovascular diseases in rural Texas counties.
ABSTRACT Most Americans living in rural areas use groundwater for drinking. Exposure to low-level (around the current U.S. standard 10 μg/L) arsenic in drinking water is associated with increased mortality of cardiovascular diseases. The current study was to determine if coronary heart disease, hypertension, and hyperlipidemia were associated with low-level arsenic exposure and AS3MT gene single nucleotide polymorphism (SNP) A35991G (rs10748835) in rural Texas. Subjects (156 men, 343 women, 40-96 years of age with a mean of 61) were residents from rural counties Cochran, Palmer, and Bailey, Texas. Groundwater arsenic concentration at each subject's home was estimated with ArcGIS inverse distance weighted interpolation based on the residential location's distances to surrounding wells with known water arsenic concentrations. The estimated groundwater arsenic concentration ranged from 2.2 to 15.3 (mean 6.2) μg/L in this cohort. Logistic regression analysis showed that coronary heart disease was associated with higher arsenic exposure (p<0.05) and with AS3MT genotype GG vs. AA (p<0.05) after adjustments for age, ethnicity, gender, education, smoking status, alcoholism, and anti-hyperlipidemia medication. Hypertension was associated with higher arsenic exposure, while hyperlipidemia was associated with genotype AG vs. AA of the AS3MT gene (p<0.05). Thus, coronary heart disease and its main risk factors were associated with low-level arsenic exposure, AS3MT polymorphism or both.
- SourceAvailable from: PubMed Central[Show abstract] [Hide abstract]
ABSTRACT: Epidemiologic studies and animal models suggest that in utero arsenic exposure affects fetal health, with a negative association between maternal arsenic ingestion and infant birth weight often observed. However, the molecular mechanisms for this association remain elusive. In the present study, we aimed to increase our understanding of the impact of low-dose arsenic exposure on fetal health by identifying possible arsenic-associated fetal tissue biomarkers in a cohort of pregnant women exposed to arsenic at low levels. Arsenic concentrations were determined from the urine samples of a cohort of 133 pregnant women from New Hampshire. Placental tissue samples collected from enrollees were homogenized and profiled for gene expression across a panel of candidate genes, including known arsenic regulated targets and genes involved in arsenic transport, metabolism, or disease susceptibility. Multivariable adjusted linear regression models were used to examine the relationship of candidate gene expression with arsenic exposure or with birth weight of the baby. Placental expression of the arsenic transporter AQP9 was positively associated with maternal urinary arsenic levels during pregnancy (coefficient estimate: 0.25; 95% confidence interval: 0.05 -- 0.45). Placental expression of AQP9 related to expression of the phospholipase ENPP2 which was positively associated with infant birth weight (coefficient estimate: 0.28; 95% CI: 0.09 -- 0.47). A structural equation model indicated that these genes may mediate arsenic's effect on infant birth weight (coefficient estimate: -0.009; 95% confidence interval: -0.032 -- -0.001; 10,000 replications for bootstrapping). We identified the expression of AQP9 as a potential fetal biomarker for arsenic exposure. Further, we identified a positive association between the placental expression of phospholipase ENPP2 and infant birth weight. These findings suggest a path by which arsenic may affect birth outcomes.Environmental Health 07/2013; 12(1):58. · 2.71 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: Oral exposure to inorganic arsenic (iAs) is associated with adverse health effects. Epidemiological studies suggest differences in susceptibility to these health effects, possibly due to genotypic variation. Genetic polymorphisms in iAs metabolism could lead to increased susceptibility by altering urinary iAs metabolite concentrations. To examine the impact of genotypic polymorphisms on iAs metabolism. We screened 360 publications from PubMed and Web of Science for data on urinary mono- and dimethylated arsenic (MMA and DMA) percentages and polymorphic genes encoding proteins that are hypothesized to play roles in arsenic metabolism. The genes we examined were arsenic (+3) methyltransferase (AS3MT), glutathione-s-transferase omega (GSTO), and purine nucleoside phosphorylase (PNP). Relevant data were pooled to determine which polymorphisms are associated across studies with changes in urinary metabolite concentration. In our review, AS3MT polymorphisms rs3740390, rs11191439, and rs11191453 were associated with statistically significant changes in percent urinary MMA. Studies of GSTO polymorphisms did not indicate statistically significant associations with methylation, and there are insufficient data on PNP polymorphisms to evaluate their impact on metabolism. Collectively, these data support the hypothesis that AS3MT polymorphisms alter in vivo metabolite concentrations. Preliminary evidence suggests that AS3MT genetic polymorphisms may impact disease susceptibility. GSTO polymorphisms were not associated with iAs-associated health outcomes. Additional data are needed to evaluate the association between PNP polymorphisms and iAs-associated health outcomes. Delineation of these relationships may inform iAs mode(s) of action and the approach for evaluating low-dose health effects for iAs. Genotype impacts urinary iAs metabolite concentrations and may be a potential mechanism for iAs-related disease susceptibility.Environmental Research 04/2014; 132C:156-167. · 3.24 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: Exposure to arsenic causes many diseases. Most Americans in rural areas use groundwater for drinking, which may contain arsenic above the currently allowable level, 10 µg/L. It is cost-effective to estimate groundwater arsenic levels based on data from wells with known arsenic concentrations. We compared the accuracy of several commonly used interpolation methods in estimating arsenic concentrations in >8000 wells in Texas by the leave-one-out-cross-validation technique. Correlation coefficient between measured and estimated arsenic levels was greater with inverse distance weighted (IDW) than kriging Gaussian, kriging spherical or cokriging interpolations when analyzing data from wells in the entire Texas (p<0.0001). Correlation coefficient was significantly lower with cokriging than any other methods (p<0.006) for wells in Texas, east Texas or the Edwards aquifer. Correlation coefficient was significantly greater for wells in southwestern Texas Panhandle than in east Texas, and was higher for wells in Ogallala aquifer than in Edwards aquifer (p<0.0001) regardless of interpolation methods. In regression analysis, the best models are when well depth and/or elevation were entered into the model as covariates regardless of area/aquifer or interpolation methods, and models with IDW are better than kriging in any area/aquifer. In conclusion, the accuracy in estimating groundwater arsenic level depends on both interpolation methods and wells’ geographic distributions and characteristics in Texas. Taking well depth and elevation into regression analysis as covariates significantly increases the accuracy in estimating groundwater arsenic level in Texas with IDW in particular.Environmental Research 01/2014; · 3.24 Impact Factor