Suchergebnisse

AbstractWhile the dose–response relationship for the carcinogenic effects of arsenic exposure indicates nonlinearity with increases only above about 150 μg/L arsenic in drinking water, similar analyses of noncarcinogenic effects of arsenic exposure remain to be conducted. We present here an alternative analysis of data on a measure of aortic elasticity, a risk factor for hypertension, and its relationship to urinary arsenic levels. An occupational health study from Ankara, Turkey by Karakulak et al. compared urinary arsenic levels and a measure of aortic elasticity (specifically, aortic strain) in workers with a linear no‐threshold model. We have examined these data with three alternative models—a fitted step‐function, a stratified, and a weighted linear regression model. Discontinuity within the data revealed two subsets of data, one for workers with urinary arsenic levels ≤ 160 μg/L whose mean aortic strain level was 11.3% and one for workers with arsenic levels > 160 μg/L whose mean aortic stain level was 5.33 % (p < 0.0001). Several alternative models were examined that indicated the best model to be the threshold model with a threshold at a urinary arsenic level of 160 μg/L. Observation of a discontinuity in the data revealed their better fit to a threshold model (at a urinary arsenic level of 160 μg/L) than to a linear‐no threshold model. Examinations with alternative models are recommended for studies of arsenic and hypertension and possibly other noncarcinogenic effects.

Background: Although inorganic arsenic in drinking water at high levels (100s–1000s μg/L [ppb]) increases cancer risk (skin, bladder, lung, and possibly prostate), the evidence at lower levels is limited. Methods: We conducted an ecologic analysis of the dose-response relationship between prostate cancer incidence and low arsenic levels in drinking water in a large study of U.S. counties (N = 710). County arsenic levels were <200 ug/L with median <100 ug/L and dependency greater than 10%. Groundwater well usage, water arsenic levels, prostate cancer incidence rates (2009–2013), and co-variate data were obtained from various U.S. governmental agencies. Poisson and negative-binomial regression analyses and stratified analysis were performed. Results: The best fitting polynomial analysis yielded a J-shaped linear-quadratic model. Linear and quadratic terms were significant (p < 0.001) in the Poisson model, and the quadratic term was significant (p < 0.05) in the negative binomial model. This model indicated a decreasing risk of prostate cancer with increasing arsenic level in the low range and increasing risk above. Conclusions: This study of prostate cancer incidence in US counties with low levels of arsenic in their well-water arsenic levels finds a j-shaped model with decreasing risk at very low levels and increasing risk at higher levels.