@article {799, title = {Body composition and arsenic metabolism: a cross-sectional analysis in the Strong Heart Study.}, journal = {Environ Health}, volume = {12}, year = {2013}, month = {2013}, pages = {107}, abstract = {

OBJECTIVE: The objective of this study was to evaluate the association between measures of body composition and patterns of urine arsenic metabolites in the 1989-1991 baseline visit of the Strong Heart Study, a cardiovascular disease cohort of adults recruited from rural communities in Arizona, Oklahoma, North Dakota and South Dakota.

METHODS: We evaluated 3,663 Strong Heart Study participants with urine arsenic species above the limit of detection and no missing data on body mass index, \% body fat and fat free mass measured by bioelectrical impedance, waist circumference and other variables. We summarized urine arsenic species patterns as the relative contribution of inorganic (iAs), methylarsonate (MMA) and dimethylarsinate (DMA) species to their sum. We modeled the associations of \% arsenic species biomarkers with body mass index, \% body fat, fat free mass, and waist circumference categories in unadjusted regression models and in models including all measures of body composition. We also considered adjustment for arsenic exposure and demographics.

RESULTS: Increasing body mass index was associated with higher mean \% DMA and lower mean \% MMA before and after adjustment for sociodemographic variables, arsenic exposure, and for other measures of body composition. In unadjusted linear regression models, \% DMA was 2.4 (2.1, 2.6) \% higher per increase in body mass index category (< 25, >=25 \& <30, >=30 \& <35, >=35 kg/m2), and \% MMA was 1.6 (1.4, 1.7) \% lower. Similar patterns were observed for \% body fat, fat free mass, and waist circumference measures in unadjusted models and in models adjusted for potential confounders, but the associations were largely attenuated or disappeared when adjusted for body mass index.

CONCLUSION: Measures of body size, especially body mass index, are associated with arsenic metabolism biomarkers. The association may be related to adiposity, fat free mass or body size. Future epidemiologic studies of arsenic should consider body mass index as a potential modifier for arsenic-related health effects.

}, issn = {1476-069X}, doi = {10.1186/1476-069X-12-107}, author = {Gribble, Matthew O and Crainiceanu, Ciprian M and Howard, Barbara V and Umans, Jason G and Francesconi, Kevin A and Goessler, Walter and Zhang, Ying and Silbergeld, Ellen K and Guallar, Eliseo and Navas-Acien, Ana} } @article {708, title = {Arsenic exposure, diabetes prevalence, and diabetes control in the Strong Heart Study.}, journal = {Am J Epidemiol}, volume = {176}, year = {2012}, month = {2012 Nov 15}, pages = {865-74}, abstract = {

This study evaluated the association of arsenic exposure, as measured in urine, with diabetes prevalence, glycated hemoglobin, and insulin resistance in American Indian adults from Arizona, Oklahoma, and North and South Dakota (1989-1991). We studied 3,925 men and women 45-74 years of age with available urine arsenic measures. Diabetes was defined as a fasting glucose level of 126 mg/dL or higher, a 2-hour glucose level of 200 mg/dL or higher, a hemoglobin A1c (HbA1c) of 6.5\% or higher, or diabetes treatment. Median urine arsenic concentration was 14.1 {\textmu}g/L (interquartile range, 7.9-24.2). Diabetes prevalence was 49.4\%. After adjustment for sociodemographic factors, diabetes risk factors, and urine creatinine, the prevalence ratio of diabetes comparing the 75th versus 25th percentiles of total arsenic concentrations was 1.14 (95\% confidence interval: 1.08, 1.21). The association between arsenic and diabetes was restricted to participants with poor diabetes control (HbA1c >=8\%). Arsenic was positively associated with HbA1c levels in participants with diabetes. Arsenic was not associated with HbA1c or with insulin resistance (assessed by homeostatic model assessment to quantify insulin resistance) in participants without diabetes. Urine arsenic was associated with diabetes control in a population from rural communities in the United States with a high burden of diabetes. Prospective studies that evaluate the direction of the relation between poor diabetes control and arsenic exposure are needed.

}, keywords = {Aged, Arizona, Arsenicals, Blood Glucose, Creatinine, Diabetes Mellitus, Environmental Exposure, Female, Hemoglobin A, Glycosylated, Humans, Indians, North American, Male, Middle Aged, North Dakota, Oklahoma, Poisson Distribution, Prevalence, Regression Analysis, Risk Factors, South Dakota}, issn = {1476-6256}, doi = {10.1093/aje/kws153}, author = {Gribble, Matthew O and Howard, Barbara V and Umans, Jason G and Shara, Nawar M and Francesconi, Kevin A and Goessler, Walter and Crainiceanu, Ciprian M and Silbergeld, Ellen K and Guallar, Eliseo and Navas-Acien, Ana} } @article {punjabi, title = {Sleep-disordered Breathing and Mortality: a Prospective Cohort Study}, journal = {PLOS Medicine, PMCID: PMC2722083}, volume = {6}, year = {2009}, author = {Punjabi, N and Caffo, B and Goodwin, J and Gottlieb, D and Newman, A and O{\textquoteright}Connor, G and Rapoport, D and Redline, S and Resnick, H and Robbins, J and Samet, J and Shahar, E and Unruh, M} } @article {pmid19750109, title = {Urine arsenic concentrations and species excretion patterns in American Indian communities over a 10-year period: the Strong Heart Study}, journal = {Environ. Health Perspect.}, volume = {117}, number = {9}, year = {2009}, month = {Sep}, pages = {1428{\textendash}1433}, author = {Navas-Acien, A. and Umans, J. G. and Howard, B. V. and Goessler, W. and Francesconi, K. A. and Crainiceanu, C and Silbergeld, E. K. and Guallar, E.} } @article {yacoub2003spin, title = {Spin-echo fMRI in humans using high spatial resolutions and high magnetic fields}, journal = {Magnetic resonance in medicine}, volume = {49}, number = {4}, year = {2003}, pages = {655{\textendash}664}, publisher = {Wiley Online Library}, author = {Yacoub, Essa and Duong, Timothy Q and De Moortele, Van and Lindquist, Martin A and Adriany, Gregor and Kim, Seong-Gi and U{\u g}urbil, K{\^a}mil and Hu, Xiaoping and others} }