The effect of low-moderate levels of arsenic exposure and of arsenic metabolism on mortality remains uncertain. We used data from a prospective cohort study in 3600 men and women aged 45 to 75 years living in Arizona, Oklahoma, and North and South Dakota. The biomarker of inorganic arsenic exposure was the sum of urine inorganic (iAs), monomethylated (MMA) and dimethylated (DMA) arsenic compounds (ƩAs) at baseline. The proportions of urine iAs, MMA and DMA over the ƩiAs, expressed as iAs%, MMA%, and DMA%, respectively, were used as biomarkers of arsenic metabolism. Arsenic exposure and arsenic metabolism were associated with all-cause, cardiovascular, and cancer mortality. For each interquartile range (IQR) increase in ƩAs (12.5 μg/L, overall range 0.7-194.1 μg/L), the adjusted hazard ratios (aHRs) were 1.28 (95% CI 1.16-1.41) for all-cause mortality, 1.28 (1.08-1.52) for cardiovascular mortality and 1.15 (0.92-1.44) for cancer mortality. The aHR for mortality for each IQR increase in MMA%, when iAs% is decreasing, was 1.52 (95% CI 1.16-1.99) for cardiovascular disease, 0.73 (0.55-0.98) for cancer, and 1.03 (0.90-1.19) for all-cause mortality. These findings at low-moderate levels of arsenic exposure highlight the need to implement public health measures to protect populations from involuntary arsenic exposure and for research to advance the biological and clinical understanding of arsenic-related health effects in general populations.

Objective

Little is known about arsenic metabolism in diabetes development. We investigated the prospective associations of low-moderate arsenic exposure and arsenic metabolism with diabetes incidence in the Strong Heart Study.

Research design and methods

A total of 1,694 diabetes-free participants aged 45-75 years were recruited in 1989-1991 and followed through 1998-1999. We used the proportions of urine inorganic arsenic (iAs), monomethylarsonate (MMA), and dimethylarsinate (DMA) over their sum (expressed as iAs%, MMA%, and DMA%) as the biomarkers of arsenic metabolism. Diabetes was defined as fasting glucose ≥ 126 mg/dL, 2-h glucose ≥ 200 mg/dL, self-reported diabetes history, or self-reported use of antidiabetic medications.

Results

Over 11,263.2 person-years of follow-up, 396 participants developed diabetes. Using the leave-one-out approach to model the dynamics of arsenic metabolism, we found that lower MMA% was associated with higher diabetes incidence. The hazard ratios (95% CI) of diabetes incidence for a 5% increase in MMA% were 0.77 (0.63-0.93) and 0.82 (0.73-0.92) when iAs% and DMA%, respectively, were left out of the model. DMA% was associated with higher diabetes incidence only when MMA% decreased (left out of the model) but not when iAs% decreased. iAs% was also associated with higher diabetes incidence when MMA% decreased. The association between MMA% and diabetes incidence was similar by age, sex, study site, obesity, and urine iAs concentrations.

Conclusions

Arsenic metabolism, particularly lower MMA%, was prospectively associated with increased incidence of diabetes. Research is needed to evaluate whether arsenic metabolism is related to diabetes incidence per se or through its close connections with one-carbon metabolism.

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.

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 µg/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.

BACKGROUND: Arsenic has been associated with hypertension, though it is unclear whether associations persist at the exposure concentrations (e.g. <100 μg/L) in drinking water occurring in parts of the Western United States. METHODS: We assessed associations between arsenic biomarkers and systolic blood pressure (SBP), diastolic blood pressure (DBP), and hypertension in the Strong Heart Family Study, a family-based cohort of American Indians from the Northern plains, Southern plains, and Southwest. We included 1910 participants from three study centers with complete baseline visit data (2001-2003) in the cross-sectional analysis of all three outcomes, and 1453 participants in the prospective analysis of incident hypertension (follow-up 2006-2009). We used generalized estimating equations with exchangeable correlation structure conditional on family membership to estimate the association of arsenic exposure biomarker levels with SBP or DBP (linear regressions) or hypertension prevalence and incidence (Poisson regressions), adjusting for urine creatinine, urine arsenobetaine, and measured confounders. RESULTS: We observed cross-sectional associations for a two-fold increase in inorganic and methylated urine arsenic species of 0.64 (95% CI: 0.07, 1.35) mm Hg for SBP, 0.49 (95% CI: 0.03, 1.02) mm Hg for DBP, and a prevalence ratio of 1.10 (95% CI: 1.01, 1.21) for hypertension in fully adjusted models. During follow-up, 14% of subjects developed hypertension. We observed non-monotonic relationships between quartiles of arsenic and incident hypertension. Effect estimates were null for incident hypertension with continuous exposure metrics. Stratification by study site revealed elevated associations in Arizona, the site with the highest arsenic levels, while results for Oklahoma and North and South Dakota were largely null. Blood pressure changes with increasing arsenic concentrations were larger for those with diabetes at baseline. CONCLUSIONS: Our results suggest a modest cross-sectional association of arsenic exposure biomarkers with blood pressure, and possible non-linear effects on incident hypertension.

Background

Arsenic (III) methyltransferase (AS3MT) has been related to urine arsenic metabolites in association studies. Other genes might also play roles in arsenic metabolism and excretion.

Objective

We evaluated genetic determinants of urine arsenic metabolites in American Indian adults from the Strong Heart Study (SHS).

Methods

We evaluated heritability of urine arsenic metabolites [percent inorganic arsenic (%iAs), percent monomethylarsonate (%MMA), and percent dimethylarsinate (%DMA)] in 2,907 SHS participants with urine arsenic measurements and at least one relative within the cohort. We conducted a preliminary linkage analysis in a subset of 487 participants with available genotypes on approximately 400 short tandem repeat markers using a general pedigree variance component approach for localizing quantitative trait loci (QTL).

Results

The medians (interquartile ranges) for %iAs, %MMA, and %DMA were 7.7% (5.4-10.7%), 13.6% (10.5-17.1%), and 78.4% (72.5-83.1%), respectively. The estimated heritability was 53% for %iAs, 50% for %MMA, and 59% for %DMA. After adjustment for sex, age, smoking, body mass index, alcohol consumption, region, and total urine arsenic concentrations, LOD [logarithm (to the base of 10) of the odds] scores indicated suggestive evidence for genetic linkage with QTLs influencing urine arsenic metabolites on chromosomes 5 (LOD = 2.03 for %iAs), 9 (LOD = 2.05 for %iAs and 2.10 for %MMA), and 11 (LOD = 1.94 for %iAs). A peak for %DMA on chromosome 10 within 2 Mb of AS3MT had an LOD of 1.80.

Conclusions

This population-based family study in American Indian communities supports a genetic contribution to variation in the distribution of arsenic metabolites in urine and, potentially, the involvement of genes other than AS3MT.

BACKGROUND: High arsenic exposure has been related to diabetes, but at low-moderate levels the evidence is mixed. Arsenic metabolism, which is partly genetically controlled and may rely on certain B vitamins, plays a role in arsenic toxicity. OBJECTIVE: We evaluated the prospective association of arsenic exposure and metabolism with type 2 diabetes and insulin resistance. METHODS: We included 1,838 American Indian men and women free of diabetes (median age, 36 y). Arsenic exposure was assessed as the sum of inorganic arsenic (iAs), monomethylarsonate (MMA), and dimethylarsinate (DMA) urine concentrations (ΣAs). Arsenic metabolism was evaluated by the proportions of iAs, MMA, and DMA over their sum (iAs%, MMA%, and DMA%). Homeostasis model assessment for insulin resistance (HOMA2-IR) was measured at baseline and follow-up visits. Incident diabetes was evaluated at follow-up. RESULTS: Median ΣAs, iAs%, MMA%, and DMA% was 4.4 μg/g creatinine, 9.5%, 14.4%, and 75.6%, respectively. Over 10,327 person-years of follow-up, 252 participants developed diabetes. Median HOMA2-IR at baseline was 1.5. The fully adjusted hazard ratio [95% confidence interval (CI)] for incident diabetes per an interquartile range increase in ΣAs was 1.57 (95% CI: 1.18, 2.08) in participants without prediabetes at baseline. Arsenic metabolism was not associated with incident diabetes. ΣAs was positively associated with HOMA2-IR at baseline but negatively with HOMA2-IR at follow-up. Increased MMA% was associated with lower HOMA2-IR when either iAs% or DMA% decreased. The association of arsenic metabolism with HOMA2-IR differed by B-vitamin intake and AS3MT genetics variants. CONCLUSIONS: Among participants without baseline prediabetes, arsenic exposure was associated with incident diabetes. Low MMA% was cross-sectional and prospectively associated with higher HOMA2-IR. Research is needed to confirm possible interactions of arsenic metabolism with B vitamins and AS3MT variants on diabetes risk. https://doi.org/10.1289/EHP2566.

Arsenic species patterns in urine are associated with risk for cancer and cardiovascular diseases. The organic anion transporter coded by the gene SLCO1B1 may transport arsenic species, but its association with arsenic metabolites in human urine has not yet been studied. The objective of this study is to evaluate associations of urine arsenic metabolites with variants in the candidate gene SLCO1B1 in adults from the Strong Heart Family Study. We estimated associations between % arsenic species biomarker traits and 5 single-nucleotide polymorphisms (SNPs) in the SLCO1B1 gene in 157 participants, assuming additive genetics. Linear regression models for each SNP accounted for kinships and were adjusted for sex, body mass index, and study center. The minor allele of rs1564370 was associated with lower %MMA (p = .0003) and higher %DMA (p = .0002), accounting for 8% of the variance for %MMA and 9% for %DMA. The rs1564370 minor allele homozygote frequency was 17% and the heterozygote frequency was 43%. The minor allele of rs2291075 was associated with lower %MMA (p = .0006) and higher %DMA (p = .0014), accounting for 7% of the variance for %MMA and 5% for %DMA. The frequency of rs2291075 minor allele homozygotes was 1% and of heterozygotes was 15%. Common variants in SLCO1B1 were associated with differences in arsenic metabolites in a preliminary candidate gene study. Replication of this finding in other populations and analyses with respect to disease outcomes are needed to determine whether this novel candidate gene is important for arsenic-associated disease risks.

Arsenic toxicokinetics are important for disease risks in exposed populations, but genetic determinants are not fully understood. We examined urine arsenic species patterns measured by HPLC-ICPMS among 2189 Strong Heart Study participants 18 years of age and older with data on ~400 genome-wide microsatellite markers spaced ~10 cM and arsenic speciation (683 participants from Arizona, 684 from Oklahoma, and 822 from North and South Dakota). We logit-transformed % arsenic species (% inorganic arsenic, %MMA, and %DMA) and also conducted principal component analyses of the logit % arsenic species. We used inverse-normalized residuals from multivariable-adjusted polygenic heritability analysis for multipoint variance components linkage analysis. We also examined the contribution of polymorphisms in the arsenic metabolism gene AS3MT via conditional linkage analysis. We localized a quantitative trait locus (QTL) on chromosome 10 (LOD 4.12 for %MMA, 4.65 for %DMA, and 4.84 for the first principal component of logit % arsenic species). This peak was partially but not fully explained by measured AS3MT variants. We also localized a QTL for the second principal component of logit % arsenic species on chromosome 5 (LOD 4.21) that was not evident from considering % arsenic species individually. Some other loci were suggestive or significant for 1 geographical area but not overall across all areas, indicating possible locus heterogeneity. This genome-wide linkage scan suggests genetic determinants of arsenic toxicokinetics to be identified by future fine-mapping, and illustrates the utility of principal component analysis as a novel approach that considers % arsenic species jointly.

BACKGROUND: Metabolism of inorganic arsenic (iAs) is subject to inter-individual variability, which is explained partly by genetic determinants. OBJECTIVES: We investigated the association of genetic variants with arsenic species and principal components of arsenic species in the Strong Heart Family Study (SHFS). METHODS: We examined variants previously associated with cardiometabolic traits (~ 200,000 from Illumina Cardio MetaboChip) or arsenic metabolism and toxicity (670) among 2,428 American Indian participants in the SHFS. Urine arsenic species were measured by high performance liquid chromatography-inductively coupled plasma mass spectrometry (HPLC-ICP-MS), and percent arsenic species [iAs, monomethylarsonate (MMA), and dimethylarsinate (DMA), divided by their sum × 100] were logit transformed. We created two orthogonal principal components that summarized iAs, MMA, and DMA and were also phenotypes for genetic analyses. Linear regression was performed for each phenotype, dependent on allele dosage of the variant. Models accounted for familial relatedness and were adjusted for age, sex, total arsenic levels, and population stratification. Single nucleotide polymorphism (SNP) associations were stratified by study site and were meta-analyzed. Bonferroni correction was used to account for multiple testing. RESULTS: Variants at 10q24 were statistically significant for all percent arsenic species and principal components of arsenic species. The index SNP for iAs%, MMA%, and DMA% (rs12768205) and for the principal components (rs3740394, rs3740393) were located near AS3MT, whose gene product catalyzes methylation of iAs to MMA and DMA. Among the candidate arsenic variant associations, functional SNPs in AS3MT and 10q24 were most significant (p < 9.33 × 10-5). CONCLUSIONS: This hypothesis-driven association study supports the role of common variants in arsenic metabolism, particularly AS3MT and 10q24. Citation: Balakrishnan P, Vaidya D, Franceschini N, Voruganti VS, Gribble MO, Haack K, Laston S, Umans JG, Francesconi KA, Goessler W, North KE, Lee E, Yracheta J, Best LG, MacCluer JW, Kent J Jr., Cole SA, Navas-Acien A. 2017. Association of cardiometabolic genes with arsenic metabolism biomarkers in American Indian communities: the Strong Heart Family Study (SHFS). Environ Health Perspect 125:15-22; http://dx.doi.org/10.1289/EHP251.