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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., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Background: Inorganic arsenic exposure is ubiquitous and both exposure and inter-individual differences in its metabolism have been associated with cardiometabolic risk. A more efficient arsenic metabolism profile (lower MMA%, higher DMA%) has been associated with reduced risk for arsenic-related health outcomes. This profile, however, has also been associated with increased risk for diabetes-related outcomes., Objectives: The mechanism behind these conflicting associations is unclear; we hypothesized the one-carbon metabolism (OCM) pathway may play a role., Methods: We evaluated the influence of OCM on the relationship between arsenic metabolism and diabetes-related outcomes (HOMA2-IR, waist circumference, fasting plasma glucose) using metabolomic data from an OCM-specific and P180 metabolite panel measured in plasma, arsenic metabolism measured in urine, and HOMA2-IR and FPG measured in fasting plasma. Samples were drawn from baseline visits (2001-2003) in 59 participants from the Strong Heart Family Study, a family-based cohort study of American Indians aged ≥14 years from Arizona, Oklahoma, and North/South Dakota., Results: In unadjusted analyses, a 5% increase in DMA% was associated with higher HOMA2-IR (geometric mean ratio (GMR)= 1.13 (95% CI: 1.03, 1.25)) and waist circumference (mean difference=3.66 (0.95, 6.38). MMA% was significantly associated with lower HOMA2-IR and waist circumference. After adjustment for OCM-related metabolites (SAM, SAH, cysteine, glutamate, lysophosphatidylcholine 18.2, and three phosphatidlycholines), associations were attenuated and no longer significant., Conclusions: These preliminary results indicate that the association of lower MMA% and higher DMA% with diabetes-related outcomes may be influenced by OCM status, either through confounding, reverse causality, or mediation., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Inorganic arsenic exposure is ubiquitous, and both exposure and interindividual differences in its metabolism have been associated with cardiometabolic risk. However, the associations of arsenic exposure and arsenic metabolism with the metabolic syndrome (MetS) and its individual components are relatively unknown. We used Poisson regression with robust variance to evaluate the associations of baseline arsenic exposure (urinary arsenic levels) and metabolism (relative percentage of arsenic species over their sum) with incident MetS and its individual components (elevated waist circumference, elevated triglycerides, reduced high-density lipoprotein cholesterol, hypertension, and elevated fasting plasma glucose) in 1,047 participants from the Strong Heart Family Study, a prospective family-based cohort study in American Indian communities (baseline visits were held in 1998-1999 and 2001-2003, follow-up visits in 2001-2003 and 2006-2009). Over the course of follow-up, 32% of participants developed MetS. An interquartile-range increase in arsenic exposure was associated with a 1.19-fold (95% confidence interval: 1.01, 1.41) greater risk of elevated fasting plasma glucose concentration but not with other individual components of the MetS or MetS overall. Arsenic metabolism, specifically lower percentage of monomethylarsonic acid and higher percentage of dimethylarsinic acid, was associated with higher risk of overall MetS and elevated waist circumference but not with any other MetS component. These findings support the hypothesis that there are contrasting and independent associations of arsenic exposure and arsenic metabolism with metabolic outcomes which may contribute to overall diabetes risk.

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., (© The Author 2015. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Background: In rodents, hypothalamic brain-derived neurotrophic factor (BDNF) expression appears to be regulated by melanocortin-4 receptor (MC4R) activity. The impact of MC4R genetic variation on circulating BDNF in humans is unknown., Objective: The objective of this study is to compare BDNF concentrations of subjects with loss-of-function (LOF) and gain-of-function (GOF) MC4R variants with those of controls with common sequence MC4R., Methods: Circulating BDNF was measured in two cohorts with known MC4R sequence: 148 subjects of Pima Indian heritage ((mean±s.d.): age, 15.7±6.5 years; body mass index z-scores (BMI-Z), 1.63±1.03) and 69 subjects of Hispanic heritage (10.8±3.6 years; BMI-Z, 1.57±1.07). MC4R variants were characterized in vitro by cell surface expression, receptor binding and cyclic AMP response after agonist administration. BDNF single-nucleotide polymorphisms (SNPs) rs12291186, rs6265 and rs7124442 were also genotyped., Results: In the Pima cohort, no significant differences in serum BDNF was observed for 43 LOF subjects versus 65 LOF-matched controls (age, sex and BMI matched; P=0.29) or 20 GOF subjects versus 20 GOF-matched controls (P=0.40). Serum BDNF was significantly associated with genotype for BDNF rs12291186 (P=0.006) and rs6265 (P=0.009), but not rs7124442 (P=0.99); BDNF SNPs did not interact with MC4R status to predict serum BDNF. In the Hispanic cohort, plasma BDNF was not significantly different among 21 LOF subjects, 20 GOF subjects and 28 controls (P=0.79); plasma BDNF was not predicted by BDNF genotype or BDNF-x-MC4R genotype interaction., Conclusions: Circulating BDNF concentrations were not significantly associated with MC4R functional status, suggesting that peripheral BDNF does not directly reflect hypothalamic BDNF secretion and/or that MC4R signaling is not a significant regulator of the bulk of BDNF expression in humans.

Hyperuricemia is associated with the metabolic syndrome, gout, renal and cardiovascular disease (CVD). American Indians have high rates of CVD and 25% of individuals in the strong heart family study (SHFS) have high serum uric acid levels. The aim of this study was to investigate the genetic determinants of serum uric acid variation in American Indian participants of the SHFS. A variance component decomposition approach (implemented in SOLAR) was used to conduct univariate genetic analyses in each of three study centers and the combined sample. Serum uric acid was adjusted for age, sex, age x sex, BMI, estimated glomerular filtration rate, alcohol intake, diabetic status and medications. Overall mean +/- SD serum uric acid for all individuals was 5.14 +/- 1.5 mg/dl. Serum uric acid was found to be significantly heritable (0.46 +/- 0.03 in all centers, and 0.39 +/- 0.07, 0.51 +/- 0.05, 0.44 +/- 0.06 in Arizona, Dakotas and Oklahoma, respectively). Multipoint linkage analysis showed significant evidence of linkage for serum uric acid on chromosome 11 in the Dakotas center [logarithm of odds score (LOD) = 3.02] and in the combined sample (LOD = 3.56) and on chromosome 1 (LOD = 3.51) in the combined sample. A strong positional candidate gene in the chromosome 11 region is solute carrier family22, member 12 (SLC22A12) that encodes a major uric acid transporter URAT1. These results show a significant genetic influence and a possible role for one or more genes on chromosomes 1 and 11 on the variation in serum uric acid in American Indian populations.

American Indians have a higher prevalence of albuminuria than the general population, likely resulting from a combination of environmental and genetic risk factors. To localize gene regions influencing variation in urinary albumin-to-creatinine ratio, we performed a linkage analysis and explored gene-by-diabetes, -hypertension, and -obesity interactions in a large cohort of American Indian families. We recruited >3600 individuals from 13 American Indian tribes from three centers (Arizona, North and South Dakota, and Oklahoma). We performed multipoint variance component linkage analysis in each center as well as in the entire cohort after controlling for center effects. We used two modeling strategies: Model 1 incorporated age, gender, and interaction terms; model 2 also controlled for diabetes, BP, body mass index, HDL, LDL, triglycerides, and smoking status. We evaluated interactions with diabetes, hypertension, and obesity using additive, interaction-specific linkage and stratified analyses. Loci suggestive for linkage to urinary albumin-to-creatinine ratio included 1q, 6p, 9q, 18q, and 20p. Gene-by-diabetes interaction was present with a quantitative trait locus specific to the diabetic stratum in the Dakotas isolated on 18q21.2 to 21.3 using model 1 (logarithm of odds = 3.3). Gene-by-hypertension interaction was present with quantitative trait loci specific to the hypertensive stratum in the Dakotas on 7q21.11 using model 1 (logarithm of odds = 3.4) and 10q25.1 using model 2 (logarithm of odds = 3.3). These loci replicate findings from multiple other genome scans of kidney disease phenotypes with distinct populations and are worthy of further study.

Background: Body fat mass distribution and deposition are determined by multiple environmental and genetic factors. Obesity is associated with insulin resistance, hyperinsulinemia, and type 2 diabetes. We previously identified evidence for genotype-by-diabetes interaction on obesity traits in Strong Heart Family Study (SHFS) participants. To localize these genetic effects, we conducted genome-wide linkage scans of obesity traits in individuals with and without type 2 diabetes, and in the combined sample while modeling interaction with diabetes using maximum likelihood methods (SOLAR 2.1.4)., Methods: SHFS recruited American Indians from Arizona, North and South Dakota, and Oklahoma. Anthropometric measures and diabetes status were obtained during a clinic visit. Marker allele frequencies were derived using maximum likelihood methods estimated from all individuals and multipoint identity by descent sharing was estimated using Loki. We used variance component linkage analysis to localize quantitative trait loci (QTLs) influencing obesity traits. We tested for evidence of additive and QTL-specific genotype-by-diabetes interactions using the regions identified in the diabetes-stratified analyses., Results: Among 245 diabetic and 704 non-diabetic American Indian individuals, we detected significant additive gene-by-diabetes interaction for weight and BMI (P < 0.02). In analysis accounting for QTL-specific interaction (P < 0.001), we detected a QTL for weight on chromosome 1 at 242 cM (LOD = 3.7). This chromosome region harbors the adiponectin receptor 1 gene, which has been previously associated with obesity., Conclusion: These results suggest distinct genetic effects on body mass in individuals with diabetes compared to those without diabetes, and a possible role for one or more genes on chromosome 1 in the pathogenesis of obesity.

To localize quantitative trait loci for insulin metabolism and obesity, genome scans/linkage analyses were performed on >900 members of 32 extended families participating in phase 3 of the Strong Heart Study, an investigation of the genetic and environmental determinants of cardiovascular disease in American-Indian populations from Arizona, Oklahoma, and North and South Dakota. Linkage analyses of fasting insulin and two obesity-related phenotypes, BMI and percent fat mass, were performed independently in each of the three populations. For log fasting insulin, we found a genome-wide maximum, robust logarithm of odds (LOD) score of 3.42 at 51 cM on chromosome 2p in the Dakotas. Bivariate linkage analyses of log fasting insulin with both BMI and fat mass indicate a situation of incomplete pleiotropy, as well as several significant bivariate LOD scores in the Dakotas.

Previous studies have demonstrated that low density lipoprotein cholesterol (LDL-C) concentration is influenced by both genes and environment. Although rare genetic variants associated with Mendelian causes of increased LDL-C are known, only one common genetic variant has been identified, the apolipoprotein E gene (APOE). In an attempt to localize quantitative trait loci (QTLs) influencing LDL-C, we conducted a genome-wide linkage scan of LDL-C in participants of the Strong Heart Family Study (SHFS). Nine hundred eighty men and women, age 18 years or older, in 32 extended families at three centers (in Arizona, Oklahoma, and North and South Dakota) were phenotyped for LDL-C concentration and other risk factors. Using a variance component approach and the program SOLAR, and after accounting for the effects of covariates, we detected a QTL influencing LDL-C on chromosome 19, nearest marker D19S888 at 19q13.41 [logarithm of odds (LOD) = 4.3] in the sample from the Dakotas. This region on chromosome 19 includes many possible candidate genes, including the APOE/C1/C4/C2 gene cluster. In follow-up association analyses, no significant evidence for an association was detected with the APOE*2 and APOE*4 alleles (P = 0.76 and P = 0.53, respectively). Suggestive evidence of linkage to LDL-C was detected on chromosomes 3q, 4q, 7p, 9q, 10p, 14q, and 17q. These linkage signals overlap positive findings for lipid-related traits and harbor plausible candidate genes for LDL-C.