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.

Telomeres shorten with age and shorter leukocyte telomere length (LTL) has been associated with various age-related diseases. Thus, LTL has been considered a biomarker of biological aging. Dyslipidemia is an established risk factor for most age-related metabolic disorders. However, little is known about the relationship between LTL and dyslipidemia. Lipidomics is a new biochemical technique that can simultaneously identify and quantify hundreds to thousands of small molecular lipid species. In a large population comprising 1843 well-characterized American Indians in the Strong Heart Family Study, we examined the lipidomic profile of biological aging assessed by LTL. Briefly, LTL was quantified by qPCR. Fasting plasma lipids were quantified by untargeted liquid chromatography-mass spectrometry. Lipids associated with LTL were identified by elastic net modeling. Of 1542 molecular lipids identified (518 known, 1024 unknown), 174 lipids (36 knowns) were significantly associated with LTL, independent of chronological age, sex, BMI, hypertension, diabetes status, smoking status, bulk HDL-C, and LDL-C. These findings suggest that altered lipid metabolism is associated with biological aging and provide novel insights that may enhance our understanding of the relationship between dyslipidemia, biological aging, and age-related diseases in American Indians.

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.

Background

Chronic exposure to arsenic (As), a human toxicant and carcinogen, remains a global public health problem. Health risks persist after As exposure has ended, suggesting epigenetic dysregulation as a mechanistic link between exposure and health outcomes.

Objectives

We investigated the association between total urinary As and locus-specific DNA methylation in the Strong Heart Study, a cohort of American Indian adults with low-to-moderate As exposure [total urinary As, mean (±SD) μg/g creatinine: 11.7 (10.6)].

Methods

DNA methylation was measured in 2,325 participants using the Illumina MethylationEPIC array. We implemented linear models to test differentially methylated positions (DMPs) and the DMRcate method to identify regions (DMRs) and conducted gene ontology enrichment analysis. Models were adjusted for estimated cell type proportions, age, sex, body mass index, smoking, education, estimated glomerular filtration rate, and study center. Arsenic was measured in urine as the sum of inorganic and methylated species.

Results

In adjusted models, methylation at 20 CpGs was associated with urinary As after false discovery rate (FDR) correction (FDR< 0.05). After Bonferroni correction, 5 CpGs remained associated with total urinary As (pBonferroni<0.05), located in SLC7A11, ANKS3, LINGO3, CSNK1D, ADAMTSL4. We identified one DMR on chromosome 11 (chr11:2,322,050-2,323,247), annotated to C11orf2; TSPAN32 genes.

Discussion

This is one of the first epigenome-wide association studies to investigate As exposure and locus-specific DNA methylation using the Illumina MethylationEPIC array and the largest epigenome-wide study of As exposure. The top DMP was located in SLC7A11A, a gene involved in cystine/glutamate transport and the biosynthesis of glutathione, an antioxidant that may protect against As-induced oxidative stress. Additional DMPs were located in genes associated with tumor development and glucose metabolism. Further research is needed, including research in more diverse populations, to investigate whether As-related DNA methylation signatures are associated with gene expression or may serve as biomarkers of disease development. https://doi.org/10.1289/EHP6263.

Background

Epigenetic dysregulation has been proposed as a key mechanism for arsenic-related cardiovascular disease (CVD). We evaluated differentially methylated positions (DMPs) as potential mediators on the association between arsenic and CVD.

Methods

Blood DNA methylation was measured in 2321 participants (mean age 56.2, 58.6% women) of the Strong Heart Study, a prospective cohort of American Indians. Urinary arsenic species were measured using high-performance liquid chromatography coupled to inductively coupled plasma mass spectrometry. We identified DMPs that are potential mediators between arsenic and CVD. In a cross-species analysis, we compared those DMPs with differential liver DNA methylation following early-life arsenic exposure in the apoE knockout (apoE^-/-) mouse model of atherosclerosis.

Results

A total of 20 and 13 DMPs were potential mediators for CVD incidence and mortality, respectively, several of them annotated to genes related to diabetes. Eleven of these DMPs were similarly associated with incident CVD in 3 diverse prospective cohorts (Framingham Heart Study, Women's Health Initiative, and Multi-Ethnic Study of Atherosclerosis). In the mouse model, differentially methylated regions in 20 of those genes and DMPs in 10 genes were associated with arsenic.

Conclusions

Differential DNA methylation might be part of the biological link between arsenic and CVD. The gene functions suggest that diabetes might represent a relevant mechanism for arsenic-related cardiovascular risk in populations with a high burden of diabetes.

Over the past 20 years, the explosion of genomic data collection and the cloud computing revolution have made computational and data science research accessible to anyone with a web browser and an internet connection. However, students at institutions with limited resources have received relatively little exposure to curricula or professional development opportunities that lead to careers in genomic data science. To broaden participation in genomics research, the scientific community needs to support these programs in local education and research at underserved institutions (UIs). These include community colleges, historically Black colleges and universities, Hispanic-serving institutions, and tribal colleges and universities that support ethnically, racially, and socioeconomically underrepresented students in the United States. We have formed the Genomic Data Science Community Network to support students, faculty, and their networks to identify opportunities and broaden access to genomic data science. These opportunities include expanding access to infrastructure and data, providing UI faculty development opportunities, strengthening collaborations among faculty, recognizing UI teaching and research excellence, fostering student awareness, developing modular and open-source resources, expanding course-based undergraduate research experiences (CUREs), building curriculum, supporting student professional development and research, and removing financial barriers through funding programs and collaborator support.

Background

C-reactive protein (CRP) is a biomarker of inflammation. Genome-wide association studies (GWAS) have identified single-nucleotide polymorphisms (SNPs) associated with CRP concentrations and inflammation-related traits such as cardiovascular disease, type 2 diabetes mellitus, and obesity. We aimed to replicate previous CRP-SNP associations, assess whether these associations generalize to additional race/ethnicity groups, and evaluate inflammation-related SNPs for a potentially pleiotropic association with CRP.

Methods and results

We selected and analyzed 16 CRP-associated and 250 inflammation-related GWAS SNPs among 40 473 African American, American Indian, Asian/Pacific Islander, European American, and Hispanic participants from 7 studies collaborating in the Population Architecture using Genomics and Epidemiology (PAGE) study. Fixed-effect meta-analyses combined study-specific race/ethnicity-stratified linear regression estimates to evaluate the association between each SNP and high-sensitivity CRP. Overall, 18 SNPs in 8 loci were significantly associated with CRP (Bonferroni-corrected P<3.1×10(-3) for replication, P<2.0×10(-4) for pleiotropy): Seven of these were specific to European Americans, while 9 additionally generalized to African Americans (1), Hispanics (5), or both (3); 1 SNP was seen only in African Americans and Hispanics. Two SNPs in the CELSR2/PSRC1/SORT1 locus showed a potentially novel association with CRP: rs599839 (P=2.0×10(-6)) and rs646776 (P=3.1×10(-5)).

Conclusions

We replicated 16 SNP-CRP associations, 10 of which generalized to African Americans and/or Hispanics. We also identified potentially novel pleiotropic associations with CRP for two SNPs previously associated with coronary artery disease and/or low-density lipoprotein-cholesterol. These findings demonstrate the benefit of evaluating genotype-phenotype associations in multiple race/ethnicity groups and looking for pleiotropic relationships among SNPs previously associated with related phenotypes.

C-reactive protein (CRP) is a circulating biomarker indicative of systemic inflammation. We aimed to evaluate genetic associations with CRP levels among non-European-ancestry populations through discovery, fine-mapping and conditional analyses. A total of 30 503 non-European-ancestry participants from 6 studies participating in the Population Architecture using Genomics and Epidemiology study had serum high-sensitivity CRP measurements and ∼200 000 single nucleotide polymorphisms (SNPs) genotyped on the Metabochip. We evaluated the association between each SNP and log-transformed CRP levels using multivariate linear regression, with additive genetic models adjusted for age, sex, the first four principal components of genetic ancestry, and study-specific factors. Differential linkage disequilibrium patterns between race/ethnicity groups were used to fine-map regions associated with CRP levels. Conditional analyses evaluated for multiple independent signals within genetic regions. One hundred and sixty-three unique variants in 12 loci in overall or race/ethnicity-stratified Metabochip-wide scans reached a Bonferroni-corrected P-value <2.5E-7. Three loci have no (HACL1, OLFML2B) or only limited (PLA2G6) previous associations with CRP levels. Six loci had different top hits in race/ethnicity-specific versus overall analyses. Fine-mapping refined the signal in six loci, particularly in HNF1A. Conditional analyses provided evidence for secondary signals in LEPR, IL1RN and HNF1A, and for multiple independent signals in CRP and APOE. We identified novel variants and loci associated with CRP levels, generalized known CRP associations to a multiethnic study population, refined association signals at several loci and found evidence for multiple independent signals at several well-known loci. This study demonstrates the benefit of conducting inclusive genetic association studies in large multiethnic populations.

Diabetic kidney disease (DKD) is the most common etiology of chronic kidney disease (CKD) in the industrialized world and accounts for much of the excess mortality in patients with diabetes mellitus. Approximately 45% of U.S. patients with incident end-stage kidney disease (ESKD) have DKD. Independent of glycemic control, DKD aggregates in families and has higher incidence rates in African, Mexican, and American Indian ancestral groups relative to European populations. The Family Investigation of Nephropathy and Diabetes (FIND) performed a genome-wide association study (GWAS) contrasting 6,197 unrelated individuals with advanced DKD with healthy and diabetic individuals lacking nephropathy of European American, African American, Mexican American, or American Indian ancestry. A large-scale replication and trans-ethnic meta-analysis included 7,539 additional European American, African American and American Indian DKD cases and non-nephropathy controls. Within ethnic group meta-analysis of discovery GWAS and replication set results identified genome-wide significant evidence for association between DKD and rs12523822 on chromosome 6q25.2 in American Indians (P = 5.74x10-9). The strongest signal of association in the trans-ethnic meta-analysis was with a SNP in strong linkage disequilibrium with rs12523822 (rs955333; P = 1.31x10-8), with directionally consistent results across ethnic groups. These 6q25.2 SNPs are located between the SCAF8 and CNKSR3 genes, a region with DKD relevant changes in gene expression and an eQTL with IPCEF1, a gene co-translated with CNKSR3. Several other SNPs demonstrated suggestive evidence of association with DKD, within and across populations. These data identify a novel DKD susceptibility locus with consistent directions of effect across diverse ancestral groups and provide insight into the genetic architecture of DKD.

Chronic kidney disease is a leading cause of death and disability globally and impacts individuals of African ancestry (AFR) or with ancestry in the Americas (AMS) who are under-represented in genome-wide association studies (GWASs) of kidney function. To address this bias, we conducted a large meta-analysis of GWASs of estimated glomerular filtration rate (eGFR) in 145,732 AFR and AMS individuals. We identified 41 loci at genome-wide significance (p < 5 × 10^-8), of which two have not been previously reported in any ancestry group. We integrated fine-mapped loci with epigenomic and transcriptomic resources to highlight potential effector genes relevant to kidney physiology and disease, and reveal key regulatory elements and pathways involved in renal function and development. We demonstrate the varying but increased predictive power offered by a multi-ancestry polygenic score for eGFR and highlight the importance of population diversity in GWASs and multi-omics resources to enhance opportunities for clinical translation for all.