Publications

BACKGROUND: Epigenetic alterations may contribute to early detection of cancer. We evaluated the association of blood DNA methylation with lymphatic-hematopoietic cancers and, for comparison, with solid cancers. We also evaluated the predictive ability of DNA methylation for lymphatic-hematopoietic cancers.

METHODS: Blood DNA methylation was measured using the Illumina Infinium methylationEPIC array in 2324 Strong Heart Study participants (41.4% men, mean age 56 years). 788,368 CpG sites were available for differential DNA methylation analysis for lymphatic-hematopoietic, solid and overall cancers using elastic-net and Cox regression models. We conducted replication in an independent population: the Framingham Heart Study. We also analyzed differential variability and conducted bioinformatic analyses to assess for potential biological mechanisms.

RESULTS: Over a follow-up of up to 28 years (mean 15), we identified 41 lymphatic-hematopoietic and 394 solid cancer cases. A total of 126 CpGs for lymphatic-hematopoietic cancers, 396 for solid cancers, and 414 for overall cancers were selected as predictors by the elastic-net model. For lymphatic-hematopoietic cancers, the predictive ability (C index) increased from 0.58 to 0.87 when adding these 126 CpGs to the risk factor model in the discovery set. The association was replicated with hazard ratios in the same direction in 28 CpGs in the Framingham Heart Study. When considering the association of variability, rather than mean differences, we found 432 differentially variable regions for lymphatic-hematopoietic cancers.

CONCLUSIONS: This study suggests that differential methylation and differential variability in blood DNA methylation are associated with lymphatic-hematopoietic cancer risk. DNA methylation data may contribute to early detection of lymphatic-hematopoietic cancers.

This paper describes the development and initial psychometric testing of the baseline Spirituality Survey (SS-1) from the Study on Stress, Spirituality, and Health (SSSH) which contained a mixture of items selected from validated existing scales and new items generated to measure important constructs not captured by existing instruments. The purpose was to establish the validity of new and existing measures in our racially/ethnically diverse sample. Psychometric properties of the SS-1 were evaluated using standard psychometric analyses in 4,634 SSSH participants. Predictive validity of SS-1 scales was assessed in relation to the physical and mental health component scores from the Short-Form 12 Health Survey (SF-12). Scales exhibited adequate to strong psychometric properties and demonstrated construct and predictive validity. Overall, the correlational findings provide solid evidence that the SS-1 scales are associated with a wide range of relevant R/S attitudes, mental health, and to a lesser degree physical health.

Social scientists have increasingly recognized the lack of diversity in survey research on American religion, resulting in a dearth of data on religion and spirituality (R/S) in understudied racial and ethnic groups. At the same time, epidemiological studies have increasingly diversified their racial and ethnic representation, but have collected few R/S measures to date. With a particular focus on American Indian and South Asian women (in addition to Blacks, Hispanic/Latinas, and white women), this study introduces a new effort among religion and epidemiology researchers, the Study on Stress, Spirituality, and Health (SSSH). This multi-cohort study provides some of the first estimates of R/S beliefs and practices among American Indians and U.S. South Asians, and offers new insight into salient beliefs and practices of diverse racial/ethnic and religious communities.

The Collaborative Cohort of Cohorts for COVID-19 Research (C4R) is a national prospective study of adults at risk for coronavirus disease 2019 (COVID-19) comprising 14 established United States (US) prospective cohort studies. For decades, C4R cohorts have collected extensive data on clinical and subclinical diseases and their risk factors, including behavior, cognition, biomarkers, and social determinants of health. C4R will link this pre-COVID phenotyping to information on SARS-CoV-2 infection and acute and post-acute COVID-related illness. C4R is largely population-based, has an age range of 18-108 years, and broadly reflects the racial, ethnic, socioeconomic, and geographic diversity of the US. C4R is ascertaining severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and COVID-19 illness using standardized questionnaires, ascertainment of COVID-related hospitalizations and deaths, and a SARS-CoV-2 serosurvey via dried blood spots. Master protocols leverage existing robust retention rates for telephone and in-person examinations, and high-quality events surveillance. Extensive pre-pandemic data minimize referral, survival, and recall bias. Data are being harmonized with research-quality phenotyping unmatched by clinical and survey-based studies; these will be pooled and shared widely to expedite collaboration and scientific findings. This unique resource will allow evaluation of risk and resilience factors for COVID-19 severity and outcomes, including post-acute sequelae, and assessment of the social and behavioral impact of the pandemic on long-term trajectories of health and aging.

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: Common genetic variation in the arsenic methyltransferase (AS3MT) gene region is known to be associated with arsenic metabolism efficiency (AME), measured as the percentage of dimethylarsinic acid (DMA%) in the urine. Rare, protein-altering variants in AS3MT could have even larger effects on AME, but their contribution to AME has not been investigated.

OBJECTIVES: We estimated the impact of rare, protein-coding variation in AS3MT on AME using a multi-population approach to facilitate the discovery of population-specific and shared causal rare variants.

METHODS: We generated targeted DNA sequencing data for the coding regions of AS3MT for three arsenic-exposed cohorts with existing data on arsenic species measured in urine: Health Effects of Arsenic Longitudinal Study (HEALS, n=2,434), Strong Heart Study (SHS, n=868), and New Hampshire Skin Cancer Study (NHSCS, n=666). We assessed the collective effects of rare (allele frequency <1%), protein-altering AS3MT variants on DMA%, using multiple approaches, including a test of the association between rare allele carrier status (yes/no) and DMA% using linear regression (adjusted for common variants in 10q24.32 region, age, sex, and population structure).

RESULTS: We identified 23 carriers of rare-protein-altering AS3MT variant across all cohorts (13 in HEALS and 5 in both SHS and NHSCS), including 6 carriers of predicted loss-of-function variants. DMA% was 6-10% lower in carriers compared with noncarriers in HEALS [β=-9.4 (95% CI: -13.9, -4.8)], SHS [β=-6.9 (95% CI: -13.6, -0.2)], and NHSCS [β=-8.7 (95% CI: -15.6, -2.2)]. In meta-analyses across cohorts, DMA% was 8.7% lower in carriers [β=-8.7 (95% CI: -11.9, -5.4)].

DISCUSSION: Rare, protein-altering variants in AS3MT were associated with lower mean DMA%, an indicator of reduced AME. Although a small percentage of the population (0.5-0.7%) carry these variants, they are associated with a 6-10% decrease in DMA% that is consistent across multiple ancestral and environmental backgrounds. https://doi.org/10.1289/EHP8152.

Genetic effects are suspected to influence cadmium internal dose. Our objective was to assess genetic determinants of urine cadmium in American Indian adults participating in the Strong Heart Family Study (SHFS). Urine cadmium levels and genotyped short tandem repeat (STR) markers were available on 1936 SHFS participants. We investigated heritability, including gene-by-sex and smoking interactions, and STR-based quantitative trait locus (QTL) linkage, using a variance-component decomposition approach, which incorporates the genetic information contained in the pedigrees. We also used available single nucleotide polymorphisms (SNPs) from Illumina's Metabochip and custom panel to assess whether promising QTLs associated regions could be attributed to SNPs annotated to specific genes. Median urine cadmium levels were 0.44 μg/g creatinine. The heritability of urine cadmium concentrations was 28%, with no evidence of gene-by-sex or -smoking interaction. We found strong statistical evidence for a genetic locus at chromosome 16 determining urine cadmium concentrations (Logarithm of odds score [LOD] = 3.8). Among the top 20 associated SNPs in this locus, 17 were annotated to ABCC1 (p-values from 0.0002 to 0.02), and attenuated the maximum linkage peak by a ∼40%. Suggestive QTL signals (LOD>1.9) in chromosomes 2, 6, 11, 14, and 19, showed associated SNPs in the genes NDUFA10, PDE10A, PLEKHA7, BAZ1A and CHAF1A, respectively. Our findings support that urinary cadmium levels are heritable and influenced by a QTL on chromosome 16, which was explained by genetic variation in ABCC1. Studies with extended sets of genome-wide markers are needed to confirm these findings and to identify additional metabolism and toxicity pathways for cadmium.

BACKGROUND: Whole-exome sequencing (WES) can expedite research on genetic variation in non-human primate (NHP) models of human diseases. However, NHP-specific reagents for exome capture are not available. This study reports the use of human-specific capture reagents in WES for olive baboons, marmosets, and vervet monkeys.

METHODS: Exome capture was carried out using the SureSelect Human All Exon V6 panel from Agilent Technologies, followed by high-throughput sequencing. Capture of protein-coding genes and detection of single nucleotide variants were evaluated.

RESULTS: Exome capture and sequencing results showed that more than 97% of old world and 93% of new world monkey protein coding genes were detected. Single nucleotide variants were detected across the genomes and missense variants were found in genes associated with human diseases.

CONCLUSIONS: A cost-effective approach based on commercial, human-specific reagents can be used to perform WES for the discovery of genetic variants in these NHP species.

BACKGROUND: Epidemiologic studies often use self-report as proxy for clinical history. However, whether self-report correctly identifies prevalence in minority populations with health disparities and poor health-care access is unknown. Furthermore, overlap of clinical vascular events with covert vascular brain injury (VBI), detected by imaging, is largely unexamined.

METHODS: The Strong Heart Study recruited American Indians from 3 regions, with surveillance and adjudication of stroke events from 1989 to 2013. In 2010-2013, all 817 survivors, aged 65-95 years, underwent brain imaging, neurological history interview, and cognitive testing. VBI was defined as imaged infarct or hemorrhage.

RESULTS: Adjudicated stroke was prevalent in 4% of participants and separately collected, self-reported stroke in 8%. Imaging-defined VBI was detected in 51% and not associated with any stroke event in 47%. Compared with adjudication, self-report had 76% sensitivity and 95% specificity. Participants with adjudicated or self-reported stroke had the poorest performance on cognitive testing; those with imaging-only (covert) VBI had intermediate performance.

CONCLUSION: In this community-based cohort, self-report for prior stroke had good performance metrics. A majority of participants with VBI did not have overt, clinically recognized events but did have neurological or cognitive symptoms. Data collection methodology for studies in a resource-limited setting must balance practical limitations in costs, accuracy, feasibility, and research goals.

BACKGROUND: Cancer mortality among American Indian (AI) people varies widely, but factors associated with cancer mortality are infrequently assessed.

METHODS: Cancer deaths were identified from death certificate data for 3516 participants of the Strong Heart Study, a population-based cohort study of AI adults ages 45-74 years in Arizona, Oklahoma, and North and South Dakota. Cancer mortality was calculated by age, sex and region. Cox proportional hazards model was used to assess independent associations between baseline factors in 1989 and cancer death by 2010.

RESULTS: After a median follow-up of 15.3 years, the cancer death rate per 1000 person-years was 6.33 (95 % CI 5.67-7.04). Cancer mortality was highest among men in North/South Dakota (8.18; 95 % CI 6.46-10.23) and lowest among women in Arizona (4.57; 95 % CI 2.87-6.92). Factors independently associated with increased cancer mortality included age, current or former smoking, waist circumference, albuminuria, urinary cadmium, and prior cancer history. Factors associated with decreased cancer mortality included Oklahoma compared to Dakota residence, higher body mass index and total cholesterol. Sex was not associated with cancer mortality. Lung cancer was the leading cause of cancer mortality overall (1.56/1000 person-years), but no lung cancer deaths occurred among Arizona participants. Mortality from unspecified cancer was relatively high (0.48/100 person-years; 95 % CI 0.32-0.71).

CONCLUSIONS: Regional variation in AI cancer mortality persisted despite adjustment for individual risk factors. Mortality from unspecified cancer was high. Better understanding of regional differences in cancer mortality, and better classification of cancer deaths, will help healthcare programs address cancer in AI communities.