Publications

Inorganic arsenic is highly toxic and carcinogenic to humans. Exposed individuals vary in their ability to metabolize arsenic, and variability in arsenic metabolism efficiency (AME) is associated with risks of arsenic-related toxicities. Inherited genetic variation in the 10q24.32 region, near the arsenic methyltransferase (AS3MT) gene, is associated with urine-based measures of AME in multiple arsenic-exposed populations. To identify potential causal variants in this region, we applied fine mapping approaches to targeted sequencing data generated for exposed individuals from Bangladeshi, American Indian, and European American populations (n = 2,357, 557, and 648 respectively). We identified three independent association signals for Bangladeshis, two for American Indians, and one for European Americans. The size of the confidence sets for each signal varied from 4 to 85 variants. There was one signal shared across all three populations, represented by the same SNP in American Indians and European Americans (rs191177668) and in strong linkage disequilibrium (LD) with a lead SNP in Bangladesh (rs145537350). Beyond this shared signal, differences in LD patterns, minor allele frequency (MAF) (e.g., rs12573221  13% in Bangladesh  0.2% among American Indians), and/or heterogeneity in effect sizes across populations likely contributed to the apparent population specificity of the additional identified signals. One of our potential causal variants influences AS3MT expression and nearby DNA methylation in numerous GTEx tissue types (with rs4919690 as a likely causal variant). Several SNPs in our confidence sets overlap transcription factor binding sites and cis-regulatory elements (from ENCODE). Taken together, our analyses reveal multiple potential causal variants in the 10q24.32 region influencing AME, including a variant shared across populations, and elucidate potential biological mechanisms underlying the impact of genetic variation on AME.

BACKGROUND: The prevalence of type 2 diabetes has dramatically increased in the past years. Increasing evidence supports that blood DNA methylation, the best studied epigenetic mark, is related to diabetes risk. Few prospective studies, however, are available. We studied the association of blood DNA methylation with diabetes in the Strong Heart Study. We used limma, Iterative Sure Independence Screening and Cox regression to study the association of blood DNA methylation with fasting glucose, HOMA-IR and incident type 2 diabetes among 1312 American Indians from the Strong Heart Study. DNA methylation was measured using Illumina's MethylationEPIC beadchip. We also assessed the biological relevance of our findings using bioinformatics analyses.

RESULTS: Among the 358 differentially methylated positions (DMPs) that were cross-sectionally associated either with fasting glucose or HOMA-IR, 49 were prospectively associated with incident type 2 diabetes, although no DMPs remained significant after multiple comparisons correction. Multiple of the top DMPs were annotated to genes with relevant functions for diabetes including SREBF1, associated with obesity, type 2 diabetes and insulin sensitivity; ABCG1, involved in cholesterol and phospholipids transport; and HDAC1, of the HDAC family. (HDAC inhibitors have been proposed as an emerging treatment for diabetes and its complications.) CONCLUSIONS: Our results suggest that differences in peripheral blood DNA methylation are related to cross-sectional markers of glucose metabolism and insulin activity. While some of these DMPs were modestly associated with prospective incident type 2 diabetes, they did not survive multiple testing. Common DMPs with diabetes epigenome-wide association studies from other populations suggest a partially common epigenomic signature of glucose and insulin activity.

Background Lead is a cardiotoxic metal with a variety of adverse health effects. In the absence of data on bone lead exposure, epigenetic biomarkers can serve as indicators of cumulative lead exposure and body burden. Herein, we leveraged novel epigenetic biomarkers of lead exposure to investigate their association with cardiovascular disease (CVD) incidence and mortality. Methods and Results Blood DNA methylation was measured using the Illumina MethylationEPIC BeadChip among 2231 participants of the Strong Heart Study (SHS) at baseline (1989-1991). Epigenetic biomarkers of lead levels in blood, patella, and tibia were estimated using previously identified cytosine-guanine dinucleotide (CpG) sites. CVD incidence and mortality data were available through 2017. Median concentrations of lead epigenetic biomarkers were 13.8 μg/g, 21.3 μg/g, and 2.9 μg/dL in tibia, patella, and blood, respectively. In adjusted models, the hazard ratio (HR) (95% CI) of CVD mortality per doubling increase in lead epigenetic biomarkers were 1.42 (1.07-1.87) for tibia lead, 1.22 (0.93-1.60) for patella lead, and 1.57 (1.16-2.11) for blood lead. The corresponding HRs for incident CVD were 0.99 (0.83-1.19), 1.07 (0.89-1.29), and 1.06 (0.87-1.30). The association between the tibia lead epigenetic biomarker and CVD mortality was modified by sex (interaction P value: 0.014), with men at increased risk (HR, 1.42 [95% CI, 1.17-1.72]) compared with women (HR, 1.04 [95% CI, 0.89-1.22]). Conclusions Tibia and blood epigenetic biomarkers were associated with increased risk of CVD mortality, potentially reflecting the cardiovascular impact of cumulative and recent lead exposures. These findings support that epigenetic biomarkers of lead exposure may capture some of the disease risk associated with lead exposure.

Dyslipidemia associates with and usually precedes the onset of chronic kidney disease (CKD), but a comprehensive assessment of molecular lipid species associated with risk of CKD is lacking. Here, we sought to identify fasting plasma lipids associated with risk of CKD among American Indians in the Strong Heart Family Study, a large-scale community-dwelling of individuals, followed by replication in Mexican Americans from the San Antonio Family Heart Study and Caucasians from the Australian Diabetes, Obesity and Lifestyle Study. We also performed repeated measurement analysis to examine the temporal relationship between the change in the lipidome and change in kidney function between baseline and follow-up of about five years apart. Network analysis was conducted to identify differential lipid classes associated with risk of CKD. In the discovery cohort, we found that higher baseline level of multiple lipid species, including glycerophospholipids, glycerolipids and sphingolipids, was significantly associated with increased risk of CKD, independent of age, sex, body mass index, diabetes and hypertension. Many lipid species were replicated in at least one external cohort at the individual lipid species and/or the class level. Longitudinal change in the plasma lipidome was significantly associated with change in the estimated glomerular filtration rate after adjusting for covariates, baseline lipids and the baseline rate. Network analysis identified distinct lipidomic signatures differentiating high from low-risk groups. Thus, our results demonstrated that disturbed lipid metabolism precedes the onset of CKD. These findings shed light on the mechanisms linking dyslipidemia to CKD and provide potential novel biomarkers for identifying individuals with early impaired kidney function at preclinical stages.

Prospective studies on the association between depression and telomere length have produced mixed results and have been largely limited to European ancestry populations. We examined the associations between depression and telomere length, and the modifying influence of religion and spirituality, in four cohorts, each representing a different race/ethnic population. Relative leukocyte telomere length (RTL) was measured by a quantitative polymerase chain reaction. Our result showed that depression was not associated with RTL (percent difference: 3.0 95% CI: -3.9, 10.5; p = 0.41; p-heterogeneity across studies = 0.67) overall or in cohort-specific analyses. However, in cohort-specific analyses, there was some evidence of effect modification by the extent of religiosity or spirituality, religious congregation membership, and group prayer. Further research is needed to investigate prospective associations between depression and telomere length, and the resources of resilience including dimensions of religion and spirituality that may impact such dynamics in diverse racial/ethnic populations.

BACKGROUND: Our aim was to investigate if moderate to vigorous physical activity (MVPA), calcium intake interacts with bone mineral density (BMD)-related single nucleotide polymorphisms (SNPs) to influence BMD in 750 Hispanic children (4-19y) of the cross-sectional Viva La Familia Study.

METHODS: Physical activity and dietary intake were measured by accelerometers and multiple-pass 24 h dietary recalls, respectively. Total body and lumbar spine BMD were measured by dual energy X-ray absorptiometry. A polygenic risk score (PRS) was computed based on SNPs identified in published literature. Regression analysis was conducted with PRSs, MVPA and calcium intake with total body and lumbar spine BMD.

RESULTS: We found evidence of statistically significant interaction effects between the PRS and MVPA on total body BMD and lumbar spine BMD (p < 0.05). Higher PRS was associated with a lower total body BMD (β = - 0.040 ± 0.009, p = 1.1 × 10- 5) and lumbar spine BMD (β = - 0.042 ± 0.013, p = 0.0016) in low MVPA group, as compared to high MVPA group (β = - 0.015 ± 0.006, p = 0.02; β = 0.008 ± 0.01, p = 0.4, respectively).

DISCUSSION: The study indicated that calcium intake does not modify the relationship between genetic variants and BMD, while it implied physical activity interacts with genetic variants to affect BMD in Hispanic children. Due to limited sample size of our study, future research on gene by environment interaction on bone health and functional studies to provide biological insights are needed.

CONCLUSIONS: Bone health in Hispanic children with high genetic risk for low BMD is benefitted more by MVPA than children with low genetic risk. Our results may be useful to predict disease risk and tailor dietary and physical activity advice delivery to people, especially children.

BACKGROUND: Smoking remains one of the leading preventable causes of death. Smoking leaves a strong signature on the blood methylome as shown in multiple studies using the Infinium HumanMethylation450 BeadChip. Here, we explore novel blood methylation smoking signals on the Illumina MethylationEPIC BeadChip (EPIC) array, which also targets novel CpG-sites in enhancers.

METHOD: A smoking-methylation meta-analysis was carried out using EPIC DNA methylation profiles in 1407 blood samples from four UK population-based cohorts, including the MRC National Survey for Health and Development (NSHD) or 1946 British birth cohort, the National Child Development Study (NCDS) or 1958 birth cohort, the 1970 British Cohort Study (BCS70), and the TwinsUK cohort (TwinsUK). The overall discovery sample included 269 current, 497 former, and 643 never smokers. Replication was pursued in 3425 trans-ethnic samples, including 2325 American Indian individuals participating in the Strong Heart Study (SHS) in 1989-1991 and 1100 African-American participants in the Genetic Epidemiology Network of Arteriopathy Study (GENOA).

RESULTS: Altogether 952 CpG-sites in 500 genes were differentially methylated between smokers and never smokers after Bonferroni correction. There were 526 novel smoking-associated CpG-sites only profiled by the EPIC array, of which 486 (92%) replicated in a meta-analysis of the American Indian and African-American samples. Novel CpG sites mapped both to genes containing previously identified smoking-methylation signals and to 80 novel genes not previously linked to smoking, with the strongest novel signal in SLAMF7. Comparison of former versus never smokers identified that 37 of these sites were persistently differentially methylated after cessation, where 16 represented novel signals only profiled by the EPIC array. We observed a depletion of smoking-associated signals in CpG islands and an enrichment in enhancer regions, consistent with previous results.

CONCLUSION: This study identified novel smoking-associated signals as possible biomarkers of exposure to smoking and may help improve our understanding of smoking-related disease risk.

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.