Publications

PURPOSE: Liver cancer incidence among American Indians/Alaska Natives has risen over the past 20 years. Peripheral blood DNA methylation may be associated with liver cancer and could be used as a biomarker for cancer risk. We evaluated the association of blood DNA methylation with risk of liver cancer.

METHODS: We conducted a prospective cohort study in 2324 American Indians, between age 45 and 75 years, from Arizona, Oklahoma, North Dakota and South Dakota who participated in the Strong Heart Study between 1989 and 1991. Liver cancer deaths (n = 21) were ascertained using death certificates obtained through 2017. The mean follow-up duration (SD) for non-cases was 25.1 (5.6) years and for cases, 11.0 (8.8) years. DNA methylation was assessed from blood samples collected at baseline using MethylationEPIC BeadChip 850 K arrays. We used Cox regression models adjusted for age, sex, center, body mass index, low-density lipoprotein cholesterol, smoking, alcohol consumption, and immune cell proportions to examine the associations.

RESULTS: We identified 9 CpG sites associated with liver cancer. cg16057201 annotated to MRFAP1) was hypermethylated among cases vs. non-cases (hazard ratio (HR) for one standard deviation increase in methylation was 1.25 (95% CI 1.14, 1.37). The other eight CpGs were hypomethylated and the corresponding HRs (95% CI) ranged from 0.58 (0.44, 0.75) for cg04967787 (annotated to PPRC1) to 0.77 (0.67, 0.88) for cg08550308. We also assessed 7 differentially methylated CpG sites associated with liver cancer in previous studies. The adjusted HR for cg15079934 (annotated to LPS1) was 1.93 (95% CI 1.10, 3.39).

CONCLUSIONS: Blood DNA methylation may be associated with liver cancer mortality and may be altered during the development of liver cancer.

BACKGROUND: Inorganic arsenic (As) may increase the risk of cardiovascular disease (CVD) and all-cause mortality through accelerated aging, which can be estimated using epigenetic-based measures.

OBJECTIVES: We evaluated three DNA methylation-based aging measures (PhenoAge, GrimAge, DunedinPACE) (epigenetic aging measures) as potential mediators of the previously reported association of As exposure with CVD incidence, CVD mortality, and all-cause mortality in the Strong Heart Study (SHS), an epidemiological cohort of American Indian adults.

METHODS: Blood DNA methylation and urinary As levels were measured in 2,323 SHS participants (41.5% men, mean age of 55 years old). PhenoAge and GrimAge values were calculated using a residual-based method. We tested the association of urinary As with epigenetic aging measures using linear regression, the association of epigenetic aging measures with the three health outcomes using additive hazards models, and the mediation of As-related CVD incidence, CVD mortality, and all-cause mortality by epigenetic aging measures using the product of coefficients method.

RESULTS: SHS participants with higher vs. lower urinary As levels had similar PhenoAge age, older GrimAge age, and faster DunedinPACE. An interquartile range increase in urinary As was associated with higher of PhenoAge age acceleration [mean difference (95% confidence interval)=0.48 (0.17, 0.80) years], GrimAge age acceleration [0.80 (0.60, 1.00) years], and DunedinPACE [0.011 (0.005, 0.018)], after adjusting for age, sex, center location, genetic components, smoking status, and body mass index. Of the 347 incident CVD events per 100,000 person-years associated with a doubling in As exposure, 21.3% (9.1, 57.1) and 22.6% (9.5, 56.9), were attributable to differences in GrimAge and DunedinPACE, respectively.

DISCUSSION: Arsenic exposure was associated with older GrimAge and faster DunedinPACE measures of biological age. Furthermore, accelerated biological aging measured from DNA methylation accounted for a relevant fraction of As-associated risk for CVD, CVD mortality, and all-cause mortality in the SHS, supporting the role of As in accelerated aging. Research of the biological underpinnings can contribute to a better understanding of the role of aging in arsenic-related disease. https://doi.org/10.1289/EHP11981.

The common marmoset (Callithrix jacchus) is one of the most widely used nonhuman primate models of human disease. Owing to limitations in sequencing technology, early genome assemblies of this species using short-read sequencing suffered from gaps. In addition, the genetic diversity of the species has not yet been adequately explored. Using long-read genome sequencing and expert annotation, we generated a high-quality genome resource creating a 2.898 Gb marmoset genome in which most of the euchromatin portion is assembled contiguously (contig N50 = 25.23 Mbp, scaffold N50 = 98.2 Mbp). We then performed whole genome sequencing on 84 marmosets sampling the genetic diversity from several marmoset research centers. We identified a total of 19.1 million single nucleotide variants (SNVs), of which 11.9 million can be reliably mapped to orthologous locations in the human genome. We also observed 2.8 million small insertion/deletion variants. This dataset includes an average of 5.4 million SNVs per marmoset individual and a total of 74,088 missense variants in protein-coding genes. Of the 4956 variants orthologous to human ClinVar SNVs (present in the same annotated gene and with the same functional consequence in marmoset and human), 27 have a clinical significance of pathogenic and/or likely pathogenic. This important marmoset genomic resource will help guide genetic analyses of natural variation, the discovery of spontaneous functional variation relevant to human disease models, and the development of genetically engineered marmoset disease models.

OBJECTIVES: To examine the associations of dietary Mg intake with inflammatory biomarkers (C-reactive protein (CRP) and interleukin 6 (IL-6)), and the interaction of dietary Mg intake with single nucleotide polymorphism (SNP) rs3740393, a SNP related to Mg metabolism and transport, on CRP and IL-6 among American Indians (AIs).

METHODS: This cross-sectional study included AI participants (n = 1,924) from the Strong Heart Family Study (SHFS). Mg intake from foods and dietary supplements was ascertained using a 119-item Block food frequency questionnaire, CRP and IL-6 were measured from blood, and SNP rs3740393 was genotyped using MetaboChip. Generalized estimating equations were used to examine associations of Mg intake, and the interaction between rs3740393 and dietary Mg, with CRP and IL-6.

RESULTS: Reported Mg intake was not associated with CRP or IL-6, irrespective of genotype. A significant interaction (p-interaction = 0.018) was observed between Mg intake and rs3740393 on IL-6. Among participants with the C/C genotype, for every 1 SD higher in log-Mg, log-IL-6 was 0.04 (95% CI: -0.10 to 0.17) pg/mL higher. Among participants with the C/G genotype, for every 1 SD higher in log-Mg, log-IL-6 was 0.08 (95% CI: -0.21 to 0.05) pg/mL lower, and among participants with the G/G genotype, for every 1 SD higher in log-Mg, log-IL-6 was 0.19 (95% CI: -0.38 to -0.01) pg/mL lower.

CONCLUSIONS: Mg intake may be associated with lower IL-6 with increasing dosage of the G allele at rs3740393. Future research is necessary to replicate this finding and examine other Mg-related genes that influence associations of Mg intake with inflammation.

BACKGROUND: Identifying lipidomic markers of diet quality is needed to inform the development of biomarkers of diet, and to understand the mechanisms driving the diet- coronary heart disease (CHD) association.

OBJECTIVE: To identify lipidomic markers of diet quality and examine whether these lipids are associated with incident CHD.

METHODS: Using LC-MS, we measured 1,542 lipid species from 1,694 American Indian adults (aged 18-75, 62% female) in the Strong Heart Family Study. Participants were followed for development of CHD through 2020. Information on past year diet was collected using the Block Food Frequency Questionnaire, and diet quality was assessed using the Alternative Healthy Eating Index-2010 (AHEI). Mixed-effects linear regression was used to identify individual lipids cross-sectionally associated with AHEI. In prospective analysis, Cox frailty model was used to estimate the hazard ratio (HR) of each AHEI-related lipid for incident CHD. All models adjusted for age, sex, center, education, BMI, smoking, alcohol drinking, level of physical activity, energy intake, diabetes, hypertension, and use of lipid-lowering drugs. Multiple testing was controlled at false discovery rate <0.05.

RESULTS: Among 1,542 lipid species measured, 71 lipid species (23 known) including acylcarnitine, cholesterol esters, glycerophospholipids, sphingomyelins and triacylglycerols, were associated with AHEI. Most of the identified lipids were associated with consumption of ω-3 fatty acids. In total, 147 participants developed CHD during a mean follow-up of 17.8 years. Among the diet-related lipids, 10 lipids (5 known, including CE(22:5)B, PC(p-14:0/22:1)/PC(o-14:0/22:1), PC(p-38:3)/PC(o-38:4)B, PE(p-18:0/20:4)/PE(o-18:0/20:4), and SM(d36:2)A) were associated with incident CHD. On average, each standard deviation increase in the baseline level of these 5 lipids was associated with 17%-23% increased risk of CHD [HR (95%CI) ranged from 1.17 (1, 1.36) to 1.23 (1.05, 1.43)].

CONCLUSION: We identified lipidomic markers of diet quality in American Indian adults. Some diet-related lipids are associated with risk of CHD beyond established risk factors.

INTRODUCTION: Native American communities suffer disproportionately from elevated metal exposures and increased risk for cardiovascular diseases and diabetes. DNA methylation is a sensitive biomarker of aging-related processes and novel epigenetic-based "clocks" can be used to estimate accelerated biological aging that may underlie increased risk. Metals alter DNA methylation, yet little is known about their individual and combined impact on epigenetic age acceleration. Our objective was to investigate the associations of metals on several DNA methylation-based aging measures in the Strong Heart Study (SHS) cohort.

METHODS: Blood DNA methylation data from 2,301 SHS participants was used to calculate age acceleration of epigenetic clocks (PhenoAge, GrimAge, DunedinPACE, Hannum, Horvath). Urinary metals [arsenic (As), cadmium (Cd), tungsten (W), zinc (Zn), selenium (Se), molybdenum (Mo)] were creatinine-adjusted and categorized into quartiles. We examined associations of individual metals through linear regression models and used Bayesian Kernel Machine Regression (BKMR) for the impact of the total metal mixture on epigenetic age acceleration.

RESULTS: The mixture of nonessential metals (W, As, Cd) was associated with greater GrimAge acceleration and DunedinPACE, while the essential metal mixture (Se, Zn, Mo) was associated with lower epigenetic age acceleration. Cd was associated with increased epigenetic age acceleration across all clocks and BKMR analysis suggested nonlinear associations between Se and DunedinPACE, GrimAge, and PhenoAge acceleration. No interactions between individual metals were observed. The associations between Cd, Zn, and epigenetic age acceleration were greater in never smokers in comparison to current/former smokers.

CONCLUSION: Nonessential metals were positively associated with greater epigenetic age acceleration, with strongest associations observed between Cd and DunedinPACE and GrimAge acceleration. In contrast, essential metals were associated with lower epigenetic aging. Examining the influence of metal mixtures on epigenetic age acceleration can provide insight into metals and aging-related diseases.

Background: The confounder-corrected chemical-shift-encoded MRI (CSE-MRI) sequence used to determine proton-density fat fraction (PDFF) for hepatic fat quantification lacks wide availability. Hepatic fat can alternatively be assessed using a 2-point Dixon method to calculate signal fat fraction (FF) from conventional T1-weighted (T1W) in-and-opposed-phase (IOP) images, although signal FF is prone to biases leading to inaccurate quantification. Objective: To compare hepatic fat quantification between PDFF inferred from conventional T1W IOP images using deep-learning convolutional neural networks (CNNs) and 2-point Dixon signal FF, using CSE-MRI PDFF as reference standard. Methods: This study entailed retrospective analysis of 292 participants (mean age, 53.7±12.0 years; 89 men, 103 women) enrolled at two sites from September 1, 2017 to December 18, 2019 in the Strong Heart Family Study (a prospective population-based study of American Indian communities). Participants underwent liver MRI (site A: 3 T; site B: 1.5 T) including T1W IOP MRI and CSE-MRI (used to reconstruct CSE-PDFF and CSE-R2* maps). Using CSEPDFF as reference, a CNN was trained in 218 (75%) randomly selected participants to infer voxel-by-voxel PDFF maps from T1W IOP images; testing was performed in the remaining 74 (25%) participants. Parametric values from the entire liver were automatically extracted. Per-participant median CNN-inferred PDFF and median 2-point Dixon signal FF were compared with reference median CSE-MRI PDFF using linear regression analysis, intraclass correlation (ICC), and Bland-Altman analysis. Code is publicly available: https://github.com/kang927/CNN-inference-of-PDFF-from-T1w-IOP-MR. Results: In the 74 test-set participants, reference CSE-PDFF ranged from 1% to 32% (mean, 11.3%±8.3%); reference CSE-R2* ranged from 31 to 457 s-1 (mean, 62.4±67.3 s-1). Agreement metrics with reference CSE-PDFF for CNN-inferred PDFF were: ICC=0.99, bias=-0.19%, 95% limits of agreement (LoA)=[-2.80%, 2.71%], and for 2-point Dixon signal FF were: ICC=0.93, bias=-1.11%, LoA=[-7.54%, 5.33%]. Conclusion: Agreement with reference CSE-PDFF was better for CNN-inferred PDFF using conventional T1W IOP images than for 2-point Dixon signal FF. Further investigation is needed in individuals with moderate-to-severe iron overload. Clinical Impact: CNN-inferred PDFF using widely available T1W IOP images may facilitate adoption of hepatic PDFF as a quantitative biomarker for liver fat assessment, expanding opportunities to screen for hepatic steatosis and non-alcoholic fatty liver disease.

Altered DNA methylation (DNAm) might be a biological intermediary in the pathway from smoking to lung cancer. In this study, we investigated the contribution of differential blood DNAm to explain the association between smoking and lung cancer incidence. Blood DNAm was measured in 2321 Strong Heart Study (SHS) participants. Incident lung cancer was assessed as time to event diagnoses. We conducted mediation analysis, including validation with DNAm and paired gene expression data from the Framingham Heart Study (FHS). In the SHS, current versus never smoking and pack-years single-mediator models showed, respectively, 29 and 21 differentially methylated positions (DMPs) for lung cancer with statistically significant mediated effects (14 of 20 available, and five of 14 available, positions, replicated, respectively, in FHS). In FHS, replicated DMPs showed gene expression downregulation largely in trans, and were related to biological pathways in cancer. The multimediator model identified that DMPs annotated to the genes AHRR and IER3 jointly explained a substantial proportion of lung cancer. Thus, the association of smoking with lung cancer was partly explained by differences in baseline blood DNAm at few relevant sites. Experimental studies are needed to confirm the biological role of identified eQTMs and to evaluate potential implications for early detection and control of lung cancer.

INTRODUCTION: Native American communities suffer disproportionately from elevated metal exposures and increased risk for cardiovascular diseases and diabetes. DNA methylation is a sensitive biomarker of aging-related processes and novel epigenetic-based "clocks" can be used to estimate accelerated biological aging that may underlie increased risk. Metals alter DNA methylation, yet little is known about their individual and combined impact on epigenetic age acceleration. Our objective was to investigate the associations of metals on several DNA methylation-based aging measures in the Strong Heart Study (SHS) cohort.

METHODS: Blood DNA methylation data from 2,301 SHS participants was used to calculate age acceleration of epigenetic clocks (PhenoAge, GrimAge, DunedinPACE, Hannum, Horvath). Urinary metals [arsenic (As), cadmium (Cd), tungsten (W), zinc (Zn), selenium (Se), molybdenum (Mo)] were creatinine-adjusted and categorized into quartiles. We examined associations of individual metals through linear regression models and used Bayesian Kernel Machine Regression (BKMR) for the impact of the total metal mixture on epigenetic age acceleration.

RESULTS: The mixture of nonessential metals (W, As, Cd) was associated with greater GrimAge acceleration and DunedinPACE, while the essential metal mixture (Se, Zn, Mo) was associated with lower epigenetic age acceleration. Cd was associated with increased epigenetic age acceleration across all clocks and BKMR analysis suggested nonlinear associations between Se and DunedinPACE, GrimAge, and PhenoAge acceleration. No interactions between individual metals were observed. The associations between Cd, Zn, and epigenetic age acceleration were greater in never smokers in comparison to current/former smokers.

CONCLUSION: Nonessential metals were positively associated with greater epigenetic age acceleration, with strongest associations observed between Cd and DunedinPACE and GrimAge acceleration. In contrast, essential metals were associated with lower epigenetic aging. Examining the influence of metal mixtures on epigenetic age acceleration can provide insight into metals and aging-related diseases.

IMPORTANCE: To our knowledge, no published studies have investigated the association of ambulatory activity with risk of death among young and middle-aged American Indian individuals. The burden of chronic disease and risk of premature death is higher among American Indian individuals than among the general US population, so better understanding of the association of ambulatory activity with risk of death is needed to inform public health messaging in tribal communities.

OBJECTIVE: To examine the association of objectively measured ambulatory activity (ie, steps per day) with risk of death among young and middle-aged American Indian individuals.

DESIGN, SETTING, AND PARTICIPANTS: The ongoing longitudinal Strong Heart Family Study (SHFS) is being conducted with participants aged 14 to 65 years in 12 rural American Indian communities in Arizona, North Dakota, South Dakota, and Oklahoma and includes up to 20 years of follow-up (February 26, 2001, to December 31, 2020). This cohort study included SHFS participants who had available pedometer data at baseline. Data analysis was performed on June 9, 2022.

EXPOSURES: Objectively measured ambulatory activity at baseline.

MAIN OUTCOMES AND MEASURES: Outcomes of interest were total and cardiovascular-related mortality. Mixed-effects Cox proportional hazards regression was used to estimate hazard ratios for risk of death, with entry at the time of the pedometer assessment and time at risk until death or the latest adjudicated date of follow-up.

RESULTS: A total of 2204 participants were included in this study. Their mean (SD) age was 41.0 (16.8) years; 1321 (59.9%) were female and 883 (40.1%) were male. During a mean follow-up of 17.0 years (range, 0-19.9 years), 449 deaths occurred. Compared with participants in the lowest quartile of steps per day (<3126 steps), individuals in the upper 3 quartiles of steps per day had lower risk of mortality, with hazard ratios of0.72 (95% CI, 0.54-0.95) for the first quartile, 0.66 (95% CI, 0.47-0.93) for the second quartile, and 0.65 (95% CI, 0.44-0.95) for the third quartile after adjustment for age, sex, study site, education, smoking status, alcohol use, diet quality, body mass index, systolic blood pressure, prevalent diabetes, prevalent cardiovascular disease, biomarker levels (fibrinogen, low-density lipoprotein cholesterol, and triglycerides), medication use (hypertensive or lipid-lowering agents), and self-reported health status. The magnitude of the hazard ratios was similar for cardiovascular mortality.

CONCLUSIONS AND RELEVANCE: In this cohort study, American Indian individuals who took at least 3126 steps/d had a lower risk of death compared with participants who accumulated fewer steps per day. These findings suggest that step counters are an inexpensive tool that offers an opportunity to encourage activity and improve long-term health outcomes.