Genetic characterization of a captive marmoset (Callithrix jacchus) colony using genotype-by-sequencing.

The marmoset is a fundamental nonhuman primate model for the study of aging, neurobiology, and many other topics. Genetic management of captive marmoset colonies is complicated by frequent chimerism in the blood and other tissues, a lack of tools to enable cost-effective, genome-wide interrogation of variation, and historic mergers and migrations of animals between colonies. We implemented genotype-by-sequencing (GBS) of hair follicle derived DNA (a minimally chimeric DNA source) of 82 marmosets housed at the Southwest National Primate Research Center (SNPRC). Our primary goals were the genetic characterization of our marmoset population for pedigree verification and colony management and to inform the scientific community of the functional genetic makeup of this valuable resource. We used the GBS data to reconstruct the genetic legacy of recent mergers between colonies, to identify genetically related animals whose relationships were previously unknown due to incomplete pedigree information, and to show that animals in the SNPRC colony appear to exhibit low levels of inbreeding. Of the >99,000 single-nucleotide variants (SNVs) that we characterized, >9800 are located within gene regions known to harbor pathogenic variants of clinical significance in humans. Overall, we show the combination of low-resolution (sparse) genotyping using hair follicle DNA is a powerful strategy for the genetic management of captive marmoset colonies and for identifying potential SNVs for the development of biomedical research models.

Assessing the use of GEE methods for analyzing binary outcomes in family studies: the Strong Heart Family Study.

The generalized estimating equations method (GEE) is commonly applied to analyze data obtained from family studies. GEE is well known for its robustness on misspecification of correlation structure. However, the unbalanced distribution of family sizes and complicated genetic relatedness structure within each family may challenge GEE performance. We focused our research on binary outcomes. To evaluate the performance of GEE, we conducted a series of simulations, on data generated adopting the kinship matrix (correlation structure within each family) from the Strong Heart Family Study (SHFS). We performed a fivefold cross-validation to further evaluate the GEE predictive power on data from the SHFS. A Bayesian modeling approach, with direct integration of the kinship matrix, was also included to contrast with GEE. Our simulation studies revealed that GEE performs well on a binary outcome from families having a relatively simple kinship structure. However, data with a binary outcome generated from families with complex kinship structures, especially with a large genetic variance, can challenge the performance of GEE.

Association of Achievement of the American Heart Association's Life's Essential 8 Goals With Incident Cardiovascular Diseases in the SHFS.

BACKGROUND: Cardiovascular disease (CVD) is a leading cause of morbidity and mortality in American Indian people. In 2022, the American Heart Association developed the Life's Essential 8 goals to promote cardiovascular health (CVH) for Americans, composed of diet, physical activity, nicotine exposure, sleep, body mass index, blood lipids, blood pressure, and blood glucose. We examined whether achievement of Life's Essential 8 goals was associated with incident CVD among SHFS (Strong Heart Family Study) participants. METHODS AND RESULTS: A total of 2139 SHFS participants without CVD at baseline were included in analyses. We created a composite CVH score based on achievement of Life's Essential 8 goals, excluding sleep. Scores of 0 to 49 represented low CVH, 50 to 69 represented moderate CVH, and 70 to 100 represented high CVH. Incident CVD was defined as incident myocardial infarction, coronary heart disease, congestive heart failure, or stroke. Cox proportional hazard models were used to examine the relationship of CVH and incident CVD. The incidence rate of CVD at the 20-year follow-up was 7.43 per 1000 person-years. Compared with participants with low CVH, participants with moderate and high CVH had a lower risk of incident CVD; the hazard ratios and 95% CIs for incident CVD for moderate and high CVH were 0.52 (95% CI, 0.40-0.68) and 0.25 (95% CI, 0.14-0.44), respectively, after adjustment for age, sex, education, and study site. CONCLUSIONS: Better CVH was associated with lower CVD risk which highlights the need for comprehensive public health interventions targeting CVH promotion to reduce CVD risk in American Indian communities.

Dyslipidemia in American Indian Adolescents and Young Adults: Strong Heart Family Study.

BACKGROUND: Although many studies on the association between dyslipidemia and cardiovascular disease (CVD) exist in older adults, data on the association among adolescents and young adults living with disproportionate burden of cardiometabolic disorders are scarce. METHODS AND RESULTS: The SHFS (Strong Heart Family Study) is a multicenter, family-based, prospective cohort study of CVD in an American Indian populations, including 12 communities in central Arizona, southwestern Oklahoma, and the Dakotas. We evaluated SHFS participants, who were 15 to 39 years old at the baseline examination in 2001 to 2003 (n=1440). Lipids were measured after a 12-hour fast. We used carotid ultrasounds to detect plaque at baseline and follow-up in 2006 to 2009 (median follow-up=5.5 years). We identified incident CVD events through 2020 with a median follow-up of 18.5 years. We used shared frailty proportional hazards models to assess the association between dyslipidemia and subclinical or clinical CVD, while controlling for covariates. Baseline dyslipidemia prevalence was 55.2%, 73.6%, and 78.0% for participants 15 to 19, 20 to 29, and 30 to 39 years old, respectively. Approximately 2.8% had low-density lipoprotein cholesterol ≥160 mg/dL, which is higher than the recommended threshold for lifestyle or medical interventions in young adults of 20 to 39 years old. During follow-up, 9.9% had incident plaque (109/1104 plaque-free participants with baseline and follow-up ultrasounds), 11.0% had plaque progression (128/1165 with both baseline and follow-up ultrasounds), and 9% had incident CVD (127/1416 CVD-free participants at baseline). Plaque incidence and progression were higher in participants with total cholesterol ≥200 mg/dL, low-density lipoprotein cholesterol ≥160 mg/dL, or non-high-density lipoprotein cholesterol ≥130 mg/dL, while controlling for covariates. CVD risk was independently associated with low-density lipoprotein cholesterol ≥160 mg/dL. CONCLUSIONS: Dyslipidemia is a modifiable risk factor that is associated with both subclinical and clinical CVD, even among the younger American Indian population who have unexpectedly high rates of significant CVD events. Therefore, this population is likely to benefit from a variety of evidence-based interventions including screening, educational, lifestyle, and guideline-directed medical therapy at an early age.

Demographic and Clinical Factors Associated With SARS-CoV-2 Spike 1 Antibody Response Among Vaccinated US Adults: the C4R Study.

This study investigates correlates of anti-S1 antibody response following COVID-19 vaccination in a U.S. population-based meta-cohort of adults participating in longstanding NIH-funded cohort studies. Anti-S1 antibodies were measured from dried blood spots collected between February 2021-August 2022 using Luminex-based microsphere immunoassays. Of 6245 participants, mean age was 73 years (range, 21-100), 58% were female, and 76% were non-Hispanic White. Nearly 52% of participants received the BNT162b2 vaccine and 48% received the mRNA-1273 vaccine. Lower anti-S1 antibody levels are associated with age of 65 years or older, male sex, higher body mass index, smoking, diabetes, COPD and receipt of BNT16b2 vaccine (vs mRNA-1273). Participants with a prior infection, particularly those with a history of hospitalized illness, have higher anti-S1 antibody levels. These results suggest that adults with certain socio-demographic and clinical characteristics may have less robust antibody responses to COVID-19 vaccination and could be prioritized for more frequent re-vaccination.

Omics feature selection with the extended SIS R package: identification of a body mass index epigenetic multi-marker in the Strong Heart Study.

The statistical analysis of omics data poses a great computational challenge given its ultra-high dimensional nature and frequent between-features correlation. In this work, we extended the Iterative Sure Independence Screening (ISIS) algorithm by pairing ISIS with elastic-net (Enet) and two versions of adaptive Enet (AEnet and MSAEnet) to efficiently improve feature selection and effect estimation in omics research. We subsequently used genome-wide human blood DNA methylation data from American Indians of the Strong Heart Study (N=2,235 participants), measured in 1989-1991, to compare the performance (predictive accuracy, coefficient estimation and computational efficiency) of SIS-paired regularization methods to Bayesian shrinkage and traditional linear regression to identify epigenomic multi-marker of body mass index. ISIS-AEnet outperformed the other methods in prediction. In biological pathway enrichment analysis of genes annotated to BMI-related differentially methylated positions, ISIS-AEnet captured most of the enriched pathways in common for at least two of all the evaluated methods. ISIS-AEnet can favor biological discovery because it identifies the most robust biological pathways while achieving an optimal balance between bias and efficient feature selection. In the extended SIS R package, we also implemented ISIS paired with Cox and logistic regression for time-to-event and binary endpoints, respectively, and bootstrap confidence intervals for the estimated regression coefficients.

The Contribution of Declines in Blood Lead Levels to Reductions in Blood Pressure Levels: Longitudinal Evidence in the Strong Heart Family Study.

BACKGROUND: Chronic lead exposure is associated with both subclinical and clinical cardiovascular disease. We evaluated whether declines in blood lead were associated with changes in systolic and diastolic blood pressure in adult American Indian participants from the SHFS (Strong Heart Family Study). METHODS AND RESULTS: Lead in whole blood was measured in 285 SHFS participants in 1997 to 1999 and 2006 to 2009. Blood pressure and measures of cardiac geometry and function were obtained in 2001 to 2003 and 2006 to 2009. We used generalized estimating equations to evaluate the association of declines in blood lead with changes in blood pressure; cardiac function and geometry measures were considered secondary. Mean blood lead was 2.04 µg/dL at baseline. After ≈10 years, mean decline in blood lead was 0.67 µg/dL. In fully adjusted models, the mean difference in systolic blood pressure comparing the highest to lowest tertile of decline (>0.91 versus <0.27 µg/dL) in blood lead was -7.08 mm Hg (95% CI, -13.16 to -1.00). A significant nonlinear association between declines in blood lead and declines in systolic blood pressure was detected, with significant linear associations where blood lead decline was 0.1 µg/dL or higher. Declines in blood lead were nonsignificantly associated with declines in diastolic blood pressure and significantly associated with declines in interventricular septum thickness. CONCLUSIONS: Declines in blood lead levels in American Indian adults, even when small (0.1-1.0 µg/dL), were associated with reductions in systolic blood pressure. These findings suggest the need to further study the cardiovascular impacts of reducing lead exposures and the importance of lead exposure prevention.

Plasma lipidomic markers of diet quality are associated with incident coronary heart disease in American Indian adults: the Strong Heart Family Study.

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. OBJECTIVES: This study aimed to identify lipidomic markers of diet quality and examine whether these lipids are associated with incident CHD. METHODS: Using liquid chromatography-mass spectrometry, we measured 1542 lipid species from 1694 American Indian adults (aged 18-75 years, 62% female) in the Strong Heart Family Study. Participants were followed up for development of CHD through 2020. Information on the 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 were adjusted for age, sex, center, education, body mass index, smoking, alcohol drinking, level of physical activity, energy intake, diabetes, hypertension, and use of lipid-lowering drugs. Multiple testing was controlled at a false discovery rate of <0.05. RESULTS: Among 1542 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 (n-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: cholesterol ester (CE)(22:5)B, phosphatidylcholine (PC)(p-14:0/22:1)/PC(o-14:0/22:1), PC(p-38:3)/PC(o-38:4)B, phosphatidylethanolamine (PE)(p-18:0/20:4)/PE(o-18:0/20:4), and sphingomyelin (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 (from HR: 1.17; 95% CI: 1, 1.36; to HR: 1.23; 95% CI: 1.05, 1.43). CONCLUSIONS: In this study, lipidomic markers of diet quality in American Indian adults are found. Some diet-related lipids are associated with risk of CHD beyond established risk factors.

Whole Genome Analysis of SNV and Indel Polymorphism in Common Marmosets (Callithrix jacchus).

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.

Arsenic Exposure and Epigenetic Aging: The Association with Cardiovascular Disease and All-Cause Mortality in the Strong Heart Study.

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.

Dietary magnesium, C-reactive protein and interleukin-6: The Strong Heart Family Study.

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.

Blood DNA methylation and liver cancer in American Indians: evidence from the Strong Heart Study.

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.

Smoking, blood DNA methylation sites and lung cancer risk.

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.

Deep Learning for Inference of Hepatic Proton Density Fat Fraction From T1-Weighted In-Phase and Opposed-Phase MRI: Retrospective Analysis of Population-Based Trial Data.

BACKGROUND. The confounder-corrected chemical shift-encoded MRI (CSE-MRI) sequence used to determine proton density fat fraction (PDFF) for hepatic fat quantification is not widely available. As an alternative, hepatic fat can be assessed by a two-point Dixon method to calculate signal fat fraction (FF) from conventional T1-weighted in- and opposed-phase (IOP) images, although signal FF is prone to biases, leading to inaccurate quantification. OBJECTIVE. The purpose of this study was to compare hepatic fat quantification by use of PDFF inferred from conventional T1-weighted IOP images and deep-learning convolutional neural networks (CNNs) with quantification by use of two-point Dixon signal FF with CSE-MRI PDFF as the reference standard. METHODS. This study entailed retrospective analysis of data from 292 participants (203 women, 89 men; mean age, 53.7 ± 12.0 [SD] years) 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 T1-weighted IOP MRI and CSE-MRI (used to reconstruct CSE PDFF and CSE R2* maps). With CSE PDFF as reference, a CNN was trained in a random sample of 218 (75%) participants to infer voxel-by-voxel PDFF maps from T1-weighted IOP images; testing was performed in the other 74 (25%) participants. Parametric values from the entire liver were automatically extracted. Per-participant median CNN-inferred PDFF and median two-point Dixon signal FF were compared with reference median CSE-MRI PDFF by means of linear regression analysis, intraclass correlation coefficient (ICC), and Bland-Altman analysis. The code is publicly available at 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% [SD]); reference CSE R2* ranged from 31 to 457 seconds-1 (mean, 62.4 ± 67.3 seconds-1 [SD]). Agreement metrics with reference to CSE PDFF for CNN-inferred PDFF were ICC = 0.99, bias = -0.19%, 95% limits of agreement (LoA) = (-2.80%, 2.71%) and for two-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 from conventional T1-weighted IOP images than for two-point Dixon signal FF. Further investigation is needed in individuals with moderate-to-severe iron overload. CLINICAL IMPACT. Measurement of CNN-inferred PDFF from widely available T1-weighted IOP images may facilitate adoption of hepatic PDFF as a quantitative bio-marker for liver fat assessment, expanding opportunities to screen for hepatic steatosis and nonalcoholic fatty liver disease.

Metal mixtures and DNA methylation measures of biological aging in American Indian populations.

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.

Maternal DNA methylation signatures of arsenic exposure is associated with adult offspring insulin resistance in the Strong Heart Study.

Exposure to low to moderate arsenic (As) levels has been associated with type 2 diabetes (T2D) and other chronic diseases in American Indian communities. Prenatal exposure to As may also increase the risk for T2D in adulthood, and maternal As has been associated with adult offspring metabolic health measurements. We hypothesized that T2D-related outcomes in adult offspring born to women exposed to low to moderate As can be evaluated utilizing a maternally-derived molecular biosignature of As exposure. Herein, we evaluated the association of maternal DNA methylation with incident T2D and insulin resistance (Homeostatic model assessment of insulin resistance [HOMA2-IR]) in adult offspring. For DNA methylation, we used 20 differentially methylated cytosine-guanine dinucleotides (CpG) previously associated with the sum of inorganic and methylated As species (ΣAs) in urine in the Strong Heart Study (SHS). Of these 20 CpGs, we found six CpGs nominally associated (p < 0.05) with HOMA2-IR in a fully adjusted model that included clinically relevant covariates and offspring adiposity measurements; a similar model that adjusted instead for maternal adiposity measurements found three CpGs nominally associated with HOMA2-IR, two of which overlapped the offspring adiposity model. After adjusting for multiple comparisons, cg03036214 remained associated with HOMA2-IR (q < 0.10) in the offspring adiposity model. The odds ratio of incident T2D increased with an increase in maternal DNA methylation at one HOMA2-IR associated CpG in the model adjusting for offspring adiposity, cg12116137, whereas adjusting for maternal adiposity had a minimal effect on the association. Our data suggests offspring adiposity, rather than maternal adiposity, potentially influences the effects of maternal DNAm signatures on offspring metabolic health parameters. Here, we have presented evidence supporting a role for epigenetic biosignatures of maternal As exposure as a potential biomarker for evaluating risk of T2D-related outcomes in offspring later in life.

DNA methylation analysis is used to identify novel genetic loci associated with circulating fibrinogen levels in blood.

BACKGROUND: Fibrinogen plays an essential role in blood coagulation and inflammation. Circulating fibrinogen levels may be determined based on interindividual differences in DNA methylation at cytosine-phosphate-guanine (CpG) sites and vice versa. OBJECTIVES: To perform an EWAS to examine an association between blood DNA methylation levels and circulating fibrinogen levels to better understand its biological and pathophysiological actions. METHODS: We performed an epigenome-wide association study of circulating fibrinogen levels in 18 037 White, Black, American Indian, and Hispanic participants, representing 14 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium. Circulating leukocyte DNA methylation was measured using the Illumina 450K array in 12 904 participants and using the EPIC array in 5133 participants. In each study, an epigenome-wide association study of fibrinogen was performed using linear mixed models adjusted for potential confounders. Study-specific results were combined using array-specific meta-analysis, followed by cross-replication of epigenome-wide significant associations. We compared models with and without CRP adjustment to examine the role of inflammation. RESULTS: We identified 208 and 87 significant CpG sites associated with fibrinogen levels from the 450K (p < 1.03 × 10-7) and EPIC arrays (p < 5.78 × 10-8), respectively. There were 78 associations from the 450K array that replicated in the EPIC array and 26 vice versa. After accounting for overlapping sites, there were 83 replicated CpG sites located in 61 loci, of which only 4 have been previously reported for fibrinogen. The examples of genes located near these CpG sites were SOCS3 and AIM2, which are involved in inflammatory pathways. The associations of all 83 replicated CpG sites were attenuated after CRP adjustment, although many remained significant. CONCLUSION: We identified 83 CpG sites associated with circulating fibrinogen levels. These associations are partially driven by inflammatory pathways shared by both fibrinogen and CRP.

Plasma lipidomic profile of depressive symptoms: a longitudinal study in a large sample of community-dwelling American Indians in the strong heart study.

Dyslipidemia has been associated with depression, but individual lipid species associated with depression remain largely unknown. The temporal relationship between lipid metabolism and the development of depression also remains to be determined. We studied 3721 fasting plasma samples from 1978 American Indians attending two exams (2001-2003, 2006-2009, mean ~5.5 years apart) in the Strong Heart Family Study. Plasma lipids were repeatedly measured by untargeted liquid chromatography-mass spectrometry (LC-MS). Depressive symptoms were assessed using the 20-item Center for Epidemiologic Studies for Depression (CES-D). Participants at risk for depression were defined as total CES-D score ≥16. Generalized estimating equation (GEE) was used to examine the associations of lipid species with incident or prevalent depression, adjusting for covariates. The associations between changes in lipids and changes in depressive symptoms were additionally adjusted for baseline lipids. We found that lower levels of sphingomyelins and glycerophospholipids and higher level of lysophospholipids were significantly associated with incident and/or prevalent depression. Changes in sphingomyelins, glycerophospholipids, acylcarnitines, fatty acids and triacylglycerols were associated with changes in depressive symptoms and other psychosomatic traits. We also identified differential lipid networks associated with risk of depression. The observed alterations in lipid metabolism may affect depression through increasing the activities of acid sphingomyelinase and phospholipase A2, disturbing neurotransmitters and membrane signaling, enhancing inflammation, oxidative stress, and lipid peroxidation, and/or affecting energy storage in lipid droplets or membrane formation. These findings illuminate the mechanisms through which dyslipidemia may contribute to depression and provide initial evidence for targeting lipid metabolism in developing preventive and therapeutic interventions for depression.

Epigenetics of type 2 diabetes and diabetes-related outcomes in the Strong Heart Study.

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.

Lipidomic profiling in the Strong Heart Study identified American Indians at risk of chronic kidney disease.

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.