Publications

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.

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.

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.

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.