Publications

BACKGROUND: Evidence on lead and the burden of cardiovascular disease (CVD) derived from National Health and Nutrition Examination Survey (NHANES) data, a general sample of the U.S. population, lacks sufficient representation of American Indians. Moreover, there is limited prospective evidence on lead and incident CVD outcomes.

OBJECTIVES: We evaluated if blood lead levels were associated with CVD mortality and incidence in American Indian adults from the Strong Heart Study (SHS).

METHODS: Whole blood samples collected in 1998-1999 among 1,818 participants was analyzed for lead using inductively coupled plasma mass spectrometry. CVD incidence and mortality were available through 2019. We used progressively adjusted multivariable Cox proportional hazards models to estimate the risk of composite CVD and coronary heart disease (CHD) mortality and incidence by baseline blood lead levels.

RESULTS: The median (p20, p80) blood lead was 22.5 (14.2, 37.3) µg/L, similar to that of a representative sample of US adults in NHANES 1999-2000. During follow-up, 578 (31.8%) participants had a composite CVD event and 454 (25.0%) participants had a CHD event. After adjustment for demographic, lifestyle, and cardiovascular risk factors, the hazard ratio (95% CI) per change across the 80th to 20th quantiles in blood lead was 1.15 (1.02-1.30) and 1.22 (1.08- 1.37) for CVD and CHD mortality, respectively, and 1.13 (1.02-1.24) and 1.12 (0.99-1.25) for CVD and CHD incidence, respectively. In flexible dose-response models, the associations appeared to be non-linear, with a clear increased risk of CVD and CHD mortality at blood lead concentrations above 35 µg/L.

DISCUSSION: Blood lead levels in American Indian adults, which are comparable to populations in the U.S. and globally, were associated with increased risk of CVD and CHD incidence and mortality. These findings highlight the importance of further reducing lead exposure, including American Indian communities. https://doi.org/10.1289/EHP16309.

BACKGROUND: The health effects of smoking vary substantially between individuals. Novel strategies are needed to identify individuals with increased risk of smoking-related morbidity. Extracellular vesicle-encapsulated microRNAs (EV-miRNAs) may be viable biomarkers of smoking-related harm. Here, we measured associations of smoking status with plasma EV-miRNA expression levels.

METHODS: Participants who were free of smoking-related diseases were sampled from the prospective Normative Aging Study (NAS). Smoking histories were evaluated using standardized smoking questionnaires. EV-miRNAs were measured in plasma. Vital status was monitored using death records. Associations of smoking status with EV-miRNA expression levels were modeled using linear regression. Results were validated in the Strong Heart Study (SHS). Associations of candidate EV-miRNAs with all-cause mortality were modeled using Cox proportional hazards models.

RESULTS: Among 88 NAS participants (mean age 70.9 ± 8.0 years), recent smoking was associated with differential expression of 16 plasma EV-miRNAs, including decreased expression of miR-30b-5p (fold change in EV-miRNA expression: 0.26, 95 % CI 0.11-0.58). The association between smoking and low miR-30b-5p expression was replicated in the SHS cohort (fold change: 0.71, 95 % CI 0.51-0.97). A pathway analysis showed miR-30b-5p targeted genes that modulate oncogenic and pro-inflammatory signaling pathways (q-value<0.05). In the NAS cohort, low miR-30b-5p expression was associated with higher all-cause mortality (adjusted Hazard Ratio 3.36, 95 % CI 1.35-8.35).

CONCLUSIONS: Recent cigarette smoking was associated with low miR-30b-5p expression in two distinct populations. Circulating miR-30b-5p is a robust biomarker of smoking-related harm and may represent a novel target for the prevention and treatment of smoking-related diseases.

BACKGROUND: American Indian populations have experienced marked disparities in respiratory disease burden. Extracellular vesicle-encapsulated microRNAs (EV-miRNAs) are a novel class of biomarkers that may improve recognition of lung damage in indigenous populations in the United States.

RESEARCH QUESTION: Are plasma EV-miRNAs viable biomarkers of respiratory health in American Indian populations?

STUDY DESIGN AND METHODS: The Strong Heart Study is a prospective cohort study that enrolled American Indian patients aged 45 to 74 years. EV-miRNA expression was measured in plasma (1993-1995). Respiratory health outcomes, including prebronchodilator FEV1, FVC, and respiratory symptom burden, were ascertained in the same study visit. Club cell secretory protein (CC-16), an antiinflammatory pneumoprotein implicated in COPD pathogenesis, was measured in serum. Linear and logistic regression were used for statistical analyses. Biological pathway analyses were used to elucidate gene targets of significant EV-miRNAs.

RESULTS: Among 853 American Indian adults, three EV-miRNAs were associated with FEV1, four EV-miRNAs were associated with FVC, and one EV-miRNA was associated with FEV1/FVC (P < .05). Increased miR-1294 expression was associated with higher odds of airflow limitation (OR, 1.29; 95% CI, 1.07-1.55), whereas increased expression of miR-1294 (OR, 1.32; 95% CI, 1.07-1.63) and miR-532-5p (OR, 1.57; 95% CI, 1.02-2.40) was associated with higher odds of restriction. Increased miR-451a expression was associated with lower odds of exertional dyspnea (OR, 0.71; 95% CI, 0.59-0.85). Twenty-two EV-miRNAs were associated with serum CC-16 levels (q < 0.05), suggesting that EV-miRNAs may play a role in the pathway linking CC-16 to COPD pathogenesis. A pathway analysis showed key EV-miRNAs targeted biological pathways that modulate inflammation, immunity, and structural integrity in the lungs.

INTERPRETATION: Circulating EV-miRNAs are novel mechanistic biomarkers of respiratory health and may facilitate the early detection and treatment of lung damage in American Indian populations that have been disproportionately affected by chronic lung diseases.

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.

UNLABELLED: Diabetes has a large medical and public health impact in American Indians. Studies have used genetic data to distinguish type 1 diabetes (T1D) and type 2 diabetes (T2D) and uncover biologic mechanisms underlying T2D clinical heterogeneity. We applied a T1D polygenic score (PS) to 3,084 American Indians (mean age 56 years, 58% women, 39% diabetes). We also calculated partitioned PS for eight clusters of T2D-associated variants and evaluated their association with 20 cardiometabolic traits and five clinical outcomes. The profile of T1D PS for individuals with diabetes was consistent with T2D. A total T2D PS was significantly associated with early age of T2D onset (P = 3.5 × 10-11). Partitioned PS for T2D clusters were significantly associated with cardiometabolic traits for the obesity cluster (increased measures of body fat and total triglycerides but lower HDL cholesterol), while the lipodystrophy cluster was associated with increased fasting insulin, waist-to-hip ratio, triglycerides, and blood pressure, and lower body fat percentage and HDL cholesterol. T2D clusters were not associated with cardiovascular and kidney outcomes. Our findings support a relationship of cluster-specific T2D partitioned PS with cardiometabolic traits described in other populations, but there are opportunities for developing improved clustering methods using genetic variation from American Indians.

ARTICLE HIGHLIGHTS: Diabetes is highly prevalent in American Indians and a main cause of morbidity and mortality, and its clinical heterogeneity can be uncovered using genetic data. We are interested in identifying type 1 versus type 2 diabetes in American Indians with diabetes and identifying biological mechanisms for type 2 diabetes that are related to clinical outcomes using genetic data. Using genetic data, we found a high probability of participants having type 2 diabetes. We identified similar associations of type 2 diabetes genetic clusters, that are related to biological mechanisms, with cardiometabolic traits as previously described in other populations, but no associations of genetic clusters with cardiovascular and kidney outcomes. Our findings support type 2 diabetes as the main cause of diabetes in our American Indian cohort and provide insights into improvements using genetic data to uncover type 2 biological mechanisms.

BACKGROUND: The association of overall cardiovascular health (CVH) with changes in DNA methylation (DNAm) has not been well characterized.

METHODS: We calculated the American Heart Association's Life's Essential 8 score to reflect CVH in 5 cohorts with diverse backgrounds (mean age 54 years, 55% women, and enrollment year ranging from 1989 to 2012). Epigenome-wide association studies (EWAS) for Life's Essential 8 score were conducted, followed by bioinformatic analyses. DNAm loci significantly associated with Life's Essential 8 score were used to calculate a CVH DNAm score. We examined the association of the CVH DNAm score with incident cardiovascular disease (CVD), cardiovascular disease-specific mortality, and all-cause mortality.

RESULTS: We identified 609 cytosine-phosphate-guanines (CpGs) associated with Life's Essential 8 score at false discovery rate<0.05 in the discovery analysis and at Bonferroni-corrected P<0.05 in the multicohort replication stage. Most had low to moderate heterogeneity (414 CpGs [68.0%] with heterogeneity <0.2) in replication analysis. Pathway enrichment analyses and a phenome-wide association study search associated these CpGs with inflammatory or autoimmune phenotypes. We observed enrichment for phenotypes in the Epigenome-Wide Association Study Catalog, with 29-fold enrichment for stroke (P=2.4e-15) and 21-fold for ischemic heart disease (P=7.4e-38). Two-sample Mendelian randomization (MR) analysis showed significant association between 141 CpGs and ten phenotypes (261 CpG-phenotype pairs) at false discovery rate<0.05. For example, hypomethylation at cg20544516 (MIR33B [microRNA 33b] and SREBF1 [sterol regulatory element-binding transcription factor 1]) is associated with a lower risk of stroke (P=8.1e-6). In multivariable prospective analyses, the CVH DNAm score was consistently associated with clinical outcomes across participating cohorts. Per SD increase in the CVH DNAm score, the decrease in risk of incident cardiovascular disease, cardiovascular disease mortality, and all-cause mortality ranged from 19% to 32%, 28% to 40%, and 27% to 45%, respectively.

CONCLUSIONS: We identified new DNAm signatures for CVH across diverse cohorts. Our analyses indicate that multiple biological pathways may be relevant to the observed association between CVH and clinical outcomes.

BACKGROUND: Environmental metals are recognized cardiovascular disease risk factors, yet the role of metal exposure in heart failure (HF) risk remains understudied.

OBJECTIVES: This study aims to evaluate the prospective association of urinary metals with incident HF across 3 geographically and ethnically/racially diverse cohorts: MESA (Multi-Ethnic Study of Atherosclerosis) and SHS (Strong Heart Study) in the United States, and the Hortega Study in Spain.

METHODS: Adults 18-85 years of age in MESA (n = 6,601), SHS (n = 2,917), and Hortega (n = 1,300) were followed up to 20 years. Urinary levels of a multi-metal panel were measured at baseline and corrected for urine dilution. Cox proportional hazards and Cox elastic-net models were used to estimate the multi-adjusted (sociodemographic/clinical/lifestyle covariates) HR of incident HF by individual metals and the mixture of 5 metals available in all cohorts, respectively. The pooled HR (95% CI) of HF by 1-unit increase in log2-transformed levels of individual metals (ie, doubling of the dose) across cohorts was estimated using a fixed effects meta-analysis. Analyses by left ventricular ejection fraction were conducted in a subset.

RESULTS: A total of 1,001 participants developed HF. In adjusted models, significant associations (pooled HRs [95% CI] per doubling of urinary metal) were identified for cadmium (HR: 1.15 [95% CI: 1.07-1.24]) molybdenum (HR: 1.13 [95% CI: 1.05-1.22]), and zinc (HR: 1.22 [95% CI: 1.14-1.32]). The HRs (95% CIs) for the association of 1 IQR increase in the multi-metal mixture levels and incident HF were 1.38 (95% CI: 1.00-1.86) in MESA, HR: 1.55 (95% CI: 1.28-1.97) in SHS, and HR: 1.08 (95% CI: 0.85-1.63) in Hortega in fully adjusted models. Stratified models by left ventricular ejection fraction were consistent with the pooled results.

CONCLUSIONS: Urinary metals are risk factors of HF across 3 diverse populations, supporting the role of reducing metal exposures to lower HF risk.

INTRODUCTION: Peripheral artery disease (PAD) and dyslipidemia are both independent predictors of cardiovascular disease, but the association between individual lipid species and subclinical PAD, assessed by ankle-brachial index (ABI), is lacking in large-scale longitudinal studies.

METHODS: We used liquid chromatography-mass spectrometry to repeatedly measure 1,542 lipid species from 1,886 American Indian adults attending 2 clinical examinations (mean  5 years apart) in the Strong Heart Family Study. We used generalized estimating equation models to identify baseline lipid species associated with change in ABI and the Cox frailty regression to examine whether lipids associated with change in ABI were also associated with incident coronary heart disease (CHD). We also examined the longitudinal association between change in lipid species and change in ABI and the cross-sectional association of individual lipids with ABI. All models were adjusted for age, sex, body mass index, smoking, alcohol use, hypertension, estimated glomerular filtration rate, diabetes, and lipid-lowering medication.

RESULTS: Baseline levels of 120 lipid species, including glycerophospholipids, glycerolipids, fatty acids, and sphingomyelins, were associated with change in ABI. Among these, higher baseline levels of 3 known lipids (phosphatidylinositol[16:0/20:4], triacylglycerol[48:2], triacylglycerol[55:1]) were associated with a lower risk of CHD (hazard ratios [95% CIs] ranged from 0.67 [0.46-0.99] to 0.76 [0.58-0.99]), while cholesterol was associated with a higher risk of CHD (hazard ratio [95% CI] = 1.37 [1.00-1.87]). Longitudinal changes in 32 lipids were significantly associated with change in ABI during 5-year follow-up. Plasma levels of glycerophospholipids, triacylglycerols, and glycosylceramides were significantly associated with ABI in the cross-sectional analysis.

CONCLUSION: Altered plasma lipidome is significantly associated with subclinical PAD in American Indians beyond traditional risk factors. If validated, the identified lipid species may serve as novel biomarkers for PAD in this high-risk but understudied population.

OBJECTIVE: To identify lipidomic markers of habitual unprocessed red meat and processed meat intake and evaluate their associations with diabetes risk in American Indians.

RESEARCH DESIGN AND METHODS: We studied 1,816 participants from the Strong Heart Family Study. Using untargeted liquid chromatography-mass spectrometry, we quantified 1,542 lipids (518 known) in fasting plasma at baseline and follow-up (∼5 years apart). Meat intake was assessed via Food Frequency Questionnaires. Mixed-effects linear regression was used to identify lipids associated with meat consumption. Mixed-effects logistic regression was used to examine whether these lipids were associated with incident diabetes, independent of conventional risk factors, or with longitudinal glucose/insulin metrics.

RESULTS: Diabetes developed in 66 of 1,076 participants with normal baseline glucose. After multiple testing correction, 15 known lipids, primarily plasmalogens, were associated with unprocessed red meat intake. Three plasmalogens were linked to incident diabetes (odds ratio [OR] 1.32 [95% CI 1.02-1.70] to 1.39 [1.08-1.78] per SD increase in baseline levels) and higher red meat intake. Eight lipids, mainly sphingomyelins, were associated with processed meat intake. Two sphingomyelins were linked to incident diabetes (OR 1.33 [95% CI 1.02-1.75] and 1.36 [1.04-1.80]) and higher processed meat intake. Of 23 meat-related lipids, 20 were associated with altered glucose/insulin metrics, and 11 mediated positive associations between red or processed meat intake and fasting glucose.

CONCLUSIONS: We identified lipidomic markers of unprocessed red and processed meat consumption. Several lipids were independently associated with increased diabetes risk, potentially by mediating the association between meat intake and glucose metabolism.

Hemophilia-A (HA) is the X-linked bleeding disorder caused by heterogeneous factor (F)VIII gene (F8)-mutations and deficiencies in plasma-FVIII-activity that prevent intrinsic-pathway mediated coagulation-amplification. Severe-HA patients (HAPs) require life-long infusions of therapeutic-FVIII-proteins (tFVIIIs) but  30% develop neutralizing-tFVIII-antibodies called "FVIII-inhibitors (FEIs)". We investigated the genetics underlying the variable risk of FEI-development in 450 North American HAPs (206 and 244 respectively self-reporting black-African- or white-European-ancestry) by analyzing the genotypes of single-nucleotide-variations (SNVs) in candidate immune-mediated-disease (IMD)-genes using a binary linear-mixed model of genetic association with baseline-FEI-status, the dependent variable, while simultaneously accounting for their genetic relationships and heterogeneous-F8-mutations to prevent the statistical problem of non-independence. We a priori selected gene-centric-association-scans of pleiotropic-IMD-genes implicated in the development of either ≥2 autoimmune-/autoinflammatory-disorders (AADs) or FEIs and ≥1 AAD. We found that baseline-FEI-status was significantly associated with NOS2A (rs117382854; p = 3.2 × 10-6) and B3GNT2 (rs10176009; p = 5.1 × 10-6)-pleiotropic-IMD-genes known previously to function in anti-microbial-/-tumoral-immunity but not in the development of FEIs-and confirmed associations with CTLA4 (rs231780; p = 2.2 × 10-5). We also found that baseline-FEI-status has a substantial heritability ( 55%) that involves (i) a F8-mutation-specific component of  8%, (ii) an additive-genetic contribution from SNVs in IMD-genes of  47%, and (iii) race, which is a significant determinant independent of F8-mutation-types and non-F8-genetics.