AESurv: autoencoder survival analysis for accurate early prediction of coronary heart disease.

Coronary heart disease (CHD) is one of the leading causes of mortality and morbidity in the United States. Accurate time-to-event CHD prediction models with high-dimensional DNA methylation and clinical features may assist with early prediction and intervention strategies. We developed a state-of-the-art deep learning autoencoder survival analysis model (AESurv) to effectively analyze high-dimensional blood DNA methylation features and traditional clinical risk factors by learning low-dimensional representation of participants for time-to-event CHD prediction. We demonstrated the utility of our model in two cohort studies: the Strong Heart Study cohort (SHS), a prospective cohort studying cardiovascular disease and its risk factors among American Indians adults; the Women's Health Initiative (WHI), a prospective cohort study including randomized clinical trials and observational study to improve postmenopausal women's health with one of the main focuses on cardiovascular disease. Our AESurv model effectively learned participant representations in low-dimensional latent space and achieved better model performance (concordance index-C index of 0.864 ± 0.009 and time-to-event mean area under the receiver operating characteristic curve-AUROC of 0.905 ± 0.009) than other survival analysis models (Cox proportional hazard, Cox proportional hazard deep neural network survival analysis, random survival forest, and gradient boosting survival analysis models) in the SHS. We further validated the AESurv model in WHI and also achieved the best model performance. The AESurv model can be used for accurate CHD prediction and assist health care professionals and patients to perform early intervention strategies. We suggest using AESurv model for future time-to-event CHD prediction based on DNA methylation features.

Sequencing-based fine-mapping and in silico functional characterization of the 10q24.32 arsenic metabolism efficiency locus across multiple arsenic-exposed populations.

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.

Arsenic Exposure, Blood DNA Methylation, and Cardiovascular Disease.

BACKGROUND: Epigenetic dysregulation has been proposed as a key mechanism for arsenic-related cardiovascular disease (CVD). We evaluated differentially methylated positions (DMPs) as potential mediators on the association between arsenic and CVD. METHODS: Blood DNA methylation was measured in 2321 participants (mean age 56.2, 58.6% women) of the Strong Heart Study, a prospective cohort of American Indians. Urinary arsenic species were measured using high-performance liquid chromatography coupled to inductively coupled plasma mass spectrometry. We identified DMPs that are potential mediators between arsenic and CVD. In a cross-species analysis, we compared those DMPs with differential liver DNA methylation following early-life arsenic exposure in the apoE knockout (apoE-/-) mouse model of atherosclerosis. RESULTS: A total of 20 and 13 DMPs were potential mediators for CVD incidence and mortality, respectively, several of them annotated to genes related to diabetes. Eleven of these DMPs were similarly associated with incident CVD in 3 diverse prospective cohorts (Framingham Heart Study, Women's Health Initiative, and Multi-Ethnic Study of Atherosclerosis). In the mouse model, differentially methylated regions in 20 of those genes and DMPs in 10 genes were associated with arsenic. CONCLUSIONS: Differential DNA methylation might be part of the biological link between arsenic and CVD. The gene functions suggest that diabetes might represent a relevant mechanism for arsenic-related cardiovascular risk in populations with a high burden of diabetes.

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.

Pilot study of a heart-healthy food box intervention for Native Americans with uncontrolled hypertension: methods and results from the Chickasaw Healthy Eating Environments Research Study.

We sought to evaluate the acceptability and feasibility of a culturally tailored food box intervention for improving blood pressure (BP), food security and Body Mass Index (BMI) among Chickasaw Nation adults with uncontrolled hypertension. As part of the Chickasaw Healthy Eating Environments Research Study (CHEERS), we administered a group randomized pilot study in four tribal communities (two intervention, two control). Participants in the intervention communities received six heart-healthy food boxes, culturally tailored to traditional Chickasaw diet and current food context. Outcomes were measured over 6 months. We enrolled 262 participants, and 204 with complete data on key variables were included in the analysis. The food boxes were very popular, and we achieved high retention for follow-up data collection. Intervention community participants had 2.6 mmHg lower mean systolic BP and improved diet quality and BMI compared with control participants, although, as expected for a pilot study, the differences were not statistically significant. The culturally tailored diet intervention and randomized trial study design were acceptable and feasible for Chickasaw Nation adults with uncontrolled hypertension. Our findings support the value of tribal-food bank partnerships as a potential approach for reducing food insecurity and hypertension-related disparities in Native American communities.

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.

Fine particulate matter composition in American Indian vs. Non-American Indian communities.

BACKGROUND: Fine particulate matter (PM2.5) exposure is a known risk factor for numerous adverse health outcomes, with varying estimates of component-specific effects. Populations with compromised health conditions such as diabetes can be more sensitive to the health impacts of air pollution exposure. Recent trends in PM2.5 in primarily American Indian- (AI-) populated areas examined in previous work declined more gradually compared to the declines observed in the rest of the US. To further investigate components contributing to these findings, we compared trends in concentrations of six PM2.5 components in AI- vs. non-AI-populated counties over time (2000-2017) in the contiguous US. METHODS: We implemented component-specific linear mixed models to estimate differences in annual county-level concentrations of sulfate, nitrate, ammonium, organic matter, black carbon, and mineral dust from well-validated surface PM2.5 models in AI- vs. non-AI-populated counties, using a multi-criteria approach to classify counties as AI- or non-AI-populated. Models adjusted for population density and median household income. We included interaction terms with calendar year to estimate whether concentration differences in AI- vs. non-AI-populated counties varied over time. RESULTS: Our final analysis included 3108 counties, with 199 (6.4%) classified as AI-populated. On average across the study period, adjusted concentrations of all six PM2.5 components in AI-populated counties were significantly lower than in non-AI-populated counties. However, component-specific levels in AI- vs. non-AI-populated counties varied over time: sulfate and ammonium levels were significantly lower in AI- vs. non-AI-populated counties before 2011 but higher after 2011 and nitrate levels were consistently lower in AI-populated counties. CONCLUSIONS: This study indicates time trend differences of specific components by AI-populated county type. Notably, decreases in sulfate and ammonium may contribute to steeper declines in total PM2.5 in non-AI vs. AI-populated counties. These findings provide potential directives for additional monitoring and regulations of key emissions sources impacting tribal lands.

Adapting a cooking, food budgeting and nutrition intervention for a rural community of American Indians with type 2 diabetes in the North-Central United States.

American Indian (AI) communities experience persistent diabetes-related disparities, yet few nutrition interventions are designed for AI with type 2 diabetes or address socio-contextual barriers to healthy eating. We describe our process of adapting the evidence-based Cooking Matters® program for use by AI adults with type 2 diabetes in a rural and resource-limited setting in the North-Central United States. We conducted three focus groups with AI adults with diabetes to (i) identify Cooking Matters® adaptations and (ii) gather feedback on appropriateness of the adapted intervention using Barrera and Castro's cultural adaptation framework. Transcripts were coded using an inductive, constant comparison approach. Queries of codes were reviewed to identify themes. Contextual considerations included limited access to grocery stores and transportation barriers, reliance on government food assistance and the intergenerational burden of diabetes. Adaptations to content and delivery included incorporating traditional and locally available foods; appealing to children or others in multigenerational households and prioritizing visual over written content. Our use of Barrera and Castro's framework adds rigor and structure to the cultural adaptation process and increases the likelihood of future intervention success. Other researchers may benefit from using this framework to guide the adaptation of evidence-based interventions in AI communities.

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.

Circulating ceramides and sphingomyelins and the risk of incident cardiovascular disease among people with diabetes: the strong heart study.

BACKGROUND: Plasma ceramides and sphingomyelins have been independently linked to diabetes risk, glucose and insulin levels, and the risk of several cardiovascular (CVD) outcomes. However, whether individual ceramide and sphingomyelin species contribute to CVD risk among people with type 2 diabetes is uncertain. Our goal was to evaluate associations of 4 ceramide and 4 sphingomyelin species with incident CVD in a longitudinal population-based study among American Indians with diabetes. METHODS: This analysis included participants with prevalent type 2 diabetes from two cohorts: a prospective cohort of 597 participants in the Strong Heart Family Study (116 incident CVD cases; mean age: 49 years; average length of follow-up: 14 years), and a nested case-control sample of 267 participants in the Strong Heart Study (78 cases of CVD and 189 controls; mean age: 61 years; average time until incident CVD in cases: 3.8 years). The average onset of diabetes was 7 years prior to sphingolipid measurement. Sphingolipid species were measured using liquid chromatography and mass spectrometry. Cox regression and logistic regression were used to assess associations of sphingolipid species with incident CVD; results were combined across cohorts using inverse-variance weighted meta-analysis. RESULTS: There were 194 cases of incident CVD in the two cohorts. In meta-analysis of the 2 cohort results, higher plasma levels of Cer-16 (ceramide with acylated palmitic acid) were associated with higher CVD risk (HR per two-fold higher Cer-16: 1.85; 95% CI 1.05-3.25), and higher plasma levels of sphingomyelin species with a very long chain saturated fatty acid were associated with lower CVD risk (HR per two-fold higher SM-22: 0.48; 95% CI 0.26-0.87), although none of the associations met our pre-specified threshold for statistical significance of p = 0.006. CONCLUSIONS: While replication of the findings from the SHS in other populations is warranted, our findings add to a growing body of research suggesting that ceramides, in particular Cer-16, not only are associated with higher diabetes risk, but may also be associated with higher CVD risk after diabetes onset. We also find support for the hypothesis that sphingomyelins with a very long chain saturated fatty acid are associated with lower CVD risk among adults with type 2 diabetes.

Air Pollution in American Indian Versus Non-American Indian Communities, 2000-2018.

Objectives. To compare fine particulate matter (PM2.5) concentrations in American Indian (AI)-populated with those in non-AI-populated counties over time (2000-2018) in the contiguous United States. Methods. We used a multicriteria approach to classify counties as AI- or non--AI-populated. We ran linear mixed effects models to estimate the difference in countywide annual PM2.5 concentrations from well-validated prediction models and monitoring sites (modeled and measured PM2.5, respectively) in AI- versus non-AI-populated counties. Results. On average, adjusted modeled PM2.5 concentrations in AI-populated counties were 0.38 micrograms per cubic meter (95% confidence interval [CI] = 0.23, 0.54) lower than in non-AI-populated counties. However, this difference was not constant over time: in 2000, modeled concentrations in AI-populated counties were 1.46 micrograms per cubic meter (95% CI = 1.25, 1.68) lower, and by 2018, they were 0.66 micrograms per cubic meter (95% CI = 0.45, 0.87) higher. Over the study period, adjusted modeled PM2.5 mean concentrations decreased by 2.13 micrograms per cubic meter in AI-populated counties versus 4.26 micrograms per cubic meter in non-AI-populated counties. Results were similar for measured PM2.5. Conclusions. This study highlights disparities in PM2.5 trends between AI- and non-AI-populated counties over time, underscoring the need to strengthen air pollution regulations and prevention implementation in tribal territories and areas where AI populations live. (Am J Public Health. 2022;112(4): 615-623. https://doi.org/10.2105/AJPH.2021.306650).

Smoking Patterns Among Urban Alaska Native and American Indian Adults: The Alaska EARTH 10-Year Follow-up Study.

INTRODUCTION: Data on cigarette smoking prevalence among Alaska Native and American Indian (ANAI) people are limited to cross-sectional studies or specific subpopulations. Using data from the Alaska Education and Research toward Health (EARTH) Study 10-year follow-up, this study assessed patterns of smoking from baseline and factors associated with current use. AIMS AND METHODS: EARTH Study urban south central ANAI participants (N = 376; 73% women) provided questionnaire data on smoking at baseline and 10-year follow-up. Multivariable-adjusted logistic regression assessed whether gender, cultural factors (Tribal identity, language spoken in the home), depressive symptoms (PHQ-9), baseline smoking status, and baseline cigarettes per day (CPD) were associated with current smoking at follow-up. RESULTS: Current smoking was 27% and 23% at baseline and follow-up, respectively. Of baseline smokers, 60% reported smoking at follow-up (77% men, 52% women). From multivariable-adjusted analyses, the odds of current smoking at follow-up were lower among women than men, those who never or formerly smoked versus currently smoked at baseline, and smoking <10 CPD compared with ≥10 CPD at baseline. PHQ-9 score or cultural variables were not associated with smoking at follow-up. Smoking fewer baseline CPD was associated with former smoking status (ie, quitting) at follow-up among women, but not men. CONCLUSIONS: Our project is among the first to longitudinally explore smoking within an ANAI cohort. While we observed persistent smoking during a 10-year period, there were important differences by gender and CPD in quitting. These differences may be important to enhance the reach and efficacy of cessation interventions for ANAI people. IMPLICATIONS: This study contributes novel longitudinal information on cigarette smoking prevalence during a 10-year period among Alaska Native and American Indian (ANAI) people. Prior data on smoking prevalence among ANAI people are limited to cross-sectional studies or specific subpopulations. Our project is among the first to longitudinally explore smoking prevalence within an ANAI cohort. We observed persistent smoking during a 10-year period. The study also contributes information on differences by gender and cigarettes smoked per day in quitting. These findings have implications for enhancing the reach and efficacy of cessation interventions for ANAI people.

Blood cadmium, lead, manganese, mercury, and selenium levels in American Indian populations: The Strong Heart Study.

BACKGROUND: Many American Indian (AI) communities are in areas affected by environmental contamination, such as toxic metals. However, studies assessing exposures in AI communities are limited. We measured blood metals in AI communities to assess historical exposure and identify participant characteristics associated with these levels in the Strong Heart Study (SHS) cohort. METHOD: Archived blood specimens collected from participants (n = 2014, all participants were 50 years of age and older) in Arizona, Oklahoma, and North and South Dakota during SHS Phase-III (1998-1999) were analyzed for cadmium, lead, manganese, mercury, and selenium using inductively coupled plasma triple quadrupole mass spectrometry. We conducted descriptive analyses for the entire cohort and stratified by selected subgroups, including selected demographics, health behaviors, income, waist circumference, and body mass index. Bivariate associations were conducted to examine associations between blood metal levels and selected socio-demographic and behavioral covariates. Finally, multivariate regression models were used to assess the best model fit that predicted blood metal levels. FINDINGS: All elements were detected in 100% of study participants, with the exception of mercury (detected in 73% of participants). The SHS population had higher levels of blood cadmium and manganese than the general U.S. population 50 years and older. The median blood mercury in the SHS cohort was at about 30% of the U.S. reference population, potentially due to low fish consumption. Participants in North Dakota and South Dakota had the highest blood cadmium, lead, manganese, and selenium, and the lowest total mercury levels, even after adjusting for covariates. In addition, each of the blood metals was associated with selected demographic, behavioral, income, and/or weight-related factors in multivariate models. These findings will help guide the tribes to develop education, outreach, and strategies to reduce harmful exposures and increase beneficial nutrient intake in these AI communities.

Subclinical atherosclerosis in adolescents and young adults and the risk of cardiovascular disease: The Strong Heart Family Study (SHFS).

BACKGROUND AND AIMS: Rates of cardiovascular disease (CVD) among American Indians (AI) have been increasing. Although we have observed an association between atherosclerosis and CVD in older adults, the potential association among young AI is unclear. Therefore, we aim to describe the prevalence of atherosclerosis among young AI and determine its association with CVD and all-cause mortality. METHODS AND RESULTS: We evaluated AI participants from the Strong Heart Family Study (SHFS), who were <40 years old and CVD free at the baseline examination, 2001-2003 (n = 1376). We used carotid ultrasound to detect baseline atherosclerotic plaque. We identified CVD events and all-cause mortality through 2019, with a median follow-up of 17.8 years. We used shared frailty Cox Proportional Hazards models to assess the association between atherosclerosis and time to CVD event or all-cause mortality, while controlling for covariates. Among 1376 participants, 71 (5.2%) had atherosclerosis at baseline. During follow-up, 120 (8.7%) had CVD events and 104 (7.6%) died from any cause. CVD incidence was higher in participants who had baseline atherosclerosis (13.51/1000 person-years) than in those who did not (4.95/1000 person-years, p = 0.0003). CVD risk and all-cause mortality were higher in participants with atherosclerosis, while controlling for covariates (CVD HR = 1.85, 95%CI = 1.02-3.37, p = 0.0420; all-cause mortality HR = 2.04, 95%CI = 1.07-3.89, p = 0.0291). CONCLUSIONS: Among young AI, atherosclerosis was independently associated with incident CVD and all-cause mortality later in life. Thus, atherosclerosis begins early in life and interventions in adolescents and young adults to slow the progression of disease could prevent or delay CVD events later in life.

Arsenic, blood pressure, and hypertension in the Strong Heart Family Study.

BACKGROUND: Arsenic has been associated with hypertension, though it is unclear whether associations persist at the exposure concentrations (e.g. <100 µg/L) in drinking water occurring in parts of the Western United States. METHODS: We assessed associations between arsenic biomarkers and systolic blood pressure (SBP), diastolic blood pressure (DBP), and hypertension in the Strong Heart Family Study, a family-based cohort of American Indians from the Northern plains, Southern plains, and Southwest. We included 1910 participants from three study centers with complete baseline visit data (2001-2003) in the cross-sectional analysis of all three outcomes, and 1453 participants in the prospective analysis of incident hypertension (follow-up 2006-2009). We used generalized estimating equations with exchangeable correlation structure conditional on family membership to estimate the association of arsenic exposure biomarker levels with SBP or DBP (linear regressions) or hypertension prevalence and incidence (Poisson regressions), adjusting for urine creatinine, urine arsenobetaine, and measured confounders. RESULTS: We observed cross-sectional associations for a two-fold increase in inorganic and methylated urine arsenic species of 0.64 (95% CI: 0.07, 1.35) mm Hg for SBP, 0.49 (95% CI: 0.03, 1.02) mm Hg for DBP, and a prevalence ratio of 1.10 (95% CI: 1.01, 1.21) for hypertension in fully adjusted models. During follow-up, 14% of subjects developed hypertension. We observed non-monotonic relationships between quartiles of arsenic and incident hypertension. Effect estimates were null for incident hypertension with continuous exposure metrics. Stratification by study site revealed elevated associations in Arizona, the site with the highest arsenic levels, while results for Oklahoma and North and South Dakota were largely null. Blood pressure changes with increasing arsenic concentrations were larger for those with diabetes at baseline. CONCLUSIONS: Our results suggest a modest cross-sectional association of arsenic exposure biomarkers with blood pressure, and possible non-linear effects on incident hypertension.

Diabetes and health-related quality of life among American Indians: the role of psychosocial factors.

PURPOSE: Little is known about the association of psychosocial factors with health-related quality of life (HRQoL) among American Indians with type 2 diabetes (T2D). This study described functional social support, emotional support, coping, resilience, post-traumatic stress disorder, and HRQoL, among American Indians by diabetes status and, among those with diabetes, examined the association of these factors with HRQoL. METHODS: Using data from the Cherokee Nation Health Survey collected between 2017 and 2019, we evaluated differences in each measure of interest according to diabetes status, using t-test and Chi-squared tests of association. We used weighted multiple logistic regression to examine associations between multiple psychosocial factors and HRQoL among those with diabetes. RESULTS: Compared to individuals without diabetes, participants with diabetes rated their functional social support (4.62 vs. 4.56, respectively) and coping (2.65 vs. 2.61, respectively) slightly lower and were more likely to report ≥ 15 days of poor physical (14% vs. 26%, respectively) and mental health (14% vs. 17%, respectively) in the past month. Odds of reporting poor overall health increased more than sixfold for those who were dissatisfied/very dissatisfied with life (AOR = 6.70). Resilience scores reduced odds of reporting ≥ 15 days with poor physical health, while experiences of post-traumatic stress doubled these odds. CONCLUSION: Our study yielded insights into the risk as well as protective factors associated with diabetes outcomes in a large sample of American Indians with T2D. Researchers should design pragmatic trials that deepen understanding of preventive as well as treatment leverage through greater attention to experiences that compromise HRQoL.

Correction to: Low-moderate arsenic exposure and respiratory health in American Indian communities in the Strong Heart Study.

The original version of this article [1], published on 28 November 2019, contained incorrect title. In this Correction the affected part of the article is shown.

Human Papillomavirus Prevalence Among American Indian Women of the Great Plains.

BACKGROUND: High-risk human papillomavirus (hrHPV) causes cervical cancer. In the United States, approximately 40% of women aged 14-59 years from all racial and ethnic groups are infected with HPV, and prevalence typically declines with age. However, American Indian (AI) women are insufficiently sampled to permit a population-specific estimate of hrHPV prevalence. METHODS: Vaginal swabs were self-collected by 698 AI women aged 21-65 years from a tribal community in the Great Plains. We estimated the population prevalence of hrHPV and identified predominant genotypes. RESULTS: The combined prevalence of hrHPV genotypes 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 66, and 68 was 34.8%. HPV-51 (7.6%), HPV-58 (5.3%), HPV-52 (4.3%), HPV-18 (4.3%), and HPV-16 (3.9%) were most prevalent. hrHPV prevalence declined with age, from 42.2% in women aged 21-24 years to 27.9% in women aged 50-65 years. CONCLUSIONS: HPV-51 was the single most prevalent oncogenic genotype. The combined prevalence of hrHPV among AI women in our sample was high, particularly among women aged 50-65 years, for whom hrHPV prevalence was approximately triple that of other races. Cervical cancer screening efforts should be increased, particularly among women from the community aged 30 years and older.

Targeted metabolomics to understand the association between arsenic metabolism and diabetes-related outcomes: Preliminary evidence from the Strong Heart Family Study.

BACKGROUND: Inorganic arsenic exposure is ubiquitous and both exposure and inter-individual differences in its metabolism have been associated with cardiometabolic risk. A more efficient arsenic metabolism profile (lower MMA%, higher DMA%) has been associated with reduced risk for arsenic-related health outcomes. This profile, however, has also been associated with increased risk for diabetes-related outcomes. OBJECTIVES: The mechanism behind these conflicting associations is unclear; we hypothesized the one-carbon metabolism (OCM) pathway may play a role. METHODS: We evaluated the influence of OCM on the relationship between arsenic metabolism and diabetes-related outcomes (HOMA2-IR, waist circumference, fasting plasma glucose) using metabolomic data from an OCM-specific and P180 metabolite panel measured in plasma, arsenic metabolism measured in urine, and HOMA2-IR and FPG measured in fasting plasma. Samples were drawn from baseline visits (2001-2003) in 59 participants from the Strong Heart Family Study, a family-based cohort study of American Indians aged ≥14 years from Arizona, Oklahoma, and North/South Dakota. RESULTS: In unadjusted analyses, a 5% increase in DMA% was associated with higher HOMA2-IR (geometric mean ratio (GMR)= 1.13 (95% CI: 1.03, 1.25)) and waist circumference (mean difference=3.66 (0.95, 6.38). MMA% was significantly associated with lower HOMA2-IR and waist circumference. After adjustment for OCM-related metabolites (SAM, SAH, cysteine, glutamate, lysophosphatidylcholine 18.2, and three phosphatidlycholines), associations were attenuated and no longer significant. CONCLUSIONS: These preliminary results indicate that the association of lower MMA% and higher DMA% with diabetes-related outcomes may be influenced by OCM status, either through confounding, reverse causality, or mediation.

Dietary determinants of inorganic arsenic exposure in the Strong Heart Family Study.

BACKGROUND: Chronic exposure to inorganic arsenic (iAs) in the US occurs mainly through drinking water and diet. Although American Indian (AI) populations have elevated urinary arsenic concentrations compared to the general US population, dietary sources of arsenic exposure in AI populations are not well characterized. METHODS: We evaluated food frequency questionnaires to determine the major dietary sources of urinary arsenic concentrations (measured as the sum of arsenite, arsenate, monomethylarsonate, and dimethylarsinate, ΣAs) for 1727 AI participants in the Strong Heart Family Study (SHFS). We compared geometric mean ratios (GMRs) of urinary ΣAs for an interquartile range (IQR) increase in reported food group consumption. Exploratory analyses were stratified by gender and study center. RESULTS: In fully adjusted generalized estimating equation models, the percent increase (95% confidence interval) of urinary ΣAs per increase in reported food consumption corresponding to the IQR was 13% (5%, 21%) for organ meat, 8% (4%, 13%) for rice, 7% (2%, 13%) for processed meat, and 4% (1%, 7%) for non-alcoholic drinks. In analyses stratified by study center, the association with organ meat was only observed in North/South Dakota. Consumption of red meat [percent increase -7% (-11%, -3%)] and fries and chips [-6% (-10%, -2%)] was inversely associated with urinary ΣAs. CONCLUSIONS: Organ meat, processed meat, rice, and non-alcoholic drinks contribute to ΣAs exposure in the SHFS population. Organ meat is a unique source of ΣAs exposure for North and South Dakota participants and may reflect local food consumption. Further studies should comprehensively evaluate drinking water arsenic in SHFS communities and determine the relative contribution of diet and drinking water to total arsenic exposure.