Adaptive selection at G6PD and disparities in diabetes complications.

Diabetes complications occur at higher rates in individuals of African ancestry. Glucose-6-phosphate dehydrogenase deficiency (G6PDdef), common in some African populations, confers malaria resistance, and reduces hemoglobin A1c (HbA1c) levels by shortening erythrocyte lifespan. In a combined-ancestry genome-wide association study of diabetic retinopathy, we identified nine loci including a G6PDdef causal variant, rs1050828 -T (Val98Met), which was also associated with increased risk of other diabetes complications. The effect of rs1050828 -T on retinopathy was fully mediated by glucose levels. In the years preceding diabetes diagnosis and insulin prescription, glucose levels were significantly higher and HbA1c significantly lower in those with versus without G6PDdef. In the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial, participants with G6PDdef had significantly higher hazards of incident retinopathy and neuropathy. At the same HbA1c levels, G6PDdef participants in both ACCORD and the Million Veteran Program had significantly increased risk of retinopathy. We estimate that 12% and 9% of diabetic retinopathy and neuropathy cases, respectively, in participants of African ancestry are due to this exposure. Across continentally defined ancestral populations, the differences in frequency of rs1050828 -T and other G6PDdef alleles contribute to disparities in diabetes complications. Diabetes management guided by glucose or potentially genotype-adjusted HbA1c levels could lead to more timely diagnoses and appropriate intensification of therapy, decreasing the risk of diabetes complications in patients with G6PDdef alleles.

Gene-environment interactions within a precision environmental health framework.

Understanding the complex interplay of genetic and environmental factors in disease etiology and the role of gene-environment interactions (GEIs) across human development stages is important. We review the state of GEI research, including challenges in measuring environmental factors and advantages of GEI analysis in understanding disease mechanisms. We discuss the evolution of GEI studies from candidate gene-environment studies to genome-wide interaction studies (GWISs) and the role of multi-omics in mediating GEI effects. We review advancements in GEI analysis methods and the importance of large-scale datasets. We also address the translation of GEI findings into precision environmental health (PEH), showcasing real-world applications in healthcare and disease prevention. Additionally, we highlight societal considerations in GEI research, including environmental justice, the return of results to participants, and data privacy. Overall, we underscore the significance of GEI for disease prediction and prevention and advocate for integrating the exposome into PEH omics studies.

Guided optimization of ToxPi model weights using a Semi-Automated approach.

The Toxicological Prioritization Index (ToxPi) is a visual analysis and decision support tool for dimension reduction and visualization of high throughput, multi-dimensional feature data. ToxPi was originally developed for assessing the relative toxicity of multiple chemicals or stressors by synthesizing complex toxicological data to provide a single comprehensive view of the potential health effects. It continues to be used for profiling chemicals and has since been applied to other types of "sample" entities, including geospatial (e.g. county-level Covid-19 risk and sites of historical PFAS exposure) and other profiling applications. For any set of features (data collected on a set of sample entities), ToxPi integrates the data into a set of weighted slices that provide a visual profile and a score metric for comparison. This scoring system is highly dependent on user-provided feature weights, yet users often lack knowledge of how to define these feature weights. Common methods for predicting feature weights are generally unusable due to inappropriate statistical assumptions and lack of global distributional expectation. However, users often have an inherent understanding of expected results for a small subset of samples. For example, in chemical toxicity, prior knowledge can often place subsets of chemicals into categories of low, moderate or high toxicity (reference chemicals). Ordinal regression can be used to predict weights based on these response levels that are applicable to the entire feature set, analogous to using positive and negative controls to contextualize an empirical distribution. We propose a semi-supervised method utilizing ordinal regression to predict a set of feature weights that produces the best fit for the known response ("reference") data and subsequently fine-tunes the weights via a customized genetic algorithm. We conduct a simulation study to show when this method can improve the results of ordinal regression, allowing for accurate feature weight prediction and sample ranking in scenarios with minimal response data. To ground-truth the guided weight optimization, we test this method on published data to build a ToxPi model for comparison against expert-knowledge-driven weight assignments.

Questionnaire-based exposome-wide association studies for common diseases in the Personalized Environment and Genes Study.

The exposome collectively refers to all exposures, beginning in utero and continuing throughout life, and comprises not only standard environmental exposures such as point source pollution and ozone levels but also exposures from diet, medication, lifestyle factors, stress, and occupation. The exposome interacts with individual genetic and epigenetic characteristics to affect human health and disease, but large-scale studies that characterize the exposome and its relationships with human disease are limited. To address this gap, we used extensive questionnaire data from the diverse North Carolina-based Personalized Environment and Genes Study (PEGS, n = 9, 429) to evaluate exposure associations in relation to common diseases. We performed an exposome-wide association study (ExWAS) to examine single exposure models and their associations with 11 common complex diseases, namely allergic rhinitis, asthma, bone loss, fibroids, high cholesterol, hypertension, iron-deficient anemia, ovarian cysts, lower GI polyps, migraines, and type 2 diabetes. Across diseases, we found associations with lifestyle factors and socioeconomic status as well as asbestos, various dust types, biohazardous material, and textile-related exposures. We also found disease-specific associations such as fishing with lead weights and migraines. To differentiate between a replicated result and a novel finding, we used an AI-based literature search and database tool that allowed us to examine the current literature. We found both replicated findings, especially for lifestyle factors such as sleep and smoking across diseases, and novel findings, especially for occupational exposures and multiple diseases.

Interactive data sharing for multiple questionnaire-based exposome-wide association studies and exposome correlations in the Personalized Environment and Genes Study.

The correlations among individual exposures in the exposome, which refers to all exposures an individual encounters throughout life, are important for understanding the landscape of how exposures co-occur, and how this impacts health and disease. Exposome-wide association studies (ExWAS), which are analogous to genome-wide association studies (GWAS), are increasingly being used to elucidate links between the exposome and disease. Despite increased interest in the exposome, tools and publications that characterize exposure correlations and their relationships with human disease are limited, and there is a lack of data and results sharing in resources like the GWAS catalog. To address these gaps, we developed the PEGS Explorer web application to explore exposure correlations in data from the diverse North Carolina-based Personalized Environment and Genes Study (PEGS) that were rigorously calculated to account for differing data types and previously published results from ExWAS. Through globe visualizations, PEGS Explorer allows users to explore correlations between exposures found to be associated with complex diseases. The exposome data used for analysis includes not only standard environmental exposures such as point source pollution and ozone levels but also exposures from diet, medication, lifestyle factors, stress, and occupation. The web application addresses the lack of accessible data and results sharing, a major challenge in the field, and enables users to put results in context, generate hypotheses, and, importantly, replicate findings in other cohorts. PEGS Explorer will be updated with additional results as they become available, ensuring it is an up-to-date resource in exposome science.

Decoding the exposome: data science methodologies and implications in exposome-wide association studies (ExWASs).

This paper explores the exposome concept and its role in elucidating the interplay between environmental exposures and human health. We introduce two key concepts critical for exposomics research. Firstly, we discuss the joint impact of genetics and environment on phenotypes, emphasizing the variance attributable to shared and nonshared environmental factors, underscoring the complexity of quantifying the exposome's influence on health outcomes. Secondly, we introduce the importance of advanced data-driven methods in large cohort studies for exposomic measurements. Here, we introduce the exposome-wide association study (ExWAS), an approach designed for systematic discovery of relationships between phenotypes and various exposures, identifying significant associations while controlling for multiple comparisons. We advocate for the standardized use of the term "exposome-wide association study, ExWAS," to facilitate clear communication and literature retrieval in this field. The paper aims to guide future health researchers in understanding and evaluating exposomic studies. Our discussion extends to emerging topics, such as FAIR Data Principles, biobanked healthcare datasets, and the functional exposome, outlining the future directions in exposomic research. This abstract provides a succinct overview of our comprehensive approach to understanding the complex dynamics of the exposome and its significant implications for human health.

Maternal educational attainment in pregnancy and epigenome-wide DNA methylation changes in the offspring from birth until adolescence.

Maternal educational attainment (MEA) shapes offspring health through multiple potential pathways. Differential DNA methylation may provide a mechanistic understanding of these long-term associations. We aimed to quantify the associations of MEA with offspring DNA methylation levels at birth, in childhood and in adolescence. Using 37 studies from high-income countries, we performed meta-analysis of epigenome-wide association studies (EWAS) to quantify the associations of completed years of MEA at the time of pregnancy with offspring DNA methylation levels at birth (n = 9 881), in childhood (n = 2 017), and adolescence (n = 2 740), adjusting for relevant covariates. MEA was found to be associated with DNA methylation at 473 cytosine-phosphate-guanine sites at birth, one in childhood, and four in adolescence. We observed enrichment for findings from previous EWAS on maternal folate, vitamin-B12 concentrations, maternal smoking, and pre-pregnancy BMI. The associations were directionally consistent with MEA being inversely associated with behaviours including smoking and BMI. Our findings form a bridge between socio-economic factors and biology and highlight potential pathways underlying effects of maternal education. The results broaden our understanding of bio-social associations linked to differential DNA methylation in multiple early stages of life. The data generated also offers an important resource to help a more precise understanding of the social determinants of health.

Multi-tissue transcriptomic and serum metabolomic assessment reveals systemic implications of acute ozone-induced stress response in male Wistar Kyoto rats.

Air pollutant exposures have been linked to systemic disease; however, the underlying mechanisms between responses of the target tissue and systemic effects are poorly understood. A prototypic inducer of stress, ozone causes respiratory and systemic multiorgan effects through activation of a neuroendocrine stress response. The goal of this study was to assess transcriptomic signatures of multiple tissues and serum metabolomics to understand how neuroendocrine and adrenal-derived stress hormones contribute to multiorgan health outcomes. Male Wistar Kyoto rats (12-13 weeks old) were exposed to filtered air or 0.8 ppm ozone for 4-hours, and blood/tissues were collected immediately post-exposure. Each tissue had distinct expression profiles at baseline. Ozone changed 1,640 genes in lung, 274 in hypothalamus, 2,516 in adrenals, 1,333 in liver, 1,242 in adipose, and 5,102 in muscle (adjusted p-value < 0.1, absolute fold-change > 50%). Serum metabolomic analysis identified 863 metabolites, of which 447 were significantly altered in ozone-exposed rats (adjusted p-value < 0.1, absolute fold change > 20%). A total of 6 genes were differentially expressed in all 6 tissues. Glucocorticoid signaling, hypoxia, and GPCR signaling were commonly changed, but ozone induced tissue-specific changes in oxidative stress, immune processes, and metabolic pathways. Genes upregulated by TNF-mediated NFkB signaling were differentially expressed in all ozone-exposed tissues, but those defining inflammatory response were tissue-specific. Upstream predictor analysis identified common mediators of effects including glucocorticoids, although the specific genes responsible for these predictors varied by tissue. Metabolomic analysis showed major changes in lipids, amino acids, and metabolites linked to the gut microbiome, concordant with transcriptional changes identified through pathway analysis within liver, muscle, and adipose tissues. The distribution of receptors and transcriptional mechanisms underlying the ozone-induced stress response are tissue-specific and involve induction of unique gene networks and metabolic phenotypes, but the shared initiating triggers converge into shared pathway-level responses. This multi-tissue transcriptomic analysis, combined with circulating metabolomic assessment, allows characterization of the systemic inhaled pollutant-induced stress response.

Race/ethnicity-stratified fine-mapping of the MHC locus reveals genetic variants associated with late-onset asthma.

Introduction: Asthma is a chronic disease of the airways that impairs normal breathing. The etiology of asthma is complex and involves multiple factors, including the environment and genetics, especially the distinct genetic architecture associated with ancestry. Compared to early-onset asthma, little is known about genetic predisposition to late-onset asthma. We investigated the race/ethnicity-specific relationship among genetic variants within the major histocompatibility complex (MHC) region and late-onset asthma in a North Carolina-based multiracial cohort of adults. Methods: We stratified all analyses by self-reported race (i.e., White and Black) and adjusted all regression models for age, sex, and ancestry. We conducted association tests within the MHC region and performed fine-mapping analyses conditioned on the race/ethnicity-specific lead variant using whole-genome sequencing (WGS) data. We applied computational methods to infer human leukocyte antigen (HLA) alleles and residues at amino acid positions. We replicated findings in the UK Biobank. Results: The lead signals, rs9265901 on the 5' end of HLA-B, rs55888430 on HLA-DOB, and rs117953947 on HCG17, were significantly associated with late-onset asthma in all, White, and Black participants, respectively (OR = 1.73, 95%CI: 1.31 to 2.14, p = 3.62 × 10-5; OR = 3.05, 95%CI: 1.86 to 4.98, p = 8.85 × 10-6; OR = 19.5, 95%CI: 4.37 to 87.2, p = 9.97 × 10-5, respectively). For the HLA analysis, HLA-B*40:02 and HLA-DRB1*04:05, HLA-B*40:02, HLA-C*04:01, and HLA-DRB1*04:05, and HLA-DRB1*03:01 and HLA-DQB1 were significantly associated with late-onset asthma in all, White, and Black participants. Conclusion: Multiple genetic variants within the MHC region were significantly associated with late-onset asthma, and the associations were significantly different by race/ethnicity group.

C/EBPß deficiency enhances the keratinocyte innate immune response to direct activators of cytosolic pattern recognition receptors.

The skin is the first line of defense to cutaneous microbes and viruses, and epidermal keratinocytes play a critical role in preventing infection by viruses and pathogens through activation of the type I interferon (IFN) response. Using RNAseq analysis, here we report that the conditional deletion of C/EBPß transcription factor in mouse epidermis (CKOß mice) resulted in the upregulation of IFNß and numerous keratinocyte interferon-stimulated genes (ISGs). The expression of cytosolic pattern recognition receptors (cPRRs), that recognize viral RNA and DNA, were significantly increased, and enriched in the RNAseq data set. cPRRs stimulate a type I IFN response that can trigger cell death to eliminate infected cells. To determine if the observed increases in cPRRs had functional consequences, we transfected CKOß primary keratinocytes with the pathogen and viral mimics poly(I:C) (dsRNA) or poly(dA:dT) (synthetic B-DNA) that directly activate PRRs. Transfected CKOß primary keratinocytes displayed an amplified type I IFN response which was accompanied by increased activation of IRF3, enhanced ISG expression, enhanced activation of caspase-8, caspase-3 and increased apoptosis. Our results identify C/EBPß as a critical repressor of the keratinocyte type I IFN response, and demonstrates that the loss of C/EBPß primes keratinocytes to the activation of cytosolic PRRs by pathogen RNA and DNA to induce cell death mediated by caspase-8 and caspase-3.

A tiered testing strategy based on in vitro phenotypic and transcriptomic data for selecting representative petroleum UVCBs for toxicity evaluation in vivo.

Hazard evaluation of substances of "unknown or variable composition, complex reaction products and biological materials" (UVCBs) remains a major challenge in regulatory science because their chemical composition is difficult to ascertain. Petroleum substances are representative UVCBs and human cell-based data have been previously used to substantiate their groupings for regulatory submissions. We hypothesized that a combination of phenotypic and transcriptomic data could be integrated to make decisions as to selection of group-representative worst-case petroleum UVCBs for subsequent toxicity evaluation in vivo. We used data obtained from 141 substances from 16 manufacturing categories previously tested in 6 human cell types (induced pluripotent stem cell [iPSC]-derived hepatocytes, cardiomyocytes, neurons, and endothelial cells, and MCF7 and A375 cell lines). Benchmark doses for gene-substance combinations were calculated, and both transcriptomic and phenotype-derived points of departure (PODs) were obtained. Correlation analysis and machine learning were used to assess associations between phenotypic and transcriptional PODs and to determine the most informative cell types and assays, thus representing a cost-effective integrated testing strategy. We found that 2 cell types-iPSC-derived-hepatocytes and -cardiomyocytes-contributed the most informative and protective PODs and may be used to inform selection of representative petroleum UVCBs for further toxicity evaluation in vivo. Overall, although the use of new approach methodologies to prioritize UVCBs has not been widely adopted, our study proposes a tiered testing strategy based on iPSC-derived hepatocytes and cardiomyocytes to inform selection of representative worst-case petroleum UVCBs from each manufacturing category for further toxicity evaluation in vivo.

Questionnaire-Based Polyexposure Assessment Outperforms Polygenic Scores for Classification of Type 2 Diabetes in a Multiancestry Cohort.

OBJECTIVE: Environmental exposures may have greater predictive power for type 2 diabetes than polygenic scores (PGS). Studies examining environmental risk factors, however, have included only individuals with European ancestry, limiting the applicability of results. We conducted an exposome-wide association study in the multiancestry Personalized Environment and Genes Study to assess the effects of environmental factors on type 2 diabetes. RESEARCH DESIGN AND METHODS: Using logistic regression for single-exposure analysis, we identified exposures associated with type 2 diabetes, adjusting for age, BMI, household income, and self-reported sex and race. To compare cumulative genetic and environmental effects, we computed an overall clinical score (OCS) as a weighted sum of BMI and prediabetes, hypertension, and high cholesterol status and a polyexposure score (PXS) as a weighted sum of 13 environmental variables. Using UK Biobank data, we developed a multiancestry PGS and calculated it for participants. RESULTS: We found 76 significant associations with type 2 diabetes, including novel associations of asbestos and coal dust exposure. OCS, PXS, and PGS were significantly associated with type 2 diabetes. PXS had moderate power to determine associations, with larger effect size and greater power and reclassification improvement than PGS. For all scores, the results differed by race. CONCLUSIONS: Our findings in a multiancestry cohort elucidate how type 2 diabetes odds can be attributed to clinical, genetic, and environmental factors and emphasize the need for exposome data in disease-risk association studies. Race-based differences in predictive scores highlight the need for genetic and exposome-wide studies in diverse populations.

Whole genome sequence analysis of apparent treatment resistant hypertension status in participants from the Trans-Omics for Precision Medicine program.

Introduction: Apparent treatment-resistant hypertension (aTRH) is characterized by the use of four or more antihypertensive (AHT) classes to achieve blood pressure (BP) control. In the current study, we conducted single-variant and gene-based analyses of aTRH among individuals from 12 Trans-Omics for Precision Medicine cohorts with whole-genome sequencing data. Methods: Cases were defined as individuals treated for hypertension (HTN) taking three different AHT classes, with average systolic BP ≥ 140 or diastolic BP ≥ 90 mmHg, or four or more medications regardless of BP (n = 1,705). A normotensive control group was defined as individuals with BP < 140/90 mmHg (n = 22,079), not on AHT medication. A second control group comprised individuals who were treatment responsive on one AHT medication with BP < 140/ 90 mmHg (n = 5,424). Logistic regression with kinship adjustment using the Scalable and Accurate Implementation of Generalized mixed models (SAIGE) was performed, adjusting for age, sex, and genetic ancestry. We assessed variants using SKAT-O in rare-variant analyses. Single-variant and gene-based tests were conducted in a pooled multi-ethnicity stratum, as well as self-reported ethnic/racial strata (European and African American). Results: One variant in the known HTN locus, KCNK3, was a top finding in the multi-ethnic analysis (p = 8.23E-07) for the normotensive control group [rs12476527, odds ratio (95% confidence interval) = 0.80 (0.74-0.88)]. This variant was replicated in the Vanderbilt University Medical Center's DNA repository data. Aggregate gene-based signals included the genes AGTPBP, MYL4, PDCD4, BBS9, ERG, and IER3. Discussion: Additional work validating these loci in larger, more diverse populations, is warranted to determine whether these regions influence the pathobiology of aTRH.

Stress Drivers of Glucose Dynamics during Ozone Exposure Measured Using Radiotelemetry in Rats.

BACKGROUND: Inhaled irritant air pollutants may trigger stress-related metabolic dysfunction associated with altered circulating adrenal-derived hormones. OBJECTIVES: We used implantable telemetry in rats to assess real-time changes in circulating glucose during and after exposure to ozone and mechanistically linked responses to neuroendocrine stress hormones. METHODS: First, using a cross-over design, we monitored glucose during ozone exposures (0.0, 0.2, 0.4, and 0.8 ppm) and nonexposure periods in male Wistar Kyoto rats implanted with glucose telemeters. A second cohort of unimplanted rats was exposed to ozone (0.0, 0.4 or 0.8 ppm) for 30 min, 1 h, 2 h, or 4 h with hormones measured immediately post exposure. We assessed glucose metabolism in sham and adrenalectomized rats, with or without supplementation of adrenergic/glucocorticoid receptor agonists, and in a separate cohort, antagonists. RESULTS: Ozone (0.8 ppm) was associated with significantly higher blood glucose and lower core body temperature beginning 90 min into exposure, with reversal of effects 4-6 h post exposure. Glucose monitoring during four daily 4-h ozone exposures revealed duration of glucose increases, adaptation, and diurnal variations. Ozone-induced glucose changes were preceded by higher levels of adrenocorticotropic hormone, corticosterone, and epinephrine but lower levels of thyroid-stimulating hormone, prolactin, and luteinizing hormones. Higher glucose and glucose intolerance were inhibited in rats that were adrenalectomized or treated with adrenergic plus glucocorticoid receptor antagonists but exacerbated by agonists. DISCUSSION: We demonstrated the temporality of neuroendocrine-stress-mediated biological sequalae responsible for ozone-induced glucose metabolic dysfunction and mechanism in a rodent model. Stress hormones assessment with real-time glucose monitoring may be useful in identifying interactions among irritant pollutants and stress-related illnesses. https://doi.org/10.1289/EHP11088.

Social isolation exacerbates acute ozone inhalation induced pulmonary and systemic health outcomes.

Psychosocially-stressed individuals might have exacerbated responses to air pollution exposure. Acute ozone exposure activates the neuroendocrine stress response leading to systemic metabolic and lung inflammatory changes. We hypothesized chronic mild stress (CS) and/or social isolation (SI) would cause neuroendocrine, inflammatory, and metabolic phenotypes that would be exacerbated by an acute ozone exposure. Male 5-week-old Wistar-Kyoto rats were randomly assigned into 3 groups: no stress (NS) (pair-housed, regular-handling); SI (single-housed, minimal-handling); CS (single-housed, subjected to mild unpredicted-randomized stressors [restraint-1 h, tilted cage-1 h, shaking-1 h, intermittent noise-6 h, and predator odor-1 h], 1-stressor/day*5-days/week*8-weeks. All animals then 13-week-old were subsequently exposed to filtered-air or ozone (0.8-ppm) for 4 h and immediately necropsied. CS, but not SI animals had increased adrenal weights. However, relative to NS, both CS and SI had lower circulating luteinizing hormone, prolactin, and follicle-stimulating hormone regardless of exposure (SI > CS), and only CS demonstrated lower thyroid-stimulating hormone levels. SI caused more severe systemic inflammation than CS, as evidenced by higher circulating cytokines and cholesterol. Ozone exposure increased urine corticosterone and catecholamine metabolites with no significant stressor effect. Ozone-induced lung injury, and increases in lavage-fluid neutrophils and IL-6, were exacerbated by SI. Ozone severely lowered circulating thyroid-stimulating hormone, prolactin, and luteinizing hormone in all groups and exacerbated systemic inflammation in SI. Ozone-induced increases in serum glucose, leptin, and triglycerides were consistent across stressors; however, increases in cholesterol were exacerbated by SI. Collectively, psychosocial stressors, especially SI, affected the neuroendocrine system and induced adverse metabolic and inflammatory effects that were exacerbated by ozone exposure.

Maternal Mediterranean Diet Adherence and Its Associations with Maternal Prenatal Stressors and Child Growth.

Background: Psychosocial and physiologic stressors, such as depression and obesity, during pregnancy can have negative consequences, such as increased systemic inflammation, contributing to chronic disease for both mothers and their unborn children. These conditions disproportionately affect racial/ethnic minorities. The effects of recommended dietary patterns in mitigating the effects of these stressors remain understudied. Objectives: We aimed to evaluate the relations between maternal Mediterranean diet adherence (MDA) and maternal and offspring outcomes during the first decade of life in African Americans, Hispanics, and Whites. Methods: This study included 929 mother-child dyads from the NEST (Newborn Epigenetics STudy), a prospective cohort study. FFQs were used to estimate MDA in pregnant women. Weight and height were measured in children between birth and age 8 y. Multivariable linear regression models were used to examine associations between maternal MDA, inflammatory cytokines, and pregnancy and postnatal outcomes. Results: More than 55% of White women reported high MDA during the periconceptional period compared with 22% of Hispanic and 18% of African American women (P < 0.05). Higher MDA was associated with lower likelihood of depressive mood (ß = -0.45; 95% CI: -0.90, -0.18; P = 0.02) and prepregnancy obesity (ß = -0.29; 95% CI: -0.57, -0.0002; P = 0.05). Higher MDA was also associated with lower body size at birth, which was maintained to ages 3-5 and 6-8 y-this association was most apparent in White children (3-5 y: ß = -2.9, P = 0.02; 6-8 y: ß = -3.99, P = 0.01). Conclusions: If replicated in larger studies, our data suggest that MDA provides a potent avenue by which effects of prenatal stressors on maternal and fetal outcomes can be mitigated to reduce ethnic disparities in childhood obesity.

Clomifene and Assisted Reproductive Technology in Humans Are Associated with Sex-Specific Offspring Epigenetic Alterations in Imprinted Control Regions.

Children conceived with assisted reproductive technology (ART) have an increased risk of adverse outcomes, including congenital malformations and imprinted gene disorders. In a retrospective North Carolina-based-birth-cohort, we examined the effect of ovulation drugs and ART on CpG methylation in differentially methylated CpGs in known imprint control regions (ICRs). Nine ICRs containing 48 CpGs were assessed for methylation status by pyrosequencing in mixed leukocytes from cord blood. After restricting to non-smoking, college-educated participants who agreed to follow-up, ART-exposed (n = 27), clomifene-only-exposed (n = 22), and non-exposed (n = 516) groups were defined. Associations of clomifene and ART with ICR CpG methylation were assessed with linear regression and stratifying by offspring sex. In males, ART was associated with hypomethylation of the PEG3 ICR [ß(95% CI) = -1.46 (-2.81, -0.12)] and hypermethylation of the MEG3 ICR [3.71 (0.01, 7.40)]; clomifene-only was associated with hypomethylation of the NNAT ICR [-5.25 (-10.12, -0.38)]. In female offspring, ART was associated with hypomethylation of the IGF2 ICR [-3.67 (-6.79, -0.55)]. Aberrant methylation of these ICRs has been associated with cardiovascular disease and metabolic and behavioral outcomes in children. The results suggest that the increased risk of adverse outcomes in offspring conceived through ART may be due in part to altered methylation of ICRs. Larger studies utilizing epigenome-wide interrogation are warranted.

Adrenal Stress Hormone Regulation of Hepatic Homeostatic Function After an Acute Ozone Exposure in Wistar-Kyoto Male Rats.

Ozone-induced lung injury, inflammation, and pulmonary/hypothalamus gene expression changes are diminished in adrenalectomized (AD) rats. Acute ozone exposure induces metabolic alterations concomitant with increases in epinephrine and corticosterone. We hypothesized that adrenal hormones are responsible for observed hepatic ozone effects, and in AD rats, these changes would be diminished. In total, 5-7 days after sham (SH) or AD surgeries, male Wistar-Kyoto rats were exposed to air or 0.8-ppm ozone for 4 h. Serum samples were analyzed for metabolites and liver for transcriptional changes immediately post-exposure. Ozone increased circulating triglycerides, cholesterol, free fatty-acids, and leptin in SH but not AD rats. Ozone-induced inhibition of glucose-mediated insulin release was absent in AD rats. Unlike diminution of ozone-induced hypothalamus and lung mRNA expression changes, AD in air-exposed rats (AD-air/SH-air) caused differential hepatic expression of ∼1000 genes. Likewise, ozone in AD rats caused differential expression of ∼1000 genes (AD-ozone/AD-air). Ozone-induced hepatic changes in SH rats reflected enrichment for pathways involving metabolic processes, including acetyl-CoA biosynthesis, TCA cycle, and sirtuins. Upstream predictor analysis identified similarity to responses produced by glucocorticoids and pathways involving forskolin. These changes were absent in AD rats exposed to ozone. However, ozone caused unique changes in AD liver mRNA reflecting activation of synaptogenesis, neurovascular coupling, neuroinflammation, and insulin signaling with inhibition of senescence pathways. In these rats, upstream predictor analysis identified numerous microRNAs likely involved in glucocorticoid insufficiency. These data demonstrate the critical role of adrenal stress hormones in ozone-induced hepatic homeostasis and necessitate further research elucidating their role in propagating environmentally driven diseases.

Comparison of National Vulnerability Indices Used by the Centers for Disease Control and Prevention for the COVID-19 Response.

OBJECTIVE: Vulnerability indices use quantitative indicators and geospatial data to examine the level of vulnerability to morbidity in a community. The Centers for Disease Control and Prevention (CDC) uses 3 indices for the COVID-19 response: the CDC Social Vulnerability Index (CDC-SVI), the US COVID-19 Community Vulnerability Index (CCVI), and the Pandemic Vulnerability Index (PVI). The objective of this review was to describe these tools and explain the similarities and differences between them. METHODS: We described the 3 indices, outlined the underlying data sources and metrics for each, and discussed their use by CDC for the COVID-19 response. We compared the percentile score for each county for each index by calculating Spearman correlation coefficients (Spearman ρ). RESULTS: These indices have some, but not all, component metrics in common. The CDC-SVI is a validated metric that estimates social vulnerability, which comprises the underlying population-level characteristics that influence differences in health risk among communities. To address risk specific to the COVID-19 pandemic, the CCVI and PVI build on the CDC-SVI and include additional variables. The 3 indices were highly correlated. Spearman ρ for comparisons between the CDC-SVI score and the CCVI and between the CCVI and the PVI score was 0.83. Spearman ρ for the comparison between the CDC-SVI score and PVI score was 0.73. CONCLUSION: The indices can empower local and state public health officials with additional information to focus resources and interventions on disproportionately affected populations to combat the ongoing pandemic and plan for future pandemics.

Questionnaire-based exposome-wide association studies (ExWAS) reveal expected and novel risk factors associated with cardiovascular outcomes in the Personalized Environment and Genes Study.

While multiple factors are associated with cardiovascular disease (CVD), many environmental exposures that may contribute to CVD have not been examined. To understand environmental effects on cardiovascular health, we performed an exposome-wide association study (ExWAS), a hypothesis-free approach, using survey data on endogenous and exogenous exposures at home and work and data from health and medical histories from the North Carolina-based Personalized Environment and Genes Study (PEGS) (n = 5015). We performed ExWAS analyses separately on six cardiovascular outcomes (cardiac arrhythmia, congestive heart failure, coronary artery disease, heart attack, stroke, and a combined atherogenic-related outcome comprising angina, angioplasty, atherosclerosis, coronary artery disease, heart attack, and stroke) using logistic regression and a false discovery rate of 5%. For each CVD outcome, we tested 502 single exposures and built multi-exposure models using the deletion-substitution-addition (DSA) algorithm. To evaluate complex nonlinear relationships, we employed the knockoff boosted tree (KOBT) algorithm. We adjusted all analyses for age, sex, race, BMI, and annual household income. ExWAS analyses revealed novel associations that include blood type A (Rh-) with heart attack (OR[95%CI] = 8.2[2.2:29.7]); paint exposures with stroke (paint related chemicals: 6.1[2.2:16.0], acrylic paint: 8.1[2.6:22.9], primer: 6.7[2.2:18.6]); biohazardous materials exposure with arrhythmia (1.8[1.5:2.3]); and higher paternal education level with reduced risk of multiple CVD outcomes (stroke, heart attack, coronary artery disease, and combined atherogenic outcome). In multi-exposure models, trouble sleeping and smoking remained important risk factors. KOBT identified significant nonlinear effects of sleep disorder, regular intake of grapefruit, and a family history of blood clotting problems for multiple CVD outcomes (combined atherogenic outcome, congestive heart failure, and coronary artery disease). In conclusion, using statistics and machine learning, these findings identify novel potential risk factors for CVD, enable hypothesis generation, provide insights into the complex relationships between risk factors and CVD, and highlight the importance of considering multiple exposures when examining CVD outcomes.