Black, Latinx, and Indigenous people in the United States experience a disproportionate burden of asthma and atopic dermatitis. The study of these disease disparities has focused on proximal socioenvironmental exposures and on the biomechanistic (including genetic) differences between racial and ethnic groups. Although biomedical research in allergy and immunology stands to benefit from the inclusion of diverse study populations, the narrow focus on biologic mechanisms disregards the complexity of interactions across biologic and structural factors, including the effects of structural racism. Structural racism is the totality of ways in which society fosters discrimination by creating and reinforcing inequitable systems through intentional policies and practices sanctioned by government and institutions. It is embedded across multiple levels, including the economic, educational, health care, and judicial systems, which are manifested in inequity in the physical and social environment. In this review, we present a conceptual framework and pull from the literature to demonstrate how structural racism is a root cause of atopic disease disparities by way of residential segregation, socioeconomic position, and mass incarceration, which may lead to aberrations in the innate and adaptive immune response and the augmentation of physiologic stress responses, contributing to a disproportionate disease burden for racial and ethnic populations.

Under the exposome paradigm all nongenetic factors contributing to disease are considered to be 'environmental' including chemicals, drugs, infectious agents, and psychosocial stress. We can consider these collectively as environmental stressors. Exposomics is the comprehensive analysis of exposure to all environmental stressors and should yield a more thorough understanding of chronic disease development. We can operationalize exposomics by studying all the small molecules in the body and their influence on biological pathways that lead to impaired health. Here, we describe methods by which this may be achieved and discuss the application of exposomics to cumulative risk assessment in vulnerable populations. Since the goal of cumulative risk assessment is to analyze, characterize, and quantify the combined risks to health from exposures to multiple agents or stressors, it seems that exposomics is perfectly poised to advance this important area of environmental health science. We should therefore support development of tools for exposomic analysis and begin to engage impacted communities in participatory exposome research. A first step may be to apply exposomics to vulnerable populations already studied by more conventional cumulative risk approaches. We further propose that recent migrants, low socioeconomic groups with high environmental chemical exposures, and pregnant women should be high priority populations for study by exposomics. Moreover, exposomics allows us to study interactions between chronic stress and environmental chemicals that disrupt stress response pathways (i.e., 'stressogens'). Exploring the impact of early life exposures and maternal stress may be an interesting and accessible topic for investigation by exposomics using biobanked samples.

Environmental exposures often occur in complex mixtures and at low concentrations. Generalized concentration addition (GCA) is a method used to estimate the joint effect of receptor ligands that vary in efficacy. GCA models have been successfully applied to mixtures of aryl hydrocarbon receptor (AhR) and peroxisome proliferator-activated receptor gamma (PPARγ) ligands, each of which can be modeled as a receptor with a single binding site. Here, we evaluated whether GCA could be applied to homodimer nuclear receptors, which have two binding sites, to predict the combined effect of full glucocorticoid receptor (GR) agonists with partial agonists. We measured transcriptional activation of GR using a cell-based bioassay. Individual concentration-response curves for dexamethasone (full agonist), prednisolone (full agonist), and medroxyprogesterone 17-acetate (partial agonist) were generated and applied in three additivity models, GCA, effect summation (ES), and relative potency factor (RPF), to generate response surfaces. GCA and RPF yielded adequate predictions of the experimental data for two full agonists. However, GCA fit experimental data significantly better than ES and RPF for all other binary mixtures. This work extends the application of GCA to homodimer nuclear receptors and improves prediction accuracy of mixture effects of GR agonists.

Objective

This study aimed to examine relationships between adverse childhood experiences (ACEs) and related life events and allostatic load (AL)-"wear and tear" from chronic stress-in a pediatric population.

Methods

Children were screened with the PEdiatric ACEs and Related Life Event Screener (PEARLS) tool, a 17-item questionnaire capturing experiences of abuse, neglect, household challenges, and related life events. Biological data were available for 207 participants, and AL was operationalized using clinical or empirical cutoff points across 4 physiological systems (i.e., cardiac, metabolic, inflammatory, neurologic). Covariate-adjusted multivariable regression models were used to examine associations between AL with adversity and health.

Results

Children (mean age = 6.5 years, range = 1-11 years) had an average AL score of 1.9 (standard deviation = 1.7), and a U-shaped relationship was observed with child's age. Continuous PEARLS and original ACE scores were not associated with AL. However, children with a reported PEARLS score of 1 to 2 or original ACEs score of 1 to 3 had 1.5 (incidence rate ratio [IRR] = 1.50, 95% confidence interval [CI] = 1.09-2.08) and 1.4 (IRR = 1.41, 95% CI = 1.08-1.84) times greater AL, respectively, compared with participants with none reported. In secondary analyses, caregiver mental illness was associated with higher child AL (adjusted IRR = 1.27, 95% CI = 1.01-1.58). AL was also associated with poorer perceived child general health (adjusted β = -0.87, 95% CI = -1.58 to -0.15) and greater odds of child obesity (adjusted odds ratio = 1.51, 95% CI = 1.23-1.89).

Conclusions

Measuring AL in a pediatric population requires careful consideration of age. Higher AL was associated with a greater number of reported adversities and worse child health.

Inter-individual differences in arsenic metabolism have been linked to arsenic-related disease risks. Arsenic (+3) methyltransferase (AS3MT) is the primary enzyme involved in arsenic metabolism, and we previously demonstrated in vitro that N-6 adenine-specific DNA methyltransferase 1 (N6AMT1) also methylates the toxic inorganic arsenic (iAs) metabolite, monomethylarsonous acid (MMA), to the less toxic dimethylarsonic acid (DMA). Here, we evaluated whether AS3MT and N6AMT1 gene polymorphisms alter arsenic methylation and impact iAs-related cancer risks. We assessed AS3MT and N6AMT1 polymorphisms and urinary arsenic metabolites (%iAs, %MMA, %DMA) in 722 subjects from an arsenic-cancer case-control study in a uniquely exposed area in northern Chile. Polymorphisms were genotyped using a custom designed multiplex, ligation-dependent probe amplification (MLPA) assay for 6 AS3MT SNPs and 14 tag SNPs in the N6AMT1 gene. We found several AS3MT polymorphisms associated with both urinary arsenic metabolite profiles and cancer risk. For example, compared to wildtypes, individuals carrying minor alleles in AS3MT rs3740393 had lower %MMA (mean difference = -1.9%, 95% CI: -3.3, -0.4), higher %DMA (mean difference = 4.0%, 95% CI: 1.5, 6.5), and lower odds ratios for bladder (OR = 0.3; 95% CI: 0.1-0.6) and lung cancer (OR = 0.6; 95% CI: 0.2-1.1). Evidence of interaction was also observed for both lung and bladder cancer between these polymorphisms and elevated historical arsenic exposures. Clear associations were not seen for N6AMT1. These results are the first to demonstrate a direct association between AS3MT polymorphisms and arsenic-related internal cancer risk. This research could help identify subpopulations that are particularly vulnerable to arsenic-related disease. Environ. Mol. Mutagen. 58:411-422, 2017. © 2017 Wiley Periodicals, Inc.