Background Given strong environmental influence on both epigenetic marks and allergic asthma in children, the epigenetic alterations in respiratory epithelia may provide insight into allergic asthma. Objective To identify DNA methylation and gene expression changes associated with childhood allergic persistent asthma. Methods We compared genomic DNA methylation patterns and gene expression in African American children with persistent atopic asthma[N=36] versus healthy controls[N=36]. Results were validated in an independent population of asthmatic children[N=30] using a shared healthy control population[N=36] and in independent population of Caucasian adult atopic asthmatics[N=12] and controls[N=12]. Results We identified 186 genes with significant methylation changes, differentially methylated regions(DMRs) or differentially methylated probes(DMPs), after adjustment for age, gender, race/ethnicity, batch effects, inflation, and multiple comparisons. Genes differentially methylated included those with established roles in asthma and atopy, genes related to extracellular matrix, immunity, cell adhesion, epigenetic regulation, and airflow obstruction. The methylation changes were substantial (median 9.5%, range:2.6–29.5%). Hypo- and hyper-methylated genes were associated with increased and decreased gene expression respectively (P<2.8x10−6 for DMRs and P<7.8x10−10 for DMPs). Quantitative analysis in 53 differentially expressed genes demonstrated that 32(60%) have significant methylation-expression relationships within 5kb of the gene. 10 loci selected based on the relevance to asthma, magnitude of methylation change, and methylation-expression relationships were validated in an independent cohort of children with atopic asthma. 67/186 genes also have significant asthma-associated methylation changes in nasal epithelia of adult Caucasian asthmatics. Conclusions Epigenetic marks in respiratory epithelia are associated with allergic asthma and gene expression changes in inner-city children.

These findings indicate that omalizumab treatment augments pDC IFN-α responses and attenuates pDC FcεRIα protein expression and provide evidence that these effects are related. These results support a potential mechanism underlying clinical observations that allergic sensitization is associated with increased susceptibility to virus-induced asthma exacerbations.

Background Treatment levels required to control asthma vary greatly across a population with asthma. The factors that contribute to variability in treatment requirements of inner-city children have not been fully elucidated. Objective To identify the clinical characteristics which distinguish difficult-to-control asthma. Methods Children with asthma aged 6-17 underwent baseline assessment and bimonthly guidelines-based management visits over one year. Difficult- versus easy-to-control asthma were defined as daily therapy with fluticasone ≥500mcg +/-LABA versus ≤100mcg assigned on at least 4 visits. Forty-four baseline variables were used to compare the 2 groups using univariate analyses and identify the most relevant features of difficult-to-control asthma using a variable selection algorithm. Nonlinear seasonal variation in longitudinal measures (symptoms, pulmonary physiology and exacerbations) was examined using generalized additive mixed-effects models. Results Among 619 recruited participants, 40.9% had difficult-to-control asthma, 37.5% had easy-to-control asthma and 21.6% fell into neither group. At baseline, FEV1 bronchodilator responsiveness was the most important characteristic distinguishing difficult- from easy-to-control asthma. Markers of rhinitis severity and atopy were among the other major discriminating features. Over time, difficult-to-control asthma was characterized by high exacerbation rates, particularly in spring and fall, greater day and night symptoms, especially in fall and winter, and compromised pulmonary physiology despite ongoing high dose controller therapy. Conclusions Despite good adherence, difficult-to-control asthma showed little improvement in symptoms, exacerbations or pulmonary physiology over the year. Besides pulmonary physiology measures, rhinitis severity and atopy were associated with high dose asthma controller therapy requirement. Clinical Implications Clinical baseline characteristics related to pulmonary physiology, rhinitis severity, and atopy prospectively distinguish difficult- from easy-to-control asthma.

Background Children with asthma in low-income urban areas have high morbidity. Phenotypic analysis in these children is lacking, but may identify characteristics to inform successful tailored management approaches. Objective To identify distinct asthma phenotypes among inner-city children receiving guidelines-based management. Methods Nine Inner City Asthma Consortium centers enrolled 717 children ages 6-17 years. Data were collected at baseline and prospectively every 2 months for 1 year. Participants’ asthma and rhinitis were optimally managed by study physicians based on guidelines. Cluster analysis using 50 baseline and 12 longitudinal variables was performed in 616 participants completing ≥4 follow-up visits. Results Five clusters (designated A through E) were distinguished by indicators of asthma and rhinitis severity, pulmonary physiology, allergy (sensitization and total serum IgE) and allergic inflammation. In comparison to other clusters, Cluster A was distinguished by lower allergy/inflammation, minimally symptomatic asthma and rhinitis and normal pulmonary physiology. Cluster B had highly symptomatic asthma despite high step-level treatment, lower allergy and inflammation, and mildly altered pulmonary physiology. Cluster C had minimally symptomatic asthma and rhinitis, intermediate allergy and inflammation and mildly impaired pulmonary physiology. Clusters D and E exhibited progressively higher asthma and rhinitis symptoms and allergy/inflammation. Cluster E had the most symptomatic asthma while receiving high step-level treatment and had the highest total serum IgE (median 733 kU/L), blood eosinophil count (median 400 cells/mm3) and allergen sensitizations (15 of 22 tested). Conclusions Allergy distinguishes asthma phenotypes in urban children. Severe asthma often co-clusters with highly allergic children. However, a symptomatic phenotype with little allergy or allergic inflammation was identified.

Background Pathway analyses can be used to determine how host and environmental factors contribute to asthma severity. Objective Investigate pathways explaining asthma severity in inner-city children. Methods Based on medical evidence in the published literature, we developed a conceptual model to describe how eight risk-factor domains (allergen sensitization, allergic inflammation, pulmonary physiology, stress, obesity, vitamin D, environmental tobacco smoke (ETS) exposure and rhinitis severity) are linked to asthma severity. To estimate the relative magnitude and significance of hypothesized relationships among these domains and asthma severity, we applied a causal network analysis to test our model in an Inner-City Asthma Consortium study. Participants comprised 6–17 year old children (n=561) with asthma and rhinitis from 9 U.S. inner-cities who were evaluated every two months for one year. Asthma severity was measured by a longitudinal composite assessment of day and night symptoms, exacerbations, and controller usage. Results Our conceptual model explained 53.4% of the variance in asthma severity. An allergy pathway (linking allergen sensitization, allergic inflammation, pulmonary physiology, and rhinitis severity domains to asthma severity) and ETS exposure pathway (linking ETS exposure and pulmonary physiology domains to asthma severity) exerted significant effects on asthma severity. Among the domains, pulmonary physiology and rhinitis severity had the largest significant standardized total effects on asthma severity (−0.51 and 0.48 respectively), followed by ETS exposure (0.30) and allergic inflammation (0.22). While vitamin D had modest but significant indirect effects on asthma severity, its total effect was insignificant (0.01). Conclusions The standardized effect sizes generated by a causal network analysis quantify the relative contributions of different domains and can be used to prioritize interventions to address asthma severity.