Asthma phenotypes in inner-city children

Abstract

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: We sought to identify distinct asthma phenotypes among inner-city children receiving guidelines-based management.

Methods: Nine inner-city asthma consortium centers enrolled 717 children aged 6 to 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 on the basis of guidelines. Cluster analysis using 50 baseline and 12 longitudinal variables was performed in 616 participants completing 4 or more 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 level (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 coclusters with highly allergic children. However, a symptomatic phenotype with little allergy or allergic inflammation was identified.

Keywords: Allergen sensitization; IgE; airway inflammation; allergy; asthma phenotypes; asthma severity; bronchial hyperresponsiveness; hierarchical cluster; inner-city asthma; rhinitis.

Copyright © 2016 American Academy of Allergy, Asthma & Immunology. All rights reserved.

Figures

Figure 1

Dendrogram produced by the hierarchical clustering algorithm. Participants in Clusters A, B, C, D and E are denoted by the colors red, blue, green, purple, and orange respectively. The y-axis is a measure of dissimilarity between clusters such that the greater the height at which two clusters break off from one another, the greater the dissimilarity between the two clusters. The grey line indicates the height of the 5-cluster solution, as it cuts through 5 vertical lines, each of which represents a cluster.

Figure 2

Variable importance plot of characteristics distinguishing the clusters. Bars plot the median z-score for each variable, a measure of importance obtained from a feature selection algorithm called Boruta where higher values indicate a higher level of importance. Only variables that were confirmed as relevant to distingushing between the five clusters are presented in this figure.

Figure 3

Descriptive summary indicating the relative degree to which asthma, pulmonary physiology, rhinitis and allergy related variables were present in each cluster. The graded color scheme contains 5 shades of blue, representing the 20th (lightest), 40th, 60th, 80th, and 100th (darkest) percentiles of the data for each row. Overall p-values indicating differences between the 5 clusters are p

Figure 4

Characteristics related to allergy by cluster. Categorical variables are summarized by bar charts with whiskers for standard errors. Continuous variables are summarized by notched boxplots, such that the box represents the inter-quartile range, the whiskers add/subtract 1.5 times the 75th/25th percentiles, the line represents the median and the notch represents the 95% confidence interval around the median. For continuous variables, mean number of allergen sensitizations or geometric mean of total serum IgE is represented by the black dot.

Figure 5

Characteristics of pulmonary physiology and inflammatory biomarkers by cluster. Categorical variables are summarized by barcharts with whiskers for standard errors. Continuous variables are summarized by notched boxplots, such that the box represents the inter-quartile range, the whiskers add/subtract 1.5 times the 75th/25th percentiles, the line represents the median and the notch represents the 95% confidence interval around the median. For continuous variables, mean FEV1/FVC (x 100) or geometric mean FeNO is represented by the black dot, with the exception of blood eosinophils and PC20, where median value is most appropriate.

Figure 6

Characteristics of asthma symptoms, exacerbations and treatment by cluster. Categorical variables are summarized by bar charts with whiskers for standard errors. Continuous variables are summarized by notched boxplots, such that the box represents the inter-quartile range, the whiskers add/subtract 1.5 times the 75th/25th percentiles, the line represents the median and the notch represents the 95% confidence interval around the median. For continuous variables, the black dot indicates the mean value.

Figure 7

Characteristics of rhinitis by cluster. Categorical variables are summarized by bar charts with whiskers for standard errors. Continuous variables are summarized by notched boxplots, such that the box represents the inter-quartile range, the whiskers add/subtract 1.5 times the 75th/25th percentiles, the line represents the median and the notch represents the 95% confidence interval around the median. For rhinitis symptom score, the black dot indicates the mean value. A detailed algorithm describing rhinitis medication management during the APIC study can be found in Pongracic et al.