Rationale: The Severe Asthma Research Program cohort includes subjects with persistent asthma who have undergone detailed phenotypic characterization. Previous univariate methods compared features of mild, moderate, and severe asthma. Objectives: To identify novel asthma phenotypes using an unsupervised hierarchical cluster analysis. Methods: Reduction of the initial 628 variables to 34 core variables was achieved by elimination of redundant data and transformation of categorical variables into ranked ordinal composite variables. Cluster analysis was performed on 726 subjects. Measurements and Main Results: Five groups were identified. Subjects in Cluster 1 (n 5 110) have early onset atopic asthma with normal lung function treated with two or fewer controller medications (82%) and minimal health care utilization. Cluster 2 (n 5 321) consists of subjects with early-onset atopic asthma and preserved lung function but increased medication requirements (29% on three or more medications) and health care utilization. Cluster 3 (n 5 59) is a unique group of mostly older obese women with late-onset nonatopic asthma, moderate reductions in FEV 1 , and frequent oral corticosteroid use to manage exacerbations. Subjects in Clusters 4 (n 5 120) and 5 (n 5 116) have severe airflow obstruction with bronchodilator responsiveness but differ in to their ability to attain normal lung function, age of asthma onset, atopic status, and use of oral corticosteroids. Conclusions: Five distinct clinical phenotypes of asthma have been identified using unsupervised hierarchical cluster analysis. All clusters contain subjects who meet the American Thoracic Society definition of severe asthma, which supports clinical heterogeneity in asthma and the need for new approaches for the classification of disease severity in asthma.

Background-Severe asthma causes the majority of asthma morbidity. Understanding mechanisms that contribute to the development of severe disease is important.

Background: Coronavirus disease 2019 is caused by SARS-coronavirus 2 (SARS-CoV-2). Angiotensin converting enzyme 2 (ACE2) and transmembrane protease serine 2 (TMPRSS2) mediate viral infection of host cells. We reasoned that differences in ACE2 or TMPRSS2 gene expression in sputum cells among asthma patients may identify subgroups at risk for COVID19 morbidity. Methods:We analyzed gene expression for ACE2 and TMPRSS2, and for intercellular adhesion molecule 1 (ICAM-1)(rhinovirus receptor as a comparator), in sputum cells from 330 participants in the Severe Asthma Research Program-3 and 79 healthy controls.Results: Gene expression of ACE2 was lower than TMPRSS2, and expression levels of both genes was similar in asthma and health. Among asthma patients, male gender, African Americans race, and history of diabetes mellitus, was associated with higher expression of ACE2 and TMPRSS2. Use of inhaled corticosteroids (ICS) was associated with lower expression of ACE2 and TMPRSS2, but treatment with triamcinolone acetonide (TA) did not decrease expression of either gene. These findings differed from those for ICAM-1, where gene expression was increased in asthma and less consistent differences were observed related to gender, race, and use of ICS. Conclusion:Higher expression of ACE2 and TMPRSS2 in males, African Americans, and patients with diabetes mellitus provides rationale for monitoring these asthma subgroups for poor COVID19 outcomes. The lower expression of ACE2 and TMPRSS2 with ICS use warrants prospective study of ICS use as a predictor of decreased susceptibility to SARS-CoV-2 infection and decreased COVID19 morbidity. the participant level with restricted maximum likelihood models. P-values <0.05 were considered statistically significant. RESULTS SubjectsThe demographic and clinical features of the asthma patients and healthy controls are shown in Table 1. Gene expression for SARS-Cov-2-and HRV-related genes in induced sputum cells from asthma patients and healthy controlsIn induced sputum cells collected at the baseline visit, the expression levels of ACE2 were lower than the expression levels of TMPRSS2, and some sputum samples had undetectable ACE2 ( Figure 1A). The expression of ACE2 and TMPRSS2 did not differ significantly in health and in asthma ( Figure 1A,B). In contrast to the SARS-Co-V2related genes, gene expression of ICAM1 was higher in asthma than in health ( Figure 1C). The expression of ACE2 was strongly associated with the expression of TMPRSS2 in the healthy control subgroup (Figure 2A) and the asthma subgroup ( Figure 2B), suggesting that these genes are expressed in similar cells(18). Relationship between clinical and demographic variables and expression levels of SARS-Cov-2-and HRV-related genes in asthma patientsHere we analyzed gene expression data in the induced sputum samples collected at the baseline visit 2 and the follow up visits 4 (year 1) and 6 (year 3). The total number was 556 samples from 330 asthma subjects. ACE2 and TMPRSS2 expression levels increased slightly with age, bu...

Background Clinical cluster analysis from the Severe Asthma Research Program (SARP) identified five asthma subphenotypes that represent the severity spectrum of early onset allergic asthma, late onset severe asthma and severe asthma with COPD characteristics. Analysis of induced sputum from a subset of SARP subjects showed four sputum inflammatory cellular patterns. Subjects with concurrent increases in eosinophils (≥2%) and neutrophils (≥40%) had characteristics of very severe asthma. Objective To better understand interactions between inflammation and clinical subphenotypes we integrated inflammatory cellular measures and clinical variables in a new cluster analysis. Methods Participants in SARP at three clinical sites who underwent sputum induction were included in this analysis (n=423). Fifteen variables including clinical characteristics and blood and sputum inflammatory cell assessments were selected by factor analysis for unsupervised cluster analysis. Results Four phenotypic clusters were identified. Cluster A (n=132) and B (n=127) subjects had mild-moderate early onset allergic asthma with paucigranulocytic or eosinophilic sputum inflammatory cell patterns. In contrast, these inflammatory patterns were present in only 7% of Cluster C (n=117) and D (n=47) subjects who had moderate-severe asthma with frequent health care utilization despite treatment with high doses of inhaled or oral corticosteroids, and in Cluster D, reduced lung function. The majority these subjects (>83%) had sputum neutrophilia either alone or with concurrent sputum eosinophilia. Baseline lung function and sputum neutrophils were the most important variables determining cluster assignment. Conclusion This multivariate approach identified four asthma subphenotypes representing the severity spectrum from mild-moderate allergic asthma with minimal or eosinophilic predominant sputum inflammation to moderate-severe asthma with neutrophilic predominant or mixed granulocytic inflammation.

Background Asthma in children is a heterogeneous disorder with many phenotypes. Although unsupervised cluster analysis is a useful tool for identifying phenotypes, it has not been applied to school-age children with persistent asthma across a wide range of severities. Objectives This study determined how children with severe asthma are distributed across a cluster analysis and how well these clusters conform to current definitions of asthma severity. Methods Cluster analysis was applied to 12 continuous and composite variables from 161 children at 5 centers enrolled in the Severe Asthma Research Program (SARP). Results Four clusters of asthma were identified. Children in Cluster 1 (n = 48) had relatively normal lung function and less atopy, while children in Cluster 2 (n = 52) had slightly lower lung function, more atopy, and increased symptoms and medication usage. Cluster 3 (n = 32) had greater co-morbidity, increased bronchial responsiveness and lower lung function. Cluster 4 (n = 29) had the lowest lung function and the greatest symptoms and medication usage. Predictors of cluster assignment were asthma duration, the number of asthma controller medications, and baseline lung function. Children with severe asthma were present in all clusters, and no cluster corresponded to definitions of asthma severity provided in asthma treatment guidelines. Conclusions Severe asthma in children is highly heterogeneous. Unique phenotypic clusters previously identified in adults can also be identified in children, but with important differences. Larger validation and longitudinal studies are needed to determine the baseline and predictive validity of these phenotypic clusters in the larger clinical setting.