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Identification of Asthma Phenotypes Using Cluster Analysis in the Severe Asthma Research Program

¹ Wake Forest University School of Medicine, Center for Human Genomics; ² The Severe Asthma Research Program, Bethesda, Maryland; and ³ Wake Forest University School of Medicine, Public Health Sciences, Winston-Salem, North Carolina

Correspondence and requests for reprints should be addressed to Wendy C. Moore, M.D., Center for Human Genomics, Wake Forest University School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157. E-mail: wmoore{at}wfubmc.edu

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 = 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 = 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 = 59) is a unique group of mostly older obese women with late-onset nonatopic asthma, moderate reductions in FEV₁, and frequent oral corticosteroid use to manage exacerbations. Subjects in Clusters 4 (n = 120) and 5 (n = 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.

Key Words: asthma phenotype • definition • cluster analysis • severe asthma

AT A GLANCE COMMENTAREY Scientific Knowledge on the Subject Current classification and management approaches in asthma do not reflect the heterogeneous characteristics of this disease. What This Study Adds to the Field Using modeling approaches, this article describes five distinct clinical phenotypes of asthma that suggest differences in pathophysiologic mechanisms.

 

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