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

Abstract

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.

Patterns of airway disease and the clinical diagnosis of asthma in the Busselton population

“…Using cluster analysis, HALDAR et al [11] examined asthmatics from an ambulatory care group and another from a hospital specialty clinic setting and found that both groups segregated according to inflammatory markers suggesting that it may be possible to identify groups of patients that respond to different forms of treatment. More recently, MOORE et al [26] examined patients from a severe asthma cohort using cluster analysis and identified five clusters based on atopy, asthma severity and healthcare utilisation. Notably, this study and that of HALDAR et al [11] identified a cluster of overweight females included in their asthma groups, similar to that seen in the present study.…”

“…This approach has been used in patients diagnosed with asthma or COPD [11,[14][15][16][17]. To a large extent, these populations have already been selected based on clinical severity, clinical setting or physiological abnormality based on lung function testing.…”

Determinants of persistent asthma in young adults

“…Common features include fluctuating respiratory symptoms associated with variable airflow limitation and bronchial hyperresponsiveness (BHR) due to inflammation of the airways. The age of asthma onset is an important factor for dividing the phenotypes and a major determinant of the prognosis [3–5], but the prognosis for adult-onset asthma is only sparsely documented [6]. In a prospective study of individuals with adult-onset asthma higher age, higher body mass index (BMI) and low lung function were associated with greater asthma severity, while non-sensitisation and a normal lung function were predictors for remission [7].…”

Asthma phenotyping, therapy, and prevention: what can we learn from systems biology?

“…In a study aimed at phenotype identification by clinical features alone, data from 726 subjects from a persistent asthma cohort were analyzed using an unsupervised hierarchical cluster analysis of 34 clinical variables, including age at onset, gender, body weight, degree of airflow limitation, reversibility of airflow limitation, and frequency of asthma exacerbation (39). The authors showed that the resulting fivepatient cluster could be correctly characterized on the basis of onlymerely three clinical parameters: pre-and postbronchodilator percentage of predicted forced expiratory volume in 1 s and age of onset of asthma.…”