Distinct Asthma Phenotypes Are Distinguished by Demographic and Clinical Variables

Cluster analysis revealed 4 distinct clinical phenotypes that varied in terms of age of onset, obesity level, gender distribution, and maximum lung function attainment among treatment-naive Indian patients diagnosed with asthma, according to a report published in the Journal of Asthma.

There is much heterogeneity in disease presentation within the asthma population, with patients experiencing varying levels of severity and differing in their abilities to achieve control. However, little guidance exists in the literature regarding the diagnosis and management of asthma in which normal lung function cannot be achieved. Investigators used cluster analysis to identify distinct patient groups that had intragroup similarities but differed from one another to categorize these phenotypes according to demographic and clinical characteristics.

A retrospective chart review enrolled 100 participants (55% female; mean age at baseline, 33.45 years; mean age at disease onset, 23.54 years; mean disease duration, 10.7 years) diagnosed with asthma confirmed by spirometry who were seen between January 2015 and February 2016 at a tertiary medical center. All patients were treatment naive at baseline and had ≥6 months of follow-up with ≥4 spirometry results during this time. The Global Initiative for Asthma and the Asthma Control Test questionnaires were used to assess asthma control prospectively, and all individuals were treated during the follow-up period according to Global Initiative for Asthma guidelines.

An agglomerative approach was employed in performing cluster analysis using Ward’s minimum-variance hierarchic clustering method. Comparison of intergroup differences between clusters was accomplished through an analysis of variance for both continuous and categoric variables using the Kruskal-Wallis and chi-square tests, respectively.

There were 4 distinct asthma phenotypes identified through cluster analysis. The first cluster (n=40) included patients with normal body weight and equal gender distribution who experienced disease onset during childhood and attained normal lung function on spirometry during follow-up. The second cluster (n=16) consisted of mostly men who were obese, who reported adolescent asthma onset and had poor achievement of maximum lung function. Cluster 3 (n=20) included mainly older men who were obese with later disease onset and suboptimal attainment of maximum lung function. Cluster 4 (n=24) included mostly women who were obese with adult-onset asthma and normal lung function. There were significant differences among the 4 clusters on many demographic and clinical variables, including baseline and postbronchodilator forced expiratory volume in 1 second on pulmonary function testing.

Study strengths included objective asthma confirmation, guidelines-based therapy, prescription compliance confirmation, and inclusion of longitudinal and cross-sectional data as well as indoor air pollution exposure information.

Study limitations included retrospective data collection, possible false negative asthma diagnoses, lack of a methacholine challenge or endogenotype analysis, and nonachievement of asthma control in a majority of patients.

“Recognition of specific sub-phenotypes may further our understanding of the pathophysiology, treatment response, prognosis and underlying genetic basis for the disease, and also pave the way for targeted therapy,” noted the authors.

Funding and Conflicts of Interest Disclosures:

There was no funding information provided.

Dr Sandhya Khurana (Division of Pulmonary & Critical Care Medicine, University of Rochester Medical Center, New York) has participated in clinical trials of biologics with GSK and Sanofi.

Reference

Bhargava S, Holla AD, Jayaraj BS, et al. Distinct asthma phenotypes with low maximal attainment of lung function on cluster analysis [published online September 3, 2019]. J Asthma. doi:10.1080/02770903.2019.1658205