Clinical phenotypes of asthma should link up with disease mechanisms.

Abstract

Asthma is a common disease which presents in various clinical forms and levels of severity. The current 'one size fits all' approach to treatment is suboptimal. Using unbiased cluster analysis has identified several asthma phenotypes. Understanding the underlying mechanisms driving these clusters may lead to better patient-orientated medicines. Clustering was initially performed on clinical features only, but the addition of biomarkers that characterize sputum and blood cellular profiles has enabled the prediction of responses to targeted therapies. Clusters of severe asthma include those on high-dose corticosteroid treatment associated with severe airflow obstruction and those with discordance between symptoms and sputum eosinophilia. Sputum eosinophilia can predict therapeutic responses to T-helper type 2 cytokine blockade. Further molecular phenotyping or endotyping of asthma will be necessary to determine new treatment strategies. Low T-helper type 2 expression may be predictive of poor therapeutic response to inhaled corticosteroids, but much less is known about this type of asthma. Phenotype-driven treatment of asthma will be further boosted by the integration of genetic, transcriptomic and proteomic technologies to defining distinct severe asthma phenotypes and biomarkers of therapeutic responses. This will lead towards stratified medicine for asthma.