Asthma is a prevalent condition that manifests itself in a variety of clinical presentations and degrees of severity. The present ‘one-size-fits-all’ therapy strategy is inadequate. Several asthma traits have been found using unbiased cluster analysis. Understanding the underlying processes that drive these clusters might lead to more patient-centered treatments. Initially, only clinical characteristics were clustered, but the inclusion of biomarkers that describe sputum and blood cellular patterns permitted the prediction of responses to targeted treatments. Clusters of severe asthma include individuals who are on high-dose corticosteroid therapy and have significant airflow obstruction, as well as those who have symptoms and sputum eosinophilia that do not match. Sputum eosinophilia can indicate how patients would respond to T-helper type 2 cytokine inhibition. To develop novel asthma treatment methods, further molecular phenotyping or endotyping will be required. Although low T-helper type 2 expression may indicate poor therapeutic response to inhaled corticosteroids, considerably less is known about this form of asthma.
The combination of genomic, transcriptomic, and proteomic technologies to define different severe asthma phenotypes and indicators of therapy responses will improve phenotype-driven treatment of asthma even further. As a result, asthma medication will become more stratified.
