Abstract
Asthma is increasingly being considered as a collection of different phenotypes that present with intermittent wheezing. Unbiased approaches to classifying asthma have led to the identification of distinct phenotypes based on age of onset of disease, atopic state, disease severity or activity, degree of chronic airflow obstruction, and sputum eosinophilia. Linking phenotypes to known disease mechanism is likely to be more fruitful in determining the potential targets necessary for successful therapies of specific endotypes. A "Th2-high expression" signature from the epithelium of patients with asthma identifies a subset of patients with high eosinophilia and good therapeutic responsiveness to corticosteroids. Other characteristic traits of asthma include noneosinophilic asthma, corticosteroid insensitivity, obesity-associated, and exacerbation-prone. Further progress into asthma mechanisms will be driven by unbiased data integration of multiscale data sets from omics technologies with those phenotypic characteristics and by using mathematical modeling. This will lead to the discovery of new pathways and their integration into endotypes and also set up further hypothesis-driven research. Continued iteration through experimentation or modeling will be needed to refine the phenotypes that relate to outcomes and also delineate specific treatments for specific phenotypes.