title: Defining phenotypes in asthma: a step towards personalized medicine.
abstract: Asthma is a common disease with a complex pathophysiology. It can present in various clinical forms and with different levels of severity. Unbiased cluster analytic methods have unravelled several phenotypes in cohorts representative of the whole spectrum of severity. Clusters of severe asthma include those on high-dose corticosteroid treatment, often with both inhaled and oral treatment, usually associated with severe airflow obstruction. Phenotypes with concordance between symptoms and sputum eosinophilia have been reported, including an eosinophilic inflammation-predominant group with few symptoms and late-onset disease who have a high prevalence of rhinosinusitis, aspirin sensitivity, and exacerbations. Sputum eosinophilia is also a biomarker that can predict therapeutic responses to antibody-based treatments to block the effects of the T-helper (Th)-2 cytokine, interleukin (IL)-5. Low Th2-expression has been predictive of poor therapeutic response to inhaled corticosteroid therapy. Current asthma schedules emphasise a step-up approach to treating asthma in relation to increasing severity, but, in more severe disease, phenotyping or endotyping of asthma will be necessary to determine new treatment strategies as severe asthma is recognized as being a particularly heterogeneous disease. Much less is known about 'non-eosinophilic' asthma. Phenotypic characterisation of corticosteroid insensitivity and chronic airflow obstruction of severe asthma is also needed. Phenotype-driven treatment of asthma will be further boosted by the advent of transcriptomic and proteomic technologies, with the application of systems biology or medicine approaches to defining phenotypes and biomarkers of disease and therapeutic response. This will pave the way towards personalized medicine and healthcare for asthma.
authors.affiliation: Experimental Studies, National Heart and Lung Institute, Imperial College London, Dovehouse St, London, SW3 6LY, UK, f.chung@imperial.ac.uk.
authors.forename: Kian Fan
authors.initials: KF
authors.lastname: Chung
mesh_terms: D000318:Adrenergic beta-Agonists / Q000627:therapeutic use*; D000893:Anti-Inflammatory Agents / Q000627:therapeutic use*; D001249:Asthma / Q000188:drug therapy* / Q000235:genetics*; D006801:Humans; D010641:Phenotype; D057285:Precision Medicine
chemical_list: D000318:Adrenergic beta-Agonists; D000893:Anti-Inflammatory Agents