Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

Chronic inflammatory airway diseases, including asthma and chronic obstructive pulmonary disease, are responsible for a large global disease burden. The recognition of airway disease phenotypes is important for the application of new therapies targeted at specific underlying biological mechanisms. Biomarkers are indicators of biological or pathogenic processes that are objectively measured. In airway disease, biomarkers will ideally provide predictive information regarding diagnosis, disease mechanisms, phenotypes, treatment responses and prognosis or future risk. Non-invasive biomarkers that aid phenotyping are crucial to the development of targeted and more efficacious treatment, leading to personalised approaches to airway disease management. Sputum and peripheral blood eosinophils and fractional exhaled nitric oxide (FeNO) are current examples of potential biomarkers. However, recent advances in technology have demonstrated the role for airway transcriptomics in biomarker discovery. This perspective piece discusses the need for biomarkers in airway disease, the use of eosinophil counts and FeNO as biomarkers, the use of transcriptomics for biomarker discovery, and the application of biomarkers in clinical and research settings. A combined approach incorporating clinical information with biological markers such as eosinophils, FeNO and inflammatory gene markers is likely to have the most success in predicting patient outcomes.

Original publication

DOI

10.5588/ijtld.14.0226

Type

Journal article

Journal

Int J Tuberc Lung Dis

Publication Date

11/2014

Volume

18

Pages

1264 - 1268

Keywords

Asthma, Biomarkers, Eosinophils, Humans, Nitric Oxide, Precision Medicine, Prognosis, Pulmonary Disease, Chronic Obstructive, Transcriptome