RT Journal Article SR Electronic T1 Functional phenotypes determined by fluctuation-based clustering of lung function measurements in healthy and asthmatic cohort participants JF Thorax JO Thorax FD BMJ Publishing Group Ltd and British Thoracic Society SP thoraxjnl-2016-209919 DO 10.1136/thoraxjnl-2016-209919 A1 Edgar Delgado-Eckert A1 Oliver Fuchs A1 Nitin Kumar A1 Juha Pekkanen A1 Jean-Charles Dalphin A1 Josef Riedler A1 Roger Lauener A1 Michael Kabesch A1 Maciej Kupczyk A1 Sven-Erik Dahlen A1 Erika von Mutius A1 Urs Frey A1 , YR 2017 UL http://thorax.bmj.com/content/early/2017/10/08/thoraxjnl-2016-209919.abstract AB Rationale Asthma is characterised by inflammation and reversible airway obstruction. However, these features are not always closely related. Fluctuations of daily lung function contain information on asthma phenotypes, exacerbation risk and response to long-acting β-agonists.Objectives In search of subgroups of asthmatic participants with specific lung functional features, we developed and validated a novel clustering approach to asthma phenotyping, which exploits the information contained within the fluctuating behaviour of twice-daily lung function measurements.Methods Forced expiratory volume during the first second (FEV1) and peak expiratory flow (PEF) were prospectively measured over 4 weeks in 696 healthy and asthmatic school children (Protection Against Allergy – Study in Rural Environments (PASTURE)/EFRAIM cohort), and over 1 year in 138 asthmatic adults with mild-to-moderate or severe asthma (Pan-European Longitudinal Assessment of Clinical Course and BIOmarkers in Severe Chronic AIRway Disease (BIOAIR) cohort). Using enrichment analysis, we explored whether the method identifies clinically meaningful, distinct clusters of participants with different lung functional fluctuation patterns.Measurements and main results In the PASTURE/EFRAIM dataset, we found four distinct clusters. Two clusters were enriched in children with well-known clinical characteristics of asthma. In cluster 3, children from a farming environment predominated, whereas cluster 4 mainly consisted of healthy controls. About 79% of cluster 3 carried the asthma-risk allele rs7216389 of the 17q21 locus. In the BIOAIR dataset, we found two distinct clusters clearly discriminating between individuals with mild-to-moderate and severe asthma.Conclusions Our method identified dynamic functional asthma and healthy phenotypes, partly independent of atopy and inflammation but related to genetic markers on the 17q21 locus. The method can be used for disease phenotyping and possibly endotyping. It may identify participants with specific functional abnormalities, potentially needing a different therapeutic approach.