University Children's Hospital (UKBB), University of Basel, Basel, Switzerland.
Endothelial Cell Biology Unit and Department of Applied Mathematics, School of Molecular & Cellular Biology, School of Mathematics, University of Leeds, Leeds, UK.
Thorax. 2018 Feb;73(2):107-115. doi: 10.1136/thoraxjnl-2016-209919. Epub 2017 Sep 2.
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.
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.
Forced expiratory volume during the first second (FEV) 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.
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 locus. In the BIOAIR dataset, we found two distinct clusters clearly discriminating between individuals with mild-to-moderate and severe asthma.
Our method identified dynamic functional asthma and healthy phenotypes, partly independent of atopy and inflammation but related to genetic markers on the 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.
哮喘的特征是炎症和可逆转的气道阻塞。然而,这些特征并不总是密切相关的。日常肺功能的波动包含了哮喘表型、加重风险和长效β激动剂反应的信息。
为了寻找具有特定肺功能特征的哮喘参与者亚组,我们开发并验证了一种新的哮喘表型聚类方法,该方法利用了两次/日肺功能测量波动行为中包含的信息。
在保护过敏-农村环境研究(PASTURE)/EFRAIM 队列中,696 名健康和哮喘学龄儿童前瞻性地测量了 4 周内的第 1 秒用力呼气容积(FEV)和呼气峰流速(PEF),在泛欧慢性气道疾病严重程度纵向评估和生物标志物研究(BIOAIR)队列中,138 名轻-中度或重度哮喘的成年哮喘患者测量了 1 年的上述指标。通过富集分析,我们探讨了该方法是否能识别具有不同肺功能波动模式的临床有意义的、不同的参与者聚类。
在 PASTURE/EFRAIM 数据集,我们发现了四个不同的聚类。两个聚类在具有哮喘典型临床特征的儿童中更为丰富。在聚类 3 中,来自农业环境的儿童居多,而聚类 4 主要由健康对照组组成。约 79%的聚类 3 携带哮喘风险等位基因 rs7216389。在 BIOAIR 数据集,我们发现两个明显不同的聚类,可清晰地区分轻-中度和重度哮喘患者。
我们的方法识别了动态功能性哮喘和健康表型,部分独立于特应性和炎症,但与 11 号染色体上的遗传标记有关。该方法可用于疾病表型分析,可能也用于确定表型。它可能会识别出具有特定功能异常的参与者,可能需要不同的治疗方法。