Youroukova Vania M, Dimitrova Denitsa G, Valerieva Anna D, Lesichkova Spaska S, Velikova Tsvetelina V, Ivanova-Todorova Ekaterina I, Tumangelova-Yuzeir Kalina D
Clinical Center for Lung Diseases, St. Sofi a Hospital for Pulmonary Diseases, Medical University of Sofi a, Sofi a, Bulgaria
Clinic of Allergy, Alexandrovska University Hospital, Medical University of Sofi a, Sofi a, Bulgaria
Folia Med (Plovdiv). 2017 Jun 1;59(2):165-173. doi: 10.1515/folmed-2017-0031.
Bronchial asthma is a heterogeneous disease that includes various subtypes. They may share similar clinical characteristics, but probably have different pathological mechanisms.
To identify phenotypes using cluster analysis in moderate to severe bronchial asthma and to compare differences in clinical, physiological, immunological and inflammatory data between the clusters.
Forty adult patients with moderate to severe bronchial asthma out of exacerbation were included. All underwent clinical assessment, anthropometric measurements, skin prick testing, standard spirometry and measurement fraction of exhaled nitric oxide. Blood eosinophilic count, serum total IgE and periostin levels were determined. Two-step cluster approach, hierarchical clustering method and k-mean analysis were used for identification of the clusters.
We have identified four clusters. Cluster 1 (n=14) - late-onset, non-atopic asthma with impaired lung function, Cluster 2 (n=13) - late-onset, atopic asthma, Cluster 3 (n=6) - late-onset, aspirin sensitivity, eosinophilic asthma, and Cluster 4 (n=7) - early-onset, atopic asthma.
Our study is the first in Bulgaria in which cluster analysis is applied to asthmatic patients. We identified four clusters. The variables with greatest force for differentiation in our study were: age of asthma onset, duration of diseases, atopy, smoking, blood eosinophils, nonsteroidal anti-inflammatory drugs hypersensitivity, baseline FEV1/FVC and symptoms severity. Our results support the concept of heterogeneity of bronchial asthma and demonstrate that cluster analysis can be an useful tool for phenotyping of disease and personalized approach to the treatment of patients.
支气管哮喘是一种异质性疾病，包括多种亚型。它们可能具有相似的临床特征，但病理机制可能不同。
通过聚类分析确定中重度支气管哮喘的表型，并比较各聚类间临床、生理、免疫和炎症数据的差异。
纳入40例处于非加重期的中重度成年支气管哮喘患者。所有患者均接受临床评估、人体测量、皮肤点刺试验、标准肺功能测定和呼出一氧化氮分数测量。测定血嗜酸性粒细胞计数、血清总IgE和骨膜蛋白水平。采用两步聚类法、层次聚类法和k均值分析来识别聚类。
我们识别出四个聚类。聚类1（n = 14）——迟发型、非特应性哮喘，肺功能受损；聚类2（n = 13）——迟发型、特应性哮喘；聚类3（n = 6）——迟发型、阿司匹林敏感性嗜酸性粒细胞性哮喘；聚类4（n = 7）——早发型、特应性哮喘。
我们的研究是保加利亚首次将聚类分析应用于哮喘患者的研究。我们识别出四个聚类。在我们的研究中，区分能力最强的变量为：哮喘发病年龄、病程、特应性、吸烟、血嗜酸性粒细胞、非甾体抗炎药超敏反应、基线FEV1/FVC和症状严重程度。我们的结果支持支气管哮喘异质性的概念，并表明聚类分析可成为疾病表型分析和患者个性化治疗方法的有用工具。
