Matabuena Marcos, Salgado Francisco Javier, Nieto-Fontarigo Juan José, Álvarez-Puebla María J, Arismendi Ebymar, Barranco Pilar, Bobolea Irina, Caballero María L, Cañas José Antonio, Cárdaba Blanca, Cruz María Jesus, Curto Elena, Domínguez-Ortega Javier, Luna Juan Alberto, Martínez-Rivera Carlos, Mullol Joaquim, Muñoz Xavier, Rodriguez-Garcia Javier, Olaguibel José María, Picado César, Plaza Vicente, Quirce Santiago, Rial Manuel J, Romero-Mesones Christian, Sastre Beatriz, Soto-Retes Lorena, Valero Antonio, Valverde-Monge Marcela, Del Pozo Victoria, Sastre Joaquín, González-Barcala Francisco Javier
Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), University of Santiago of Compostela, Santiago de Compostela, Spain.
Department of Biochemistry and Molecular Biology, School of Biology-Biological Research Centre (CIBUS), University of Santiago de Compostela, Santiago de Compostela, Spain; Translational Research in Airway Diseases Group (TRIAD) - Health Research Institute of Santiago de Compostela (IDIS), Spain.
Arch Bronconeumol. 2023 Apr;59(4):223-231. doi: 10.1016/j.arbres.2023.01.007. Epub 2023 Jan 18.
The definition of asthma phenotypes has not been fully established, neither there are cluster studies showing homogeneous results to solidly establish clear phenotypes. The purpose of this study was to develop a classification algorithm based on unsupervised cluster analysis, identifying clusters that represent clinically relevant asthma phenotypes that may share asthma-related outcomes.
We performed a multicentre prospective cohort study, including adult patients with asthma (N=512) from the MEGA study (Mechanisms underlying the Genesis and evolution of Asthma). A standardised clinical history was completed for each patient. Cluster analysis was performed using the kernel k-groups algorithm.
Four clusters were identified. Cluster 1 (31.5% of subjects) includes adult-onset atopic patients with better lung function, lower BMI, good asthma control, low ICS dose, and few exacerbations. Cluster 2 (23.6%) is made of adolescent-onset atopic asthma patients with normal lung function, but low adherence to treatment (59% well-controlled) and smokers (48%). Cluster 3 (17.1%) includes adult-onset patients, mostly severe non-atopic, with overweight, the worse lung function and asthma control, and receiving combination of treatments. Cluster 4 (26.7%) consists of the elderly-onset patients, mostly female, atopic (64%), with high BMI and normal lung function, prevalence of smokers and comorbidities.
We defined four phenotypes of asthma using unsupervised cluster analysis. These clusters are clinically relevant and differ from each other as regards FEV1, age of onset, age, BMI, atopy, asthma severity, exacerbations, control, social class, smoking and nasal polyps.
哮喘表型的定义尚未完全确立，也没有聚类研究能得出一致结果来确凿地确立明确的表型。本研究的目的是基于无监督聚类分析开发一种分类算法，识别出代表可能具有共同哮喘相关结局的临床相关哮喘表型的聚类。
我们进行了一项多中心前瞻性队列研究，纳入了来自MEGA研究（哮喘发生和演变的潜在机制）的成年哮喘患者（N = 512）。为每位患者完成了标准化的临床病史记录。使用核k组算法进行聚类分析。
识别出四个聚类。聚类1（占受试者的31.5%）包括成年起病的特应性患者，肺功能较好，体重指数较低，哮喘控制良好，吸入性糖皮质激素剂量低，且发作次数少。聚类2（23.6%）由青少年起病的特应性哮喘患者组成，肺功能正常，但治疗依从性低（59%控制良好）且吸烟者比例高（48%）。聚类3（17.1%）包括成年起病的患者，大多为重度非特应性，超重，肺功能和哮喘控制较差，且接受联合治疗。聚类4（26.7%）由老年起病的患者组成，大多为女性，特应性（64%），体重指数高，肺功能正常，吸烟者和合并症患病率高。
我们使用无监督聚类分析定义了四种哮喘表型。这些聚类在临床方面具有相关性，在第一秒用力呼气容积、起病年龄、年龄、体重指数、特应性、哮喘严重程度、发作、控制、社会阶层、吸烟和鼻息肉方面彼此不同。
