Multiview Cluster Analysis Identifies Variable Corticosteroid Response Phenotypes in Severe Asthma.
Multiview Cluster Analysis Identifies Variable Corticosteroid Response Phenotypes in Severe Asthma.
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DOI：
10.1164/rccm.201808-1543oc
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发表时间：
2019-05
影响因子：
24.7
通讯作者：
Wei Wu;Seo-Jin Bang;E. Bleecker;M. Castro;L. Denlinger;S. Erzurum;J. Fahy;A. Fitzpatrick;B. Gaston;A. Hastie;E. Israel;N. Jarjour;B. Levy;D. Mauger;D. Meyers;W. Moore;M. Peters;B. Phillips;W. Phipatanakul;R. Sorkness;S. Wenzel
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文献类型：
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作者：
Wei Wu;Seo-Jin Bang;E. Bleecker;M. Castro;L. Denlinger;S. Erzurum;J. Fahy;A. Fitzpatrick;B. Gaston;A. Hastie;E. Israel;N. Jarjour;B. Levy;D. Mauger;D. Meyers;W. Moore;M. Peters;B. Phillips;W. Phipatanakul;R. Sorkness;S. Wenzel
基本原理：皮质类固醇（CS）是最有效的哮喘治疗方法，但反应是异质性的，全身CS导致长期副作用。因此，更好地了解CS响应中的影响因素可以提高精确度管理。虽然有几个因素与CS反应，还没有综合/集群的方法来确定差异CS反应。目的：使用无监督多视图学习方法识别对CS治疗有不同反应的哮喘亚表型。研究方法：将多核k均值聚类应用于严重哮喘研究项目中346名成人哮喘参与者的100个临床、生理、炎症和人口统计学变量，并配对（曲安奈德给药前和给药后2-3周）痰液数据。使用机器学习技术来选择预测新患者的聚类分配的顶级基线变量。测量和主要结果：多核聚类揭示了四个集群的个人与哮喘和不同的CS反应。第1组和第2组由患有过敏性哮喘和相对正常肺功能的年轻、中度CS反应个体组成，通过对比CS治疗后的痰中性粒细胞和巨噬细胞百分比来区分。第3组的受试者有迟发性哮喘和低肺功能，基线嗜酸性粒细胞增多症高，CS反应性最高。第4组主要由年轻肥胖女性组成，伴有严重气流受限、少量嗜酸性粒细胞炎症和最低CS反应性。确定了前12个基线变量，并使用独立的严重哮喘研究计划测试集验证了聚类。结论：我们基于机器学习的方法为哮喘中CS反应性的机制提供了新的见解，并有可能改善疾病治疗。
Rationale: Corticosteroids (CSs) are the most effective asthma therapy, but responses are heterogeneous and systemic CSs lead to long-term side effects. Therefore, an improved understanding of the contributing factors in CS responses could enhance precision management. Although several factors have been associated with CS responsiveness, no integrated/cluster approach has yet been undertaken to identify differential CS responses. Objectives: To identify asthma subphenotypes with differential responses to CS treatment using an unsupervised multiview learning approach. Methods: Multiple-kernel k-means clustering was applied to 100 clinical, physiological, inflammatory, and demographic variables from 346 adult participants with asthma in the Severe Asthma Research Program with paired (before and 2-3 weeks after triamcinolone administration) sputum data. Machine-learning techniques were used to select the top baseline variables that predicted cluster assignment for a new patient. Measurements and Main Results: Multiple-kernel clustering revealed four clusters of individuals with asthma and different CS responses. Clusters 1 and 2 consisted of young, modestly CS-responsive individuals with allergic asthma and relatively normal lung function, separated by contrasting sputum neutrophil and macrophage percentages after CS treatment. The subjects in cluster 3 had late-onset asthma and low lung function, high baseline eosinophilia, and the greatest CS responsiveness. Cluster 4 consisted primarily of young, obese females with severe airflow limitation, little eosinophilic inflammation, and the least CS responsiveness. The top 12 baseline variables were identified, and the clusters were validated using an independent Severe Asthma Research Program test set. Conclusions: Our machine learning-based approaches provide new insights into the mechanisms of CS responsiveness in asthma, with the potential to improve disease treatment.