Identification of cough-variant asthma phenotypes based on clinical and pathophysiologic data

Background Cough-variant asthma (CVA) may respond differently to antiasthmatic treatment. There are limited data on the heterogeneity of CVA. Objective We aimed to classify patients with CVA using cluster analysis based on clinicophysiologic parameters and to unveil the underlying molecular pathways of these phenotypes with transcriptomic data of sputum cells. Methods We applied k-mean clustering to 342 newly physician-diagnosed patients with CVA from a prospective multicenter observational cohort using 10 prespecified baseline clinical and pathophysiologic variables. The clusters were compared according to clinical features, treatment response, and sputum transcriptomic data. Results Three stable CVA clusters were identified. Cluster 1 (n = 176) was characterized by female predominance, late onset, normal lung function, and a low proportion of complete resolution of cough (60.8%) after antiasthmatic treatment. Patients in cluster 2 (n = 105) presented with young, nocturnal cough, atopy, high type 2 inflammation, and a high proportion of complete resolution of cough (73.3%) with a highly upregulated coexpression gene network that related to type 2 immunity. Patients in cluster 3 (n = 61) had high body mass index, long disease duration, family history of asthma, low lung function, and low proportion of complete resolution of cough (54.1%). TH17 immunity and type 2 immunity coexpression gene networks were both upregulated in clusters 1 and 3. Conclusion Three clusters of CVA were identified with different clinical, pathophysiologic, and transcriptomic features and responses to antiasthmatics treatment, which may improve our understanding of pathogenesis and help clinicians develop individualized cough treatment in asthma. Cough-variant asthma (CVA) may respond differently to antiasthmatic treatment. There are limited data on the heterogeneity of CVA. We aimed to classify patients with CVA using cluster analysis based on clinicophysiologic parameters and to unveil the underlying molecular pathways of these phenotypes with transcriptomic data of sputum cells. We applied k-mean clustering to 342 newly physician-diagnosed patients with CVA from a prospective multicenter observational cohort using 10 prespecified baseline clinical and pathophysiologic variables. The clusters were compared according to clinical features, treatment response, and sputum transcriptomic data. Three stable CVA clusters were identified. Cluster 1 (n = 176) was characterized by female predominance, late onset, normal lung function, and a low proportion of complete resolution of cough (60.8%) after antiasthmatic treatment. Patients in cluster 2 (n = 105) presented with young, nocturnal cough, atopy, high type 2 inflammation, and a high proportion of complete resolution of cough (73.3%) with a highly upregulated coexpression gene network that related to type 2 immunity. Patients in cluster 3 (n = 61) had high body mass index, long disease duration, family history of asthma, low lung function, and low proportion of complete resolution of cough (54.1%). TH17 immunity and type 2 immunity coexpression gene networks were both upregulated in clusters 1 and 3. Three clusters of CVA were identified with different clinical, pathophysiologic, and transcriptomic features and responses to antiasthmatics treatment, which may improve our understanding of pathogenesis and help clinicians develop individualized cough treatment in asthma.