Comparing Heterogenous Phenotypes of Chronic Obstructive Pulmonary Disease: Network Analysis and Penalized Generalized Linear Model.

Background and objective: Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease, with chronic bronchitis (CB) and emphysema phenotypes. The aim of our study was to compare the distinct patterns of correlation networks for respiratory symptoms and predictors of future exacerbations of different COPD phenotypes.

Methods: CB and emphysema were identified using a questionnaire and computed tomography images, respectively, and also included patients with preserved ratio impaired spirometry (PRISm). We constructed separate correlation networks for each subgroup using Spearman correlation coefficients. Predictors of future exacerbations were selected via least absolute shrinkage and selection operation regression analyses in multivariable analysis.

Results: Among the 3436 patients, 2232 were non-CB, 1131 were CB, 1116 were emphysema, and 73 were PRISm groups. The forced expiratory volume in one second (FEV1) and respiratory symptoms worsened in the following order: PRISm, non-CB, emphysema, and CB groups. During the 1-year follow-up, 17.3%, 21.3%, and 18.9% of patients in the non-CB, CB, and emphysema groups, respectively, experienced exacerbation. Each group showed a distinct correlation pattern between demographic characteristics, comorbidities, pulmonary function, blood biomarkers, respiratory symptoms, and exercise capacity. Across all groups, lower FEV1 (%), higher white blood cell count, higher erythrocyte sedimentation rate, and worse Saint George's Respiratory Questionnaire symptom and total scores were identified as common risk factors for future exacerbations. However, each group showed distinct predictors for future exacerbations.

Conclusion: The correlation network patterns and predictors of future exacerbations varied significantly depending on the COPD phenotype. Further research is required to understand the heterogeneous COPD pathophysiology and facilitate personalized medicine.