Chronic obstructive pulmonary disease (COPD) exhibits substantial clinical heterogeneity, with a considerable proportion of patients experiencing persistent exacerbations despite guideline-recommended optimal inhaled therapy. These unmet medical needs underscore the necessity for comprehensive phenotypic assessment to identify clinical and molecular factors associated with exacerbation strategies for achieving disease stability. We hypothesized that multidimensional profiling integrating clinical phenotyping, quantitative imaging, and molecular analyses would identify factors associated with subsequent disease stability in optimally treated patients. This Japanese prospective observational cohort, involving five tertiary respiratory centers, will enroll patients with COPD: 300 receiving optimal inhaled therapy for at least six months, 100 on monotherapy, and 100 non-COPD controls. Patients will undergo comprehensive assessment at enrollment and annual follow-up for three years, including spirometry, computed tomography for emphysema, airway wall thickening, and mucus plug quantification, blood eosinophil counts and inflammatory markers, peripheral blood mononuclear cell transcriptomics and plasma proteomics, cardiovascular biomarkers, and frailty evaluation. The primary endpoint compares three-year exacerbation frequencies stratified by blood eosinophil counts. Secondary endpoints will identify clinical and molecular characteristics predicting treatment response and disease stability. This multidimensional assessment will identify factors associated with exacerbation risk across inflammatory, structural, cardiovascular, and functional domains. Findings will inform biomarker-guided precision medicine strategies and enable intervention trial design targeting disease stability, advancing the paradigm from reactive management to proactive, personalized treatment approaches. This study was approved by the Ethics Committee at Tohoku University Graduate School of Medicine (approval number 2025-1-757; approval date, Dec 17, 2025). The University Hospital Medical Information Network (UMIN 000059413).
Fujino et al. (Tue,) studied this question.