Plasma metabolomic signatures enable the diagnosis and prognosis of chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a progressive heterogeneous lung disease driving global illness and death, yet reliable biomarkers for its early diagnosis, molecular subtyping and prognosis are lacking. Here, we conduct targeted plasma metabolomic profiling in two independent, deeply phenotyped cohorts comprising 1344 participants across the full spectrum of COPD severity, with 3-year longitudinal follow-up for 651 subjects. Machine learning integration screens metabolite signatures associated with disease presence, clinical subtypes, and longitudinal outcomes. We identify unique plasma metabolic profiles distinguish COPD patients from healthy people and separate emphysema and small-airway-dominant subtypes with high accuracy in test and validation cohorts. A 27-metabolite panel detects early COPD by capturing metabolic shifts prior to lung function loss. Moreover, specific metabolite subsets independently predicted lung function decline, occurrence of acute exacerbations, and dyspnoea severity. Together, plasma metabolomics yields robust multi-purpose COPD biomarkers, offering an accessible strategy for early screening and personalized clinical risk stratification. This study combines plasma metabolomics and machine learning to identify blood biomarkers that improve the diagnosis, classification and prognosis of chronic obstructive pulmonary disease, supporting earlier detection and more precise patient care.