A Pilot Study on Proteomic Predictors of Mortality in Stable COPD

Area of Science:

• Pulmonary Medicine
• Proteomics
• Biomarkers

Background:

• Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of death globally.
• Existing clinical predictors for COPD survival are limited.
• Proteomics offers a deeper understanding of COPD pathophysiology and prognosis.

Purpose of the Study:

• To identify proteomic signatures for predicting mortality in stable COPD patients.
• To evaluate the utility of proteomics combined with artificial intelligence (AI) for mortality prediction.
• To explore the role of specific protein groups in COPD prognosis.

Main Methods:

• Pilot study involving 40 stable COPD patients with 4-year follow-up.
• Plasma proteomic analysis using liquid chromatography-mass spectrometry and multiplex immunoassays.
• AI-supported analysis for predictive modeling of mortality.

Main Results:

• 363 proteins/peptides identified; 31 showed significant differences between survivors and non-survivors.
• Proteins involved in immune response, hemostasis, and proinflammatory cytokines were prominent.
• AI-enhanced predictive modeling achieved 95% accuracy for mortality prediction.

Conclusions:

• Proteomic footprints in stable COPD patients can effectively predict 4-year mortality.
• Inflammatory, immune, and cardiovascular events are key factors in COPD prognosis.
• Future applications may improve prognostic precision and guide preventive strategies for COPD.