Proteomics-based risk prediction and drug targets identification for chronic obstructive pulmonary disease

Abstract

Background Chronic obstructive pulmonary disease (COPD) is a leading cause of global mortality, yet existing risk prediction models remain limited. This study aimed to develop and validate a protein-based risk score for COPD, comparing its performance against COPD polygenic risk scores (PRSs) and clinical risk factors, while exploring underlying biological pathways and causal protein-disease associations. Methods The study analysed 27 796 UK Biobank participants from England (70% training and 30% testing set) and 3534 from Scotland/Wales (validation cohort). Least absolute shrinkage and selection operator regression identified predictive proteins in the training set, with model performance assessed using Harrell’s C-index, Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement Index (IDI). Pathway and Mendelian randomisation (MR) analyses explored biological mechanisms and causal effects. Results In the testing set, a developed 32-protein risk score strongly predicted incident COPD with high accuracy (C-index 0.826, 95% CI 0.803 to 0.849). It outperformed PRS (C-index 0.510, 95% CI 0.478 to 0.542) and matched clinical models (C-index 0.845, 95% CI 0.823 to 0.867). A simplified 10-protein panel retained robust performance (C-index 0.816, 95% CI 0.792 to 0.840). Adding the protein scores to clinical factors improved risk reclassification (NRI 0.251–0.318; IDI: 0.042–0.064). MR analysis identified ADM and SCGB1A1 as protective, while MMP12 and TNFRSF10A increased risk. Pathway analysis implicated inflammation and extracellular remodelling. Chitinase-3-like protein 1 and matrix metalloproteinase-9 were central players in the protein–protein interaction network. Similar results were found in the validation cohort. Conclusion Protein biomarkers outperform genetic risk scores and complement clinical factors for COPD prediction, with a streamlined 10-protein panel offering clinical feasibility. The study identifies novel pathways and causal therapeutic targets. Further validation is needed prior to routine clinical implementation. Data may be obtained from a third party and are not publicly available. Data may be obtained from a third party and are not publicly available (UK Biobank, ). The analytical methods supporting the findings of this study can be obtained from the corresponding authors upon reasonable request.