Thorax · 2026
Proteomics-based risk prediction and drug targets identification for chronic obstructive pulmonary disease.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Zhang Yuanyuan, Ye Ziliang, Yang Sisi, Zhang Yanjun, Huang Yu, Xiang Hao, Wu Yiting, Zhang Yiwei, Gan Xiaoqin, Qin Xianhui
Аннотация
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.
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.