Hospital del Mar Research Institute, Respiratory Medicine Department, Hospital del Mar. Medicine and Life Sciences Department, Universitat Pompeu Fabra (UPF), BRN, 08018 Barcelona, Spain.
Centro de Investigación Biomédica en Red, Área de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, 28029 Madrid, Spain.
Cells. 2024 Aug 14;13(16):1351. doi: 10.3390/cells13161351.
Chronic Obstructive Pulmonary Disease (COPD) is the third leading cause of global mortality. Despite clinical predictors (age, severity, comorbidities, etc.) being established, proteomics offers comprehensive biological profiling to obtain deeper insights into COPD pathophysiology and survival prognoses. This pilot study aimed to identify proteomic footprints that could be potentially useful in predicting mortality in stable COPD patients. Plasma samples from 40 patients were subjected to both blind (liquid chromatography-mass spectrometry) and hypothesis-driven (multiplex immunoassays) proteomic analyses supported by artificial intelligence (AI) before a 4-year clinical follow-up. Among the 34 patients whose survival status was confirmed (mean age 69 ± 9 years, 29.5% women, FEV 42 ± 15.3% ref.), 32% were dead in the fourth year. The analysis identified 363 proteins/peptides, with 31 showing significant differences between the survivors and non-survivors. These proteins predominantly belonged to different aspects of the immune response (12 proteins), hemostasis (9), and proinflammatory cytokines (5). The predictive modeling achieved excellent accuracy for mortality (90%) but a weaker performance for days of survival (Q 0.18), improving mildly with AI-mediated blind selection of proteins (accuracy of 95%, Q of 0.52). Further stratification by protein groups highlighted the predictive value for mortality of either hemostasis or pro-inflammatory markers alone (accuracies of 95 and 89%, respectively). Therefore, stable COPD patients' proteomic footprints can effectively forecast 4-year mortality, emphasizing the role of inflammatory, immune, and cardiovascular events. Future applications may enhance the prognostic precision and guide preventive interventions.
慢性阻塞性肺疾病（COPD）是全球第三大致死原因。尽管已经确定了临床预测因素（年龄、严重程度、合并症等），但蛋白质组学提供了全面的生物学分析，以更深入地了解 COPD 的病理生理学和生存预后。这项初步研究旨在确定蛋白质组学特征，这些特征可能有助于预测稳定型 COPD 患者的死亡率。40 名患者的血浆样本接受了盲法（液相色谱-质谱）和假设驱动（多重免疫分析）蛋白质组学分析，并得到人工智能（AI）的支持，随后进行了 4 年的临床随访。在 34 名生存状况得到确认的患者中（平均年龄 69 ± 9 岁，29.5%为女性，FEV 42 ± 15.3%参考值），4 年后有 32%死亡。分析确定了 363 种蛋白质/肽，其中 31 种在幸存者和非幸存者之间存在显著差异。这些蛋白质主要属于不同的免疫反应（12 种蛋白质）、止血（9 种）和促炎细胞因子（5 种）方面。预测模型对死亡率的准确性非常高（90%），但对生存天数的准确性较低（Q 0.18），通过 AI 介导的盲法蛋白质选择略有提高（准确率为 95%，Q 值为 0.52）。进一步按蛋白质组分层，突出了止血或促炎标志物单独预测死亡率的价值（准确率分别为 95%和 89%）。因此，稳定型 COPD 患者的蛋白质组学特征可以有效预测 4 年死亡率，强调了炎症、免疫和心血管事件的作用。未来的应用可能会提高预后的准确性，并指导预防干预措施。
