Bibliographic Details
| Title: |
A Pilot Study on Proteomic Predictors of Mortality in Stable COPD. |
| Authors: |
Enríquez-Rodríguez, Cesar Jessé1,2 (AUTHOR) carme.casadevall@upf.edu, Casadevall, Carme1,2 (AUTHOR) ady@12o.es, Faner, Rosa2,3 (AUTHOR) lcampos@separ.es, Pascual-Guardia, Sergi1,2 (AUTHOR) gpecesbarba@gmail.com, Castro-Acosta, Ady2,4 (AUTHOR) lseijo@unav.es, López-Campos, José Luis2,5 (AUTHOR) eduardmonsomolas@gmail.com, Peces-Barba, Germán2,6 (AUTHOR) borja.cosio@ssib.es, Seijo, Luis2,6,7 (AUTHOR) aagusti@clinic.cat, Caguana-Vélez, Oswaldo Antonio1,2 (AUTHOR) ebarreiro@researchmar.net, Monsó, Eduard2,8 (AUTHOR), Rodríguez-Chiaradia, Diego1,2 (AUTHOR), Barreiro, Esther1,2 (AUTHOR), Cosío, Borja G.2,9 (AUTHOR), Agustí, Alvar2,3 (AUTHOR), Gea, Joaquim1,2 (AUTHOR) cesarjesse.enriquez01@alumni.upf.edu, Group, on behalf of the BIOMEPOC1 (AUTHOR) |
| Source: |
Cells (2073-4409). Aug2024, Vol. 13 Issue 16, p1351. 21p. |
| Subject Terms: |
*CHRONIC obstructive pulmonary disease, *PEPTIDE mass fingerprinting, *ARTIFICIAL intelligence, *PROTEOMICS, *DEATH forecasting |
| Abstract: |
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, FEV1 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 (Q2 0.18), improving mildly with AI-mediated blind selection of proteins (accuracy of 95%, Q2 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. [ABSTRACT FROM AUTHOR] |
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