A Pilot Study on Proteomic Predictors of Mortality in Stable COPD DOI Creative Commons
César Jessé Enríquez-Rodríguez, Carme Casadevall, Rosa Faner

и другие.

Cells, Год журнала: 2024, Номер 13(16), С. 1351 - 1351

Опубликована: Авг. 14, 2024

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 identify proteomic footprints that could be potentially useful in predicting mortality stable patients. Plasma samples from 40 patients were subjected both blind (liquid chromatography–mass spectrometry) hypothesis-driven (multiplex immunoassays) analyses supported by artificial intelligence (AI) before a 4-year follow-up. Among 34 whose status was confirmed (mean age 69 ± 9 years, 29.5% women, FEV1 42 15.3% ref.), 32% dead fourth year. The analysis identified 363 proteins/peptides, with 31 showing significant differences between survivors non-survivors. These proteins predominantly belonged different aspects immune response (12 proteins), hemostasis (9), proinflammatory cytokines (5). predictive modeling achieved excellent accuracy for (90%) but weaker performance days (Q2 0.18), improving mildly AI-mediated selection (accuracy 95%, Q2 0.52). Further stratification protein groups highlighted value either or pro-inflammatory markers alone (accuracies 95 89%, respectively). Therefore, patients’ can effectively forecast mortality, emphasizing role inflammatory, immune, cardiovascular events. Future applications may enhance prognostic precision guide preventive interventions.

Язык: Английский

A Pilot Study on Proteomic Predictors of Mortality in Stable COPD DOI Creative Commons
César Jessé Enríquez-Rodríguez, Carme Casadevall, Rosa Faner

и другие.

Cells, Год журнала: 2024, Номер 13(16), С. 1351 - 1351

Опубликована: Авг. 14, 2024

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 identify proteomic footprints that could be potentially useful in predicting mortality stable patients. Plasma samples from 40 patients were subjected both blind (liquid chromatography–mass spectrometry) hypothesis-driven (multiplex immunoassays) analyses supported by artificial intelligence (AI) before a 4-year follow-up. Among 34 whose status was confirmed (mean age 69 ± 9 years, 29.5% women, FEV1 42 15.3% ref.), 32% dead fourth year. The analysis identified 363 proteins/peptides, with 31 showing significant differences between survivors non-survivors. These proteins predominantly belonged different aspects immune response (12 proteins), hemostasis (9), proinflammatory cytokines (5). predictive modeling achieved excellent accuracy for (90%) but weaker performance days (Q2 0.18), improving mildly AI-mediated selection (accuracy 95%, Q2 0.52). Further stratification protein groups highlighted value either or pro-inflammatory markers alone (accuracies 95 89%, respectively). Therefore, patients’ can effectively forecast mortality, emphasizing role inflammatory, immune, cardiovascular events. Future applications may enhance prognostic precision guide preventive interventions.

Язык: Английский

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