Metabolomic Signatures Predict Seven-Year Mortality in Clinically Stable COPD Patients.
Chronic Obstructive Pulmonary Disease (COPD) is a complex condition with high mortality. Early identification of patients at increased risk of death remains a major clinical challenge. This pilot study aimed to explore whether plasma metabolomic profiling could aid in the prediction of long-term (7-...
| Autores: | , , , , , , , , , , , , , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Institut d'Investigació i Innovació Parc Taulí (I3PT) |
| Repositorio: | r-I3PT. Repositorio Institucional Producción Científica del Institut d'Investigació i Innovació Parc Taulí |
| OAI Identifier: | oai:i3pt.fundanetsuite.com:p6874 |
| Acceso en línea: | https://i3pt.portalinvestigacion.com/publicaciones/6874 https://www.scopus.com/inward/record.uri?eid=2-s2.0-105011221581&doi=10.3390%2Fijms26136373&partnerID=40&md5=ae46b9217265da4663239a415ddad9fc |
| Access Level: | acceso abierto |
| Palabra clave: | COPD, amino acids, energy, metabolomics, microbiota, mortality, redox |
| Sumario: | Chronic Obstructive Pulmonary Disease (COPD) is a complex condition with high mortality. Early identification of patients at increased risk of death remains a major clinical challenge. This pilot study aimed to explore whether plasma metabolomic profiling could aid in the prediction of long-term (7-year) mortality and provide insight into potential underlying mechanisms. Plasma samples from 54 randomly selected stable COPD patients were analyzed using both untargeted and semi-targeted LC-MS approaches. After excluding patients with unclear death data, non-COPD-related deaths and metabolomic outliers, 41 individuals were included in the final analysis. During follow-up, 13 patients (32%) died, and 28 survived. Univariate analysis identified 12 metabolites-mainly amino acids-that differed significantly between the two groups. Functional analysis suggested a significant disruption in energy production pathways. Predictive models developed using machine learning algorithms, consisting of either ten metabolites alone or nine metabolites plus FEV 1 , achieved high accuracy for 7-year mortality prediction, with the latter model performing slightly better. Internal validation was conducted using five-fold cross-validation. While exploratory, these findings support the hypothesis that early metabolic alterations, particularly in energy pathways, may contribute to long-term mortality risk in stable COPD patients, and could complement traditional prognostic markers such as FEV 1 . |
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