Metabolomic Signatures Predict Seven-Year Mortality in Clinically Stable COPD Patients
Enríquez-Rodríguez, César Jessé (Hospital del Mar (Barcelona, Catalunya))
Agranovich, B. (The Ruth and Bruce Rappaport Faculty of Medicine)
Pascual-Guardia, Sergi (Hospital del Mar (Barcelona, Catalunya))
Faner, Rosa (Hospital Clínic i Provincial de Barcelona)
Camps-Ubach, Ramon (Hospital del Mar (Barcelona, Catalunya))
Castro-Acosta, A. (Hospital 12 de Octubre (Madrid))
López-Campos, J.L. (Universidad de Sevilla)
Peces-Barba, G. (Universidad Autónoma de Madrid)
Seijo, L. (Clínica Universidad de Navarra)
Caguana-Vélez, O.A. (Universitat Pompeu Fabra)
Rodríguez-Chiaradia, Diego A. (Hospital del Mar (Barcelona, Catalunya))
Barreiro, Esther (Hospital del Mar (Barcelona, Catalunya))
Monso, Eduard (Parc Taulí Hospital Universitari. Institut d'Investigació i Innovació Parc Taulí (I3PT))
Cosio, Borja G. (Universitat de les Illes Balears)
Abramovich, I. (The Ruth and Bruce Rappaport Faculty of Medicine)
Agustí García-Navarro, Àlvar (Hospital Clínic i Provincial de Barcelona)
Casadevall, Carme (Hospital del Mar (Barcelona, Catalunya))
Gea Guiral, Joaquim (Hospital del Mar (Barcelona, Catalunya))
Universitat Autònoma de Barcelona

Date:	2025
Abstract:	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, 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.
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	COPD ; Amino acids ; Energy ; Metabolomics ; Microbiota ; Mortality ; Redox
Published in:	International journal of molecular sciences, Vol. 26 Núm. 13 (july 2025) , p. 6373, ISSN 1422-0067

DOI: 10.3390/ijms26136373
PMID: 40650154

21 p, 1.9 MB

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Research literature > UAB research groups literature > Research Centres and Groups (research output) > Health sciences and biosciences > Parc Taulí Research and Innovation Institute (I3PT
Articles > Research articles
Articles > Published articles