Metabolomic signatures predict seven-year mortality in clinically stable COPD patients

Enríquez Rodríguez, César Jessé; Agranovich, Bella; Pascual Guàrdia, Sergi, 1979-; Faner, Rosa; Camps-Ubach, Ramon; Castro-Acosta, Ady Angélica; Lopez-Campos, Jose Luis; Peces-Barba Romero, Germán; Seijó Maceiras, Luis; Caguana Vélez, Oswaldo Antonio; Rodriguez-Chiaradia, Diego Agustín; Barreiro Portela, Esther; Monsó Molas, Eduard; Cosío, Borja G.; Abramovich, Ifat; Agustí, Alvar; Casadevall Fusté, Carme; Gea Guiral, Joaquim

doi:http://dx.doi.org/10.3390/ijms26136373

Metabolomic signatures predict seven-year mortality in clinically stable COPD patients

Citation

Enríquez-Rodríguez CJ, Agranovich B, Pascual-Guàrdia S, Faner R, Camps-Ubach R, Castro-Acosta A, et al. Metabolomic signatures predict seven-year mortality in clinically stable COPD patients. Int J Mol Sci. 2025 Jul 1;26(13):6373. DOI: 10.3390/ijms26136373

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 FEV1, 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 FEV1.