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Related Experiment Video

Updated: Sep 16, 2025

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
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Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics

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Metabolomic Signatures Predict Seven-Year Mortality in Clinically Stable COPD Patients.

César Jessé Enríquez-Rodríguez1,2,3, Bella Agranovich4, Sergi Pascual-Guàrdia1,2,3

  • 1Hospital del Mar Research Institute, Servei de Pneumologia, Hospital del Mar, 08003 Barcelona, Spain.

International Journal of Molecular Sciences
|July 12, 2025
PubMed
Summary

Plasma metabolomic profiling can predict long-term mortality in Chronic Obstructive Pulmonary Disease (COPD) patients. Early metabolic changes, especially in energy pathways, may indicate higher risk and complement existing markers like FEV1.

Keywords:
COPDamino acidsenergymetabolomicsmicrobiotamortalityredox

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Area of Science:

  • Pulmonary Medicine
  • Metabolomics
  • Biochemistry

Background:

  • Chronic Obstructive Pulmonary Disease (COPD) presents significant mortality challenges.
  • Early identification of high-risk patients is crucial for improved outcomes.
  • Prognostic markers for COPD mortality require further refinement.

Purpose of the Study:

  • To investigate plasma metabolomic profiling for predicting 7-year mortality in stable COPD patients.
  • To explore potential underlying biological mechanisms associated with COPD mortality.
  • To assess the utility of metabolomic data in conjunction with FEV1 for risk stratification.

Main Methods:

  • Plasma samples from 41 stable COPD patients were analyzed using untargeted and semi-targeted LC-MS.
  • Metabolomic data was correlated with 7-year mortality outcomes.
  • Machine learning algorithms were employed to build predictive models for mortality.
  • Five-fold cross-validation was used for internal validation.

Main Results:

  • Twelve metabolites, predominantly amino acids, significantly differed between survivors and non-survivors.
  • Functional analysis indicated disruptions in energy production pathways.
  • Machine learning models incorporating metabolites and FEV1 demonstrated high accuracy in predicting 7-year mortality.
  • A model combining nine metabolites and FEV1 showed slightly superior predictive performance.

Conclusions:

  • Early alterations in plasma metabolomic profiles, particularly in energy metabolism, may predict long-term mortality in COPD.
  • Metabolomic profiling offers potential as a complementary tool to traditional markers like FEV1 for COPD risk assessment.
  • These findings warrant further investigation in larger cohorts to validate the clinical utility of metabolomic biomarkers.