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Updated: Oct 18, 2025
Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
Published on: November 29, 2024
Biological and Clinical Factors Contributing to the Metabolic Heterogeneity of Hospitalized Patients with and without
Angelo D'Alessandro1, Tiffany Thomas2, Imo J Akpan3
1Department of Biochemistry and Molecular Genetics, Anschutz Medical Campus, University of Colorado Denver, Aurora, CO 80045, USA.
Insights
Metabolic changes in amino acids and fatty acids are key indicators of COVID-19 severity and prognosis. This study analyzed plasma from over 800 patients, revealing metabolic markers for disease progression and aiding future research.
Area of Science:
- Biochemistry
- Metabolomics
- Infectious Disease Epidemiology
Background:
- The COVID-19 pandemic poses a significant global health challenge.
- Understanding the metabolic alterations in patients with COVID-19 is crucial for managing disease severity and outcomes.
Purpose of the Study:
- To investigate the metabolic features associated with COVID-19 infection.
- To identify metabolic markers for disease severity, progression, and prognosis.
- To develop predictive models for COVID-19 using metabolomic and clinical data.
Main Methods:
- High-throughput metabolomics, lipidomics, and enzymatic activity assays on plasma samples from acutely ill patients.
- Analysis of patients who tested positive (n=543) or negative (n=288) for COVID-19.
- Machine learning models trained and tested on metabolomic and clinical data.
Main Results:
- Significant alterations in amino acid and fatty acid/acylcarnitine metabolism were identified as relevant markers.
- Lipidomics revealed metabolic differences in relation to body mass index in COVID-19 patients.
- Machine learning models achieved approximately 78% accuracy in predicting outcomes based on integrated data.
Conclusions:
- Plasma metabolic profiles, particularly amino acid and fatty acid pathways, offer insights into COVID-19 severity and prognosis.
- Metabolomic data, combined with clinical information, can support predictive modeling for COVID-19.
- This study provides a foundation for further mechanistic research and data sharing to advance understanding of critical illness metabolism.
Abstract:
The Corona Virus Disease 2019 (COVID-19) pandemic represents an ongoing worldwide challenge. The present large study sought to understand independent and overlapping metabolic features of samples from acutely ill patients (n = 831) that tested positive (n = 543) or negative (n = 288) for COVID-19. High-throughput metabolomics analyses were complemented with antigen and enzymatic activity assays on plasma from acutely ill patients collected while in the emergency department, at admission, or during hospitalization. Lipidomics analyses were also performed on COVID-19-positive or -negative subjects with the lowest and highest body mass index (n = 60/group). Significant changes in amino acid and fatty acid/acylcarnitine metabolism emerged as highly relevant markers of disease severity, progression, and prognosis as a function of biological and clinical variables in these patients. Further, machine learning models were trained by entering all metabolomics and clinical data from half of the COVID-19 patient cohort and then tested on the other half, yielding ~78% prediction accuracy. Finally, the extensive amount of information accumulated in this large, prospective, observational study provides a foundation for mechanistic follow-up studies and data sharing opportunities, which will advance our understanding of the characteristics of the plasma metabolism in COVID-19 and other acute critical illnesses.
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