Metabolomic biomarkers predict long-term physical function in survivors of acute respiratory failure
Our findings highlight persistent metabolic dysfunction at discharge, particularly in pathways related to bioenergetics. To our knowledge, this is the first study to employ a metabolite-based machine learning model to predict physical function outcomes in ARF survivors using serum metabolites measured at discharge. Further insights on dysregulated pathways suggest that nutritional interventions targeting these metabolic pathways, such as supplementation with β-alanine, could potentially improve post-ICU recovery outcomes.
Authors
- Ryan Benton (ORCID: https://orcid.org/0000-0001-9616-6221)
- D. Clark Files (ORCID: https://orcid.org/0000-0002-1595-4080)
- Sheetal Gandotra (ORCID: https://orcid.org/0000-0002-5475-9704)
- Peter E. Morris
- Lina D. Purcell
- Raymond J. Langley (ORCID: https://orcid.org/0000-0001-8849-9325)
- Justin T. Roberts (ORCID: https://orcid.org/0000-0003-4433-1234)
- Troy Stevens (ORCID: https://orcid.org/0000-0001-9689-929X)
- Grant Daly (ORCID: https://orcid.org/0000-0002-4109-0546)
- Emily M. Hartsell (ORCID: https://orcid.org/0000-0003-0268-9916)
- Adeyeye I. Haastrup
- Mark N. Gillespie
- Viktor Pastukh
Institutions
- University of Maryland, Baltimore (US)
- Atrium Health Wake Forest Baptist (US)
- University of Alabama at Birmingham (US)
- University of South Alabama (US)
Publication Details
- Journal
- Respiratory Research
- Published
- 2025-11-21
- DOI
- https://doi.org/10.1186/s12931-025-03423-2
- Primary Topic
- Respiratory Support and Mechanisms
- Type
- article
- Field-Weighted Citation Impact
- 0.00