Language
English
Publication Date
11-3-2025
Journal
Journal of Clinical Investigation
DOI
10.1172/JCI193698
PMID
40924481
PMCID
PMC12582403
PubMedCentral® Posted Date
9-9-2025
PubMedCentral® Full Text Version
Post-print
Abstract
Background: Following SARS-CoV-2 infection, approximately 10%–35% of patients with COVID-19 experience long COVID (LC), in which debilitating symptoms persist for at least 3 months. Elucidating the biologic underpinnings of LC could identify therapeutic opportunities.
Methods: We utilized machine learning methods on biologic analytes provided over 12 months after hospital discharge from more than 500 patients with COVID-19 in the IMPACC cohort to identify a multiomics “recovery factor,” trained on patient-reported physical function survey scores. Immune profiling data included PBMC transcriptomics, serum O-link and plasma proteomics, plasma metabolomics, and blood mass cytometry by time of flight (CyTOF) protein levels. Recovery factor scores were tested for association with LC, disease severity, clinical parameters, and immune subset frequencies. Enrichment analyses identified biologic pathways associated with recovery factor scores.
Results: Participants with LC had lower recovery factor scores compared with recovered participants. Recovery factor scores predicted LC as early as hospital admission, irrespective of acute COVID-19 severity. Biologic characterization revealed increased inflammatory mediators, elevated signatures of heme metabolism, and decreased androgenic steroids as predictive and ongoing biomarkers of LC. Lower recovery factor scores were associated with reduced lymphocyte and increased myeloid cell frequencies. The observed signatures are consistent with persistent inflammation driving anemia and stress erythropoiesis as major biologic underpinnings of LC.
Conclusion: The multiomics recovery factor identifies patients at risk of LC early after SARS-CoV-2 infection and reveals LC biomarkers and potential treatment targets.
Keywords
Humans, COVID-19, Male, Female, SARS-CoV-2, Middle Aged, Aged, Adult, Metabolomics, Machine Learning, Multiomics, Biomarkers, COVID-19, Machine learning, Immunology, Infectious disease
Published Open-Access
yes
Recommended Citation
Gabernet, Gisela; Maciuch, Jessica; Gygi, Jeremy P; et al., "A Multiomics Recovery Factor Predicts Long COVID in the Impacc Study" (2025). Faculty and Staff Publications. 4650.
https://digitalcommons.library.tmc.edu/baylor_docs/4650
Graphical Abstract
Included in
Clinical Epidemiology Commons, COVID-19 Commons, Health Services Research Commons, Infectious Disease Commons, Medical Sciences Commons