A multiomics recovery factor predicts long COVID in the IMPACC study

BACKGROUNDFollowing 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.METHODSWe 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.RESULTSParticipants 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.CONCLUSIONThe multiomics recovery factor identifies patients at risk of LC early after SARS-CoV-2 infection and reveals LC biomarkers and potential treatment targets.TRIAL REGISTRATIONClinicalTrials.gov NCT04378777.FUNDINGNational Institute of Allergy and Infectious Diseases (NIAID), NIH (3U01AI167892-03S2, 3U01AI167892-01S2, 5R01AI135803-03, 5U19AI118608-04, 5U19AI128910-04, 4U19AI090023-11, 4U19AI118610-06, R01AI145835-01A1S1, 5U19AI062629-17, 5U19AI057229-17, 5U19AI057229-18, 5U19AI125357-05, 5U19AI128913-03, 3U19AI077439-13, 5U54AI142766-03, 5R01AI104870-07S1, 3U19AI089992-09, 3U19AI128913-03, and 5T32DA018926-1, 3U19AI1289130, U19AI128913-04S1, R01AI122220); NIH (UM1TR004528); and National Science Foundation (NSF) (DMS2310836).

Authors

Institutions

Publication Details

Journal
Journal of Clinical Investigation
Published
2025-09-09
DOI
https://doi.org/10.1172/jci193698
Citations
1
Primary Topic
Long-Term Effects of COVID-19
Type
article
Field-Weighted Citation Impact
1.39

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A multiomics recovery factor predicts long COVID in the IMPACC study

Grace A. McComsey, Walter L. Eckalbar, Joann Diray‐Arce, Jeremy P. Gygi et al.
1 citations
Journal of Clinical Investigation
Long-Term Effects of COVID-19
1.39
article

A multiomics recovery factor predicts long COVID in the IMPACC study

Grace A. McComsey, Walter L. Eckalbar, Joann Diray‐Arce, Jeremy P. Gygi, Hanno Steen, Leying Guan, David J. Erle, Chris Bime, Ruth R. Montgomery, Annmarie Hoch, William B. Messer, Esther Melamed, Albert C. Shaw, Kari Christine Nadeau, Jordan P. Metcalf, Kinga K. Smolen, Bali Pulendran, Slim Fourati, Rafick‐Pierre Sékaly, Charles B. Cairns, Nadine Rouphael, Mark Davis, Steven E. Bosinger, Farrah Kheradmand, John F. Moore, Matthew C. Altman, Catherine L. Hough, Timothy Shun Man Chu, Seunghee Kim‐Schulze, Elaine F. Reed, Kerstin Westendorf, Mark A. Atkinson, Joanna Schaenman, Carolyn S. Calfee, Lauren I.R. Ehrlich, Gisela Gabernet, Steven H. Kleinstein, Ofer Levy, Holden T. Maecker, Naresh Doni Jayavelu, Patrice M. Becker, David B. Corry, Ana Fernández-Sesma, Nelson Iván Agudelo Higuita, Al Ozonoff, Scott C. Brakenridge, Alison D. Augustine, Lindsey Robert Baden, Caitlin Syphurs, Monica Kraft, Viviana Simon, Elias K. Haddad, Florian Krammer, Bjoern Peters, David A. Hafler, Jessica Maciuch
article en
1 citations

Abstract

BACKGROUNDFollowing 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.METHODSWe 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.RESULTSParticipants 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.CONCLUSIONThe multiomics recovery factor identifies patients at risk of LC early after SARS-CoV-2 infection and reveals LC biomarkers and potential treatment targets.TRIAL REGISTRATIONClinicalTrials.gov NCT04378777.FUNDINGNational Institute of Allergy and Infectious Diseases (NIAID), NIH (3U01AI167892-03S2, 3U01AI167892-01S2, 5R01AI135803-03, 5U19AI118608-04, 5U19AI128910-04, 4U19AI090023-11, 4U19AI118610-06, R01AI145835-01A1S1, 5U19AI062629-17, 5U19AI057229-17, 5U19AI057229-18, 5U19AI125357-05, 5U19AI128913-03, 3U19AI077439-13, 5U54AI142766-03, 5R01AI104870-07S1, 3U19AI089992-09, 3U19AI128913-03, and 5T32DA018926-1, 3U19AI1289130, U19AI128913-04S1, R01AI122220); NIH (UM1TR004528); and National Science Foundation (NSF) (DMS2310836).

Journal of Clinical InvestigationVol. 135(21)
Northwestern University (US), Brigham and Women's Hospital (US), Boston Children's Hospital (US), La Jolla Institute for Immunology (US), Harvard University (US), University of Arizona (US), Emory University (US), City College of San Francisco (US), University of California, Los Angeles (US), Oregon Health & Science University (US), Palo Alto University (US), University of California, San Francisco (US), Baylor College of Medicine (US), University Hospitals of Cleveland (US), Yale University (US), University of Florida (US), Mercedes-Benz Research and Development North America (United States) (US), Center for Assessment (US), National Institute of Allergy and Infectious Diseases (US), University of Washington Applied Physics Laboratory (US), OU Health, University of Oklahoma Health Sciences Center (US), Case Western Reserve University (US), Drexel University (US), The University of Texas at Austin (US), Stanford University (US), Icahn School of Medicine at Mount Sinai (US)
National Science Foundation, National Institutes of Health, National Institute of Allergy and Infectious Diseases
Openalex Percentile: Top 16%
Long-Term Effects of COVID-19
1.39
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.