A multiomics recovery factor predicts long COVID in the IMPACC study
Gisela Gabernet, et al.
Gisela Gabernet, et al.
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Clinical Research and Public Health Immunology Infectious disease

A multiomics recovery factor predicts long COVID in the IMPACC study

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.TRIAL REGISTRATION ClinicalTrials.gov NCT04378777.FUNDING National 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

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

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Figure 2

Identification of a convalescent multiomics recovery factor that discriminates LC.

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Identification of a convalescent multiomics recovery factor that discrim...
(A) Predictive performance of a lasso model trained on the MOFA and SPEAR factors to discriminate LC versus MIN at the event level. The mean AUROC of a 10-fold cross-validation on the train cohort for 100 bootstrapped model training repetitions is shown. Significance was calculated by standard normal approximation of bootstrapped differences between models (t test, adj. ****P ≤ 0.0001). CV, cross validation. (B) Predictive performance of the SPEAR Physical model to discriminate LC versus MIN on the test cohort. The ROC curve of model (solid line), random classifier (dashed line), and AUROC value are shown. TPR, true-positive rate; FPR, false-positive rate. (C) Recovery factor scores for the test cohort of the MIN and LC groups at 3 months (visit 7), 6 months (visit 8), 9 months (visit 9), and 12 months (visit 10) after hospital discharge. (D) Recovery factor scores of the individual PRO clusters by visit for the test cohort. P values in C and D show the significance of the recovery factor score association with MIN versus LC and pairwise PRO cluster combinations using a goodness-of-fit χ2 test. See also Supplemental Figure 3–5.