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