{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Gabernet G"],"funding":["NCATS NIH HHS","NIDA NIH HHS","NIAID NIH HHS","National Institutes of Health/ National INstitute of Allergy and INfectious Diseases","National Institutes of health/National Institute of Allergy and Infectious Diseases","National Science Foundation"],"pagination":["e193698"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12582403"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["135(21)"],"pubmed_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 severit"],"journal":["The Journal of clinical investigation"],"pubmed_title":["A multiomics recovery factor predicts long COVID in the IMPACC study."],"pmcid":["PMC12582403"],"funding_grant_id":["R01 AI135803","R01 AI104870","R01 AI132774","UM1 TR004528","T32 DA018926","U19 AI090023","U19 AI062629","5R01AI135803,5U19AI118608,3U01AI167892,5  U19 AI128910,4 U19AI090023,4 U19AI118610,R01 AI145835,5 U19AI062629,5 U19AI057229,5  U19  AI125357,5 U19AI128913,3U19AI077439,5 U54AI142766,5 R01AI104870,3U19AI089992,5R01AI132774,5T32DA018926,UM1TR004528","U01 AI167892","U54 AI142766","U19 AI118610","DMS2310836","R01AI122220","U19 AI077439","R01 AI145835","U19 AI118608","U19 AI125357","U19 AI057229","R01 AI122220","U19 AI128910","U19 AI089992","U19 AI128913"],"pubmed_authors":["Peters B","Ehrlich LI","Simon V","Kheradmand F","Moore JF","Schaenman J","Augustine AD","Rouphael N","Maecker HT","Corry DB","Sekaly RP","McComsey GA","Davis MM","Reed EF","Doni Jayavelu N","Metcalf JP","Haddad EK","Diray-Arce J","Ozonoff A","Chu T","Melamed E","Cairns CB","Baden LR","Kim-Schulze S","Pulendran B","Brakenridge SC","Syphurs C","Hough CL","Smolen KK","Nadeau KC","Montgomery RR","Kraft M","Bime C","Levy O","IMPACC Network","Gygi JP","Steen H","Eckalbar W","Hoch A","Hafler DA","Agudelo Higuita NI","Calfee CS","Krammer F","Gabernet G","Maciuch J","Erle DJ","Kleinstein SH","Shaw AC","Altman MC","Messer WB","Guan L","Fourati S","Bosinger SE","Westendorf K","Atkinson MA","Fernandez-Sesma A","Becker PM"],"additional_accession":[]},"is_claimable":false,"name":"A multiomics recovery factor predicts long COVID in the IMPACC study.","description":"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 severit","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Nov","modification":"2026-06-05T11:54:02.752Z","creation":"2026-05-16T03:12:42.531Z"},"accession":"S-EPMC12582403","cross_references":{"pubmed":["40924481"],"doi":["10.1172/JCI193698"]}}