biostudies-literatureUnknownGabernet GNCATS NIH HHSNIDA NIH HHSNIAID NIH HHSFollowing SARS-CoV-2 infection, ~10-35% of COVID-19 patients experience long COVID (LC), in which often debilitating symptoms persist for at least three months. Elucidating the biologic underpinnings of LC could identify therapeutic opportunities. We utilized machine learning methods on biologic analytes and patient reported outcome surveys provided over 12 months after hospital discharge from >500 hospitalized COVID-19 patients in the IMPACC cohort to identify a multi-omics "recovery factor". IMPACC participants who experienced LC had lower recovery factor scores compared to participants without LC. Biologic characterization revealed increased levels of plasma proteins associated with inflammation, elevated transcriptional signatures of heme metabolism, and decreased androgenic steroids ibioRxiv : the preprint server for biology2025.02.12.637926https://www.ebi.ac.uk/biostudies/studies/S-EPMC11844572biostudies-literatureIdentification of a multi-omics factor predictive of long COVID in the IMPACC study.PMC11844572R01 AI135803R01 AI104870UM1 TR004528U19 AI062629T32 DA018926U19 AI090023U01 AI167892U54 AI142766U19 AI118610U19 AI077439R01 AI145835U19 AI118608U19 AI125357U19 AI057229U19 AI128910R01 AI122220U19 AI089992U19 AI128913Peters BSimon VKheradmand FMoore JFSchaenman JRouphael NMaecker HTCorry DBSekaly RPMcComsey GADavis MMReed EFMetcalf JPEhrlich LIRHaddad EKDiray-Arce JOzonoff AChu TMelamed ECairns CBBaden LRKim-Schulze SPulendran BSyphurs CHough CLSmolen KKNadeau KCMontgomery RRKraft MBime CLevy OIMPACC NetworkBrackenridge SCGygi JPSteen HEckalbar WHoch AHafler DAAgudelo Higuita NICalfee CSKrammer FGabernet GMaciuch JErle DJKleinstein SHShaw ACAltman MCMesser WBGuan LFourati SJayavelu NDBosinger SEWestendorf KAtkinson MAFernandez-Sesma AfalseIdentification of a multi-omics factor predictive of long COVID in the IMPACC study.Following SARS-CoV-2 infection, ~10-35% of COVID-19 patients experience long COVID (LC), in which often debilitating symptoms persist for at least three months. Elucidating the biologic underpinnings of LC could identify therapeutic opportunities. We utilized machine learning methods on biologic analytes and patient reported outcome surveys provided over 12 months after hospital discharge from >500 hospitalized COVID-19 patients in the IMPACC cohort to identify a multi-omics "recovery factor". IMPACC participants who experienced LC had lower recovery factor scores compared to participants without LC. Biologic characterization revealed increased levels of plasma proteins associated with inflammation, elevated transcriptional signatures of heme metabolism, and decreased androgenic steroids i2025-01-01T00:00:00Z2025 Feb2026-05-22T03:18:48.938Z2025-04-04T13:46:37.934ZS-EPMC118445723999044210.1101/2025.02.12.637926