Distinguishing features of Long COVID identified through immune profiling.
Jonathan KleinJamie WoodJillian JaycoxRahul Madhav DhodapkarPeiwen LuJeff R GehlhausenAlexandra TabachnikovaKerrie GreeneLaura TabacofAmyn A MalikValter Vinícius Silva MonteiroJulio SilvaKathy KamathMinlu ZhangAbhilash DhalIsabel M OttGabrielee ValleMario Peña-HernandezTianyang MaoBornali BhattacharjeeTakehiro TakahashiCarolina LucasEric SongDayna MccarthyErica BreymanJenna Tosto-MancusoYile DaiEmily PerottiKoray AkdumanTiffany J TzengLan XuAnna C GeraghtyMichelle MonjeInci B YildirimJohn ShonRuslan MedzhitovDenyse LutchmansinghJennifer D PossickNaftali KaminskiSaad B OmerHarlan M KrumholzLeying GuanCharles Dela CruzDavid van DijkAaron M RingDavid F PutrinoAkiko IwasakPublished in: Nature (2023)
Post-acute infection syndromes (PAIS) may develop after acute viral disease 1 . Infection with SARS-CoV-2 can result in the development of a PAIS known as "Long COVID" (LC). Individuals with LC frequently report unremitting fatigue, post-exertional malaise, and a variety of cognitive and autonomic dysfunctions 2-4 ; however, the biological processes associated with the development and persistence of these symptoms are unclear. Here, 273 individuals with or without LC were enrolled in a cross-sectional study that included multi-dimensional immune phenotyping and unbiased machine learning methods to identify biological features associated with LC. Marked differences were noted in circulating myeloid and lymphocyte populations relative to matched controls, as well as evidence of exaggerated humoral responses directed against SARS-CoV-2 among participants with LC. Further, higher antibody responses directed against non-SARS-CoV-2 viral pathogens were observed among individuals with LC, particularly Epstein-Barr virus. Levels of soluble immune mediators and hormones varied among groups, with cortisol levels being lower among participants with LC. Integration of immune phenotyping data into unbiased machine learning models identified key features most strongly associated with LC status. Collectively, these findings may help guide future studies into the pathobiology of LC and aid in developing relevant biomarkers.
Keyphrases
- sars cov
- simultaneous determination
- machine learning
- epstein barr virus
- respiratory syndrome coronavirus
- mass spectrometry
- liquid chromatography
- coronavirus disease
- immune response
- tandem mass spectrometry
- physical activity
- big data
- high throughput
- artificial intelligence
- acute myeloid leukemia
- dendritic cells
- heart rate
- risk factors
- depressive symptoms
- liver failure
- heat stress
- single cell
- current status
- genetic diversity