Klein, Jon and Wood, Jamie and Jaycox, Jillian R. and Dhodapkar, Rahul M. and Lu, Peiwen and Gehlhausen, Jeff R. and Tabachnikova, Alexandra and Greene, Kerrie and Tabacof, Laura and Malik, Amyn A. and Silva Monteiro, Valter and Silva, Julio and Kamath, Kathy and Zhang, Minlu and Dhal, Abhilash and Ott, Isabel M. and Valle, Gabrielee and Peña-Hernández, Mario and Mao, Tianyang and Bhattacharjee, Bornali and Takahashi, Takehiro and Lucas, Carolina and Song, Eric and McCarthy, Dayna and Breyman, Erica and Tosto-Mancuso, Jenna and Dai, Yile and Perotti, Emily and Akduman, Koray and Tzeng, Tiffany J. and Xu, Lan and Geraghty, Anna C. and Monje, Michelle and Yildirim, Inci and Shon, John and Medzhitov, Ruslan and Lutchmansingh, Denyse and Possick, Jennifer D. and Kaminski, Naftali and Omer, Saad B. and Krumholz, Harlan M. and Guan, Leying and Dela Cruz, Charles S. and van Dijk, David and Ring, Aaron M. and Putrino, David and Iwasaki, Akiko (2023) Distinguishing features of long COVID identified through immune profiling. Nature, 623 (7985). pp. 139-148. ISSN 0028-0836
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Abstract
Post-acute infection syndromes may develop after acute viral disease1. Infection with SARS-CoV-2 can result in the development of a post-acute infection syndrome known as long COVID. Individuals with long COVID frequently report unremitting fatigue, post-exertional malaise, and a variety of cognitive and autonomic dysfunctions2,3,4. However, the biological processes that are associated with the development and persistence of these symptoms are unclear. Here 275 individuals with or without long COVID were enrolled in a cross-sectional study that included multidimensional immune phenotyping and unbiased machine learning methods to identify biological features associated with long COVID. Marked differences were noted in circulating myeloid and lymphocyte populations relative to the matched controls, as well as evidence of exaggerated humoral responses directed against SARS-CoV-2 among participants with long COVID. Furthermore, higher antibody responses directed against non-SARS-CoV-2 viral pathogens were observed among individuals with long COVID, particularly Epstein–Barr virus. Levels of soluble immune mediators and hormones varied among groups, with cortisol levels being lower among participants with long COVID. Integration of immune phenotyping data into unbiased machine learning models identified the key features that are most strongly associated with long COVID status. Collectively, these findings may help to guide future studies into the pathobiology of long COVID and help with developing relevant biomarkers.
Item Type: | Article |
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Subjects: | Academic Digital Library > Medical Science |
Depositing User: | Unnamed user with email info@academicdigitallibrary.org |
Date Deposited: | 14 Nov 2023 06:34 |
Last Modified: | 14 Nov 2023 06:34 |
URI: | http://publications.article4sub.com/id/eprint/2842 |