Distinguishing features of long COVID identified through immune profiling

Jon Klein, Jamie Wood, Jillian R. Jaycox, Rahul M. Dhodapkar, Peiwen Lu, Jeff R. Gehlhausen, Alexandra Tabachnikova, Kerrie Greene, Laura Tabacof, Amyn A. Malik, Valter Silva Monteiro, Julio Silva, Kathy Kamath, Minlu Zhang, Abhilash Dhal, Isabel M. Ott, Gabrielee Valle, Mario Peña-Hernández, Tianyang Mao, Bornali Bhattacharjee, Takehiro Takahashi, Carolina Lucas, Eric Song, Dayna McCarthy, Erica Breyman, Jenna Tosto-Mancuso, Yile Dai, Emily Perotti, Koray Akduman, Tiffany J. Tzeng, Lan Xu, Anna C. Geraghty, Michelle Monje, Inci Yildirim, John Shon, Ruslan Medzhitov, Denyse Lutchmansingh, Jennifer D. Possick, Naftali Kaminski, Saad B. Omer, Harlan M. Krumholz, Leying Guan, Charles S. Cruz, David Dijk (), Aaron M. Ring (), David Putrino () and Akiko Iwasaki ()
Additional contact information
Jon Klein: Yale School of Medicine
Jamie Wood: Icahn School of Medicine at Mount Sinai
Jillian R. Jaycox: Yale School of Medicine
Rahul M. Dhodapkar: Yale School of Medicine
Peiwen Lu: Yale School of Medicine
Jeff R. Gehlhausen: Yale School of Medicine
Alexandra Tabachnikova: Yale School of Medicine
Kerrie Greene: Yale School of Medicine
Laura Tabacof: Icahn School of Medicine at Mount Sinai
Amyn A. Malik: Yale School of Public Health
Valter Silva Monteiro: Yale School of Medicine
Julio Silva: Yale School of Medicine
Kathy Kamath: SerImmune
Minlu Zhang: SerImmune
Abhilash Dhal: SerImmune
Isabel M. Ott: Yale School of Medicine
Gabrielee Valle: Yale School of Medicine
Mario Peña-Hernández: Yale School of Medicine
Tianyang Mao: Yale School of Medicine
Bornali Bhattacharjee: Yale School of Medicine
Takehiro Takahashi: Yale School of Medicine
Carolina Lucas: Yale School of Medicine
Eric Song: Yale School of Medicine
Dayna McCarthy: Icahn School of Medicine at Mount Sinai
Erica Breyman: Icahn School of Medicine at Mount Sinai
Jenna Tosto-Mancuso: Icahn School of Medicine at Mount Sinai
Yile Dai: Yale School of Medicine
Emily Perotti: Yale School of Medicine
Koray Akduman: Yale School of Medicine
Tiffany J. Tzeng: Yale School of Medicine
Lan Xu: Yale School of Medicine
Anna C. Geraghty: Stanford University
Michelle Monje: Stanford University
Inci Yildirim: Yale School of Public Health
John Shon: SerImmune
Ruslan Medzhitov: Yale School of Medicine
Denyse Lutchmansingh: Yale School of Medicine
Jennifer D. Possick: Yale School of Medicine
Naftali Kaminski: Yale School of Medicine
Saad B. Omer: Yale School of Public Health
Harlan M. Krumholz: Yale School of Medicine
Leying Guan: Yale School of Medicine
Charles S. Cruz: Yale School of Medicine
David Dijk: Yale School of Medicine
Aaron M. Ring: Yale School of Medicine
David Putrino: Icahn School of Medicine at Mount Sinai
Akiko Iwasaki: Yale School of Medicine

Nature, 2023, vol. 623, issue 7985, 139-148

Abstract: 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 dysfunctions2–4. 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.

Date: 2023
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DOI: 10.1038/s41586-023-06651-y

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