Kovarik, Johannes J, Bileck, Andrea, Hagn, Gerhard et al. · iScience · 2023 · DOI
Researchers compared blood samples from three groups: healthy people, people who recovered from COVID-19 without ongoing symptoms, and people with long COVID who have chronic fatigue and other persistent symptoms. They found that people with long COVID have lower levels of inflammation markers and higher levels of compounds that reduce inflammation, which is unexpected since many assumed long COVID involved high inflammation.
This study challenges the assumption that long COVID and ME/CFS are primarily driven by excessive inflammation, suggesting instead that dysregulated or alternative immune activation may underlie persistent symptoms. Understanding these distinct immunological patterns could lead to targeted treatments tailored to the actual immune dysfunction rather than broad anti-inflammatory approaches.
This cross-sectional study cannot establish causality or determine whether the observed immune signature causes symptoms, results from them, or represents an epiphenomenon. The small sample size and lack of longitudinal follow-up mean we cannot determine if these markers change with disease progression or recovery. The proposed macrophage model remains theoretical and requires functional validation.
About the PEM badge: “PEM required” means post-exertional malaise was an explicit required diagnostic criterion for participant inclusion in this study — not that PEM was studied, observed, or discussed. Studies using criteria that do not require PEM (e.g. Fukuda, Oxford) are tagged “PEM not required”. How the atlas works →
The first block is for the primary paper and is the citation you should use in research work. The atlas-snapshot line only applies if you are specifically referring to this atlas’s reading of the paper on the date shown.
Primary citation
Kovarik, Johannes J, Bileck, Andrea, Hagn, Gerhard, Meier-Menches, Samuel M, Frey, Tobias, Kaempf, Anna, et al. (2023). A multi-omics based anti-inflammatory immune signature characterizes long COVID-19 syndrome.. iScience. https://doi.org/10.1016/j.isci.2022.105717
BibTeX
@article{mecfsatlas-kovarik-2023-multi-omics,
author = {Kovarik, Johannes J and Bileck, Andrea and Hagn, Gerhard and Meier-Menches, Samuel M and Frey, Tobias and Kaempf, Anna and Hollenstein, Marlene and Shoumariyeh, Tarik and Skos, Lukas and Reiter, Birgit and Gerner, Marlene C and Spannbauer, Andreas and Hasimbegovic, Ena and Schmidl, Doreen and Garhöfer, Gerhard and Gyöngyösi, Mariann and Schmetterer, Klaus G and Gerner, Christopher},
title = {A multi-omics based anti-inflammatory immune signature characterizes long COVID-19 syndrome.},
journal = {iScience},
year = {2023},
doi = {10.1016/j.isci.2022.105717},
note = {PubMed: 36507225},
url = {https://www.mecfsatlas.com/evidence/kovarik-2023-multi-omics},
}Atlas snapshot reference
ME/CFS Atlas. Generator v1 / Scanner v1.4 / policy v0.1. Accessed 2026-05-28. https://www.mecfsatlas.com/evidence/kovarik-2023-multi-omics
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