Cai, Ethan, Kouznetsova, Valentina L, Tsigelny, Igor F · Metabolites · 2025 · DOI
Researchers developed a computer program that can identify Long COVID (PASC) by analyzing chemical markers in the blood called metabolites. The program successfully distinguished Long COVID from several similar conditions like Lyme disease and POTS, but had difficulty telling Long COVID apart from fibromyalgia, suggesting these two conditions may share similar chemical patterns in the body.
This research provides potential support for a biological basis of Long COVID through metabolomic profiling and offers a novel diagnostic approach that could reduce diagnostic delays—a significant challenge in both PASC and ME/CFS. The noted metabolomic similarity between fibromyalgia and Long COVID is particularly relevant for ME/CFS patients, as these conditions share clinical features and diagnostic ambiguity.
This study does not establish causation for any metabolite abnormalities—only association. It does not determine whether metabolomic patterns are primary disease mechanisms or secondary consequences of illness. The inability to distinguish fibromyalgia from PASC raises questions about whether the model identifies disease-specific biology or shared features among post-infectious/chronic conditions, and clinical validation in real-world diagnostic settings remains needed.
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
Cai, Ethan, Kouznetsova, Valentina L, & Tsigelny, Igor F (2025). Metabolomics-Based Machine Learning Diagnostics of Post-Acute Sequelae of SARS-CoV-2 Infection.. Metabolites. https://doi.org/10.3390/metabo15120801
BibTeX
@article{mecfsatlas-cai-2025-metabolomics-based,
author = {Cai, Ethan and Kouznetsova, Valentina L and Tsigelny, Igor F},
title = {Metabolomics-Based Machine Learning Diagnostics of Post-Acute Sequelae of SARS-CoV-2 Infection.},
journal = {Metabolites},
year = {2025},
doi = {10.3390/metabo15120801},
note = {PubMed: 41441042},
url = {https://www.mecfsatlas.com/evidence/cai-2025-metabolomics-based},
}Atlas snapshot reference
ME/CFS Atlas. Generator v1 / Scanner v1.4 / policy v0.1. Accessed 2026-05-30. https://www.mecfsatlas.com/evidence/cai-2025-metabolomics-based
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