Yagin, Fatma Hilal, Colak, Cemil, Al-Hashem, Fahaid et al. · Diagnostics (Basel, Switzerland) · 2025 · DOI
Researchers used advanced computer learning and blood tests to find chemical markers that could help diagnose ME/CFS. They analyzed 888 different chemicals in blood samples from 106 ME/CFS patients and 91 healthy people, and found a pattern of chemical imbalances that correctly identified ME/CFS patients 87% of the time. The key chemical imbalances involved energy production in cells, inflammation, and gut health—areas that have long been suspected in ME/CFS.
This research addresses a critical clinical need—ME/CFS currently lacks objective diagnostic biomarkers, forcing clinicians to rely on symptom-based diagnosis. A validated metabolic signature could improve diagnostic accuracy and speed of diagnosis, reducing the diagnostic odyssey many patients experience. Additionally, identifying dysregulated metabolic pathways provides mechanistic insights that could inform the development of targeted treatments.
This study does not prove these metabolites *cause* ME/CFS or that they are specific to ME/CFS alone; they may be altered in other conditions. The cross-sectional design cannot establish temporal relationships or causality. The findings require validation in independent cohorts and real-world clinical settings before the model could be used in clinical practice.
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 →
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Primary citation
Yagin, Fatma Hilal, Colak, Cemil, Al-Hashem, Fahaid, Alzakari, Sarah A, Alhussan, Amel Ali, & Aghaei, Mohammadreza (2025). Leveraging Explainable Automated Machine Learning (AutoML) and Metabolomics for Robust Diagnosis and Pathophysiological Insights in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS).. Diagnostics (Basel, Switzerland). https://doi.org/10.3390/diagnostics15212755
BibTeX
@article{mecfsatlas-yagin-2025-leveraging-explainable,
author = {Yagin, Fatma Hilal and Colak, Cemil and Al-Hashem, Fahaid and Alzakari, Sarah A and Alhussan, Amel Ali and Aghaei, Mohammadreza},
title = {Leveraging Explainable Automated Machine Learning (AutoML) and Metabolomics for Robust Diagnosis and Pathophysiological Insights in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS).},
journal = {Diagnostics (Basel, Switzerland)},
year = {2025},
doi = {10.3390/diagnostics15212755},
note = {PubMed: 41226047},
url = {https://www.mecfsatlas.com/evidence/yagin-2025-leveraging-explainable},
}Atlas snapshot reference
ME/CFS Atlas. Generator v1 / Scanner v1.4 / policy v0.1. Accessed 2026-05-29. https://www.mecfsatlas.com/evidence/yagin-2025-leveraging-explainable
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