Yagin, Fatma Hilal, Alkhateeb, Abedalrhman, Raza, Ali et al. · Diagnostics (Basel, Switzerland) · 2023 · DOI
Researchers used artificial intelligence and a type of blood analysis called metabolomics to find chemical markers that could help identify ME/CFS patients. They studied 26 people with ME/CFS and 26 healthy people, testing 768 different chemicals in their blood. The AI model identified four key chemicals (C-glycosyltryptophan, oleoylcholine, cortisone, and 3-hydroxydecanoate) that were different between the two groups and could help diagnose ME/CFS with high accuracy.
ME/CFS lacks objective diagnostic biomarkers, making this identification of metabolomic signatures clinically significant. This work provides a potential diagnostic tool and advances mechanistic understanding of ME/CFS by highlighting dysregulation in amino acid metabolism, energy production, and endocrine pathways. The interpretable AI approach bridges the gap between complex biochemical data and clinical utility.
This study does not establish that these metabolites cause ME/CFS or prove these are the only biomarkers relevant to the disease. The small sample size and cross-sectional design prevent determination of whether metabolite changes occur before symptom onset or result from the illness. Results require validation in larger, diverse, independent populations before clinical implementation.
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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