E2 ModeratePreliminaryPEM unclearCross-SectionalPeer-reviewedReviewed
Developing a Blood Cell-Based Diagnostic Test for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Using Peripheral Blood Mononuclear Cells.
Xu, Jiabao, Lodge, Tiffany, Kingdon, Caroline et al. · Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023 · DOI
Quick Summary
Researchers used a new technology called Raman spectroscopy combined with artificial intelligence to analyze blood cells from ME/CFS patients and healthy people. The test was able to correctly identify ME/CFS patients about 91% of the time and could even distinguish between mild, moderate, and severe cases with 84% accuracy. This could eventually help doctors diagnose ME/CFS with a simple blood test instead of relying only on patient symptoms.
Why It Matters
ME/CFS lacks objective diagnostic biomarkers, forcing many patients to pursue years of testing before diagnosis. A validated blood-based test could reduce diagnostic delays, improve clinical management, and enable better patient stratification for research and treatment trials. The methodology may also be applicable to other unexplained post-infectious conditions like long COVID.
Observed Findings
- Raman spectral profiles of blood cells differentiated ME/CFS patients from healthy controls with 91% accuracy.
- The same technology distinguished mild, moderate, and severe ME/CFS cases with 84% accuracy.
- Specific Raman peaks were identified that correlate with ME/CFS phenotypes and disease severity.
- Disease control participants were successfully distinguished from both ME/CFS patients and healthy controls.
- The analysis revealed potential biochemical markers in blood cells associated with ME/CFS.
Inferred Conclusions
- Single-cell Raman spectroscopy combined with AI can identify objective blood-based signatures of ME/CFS with clinically promising accuracy.
- Blood cell spectral profiles reflect disease severity and could potentially support disease stratification and monitoring.
- This technology may provide insights into the biological mechanisms underlying ME/CFS and guide therapeutic development.
Remaining Questions
- Will these Raman signatures remain stable over time, and can they be used to monitor disease progression or response to treatment?
- How do these blood cell changes relate to the underlying pathophysiology of ME/CFS—are they primary causes or secondary effects?
What This Study Does Not Prove
This study does not establish causation—it identifies correlations between blood cell spectral profiles and ME/CFS status. The cross-sectional design cannot determine whether observed cellular changes cause ME/CFS or result from it. The findings require independent validation in larger, prospectively-designed studies before this test can be used clinically.
Tags
Symptom:Fatigue
Biomarker:Blood Biomarker
Phenotype:SevereLong COVID Overlap
Method Flag:Small SampleExploratory OnlyMixed CohortWeak Case DefinitionSevere ME Included
Metadata
- DOI
- 10.1002/advs.202302146
- PMID
- 37653608
- Review status
- Editor reviewed
- Evidence level
- Single-study or moderate support from human research
- Last updated
- 12 April 2026
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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