Discriminating Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and comorbid conditions using metabolomics in UK Biobank.

Observed Findings

• Nine blood NMR biomarkers combined with 19 baseline characteristics discriminated ME/CFS from other conditions with 83% accuracy and 70% sensitivity.
• Lipoprotein-related markers and ketone bodies showed significant associations with ME/CFS, with gender-specific differences in these patterns.
• Overlapping metabolic signatures were identified between ME/CFS and specific comorbidities, particularly regarding surface lipids.
• A multi-variable predictive score derived from the same 28 features showed 2.5 times greater association strength than any single individual biomarker.
• 168 individual biomarkers demonstrated significant associations with ME/CFS across the study cohorts.

Inferred Conclusions

• Blood metabolomics can identify metabolic signatures distinguishing ME/CFS from both healthy individuals and those with common comorbidities.
• Gender-specific metabolic differences in ME/CFS warrant separate analysis and potentially sex-stratified diagnostic approaches.
• Combined biomarker panels are substantially more informative than single markers for ME/CFS classification, suggesting multifactorial metabolic dysregulation.
• Metabolomics-based association scores may offer clinical utility for diagnosing ME/CFS and provide a generalizable analytical framework for other conditions lacking definitive laboratory tests.

Remaining Questions