Research abstract:

Integration of DNA methylation & health scores identifies subtypes in myalgic encephalomyelitis/chronic fatigue syndrome, by Wilfred C de Vega, Lauren Erdman, Suzanne D Vernon, Anna Goldenberg & Patrick O McGowan in Epigenomics. 2018 Apr 25. doi: 10.2217/epi-2017-0150. [Epub ahead of print]

If your body cannot methylate properly, toxins build up in your bloodstream and will eventually cause disease. … In fact, methylation can turn genes on or off, which can be good or bad for our health, depending on the gene.  Read more


To identify subtypes in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) based on DNA methylation profiles and health scores.


DNA methylome profiles in immune cells were integrated with symptomatology from 70 women with ME/CFS using similarity network fusion to identify subtypes.


We discovered four ME/CFS subtypes associated with DNA methylation modifications in 1939 CpG sites, three RAND-36 categories and five DePaul Symptom Questionnaire measures. Methylation patterns of immune response genes and differences in physical functioning and postexertional malaise differentiated the subtypes.


ME/CFS subtypes are associated with specific DNA methylation differences and health symptomatology and provide additional evidence of the potential relevance of metabolic and immune differences in ME/CFS with respect to specific symptoms.

Summary points

  • DNA methylation data and health score data using the RAND-36 survey and the DePaul Symptom Questionnaire were collected in 70 women diagnosed with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS).
  • Potential clinical subtypes integrating DNA methylation differences in ME/CFS biomarkers (4699 sites in total) and health score data were identified using similarity network fusion, a clustering approach that preserves the data structures unique to each level of data.
  • Similarity network fusion analysis revealed the presence of four clinical subtypes among the ME/CFS patients based on methylation differences in 1939 sites, three RAND-36 categories and five DePaul measures (normalized mutual information [NMI] >0.2, Kruskal–Wallis FDR <0.05).
  • RAND-36 scores that significantly contributed to clustering corresponded to physical functioning (NMI = 0.26), social functioning (NMI = 0.23) and role limitations due to physical health (NMI = 0.23).
  • DePaul Symptom Questionnaire measures that were significantly different between ME/CFS clinical subtypes were associated with frequency of postexertional malaise, severity of pain/muscle weakness and severity of unrefreshing sleep.
  • Gene set enrichment analysis of the top 50 sites that contributed to differentiating the ME/CFS subtypes were mainly annotated to immune response.
  • The results are consistent with previous work that described immune and metabolic dysfunction in ME/CFS.
  • The described subtypes of our study are also generally consistent with previous ME/CFS work that identified immune gene and degree of symptom severity as highly relevant factors in differentiating ME/CFS subtypes.

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