Epigenome-wide profiling identifies distinct DNA methylation architecture underlying ME/CFS and fibromyalgia symptom burden, 2026, Polli et al

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Epigenome-wide profiling identifies distinct DNA methylation architecture underlying ME/CFS and fibromyalgia symptom burden

Polli, Andrea; Hendrix, Jolien; Wyns, Arne; Van Campenhout, Jente; Allard, Sabine; Aerts, Joeri L.; Laeremans, Thessa; Xiong, Huanyu; Buntinx, Yanthe; Michiels, Joni; Ben Amar, Jinane; Godderis, Lode; Thienpont, Bernard; Nijs, Jo

Background
Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and fibromyalgia are overlapping chronic disorders characterized by fatigue, pain, cognitive dysfunction, sleep disturbance, and multisystem symptoms.

Whether peripheral blood DNA methylation reflects diagnostic categories, quantitative symptom burden, or both remains unclear. We aimed to identify DNA methylation axes associated with diagnosis, symptom dimensions, and clinical differences between these conditions.

Methods
This cross-sectional study included 188 women: 73 healthy controls, 71 with ME/CFS, and 44 with fibromyalgia. DNA methylation was profiled in peripheral blood mononuclear cells using the Illumina MethylationEPIC v2 array. Principal component analysis identified latent methylation axes.

Linear models adjusted for age, body mass index, and estimated immune-cell composition tested associations with diagnostic group and clinical measures. Region-level methylation analyses, functional enrichment, bootstrap resampling, permutation testing, leave-one-out analyses, and medication/comorbidity sensitivity analyses were performed.

Results
Both patient groups had greater symptom burden than healthy controls but differed clinically. Fibromyalgia showed greater widespread pain, pain catastrophizing, central sensitization inventory scores, and temporal summation, whereas ME/CFS showed greater post-exertional malaise, cognitive symptoms, and lower physical activity.

Two methylation axes showed clinically relevant associations. PC5 differentiated ME/CFS from fibromyalgia and healthy controls and was associated mainly with post-exertional malaise and cognitive symptoms. PC6 separated both patient groups from healthy controls but not from each other, and was associated with broader symptom burden, including widespread pain, pain impact, sleep disturbance, visceral symptoms, and temporal summation.

The temporal summation association, together with higher widespread pain and temporal summation in fibromyalgia, suggests that PC6 includes a pain-related component extending to experimentally assessed nociceptive summation.

Region-level analyses identified 591 PC5-associated and 54 PC6-associated high-confidence differentially methylated regions. Enrichment implicated neuroimmune, metabolic, cytokine, NF-κB, JAK-STAT, TGF-β, and immune-regulatory pathways. Sensitivity analyses supported the stability of the main associations.

Conclusions
Peripheral blood DNA methylation profiles identified partly distinct but overlapping DNA methylation axes in ME/CFS and fibromyalgia. PC5 was aligned with post-exertional malaise and cognitive symptoms, whereas PC6 was aligned with broader pain-related multisystem burden. Independent replication and longitudinal studies are needed to establish clinical utility.

Web | DOI | PDF | Journal of Translational Medicine | Open Access
 
Epigenome-wide profiling identifies distinct DNA methylation architecture underlying ME/CFS and fibromyalgia symptom burden

Polli, Andrea; Hendrix, Jolien; Wyns, Arne; Van Campenhout, Jente; Allard, Sabine; Aerts, Joeri L.; Laeremans, Thessa; Xiong, Huanyu; Buntinx, Yanthe; Michiels, Joni; Ben Amar, Jinane; Godderis, Lode; Thienpont, Bernard; Nijs, Jo

Background
Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and fibromyalgia are overlapping chronic disorders characterized by fatigue, pain, cognitive dysfunction, sleep disturbance, and multisystem symptoms.

Whether peripheral blood DNA methylation reflects diagnostic categories, quantitative symptom burden, or both remains unclear. We aimed to identify DNA methylation axes associated with diagnosis, symptom dimensions, and clinical differences between these conditions.

Methods
This cross-sectional study included 188 women: 73 healthy controls, 71 with ME/CFS, and 44 with fibromyalgia. DNA methylation was profiled in peripheral blood mononuclear cells using the Illumina MethylationEPIC v2 array. Principal component analysis identified latent methylation axes.

Linear models adjusted for age, body mass index, and estimated immune-cell composition tested associations with diagnostic group and clinical measures. Region-level methylation analyses, functional enrichment, bootstrap resampling, permutation testing, leave-one-out analyses, and medication/comorbidity sensitivity analyses were performed.

Results
Both patient groups had greater symptom burden than healthy controls but differed clinically. Fibromyalgia showed greater widespread pain, pain catastrophizing, central sensitization inventory scores, and temporal summation, whereas ME/CFS showed greater post-exertional malaise, cognitive symptoms, and lower physical activity.

Two methylation axes showed clinically relevant associations. PC5 differentiated ME/CFS from fibromyalgia and healthy controls and was associated mainly with post-exertional malaise and cognitive symptoms. PC6 separated both patient groups from healthy controls but not from each other, and was associated with broader symptom burden, including widespread pain, pain impact, sleep disturbance, visceral symptoms, and temporal summation.

The temporal summation association, together with higher widespread pain and temporal summation in fibromyalgia, suggests that PC6 includes a pain-related component extending to experimentally assessed nociceptive summation.

Region-level analyses identified 591 PC5-associated and 54 PC6-associated high-confidence differentially methylated regions. Enrichment implicated neuroimmune, metabolic, cytokine, NF-κB, JAK-STAT, TGF-β, and immune-regulatory pathways. Sensitivity analyses supported the stability of the main associations.

Conclusions
Peripheral blood DNA methylation profiles identified partly distinct but overlapping DNA methylation axes in ME/CFS and fibromyalgia. PC5 was aligned with post-exertional malaise and cognitive symptoms, whereas PC6 was aligned with broader pain-related multisystem burden. Independent replication and longitudinal studies are needed to establish clinical utility.

Web | DOI | PDF | Journal of Translational Medicine | Open Access
This all sounds very interesting to me!

Anyone else had a look?

TGF-B has come up before, and it's broadly the sort of pathways we might expect to be involved.

There is a DNA methylation aspect to SequenceME too isn't there.
 
From the Pain in Motion research group in Brussels, Belgium. I had compiled a list of some of s4me threads for some of their publications when I wanted to attend a talk by one of the researchers, but couldn't make it in the end.

 
From the Pain in Motion research group in Brussels, Belgium. I had compiled a list of some of s4me threads for some of their publications when I wanted to attend a talk by one of the researchers, but couldn't make it in the end.

Seems like people on here weren't too impressed with this teams other papers
 
"Fibromyalgia showed greater widespread pain, pain catastrophizing, central sensitization inventory scores, and temporal summation, whereas ME/CFS showed greater..."

They lost me at pain catastrophizing and central sensitization. Neither should be used in any research into ME/CFS or fibro. When they are, for me at least, they represent a concrete tell.
 
Enrichment implicated […], TGF-β
TGF-B has come up before, and it's broadly the sort of pathways we might expect to be involved.

Yep, according to one of @ME/CFS Science Blog ‘s recent blogs it’s the cytokine most consistently found to be increased in ME/CFS so interesting to see it come up again.

(It’s disappointing to see the authors get mixed up in the central sensitisation/catastrophising concepts but I don’t think that takes away from the biological data they generated).
 
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