A paper published in the Journal of Translational Medicine has attracted significant media attention over the past week. The study, led by Prof. Dmitri Pchejetski, identified “shared biology” across five fatigue-associated illnesses: ME/CFS, long COVID, post-traumatic stress disorder (PTSD), multiple sclerosis (MS), and rheumatoid arthritis (RA).
What did the researchers do?
The methods used in this study were very complex. In simple terms, the researchers looked at existing genetic data from large studies called genome-wide association studies, or GWAS. These studies typically compare the DNA of thousands of people to look for genetic differences that may be linked to health conditions or behaviours. For ME/CFS, the researchers used data from DecodeME, the world’s largest DNA study of ME/CFS.
The team then used computer-based tools called ‘EpiSwitch Orion‘ to compare the genetic datasets for the five illnesses. They wanted to see whether any of the same genes or biological processes appeared across more than one illness.
What were the key findings?
- There was little genetic overlap between ME/CFS, long COVID, and PTSD (RA and MS, both known to be autoimmune diseases, did have some gene overlap).
- Despite this, ‘high network-level interconnectivity’ was identified, indicating that biological mechanisms in the illnesses may overlap.
- Central ‘hub genes’ were identified and may provide targets for understanding how diseases with different triggers lead to “the same clinical exhaustion”.
The shared biological pathways, all of which have previously been linked with ME/CFS, related to:
- The immune system
- Mitochondrial function
- Metabolic regulation
- Neuroendocrine processes, which communication between the nervous system and hormone – endocrine – system
It is essential to recognise that the findings in this paper are ‘hypothesis generating”, rather than conclusive. This means that the observations made by the researchers should be used to inform future studies, rather than being interpreted as fact at this stage.
What are the limitations?
The research team highlights several limitations of the study, these include:
- The findings should be seen as a starting point for future research, rather than as firm proof.
- The study mainly used computer-based analysis of existing genetic datasets, so the results depend on the quality of those datasets and the assumptions built into the analysis tools.
- The proposed biomarkers and biological pathways still need to be tested and confirmed in independent studies.
- Because the five conditions and the datasets used to study them are different, comparisons between them may be affected by differences in how the data were collected, processed, or analysed.
Additional limitations of the paper have also been highlighted by Prof. Chris Ponting and Dr Sjoerd Beentjes from Edinburgh University in an article by the Science Media Center, including that:
- The study used computational tools that are “not open to scrutiny by the scientific community”, and because of this, researchers are not allowed to reproduce the study independently.
- The study may compare data from different types of cells that do not behave in the same way. The chromosome data came from blood cells, but the genetic studies point to other cells being important. This matters because different cell types, such as immune cells and nerve cells, organise their DNA differently.
- The authors used a much lower cut off for deciding whether a result was significant. They treated results with a p value below 0.01 as important, whereas, according to Prof. Ponting, this type of study would usually use a much stricter cut-off of p < 0.00000005. This means there is a greater risk that some of the findings could have occurred by chance.
Conclusions
While the observations made in this study are interesting, and the biological mechanisms identified echo those which have already been found to be involved in ME/CFS, more research is needed to validate the findings.
Notably, this paper comes from the same team of researchers who, in 2025, published a small exploratory study which identified a blood test that could diagnose ME/CFS with 96% accuracy. In their new paper the research team explain than unlike this initial study, which aimed to look at how well the blood test worked, the latest paper aimed to investigate potential overlaps in genetics and biological mechanisms between illnesses including ME/CFS, with fatigue as a major symptom.
Press coverage of the article includes:
- BBC Radio 4’s Today Programme
- Science Media Centre
- The Australian
- University of East Anglia, Norwich
- The Times
