For some people, rising from bed or completing daily tasks can feel like extricating oneself from quicksand or the grip of a coiling anaconda.
Those with chronic fatigue syndrome, sometimes known as myalgic encephalomyelitis (CFS/ME), suffer from symptoms like extreme and persistent exhaustion and cognitive issues, including 'brain fog'.
CFS/ME is an 'unambiguously biological' condition for which there is no known cause or cure.
Yet long-lasting, chronic fatigue is a hallmark of numerous other commonly debilitating illnesses, including long COVID, post-traumatic stress disorder (PTSD), rheumatoid arthritis (RA), and multiple sclerosis (MS).
Now, researchers from the University of East Anglia (UEA) and Oxford BioDynamics – a biotech company designing commercial diagnostic molecular tests – say they have revealed the underlying mechanisms shared by these five major illnesses.
These conditions have vastly different pathologies. Long COVID emerges from a viral infection (which is a possibility for CFS/ME too); PTSD results from traumatic experiences and has been linked to chronic inflammation, while RA and MS are considered autoimmune disorders caused by the body mistakenly breaking down its own tissues.
"But one thing that links them all is that patients frequently report remarkably similar symptoms – overwhelming fatigue, brain fog, poor concentration, disturbed sleep, autonomic dysfunction and a dramatic reduction in everyday functioning," explains Dmitry Pshezhetskiy, a clinician-scientist at UEA and the study's lead researcher.
"We wanted to find out why this is."
So Pshezhetskiy and colleagues curated data from existing genome-wide association studies (GWAS) encompassing thousands of cases across these five illnesses. GWAS look for links (associations) between genetic variants and disease at a population level.
Then the researchers turned to their EpiSwitch® platform, a set of machine learning algorithms developed by Oxford BioDynamics and previously used to develop a prototype blood test for CFS/ME – which raised both hope and skepticism when preliminary results were announced late last year.
This platform allowed the researchers to look beyond linear DNA sequences encoding individual genes and explore the genome's 3D architecture to see how spatially distinct genes interact, something that can be affected by the shape of chromosomes or epigenetic markings, for example.
"What we discovered is something approaching a biological unifying theory of fatigue," says Pshezhetskiy.
There was little direct overlap in the genes associated with each condition, but the illnesses appear to be deeply connected.
Like the confluence of streams, each carrying its sediments into a river, the genes linked to each of the five illnesses feed into the same biological networks.
Through their network analysis, the researchers identified several highly influential 'hub' genes, located at the busiest points, or confluences, of these networks.
Further experimental and clinical validation is required to confirm their association to each illness, but we know these genes and pathways control major bodily functions.
They regulate life-shaping processes like immune and inflammatory responses, brain-mediated hormonal signaling, and mitochondrial energy production – processes that have been implicated in ME/CFS and long COVID before.
Importantly, many of these gene 'hubs' weren't previously identified as standout candidates in the GWAS data.
"This is not something you can see by reading the genetic sequence alone, which is why these conditions may have looked unrelated for so long," Pshezhetskiy notes.
"Although these conditions are triggered by completely different events, they may ultimately disrupt the same fundamental biological systems and produce the similarly devastating exhaustion experienced by millions worldwide."
Similar to other recent analyses of gene expression, the study highlighted immune cell exhaustion as a central mechanism potentially connecting these five conditions.
"These findings suggest a state of chronic immune activation followed by functional exhaustion, which may contribute to persistent symptoms," the researchers write in their paper.
Overall, they hope their findings, if validated, might pave the way towards diagnostics that use laboratory tests rather than more subjective means to assess CFS/ME and long COVID.
But there's a lot this study could still be missing: The known disease associations are from GWAS data, which capture only select populations and a tiny fraction of the functional genome (many regulatory elements hide in 'dark', non-coding regions of DNA).
However, the EpiSwitch® platform looks beyond linear DNA at the genome's 3D architecture. This is promising because it encompasses epigenetic modifications – molecular tags that can change how DNA is bundled up, altering gene expression.
These epigenetic markings are thought to reflect environmental exposures, including stress and infections, and although they aren't permanent, they can have long-lasting effects.
"We hope our work could pave the way for objective blood tests capable of identifying underlying biological signatures rather than relying solely on patient-reported symptoms," Pshezhetskiy says.
Further down the line, work like this could also guide the development of new therapies that might try to calm overactive immune cells or boost mitochondria's ability to produce energy, as a way to fight fatigue.
In the meantime, this work may inspire new thinking about specific conditions as manifestations of broader dysfunctions in biological networks.
Related: In a First, Chronic Fatigue Syndrome Linked to The Brain's Clearing System
"In that scenario, chronic exhaustion is not simply a symptom. It is the visible consequence of a deeper systems failure affecting immune function, metabolism, and stress-response pathways," concludes Pshezhetskiy.
"This study offers a framework for understanding how different triggers can converge to cause the exact same profound clinical exhaustion."
This research was published in the Journal of Translational Medicine.
This article was fact-checked by Clare Watson and edited by Clare Watson. While we pride ourselves on our process, we are only human. If you spot a mistake, please let us know.
Facts Only
* Dmitry Pshezhetskiy is a clinician-scientist at the University of East Anglia (UEA).
* Oxford BioDynamics is a biotech company that developed the EpiSwitch® platform.
* The study analyzed five conditions: chronic fatigue syndrome (CFS/ME), long COVID, post-traumatic stress disorder (PTSD), rheumatoid arthritis (RA), and multiple sclerosis (MS).
* Researchers used data from existing genome-wide association studies (GWAS).
* The EpiSwitch® platform uses machine learning algorithms to analyze the 3D architecture of the genome and epigenetic markings.
* The analysis identified "hub" genes that regulate immune and inflammatory responses, hormonal signaling, and mitochondrial energy production.
* The study suggests immune cell exhaustion as a potential connecting mechanism across the five conditions.
* Findings were published in the Journal of Translational Medicine.
* The research was fact-checked and edited by Clare Watson.
Executive Summary
Researchers from the University of East Anglia and Oxford BioDynamics have identified shared biological networks linking five distinct conditions: CFS/ME, long COVID, PTSD, rheumatoid arthritis, and multiple sclerosis. While these illnesses have diverse origins—ranging from viral infections and traumatic stress to autoimmune malfunctions—they manifest similar symptoms, including profound exhaustion and cognitive impairment. Using the EpiSwitch® platform to analyze the genome's 3D architecture, the team discovered "hub" genes that regulate critical systemic functions, suggesting that different triggers may converge on the same biological failures.
The findings point toward a state of chronic immune activation followed by functional exhaustion as a central mechanism. While this offers a potential framework for developing objective blood tests and targeted therapies for mitochondrial or immune support, the results require further experimental and clinical validation. A noted limitation is that the original GWAS data may only represent select populations and omit non-coding regions of the functional genome.
Full Take
This study employs a systems-biology approach to move beyond linear genetic sequencing, attempting to find a "unifying theory" for chronic exhaustion. By shifting the focus from individual genes to network "hubs" and 3D genomic architecture, the research suggests that disparate pathologies can result in an identical clinical phenotype.
**Methodology Check:** The study relies on secondary data from GWAS, which introduces inherent sampling biases and may omit critical non-coding DNA. The primary "discovery" is a correlation within a computational model (EpiSwitch®), not a direct clinical observation. A peer reviewer would flag the lack of primary experimental validation for the identified hub genes as a significant gap between the computational hypothesis and clinical reality.
**Claims vs. Evidence:** There is a tension between the bold framing of a "biological unifying theory" and the admission that "further experimental and clinical validation is required." The data shows network convergence in a model; it does not yet prove a causal biological mechanism in patients.
**Real-World Implications:** If validated, this shifts the paradigm of chronic fatigue from a subjective symptom to a measurable system failure. This would strip away the stigma often associated with CFS/ME and long COVID, moving diagnosis from patient-reported narratives to objective biomarkers.
**Bridge Questions:** To what extent does the 3D architecture of the genome respond to environmental triggers versus innate genetic predisposition? Would treating the "hub" genes produce the same therapeutic result across all five conditions, or would the different triggers require distinct intervention strategies?
**Counterstrike Scan:** A coordinated influence campaign would use this "unifying theory" to prematurely market a specific diagnostic test or supplement, bypassing the validation phase to capitalize on the desperation of chronic illness sufferers. The current content does not match this pattern; it explicitly acknowledges the need for further validation and the limitations of the data.
Patterns detected: none