ME/CFS and Long COVID May Share Biological Pathways With Other Fatigue-Related Conditions

For many people living with chronic illness, severe fatigue can make basic daily activities extraordinarily difficult. Persistent exhaustion, cognitive difficulties and sleep disturbances are prominent features of conditions such as myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS).

ME/CFS is recognized as a complex biological illness, although its underlying causes remain incompletely understood and there is currently no established cure. Significant fatigue can also occur in conditions including long COVID, post-traumatic stress disorder, rheumatoid arthritis and multiple sclerosis, prompting researchers to investigate whether some biological processes might overlap across these otherwise distinct disorders.

Searching for shared biological pathways

Researchers from the University of East Anglia and UK biotechnology company Oxford BioDynamics analyzed data from large genome-wide association studies covering the five conditions. Genome-wide association studies identify genetic variants statistically associated with particular diseases or traits.

Rather than considering associated genes independently, the researchers used a network-based approach incorporating Oxford BioDynamics’ EpiSwitch technology and information about three-dimensional genome organization. DNA is folded within the nucleus, allowing physically distant genomic regions to interact and potentially influence gene regulation.

The researchers used these relationships to investigate whether genes associated with different disorders converged within common biological networks.

The central question was whether conditions that can involve fatigue, cognitive difficulties and sleep problems might share aspects of their molecular architecture despite having different clinical features and causes.

A proposed network model of fatigue

The analysis found relatively little direct overlap among the specific disease-associated genes. However, when the researchers examined broader networks, genes associated with the different conditions appeared to converge around shared regulatory hubs.

Some hub genes identified through this network analysis were not among the strongest signals in the original genome-wide association studies. Their potential importance emerged from their position and connections within the modeled networks rather than from strong disease associations on their own.

The implicated networks involved biological processes related to immune regulation, neuroendocrine signaling and cellular energy metabolism. These systems have previously been investigated in relation to fatigue and several of the conditions included in the analysis.

The findings therefore suggest possible points of biological convergence. They do not demonstrate that ME/CFS, long COVID, PTSD, rheumatoid arthritis and multiple sclerosis share a single underlying disease mechanism.

Immune function and energy metabolism

The researchers proposed that altered immune regulation could contribute to some of the shared biological patterns. Immune-cell exhaustion, a state in which prolonged stimulation changes immune-cell function, is one potential mechanism considered within this framework.

Processes involving mitochondrial function and energy metabolism also appeared in the network analysis. Because mitochondria participate in cellular energy production, alterations in these pathways are relevant candidates for further investigation.

Neuroendocrine and autonomic processes could also potentially contribute to symptoms involving sleep, concentration and physiological regulation. However, the current analysis does not establish a causal sequence in which immune activation produces mitochondrial dysfunction and subsequently leads to chronic fatigue.

Likewise, describing chronic fatigue as evidence of a generalized “systems failure” would be stronger than the findings support. Fatigue is a complex symptom that can arise through different mechanisms in different diseases, even when some molecular pathways overlap.

What the findings could mean for diagnosis

The researchers suggest that identifying shared molecular networks could eventually contribute to biomarker development for conditions including ME/CFS and long COVID.

EpiSwitch technology examines chromosome conformations associated with gene regulation, providing information about aspects of three-dimensional genome organization. Such patterns can potentially serve as biomarkers, but candidate signatures identified through research require independent validation before they can become reliable diagnostic tests.

It is therefore premature to conclude that this study has produced an objective blood test for ME/CFS or long COVID. Both conditions currently require clinical assessment, and biomarker research remains an active field.

Similarly, the analysis does not demonstrate that infections, psychological stress or environmental exposures directly created the chromosome patterns examined in the study or that such changes necessarily persist after a triggering event.

Potential treatment implications remain preliminary

Mapping shared networks could also help researchers prioritize biological pathways for laboratory investigation and future drug development.

Immune regulation, cellular metabolism and other identified pathways could provide starting points for additional research. However, strategies such as “reversing immune exhaustion” or boosting mitochondrial function should not yet be presented as established therapeutic approaches arising from these findings.

A network hub is also not automatically a safe or effective drug target. Genes occupying central positions in biological networks can participate in numerous normal physiological processes, meaning that altering their activity could have unintended consequences.

The study also has broader limitations. Computational network analyses can generate biologically plausible hypotheses but require experimental confirmation, while the genetic datasets used in such analyses may not adequately represent all ancestral and geographic populations.

Published in the Journal of Translational Medicine, the research offers a framework for investigating biological similarities among conditions in which persistent fatigue can be prominent. Its findings are best viewed as hypothesis-generating rather than evidence that researchers have discovered one common biological cause of chronic fatigue.

For people with ME/CFS and other conditions involving disabling fatigue, the research nevertheless reinforces an important scientific direction: severe persistent fatigue can be investigated through measurable biological processes. Determining which of the proposed networks actually contribute to symptoms — and whether they can eventually improve diagnosis or treatment — will require further laboratory studies and clinical validation.