Cornell University researchers have discovered a method to identify biomarkers for chronic fatigue syndrome, a breakthrough that could lead to the first reliable diagnostic tests for the complex disease.
Through innovative use of machine learning and RNA analysis, Cornell University researchers have discovered a method to identify biomarkers for myalgic encephalomyelitis, also known as chronic fatigue syndrome (ME/CFS).
The study, published on Aug. 11 in Proceedings of the National Academy of Sciences, offers a promising new approach to diagnosing a debilitating disease that has been difficult to diagnose, as its symptoms are often mistaken for those of other conditions.
By analyzing cell-free RNA found in blood plasma, the research team, led by Anne Gardella, a doctoral student in biochemistry, molecular and cell biology in the De Vlaminck Lab, pinpointed unique biomarkers that could distinguish ME/CFS patients from healthy individuals.
“By reading the molecular fingerprints that cells leave behind in blood, we’ve taken a concrete step toward a test for ME/CFS,” co-senior author Iwijn De Vlaminck, an associate professor of biomedical engineering in Cornell Engineering, said in a news release. “This study shows that a tube of blood can provide clues about the disease’s biology.”
A Collaborative Breakthrough
The project was a collaborative effort between the labs of De Vlaminck and co-senior author Maureen Hanson, the Liberty Hyde Bailey Professor in the Department of Molecular Biology and Genetics in the College of Agriculture and Life Sciences.
Hanson, who directs the Cornell Center for Enervating NeuroImmune Disease, emphasized the systemic nature of ME/CFS, noting that it affects the nervous, immune and cardiovascular systems.
“ME/CFS affects a lot of different parts of the body,” Hanson added. “Analyzing plasma gives you access to what’s going on in those different parts.”
There are no specific lab tests for diagnosing ME/CFS, so doctors must base their diagnosis on a combination of symptoms, including fatigue, dizziness, sleep disturbances and “brain fog.”
“The problem is a lot of the symptoms that a patient might come to a primary care physician complaining about could be many different things,” added Hanson. “And what that primary care physician would really like to have would be a blood test.”
Identifying Key Biomarkers
Using blood samples from ME/CFS patients and a control group of healthy, albeit sedentary, individuals, the team spun down the blood plasma to isolate and sequence the RNA molecules released during cellular damage and death.
They identified over 700 significantly different transcripts and used machine-learning algorithms to develop a tool that revealed signs of immune system dysregulation, extracellular matrix disorganization and T cell exhaustion in ME/CFS patients.
“We identified six cell types that were significantly different between ME/CFS cases and controls,” added Gardella. “The topmost elevated cell type in patients is the plasmacytoid dendritic cell. These are immune cells that are involved in producing type 1 interferons, which could indicate an overactive or prolonged antiviral immune response in patients. We also observed differences in monocytes, platelets and other T cell subsets, pointing to broad immune dysregulation in ME/CFS patients.”
The cell-free RNA classifier models showed a 77% accuracy rate in detecting ME/CFS — not yet sufficient for a diagnostic test but a considerable advancement in the field.
Broader Implications
The implications of this research extend beyond ME/CFS. The team hopes this method could also help differentiate ME/CFS from long COVID, another condition that has garnered renewed interest in infection-associated chronic conditions.
“While long COVID has raised awareness of infection-associated chronic conditions, it’s important to recognize ME/CFS, because it’s actually more common and more severe than many people might realize,” Gardella added.
Source: Cornell University