Breakthrough Blood Test Shows Promise for Diagnosing Chronic Fatigue Syndrome
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A novel approach analyzing cell-free RNA in blood plasma is offering a potential breakthrough in the diagnosis of myalgic encephalomyelitis, commonly known as chronic fatigue syndrome (ME/CFS), a debilitating illness that has long evaded accurate medical assessment. Researchers at Cornell University have developed machine-learning models capable of identifying key biomarkers for the condition, paving the way for a much-needed objective diagnostic test.
Cells, as they naturally expire, release RNA into the bloodstream, essentially leaving a molecular record of their activity. This record contains valuable information about changes in gene expression, cellular signaling, and tissue injury. Cornell researchers are now learning to decipher this “molecular fingerprint” to unlock the secrets of complex diseases.
Unraveling the Mystery of ME/CFS
The findings, published on august 11 in Proceedings of the National Academy of Sciences, represent a notable step forward in understanding ME/CFS, a condition characterized by profound fatigue, cognitive dysfunction (“brain fog”), and a range of other symptoms that often mimic those of other illnesses.This makes accurate diagnosis notoriously difficult.
the project was a collaborative effort led by doctoral student Anne Gardella, working in the De Vlaminck Lab, and co-senior authors Iwijn De Vlaminck, associate professor of biomedical engineering, and Maureen Hanson, Liberty Hyde Bailey Professor in the Department of Molecular biology and Genetics. “By reading the molecular fingerprints that cells leave behind in blood, we’ve taken a concrete
To identify these potential biomarkers, researchers collected blood samples from individuals diagnosed with ME/CFS and a control group of healthy, sedentary individuals. De Vlaminck’s team then isolated and sequenced the RNA molecules released from cells during damage and death.
Identifying Key Molecular Signatures
the analysis revealed over 700 significantly diffrent RNA transcripts between the ME/CFS patients and the control group. These results were then analyzed using various machine-learning algorithms, leading to the growth of a classifying tool that highlighted signs of immune system dysregulation, extracellular matrix disorganization, and T cell exhaustion in ME/CFS patients.
Statistical analysis further pinpointed the cellular origins of these RNA molecules, building upon previous single-cell RNA sequencing data from the Grimson Lab at Cornell. “We identified six cell types that were significantly different between ME/CFS cases and controls,” Gardella reported. “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.”
Promising,But Not Yet Definitive
the resulting cell-free RNA classifier models demonstrated 77% accuracy in detecting ME/CFS. While this is not yet sufficient for a definitive diagnostic test, researchers view it as a substantial advancement. They are optimistic that this approach will not only improve understanding of ME/CFS but also shed light on other chronic illnesses, including differentiating ME/CFS from the symptoms of long COVID.
“While long COVID has raised awareness of infection-associated chronic conditions, it’s significant to recognize ME/CFS, because it’s actually more common and more severe than manny people might realise,” Gardella concluded.
Reference: Gardella AE, Eweis-LaBolle D, Loy CJ, et al. Circulating cell-free RNA signatures for the characterization and diagnosis of myalgic encephalomyelitis/chronic fatigue syndrome.PNAS. 2025;122(33):e2507345122. doi: 10.1073/pnas.2507345122.
