New Blood Test For Chronic Fatigue Syndrome Has 91% Accuracy
It can take years for people living with chronic fatigue syndrome to receive a formal diagnosis, and they are a favored few. Experts suggest up to 91 percent of people in the US remain undiagnosed, living without medical support for a condition that robs them of energy, brain-power, and a care-free life.
But those statistics could in time improve, if a newly developed diagnostic test stands up to scrutiny.
A team of scientists led by the University of Oxford has just published their preliminary results of a blood cell-based test that can distinguish between unaffected individuals and those with chronic fatigue syndrome (also known as myalgic encephalomyelitis or ME/CFS) with 91 percent accuracy.
The development of a simple test with the potential for early diagnosis [of ME/CFS is] a critical goal,” Jiabao Xu and colleagues write in their open-access, peer-reviewed paper.
Early diagnosis would enable patients to manage their conditions more effectively, potentially leading to new discoveries in disease pathways and treatment development”, they say, especially if such a blood test can reveal changes over time.
The blood test differentiates between the properties of a type of blood cell called peripheral blood mononuclear cells (PBMCs) in people with and without ME/CFS, using a technique called Raman spectroscopy and an artificial intelligence (AI) tool.
Previous studies have suggested PMBCs from people with ME/CFS have reduced energetic function; results which fit with an emerging theory that the condition is one of impaired energy production.
Building on their pilot study, and the research suggesting PBMCs are perturbed in ME/CFS, Xu and colleagues tested their diagnostic approach in nearly 100 people: including 61 individuals with ME/CFS, 16 healthy controls, and 21 people with multiple sclerosis, an autoimmune disorder that has many similar symptoms to ME/CFS.
If the blood test could distinguish between people with ME/CFS and those with MS, as well as healthy folks, then it might bode well for its use in differentiating ME/CFS from other illnesses, such as fibromyalgia, chronic Lyme disease, and long COVID.
The team profiled more than 2,000 cells across 98 patient samples, analyzing the molecular vibrations of single cells. The resulting spectra, much like those astronomers use to look at the chemical composition of stars, reflect changes in levels of intracellular metabolites produced when cells metabolize fuel.
Xu and colleagues observed clear metabolic differences between ME/CFS patients and the two control groups.
Applying the AI algorithm, the test could accurately classify 91 percent of patients, and could even differentiate between mild, moderate, and severe ME/CFS patients with 84 percent accuracy.
Further studies to validate the findings in larger cohorts will take some time. Xu and colleagues hope their method overcomes problems that other teams have encountered with sample processing. However, single-cell Raman spectroscopy is not readily available in certified diagnostic laboratories.
Similar blood cell-based tests using different analytical techniques have shown promise before. In 2019, Stanford University scientists published results from a pilot study of a test analyzing PBMCs, yet there has been nothing of its progress since. (Members of the Stanford team are continuing their studies on ME/CFS.)
In the meantime, untold numbers of people living with ME/CFS are still aching for a diagnosis and appropriate, evidence-based treatment options.
“ME/CFS is still viewed with skepticism by many [medical professionals] with no effective treatment options or clear pathology,” Xu and colleagues note.
Let’s hope that soon changes, with studies like this pointing to detectable biological changes in the energy-limiting, life-altering condition.
The study was published in Advanced Science.
Diagnostic tools for complex conditions like myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) have been elusive. But a novel approach highlighted in a recent study has the potential to someday transform ME/CFS diagnosis and patient care.
ME/CFS is characterized by debilitating, yet unexplained, fatigue that profoundly impacts patients’ lives. Symptoms usually include sleep disorders, difficulties with concentration and memory, and persistent muscle or joint pain.
According to the U.S. Centers for Disease Control and Prevention (CDC), ME/CFS affects up to 2.5 million Americans. However, it’s likely that the number is much higher. Many cases go unrecognized due to the complexity of the condition. Diagnosis has relied on patient self-reporting and ruling out other causes for exhaustion. Some patients search for years and never get definitive answers.
A New Approach
Scientists aren’t clear what causes chronic fatigue, but some evidence suggests it may be energy malfunction at the cellular level. Because there was no existing way to accurately measure cellular energy levels, the Advanced Science study looked for a new way to test the theory.
Using a technique known as the single-cell Raman platform, a specialized analytical tool that blends sophisticated spectroscopy with artificial intelligence, they were able to analyze the blood cells from 98 human subjects, including 61 ME/CFS patients of varying disease severity and 37 healthy and disease controls, in great detail. Raman platform methods have been widely used in tasks like fingerprint analysis but rarely used in medical testing.
The University of Oxford-led research team focused on a blood cell type known as Peripheral Blood Mononuclear Cells, or PBMCs for short. PBMCs are a critical component of the immune system, serving as frontline defenders against infections and inflammation. In the context of chronic fatigue, PBMCs were of particular interest to the scientists because they could potentially serve as a biomarker for the condition. In fact, the study found clear differences in PBMC activity between patients with ME/CFS and healthy controls.
Unprecedented Accuracy
The Raman profiles distinguished between healthy individuals, disease controls, and ME/CFS patients with a 91 percent accuracy. The test was so sensitive it was even able to differentiate between mild, moderate, and severe ME/CFS patients.
This level of precision could be a game-changer, the researchers said. For a condition often considered purely psychological, a reliable blood test could take the guesswork out of diagnosis. Having an objective biomarker could reinforce the reality of the illness and lend credibility to the patient experience.
Verifiable diagnostics could also enable early and more effective intervention. Because the research explored the underlying molecular mechanisms of ME/CFS, it offered insights into the very nature of the condition. This could be the key for developing targeted therapies. By identifying specific gene expression patterns, clinicians might be able to tailor treatment plans to address the unique biological profiles of each individual patient.
Beyond ME/CFS
The researchers noted that these results don’t just apply to ME/CFS either. The methodology could lead to definitive assessments in other chronic conditions that share similar diagnostic challenges. For example, many people with long COVID experience symptoms remarkably similar to ME/CFS. And Lyme disease, rife with fatigue-related symptoms, has its own set of diagnostic hurdles.
The study results mark a significant step forward in understanding ME/CFS, but the researchers caution that it’s not a panacea. At least not yet. They said that they need further validation in much larger cohorts before any tests can be widely implemented. But in the short term, their work does offer realistic hope for a reliable diagnostic tool in chronic fatigue conditions.
