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.
