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Virginia Gewin gets to grips with a fundamental problem in chronic fatigue syndrome research – how to know it when you see it.

After 25 years living with chronic fatigue syndrome (also known as myalgic encephalomyelitis, or CFS-ME), researcher Leonard Jason maintains a stalwart professional focus on improving the diagnosis of the disease. That requires doggedly analysing cases and symptoms to refine the criteria necessary for an appropriate definition of what constitutes a case of CFS-ME. Yet ongoing controversy over the criteria illustrates how much there is still to learn about it.

For example, in 2011 Jason and his colleagues showed that the most commonly used criteria identified only 79% of patients with CFS-ME. These findings underpin Jason’s repeated calls for government bodies to test any proposed disease criteria using patient data – and his continued dismay that they do not.

One problem is that people who also have other conditions, such as mood disorders, should be omitted when trying to define diagnostic criteria for CFS-ME. The goal is to hone the patient and disease criteria to their very essence: only then will researchers be able to tease out the biological mechanisms specific to this disease and find potentially effective treatments.

Jason and his colleagues are currently experimenting with machine learning techniques to see if they can distinguish patients from controls and, eventually, classify patients into distinct subtypes. Preliminary findings suggest that those who are bedbound, and arguably suffer the most, experience some unique symptoms such as orthostatic intolerance, which means their nervous systems shut down when they stand upright.

Their most recent work found that only 67% of study participants reported orthostatic intolerance, compared to 93% reporting cognitive impairment – yet in criteria proposed by the Institute of Medicine in 2015, having either of these symptoms meets its case definition. It could be, however, that orthostatic intolerance is indicative of a distinct subtype.

But perhaps Jason’s most important contributions are yet to come. In 2013, he started two studies that will monitor people over time to document risk factors and how the disease unfolds. The first will follow college students who experience infectious mononucleosis (also known as glandular fever), a commonly acknowledged trigger, to identify those who eventually go on to be diagnosed with CFS-ME. He is also overseeing a study that will document children with CFS-ME in a community sample of 20,000 families.

His work is slow but prodding, securing the data needed to understand the disease. And we all know it’s the tortoise that wins the race in the long run.

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