ME/CFS is a devastating disease that affects over 250,0001 individuals in the UK, yet there is no reliable biomarker and diagnosis is based on manifesting clinical symptoms, coupled with high inter-patient variability.
Developing an early diagnostic test is of fundamental importance in the treatment of any illness. In ME/CFS a diagnostic test would help not only in the clinical management of patients and but give patients hope that we are moving closer to understanding a condition which is currently is very much a mystery illness.
Using modern statistical approaches and machine learning Dr Karl Morten (Nuffield Department of Women's and Reproductive Health) and his colleagues have identified a series of variables including the micro RNA’s of blood cells and small extracellular vesicles which can distinguish a group of severe ME/CFS patients from healthy controls with 100% accuracy. These two groups cannot be readily separated by a standard blood test. Standard tests return as negative for the severely ill group. Their next step is to apply this approach to mild and moderately affected ME/CFS patients with different levels of disability and compare to other disease groups as well as healthy controls. This will determine if we have a potential panel of biomarkers which could be used to developed a diagnostic test.