ME/CFS (Myalgic Encephalomyelitis / Chronic Fatigue Syndrome) is a chronic, complex, heterogeneous disease that affects millions and lacks both diagnostics and treatments. Big data, or the collection of vast quantities of data that can be mined for information, has transformed the understanding of many complex illnesses like cancer (1,2) and multiple sclerosis (3,4), by dissecting heterogeneity, identifying subtypes, and enabling the development of personalized treatments. It is possible that big data can reveal the same for ME/CFS. Solve M.E. developed and launched the You + ME Registry to collect longitudinal health data from people with ME/CFS, people with Long COVID (LC) and control volunteers using rigorous protocols designed to harmonize with other groups collecting data from similar groups of people. The Registry is an invaluable resource because it integrates with a symptom tracking app, as well as a biorepository, to provide a robust and rich dataset that is available to qualified researchers. Accordingly, it facilitates collaboration that may ultimately uncover causes and help accelerate the development of therapies.

We propose a framework for the understanding of the pathophysiology and management of Myalgic Encephalomyelitis / Chronic Fatigue Syndrome (ME/CFS) that considers wider determinants of health and long-term temporal variation in pathophysiological features and disease phenotype throughout the natural history of the disease. As in other chronic diseases, ME/CFS evolves through different stages, from asymptomatic predisposition, progressing to a prodromal stage, and then to symptomatic disease. Disease incidence depends on genetic makeup and environment factors, the exposure to an insult, or repeated insults, and the nature of the host response. In people who develop ME/CFS, normal homeostatic processes in response to adverse insults may be replaced by aberrant responses leading to dysfunctional states. Thus, the predominantly neuro-immune and autonomic manifestations, underlined by a hyper-metabolic state, that characterise early disease, may be followed by various processes leading to multi-systemic related symptoms. This abnormal metabolic state and the effects of a range of mediators such as products of oxidative and nitrosamine stress, may lead to progressive cell and metabolic dysfunction culminating in a hypometabolic state with low energy production. These processes do not seem to happen uniformly; although a spiralling of progressive inter-related and self-sustaining abnormalities may ensue, reversion to states of milder abnormalities is possible if the host is able to restate responses to improve homeostatic equilibrium. Disease management and research efforts should seek to identify and apply strategies targeted at the different pathophysiological dysfunctions that characterise different disease stages. As disease presentation varies over time, no single case description, set of diagnostic criteria, or molecular feature is currently diagnostic for all patients at all times. While acknowledging its limitations due to the incomplete research evidence, we suggest the proposed framework may improve research design and health care interventions for people with ME/CFS.

The European Network on Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (EUROMENE) was established after a successful grant application to the European Cooperation is Science and Technology (COST). This network aimed to assess the existing knowledge and/or experience on health care delivery for people with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) in the European countries and worldwide, and to enhance coordinated research and health care provision in this field. The EUROMENE proposal, was based on the establishment of interrelated working groups (WGs), where the participants contributed with specific knowledge and viewpoints according to their specialties and/or areas of interest. In this paper we outline the work of a multidisciplinary team of researchers, including epidemiologists, clinicians, statisticians, biomedical scientist and heath economists, who set out their recommendations to guide data acquisition for ME/CFS research, aiming to standardise data collection and improve epidemiological research.