Selection of biomarkers in ME/CFS for patient stratification and treatment surveillance / optimisation

Project Details


The aim of the study is to examine diagnostic potential of serum biomarkers depending on the presence or absence of viral (HHV-6, HHV-7, B19) infection biomarkers by direct comparing ME/CFS cases to healthy controls and applying the results from clinical testing and pathology to the pattern recognition algorithm random forest (RF), to widen marker patterns usable in ME/CFS patient stratification. A longer-term aim is to develop simpler diagnostic tools from routine data to assist health professionals to diagnose ME/CFS and to monitor therapeutic approaches. Examination of the diagnostic potential of serum biomarkers, both individually and in combination with viral infection markers will allow stratify ME/CFS patients and seek appropriate therapy and evaluate its efficacy. The investigation by measuring of symptom severity will also allow stratify the ME/CFS cohort into mild to severe disease classes. We hypothesize the cohort of ME/CFS patients is divided into a number of groups/subsets, one of which is a group/subset having primary autoimmune aetiology, another one which is triggered by viral infection. The group/subset triggered by viral infection could have also markers of autoimmunity. It is possible to find a set of markers of autoimmunity pointing towards ME/CFS, especially later after disease onset. It should be possible to find a set of markers allowing the stratification of ME/CFS patients according to pathomechanisms and also severity of clinical manifestations.
Effective start/end date1/01/2030/06/23

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

  • SDG 3 - Good Health and Well-being
  • SDG 10 - Reduced Inequalities


  • ME/CFS,
  • clinical testing
  • markers of viral infection
  • patients stratification
  • serological biomarkers
  • machine learning

Field of Science

  • 3.5 Other medical sciences

Smart Specialization Area

  • Biomedicine, medical technologies and biotechnology


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