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Related Experiment Video

Updated: Jul 12, 2025

Induction of Experimental Autoimmune Encephalomyelitis in Mice and Evaluation of the Disease-dependent Distribution of Immune Cells in Various Tissues
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Typing myalgic encephalomyelitis by infection at onset: A DecodeME study.

Andrew D Bretherick1,2, Simon J McGrath3, Andy Devereux-Cooke3

  • 1MRC Human Genetics Unit, The University of Edinburgh, Edinburgh, Scotland, EH4 2XU, UK.

NIHR Open Research
|October 26, 2023
PubMed
Summary

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) patient characteristics like sex, age, and onset type are linked to symptom severity and comorbidities. This large study reveals significant differences in ME/CFS presentation based on these factors.

Keywords:
Myalgic encephalomyelitisPost-exertional malaisePost-viral syndromeSex-biasSub-types

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Area of Science:

  • Investigating the heterogeneity of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) in a large UK cohort.
  • Utilizing population-based data to explore associations between patient demographics, illness onset, and ME/CFS characteristics.

Background:

  • Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is characterized by post-exertional malaise, unrefreshing sleep, and cognitive impairment.
  • Despite affecting 0.2-0.4% of the population, ME/CFS lacks diagnostic laboratory tests, effective treatments, and clear understanding of its pathogenesis.
  • Previous small-scale studies have not definitively determined if ME/CFS presents differently based on onset type, sex, or age.

Purpose of the Study:

  • To investigate potential associations between ME/CFS severity or onset type and demographic factors (sex, age, illness duration, comorbidities, symptoms).
  • To leverage the large DecodeME cohort for robust statistical analysis of ME/CFS patient characteristics.

Main Methods:

  • The DecodeME study recruited 17,074 participants diagnosed with ME/CFS by a health professional.
  • Data were collected via detailed questionnaires from UK-based participants.
  • Logistic regression analysis was employed to examine associations between ME/CFS characteristics and patient factors.

Main Results:

  • The cohort comprised 83.5% females, confirming the known sex bias in ME/CFS.
  • Females reported more comorbidities than males.
  • Being female, older age, and over 10 years post-onset were significantly associated with greater ME/CFS severity.
  • ME/CFS onset following an unknown infection was significantly linked to active fibromyalgia.
  • Five distinct ME/CFS onset types were analyzed: post-glandular fever, post-COVID-19, post-other infections, non-infectious onset, and unknown infection status.

Conclusions:

  • DecodeME participants exhibit variations in symptoms, comorbidities, and illness severity.
  • These differences are significantly stratified by sex-at-birth and the presence of infection around the time of ME/CFS onset.