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. 2023 Aug 21:3:20.
doi: 10.3310/nihropenres.13421.4. eCollection 2023.

Typing myalgic encephalomyelitis by infection at onset: A DecodeME study

Andrew D Bretherick  1   2 , Simon J McGrath  3 , Andy Devereux-Cooke  3 , Sian Leary  3 , Emma Northwood  3 , Anna Redshaw  3 , Pippa Stacey  3 , Claire Tripp  3 , Jim Wilson  3 , Sonya Chowdhury  4 , Isabel Lewis  4 , Øyvind Almelid  1 , Sumy V Baby  1 , Tom Baker  1 , Hannes Becher  1 , Thibaud Boutin  1 , Malgorzata Clyde  1 , Diana Garcia  1 , John Ireland  1 , Shona M Kerr  1 , Ewan McDowall  1 , David Perry  1 , Gemma L Samms  1 , Veronique Vitart  1 , Jareth C Wolfe  1 , Chris P Ponting  1
Affiliations

Typing myalgic encephalomyelitis by infection at onset: A DecodeME study

Andrew D Bretherick et al. NIHR Open Res. .

Abstract

Background: People with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) experience core symptoms of post-exertional malaise, unrefreshing sleep, and cognitive impairment. Despite numbering 0.2-0.4% of the population, no laboratory test is available for their diagnosis, no effective therapy exists for their treatment, and no scientific breakthrough regarding pathogenesis has been made. It remains unknown, despite decades of small-scale studies, whether individuals experience different types of ME/CFS separated by onset-type, sex or age.

Methods: DecodeME is a large population-based study of ME/CFS that recruited 17,074 participants in the first 3 months following full launch. Detailed questionnaire responses from UK-based participants who all reported being diagnosed with ME/CFS by a health professional provided an unparalleled opportunity to investigate, using logistic regression, whether ME/CFS severity or onset type is significantly associated with sex, age, illness duration, comorbid conditions or symptoms.

Results: The well-established sex-bias among ME/CFS patients is evident in the initial DecodeME cohort: 83.5% of participants were females. What was not known previously was that females tend to have more comorbidities than males. Moreover, being female, being older and being over 10 years from ME/CFS onset are significantly associated with greater severity. Five different ME/CFS onset types were examined in the self-reported data: those with ME/CFS onset (i) after glandular fever (infectious mononucleosis); (ii) after COVID-19 infection; (iii) after other infections; (iv) without an infection at onset; and, (v) where the occurrence of an infection at or preceding onset is not known. Among other findings, ME/CFS onset with unknown infection status was significantly associated with active fibromyalgia.

Conclusions: DecodeME participants differ in symptoms, comorbid conditions and/or illness severity when stratified by their sex-at-birth and/or infection around the time of ME/CFS onset.

Keywords: Myalgic encephalomyelitis; Post-exertional malaise; Post-viral syndrome; Sex-bias; Sub-types.

Plain language summary

Myalgic Encephalomyelitis / Chronic Fatigue Syndrome (ME/CFS) is a chronic disease that affects an estimated 250,000 people in the UK. Its defining symptom is post-exertional malaise, an excessive delayed worsening of symptoms following even minor physical or mental exertion. For those with it, ME/CFS means disability and poor quality of life. DecodeME is a research study which is looking for DNA differences between people with ME/CFS and people without any health problems. People with ME/CFS who take part in DecodeME complete a questionnaire that assesses their symptoms and whether they will then be invited to donate a DNA sample. This paper analyses the answers to this questionnaire; we will publish results of the DNA analysis separately. So far, more than 17 thousand people with ME/CFS have completed the DecodeME questionnaire. Their answers help us to address the question: “Are there different types of ME/CFS linked to different causes and how severe it becomes?” Results show that people with ME/CFS do not form a single group reporting similar symptoms and additional medical conditions. Instead, participants who had an infection at the start of their ME/CFS reported a different pattern of symptoms and conditions compared to those without an infection. It is well known that most people with ME/CFS are females. What was not clear previously was that females tend to have more additional health conditions. Also, being female, being older and being over 10 years from ME/CFS onset all make it more likely that someone is more severely affected by their ME/CFS. These findings could indicate that by studying people with different ME/CFS onset-types separately – rather than analysing all people with ME/CFS together – it will be easier to understand what is going wrong.

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Conflict of interest statement

Competing interests: ADC is a committee member of the Science for ME online support discussion forum. CPP is Deputy Chair of the ME/CFS Research Collaborative and has had PhD studentships funded by Action for ME & the Chief Scientist Office (Scotland), or by ME Research UK. JW was on the ME/CFS Research Collaborative Patient Advisory Group and received travel expenses for participation in Board Meetings. SC is CEO of Action for ME; Co-Chair, World ME Alliance; and, Non-Executive Director, Curo Group. SL is Head of Advocacy and Communications of the World ME Alliance.

Figures

Figure 1.
Figure 1.. Onset type, illness course, duration of illness and severity of the DecodeME cohort.
Numbers of DecodeME participants reporting whether they had an infection prior to ME/CFS onset (top left) as well as their illness’ course (top right), duration (bottom left) and severity (bottom right; n = 17,074 participants).
Figure 2.
Figure 2.. Numbers of DecodeME participants reporting conditions co-occurring with ME/CFS (comorbidities); total, 17,074 participants.
In ( A) numbers are shown in log 10-scale and those with active or inactive comorbidities are indicated in blue or green, respectively. The UpSet plot ( B) shows numbers of participants with five conditions that most frequently co-occur with ME/CFS. These either co-occur together with others (indicated by filled circles linked by lines) or else separately (filled circles not linked by lines).
Figure 3.
Figure 3.. Numbers of DecodeME participants reporting symptoms (Radar chart); total, 17074 participants.
Most frequently reported symptoms are furthest from these circles’ centre. Twelve different groups of questions are indicated in separate colours; for each symptom group, the most and least frequently reported symptoms are listed and indicated as unfilled circles. With reference to the DecodeME questionnaire ( www.decodeme.org.uk/app/uploads/2022/08/DecodeME-Questionnaire.pdf) the questions (Q) are, clockwise: Fatigue (Q8-answer 3 [Q8-3], Q9-3, Q3-1, Q10-1), Post-exertional malaise (PEM, Q12-1 AND Q13-1), Cold or flu-like (Q14-4, -2, -1, -5, -6, -3), Sensitivities (Q15-1, -2, -3, -5, -7, -9, -4, -6, -8), Pain (Q16-4, -6, -5, -3, -2, -7, -1), Gut (Q17-1, -2, -3), Headaches (Q18-1, -4, -2, -3), Cognition (Q19-15, -7, -8, -9, -12, -3, -1, -2, -6, -10, -5, -13, -4, -14, -11), Sleep (Q20-4, -3, -2, -1), Autonomic (Q21-3, -6, -11, -10, -9, -4, -2, -1, -5, -7, -12, -8), Neuroendocrine (Q22-3, -1, -2, -4), and Mood (Q23-2, -3, -1, -5, -6, -4).
Figure 4.
Figure 4.. Most symptoms are strongly associated with female sex at birth and younger age.
The question asked was: “In the last 6 months, have you had any of the symptoms below often, repeatedly, or constantly? Please mark any that apply.” Sex-biased (X-axis) and/or Age-biased (Y-axis) associations in a logistic regression analysis ( Symptom ~ age + sex + intercept) are shown as data points. Data points within the blue-shaded areas are not significant after accounting for 82 tests ( p<0.05/82, or |Z|<3.427. Only one symptom (“Feeling easily annoyed or irritable”) was male-biased; 3 symptoms (sensitivities to chemicals or medicine, or bladder problems) were associated with older age. Results for 80 symptoms are shown.
Figure 5.
Figure 5.. Questionnaire responses that significantly associate with ME/CFS symptom severity.
Z-scores are shown for symptoms that significantly associate with severity ( p<0.05 after Bonferroni correction for 82 tests, including age and sex). Here severity is defined by self-report of moderate or severe or very severe symptoms versus self-report of mild symptoms (see Figure 1). Responses to questions 14 and 15 (Q14, Q15) are significantly associated with mild symptoms. Responses relate to DecodeME Questionnaire questions (e.g. question 10, Q10).
Figure 6.
Figure 6.
Associations of symptoms or age to 5 ME/CFS onset types: ( A) Fatigue symptoms (10 tests), ( B) Non-fatigue symptoms (74 tests), and ( C) Illness course descriptions (7 tests). These were considered in a logistic regression model of the form OnsetType ~ age + sex + symptoms/descriptions and an intercept. A covariate is only shown if it survived Bonferroni multiple testing correction ( p<0.05) per regression for one or more symptom/description. Significant associations are indicated with an asterisk (*); their Z-scores lie outside of non-significant values, bounded by the red dashed lines, after Bonferroni multiple testing correction. The z-score (Y-axis) is the effect-size estimate in standard deviation units.
Figure 7.
Figure 7.. Associations of comorbidities or age to 5 ME/CFS onset types.
Thirty-four comorbidities were considered in a logistic regression model of the form OnsetType ~ age + sex + comorbidities and an intercept. A covariate is only shown if it survived Bonferroni multiple testing correction ( p<0.05) for one or more onset type. Active and inactive comorbidities were considered independently: Active, if the condition has given symptoms in the past 6 months, or Inactive, if the condition has not given symptoms in the past 6 months, either because it has died down or treatment has controlled it. The z-score (Y-axis) is the effect-size estimate in standard deviation units.
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