Typing myalgic encephalomyelitis by infection at onset: A DecodeME study

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.


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

Suzanne Vernon , The Bateman Horne
Center of Excellence, Salt Lake City, USA 2.
Any reports and responses or comments on the 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.

Plain English 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 article can be found at the end of the article.

Plain English 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

Introduction
Myalgic encephalomyelitis / chronic fatigue syndrome (ME/CFS) is a chronic multisystem disorder that affects an estimated 0.2-0.4% of the UK population Over many decades, ME/CFS studies have addressed similar questions using symptom data for tens or hundreds of participants recruited using various inclusion and exclusion criteria 8,11,12 6A) or 72 non-fatigue symptoms (Figure 6B) or 5 illness courses (Figure 6C)?" we used the model: OnsetType ~ age + sex + symptoms + intercept.To address "What onset types are associated with 34 comorbidities (active and inactive)?"we used the model: Onset-Type ~ age + sex + comorbidities + intercept -Figure 7.
For the relevant analyses, severity was coded as mild versus others (i.e.moderate or severe or very severe) -Figure 5; 5 illness courses were compared with 'Fluctuating', the majority response -Figure 6C.  1).
Over half (58.0%; n=9,909) indicated that their ME/CFS is "Fluctuating (my symptoms vary day to day but don't go away)", 12.7% (n=2,175) describe their symptoms as "Relapsing and remitting (good periods with no symptoms alternating with symptomatically bad periods)" and 15.3% (n=2,614) indicate their symptoms are "Getting worse" (Figure 1).
Most (61.3%; n=10,463) participants have had ME/CFS for over 10y, and 81.6% (n=13,924) over 5y (Figure 1).Together, study participants have experienced over 1.3×10 5 years of ME/CFS symptoms.3).Almost all answered that once they had exceeded their energy limit their change in symptoms lasts "a long time, which can be more than 24 hours" (97.5%; n=16,649) and agreed that their fatigue affected them both physically and mentally (96.2%; n=16,433).For 88.7% (n=15,142), their fatigue occurs more than half of the time and 87.3% (n=14,921) report their fatigue as disabling.

ME/CFS after glandular fever mostly affects adults a decade after peak incidence
We first analysed participants' ages at ME/CFS onset.Onsets occurring >5y ago do not allow fine resolution of their dates, especially for those responding "Between 5 and 10 years" or "Over 10 years" to "How long have you had your illness?"Consequently, we only considered participants reporting onsets within the last 5y (n=3,150).The median age of this group was 40y, IQR=31y-51y, implying that most participants' onsets occurred between 25y and 50y of age.This is older than the peak incidence of glandular fever in the UK (15y-19y old) 15 .Rather than most participants reporting ME/CFS onset in the last 5y after glandular fever being in their early twenties, as expected, they were a decade older (median ages 30.5y [IQR=23y-41y]).This difference is consistent with adolescents being less likely, than older people, to develop ME/CFS after glandular fever.

ME/CFS comorbidities and symptoms are sex-and age-biased
The substantial number of males participating in DecodeME (n=2,827) allowed the study to reveal previously unreported sex-biases in comorbidities or symptoms.Females with ME/CFS reported more comorbidities and symptoms than males in the DecodeME questionnaire.Two-thirds (66.7%; n= 9,507) of females, but a half (52.7%; n=1,489) of males, reported at least one active comorbidity; similarly 39.2% (n=5,588) of females and 28.6% (n=809) of males reported at least one inactive comorbidity.Female participants reported, on average, more symptoms than males (42 versus 36).
Testing all symptoms simultaneously with sex and age, showed strong association between ME/CFS severity and 18 factors including fatigue, age, difficulty remaining standing, and sleep problems (Figure 5). ).
The type of infectious or non-infectious disease onset does not explain these strong and pervasive sex-biases because proportions of females were not significantly different across the five onset types (83.1%-84.5%;χ 2 = 1.707, df = 4, p = 0.79).The statistical significance of this difference is strong.When testing for association between those with an infection around the time of ME/CFS onset and duration (<10y vs. >10 years since time of onset), age and sex, only association with duration was significant (p = 4×10 -67 ).This relative paucity of participants not reporting an infection around the time of onset of their ME/CFS over 10y ago is unexpected, and not easily explained by historic variation in ME/CFS triggers because association with age was not significant in this analysis (p > 0.05).When analysed separately, each onset type was not associated with participants' sex at birth, when including age and ME/CFS duration over 10y in the analysis.
Significant differences occurred between the 5 ME/CFS onset types and 4 fatigue symptoms (Figure 6A), 16 other symptoms (Figure 6B) and 3 different types of illness course (Figure 6C).Those with glandular fever onset were significantly more likely than others to report swollen or tender glands and viral infections with long recovery periods within the last 6 months, and to experience relapsing and remitting symptoms (relative to 'Fluctuating', the majority response).Others with COVID-19 infection at ME/CFS onset preferentially reported a tight feeling in the chest, sensitivity to alcohol and a feeling of burning in the lungs.Participants with other types of infection onset more frequently reported feeling mentally fatigued, feeling fatigued less than half the time, and difficulties remaining standing, and less frequently reported feeling more sleepy than is normal, having worsening symptoms (relative to 'Fluctuating'), unusual changes in appetite and mood swings.
Participants reporting an infectious onset (when compared to those who did not) were also significantly more likely to report: improving symptoms, relapsing/remitting, or recovered (relative to 'Fluctuating') symptoms, and less likely to report worsening symptoms (again, relative to 'Fluctuating').They were more likely, among other things, to report viral infections with long recovery periods, fewer viral infections than they used to get, and having a pale face.Other symptoms that were significantly more likely to be reported by participants without an identified infection at onset were fatigue more than half the time, reduced libido, and unusual changes in appetite.They were also less likely to report symptoms common during infection: flu-like feelings, and swollen or tender glands.
Those with an infection around the time of onset of ME/CFS more frequently reported symptoms typical of infection in the last 6 months, whereas those reporting no infection at onset less frequently indicated these symptoms.This was unexpected because of the long time-lag between onset (mostly >10y ago) and participants' recent questionnaire responses.Even though most participants report a long interval between their onset of ME/CFS (mostly >10y ago) and their recent symptoms characteristic of infection, our results cannot distinguish between whether these recent symptoms are a natural consequence of their ME/CFS onset, for example because of viral persistence in some individuals 17 , or else they are independent of onset.
In our final analysis, we tested for association between participants' onset type and their comorbidities, age and sex.Only younger age, rather than any comorbidity, was significantly associated with glandular fever onset (Figure 7).Among all onset types, only coronavirus disease 2019 (COVID-19 caused by SARS-CoV-2) infection was significantly associated active Mast Cell Activation Syndrome (MCAS), i.e.MCAS symptoms within the previous 6 months.COVID-19 related onset was also negatively associated with active fibromyalgia.Onset with another infection was positively associated with inactive Shingles or active Lyme disease, and negatively associated with fibromyalgia or clinical depression.Onset without reported infection at onset was significantly associated with recent clinical depression symptoms; and, onset with unknown infection status was significantly associated with active fibromyalgia as a comorbidity (Figure 7).
In summary, we report significant associations to five onset types derived from participants' responses to the question 'Did you have an infection when, or just before, your first ME/CFS symptoms started?': 1. 'Yes, glandular fever' (17%; n=2,936): These participants were more likely to report swollen or tender glands and viral infections with long recovery periods, and to experience relapsing and remitting symptoms.This initial DecodeME cohort has a comparable age-distribution to previous USA-based studies [22][23][24] , the reported comorbidities (Figure 2) are similar to those of a previous study 25 , and proportions of participants reporting glandular fever or another infectious disease around the time of onset are similar to those previously reported 11,25,26 .The DecodeME cohort's females outnumber males by over five-to-one, which is one of the highest female-bias among those with ME/CFS yet reported internationally 3,9,27-32 .Cohorts of these previous questionnaire studies numbered in the hundreds.DecodeME's larger cohort thus provides robust statistical support to these previous findings from less well-powered studies.
Studies involving hundreds of participants previously concluded that ME/CFS exhibits few sex differences in illness patterns 33,34 .Smaller studies indicated older age as associated with greater ME/CFS symptom severity, but other studies found no such association (reviewed in 12,35).These previously limited cohort sizes did not permit comprehensive analysis.
In a previous study, three symptoms were reported significantly more often by females than males: fever, swollen glands, and sore throat 34 .In our study, we replicated these findings, and found a further 59 of 80 ME/CFS symptoms that are also female-biased.Our analyses additionally found 61 symptoms biased towards younger age, with only 5 biased towards older age.
The raw number of symptoms may not be meaningful, however, as symptoms can be overlapping, and people with ME/ CFS may, over time, pace sufficiently to avoid triggering some symptoms or may begin to describe their symptoms with fewer labels, particularly when interventions are not available to treat each symptom effectively.Indeed, rather than younger participants reporting increased severity, we found that being female, older and over 10y from onset are all risk factors for ME/CFS severity.
The median time to receive a clinical diagnosis in the UK is 2 years which is reflected in DecodeME participants' responses.Specifically, participants whose illness started within the last 1-3y or 0.5-1y (n = 1,287 and 177) were respectively 21% and 57% fewer per year than the study's participants from the 3-5y recruitment interval (n=1,634).
Despite its large cohort size (N=17,074), extensive community reach and use of paper, as well as online, questionnaires, the analysis presented here -of the December 2022 DecodeME data freeze -has four main limitations.First, recruitment is restricted to participants over the age of 16y, which limited investigation of paediatric ME/CFS.Second, when asking participants if they were diagnosed by a health professional we did not require clinical confirmation of reported answers.Nevertheless, our extensive engagement with participants and the internal consistency of their responses encourage us to believe that questionnaire answers have been given in good faith, noting that inconsistent responses may result from respondents' ME/CFS symptoms including their cognitive dysfunction.Third, most ME/CFS symptoms are not independent of one another.Consequently, multicollinearity should be borne in mind for those analyses considering multiple symptoms in the same analysis.Fourth, regrettably DecodeME has not yet been successful in recruiting proportionately from minoritised groups.There is little consensus on whether ME/CFS prevalence differs among these and other groups 36 .
Other recruitment and representativeness biases are also possible, as with all research cohorts.
A previous study indicated that ME/CFS onset type associates with severity 37 although this was not replicated by our larger study.Instead, we identified large numbers of comorbidities and symptoms that are each more likely to be reported by participants with a specific onset type.
These onset types reveal differences amongst those with ME/CFS regarding their symptoms and comorbidities (Figure 4).However, these distinctions are not absolute.For example, those reporting no infection at onset (Type 4, above) are not cleanly distinguished from all others by active clinical depression.Rather, they were the only onset type that was more likely to report this diagnosis (25.4%; n=667) than all other participants were (19.6%; n=2829).Similarly, Type 3 ("other infection") contains a higher proportion (9.4%; n=710) of those who report inactive shingles, than all other participants (7.3%; n=692).Shingles is caused by reactivation of latent varicellazoster virus (a herpesvirus).People with herpes zoster infection are known to have a significantly higher risk of ME/CFS up to at least 6 years 38 fuelling speculation that varicella-zoster virus infection is a cause of ME/CFS that may be prevented by vaccination.2.5% of ME/CFS cases have been attributed to varicella-zoster virus infection 11 .We note that among those reporting no infection around the time of onset (Type 4) some may have developed ME/CFS secondary to an infection without an obvious acute phase, such as can occur with Epstein-Barr virus 39 .However, we are unable to test this hypothesis here.
ME/CFS' poor long-term prognosis, its severe symptoms -especially for older females, its profound impact on the quality of life of people with ME/CFS and their family members 9,32 , and its high population prevalence (>0.2%) 1 present formidable healthcare and research challenges.Considering that 64% (n=10,853) of DecodeME participants reported an infection around the time of onset, any vaccination against the major infectious agents triggering ME/CFS, including Epstein-Barr virus 40 , SARS-CoV-2 41 and influenza viruses 42 may help reduce ME/CFS incidence in the future, especially for individuals more susceptible to severe disease, or those more likely to be exposed to the infectious agents.
It would be premature to propose that ME/CFS onset definitively defines clinically relevant disease types.However, as highlighted here, there are clear differences, in the symptoms experienced and associated co-morbidities, between subcategories defined by onset type.Our ongoing genetic analyses seek to establish the relative merit of onset type versus other features, such as disease severity or symptom clusters, to stratify people with ME/CFS.In order to address effectively the devastating impact that ME/CFS has on the millions of people worldwide affected, the research community and policy-makers will need to give sustained focus on disease classification and aetiology.Recruitment to the DecodeME study is ongoing.Results will be updated after the project's recruitment phase has ended.

subcategorising ME/CFS by onset type may help to reduce cohort heterogeneity and aid scientific discovery..."
Undoubtedly finding an effective way to categorise ME/CFS would be a big step forward, but the data presented in this paper do not support onset type for this use, at present.
The data indicate that onset groups show some differences in presentation, and many similarities.Further research is needed to establish whether onset type is a useful way to categorise pwME/CFS and to compare it to other methods, such as disease severity or symptom clusters before it can recommended for use in clinical practice or research.
It is fine to raise the potential benefits of an effective way to categorise ME/CFS, but greater caution is needed in suggesting onset type as the way of doing so.
Competing Interests: No competing interests were disclosed.I am not really clear whether the authors have answered the original research question -are there different types of ME/CFS?Are they saying -yes, illness onset defines different types of ME?There seems to be some leaning towards this interpretation but it isn't stated explicitly.If so, I am not convinced.Not because of anything in the method or analysis (as far as I can tell, that is fine), but because they started with the assumption that onset type (and they predefined the types of onset) was the definitive defining variable.I'm no statistician, but I think in a cohort and dataset of this size, one is bound to find some associations and significant differences.The issue is the importance of those found.

Reviewer
The essential section of the discussion regarding the importance of these findings and their potential application in future research and clinical practice is missing, possibly because the authors aren't sure themselves.
I think this comes down to the founding basis of the paper; that it started with illness type as the defining characteristic, and had pre-defined the types of onset.What was the rationale for this choice?There is a logic to it, but would other characteristics provide clearer differentiation, or be more relevant to future research efforts or clinical applications?Viral/non-viral onset; severity of symptoms or disability; outcome/course of illness (in terms of recovery, fluctuations deterioration etc); or symptomology according to the body system(s) affected are also obviously clinically applicable and meaningful.I'm not saying that illness onset isn't a (or maybe THE) way to categorise ME, but that to be adopted in practice or research it needs to show a clearer differentiation between types than other possible dependent/ defining variables.I.e. it needs to be the best way of typing PwME, not just a possible way of doing so.
From what I can gather, the upshot of a section on the importance and application of the findings might be something along the lines of …. "The findings show that sex and age, although associated with symptomology, co-morbidities and disease severity, are not factors that define different types of ME.Type of onset does show some significant differences in terms of symptomology, co-morbidity and illness severity, however these differences are not absolute and using 'type of onset' to define different types of ME would be premature.Further research is needed to investigate the accuracy with which 'type of onset' (probably with additional variables) can categorise pwME, and also to explore other potential ways defining variables, before comparing these with onset type to establish the most effective way to categorise type of ME." The results of the DeCode have been eagerly awaited, even the interim results so there is bound to be a lot of interest in this paper.However I feel it needs some more work to make it as easy to understand as possible.The lack of clarity stems primarily (I think) from a uncertainty about the aim/objective/research questions addressed in this paper.It has a feel of something of a fishing exercise, and having gotten some results, the authors are keen to tell people about them (which is admirable) but aren't really clear about what and why.The methods section, in particular needs more details so the reader can understand what was done, why and how.
To look at each detail in turn:

Abstract
The abstract needs a clear aim/objective/res question and further methodological detail.At present there is no info about the questionnaire, except that there was one.An indication of the selection criteria, recruitment methods, and details of the questionnaire (what does it measure, how?)And the analysis methods (which need to be linked research questions) are needed.In the results section there needs to be some data and the unique findings made clearer.It is hardly news that ME/CFS is a heterogeneous condition.

○
The plain English summary includes rather lot of jargon.For example -"For many people, ME/CFS means a substantial impairment of their activity levels, a high level of disability and a poor quality of life" rather than "for many people, ME/CFS means disability and poor quality of life .." NB.The PSP included a range of HCPs not just Doctors, ○ Methods I appreciate that PPI has been central to the DeCode project but there needs to be further details of the actual methods used.The 3 rd paragraph of the methods isn't really relevant to what was done in this project and can be removed.

○
The description of the questionnaire needs to include its aim; an outline of the content ○ (what does it ask about?), its size/length, types of questions and response format(s).The details of how it was developed and validated and any other psychosomatics would be useful.The reader needs to know what data were collected and how, and be reassured that the information obtained was relevant and accurate ie psychometrically robust.
The paragraph on the 'cohort' describes the process of completing part of the questionnaire.It would be easier to understand who was recruited to the study and how by listing the selection criteria, and stating the recruitment strategies-where and how were people recruited?I think it was largely via social media and support groups which is fine.Just say so.

○
The paragraph on significance testing needs to be replaced by an explanation of the analysis used, linked to the objectives/ research questions.It might be helpful to get a statistician to help with this.I see that logistic regression model(s) was/were used.This needs further detail, particularly the dependent and independent variables.There is reference to figures which are not explained and I cannot really follow.Also Onset Type, symptoms (in groups with different numbers), severity, illness courses, 'Fluctuating', the majority response, and comorbidities are unexplained, undefined and unconnected to any objective or research question.This section is key to understanding the whole paper so needs to clearly describe what was analysed, why and how.

Results
The 1 st paragraph (after the 1 st sentence) describes what participants did rather than what was found, and so belongs in the method section.The results need to start with a summary description of the sample -the number, mean age; sex ratio; time since diagnosis +/or duration of symptoms.Then the main co-morbidities; frequency of symptoms etc.The text needs to be understandable without the tables/figures and vice versa.The figures /tables add extra detail but there needs to summary in the text.

○
The results section needs to only include what was found in this study.Reflections on how they relate to other studies (or not) are for the discussion (see paragraph 2).

○
In the summary of results, try to avoid copy and pasting the results as they were churned out.Eg rather than "Participants were asked: "Did you have an infection when, or just before, your first ME/CFS symptoms started?" with five possible responses: (i) Yes, glandular fever (n=2,936), (ii) Yes, COVID-19 (n=380), (iii) Yes, another infection (n=7,537), (iv) No (n =2,625), or (v) Don't know (n=3,596; Figure 3).Proportions of people in the first 3 categories reporting a positive laboratory test of their infection prior to ME/CFS were 68.4%, 50.5% and 25.9%, respectively" -which is lengthy and unfocussed Summarise this for the reader by saying (for example) " Most (n, %) reported an infectious onset to their symptoms, most commonly glandular fever (17%, n=xx) this was glandular fever, followed by covid-19 (n,%).However only 68% and 51% respectively of respondents with these triggers reported a positive laboratory test confirming the diagnosis".
○ Do not include reference/comparison to other studies in the results section; this is for the discussion section.

○
Several of the sentences are rather convoluted and a bit more plain English would not go amiss.For example, I eventually managed to work out that "Participants who started their ○ illness within the last 1-3y or 0.5-1y numbered 1,287 or 354, respectively.These numbers are 57% and 21% fewer, per year, than the study's 1,634 participants from the 3-5y recruitment interval."Actually meant "Most 1,634 (xx%) were diagnosed 3-5 years previously, followed by 1287 (x%) with 1-3 year duration and 354 (X%) were more recently diagnosed (0.5-1 year)".
Always include both the number and % of participants being referred to.
○ Avoid any interpretation in the results.

○
Whenever presenting an average value an indication of the variability (whether standard deviations for means, or inter-quartile ranges for medians) is needed.
○ Why was a sub-group used to assess incidence?I don't think 'incidence' is the proper term.
The reference to the frequency with which ME/CFS occurs within a defined general population and this dataset doe sot include that data.

○
My comments above also apply to the rest of the results.At present they are difficult to read and to work out the main message from the rests.The reader is left to work out the main gist of the results themselves and it is far from easy.

Discussion
The 1 st paragraph of the discussion needs to summarise the main findings; the headline news in sufficient detail to the reader to have an insight into the significance of the results.Merely saying ME/CFS is heterogeneous with substantial differences in onset; symptoms, comorbidities and severity and sex differences isn't new, we already know this.The headline news needs some (summary) details about what the differences were.

○
Then you compare your findings with previous studies -which is great.Make sure all the comparisons and interpretation are here and not in the results.Also try to include any possible explanation for differing/contrasting results (usually due to sample size, method of data collection or selection criteria)

○
The limitations section is good.

○
The list of symptoms and other characteristics associated with different onset types are new data and belong in the results section -and would help a great deal to understand the results.Although I am a bit confused -why/how was onset type used to sub-group pwME?why not look at cluster/co-occurrence of symptoms and/or co-morbidities +/-onset?It may be that this was done and onset was the strongest factor, if so it needs to be explained more clearly.

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 16 (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 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 Figure1).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 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 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.

2 Reviewer
Expertise: pwME, clinical researcher academic I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.Version Report 13 July 2023 https://doi.org/10.3310/nihropenres.14591.r29746© 2023 Tyson S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Sarah Tyson Division of Nursing, Midwifery & Social Work, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, England, UK Thank you for asking me to review this resubmission.The authors have obviously undertaken a lot of work and the paper is much improved.Congratulations.It is now much easier to follow and the scientific merit is clear.I am happy to accept the changes made, except I would like the authors to reconsider one point, which is now starker given the helpful revisions in the paper.
4,5orthostatic intolerance that may each change across the life-course 3 .Many people with ME/CFS report an infectious episode prior to their initial symptoms.Up to 10% of people with glandular fever (also known as infectious mononucleosis) are eventually diagnosed with ME/CFS4,5, with similar fractions of people with Ross River virus or Coxiella burnetii infections also developing ME/CFS 4 .
1,2.Its core symptoms are post-exertional malaise, pain, fatigue, unrefreshing sleep, cognitive impairment and/One priority from a 2022 priority setting exercise facilitated by the James Lind Alliance 10 was "Are there different types of ME/CFS linked to different causes and how severe it becomes?Do different types of ME/CFS need different treatments or have different chances of recovery?"To address this question, we took advantage of questionnaire data from DecodeME, a study launched in the UK in September 2022.Before the end of the year, over 17,000 people with a ME/CFS diagnosis from a health professional, and at least 16 years (y) old, had been recruited and completed the study questionnaire.

further responses from the reviewers can be found at the end of the article
We have responded further to a Reviewer's comment that the data presented in this paper do not support onset type for effectively categorising ME/CFS.This was stated previously (v3) for clinical studies, but for this new version (v4) we now also state in the final paragraph (Discussion) that DecodeME is seeking genetic evidence of the relative merit of onset type versus other features, such as disease severity or symptom clusters, for stratifying people with ME/CFS.Group.People with lived experience of ME/CFS led the co-creation of a new DecodeME questionnaire.This resulted in substantial improvements in comprehension and accuracy compared to initial drafts and reduced the burden on participants, thereby boosting recruitment.What self-reported symptoms are associated with sex and/or age?" for each of 80 symptoms we used the linear model: Symptom ~ age + sex + intercept -Figure4.To address "What symptoms are associated with severity?"for80symptoms we used the model: Severity ~ age + sex + symptoms + intercept -Figure5.To address the questions "What onset types are associated with each of 8 fatigue symptoms (Figure . However, they remain inconclusive on whether different ME/CFS types exist and whether symptoms are sex-biased.The DecodeME project provided a unique opportunity to perform adequately-powered analyses for detecting differences within a single large ME/CFS cohort, under an assumption that ME/CFS type is delineated by onset type.and for their breadth of experience.The project's name was suggested and decided by PPI Steering Group members.In DecodeME, PPI representatives serve on each of its delivery groups, lead on marketing and communication (including social media), and contribute the majority (two of three) members of the decision-making body, the Management Amendments from Version 3 categories were consistent with participants' reports of their comorbidities and symptoms (Results).Participants indicated the duration of their ME/CFS illness by selecting from a set of predefined ranges, for example between 5 and 10 years, or over 10 years, since onset of symptoms.Questionnaire responses from participants who both consented to participate and self-reported being given a diagnosis of ME, CFS, ME/CFS or CFS/ME by a health professional (as of 19 December 2022) were analysed.Only those whose sex assigned at birth was male or female were analysed due to insufficient numbers of other identities.Participant ages were as of 19 December 2022.Further analyses of questionnaire and genotype data will be undertaken for the full DecodeME cohort once the recruitment phase of the project is completed.Significance testing.Logistic regression analyses were used to evaluate the relationship between various predictor variables (e.g.age or sex or comorbidities) and a binary outcome (e.g.symptom or onset type).For this we used the glm function in R version 4.2.2.Only p-values surviving Bonferroni correction for multiple tests (nominal p-value, here 0.05, divided by the number of tests per analysis) are shown.To address the question " , far fewer than the 18.3% in England and Wales who identify as non-White (https://www.ethnicityfacts-figures.service.gov.uk/).Most DecodeME participants' severity levels are categorised as Mild or Moderate, but Severe and Very Severe individuals are also represented (Figure DecodeME cohort contained 17,074 participants (83.5% females) whose median age was 49y (interquartile range [IQR] 37y-59y).Male participants tended to be older than females (median 52y [IQR=40y-63y] and 48y [IQR=37y-59y] respectively; p< 2.2×10 -16 , Wilcoxon rank sum test).Only 3.3% (n=557) of 17,074 participants did not self-report their ethnicity as White

the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? No If applicable, is the statistical analysis and its interpretation appropriate
What is the clinical or significance/ implications of the findings?Are you saying onset is such an important/explanatory factor that it should be used to group different types of ME/CFS?Or that they seem the best bet (if so, I didn't pick that up) or what??I cannot comment.A qualified statistician is required.
○ ○I'm presuming there isn't a conclusion section where the authors relate the findings to the original objectives /research questions and whether they have been fulfilled, because the journal specifically excludes one.If not, please add a conclusion.○Is

all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly
Competing Interests: No competing interests were disclosed.Reviewer Expertise: pwME, clinical researcher academicI confirm that I