Preprint Cluster analysis of ME/CFS symptoms in DecodeME reveals two subgroups and a link to onset type, 2026, St-Jean et al

FOr me the absence of a different genetic signature is the key thing. I am also sceptical about the significance or reliability of the history of infectious onset but I think it is very interesting that BTN2A1, in particular, is not differentially linked either to severity group or infectious history.

I agree with others that if anything this again suggests that ME/CFS is rather homogeneous mechanistically. As for the lack of different in gene variants for sexes, it seems to point to everyone having the same route to disease. And yes, it suggests that the gene variants are telling us about critical ppints in regulatory failure rather than just downstream tissue sensitivity. If genes were linked to how susceptible a tissue like brain is to bombardment with signals then they should link even more to severe cases.

All in all I think it tends to be reassuring - that there is probably only one major route to ME/CFS to find and the gene variants are pointing at critical pathway points.
 
I think severity groups are meaningful. I would not call them subtypes - I don't think that makes a lot of sense either clinically or scientifically. But severity levels are relevant to unpicking mechanism, especially if the ppopulation is bimodal in this respect (if that can be reliably said).
 
Jonathan Edwards said:
FOr me the absence of a different genetic signature is the key thing.
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Isn’t that too soon to say, though? The current analysis might be underpowered, it was 8k vs 10k instead of 15k vs 250k or whatever DecodeME was.

The paper also talks about three suggestive differences in genes:
In contrast, the GWAS conducted as part of the present analysis did not identify any genome-wide significant subtype-associated variants, and the three suggestive associations should therefore be interpreted cautiously, particularly as approximately one would be expected to be false positive at the threshold used.
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Nevertheless, the proximity of these suggestive loci to genes implicated in circadian regulation, inflammatory signalling, and sensory or pain processing is notable in relation to the clinical profile of the HSBC, which was marked by greater illness severity, sleep-related symptoms, sensory sensitivities, pain-associated comorbidities, and higher prevalence of infectious or uncertain onset.
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Taken together, the genetic findings do not validate the symptom clusters directly, but they provide a plausible biological frame for interpreting them and suggest that symptom burden, onset type, sex, and comorbidity profile may be important stratification variables in future genetic and mechanistic studies of ME/CFS.
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Utsikt said:
The current analysis might be underpowered, it was 8k vs 10k instead of 15k vs 250k or whatever DecodeME was.
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The methods say the GWAS cohorts were smaller:
Genome-Wide Association Study
Genetic data from DecodeME participants was extracted from the jointly imputed case-control dataset for GWAS-1 (4). For this analysis, we retained only participants who were included in DecodeME GWAS-1 and had matching questionnaire data, leading to a dataset with 15,328 participants. Of these, 2,401 participants were allocated to the LSBC, and 3,264 to the HSBC.
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Though I don't understand what those smaller numbers are based on, and why not all 15,328 would be included.
 
Utsikt said:
That goes beyond the evidence due to the limited sample sizes when using subgroups. We know from other genetic studies that reaching a critical mass is crucial to be able to find the relevant genes. We need larger cohorts to say anything more definitively.
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They have a cohort of 10,000 patients. Leading immunologists like Selin and Kumar who work with a research question (which is key if you want to find out something) make proof of concept studies with 6 patients and find out extremely interesting and valuable things.

Blaming limited severity subgroup sizes at this scale sounds like a particle physicist arguing that the only reason their field hasn't made a major breakthrough since the 1970s is because nobody built them a bigger Large Hadron Collider.
 
PageofME said:
They have a cohort of 10,000 patients. Leading immunologists like Selin and Kumar who work with a research question (which is key if you want to find out something) make proof of concept studies with 6 patients and find out extremely interesting and valuable things.
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In a GWAS, they're doing multiple test correction for around a million tests (about that many independent loci on the genome). If Selin and Kumar ran a million tests with 6 individuals and corrected the p-values, there would almost certainly not be any significant findings.

It's the reason GWAS sample sizes are usually at least in the tens of thousands, and sometimes in the millions.
 
On a very brief skim through (all I can manage these days) this seems like useful work; it's good to see further analyses of the DecodeME questionnaire data. Symptomatic burden and self-reported severity do look strongly associated but not identical, with higher severity overall being more likely to fall into the higher symptom burden cluster; but it's not one-to-one - the illness course also seems to track well, with the higher burden cluster enriched for deterioration and improvement being more common in the lower burden one.

Perhaps a median or mean symptom count by severity category analysis would be useful, e.g. some histograms & density plots of symptom count by cluster and severity, or a symptom-count adjusted enrichment analysis to see if any subtype remains after removing the burden dimension? The adjusted regression gives higher odds for infectious vs. non, but the cluster table (Suppl. Tab. 2) has an excess of unknown-onset and lower non-infectious onset in the HSBC.

The cluster separation does seem modest, more of a broad symptom-burden gradient. I think the authors' interpretation that these are "clinically meaningful subtypes, potentially reflecting distinct biological mechanisms" is too strong; if such an analysis had produced one cluster that was, say, enriched for pain and another cluster for orthostatic symptoms, that would be a different matter.
 
"Nevertheless, the proximity of these suggestive loci to genes implicated in circadian regulation, inflammatory signalling, and sensory or pain processing is notable in relation to the clinical profile of the HSBC, which was marked by greater illness severity, sleep-related symptoms, sensory sensitivities, pain-associated comorbidities"

These are further indications of the mecanism. I don’t think this means that the disease does not fluctuate and that there would be two very distinct groups, as variants would have emerged significantly. Either protective in the mild group, or aggravating in the other, or both.
 
PageofME said:
First, they separated the participants into two groups based entirely on symptom severity. They found that those in the more severe group were about 1.24 times more likely to have had an infectious trigger at the start of their ME/CFS compared to those with lower symptom burden.
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Nightsong said:
Symptomatic burden and self-reported severity do look strongly associated but not identical, with higher severity overall being more likely to fall into the higher symptom burden cluster; but it's not one-to-one
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Nightsong said:
Perhaps a median or mean symptom count by severity category analysis would be useful, e.g. some histograms & density plots of symptom count by cluster and severity, or a symptom-count adjusted enrichment analysis to see if any subtype remains after removing the burden dimension?
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I agree with the notes of caution about claiming subtypes; it doesn't sound as though much was found to support that idea.

I want to make sure that everyone understands that the symptom burden talked about here is the number of symptoms, as Nightsong explains. So, they didn't separate the groups based on symptom severity, they separated them on symptom number. The people reporting a higher number of symptoms also tended to report a higher illness severity but there was plenty of variation.

I'd also like to throw in the usual comments about the uncertainties related to self-selection of participants and self-reporting. It's conceivable that women have spent more time in support groups chatting with others and might therefore know about the co-morbidities that get talked about, things like POTS and so be more likely to report them. Probably parents of young people with ME/CFS might tend to be across the ME/CFS literature and Facebook chat groups and assist their young person to tick more symptom boxes.

I haven't looked at the symptom descriptions, but it's possible that there may be more options relevant to women that more or less exclude men e.g. period pain, urinary tract infections, that could skew the symptom count to female.
 
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Utsikt said:
Isn’t that too soon to say, though? The current analysis might be underpowered, it was 8k vs 10k instead of 15k vs 250k or whatever DecodeME was.
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I have not had time to read through the paper but my thought was that it made the original 8 gene links look more robust in terms of critical pathways. There will no doubt be some other variants picked up with big enough numbers that reflect end organ susceptibilities. There might be some subgroup discriminating variants linked to process too but at least we are not seeing major subgroup separation.
 
forestglip said:
In a GWAS, they're doing multiple test correction for around a million tests (about that many independent loci on the genome). If Selin and Kumar ran a million tests with 6 individuals and corrected the p-values, there would almost certainly not be any significant findings.

It's the reason GWAS sample sizes are usually at least in the tens of thousands, and sometimes in the millions.
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In academic writing, the first rule I taught my students was basic research design: you must formulate a relevant question that you can actually answer within the limits of your time, scope, and resources.

What @Utsikt seems to be suggesting is that because these researchers failed at this foundational step – asking a question too large for their 10,000-patient cohort – we should reward the poor study design by giving them even more funding just so they can finally answer it.
 
bspielvogel94 said:
@EndME

Strictly speaking, "clinical subtypes" simply means that different patients cluster differently based on symptoms. Definitionally, it does not suggest that there are different disease mechanisms or underlying biology behind those subtypes.

And yet, that's all I seem to see and hear. We explain away every failed trial with the notion that we included multiple subtypes with different disease mechanisms, and so of course the treatment wouldn't work across all of them. Every research organization stresses the importance of subtyping, and yet there is zero evidence that distinct subtypes actually exist beyond symptom presentation.

If two people contract the flu and one is sick for a few days while the other is severely ill and ends up with pneumonia, are those two different subtypes of the virus influenza? No, of course not, and yet that's what this study seems to be saying.
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The subtype may lie in the pathway taken after the viral flu. This may relate to genetics, further exposures leading to co morbidities , involve much chance etc. A subgroup may amount practically to whether sth recommended by Teitelbaum might help or in deed by Myhill or whether you have ended up with LP responsive or MT responsive syndromes. Practically these are subgroups in a criteria based condition and sth that helped me e.g antifungals may be useless for others. Clinical subgroups do exist imo but the anecdotes do not amount to scientific data, of course. Some may never have fitted the criteria , true, but they/we are still caught up in the patient group, which may indicate the necessity of diagnosis by stronger criteriai at the risk of implying that only those who meet such have true ME. The situation remains very difficult and gold- standarding one means of establishing subgroups, even if such a means were currently available might leave other important ways of looking at things undervalued.
 
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Kitty said:
Yup.

How could anyone know their onset was not infection-associated? Even if they have good reason to think it was triggered by a different type of event?
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Long Covid has blown this issue wide open, and unfortunately no one seems to have adapted to it, on either side of the divide. A huge % of LC had a completely unremarkable acute illness, closer to a typical cold most people would get 2-3 times per year. Not even something most people would remember to mention, even when prompted they might not even remember.

What this looks like frankly is not that we don't have enough data, but rather that we have too much data, making it extra hard to make sense of it. The size of the effort required is simply too large.

At least this is something that time, and the progress of technology, eventually solves, but it probably means decades before we see anything useful.
 
forestglip said:
The methods say the GWAS cohorts were smaller:

Though I don't understand what those smaller numbers are based on, and why not all 15,328 would be included.
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Well, in that case we can probably not take the lack of significant hits as evidence of the absence of genes affecting the number of symptoms you have.
Jonathan Edwards said:
I have not had time to read through the paper but my thought was that it made the original 8 gene links look more robust in terms of critical pathways. There will no doubt be some other variants picked up with big enough numbers that reflect end organ susceptibilities. There might be some subgroup discriminating variants linked to process too but at least we are not seeing major subgroup separation.
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My point was that I’m not sure we would have been able to find the genes with this small of a study.

But I agree that if there are no hits when comparing the subgroups that exist, the interpretation of the 8 hits in the main DecodeME analysis is that those are more likely to reflect critical ME/CFS pathways.
PageofME said:
In academic writing, the first rule I taught my students was basic research design: you must formulate a relevant question that you can actually answer within the limits of your time, scope, and resources.

What @Utsikt seems to be suggesting is that because these researchers failed at this foundational step – asking a question too large for their 10,000-patient cohort – we should reward the poor study design by giving them even more funding just so they can finally answer it.
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You’ve misunderstood me.

You previously claimed that this study was proof that there are no genes that influence the number of symptoms you get.

I said that the study is likely underpowered to answer that question so it might have missed genes because of that.

I also don’t think the study design was poor, and I doubt the GWAS was the primary aim of this study. They did what they were able to with the data they had, because why not - you might as well check when you can? Keep in mind that the power calculations were done for the main GWAS of patients vs healthy controls because that was the priority and the only financially viable option.

If the problem for this particular analysis is that the study cohorts were too small, not that the methodology was inherently flawed, then the only viable solution if you want the answer is to use larger cohorts. That would not be to reward a poor design, but rather to recognise the potential of the methodology and the importance of the question they tried to answer in the secondary analysis of the data.

What would be a poorly designed study, is to repeat a GWAS of the same size given what we now know. But I don’t see anyone arguing for that.
 
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PageofME said:
No, that’s not quite it. The study actually shows two key things:

First, they separated the participants into two groups based entirely on symptom severity. They found that those in the more severe group were about 1.24 times more likely to have had an infectious trigger at the start of their ME/CFS compared to those with lower symptom burden.

Second, and perhaps most interestingly, they found that these two groups do not differ genetically. This means human genes are not the reason some people end up with a much higher symptom burden.

To us patients, this might seem trivial. We already know firsthand that the main cause of severe deterioration is overexertion relative to our individual baseline. But researchers have a habit of making things more complicated because they have to prove everything objectively. In the end, I guess it’s still a good thing we have them doing this work.
:)
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I don't put much weight on the infectious trigger odds ratio because patients self-reporting triggers can be very messy (e.g., asymptomatic infections, recall bias, etc.). If it were 3x or something huge like that, that would be something, but 24% is quite modest.

But that's a good point on whether genetics are a driver of disease severity - that is a good thing to explore.

My original comment was a reaction to the conclusion that ME/CFS is a heterogeneous condition and that we must "tailor future interventions to these subgroups." In terms of symptom management, yes of course tailor specifically to what the patient is experiencing (e.g., pain meds for pain, sleep meds for insomnia, etc.). But I personally have not seen compelling evidence to suggest that there are subgroups for the actual disease mechanism that would require different disease-modifying medications (when that day ultimately arrives).
 
Jonathan Edwards said:
I think severity groups are meaningful. I would not call them subtypes - I don't think that makes a lot of sense either clinically or scientifically. But severity levels are relevant to unpicking mechanism, especially if the ppopulation is bimodal in this respect (if that can be reliably said).
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Re mechanism
Would it be interesting to compare those who
  1. Were initially severe and improved
  2. Were initially severe and did not improve
  3. Became severe and improved
  4. Became severe and did not improved
Ie move up or moved down over time . Could there be a clue to bistability / mechanism .
I don't know if we have data for this.

Would longitudinal study to capture something like this combined with the genetics be worthwhile ?

My daughter is sadly deteriorating . What is noticeable is the impact on existing symptoms and the development of some new ones .
 
Amw66 said:
Would longitudinal study to capture something like this combined with the genetics be worthwhile ?
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I think so. We've discussed such ideas for example here. There's some difficulties with variouses biases possibly being introduced (like recovered people possibly being less likely to respond), but I think a follow-up to DecodeME will still be worth the effort if funding can be secured.
 
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