Yes, I think it is important to take into account the fact that when categorising diseases, like animal species, it is very often combinations that are crucial.
Having wings is pretty non-specific for creatures. But a four-legged creature with wings is unique - a Hippogriff. Insects have six legs and birds, angels and bats two.
NRS for me would be a way of picking out ME from other conditions where fatigue or exhaustion was not associated with any features that would make one think it was likely to be in a causal set with those with PEM, for instance. For arthritis, a rash allows you to put a patient in a group in this way, despite rashes being two a penny.
Another angle we could try with this is start with big data which has self reported symptoms with fine granularity, analyse symptom distribution, create a new subtype for every different symptom combination, then count those belonging to each subtype and derive research subtypes with defining symptoms from the patient data.
With a little statistical wisdom, like nesting subtypes with identical key symptoms, I think that could also yield useful clinical subtypes.
I dont think anyone has actually done this systematically yet have they? All the criteria are subjective attempts to do this intuitively, which is born of noble intent but a notoriously unreliable method.
One hopes DecodeME is trying to make some progress with this in relation to GWAS.
It is the sort of study which could also be done online as an open questionnaire. It would be less rigorous if not linked to GWAS but not entirely useless and we dont know what GWAS will turn up yet.
I think we are at a point where we don't have enough empirical data to make much progress on the definition, for now.
Lets face it, ME/CFS is a bucket diagnosis. Assuming the average ME cohort is a heterogenous mix of conditions, of course we are not getting consistent results.
Assuming the ‘fatigue’ nonsense assumptions come from somewhere (like another condition)
I don’t think we have enough evidence to assert this. I accept ME is a fuzzy set with blurring at the fringes and there is high levels of variability between different patients. However there is also high levels of variation within individuals; over the thirty years of my ME my condition has varied enormously and looking just at different cross sections I could have been thought to have different conditions, but longitudinally there is a continuity that I believe makes it clear that this is one condition. Given the variation between different individuals it alway surprises me how much we in fact have in common.
I think it is worse asking if the focus on ‘fatigue’ was/is a deliberate attempt to mask the distinguishing features of ME. I think it is worth looking at the concept of ‘common health problems’ that Joanne Hunt (see https://www.s4me.info/threads/inheriting-discriminatory-socio-political-landscapes-as-‘undeserving’-disabled-people-legacy-of-common-health-problems-future-for-lc-2024-hunt.37264/ ) argues was introduced to sell the idea that many chronic conditions were in effect a life style choice rather than reflecting underlying medical conditions, as part of government and insurers attempts at denying benefits.
Let's look at other diseases. Has it ever happened for a disease definition where there aren't prominent unique signs to precede an understanding of the pathophysiology? Reaching a consensus?I think we are at a point where we don't have enough empirical data to make much progress on the definition, for now.
[just commenting on the abstract, haven't been able to catch up on the whole thread yet]abstract said:Method: Respondents were asked questions about core symptoms and other critical case definition issues.
Results: Overall, post-exertional malaise, cognitive impairment, fatigue, and unrefreshing sleep were the most endorsed core symptoms with at least 80% consensus among participants.
Could there not be substantial differences to what is meant by unrefreshing sleep in the general context? The study for example suggests that "nonrestorative sleep (NRS)...is associated with greater daytime sleepiness", however I'm not aware that, that would be the case for pwME. From what I've gathered they are typically awake and not sleepy in a classical sense, more so fatigued irrespectively of how their sleep was.
"Furthermore Nonrestorative sleep...is described as the feeling that sleep is restless, light, or of poor quality even though the duration may appear normal." I was under the impression that for pwME it is often more than that, they wake up more fatigued then when they initally went to bed, a sort of feeling as being hit by a truck when the morning arrives, that for a subset of people improves as the day goes by.
Another angle we could try with this is start with big data which has self reported symptoms with fine granularity, analyse symptom distribution, create a new subtype for every different symptom combination, then count those belonging to each subtype and derive research subtypes with defining symptoms from the patient data.
With a little statistical wisdom, like nesting subtypes with identical key symptoms, I think that could also yield useful clinical subtypes.
I dont think anyone has actually done this systematically yet have they? All the criteria are subjective attempts to do this intuitively, which is born of noble intent but a notoriously unreliable method.
One hopes DecodeME is trying to make some progress with this in relation to GWAS.
It is the sort of study which could also be done online as an open questionnaire. It would be less rigorous if not linked to GWAS but not entirely useless and we dont know what GWAS will turn up yet.
What is long overdue, on the other hand, is improving the way we use the existing criteria.
[just commenting on the abstract, haven't been able to catch up on the whole thread yet]
Is this yet another one of that seemingly endless stream of papers from that group, always pushing the DSQs, always in a slightly different context so as to justify publishing a separate paper?
Anyway, I don't think now is the time for yet another research definition; to make worthwhile improvements on the existing ones we first need some new knowledge based on solid data.
What is long overdue, on the other hand, is improving the way we use the existing criteria.
First, we could do with better symptom definitions. The term PEM (which would better be called a feature or a phenomenon than a symptom) is applied to everything from early fatiguability during exercise to delayed-onset muscle soreness. The term cognitive impairment is applied to everything from being generally too exhausted to think clearly to highly specific memory problems. The term fatigue is applied to everything from sleepiness to feeling poisoned. And the term unrefreshing sleep is applied to everything from waking up exhausted despite sleeping through the night to day-night reversal and insomnia. Trouble is almost everybody assumes, wrongly, that everybody else shares their particular definition of the terms.
Second, we need to find reasonably foolproof ways of operationalising the criteria so even novices to the field can identify symptom patterns in a consistent & meaningful manner. And sorry, authors, but the DSQ ain't it (as discussed at length elsewhere). Most crucially we need to figure out how to ask questions about PEM that don't mislead people to confuse PEM with early fatiguability during exercise, DOMS, deconditioning and all the other stuff we've seen it confused with. Plenty of room for a whole horde of devils to slip into the detail here unfortunately. And that's not even counting wilful misinterpretations.
Third, we need to get better at comprehensively documenting individual phenotypes, irrespective of diagnostic criteria used (as long as the criteria include PEM). Yes, that takes more work and therefore funding but currently we're trying to interpret conclusions from Fukuda studies with unknown numbers of participants with and without PEM, CCC studies who use PEM synonymous with DOMS, and heaven knows what other confounders. Sure, there's a limit to how much detail can feasibly be recorded but we can surely do better than that.
By using existing criteria more wisely, if we later suspect a particular symptom or pattern may be really critical, or is defining of a subgroup, we can trawl back through the data to check. Plus it would give AI something to get its teeth into without risking too much GIGO. This, hopefully, can contribute to generating more solid knowledge about the shape of ME and help get us out of the chicken and egg situation we're in right now. Then we can refine the diagnostic criteria.