What are the necessary conditions and criteria for a theoretical model of ME/CFS?

[MelbME]

There are a couple of groups who have sort of gone down this route. Programms such as LIINC have been setup to track people right from their Covid infection until whatever outcome they arrive at and the recent ME/CFS biobank in Germany is also setup to do something similar.

However, some of those studies have shown that even people that meet the CCC definition at 6 months or at least look very similar to ME/CFS often turn out to look rather different at a later time, as for example seen in Long-term symptom severity and clinical biomarkers in post-COVID-19/chronic fatigue syndrome:results from a prospective observational cohort. This German group surrounding Scheibenbogen is also tracking ME/CFS post EBV, but those studies are probably extremely underpowered to reveal much from an epidemiological stand-point as seen in One-Year Follow-up of Young People with ME/CFS Following Infectious Mononucleosis by Epstein-Barr Virus.

If the studies are anyways severly underpowered from an epidemiological stand-point, for example to figure out how large of a risk factor EBV infection is for ME/CFS, I do agree that it might just be sensible too just look at 1000 ME/CFS patients with a longer disease history and do a deep phenotyping study of those patients.

I do wonder though why there is so much focus on trying to look at signatures that seperate somewhere around 90% of ME/CFS patients from healthy controls and then often do so via something like a random forest classifier that just ends up looking rather random, finding marginal differences in a larger set of people without telling us anything fruitful.

I'm far more interested in a result that shows stark differences between two groups and that tells us something meaningful about pathology even if that result only applies to say 50% of ME/CFS patients. But then I wonder how large the sample set of patients really has to be. Depending on what is done it might sometimes be more important to have someone that look at a problem under a novel angle. There are certainly things wrong with the intramural study by the NIH, however what it did show us that a study expecting somewhere around 90% accuracy from a signature is destined to fail from the get go if it is really supposed to distinguish ME/CFS from HC.

We have a paper submitted to a Nature journal that you will probably be interested in. We're hoping it gets published soon. We looked at common co-morbid conditions in an ME cohort and then compared ME to cohorts of that co-morbidity to see if we could find differences that had more to do with an ME signature than the co-morbidity. We also looked for a diagnostic signature that would separate ME from these comorbid diseases. We got one that was ~75% accurate. So we think there is potential here.

 

[EndME]

We have a paper submitted to a Nature journal that you will probably be interested in. We're hoping it gets published soon. We looked at common co-morbid conditions in an ME cohort and then compared ME to cohorts of that co-morbidity to see if we could find differences that had more to do with an ME signature than the co-morbidity. We also looked for a diagnostic signature that would separate ME from these comorbid diseases. We got one that was ~75% accurate. So we think there is potential here.

Thanks a lot for sharing! I'm very much looking forward to reading it once it is published.

With AI classifiers seemingly becoming the norm, are you aware of any work going into something along the lines of "instead of trying to distinguish as many LC or ME patients from HC as possible, even if the differences in actual measurement values might be very marginal and might require combinations of things that apparently aren't pathological connected (say:cortisol+EBV), we are looking at trying to find a diagnostic signature that yield relatively consistent values in HCs and can explain pathology but who's measurement values differ drastically in LC or ME from HC, but only does so for a subset of patients".

The danger of this approach is of course that one could be identifying differences attributable to co-morbidities rather than "true" ME or LC, but it still seems worthy exploring for me, especially if one then can then look at it in a follow-up study in a similar fashion to the one you submitted to a Nature journal.

In the past, I have been rather perplexed when even the most brilliant researchers have made claims surrounding their diagnostics, for example claims surrounding "cortisol being a biomarker for LC" when they should be well aware that AI classifiers cannot distinguish signal from noise but are simply very good at finding structures independently of whether those structure represent signal or noise and have been wondering whether there shouldn't be far more focus on the explainability power of a diagnostic signature rather than purely being focusing on its differentiating capacity and gobbling up those p-values that some statistican throws at you.

 

[MelbME]

Thanks a lot for sharing! I'm very much looking forward to reading it once it is published.

With AI classifiers seemingly becoming the norm, are you aware of any work going into something along the lines of "instead of trying to distinguish as many LC or ME patients from HC as possible, even if the differences in actual measurement values might be very marginal and might require combinations of things that apparently aren't pathological connected (say:cortisol+EBV), we are looking at trying to find a diagnostic signature that yield relatively consistent values in HCs and can explain pathology but who's measurement values differ drastically in LC or ME from HC, but only does so for a subset of patients".

The danger of this approach is of course that one could be identifying differences attributable to co-morbidities rather than "true" ME or LC, but it still seems worthy exploring for me, especially if one then can then look at it in a follow-up study in a similar fashion to the one you submitted to a Nature journal.

In the past, I have been rather perplexed when even the most brilliant researchers have made claims surrounding their diagnostics, for example claims surrounding "cortisol being a biomarker for LC" when they should be well aware that AI classifiers cannot distinguish signal from noise but are simply very good at finding structures independently of whether those structure represent signal or noise and have been wondering whether there shouldn't be far more focus on the explainability power of a diagnostic signature rather than purely being focusing on its differentiating capacity and gobbling up those p-values that some statistican throws at you.

Well one does "sell" the impact of work to get published in a good paper from time to time and to help get future grants/funding, that's probably most of the reason. And maybe sometimes they're just not as experienced in the diagnostic space. In 2015 we did the first metabolomics profile of ME vs controls, decent numbers for the time. We could get 100% separation of groups using 6 metabolites. We did explore the idea of a biomarker but in that process quickly realized how far away we were (had no mechanism we were building off and hadn't validated it nor compared it to disease cohorts). I was mid-PhD but it was a good learning process to at least speak to people in the diagnostic area about how realistic the chances were.

POT (postural orthostatic tachycardia) occurs in healthy controls. POTS is when it is produces symptoms chronically and comes with an array of symptoms that aren't obviously related. So is that what you refer to as a pathology. I mean why not separate patients by comorbidities?

 

[alex3619]

But would this design be more fruitful than deeply characterising 1000 ME/CFS patients to look for pathologies and signatures to cluster?

I'm far more interested in a result that shows stark differences between two groups and that tells us something meaningful about pathology even if that result only applies to say 50% of ME/CFS patients.

A prospective study would start with healthy people and test them every time they get sick, or at least a blood sample. No waiting. At even six months the key biochemical processes may be much less apparent. This should probably be a much larger study looking at a much larger range of diseases. No point in just tracking ME or LC, especially since funding might be easier.

 

[EndME]

A prospective study would start with healthy people and test them every time they get sick, or at least a blood sample. No waiting. At even six months the key biochemical processes may be much less apparent. This should probably be a much larger study looking at a much larger range of diseases. No point in just tracking ME or LC, especially since funding might be easier.

From my understanding that is essentially what LIINC has done for LC.

It was done for EBV and MS and took somewhere around 40 years and more than 10 million participants (and yet one tiny wrong test result in that study completely changes the risk factor they had calculated). Allegedly those authors thought about ME at the same time but decided against it due to the necessary efforts and unknown results. With Covid known to cause ME/CFS as well you'd likely have to control for Covid infections as well and I think that is extremely hard.

It's just very hard to get sensible data on something that affects almost everyone (EBV & Covid) but where only a much smaller minority develop symptoms afterwards (ME/CFS), especially when you don't even know whether negative controls are actually negative controls.

It seems more sensible to me to just start off with those that are already affected. Possibly someone has a clever way around those problems but I haven't seen anyone suggest anything sensible.

 

[MelbME]

From my understanding that is essentially what LIINC has done for LC.

It was done for EBV and MS and took somewhere around 40 years and more than 10 million participants (and yet one tiny wrong test result in that study completely changes the risk factor they had calculated). Allegedly those authors thought about ME at the same time but decided against it due to the necessary efforts and unknown results. With Covid known to cause ME/CFS as well you'd likely have to control for Covid infections as well and I think that is extremely hard.

It's just very hard to get sensible data on something that affects almost everyone (EBV & Covid) but where only a much smaller minority develop symptoms afterwards (ME/CFS), especially when you don't even know whether negative controls are actually negative controls.

It seems more sensible to me to just start off with those that are already affected. Possibly someone has a clever way around those problems but I haven't seen anyone suggest anything sensible.

Well I think characterising 500-1000 ME/CFS would give a great overview of the heterogeneity and it would be a target for developing severity markers.

Obviously decode targeted 20k+ people, this wouldn't be to that level but could be a start. Need more of these large-scale data projects.

 

[Creekside]

This report caught my attention: https://www.sciencedaily.com/releases/2024/07/240726113357.htm It's about how microglia connect to neurons via tunneling nanotubes to pump waste proteins out and mitochondria in. I've thought up a model for ME based on that.

Microglia are a good candidate for playing a major role in ME, connecting immune system activation with neurological symptoms. When a glial cell connects to a neuron with a nanotube, it transfer proteins and mitochondria. That makes it likely that other material is transferred: signalling molecules, building blocks, waste molecules; held in vesicles or floating free. ME might alter the contents of microglia, and thus what gets transferred to neurons. Immune activation in the body makes that worse, so PEM might be the result of more of <whatever> being transferred into neurons, hampering their function, resulting in symptoms. If this nanotube activity depends on neural activity, that could explain why cognitive exertion results in the same PEM symptoms, but with a shorter delay.

Another complication: microglial may use these nanotubes to connect with each other, in response to stressors. Maybe ME affects how microglia connect with each other and with neurons. Maybe ME makes them connect with each other, leaving neurons with excess waste. Maybe they connect too much with neurons, preventing the microglial interconnections necessary for their proper function.

Now that these nanotubes have been discovered, models such as this can be tested. Does nanotube activity depend on neural activity? Does the rate and/or duration of connections between different cells change in ME? Measuring what gets transferred might be tricky, since these nanotubes are tiny and probably short-lived (maybe microseconds?).

Any thoughts? Assuming your tunneling nanotubes are working properly...

 

[SNT Gatchaman]

I thought that paper was worth a thread @Creekside.