Preprint Replicated blood-based biomarkers for Myalgic Encephalomyelitis not explicable by inactivity, 2024, Beentjes, Ponting et al

Hutan said:
The z score for BCHE in ME/CFS men is over 5 (for women it's heading towards 2). Given the z score of 5, I'm not understanding your hypothetical scatter plot. Shouldn't the ME/CFS data points be a lot higher? I could easily be misunderstanding something there.
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Good point. My hypothetical plot is simply based on the cohen's d effect size value from the raw data, the sample size they report for males and imagining that the mean is zero and SD is 1. The Z score values they report comes after accounting for age and an activity mediator so it would be a bit different.

My main guess for why it would be different is because I think to get their Z scores they have divided their estimates not by the standard deviation (which would indicate the variation in the data), but by the standard error of the mean (which represents the variation around your estimate of the true difference between hc and me).

Sorry it's a bit confusing to write out, but basically I think it doesn't so much represent the magnitude of the difference in the data, but instead represents the confidence that there is a real difference between the groups. When I divide the estimated difference by the SEM in the plot I made, I get a value around 5 - but of course we know the effect size cohen's d is only 0.8. I hope that makes sense.

I should say that while i've got some experience with generalised linear models I haven't any experience with the model they're using in this paper - so I'm just making an educated guess.
 
@Thanks @chillier. Yes, what you have said makes sense to me.

I'm not yet completely clear whether the situation is
1. most likely, the BCHE values for both males and females PwME sit within the 95% confidence interval for healthy people, albeit the male PwME data is mostly above the healthy mean, or
2. BCHE values for female PwME are at the top of the 95% confidence interval for healthy people and the values for male PwME are almost all way above it, or
3. something else.

Of course, it makes an enormous difference to what interpretation we might put on the result.

I find the descriptions of some of the charts less than clear - it is often hard to know what is being compared. I know there is a lot of analysis and it's complicated, but I think/hope the results can be presented in a way that is easier to understand. Going through the captions of the figures and the statements of results and checking that what is being compared is always clearly stated might help.

I need to read the paper again.
 
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@chillier 's comparison the Hoel paper is with serum, the rest are plasma, this could have an effect.

chillier said:
EDIT: Also one of the significant proteins in the combined analysis in this thread's paper is the maybe relevant seeming 'Phosphatidylcholine-sterol acyltransferase' (adjusted p = 0.023).
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Jonathan Edwards said:
I know very little about this sort of biochemistry but choline seems to crop up everywhere.
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Uniprot entries describing the concerned enzymes:
https://www.uniprot.org/uniprotkb/P06276/entry
https://www.uniprot.org/uniprotkb/P04180/entry

IMO it is kind of hard to draw conclusions from snippets of lipid related data alone especially from biofluids, where the lipids in circulation may be impacted by processes within cells / other tissues, rates of secretion or uptake, all in combination with reactions in circulation itself, such as those catalysed by Phosphatidylcholine-sterol acyltransferase (LCAT) (link two above). And then within that, we don't really know the exact roles of all of these lipids or all of the specific implications of them rising and falling (and implications for which tissues?)

An approach to look for a meaningful thread to tug on related to lipid data might be to try and associate it with related enzyme levels or activity. Then we can account for tissue/circulation specificity as well depending on the attributes of the enzyme (order of the following points doesn't matter):

eg:
a) Identify whether particular groups of lipids are affected in their levels, or in their composition (length, saturation, ether bond, lyso species, etc)
b) In parallel, check whether this is accompanies directionally-concordant dysregulation of enzymes known to drive whatever specific change you are seeing (or vice versa)

Random illustrative example: if one sees an accumulation of PS lipids and a reduction of PE lipids, and in parallel a downregulation of an enzyme that catalyses a key PS -> PE reaction, bob's your uncle and you have a clear hypothesis to test.

So from biofluid data I think a useful exercise would be to take an enzyme like LCAT that is active in reactions in circulation, that there are differences in here, and then look at the levels of the molecules that it's working on (input & output).

Jonathan Edwards said:
I know very little about this sort of biochemistry but choline seems to crop up everywhere.
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What is it you say, "analysis by-meme" ;) (joking not critiquing)

Anyway now for some example interpretation, at the very least as a curiosity

LCAT catalyses PAF -> Lyso-PAF. The lyso form of PAF is inactivated. PAF ("Platelet-activating factor") is a very influential molecule. @chillier I can't find it in the paper, were LCAT levels elevated or reduced? The paper seems to report elevated platelet count. This could be caused by elevated levels of active PAF (PAF mobilises platelets). This could be driven by reduced PAF inactivation by LCAT. If LCAT levels are reduced there could be a testable hypothesis in here. The whole chain of events isn't completely evidenced in the dataset from what I can see (although I have only skimmed due to time constraints) but it's something.

Putting together a table of altered metabolites, putting together another table of altered gene products annotated with the metabolites involved in reactions that they catalyse, and then cross checking everything could be really useful. Having metabolite and protein data together is a goldmine. Hell you could even look for correlations between altered enzymes and metabolites and then check the significant outcomes for biological relevance.

I am doing something related in a paper draft atm but using the LCL collection so more removed from the body than here, but more linked to one kind of cell's metabolism. Please please please someone do this in biofluids, the datasets seem to be in place?

Is anyone aware of any publicly available combined metabolite/gene expression datasets for ME/CFS? I'm tempted to have a play around if so :nailbiting::nailbiting::nailbiting:
 
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forestglip said:
Germain et al 2020 reported higher sphingolipids than controls. It seems phosphatidylcholine wasn't significant.
View attachment 22982
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Thanks that's interesting. They don't report on triglycerides or phosphatidylcholine in Germain et al 2020 but they do mention they are detectable in their lipid panel so it follows they would probably be insignificant.

I wanted to check anyway so went into the supplementary and calculated the p values for the phosphatidycholines and triglycerides (I log2 transformed the data like they do in the paper I believe). Here's a volcano plot for the phosphatidylcholines (each dot is a unique phosphatidylcholine):
upload_2024-9-1_13-48-52.png
Sure enough nothing significant, and also no particular fold change bias either way.

Triglycerides also aren't mentioned and also aren't significant (besides one but it won't survive multiple test correction).

However, across ~500 unique triglycerides there is a global bias towards increased levels. Only a small handful are lower and they contain big outliers. in a linear model using all triglycerides as a random effect to test whether there are global differences in triglyceride levels, it comes out as highly significant <2e-16. I tried summing them together too to compare total triglyceride levels, and while this trended up it was not significant.
upload_2024-9-1_13-55-58.png
 
DMissa said:
@chillier 's comparison the Hoel paper is with serum, the rest are plasma, this could have an effect.
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For sure, there's evidence that in general serum contains higher levels of many amino acids and differing levels of pyruvate:lactate than plasma - possibly due to platelet activation in serum. I don't know about lipids.


DMissa said:
IMO it is kind of hard to draw conclusions from snippets of lipid related data alone especially from biofluids, where the lipids in circulation may be impacted by processes within cells / other tissues, rates of secretion or uptake, all in combination with reactions in circulation itself, such as those catalysed by Phosphatidylcholine-sterol acyltransferase (LCAT) (link two above). And then within that, we don't really know the exact roles of all of these lipids or all of the specific implications of them rising and falling (and implications for which tissues?)

An approach to look for a meaningful thread to tug on related to lipid data might be to try and associate it with related enzyme levels or activity. Then we can account for tissue/circulation specificity as well depending on the attributes of the enzyme (order of the following points doesn't matter):

eg:
a) Identify whether particular groups of lipids are affected in their levels, or in their composition (length, saturation, ether bond, lyso species, etc)
b) In parallel, check whether this is accompanies directionally-concordant dysregulation of enzymes known to drive whatever specific change you are seeing (or vice versa)

Random illustrative example: if one sees an accumulation of PS lipids and a reduction of PE lipids, and in parallel a downregulation of an enzyme that catalyses a key PS -> PE reaction, bob's your uncle and you have a clear hypothesis to test.

So from biofluid data I think a useful exercise would be to take an enzyme like LCAT that is active in reactions in circulation, that there are differences in here, and then look at the levels of the molecules that it's working on (input & output).
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This would be great, and of course it's difficult to get a mechanistic understanding from just metabolomics. I'd love it if something like this was possible. I wonder though whether a lot of these things could be fallout from another upstream problem (eg Liver issues). The effect sizes are small and the differences in lipid levels seem like they might be quite broad spectrum within their classes (PC, TAG, CE etc).

There are other proteins in the data like Apolipoproteins which sit in lipoproteins and therefore may give clues as to what kind of density lipoproteins (carrying what kinds of lipids) are involved - but they're not enzymes so probably not great for anything mechanistic. In this study Apolipoprotein A(1) is low according to both blood biochemistry and NMR data, and Apolipoprotein D is low in men only in the proteomics data. These are both associated with HDL, and accordingly HDL cholesterol as well as HDL concentration in general is down in the data.

Interestingly ApoD is also associated with LCAT and they are co-expressed together supposedly.
from wikipedia said:
Apo-D is closely associated with the enzyme lecithin-cholesterol acyltransferase (LCAT) - an enzyme involved in lipoprotein metabolism.[7] ApoD has also been shown to be an important link in the transient interaction between HDL and low-density lipoprotein (LDL) particles and between HDL particles and cells.[13]
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DMissa said:
LCAT catalyses PAF -> Lyso-PAF. The lyso form of PAF is inactivated. PAF ("Platelet-activating factor") is a very influential molecule. @chillier I can't find it in the paper, were LCAT levels elevated or reduced? The paper seems to report elevated platelet count. This could be caused by elevated levels of active PAF (PAF mobilises platelets). This could be driven by reduced PAF inactivation by LCAT. If LCAT levels are reduced there could be a testable hypothesis in here. The whole chain of events isn't completely evidenced in the dataset from what I can see (although I have only skimmed due to time constraints) but it's something.
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Interesting! LCAT levels are increased, but it's one of the proteins that is only significant after multiple testing correction in the combined analysis. It is significant in both male and female cohorts analyzed separately but it does not survive multiple test correction in either. The Cohen's d for men in the raw data is 0.55 in male and 0.33 in female cohorts.
 
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I am interested in the SOd3 finding, but can’t quite follow the information in the article. Was it raised or lowered? And what might this mean?

“Repeating this NDE analysis using the UKB 874 mediator on levels of 2,923 proteins, measured using antibody-based assays, yielded only a single protein, extracellular superoxide dismutase or SOD3, whose abundance was significantly altered (FDR < 0.05) between cases and controls in both females and males.”
 
That's pretty unhelpful coverage I think.
Ponting's study does not claim to provide any sort of diagnostic test. Moreover, diagnostic tests are not what we need. The comment shows a lack of understanding of the basic process of clinical decision-making.

The point of such studies is to give us at least some clues as to what is actually going on in at least some people with ME/CFS.


The discussion arising from this has been moved to Biomarkers for ME/CFS - Discussion thread on the next steps for testing biomarkers and why we need them
 
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To come back to the paper itself, I think what it shows that Is valuable is a kind of biological footprint of the illness – even if you can only see it in a large sample.

Although the pre-print says it shows it’s not a psychological illness, I don’t think that will convince doubters without showing data that shows, for example, anxiety and depression have a different biomarker profile (depression at least will have some biomarkers).

But evidence that shows what causes at least a subset of ME is, sadly, I think the only thing that will start to shift minds away from Psychosocial explanations that prevail.

Maybe a diagnostic test would also come along at that point, but the bigger gain is showing biological causation. Unfortunately we’re not there yet.

Perhaps Decode ME will find something (genetic studies are good at finding causal signals).

ADDED: I guess I’m just agreeing with @Jonathan Edwards.
 
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Simon M said:
To come back to the paper itself, I think what it shows that Is valuable is a kind of biological footprint of the illness – even if you can only see it in a large sample.
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I agree that if specific findings can be replicated in a population with tighter diagnostic ascertainment they may be a great importance. And if they can be linked to DecodeME findings in terms of genes all the better.

My worry about the sample size is that the bigger your sample the more likely you are to get significant differences due to systematic confounders related to the way cohorts have been gathered. As has been pointed out by others, only a tiny proportion of 'ME' subjects had a CRP above 1, so we can be reasonably sure that whatever is driving ME/CFS symptoms it doesn't normally involve any chronic inflammation.
 
Is it actually sensible to move thee posts since the discussion is central to analysis of this paper? I appreciate that it may seem tidier to have topics all in one place but if we are going to have dialogue with scientists on specific projects then it becomes a bit pointless if discussion of key points gets removed. The more general parts of the discussion have been covered several times before so the thread for biomarkers is likely just to become a repetition.
 
It is a question we wrestle with in the moderation team. I don't think it's just a question of being tidier. If someone wants to think about biomarkers and diagnostic tests, it seems sensible to direct them to a thread where other useful points may have been made. If people want to understand what this specific paper has found, they may well not want to wade through a lot of discussion that is peripheral to the paper. I guess it comes down to a decision about what is central and what is peripheral.

The 27 posts can still be found, and perhaps the move will result in people following the link and reading the more expansive discussion of the topic. If someone did want to think about biomarkers and diagnostic tests (or some other discussion topic) and we didn't move substantial discussions to the dedicated thread, it means that content on a single topic is in all sorts of obscure threads and can't easily be found.
 
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Hutan said:
they may well not want to wade through a lot of discussion that is peripheral to the paper.
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Except that this discussion is central to the significance of the paper. Individuals may disagree about the implications, but that is what make a discussion valuable. My concern is that by not appreciating the complexity of the 'diagnostic' concept the authors may have significantly misinterpreted their data. That would be a huge pity because they data are very valuable. If much of the paper focuses on mathematical analysis that is misconceived and the genuinely important facts are not brought out key information may be buried in confusion.

Hutan said:
The posts can still be found, and perhaps the move will result in people following the link and reading the more expansive discussion of the topic. If someone did want to think about biomarkers and diagnostic tests (or some other discussion topic) and we didn't move substantial discussions to the dedicated thread, it means that content on a single topic is in all sorts of obscure threads and can't easily be found.
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But at the cost of losing important discussion in relation to this particular study, because most people will not realise that moved threads may be crucial to the discussion. No topic exists in isolation., It is always relevant in different ways to many different problems. Dumping the same arguments repeatedly in one thread, dislocated from the situations they relate to seems to me unhelpful.
 
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