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Steroid dynamics in myalgic encephalomyelitis / chronic fatigue syndrome: a case-control study [...], 2025, Thomas, Armstrong, Bergquist et al
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wigglethemouse
Senior Member (Voting Rights)
When I looked the other day I couldn't find good information. Most of the information returned in my search related to absolute levels. I think diabetes and cholesterol kept coming up, and I think there might be lipid changes in diabetes, but I couldn't find detailed info on network correlations.
The misrepresentation in the abstract is worse than that. It claims that steroid ratios were altered. In fact, the text notes that steroid ratios were not different.
When I see things like that, and the biased misrepresentation of the literature, I get concerned that the researchers have not approached the question with the required level of equipoise. And we know that this paper was written in order to help secure funds for a bigger study.
Not necessarily. We see everywhere in biology that different attributes of the person can affect what the metabolites are, and how quickly they are processed into other things. After all, that is what is being argued here - that the ME/CFS status is altering the relationships. I think then it has to be acknowledged that, in the case of many of these hormones, for example the stage of the menstrual cycle, whether someone is menopausal, whether they have been treated with steroids including those in oral contraceptives, gosh possibly even hormonal replacement therapy which we also don't know about, are of such fundamental importance that they should not be ignored.
It makes no sense to suggest that loading the body up on levels of synthetic progesterone in sufficient quantities that it makes someone infertile, or including a menopausal person where the hormonal milieu is so fundamentally changed, can safely be ignored, while having ME/CFS is responsible for the variation in the correlations found. Particularly since we have virtually no evidence for ME/CFS symptoms fluctuating with the menstrual cycle (I'm thinking of the Visible study) or changing with the onset of menopause or even varying between men and women.
Thanks for explaining that. But, it makes me even more concerned.
So, this group did not find differences in absolute levels of hormones, nor in the ratios of them. They did not report straightforward correlations between pairs of hormones - as I said, it would be interesting to see plots of the actual data for pairs of hormones, to understand the shortcomings of the data better.
If I'm understanding you correctly @jnmaciuch, what they did find is some differences when they applied a method that makes the results for all the comparisons even more vulnerable to some chance differences in some hormones. And there is a lot of chance here. The standard deviations of many of the measures are as big as the means! To try to get meaningful data about comparisons on one uncertain data set against another strikes me as grasping at straws.
Table 8- labelled Significant partial correlations within the control group only shows 27 hormone pairs. The p value goes up to 0.043, so I think all of the significant correlations are there. So, not 52. Despite the abstract claiming that.
I think the 52 number was before adjustment for FDR.
If only 27 of the correlations were significant in the control group, how can you meaningfully say that there were 57 correlations that were significantly different when comparing the control and ME/CFS groups? If a correlation is not significant in the controls, and not significant in the ME/CFS group, you can't then say that the correlations are different.
Is it reported how many hormones were quantified? I think it was a lot. And then, how many hormone pairs were assessed?
I have to head off now, and I've written this in a hurry, so sorry if it is not clear.
But, there is a lot that is troubling about this paper.
jnmaciuch
Senior Member (Voting Rights)
I’m sorry @Hutan, that’s nearly the opposite of what I’ve been trying to explain. It must be something in my explanation that is not coming across, maybe some background that I don’t realize needs to be laid out explicitly. But I don’t really know how else to explain without investing time that I don’t have at the moment.
I can really only offer the assurance of one person who is fully aware of the potential confounding effects on hormones (and is generally quite skeptical of hormone-related findings) that this particular finding I have highlighted is very unlikely to be caused by the confounders you identified.
I can fully understand why someone would be unwilling to just take my word on it, but since it seems that my further attempts to explain would still be insufficient, it’s probably best just to leave it be.
[Edit: the difference between the number of features reported in table 8 and the number reported in the text should be rectified, though. @MelbME].
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I think I understood you correctly, in broad terms But, removing the influence of other factors inevitably changes the factor of interest. So, I think it does make the factor of interest even more vulnerable to random oddities or problems related to the confounders that we can't properly quantify. There are not enough significant data points and too much uncertainty about them to get carried away with clever adjustments and still be certain that there is something true. It's very hard to make a silk purse out of a sow's ear.
I think we would need to see the charts of the pairs of the actual steroid levels (with male/female and age points identified) to understand the data better.
Also, it's not just the problem with the number of features in table 8, but how that flows through to the suggestion of 57 differences between correlations the ME/CFS and control cohorts. For example, in Table 8 of significant partial correlations for the Controls, there is no steroid pair that includes DOC. And yet in Table 10, my rough count gets to 11 steroid pairs including DOC where they are claiming significant differences between steroid relationships for the Controls and ME/CFS cohort . I can't see how that can be valid.
Do you mean that it doesn't appear to be statistically valid? Each group on its own might have a correlation that is too small to be statistically significant when you're testing if the correlation is different from zero correlation. But the distance between the correlation in ME/CFS and the correlation in controls can be large enough that it is significant.
A similar example: You're testing change in a metabolite after stimulation. In ME/CFS it goes up 1 unit, and this is so small that it is not significant when seeing if it's different from 0 units change. Controls go down 1 unit. Again not significant. But if comparing the change in ME/CFS to the change in controls, the difference between them is 2 units, which is large enough to be significant.
I think I mean something along the lines of
Table 8 tells us that, in the controls, levels of DOC are not significantly related to any other hormone
So, given that, then it doesn't seem reasonable to suggest that there is something wrong with the relationships between DOC and 11 other hormones in the ME/CFS group just because the trend of the relationship is different to that in the controls. The relationships between DOC and other hormones are not significant for either group.
And so on for other hormone pairings.
Table 8 tells us that, in the controls, levels of DOC are not significantly related to any other hormone
So, given that, then it doesn't seem reasonable to suggest that there is something wrong with the relationships between DOC and 11 other hormones in the ME/CFS group just because the trend of the relationship is different to that in the controls. The relationships between DOC and other hormones are not significant for either group.
And so on for other hormone pairings.
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One test is testing if either group's correlation is different from zero. The other is testing if the groups' correlations are different from each other.
The first not being significant doesn't mean there's definitely no correlation between these metabolites, just that with the data we have (one group's data compared to zero) we can't be sure there is a non-zero correlation.
The second being significant means that there is enough of a difference between groups to say there's likely a difference between them.
(This implies that at least one of the groups must indeed actually have a non-zero correlation, since they can't both be zero and different from each other. But the nature of the data - small sample, large variance - didn't allow for it to be significant for either group in the first test.)
Yes. And I'd say with that example, if there is no statistically significant response in the ME/CFS group, and no statistically significant response in the controls, then I don't think we can say that the ME/CFS response was faulty. All we can say is that either the metabolite isn't related to the stimulation or the sample wasn't big enough to get a sufficiently strong signal. The 1 unit up in the ME/CFS group and the 1 unit down in the controls might just be noise.
And, in the example of this paper, there are a lot of possible sources of a lot of noise.
I understand the 'different from zero' versus 'different from each other' tests.
My point is, that if we have no evidence of relationships between DOC and other hormones in the controls, and we have no evidence of relationships between DOC and other hormones in the ME/CFS group, and the levels of DOC and the other hormones are not different between the groups, and the ratios between DOC and the other hormones are not different between the groups, then really the analysis should stop there.
There is a very poor basis for then saying that because the non-significant trends in relationships in the two groups are different, there is a faulty relationship between DOC and another hormone in the ME/CFS group. It's torturing the data.
I think we need the raw data.
If by noise you mean confounders like people with ME/CFS might be more likely to take contraceptives, that's still totally possible, no disagreement here. That's not the kind of noise this kind of statistical test is testing for though.
The p-value is small enough to say that the reason the ME/CFS correlation is far from the control correlation is not just because of random noise. As in factors that have nothing to do with their group status. (e.g. if everyone was tested at a totally random point in their menstrual cycle, that'd be this kind of random noise that a low p-value would rule out as the reason for the difference.)
It tells us there's a non-random difference. The cause of that difference could be some biological factor or contraceptives or time they woke up. But some factor that presents differently because of the group they're in.
Totally possible for this to be significant but the other tests not since it's looking at different data.
They don't really seem to focus on the differences between group correlations from Table 10 anyway, from what I could see. It just got a couple passing mentions. The bulk of their conclusion seems to be based on Tables 8 and 9.