Steroid dynamics in myalgic encephalomyelitis / chronic fatigue syndrome: a case-control study [...], 2025, Thomas, Armstrong, Bergquist et al

[Utsikt]

Do we know how these relationships behave in other diseases? Are they maintained in MS, RA, Lupus, etc.?

 

[wigglethemouse]

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.

 

[Hutan]

I do share the concern expressed by others about the abstract conclusion, particularly the bit I've highlighted in bold:

Conclusions Despite no significant differences in absolute steroid levels, network analysis revealed profound disruptions in steroid-steroid relationships in ME/CFS compared to controls, suggesting disrupted steroid homeostasis. Collectively the results suggest dysregulation of HPA axis function and progestogen pathways, as demonstrated by altered partial correlations, centrality profiles, and steroid ratios. These findings illustrate the importance of hormone network dynamics in ME/CFS pathophysiology and underscores the need for more research into steroid metabolism.

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.

But even with that effect, you’d still expect (for example) the relationship between progesterone and its downstream metabolites to hold up strongly.

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.

I am guessing that a lot of the confusion originates from understanding of the method used. A partial spearman correlation is a rank-based (not value-based) correlation which effectively regresses out the effects of other measured variables to try to quantify the direct influence of one variable on another as much as possible.

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.

However, network analysis revealed a marked reduction in direct steroid-steroid relationships in ME/CFS, with controls exhibiting 52 significant partial correlations

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]

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.

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].

 

[Hutan]

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 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.