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

Some questions that were going around my head today.

What sort of sample size would be needed to replicate this do we think? This has ~1500 people with ME, could a study be done with this (or fewer) people with ME and compared to the healthy controls in the biobank data?

Would it be possible to do a study with say, a subset of the DecodeME cohert, to see what the results were in a more clearly defined group and across different severities?

What are the minimum blood tests needed to compare?

Given most of us in DecodeME will have had common blood test results done is there anything that can be analysed with access to our NHS records as a starting point?

That would be nice! Presumably that would give people a clearer idea of what to focus on but also encourage and support research and funding?

 

I think you could try to replicate findings for individual markers with quite a small sample of maybe just 100 people from DecodeME. But there are issues with controls. The UK Biobank is OK to use as a source of healthy controls in terms of genes. But I think the age window would be different from that for DecodeME. So you might well not be comparing like with like. Possibly you could look at DecodeME cases aged 40-70, but the population being drawn from may be different.

Individual results for any given person do not tell us much because all these values are probably within the normal range. The meaningful result is the difference across a population/cohort. Results from routine care are probably not useful because all these tests are likely to be calibrated slightly differently in different labs.

There are lots of test that one could compare. It is not clear which of these are truly related to ME/CFS and in what way.

 

Interesting, thank you @Jonathan Edwards

 

I can’t remember what has been discussed on this thread already so I apologise if this is a repeat but a thought occurs to me: Samples were taken from the UK biobank. Given reported high rates of misdiagnosis by GPs, and the lack of requirement to have even been diagnosed by a doctor in order to be categorised as having ME/CFS for the biobank, it seems likely that there may be a high percentage of people in this study who are classed as having ME/CFS but do not meet any useful diagnostic criteria for the illness. What I’m wondering is whether a significant percentages of those cases may have the same undiagnosed conditions which are causing patients to wrongly think they have ME/CFS. Is it possible that it is the signals from conditions which are commonly misdiagnosed (and mis-self-diagnosed) as ME/CFS which are showing up in the data?

If it is possible, I hope* DecodeME may be able to tell us whether or not it is the case.

*Edit: changed wording.

 

Yes, and I have discussed this with Chris in detail. There is actually a problem the other way around too. If ME/CFS is underdiagnosed the diagnosed Biobank cases may not be representative of people with ME/CFS because they may have got their diagnosis for confounding reasons. One simple scenario is that if they also have another problem, whether or not diagnosed, that takes them to doctors frequently they may see more doctors and since only a proportion of doctors use the ME or CFS labels they will be more likely to be (correctly) diagnosed with ME/CFS.

And with a large sample you are almost guaranteed to pick up confounding associations of that sort which, paradoxically, would not show up on smaller studies.

DecodeME should help because the cohort ascertainment was better. There is still the same risk to a degree but I think it will be less. Also the risk is likely to be much less for gene associations I think. And of course the causal analysis is much easier because genes have to be upstream. In a way I see this study as a reassurance that one can demarcate how big an influence confounding factors are likely to have under what circumstances.

 

"Trial By Error: Professor Chris Ponting on “Replicated Blood-Based Biomarkers” for ME in Big Data Pre-Print Study" (videoed interview):

https://virology.ws/2024/11/19/tria...iomarkers-for-me-in-big-data-pre-print-study/

 

The paper found high ALT/ALAT levels to be associated with ME. I looked through my blood tests and that doesn’t match my n=1 results. And I have a lot of blood tests done lol. For reference according to most websites a normal level is from around 7-50 U/l. These are my blood test results for ALT:


A month after getting COVID: 6 (ME/CFS like two month long “crash”)

Three months after: 7 (mostly recovered)

Six months after: 13 (mild ME)

1 year three months: 9 (severe ME)

1 year four months: 6 (severe ME)

2 years after: 12 (very severe ME)

 

Mine are normal too, except for one time ALT was randomly high at 92. My doc immediately retested and got 20.

TIBC, AST were also higher than my normal values for this test. AST returned to normal as well for the followup, and she didn't retest TIBC.

 

My second last alt was a little high (56).
That was two years ago but my GP only mentioned it recently.
Latest one was normal (31).

 

Not really much going on for ALT in the deep phenotyping study.

There's one outlier with 548, so the second plot is just zoomed in to everyone else. No one else has abnormal values, and not much difference between groups except maybe 2 participants are a little higher and 1 is very high.

 

Trying to have another look at this paper.

For the proteomics, the following seem to be the values that stand out most. I've extracted the following values from the data (Using direct effects).

Name(abbreviation), cohen_d_raw_data, z_value_modelled, corrected_p_value

Males
Butyrylcholinesterase (BCHE), 0.796, +5.3, 0.000313,
Apolipoprotein D (APOD), 0.427, -4.91, 0.00130
Leptin (LEP) - increased, 0.652, 4.81, 0.00142

Females
Neutral ceramidase (ASAH2), 0.315, +4.75, 0.00517
Inactive serine protease (PAMR1), 0.314, +4.59, 0.00517
Coiled-coil domain-containing protein 50 (CCDC50), 0.280, -4.55, 0.00517

Combined
Complement factor H (CFH), +0.694 male, +0.491 female, 5.85, 0.0000143
Extracellular superoxide dismutase (SOD3), -0.274 male, -0.21 female, -4.88, 0.00159

 

The biggest differences for the NMR-metabolites seem to involve high-density lipoprotein (HDL), which also pops up in the proteomics above and in the blood where for example hdl_cholesterol and apolipoprotein_a were reduced.

 

Just to point out that there is some brief discussion of this paper, in particular the utility & potential limitations of using the UK Biobank CFS cohort, on this thread:

https://s4me.info/threads/does-the-...e-one-well-eventually-find.41620/#post-571975

 

Leptin was also increased in plasma of ME/CFS patients in this Spanish study:
Association of circulating biomarkers with illness severity measures differentiates myalgic encephalomyelitis/chronic fatigue syndrome and post-COVID-19 condition: a prospective pilot cohort study - PubMed


https://www.s4me.info/threads/assoc...dition-2023-castro-marrero.36606/#post-526199

 

Not sure if these plasma leptin measurements are related to the proteomics analysis but here's another one that found higher levels in ME/CFS patients compared to controls although the difference was not statistically significant:



Plasma leptin in chronic fatigue syndrome and a placebo-controlled study of the effects of low-dose hydrocortisone on leptin secretion - PubMed

 

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Wikipedia

ME/CFS and Long COVID

SARS-CoV-2 hijacks host CD55, CD59 and factor H to impair antibody- dependent complement-mediated lysis, 2024, Gebetsberger et al

Preprint - Role of the complement system in Long COVID, 2024, Farztdinov, Scheibenbogen et al.

Persistent complement dysregulation with signs of thromboinflammation in active Long Covid, 2024, Boyman et al

Pathway-focused genetic evaluation of immune and inflammation related genes with chronic fatigue syndrome, 2015, Rajeevan et al

Targeted proteomics identifies circulating biomarkers associated with active COVID-19 and post-COVID-19, 2022, Zoodsma et al

Complement dysregulation is a predictive and therapeutically amenable feature of long COVID, 2023, Morgan et al

Other conditions

Increased plasma complement factor H is associated with geriatric depression, 2019, Shin et al

The human complement factor H: functional roles, genetic variations and disease associations, 2004, Rodrı́guez de Córdoba et al

The role of complement Factor H in age-related macular degeneration: a review, 2010, Donoso et al

Complement 3 and factor h in human cerebrospinal fluid in Parkinson's disease, Alzheimer's disease, and multiple-system atrophy, 2011, Wang et al

Complement regulator factor H as a serum biomarker of multiple sclerosis disease state, 2010, Ingram et al

 

Maybe related.

There is a relationship between mutations in SOD1 and complement activation in mice models.

https://pmc.ncbi.nlm.nih.gov/articles/PMC3170534/

CFH also protects against oxidative stres through Malondialdehyde binding:
https://pubmed.ncbi.nlm.nih.gov/21979047/

SOD3 secretion by MSCs is increased by activated microglial cells and protects against nerve damage:
https://link.springer.com/article/10.1007/s12015-010-9178-6

 

In the absence of clear evidence of inflammation I am doubtful that 'protection against damage' is likely to be what we are looking for, although it is conceivable.

What I find more interesting is the suggestion that complement may be involved in normal day to day neural plasticity (I don't have a citation at hand). If SOD3 and Factor H both have a moderating role in complement mediated events then that might be important.

 

Obviously BChE hydrolyses acetylcholine. ApoD has a role in cholesterol metabolism and transport of lipids; leptin obviously affects hunger, body weight, energy use & feeding. ASAH2 is involved in hydrolysing ceramides to sphingosine. I may be completely off-base with this (been a very long time) but something that strikes me is that sex hormones in some way regulate most of the things on that list - if I remember correctly levels of leptin are altered by androgens & oestrogen (?testosterone suppresses), BChE expression is partially regulated by androgens, I think, as is ApoD - not sure about ASAH2 but doesn't oestrogen have an effect on sphingolipid metabolism as well...? Something to look up.

Some papers discussing the role of complement in CNS that I've started to read through:

"The role of astrocytes and complement system in neural plasticity"

"Complement peptide C3a stimulates neural plasticity after experimental brain ischaemia"

"Complement in the Brain"

"Role of Complement in neurodegeneration and neuroinflammation"

"The complement cascade in the regulation of neuroinflammation, nociceptive sensitization, and pain"

 

But aren't complement mediated events inflammatory?