Deep phenotyping of post-infectious myalgic encephalomyelitis/chronic fatigue syndrome, 2024, Walitt et al

[forestglip]

Just as a sanity check, since I'm noticing something counterintuitive. The worse the fatigue severity, the more Earnings from the EEFRT study.

I pulled up the raw data, and looked at Earnings vs some of the scores of the MFI-20 or MASQ surveys. (They aren't summed together in the raw data, they're split into things like "mental score", "physical score", "verbal memory".)



It does seem that way. I never really looked at the EEFRT portion too deeply, so I don't really remember what all the "Median_Trial_Time_Hard_Trials", "Total_Trials_Completed_Hard_Trials", etc actually means.

But it seems like the spreadsheet above is showing that the more severe the fatigue, the less time spent per hard trial, but the more hard trials total completed?

 

[Murph]

Couple of points @forestglip

1. I'm loving the embedded google sheets, that works really well for me.
2. I'm very impressed by your interrogation of the data and your commitment to extracting the true value from it. I notice you're not afraid to put in the effort.
3. I believe Benjamini Hochberg is for independent tests, some of those cognitive tests are probably not independent. So the BH correction could be a bit strong.
I haven't used it but there is a test that you can use to control false discovery rate under conditions of dependence, called Benjamini-Yekutieli. https://projecteuclid.org/journals/...-multiple-testing/10.1214/aos/1013699998.full

 

[forestglip]

SF-36 seems like a better metric than fatigue for disability severity.

Here's the SF-36.

I might exclude this question and make a custom total since this seems irrelevant for the level of disability one is experiencing at the moment:

2. Compared to one year ago [your health is]:
Much better now than one year ago 1
Somewhat better now than one year ago 2
About the same 3
Somewhat worse now than one year ago 4
Much worse now than one year ago 5

And it looks like the "RN Polysymptom Index" has a bunch of yes or no questions about PEM that I could add to the SF-36 score. These seven are the ones where not everyone put yes and there are no missing values:

PEM_MOD_VERY_SEVERE
PEM rated as moderate to very severe

PEM_SORENESS
Next day soreness or fatigue after non-strenuous everyday activities

PEM_MENTAL_TIRE_SLIGHT_EFFORT
Mentally tired after the slightest effort

PEM_PHYS_DRAIN
Physically drained or sick after mild activity

PEM_BREATHLESS_EXERTION
Breathless with exertion

PEM_LONG_RECOVER_EXERTION
Long recovery period from exertion: takes more than 24 hours to recover to pre-exertion level

PEM_WORSE
PEM getting worse over time

PEM_CONCERN
PEM is concerning

Here is the what the spread of summed PEM answers looks like:


Maybe I should multiply the PEM score to be higher than the SF-36 score before adding them together since it's the main symptom of ME/CFS?

Edit: Missed one PEM question, it's the last one added above. Also updated the plot.

 

[forestglip]

1. I'm loving the embedded google sheets, that works really well for me.

Yeah I was really excited to learn the forum could do that!

2. I'm very impressed by your interrogation of the data and your commitment to extracting the true value from it. I notice you're not afraid to put in the effort. NIH could learn from you!

I have a ton of fun trying to treasure hunt in data. I'm not very good at it, but I can imagine the possibilities.

3. I believe Benjamini Hochberg is for independent tests, some of those cognitive tests are probably not independent. So the BH correction could be a bit strong.
I haven't used it but there is a test that you can use to control false discovery rate under conditions of dependence, called Benjamini-Yekutieli. https://projecteuclid.org/journals/...-multiple-testing/10.1214/aos/1013699998.full

Thanks, you're probably right. I'll look at that. Though I didn't really care about multiple test correction with this massive dataset. With only 17 participants and 3000 tests, I think there's very little hope of much being significant after correction. I mainly just wanted to get them in order to see what kind of stuff is near the top for most correlated.

 

[Murph]

Thanks, you're probably right. I'll look at that. Though I didn't really care about multiple test correction with this massive dataset. With only 17 participants and 3000 tests, I think there's very little hope of much being significant after correction. I mainly just wanted to get them in order to see what kind of stuff is near the top for most correlated.

I agree with this approach. Worrying about absolute p values isn't as important as ranking things and seeing if those things show up near the top in other studies. That's when there's actual signal.

Another bit of info for anyone scrolling the mega list of correlates with severity, the acronym VM seems to stand for Vector Movements. It's about whether accelerometers attached to people's wrists and hips moved. Makes sense that more severe people stayed more still post-CPET.

 

[forestglip]

Another bit of info for anyone scrolling the mega list of correlates with severity, the acronym VM seems to stand for Vector Movements. It's about whether accelerometers attached to people's wrists and hips moved. Makes sense that more severe people stayed more still post-CPET.

Thanks I didn't know what VM was.

But weirdly, I think the correlation is the opposite. The higher the severity, the more movements. I think it's mainly because of the MASQ, which has a higher score so weighs more in the final score, and is more for cognitive dysfunction.

I wasn't sure if maybe I was reading the MASQ scores backwards, but it seems right. Here is the raw data for the mental fatigue scale from the MFI-20 and "visual/perceptual ability" from MASQ for both groups. ME/CFS is red.

It does look like higher values for both scales are worse because both are higher in the ME/CFS group:


And yet a worse visual/perceptual ability is correlated to higher wrist and hip movements:


Much less correlation of movement with the physical fatigue scale:


Also, it doesn't even seem like the ME/CFS group is moving their hip less than the healthy group at this time point, which is a bit weird.


----

Edit: And because it was bugging me if maybe I was wrong and higher MASQ scores mean less cognitive problems, I searched and found a paper that says:

Results indicated that twin A had substantially more cognitive complaints (MASQ score 134) than her unaffected twin (MASQ score 63; higher MASQ scores denote more cognitive complaints; Table 1).

I can't find any official instructions, but this study says higher equals worse. And in the NIH study, ME/CFS had a higher score for all MASQ domains.

So I can probably safely add it to the MFI-20 scores where higher also means worse there. So the correlations should be good in the previous post, they're just heavily weighted to cognitive complaints since that is all of MASQ and a portion of MFI.

But I'm going to do a new score without these surveys anyway.

 

[forestglip]

I might exclude this question and make a custom total since this seems irrelevant for the level of disability one is experiencing at the moment:

Oh, I looked up the instructions for SF-36 and question 2 about how your health today compares to a year ago isn't actually included in any of the composite scores. Table 2 shows how to add them together, and it's the only question missing.

And I found a paper that says it's basically just used for validating the test. For people that say they improved over the past year, their total disability scores should on average be higher than those who didn't:

The SF 36 questionnaire contains a transition question which is not used to score any of the eight scales. This question ("Compared to one year ago, how would you rate your health in general now: much better, somewhat better, about the same, somewhat worse, much worse?") was used in this study as a criterion by which to judge the responsive- ness of the questionnaire. Such questions are a valid way of measuring changes in perceived health2' and were used to assess the responsive- ness of instruments designed to measure outcomes.3 4 22 23 For the SF 36 questionnaire to be a valid measure of outcome which reflects perceived changes in health status a significant relation would be expected between changes on the eight scales over the year and the responses to the transition question. Patients indicating an improvement in health on the transition question would be expected to have higher standardized response means across the eight scales than patients who stated that their health remained the same.

Garratt AM, Ruta DA, Abdalla MI, et al. SF 36 health survey questionnaire: II. Responsiveness to changes in health status in four common clinical conditions.. BMJ Quality & Safety 1994;3:186-192.

So the SF-36 has these eight domain scores:

Physical functioning
Role limitations due to physical health
Role limitations due to emotional problems
Energy/fatigue
Emotional well-being
Social functioning
Pain
General health

What I'm thinking is to mainly use the PEM score in the post above, but since a lot of participants are tied, the SF-36 will just kind of act as a tie-breaker. I added up all the scores for the SF-36 domains above which gives a score around 200-500. I scaled them so that they are between 0 and 0.99. It's less than 1 because the PEM scores are separated by 1, and I don't want adding the SF-36 score to be able to change the order, only break ties. And since higher scores on SF-36 indicate better health/less disabled, I will subtract them from the PEM severity score. So people who are less disabled (higher SF-36 score) but same PEM severity will end up with a lower total "severity score".

So here is a plot of the PEM score and the transformation after subtracting the SF-36 score. The third column is after I converted them to equidistant ranked scores to visualize them better, since for Spearman correlation, only the order matters, not the spacing between them.


Amazingly, two participants are still tied. They had the same PEM and SF-36 scores (not all SF-36 domains were identical, but they happened to add up to the same number). I don't know if I'll just let them be tied or try to think of another tiebreaker.

Edit: If anyone has thoughts on this PEM + SF36 not being the optimal way to measure severity, I'd love to hear them. I don't want to create many sets of correlations from tweaking the severity score over and over as I think of improvements.

 

[forestglip]

Hopefully this is the final product. I used the PEM - SF-36 metric as described in the last post, as well as in the Notes tab of the spreadsheet below.

I used Kendall's Tau test for correlations because I read that it is more robust to tied values (two participants having the same severity or lab result) than Spearman's rho. This may or may not be accurate, as I had trouble finding scientific papers specifically supporting or disputing this.

The thinking with the severity metric was first ranking by PEM score since that is probably the best available data for "severity" of ME/CFS. Since there are a lot of ties, I broke the ties based on their score on the SF-36 questionnaire, with the thinking that if they score worse on a general healthy survey, there is a good chance they have worse ME/CFS.

Still, they may have other comorbid conditions which are impacting their health or life which may cause lower scores on the SF-36 without necessarily meaning worse ME/CFS. It may be better to only use the PEM score, but there may be less power to find correlations as there are only 6 unique scores split across 17 participants, i.e. there are many ties.

Description of SF-36

one of the most widely used generic measures of health-related quality of life and has been shown to discriminate between subjects with different chronic conditions and between subjects with different severity levels of the same disease.

All 8 of these subscales were added together. It may be better to only use some of these subscales.

The 8 subscales are: physical functioning, role limitations due to physical problems, bodily pain, general health perceptions, vitality, social functioning, role-limitations due to emotional problems, and mental health.

A red/positive correlation indicates that as severity increases, the given test's value also increases. A blue/negative correlation indicates that as severity increases, the test's value decreases.

Link to browser version


Edit: I decided to add in a "pure" PEM score, since I want the correlations to be as "clean" as possible. It's the second tab of the spreadsheet. It's only based on the 8 yes or no questions mentioned that relate to PEM.

 

[forestglip]

The PEM - SF-36 severity metric seems to correlate to physical fatigue from the MFI-20:


Not as much for mental fatigue:




Again weirdly one of the wrist movement counts after CPET was one of the most correlated, with more severe PEM meaning higher wrist movements. This time 27-43 hours post-CPET:


But compared to the healthy group, the ME/CFS group as a whole looks slightly lower, though it's not significant.

 

[forestglip]

I decided to just rerun it only based on the PEM score. I want the score to match ME/CFS as well as possible, and I can't be sure the SF-36 is doing that. I added the new correlations as the second tab in the spreadsheet above.

One thing I've noticed so far is that pretty much everything from the "Seahorse Mitochondrial Function Baseline" dataset is negatively correlated with PEM severity.


Here are the descriptions of these tests:

Spare Respiratory Capacity (%)
Spare respiratory capacity

Metabolic Potential (% Baseline OCR)
Oxygen consumption rate

Spare Respiratory Capacity
The ability of the cell to respond to increased energy demand or under stress.

Metabolic Potential (% Baseline ECAR)
Extracellular acidification rate

Non-mitochondrial Oxygen Consumption
Oxygen consumption that persists due to a subset of cellular enzymes that continue to consume oxygen after the addition of rotenone and antimycin A.

Maximal Respiration
The maximum rate of respiration that the cell can achieve.

Coupling Efficiency (%)
ATP/O ratio

ATP Production
The decrease in oxygen consumption rate upon injection of the ATP synthase inhibitor oligomycin represents the portion of basal respiration that was being used to drive ATP production

Proton Leak
Remaining basal respiration not coupled to ATP production.

Basal
Energetic demand of the cell under baseline conditions

I know pretty much nothing about these tests, so I hope someone else can chip in. But the only one that sounds "bad" to me is "Proton Leak", and that's the only one that is higher with worse PEM. The rest that sound like things that would be good, like "Spare Respiratory Capacity" are lower with worse PEM.

And then here we are with the same tests before and after a CPET. I guess they did a baseline twice? Because the baseline values in the CPET file don't match the values in the "baseline" file above.


Pretty much the same ones are correlated negatively or positively here.

What I'm noticing is that tests at baseline, 72 hour, and 48 hour seem to be the most correlated, but most of the tests at 24 hours are barely correlated. Maybe some temporary improvement immediately after exercise? Potentially a marker of the "adrenaline" effect?

ATP production has a negative correlation of at least -0.24 at all time points, including the other baseline value in the table above. Maybe this is a more stable marker of ME/CFS?

 

[forestglip]

Looking at metrics between groups, it's definitely not that straightforward. While those with worse PEM tend to have lower ATP production at baseline, healthy people's ATP production seems to be lower than ME/CFS.


I quickly looked at the rest of the comparisons. ATP production is either the same or higher in the ME/CFS group at all time points. Pretty much all of the tests are no difference or the opposite of what I would expect if basing it on the correlations with PEM.

I've double, triple checked I did everything right. This plot of ATP from the baseline file is about as raw from the downloaded data files as I can get it:

It does look like a somewhat negative correlation where ATP production gets lower as PEM increases in the ME/CFS group, and yet the healthy group is lower on average, even though they have zero PEM.

Here's another, Spare Respiratory Capacity (%), which was most correlated in the first table above. Same pattern:


So it'll either take something quite unintuitive to explain this or it's nothing.

Edit: This makes me wonder. What if I include healthy controls and set all their PEM scores to zero and run the correlations with everyone?

I guess the way I have it now might be better because I have a "validation set", to see if the top correlations from the only ME/CFS group extend to healthy controls. Which was useful here for providing contrary evidence for mitochondrial function.

But maybe just use half the healthy controls in the correlations and half as validation?

 

[forestglip]

I thought including half the healthy controls was a decent enough idea so I went ahead and did that. Everything the same, except half the controls are tied at a PEM score of zero and the other half are excluded for later validation.

Browser link


First impressions: Now all the top correlations are surveys. SF-36 questions, physical fatigue scale, etc. Which makes sense. Those surveys separate the two groups best, and some questions are bound to correlate with PEM.

Taking a peak at how the top correlation looks:

I added a bit of "jitter" to the dots. It just moves them a tiny bit at random so they aren't overlapping, since so many of them have the exact same value.

So that's question 15 of the SF-36:

During the past 4 weeks, have you had any of the following problems with your work or other regular daily activities
as a result of your physical health ?
Were limited in the kind of work or other activities?

1. All of the time
2. Most of the time
3. Some of the time
4. A little of the time
5. None of the time

The top 69 correlations are all surveys.

There's a few things from the heart monitor files near the top: "Frequency domain measures of heart rate variability collected using a 24 hour Holter monitor." But there are at least 631 measures related to HRV, so some are bound to be highly correlated.

Next category I see going down is CSF metabolomics. First one is at row 109. (You'll have to follow the browser link to see row numbers.) The metabolite is X-22162. Whatever that is.

But in any case, I'm taking a position of ignoring everything from the CSF metabolomics portion for now, for the reasons I gave in a previous post:

I'm starting to wonder if there was a methodological issue with the CSF metabolite lab test in the NIH study. 92% (Edit: 88%) of the 445 chemicals tested had a lower median in ME/CFS. All of the metabolites they reported as significant were lower.

View attachment 23942

I don't know enough about the field, but does anyone know if most/all of the metabolites in the chart above have a reason to be correlated to each other? It seems too high to be due to chance, but that's just a feeling, could be wrong. Is there any physiological reason this could happen?

I made a chart to visualize the skew. This is just the difference in medians between the two groups for each metabolite. It's probably not ideal for comparing individual metabolites to each other, but it gives an idea of how many are higher vs lower.

View attachment 23941
I mean, if that's not lab error, then it looks like something very significant just in the combination of all metabolites.

Edit: Or a histogram:
View attachment 23945
That looks like a normal distribution, just shifted left by about 0.25 for some reason.

Edit 2: And just to double check if this could be due to chance: The mean of the differences is -0.233. I did a Shapiro-Wilk test, these differences are normally distributed (p = 1.35e-16) (Edit: They are not normally distributed. I mistakenly thought low p-value meant normal.), and then a one sample t-test with a null hypothesis mean of 0, and it is significantly different (p = 2.08e-72).

Edit 3: This might be a better way to visualize it, showing the metabolite concentrations for each group separately.
View attachment 23952
The mean of all median concentrations for ME/CFS is 0.785. For HV it is 1.02. ME/CFS values seem to be shifted down by about 0.234.

Edit 4: I realized I counted changes of zero as downregulated in the "92% downregulated" figure. The correct numbers are 88% downregulated, 4% zero difference, 8% upregulated.

I think there was some sort of technical artifact making all metabolites downregulated in ME/CFS. Whether during sampling, during the actual lab measurements, maybe just something like all ME/CFS were lying down and all HV were sitting up during testing. Not sure, but artifact seems most likely. And if it's not an artifact, then all encompassing CSF metabolite downregulation is potentially a big finding. But I think that's the less likely option.

Next category I see is something from the blood labs: MCV (fL) (mean corpuscular volume or average size of red blood cells)

(The spacing on the x axis is arbitrary. The red dots could be a million units farther right and it'd be the same correlation since the spacing doesn't matter for Kendall's tau and there's no way for me to say how much "PEM severity" is between any two participants or between the groups.)

It looks interesting.

Next a couple things from the lipidomics study. A triglyceride and a diglyceride. (positive correlation)

Then I see something from the "Free living accelerometry" study where they wore an activity monitor at home for at least five days and during the exercise test: (Hip Moderate [2020 - 5998 cnts; 3-5.9 METs] Time (min) negative)

The mean number of moderate intensity minutes per valid day defined as the number of minutes with a count value > 2020 and < 5999 counts per minute from the waist-worn device

Peak VO2 during CPET is at row 179. (negative)

More lipidomics. (all positive)

Another from the accelerometry: (Hip Avg Wear Time METs, negative)

The mean metabolic equivalents of task (METs) for all valid days normalized to average wear time per valid day from the waist-worn device

Negative for a hand grip metric.

There's something from CSF flow cytometry at row 277: CD4+ T cell subset Memory (%) (positive)
This CSF study doesn't seem skewed like CSF metabolomics. In this one, 49% of tests are higher in ME/CFS, 49% are lower, 2% the same. Seems more realistic.

Something from CSF catecholamine study at 283: concentration of DOPA
In this study, it's 8 catecholamines. Mostly lower in ME/CFS.

Next thing that looks interesting to me at 384: Lymphocyte NK CD56dim (cells/ul) (negative)

Oh fun, we got a stool metabolite at 472: Xylose (negative)

At 506, from clinical master labs: Triglycerides (mg/dL) (I assume in blood) (positive)

Another from CSF flow cytometry at 530: Lymphocyte NK cell (cells/ul) (negative)

At 544 from tilt catecholamine study: Plasma concentration of dopamine at the end of head-up tilt, in pg/mL (negative) Highly correlated at -0.72 but there's only data for 7 participants. Just to see what the correlations look like at row 544:


For reference, these are sorted by p value, and that last one at 544 I mentioned has an uncorrected p value of 0.02. And there are about 3300 total tests, though many are correlated to each other.

So yeah, might be something interesting in there. Since I have the other 11 healthy controls that weren't included I can eventually test to see if any of these correlations hold up.

Edit: I thought about it more and realized my logic wasn't logicing. I can't truly validate with only healthy controls in the validation set. I can do like a "half validation" by replacing the controls and checking the correlation, but it's possible that random variation in the ME/CFS group caused a non-real effect, and I can't check that. Should have done half of both groups from the start.

 

[forestglip]

Okay, hopefully I'm done. Here's spreadsheet_final_v2(1)_20. (relevant comic)

I split the data into two datasets. Details of how I split are in the Notes tab. I tested correlations for each split and the full dataset. The correlations for the full dataset are the first tab, and the others are the next two.

On the first tab, I marked which tests had a p<0.15 in all three datasets. (0.15 was just an arbitrary cutoff I chose to get a decent number of tests past the cutoff.) It's the first column. It shows the higher of the two p values for that test from the two splits. Significance is also indicated by the color. The more green, the more significant.

I further colorcoded the tests that met this significance cutoff in the Lab Test column based on if they are a subjective survey or an objective test. Bluish color for survey and purple for objective. So the most interesting ones should be the purple tests in the first tab. And the greener the first column, the more significant in both splits.

Browser link


So what do we have that is an objective test that reaches p<0.15 in both data splits?

178 total tests

2 from accelerometry:

Hip Moderate [2020 - 5998 cnts; 3-5.9 METs] Time (min)
Hip Total Steps

2 from microbiome "Alpha Diversity" file:

Chao1
Observed

I don't know what these mean.

1 from before and after CPET datafile:

Test of Variables of Attention Comparison Score: 48 hours after Exercise Stress

1 from blood flow cytometry:

B cell subset Switched memory (%)

1 from body composition:

Body fat (%)

7 from CPET:

PeakVO2rel
PeakRR
APMHR
PeakVE
PeakVCO2
Ramp
ATVO2rel

2 from CSF catecholamine (but really just 1 since it's just different units):

Cerebrospinal fluid concentration of DOPA, in pmol/mL
Cerebrospinal fluid concentration of DOPA, in pg/mL

4 from EEfRT:

Median_Trial_Time_Hard_Trials
Mean_Button_Press_Rate_Hard_Trials
Median_Button_Press_Rate_Hard_Trials
EFFORT_T4

1 from food records:

Percent of daily calories consumed from saturated fat

70 from the 5 heart rate variability files (some of these you'll have to look at the spreadsheet to see if it's from the LF, HF, or the other files):

SD2 (msec) at 7.5 minutes
20:15
22:50
2:25
23:45
1:45
19:20
6:20
20:20
14:30
21:40
16:30
8:00
2:30
SD1 (msec) at 37.5 minutes
18:30
SD1 (msec) at 35 minutes
3:55
14:30
19:15
2:20
SD1 (msec) at 47.5 minutes
23:45
0:15
21:50
19:25
8:00
17:45
3:50
21:55
21:20
2:15
15:50
SDNNI
5:35
2:10
5:55
17:50
23:00
19:10
19:45
15:25
2:15
15:00
rMSSD
3:50
0:40
23:05
12:45
5:50
2:10
pNN50
15:40
SD1 (msec) at 52.5 minutes
12:45
11:25
6:30
20:40
5:40
12:30
11:05
5:35
SD1 (msec) at 15 minutes
SD2 (msec) at 50 minutes
7:30
18:55
18:45
12:15
3:35
11:00

14 from lipidomics (all increased with worse PEM except LPC):

TAG51:3-FA15:0
TAG52:3-FA16:0
TAG50:3-FA14:0
TAG52:3-FA18:2
DAG(16:0/18:1)
TAG50:2-FA18:1
DAG(16:1/18:1)
TAG50:3-FA18:2
DAG(18:1/18:2)
TAG51:4-FA18:2
LPC(20:5)
TAG50:2-FA16:1
DAG(16:1/18:2)
TAG48:2-FA16:0

69 from CSF metabolomics:

dimethyl sulfone
phenyllactate (PLA)
4-methylcatechol sulfate
N-delta-acetylornithine
argininosuccinate
2-hydroxy-3-methylvalerate
5-methylthioadenosine (MTA)
5-hydroxyindoleacetate
alpha-hydroxyisovalerate
orotidine
kynurenine
guaiacol sulfate
X-25790
5-methylcytidine
N-acetyl-beta-alanine
N-acetylputrescine
2,3-dihydroxy-5-methylthio-4-pentenoate (DMTPA)*
4-acetamidobutanoate
picolinate
X-24408
gluconate
X-18887
imidazole lactate
N-acetyltaurine
N2,N2-dimethylguanosine
N-formylmethionine
X-12007
lactate
arabitol/xylitol
glutamine
pseudouridine
N-acetylarginine
N-acetylglucosamine/N-acetylgalactosamine
orotate
aconitate [cis or trans]
4-guanidinobutanoate
N6,N6,N6-trimethyllysine
succinylglutamine
3-hydroxyhexanoate
3-(3-amino-3-carboxypropyl)uridine*
choline phosphate
isobutyrylcarnitine (C4)
X-23666
arabinose
X-12411
gamma-glutamylmethionine
N-acetylvaline
betaine
3-methylglutaconate
7-methylguanine
tiglylcarnitine (C5:1-DC)
phenylacetate
adenine
S-1-pyrroline-5-carboxylate
X-12680
O-sulfo-L-tyrosine
dimethylarginine (SDMA + ADMA)
methionine sulfone
N,N,N-trimethyl-alanylproline betaine (TMAP)
mannonate*
trigonelline (N'-methylnicotinate)
X-12101
X-23195
mannitol/sorbitol
ethyl beta-glucopyranoside
arginine
3-hydroxystachydrine*
methyl glucopyranoside (alpha + beta)
N-methylproline

That's 15% of all tests in CSF metabolomics. Very over-represented compared to all the other studies here. All but one (X-23195) lower with worse PEM. Which, hey, if all ME/CFS tests were shifted down for some reason, and this X thing is still higher, then maybe that means it's very significant.

1 lower in stool metabolites:

Hypoxanthine

3 lower in tilt catecholamine

Plasma concentration of DHPG at 12 minutes of head-up tilt, in pg/mL
Plasma concentration of DOPAC at 12 minutes of head-up tilt, in pmol/mL
Plasma concentration of DOPA at 12 minutes of head-up tilt, in pg/mL

Minutes is a measure of angle here.

--------

So yeah, those are the ones I'd say are maybe interesting just based on this study alone. But I think the first tab can be useful for comparing other study results. The first tab has correlations from the whole dataset, so it has the most power to find correlations. The correlation/significance data there can be used to compare to tests from other studies, even if the data splits didn't necessarily show high significance for the tests in question. The splits have very few participants, so it's likely some correlations were missed in those due to random variation.

 

[Amw66]

Looking at metrics between groups, it's definitely not that straightforward. While those with worse PEM tend to have lower ATP production at baseline, healthy people's ATP production seems to be lower than ME/CFS.
View attachment 24622

I quickly looked at the rest of the comparisons. ATP production is either the same or higher in the ME/CFS group at all time points. Pretty much all of the tests are no difference or the opposite of what I would expect if basing it on the correlations with PEM.

I've double, triple checked I did everything right. This plot of ATP from the baseline file is about as raw from the downloaded data files as I can get it:
View attachment 24623
It does look like a somewhat negative correlation where ATP production gets lower as PEM increases in the ME/CFS group, and yet the healthy group is lower on average, even though they have zero PEM.

Here's another, Spare Respiratory Capacity (%), which was most correlated in the first table above. Same pattern:
View attachment 24624

So it'll either take something quite unintuitive to explain this or it's nothing.

Edit: This makes me wonder. What if I include healthy controls and set all their PEM scores to zero and run the correlations with everyone?

I guess the way I have it now might be better because I have a "validation set", to see if the top correlations from the only ME/CFS group extend to healthy controls. Which was useful here for providing contrary evidence for mitochondrial function.

But maybe just use half the healthy controls in the correlations and half as validation?

ATP is also a key signalling molecule .
So if something is going awry, it's function can alter .
Most molecules multi task , but we tend to focus on their primary association

 

[forestglip]

Since low fecal hypoxanthine was flagged above, I did a search.

Plots of fecal hypoxanthine from the NIH study:


Plots of hypoxanthine vs other metabolites mentioned in other studies below as lower. Propionate, uracil, and butyrate look like they might be positively correlated with hypoxanthine.


Also, one of the studies found higher fecal taurine in ankylosing spondylitis and rheumatoid arthritis. The plot for taurine in the NIH study is interesting. Everyone along the bottom has zero, there was just jitter added to the dots. Only three had taurine. Notice the dot in the top right corner who had the max possible PEM score.


Fecal hypoxanthine

The faecal metabolome in COVID-19 patients is altered and associated with clinical features and gut microbes, Lv et al, 2021

negative correlations were observed between blood neutrophils and faecal hypoxanthine

Our results showed that four purine metabolites, deoxyinosine, 7H-purine, hypoxanthine and inosine, were depleted in the faeces of COVID-19 patients. On the one hand, there is a substantial need for nucleotide genesis in the gut because intestinal epithelial cells have an extremely short lifetime and are almost completely renewed every 2–6 days [29]. Therefore, a reduction in faecal purine metabolites may result from increasing demands for nucleic acids to repair the intestinal epithelial damage caused by SARS-CoV-2 infection. On the other hand, as the gut microbiota can produce and release large quantities of purines [30], the reduction in faecal purine metabolites may also be related to alterations in the gut microbiota. Consistent with this phenomenon, our results revealed positive correlations between several COVID-19-depleted microbes and purine metabolites, such as between Anaerostipes and Faecalibacterium and hypoxanthine and between Aspergillus tritici and inosine.

Longitudinal Multi-omics Reveals Subset-Specific Mechanisms Underlying Irritable Bowel Syndrome, 2020, Mars et al

Lysine, uracil, and hypoxanthine were all found to be significantly lower in stool samples from IBS-C patients compared to HC (Figures 3A–3C; Figures S4C–S4F). Hypoxanthine was also lower in IBS-D patients albeit not at the same significance as in IBS-C. Hypoxanthine can serve as an energy source for intestinal epithelial cells and promotes intestinal cellular barrier development and recovery following injury or hypoxia (Lee et al., 2018; Lee et al., 2020). Lower fecal hypoxanthine levels could reflect decreased production or elevated breakdown of hypoxanthine by the microbiome in the gut of IBS patients.

Untargeted Metabolomics of Feces Reveals Diagnostic and Prognostic Biomarkers for Active Tuberculosis and Latent Tuberculosis Infection: Potential Application for Precise and Non-Invasive Identification, 2023, Luo et al

In this study, hypoxanthine and xanthine levels were lower in the LTBI [latent tuberculosis infection] group than in the HC group, and lower in the TB [active tuberculosis infection] group than in the LTBI group. At the functional level, purine metabolism was significantly highlighted in all three comparison groups. Ye et al compared the composition of gut microbes between pulmonary TB patients and healthy controls and discovered that Lactobacillus was found in substantial amounts in the feces of pulmonary tuberculosis patients.Citation21 Purines, acting as the precursors for DNA synthesis, are vital to the growth and propagation of Lactobacillus.Citation22 And it was proved that Lactobacillus could inhibit the absorption of purines through intestinal epithelial cells, including hypoxanthine, IMP, and inosine; therefore, this bacterial strain has the ability to enhance the health of hyperuricemia patients.Citation22,Citation23 These findings suggest that the decreased levels of purines in the feces of TB patients may be due to the enrichment of Lactobacillus in the gut, which could explain the results of our study. Besides, purines were found to be absorbed largely by M.tb to synthesize DNA.Citation24 And The key enzyme in purine salvage pathway, hypoxanthine-guanine phosphoribosyltransferase (HGPRT), is indispensable to the survival and propagation of M.tb.Citation25 Additionally, HGPRT inhibitors may be the new anti-TB drugs for its interference with the process of purine utilization in M.tb.Citation25,Citation26 In the study of Huang H et al, it was discovered that the level of xanthine in plasma was lower in TB patients, which further confirmed the above findings.Citation25 Therefore, according to the “gut-lung axis” theory, we assumed that the decrease of intestinal purines, especially hypoxanthine and xanthine, corresponded with the large consumption of purines in M.tb and may be the risk markers indicating the aggravation of TB.

Characterizing the metabolomic signature of attention-deficit hyperactivity disorder in twins, 2023, Swann et al

Here, we perform unbiased metabolomic profiling of urine and fecal samples collected from a well-characterized Swedish twin cohort enriched for ADHD (33 ADHD, 79 non-ADHD)

The fecal profile of ADHD individuals was characterized by increased excretion of stearoyl-linoleoyl-glycerol, 3,7-dimethylurate, and FAD and lower amounts of glycerol 3-phosphate, thymine, 2(1H)-quinolinone, aspartate, xanthine, hypoxanthine, and orotate.

Characterization of ankylosing spondylitis and rheumatoid arthritis using 1H NMR-based metabolomics of human fecal extracts, 2016, Shao et al

Compared with healthy controls, both AS and RA patients had significantly higher concentrations of taurine, methanol, fumarate, and tryptophan as well as relatively lower concentrations of butyrate, propionate, methionine, and hypoxanthine.

Blood and urine hypoxanthine

Untargeted Metabolomics and Quantitative Analysis of Tryptophan Metabolites in [ME] Patients and Healthy Volunteers, 2024, Abujrais+

Hypoxanthine is a naturally occurring purine derivative involved in nucleic acid metabolism. In hypoxic conditions, where oxygen supply to tissues is reduced, hypoxanthine levels can rise due to increased ATP breakdown. (27) Consequently, hypoxanthine serves as a biomarker for cellular hypoxia, which may be relevant to reduced oxygen extraction in ME/CFS. (28) In our study, elevated hypoxanthine levels in ME/CFS patients compared to HC, (p = 0.002) [plasma] suggests a potential link between hypoxia and ME/CFS pathology. This finding aligns with the understanding that ME/CFS patients often exhibit metabolic dysregulation, leading to cellular stress and hypoxic conditions, which correlate with symptoms like fatigue and reduced energy metabolism. (7,29) In a study by Shida et al. they found that hypoxanthine disrupts muscle energy metabolism by reducing ATP levels crucial for muscle contraction. (30) This disruption activates uncoupling protein 2 (UCP2), leading to mitochondrial decoupling and muscle weakness so elevated hypoxanthine levels in ME/CFS patients, may exacerbate muscle degradation. It is worth noting that the recurring presence of hypoxanthine as a metabolite varies significantly between studies. Furthermore, Naviaux et al. have proposed that the elevated efflux of purine metabolites may be a stress signal that propagates reduced energy production in ME/CFS. (31)

Metabolic profiling reveals anomalous energy metabolism and oxidative stress pathways in CFS, 2015, Armstrong et al

A decrease in blood hypoxanthine and an increase in urine allantoin further suggest the elevation of reactive oxygen species in ME/CFS patients.

Development of an expert system for the classification of myalgic encephalomyelitis/chronic fatigue syndrome 2024 Yagin et al

SHAP results showed that decreased levels of alpha-CEHC sulfate, hypoxanthine, and phenylacetylglutamine, as well as increased levels of N-delta-acetylornithine and oleoyl-linoloyl-glycerol (18:1/18:2)[2], increased the risk of ME/CFS. [Blood, Fukuda]

Post-Exertional Malaise Is Associated with Hypermetabolism, Hypoacetylation and Purine Metabolism Deregulation in ME/CFS Cases, 2019, McGregor et al

The principal biochemical change related to the 7-day severity of PEM was the fall in the purine metabolite, hypoxanthine.

As serum hypoxanthine was the prime predictive variable for alterations in the PEM scores, we assessed the relationships between serum Hypoxanthine and the purine related metabolites (Table 4). Serum and urine hypoxanthines were lower in the PEM subgroups versus the controls. Whilst there was no difference in the serum urate levels, the marker of purine degradation in the liver, the serum hypoxanthine: urate ratio was lower in the ME/CFS group. The ratio in the NoPEM subgroup was 5.4-fold lower whilst in the PEM group the ratio was 3.5-fold lower. The hypoxanthine:urate ratio was negatively correlated with serum glucose (r = −0.48, p < 0.001) and positively correlated with serum lactate (r = 0.77, p < 0.001), the purine ring precursor amino acids (r = 0.54, p < 0.001), acetate (r = 0.49, p < 0.001), and the total serum amino acids (r = 0.38, p < 0.006). The correlation between serum hypoxanthine and the purine ring precursors, indicative of purine synthesis, was not different between the ME/CFS cases and the controls (ME/CFS r = 0.66, p < 0.001, control r = 0.61, p < 0.001). However, the ratio was significantly lower in the ME/CFS group (Table 4 and Figure S2) and the purine ring precursor amino acids correlated positively with serum acetate (r = 0.52, p < 0.001). Thus, the synthesis and possibly the salvage of hypoxanthine were reduced whilst purine degradation was in the normal range. The levels of hypoxanthine in the serum were associated with the availability of the purine ring precursors, the glucose: lactate ratio and acetate. This suggests that acetylation is a major factor in the change in the purine metabolism deregulation in ME/CFS. Thus, the increase in urine metabolite loss during exercise events in ME/CFS cases results in a loss of purine ring precursors and a fall in acetate and hypoxanthine.

The increase in fecal uracil was also correlated with the serum hypoxanthine level in the PEM group (r = +0.39, p < 0.03) showing that they rose together as part of the PEM-associated hypermetabolic event. Uracil is a breakdown product of RNA but may also be of bacterial origin. Whether this indicates a breakdown in enterocytes or an alteration in the fecal flora or their metabotoxins/toxins is not known. Further investigation of these changes is warranted.

Purinergic signaling elements are correlated with coagulation players in peripheral blood and leukocyte samples from COVID-19 patients, 2022, Schultz et al

Also, ATP, ADP, inosine, and hypoxanthine had positive and negative correlations with clinical features.

High-Intensity Interval Training Decreases Resting Urinary Hypoxanthine Concentration in Young Active Men—A Metabolomic Approach, 2019, Kistner et al

One day after HIIT, no overall change in resting urinary metabolome, except a significant difference with decreasing means in urinary hypoxanthine concentration, was documented in the experimental group.

 

[forestglip]

So low hypoxanthine in the stool has been seen in many quite different health conditions (acute COVID, TB, ADHD, IBS, ankylosing spondylitis, RA). One more if you include ME/CFS. So maybe it is a marker of poor health in general. The plot of hypoxanthine in the two groups of this study above shows much more variability in the healthy controls, overlapping with ME/CFS on the low end, but also going much higher. Maybe this indicates that there is a spectrum of healthiness in the controls, with some dealing with other health problems and some fairly healthy, but most or all ME/CFS would be classified as "unhealthy" based on this marker.

Edit: "Unhealthy" could just mean some specific subset of health conditions, not all.

Edit 2: Updated disease list.

 

[forestglip]

The deep phenotyping study has the stool metabolomics statistics in Supplementary File 21. These are sorted by significance.



Tyrosine, phenylacetate, and threonine are the other ones they found were significant. (All increased according to the spreadsheet above.)

Edit: Unclear if they did multiple test correction, but probably not since I got similar p values before correction.

 

[Yann04]

I don’t have the energy to follow but thank you for doing these literature searches @forestglip it is so important.

 

[Turtle]

So low hypoxanthine in the stool has been seen in at least four quite different health conditions (acute COVID, TB, ADHD, IBS). Five if you include ME/CFS. So maybe it is a marker of poor health in general. The plot of hypoxanthine in the two groups of this study above shows much more variability in the healthy controls, overlapping with ME/CFS on the low end, but also going much higher. Maybe this indicates that there is a spectrum of healthiness in the controls, with some dealing with other health problems and some fairly healthy, but most or all ME/CFS would be classified as "unhealthy" based on this marker.

Edit: "Unhealthy" could just mean some specific subset of health conditions, not all.

Thanks for your deep diving into all of those data @forestglip.
Could hypoxia be a common factor in all those hypoxanthine findings?

 

[forestglip]

Could hypoxia be a common factor in all those hypoxanthine findings?

No idea. Hard to think of what could cause the same thing in so many conditions. My mind usually goes to confounders like less physical activity in many diseases, but I don't typically think of people with ADHD as being less active. (Edit: maybe poor diet both causing low hypoxanthine and being a risk factor for or result of many conditions?)

I haven't yet found any studies in humans where fecal hypoxanthine wasn't lower in the unhealthy group. (All the ones I found are listed above, and I'll add to the post if I find more.)