Discriminating Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and comorbid conditions using metabolomics in UK Biobank, 2024, Huang et al

[Hutan]

The production of the model was using 7 disease populations.

That means that the model is definitely not useful for diagnosing ME/CFS then. All of those highly selected homogeneous disease populations exclude many people with diseases associated with dislipidemia; for example people who are obese. The ME/CFS group, as a heterogenous cohort, does not.

Of course, if you have data where the ME/CFS group is the only group allowed to have obese people, you will find that levels of blood lipids are different in the ME/CFS group compared to the healthy group.

From Table 2, the number of people on cholesterol-lowering medication in the ME/CFS group is 15.7%. This is probably normal for people of this age in the UK who are educated and concerned about health enough to be part of the UK Biobank. Indeed a study on the UK Biobank data reported statin use at baseline of 15.4% for its whole population.
(from Association of statin use with risk of depression and anxiety: A prospective large cohort study)

Here are the percentages of people on cholesterol-lowering medication in the other groups of people used as comparator groups in this paper (also Table 2)

Hypertension 9.7%
Depression 0.7%
Asthma 0.9%
IBS 0.5%
Hypothyroidism 0.6%
Migraine 1.7%
No health conditions (C2) 0.8%

Can you see how highly selected the comparison groups are? By not allowing people in the comparator groups to have any health condition other than the one the group is labelled with, you are badly skewing the comparison.

Taking hypothyroidism for example, there's a paper 'Prevalence of Hyperlipidaemia in Adult Patients with Hypothyroidism: A Systematic Review'. It lists out the findings of a whole lot of papers in hypothyroidism.
One study reports the prevalence of Hypercholesterolemia: 48.4%. Hypertriglyceridemia: 32.3%
Another notes that low HDL-C is present in 69.2% of people with hypothyroidism.
I haven't checked the details of those studies, but there is overwhelming evidence that people who are hypothyroid are very likely to have issues with weight control and issues with blood lipids. The selected people with Hypothyroidism control group are not at all normal for people with hypothyroidism.

It's like, I don't know.... having two jars of M&Ms filled straight from the packet, calling one ME/CFS and the other Hypothyroidism. And then deliberately taking nearly all of the yellow M&Ms out of the Hypothyroidism jar. And then saying that 'we can diagnose if a jar of M&Ms has ME/CFS with a reasonable degree of accuracy by looking at how many yellow M&Ms are in it'. This paper is like that.

 

[Jonathan Edwards]

Even 5 years ago our discussion with the business groups at the University had pointed to problems they'd had in translating AI tools to GPs, that largely fallen away on the past 5 years.

I am sceptical of that. GPs were taught to diagnose primary hyperparathyroidism by asking for a 'bone profile' of calcium, phosphate and alkaline phosphatase fifty years ago without most of them remembering why those tests were useful.

Maybe wariness of black box sets of results where none of the individual results is outside the normal range reflected a healthy caution about diagnostic tests that might not be all they seem and maybe everyone has got so obsessed with tech now they have lost that caution?

Comorbids we used were the common ones that mecfs patients in ukbiobank actually had and were enriched against a general population background in ME. We added hypertension to account for the lipid and lipoprotein elevations. Interesting thing is that mecfs group (25% hypertension) had lipoprotein profiles that were close to equal to a group where 100% of people had hypertension.

OK, so you are using comorbidity to mean something found in statistical association with ME/CFS that may confound attempts to identify markers that sort with ME/CFS because of ME/CFS biology specifically. If you can do that you have some biological explanatory data. That is what Beentjes and co hope they had done but I fear may have been due to confounders.

But I thin this is actually a different problem from discriminating diagnoses in an individual. It may translate to that if the biological link is robust but again, the question is whether anyone has ever found a set of markers with a pattern of values within normal ranges that is robust enough that does not make sense biologically. I don't know of a case. It seems you don't either, since none has been mentioned so far?

If we are allowing AI why not tell patients just to log on to Google and ask it 'Have I got ME/CFS?' No doubt Goggle has the sense to go through a history including questions that test for fitting CCC. You cannot do better than that if that is how ME/CFS is defined.