The Causes of Long Covid

This has been my experience. After not getting answers for a long time from conventional doctors, I went to a naturopath out of desperation. I was diagnosed with "mold toxicity", and took a bunch of supplements to boost all the low levels I had (B12 and cortisol). I also took flax seeds to help with "detox". All the stuff helped a bit, but only a bit. It was still just symptom fighting.

Now to be fair, there are people whose lives have been changed by these treatments, because in some cases someone just happens to be low in some essential micronutrient, and seeing a naturopath solves that when a traditional doctor didn't do a broad assay. But it still doesn't help people like me where whatever is happening can't be described by surface level blood tests and treatments. Naturopaths talk about "wholistic health", but if it's so wholistic, why don't they consider intermediate reactions? So it's become a life goal of mine to build a quantitative metabolic reaction database. I'm currently a applied math major with a chemistry minor, so in a couple years I hope to be able to make some headway on this.

> You are just ignoring the evidence, being unscientific, and unless you work for a top medical lab somewhere, plain arrogant.

If you don't know how to interpret evidence, then I suppose it would sound like I am being overly critical. I didn't bother to pick on just one, but since you chose it [1]...

> The UK Biobank study scanned participants before and after infection with matched controls. The difference is measured against their own pre-infection brain. That is the opposite of what you're describing.

It is not. The longitudinal nature of the study is a distraction from the fundamental issues with the approach.

They did a longitudinal case-control study, one group of which had positive covid tests in the past, and the other one did not at the time of the second scan (2021). That's the entire evidence base that this study is built upon -- it has nothing to do with "long Covid", and it's only barely plausible that the control group is actually a control for the factors of interest.

Next, they took two scans for all participants - one from before the pandemic, and one made after (again, in 2021). They made over 6000 different images, and then cherry-picked the ones with differences for further analysis (~70). Ultimately only 6 of these fishing expeditions survived family-wise error correction:

> The main case-versus-control analysis between the 401 SARS-CoV-2 cases and 384 controls (Model 1) on 297 olfactory-related cerebral IDPs yielded 68 significant results after FDR correction for multiple comparisons, including 6 that survived FWE correction

So first off, no statistical correction can compensate for this fundamental bias. You cannot start with thousands of different samples - even if they're taken from the same people at different time points - and winnow that down to a handful by filtering on the outcome of interest, Applying a multiple-sample correction will not fix it. It's not even clear that there is such a correction that is valid for the underlying distribution of the data involved.

But setting that aside, the differences observed, even between longitudinal samples, do not have to be due to Covid! Even if they're not random (which we cannot grant; see previous paragraph) they could be due to everyone being locked inside during 2020. They could be due to factors completely unexamined by the study, like, say, increases in drinking or drug use, or lack of exercise. Or any of a million other things. We don't know. The authors don't know. They're just not intellectually honest enough to admit that they don't know.

I could go on, and point out more flaws (e.g. the "significant" results mostly disappear when you exclude hospitalized patients, yet oddly, the difference between "hostipitalized" and "control" cohorts is not itself significant, indicating inadequate statistics), but this post is already too long.

I'm sorry that you think this is arrogant, but this is how we actually read papers.

[1] https://pmc.ncbi.nlm.nih.gov/articles/PMC9046077

This seems to me like a performance at this point and not serious analysis.

It’s true I conflated this with long covid. It’s not a long covid study.

I am tired and done with this. You made several errors in this comment.

Your biggest error is the lockdown one.

This makes no sense whatsoever - the controls also lived through lockdown. If this is the rigorous analysis you’re bringing to the studies you read, I’m not surprised none of them pass the muster.

“No correction can fix it” is wrong because the olfactory IDPs were pre-specified. “Could be lockdown” is wrong because controls lived through the same lockdown. “Results disappear excluding hospitalized” is wrong because the paper says they persisted.

The statistical weaknesses you describe are in the papers own limitations section. You just read them back as limitations that can’t be surpassed while evidence based researchers in the field disclose them as meaningful but not exclusionary.

Unless you want to continue with debunking every other strong paper I’ve posted with similar limited and likely to be demonstrably wrong takedowns, then I can’t help you. You have unfalsifiable priors, are constantly ignoring evidence and seem to believe you know better than the top researchers in the field - people who are saving lives - because you catch some statistical limitations and imply that they debunk the entire thing, instead of accepting them as limits of incomplete research into a real condition that’s crippling millions of people.

> the controls also lived through lockdown. If this is the rigorous analysis you’re bringing to the studies you read, I’m not surprised none of them pass the muster.

You've missed the point. I'm not suggesting that the other factor or factors has to be "lockdown". I'm just giving examples that illustrate the idea: even if you assume that the differences between the control and the experimental group are non-random and significant, you still cannot attribute the longitudinal difference to the one factor alone. If you don't like my theory, it's easy to find another, if you're even a little bit imaginative.

> “Results disappear excluding hospitalized” is wrong because the paper says they persisted.

No. They lose all but one. The final "significant" result is teetering on the edge of insignificance. See table 4 [1]. Models 2-4.

> the statistical weaknesses you describe are in the papers own limitations section.

Yes, because they're real. It's great that they wrote them in the paper, but they're fatal flaws.

"We openly disclosed the reason our study is nonsense!" is not the damning indictment you're suggesting that it is.

[1] https://pmc.ncbi.nlm.nih.gov/articles/PMC9046077/table/Tab4/

Thanks. Unfortunately, not really, especially where diet is concerned. For example, I saw significant improvement after removing wheat, white rice, red meat, and dairy entirely, which is not something your typical US doctor would suggest. The first doctor I saw wanted to put me on antidepressants.

I also think exercise recommendations are generally too low, especially with respect to high intensity cardio.