The Causes of Long Covid(science.org) |
The Causes of Long Covid(science.org) |
Ended up doing a paleo diet, avoiding stressors (some of which are not obvious like just being on your phone scrolling, bad posture/circulation/sitting for too long), improving sleep hygiene, and ramping up consistent cardio exercise, with an emphasis on getting up to 4x/week zone 5 cardio without triggering intolerance.
Since then I've discovered a lot of other things that are great for overall health, like HRV-reset breathing and long-duration water fasts (around 3 days is optimal for me). I imagine those would have been very helpful if I had tried them earlier. A water fast is a complete metabolic and inflammatory reset of the body, and it's not as hard as you might think.
Hopefully most affected folks have recovered and are living normal lives by now, but if not, there are things you can do! It seems like the more challenging those things are, the more efficacious.
Can I read up on this anywhere? I'd welcome a suggestion over surfing the many many pages I found through a simple search. A book or paper reccomendation to read up on would be nice as well
You can expect to feel colder as your body doesn't conserve as much heat, and after ~2 days more lethargic physically, but your mental energy may actually be higher. I don't sleep as well when fasting, so 3 nights is about my limit. That being said, you feel rested on less sleep, because your body is probably producing a lot less waste.
I thought up to 3x a week and never consecutive days is the maximum.
From my own experience, it seems like hitting that Zone 5 briefly is a good nervous system reset (overrides any dysfunctional breathing and heart rate effects from long covid); it's less about training the heart, although that's an excellent side effect.
I also think exercise recommendations are generally too low, especially with respect to high intensity cardio.
The problem with “Long Covid” as it exists today is that there’s no such definition. Literally anyone who had Covid once and feels bad today (and quite a few people who never had a confirmed case at all) includes their set of symptoms in the communal diagnosis. Thus, if you dig into these studies, you always find that the syndrome is a wide-ranging and variable constellation of symptoms, making it impossible for a study to have any systematic legitimacy. Moreover, the results of any particular study are more strongly influenced by the inclusion criterion (if there even is one) than by any other factor.
It’s perfectly possible to evaluate treatments in this situation, and would be a better use of resources - pick symptoms, make an inclusion criteria, and run a randomized trial of existing drugs or therapies. But this is likely to fail, and it’s much, much easier to write papers with unprovable theories and retrospective analysis.
This person told me it was sure it was related to COVID because there was nothing before or after it and that was the only thing that happened.
Kinda sucks to thing that everytime it might be a chance for that or worse
It has been cheaper to not work for the healthcare system lately. Also because people are a**oles to nurses and the rest of the staff.
The results follow a 2024 study led by Dr Mulu Woldegiorgis that surveyed 11,000 people from Western Australia three months after they contracted COVID-19. Almost one-in-five (18.2 per cent) had developed long COVID. The researchers then monitored the same group of people six months after their initial infection.
“We wanted to get a sense of the trajectory for people with long COVID. After six months, the average number of symptoms remained stable, indicating little improvement,” she said.
and from that study:
In multivariable analyses, pre-existing health conditions at the time of initial SARS-CoV-2 infection and reporting fatigue, shortness of breath, and cough 3 months post-infection were independent predictors of persistent long COVID.
Age, sex, and number of COVID vaccinations were not significantly associated with persistent long COVID.
Personally, the only long Covid symptom I know of is that I have a coughing fit after every meal (and sometimes during). Some foods seem to lead to worse fits, but anything other than liquid will make me cough to some degree. Sometimes, it’s to the point that I see stars and nearly pass out.
All in all, I got off easy with Covid. It could have been worse.
This might be stomach acid aspirating into your lungs, from GERD or similar caused/exacerbated by Covid, and that may have negative consequences for your health apart from the coughing.
This has led me to conclude that perhaps in most cases chronic illness is an emergent behavior from a complex system, namely our body. Now tbh this is kind of a cheap take, because it's not that hard to conclude. But gosh darn it, we're programmers and we deal with complex systems all the time! What I want to see is a complete quantitative mapping of human metabolism, so that we can see all the in-between steps, not just the surface levels. That way curing chronic illness is more about comparing metabolite levels against known pathways and seeing what's regulated incorrectly. There's just not enough introspective capability currently.
My vision is some day a person who's been chronically ill can walk into a clinic, take a blood test, and with mass spectrometry get the level of the around 1800 different intermediate metabolites. That gets mapped to a known good metabolic graph, and it's optimized to find what in-between step is off kilter. They're then prescribed a drug that resets the bad state, and it 6 weeks they're back to normal.
I also doubt that AI will substantially help either. It still doesn't bring any more introspection capability, and if we can't figure out why someone is sick, I have little faith that a predictive AI can figure it out either.
Long COVID may be fueled by inflammation and tiny clots (2026)
https://www.sciencedaily.com/releases/2026/01/260107225532.h...
And of course people can have both.
There are actually good, reliable tests for it. However Lyme disease (not Lymes disease) became an alternative medicine explanation for everything vague and many people became obsessed with thinking they had it based on vague symptoms like fatigue. When they couldn’t get positive test results to confirm their belief, the Lyme disease online communities established the idea that the tests cannot detect their version of the disease. It’s a belief that allows anyone to diagnose with Lyme disease in a completely unprovable way.
> and the symptoms vary wildly from person to person.
This belief is an unfortunate result of the online Lyme communities encouraging everyone with any unexplained symptoms to believe it’s caused by Lyme disease that can’t be detected. When the disease becomes redefined as being untestable and causing wildly different symptoms in everyone, it becomes impossible to say that anyone doesn’t have it. If you have any vague symptoms like feeling tired, a Lyme disease community will encourage you do believe that it’s caused by an undetectable case of Lyme disease.
There is a lot of strong evidence that these patients do not have Lyme disease, but they’re always good at coming up with another reason why they have it but it can’t be detected in them specifically
Massaging this kind of data (clustering, etc.) is much lower value than finding fixed criteria that define a consistent group of patients who have objectively defined symptoms that cannot be more readily explained by another diagnosis. This is a pre-requisite for any further study. It can be done, but it’s hard, and it tends to lead to criticisms because you end up excluding a large number of people who fervently believe they have the illness, but don’t fit the objective standards.
Just for example: it’s not enough to claim that you have “brain fog”. A more valid endpoint might instead attempt to classify people based on standardized tests of thinking. Even that has problems, of course, but if you can just claim that you are fatigued and unable to think clearly, there’s a huge problem of confounding (i.e. maybe your symptoms are caused by something else), let alone the unverified nature of the original claim.
Yes, it's many variants from a disease, but still, like cancer we can tackle them one by one.
https://www.youtube.com/watch?v=ldg2AdVnlOs
https://www.nature.com/articles/s41598-026-46965-1
If it would be easy already data from 2016 would have "solved" it, right?
For example Organic Acids in Urine Test gives you some 70-80 metabolic markers, which some folks interpret. There's no large scale RCTs or studies on this, so it's a bit dubious. But I did one and the practitioner correctly read the leaves to suggest some things that were missing and which helped me (glutathione and B1).
The alternative medicine people use these tests because when you measure 80 different things you are almost guaranteed that some of them will come up high or low.
What they don’t explain is that many of them are expected to fluctuate and will show up with very different values on different days or depending on what you’ve eaten or when you’re taking the test.
They like it because they can tell you that you are too low in this thing and therefore you need to take this other supplement, every time someone takes the test.
For someone who isn’t getting answers from regular doctors it feels like a miracle that someone finally tested them, diagnosed them, and gave them something to take with a simple explanation of why it explains everything. This is the perfect recipe for placebo responses, which are common.
The forums are interesting to read because most people who do these and take the supplements will be very positive at first, but then over time they go back and take another test and find the results are completely different because it’s so random.
Organic acids tests are mostly only useful in the context of diagnosing specific genetic deficiencies which produce severe changes in the test results. The minor ups and downs that the alternative medicine people try to use are not diagnostic, especially with only a single test.
I ended up doing this inadvertently earlier this week due to some medication side effects (yay akathisia!) making me want to go outside and just seriously run fast, including up the nasty hill nearby. Which I could, so I did. I'm amazed at how much it seems to have helped, for how simple it was. (Another one from the past, though for different situations: alternating hot and cold showers.)
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.
I think what you are looking for is more like a model of the metabolome, showing the flow through the network under certain conditions (steady-state, growth, cell stress, etc). Not sure if there is a readily available database of such models, or how easy it would be just to run them and get meaningful results.
Everything you are saying is happening. But because the suspicion seems more and more that it’s an auto immune condition of some sort, and that we are only catching the downstream effects as some of the immune dysfunction isn’t mapped yet, we are seeing the clusters that you say emerge - overwhelming numbers of symptoms, relatively incoherent connection.
But autonomic dysfunction, small fiber neuropathic and detectable auto immune dysfunction are all known and increasingly mapped positive markers for the condition. Have you read the latest studies ?
Everything I've personally seen in this space is exactly what I described: they start with a set of people who claim to have the illness, then go on a statistical fishing expedition to look for "signs of immune disfunction" (or whatever, but you're right that these researchers tend to focus on immune-related metrics), then use whatever signals they happen to find to create a class. This is not the same thing as what I'm talking about, and it isn't valid.
I'm not going to claim comprehensive knowledge of the space, but the papers I've read that make it into the high-profile journals are of this sort.
The papers cited by this Lowe article are better than most at least in the sense that they have control groups and are doing experiments. But let's be clear -- the first one is claiming to see "long covid" pain symptoms in mice who are injected with whole human IgG (a notoriously messy and subjective approach) [1], and the other is exactly the kind of fishing expedition I'm describing, where they indiscriminately look for "targets" of said antibodies [2]. The former is at least doing an experiment that I suppose could lead to some kind of claim of cause, but the latter (despite the exaggerated title) provides no evidence that the correlations they're seeing are meaningful in any disease process.
I guarantee that using the high-dimensional screening that the latter paper in particular is doing, I can take 1000 random people, split them into two arbitrary classes ("fooists" and "non-fooists"), and find some "statistically significant" difference in immune marker profile between them. That is the fundamental problem with the approach.
When I say that you have to start from an objective measurement of symptoms, it means literally that -- not starting from an assay result that is unlinked to any symptom.
[1] https://www.sciencedirect.com/science/article/pii/S266637912...
[2] https://www.sciencedirect.com/science/article/abs/pii/S00928...
Aside: this lab is becoming infamous for this kind of statistical fishing expedition. It makes me cry for the state of science.
There are two fairly strong clusters of findings that are objective, repeatable, and consistent. And that is the autonomic testing in long COVID patients is coherent in its dysfunction, and so is the Small Fiber Neuropathy testing that is now consistently showing abnormalities.
Lets go step by step.
Small Fiber Neuropathy. Nerve fiber density is a count with age/sex-normed reference ranges. In previously healthy post-COVID patients with no diabetes and no risk factor, then the test shows whether the nerves are there or they aren't.
https://jdc.jefferson.edu/cgi/viewcontent.cgi?article=1284&c...
https://www.medrxiv.org/content/10.1101/2025.03.04.25323101v...
https://www.neurology.org/doi/pdf/10.1212/NXI.00000000002002...
https://pmc.ncbi.nlm.nih.gov/articles/PMC12847426/pdf/fnhum-...
We have brain structure changes showing in the UK Biobank studies https://pmc.ncbi.nlm.nih.gov/articles/PMC9046077/
Associations with complement dysregulation https://www.cell.com/med/fulltext/S2666-6340(24)00041-2
Muscular abnormalities in long COVID patients reporting reduced exercise function https://www.sciencedirect.com/science/article/pii/S104327602...
Potential that persistent infection shows up in Long Covid patients in abnormal rates https://www.massgeneralbrigham.org/en/about/newsroom/press-r...
If your argument is that people are showing up with abnormalities, then diagnosed with Long Covid, then spurious biomarkers are associated to it - you are just wrong. Wrong multiple times. Demonstrably so.
What we are seeing is more likely to be exactly what it looks like - an novel condition being captured by downstream effects of previously unknown or understudied mechanisms.
Thanks for this, reading "water fast" and "3 days" gave me a shot of adrenaline. The "water" prefix is just confusing, the word for abstaining from food is just "fast" for those interested.
If this is engagement bait, then well played..
The MRI studies are particularly egregious examples of this. Just because you see a difference on an MRI does not mean that the difference is due to the thing you’re blaming. In fact, it almost never is.
> If your argument is that people are showing up with abnormalities, then diagnosed with Long Covid, then spurious biomarkers are associated to it - you are just wrong. Wrong multiple times. Demonstrably so.
I am? I have now followed every link. Literally every paper you posted is following this exact pattern. I don't know how you could possibly conclude otherwise, unless you just didn't read past the titles.
They each take a (typically small) cohort of people who self-identify as "long covid sufferers", they subject them to random combinations of tests, and report only what they find to be significant. It's literally the XKCD comic about jelly beans.
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.
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.
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.
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/
It’s lockdown and now no lockdown. Could be anything. All observational studies are wrong. The stated limitations are fatal flaws. You heard it here first in HN. All medical research is fatally flawed, says user “timr”.
Good luck with that.
Afaik evidence based medicine ranks mechanistic analysis near the bottom of the hierarchy — below controlled trials and systematic observation. I believe that ordering was a deliberate choice.
You seem obsessed with something that modern medical research often doesn’t focus on - by design. We still don’t know how lithium works 50 years past its introduction. We don’t know how the conditions that it treats - psychosis or bipolar - work either. Yet lithium is used all over the world- because the effects data and reports show that it works. Your mechanistic obsession isn’t just wrong - it’s directionally incorrect as far as a lot of medical research goes.
No, but most of them are wrong, and all of them need to be treated with an incredibly high degree of skepticism. This is critical review 101. When you push on this paper, even lightly, it falls over.
Not all papers are bad, but this one is bad, and while there are a great many well-done studies in the world, the subject of "long covid", to date, has essentially ~none of them.
If you’re right and everyone else is wrong about hundreds if not thousands of studies, then you should be writing a book, not comments in HN.
We started at “some studies have errors” and we ended in “an entire field of research is wrong”.
You have already decided the field has no valid studies. Even when given dozens of examples you picked one and made up a series of points about one study. You made mistakes, never admitted it, and now are calling into question an entire field of medical research.
Again. Good luck with that.