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Complex patterns of multimorbidity associated with severe COVID-19 and long COVID, 2024, Pietzner et al.

Discussion in 'Long Covid research' started by SNT Gatchaman, Jul 9, 2024.

  1. SNT Gatchaman

    SNT Gatchaman Senior Member (Voting Rights)

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    Complex patterns of multimorbidity associated with severe COVID-19 and long COVID
    Pietzner, Maik; Denaxas, Spiros; Yasmeen, Summaira; Ulmer, Maria A.; Nakanishi, Tomoko; Arnold, Matthias; Kastenmüller, Gabi; Hemingway, Harry; Langenberg, Claudia

    BACKGROUND
    Early evidence that patients with (multiple) pre-existing diseases are at highest risk for severe COVID-19 has been instrumental in the pandemic to allocate critical care resources and later vaccination schemes. However, systematic studies exploring the breadth of medical diagnoses are scarce but may help to understand severe COVID-19 among patients at supposedly low risk.

    METHODS
    We systematically harmonized >12 million primary care and hospitalisation health records from ~500,000 UK Biobank participants into 1448 collated disease terms to systematically identify diseases predisposing to severe COVID-19 (requiring hospitalisation or death) and its post-acute sequalae, Long COVID.

    RESULTS
    Here we identify 679 diseases associated with an increased risk for severe COVID-19 (n = 672) and/or Long COVID (n = 72) that span almost all clinical specialties and are strongly enriched in clusters of cardio-respiratory and endocrine-renal diseases. For 57 diseases, we establish consistent evidence to predispose to severe COVID-19 based on survival and genetic susceptibility analyses. This includes a possible role of symptoms of malaise and fatigue as a so far largely overlooked risk factor for severe COVID-19. We finally observe partially opposing risk estimates at known risk loci for severe COVID-19 for etiologically related diseases, such as post-inflammatory pulmonary fibrosis or rheumatoid arthritis, possibly indicating a segregation of disease mechanisms.

    CONCLUSIONS
    Our results provide a unique reference that demonstrates how 1) complex cooccurrence of multiple – including non-fatal – conditions predispose to increased COVID-19 severity and 2) how incorporating the whole breadth of medical diagnosis can guide the interpretation of genetic risk loci.



    Link | PDF (Nature Communications Medicine) [Open Access]
     
    SNT Gatchaman, Jul 9, 2024
    #1
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  2. SNT Gatchaman

    SNT Gatchaman Senior Member (Voting Rights)

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    SNT Gatchaman, Jul 9, 2024
    #2
    Murph, Deanne NZ, Michelle and 5 others like this.
  3. Hutan

    Hutan Moderator Staff Member

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    Screen Shot 2024-07-09 at 11.23.37 pm.png
     
    Hutan, Jul 9, 2024
    #3
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  4. rvallee

    rvallee Senior Member (Voting Rights)

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    So, following this analysis, COVID causes a huge number of cases where fatigue and malaise are prominent disabling symptoms, symptoms which themselves represent higher risk factors for severe acute COVID and Long Covid, as well as across the board worsening of all conditions, which also represent higher risk factors for worsening, hence feeding into a massive crisis of disability. But of course decades of mislabeling of many of those risk factors as anxiety and mental health is feeding into the beliefs about self-perpetuating behavioral conditions, so it's unlikely that a course correction will happen any time soon, since that massive crisis of disability is simply blamed on people being lazy, based on incorrect data and flawed assumptions.

    Sounds very not smart to have chosen a strategy of endless mass reinfections, having lied their ass off that it would do no such thing, that in fact it would be beneficial for health, or whatever. But of course it was such an incompetent and obviously failed decision that there is almost no way to backtrack on it, as it would embarrass far too many people in a profession that values conforming to doing the wrong thing above doing the right thing without authorization.

    Glad to see we're in such good hands with great leadership acting in good faith, and all that. But at least billionaires and multimillionaires, our modern aristocracy in all but name, are doing better than ever, and that has to be worth all of this. We're even back to public sacrifices, just without the ceremony and stuff. Well, I guess there's a lot of cheering and rejoicing, it's just happening independently of it.
     
    rvallee, Jul 9, 2024
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  5. Hutan

    Hutan Moderator Staff Member

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    This sort of a study (looking at medical records and predicting risks of future illness) is a goldmine for health insurers as well as for health service providers.

    Pietzner, Maik 1,2, 3; Denaxas, Spiros 4,5,6,7; Yasmeen, Summaira 1; Ulmer, Maria A. 8; Nakanishi, Tomoko 2; Arnold, Matthias 8,9; Kastenmüller, Gabi 8; Hemingway, Harry 4,5,7; Langenberg, Claudia 1,2,3


    1 Computational Medicine, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany.
    2 Precision Healthcare University Research Institute, Queen Mary University of London, London, UK.
    3 MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.
    4 Institute of Health Informatics, University College London, London, UK.
    5 Health Data Research UK, London, UK.
    6 British Heart Foundation Data Science Centre, London, UK.
    7 National Institute of Health Research University College London Hospitals Biomedical Research Centre, London, UK.
    8 Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.
    9 Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA. 10
     
    Last edited: Jul 9, 2024
    Hutan, Jul 9, 2024
    #5
    Peter Trewhitt, Sean, Deanne NZ and 1 other person like this.
  6. Hutan

    Hutan Moderator Staff Member

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    In principle, I like the idea of looking very broadly for risk factors in medical records. But, we know how bad medical records can be. The definition of Long Covid, which is a vague term at best, will include people with all sorts of health issues. Unfortunately, it's probably going to be a garbage-in garbage out situation.
     
    Hutan, Jul 9, 2024
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  7. Hutan

    Hutan Moderator Staff Member

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    I think it's worth noting the much smaller sample size for the Long Covid analysis.

    Screen Shot 2024-07-10 at 8.40.10 am.png

    Looking a bit closer at Figure 2, I see that the y axes are different for each of the four analyses.
    Hospitalisation 0-300
    Respiratory failure 0-50
    Death 0-200
    Long Covid 0-25

    The y axis is log10 (p value), so it's a measure of how certain they are that there is an effect there. It isn't a measure of the estimated hazard ratio. The maximum strength of the associations are much lower for Long Covid, and yet, with the variable y-axes, they are presented as being equivalent. They definitely aren't.

    It's a shame that they don't report the hazard ratios for the most significant correlations in the main paper.
     
    Hutan, Jul 9, 2024
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  8. Hutan

    Hutan Moderator Staff Member

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    There's a lot of interesting data in Supplementary Information 2 (bearing in mind the problems with the definition of Long Covid and the accuracy of the medical records).

    Mental disorders:
    Not significant and trending towards reducing risk of Long Covid
    Not significant and trending towards increasing risk of Long Covid
    Significant (Hazard ratio and p value)
    No data - rows show "NA"

    What to conclude from all of that? First, the data is messy. And, there's a really weird inconsistency in the attribution of 'mental disorder' or 'neurological' to the items. Alzheimers, for example, is classed as a mental disorder. Parkinsons is 'neurological'.

    There are a whole lot of disorders for which there is no information and it's not clear why. For example, it should have been possibly to identify 'autism' in someone's medical records and it's common enough that there should have been some data to present.

    There's a possibility that some disorders reduced the risk of getting Covid-19 during the period of the study e.g. OCD. And, if you don't get Covid-19, in theory you don't get Long Covid. A diagnosis of somatoform disorder was not significant for the risk of later having a diagnosis of Long Covid, but a diagnosis of psychogenic disorder was. Having a medical history of depression seemed to reduce the risk of later being diagnosed with Long Covid - was that because symptoms were just attributed to depression, or because people with depression don't go to super spreader events so much?

    Anxiety and its various forms in medical histories seems to be a major risk for a Long Covid diagnosis. (e.g. Anxiety Disorder HR 2.4, with an extremely small p value of 3.85 ^-17). How to explain it?

    Some of the people had diagnoses of Chronic fatigue syndrome and symptoms of fatigue and psychogenic disorder in their records, so they already had symptoms that might later qualify them for a Long Covid diagnosis. If people had pre-Covid fatigue and orthostatic intolerance symptoms and had been trying to get their symptoms investigated, it's likely that they would have ended up with at least one doctor putting their symptoms down to anxiety.

    Perhaps women in general are more likely to get a diagnosis of anxiety in their records, and so a correlation between anxiety and Long Covid is just partly a reflection of women being more likely to get ME/CFS-like Long Covid?


    There are other things to look at in the data. There seems to be quite a few skin infection, urinary tract disorder and musculoskeletal correlations.
     
    Last edited: Jul 10, 2024
    Hutan, Jul 10, 2024
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  9. Sean

    Sean Moderator Staff Member

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    I have no doubt this problem has massively skewed and corrupted attempts to understand a whole bunch of health issues and their causes, including mental health.
     
    Sean, Jul 10, 2024
    #9
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