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Persistent Overactive Cytotoxic Immune Response in a Spanish Cohort of Individuals With Long-COVID: Identification of Diagnostic Biomarkers, 2022

Discussion in 'Long Covid research' started by SNT Gatchaman, Mar 25, 2022.

  1. SNT Gatchaman

    SNT Gatchaman Senior Member (Voting Rights)

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    Persistent Overactive Cytotoxic Immune Response in a Spanish Cohort of Individuals With Long-COVID: Identification of Diagnostic Biomarkers
    Miguel Galan, Lorena Vigon, Daniel Fuertes, Marıa Aranzazu Murciano-Anton, Guiomar Casado-Fernandez, Susana Domınguez-Mateos, Elena Mateos, Fernando Ramos-Martın, Vicente Planelles, Montserrat Torres, Sara Rodrıguez-Mora, Marıa Rosa Lopez-Huertas and Mayte Coiras

    Abstract
    Long-COVID is a new emerging syndrome worldwide that is characterized by the persistence of unresolved signs and symptoms of COVID-19 more than 4 weeks after the infection and even after more than 12 weeks. The underlying mechanisms for Long-COVID are still undefined, but a sustained inflammatory response caused by the persistence of SARS-CoV-2 in organ and tissue sanctuaries or resemblance with an autoimmune disease are within the most considered hypotheses.

    In this study, we analyzed the usefulness of several demographic, clinical, and immunological parameters as diagnostic biomarkers of Long-COVID in one cohort of Spanish individuals who presented signs and symptoms of this syndrome after 49 weeks post-infection, in comparison with individuals who recovered completely in the first 12 weeks after the infection.

    We determined that individuals with Long-COVID showed significantly increased levels of functional memory cells with high antiviral cytotoxic activity such as CD8+ TEMRA cells, CD8±TCRgd+ cells, and NK cells with CD56+CD57+NKG2C+ phenotype.

    The persistence of these long-lasting cytotoxic populations was supported by enhanced levels of CD4+ Tregs and the expression of the exhaustion marker PD-1 on the surface of CD3+ T lymphocytes.

    With the use of these immune parameters and significant clinical features such as lethargy, pleuritic chest pain, and dermatological injuries, as well as demographic factors such as female gender and O+ blood type, a Random Forest algorithm predicted the assignment of the participants in the Long-COVID group with 100% accuracy. The definition of the most accurate diagnostic biomarkers could be helpful to detect the development of Long-COVID and to improve the clinical management of these patients.

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    Hutan, John Mac, ukxmrv and 4 others like this.
  2. SNT Gatchaman

    SNT Gatchaman Senior Member (Voting Rights)

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  3. Trish

    Trish Moderator Staff Member

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    Surely their model needs to be tested on a separate group of patients and controls before they start claiming 100% predictive accuracy.
     
  4. Peter Trewhitt

    Peter Trewhitt Senior Member (Voting Rights)

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    My thoughts exactly. I biomarker must do more than distinguish between recovered Covid patients and those with persisting symptoms, it must distinguish between people with other non Covid related conditions too. At present all you can say is that they are distinguishing the well and the unwell in their sample.

    Also given their ‘algorithm’ seems to use both reported symptoms and patient demographics as well as the biomarkers, how robust would diagnosis using their biomarkers alone be?

    Having said that it is a potentially exciting result and I look forward to research with different control groups and to seeing if these markers tell us anything about the relationship between Long Covid and ME/CFS and if they vary over time or with severity of symptoms.
     
  5. John Mac

    John Mac Senior Member (Voting Rights)

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    alktipping, Peter Trewhitt and Mij like this.
  6. SNT Gatchaman

    SNT Gatchaman Senior Member (Voting Rights)

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    Yes agree. Although we have commented in the past the opposite too — that evaluations of HC vs LC would be much better complemented by including post COVID but recovered patients. Seems like ideally - as you say - you need all three groups. Hopefully other investigators will follow on.

    Yes, there's a lot of interest in applying machine learning classification algorithms. It does seem a bit unhelpful here to conclude that patients with persisting symptoms (in which fatigue is a very common feature) can be discriminated on the basis of fatigue. On one hand, you're basically dividing the patients up into fatigued vs non-fatigued and then saying the fatigued ones are 100% in group A (duh). On the other hand, they are confirming that fatigue is a major feature of LC, when considering all the available reported persistent symptoms (usually quoted as 200+).

    The more interesting part is obviously the suggestion of discriminatory signals relating to T cell function/regulation.

    ---
    If I'm following along correctly (and this is all complex, to say the least):

    Memory T cells can be divided into central memory (Tcm) and effector memory (Tem).
    Temra cells are CD8+ memory T cells, that can be considered as terminally differentiated effector memory cells. EMRA stands for "effector memory re-expressing CD45RA".

    There is debate around effector and memory T cell lineage, which is noted in the Wikipedia page. Also, some consider TEMRAs to be not just terminally differentiated but exhausted or senescent. See this paper for discussion around what "senescence" and "exhaustion" can mean in this context.
     

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