Ct threshold values, a proxy for viral load in community SARS-CoV-2 cases, demonstrate wide variation across populations and over time

  1. A Sarah Walker  Is a corresponding author
  2. Emma Pritchard
  3. Thomas House
  4. Julie V Robotham
  5. Paul J Birrell
  6. Iain Bell
  7. John Bell
  8. John Newton
  9. Jeremy Farrar
  10. Ian Diamond
  11. Ruth Studley
  12. Jodie Hay
  13. Karina-Doris Vihta
  14. Timothy EA Peto
  15. Nicole Stoesser
  16. Philippa C Matthews
  17. David W Eyre
  18. Koen Pouwels
  19. COVID-19 Infection Survey team
  1. University of Oxford, United Kingdom
  2. University of Manchester, United Kingdom
  3. Public Health England, United Kingdom
  4. Office for National Statistics, United Kingdom
  5. The Wellcome Trust, United Kingdom
  6. University of Glasgow, United Kingdom

Abstract

Background: Information on SARS-CoV-2 in representative community surveillance is limited, particularly cycle threshold (Ct) values (a proxy for viral load).

Methods: We included all positive nose and throat swabs 26-April-2020 to 13-March-2021 from the UK's national COVID-19 Infection Survey, tested by RT-PCR for the N, S and ORF1ab genes. We investigated predictors of median Ct value using quantile regression.

Results: Of 3,312,159 nose and throat swabs, 27,902(0.83%) were RT-PCR-positive, 10,317(37%), 11,012(40%) and 6,550(23%) for 3, 2 or 1 of the N, S and ORF1ab genes respectively, with median Ct=29.2 (~215 copies/ml; IQR Ct=21.9-32.8, 14-56,400 copies/ml). Independent predictors of lower Cts (i.e. higher viral load) included self-reported symptoms and more genes detected, with at most small effects of sex, ethnicity and age. Single-gene positives almost invariably had Ct>30, but Cts varied widely in triple-gene positives, including without symptoms. Population-level Cts changed over time, with declining Ct preceding increasing SARS-CoV-2 positivity.Of 6,189 participants with IgG S-antibody tests post-first RT-PCR-positive, 4,808(78%) were ever antibody-positive; Cts were significantly higher in those remaining antibody-negative.

Conclusions: Marked variation in community SARS-CoV-2 Ct values suggest that they could be a useful epidemiological early-warning indicator.

Funding: Department of Health and Social Care, National Institutes of Health Research, Huo Family Foundation, Medical Research Council UK; Wellcome Trust.

Data availability

De-identified study data are available for access by accredited researchers in the ONS Secure Research Service (SRS) for accredited research purposes under part 5, chapter 5 of the Digital Economy Act 2017. Individuals can apply to be an accredited researcher using the short form on https://researchaccreditationservice.ons.gov.uk/ons/ONS_registration.ofml. Accreditation requires completion of a short free course on accessing the SRS. To request access to data in the SRS, researchers must submit a research project application for accreditation in the Research Accreditation Service (RAS). Research project applications are considered by the project team and the Research Accreditation Panel (RAP) established by the UK Statistics Authority. Project application example guidance and an exemplar of a research project application are available. A complete record of accredited researchers and their projects is published on the UK Statistics Authority website to ensure transparency of access to research data. For further information about accreditation, contact Research.Support@ons.gov.uk or visit the SRS website.Data points underlying Figures are provided in Supplementary File 4 and Stata code in Supplementary File 3.

Article and author information

Author details

  1. A Sarah Walker

    Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    For correspondence
    sarah.walker@ndm.ox.ac.uk
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0412-8509
  2. Emma Pritchard

    Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    Competing interests
    No competing interests declared.
  3. Thomas House

    University of Manchester, Manchester, United Kingdom
    Competing interests
    No competing interests declared.
  4. Julie V Robotham

    Modelling and Economics Unit, Public Health England, London, United Kingdom
    Competing interests
    No competing interests declared.
  5. Paul J Birrell

    Modelling and Economics Unit, Public Health England, London, United Kingdom
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8131-4893
  6. Iain Bell

    Office for National Statistics, Office for National Statistics, London, United Kingdom
    Competing interests
    No competing interests declared.
  7. John Bell

    Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
    Competing interests
    No competing interests declared.
  8. John Newton

    Public Health England, Public Health England, London, United Kingdom
    Competing interests
    No competing interests declared.
  9. Jeremy Farrar

    The Wellcome Trust, London, United Kingdom
    Competing interests
    No competing interests declared.
  10. Ian Diamond

    Office for National Statistics, Office for National Statistics, London, United Kingdom
    Competing interests
    No competing interests declared.
  11. Ruth Studley

    Office for National Statistics, Office for National Statistics, London, United Kingdom
    Competing interests
    No competing interests declared.
  12. Jodie Hay

    Virology, University of Glasgow, Glasgow, United Kingdom
    Competing interests
    No competing interests declared.
  13. Karina-Doris Vihta

    Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    Competing interests
    No competing interests declared.
  14. Timothy EA Peto

    Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    Competing interests
    No competing interests declared.
  15. Nicole Stoesser

    Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4508-7969
  16. Philippa C Matthews

    Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4036-4269
  17. David W Eyre

    Big Data Institute, University of Oxford, Oxford, United Kingdom
    Competing interests
    David W Eyre, declares lecture fees from Gilead, outside the submitted work..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5095-6367
  18. Koen Pouwels

    Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7097-8950
  19. COVID-19 Infection Survey team

Funding

Department of Health and Social Care (-)

  • A Sarah Walker
  • Emma Pritchard
  • Thomas House
  • Iain Bell
  • Ian Diamond
  • Ruth Studley
  • Jodie Hay
  • Karina-Doris Vihta
  • Koen Pouwels

National Institutes of Health Research (NIHR200915)

  • A Sarah Walker
  • Emma Pritchard
  • Julie V Robotham
  • Karina-Doris Vihta
  • Timothy EA Peto
  • Nicole Stoesser
  • David W Eyre
  • Koen Pouwels

Huo Family Foundation

  • Emma Pritchard
  • Koen Pouwels

Medical Research Council (MC_UU_12023/22)

  • A Sarah Walker

Wellcome Trust (110110/Z/15/Z)

  • Philippa C Matthews

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: Written informed consent was obtained from participants aged 16 years and older, and from parents/carers for those aged 2-15 years; those aged 10-15 years provided written assent. The study received ethical approval from the South Central Berkshire B Research Ethics Committee (20/SC/0195).

Copyright

© 2021, Walker et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. A Sarah Walker
  2. Emma Pritchard
  3. Thomas House
  4. Julie V Robotham
  5. Paul J Birrell
  6. Iain Bell
  7. John Bell
  8. John Newton
  9. Jeremy Farrar
  10. Ian Diamond
  11. Ruth Studley
  12. Jodie Hay
  13. Karina-Doris Vihta
  14. Timothy EA Peto
  15. Nicole Stoesser
  16. Philippa C Matthews
  17. David W Eyre
  18. Koen Pouwels
  19. COVID-19 Infection Survey team
(2021)
Ct threshold values, a proxy for viral load in community SARS-CoV-2 cases, demonstrate wide variation across populations and over time
eLife 10:e64683.
https://doi.org/10.7554/eLife.64683

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https://doi.org/10.7554/eLife.64683

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    Background:

    Subarachnoid hemorrhage (SAH) is characterized by intense central inflammation, leading to substantial post-hemorrhagic complications such as vasospasm and delayed cerebral ischemia. Given the anti-inflammatory effect of transcutaneous auricular vagus nerve stimulation (taVNS) and its ability to promote brain plasticity, taVNS has emerged as a promising therapeutic option for SAH patients. However, the effects of taVNS on cardiovascular dynamics in critically ill patients, like those with SAH, have not yet been investigated. Given the association between cardiac complications and elevated risk of poor clinical outcomes after SAH, it is essential to characterize the cardiovascular effects of taVNS to ensure this approach is safe in this fragile population. Therefore, this study assessed the impact of both acute and repetitive taVNS on cardiovascular function.

    Methods:

    In this randomized clinical trial, 24 SAH patients were assigned to either a taVNS treatment or a sham treatment group. During their stay in the intensive care unit, we monitored patient electrocardiogram readings and vital signs. We compared long-term changes in heart rate, heart rate variability (HRV), QT interval, and blood pressure between the two groups. Additionally, we assessed the effects of acute taVNS by comparing cardiovascular metrics before, during, and after the intervention. We also explored acute cardiovascular biomarkers in patients exhibiting clinical improvement.

    Results:

    We found that repetitive taVNS did not significantly alter heart rate, QT interval, blood pressure, or intracranial pressure (ICP). However, repetitive taVNS increased overall HRV and parasympathetic activity compared to the sham treatment. The increase in parasympathetic activity was most pronounced from 2 to 4 days after initial treatment (Cohen’s d = 0.50). Acutely, taVNS increased heart rate, blood pressure, and peripheral perfusion index without affecting the corrected QT interval, ICP, or HRV. The acute post-treatment elevation in heart rate was more pronounced in patients who experienced a decrease of more than one point in their modified Rankin Score at the time of discharge.

    Conclusions:

    Our study found that taVNS treatment did not induce adverse cardiovascular effects, such as bradycardia or QT prolongation, supporting its development as a safe immunomodulatory treatment approach for SAH patients. The observed acute increase in heart rate after taVNS treatment may serve as a biomarker for SAH patients who could derive greater benefit from this treatment.

    Funding:

    The American Association of Neurological Surgeons (ALH), The Aneurysm and AVM Foundation (ALH), The National Institutes of Health R01-EB026439, P41-EB018783, U24-NS109103, R21-NS128307 (ECL, PB), McDonnell Center for Systems Neuroscience (ECL, PB), and Fondazione Neurone (PB).

    Clinical trial number:

    NCT04557618.