Assessment of background levels of autoantibodies as a prognostic marker for severe SARS-CoV-2 infection

Authors

  • Frank M. Sullivan Division of Population and Behavioural Sciences, St Andrews University Medical School, St Andrews - United Kingdom https://orcid.org/0000-0002-6623-4964
  • Agnes Tello Division of Population and Behavioural Sciences, St Andrews University Medical School, St Andrews and Edinburgh Clinical Trials Unit, Usher Institute, University of Edinburgh, Edinburgh - United Kingdom https://orcid.org/0000-0002-0602-472X
  • Petra Rauchhaus Tayside Clinical Trials Unit, University of Dundee, Dundee - United Kingdom https://orcid.org/0000-0003-4994-155X
  • Virginia Hernandez Santiago Division of Population and Behavioural Sciences, St Andrews University Medical School, St Andrews - United Kingdom https://orcid.org/0000-0002-8544-1483
  • Fergus Daly Division of Population and Behavioural Sciences, St Andrews University Medical School, St Andrews - United Kingdom

DOI:

https://doi.org/10.33393/jcb.2022.2337

Keywords:

COVID-19, Current or ex-smokers, Lung cancer, Mortality prediction, Serum biomarkers

Abstract

Background: Patients with more severe forms of SARS-CoV-2 exhibit activation of immunological cascades. Participants (current or ex-smokers with at least 20 years pack history) in a trial (Early Diagnosis of Lung Cancer, Scotland [ECLS]) of autoantibody detection to predict lung cancer risk had seven autoantibodies measured 5 years before the pandemic. This study compared the response to Covid infection in study participants who tested positive and negative to antibodies to tumour-associated antigens: p53, NY-ESO-1, CAGE, GBU4-5, HuD, MAGE A4 and SOX2.

Methods: Autoantibody data from the ECLS study was deterministically linked to the EAVE II database, a national, real-time prospective cohort using Scotland’s health data infrastructure, to describe the epidemiology of SARS-CoV-2 infection, patterns of healthcare use and outcomes. The strength of associations was explored using a network algorithm for exact contingency table significance testing by permutation.

Results: There were no significant differences discerned between SARS-CoV-2 test results and EarlyCDT-Lung test results (p = 0.734). An additional analysis of intensive care unit (ICU) admissions detected no significant differences between those who tested positive and negative. Subgroup analyses showed no difference in COVID-19 positivity or death rates amongst those diagnosed with chronic obstructive pulmonary disease (COPD) with positive and negative EarlyCDT results.

Conclusions: This hypothesis-generating study demonstrated no clinically valuable or statistically significant associations between EarlyCDT positivity in 2013-15 and the likelihood of SARS-CoV-2 positivity in 2020, ICU admission or death in all participants (current or ex-smokers with at least 20 years pack history) or in those with COPD or lung cancer.

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Published

2022-05-03

How to Cite

1.
Sullivan FM, Tello A, Rauchhaus P, Hernandez Santiago V, Daly F. Assessment of background levels of autoantibodies as a prognostic marker for severe SARS-CoV-2 infection. J Circ Biomark [Internet]. 2022 May 3 [cited 2022 May 20];11(1):24-7. Available from: https://journals.aboutscience.eu/index.php/jcb/article/view/2337

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Section

Original research article