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

Frank M Sullivan, Agnes Tello, Petra Rauchhaus, Virginia Hernandez Santiago, Fergus Daly

Research output: Contribution to journalArticlepeer-review

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 admis-sion or death in all participants (current or ex-smokers with at least 20 years pack history) or in those with COPD or lung cancer.

Original languageEnglish
Pages (from-to)24-27
Number of pages4
JournalJournal of circulating biomarkers
Volume11
Issue number1
DOIs
Publication statusPublished - 3 May 2022

Keywords

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

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