ERJ Open Research (Dec 2022)

Immunophenotypes of anti-SARS-CoV-2 responses associated with fatal COVID-19

  • Julij Šelb,
  • Barbara Bitežnik,
  • Urška Bidovec Stojković,
  • Boštjan Rituper,
  • Katarina Osolnik,
  • Peter Kopač,
  • Petra Svetina,
  • Kristina Cerk Porenta,
  • Franc Šifrer,
  • Petra Lorber,
  • Darinka Trinkaus Leiler,
  • Tomaž Hafner,
  • Tina Jerič,
  • Robert Marčun,
  • Nika Lalek,
  • Nina Frelih,
  • Mojca Bizjak,
  • Rok Lombar,
  • Vesna Nikolić,
  • Katja Adamič,
  • Katja Mohorčič,
  • Sanja Grm Zupan,
  • Irena Šarc,
  • Jerneja Debeljak,
  • Ana Koren,
  • Ajda Demšar Luzar,
  • Matija Rijavec,
  • Izidor Kern,
  • Matjaž Fležar,
  • Aleš Rozman,
  • Peter Korošec

DOI
https://doi.org/10.1183/23120541.00216-2022
Journal volume & issue
Vol. 8, no. 4

Abstract

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Background The relationship between anti-SARS-CoV-2 humoral immune response, pathogenic inflammation, lymphocytes and fatal COVID-19 is poorly understood. Methods A longitudinal prospective cohort of hospitalised patients with COVID-19 (n=254) was followed up to 35 days after admission (median, 8 days). We measured early anti-SARS-CoV-2 S1 antibody IgG levels and dynamic (698 samples) of quantitative circulating T-, B- and natural killer lymphocyte subsets and serum interleukin-6 (IL-6) response. We used machine learning to identify patterns of the immune response and related these patterns to the primary outcome of 28-day mortality in analyses adjusted for clinical severity factors. Results Overall, 45 (18%) patients died within 28 days after hospitalisation. We identified six clusters representing discrete anti-SARS-CoV-2 immunophenotypes. Clusters differed considerably in COVID-19 survival. Two clusters, the anti-S1-IgGlowestTlowestBlowestNKmodIL-6mod, and the anti-S1-IgGhighTlowBmodNKmodIL-6highest had a high risk of fatal COVID-19 (HR 3.36–21.69; 95% CI 1.51–163.61 and HR 8.39–10.79; 95% CI 1.20–82.67; p≤0.03, respectively). The anti-S1-IgGhighestTlowestBmodNKmodIL-6mod and anti-S1-IgGlowThighestBhighestNKhighestIL-6low cluster were associated with moderate risk of mortality. In contrast, two clusters the anti-S1-IgGhighThighBmodNKmodIL-6low and anti-S1-IgGhighestThighestBhighNKhighIL-6lowest clusters were characterised by a very low risk of mortality. Conclusions By employing unsupervised machine learning we identified multiple anti-SARS-CoV-2 immune response clusters and observed major differences in COVID-19 mortality between these clusters. Two discrete immune pathways may lead to fatal COVID-19. One is driven by impaired or delayed antiviral humoral immunity, independently of hyper-inflammation, and the other may arise through excessive IL-6-mediated host inflammation response, independently of the protective humoral response. Those observations could be explored further for application in clinical practice.