Immunoglobulin signature predicts risk of post-acute COVID-19 syndrome

Carlo Cervia, Yves Zurbuchen, Patrick Taeschler, Tala Ballouz, Dominik Menges, Sara Hasler, Sarah Adamo, Miro E. Raeber, Esther Bächli, Alain Rudiger, Melina Stüssi-Helbling, Lars C. Huber, Jakob Nilsson, Ulrike Held, Milo A. Puhan and Onur Boyman ()
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Carlo Cervia: University of Zurich
Yves Zurbuchen: University of Zurich
Patrick Taeschler: University of Zurich
Tala Ballouz: University of Zurich
Dominik Menges: University of Zurich
Sara Hasler: University of Zurich
Sarah Adamo: University of Zurich
Miro E. Raeber: University of Zurich
Esther Bächli: Uster Hospital
Alain Rudiger: Limmattal Hospital
Melina Stüssi-Helbling: City Hospital Triemli Zurich
Lars C. Huber: City Hospital Triemli Zurich
Jakob Nilsson: University of Zurich
Ulrike Held: University of Zurich
Milo A. Puhan: University of Zurich
Onur Boyman: University of Zurich

Nature Communications, 2022, vol. 13, issue 1, 1-12

Abstract: Abstract Following acute infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) a significant proportion of individuals develop prolonged symptoms, a serious condition termed post-acute coronavirus disease 2019 (COVID-19) syndrome (PACS) or long COVID. Predictors of PACS are needed. In a prospective multicentric cohort study of 215 individuals, we study COVID-19 patients during primary infection and up to one year later, compared to healthy subjects. We discover an immunoglobulin (Ig) signature, based on total IgM and IgG3 levels, which – combined with age, history of asthma bronchiale, and five symptoms during primary infection – is able to predict the risk of PACS independently of timepoint of blood sampling. We validate the score in an independent cohort of 395 individuals with COVID-19. Our results highlight the benefit of measuring Igs for the early identification of patients at high risk for PACS, which facilitates the study of targeted treatment and pathomechanisms of PACS.

Date: 2022
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DOI: 10.1038/s41467-021-27797-1

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