Department of Immunology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.
Nat Commun. 2022 Jan 25;13(1):446. doi: 10.1038/s41467-021-27797-1.
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.
在严重急性呼吸综合征冠状病毒 2（SARS-CoV-2）急性感染后，相当一部分患者出现长期症状，这种严重情况被称为急性冠状病毒病 2019（COVID-19）后综合征（PACS）或长 COVID。需要预测 PACS 的指标。在一项针对 215 名患者的前瞻性多中心队列研究中，我们在原发性感染期间以及之后一年与健康受试者比较了 COVID-19 患者。我们发现了一种免疫球蛋白（Ig）特征，基于总 IgM 和 IgG3 水平，与年龄、支气管哮喘病史和原发性感染期间的五个症状相结合，能够独立于采血时间点预测 PACS 的风险。我们在一个由 395 名 COVID-19 患者组成的独立队列中验证了该评分。我们的结果强调了测量 Igs 的益处，用于早期识别 PACS 高危患者，这有助于研究 PACS 的靶向治疗和发病机制。
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