Measurement of SARS-CoV-2 Antibody Titers Improves the Prediction Accuracy of COVID-19 Maximum Severity by Machine Learning in Non-Vaccinated Patients
Numerous studies have suggested that the titers of antibodies against SARS-CoV-2 are associated with the COVID-19 severity, however, the types of antibodies associated with the disease maximum severity and the timing at which the associations are best observed, especially within one week after sympt...
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Published in | Frontiers in immunology Vol. 13; p. 811952 |
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Main Authors | , , , , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
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Frontiers Media S.A
21.01.2022
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Abstract | Numerous studies have suggested that the titers of antibodies against SARS-CoV-2 are associated with the COVID-19 severity, however, the types of antibodies associated with the disease maximum severity and the timing at which the associations are best observed, especially within one week after symptom onset, remain controversial. We attempted to elucidate the antibody responses against SARS-CoV-2 that are associated with the maximum severity of COVID-19 in the early phase of the disease, and to investigate whether antibody testing might contribute to prediction of the disease maximum severity in COVID-19 patients. We classified the patients into four groups according to the disease maximum severity (severity group 1 (did not require oxygen supplementation), severity group 2a (required oxygen supplementation at low flow rates), severity group 2b (required oxygen supplementation at relatively high flow rates), and severity group 3 (required mechanical ventilatory support)), and serially measured the titers of IgM, IgG, and IgA against the nucleocapsid protein, spike protein, and receptor-binding domain of SARS-CoV-2 until day 12 after symptom onset. The titers of all the measured antibody responses were higher in severity group 2b and 3, especially severity group 2b, as early as at one week after symptom onset. Addition of data obtained from antibody testing improved the ability of analysis models constructed using a machine learning technique to distinguish severity group 2b and 3 from severity group 1 and 2a. These models constructed with non-vaccinated COVID-19 patients could not be applied to the cases of breakthrough infections. These results suggest that antibody testing might help physicians identify non-vaccinated COVID-19 patients who are likely to require admission to an intensive care unit. |
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AbstractList | Numerous studies have suggested that the titers of antibodies against SARS-CoV-2 are associated with the COVID-19 severity, however, the types of antibodies associated with the disease maximum severity and the timing at which the associations are best observed, especially within one week after symptom onset, remain controversial. We attempted to elucidate the antibody responses against SARS-CoV-2 that are associated with the maximum severity of COVID-19 in the early phase of the disease, and to investigate whether antibody testing might contribute to prediction of the disease maximum severity in COVID-19 patients. We classified the patients into four groups according to the disease maximum severity (severity group 1 (did not require oxygen supplementation), severity group 2a (required oxygen supplementation at low flow rates), severity group 2b (required oxygen supplementation at relatively high flow rates), and severity group 3 (required mechanical ventilatory support)), and serially measured the titers of IgM, IgG, and IgA against the nucleocapsid protein, spike protein, and receptor-binding domain of SARS-CoV-2 until day 12 after symptom onset. The titers of all the measured antibody responses were higher in severity group 2b and 3, especially severity group 2b, as early as at one week after symptom onset. Addition of data obtained from antibody testing improved the ability of analysis models constructed using a machine learning technique to distinguish severity group 2b and 3 from severity group 1 and 2a. These models constructed with non-vaccinated COVID-19 patients could not be applied to the cases of breakthrough infections. These results suggest that antibody testing might help physicians identify non-vaccinated COVID-19 patients who are likely to require admission to an intensive care unit. |
Author | Kurano, Makoto Yu, Yi Ohmiya, Hiroko Nakano, Yuki Xia, Fuzhen Okada, Jun Jubishi, Daisuke Yatomi, Yutaka Okamoto, Koh Kodama, Tatsuhiko Qian, Chungen He, Fan Yokoyama, Rin Moriya, Kyoji Zheng, Liang Kishi, Yoshiro |
AuthorAffiliation | 2 Department of Clinical Laboratory Medicine, Graduate School of Medicine, The University of Tokyo , Tokyo , Japan 7 Laboratory for Systems Biology and Medicine, Research Center for Advanced Science and Technology, The University of Tokyo , Tokyo , Japan 3 Business Planning Department, Sales & Marketing Division, Medical & Biological Laboratories Co., Ltd , Tokyo , Japan 6 Department of Infection Control and Prevention, The University of Tokyo , Tokyo , Japan 1 Department of Clinical Laboratory, The University of Tokyo Hospital , Tokyo , Japan 4 The Key Laboratory for Biomedical Photonics of Ministry of Education at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology , Wuhan , China 5 Reagent R&D Center, Shenzhen YHLO Biotech Co., Ltd , Shenzhen , China |
AuthorAffiliation_xml | – name: 6 Department of Infection Control and Prevention, The University of Tokyo , Tokyo , Japan – name: 2 Department of Clinical Laboratory Medicine, Graduate School of Medicine, The University of Tokyo , Tokyo , Japan – name: 5 Reagent R&D Center, Shenzhen YHLO Biotech Co., Ltd , Shenzhen , China – name: 7 Laboratory for Systems Biology and Medicine, Research Center for Advanced Science and Technology, The University of Tokyo , Tokyo , Japan – name: 4 The Key Laboratory for Biomedical Photonics of Ministry of Education at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology , Wuhan , China – name: 1 Department of Clinical Laboratory, The University of Tokyo Hospital , Tokyo , Japan – name: 3 Business Planning Department, Sales & Marketing Division, Medical & Biological Laboratories Co., Ltd , Tokyo , Japan |
Author_xml | – sequence: 1 givenname: Makoto surname: Kurano fullname: Kurano, Makoto organization: Department of Clinical Laboratory Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan – sequence: 2 givenname: Hiroko surname: Ohmiya fullname: Ohmiya, Hiroko organization: Business Planning Department, Sales & Marketing Division, Medical & Biological Laboratories Co., Ltd, Tokyo, Japan – sequence: 3 givenname: Yoshiro surname: Kishi fullname: Kishi, Yoshiro organization: Business Planning Department, Sales & Marketing Division, Medical & Biological Laboratories Co., Ltd, Tokyo, Japan – sequence: 4 givenname: Jun surname: Okada fullname: Okada, Jun organization: Business Planning Department, Sales & Marketing Division, Medical & Biological Laboratories Co., Ltd, Tokyo, Japan – sequence: 5 givenname: Yuki surname: Nakano fullname: Nakano, Yuki organization: Department of Clinical Laboratory, The University of Tokyo Hospital, Tokyo, Japan – sequence: 6 givenname: Rin surname: Yokoyama fullname: Yokoyama, Rin organization: Department of Clinical Laboratory, The University of Tokyo Hospital, Tokyo, Japan – sequence: 7 givenname: Chungen surname: Qian fullname: Qian, Chungen organization: The Key Laboratory for Biomedical Photonics of Ministry of Education at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China – sequence: 8 givenname: Fuzhen surname: Xia fullname: Xia, Fuzhen organization: Reagent R&D Center, Shenzhen YHLO Biotech Co., Ltd, Shenzhen, China – sequence: 9 givenname: Fan surname: He fullname: He, Fan organization: Reagent R&D Center, Shenzhen YHLO Biotech Co., Ltd, Shenzhen, China – sequence: 10 givenname: Liang surname: Zheng fullname: Zheng, Liang organization: Reagent R&D Center, Shenzhen YHLO Biotech Co., Ltd, Shenzhen, China – sequence: 11 givenname: Yi surname: Yu fullname: Yu, Yi organization: Reagent R&D Center, Shenzhen YHLO Biotech Co., Ltd, Shenzhen, China – sequence: 12 givenname: Daisuke surname: Jubishi fullname: Jubishi, Daisuke organization: Department of Infection Control and Prevention, The University of Tokyo, Tokyo, Japan – sequence: 13 givenname: Koh surname: Okamoto fullname: Okamoto, Koh organization: Department of Infection Control and Prevention, The University of Tokyo, Tokyo, Japan – sequence: 14 givenname: Kyoji surname: Moriya fullname: Moriya, Kyoji organization: Department of Infection Control and Prevention, The University of Tokyo, Tokyo, Japan – sequence: 15 givenname: Tatsuhiko surname: Kodama fullname: Kodama, Tatsuhiko organization: Laboratory for Systems Biology and Medicine, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan – sequence: 16 givenname: Yutaka surname: Yatomi fullname: Yatomi, Yutaka organization: Department of Clinical Laboratory Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan |
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Copyright | Copyright © 2022 Kurano, Ohmiya, Kishi, Okada, Nakano, Yokoyama, Qian, Xia, He, Zheng, Yu, Jubishi, Okamoto, Moriya, Kodama and Yatomi. Copyright © 2022 Kurano, Ohmiya, Kishi, Okada, Nakano, Yokoyama, Qian, Xia, He, Zheng, Yu, Jubishi, Okamoto, Moriya, Kodama and Yatomi 2022 Kurano, Ohmiya, Kishi, Okada, Nakano, Yokoyama, Qian, Xia, He, Zheng, Yu, Jubishi, Okamoto, Moriya, Kodama and Yatomi |
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Keywords | COVID-19 severity IgA spike protein IgG nucleocapsid protein machine learning IgM |
Language | English |
License | Copyright © 2022 Kurano, Ohmiya, Kishi, Okada, Nakano, Yokoyama, Qian, Xia, He, Zheng, Yu, Jubishi, Okamoto, Moriya, Kodama and Yatomi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
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Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Edited by: Milos Jesenak, Comenius University, Slovakia These authors have contributed equally to this work This article was submitted to Viral Immunology, a section of the journal Frontiers in Immunology Reviewed by: Siddappa N. Byrareddy, University of Nebraska Medical Center, United States; Ciputra Hartana, Ragon Institute of MGH, MIT and Harvard, United States |
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SubjectTerms | Antibodies, Viral - blood Antibody Formation - immunology Coronavirus Nucleocapsid Proteins - immunology COVID-19 COVID-19 - blood COVID-19 - immunology COVID-19 - pathology COVID-19 Vaccines - blood COVID-19 Vaccines - immunology Humans IgA IgG IgM Immunoglobulin A - blood Immunoglobulin G - blood Immunoglobulin M - blood Immunology Machine Learning Protein Domains - immunology SARS-CoV-2 - immunology severity Severity of Illness Index Spike Glycoprotein, Coronavirus - immunology Time Factors Vaccination Vaccination Hesitancy |
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Title | Measurement of SARS-CoV-2 Antibody Titers Improves the Prediction Accuracy of COVID-19 Maximum Severity by Machine Learning in Non-Vaccinated Patients |
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