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 inFrontiers in immunology Vol. 13; p. 811952
Main Authors Kurano, Makoto, Ohmiya, Hiroko, Kishi, Yoshiro, Okada, Jun, Nakano, Yuki, Yokoyama, Rin, Qian, Chungen, Xia, Fuzhen, He, Fan, Zheng, Liang, Yu, Yi, Jubishi, Daisuke, Okamoto, Koh, Moriya, Kyoji, Kodama, Tatsuhiko, Yatomi, Yutaka
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Published Switzerland 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.
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
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Copyright Copyright © 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
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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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Snippet 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...
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StartPage 811952
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
URI https://www.ncbi.nlm.nih.gov/pubmed/35126396
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Volume 13
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