Prediction models for neutralization activity against emerging SARS-CoV-2 variants: A cross-sectional study.

COVID-19 Omicron sub-lineage SARS-CoV-2 neutralizing antibody prediction model

Journal

Frontiers in microbiology
ISSN: 1664-302X
Titre abrégé: Front Microbiol
Pays: Switzerland
ID NLM: 101548977

Informations de publication

Date de publication:
2023
Historique:
received: 19 12 2022
accepted: 13 03 2023
medline: 28 4 2023
pubmed: 28 4 2023
entrez: 28 4 2023
Statut: epublish

Résumé

Despite extensive vaccination campaigns to combat the coronavirus disease (COVID-19) pandemic, variants of concern, particularly the Omicron variant (B.1.1.529 or BA.1), may escape the antibodies elicited by vaccination against SARS-CoV-2. Therefore, this study aimed to evaluate 50% neutralizing activity (NT We used a random 10% of samples from 1,277 participants in a population-based cross-sectional survey conducted in January and February 2022 in Yokohama City, the most populous municipality in Japan. We measured NT Among 123 participants aged 20-74, 93% had received two doses of SARS-CoV-2 vaccine. The geometric means (95% confidence intervals) of NT In a general Japanese population with 93% of the population vaccinated with two doses of SARS-CoV-2 vaccine, neutralizing activity against Omicron BA.1 and BA.2 were substantially lower than those against D614G or the Delta variant. The prediction models for Omicron BA.1 and BA.2 showed moderate predictive ability and the model for BA.1 performed well in validation data.

Identifiants

pubmed: 37113226
doi: 10.3389/fmicb.2023.1126527
pmc: PMC10126441
doi:

Types de publication

Journal Article

Langues

eng

Pagination

1126527

Informations de copyright

Copyright © 2023 Goto, Miyakawa, Nakayama, Yagome, Xu, Kaneko, Ohtake, Kato and Ryo.

Déclaration de conflit d'intérêts

SY was employed by Integrity Healthcare Co., Ltd. NO was employed by Tosoh Corporation. HK received grants from Shionogi & Company, Limited, and Asahi Kasei Pharma Co., Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

Atsushi Goto (A)

Department of Public Health, School of Medicine, Yokohama City University, Yokohama, Japan.
Department of Health Data Science, Graduate School of Data Science, Yokohama City University, Yokohama, Japan.

Kei Miyakawa (K)

Department of Microbiology, Graduate School of Medicine, Yokohama City University, Yokohama, Japan.
Center for Influenza and Respiratory Virus Research, National Institute of Infectious Diseases, Musashimurayama, Japan.

Izumi Nakayama (I)

Department of Public Health, School of Medicine, Yokohama City University, Yokohama, Japan.

Susumu Yagome (S)

Department of Health Data Science, Graduate School of Data Science, Yokohama City University, Yokohama, Japan.
Integrity Healthcare Co., Ltd., Tokyo, Japan.

Juan Xu (J)

Department of Endocrinology and Metabolism, Graduate School of Medicine, Yokohama City University, Yokohama, Japan.

Makoto Kaneko (M)

Department of Health Data Science, Graduate School of Data Science, Yokohama City University, Yokohama, Japan.

Norihisa Ohtake (N)

Bioscience Division, Research and Development Department, Tosoh Corporation, Tokyo Research Center, Ayase, Japan.

Hideaki Kato (H)

Infection Prevention and Control Department, Yokohama City University Hospital, Yokohama, Japan.

Akihide Ryo (A)

Department of Microbiology, Graduate School of Medicine, Yokohama City University, Yokohama, Japan.
Department of Virology III, National Institute of Infectious Diseases, Musashimurayama, Japan.

Classifications MeSH