在法国2023-2024年非典型疫情期间分离出的细小病毒B19的病毒学特征

Virological characterization of Parvovirus B19 isolated during the atypical 2023-2024 outbreak in France.

作者信息

Veyrenche Nicolas, Fourgeaud Jacques, Burgard Marianne, Allali Slimane, Toubiana Julie, Pinhas Yaël, Frange Pierre, Guilleminot Tiffany, Derridj Neil, Cohen Jérémie F, Leruez-Ville Marianne

机构信息

Microbiology department, Necker-Enfants malades Hospital, AP-HP, Université Paris Cité, Paris, France; Université Paris Cité, URP 7328 FETUS, Paris, France.

出版信息

J Infect. 2025 Feb;90(2):106409. doi: 10.1016/j.jinf.2025.106409. Epub 2025 Jan 4.

DOI:10.1016/j.jinf.2025.106409

PMID:39761699

Abstract

BACKGROUND

A Parvovirus B19 (B19V) outbreak has been reported in Europe in 2023-2024. The aims of this study were 1) to describe the incidence of primary cases from 2012 to 2024 in one French hospital 2) to analyze the genome of 2023 strains 3) to identify virological profiles according to the clinical presentations of B19V infection.

METHODS

The incidence of B19V primary cases was studied through an interrupted time-series analysis. Genomes of 2023 strains were sequenced in the NS1-VP1u region. Blood viral loads, IgG and IgM levels were analyzed in 158 cases according to clinical manifestations with Kruskal-Wallis test and a machine learning approach based on k-nearest neighbors.

RESULTS

During the 2023-2024 B19V outbreak, there was an 8-time increase in the incidence of B19V infections compared with pre-pandemic levels (8.25 (95%CI: 5.79-11.76)). The 2023 strains belonged to genotype 1a and were closely related to pre-2019 strains. Blood viral loads were significantly different between clinical presentations (p<0.0001). Machine learning allowed us to classify 68.8% (95% CI: 60.9-75.9) patients into the correct clinical group.

CONCLUSIONS

The 2023-24 epidemic is probably due to the reemergence of the pre-2019 strain. The virological profiles highlighted in this study could assist in accurately interpreting virology results.

摘要

背景

欧洲在2023 - 2024年报告了一起B19微小病毒（B19V）疫情。本研究的目的是：1）描述2012年至2024年期间一家法国医院原发性病例的发病率；2）分析2023年毒株的基因组；3）根据B19V感染的临床表现确定病毒学特征。

方法

通过间断时间序列分析研究B19V原发性病例的发病率。对2023年毒株的基因组在NS1 - VP1u区域进行测序。根据临床表现，采用Kruskal - Wallis检验和基于k近邻的机器学习方法，对158例患者的血液病毒载量、IgG和IgM水平进行分析。

结果

在2023 - 2024年B19V疫情期间，B19V感染的发病率与疫情前水平相比增加了8倍（8.25（95%CI：5.79 - 11.76））。2023年的毒株属于1a基因型，与2019年前的毒株密切相关。不同临床表现之间的血液病毒载量存在显著差异（p<0.0001）。机器学习使我们能够将68.8%（95%CI：60.9 - 75.9）的患者正确分类到临床组。