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Adult Outpatients with Long COVID Infected with SARS-CoV-2 Omicron Variant. Part 1: Oral Microbiota Alterations.
Xu, Jianchao; Wu, Di; Yang, Jie; Zhao, Yinuo; Liu, Xuzhao; Chang, Yingying; Tang, Yao; Sun, Feng; Zhao, Yubin.
Afiliación
  • Xu J; Hebei University of Chinese Medicine, Shijiazhuang, China; Shijiazhuang People's Hospital, Shijiazhuang, China.
  • Wu D; Hebei University of Chinese Medicine, Shijiazhuang, China; The Traditional Chinese Medicine Hospital of Shijiazhuang, Shijiazhuang, China.
  • Yang J; Hebei General Hospital, Shijiazhuang, China.
  • Zhao Y; Faculty of Biology, Medicine and Health, School of Biological Sciences, The University of Manchester, Manchester, UK.
  • Liu X; Handan Hospital of Integrated Chinese and Western Medicine, Handan, China.
  • Chang Y; The Traditional Chinese Medicine Hospital of Shijiazhuang, Shijiazhuang, China.
  • Tang Y; Wuhan Metware Biotechnology Co, Ltd, Wuhan, China.
  • Sun F; Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Beijing, China.
  • Zhao Y; Hebei University of Chinese Medicine, Shijiazhuang, China; Shijiazhuang People's Hospital, Shijiazhuang, China; Shijiazhuang College of Applied Technology, China. Electronic address: drzhyubin@163.com.
Am J Med ; 2024 Aug 14.
Article en En | MEDLINE | ID: mdl-39151680
ABSTRACT

BACKGROUND:

Many individuals experience long COVID after SARS-CoV-2 infection. As microbiota can influence health, it may change with COVID-19. This study investigated differences in oral microbiota between COVID-19 patients with and without long COVID.

METHODS:

Based on a prospective follow-up investigation, this nested case-control study evaluated the differences in oral microbiota in individuals with and without long COVID (Symptomatic and Asymptomatic groups), which were assessed by 16S rRNA sequencing on tongue coating samples. A predictive model was established using machine learning based on specific differential microbial communities.

RESULTS:

One-hundred-and-eight patients were included (n=54 Symptomatic group). The Symptomatic group had higher Alpha diversity indices (observed_otus, Chao1, Shannon, and Simpson indices), differences in microbial composition (Beta diversity), and microbial dysbiosis with increased diversity and relative abundance of pathogenic bacteria. Marker bacteria (c__Campylobacterota, o__Coriobacteriales, o__Pseudomonadales, and o__Campylobacterales) were associated with long COVID by linear discriminant analysis effect size and receiver operating characteristic curves (AUC 0.821).

CONCLUSION:

There were distinct variations in oral microbiota between COVID-19 patients with and without long COVID. Changes in oral microbiota may indicate long COVID.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Am J Med Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Am J Med Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos