Adult Outpatients with Long COVID Infected with SARS-CoV-2 Omicron Variant. Part 1: Oral Microbiota Alterations - 14/09/24

Doi : 10.1016/j.amjmed.2024.07.030

Jianchao Xu, MD ^a,^b,^{^{†
These authors contributed equally to this manuscript.}}, Di Wu, MD ^a,^c,^{^{†
These authors contributed equally to this manuscript.}}, Jie Yang, MD ^d, Yinuo Zhao, BS ^e, Xuzhao Liu, MD ^f, Yingying Chang, MD ^c, Yao Tang, MD ^g, Feng Sun, PhD ^h,^i,^⁎, Yubin Zhao, PhD ^a,^b,^j,^⁎
^a Hebei University of Chinese Medicine, Shijiazhuang, China
^b Shijiazhuang People's Hospital, Shijiazhuang, China
^c The Traditional Chinese Medicine Hospital of Shijiazhuang, Shijiazhuang, China
^d Hebei General Hospital, Shijiazhuang, China
^e Faculty of Biology, Medicine and Health, School of Biological Sciences, The University of Manchester, Manchester, UK
^f Handan Hospital of Integrated Chinese and Western Medicine, Handan, China
^g Wuhan Metware Biotechnology Co, Ltd, Wuhan, China
^h Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
ⁱ Key Laboratory of Epidemiology of Major Diseases (Peking University), Beijing, China
^j Shijiazhuang College of Applied Technology, China

^⁎Requests for reprints should be addressed to Yubin Zhao, PhD, Shijiazhuang People's Hospital, Hebei University of Chinese Medicine, 36 Fan Xi Road, Chang 'an, Shijiazhuang 050000, Hebei, China.Shijiazhuang People's HospitalHebei University of Chinese Medicine36 Fan Xi RoadChang 'anShijiazhuangHebei050000China

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Sous presse. Épreuves corrigées par l'auteur. Disponible en ligne depuis le Saturday 14 September 2024

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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Keywords : Coronavirus disease 2019 (COVID-19), SARS-CoV-2 Omicron variant, Long COVID, Oral microbiota, 16S, Sequence analysis, RNA

Plan

Introduction

Materials and methods

Participants and Study Design

Tongue Coating Sample Collection and DNA Extraction

16S rRNA Sequencing and Bioinformatics Analysis

Statistical Analysis

Results

Patient Inclusion and Characteristics of the Participants

Sequence Analysis

Bacterial Diversity in the Oral Microbiota of the Two Groups

Differences in Oral Microbiota Between the Two Groups

Predictive Models

Discussion

Conclusion

	Funding: The study was supported the National Key Research and Development Program of China: (2021YFC0863200). National Administration of Traditional Chinese Medicine's Emergency Special Project on Traditional Chinese Medicine for COVID-19:(2022ZYLCYJ02-1). Hebei Provincial Key Research and Development Project:(20277709D). Scientific and Technological Research and Development Plan of Shijiazhuang city. (201460463A). Research Project of Traditional Chinese Medicine of Hebei Administration of Traditional Chinese Medicine (2024129).
	Conflict of Interest: All the authors declare that they have no conflict of interest.
	Authorship: Jianchao Xu, Di Wu and Yubin Zhao designed and performed the experiments, analyzed the data, and drafted the manuscript. Feng Sun participated in the design and direction of the study. Jie Yang, Xuzhao Liu, Yinuo Zhao and Yingying Chang participated in the follow-up examination of this study. Yao Tang provided technical support and statistical analysis of the manuscript. Yubing Zhao and Feng Sun revised the manuscript. All the authors approved the final version of the manuscript. Jianchao Xu and Di Wu contributed equally to this work.