The gut microbiome associates with phenotypic manifestations of post-acute COVID-19 syndrome.

Journal: Cell host & microbe
PMID: 38657605

Abstract

The mechanisms underlying the many phenotypic manifestations of post-acute COVID-19 syndrome (PACS) are poorly understood. Herein, we characterized the gut microbiome in heterogeneous cohorts of subjects with PACS and developed a multi-label machine learning model for using the microbiome to predict specific symptoms. Our processed data covered 585 bacterial species and 500 microbial pathways, explaining 12.7% of the inter-individual variability in PACS. Three gut-microbiome-based enterotypes were identified in subjects with PACS and associated with different phenotypic manifestations. The trained model showed an accuracy of 0.89 in predicting individual symptoms of PACS in the test set and maintained a sensitivity of 86% and a specificity of 82% in predicting upcoming symptoms in an independent longitudinal cohort of subjects before they developed PACS. This study demonstrates that the gut microbiome is associated with phenotypic manifestations of PACS, which has potential clinical utility for the prediction and diagnosis of PACS.

Authors

  • Qi Su
    School of Foreign Languages, Peking University, Beijing, China; MOE Key Laboratory of Computational Linguistics, School of EECS, Peking University, Beijing, China. Electronic address: sukia@pku.edu.cn.
  • Raphaela I Lau
    Microbiota I-Center (MagIC), Hong Kong SAR, China; Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China.
  • Qin Liu
    School of Public Health and Management, Research Center for Medicine and Social Development, Innovation Center for Social risk Governance in Health, Chongqing Medical University, Chongqing 400016, China.
  • Moses K T Li
    Microbiota I-Center (MagIC), Hong Kong SAR, China.
  • Joyce Wing Yan Mak
    Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China.
  • Wenqi Lu
    Tissue Image Analytics Centre, University of Warwick, Coventry, UK.
  • Ivan S F Lau
    Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China.
  • Louis H S Lau
    Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China.
  • Giann T Y Yeung
    Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China.
  • Chun Pan Cheung
    Microbiota I-Center (MagIC), Hong Kong SAR, China; Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China.
  • Whitney Tang
    Microbiota I-Center (MagIC), Hong Kong SAR, China; Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China.
  • Chengyu Liu
    Department of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.
  • Jessica Y L Ching
    Microbiota I-Center (MagIC), Hong Kong SAR, China; Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China.
  • Pui Kuan Cheong
    Microbiota I-Center (MagIC), Hong Kong SAR, China; Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China.
  • Francis K L Chan
    Microbiota I-Center (MagIC), Hong Kong SAR, China; Centre for Gut Microbiota Research, The Chinese University of Hong Kong, Hong Kong SAR, China.
  • Siew C Ng
    Microbiota I-Center (MagIC), Hong Kong SAR, China; Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China; Li Ka Shing Institute of Health Sciences, State Key Laboratory of Digestive Disease, Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR, China. Electronic address: siewchienng@cuhk.edu.hk.

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