Evidence map›Paper›PMID 35838111›Full record

ArticleJournal of medical virology2022

Comparison of the respiratory tract microbiome in hospitalized COVID-19 patients with different disease severity.

Jiali Chen, Xiong Liu, Wei Liu, Chaojie Yang, Ruizhong Jia, Yuehua Ke, Jinpeng Guo, Leili Jia, Changjun Wang, Yong Chen

Abstract read
In one paragraph

Article in Journal of medical virology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Characteristics of the gut-lung axis in patients with viral pneumonia and respiratory failure.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2025
    Article
  5. Article
  6. Article
  7. Article
  8. Observational
  9. A pilot study of the impact of bacterial and fungal coinfections on mildly ill COVID-19 patients.Canadian journal of respiratory therapy : CJRT = Revue canadienne de la therapie respiratoire : RCTR · 2025
    Article
  10. Review
  11. Article
  12. Article
  13. The nasopharyngeal microbiome in COVID-19.Emerging microbes & infections · 2023
    Article
  14. Article
  15. Clinical identification and microbiota analysis ofFrontiers in cellular and infection microbiology · 2023
    Article
  16. Article
  17. Article
  18. Article
  19. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Jiali ChenDepartment of Emergency Response, Chinese PLA Center for Disease Control and Prevention, Beijing, China.
Xiong LiuDepartment of Emergency Response, Chinese PLA Center for Disease Control and Prevention, Beijing, China.
Wei LiuDepartment of Emergency Response, Chinese PLA Center for Disease Control and Prevention, Beijing, China.
Chaojie YangDepartment of Emergency Response, Chinese PLA Center for Disease Control and Prevention, Beijing, China.
Ruizhong JiaDepartment of Emergency Response, Chinese PLA Center for Disease Control and Prevention, Beijing, China.
Yuehua KeDepartment of Emergency Response, Chinese PLA Center for Disease Control and Prevention, Beijing, China.
Jinpeng GuoDepartment of Emergency Response, Chinese PLA Center for Disease Control and Prevention, Beijing, China.
Leili JiaDepartment of Emergency Response, Chinese PLA Center for Disease Control and Prevention, Beijing, China.
Changjun WangDepartment of Emergency Response, Chinese PLA Center for Disease Control and Prevention, Beijing, China.ORCID 0000-0002-1996-5991
Yong ChenDepartment of Emergency Response, Chinese PLA Center for Disease Control and Prevention, Beijing, China.ORCID 0000-0002-5086-6377

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Little is known about the characteristics of respiratory tract microbiome in Coronavirus disease 2019 (COVID-19) inpatients with different severity. We conducted a study that expected to clarify these characteristics as much as possible. A cross-sectional study was conducted to characterize respiratory tract microbial communities of 69 COVID-19 inpatients from 64 nasopharyngeal swabs and 5 sputum specimens using 16S ribosomal RNA gene V3-V4 region sequencing. The bacterial profiles were analyzed to find potential biomarkers by the two-step method, the combination of random forest model and the linear discriminant analysis effect size, and explore the connections with clinical characteristics by Spearman's rank test. Compared with mild COVID-19 patients, severe patients had significantly decreased bacterial diversity (p-values were less than 0.05 in the alpha and beta diversity) and relative lower abundance of opportunistic pathogens, including Actinomyces, Prevotella, Rothia, Streptococcus, Veillonella. Eight potential biomarkers including Treponema, Leptotrichia, Lachnoanaerobaculum, Parvimonas, Alloprevotella, Porphyromonas, Gemella, and Streptococcus were found to distinguish the mild COVID-19 patients from the severe COVID-19 patients. The genera of Actinomyces and Prevotella were negatively correlated with age in two groups. Intensive care unit admission, neutrophil count, and lymphocyte count were significantly correlated with different genera in the two groups. In addition, there was a positive correlation between Klebsiella and white blood cell count in two groups. The respiratory tract microbiome had significant differences in COVID-19 patients with different severity. The value of the respiratory tract microbiome as predictive biomarkers for COVID-19 severity deserves further exploration.

Indexed as

COVID-19MicrobiotaBacteriaCross-Sectional StudiesHumansRespiratory SystemSeverity of Illness IndexbiomarkerCOVID-19disease severityrespiratory tract microbiome

Identifiers

PMID35838111
PMCPMC9349541

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.