Evidence map›Paper›PMID 38811936›Full record

ArticleRespiratory research2024

Highly diverse sputum microbiota correlates with the disease severity in patients with community-acquired pneumonia: a longitudinal cohort study.

Jing Yang, Jinman Li, Linfeng Zhang, Zijie Shen, Yan Xiao, Guoliang Zhang, Mingwei Chen, Fuhui Chen, Ling Liu, Ying Wang and 8 more

Abstract readMulticenter Study
In one paragraph

Article in Respiratory research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Article
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  7. Article
  8. A Pilot Study ofDiagnostics (Basel, Switzerland) · 2026
    Article
  9. The relationship betweenFrontiers in medicine · 2026
    Article
  10. Respiratory Microbiome of Carbapenem-ResistantPathogens (Basel, Switzerland) · 2025
    Article
  11. Article
  12. Article
  13. Article
  14. Review
  15. Article
  16. Article
  17. 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

18 authors.

Jing Yang *Beijing Institute of Genomics, Chinese Academy of Sciences, China National Center for Bioinformation, Beijing, 100101, China.
Jinman Li *NHC Key Laboratory of Systems Biology of Pathogens and Christophe Mérieux Laboratory, National Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
Linfeng Zhang *Beijing Institute of Genomics, Chinese Academy of Sciences, China National Center for Bioinformation, Beijing, 100101, China.
Zijie ShenBeijing Institute of Genomics, Chinese Academy of Sciences, China National Center for Bioinformation, Beijing, 100101, China.
Yan XiaoNHC Key Laboratory of Systems Biology of Pathogens and Christophe Mérieux Laboratory, National Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
Guoliang ZhangShenzhen Third People's Hospital, Shenzhen, 518112, China.
Mingwei ChenThe First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China.
Fuhui ChenThe Second Affiliated Hospital of Harbin Medical University, Harbin, 150001, China.
Ling LiuJiangsu Provincial Key Laboratory of Critical Care Medicine, Department of Critical Care Medicine, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, 210009, China.
Ying WangNHC Key Laboratory of Systems Biology of Pathogens and Christophe Mérieux Laboratory, National Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
Lan ChenNHC Key Laboratory of Systems Biology of Pathogens and Christophe Mérieux Laboratory, National Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
Xinming WangNHC Key Laboratory of Systems Biology of Pathogens and Christophe Mérieux Laboratory, National Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
Li ZhangBeijing Institute of Genomics, Chinese Academy of Sciences, China National Center for Bioinformation, Beijing, 100101, China.
Lu WangBeijing Institute of Genomics, Chinese Academy of Sciences, China National Center for Bioinformation, Beijing, 100101, China.
Zhang WangInstitute of Ecological Sciences, South China Normal University, Guangzhou, 510631, China.
Jianwei Wang *NHC Key Laboratory of Systems Biology of Pathogens and Christophe Mérieux Laboratory, National Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China. wangjw28@163.com.
Mingkun Li *Beijing Institute of Genomics, Chinese Academy of Sciences, China National Center for Bioinformation, Beijing, 100101, China. limk@big.ac.cn.
Lili Ren *NHC Key Laboratory of Systems Biology of Pathogens and Christophe Mérieux Laboratory, National Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China. renlili@ipbcams.ac.cn.

Funding

Beijing Municipal Natural Science Foundation of China Z190017Beijing Nova Program Z191100006619102Beijing Nova Program Z211100002121135Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Sciences (CIFMS) 2021-I2M-1-038Fundamental Research Funds for the Central Universities 3332021092National Key R&D Program of China 2022YFA1304300National Natural Science Foundation of China 32100098Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences 2019PT310029
6 · The paper itself

Abstract

backgroundCommunity-acquired pneumonia (CAP) is a common and serious condition that can be caused by a variety of pathogens. However, much remains unknown about how these pathogens interact with the lower respiratory commensals, and whether any correlation exists between the dysbiosis of the lower respiratory microbiota and disease severity and prognosis.

methodsWe conducted a retrospective cohort study to investigate the composition and dynamics of sputum microbiota in patients diagnosed with CAP. In total, 917 sputum specimens were collected consecutively from 350 CAP inpatients enrolled in six hospitals following admission. The V3-V4 region of the 16 S rRNA gene was then sequenced.

resultsThe sputum microbiota in 71% of the samples were predominately composed of respiratory commensals. Conversely, 15% of the samples demonstrated dominance by five opportunistic pathogens. Additionally, 5% of the samples exhibited sterility, resembling the composition of negative controls. Compared to non-severe CAP patients, severe cases exhibited a more disrupted sputum microbiota, characterized by the highly dominant presence of potential pathogens, greater deviation from a healthy state, more significant alterations during hospitalization, and sparser bacterial interactions. The sputum microbiota on admission demonstrated a moderate prediction of disease severity (AUC = 0.74). Furthermore, different pathogenic infections were associated with specific microbiota alterations. Acinetobacter and Pseudomonas were more abundant in influenza A infections, with Acinetobacter was also enriched in Klebsiella pneumoniae infections.

conclusionCollectively, our study demonstrated that pneumonia may not consistently correlate with severe dysbiosis of the respiratory microbiota. Instead, the degree of microbiota dysbiosis was correlated with disease severity in CAP patients.

Indexed as

Community-Acquired InfectionsMicrobiotaSeverity of Illness IndexSputumAdultAgedAged, 80 and overCohort StudiesDysbiosisFemaleHumansLongitudinal StudiesMaleMiddle AgedPneumoniaPneumonia, Bacterial16S rRNACommunity-acquired pneumoniaDisease severityLongitudinal studySputum microbiota

Identifiers

PMID38811936
PMCPMC11137881

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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.