Evidence map›Paper›PMID 36952529›Full record

ArticlePloS one2023

Changes in gut microbial community upon chronic kidney disease.

Wu Liu, Jiaqi Huang, Tong Liu, Yutian Hu, Kaifeng Shi, Yi Zhou, Ning Zhang

Open access · goldFull text read
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
4.2field-weighted citation impact, top 6% of its field
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

25 citing papers in PubMed, 27 citations in OpenAlex.

  1. Trial
  2. Article
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  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. The impact of progressive chronic kidney disease on hepatic drug metabolism.Drug metabolism and disposition: the biological fate of chemicals · 2025
    Article
  13. Observational
  14. Review
  15. Review
  16. Review
  17. Article
  18. Article
  19. Gut Microbiota in Patients Receiving Dialysis: A Review.Pathogens (Basel, Switzerland) · 2024
    Review
  20. Review
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

7 authors at 4 institutions in 1 country.

Wu LiuWangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, China.ORCID 0000-0001-9253-4576
Jiaqi HuangDepartment of Radiation Oncology, Peking University Third Hospital, Beijing, China.
Tong LiuSchool of Water Resources and Environment, China University of Geosciences (Beijing), Beijing, China.
Yutian HuSchool of Water Resources and Environment, China University of Geosciences (Beijing), Beijing, China.
Kaifeng ShiWangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
Yi ZhouWangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
Ning ZhangWangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
Chinese Academy of Medical Sciences & Peking Union Medical College · CNChina University of Geosciences (Beijing) · CNBeijing University of Chinese Medicine · CNPeking University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the increasing incidence and mortality of chronic kidney disease (CKD), targeted therapies for CKD have been explored constantly. The important role of gut microbiota on CKD has been emphasized increasingly, it is necessary to analyze the metabolic mechanism of CKD patients from the perspective of gut microbiota. In this study, bioinformatics was used to analyze the changes of gut microbiota between CKD and healthy control (HC) groups using 315 samples from NCBI database. Diversity analysis showed significant changes in evenness compared to the HC group. PCoA analysis revealed significant differences between the two groups at phylum level. In addition, the F/B ratio was higher in CKD group than in HC group, suggesting the disorder of gut microbiota, imbalance of energy absorption and the occurrence of metabolic syndrome in CKD group. The study found that compared with HC group, the abundance of bacteria associated with impaired kidney was increased in CKD group, such as Ralstonia and Porphyromonas, which were negatively associated with eGFR. PICRUSt2 was used to predict related functions and found that different pathways between the two groups were mainly related to metabolism, involving the metabolism of exogenous and endogenous substances, as well as Glycerophospholipid metabolism, which provided evidence for exploring the relationship between gut microbiota and lipid metabolism. Therefore, in subsequent studies, special attention should be paid to these bacteria and metabolic pathway, and animal experiments and metabolomics studies should be conducted explore the association between bacterial community and CKD, as well as the therapeutic effects of these microbial populations on CKD.

Indexed as

Gastrointestinal MicrobiomeRenal Insufficiency, ChronicAnimalsBacteriaKidneyLipid MetabolismMetabolomics

Identifiers

PMID36952529
PMCPMC10035866
OpenAlexW4360608914

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read20
identifiers read9
Read underepoch 390

Registered trials

None linked

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.