Evidence map›Paper›PMID 32246318›Full record

ArticleEndocrine2020

The oral microbiome profile and biomarker in Chinese type 2 diabetes mellitus patients.

Bin Chen, Zhe Wang, Jingwen Wang, Xinhuan Su, Junjie Yang, Qunye Zhang, Lei Zhang

Abstract read
PubMed Publisher
In one paragraph

Article in Endocrine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
57citing papers in PubMed, 1 pooled it
4.8field-weighted citation impact, top 4% 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

57 citing papers in PubMed, 1 synthesis or guideline pooled it, 62 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Article
  6. Review
  7. Article
  8. Review
  9. Article
  10. SupragingivalJournal of oral microbiology · 2026
    Article
  11. Review
  12. Article
  13. Review
  14. Review
  15. Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

7 authors at 6 institutions in 1 country.

Bin Chen *Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, School of Chemistry, & Key Laboratory of Big Data-Based Precision Medicine (Beihang University), the Ministry of Industry and Information Technology of the People's Republic of China, Beihang University, Beijing, China.
Zhe Wang *Division of Endocrinology and Metabolism, Shandong Provincial Hospital affiliated to Shandong University, Jinan, China.
Jingwen WangQingdao Human Microbiome Center, The Affiliated Central Hospital of Qingdao University, Qingdao, China.
Xinhuan SuDivision of Endocrinology and Metabolism, Shandong Provincial Hospital affiliated to Shandong University, Jinan, China.
Junjie YangCollege of Life Science, Qilu Normal University, Jinan, China.
Qunye ZhangThe Key Laboratory of Cardiovascular Remodeling and Function Research, Chinese Ministry of Education and Chinese Ministry of Health, and The State and Shandong Province Joint Key Laboratory of Translational Cardiovascular Medicine, Qilu Children's Hospital of Shandong University, Jinan, China. wz.zhangqy@sdu.edu.cn.
Lei ZhangBeijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, School of Chemistry, & Key Laboratory of Big Data-Based Precision Medicine (Beihang University), the Ministry of Industry and Information Technology of the People's Republic of China, Beihang University, Beijing, China. microbiome@foxmail.com.
Shandong University · CNBeihang University · CNLinyi People's Hospital · CNQilu Normal University · CNQingdao University · CNShandong Provincial Hospital · CN

Funding

National Natural Science Foundation of China 31471202Shandong Provincial Key Research and Development Program 2016YYSP009
6 · The paper itself

Abstract

purposeOral microbiota maintains a dynamic ecological balance with the host. However, a disruption in this balance can lead to oral diseases such as dental caries and periodontitis. Several studies suggest differences in microbial composition in the oral cavity between patients with T2DM and nondiabetic patients. However, there is inadequate oral microbiome-related data from Chinese patients with T2DM, and the difference in microbiome profile between Chinese patients with T2DM and other ethnicities needs to be investigated further.

methodOral swab samples were collected from 280 adult patients with T2DM and 162 healthy controls. Illumina sequencing was performed on oral samples targeting V1-V2 region of 16S rRNA gene and sequence analysis was carried in the QIIME.

resultsPatients with T2DM and healthy cohorts exhibited distinct oral microbial clusters based on principal coordinate analysis (PCoA). The Firmicutes/Bacteroidetes ratio increased in T2DM and T2DM patients presented significantly higher numbers of Neisseria, Streptococcus, Haemophilus, and Pseudomonas genera, and lower numbers of Acinetobacteria compared with healthy controls. When compared with the available published data of oral and gut microbiome associated with T2DM patients, we found the ratio of Firmicutes/Bacteroidetes and the abundance of Haemophilus could be a specific microbial biomarker in Chinese patients with T2DM.

conclusionsOur study revealed a significant difference in the oral microbiota between T2DM patients and healthy individuals. We identified 25 taxa, including 6 genera, with significant difference in abundance between T2DM and healthy controls.

Indexed as

Dental CariesDiabetes Mellitus, Type 2MicrobiotaAdultBiomarkersChinaHumansRNA, Ribosomal, 16SBiomarkersRNA, Ribosomal, 16SDysbiosisOral microbiotaType 2 diabetes mellitus

Identifiers

PMID32246318
OpenAlexW3015158687

What OpenQuestion holds

Textmetadata
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.