Evidence map›Paper›PMID 39135686›Full record

ArticleiMeta2024

A universal oral microbiome-based signature for periodontitis.

Mingyan Geng, Min Li, Yun Li, Jiaying Zhu, Chuqing Sun, Yan Wang, Wei-Hua Chen

Abstract read
In one paragraph

Article in iMeta, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Review
  6. Article
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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.

Mingyan GengInstitution of Medical Artificial Intelligence Binzhou Medical University Yantai China.
Min LiKey Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-Imaging, Center for Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology Huazhong University of Science and Technology Wuhan China.
Yun LiKey Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-Imaging, Center for Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology Huazhong University of Science and Technology Wuhan China.
Jiaying ZhuKey Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-Imaging, Center for Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology Huazhong University of Science and Technology Wuhan China.
Chuqing SunKey Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-Imaging, Center for Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology Huazhong University of Science and Technology Wuhan China.
Yan WangInstitution of Medical Artificial Intelligence Binzhou Medical University Yantai China.
Wei-Hua ChenKey Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-Imaging, Center for Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology Huazhong University of Science and Technology Wuhan China.ORCID 0000-0003-2168-6147

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We analyzed eight oral microbiota shotgun metagenomic sequencing cohorts from five countries and three continents, identifying 54 species biomarkers and 26 metabolic biomarkers consistently altered in health and disease states across three or more cohorts. Additionally, machine learning models based on taxonomic profiles achieved high accuracy in distinguishing periodontitis patients from controls (internal and external areas under the receiver operating characteristic curves of 0.86 and 0.85, respectively). These results support metagenome-based diagnosis of periodontitis and provide a foundation for further research and effective treatment strategies.

Identifiers

PMID39135686
PMCPMC11316925

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.