Evidence map›Paper›PMID 24646694›Full record

ArticleThe ISME journal2014

Predictive modeling of gingivitis severity and susceptibility via oral microbiota.

Shi Huang, Rui Li, Xiaowei Zeng, Tao He, Helen Zhao, Alice Chang, Cunpei Bo, Jie Chen, Fang Yang, Rob Knight and 3 more

Open access · bronzeAbstract read
In one paragraph

Article in The ISME journal, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
76citing papers in PubMed, 2 pooled it
7.6field-weighted citation impact, top 2% 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

76 citing papers in PubMed, 2 syntheses or guidelines pooled it, 145 citations in OpenAlex.

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  12. Innate Immunity Gene Variants Affect Periodontitis Susceptibility.Advances in experimental medicine and biology · 2026
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16 more citing papers are in PubMed but not listed here.

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

13 authors at 4 institutions in 2 countries.

Shi Huang1] Single-Cell Center, CAS Key Laboratory of Biofuels and Shandong Key Laboratory of Energy Genetics, Qingdao, China [2] University of Chinese Academy of Sciences, Beijing, China.
Rui LiDepartments of Microbiology Capability Organization and Oral Care Clinical Operation, Procter & Gamble Innovation Center, Beijing, China.
Xiaowei ZengSingle-Cell Center, CAS Key Laboratory of Biofuels and Shandong Key Laboratory of Energy Genetics, Qingdao, China.
Tao HeDepartment of Oral Care Clinical Operation, Procter & Gamble Mason Business Center, Cincinnati, OH, USA.
Helen ZhaoDepartments of Microbiology Capability Organization and Oral Care Clinical Operation, Procter & Gamble Innovation Center, Beijing, China.
Alice ChangDepartments of Microbiology Capability Organization and Oral Care Clinical Operation, Procter & Gamble Innovation Center, Beijing, China.
Cunpei BoSingle-Cell Center, CAS Key Laboratory of Biofuels and Shandong Key Laboratory of Energy Genetics, Qingdao, China.
Jie ChenSingle-Cell Center, CAS Key Laboratory of Biofuels and Shandong Key Laboratory of Energy Genetics, Qingdao, China.
Fang YangOral Center, Qingdao Municipal Hospital, Qingdao, China.
Rob KnightDepartments of Chemistry and Biochemistry and Computer Science, BioFrontiers Institute, Howard Hughes Medical Institute, University of Colorado at Boulder, Boulder, CO, USA.
Jiquan LiuDepartments of Microbiology Capability Organization and Oral Care Clinical Operation, Procter & Gamble Innovation Center, Beijing, China.
Catherine DavisDepartment of Global Product Stewardship, Procter & Gamble Winton Hill Business Center, Cincinnati, OH, USA.
Jian XuSingle-Cell Center, CAS Key Laboratory of Biofuels and Shandong Key Laboratory of Energy Genetics, Qingdao, China.
Procter & Gamble (United States) · USHoward Hughes Medical Institute · USQingdao University · CNUniversity of Chinese Academy of Sciences · CN

Funding

Howard Hughes Medical Institute
6 · The paper itself

Abstract

Predictive modeling of human disease based on the microbiota holds great potential yet remains challenging. Here, 50 adults underwent controlled transitions from naturally occurring gingivitis, to healthy gingivae (baseline), and to experimental gingivitis (EG). In diseased plaque microbiota, 27 bacterial genera changed in relative abundance and functional genes including 33 flagellar biosynthesis-related groups were enriched. Plaque microbiota structure exhibited a continuous gradient along the first principal component, reflecting transition from healthy to diseased states, which correlated with Mazza Gingival Index. We identified two host types with distinct gingivitis sensitivity. Our proposed microbial indices of gingivitis classified host types with 74% reliability, and, when tested on another 41-member cohort, distinguished healthy from diseased individuals with 95% accuracy. Furthermore, the state of the microbiota in naturally occurring gingivitis predicted the microbiota state and severity of subsequent EG (but not the state of the microbiota during the healthy baseline period). Because the effect of disease is greater than interpersonal variation in plaque, in contrast to the gut, plaque microbiota may provide advantages in predictive modeling of oral diseases.

Indexed as

MicrobiotaAdultBacteriaDisease ProgressionFemaleGingivitisHumansMaleModels, BiologicalMouthPeriodontal IndexPhenotype

Identifiers

PMID24646694
PMCPMC4139724
OpenAlexW2091433858

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.