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ISME J. 2014 Sep;8(9):1768-80. doi: 10.1038/ismej.2014.32. Epub 2014 Mar 20.

Predictive modeling of gingivitis severity and susceptibility via oral microbiota.

Author information

1
1] 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.
2
Departments of Microbiology Capability Organization and Oral Care Clinical Operation, Procter & Gamble Innovation Center, Beijing, China.
3
Single-Cell Center, CAS Key Laboratory of Biofuels and Shandong Key Laboratory of Energy Genetics, Qingdao, China.
4
Department of Oral Care Clinical Operation, Procter & Gamble Mason Business Center, Cincinnati, OH, USA.
5
Oral Center, Qingdao Municipal Hospital, Qingdao, China.
6
Departments of Chemistry and Biochemistry and Computer Science, BioFrontiers Institute, Howard Hughes Medical Institute, University of Colorado at Boulder, Boulder, CO, USA.
7
Department of Global Product Stewardship, Procter & Gamble Winton Hill Business Center, Cincinnati, OH, USA.

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.

PMID:
24646694
PMCID:
PMC4139724
DOI:
10.1038/ismej.2014.32
[Indexed for MEDLINE]
Free PMC Article
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