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Article: Predictive modeling of gingivitis severity and susceptibility via oral microbiota

TitlePredictive modeling of gingivitis severity and susceptibility via oral microbiota
Authors
Keywordsgingivitis
metagenomics
microbial indices of gingivitis
plaque
Issue Date2014
Citation
ISME Journal, 2014, v. 8, n. 9, p. 1768-1780 How to Cite?
AbstractPredictive 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. © 2014 International Society for Microbial Ecology.
Persistent Identifierhttp://hdl.handle.net/10722/311388
ISSN
2023 Impact Factor: 10.8
2023 SCImago Journal Rankings: 3.692
PubMed Central ID
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorHuang, Shi-
dc.contributor.authorLi, Rui-
dc.contributor.authorZeng, Xiaowei-
dc.contributor.authorHe, Tao-
dc.contributor.authorZhao, Helen-
dc.contributor.authorChang, Alice-
dc.contributor.authorBo, Cunpei-
dc.contributor.authorChen, Jie-
dc.contributor.authorYang, Fang-
dc.contributor.authorKnight, Rob-
dc.contributor.authorLiu, Jiquan-
dc.contributor.authorDavis, Catherine-
dc.contributor.authorXu, Jian-
dc.date.accessioned2022-03-22T11:53:49Z-
dc.date.available2022-03-22T11:53:49Z-
dc.date.issued2014-
dc.identifier.citationISME Journal, 2014, v. 8, n. 9, p. 1768-1780-
dc.identifier.issn1751-7362-
dc.identifier.urihttp://hdl.handle.net/10722/311388-
dc.description.abstractPredictive 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. © 2014 International Society for Microbial Ecology.-
dc.languageeng-
dc.relation.ispartofISME Journal-
dc.subjectgingivitis-
dc.subjectmetagenomics-
dc.subjectmicrobial indices of gingivitis-
dc.subjectplaque-
dc.titlePredictive modeling of gingivitis severity and susceptibility via oral microbiota-
dc.typeArticle-
dc.description.naturelink_to_OA_fulltext-
dc.identifier.doi10.1038/ismej.2014.32-
dc.identifier.pmid24646694-
dc.identifier.pmcidPMC4139724-
dc.identifier.scopuseid_2-s2.0-84906942512-
dc.identifier.volume8-
dc.identifier.issue9-
dc.identifier.spage1768-
dc.identifier.epage1780-
dc.identifier.eissn1751-7370-
dc.identifier.isiWOS:000341212600002-

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