The development of a new predictive model for perinatal complications targeting the oral microbiome

2023 Fiscal Year Final Research Report

• Project/Area Number: 21K17208
• Research Category: Grant-in-Aid for Early-Career Scientists
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 57080:Social dentistry-related
• Research Institution: Tohoku University
• Principal Investigator: Tamahara Toru 東北大学, 東北メディカル・メガバンク機構, 講師 (40756235)
• Project Period (FY): 2021-04-01 – 2024-03-31
• Keywords: 口腔細菌叢 / 周産期 / コホート / 機械学習

Outline of Final Research Achievements

This study focused on changes in the oral microbiome during the perinatal period to investigate the relationship with perinatal complications. We analyzed the microbiomes of 269 pregnant women using saliva (807 samples) and dental plaque (807 samples) collected in each perinatal semester. The variation in the microbiomes between samples was calculated using Unifrac distances, and this was used to investigate the relationship with perinatal complications.
As a result, the patterns of microbiome changes during the perinatal period were identified. These patterns were combined with physiological and biochemical data to perform machine learning, leading to the creation of a risk prediction model for perinatal complications using a multimodal Bayesian network. This risk prediction model demonstrated that improving the oral environment from the early perinatal period can contribute to reducing the risk of perinatal complications.

Free Research Field

歯科

Academic Significance and Societal Importance of the Research Achievements

1996年にOffenbacherが妊婦の歯周炎と早産・低体重児出産の関連を報告して以来、この領域では多くの研究が行われているが、研究間での人種多様性や歯周病の定義の違いで議論が続いていた。そこで本研究では歯周炎だけではなく、周産期の各セメスターにおける口腔内細菌叢の変化を絡めることで、口腔と周産期合併症の関連を詳細に解析することができた。これにより信頼性の高いデータが得られ、今後の本領域の研究に寄与することが予想される。