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Novel approach on obesity risk stratification by combining human gut microbiome and genetic predisposition

Research Project

Project/Area Number 17K17614
Research Category

Grant-in-Aid for Young Scientists (B)

Allocation TypeMulti-year Fund
Research Field Epidemiology and preventive medicine
Metabolomics
Research InstitutionKanagawa University of Human Services (2019)
Yamagata University (2017-2018)

Principal Investigator

Nakamura Sho  神奈川県立保健福祉大学, ヘルスイノベーション研究科, 准教授 (00740656)

Project Period (FY) 2017-04-01 – 2020-03-31
Project Status Completed (Fiscal Year 2019)
Budget Amount *help
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2018: ¥2,600,000 (Direct Cost: ¥2,000,000、Indirect Cost: ¥600,000)
Fiscal Year 2017: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Keywords腸内細菌叢解析 / 肥満リスク / 遺伝的リスクスコア / 肥満 / 社会医学 / 医療・福祉 / 細菌
Outline of Final Research Achievements

Analysis of human gut microbiome was performed for a total of 171 participants of ME-BYO cohort study (Kanagawa site of the J-MICC study). Owing to the support from Platforms for Advanced Technologies and Research Resources Platform, we were able to perform an analysis of 72 patients combined with the genetic risk score for obesity. Based on the results of this research, we are making adjustments so that microbiome analysis be carried out continuously in the ME-BYO cohort. By this, we will be able to update the results from this study and finally leading to the realization and clinical utilization of the risk stratification strategy using microbiome and genetic risk in combination. We are currently summarizing the additional results and planning to publish the final results in a peer-reviewed English journal in a mean time.

Academic Significance and Societal Importance of the Research Achievements

これまで、BMIを目的変数として肥満関連GWASによる肥満関連SNPsあるいは、腸内細菌叢解析の結果を用いて肥満のリスク評価を試みた報告はあるもののこれらを組み合わせて評価を試みる報告はなされてこなかった。本研究はサンプル数の問題を含めてプレリミナリーな結果ではあるものの、新しいアプローチによって肥満のリスク評価を試みた。
今後最終解析の結果も踏まえてにはなるが、遺伝的リスクスコアおよび腸内細菌叢解析の両結果を用いることでこれまでよりも詳細に肥満リスクを層別化をすることができ、介入対象の細分化をすることができるので、これまでよりも効率的に肥満の予防介入を行うことができる可能性を示唆している。

Report

(4 results)
  • 2019 Annual Research Report   Final Research Report ( PDF )
  • 2018 Research-status Report
  • 2017 Research-status Report

URL: 

Published: 2017-04-28   Modified: 2025-01-30  

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