| Project/Area Number |
17K12647
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Statistical science
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| Research Institution | The University of Tokyo |
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Principal Investigator |
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| Project Period (FY) |
2017-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
Fiscal Year 2018: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2017: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Keywords | 統計的時系列解析 / 状態空間モデル / リスク予測 / ゲノムデータ / 腸内細菌叢ゲノム / 健康診断データ / 統計科学 / 生体生命情報学 / 健康情報学 / 計算機統計学 / 応用統計学 |
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| Outline of Final Research Achievements |
In recent years, in many developed countries including Japan, an increase in the ratio of elderly people to the total population and an increase in patients with lifestyle-related diseases have become a global issue. Through the improvement and extension of existing risk prediction methods for heart disease whose research has been advanced mainly in western countries, we newly develop a risk prediction method for japanese population that integrates genome and bacterial flora metagenomic data. In particular, we developed a statistical time series analysis method with high prediction accuracy, taking into account non-linear effects of vital values for lifestyle related diseases.
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| Academic Significance and Societal Importance of the Research Achievements |
世界的に大きな注目を浴びる生活習慣病の早期リスク予測手法の開発に対して，データ同化手法を適用することで重篤化のシミュレートを行い，状態推定とリスク予測を行う点に特色がある．また本研究は臨床応用へのインパクトが高いと思われる．アジア人に対する慢性疾患のリスク予測手法の開発は遅れており，指標の提案は高齢化先進国と呼ばれる日本のみならず，今後高齢化が加速するアジア圏内において１つの重要な指標として取り入れられる可能性がある．
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