| Project/Area Number |
18K06759
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 47060:Clinical pharmacy-related
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| Research Institution | Keio University (2019-2022)
Showa Pharmaceutical University (2018) |
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Principal Investigator |
Hashimoto Azusa 慶應義塾大学, 薬学部(芝共立), 准教授 (20740426)
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| Project Period (FY) |
2018-04-01 – 2023-03-31
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| Project Status |
Completed (Fiscal Year 2022)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2020: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2019: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2018: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | 生活習慣病 / 大規模医療情報データベース / 高血圧 / 高脂血症 / 糖尿病 / 薬剤疫学 / 降圧薬 / 糖尿病治療薬 / 脂質異常症治療薬 |
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| Outline of Final Research Achievements |
This study was the international collaborative pharmacoepidemiological study on drugs for the treatment of lifestyle-related diseases.
First, we assessed antihypertensive drug effects according to the pretreatment self-measured home blood pressure using data from the HOMED-BP study. Second, we identified lifestyle behaviors that were associated with the initiation of renal replacement therapy among chronic kidney disease patients using a large-scale medical database. We also applied machine learning to large-scale medical data to try to predict the safety of drugs for the treatment of the lifestyle-related diseases.
Although research activities in Europe were not possible due to the COVID-19 pandemic, I performed several international collaborative activities, including a presentation about the joint research at an international conference and the publication of a review paper written together with international collaborators.
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| Academic Significance and Societal Importance of the Research Achievements |
薬剤疫学研究の手法を用いて、生活習慣病薬の効果および副作用発現やそれらに関わる遺伝子多型を検討することで、生活習慣病薬の安全性および有効性に関するエビデンスを創出することができ、個人の特性にあった最適な個別化医療の実現へ貢献するものと期待される。
本研究で用いたHOMED-BP研究は、家庭血圧データを含む、精度の高い詳細な情報を有した類まれなデータベースを構築しており、本データに基づいて解析した結果は、非常に妥当性・信頼性の高いエビデンスとなることが期待できる。また、大規模診療報酬請求データを用いることで、発生数が少ない有害事象についての検討や機械学習の手法を用いた検討を行うことが可能となった。
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