探索多因素精确干预在阿尔茨海默病的潜在影响与系统动态的系统动态
Jeroen F Uleman1, René J F Melis2, Alfons G Hoekstra3
1Department of Geriatric Medicine, Radboudumc Alzheimer Center, Donders Institute for Medical Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands; Institute for Advanced Study, University of Amsterdam, Amsterdam, the Netherlands.
Journal of biomedical informatics
|July 29, 2023
概括
针对阿尔茨海默病 (AD) 和精准医学 (PM) 的新系统动态模型 (SDM) 是有前途的. 使用这种模式的多因素干预措施可以显著改善AD预防与单一因素或一种适合所有人的方法相比.
科学领域:
- 神经科学是一个神经科学.
- 计算生物学 计算生物学
- 公共卫生 公共卫生
背景情况:
- 针对单一原因的众多阿尔茨海默病 (AD) 临床试验未能产生有效的治疗方法.
- 最近的多因素预防试验显示出混合的结果,强调了改善研究设计的必要性.
- 试点精准医学 (PM) 试验有希望,但可能缺乏对更广泛的人群的概括性.
研究的目的:
- 开发一种基于模型的方法,用于预防阿尔茨海默病 (AD) 的多因素精准医学 (PM).
- 使用纵向数据系统地开发AD PM的系统动态模型 (SDM).
- 探索单因素和多因素干预对可修改的风险因素的潜在影响.
主要方法:
- 开发了AD PM的系统动态模型 (SDM),使用两个纵向研究中的3660名参与者的数据.
- 采用模型选择程序来识别交互条款并估计SDM内的个性化参数.
- 模拟单因素和双因素干预,使用开发的SDM针对14个可修改的风险因素.
主要成果:
- 量化了双因素干预的潜在影响是单因素干预的1.5倍 (95% CI:1.5-2.6).
- 证明基于SDM的PM可以在12年内在ADAS-cog-13尺度上获得3.5个点的改进,而不是一种适合所有人的方法 (95%CI:3.1-3.8).
- 确定了阿尔茨海默病预防中多因素PM的显著潜力.
结论:
- 基于模型的多因素精准医学 (PM) 方法对于预防阿尔茨海默病 (AD) 是有必要的.
- 由SDM指导的多因素PM干预,显示出比传统方法更高效的潜力.
- 虽然需要验证,但多因素PM对未来的AD预防策略有希望.
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