Integrative modeling and dynamic prediction of Alzheimer's disease

阿尔茨海默病的综合建模与动态预测

• 批准号: 10255992
• 负责人: Sheng Luo
• 金额: $ 45.82万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10255992
• 关键词: Address; Affect; Alzheimer' s Disease; Alzheimer' s disease model; Alzheimer' s disease patient; Alzheimer' s disease risk; Behavior; Behavior assessment; Biological Markers; Brain region; Categories; Clinical; Clinical Data; Clinical Research; Clinical Trials Design; Cognition; Computer software; Data; Data Sources; Disease; Disease Progression; Enrollment; Evaluation; Event; Future; Genetic; Genetic Markers; Goals; Grant; Heritability; Impairment; Individual; Internet; Joints; Magnetic Resonance Imaging; Measurement; Measures; Medical History; Methodology; Methods; Modeling; Morphology; Nature; Neurodegenerative Disorders; Neuropsychology; Online Systems; Onset of illness; Outcome; Pathogenicity; Prognosis; Quality of life; Risk; Risk Factors; Selection for Treatments; Severity of illness; Single Nucleotide Polymorphism; Structure; Therapeutic Intervention; Thick; Time; Translational Research; Update; Variant; base; behavioral/social science; data structure; design; genetic predictors; genome wide association study; high dimensionality; high risk; interest; longitudinal analysis; model development; multimodal data; multimodality; multiple data sources; neuroimaging; novel; open source; personalized management; personalized predictions; protective factors; response; risk variant; software development; survival prediction; tool; user-friendly

Project Summary/Abstract The proposed R01 grant is in direct response to PAR-18-352 “Methodology and Measurement in the Behavioral and Social Sciences (R01)”. Alzheimer's disease (AD) is a progressive, neurodegenerative disorder that causes impairment in multiple domains (e.g., cognition, behavior, and quality of life) and progresses heterogeneously in time and across domains and individuals. No single biomarker provides sufficient information to capture the underlying severity of disease across the entire spectrum. Hence, AD studies collect data from multiple sources (e.g., clinical, neuroimaging, and genetic; multi-modal data). We propose a novel integrative modeling framework to provide statistically-principled inference, accurate personalized prediction of disease progression, and dynamic prediction update, based on new subject-specific data. This novel model development is important to identify risk and protective factors for AD and target high risk individuals, as well as to personalize the management, prognosis, and treatment selections. The overall objectives are to: (1) develop a multivariate functional mixed model (MFMM) for the integrative modeling of the longitudinal clinical data; (2) use such model to provide personalized prediction of future outcome trajectories and risks of target events; (3) advance the integrative model by incorporating the high-dimensional neuroimaging and genetic data; (4) make this methodology easily accessible via professional software development and web deployment. Our methods can be broadly applied to other clinical studies with similar multi-modal data structure.

项目概要/摘要 拟议的 R01 拨款是对 PAR-18-352“方法论和测量”的直接回应 行为和社会科学 (R01)”。阿尔茨海默病 (AD) 是一种进行性神经退行性疾病 导致多个领域（例如认知、行为和生活质量）受损并取得进展 在时间上、跨领域和个人上是异构的。没有单一的生物标志物能够提供足够的 信息来捕获整个范围内疾病的潜在严重程度。因此，AD 研究收集 来自多个来源的数据（例如临床、神经影像和遗传；多模式数据）。我们提议写一本小说 综合建模框架，提供统计原理的推理，准确的个性化预测 基于新的特定主题数据的疾病进展和动态预测更新。这个新颖的模型 开发对于识别 AD 的风险和保护因素以及针对高风险人群非常重要 个性化管理、预后和治疗选择。总体目标是：（1） 开发多变量功能混合模型（MFMM），用于纵向临床的综合建模 数据; （2）利用该模型对目标的未来结果轨迹和风险进行个性化预测 事件； （3）结合高维神经影像和遗传学，推进整合模型 数据; (4) 通过专业软件开发和网络使该方法易于使用 部署。我们的方法可以广泛应用于具有类似多模式数据的其他临床研究 结构。