Integrative analysis for patient-centered outcomes and time-to-event data in Alzheimer's disease

项目摘要 该项目的总体目标是开发创新、稳健和可信的分析方法，以 在无症状期间发现与阿尔茨海默病发病相关的个性化生物标志物轨迹 阶段，分析其相关的遗传基础，并动态预测合成的总体疾病风险 通过海量和时变的健康和生物医学概况来提高生活质量。阿尔茨海默病 (AD)是不治之症，其发病率飙升已引发全球健康和财政危机。近期 研究表明，阿尔茨海默病是一个连续体，在出现前几年就开始出现病理变化。 临床症状。正在进行的生物标记物研究在跟踪疾病演变和 预测与AD相关的结果，以及如今更容易获得的电子健康记录(EHR) 进一步为迅速管理疾病进展提供未开发的资源。然而，现有的 疾病动力学和预测性研究1)忽视了生物标记物动力学之间的相互作用 和疾病特征，2)稀疏和不规则测量下的功率不足，3)处理失败 具有特定主题里程碑的依赖时间的EHR，以及4)对风险状况预测的监督 考虑患者的生活质量。为了解决这些障碍，当前的项目提出了以下建议 目的：1)构建无症状AD发病相关的生物标志物轨迹 阶段和分析相关的遗传风险概况；目标2)建立动态风险预测和生活质量 AD相关事件的评估工具，集成了电子健康记录、脑成像特征和 神经心理测量学；目的3)通过以下方式对所建议的方法进行系统评估 广泛的模拟和真实数据分析，并开发用户友好的分析管道 建议的方法。该项目在AD医疗和生物医学方面具有多方面的创新 研究包括但不限于a)建立与疾病相互作用的多领域生物标志物轨迹 开始，b)考虑年龄和事件发生时间指数作为标记物的动态，以及灵活性和知识- 驱动形状，c)揭示相关的遗传基础d)解释由于延迟进入造成的抽样偏差， E)利用特定主题的地标开发动态预测，f)预测占生命周期的风险概况 质量，g)为我们的产品开发高效和用户友好的管道。我们会落实建议的 包含多领域重复测量生物医学的三项大规模AD队列研究的范例 和临床数据，其中一项与超过250万患者的大规模EHR数据集相关联。一个 该项目的成功完成将为实现早期发现、干预和 AD的管理。通过为基于以下内容的主动式疾病建模奠定基础 对于多域数据源，我们期待所提出的研究将同时提供有价值的 关于公共健康结果的更一般的神经学和精神病学研究的见解。

Project Summary The overarching goal of this project is to develop innovative, robust and plausible analytical methods to uncover individualized biomarker trajectories that interrelate with Alzheimer's onset during asymptomatic stage, dissect their associated genetic bases, and dynamically predict the overall disease risk composited with quality of life through massive and time-varying health and biomedical profiles. Alzheimer's disease (AD) is incurable, and its soaring prevalence has induced a global crisis on health and finances. Recent research reveals that AD is a continuum with pathological changes launched years before the emergence of clinical symptoms. The ongoing biomarker research plays a dominate role in tracking disease evolution and predicting AD-related outcomes, and the more accessible electronic health records (EHRs) nowadays further provide an untapped resource for a prompt management of disease progression. However, existing disease dynamics and predictive studies suffer with 1) ignoring the interplay between biomarker dynamics and disease hallmarks, 2) inadequate power under sparse and irregular measurements, 3) failure to handle time-dependent EHRs with subject-specific landmarks, and 4) oversight on predicting risk profiles accounting for patients’ quality of life. To address these barriers, the current project proposes the following aims: Aim 1) to construct AD biomarker trajectories interrelated with disease onset during asymptomatic stage and dissect associated genetic risk profiles; Aim 2) to build dynamic risk prediction and quality of life assessment tools for AD-related events integrating electronic health records, brain imaging traits and neuropsychological metrics; Aim 3) to perform systematic evaluation for the proposed methods through extensive simulations and real data analyses, and develop user-friendly analytical pipelines for the proposed methods. This project is innovative in multiple aspects for and beyond AD medical and biomedical research including but not limited to a) establish multi-domain biomarker trajectories interacted with disease onset, b) consider age and time-to-event indices for marker dynamics as well as flexible and knowledge- driven shapes, c) uncover relevant genetic underpinnings d) account for sampling bias due to delayed entry, e) develop dynamic prediction with subject-specific landmarks, f) predict risk profiles accounting for the life quality, g) develop efficient and user-friendly pipelines for our products. We will implement the proposed paradigms on three large-scale AD cohort studies containing multi-domain repeatedly measured biomedical and clinical data, with one of them linked with a massive EHR dataset of over 2.5 million patients. A successful completion of this project will pave unique ways to achieve early detection, intervention and management for AD. By contributing on laying the groundwork for proactive disease modeling based on multi-domain data sources, we anticipate the proposed research will simultaneously provide valuable insights for more general neurological and psychiatric research for public health outcomes.