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

阿尔茨海默病以患者为中心的结果和事件发生时间数据的综合分析

• 批准号: 10634872
• 负责人: Yifei Sun
• 金额: $ 233.03万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10634872
• 关键词: Accounting; Address; Affect; Age; Alzheimer' s Disease; Alzheimer’s disease biomarker; Assessment tool; Attention; Biological Markers; Biomedical Research; Brain imaging; Caring; Clinical; Clinical Data; Cohort Studies; Data; Data Analyses; Data Set; Data Sources; Decision Making; Deterioration; Development; Disease; Disease Management; Disease Marker; Disease Progression; Disease model; Early Diagnosis; Early Intervention; Electronic Health Record; Evaluation; Event; Evolution; Failure; Future; Genetic; Genetic Risk; Goals; Harvest; Health; Health Status; Healthcare; Individual; International; Intervention; Knowledge; Link; Measurement; Measures; Medical; Medical Research; Methods; Nature; Neurologic; Neuropsychology; Onset of illness; Outcome; Pathologic; Pathology; Patient-Focused Outcomes; Patients; Pattern; Personal Satisfaction; Persons; Play; Prevalence; Primary Care; Progressive Disease; Quality of Life Assessment; Quality of life; Records; Research; Research Infrastructure; Resources; Risk; Role; Sampling Biases; Shapes; Survival Analysis; Symptoms; System; Time; Vulnerable Populations; analytical method; base; caregiving; disorder risk; effective intervention; flexibility; high risk; improved; indexing; innovation; insight; learning strategy; multimodality; pre-clinical; public health research; risk prediction; screening; semiparametric; simulation; trait; user-friendly

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.

项目摘要 该项目的总体目标是开发创新的、稳健的和合理的分析方法， 发现与阿尔茨海默病发作相关的个体化生物标志物轨迹， 阶段，剖析其相关的遗传基础，并动态预测综合的总体疾病风险 通过大量的、随时间变化的健康和生物医学概况来提高生活质量。阿尔茨海默病 (AD)这种疾病是无法治愈的，其流行率的飙升已经引发了一场全球性的健康和财政危机。最近 研究表明，AD是一个连续的病理变化，在AD出现前几年就开始了。 临床症状。正在进行的生物标志物研究在跟踪疾病演变和 预测AD相关的结果，以及如今更容易获得的电子健康记录（EHR） 进一步提供了用于迅速控制疾病进展的未开发资源。但现有 疾病动力学和预测性研究受到以下影响：1）忽视生物标志物动力学之间的相互作用 和疾病标志，2）稀疏和不规则测量下功率不足，3）无法处理 具有受试者特定标志的时间依赖性EHR，以及4）对预测风险概况的监督 考虑到病人的生活质量。为克服这些障碍，本项目提出以下建议 目的：1）构建与无症状期间疾病发作相关的AD生物标志物轨迹， 阶段和解剖相关遗传风险概况;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，整合电子健康记录、脑成像特征和 神经心理学指标;目的3）通过以下方法对所提出的方法进行系统评价： 广泛的模拟和真实的数据分析，并开发用户友好的分析管道 建议的方法。该项目在AD医学和生物医学领域的多个方面都具有创新性 研究包括但不限于a）建立与疾病相互作用的多域生物标志物轨迹 发作，B）考虑标记物动态的年龄和事件发生时间指数以及灵活性和知识- 驱动的形状，c）揭示相关的遗传基础d）解释由于延迟进入而导致的采样偏差， e）开发具有受试者特定标志的动态预测， 质量，g）为我们的产品开发高效和用户友好的管道。我们会实施建议的 三项大规模AD队列研究的范例，包括多领域重复测量的生物医学 和临床数据，其中一个与超过250万患者的庞大EHR数据集相关联。一 该项目的成功完成将为实现早期发现、干预和 管理AD。通过为基于以下内容的主动疾病建模奠定基础做出贡献 多领域数据源，我们预计提出的研究将同时提供有价值的 为公共卫生结果提供更广泛的神经学和精神病学研究的见解。