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; 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，健壮和合理的分析方法 发现个性化的生物标志物轨迹，与阿尔茨海默氏症的发作相互关联。 阶段，剖析其相关的遗传基础，并动态预测组成的总体疾病风险 通过大规模和随时间变化的健康和生物医学概况，生活质量。阿尔茨海默氏病 （AD）是无法治愈的，其飙升的流行率引起了全球健康和财务危机。最近的 研究表明，AD是一个连续性，在出现之前的病理变化几年 临床症状。正在进行的生物标志物研究在跟踪疾病进化和 预测与广告相关的结果，以及如今更容易获得的电子健康记录（EHRS） 进一步提供了未开发的资源，以促进疾病进展。但是，存在 疾病动力学和预测性研究遭受1）忽略生物标志物动力学之间的相互作用 和疾病标志，2）在稀疏和不规则测量下的功率不足，3）未能处理 具有特定于主题的地标的时间依赖的EHR，以及4）预测风险概况的监督 计算患者的生活质量。为了解决这些障碍，当前的项目提议以下 目的：目的1）构建与疾病发作相关的AD生物标志物轨迹 阶段并剖析相关的遗传风险特征；目标2）建立动态风险预测和生活质量 与广告相关事件的评估工具整合了电子健康记录，大脑成像特征和 神经心理学指标；目标3）通过 广泛的模拟和实际数据分析，并为用户友好的分析管道开发 建议的方法。该项目在广告医学和生物医学的多个方面具有创新性 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）考虑标记动态的年龄和事件索引以及灵活和知识 - 驱动的形状，c）发现相关的遗传基础d）说明由于进入延迟而导致的采样偏差， e）使用特定于主题的地标制定动态预测，f）预测造成生命的风险概况 质量，g）为我们的产品开发高效且用户友好的管道。我们将实施提议的 三个大规模AD队列研究的范例，其中包含多域的多域反复测量的生物医学 和临床数据，其中一个与超过250万患者的大量EHR数据集有关。一个 成功完成该项目将铺平独特的方法来实现早期检测，干预和 AD管理。通过为基于主动疾病建模的基础做出贡献 多域数据源，我们预计拟议的研究将仅提供有价值的研究 对公共卫生结果的更一般神经和精神病研究的见解。