Statistical Models of Alzheimer's Disease Pathological Cascade

阿尔茨海默病病理级联的统计模型

• 批准号: 10636802
• 负责人: Zheyu Wang
• 金额: $ 40.94万
• 依托单位: JOHNS HOPKINS 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/10636802
• 关键词: Address; Affect; Aging; Alzheimer' s Disease; Alzheimer' s disease model; Alzheimer' s disease pathology; Alzheimer’s disease biomarker; Biological; Biological Markers; Clinical; Complex; Data; Data Coordinating Center; Data Set; Data Sources; Databases; Dedications; Disease; Early Intervention; Early treatment; Evaluation; Evolution; Formulation; Foundations; Functional disorder; Future; Guidelines; Health; Impaired cognition; Individual; Influentials; Intervention Trial; Joints; Link; Machine Learning; Measurable; Measures; Memory impairment; Mental Health; Methodology; Methods; Modeling; National Institute on Alcohol Abuse and Alcoholism; Nature; Observational Study; Outcome; Outcome Study; Pathogenicity; Pathologic; Pathologic Processes; Pathway interactions; Patients; Performance; Phase; Population; Population Heterogeneity; Process; Process Measure; Recording of previous events; Research; Series; Shapes; Sigmoid colon; Statistical Models; Structure; Symptoms; Syndrome; System; Techniques; Testing; Time; Universities; Validation; Variant; Visualization; clinical diagnosis; design; evidence base; improved; multiple data sources; neuroimaging; personalized predictions; pre-clinical; predictive modeling; research and development; semiparametric; software development; statistical learning; theories; therapeutic development; user-friendly; web app

Abstract Enormous effort has been made to uncover the series of changes in biomarkers along Alzheimer’s disease (AD) pathophysiological pathway and its later clinical manifestations. The most influential hypothetical model proposed by Jack and colleagues has greatly shaped AD research in the past decade, whereas it remains a hypothesis to be validated. The key challenge in the validation is the fact that AD pathophysiological process is not directly observable. The temporal biomarker profile is therefore usually examined against discrete clinical diagnoses, estimated years from clinical symptom onset or test score of cognitive impairment – neither is a good measure of the AD pathogenic process, but merely clinical consequences that have been shown to vary greatly among individuals and also to be affected by other diseases. In this proposal, we will tackle this topic in the following aspects. (1) We will develop appropriate statistical models that directly address the unobservable nature of the AD pathophysiological process and therefore provide the foundation to operationalize and validate hypothetical AD biomarker models. (2) We will utilize data across multiple AD database to provide data-based evidence on the AD biomarker cascade and its clinical manifestations, as well as inform the link between the newly proposed biological AD definition in the 2018 NIA-AA research guideline and the current syndromic AD definition. (3) We will develop a statistical framework for dynamic prediction of AD pathophysiological progression trajectory and its clinical manifestations based on the history of a patient’s biomarker profiles. (4) We will develop a web-based application that allows for expedited delivery of statistical learning into practice. Although the scientific questions are focused, the proposed statistical model is applicable to many observational studies with longitudinal, multivariate biomarker measures to capture an unobservable structure, such as in aging or mental health studies.

摘要 为了揭示沿着阿尔茨海默病的生物标志物的一系列变化，人们付出了巨大的努力 (AD)病理生理途径及其后期临床表现。最具影响力的假设模型 在过去的十年里，杰克和他的同事们提出的这一观点极大地塑造了AD研究，而它仍然是一个 待验证的假设。验证中的关键挑战是AD病理生理过程是 不能直接观察到。因此，时间生物标志物谱通常针对离散的临床特征进行检查。 诊断，从临床症状发作或认知障碍的测试评分估计年数-两者都不是一个 AD致病过程的良好测量，但仅仅是临床结果，已被证明不同 个体之间的差异很大，也会受到其他疾病的影响。在这份提案中，我们将在以下方面处理这一问题： 以下方面。(1)我们将开发适当的统计模型，直接解决不可观察的问题。 AD病理生理过程的性质，因此为操作和 验证假设的AD生物标志物模型。(2)我们将利用多个AD数据库中的数据， 关于AD生物标志物级联及其临床表现的基于数据的证据，以及告知 在2018年NIA-AA研究指南中新提出的生物AD定义与当前的 综合征AD定义。(3)我们将建立一个动态预测AD的统计框架 病理生理学进展轨迹及其基于患者病史的临床表现 生物标志物谱。(4)我们将开发一个基于网络的应用程序，以便加快提供统计数据， 学习到实践中去。尽管科学问题是集中的，但所提出的统计模型是 适用于许多具有纵向、多变量生物标志物测量的观察性研究，以捕获 不可观察的结构，如在老龄化或心理健康研究。