Statistical Models of Alzheimer's Disease Pathological Cascade

摘要 已经做出了巨大的努力来揭示阿尔茨海默病的一系列生物标记物的变化 (D)病理生理途径及其后期临床表现。最具影响力的假设模型 杰克和他的同事提出的在过去十年里极大地塑造了AD的研究，而它仍然是一个 假设有待验证。验证中的关键挑战是AD的病理生理过程 不可直接观察到的。因此，临床期的生物标记物特征通常要对照离散的临床进行检查。 诊断，从临床症状出现起估计的年数或认知障碍的测试分数-两者都不是 AD致病过程的良好衡量标准，但仅仅是临床后果已被证明是不同的 在个体之间有很大的差异，也可能受到其他疾病的影响。在这项建议中，我们将在 主要有以下几个方面。(1)我们将开发适当的统计模型，直接处理不可观测的 阿尔茨海默病的病理生理过程的性质，因此提供了操作和 验证假想的AD生物标志物模型。(2)我们将利用跨多个AD数据库的数据来提供 AD生物标记物级联及其临床表现的基于数据的证据，以及相关信息 2018年NIA-AA研究指南中新提出的生物性AD定义与当前 综合症AD定义。(3)建立AD动态预测的统计框架 基于患者病史的病理生理进展轨迹及其临床表现 生物标记物档案。(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 学以致用。虽然科学问题是集中的，但提出的统计模型是 适用于许多观察性研究，采用纵向、多变量生物标记物措施来捕捉 不可观察的结构，如在衰老或心理健康研究中。

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.