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Statistical Models of Alzheimer's Disease Pathological Cascade

阿尔茨海默病病理级联的统计模型

基本信息

批准号：
10026353
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
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-15 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10026353
关键词：
Address Affect Aging Alzheimer&apos s Disease Alzheimer&apos s disease model Alzheimer&apos s disease pathology Alzheimer’s disease biomarker Biological Biological Markers Clinical Complex Data Data Coordinating Center Data Set Data Sources Databases 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 Nature Observational Study Online Systems 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 base clinical Diagnosis design evidence base improved multiple data sources neuroimaging patient biomarkers personalized predictions pre-clinical predictive modeling research and development semiparametric software development statistical learning theories therapeutic development user-friendly

项目摘要

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