Mathematical Models of Tau-PET Measures and Cognitive Decline in Alzheimer’s Disease Across the Lifespan

Tau-PET 测量的数学模型和阿尔茨海默病整个生命周期中的认知衰退

基本信息

批准号：
10448899
负责人：
Sarah Frances Ackley
金额：
$ 12.5万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-05-01 至 2024-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10448899
关键词：
Affect Age Alzheimer&apos s Disease Alzheimer&apos s disease diagnosis Alzheimer&apos s disease related dementia Amyloid Atrophic Biological Biological Markers Biology Blood Vessels Brain California Clinical Cognition Communicable Diseases Complex Computational Biology Data Drug Targeting Early Onset Alzheimer Disease Functional disorder Goals Guidelines Heterogeneity Impaired cognition Individual Infectious Disease Epidemiology Intervention Knowledge Lead Light Link Location London Longevity Magnetic Resonance Imaging Mathematics Measurement Measures Mentors Mentorship Modeling Neurofibrillary Tangles Neuropsychology Onset of illness Outcome Pathologic Processes Pathology Physiological Positron-Emission Tomography Reading Research Research Activity Structure System Techniques Therapeutic Trials Time Training Training Activity Universities White Matter Hyperintensity age related base blood-based biomarker cognitive change cohort college computerized tools experience hands on research improved mathematical model multimodality neurofilament neuroimaging professor targeted treatment tau Proteins tau aggregation therapeutic development treatment duration trial design

项目摘要

PROJECT SUMMARY/ ABSTRACT Many specifics of the pathological process of Alzheimer’s disease (AD) remain unknown, such as the precise, functional relationship between tau accumulation and cognitive decline as a function of age, as well as other biomarkers that may modify these relationships. Conventional statistical approaches cannot easily answer questions about the relationship between tau and cognition, due to their dynamic relationship, unknown time lags, and complex measurement error structures. Mathematical modeling techniques—commonly used in infectious disease epidemiology and computational biology—are specialized for the study of complex relationships between biological variables, while incorporating prior knowledge about the relevant physiologic system. The proposed project leverages my quantitative expertise from dissertation research on infectious disease, using data from across the age span of AD onset to elucidate the relationship between tau-PET measures and cognition. As more tau-targeting drugs move through the pipeline, it is important to determine the optimal timing and duration of treatment for trial design and for post-approval clinical guidelines. The ideal timing for tau-targeting therapies may depend on factors such as age, amyloid, or vascular burden. Existing and emerging blood- based biomarkers may offer important information about how tau spreads in the brain and the timing of subsequent atrophy and cognitive decline longitudinally. A growing number of studies now perform tau-PET, and including repeated neuroimaging, making it possible for an improved understanding of the dynamics of tau and cognition in relation to other biomarkers. We propose a biologically motivated, mathematical modeling approach to understand how neuroimaging and other biomarkers can be used to better understand Alzheimer’s disease biology. We plan to fit mechanistic models to data from three cohorts across the age span of AD diagnosis: Alzheimer’s Disease Neuroimaging Initiative (ADNI), Longitudinal Early-onset Alzheimer's Disease Study (LEADS), and The 90+ Study. The long- term objective of this research is to improve our understanding of the age-specific pathophysiology of AD, determining the precise relationship between tau and cognition, with the ultimate goal of guiding therapeutic development and trials for AD treatment. The proposed training activities include hands-on research experience, as well as didactics, advanced coursework, and directed readings and mentorship with the primary mentor Professor M. Maria Glymour and co-mentor Professor Gil Rabinovici, MD. Scientific advisors Professors María Corrada (MPI: The 90+ Study; University of California, Irvine), clinical neuropsychologist and Professor Adam Staffaroni, and Professor Roy Anderson (Imperial College London) will also contribute their expertise.

项目概要/摘要阿尔茨海默病 (AD) 病理过程的许多细节仍然未知，例如精确的、 tau 蛋白积累与认知能力下降之间的函数关系作为年龄以及其他因素的函数可能改变这些关系的生物标志物无法轻易回答。关于 tau 和认知之间关系的问题，由于它们的动态关系，时间未知滞后和复杂的测量误差结构——常用于传染病流行病学和计算生物学——专门用于研究复杂的疾病生物变量之间的关系，同时结合相关生理学的先验知识拟议的项目利用了我在传染病论文研究中的定量专业知识。疾病，使用 AD 发病年龄跨度的数据来阐明 tau-PET 之间的关系措施和认知。随着越来越多的 tau 靶向药物通过管道，确定最佳时机和试验设计和批准后临床指南的治疗持续时间 tau 靶向的理想时机。治疗可能取决于年龄、淀粉样蛋白或现有和新出现的血管负荷等因素。基于生物标记物可能提供有关 tau 蛋白如何在大脑中传播以及其发生时间的重要信息。随后的纵向萎缩和认知能力下降现在越来越多的研究进行了 tau-PET，包括重复的神经影像学，可以更好地了解 tau 蛋白的动态以及与其他生物标志物相关的认知。我们提出了一种基于生物学的数学建模方法来理解神经影像学和我们计划使用其他生物标志物来更好地了解阿尔茨海默病的生物学机制。 AD 诊断中三个队列数据的模型：阿尔茨海默病神经影像学倡议 (ADNI)、纵向早发性阿尔茨海默病研究 (LEADS) 和 90+ 研究。这项研究的长期目标是提高我们对 AD 年龄特异性病理生理学的理解，确定 tau 与认知之间的精确关系，最终目标是指导治疗 AD 治疗的开发和试验。拟议的培训活动包括实践研究经验以及教学、高级课程作业、定向阅读以及由主要导师 M. Maria Glymour 教授和共同导师 Gil Rabinovici 教授，医学博士。科学顾问 María Corrada 教授（MPI：90+ 研究；加州大学欧文分校）临床神经心理学家 Adam Staffaroni 教授和 Roy 教授安德森（伦敦帝国学院）也将贡献他们的专业知识。