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

• 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
• 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' s Disease; Alzheimer' s disease diagnosis; Alzheimer' 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

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.

项目总结/摘要 阿尔茨海默病（AD）的病理过程的许多细节仍然未知，例如精确的， tau积累和认知下降之间的函数关系作为年龄的函数，以及其他 生物标志物可以改变这些关系。传统的统计方法无法轻易回答 关于tau和认知之间关系的问题，由于它们的动态关系，未知时间 滞后和复杂的测量误差结构。数学建模技术-通常用于 传染病流行病学和计算生物学-专门研究复杂的 生物学变量之间的关系，同时结合有关相关生理学的先验知识， 系统拟议的项目利用我的定量专业知识，从论文研究传染病 疾病，使用来自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。 随着更多的tau靶向药物通过管道，重要的是要确定最佳时机， 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 治疗可能取决于诸如年龄、淀粉样蛋白或血管负荷等因素。现有和新兴的血液- 生物标记物可以提供关于tau蛋白如何在大脑中传播以及 随后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， 包括重复的神经成像，使人们有可能更好地了解tau蛋白的动态， 与其他生物标志物的关系。 我们提出了一个生物学动机，数学建模方法来了解如何神经成像和 其他生物标志物可用于更好地了解阿尔茨海默病生物学。我们计划适应机械 AD诊断年龄跨度的三个队列的数据模型：阿尔茨海默病神经影像学 主动（ADNI），纵向早发性阿尔茨海默病研究（LEADS）和90+研究。很长的- 本研究的长期目标是提高我们对AD的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 开发和试验AD治疗。 拟议的培训活动包括实践研究经验，以及教学法，先进的 课程作业，并指导阅读和指导与主要导师教授M。玛丽亚·加图尔和 共同导师吉尔·拉宾诺维奇教授，医学博士。科学顾问María Corrada教授（MPI：90+研究; 加州尔湾大学）、临床神经心理学家兼亚当·斯塔法罗尼教授、罗伊教授 安德森（帝国理工学院伦敦）也将贡献他们的专业知识。