Multi-cohort study of factors that influence Alzheimer's disease biomarker and dementia timing

影响阿尔茨海默病生物标志物和痴呆时间的因素的多队列研究

• 批准号: 10591179
• 负责人: TOBEY JAMES BETTHAUSER
• 金额: $ 77.04万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-01 至 2027-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10591179
• 关键词: Acceleration; Accounting; Affect; Age; Age of Onset; Aging; Alzheimer disease prevention; Alzheimer' s Disease; Alzheimer' s disease brain; Alzheimer' s disease pathology; Alzheimer' s disease patient; Alzheimer' s disease risk; Alzheimer’s disease biomarker; Amyloid; Amyloid beta-Protein; Amyloid deposition; Autopsy; Biological; Biological Aging; Biological Assay; Biological Factors; Biological Markers; Brain; Brain scan; Cerebrospinal Fluid; Clinic; Clinical; Clinical Trials; Clinical Trials Design; Cognition; Cohort Studies; Combined Modality Therapy; Data; Dementia; Demographic Factors; Detection; Disease; Disease Progression; Economic Conditions; Education; Environmental Risk Factor; Event; Genetic; Health; Health behavior; Heterogeneity; Image; Impaired cognition; Individual; Intervention; Investigation; Link; Liquid substance; Magnetic Resonance Imaging; Measurable; Measures; Mediator; Memory; Methods; Modeling; Modification; Monitor; Nerve Degeneration; Neurobehavioral Manifestations; Neurofibrillary Tangles; Onset of illness; Participant; Pathologic; Pathology; Patients; Persons; Phase; Positron-Emission Tomography; Prevention therapy; Proteins; Public Health; Registries; Research; Research Design; Risk; Risk Factors; Sampling; Shapes; Social Conditions; Source; Standardization; Stereotyping; Sum; Symptoms; Techniques; Testing; Thinking; Time; Translating; Translations; Universities; Variant; Vascular Diseases; Washington; Wisconsin; Work; apolipoprotein E-4; brain based; brain volume; cohort; comorbidity; cost; dementia risk; demographics; effective therapy; experience; improved; insight; neighborhood disadvantage; neuroimaging; neuroimaging marker; neuropathology; novel; novel strategies; optimal treatments; polygenic risk score; pre-clinical; prevent; recruit; response; risk prediction; sex; social factors; social health determinants; sociocultural determinant; sociodemographics; tau Proteins; timeline; tool; uptake

PROJECT SUMMARY/ABSTRACT Advances in imaging and fluid-based biomarkers of Alzheimer’s disease (AD) including amyloid (A), tau (T) and neurodegeneration (N), allow detection of underlying disease pathology decades before the onset of dementia. Despite over a decade of AD biomarker research, the field still lacks the ability to accurately predict if and when individuals with preclinical AD (those with biomarker detectable pathology in the absence of cognitive symptoms) will experience dementia. Identifying factors that slow or quicken this preclinical timeframe is needed to improve dementia risk prediction for preclinical AD patients and to inform optimal treatment windows for clinical trials aiming to slow or prevent cognitive decline and impairment. Until recently, studying these factors was precluded by observing different people for short periods of time that began studies in different disease stages with no way to identify when disease began for individual participants. Our team developed and validated new methods that provide individualized estimated amyloid onset age (EAOA) from amyloid biomarkers. EAOA can be used to rearrange biomarker and clinical observations along an AD-specific timeline (i.e., an Amyloid Clock) anchored to the start of preclinical AD. This project will apply this novel approach to existing data from that Washington University Knight ADRC, the Wisconsin Alzheimer’s Disease Research Center, the Wisconsin Registry for Alzheimer’s Prevention, the, the Mayo Clinic Study of Aging and the Alzheimer’s Disease Neuroimaging Initiative to investigate factors across cohorts that influence the timing and trajectories of AD biomarkers and dementia. This study was initiated based on our preliminary findings showing considerable differences between individuals and cohorts regarding 1) when amyloid onset occurs, 2) the time between amyloid onset and dementia onset, and 3) factors that affect AD biomarker and dementia trajectories in AD. In addition, studies from our center and others have begun to link AD pathology, change in brain volume, and changes in cognition to social determinants of health (SDoH) like neighborhood disadvantage. However, possible links between SDoH and the timing and trajectories of AD biomarkers and dementia are not well-understood. Our hypothesis is that observed individual and cohort differences in AD trajectories are due to a combination of demographic, environmental, sociocultural, and biologic factors, and study design and sample composition. We will test this overall hypothesis in three specific aims: 1) identify common factors across multiple cohorts that influence the timing and trajectories of ATN biomarkers; 2) identify common factors across multiple cohorts that affect the time from amyloid onset to dementia; and 3) explore inter-cohort differences in AD biomarker and dementia trajectories. This study will leverage existing data in several well-characterized studies to provide new insights into mechanisms that explain when preclinical AD begins and how long this preclinical phase lasts. This is expected to improve AD dementia risk prediction for individuals and identify optimal windows for disease modifying and prevention therapies.

项目总结/摘要 阿尔茨海默病（AD）的成像和基于液体的生物标志物的进展，包括淀粉样蛋白（A）、tau（T）和 神经变性（N），允许在痴呆发作前几十年检测潜在的疾病病理。 尽管AD生物标志物研究已经超过十年，但该领域仍然缺乏准确预测AD是否以及何时发生的能力。 临床前AD患者（在无认知症状的情况下具有生物标志物可检测病理学的患者） 会患上痴呆症需要确定减缓或加快这一临床前时间表的因素，以改善 临床前AD患者的痴呆风险预测，并为临床试验提供最佳治疗窗口 旨在减缓或预防认知能力下降和损伤。直到最近，研究这些因素都被排除在外 通过观察不同的人在不同的疾病阶段开始研究， 以确定个体参与者的疾病开始时间。我们的团队开发并验证了新的方法， 根据淀粉样蛋白生物标志物提供个体化估计的淀粉样蛋白发病年龄（EAOA）。EAOA可用于 沿着AD特异性时间轴重新排列生物标志物和临床观察结果（即，淀粉样蛋白时钟） 到临床前AD的开始。该项目将把这种新颖的方法应用于来自华盛顿的现有数据 奈特大学ADRC，威斯康星州阿尔茨海默病研究中心，威斯康星州登记处， 阿尔茨海默氏症预防、马约诊所衰老研究和阿尔茨海默氏症神经影像学倡议 研究影响AD生物标志物和痴呆的时间和轨迹的跨队列因素。 这项研究是基于我们的初步发现，显示出相当大的差异，个人 以及关于1）何时发生淀粉样蛋白发作，2）淀粉样蛋白发作和痴呆发作之间的时间， 影响AD生物标志物和AD痴呆轨迹的因素。此外，我们中心的研究和 其他人已经开始将AD病理学、脑容量变化和认知变化与社会决定因素联系起来 健康（SDoH）像邻居的缺点。然而，SDoH和时间之间的可能联系， AD生物标志物和痴呆的轨迹还不清楚。我们的假设是观察到的个体 AD轨迹的群体差异是由于人口统计学，环境，社会文化， 和生物因素，以及研究设计和样品组成。我们将在三个方面来检验这一总体假设。 具体目标：1）确定影响ATN时间和轨迹的多个队列的共同因素 生物标志物; 2）确定多个队列中影响从淀粉样蛋白发病到死亡的时间的共同因素。 痴呆;和3）探索AD生物标志物和痴呆轨迹的队列间差异。本研究将 利用几项特征鲜明的研究中的现有数据，为解释 临床前AD何时开始以及该临床前阶段持续多长时间。这有望改善AD痴呆症 风险预测的个人和确定最佳窗口疾病的修改和预防治疗。