Building predictive algorithms to identify resilience and resistance to Alzheimer's disease

构建预测算法来识别对阿尔茨海默病的恢复力和抵抗力

• 批准号: 10659007
• 负责人: Rachel Frances Buckley
• 金额: $ 104.29万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2028-02-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10659007
• 关键词: Address; African American; Age; Alzheimer disease prevention; Alzheimer' s Disease; Alzheimer' s disease pathology; Alzheimer' s disease risk; Amyloid; Area; Biometry; Black race; Blood Vessels; Brain; Calibration; Classification; Clinical; Clinical Trials; Clinical Trials Design; Cognition; Cognitive; Data; Decision Making; Diagnostic; Education; Elderly; Epidemiology; Ethnic Origin; Ethnic Population; Exhibits; Female; Gender; Genetic; Goals; Impaired cognition; Individual; Knowledge; Life; Longitudinal cohort; Machine Learning; Maps; Measures; Medical; Modeling; Neurobehavioral Manifestations; Neuropsychology; Not Hispanic or Latino; Pathologic; Pathology; Patients; Phenotype; Population; Positioning Attribute; Positron-Emission Tomography; Prevention trial; Proteins; Protocols documentation; Publishing; Race; Resistance; Resistance profile; Risk; Risk Estimate; Risk Factors; Sample Size; Signal Transduction; Structure; Symptoms; Theoretical model; Therapeutic Intervention; White Matter Hyperintensity; Woman; apolipoprotein E-4; behavioral neurology; burden of illness; clinical decision-making; clinical practice; clinical trial recruitment; cognitive change; cognitive neuroscience; cognitive performance; cohort; data harmonization; dementia risk; demographics; flexibility; gray matter; human old age (65+); improved; innovation; intersectionality; male; men; neuroimaging; prediction algorithm; predictive modeling; preservation; profiles in patients; racial diversity; racial population; resilience; response; screening; sex; social culture; tau Proteins; theories; vascular factor; β-amyloid burden

PROJECT SUMMARY There are two observed phenomena that defy the traditional Alzheimer’s disease (AD) trajectory; those who resist the accumulation of AD pathology (amyloid and/or tau) despite evidence of risk factors, and those who present with AD pathology but remain resilient to cognitive decline. Classifying these individuals who will likely manifest resistance or resilience to AD over their lifetime is critical for informing clinical practice and transforming clinical trial recruitment. It remains unclear how combinations of risk factors, whether demographic, vascular or neuroimaging, may help to increase accuracy for predicting an individuals’ likelihood of manifesting resistance or resilience to AD. Further, very little is understood about how sex, race and their interaction influence these phenomena. Relatively limited sample sizes and low racial diversity have so far hampered studies. The overall goal of this proposal is to develop and validate robust predictive algorithms of resistance and resilience to AD by harmonizing data from 13 well characterized and racially diverse cohorts of clinically normal older adults (n=~15,000). This innovative proposal could transform approaches for both clinical decision making and clinical trials. Based on a simple set of easily accessible medical information, such as demographics, vascular risk, APOEe4 status, and brain volumetric data when available, our validated models will provide interpretable patient- level predictions of resistance and resilience with 10-year risk estimates of AD pathological burden and cognitive decline given a patient’s profile. Similarly, our predictive algorithms will provide a predictive framework of who should be invited for initial screening and serve to predict those most likely to accumulate Ab/tau or exhibit short term decline within the course of a clinical trial. We propose to harmonize data from 13 cohorts of ~15,000 clinically normal individuals, to accomplish the following aims: (1) build predictive algorithms to classify those who are resistant to either amyloid or tau and validate these models to demonstrate their utility in clinical practice and AD prevention trials, (2) build and validate predictive algorithms to classify those who are cognitively resilient in the face of abnormal levels of amyloid or tau, and (3) examine how intersections between sex and race can produce more refined individualized risk profiles that are reflective of these two critical population strata that are known risk factors for AD. Our strong interdisciplinary team spans the breadth of cognitive neuroscience, PET and MR neuroimaging, biostatistics, behavioral neurology, and epidemiology. Our multi-PI team reflect four critical areas of expertise that are essential to this proposal: (1) data harmonization, (2) neuroimaging, (3) machine learning, and (4) cognitive resilience. We have published a range of data harmonization approaches for both cognitive and PET neuroimaging data, which can be flexibly applied to different data types. Using these approaches, we will identify higher-order interactions between multiple risk factors (demographics, vascular risk, neuroimaging) to build individualized risk profiles of both resistance and resilience to AD. This innovative proposal has the potential to transform the way we approach clinical practice and clinical trial design.

项目概要 有两种观察到的现象与传统的阿尔茨海默病 (AD) 轨迹相悖： 尽管存在风险因素，但仍能抵抗 AD 病理（淀粉样蛋白和/或 tau 蛋白）的积累，以及那些 对这些可能患有 AD 病理但仍能抵抗认知能力下降的人进行分类。 在其一生中对 AD 的抵抗力或恢复力对于指导临床实践和改变症状至关重要 目前尚不清楚风险因素（无论是人口统计学、血管还是风险因素）的组合如何。 神经影像学，可能有助于提高预测个体表现出抵抗力的可能性的准确性 此外，人们对性别、种族及其相互作用如何影响这些因素知之甚少。 迄今为止，相对有限的样本量和较低的种族多样性阻碍了研究。 该提案的目标是通过以下方式开发和验证对 AD 的抵抗力和恢复力的鲁棒预测算法 协调来自 13 个特征明确且种族多样化的临床正常老年人队列的数据 （n=~15,000）。这项创新提案可以改变临床决策和临床方法。 试验基于一组简单且易于获取的医疗信息，例如人口统计、血管风险、 APOEe4 状态和脑容量数据（如果可用），我们经过验证的模型将提供可解释的患者- 通过 AD 病理负担和认知的 10 年风险估计来预测抵抗力和复原力水平 考虑到患者的概况，我们的预测算法将提供一个预测框架 应邀请进行初步筛选，并用于预测那些最有可能积累 Ab/tau 或表现出短期症状的人 我们建议协调 13 个队列（约 15,000 人）的数据。 临床正常个体，以实现以下目标：（1）构建预测算法来对这些个体进行分类 对淀粉样蛋白或 tau 蛋白具有抗性并验证这些模型以证明其在临床实践中的实用性 和 AD 预防试验，(2) 构建并验证预测算法以对认知弹性强的人进行分类 面对淀粉样蛋白或 tau 蛋白的异常水平，以及 (3) 研究性别和种族之间的交叉如何影响 生成更精细的个性化风险概况，反映这两个关键人口阶层 我们强大的跨学科团队涵盖认知神经科学、PET 等领域。 我们的多 PI 团队涵盖四个领域。 对于本提案至关重要的关键专业领域：(1) 数据协调，(2) 神经影像，(3) 机器学习，（4）认知弹性我们已经发布了一系列数据协调方法。 适用于认知和 PET 神经影像数据，可以灵活地应用于不同的数据类型。 方法，我们将确定多个风险因素（人口统计、血管风险、 神经影像学）来建立个性化的 AD 抵抗力和恢复力风险概况。 该提案有可能改变我们临床实践和临床试验设计的方式。