Precision Brain Health Monitoring for Alzheimer's Disease Risk Detection in the Framingham Study

项目摘要 有效治疗和预防阿尔茨海默病（AD）的途径取决于疾病检测， 发生之前，为时已晚，以扭转进展。淀粉样蛋白β（AAPs）是被广泛接受的“黄金标准” AD的生物标志物和目前用于测量该生物标志物的方法依赖于正电子发射断层扫描（PET）， 扫描和/或脑脊液（CSF）分析。然而，这些AD生物标志物获取方法是不可行的。 昂贵的、侵入性的、难以推广的以及对这些方法的依赖，加剧了种族和民族的 AD研究中的差异。数字技术为临床表型分析提供了一种替代方法， 早在临床症状严重程度阈值达到诊断标准之前AD相关变化。此外，本发明还 数字表型分析使得通过确定数字指数来识别和验证数字生物标志物成为可能 与更广泛接受的生物标志物高度相关。在此背景下，本申请寻求 利用心脏脆弱性研究（FHS）中年第三代和全方位的机会时机， 第二代队列作为参与者返回他们的NHLBI资助的第四次健康检查。NHLBI的资金， 然而，仅涵盖与约20%的健康检查组成部分相关的费用。剩下的80% 健康检查将由辅助研究确定，例如这里提议的项目。该项目旨在 在Gen 3/OmniGen 2健康检查中添加两个新组件。目标1提出使用一种新型透镜A β 眼扫描，将局部应用的荧光A β结合配体与专门的光谱眼配对 扫描仪可以检测眼睛的透镜中的晶状体沉积，并已证明具有更高的灵敏度， 与淀粉样蛋白-PET脑扫描相比，检测早期AD相关A β病理的特异性。目标2旨在使用 智能手机应用程序收集3年的纵向认知指标，从中表征那些 认知稳定与认知下降。对这两个目标的拟议分析将测试总体 假设作为数字特征（例如，商品特定 反应、潜伏期、错误率、声学和语言测量）可以检测出透镜A β阳性者 和/或处于高AD风险（例如，高心血管风险，ApoE 4+，痴呆家族史，女性，年龄> 60+）。 Aim 3将进一步应用传统的先验和新颖的数据驱动的机器学习计算工具来构建 多标志物谱高度预测（AUC> 0.85）稳定认知和认知下降。我们断定 机器学习方法将产生更高的预测模型， 与先验方法相比。

Project Summary The path to effective treatment and prevention of Alzheimer's disease (AD) depends on disease detection that occurs before it is too late to reverse progression. Amyloid beta (Aß) is the widely accepted "gold standard" biomarker of AD and current methods for measuring this biomarker rely on positron emission tomography (PET) scans and/or analysis of cerebrospinal fluid (CSF). These AD biomarker acquisition methods, however, are expensive, invasive, and difficult to scale and reliance on these approaches have exacerbated racial and ethnic disparities in AD research. Digital technologies offer an alternative method for clinical phenotyping that can detect AD-related changes well before the threshold of clinical symptom severity meets diagnostic criteria. Further, digital phenotyping makes possible the identification and validation of digital biomarkers by determining digital indices that correlate highly with more widely-accepted biological biomarkers. Within this context, this application seeks to capitalize on the opportunistic timing of the Framingham Heart Study (FHS) middle-aged Generation 3 and Omni Generations 2 cohorts as participants return for their NHLBI-funded 4th health examination. The NHLBI funding, however, only covers costs associated with about 20% of the health exam components. The remaining 80% of the health exam will be determined by ancillary studies such as the project proposed here. This project aims to add two new components to the Gen 3/OmniGen 2 health exam. Aim 1 proposes conducting a novel lens Aβ eye scan that pairs a topically-applied fluorescent Aβ-binding ligand with a specialized spectroscopic eye scanner that can detect Aß deposition in the lens of the eye and has demonstrated higher sensitivity and specificity to detect early AD-related Aβ pathology compared to amyloid-PET brain scans. Aim 2 seeks to use a smartphone application to collect 3 years of longitudinal cognitive metrics from which to characterize those with stable cognition versus declining cognition. Proposed analyses across these two aims will test the overall hypothesis that novel digital cognitive profiles that are unique combinations of digital features (e.g., item-specific responses, latencies, error rates, acoustic and linguistic measures) can detect those who are lens Aβ positive and/or at high AD risk (e.g., high cardiovascular risk, ApoE4+, family history of dementia, women, age >60+). Aim 3 will further apply traditional a priori and novel data-driven machine learning computational tools to construct multi-marker profiles that are highly predictive (AUC > .85) of stable cognition and cognitive decline. We posit that machine learning methods will generate more highly predictive models specific to digital cognitive profiles as compared to a priori methods.