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 发生在逆转进程为时已晚之前。淀粉样β蛋白(A？)是广泛接受的“黄金标准” 阿尔茨海默病的生物标志物和目前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/或分析脑脊液(CSF)。然而，这些AD生物标记物的获取方法是 昂贵、侵入性和难以扩展的方法以及对这些方法的依赖加剧了种族和民族 AD研究中的差异。数字技术为临床表型鉴定提供了一种替代方法，可以检测到 在临床症状严重程度达到诊断标准的阈值之前，AD相关的变化就已经很久了。此外， 数字表型通过确定数字指数，使数字生物标志物的识别和验证成为可能 这与更广泛接受的生物标志物高度相关。在此上下文中，此应用程序寻求 利用弗雷明翰心脏研究(FHS)的机会主义时机，中年第三代和OMNI 第二代人作为参与者返回参加由NHLBI资助的第四次健康检查。NHLBI基金， 然而，只覆盖了大约20%的健康检查组件的相关费用。剩下的80% 健康检查将由辅助研究决定，例如这里提出的项目。该项目旨在 在Gen 3/OmniGen 2健康检查中增加两个新组件。目标1建议进行一种新的透镜Aβ 将局部应用的荧光A-β结合配体与特殊的光谱眼睛配对的眼睛扫描 扫描仪，可以检测眼睛晶状体中的沉积，并已显示出更高的灵敏度和 与淀粉样脑部扫描相比，检测早期AD相关A-β病理的特异性。Aim 2试图使用一种 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 稳定认知与下降认知。拟议的跨这两个目标的分析将测试总体 假设新颖的数字认知简档是数字特征(例如，特定于项目)的唯一组合 反应、潜伏期、错误率、声学和语言测量)可以检测到那些镜头Aβ阳性的人 和/或AD风险高(例如，高心血管风险、ApoE4+、痴呆症家族史、女性、年龄和60岁以上)。 目标3将进一步应用传统的先验和新颖的数据驱动机器学习计算工具来构建 高度预测(AUC&GT；.85)认知稳定和认知衰退的多标记物特征。我们假设 机器学习方法将生成特定于数字认知特征的更高预测性的模型 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.