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

弗雷明汉研究中用于阿尔茨海默病风险检测的精确大脑健康监测

基本信息

批准号：
10214162
负责人：
Rhoda Au
金额：
$ 183.64万
依托单位：
BOSTON UNIVERSITY MEDICAL CAMPUS
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-05-01 至 2025-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10214162
关键词：
Acoustics Age Alzheimer disease detection Alzheimer disease prevention Alzheimer&apos s Disease Alzheimer&apos s disease related dementia Alzheimer&apos s disease risk Alzheimer’s disease biomarker Amyloid Amyloid beta-Protein Ancillary Study Biological Biological Markers Brain scan Cardiovascular system Cellular Phone Cerebrospinal Fluid Clinical Cognition Cognitive Communities Contracts Coupled Crystalline Lens Data Dementia Detection Devices Diagnosis Diagnostic Disease Disease Pathway Drug usage Early Diagnosis Elderly Eye Family history of Framingham Heart Study Funding Funding Agency Generations Genetic Gold Health Heterogeneity Image Impaired cognition Intervention Letters Ligand Binding Linguistics Machine Learning Measurement Measures Methods Monitor National Heart, Lung, and Blood Institute Neuropsychological Tests Onset of illness Participant Pathology Pattern Performance Phenotype Positron-Emission Tomography Reporting Research Response Latencies Scanning Sensitivity and Specificity Severities Specificity Spectrum Analysis Symptoms System Technology Testing Topical application Validation Voice Woman Work abeta accumulation abeta deposition apolipoprotein E-4 base brain health cardiovascular risk factor clinical phenotype clinical risk cognitive function cognitive testing cohort computerized tools cost data resource dementia risk digital disease diagnosis disorder control effective therapy endophenotype indexing lens machine learning method men middle age novel phase 2 study pre-clinical predictive modeling racial and ethnic disparities sex smartphone Application β-amyloid burden

项目摘要

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.

项目总结