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

项目总结

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.