Cognitive heterogeneity in those with high Alzheimer's Disease Risk

Boston University Medical Campus, Boston MA

Investigators

Linked publications, trials & patents

Abstract

PROJECT SUMMARY The insidious nature of Alzheimer’s disease (AD) spans decades and adds complexity to detect the disease earlier in its course. A significant consideration is that those identified as at risk for AD may not necessarily develop clinically expressed disease. Traditional methods for detecting behavioral indices associated with those with AD risk have relied mainly on cognitive changes characterized by neuropsychological (NP) test performances. But heterogeneity exists both within and across cognitive performance measures, suggesting that NP tests themselves are not sufficiently reliable in differentiating those at risk of progressing from preclinical to clinical AD versus those who do not. Thus, the primary objective of this renewal application is to develop a data-driven framework to better elucidate the cognitive heterogeneity associated with the earliest stages of AD, particularly at the preclinical (pre-symptomatic) and prodromal (mild cognitive impairment; MCI) stages. To meet this objective, we will profile the cognitive characteristics of persons with high risk AD (e.g., A+ and/or T+; amyloid / tau) from 1) plasma, 2) post-mortem tissue and 3) positron emission tomography (PET) scans to determine what cognitive profiles differentiate those who will and will not progress (e.g., asymptomatic [does not progress] versus pre-symptomatic [does progress]; MCI stable versus MCI progressors). Cognitive characterization will utilize standardized scores from NP tests and novel digital cognitive metrics. We will also evaluate whether the addition of neuroimaging measures as a biomarker of “N” (neurodegeneration) combined with cognitive metrics will increase high AD risk group differentiation. We will further leverage the richness of the Framingham Heart Study’s 7+ decades of demographic, clinical and other biomarker data to link cognitive signatures of those with high-risk AD at their symptomatic origins. As an attempt to tie our findings with the frameworks conducting population-level risk assessments, in an exploratory analysis, we will fuse information collected at the pre-symptomatic/MCI stages and develop multimodal data analytic approaches to predict those who are at high risk of developing the disease and compare our findings with traditional statistical methods. The scientific importance of outcomes from our approach is the potential contribution to the growing body of literature focused on precision identification of individuals who are at high disease risk and most likely to progress to clinically expressed disease, and thus on whom intervention is most likely to be warranted and effective.