Genomic, physiological, and environmental predictors of AD risk, resilience and resistance

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The lack of an effective treatment for Alzheimer's disease (AD) has led to a call to detect the disease earlier in its course. However, AD's insidious onset that can span many years, adds complexity to making an early diagnosis. It is widely accepted that even among individuals with well-documented AD risk factors (e.g., age, sex, low education, APOE ε4, high cardiovascular risk, high plasma Aβ40/42 ratio, tau pathology), diagnosis is not inevitable. By way of its longstanding investigation of cognitive aging and dementia/AD, the Framingham Heart Study (FHS) has amassed arguably one of richest databases acquired from a community-based cohort. Across its multi- generational cohorts, participants have undergone up to 7 decades of regular health examinations that document many co-morbid features linked to future risk of late life cognitive decline and dementia. Because AD- related processes are likely initiated many years before onset of disease symptoms, one primary objective of this project is to better elucidate mid-life vascular and inflammatory traits that are associated with AD risk. Additional unique goals of this project are to leverage this unprecedented resource to identify factors associated with longitudinal trajectories of cognitive decline, with longitudinal trajectories of neurodegeneration as measured by MRI, and with resilience to developing cognitive decline. To achieve these goals, we will first apply prediction modeling approaches to identify measured and derived traits associated with AD and related endophenotypes. From the extensive list of demographic, lifestyle, vascular/metabolic, plasma and omics measures (including whole genome, transcriptome, and methylome) already captured as part of the FHS, we will use traditional model building (guided by a priori determined AD pathways) and data driven approaches to identify traits associated with (a) MCI, dementia and AD, (b) longitudinal trajectories of cognitive decline, (c) longitudinal trajectories of structural MRI indices, and (d) AD-related neuropathological indices. We will perform pleiotropy GWAS to identify shared genetic underpinnings of significantly correlated traits in initial analyses and test whether using digital neuropsychological phenotypes strengthen findings. Next, using the same database of previously measured traits, we will apply prediction modeling approaches to identify measured and derived traits associated with cognitive resistance, as defined by lack of conversion to dementia. Finally, we will identify vascular and inflammatory moderators of genetic influences by performing Mendelian randomization to assess the causal relationship between vascular risk factors (e.g., blood glucose, lipid fractions, blood pressure, BMI, cigarette smoking) and inflammatory markers (e.g., CRP, IL-β, TNFα, IL6) and AD using existing GWAS summary statistics. For vascular and inflammatory risk factors with significant causal effects, we will assess gene ˣ environment interactions with variants in targeted genes previously implicated in AD. The novel factors identified in this project will inform AD prognostication as well as provide insight into disease mechanisms and new targets for prevention and therapy, heralding a personalized medicine approach to AD.