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

> **NIH NIH U19** · BOSTON UNIVERSITY MEDICAL CAMPUS · 2020 · $370,535

## Abstract

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 interacti...

## Key facts

- **NIH application ID:** 10047358
- **Project number:** 1U19AG068753-01
- **Recipient organization:** BOSTON UNIVERSITY MEDICAL CAMPUS
- **Principal Investigator:** Lindsay A. Farrer
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $370,535
- **Award type:** 1
- **Project period:** 2020-09-15 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10047358

## Citation

> US National Institutes of Health, RePORTER application 10047358, Genomic, physiological, and environmental predictors of AD risk, resilience and resistance (1U19AG068753-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10047358. Licensed CC0.

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