# Genomic and Metabolomic Data Integration in a Longitudinal Cohort at Risk for Alzheimer's Disease

> **NIH NIH RF1** · UNIVERSITY OF WISCONSIN-MADISON · 2022 · $2,285,690

## Abstract

PROJECT SUMMARY
A longitudinal multi-omics examination of beta amyloid deposition (A), pathologic tau (T), neurodegeneration (N),
and cognitive decline in the years prior to Alzheimer’s disease (AD) diagnosis is critical to better understand,
predict, prevent, diagnose, and treat the disease. Gaps in knowledge include the timing, trajectory, and etiology
of metabolite changes in the disease process. This renewal application augments two existing longitudinal cohort
studies of preclinical and clinical AD, the Wisconsin Registry for Alzheimer’s Prevention and the Wisconsin Alz-
heimer’s Disease Research Center, with rich phenotypic data from blood, cerebrospinal fluid (CSF), imaging,
lifestyle questionnaires, and neuropsychological testing. The overall objective is to measure plasma and CSF
metabolomics in additional longitudinal samples and use sophisticated data analysis approaches to establish
the timing, trajectory, and etiology of metabolite changes in the disease process. The central hypothesis is that
changes in metabolites are influenced by genetics and lifestyle and occur at distinct stages of AD pathology. The
rationale for the proposed research is that a better understanding of the timing, trajectory, and etiology of AD-
related metabolomic changes is critical to prevent (e.g., lifestyle interventions), diagnose (metabolomic bi-
omarkers), and treat (new therapeutic targets) the disease. The central hypothesis will be tested by executing
the following specific aims: 1) determine the timing and trajectory of plasma and CSF metabolites throughout the
AD process using sophisticated longitudinal modeling approaches, 2) integrate genomics and metabolomics to
determine which AD-associated metabolites are in the causal pathway to AD using Mendelian randomization
analyses, and 3) determine which AD-associated plasma and CSF metabolites mediate the relationships be-
tween AD-associated lifestyle factors and AD-related outcomes. At the conclusion of this project, expected out-
comes include: 1) identification of metabolites/pathways that are precursors to AD pathologic changes and may
be therapeutic targets versus those that change in the early stages and can be used as early biomarkers versus
diagnostic/prognostic metabolites that are markers of more advanced disease, 2) identification of metabolites
that are in the causal pathway and may inform therapeutic targets, 3) a better understanding of mechanisms
linking metabolic and vascular disease processes with AD, and 3) identification of metabolites linking lifestyle
factors to AD risk that can inform future intervention trials and clinical practice by identifying more specific be-
havior changes and providing biomarkers of biologic change to monitor the effectiveness of interventions with a
shorter period of follow up.

## Key facts

- **NIH application ID:** 10528813
- **Project number:** 2RF1AG054047-06A1
- **Recipient organization:** UNIVERSITY OF WISCONSIN-MADISON
- **Principal Investigator:** Corinne D. Engelman
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $2,285,690
- **Award type:** 2
- **Project period:** 2016-08-15 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10528813

## Citation

> US National Institutes of Health, RePORTER application 10528813, Genomic and Metabolomic Data Integration in a Longitudinal Cohort at Risk for Alzheimer's Disease (2RF1AG054047-06A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10528813. Licensed CC0.

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