# Identifying Metabolomic Risk Factors in Plasma and Cerebrospinal Fluid for Alzheimer's Disease

> **NIH NIH R21** · UNIVERSITY OF WISCONSIN-MADISON · 2020 · $374,959

## Abstract

PROJECT SUMMARY
Genome-wide association studies (GWAS) have identified many genetic loci associated with Alzheimer's
disease (AD). However, interpretation of these associations remains challenging, in part due to a lack of
consideration of molecular mechanisms linking the genome and AD phenome in existing analytic approaches.
In addition, with the failure of numerous drug trials, it is of great interest to identify novel, causal, and targetable
risk factors for AD. Metabolomic changes in plasma and cerebrospinal fluid (CSF) may be robust biomarkers
for AD. If causality can be demonstrated, they also have the potential to be therapeutic targets. Further,
systematic integration of metabolomic data with GWAS associations may provide mechanistic insights into the
genetic basis of AD and further our understanding of the genetic variants associated with AD outcomes. Here,
we propose a set of projects that will integrate highly original genetic and multi-tissue metabolomic data from
the Wisconsin Registry of Alzheimer's Prevention (WRAP) and Wisconsin Alzheimer's Disease Research
Center (W-ADRC) with summary data from large-scale AD GWAS conducted by consortia to identify novel
metabolomic risk factors for AD. The overarching goal of this study is to further scientific understanding
of the genetic regulation of metabolome, identify robust metabolite-AD associations in plasma and
CSF, and estimate the causal effects of metabolomic variations on AD outcomes. Innovations in analytic
methods will manifest in novel statistical approaches to conduct metabolome-wide association studies using
GWAS summary statistics and robust Mendelian randomization approaches for causal inference. Leveraging
these advanced statistical methods developed by the investigators, our proposed study will enhance the
statistical power by using summary statistics from large-scale AD GWAS, reduce the impact of confounding
and reverse causality by utilizing genetic prediction models for metabolites built from dementia-free reference
cohorts, and assess metabolite-AD associations and possible causal effects through hypothesis-free scans.
These advances in data and statistical methodology provide a unique opportunity to identify metabolomic risk
factors that are both statistically and clinically meaningful for AD. These results will reveal fundamental new
insights into AD etiology and provide analytic tools that are widely applicable in human genetics research.

## Key facts

- **NIH application ID:** 9955899
- **Project number:** 1R21AG067092-01
- **Recipient organization:** UNIVERSITY OF WISCONSIN-MADISON
- **Principal Investigator:** Qiongshi Lu
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $374,959
- **Award type:** 1
- **Project period:** 2020-09-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9955899

## Citation

> US National Institutes of Health, RePORTER application 9955899, Identifying Metabolomic Risk Factors in Plasma and Cerebrospinal Fluid for Alzheimer's Disease (1R21AG067092-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9955899. Licensed CC0.

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