# Assessing Alzheimer disease risk and heterogeneity using multimodal machine learning approaches

> **NIH NIH U01** · BOSTON UNIVERSITY MEDICAL CAMPUS · 2021 · $616,925

## Abstract

PROJECT SUMMARY/ABSTRACT
Alzheimer's disease (AD) is the most common form of dementia characterized by progressive loss of cognitive
function. Unfortunately, currently there is no effective treatment for AD and clinical interventions of AD have
largely failed despite enormous efforts. For the current application, we seek to develop multimodal machine
learning models by leveraging the rich collection of AD-related omics data and phenotypical data recently
generated from large-scale collaborative projects such as Alzheimer Disease Neuroimaging Initiative (ADNI),
Accelerating Medicines Partnership-AD (AMP-AD) and the Alzheimer's Disease Sequencing Project (ADSP).
Three aims will be pursued in the current application. Aim 1. We will build an expandable multimodal
unsupervised machine learning framework to investigate AD heterogeneity. Given the multifactorial nature of
AD, we will perform AD subtyping by harnessing the rich information across multiple spectrum of data. Aim 2.
We will build an expandable multimodal supervised machine learning framework to quantify AD risk from
longitudinal follow up of cognitively normal elders. The models will be built from genetic susceptibility and gene
regulatory information as well as endophenotypes measured when participants were cognitive normal. Aim 3.
We will build AD-related gene interaction networks in post-mortem human brain samples. We will examine the
association of multiple omics data with AD in brain samples, and build tissue-specific interaction networks to
understand potential molecular mechanisms underlying AD pathogenesis. The present application represents
an innovative approach to identify individuals at high risk of AD from both clinical and genetic risk factors in
ethnically diverse populations. The outlined strategy will provide new insights into the risk stratification and
prevention strategies for AD. We also commit to share our methods through GitHub or CRAN for free access
across the scientific community.

## Key facts

- **NIH application ID:** 10296695
- **Project number:** 1U01AG068221-01A1
- **Recipient organization:** BOSTON UNIVERSITY MEDICAL CAMPUS
- **Principal Investigator:** ANITA L DESTEFANO
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $616,925
- **Award type:** 1
- **Project period:** 2021-09-15 → 2021-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10296695

## Citation

> US National Institutes of Health, RePORTER application 10296695, Assessing Alzheimer disease risk and heterogeneity using multimodal machine learning approaches (1U01AG068221-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10296695. Licensed CC0.

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