# Data-driven Subtypes of Alzheimer's disease progression for targeted treatment

> **NIH NIH R01** · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · 2024 · $778,499

## Abstract

Alzheimer's disease (AD) is the only of the top ten leading causes of death that cannot be
cured. AD drug development has been impeded by significant disease heterogeneity. Despite
the massive information that we have been collecting from patients in the last decade, there is
still a lack of understanding of why some AD patients are fast progressors and others are slow
progressors; such gaps in knowledge have posed many barriers to conducting targeted clinical
trials or developing effective personalized therapies. If we can select more homogenous
patients (based on neuropathology, biomarkers, demographics, and clinical presentations) who
might benefit from a specific intervention, more focused trials would then be made possible to
accelerate new therapy development. However, identifying homogeneous patient
subpopulations is a non-trivial problem. Most of the existing research strategies lack a
longitudinal and holistic consideration of multi-modal, multi-resolution, and multi-source data.
We believe there are unique opportunities for us to address this challenge with advanced
machine learning by integrating heterogeneous information. Clinical trials are the de facto gold
standard for monitoring the progression of AD. Our team has access to 14 AD randomized trials
data, as well as three observational trials and registry data. An innovative exploration of big data
through advanced informatics models will address the heterogeneity of AD. Our goal is to
develop novel machine learning models to reveal and stratify heterogeneous subpopulations
based on risk and progression patterns. We will transform them into potentially targetable
groups to enable focused trials, support future therapeutic development, and promote
personalized AD treatment. Specifically, we will develop novel deep temporal clustering models
to identify subtypes of progression in AD patients using brain atrophy, fluid biomarkers, and
cognitive decline (Aim 1). We will also develop a scalable casual structure model to clinical
trajectory to AD (Aim 2). Finally, we will verify and connect AD subpopulation identified in Aim 1
and 2 to targetable population.

## Key facts

- **NIH application ID:** 10977900
- **Project number:** 1R01AG084637-01A1
- **Recipient organization:** UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
- **Principal Investigator:** Xiaoqian Jiang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $778,499
- **Award type:** 1
- **Project period:** 2024-09-30 → 2029-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10977900

## Citation

> US National Institutes of Health, RePORTER application 10977900, Data-driven Subtypes of Alzheimer's disease progression for targeted treatment (1R01AG084637-01A1). Retrieved via MORVS 2026-10-26 from https://api.morvs.ai/grant/nih/10977900. Licensed CC0.

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