# Identifying Undiagnosed Alzheimer’s Disease in Understudied Populations

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2024 · $767,193

## Abstract

Project Abstract
Diagnosis of Alzheimer’s disease (AD) is crucial for individuals to pursue treatments and plan for the future.
Unfortunately, AD is underdiagnosed in community settings compared to the estimated prevalence from
longitudinal cohort studies. AD underdiagnosis is exacerbated in understudied populations, including
Hispanic/Latino (HL) and non-Hispanic African American (NH-AfAm) groups. Mining patients’ electronic health
records (EHR) using machine learning may help identify patients with undiagnosed AD.
 Prior studies have identified individual comorbidities associated with AD. However, patients accumulate
disease conditions sequentially accumulate over time. These pathways of disease accumulation are known as
disease trajectories. Few studies have investigated disease trajectories in AD and none have comprehensively
evaluated them in understudied populations. Disease trajectories derived from the EHR have the potential to
predict undiagnosed AD as the next diagnosis follows from the prior diagnoses. Current models that predict AD
do not utilize disease trajectories or account for AD underdiagnosis. EHR data can be complemented by genetics
to determine AD risk. Incorporating polygenic risk with EHR data to identify undiagnosed AD has not been
evaluated.
 To address these challenges, our objectives are to identify undiagnosed AD in non-Hispanic white (NH-
white), HL, and NH-AfAm groups utilizing disease trajectories and genetics followed by validation and replication.
We leverage existing resources from millions of patients with EHR and hundreds of thousands with genetic data
linked to the EHR. We propose the following aims. Aim 1 will identify disease trajectories in AD. Aim 2 will identify
undiagnosed AD based on disease trajectory while mitigating the bias of underdiagnosis in HL and NH-AfAm
groups. We will validate patients predicted to have undiagnosed AD via multiple outcome measures and replicate
our results in another EHR cohort, All of Us. Aim 3 will integrate polygenic risk with disease trajectories to detect
undiagnosed AD. We will validate these patients predicted to have undiagnosed AD via multiple outcome
measures, and replicate our results in All of Us.
 This proposal aligns with our long-term goal to realize precision medicine in dementias for all racial and ethnic
groups by leveraging EHR, genomics, and computational tools. Our contributions will enable the application of
disease trajectories in precision medicine, the detection of undiagnosed AD at UCLA and other health systems,
and future work to reduce the bias of underdiagnosis in understudied populations.

## Key facts

- **NIH application ID:** 10985209
- **Project number:** 1R01AG085518-01A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Timothy S Chang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $767,193
- **Award type:** 1
- **Project period:** 2024-09-15 → 2029-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10985209

## Citation

> US National Institutes of Health, RePORTER application 10985209, Identifying Undiagnosed Alzheimer’s Disease in Understudied Populations (1R01AG085518-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10985209. Licensed CC0.

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