# Leveraging the Electronic Health Record and Integrating Social and Biological Data to Expand Dementia Research in Understudied Populations in Los Angeles County

> **NIH NIH UH2** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2023 · $549,791

## Abstract

PROJECT SUMMARY
This proposal requests UH2/UH3 funding to build a collaborative research program to study Alzheimer’s disease
(AD) and AD related dementias (ADRD) in diverse populations through the UCLA Health System in Los Angeles
County. This program is led by a multi-disciplinary team with expertise in AD/ADRD clinical and neurobiological
(PI Vossel, Co-I Chang); social and environmental (PI Mayeda); sociocultural (Co-I Díaz-Santos, Adrissi); and
genomic (Co-I Chang, Deters) research. The program is based in the Mary S. Easton Center for Alzheimer’s
Research and Care (Director Vossel), which has robust ties with greater L.A. communities and ongoing
education and outreach activities. AD and ADRD comprise syndromes with a spectrum of environmental, social,
genomic, and clinical mechanisms. To improve our understanding of the heterogeneity of AD/ADRD, individuals
from all groups, including traditionally understudied groups, must be studied. The UCLA Health System serves
one of the largest Hispanic/Latinx (HL), Black, and Asian American/Pacific Islander (AAPI) populations in the
United States. Traditional AD/ADRD recruitment of these understudied populations has been challenging.
Leveraging electronic health records (EHR) tools to recruit understudied populations and analyzing routinely
collected EHR data would lower the barrier to entry. Our objective is to recruit HL, Black, and AAPI AD/ADRD
cohorts via EHR tools and partnerships with primary care clinics and communities. Next, we will evaluate
recruitment efficiency of EHR tools and partnerships, followed by augmentation of EHR with social determinants
of health (SDOH), genetic, blood biomarker, neuroimaging, and neurophysiology data to study mechanisms of
AD/ADRD in these understudied populations. Our long-term goal is to develop and scale an EHR-linked
AD/ADRD research infrastructure in L.A. County through UCLA Health’s network sites, allowing integration of
SDOH, neurobiological and genomic data. We will improve recruitment and retention of understudied ADRD
populations in research by enrolling 160 HL, 160 Black, and 100 AAPI AD/ADRD individuals and controls. In
preliminary work, our team has integrated dementia screening in the EHR to improve AD and ADRD diagnosis
in primary care and studied genomic, social, and environmental risk factors of AD and ADRD from the EHR.
During the UH2 phase we will 1) utilize EHR tools in HL, Black, and AAPI AD/ADRD participant recruitment, 2)
engage HL, Black, and AAPI community partners to improve study recruitment and design, and 3) share clinical,
social, and genomic data on NIA-supported infrastructures. During the UH3 phase we will 4) evaluate dementia
screening and EHR tools on AD and ADRD recruitment in HL, Black, and AAPI individuals, 5) identify AD
endophenotypes from social determinants of health, blood biomarker, neurophysiologic, and comorbidity factors
in HL, Black and AAPI individuals, and 6) predict AD among HL, Black, and AAPI individuals usin...

## Key facts

- **NIH application ID:** 10729950
- **Project number:** 1UH2AG083254-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Keith Alan Vossel
- **Activity code:** UH2 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $549,791
- **Award type:** 1
- **Project period:** 2023-09-01 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10729950

## Citation

> US National Institutes of Health, RePORTER application 10729950, Leveraging the Electronic Health Record and Integrating Social and Biological Data to Expand Dementia Research in Understudied Populations in Los Angeles County (1UH2AG083254-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10729950. Licensed CC0.

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