# Evaluating social and clinical risk factors for the progression from Mild CognitiveImpairment to Alzheimer’s Disease and Related Dementias using Real World ClinicalData

> **NIH NIH F31** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2024 · $44,892

## Abstract

PROJECT SUMMARY
Uncovering factors influencing progression from Mild Cognitive Impairment (MCI) to Alzheimer’s Disease and
Alzheimer’s Disease Related Dementias (AD/ADRD) represents a valuable opportunity to prevent clinical
AD/ADRD and prolong functional independence of people along the AD/ADRD continuum. Identifying this
progression is particularly difficult due to the diverse clinical criteria used to diagnose MCI and extensive
underdiagnosis leaving a nebulous path for researchers attempting to learn from its phenotypic characteristics.
Leveraging informatics tools to identify MCI cases in Electronic Medical Records (EMR) offers a great
opportunity to mitigate the heterogeneity and underdiagnosis of MCI. Risk factors exacerbating the progression
from MCI to AD/ADRD have been previously studied in the context of clinical measures. However, few have
included neighborhood context measures as risk factors, which may be key to understanding the higher
AD/ADRD incidence rates among underrepresented populations. The objective of this F31 proposal is to equip
me with the necessary skills to pursue a career as an independent investigator using clinical data and other
large, complex information systems to identify opportunities to prevent and delay progression to AD/ADRD. To
achieve this goal, I will use longitudinal data from a large, diverse EMR system to identify MCI cases using
structured and unstructured data to evaluate whether neighborhood measures of resource availability and
indicators of cardiovascular risk factors predict time to progression from MCI to AD/ADRD. Aim 1 will identify
patients with MCI diagnosis via diagnostic codes and MCI early symptoms in clinical notes using EMR data
from the University of California, San Francisco (UCSF) through the construction of a specific natural language
processing (NLP) algorithm. This will be validated by comparing association with future AD/ADRD diagnosis
and ground-truth chart review on a selected sample. Aim 2 evaluates the independent and mediated effects of
neighborhood deprivation indices and cardiovascular disease (CVD) risk factors on time to progression from
MCI to AD/ADRD. Independent effects will be evaluated with time-varying-models of repeated measures of
CVD factors and neighborhood deprivation indices. Mediation will be used to determine whether place-level
deprivation effects are mediated by CVD risk burden on time to progression from MCI to AD/ADRD. The
proposed research contributes to the NIA goals to improve assessment of MCI and AD related conditions and
understand the effects of societal factors and health disparities on AD/ADRD. Under the guidance of a highly
qualified mentorship team, my training aims will augment my research aims by enhancing my understanding of
MCI and AD/ADRD clinical processes and phenotypes, provide training on causal inference methods using
longitudinal data with integration to machine learning models. With my highly committed mentorship team and
trainin...

## Key facts

- **NIH application ID:** 10824737
- **Project number:** 1F31AG085938-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Silvia Miramontes Lizarraga
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $44,892
- **Award type:** 1
- **Project period:** 2024-02-12 → 2027-02-11

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10824737

## Citation

> US National Institutes of Health, RePORTER application 10824737, Evaluating social and clinical risk factors for the progression from Mild CognitiveImpairment to Alzheimer’s Disease and Related Dementias using Real World ClinicalData (1F31AG085938-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10824737. Licensed CC0.

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