# Leveraging 30 years of Alzheimer's disease clinical trials data to decipher phenotypic response to treatment

> **NIH NIH RF1** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2024 · $3,281,385

## Abstract

PROJECT SUMMARY
 Looking back to move forward: The overall goal of this proposal is to examine the role of
specific phenotypes, or subgroups of people based on a given characteristic, such as sex/gender,
APOE4 status, baseline AD biomarker levels, or polygenic hazard scores (PHS), on response to drug or
lifestyle interventions in existing Alzheimer’s disease clinical trials datasets. The precision medicine
approach of understanding the role of different phenotypes such as men vs. women, APOE4 carrier vs. non-
carrier, and AD polygenic hazard score in understanding Alzheimer’s risk and pathology has gained traction.
There is clearer recognition of sex/gender and APOE genotype differences in tau accumulation, cognitive
decline, and most recently in anti-amyloid immunotherapies. Findings from our studies began to hint at critical
sex/gender and genetic differences that reside in Alzheimer’s disease risk. Additionally, our team has
developed polygenic hazard scores using factors relevant to AD risk, such as sex/gender, tau PET burden, and
multiple risk genotypes.
 With tremendous strides in advanced technology and methods for genetic analyses, plasma-
based biomarkers, and harmonized brain imaging, in this proposal, we will examine the role of key
factors in clinical trials outcomes by leveraging existing Alzheimer’s clinical trials datasets from the
last 30 years. These clinical trials largely followed a harmonized set of outcomes focused on cognition and
function, with a large subset containing neuroimaging and fluid biomarkers, both in derived and raw form. Data
from 14,602 participants who were randomized to treatment or placebo will be included in this proposal. All
datasets are de-identified and were collected within the guidelines of their respective institutional regulations.
This sample will be augmented as more data become available from newly completed trials over the project
period. Statistical models will include interaction terms for phenotype-by-arm on ADAS-Cog, PACC (Preclinical
Alzheimer’s Cognitive Composite), CDR-SOB (Clinical Dementia Rating Scale Sum-of-Boxes), ADCS-ADL
(Activities of Daily Living), in addition to brain MRI, PET, and plasma/CSF levels. Datasets will be analyzed
individually, and collectively in meta-analyses by grouping datasets according to primary mechanism of action.
All derived data from biofluids, genetics, and harmonized data will be openly shared with the scientific
community as a new resource.

## Key facts

- **NIH application ID:** 10950572
- **Project number:** 1RF1AG088811-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Judy Pa
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $3,281,385
- **Award type:** 1
- **Project period:** 2024-09-01 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10950572

## Citation

> US National Institutes of Health, RePORTER application 10950572, Leveraging 30 years of Alzheimer's disease clinical trials data to decipher phenotypic response to treatment (1RF1AG088811-01). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10950572. Licensed CC0.

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