# Integrative analysis for patient-centered outcomes and time-to-event data in Alzheimer's disease

> **NIH NIH RF1** · YALE UNIVERSITY · 2023 · $2,330,283

## Abstract

Project Summary
The overarching goal of this project is to develop innovative, robust and plausible analytical methods to
uncover individualized biomarker trajectories that interrelate with Alzheimer's onset during asymptomatic
stage, dissect their associated genetic bases, and dynamically predict the overall disease risk composited
with quality of life through massive and time-varying health and biomedical profiles. Alzheimer's disease
(AD) is incurable, and its soaring prevalence has induced a global crisis on health and finances. Recent
research reveals that AD is a continuum with pathological changes launched years before the emergence of
clinical symptoms. The ongoing biomarker research plays a dominate role in tracking disease evolution and
predicting AD-related outcomes, and the more accessible electronic health records (EHRs) nowadays
further provide an untapped resource for a prompt management of disease progression. However, existing
disease dynamics and predictive studies suffer with 1) ignoring the interplay between biomarker dynamics
and disease hallmarks, 2) inadequate power under sparse and irregular measurements, 3) failure to handle
time-dependent EHRs with subject-specific landmarks, and 4) oversight on predicting risk profiles
accounting for patients’ quality of life. To address these barriers, the current project proposes the following
aims: Aim 1) to construct AD biomarker trajectories interrelated with disease onset during asymptomatic
stage and dissect associated genetic risk profiles; Aim 2) to build dynamic risk prediction and quality of life
assessment tools for AD-related events integrating electronic health records, brain imaging traits and
neuropsychological metrics; Aim 3) to perform systematic evaluation for the proposed methods through
extensive simulations and real data analyses, and develop user-friendly analytical pipelines for the
proposed methods. This project is innovative in multiple aspects for and beyond AD medical and biomedical
research including but not limited to a) establish multi-domain biomarker trajectories interacted with disease
onset, b) consider age and time-to-event indices for marker dynamics as well as flexible and knowledge-
driven shapes, c) uncover relevant genetic underpinnings d) account for sampling bias due to delayed entry,
e) develop dynamic prediction with subject-specific landmarks, f) predict risk profiles accounting for the life
quality, g) develop efficient and user-friendly pipelines for our products. We will implement the proposed
paradigms on three large-scale AD cohort studies containing multi-domain repeatedly measured biomedical
and clinical data, with one of them linked with a massive EHR dataset of over 2.5 million patients. A
successful completion of this project will pave unique ways to achieve early detection, intervention and
management for AD. By contributing on laying the groundwork for proactive disease modeling based on
multi-domain data sources, we anticipate t...

## Key facts

- **NIH application ID:** 10634872
- **Project number:** 1RF1AG081413-01
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Yifei Sun
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $2,330,283
- **Award type:** 1
- **Project period:** 2023-05-01 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10634872

## Citation

> US National Institutes of Health, RePORTER application 10634872, Integrative analysis for patient-centered outcomes and time-to-event data in Alzheimer's disease (1RF1AG081413-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10634872. Licensed CC0.

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