# Development of novel polysomnography-based digital biomarkers to predict Alzheimer’s disease and Parkinson’s disease in real world settings

> **NIH NIH R21** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2023 · $469,214

## Abstract

PROJECT SUMMARY/ ABSTRACT
One of the greatest unmet challenges in the management of neurodegenerative diseases is the early diagnosis
of Alzheimer's disease and related dementias (ADRD) and Parkinson's disease (PD). Given their high
prevalence, long prodromal period, and lack of disease-modifying therapies, early detection of ADRD and PD is
of critical importance. Facilitated by recent advances in artificial intelligence (AI) methods, this proposal will break
new ground by developing novel data-driven screening biomarkers for ADRD and PD, using multimodal,
multidimensional, real-time polysomnography (PSG) sleep signals. Despite the growing evidence that suggests
a bi-directional relationship between sleep and ADRD/PD, little is known about the utility of the multimodal PSG
sleep signals [e.g., electroencephalogram (EEG) for the brain, electrocardiogram (ECG) for the heart,
electromyogram (EMG) for the muscle, and respiratory flow and effort for breathing] for identifying future ADRD
and PD cases. As a multidisciplinary team with strong preliminary data and extensive experiences in research
of sleep and neurodegeneration, we are uniquely positioned to address this gap. The goal of this proposal is to
use data-driven AI approaches to generate cost-effective and user-friendly PSG-based digital biomarkers for the
prediction of ADRD and PD in clinical and at-home settings. Our hypothesis is that PSG sleep signals could be
used to develop prediction algorithms that identify ADRD and PD, years before clinical diagnoses, and that the
prediction algorithms can generalize from clinical to community settings. We have an unprecedented opportunity
to leverage data from three NIH-supported multicenter longitudinal cohorts: a diverse clinical sleep cohort, the
Complete AI Sleep Report (CAISR) study, consisting of over 70K subjects aged 50 years and older with 15 years
of follow-up, and two community-based cohorts, the Osteoporotic Fractures in Men (MrOS) Sleep Study and the
Study of Osteoporotic Fractures (SOF), with over 3500 community-dwelling older adults followed for up to 13
years. Using state-of-the-art AI models, we will pursue two specific aims: 1) discover PSG biomarkers that
identify current and future diagnoses of ADRD and PD in clinical settings; and 2) validate the performance and
generalizability of the PSG biomarkers for detecting ADRD and PD using in-home PSG in community settings.
This will be the first study to create cost-effective, non-invasive PSG-based screening biomarkers for identifying
ADRD and PD in real-world settings. This will set the foundation for further studying whether non-invasive PSG
biomarkers are predictive of ADRD/PD pathogenesis and may be integrated with other innovative biomarkers
for improved characterization of ADRD and PD phenotypes. By identifying specific PSG modalities with the best
predictive value, this work will directly inform the development of new user-friendly devices for long-term
monitoring of sleep bioma...

## Key facts

- **NIH application ID:** 10807908
- **Project number:** 1R21AG085495-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Yue Leng
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $469,214
- **Award type:** 1
- **Project period:** 2023-09-30 → 2025-09-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10807908

## Citation

> US National Institutes of Health, RePORTER application 10807908, Development of novel polysomnography-based digital biomarkers to predict Alzheimer’s disease and Parkinson’s disease in real world settings (1R21AG085495-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10807908. Licensed CC0.

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