# Photoplethysmography Analysis to Assess Cardio-Cerebrovascular Impact of Sleep

> **NIH NIH R21** · UNIVERSITY OF WASHINGTON · 2020 · $256,550

## Abstract

Project Summary
Obstructive sleep apnea (OSA) is common in the aging population and is associated with increased risk of
cardiovascular (CV) disease, including cerebrovascular injury. Repetitive exposure to acute CV response, such
as sympathetic surge and blood pressure (BP) rise following obstructive respiratory events, is an important
mediating mechanism linking OSA to CV disease and cerebrovascular injury. However, such acute CV
responses are not effectively captured by conventional polysomnography metrics, such as the apnea
hypopnea index (AHI) commonly used in OSA evaluation. This results in imprecise classification of patients in
terms of their CV risk and may be responsible for inconsistent results in epidemiologic and clinical studies.
More importantly, the uncertainty of the effectiveness of continuous positive airway pressure (CPAP) therapy in
reducing the CV risk poses a significant challenge in therapeutic decision-making in older individuals.
Therefore, the identification of additional phenotypic markers that better quantify the unfavorable CV effects of
OSA and provide improved prediction of CV outcomes is crucial to improving risk stratification and clinical
therapeutic decision-making.
Herein, we propose to study novel physiologic measurements that can readily be retrieved from a single
photoplethysmography (PPG) sensor and investigate whether PPG features are associated with markers of
subclinical, clinical CV disease, and cerebrovascular injury. We will extract PPG features from
polysomnography obtained in the Multi-Ethnic Study of Atherosclerosis (MESA) Sleep study and examine
whether PPG features are associated with CV outcomes, including left ventricular mass, aortic stiffness, and
incident cardiovascular events, as well as markers of cerebrovascular injury, including brain structural
abnormalities by MRI and cognitive impairment by Cognitive Abilities Screening Instrument in the older men
and women. We will compare the association of PPG features with these outcomes with that of conventional
OSA assessment metrics (such as apnea hypopnea index). The main PPG feature of interest is slope transit
time. To assess the utility of the various PPG features for use in estimating and classifying outcomes, we will
use novel machine learning methods, such as are based on the GNOSIS (Generalized Networks for the
Optimal Synthesis of Information Systems) information-theory-based modeling tool.
Subsequently, we will determine how PPG features predict BP treatment response to CPAP and O2 therapy
using data from the HeartBEAT randomized controlled trial.
This study attempts to identify novel PSG metrics that are cardiovascular-centric and to assess their clinical
utility in the older adults. The findings of the study will provide a basis for further development of a simpler
method by which to assess and monitor OSA. Considering the high prevalence of OSA in older populations,
coupled with its impact on adverse health outcomes, the proposal ...

## Key facts

- **NIH application ID:** 10107688
- **Project number:** 1R21AG070576-01
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Younghoon Kwon
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $256,550
- **Award type:** 1
- **Project period:** 2020-09-30 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10107688

## Citation

> US National Institutes of Health, RePORTER application 10107688, Photoplethysmography Analysis to Assess Cardio-Cerebrovascular Impact of Sleep (1R21AG070576-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10107688. Licensed CC0.

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