# Cardiovascular implications of sleep characteristics using real-world objective sleep data

> **NIH NIH R21** · UNIVERSITY OF WASHINGTON · 2020 · $137,730

## Abstract

Project Summary
Cardiovascular disease (CVD) is the leading cause of mortality in the U.S. Impaired sleep is recognized as a
strong risk factor of CVD. Sleep disorders such as obstructive sleep apnea (OSA), insomnia, abnormal sleep
duration, and poor sleep quality have each been associated with CVD-related morbidity and mortality. Major
limitations of existing studies on sleep and CVD is the lack of objective sleep measurement and the lack of
understanding of the multi-dimensional nature of sleep and its complex interactions with CVD. In addition,
emerging evidence also suggests potential relationships between sleep disorders and preclinical CV
conditions, which often occur before the clinical manifestation of CVD. Blood pressure parameters including
systolic blood pressure variability (SBPV) and mean systolic blood pressure (SBP) are examples of
preclinical CVD that have prognostic value for future CV events. However, no research has yet explored if
sleep disorders beyond OSA (such as impaired sleep quality and abnormal sleep duration) are risk factors
attributable to preclinical CV conditions. This is a critical area of inquiry since understanding the complex
relationships between sleep and preclinical and clinical CV conditions will allow healthcare providers to
implement targeted interventions to reduce CVD.
The proposed study will examine whether PSG-derived objective measures of sleep obtained in the clinical
setting would be predictive of CVD and preclinical CVD. Given that hypertension is one of the major
important CV risks and has been most well studied CV risk factor in relation to sleep, SBPV, mean SBP, and
other blood pressure metrics will be the focus of the preclinical CVD. Our main aim is to examine the
relationships between multidimensional sleep characteristics (in terms of duration, efficiency, quality, and
disordered sleep breathing) and clinical CV conditions, after adjusting for personal, clinical, and other
confounders. We will use sleep data collected from more than 7,000 individuals who completed a diagnostic
sleep study at the University of Virginia Health System in 2010 - 2018. Machine learning (ML) models will be
used to analyze the multidimensional PSG measures. This proposed study represents the largest real-world
dataset on objective sleep measures, which will allow us to simultaneously examine the entire spectrum of
sleep and advance our understanding about the impact of sleep on CV outcomes.

## Key facts

- **NIH application ID:** 10048498
- **Project number:** 1R21HL150502-01A1
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Younghoon Kwon
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $137,730
- **Award type:** 1
- **Project period:** 2020-09-15 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10048498

## Citation

> US National Institutes of Health, RePORTER application 10048498, Cardiovascular implications of sleep characteristics using real-world objective sleep data (1R21HL150502-01A1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10048498. Licensed CC0.

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