# Mechanisms that Account for Different Symptom Subtypes of OSA

> **NIH NIH P01** · UNIVERSITY OF PENNSYLVANIA · 2024 · $250,014

## Abstract

ABSTRACT
 Obstructive sleep apnea (OSA) is a common, heritable, and serious medical condition associated with a wide
range of adverse health consequences. OSA presents with a heterogeneous set of symptoms that form subtypes
characterized by degrees of excessive sleepiness, disturbed sleep, and a lack of traditional symptoms. Further,
downstream disease risk differs by symptom subtypes. For example, increased cardiovascular risk conferred by
OSA is driven by patients in the excessively sleepy subtype, highlighting the importance of this symptom. This
Project builds off the established existence and relevance of OSA symptom subtypes, and seeks to expand
knowledge by understanding the physiology, genetic architecture and multi-Omics signatures associated with
OSA symptom subtypes, with a particular focus on excessive sleepiness. Aim 1 leverages traditional and novel
electroencephalographic (EEG)-based metrics to assess whether differences in the physiologic response to OSA
explain different symptom manifestations. Prior data suggest patients in different subtypes have similar apnea-
hypopnea index (AHI), but there has not been a detailed examination of other physiological traits. Aim 2 uses
existing data resources to examine the underlying common and rare frequency genetic variation associated with
OSA symptom subtypes, a quantitative measure of excessive sleepiness, and physiological features of OSA
symptom subtypes. Previous studies and our own preliminary data have identified several loci genetic risk loci
for OSA and sleepiness, and highlight a possible relationship of changes in methylomic, transcriptomic, and
metabolomic profiles with OSA and related symptoms. Thus, Aim 3 will apply integrative methods to combine
sequencing data with multi-Omics data from NHLBI's TOPMed Program to identify potential causal genes and
biological pathways through which genetic risk loci operate to affect OSA symptom subtypes and risk factors.
We propose an efficient and cost-effective approach to identify physiological, genetic and other Omic predictors
of OSA symptom subtypes and excessive sleepiness. Ultimately, results will move the field forward by connecting
genetic variation to biological mechanisms and physiological indicators, thus improving our scientific
understanding while identifying new predictors and biological targets for early and precise interventions. The
National Center on Sleep Disorders Research (NCSDR) has recently identified high priority critical opportunities
to help reach its strategic goals, including identifying biomarkers for sleep disorders and using Omics approaches
to advance precision medicine. This Project speaks directly to these opportunities.

## Key facts

- **NIH application ID:** 10880330
- **Project number:** 5P01HL160471-02
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Anne Justice
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $250,014
- **Award type:** 5
- **Project period:** 2023-07-01 → 2028-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10880330

## Citation

> US National Institutes of Health, RePORTER application 10880330, Mechanisms that Account for Different Symptom Subtypes of OSA (5P01HL160471-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10880330. Licensed CC0.

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