Funded Projects

The AASM Foundation funds high-impact projects that are aimed at improving sleep health for all. In the past 20 years, the AASM Foundation has invested more than $13.5 million in funding career development, high-impact research, clinical training and community initiatives. These cross-cutting sleep research projects range from molecular mechanisms of sleep to population sleep health.

Congratulations to the recipients of our 2019 award cycle.

2019 Physician Scientist Training Award Recipients

David Kim, MD
David Kim, MD Johns Hopkins University
The Effect of Beta-Adrenergic Blockade on the Cardiometabolic Consequences of Obstructive Sleep Apnea

Obstructive sleep apnea (OSA) is associated with diabetes and cardiovascular disease by unknown mechanisms. We have shown that OSA induces adipose tissue lipolysis, which increases plasma free fatty acids (FFA) during sleep. Excess FFA may cause metabolic dysfunction. We hypothesize that beta adrenergic blockade will mitigate this effect of OSA. Our study is a randomized clinical trial of propranolol versus placebo on nocturnal FFA levels during sleep in subjects exposed acutely to OSA (CPAP withdrawal).

Vaishnavi Kundel, MD
Vaishnavi Kundel, MD Icahn School of Medicine at Mount Sinai
Investigating Sleep Duration and Vascular Inflammation in Patients with Sleep Apnea using Multi-Modality Imaging: Hybrid Positron Emission Tomography/Magnetic Resonance Imaging

Both short sleep duration and sleep apnea are associated with an increased risk of atherosclerosis. This proposal will investigate the relationship between actigraphically-estimated sleep duration and carotid vascular inflammation in patients with sleep apnea, employing hybrid positron emission tomography/magnetic resonance imaging (PET/MRI) with 18-F-fluorodeoxyglucose tracer as a robust measure of plaque inflammation. The study will help delineate the influence of short sleep duration on atherosclerosis and lay the groundwork for investigating short sleep duration as an under-appreciated risk factor for cardiovascular risk in patients with sleep apnea.

2019 Bridge to Success Award Recipients

Carrie Mahoney, PhD
Carrie Mahoney, PhD Beth Israel Deaconess Medical Center
The Role of Oxytocin in the Lateral Hypothalamus

Neurodevelopmental and neuropsychiatric disorders often show comorbid sleep disturbances and impaired social behavior. Whether a causal relationship or a common etiology underlies these impairments remains unknown. This project uses genetically targeted techniques in mice to test whether oxytocin neurons in the paraventricular hypothalamus recruit arousal-promoting and sleep-promoting neurons from the lateral hypothalamus to promote social interactions and enhance social memory. These are essential first steps in defining the influence of oxytocin on sleep and memory.

2019 Strategic Research Award Recipients

Michelle Zeidler, MD, MS
Michelle Zeidler, MD, MSUniversity of California, David Geffen School of Medicine
Utilizing Artificial Intelligence to Optimize Diagnosis of Obstructive Sleep Apnea

Essential to addressing the epidemic of obstructive sleep apnea (OSA) is an efficient process for diagnosing OSA through either a home sleep apnea test (HSAT) or attended polysomnogram (PSG). The goal of this project is to leverage machine learning to optimize the diagnosis of OSA by creating a predictive model for identifying which patients with suspected OSA should be directly referred to PSG due to high likelihood of a non-diagnostic HSAT.

Dennis Hwang, MD
Dennis Hwang, MD Kaiser Permanente
Using Machine Learning to Predict PAP Adherence After Therapy Initiation

Achieving proper positive airway pressure (PAP) therapy adherence in the treatment of obstructive sleep apnea (OSA) is a significant challenge. Developing artificial intelligence mechanisms to predict likelihood of PAP adherence at multiple time-points and timing of non-adherence has the potential to improve OSA management. Machine learning will be applied to a large diverse patient dataset with the goal of implementing the developed prediction algorithms as clinician decision support tools to assist sleep medicine clinicians and enhance personalization of patient care.

Michael Westover, MD, PhD
Michael Westover, MD, PhD Massachusetts General Hospital
Redefining Sleep: Data Driven Biomarkers of Sleep Quality

There is no agreed upon method to measure sleep quality. Conventional metrics are limited by simplistic electroencephalogram features and ignore other sleep signals. Deep learning (DL) algorithms can extract rich information from signals to predict specified outcomes. This project will use >29,000 polysomnograms and DL algorithms to develop measures of sleep quality that reflect cognitive and cardiovascular risks of disrupted sleep, which are named data driven sleep quality biomarkers.

Kirsi-Marja Zitting, PhD
Kirsi-Marja Zitting, PhDBrigham and Women's Hospital
Deep Neural Network Model for Automatic Detection of Sleep-Disordered Events from Out-of-Center Level 3 Sleep Tests

Obstructive sleep apnea (OSA) is a highly prevalent disorder that can have serious health risks if left untreated. The aim of this project is to develop a deep neural network model to automatically detect apneas and hypopneas from the electrocardiogram signal and apply this technique to the scoring of out-of-center sleep tests (OCSTs) . This technique will improve the accuracy of OCST for the diagnosis of OSA and result in more patients being identified and treated.