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
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).
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
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
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.
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.
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.
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.