Development of novel polysomnography-based digital biomarkers to predict Alzheimer’s disease and Parkinson’s disease in real world settings

PROJECT SUMMARY/ ABSTRACT One of the greatest unmet challenges in the management of neurodegenerative diseases is the early diagnosis of Alzheimer's disease and related dementias (ADRD) and Parkinson's disease (PD). Given their high prevalence, long prodromal period, and lack of disease-modifying therapies, early detection of ADRD and PD is

of critical importance. Facilitated by recent advances in artificial intelligence (AI) methods, this proposal will break new ground by developing novel data-driven screening biomarkers for ADRD and PD, using multimodal, multidimensional, real-time polysomnography (PSG) sleep signals. Despite the growing evidence that suggests

a bi-directional relationship between sleep and ADRD/PD, little is known about the utility of the multimodal PSG sleep signals [e.g., electroencephalogram (EEG) for the brain, electrocardiogram (ECG) for the heart, electromyogram (EMG) for the muscle, and respiratory flow and effort for breathing] for identifying future ADRD

and PD cases. As a multidisciplinary team with strong preliminary data and extensive experiences in research of sleep and neurodegeneration, we are uniquely positioned to address this gap. The goal of this proposal is to use data-driven AI approaches to generate cost-effective and user-friendly PSG-based digital biomarkers for the

prediction of ADRD and PD in clinical and at-home settings. Our hypothesis is that PSG sleep signals could be used to develop prediction algorithms that identify ADRD and PD, years before clinical diagnoses, and that the prediction algorithms can generalize from clinical to community settings. We have an unprecedented opportunity

to leverage data from three NIH-supported multicenter longitudinal cohorts: a diverse clinical sleep cohort, the Complete AI Sleep Report (CAISR) study, consisting of over 70K subjects aged 50-years and older with 15-years of follow-up, and two community-based cohorts, the Osteoporotic Fractures in Men (MrOS) Sleep Study and the

Study of Osteoporotic Fractures (SOF), with over 3500 community-dwelling older adults followed for up to 13-years. Using state-of-the-art AI models, we will pursue two specific aims: 1) discover PSG biomarkers that identify current and future diagnoses of ADRD and PD in clinical settings; and 2) validate the performance and

generalizability of the PSG biomarkers for detecting ADRD and PD using in-home PSG in community settings. This will be the first study to create cost-effective, non-invasive PSG-based screening biomarkers for identifying ADRD and PD in real-world settings. This will set the foundation for further studying whether non-invasive PSG

biomarkers are predictive of ADRD/PD pathogenesis and may be integrated with other innovative biomarkers for improved characterization of ADRD and PD phenotypes. By identifying specific PSG modalities with the best predictive value, this work will directly inform the development of new user-friendly devices for long-term

monitoring of sleep biomarkers and ADRD/PD risk in home settings. This offers a transformative public health impact, as screening ADRD and PD through daily sleep monitoring is scalable and will identify high-risk individuals for early diagnosis and early intervention.