The elderly are the fasting growing segment of the US population and consume an increasing proportion of healthcare resources. Age-related physiologic changes are to be expected, but some warn of preventable incident serious physical or cognitive declines. Unquestionably, a significant degree of variance remains unexplained with regard to how clinical and demographic risk factors affect the propensity for adverse health outcomes. Identification of subclinical biomarkers for incident decline can lead to early interventions and insights into underlying pathologic processes, resulting in healthier lives for the elderly. Aging is associated with changes in both sleep and autonomic function which may serve as markers for underlying physiologic decline. Because sleep is a time of dynamic, centrally-mediated autonomic changes, changes in sleep organization and autonomic function during sleep are likely biomarkers for adverse changes in the functioning and integration of the central and autonomic nervous systems. The fundamental purpose of the proposed research will be to utilize readily-available and easily-acquired ECG signals from polysomnograms (PSGs), to derive novel autonomic function derived from heart rate patterns, in order to prognosticate the development of adverse cardiovascular and cerebrovascular outcomes, cognitive impairment and mortality. The novel aspect of this application is that it will use a national repository of sleep and electrocardiographic data on a cohort of elderly subjects (the Sleep Heart Health Study) who have also been extensively characterized with regard to cardiovascular, cerebrovascular risk factors, morbidity and mortality in the Cardiovascular Health Study. Additionally, 2/3 of the cohort has had serial brain MRIs and cognitive function assessments in the Cardiovascular Health Cognition Study. The proposed study will be able to address vital hypotheses that have not previously been examined at a population level. Specifically, conventional and newly-developed PSG heart rate parameters will be combined to characterize abnormalities in autonomic function as potential intermediates in the putative causal pathway between known risk factors and health outcomes. Measures proposed in this application will provide more sensitive assessments of health-related risk and define mechanisms that will link sleep-related disorders to increased morbidity and possible mortality. The implications of this work extend to identifying individuals at the highest risk of adverse outcomes associated with sleep-related disorders. Appropriate identification and treatment of the high-risk elderly will have benefits both in quality of life and in optimization of the use of health resources. The field of sleep medicine is now ripe for a paradigm shift that needs to include the use of new technologies to address important questions about the health significance of cardiac autonomic function during sleep. Finally, we also expect to provide new and much-needed normative data for age-related changes in different aspects of autonomic function during sleep.
This project will determine if information that can be extracted from heart rate patterns from overnight sleep studies can provide biomarkers that help identify elderly adults vulnerable to declines in their cardiovascular or cerebrovascular health, declines in their mental function or death. These biomarkers will be used in combination with existing results from the overnight sleep studies and clinical and demographic data to identify high risk people. Earlier identification of high risk people can results in preventive interventions that improve quality of life, promote efficient use of healthcare resources and provide a greater understanding of the processes underlying physical and mental decline in the elderly.
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