Enhanced Measurement and Modeling of Sleep Electrophysiology to Better Understand Sleep Disparities
Enhanced Measurement and Modeling of Sleep Electrophysiology to Better Understand Sleep Disparities
批准号:
10020195
负责人:
Shaun M Purcell
金额:
$21.68万
依托单位国家:
美国
项目类别:
财政年份:
2019
资助国家:
美国
项目状态:
已结题
起止时间:
2019-09-17 至 2023-06-30
关键词:
African AmericanAgeAttenuatedCircadian RhythmsCognitiveCommunitiesComplementComplexCouplingDataData ScienceDevelopmentDiseaseElectrophysiology (science)Ethnic OriginEthnic groupFamilyFrequenciesGenderHealthIndividualIndividual DifferencesInheritedInterventionLife Cycle StagesLife StyleLinkMachine LearningMeasurementMeasuresMediatingMediator of activation proteinMental HealthMethodsModelingNatureOutcomePatient Self-ReportPharmaceutical PreparationsPhenotypePhysiologicalPolysomnographyPositioning AttributeProcessPublic HealthRaceRecordsReportingResearchResearch PersonnelResourcesRoleSamplingSignal TransductionSleepSleep ArchitectureSleep disturbancesSlow-Wave SleepSocial EnvironmentSocioeconomic StatusTechniquesTestingUnited StatesUrsidae FamilyVariantage effectage relatedallostatic loadanalytical methodattenuationbasebehavioral pharmacologycardiovascular disorder riskcausal modelcohortcomputational neurosciencecontextual factorsdemographicsdensityethnic differenceethnic health disparityethnic minority populationgender differencegenetic makeuphealth disparityhealthy agingimprovedindexinglifestyle factorsnovelphysical conditioningphysical statepopulation healthprimary outcomepsychosocialracial and ethnicracial and ethnic disparitiesracial differenceracial minoritysexsignal processingsleep behaviorsleep patternsleep physiologysleep qualitysleep quantitysleep spindlesocialsocial disparitiessocioeconomicstrait
中文摘要
项目总结
英文摘要
Project Summary
Health disparities associated with race and ethnicity are a major public health issue in the United
States. Multiple lines of evidence suggest that differences in people's quality and quantity of sleep may
account for part of this problem. Here we wish to study patterns of sleep, using full polysomnography
data in a large and diverse cohort, to better understand what causes some individuals and groups to
differ in their typical sleep, and to link this variation in sleep to relevant health outcomes.
In order to study sleep as a mediator of health disparities associated with race/ethnicity, we propose to
first unpack the “sleep” term into profiles of quantitative traits (Aim 1). Second, we will test for
demographic moderators of the associations between race/ethnicity and sleep traits, which can point to
likely socioeconomic and social/environmental contextual mediators (Aim 2). Finally, we will
demonstrate the relevance of these findings by testing for association with health outcomes – primarily
cardiovascular disease risk and measures of allostatic load – and estimating the extent to which sleep
accounts for racial/ethnic health disparities (Aim 3).
We will apply a targeted array of data-driven analytic methods, from data science, machine learning,
computational neuroscience and causal modeling, to characterize individual differences in patterns of
sleep. A more nuanced understanding of what constitutes “poor sleep” is important because different
aspects of sleep are likely to have qualitatively different links to demographic and biomedical factors,
including age, sex, lifestyle factors, inherited genetic makeup, circadian rhythms, medication effects
and current state of physical and mental health. By disentangling this network of inter-related variables,
we will be better positioned to understand – and ameliorate – the effects of the numerous factors that
can induce racial/ethnic disparities in sleep, and consequently, in health too.
期刊论文(0)
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科研奖励(0)
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