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.