Background: Blacks have higher rates of cardiovascular disease (CVD), CVD risk markers (elevated blood pressure, cholesterol, and unhealthy glucose levels), and CVD mortality, compared to whites. Despite advances in understanding how CVD disparity is engendered and preventing CVD, blacks compared to whites are still disproportionately burdened by CVD, particularly CVD risk markers. Therefore, more novel targets that drive racial disparity in CVD risk markers must be investigated in order to reduce and eliminate CVD disparity. Evidence indicating that insufficient sleep (sleep duration ?6hrs/24 hr. cycle), which disproportionately affects blacks, is linked to concurrent and future CVD risk may offer a potentially novel mechanism explaining CVD risk disparity between blacks and whites. However, it is unclear whether insufficient sleep directly or indirectly drives disparity in CVD risk between blacks and whites? Therefore, the purpose of the proposed K01 study is to investigate whether insufficient sleep independently explains a significant portion of racial disparity in CVD risk markers, while adjusting for the confounding effects of sleep apnea and insomnia (two sleep disorders that cause insufficient sleep). Additionally, since 55% of insufficient sleepers are obese and blacks are twice as likely to be obese compared to whites, the candidate will investigate the independent and combined effects of insufficient sleep and obesity on the disparity in CVD risk markers between blacks and whites. Research: In the proposed K01, the candidate, Dr. Azizi Seixas, will investigate: 1) whether insufficient sleep and/or obesity play an important role in the relationship between race/ethnicity and CVD risk, controlling for sleep apnea and insomnia; and 2) what sleep duration and body mass index profile is associated with lowest probability of CVD risk among blacks and whites, using advanced mathematical analytic methods and complex system simulation modeling. Training: The training plan builds upon the candidate's background and experience in conducting research in sleep disorders and cardiovascular disease (e.g. hypertension and stroke), among minority populations. During the K01, the candidate will receive training in sleep science and physiology, cardiovascular disease and advanced analytical, machine learning, and simulation modeling. He will perform secondary analyses of data from the Sleep Heart Health Study, a NHLBI-funded study. The pedagogical approach includes one-on-one mentorship, course work, mentored laboratory training (practicum), and attendance to targeted conferences and seminars, with the common goal of supporting his transition to being an independent scientist.

Public Health Relevance

The overarching objective of this K01 career development award is to foster the transition of Dr. Azizi Seixas into an independent scientist in the area of sleep and cardiovascular disease and health disparities. The proposed study will investigate a novel mechanism underlying associations between insufficient sleep, obesity, and disparity in CVD risk markers between blacks and whites. Findings from the study will be the basis of sleep and obesity interventions to reduce CVD risk, especially among diverse groups burdened by cardiovascular disease.

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Research Scientist Development Award - Research & Training (K01)
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Special Emphasis Panel (ZHL1)
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Nelson, Cheryl R
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New York University
Internal Medicine/Medicine
Schools of Medicine
New York
United States
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Robbins, Rebecca; Seixas, Azizi; Schoenthaler, Antoinette (2018) The nature and scope of patient-sharing network research: a novel, important area for network science. Transl Behav Med 8:626-628
Seixas, Azizi A; Gyamfi, Lloyd; Newsome, Valerie et al. (2018) Moderating effects of sleep duration on diabetes risk among cancer survivors: analysis of the National Health Interview Survey in the USA. Cancer Manag Res 10:4575-4580
Seixas, Azizi A; Henclewood, Dwayne A; Williams, Stephen K et al. (2018) Sleep Duration and Physical Activity Profiles Associated With Self-Reported Stroke in the United States: Application of Bayesian Belief Network Modeling Techniques. Front Neurol 9:534