PRIDE Summer Institute in Cardiovascular Disease-Comorbidities Genetics and Epidemiology (CVD-CGE)

ABSTRACT There have been many preventative research studies and intervention strategies to address the gap in cardiometabolic-associated morbidity, i.e., overweight, obesity, hypertension, diabetes, stroke and mortality, between racial/ethnic minorities and individuals with low socioeconomic status (SES) compared to non-Hispanic whites and individuals with higher SES in the US. However, few studies have investigated geographic sleep and cardiometabolic health disparities between rural versus urban residents. Cardiovascular disease (CVD) remains the leading cause of death worldwide and has its origins in early life. Some hard-to-reach rural communities, Appalachian regions in particular, may be disproportionately burdened with higher rates of cardiovascular morbidity and mortality. The American “stroke and diabetes belts” are characterized by a set of contiguous states with characteristically high age-adjusted stroke and diabetes mortality rates. Ohio, particularly a cluster of counties in the southeast region (see Figure 1) (1), is included among the 15 “diabetes belt” states (2). In 2014, the state of Ohio had the 12th highest rate of CVD-related mortality among US states (3). A recent finding reported the rate of heart disease in Appalachian Ohio to be 22% higher than the national average rate and 15% higher than the rate in non-Appalachian Ohio (1). Identification of individuals susceptible to adverse cardiometabolic health in early life, rather than in adulthood, a poorly understood period in the pathogenesis of CVD, is fundamental in prevention efforts (4). Although there is little research on the influence of exposure to SHS on sleep patterns in childhood, studies have reported cross-sectional associations between SHS and poor sleep at different ages throughout childhood and adolescence (11), including longer sleep onset delay, sleep disordered breathing (SDB), parasomnias, daytime sleepiness, overall sleep disturbance (e.g., night awakenings), and obstructive sleep apnea syndrome (11-14), and snoring (15), a symptom of SDB. Prior studies have found no association between serum cotinine and short sleep duration (14) while other studies have found longitudinal associations between SHS exposure and shorter sleep among young children (11), and shorter sleep among school-aged children even exposed to smoke residue (16). The SHS-sleep and SHS-cardiometabolic health relationships, and the role of poor sleep behaviors among child and adolescent rural residents in the latter relationship have not been specifically characterized in the literature. Therefore, this study seeks to determine the prevalence and impact of SHS on sleep outcomes (Aim 1) while further examining the impact of SHS on cardiometabolic health (Aim 2) in a rural Appalachian pediatric cohort of children. A further analysis will be conducted to elucidate the role of sleep in the SHS-cardiometabolic health relationship (Sub Aim 2). My long-term career goal is to contribute to the better characterization and understanding of how upstream socio- ecological factors contribute to poor sleep and cardiometabolic health disparities. I also plan to engage in collaborative work to elucidate gene-environment interactions and use the combination of findings to support effective non-invasive interventions among rural populations. Initially, as a postdoctoral fellow in the Social and Environmental Determinants of Health Equity research group in the Epidemiology Branch at NIEHS, and currently as an Assistant Professor of Epidemiology, I have investigated the impact of social and environmental exposures (e.g., traffic-related air pollution) on parent-reported snoring frequency, a symptom consistent with SDB among children in an urban area. To enhance my current approach, I am proposing to (1) characterize ‘exposed’ versus ‘not exposed’ to SHS among rural Appalachian child participants by assessing biological measures of serum cotinine levels, (2) measure prevalence of ’poor’ versus ‘appropriate’ sleep behavior using 3 sleep dimensions, (3) use statistical tools to examine the potential association between SHS exposure and each sleep dimension, (4) use statistical tools to examine the association between SHS and BMI and BP>90th percentiles, and lastly (5) explore the role of sleep behavior as a potential mediator in the SHS-cardiometabolic health relationship.