LAUSR

Individuals with type 1 diabetes (T1D) are at a heightened risk for cardiometabolic disease and associated risk factors. Insufficient sleep duration (< 7 hours) may accentuate this risk, but little is known about its geographic distribution. Insufficient sleep is disproportionately distributed across the United States. We sought to determine whether the geographic distribution of insufficient sleep was associated with individual cardiometabolic risk among 18,620 adolescents and adults ages 13-90 years in the T1D Exchange Clinic Registry. We linked residence zip codes with prevalence of insufficient sleep generated from the Population Level Analysis and Community Estimates (PLACES) dataset. We examined associations between geographic patterns of sleep duration (area-level) and individual cardiometabolic risk factors (hemoglobin A1c, low-density lipoprotein, blood pressure, body mass index) using multivariable linear regression adjusting for age, sex at birth, T1D duration, and race/ethnicity. Geographic areas with shorter sleep duration experienced multiple individual cardiometabolic risk factors. Specifically, shorter geographic level sleep duration was associated with higher glycemia (□= .156, p < .001, R2 = .144), higher body mass index (□ = .037, p < .001, R2 = .125), higher low density lipoprotein cholesterol (□ = .022, p < .001, R2 = .031), and higher systolic blood pressure (□ = .021, p < .001, R2 = .142), even after adjusting for covariates (age, sex, type 1 diabetes duration, and race/ethnicity). There were significant differences in age, race/ethnicity, and sex. The association between shorter area-level sleep duration and diastolic blood pressure was no longer significant after adjusting for covariates. Adolescents and adults with T1D living in areas with a higher prevalence of insufficient sleep had higher cardiometabolic risk even after considering covariates. Future studies are warranted to unravel underlying mechanisms contributing to cardiometabolic risk at the individual level (e.g., sympathetic dysregulation, discrimination, insulin insensitivity, and inflammation) as well as other geographic contributions (e.g., air, light noise pollution, redlining). Understanding the determinants of geographic variability would enhance the utility of these data for public health campaigns. This research is supported by the National Institute of Nursing Research (R00NR018886).