(P01-060-20) Effects of Habitual Sleep on Glucose Regulation in Individuals at Risk for Type 2 Diabetes

Objectives: This study sought to advance understanding of the impact of habitual sleep patterns on glucose regulation in individuals at risk for type 2 diabetes (T2D).

Methods: To achieve this aim, we examined associations between a comprehensive panel of sleep parameters and glucose metabolism marker among people with prediabetes (n=19, age=60.0y, male=47.4%) using wearable technology. Briefly, participants underwent fasting plasma glucose (FPG) test and wore Fitbit Ionic band to assess their habitual sleep patterns. Sleep parameters were obtained for a median of 50 days per each participant such as total sleep duration, duration of each sleep stage per night, bed-time, wake-time, etc. To examine associations of sleep parameters with blood glucose levels, a least absolute shrinkage and selection operator (LASSO) regression was used to identify sleep parameters that predict FPG levels with the enhanced prediction accuracy. Mixed effects regression was also performed.

Results: In LASSO regression of FPG levels, wake-time (β= -0.013) and percentage of the rapid eye movement (REM) sleep duration out of the total sleep duration (REM%; β= -0.231) were found to inversely predict FPG levels among participants. Mixed effects regression model also showed that REM% is inversely associated with FPG levels (R²=0.61; β= -1.57, p=0.058) after adjusting other covariates. In sum, people with prediabetes who have earlier wake-time and shorter REM proportion have shown higher FPG levels.

Conclusions: Overall, these findings suggest that habitual sleep patterns may influence physiologic defect underlying dysglycemia and progression to T2D in individuals with prediabetes.

Funding Sources: Precision Health and Integrated Diagnostic (PHIND) Center at Stanford University