Impact of genetic variants on sleep timing and type 2 diabetes risk

Massachusetts General Hospital, Boston MA

Investigators

Linked publications, trials & patents

Abstract

DESCRIPTION (provided by applicant): 25. 8 million Americans (8. 3%) suffer from type 2 diabetes, which predisposes to stroke and heart failure. Timing of sleep is an understudied risk factor for development of type 2 diabetes, with later bedtime associated with increased risk of diabetes. Recent evidence suggests that morning type individuals have lower HbA1c levels compared to evening types. Sleep timing traits (bedtime, wake time, and sleep midpoint) are heritable (12-42%), but the underlying genetic variation is poorly understood. This proposal aims to identify new genetic factors that affect sleep timing, and interact with and influence the development of type 2 diabetes. The first step is conducting well-powered genome-wide association studies of sleep timing traits in healthy and diabetic subjects across multiple ethnic groups. We will employee innovate admixture analysis techniques to perform a genome-wide association study in African American subjects, as well as subjects of European Ancestry. This will mark the largest genome-wide association study of sleep traits to date and will identify genetic variants which relate to sleep timing. The results will point to new genes and pathways involved in sleep timing in healthy and diabetic subjects. Previously, genetic variants in sleep and circadian genes were found to associate with glycemic traits, but it is unknown if changes to sleep timing mediate the relationship. To test this, mediation analysis will be performed between genetic variants associated with type 2 diabetes, sleep timing, and type 2 diabetes status. This will test if the mechanism by which genetic variants influence diabetes risk is through sleep timing, opening the door for treatment of sleep timing as a novel avenue of diabetes treatment. Lastly, sleep timing may act as a sensitizing environment for genetic risk on diabetes development in some cases. To test this, variants previously associates with type 2 diabetes will be tested for interaction with sleep timing variables. Using this strategy genetic variation will b identified which impacts diabetes risk under specific sleep timing conditions only. The results of this study will identify new genetic factors that affect sleep timing, and interact with and influence the development of type 2 diabetes. This work will bring attention to the public health importance of sleep timing regulation in managing risk of chronic disease. Through these results we will also elucidate pathways underlying sleep and circadian regulation to understand their mechanistic link to cardio-metabolic disease opening potential new avenues of treatment.