Pulling the covers in electronic health records for an association study with self-reported sleep behaviors. (2nd December 2018)

• Record Type: Journal Article
• Authors: Rhoades, Seth D.; Bastarache, Lisa; Denny, Joshua C.; Hughey, Jacob J.
• Is Part Of: Chronobiology international. Volume 35:Number 12(2018)
• Page Start: 1702
• Page End: 1712
• Publication Date: 2018-12-02
• Subjects: Sleep -- electronic health records -- phenome-wide association study -- genetics; Chronobiology -- Periodicals; Biological rhythms -- Periodicals; Circadian rhythms -- Periodicals; 571.77
• DOI: 10.1080/07420528.2018.1508152
• Languages: English
• ISSNs: 0742-0528

Abstract

ABSTRACT: The electronic health record (EHR) contains rich histories of clinical care, but has not traditionally been mined for information related to sleep habits. Here, we performed a retrospective EHR study based on a cohort of 3, 652 individuals with self-reported sleep behaviors documented from visits to the sleep clinic. These individuals were obese (mean body mass index 33.6 kg/m 2 ) and had a high prevalence of sleep apnea (60.5%), however we found sleep behaviors largely concordant with prior prospective cohort studies. In our cohort, average wake time was 1 hour later and average sleep duration was 40 minutes longer on weekends than on weekdays ( p < 10 −12 ). Sleep duration varied considerably as a function of age and tended to be longer in females and in whites. Additionally, through phenome-wide association analyses, we found an association of long weekend sleep with depression, and an unexpectedly large number of associations of long weekday sleep with mental health and neurological disorders ( q < 0.05). We then sought to replicate previously published genetic associations with morning/evening preference on a subset of our cohort with extant genotyping data ( n = 555). While those findings did not replicate in our cohort, a polymorphism (rs3754214) in high linkage disequilibrium with a previously published polymorphism near TARS2 was associated with long sleep duration ( p < 0.01). Collectively, our results highlight the potential of the EHR for uncovering the correlates of human sleep in real-world populations.