Study Objectives Implementation of electronic health record biobanks has facilitated linkage between clinical and questionnaire data and enabled assessments of relationships between sleep health and diseases in phenome-wide association studies (PheWAS). In the Mass General Brigham Biobank, a large health system-based study, we aimed to systematically catalog associations between time in bed, sleep timing and weekly variability with diseases derived from ICD-9/10 codes. Methods Self-reported habitual bed and wake times were used to derive variables: short (<7hr) and long (≥9hr) time in bed, sleep midpoint, social jetlag, and sleep debt. Logistic regression and Cox proportional hazards models were used to test cross-sectional and prospective associations, respectively, adjusted for age, gender, race/ethnicity, and employment status, and further adjusted for BMI. Results In cross-sectional analysis (n=34,651), sleep variable associations were most notable for circulatory system, mental disorders, and endocrine/metabolic diseases. We observed the strongest associations for short time in bed with obesity, for long time in bed and sleep midpoint with Major depressive disorder, for social jetlag with Hypercholesterolemia, and for sleep debt with Acne. In prospective analysis (n=24,065), we observed short time in bed associations with higher incidence of Acute pain, and later sleep midpoint and higher sleep debt and social jetlag associations with higher incidence of Major depressive disorder. Conclusion Our analysis reinforced that sleep health is a multidimensional construct, corroborated robust known findings from traditional cohort studies, and supported the application of PheWAS as a promising tool for advancing sleep research. Considering the exploratory nature of PheWAS, careful interrogation of novel findings is imperative.
Introduction Implementation of electronic health records (EHR) across healthcare systems linking clinical to survey data has enabled systematic assessments of longitudinal relationships between sleep traits and diseases classified by PheWAS codes where ICD-9/10 codes are collapsed to categories based on clinical similarity. In the Partners Biobank, a hospital-based virtual cohort from Mass General Brigham in greater Boston, MA, we aimed to assess associations between sleep traits and incident diseases. Methods Self-reported weekday/weekend bed and wake times from a survey at consent were used to derive sleep traits. Incident diseases were defined as two incident PheWAS codes on separate dates ≥1y after consent. Cox proportional hazards models compared short (<7h) and long (≥9h) sleep duration, with 7-8h (referent group), adjusted for age, gender, race/ethnicity, and employment status, then further adjusted for BMI. Similarly, sleep midpoint (midpoint between weekend wake/bed times), sleep debt (difference in weekend/weekday sleep duration), and social jetlag (difference in weekend/weekday sleep midpoint) were assessed. Results The analytical sample consisted of 24,065 adults (mean sleep duration =8.12h) seeking regular care with sleep data. Participants had a total of 7,513,649 ICD codes of which incident 323,946 ICD codes mapped to 137,137 PheWAS codes. Over a median follow-up of 2.73 years (interquartile range: 1.82-3.98), participants sleeping <7h had a significantly higher risk of incident Acute pain [hazard ratio(95% confidence interval)=1.46(1.2-1.78)], Tobacco use disorder [1.42(1.18-1.71)], Sciatica [1.72(1.3-2.27)], and Edema [1.69(1.25-2.28)]. Each additional hour of later sleep midpoint and increased sleep debt and social jetlag associated with higher risk of incident Major depressive disorder [midpoint:1.30(1.14-1.49); debt:1.23(1.09-1.38); jetlag:1.54(1.27-1.84)]. Associations retained significance upon further adjustment for BMI, except for Edema, and no other associations were observed at the Bonferroni threshold (P=0.0125). Conclusion Our findings in a large hospital-based virtual cohort support unique inter-relationships between sleep duration/timing on somatic, behavioral, and mental health outcomes. Support H.S.D. and R.S. are supported by NIDDK grant R01DK107859. B.C. is supported by K01-HL135405-01. S.R. and R.S. are partially supported by R35 NHLBI HL 135816.
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