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Wainberg M, Jones SE, Beaupre LM, Hill SL, Felsky D, Rivas MA, Lim ASP, Ollila HM, Tripathy SJ. Association of accelerometer-derived sleep measures with lifetime psychiatric diagnoses: A cross-sectional study of 89,205 participants from the UK Biobank. PLoS Med 2021; 18:e1003782. [PMID: 34637446 PMCID: PMC8509859 DOI: 10.1371/journal.pmed.1003782] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 08/25/2021] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Sleep problems are both symptoms of and modifiable risk factors for many psychiatric disorders. Wrist-worn accelerometers enable objective measurement of sleep at scale. Here, we aimed to examine the association of accelerometer-derived sleep measures with psychiatric diagnoses and polygenic risk scores in a large community-based cohort. METHODS AND FINDINGS In this post hoc cross-sectional analysis of the UK Biobank cohort, 10 interpretable sleep measures-bedtime, wake-up time, sleep duration, wake after sleep onset, sleep efficiency, number of awakenings, duration of longest sleep bout, number of naps, and variability in bedtime and sleep duration-were derived from 7-day accelerometry recordings across 89,205 participants (aged 43 to 79, 56% female, 97% self-reported white) taken between 2013 and 2015. These measures were examined for association with lifetime inpatient diagnoses of major depressive disorder, anxiety disorders, bipolar disorder/mania, and schizophrenia spectrum disorders from any time before the date of accelerometry, as well as polygenic risk scores for major depression, bipolar disorder, and schizophrenia. Covariates consisted of age and season at the time of the accelerometry recording, sex, Townsend deprivation index (an indicator of socioeconomic status), and the top 10 genotype principal components. We found that sleep pattern differences were ubiquitous across diagnoses: each diagnosis was associated with a median of 8.5 of the 10 accelerometer-derived sleep measures, with measures of sleep quality (for instance, sleep efficiency) generally more affected than mere sleep duration. Effect sizes were generally small: for instance, the largest magnitude effect size across the 4 diagnoses was β = -0.11 (95% confidence interval -0.13 to -0.10, p = 3 × 10-56, FDR = 6 × 10-55) for the association between lifetime inpatient major depressive disorder diagnosis and sleep efficiency. Associations largely replicated across ancestries and sexes, and accelerometry-derived measures were concordant with self-reported sleep properties. Limitations include the use of accelerometer-based sleep measurement and the time lag between psychiatric diagnoses and accelerometry. CONCLUSIONS In this study, we observed that sleep pattern differences are a transdiagnostic feature of individuals with lifetime mental illness, suggesting that they should be considered regardless of diagnosis. Accelerometry provides a scalable way to objectively measure sleep properties in psychiatric clinical research and practice, even across tens of thousands of individuals.
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Affiliation(s)
- Michael Wainberg
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Canada
| | - Samuel E. Jones
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- University of Exeter Medical School, Exeter, United Kingdom
| | - Lindsay Melhuish Beaupre
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, Canada
| | - Sean L. Hill
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, Canada
- Department of Psychiatry, University of Toronto, Toronto, Canada
- Department of Physiology, University of Toronto, Toronto, Canada
| | - Daniel Felsky
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, Canada
- Department of Psychiatry, University of Toronto, Toronto, Canada
- Biostatistics Division, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Manuel A. Rivas
- Department of Genetics, Stanford University, Stanford, California, United States of America
| | - Andrew S. P. Lim
- Sunnybrook Health Sciences Centre, Toronto, Canada
- Division of Neurology, Department of Medicine, University of Toronto, Toronto, Canada
| | - Hanna M. Ollila
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, United States of America
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Shreejoy J. Tripathy
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, Canada
- Department of Psychiatry, University of Toronto, Toronto, Canada
- Department of Physiology, University of Toronto, Toronto, Canada
- * E-mail:
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