Phenome-wide Analysis of Diseases in Relation to Objectively Measured Sleep Traits and Comparison with Subjective Sleep Traits in 88,461 Adults.

Research overview

The text below is the source article’s original abstract under a verified reuse license. cc by

Background: Sleep traits have been suggested to correlate with various diseases, but most evidence is based on subjective sleep measurement. We investigated the associations of accelerometer-derived objective sleep traits with diseases throughout physiological systems to ascertain whether the disease spectrum related to objective sleep traits differs from that related to subjective sleep traits. Methods: In 88,461 UK Biobank (UKB) adults wearing accelerometers, multiple dimensions of sleep were objectively derived: (a) nocturnal sleep duration and onset timing, (b) sleep rhythm (relative amplitude and interdaily stability), and (c) sleep fragmentation (sleep efficiency and waking numbers). Associations with International Classification of Diseases, 10th Revision-decoded diseases during follow-up were estimated using the Cox model, and the results were compared with those of a published literature search of subjectively measured sleep traits and diseases. National Health and Nutrition Examination Survey (NHANES) data were used to validate the newly identified associations unreported by previous studies. For the meta-analysis-reported associations (with subjective sleep traits) that were negative (with objective sleep traits) in our study, reanalysis was done in UKB with subjective sleep traits, stratified by objective measurements. Results: During the average 6.8-year follow-up, 172 diseases were associated with sleep traits. Among them, 42 showed at least doubled disease risk, including age-related physical debility (lowest versus highest quartile of relative amplitude, hazard ratio [HR] = 3.36, 95% confidence interval [CI]: 2.25, 5.02), gangrene (lowest versus highest quartile of interdaily stability, HR = 2.61, 95% CI: 1.41, 4.83), and fibrosis and cirrhosis of the liver (sleep onset timing ≥0030 versus 2300 to 2330, HR = 2.57, 95% CI: 1.42, 4.67). A total of 92 diseases had >20% burden attributable to sleep, such as Parkinson's disease (37.05%, 95% CI: 21.02%, 49.83%), type 2 diabetes (36.12%, 95% CI: 29.00%, 42.52%), and acute kidney failure (21.85%, 95% CI: 13.47%, 29.42%). Notably, 83 (48.3%) disease associations were sleep rhythm specific, distinct from existing subjective-measure literature that focused on sleep duration. Reanalysis in UKB showed a contamination of objectively short sleepers in self-report long sleepers, which induced false-positive associations in subjective meta-analyses, including for ischemic heart disease and depressive disorder. Newly identified associations of sleep rhythm with 4 diseases including chronic obstructive pulmonary disease and diabetes were successfully replicated in NHANES. A mediation analysis showed that inflammatory factors including leukocytes, eosinophils, and C-reactive protein contributed significantly to all these newly identified sleep-disease associations. Conclusions: Objective sleep traits showed a disease spectrum similar to but not identical to that of subjective sleep traits. Objective measurement can be a useful complement to sleep-disease studies as it may help overcome false-positive associations caused by misclassification bias of some subjective measurement such as sleep duration. Comprehensive control of multiple sleep traits may be important for health as substantial disease burden was attributed to different sleep traits.

Related topics