» Articles » PMID: 36304418

Machine Learning Analyses Reveal Circadian Features Predictive of Risk for Sleep Disturbance

Overview
Journal Nat Sci Sleep
Publisher Dove Medical Press
Date 2022 Oct 28
PMID 36304418
Authors
Affiliations
Soon will be listed here.
Abstract

Introduction: Sleep disturbances often co-occur with mood disorders, with poor sleep quality affecting over a quarter of the global population. Recent advances in sleep and circadian biology suggest poor sleep quality is linked to disruptions in circadian rhythms, including significant associations between sleep features and circadian clock gene variants.

Methods: Here, we employ machine learning techniques, combined with statistical approaches, in a deeply phenotyped population to explore associations between clock genotypes, circadian phenotypes (diurnal preference and circadian phase), and risk for sleep disturbance symptoms.

Results: As found in previous studies, evening chronotypes report high levels of sleep disturbance symptoms. Using molecular chronotyping by measuring circadian phase, we extend these findings and show that individuals with a mismatch between circadian phase and diurnal preference report higher levels of sleep disturbance. We also report novel synergistic interactions in genotype combinations of and variants (PER3B (rs17031614)/ CRY1 (rs228716) and CLOCK3111 (rs1801260)/ CRY2 (rs10838524)) that yield strong associations with sleep disturbance, particularly in males.

Conclusion: Our results indicate that both direct and indirect mechanisms may impact sleep quality; sex-specific clock genotype combinations predictive of sleep disturbance may represent direct effects of clock gene function on downstream pathways involved in sleep physiology. In addition, the mediation of clock gene effects on sleep disturbance indicates circadian influences on the quality of sleep. Unraveling the complex molecular mechanisms at the intersection of circadian and sleep physiology is vital for understanding how genetic and behavioral factors influencing circadian phenotypes impact sleep quality. Such studies provide potential targets for further study and inform efforts to improve non-invasive therapeutics for sleep disorders.

Citing Articles

Machine learning analyses reveal circadian clock features predictive of anxiety among UK biobank participants.

Ventresca C, Mohamed W, Russel W, Ay A, Ingram K Sci Rep. 2023; 13(1):22304.

PMID: 38102312 PMC: 10724169. DOI: 10.1038/s41598-023-49644-7.

References
1.
Archer S, Oster H . How sleep and wakefulness influence circadian rhythmicity: effects of insufficient and mistimed sleep on the animal and human transcriptome. J Sleep Res. 2015; 24(5):476-93. DOI: 10.1111/jsr.12307. View

2.
Russo P, Biasi V, Cipolli C, Mallia L, Caponera E . Sleep habits, circadian preference, and school performance in early adolescents. Sleep Med. 2017; 29:20-22. DOI: 10.1016/j.sleep.2016.09.019. View

3.
Liberman A, Halitjaha L, Ay A, Ingram K . Modeling Strengthens Molecular Link between Circadian Polymorphisms and Major Mood Disorders. J Biol Rhythms. 2018; 33(3):318-336. DOI: 10.1177/0748730418764540. View

4.
Soria V, Martinez-Amoros E, Escaramis G, Valero J, Perez-Egea R, Garcia C . Differential association of circadian genes with mood disorders: CRY1 and NPAS2 are associated with unipolar major depression and CLOCK and VIP with bipolar disorder. Neuropsychopharmacology. 2010; 35(6):1279-89. PMC: 3055337. DOI: 10.1038/npp.2009.230. View

5.
Ebisawa T, Uchiyama M, Kajimura N, Mishima K, Kamei Y, Katoh M . Association of structural polymorphisms in the human period3 gene with delayed sleep phase syndrome. EMBO Rep. 2001; 2(4):342-6. PMC: 1083867. DOI: 10.1093/embo-reports/kve070. View