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

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 Period 3, Clock and Cryptochrome 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.

Keywords:

• circadian clock
• chronotype
• sleep disturbance
• sleep quality
• machine learning
• circadian misalignment

Data Sharing Statement

All data and coding software are available on Github: https://github.com/aziz-zafar/Mood-Disorder.

Disclosure

The authors declare no competing interests.

Additional information

Funding

Funding was provided by Colgate University Research Council and Picker Interdisciplinary Science Institute grants to KKI.