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 mea

SUBMITTER: Overton R 

PROVIDER: S-EPMC9595061 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature