Seasonal Variation in Sleep Apnea and Slow-Wave Sleep in Retrospective Polysomnography data

Music City Center 

Objective: The primary objective of this research is to examine how sleep apnea, measured by the apnea-hypopnea index (AHI) and the percentage of slow wave sleep, varies across different seasons. We hypothesize that sleep parameters can be seasonal.
Methods: In this study, we analyzed diagnostic in-laboratory polysomnography (PSG) data from 27,760 patients from sleep studies. We obtained the apnea-hypopnea index (AHI) for each patient, measured as the number of apnea/hypopnea events per hour during sleep. We also obtained the percentage of sleep time each patient spent in slow wave sleep (SWS) (also known as the deepest non-rapid eye movement sleep). We applied a generalized linear model with harmonic terms to model the seasonality of the data adjusting for covariates age and body mass index. We analyzed data from men and women separately to see if sex differences could be found.
Results: AHI is higher in spring/winter and lower in fall, with men generally exhibiting a higher AHI than women across all seasons. Conversely, the proportion of SWS was greater in the fall and lower in the spring/winter, with women having a higher proportion of SWS than men across all seasons. Significant seasonality of AHI was found for both men (P=0.0001) and women (P=0.0053). Similarly, significant seasonality of SWS was also observed for women (P=0.0099), but interestingly not for men (P=0.13).
Conclusion and Discussion: Clear seasonal effects have been identified for both the AHI and the percentage of SWS outcome variables. The fall appears to be the best season for sleep quality (with the lowest sleep apnea and the highest proportion of SWS), whereas the winter and spring appear to be characterized by more sleep disturbance. The consistency in our findings between men and women strongly suggests that the seasonal effects on sleep are real. We suspect that spring allergy and cold/flu season in the winter may contribute to the seasonal differences. SWS is essential for cells repair and regenerate. Disruption in SWS is linked to an increased risk of metabolic disorders, such as type 2 diabetes. Recently, a few high-profile publications demonstrated that SWS contributes to the homeostasis of various physiological processes, including heart recovery after a myocardial infarction and the brain waste clearance. Our novel findings on the seasonality of sleep characteristics will be informative to clinicians and sleep researchers for assessing seasonal sleep health.

apnea-hypopnea index



polysomnography (PSG) studies

sleep patterns

circadian rhythm

generalized linear model 

Section on Statistics and the Environment