Phenome-wide associations of sleep characteristics in the Human Phenotype Project

Abstract

Sleep tests commonly diagnose sleep disorders, but the diverse sleep-related biomarkers recorded by such tests can also provide broader health insights. In this study, we leveraged the uniquely comprehensive data from the Human Phenotype Project cohort, which includes 448 sleep characteristics collected from 16,812 nights of home sleep apnea test monitoring in 6,366 adults (3,043 male and 3,323 female participants), to study associations between sleep traits and body characteristics across 16 body systems. In this analysis, which identified thousands of significant associations, visceral adipose tissue (VAT) was the body characteristic that was most strongly correlated with the peripheral apnea–hypopnea index, as adjusted by sex, age and body mass index (BMI). Moreover, using sleep characteristics, we could predict over 15% of body characteristics, spanning 15 of the 16 body systems, in a held-out set of individuals. Notably, sleep characteristics contributed more to the prediction of certain insulin resistance, blood lipids (such as triglycerides) and cardiovascular measurements than to the characteristics of other body systems. This contribution was independent of VAT, as sleep characteristics outperformed age, BMI and VAT as predictors for these measurements in both male and female participants. Gut microbiome-related pathways and diet (especially for female participants) were notably predictive of clinical obstructive sleep apnea symptoms, particularly sleepiness, surpassing the prediction power of age, BMI and VAT on these symptoms. Together, lifestyle factors contributed to the prediction of over 50% of the sleep characteristics. This work lays the groundwork for exploring the associations of sleep traits with body characteristics and developing predictive models based on sleep monitoring. In the Human Phenotype Project, home sleep apnea testing data were collected for a total of 16,812 nights in 6366 individuals, allowing for a comprehensive study of the association of sleep traits with physiological features across 16 body systems.