The 103,200-arm acceleration dataset in the UK Biobank revealed a landscape of human sleep phenotypes

Human sleep phenotypes can be defined and diversified by both genetic and envi-ronmental factors. However, some sleep phenotypes are difficult to evaluate without long-term, precise sleep monitoring, for which simple yet accurate sleep measure-ment is required. To solve this problem, we recently developed a state-of-the-art sleep/wake classification algorithm based on wristband-type accelerometers, termed ACCEL (acceleration-based classification and estimation of long-term sleep-wake cy-cles). In this study, we optimized and applied ACCEL to large-scale analysis of human sleep phenotypes. The clustering of an about 100,000-arm acceleration dataset in the UK Biobank using uniform manifold approximation and projection (UMAP) dimension reduction and density-based spatial clustering of applications with noise (DBSCAN) clustering methods identified 16 sleep phenotypes, including those related to social jet lag, chronotypes (morning/night person), and seven different insomnia-like phenotypes. Considering the complex relationship between sleep disorders and other psychiatric disorders, these unbiased and comprehensive analyses of sleep phe-notypes in humans will not only contribute to the advancement of biomedical research on genetic and environmental factors underlying human sleep patterns but also, allow for the development of better digital biomarkers for psychiatric disorders.

Automated summary

By analyzing a large-scale dataset of human sleep phenotypes, we identified 16 sleep phenotypes, including social jet lag, chronotypes (morning/night person), and seven different insomnia-like phenotypes. These analyses contribute to the advancement of research on genetic and environmental factors underlying human sleep patterns, and offer potential for the development of digital biomarkers for psychiatric disorders.