期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2022
卷号：119
期号：12
DOI：10.1073/pnas.2116729119
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：Significance
Human sleep phenotypes are diversified by genetic and environmental factors, and a quantitative classification of sleep phenotypes would lead to the advancement of biomedical mechanisms underlying human sleep diversity. To achieve that, a pipeline of data analysis, including a state-of-the-art sleep/wake classification algorithm, the uniform manifold approximation and projection (UMAP) dimension reduction method, and the density-based spatial clustering of applications with noise (DBSCAN) clustering method, was applied to the 100,000-arm acceleration dataset. This revealed 16 clusters, including seven different insomnia-like phenotypes. This kind of quantitative pipeline of sleep analysis is expected to promote data-based diagnosis of sleep disorders and psychiatric disorders that tend to be complicated by sleep disorders.
Human sleep phenotypes can be defined and diversified by both genetic and environmental factors. However, some sleep phenotypes are difficult to evaluate without long-term, precise sleep monitoring, for which simple yet accurate sleep measurement 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 cycles). 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 phenotypes 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.
