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

Proc Natl Acad Sci U S A. 2022 Mar 22;119(12):e2116729119. doi: 10.1073/pnas.2116729119. Epub 2022 Mar 18.


SignificanceHuman 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.

Keywords: UMAP; clustering; insomnia; sleep; sleep landscape.

MeSH terms

  • Acceleration
  • Biological Specimen Banks*
  • Humans
  • Phenotype
  • Sleep
  • Sleep Wake Disorders*
  • United Kingdom