Health-oriented sleep states: making sleep states reflect health conditions.

STUDY

objectivesThe rich information in sleep offers insights into brain function and overall health. The current guidelines for sleep staging by the American Academy of Sleep Medicine rely on relatively broad categorizations. These traditional sleep stages are not optimized to reflect health status. Here, we propose health-oriented sleep states to better associate with pre-existing health conditions.

methodsThis observational retrospective cohort study involved 8673 participants from the Massachusetts General Hospital sleep laboratory. We examined seven pre-existing conditions: mild cognitive impairment, ischemic stroke, atrial fibrillation, myocardial infarction, type 2 diabetes, hypertension, and depression. We clustered a sleep staging model's hidden layer within each stage, where clusters represent sleep states. The number of sleep states was selected to maximize the average association with the health conditions, using the average area under the receiver operating characteristic curve across outcomes based on time spent in these states. We also assessed the area under the precision-recall curve.

resultsWe identified three states within N3, 14 in N2, 6 in N1, 3 in R, and 9 in W. Average area under the receiver operating characteristic curve ranged from 0.608 to 0.723 across the seven outcomes, and area under the precision-recall curve from 0.064 to 0.524. Among these outcomes, mild cognitive impairment/dementia, atrial fibrillation, myocardial infarction, and hypertension demonstrated significantly stronger associations with the health conditions compared to conventional American Academy of Sleep Medicine sleep stages.

conclusionsNovel sleep states are linked to health conditions. A better understanding of the physiology behind these sleep states may further enhance the concept of using sleep as a window into overall health. Statement of Significance The conventional sleep staging describes sleep physiology rather than indicating health conditions. In contrast to the macrostructure (i.e. sleep stages), the microstructure of sleep, as reflected in multi-organ physiological signals during sleep, contains profound information about health. It would be a conceptual innovation to summarize the multi-organ microstructure of sleep into novel sleep states that better reflect health conditions than the current sleep stages. These sleep states should still align with the conventional sleep stages. We propose health-oriented sleep states, which are data-driven states optimized to associate with health conditions. This approach directly links health to sleep states and interprets them similarly to sleep stages, marking a significant step toward a more comprehensive understanding of the clinical relevance of sleep.