No abstract
Introduction Sleep architecture is influenced by age and sex and is disrupted by obstructive sleep apnea (OSA) and periodic limb movements (PLM) of sleep. Although increasing OSA severity is thought to decrease both REM and slow wave sleep (SWS), it may do so in non-linear ways. Here, we aim to 1) compare sleep macrostructure between older men and women, 2) compare metrics of total and REM-specific OSA severity between older men and women, and 3) examine associations between metrics of OSA severity and REM sleep and SWS in a clinical sample. Methods Clinical in-lab diagnostic polysomnography (PSG) in adults ≥64 years of age from the greater New York area recorded between 2006- 2016 were collated including demographic and traditional sleep scoring metrics. Studies where TST < 4 hours were removed. Demographic, sleep macrostructure, OSA (AHI4% & AHI3A criteria), pulse oximetry (SpO2) nadir and PLM measures were compared according to sex. Results PSGs from 1282 older adults (average age 70 years in both sexes, 41% female) were included in the analyses. Women had a significantly greater SWS% (14.5 vs 7.9, p<0.001) and less N1% (18.2 vs 24.4, p<0.001), without significant differences in TST, N2%, REM%, sleep efficiency or SpO2 nadir. Men had significantly higher all-sleep OSA (median AHI4% 8.8 vs 11.1, p=0.0004; median AHI3A 24.4 vs 27.9, p=0.003) and PLM’s (4.0 vs 7.6/hour, p=0.008) but women had significantly more OSA during REM sleep (median REM AHI4% 16.7 vs 14.0, p=0.01; median REM AHI3A 32.6 vs 27.4, p=0.0002). Inverse non-linear associations were observed between OSA severity and %SWS and %REM with a unique pattern for each sleep stage. The pattern between men and women within each stage appeared similar. Conclusion In this clinical sample of older adults, women exhibit a greater proportion of SWS and worse REM-related OSA then men. Increasing OSA severity is associated with non-linear reductions in %SWS and %REM, and we plan to further investigate these relationships and sexual dimorphism by using quantitative analysis of PSG signals for more precise measures of slow wave activity and breathing physiology than traditional sleep scoring metrics. Support R01AG056682
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