Characteristics associated with hypersomnia and excessive daytime sleepiness identified by extended polysomnography recording

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Evangelista, Elisa | Rassu, Anna Laura | Barateau, Lucie | Lopez, Régis | Chenini, Sofiène | Jaussent, Isabelle | Dauvilliers, Yves

Edité par CCSD ; Oxford University Press -

International audience. Abstract Study Objectives Hypersomnolence, defined by excessive daytime sleepiness (EDS) or excessive quantity of sleep (EQS), has been associated with increased morbidity. The aim of this study was to determine the clinical and polysomnographic characteristics associated with EQS and EDS assessed objectively during extended polysomnography recording. Methods A total of 266 drug-free subjects (201 women; mean age: 26.5 years [16.08; 60.87]) underwent 32-h bed-rest polysomnography recording preceded by polysomnography and modified multiple sleep latency test (mMSLT). Participants were categorized according to their total sleep time (bed-rest TST ≥19 h, hypersomnia), objective EDS (mean sleep latency on MSLT ≤8 min), and self-reported EDS (Epworth sleepiness scale score >10) and EQS (≥9 h/24 h per week). Results Subjects with hypersomnia were often younger, with normal sleep architecture, high nighttime sleep efficiency, and severe objective EDS. No association with sex, body mass index, Epworth sleepiness scale, EQS, and depressive symptoms was detected. Subjects with objective EDS had less EQS, higher sleep efficiency, and increased hypersomnia. Discrepancies were observed between objective and self-reported measures of sleep duration and EDS. Finally, 71 subjects were identified who had objective hypersomnia and/or EDS, no medical and psychiatric conditions and normal polysomnography parameters, and therefore met the stringent criteria of idiopathic hypersomnia, an orphan disorder. Conclusions Sleep duration and EDS should be quantified using self-reported and objective measures in a controlled procedure to differentiate long sleepers, patients with hypersomnia, and patients with idiopathic hypersomnia. This will help to better understand their biology, to identify specific biomarkers, and to assess related health outcomes.

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