0332 Validation of the Alliance Sleep Questionnaire (Asq) Insomnia Module in Sleep Disordered Patients

Leary, E; Joergensen, SH; Malunjkar, S; Kim, S; Mignot, Emmanuel; Barwick, Fiona H.

doi:10.1093/sleepj/zsx050.331

Cited by 3 publications

(2 citation statements)

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“…Nevertheless, it appears imperative to systematize the use of standardized and validated cataplexy measures (e.g. Leary et al, 2014) as well as non-invasive biomarker tests when available, such as HLA typing.…”

Section: Discussionmentioning

confidence: 99%

Comparison of the macro and microstructure of sleep in a sample of sleep clinic hypersomnia cases

Cairns

Bogan

2019

Neurobiology of Sleep and Circadian Rhythms

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The purpose of this study was to elucidate the differentiating or grouping EEG characteristics in various hypersomnias (type 1 and type 2 narcolepsy (N-1 and N-2) and idiopathic hypersomnia (IH) compared to an age-matched snoring reference group (SR). Polysomnogram sleep EEG was decomposed into a 4-frequency state model. The IH group had higher sleep efficiency (SE; 92.3% vs. 85.8%; sp < 0.05), lower WASO (IH = 35.4 vs. N-1 = 65.5 min; p < 0.01), but similar (i.e. high) arousal indices as N-1 (~33/h). N-1 and N-2 had earlier REM latency than IH and SR (N-1 = 64.8, N-2 = 76.3 vs. IH/SR = 118 min, p < 0.05). N-1 and N-2 showed an increase in MF1 segments (characteristic of stage 1 and REM) across the night as well as distinct oscillations every 2 h, but MF1 segment timing was advanced by 30 min compared to the SR group (p < 0.05). This suggests the presence of circadian organization to sleep that is timed earlier or of increased pressure and/or lability. MF1 demonstrated a mixed phenotype in IH, with an early 1 st oscillation (like N-1 and N-2), 2 nd oscillation that overlapped with the SR group, and a surge prior to wake (higher than all groups). This phenotype may reflect a heterogeneous group of individuals, with some having more narcolepsy-like characteristics (i.e. REM) than others. LF domain (delta surrogate) was enhanced in IH and N-1 and more rapidly dissipated compared to N-2 and SR (p < 0.05). This suggests an intact homeostatic sleep pattern that is of higher need/reduced efficiency whereas rapid dissipation may be an underlying mechanism for sleep disruption.

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Section: Discussionmentioning

confidence: 99%

Comparison of the macro and microstructure of sleep in a sample of sleep clinic hypersomnia cases

Cairns

Bogan

2019

Neurobiology of Sleep and Circadian Rhythms

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“…The ASQ is an electronic, comprehensive sleep disorder questionnaire that includes questions and validated measures designed to collect standardized subjective sleep data [ 39 , 40 ]. The questionnaire uses complex branching logic in a modular fashion to guide participants through a comprehensive set of questions to quantify demographic and clinical characteristics, including age, sex, body mass index (BMI), race, ethnicity, and associated medical history (e.g., medication use, presence of comorbidities, previous diagnosis of sleep conditions, sleep complaints).…”

Section: Methodsmentioning

confidence: 99%

Proteomic Biomarkers of the Apnea Hypopnea Index and Obstructive Sleep Apnea: Insights into the Pathophysiology of Presence, Severity, and Treatment Response

Cederberg

Hanif

Sempere

et al. 2022

IJMS

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Obstructive sleep apnea (OSA), a disease associated with excessive sleepiness and increased cardiovascular risk, affects an estimated 1 billion people worldwide. The present study examined proteomic biomarkers indicative of presence, severity, and treatment response in OSA. Participants (n = 1391) of the Stanford Technology Analytics and Genomics in Sleep study had blood collected and completed an overnight polysomnography for scoring the apnea–hypopnea index (AHI). A highly multiplexed aptamer-based array (SomaScan) was used to quantify 5000 proteins in all plasma samples. Two separate intervention-based cohorts with sleep apnea (n = 41) provided samples pre- and post-continuous/positive airway pressure (CPAP/PAP). Multivariate analyses identified 84 proteins (47 positively, 37 negatively) associated with AHI after correction for multiple testing. Of the top 15 features from a machine learning classifier for AHI ≥ 15 vs. AHI < 15 (Area Under the Curve (AUC) = 0.74), 8 were significant markers of both AHI and OSA from multivariate analyses. Exploration of pre- and post-intervention analysis identified 5 of the 84 proteins to be significantly decreased following CPAP/PAP treatment, with pathways involving endothelial function, blood coagulation, and inflammatory response. The present study identified PAI-1, tPA, and sE-Selectin as key biomarkers and suggests that endothelial dysfunction and increased coagulopathy are important consequences of OSA, which may explain the association with cardiovascular disease and stroke.

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Fatigued but not sleepy? An empirical investigation of the differentiation between fatigue and sleepiness in sleep disorder patients in a cross-sectional study

Suh,

Lok,

Weed

et al. 2024

Journal of Psychosomatic Research

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0332 Validation of the Alliance Sleep Questionnaire (Asq) Insomnia Module in Sleep Disordered Patients

Cited by 3 publications

References 0 publications

Comparison of the macro and microstructure of sleep in a sample of sleep clinic hypersomnia cases

Comparison of the macro and microstructure of sleep in a sample of sleep clinic hypersomnia cases

Proteomic Biomarkers of the Apnea Hypopnea Index and Obstructive Sleep Apnea: Insights into the Pathophysiology of Presence, Severity, and Treatment Response

Fatigued but not sleepy? An empirical investigation of the differentiation between fatigue and sleepiness in sleep disorder patients in a cross-sectional study

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