Repeated polysomnography and multiple sleep latency test in narcolepsy type 1 and other hypersomnolence disorders

Nocturnal sleep phenotypes in idiopathic hypersomnia - A data-driven cluster analysis

INTRODUCTION

The diagnostic process for idiopathic hypersomnia (IH) is complex due to the diverse aetiologies of daytime somnolence, ambiguous pathophysiological understanding, and symptom variability. Current diagnostic instruments, such as the multiple sleep latency test (MSLT), are limited in their ability to fully represent IH's diverse nature. This study endeavours to delineate subgroups among IH patients via cluster analysis of polysomnographic data and to examine the temporal evolution of their symptomatology, aiming to enhance the granularity of understanding and individualized treatment approaches for IH.

METHODS

This study included individuals referred to the Uppsala Centre for Sleep Disorders from 2010 to 2019, who were diagnosed with IH based on the International Classification of Sleep Disorders-3 (ICSD-3) criteria, following a thorough diagnostic evaluation. The final cohort, after excluding participants with incomplete data or significant comorbid sleep-related respiratory conditions, comprised 69 subjects, including 49 females and 20 males, with an average age of 40 years. Data were collected through polysomnography (PSG), MSLT, and standardized questionnaires. A two-step cluster analysis was employed to navigate the heterogeneity within IH, focusing on objective time allocation across different sleep stages and sleep efficiency derived from PSG. The study also aimed to track subgroup-specific changes in symptomatology over time, with follow-ups ranging from 21 to 179 months post-diagnosis.

RESULTS

The two-step cluster analysis yielded two distinct groups with a satisfactory silhouette coefficient: Cluster 1 (n = 29; 42 %) and Cluster 2 (n = 40; 58 %). Cluster 1 exhibited increased deep sleep duration, reduced stage 2 sleep, and higher sleep maintenance efficiency compared to Cluster 2. Further analyses of non-clustering variables indicated shorter wake after sleep onset in Cluster 1, but no significant differences in other sleep parameters, MSLT outcomes, body mass index, age, or self-reported measures of sleep inertia or medication usage. Long-term follow-up assessments showed an overall improvement in excessive daytime sleepiness, with no significant inter-cluster differences.

CONCLUSION

This exploratory two-step cluster analysis of IH-diagnosed patients discerned two subgroups with distinct nocturnal sleep characteristics, aligning with prior findings and endorsing the notion that IH may encompass several phenotypes, each potentially requiring tailored therapeutic strategies. Further research is imperative to substantiate these findings.

The effects of daylight duration on the multiple sleep latency test (MSLT) results: A pilot study

OBJECTIVE

MSLT results are known to be affected by multiple factors including sleep time, frequency of nighttime arousals, and medications intake. Although being the main synchronizer of sleep and wakefulness, daylight duration effects on MSLT have not been examined. Burlington, Vermont, USA experiences great variations in daylight duration, ranging from 8 h 50 min to 15 h 33 min of daylight. The aim of this study was to test the hypothesis that there would be photoperiod duration effects on MSLTs performed during short daylight (short daylight studies, SDS) vs. long daylight (long daylight studies, LDS) from 2013 to 2023 in our sleep laboratory.

METHODS

We identified and analyzed 37 SDS (daylight 530-560 min) and 36 LDS (daylight 903-933 min) from our database. Groups of SDS and LDS results were compared using non-paired student T test, Chi-Square and non-parametric Mann Whitney U Test.

RESULTS

Average daylight duration was 15 h 18 ± 14.6 min for LDS and 8 h 57 ± 18 min for SDS. Two groups did not differ in terms of the age, gender, BMI and race of patients studied. Mean total sleep time and sleep efficiency during PSG preceding MSLT, and MSLT mean sleep onset latency did not significantly differ for the two groups. However, SDS MSLT naps had significantly more sleep onset REM periods (SOREMP), and distribution of the number of SOREMP captured during MSLT was different for SDS and LDS groups. Differences of SDS and LDS results did not relate to sleep architecture of the overnight PSG as analysis of sleep and REM latency and relative percentages of N1, N2, REM, and N3 was not significantly different between SDS and LDS. The two groups showed difference in arousal indexes during N1 and REM sleep.

CONCLUSIONS

Daylight duration may impact MSLT results and should probably be accounted for in MSLT interpretation. Attention to photoperiod could be considered in MSLT guidelines, if our results are replicated in larger samples.

Sleep architecture in idiopathic hypersomnia: the influence of age, sex, and body mass index

This study aimed to progress the understanding of idiopathic hypersomnia (IH) by assessing the moderating influence of individual characteristics, such as age, sex, and body mass index (BMI) on sleep architecture. In this retrospective study, 76 IH participants (38.1 ± 11.3 years; 40 women) underwent a clinical interview, an in-laboratory polysomnography with a maximal 9-h time in bed and a multiple sleep latency test (MSLT). They were compared to 106 healthy controls (38.1 ± 14.1 years; 60 women). Multiple regressions were used to assess moderating influence of age, sex, and BMI on sleep variables. We used correlations to assess whether sleep variables were associated with Epworth Sleepiness Scale scores and mean sleep onset latency on the MSLT in IH participants. Compared to controls, IH participants had shorter sleep latency (p = 0.002), longer total sleep time (p < 0.001), more time spent in N2 sleep (p = 0.008), and showed trends for a higher sleep efficiency (p = 0.023) and more time spent in rapid eye movement (REM) sleep (p = 0.022). No significant moderating influence of age, sex, or BMI was found. More severe self-reported sleepiness in IH patients was correlated with shorter REM sleep latency and less N1 sleep in terms of proportion and duration (ps < 0.01). This study shows that, when compared to healthy controls, patients with IH had no anomalies in their sleep architecture that can explain their excessive daytime sleepiness. Moreover, there is no moderating influence of age, sex, and BMI, suggesting that the absence of major group differences is relatively robust.

Is there a role for repeating the multiple sleep latency test across childhood when initially non-diagnostic?

BACKGROUND

The gold standard investigation for central disorders of hypersomnolence is the Multiple Sleep Latency Test (MSLT). As the clinical features of these disorders of hypersomnolence evolve with time in children, clinicians may consider repeating a previously non-diagnostic MSLT. Currently there are no guidelines available regards the utility and timing of repeating paediatric MSLTs.

METHODS

Retrospective review of children aged 3-18years with ≥2MSLTs between 2005 and 2022. Narcolepsy was defined as mean sleep latency (MSL) <8min with ≥2 sleep onset REM (SOREM); idiopathic hypersomnia (IH) was defined as MSL <8min with <2 SOREM. MSLTs not meeting these criteria were labelled non-diagnostic.

RESULTS

19 children (9 female) with initial non-diagnostic MSLT underwent repeat MSLT, with 6 proceeding to a 3rd MSLT following 2 non-diagnostic MSLTs. The 2nd MSLT resulted in diagnosis in 6/19 (32 %) (3 narcolepsy, 3 IH); and 2/6 (33 %) 3rd MSLT were diagnostic (2 IH). Median age at initial MSLT was 7.5y (range 3.4-17.8y), with repeat performed after median of 2.9y (range 0.9-8.2y), and 3rd after a further 1.9 years (range 1.2-4.2y). Mean change in MSL on repeat testing was -2min (range -15.5min to +4.9min, p = 0.18). Of the 8 diagnostic repeat MSLTs, in addition to the MSL falling below 8 min, 2 children also developed ≥2 SOREM that had not been previously present.

CONCLUSIONS

A third of repeat MSLTs became diagnostic, suggesting repeat MSLT should be considered in childhood if clinical suspicion persists. Further work needs to address the ideal interval between MSLTs and diagnostic cut-points specific to the paediatric population.

Identification of sleep quality clusters among stroke patients: A multi-center Latent Profile Analysis study

BACKGROUND

Quality sleep plays a crucial role in maintaining good health. Nevertheless, sleep disruption is a common and complex issue after a stroke. It can increase the likelihood of stroke recurrence by influencing modifiable risk factors. However, there is currently a lack of research on the latent classes or clusters of sleep quality and its predictive factors among stroke patients.

OBJECTIVES

This study aims to identify latent classes of sleep quality and explore the predictive factors associated with different sleep quality clusters among stroke patients.

METHODS

A total of 500 participants were recruited through cluster random sampling from January 2023 to May 2023. Latent profile analysis was conducted to identify latent classes of sleep quality within the sample of stroke patients. Additionally, multinomial regression analyses were employed to investigate the predictors associated with the different latent classes identified in the analysis.

RESULTS

Out of the 500 participants, 458 (91.6 %) completed the survey, and 71 % of them reported experiencing sleep problems. The analysis revealed three latent profile classes: the "good sleep quality-deficient duration" group (65.4 %), the "moderate sleep quality-more disturbances" group (14.1 %), and the "poor sleep quality-low efficiency" group (20.5 %). Factors associated with sleep quality were identified. Protective factors for sleep quality included being male, having the TOAST type of large-artery atherosclerosis, having a good education, high household income, no family history of stroke, residing in rural areas, and having better environmental and social support (all p < 0.05). Risk factors for sleep quality included smoking, high perceived stress, and a greater number of comorbidities (all p < 0.05).

CONCLUSIONS

This study has successfully identified three distinct latent profile classes of sleep quality and their associated predictors among stroke patients in China. The findings offer both theoretical guidance and practical insights for the development of targeted intervention programs aimed at enhancing the sleep quality of stroke patients.