Data-Driven Phenotyping of Central Disorders of Hypersomnolence With Unsupervised Clustering

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.

Sleep schedules and MSLT-based diagnosis of narcolepsy type 2 and idiopathic hypersomnia: Exploring potential associations in a large clinical sample

Differential diagnosis of narcolepsy type 2 and idiopathic hypersomnia relies on the frequency of sleep-onset rapid eye movement periods observed on the Multiple Sleep Latency Test. This study investigated whether variations in sleep schedules, particularly delayed sleep-wake patterns, contribute to the diagnostic distinction between narcolepsy type 2 and idiopathic hypersomnia. The study included 871 patients aged 18-39 years (316 with narcolepsy type 2, 555 with idiopathic hypersomnia). These patients were diagnosed based on MSLT results following polysomnography from November 2013 to November 2017. Patients' sleep habits, including bedtime and wake-up times on weekdays and weekends during the 2 weeks preceding the polysomnography/Multiple Sleep Latency Test, were assessed using self-reported sleep logs. A multivariate logistic regression analysis was conducted to identify factors associated with narcolepsy type 2 diagnosis. The analysis revealed that being male (p < 0.001), younger age (p < 0.001), shorter weekday sleep duration (p < 0.05), and a delayed weekday sleep midpoint time (p < 0.01) were significantly associated with a diagnosis of narcolepsy type 2. The study suggests that the conventionally fixed schedule of polysomnography and Multiple Sleep Latency Test administration may have contributed to the increased occurrence of sleep-onset rapid eye movement periods particularly in individuals with a delayed sleep-wake schedule.

Daytime sleepiness and BMI exhibit gender and age differences in patients with central disorders of hypersomnolence

The aim of the present study was to examine gender and age-specific effects on subjective daytime sleepiness (as measured by the Epworth Sleepiness Scale), body weight and eating behaviour in patients with central disorders of hypersomnolence. Based on the European Narcolepsy Network database, we compared 1035 patients with narcolepsy type I and 505 patients with other central disorders of hypersomnolence ("narcoleptic borderland"), including narcolepsy type II (N = 308) and idiopathic hypersomnia (N = 174), using logistic regression and general linear models. In the entire study population, the Epworth Sleepiness Scale was higher in women (N = 735, mean age = 30 years, mean Epworth Sleepiness Scale = 16.6 ± SD 3.9) than in men (N = 805, mean age = 32 years, mean Epworth Sleepiness Scale = 15.8 ± SD 4.4). In women with narcolepsy type I (N = 475), both Epworth Sleepiness Scale and body mass index increased in parallel with age. In women of the narcoleptic borderland (N = 260), the Epworth Sleepiness Scale markedly peaked in their early 30s, while body mass index only started to rise at that age. This rise in body mass index following the Epworth Sleepiness Scale peak cannot be explained by sleepiness-induced uncontrolled eating, as self-reported uncontrolled eating was negatively associated with the Epworth Sleepiness Scale in this group. We propose that the narcoleptic borderland harbours a unique cluster of women in their fertile years with an unexplored aetiology requiring further investigation towards tailored interventions.

The Swiss Primary Hypersomnolence and Narcolepsy Cohort Study: feasibility of long-term monitoring with Fitbit smartwatches in central disorders of hypersomnolence and extraction of digital biomarkers in narcolepsy

The Swiss Primary Hypersomnolence and Narcolepsy Cohort Study (SPHYNCS) is a multicenter research initiative to identify new biomarkers in central disorders of hypersomnolence (CDH). Whereas narcolepsy type 1 (NT1) is well characterized, other CDH disorders lack precise biomarkers. In SPHYNCS, we utilized Fitbit smartwatches to monitor physical activity, heart rate, and sleep parameters over 1 year. We examined the feasibility of long-term ambulatory monitoring using the wearable device. We then explored digital biomarkers differentiating patients with NT1 from healthy controls (HC). A total of 115 participants received a Fitbit smartwatch. Using a adherence metric to evaluate the usability of the wearable device, we found an overall adherence rate of 80% over 1 year. We calculated daily physical activity, heart rate, and sleep parameters from 2 weeks of greatest adherence to compare NT1 (n = 20) and HC (n = 9) participants. Compared to controls, NT1 patients demonstrated findings consistent with increased sleep fragmentation, including significantly greater wake-after-sleep onset (p = .007) and awakening index (p = .025), as well as standard deviation of time in bed (p = .044). Moreover, NT1 patients exhibited a significantly shorter REM latency (p = .019), and sleep latency (p = .001), as well as a lower peak heart rate (p = .008), heart rate standard deviation (p = .039) and high-intensity activity (p = .009) compared to HC. This ongoing study demonstrates the feasibility of long-term monitoring with wearable technology in patients with CDH and potentially identifies a digital biomarker profile for NT1. While further validation is needed in larger datasets, these data suggest that long-term wearable technology may play a future role in diagnosing and managing narcolepsy.

Diagnostic challenges and burden of idiopathic hypersomnia: a systematic literature review

Idiopathic hypersomnia (IH) is a rare neurological sleep disorder, characterized by excessive daytime sleepiness despite normal sleep duration, that can significantly impact patient's lives. The burden of IH goes beyond excessive daytime sleepiness, pervading all aspects of everyday life. Characteristic and burdensome symptoms of IH include sleep inertia/drunkenness, long sleep duration, and daytime cognitive dysfunction. This systematic review assessed current knowledge regarding IH diagnostic challenges and burden of illness. Literature searches for original epidemiological, clinical, humanistic, or economic research relevant to IH published between 2012 and 2022 in MEDLINE, Embase, Cochrane, gray literature (diagnostic criteria and treatment guidelines), conferences (2019-2022), and clinical trial databases yielded 97 articles. Findings indicate that IH remains a poorly defined diagnosis of exclusion that is difficult to distinguish from narcolepsy type 2 because of symptom overlap and inadequacies of objective testing. Consequently, individuals with IH endure diagnostic delays of up to 9 years. The economic burden of IH has not been characterized to any appreciable extent. Pharmacological treatment options can improve symptoms and functional status, but rarely restores normal levels of functioning. These findings highlight the need to reclassify central disorders of hypersomnolence. Further collaboration is now required between research groups to identify and validate objective markers to help redefine diagnostic criteria for IH. This would move IH into a position that could benefit from future targeted therapeutic interventions. The study was funded by Takeda Development Center Americas, Inc.

A review of information sources and analysis methods for data driven decision aids in child and adolescent mental health services

OBJECTIVE

Clinical data analysis relies on effective methods and appropriate data. Recognizing distinctive clinical services and service functions may lead to improved decision-making. Our first objective is to categorize analytical methods, data sources, and algorithms used in current research on information analysis and decision support in child and adolescent mental health services (CAMHS). Our secondary objective is to identify the potential for data analysis in different clinical services and functions in which data-driven decision aids can be useful.

MATERIALS AND METHODS

We searched related studies in Science Direct and PubMed from 2018 to 2023(Jun), and also in ACM (Association for Computing Machinery) Digital Library, DBLP (Database systems and Logic Programming), and Google Scholar from 2018 to 2021. We have reviewed 39 studies and extracted types of analytical methods, information content, and information sources for decision-making.

RESULTS

In order to compare studies, we developed a framework for characterizing health services, functions, and data features. Most data sets in reviewed studies were small, with a median of 1,176 patients and 46,503 record entries. Structured data was used for all studies except two that used textual clinical notes. Most studies used supervised classification and regression. Service and situation-specific data analysis dominated among the studies, only two studies used temporal, or process features from the patient data. This paper presents and summarizes the utility, but not quality, of the studies according to the care situations and care providers to identify service functions where data-driven decision aids may be relevant.

CONCLUSIONS

Frameworks identifying services, functions, and care processes are necessary for characterizing and comparing electronic health record (EHR) data analysis studies. The majority of studies use features related to diagnosis and assessment and correspondingly have utility for intervention planning and follow-up. Profiling the disease severity of referred patients is also an important application area.

Gender medicine and sleep disorders: from basic science to clinical research

Several pivotal differences in sleep and sleep disorders are recognized between women and men. This is not only due to changes in hormonal balance during women's reproductive life, such as in pregnancy and menopause. Women are more likely to report insomnia and non-specific symptoms of apneas, such as fatigue or mood disturbance, compared to men. Thus, it is important for clinicians and researchers to take sex and gender differences into account when addressing sleep disorders in order to acknowledge the biology unique to women. We present a narrative review that delves into the primary sleep disorders, starting from basic science, to explore the impact of gender differences on sleep and the current status of research on women's sleep health.

Narcolepsy and rapid eye movement sleep

Since the first description of narcolepsy at the end of the 19th Century, great progress has been made. The disease is nowadays distinguished as narcolepsy type 1 and type 2. In the 1960s, the discovery of rapid eye movement sleep at sleep onset led to improved understanding of core sleep-related disease symptoms of the disease (excessive daytime sleepiness with early occurrence of rapid eye movement sleep, sleep-related hallucinations, sleep paralysis, rapid eye movement parasomnia), as possible dysregulation of rapid eye movement sleep, and cataplexy resembling an intrusion of rapid eye movement atonia during wake. The relevance of non-sleep-related symptoms, such as obesity, precocious puberty, psychiatric and cardiovascular morbidities, has subsequently been recognized. The diagnostic tools have been improved, but sleep-onset rapid eye movement periods on polysomnography and Multiple Sleep Latency Test remain key criteria. The pathogenic mechanisms of narcolepsy type 1 have been partly elucidated after the discovery of strong HLA class II association and orexin/hypocretin deficiency, a neurotransmitter that is involved in altered rapid eye movement sleep regulation. Conversely, the causes of narcolepsy type 2, where cataplexy and orexin deficiency are absent, remain unknown. Symptomatic medications to treat patients with narcolepsy have been developed, and management has been codified with guidelines, until the recent promising orexin-receptor agonists. The present review retraces the steps of the research on narcolepsy that linked the features of the disease with rapid eye movement sleep abnormality, and those that do not appear associated with rapid eye movement sleep.

The nature and magnitude of cognitive impairment in narcolepsy type 1, narcolepsy type 2, and idiopathic hypersomnia: a meta-analysis

People with narcolepsy type 1 (NT1), narcolepsy type 2 (NT2), and idiopathic hypersomnia (IH) often report cognitive impairment which can be quite burdensome but is rarely evaluated in routine clinical practice. In this systematic review and meta-analysis, we assessed the nature and magnitude of cognitive impairment in NT1, NT2, and IH in studies conducted from January 2000 to October 2022. We classified cognitive tests assessing memory, executive function, and attention by cognitive domain. Between-group differences were analyzed as standardized mean differences (Cohen's d), and Cohen's d for individual tests were integrated according to cognitive domain and clinical disease group. Eighty-seven studies were screened for inclusion; 39 satisfied inclusion criteria, yielding 73 comparisons (k): NT1, k = 60; NT2, k = 8; IH, k = 5. Attention showed large impairment in people with NT1 (d = -0.90) and IH (d = -0.97), and moderate impairment in NT2 (d = -0.60). Executive function was moderately impaired in NT1 (d = -0.30) and NT2 (d = -0.38), and memory showed small impairments in NT1 (d = -0.33). A secondary meta-analysis identified sustained attention as the most impaired domain in NT1, NT2, and IH (d ≈ -0.5 to -1). These meta-analyses confirm that cognitive impairments are present in NT1, NT2, and IH, and provide quantitative confirmation of reports of cognitive difficulties made by patients and clinicians. These findings provide a basis for the future design of studies to determine whether cognitive impairments can improve with pharmacologic and nonpharmacologic treatments for narcolepsy and IH.

Differences in Polysomnographic and Craniofacial Characteristics of Catathrenia Phenotypes: A Cluster Analysis

Purpose

Catathrenia is a rare sleeping disorder characterized by repetitive nocturnal groaning during prolonged expirations. Patients with catathrenia had heterogeneous polysomnographic, comorbidity, craniofacial characteristics, and responses to treatment. Identifying phenotypes of catathrenia might benefit the exploration of etiology and personalized therapy.

Patients and Methods

Sixty-six patients diagnosed with catathrenia by full-night audio/video polysomnography seeking treatment with mandibular advancement devices (MAD) or continuous positive airway pressure (CPAP) were included in the cohort. Polysomnographic characteristics including sleep architecture, respiratory, groaning, and arousal events were analyzed. Three-dimensional (3D) and 2D craniofacial hard tissue and upper airway structures were evaluated with cone-beam computed tomography and lateral cephalometry. Phenotypes of catathrenia were identified by K-mean cluster analysis, and inter-group comparisons were assessed.

Results

Two distinct clusters of catathrenia were identified: cluster 1 (n=17) was characterized to have more males (71%), a longer average duration of groaning events (18.5±4.8 and 12.8±5.7s, p=0.005), and broader upper airway (volume 41,386±10,543 and 26,661±6700 mm3, p<0.001); cluster 2 (n=49) was characterized to have more females (73%), higher respiratory disturbance index (RDI) (median 1.0 [0.3, 2.0] and 5.2 [1.2, 13.3]/h, p=0.009), more respiratory effort-related arousals (RERA)(1 [1, 109] and 32 [13, 57)], p=0.005), smaller upper airway (cross-sectional area of velopharynx 512±87 and 339±84 mm2, p<0.001) and better response to treatment (41.2% and 82.6%, p=0.004).

Conclusion

Two distinct phenotypes were identified in patients with catathrenia, primary catathrenia, and catathrenia associated with upper airway obstruction, suggesting respiratory events and upper airway structures might be related to the etiology of catathrenia, with implications for its treatment.

Long sleep time and excessive need for sleep: State of the art and perspectives

The mechanisms underlying the individual need for sleep are unclear. Sleep duration is indeed influenced by multiple factors, such as genetic background, circadian and homeostatic processes, environmental factors, and sometimes transient disturbances such as infections. In some cases, the need for sleep dramatically and chronically increases, inducing a daily-life disability. This "excessive need for sleep" (ENS) was recently proposed and defined in a European Position Paper as a dimension of the hypersomnolence spectrum, "hypersomnia" being the objectified complaint of ENS. The most severe form of ENS has been described in Idiopathic Hypersomnia, a rare neurological disorder, but this disabling symptom can be also found in other hypersomnolence conditions. Because ENS has been defined recently, it remains a symptom poorly investigated and understood. However, protocols of long-term polysomnography recordings have been reported by expert centers in the last decades and open the way to a better understanding of ENS through a neurophysiological approach. In this narrative review, we will 1) present data related to the physiological and pathological variability of sleep duration and their mechanisms, 2) describe the published long-term polysomnography recording protocols, and 3) describe current neurophysiological tools to study sleep microstructure and discuss perspectives for a better understanding of ENS.

Prevalence and Course of Idiopathic Hypersomnia in the Wisconsin Sleep Cohort Study

BACKGROUND AND OBJECTIVES

Idiopathic hypersomnia (IH) is a CNS disorder of hypersomnolence of unknown etiology. Due to the requirement for objective sleep testing to diagnose the disorder, there are currently no population-based estimates of the prevalence of IH nor data regarding the longitudinal course of IH in naturalistic settings.

METHODS

Subjective and objective data from the Wisconsin Sleep Cohort study were used to identify cases with probable IH from participants with polysomnography and multiple sleep latency test data. Demographic, polysomnographic, and symptom-level data were compared between those with and without IH. Longitudinal trajectories of daytime sleepiness among those with IH were assessed to evaluate symptom persistence or remission over time.

RESULTS

From 792 cohort study participants with available polysomnography and multiple sleep latency test data, 12 cases with probable IH were identified resulting in an estimated prevalence of IH of 1.5% (95% CI 0.7-2.5, p < 0.0001). Consistent with inclusion/exclusion criteria, cases with IH had more severe sleepiness and sleep propensity, despite similar or longer sleep times. Longitudinal data (spanning 12.1 ± 4.3 years) demonstrated a chronic course of sleepiness for most of the cases with IH, though pathologic somnolence remitted in roughly 40% of cases.

DISCUSSION

These results demonstrate IH is more common in the working population than generally assumed with a prevalence on par with other common neurologic and psychiatric conditions. Further efforts to identify and diagnose those impaired by unexplained daytime somnolence may help clarify the causes of IH and the mechanisms underlying symptomatic remission.

Disentangling the complex landscape of sleep-wake disorders with data-driven phenotyping: A study of the Bernese center

BACKGROUND AND PURPOSE

The diagnosis of sleep-wake disorders (SWDs) is challenging because of the existence of only few accurate biomarkers and the frequent coexistence of multiple SWDs and/or other comorbidities. The aim of this study was to assess in a large cohort of well-characterized SWD patients the potential of a data-driven approach for the identification of SWDs.

METHODS

We included 6958 patients from the Bernese Sleep Registry and 300 variables/biomarkers including questionnaires, results of polysomnography/vigilance tests, and final clinical diagnoses. A pipeline, based on machine learning, was created to extract and cluster the clinical data. Our analysis was performed on three cohorts: patients with central disorders of hypersomnolence (CDHs), a full cohort of patients with SWDs, and a clean cohort without coexisting SWDs.

RESULTS

A first analysis focused on the cohort of patients with CDHs and revealed four patient clusters: two clusters for narcolepsy type 1 (NT1) but not for narcolepsy type 2 or idiopathic hypersomnia. In the full cohort of SWDs, nine clusters were found: four contained patients with obstructive and central sleep apnea syndrome, one with NT1, and four with intermixed SWDs. In the cohort of patients without coexisting SWDs, an additional cluster of patients with chronic insomnia disorder was identified.

CONCLUSIONS

This study confirms the existence of clear clusters of NT1 in CDHs, but mainly intermixed groups in the full spectrum of SWDs, with the exception of sleep apnea syndromes and NT1. New biomarkers are needed for better phenotyping and diagnosis of SWDs.

Idiopathic hypersomnia and Kleine-Levin syndrome

Idiopathic hypersomnia (IH) and Kleine-Levin syndrome (KLS) are rare disorders of central hypersomnolence of unknown cause, affecting young people. However, increased sleep time and excessive daytime sleepiness (EDS) occur daily for years in IH, whereas they occur as relapsing/remitting episodes associated with cognitive and behavioural disturbances in KLS. Idiopathic hypersomnia is characterized by EDS, prolonged, unrefreshing sleep at night and during naps, and frequent morning sleep inertia, but rare sleep attacks, no cataplexy and sleep onset in REM periods as in narcolepsy. The diagnosis requires: (i) ruling out common causes of hypersomnolence, including mostly sleep apnea, insufficient sleep syndrome, psychiatric hypersomnia and narcolepsy; and (ii) obtaining objective EDS measures (mean latency at the multiple sleep latency test≤8min) or increased sleep time (sleep time>11h during a 18-24h bed rest). Treatment is similar to narcolepsy (except for preventive naps), including adapted work schedules, and off label use (after agreement from reference/competence centres) of modafinil, sodium oxybate, pitolisant, methylphenidate and solriamfetol. The diagnosis of KLS requires: (i) a reliable history of distinct episodes of one to several weeks; (ii) episodes contain severe hypersomnia (sleep>15h/d) associated with cognitive impairment (mental confusion and slowness, amnesia), derealisation, major apathy or disinhibited behaviour (hypersexuality, megaphagia, rudeness); and (iii) return to baseline sleep, cognition, behaviour and mood after episodes. EEG may contain slow rhythms during episodes, and rules out epilepsy. Functional brain imaging indicates hypoactivity of posterior associative cortex and hippocampus during symptomatic and asymptomatic periods. KLS attenuates with time when starting during teenage, including less frequent and less severe episodes. Adequate sleep habits, avoidance of alcohol and infections, as well as lithium and sometimes valproate (off label, after agreement from reference centres) help reducing the frequency and severity of episodes, and IV methylprednisolone helps reducing long (>30d) episode duration.

Pharmacological options for narcolepsy: are they the way forward?

INTRODUCTION

Narcolepsy is an under-recognized, rare neurologic disorder of hypersomnolence that is associated with increased mortality and medical and psychiatric co-morbidities. Narcolepsy exerts a substantial economic burden on patients and society. There is currently no cure, and life-long symptomatic therapy is needed. Available drugs do not modify the disease course.

AREAS COVERED

This manuscript provides an overview of narcolepsy symptoms, diagnosis, pathophysiology, current pharmacotherapies, and emerging treatments. Gaps and unresolved issues in diagnosis and management of narcolepsy are discussed to answer whether pharmacological options are the way forward.

EXPERT OPINION

Diagnostic criteria for narcolepsy (ICSD-3) need revision and greater clarity. Improved recognition of cataplexy and other symptoms through educational outreach, new biomarkers, improved test scoring through artificial intelligence algorithms, and use of machine learning may facilitate earlier diagnosis and treatment. Pharmacological options need improved symptomatic therapy in addition to targeted therapies that address the loss of hypocretin signaling. Optimal narcolepsy care also needs a better understanding of the pathophysiology, recognition of the different phenotypes in narcolepsy, identification of at-risk individuals and early recognition of symptoms, better diagnostic tools, and a database for research and disease monitoring of treatment, side-effects, and comorbidities.

Update on the treatment of idiopathic hypersomnia: Progress, challenges, and expert opinion

Idiopathic hypersomnia is a central hypersomnolence disorder of unknown origin characterized by excessive daytime sleepiness despite normal or long sleep time, and frequent severe sleep inertia. Management strategies have been largely derived from expert consensus, due to a lack of disease-specific assessments and reliance on case series and rare randomized controlled studies. Guidelines recommend treatment with off-label medications. Modafinil, which was approved for idiopathic hypersomnia until 2011 in Europe, is the most commonly used treatment and improved sleepiness in two recent randomized placebo-controlled trials. In 2021, low-sodium oxybate (LXB) was approved in the United States for idiopathic hypersomnia. In a placebo-controlled, double-blind, randomized withdrawal study, LXB reduced daytime sleepiness and sleep inertia, and improved daily functioning. Here, treatment options are reviewed considering the authors' professional experience, current guidelines, and the latest research developments. The choice of pharmacotherapy should be guided by symptom profile, age, comorbidities (eg, depressive symptoms, cardiovascular problems), and concomitant medications (eg, oral contraceptives). Nonpharmacologic approaches have a role in management. An instrument (idiopathic hypersomnia severity scale) has been validated in idiopathic hypersomnia specifically, opening a path to better assessment of symptoms, impact, and response to treatment. Continued research on idiopathic hypersomnia is needed to support treatment algorithms.

The evolving diagnosis and classification of CNS hypersomnolence disorders

Purpose of Review

We describe the evolution and limitations of current diagnostic criteria and classification systems of CNS hypersomnolence disorders and propose some changes.

Recent Findings

An unsupervised cluster analysis of 1100 Europeans with hypersomnolence identified the narcolepsy type 1 phenotype but not other categories listed in ICSD-3.

Summary

There are long-standing unsolved issues regarding the diagnosis and classification of central disorders of hypersomnolence. These include delineating and identifying phenotypes and unique conditions (“sui generis”), sleep deprivation’s impact on phenotypes and how to separate sleep deprivation as a trigger from other causes, as well as the association of excessive sleepiness with other disorders. We discuss these issues and present a novel, straightforward classification system with consistent terminology to get out of the impasse and do justice to people with hypersomnolence.

Differentiation of central disorders of hypersomnolence with manual and artificial-intelligence-derived polysomnographic measures

Differentiation of central disorders of hypersomnolence (DOH) is challenging but important for patient care. This study aimed to investigate whether biomarkers derived from sleep structure evaluated both by manual scoring as well as with artificial intelligence (AI) algorithms allow distinction of patients with different DOH. We included video-polysomnography data of 40 narcolepsy type 1 (NT1), 26 narcolepsy type 2 (NT2), 23 patients with idiopathic hypersomnia (IH) and 54 participants with subjective excessive daytime sleepiness (sEDS). Sleep experts manually scored sleep stages. A previously validated AI algorithm was employed to obtain automatic hypnograms and hypnodensity graphs (where each epoch is represented as a mixture of sleep stage probabilities). One-thousand-three features describing sleep architecture and instability were extracted from manual/automatic hypnogram and hypnodensity graphs. After feature selection, random forest classifiers were trained and tested in a 5-fold-cross-validation scheme to distinguish groups pairwise (NT1-vs-NT2, NT1-vs-IH, …) and single groups from the pooled remaining ones (NT1-vs-rest, NT2-vs-rest,…). The accuracy/F1-score values obtained in the test sets were: 0.74 ± 0.04/0.79 ± 0.05 (NT1-vs-NT2), 0.89 ± 0.09/0.91 ± 0.08 (NT1-vs-IH), 0.93 ± 0.06/0.91 ± 0.07 (NT1-vs-sEDS), 0.88 ± 0.04/0.80 ± 0.07 (NT1-vs-rest), 0.65 ± 0.10/0.70 ± 0.09 (NT2-vs-IH), 0.72 ± 0.12/0.60 ± 0.10 (NT2-vs-sEDS), 0.54 ± 0.19/0.38 ± 0.13 (NT2-vs-rest), 0.57 ± 0.11/0.35 ± 0.18 (IH-vs-sEDS), 0.71 ± 0.08/0.35 ± 0.10 (IH-vs-rest) and 0.76 ± 0.08/0.71 ± 0.13 (sEDS-vs-rest). The results confirm previous findings on sleep instability in patients with NT1 and show that combining manual and automatic AI-based sleep analysis could be useful for better distinction of NT2 from IH, but no precise sleep biomarker of NT2 or IH could be identified. Validation in a larger and multi-centric cohort is needed to confirm these findings.

Gender differences in narcolepsy: What are recent findings telling us?

Three papers currently published in SLEEP using two different mouse models of narcolepsy, including either Hcrt-tTa;TetO diptheria toxin-A (DTA) or Hypocretin knock-out (Hcrt-KO) mice, suggest important gender differences in narcolepsy expression. Specifically, these recent data corroborate previous findings in mice demonstrating that females show more cataplexy events and more total cataplexy expression than males. Moreover, in the neurotoxic DTA mouse model, females show earlier onset of cataplexy expression than males during active Hcrt cell loss. Finally, females show a doubling of cataplexy during estrous compared to other phases of the estrous cycle. These findings are reviewed in the broader context of prior published literature, including reported gender differences in Hcrt expression and hormonal influences on sleep and wakefulness. Although similar findings have not been reported in humans, a systematic evaluation of gender differences in human narcolepsy has yet to be performed. Taken together, these animal data suggest that more research exploring gender differences in human narcolepsy is warranted.

Clinical considerations for the diagnosis of idiopathic hypersomnia

Idiopathic hypersomnia is a sleep disorder of neurologic origin characterized by excessive daytime sleepiness, with sleep inertia, long, unrefreshing naps, and prolonged nighttime sleep being key symptoms in many patients. Idiopathic hypersomnia is described in the International Classification of Sleep Disorders, 3rd Edition as a central disorder of hypersomnolence with distinct clinical features and diagnostic criteria; however, confirming the diagnosis of idiopathic hypersomnia is often challenging. Diagnosis of idiopathic hypersomnia is based on objective sleep testing and the presence of associated clinical features but may be difficult for clinicians to recognize and correctly diagnose because of its low prevalence, clinical heterogeneity, and symptoms, which are similar to those of other sleep disorders. The testing required for diagnosis of idiopathic hypersomnia also presents logistical barriers, and reliability of objective sleep measures is suboptimal. The pathophysiology of idiopathic hypersomnia remains unknown. In this review, clinical considerations related to the pathogenesis, diagnosis, and management of idiopathic hypersomnia will be discussed, including perspectives from the European Union and United States.

Central Disorders of Hypersomnolence: Association with Fatigue, Depression and Sleep Inertia Prevailing in Women

Fatigue, depression, and sleep inertia are frequently underdiagnosed manifestations in narcolepsy and idiopathic hypersomnia. Our cross-sectional study design included diagnostic interview accompanied by assessment instruments and aimed to explore how these factors influence disease severity as well as to elucidate any sex predisposition. One hundred and forty-eight subjects (female 63%) were divided into narcolepsy type 1 (NT1; n = 87, female = 61%), narcolepsy type 2 (NT2; n = 22, female = 59%), and idiopathic hypersomnia (IH; n = 39, female = 69%). All subjects completed a set of questionnaires: Epworth Sleepiness Scale (ESS), Hospital Anxiety and Depression Scales (HADS), Fatigue Severity Scale (FSS), and Sleep Inertia Questionnaire (SIQ). In narcoleptic subjects, questionnaire data were correlated with the Narcolepsy Severity Scale (NSS), and in subjects with idiopathic hypersomnia, with the Idiopathic Hypersomnia Severity Scale (IHSS). The highest correlation in narcoleptic subjects was found between NSS and ESS (r = 0.658; p < 0.0001), as well as FSS (r = 0.506; p < 0.0001), while in subjects with idiopathic hypersomnia, the most prominent positive correlations were found between IHSS and SIQ (r = 0.894; p < 0.0001), FSS (r = 0.812; p < 0.0001), HADS depression scale (r = 0.649; p = 0.0005), and HADS anxiety scale (r = 0.528; p < 0.0001). ESS showed an analogic correlation with disease severity (r = 0.606; p < 0.0001). HADS anxiety and depression scores were higher in females (p < 0.05 and p < 0.01), with similar results for FSS and SIQ scales (p < 0.05 for both), and a trend toward higher ESS values in females (p = 0.057). Our study illustrates that more attention should be focused on pathophysiological mechanisms and associations of fatigue, depression, as well as sleep inertia in these diseases; they influence the course of both illnesses, particularly in women.