Comparative polysomnography parameters between narcolepsy type 1/type 2 and idiopathic hypersomnia: A systematic review and meta-analysis

High-resolution HLA sequencing and hypocretin receptor 2 autoantibodies in narcolepsy type 1 and type 2

Narcolepsy is a sleep disorder caused by an apparent degeneration of orexin/hypocretin neurons in the lateral hypothalamic area and a subsequent decrease in orexin/hypocretin levels in the cerebrospinal fluid. Narcolepsy is classified into type 1 (NT1) and type 2 (NT2). While genetic associations in the human leukocyte antigen (HLA) region and candidate autoantibodies have been investigated in NT1 to imply an autoimmune origin, less is known about the pathogenesis in NT2. Twenty-six NT1 and 15 NT2 patients were included, together with control groups of 24 idiopathic hypersomnia (IH) patients and 778 general population participants. High-resolution sequencing was used to determine the alleles, the extended haplotypes, and the genotypes of HLA-DRB3, -DRB4, -DRB5, -DRB1, -DQA1, -DQB1, -DPA1, and -DPB1. Radiobinding assay was used to determine autoantibodies against hypocretin receptor 2 (anti-HCRTR2 autoantibodies). NT1 was associated with HLA-DRB5*01:01:01, -DRB1*15:01:01, -DQA1*01:02:01, -DQB1*06:02:01, -DRB5*01:01:01, -DRB1*15:01:01, -DQA1*01:02:01, -DQB1*06:02:01 (odds ratio [OR]: 9.15; p = 8.31 × 10-4) and HLA-DRB5*01:01:01, -DRB1*15:01:01, -DQA1*01:02:01, -DQB1*06:02:01, -DRB4*01:03:01, -DRB1*04:01:01, -DQA1*03:02//03:03:01, -DQB1*03:01:01 (OR: 23.61; p = 1.58 × 10-4) genotypes. Lower orexin/hypocretin levels were reported in the NT2 subgroup (n = 5) that was associated with the extended HLA-DQB1*06:02:01 haplotype (p = .001). Anti-HCRTR2 autoantibody levels were not different between study groups (p = .8524). We confirmed the previous association of NT1 with HLA-DQB1*06:02:01 extended genotypes. A subgroup of NT2 patients with intermediate orexin/hypocretin levels and association with HLA-DQB1*06:02:01 was identified, indicating a possible overlap between the two distinct narcolepsy subtypes, NT1 and NT2. Low anti-HCRTR2 autoantibody levels suggest that these receptors might not function as autoimmune targets in either NT1 or NT2.

Correlation analysis between HLA-DQA1*0102/DQB1*0602 genotypes and narcolepsy patients in China

Background and objective

At present, the etiology of narcolepsy is not fully understood, and it is generally believed to be an autoimmune reaction caused by interactions between environmental and genetic factors. Human leukocyte antigen (HLA) class II genes are strongly associated with this gene, especially HLA-DQB1*0602/DQA1*0102. In this study, we mainly analyzed the correlation between different genotypes of HLA-DQB1*0602/DQA1*0102 and clinical manifestations in Chinese patients with narcolepsy.

Experimental method

Narcolepsy patients who were treated at the Department of Neurology, The First Affiliated Hospital of Shandong First Medical University from January 2021 to September 2023 were selected. General information, sleep monitoring data, cerebrospinal fluid (CSF) orexin levels, and human leukocyte antigen gene typing data were collected. The statistical analysis was performed using SPSS 26.0, and the graphs were drawn using GraphPad Prism 9.5.

Main results

A total of 78 patients were included in this study. The DQA1 and DQB1 gene loci were detected in 54 patients, and only the DQB1 gene locus was detected in 24 narcoleptic patients. The most common allele at the HLA-DQB1 locus was *0602 (89.7%), and the most common genotype at this locus was *0602*0301 (19.2%), followed by *0602*0602 (17.9%). The most common phenotype of the HLA-DQA1 locus is *0102 (92.6%), and the most common genotype of this locus is *0102*0102 (27.8%), followed by *0102*0505 (14.8%). There were significant differences (p < 0.05) between HLA-DQB1*0602-positive and HLA-DQB1*0602-negative patients in terms of orexin-A levels, presence or absence of cataplexy, UNS, PSG sleep latency, REM sleep latency, N1 sleep percentage, oxygen depletion index, and average REM latency on the MSLT. The HLA-DQA1*0102-positive and HLA-DQA1*0102-negative patients showed significant differences (p < 0.05) in disease course, presence or absence of sudden onset, PSG REM sleep latency, N1 sleep percentage, and average REM latency on the MSLT. There were significant differences in the average REM latency of the MSLT between HLA-DQB1*0602/DQA1*0102 homozygous and heterozygous patients p < 0.05, and no differences were found in the baseline data, orexin-A levels, scale scores, or other sleep parameters.

Conclusion

Different genotypes of HLA-DQA1*0102/DQB1*0602 are associated with symptoms of cataplexy in Chinese narcoleptic patients. Homozygous individuals have a shorter mean REM latency in the MSLT, greater genetic susceptibility, and relatively more severe sleepiness.

Ultradian sleep cycles: Frequency, duration, and associations with individual and environmental factors-A retrospective study

OBJECTIVE

Sleep varies between individuals in response to sleep-wake history and various environmental factors, including light and noise. Here we report on the intranight variation of the ultradian nonrapid eye movement-rapid eye movement (NREM-REM) sleep cycle in 369 participants who have contributed to different laboratory studies from 1994 to 2020 at the Centre for Chronobiology, Basel, Switzerland.

RESULTS

We observed a large interindividual variability in sleep cycle duration, including NREM and REM sleep episodes in healthy participants who were given an 8-hour sleep opportunity at habitual bedtime in controlled laboratory settings. The median sleep cycle duration was 96 minutes out of 6064 polysomnographically-recorded cycles. The number and duration of cycles were not normally distributed, and the distribution became narrower for NREM sleep and wider for REM sleep later in the night. The first cycle was consistently shorter than subsequent cycles, and moderate presleep light or nocturnal noise exposure had no significant effects on ultradian sleep cycle duration. Age and sex significantly affected NREM and REM sleep duration, with older individuals having longer NREM and shorter REM sleep particularly in the end of the night, and females having longer NREM sleep episodes. High sleep pressure (ie, sleep deprivation) and low sleep pressure (ie, multiple naps) altered ultradian sleep cycles, with high sleep pressure leading to longer NREM sleep in the first cycle, and low sleep pressure leading to longer REM sleep episodes. Positive correlations were observed between N2 and NREM duration, and between N1 and REM duration. Weak intrasleep REM sleep homeostasis was also evident in our data set.

CONCLUSIONS

We conclude that ultradian sleep cycles are endogenous biological rhythms modulated by age, sex, and sleep homeostasis, but not directly responsive to (moderate levels of) environmental cues in healthy good sleepers.

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.

Narcolepsy Diagnosis With Sleep Stage Features Using PSG Recordings

Narcolepsy is a sleep disorder affecting millions of people worldwide and causes serious public health problems. It is hard for doctors to correctly and objectively diagnose narcolepsy. Polysomnography (PSG) recordings, a gold standard for sleep monitoring and quality measurement, can provide abundant and objective cues for the narcolepsy diagnosis. There have been some studies on automatic narcolepsy diagnosis using PSG recordings. However, the sleep stage information, an important cue for narcolepsy diagnosis, has not been fully utilized. For example, some studies have not considered the sleep stage information to diagnose narcolepsy. Although some studies consider the sleep stage information, the stages are manually scored by experts, which is time-consuming and subjective. And the framework using sleep stages scored automatically for narcolepsy diagnosis is designed in a two-phase learning manner, where sleep staging in the first phase and diagnosis in the second phase, causing cumulative error and degrading the performance. To address these challenges, we propose a novel end-to-end framework for automatic narcolepsy diagnosis using PSG recordings. In particular, adopting the idea of multi-task learning, we take the sleep staging as our auxiliary task, and then combine the sleep stage related features with narcolepsy related features for our primary task of narcolepsy diagnosis. We collected a dataset of PSG recordings from 77 participants and evaluated our framework on it. Both of the sleep stage features and the end-to-end fashion contribute to diagnosis performance. Moreover, we do a comprehensive analysis on the relationship between sleep stages and narcolepsy, correlation of different channels, predictive ability of different sensing data, and diagnosis results in subject level.

Novel Objective Measures of Hypersomnolence

Purpose of review

To provide a brief overview of current objective measures of hypersomnolence, discuss proposed measure modifications, and review emerging measures.

Recent findings

There is potential to optimize current tools using novel metrics. High-density and quantitative EEG-based measures may provide discriminative informative. Cognitive testing may quantify cognitive dysfunction common to hypersomnia disorders, particularly in attention, and objectively measure pathologic sleep inertia. Structural and functional neuroimaging studies in narcolepsy type 1 have shown considerable variability but so far implicate both hypothalamic and extra-hypothalamic regions; fewer studies of other CDH have been performed. There is recent renewed interest in pupillometry as a measure of alertness in the evaluation of hypersomnolence.

Summary

No single test captures the full spectrum of disorders and use of multiple measures will likely improve diagnostic precision. Research is needed to identify novel measures and disease-specific biomarkers, and to define combinations of measures optimal for CDH diagnosis.

What respiratory physicians should know about narcolepsy and other hypersomnias

Narcolepsy and related central disorders of hypersomnolence may present to the sleep clinic with excessive daytime sleepiness. A strong clinical suspicion and awareness of the diagnostic clues, such as cataplexy, are essential to avoid unnecessary diagnostic delay. This review provides an overview of the epidemiology, pathophysiology, clinical features, diagnostic criteria and management of narcolepsy and related disorders, including idiopathic hypersomnia, Kleine-Levin syndrome (recurrent episodic hypersomnia) and secondary central disorders of hypersomnolence.