Baier Paul Christian, Sahlström Hildur, Markström Agneta, Furmark Tomas, Bothelius Kristoffer
University Hospital Schleswig-Holstein, Department of Psychiatry and Psychotherapy, Kiel, Germany.
Uppsala University, Department of Psychology, Uppsala, Sweden.
Sleep Med. 2024 Dec;124:127-133. doi: 10.1016/j.sleep.2024.09.026. Epub 2024 Sep 16.
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.
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.
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.
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.
特发性嗜睡症（IH）的诊断过程较为复杂，原因在于白天嗜睡的病因多样、病理生理理解不明确以及症状存在变异性。当前的诊断工具，如多次睡眠潜伏期试验（MSLT），在充分体现IH的多样性方面能力有限。本研究旨在通过对多导睡眠图数据进行聚类分析来划分IH患者的亚组，并研究其症状的时间演变，以提高对IH的理解粒度和个性化治疗方法。
本研究纳入了2010年至2019年转诊至乌普萨拉睡眠障碍中心的个体，这些个体在经过全面诊断评估后，根据《国际睡眠障碍分类第3版》（ICSD - 3）标准被诊断为IH。在排除数据不完整或患有严重合并症睡眠相关呼吸疾病的参与者后，最终队列包括69名受试者，其中49名女性和20名男性，平均年龄为40岁。数据通过多导睡眠图（PSG）、MSLT和标准化问卷收集。采用两步聚类分析来梳理IH内部的异质性，重点关注不同睡眠阶段的客观时间分配以及从PSG得出的睡眠效率。该研究还旨在追踪症状随时间的亚组特异性变化，随访时间为诊断后21至179个月。
两步聚类分析产生了两个轮廓系数令人满意的不同组：第1组（n = 29；42%）和第2组（n = 40；58%）。与第2组相比，第1组表现出深睡眠持续时间增加、浅睡眠2期减少以及睡眠维持效率更高。对非聚类变量的进一步分析表明，第1组睡眠起始后觉醒时间较短，但在其他睡眠参数、MSLT结果、体重指数、年龄或自我报告的睡眠惯性或药物使用测量方面无显著差异。长期随访评估显示白天过度嗜睡总体有所改善，组间无显著差异。
对诊断为IH的患者进行的这项探索性两步聚类分析识别出了两个具有不同夜间睡眠特征的亚组，与先前的研究结果一致，并支持IH可能包含几种表型的观点，每种表型可能都需要量身定制的治疗策略。有必要进行进一步研究以证实这些发现。
