Maruo Takashi, Takagi Shunsuke, Uchida Sunao, Takahashi Hidehiko, Sugihara Genichi
Department of Psychiatry and Behavioral Sciences, Tokyo Medical and Dental University Graduate School, Tokyo, Japan.
Sunao Clinic, Saitama, Japan.
Front Psychiatry. 2024 Mar 13;15:1361140. doi: 10.3389/fpsyt.2024.1361140. eCollection 2024.
Excessive daytime sleepiness (EDS) is a crucial symptom that diminishes the quality of life. The primary causes of EDS are central hypersomnia, including narcolepsy type 1 (NT1), type 2 (NT2), and idiopathic hypersomnia (IH). EDS is often associated with other psychiatric disorders, particularly attention deficit hyperactivity disorder (ADHD). The Multiple Sleep Latency Test (MSLT) is the standard assessment tool for EDS. Although the MSLT yields numerous parameters, most are not employed in clinical practice. In this study, we leveraged novel MSLT parameters to discern central hypersomnia and ADHD presence. Our analysis focused on sleep latency variability and employed cluster analysis to identify unique temporal patterns.
We examined the MSLT data from 333 patients; of these, 200 (aged 14-54, mean: 24.9 ± 8.1, years; 114 females) met the inclusion criteria comprising comprehensive data an Apnea-Hypopnea Index (AHI) below 5, and no prior diagnosis of sleep apnea syndrome. We employed a time-course cluster approach that specifically targeted sleep latency variability during the MSLT.
Considering both multiple clustering quality evaluations and the study's objectives, we identified 9 distinct clusters. Clusters 1 and 3 predominantly had MSLT-positive results; Cluster 2 was entirely MSLT-positive; Clusters 4, 5, 6, 8, and 9 were mainly MSLT-negative; and Cluster 7 had mixed results. The diagnosis of hypersomnia varied notably among Clusters 1, 2, 3, and 7, with Cluster 2 demonstrating a pronounced tendency towards NT1 and NT2 diagnoses (p < 0.005). However, no significant correlation was observed between ADHD diagnoses and specific sleep latency patterns in any cluster.
Our study highlights the value of time-course clustering in understanding sleep latency patterns of patients with central hypersomnia.
日间过度嗜睡（EDS）是一种会降低生活质量的关键症状。EDS的主要病因是中枢性过度嗜睡，包括1型发作性睡病（NT1）、2型发作性睡病（NT2）和特发性过度嗜睡（IH）。EDS常与其他精神障碍相关，尤其是注意力缺陷多动障碍（ADHD）。多次睡眠潜伏期试验（MSLT）是评估EDS的标准工具。尽管MSLT会产生众多参数，但大多数参数在临床实践中并未得到应用。在本研究中，我们利用新的MSLT参数来辨别中枢性过度嗜睡和ADHD的存在情况。我们的分析聚焦于睡眠潜伏期变异性，并采用聚类分析来识别独特的时间模式。
我们检查了333例患者的MSLT数据；其中200例（年龄14 - 54岁，平均：24.9 ± 8.1岁；114例女性）符合纳入标准，包括完整数据、呼吸暂停低通气指数（AHI）低于5，且既往无睡眠呼吸暂停综合征诊断。我们采用了一种时间进程聚类方法，专门针对MSLT期间的睡眠潜伏期变异性。
综合考虑多种聚类质量评估和研究目标，我们识别出9个不同的聚类。聚类1和3主要为MSLT阳性结果；聚类2完全为MSLT阳性；聚类4、5、6、8和9主要为MSLT阴性；聚类7结果混杂。聚类1、2、3和7之间，过度嗜睡的诊断差异显著，聚类2表现出明显的NT1和NT2诊断倾向（p < 0.005）。然而，在任何聚类中，ADHD诊断与特定睡眠潜伏期模式之间均未观察到显著相关性。
我们的研究突出了时间进程聚类在理解中枢性过度嗜睡患者睡眠潜伏期模式方面的价值。
