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Network Analysis in Central Disorders of Hypersomnolence: Insight Into Relationships Between Symptoms, Mental Health,
Denise Bijlenga1,2, Tessa F Blanken3, Josephine J de Boer1,2
1Stichting Epilepsie Instellingen Nederland (SEIN), Sleep-Wake Center, Heemstede, the Netherlands.
Abstract:
In the central disorders of hypersomnolence (CDH) narcolepsy types 1 and 2 (NT1, NT2) and idiopathic hypersomnia (IH), mental health problems are highly prevalent, and quality of life is decreased. The aim was to gain insight into relationships between CDH-related symptoms, mental health, and quality of life (QoL) in CDH, using network analysis. Variables involved CDH symptoms, levels of self-reported sleepiness, fatigue, mood, anxiety, attention, hyperactivity/impulsivity, apathy, and QoL indicators in CDH. Associations with diagnosis, sex, and treatment status were examined. The network analysis was on cross-sectional questionnaire data of N = 314 with CDH (NT1 n = 203; NT2/IH n = 111). A Mixed Graphical Model (MGM) with 16 nodes was estimated. Edges were selected using Regularization with EBIC-LASSO; network stability was determined with bootstrapping. Network comparison tests (NCTs) were performed between sub-groups for diagnosis (NT1 vs. NT2/IH), sex, and treatment status. We found that fatigue and depressive symptoms had the highest conditional associations with QoL variables. QoL outcome 'Energy, attention, and activities' had the most conditional associations with other variables in the network, indicating relative importance. Typical CDH symptoms clustered together, of which sleep inertia had the highest conditional association with QoL 'Coping with CDH'. Subjective sleepiness was not directly related to any variables in the network. NCTs showed no significant differences between diagnoses, sexes, or treatment statuses. This study indicates that, within the examined network, fatigue and depressive symptoms are most associated with QoL in CDH, signalling potential relevance in treatment. The impact of treatment on the symptom network should be further investigated using longitudinal data.
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