Multiple Sleep Latency Test and Polysomnography in Patients with Central Disorders of Hypersomnolence

A multiple sleep latency test (MSLT) with occurrence of sleep onset REM periods (SOREMP) is considered one of the central diagnostic criteria for narcolepsy according to the International Classification of Sleep Disorders, but its sensitivity and specificity have been questioned. This study aims to describe MSLT and polysomnography (PSG) findings, including frequency and distribution of SOREMP during the day, in a large cohort of patients with central disorders of hypersomnolence (CDH). We retrospectively analyzed electrophysiological data from MSLT and PSG in 370 consecutive patients with narcolepsy type 1 (NT1, n = 97), type 2 (NT2, n = 31), idiopathic hypersomnia (IH, n = 48), nonorganic hypersomnia (NOH, n = 116) and insufficient sleep syndrome (ISS, n = 78). NT1 and NT2 patients had a significantly shorter mean Sleep Latency (mSL) and REM-Latency (REML) in MSLT and PSG. SOREMP occurred more frequently in narcoleptic vs. non-narcoleptic patients in MSLT and PSG. Occurrence of 3 or more SOREMP in MSLT and a SOREMP in PSG had a very high specificity and positive predictive value (98%/96% and 100% respectively), however relatively low sensitivity (65% and 45% respectively). NT1 more than NT2 patients have shorter mSL and more frequent SOREMP in MSLT and shorter SL as well as REML during nocturnal PSG. Increasing numbers of SOREMP in MSLT and especially SOREMP during PSG increase specificity on the expense of sensitivity in diagnosing narcolepsy. Therefore, frequency of SOREMP in MSLT naps and PSG can help to discriminate but not clearly separate narcoleptic from non-narcoleptic patients

Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning

Narcolepsy is a rare life-long disease that exists in two forms, narcolepsy type-1 (NT1) or type-2 (NT2), but only NT1 is accepted as clearly defined entity. Both types of narcolepsies belong to the group of central hypersomnias (CH), a spectrum of poorly defined diseases with excessive daytime sleepiness as a core feature. Due to the considerable overlap of symptoms and the rarity of the diseases, it is difficult to identify distinct phenotypes of CH. Machine learning (ML) can help to identify phenotypes as it learns to recognize clinical features invisible for humans. Here we apply ML to data from the huge European Narcolepsy Network (EU-NN) that contains hundreds of mixed features of narcolepsy making it difficult to analyze with classical statistics. Stochastic gradient boosting, a supervised learning model with built-in feature selection, results in high performances in testing set. While cataplexy features are recognized as the most influential predictors, machine find additional features, e.g. mean rapid-eye-movement sleep latency of multiple sleep latency test contributes to classify NT1 and NT2 as confirmed by classical statistical analysis. Our results suggest ML can identify features of CH on machine scale from complex databases, thus providing 'ideas' and promising candidates for future diagnostic classifications.</p

Narcolepsy risk loci outline role of T cell autoimmunity and infectious triggers in narcolepsy

Narcolepsy has genetic and environmental risk factors, but the specific genetic risk loci and interaction with environmental triggers are not well understood. Here, the authors identify genetic loci for narcolepsy, suggesting infection as a trigger and dendritic and helper T cell involvement. Narcolepsy type 1 (NT1) is caused by a loss of hypocretin/orexin transmission. Risk factors include pandemic 2009 H1N1 influenza A infection and immunization with Pandemrix (R). Here, we dissect disease mechanisms and interactions with environmental triggers in a multi-ethnic sample of 6,073 cases and 84,856 controls. We fine-mapped GWAS signals within HLA (DQ0602, DQB1*03:01 and DPB1*04:02) and discovered seven novel associations (CD207, NAB1, IKZF4-ERBB3, CTSC, DENND1B, SIRPG, PRF1). Significant signals at TRA and DQB1*06:02 loci were found in 245 vaccination-related cases, who also shared polygenic risk. T cell receptor associations in NT1 modulated TRAJ*24, TRAJ*28 and TRBV*4-2 chain-usage. Partitioned heritability and immune cell enrichment analyses found genetic signals to be driven by dendritic and helper T cells. Lastly comorbidity analysis using data from FinnGen, suggests shared effects between NT1 and other autoimmune diseases. NT1 genetic variants shape autoimmunity and response to environmental triggers, including influenza A infection and immunization with Pandemrix (R)

Dopaminergic treatment in idiopathic restless legs syndrome: effects on subjective sleepiness

OBJECTIVES: To assess frequency and characteristics of excessive daytime sleepiness (EDS) in restless legs syndrome (RLS) and the evolution of EDS under different RLS therapies. METHODS: We analyzed data from the "Swiss RLS" study, which was conducted to compare treatment efficacy and safety of the dopamine agonist pramipexole (PPX) versus L-dopa/benserazide (L/B) in de novo patients with idiopathic RLS and performed as a randomized, double-dummy, comparative crossover trial. Primary outcome measure of the present study was the change in subjective sleepiness (as measured by Epworth sleepiness scale [ESS] score). There were 37 patients (21 women) included. Mean age was 56.6 years (range, 25-85 years), and mean body mass index was 24.6 (SD, ±3.5). RESULTS: At baseline, EDS (as determined by an ESS score of >10) was found in 32% of the patients. Sleepy RLS patients were younger (P < 0.001) than non-sleepy patients. Pramipexole and L/B both were effective in the treatment of RLS symptoms (IRLS score, P < 0.001 and P = 0.002). Overall, ESS was reduced (main effect for "time", P = 0.02) independent from the dopaminergic substance. In 5 of 37 patients, ESS score deteriorated to greater than 10 under treatment (PPX = 3 patients, L/B = 2 patients). No sleep attack occurred. CONCLUSIONS: Excessive daytime sleepiness is frequent in RLS patients. Dopaminergic treatment usually promotes wakefulness, but infrequently leads to daytime sleepiness

Isolated mediotegmental lesion causing narcolepsy and rapid eye movement sleep behaviour disorder: a case evidencing a common pathway in narcolepsy and rapid eye movement sleep behaviour disorder

Narcolepsy is usually an idiopathic disorder, often with a genetic predisposition. Symptomatic cases have been described repeatedly, often as a consequence of hypothalamic lesions. Conversely, REM (rapid eye movement) sleep behaviour disorder (RBD) is usually a secondary disorder, often due to degenerative brain stem disorders or narcolepsy. The case of a hitherto healthy man is presented, who simultaneously developed narcolepsy and RBD as the result of an acute focal inflammatory lesion in the dorsomedial pontine tegmentum in the presence of normal cerebrospinal fluid hypocretin‐1 levels and in the absence of human lymphocyte antigen haplotypes typically associated with narcolepsy and RBD (DQB1*0602, DQB1*05). This first observation of symptomatic narcolepsy with RBD underlines the importance of the mediotegmental pontine area in the pathophysiology of both disorders, even in the absence of a detectable hypocretin deficiency and a genetic predisposition

Post Tick-Borne Encephalitis Virus Vaccination Narcolepsy with Cataplexy.

STUDY OBJECTIVES Narcolepsy with cataplexy (NC) is a chronic neurological disorder thought to result from an altered immune response based on a genetic predisposition coupled with environmental factors. Pandemrix vaccination has been reported to increase the risk of narcolepsy. We aimed at identifying other vaccines associated with the onset of narcolepsy. METHODS Case series and retrospective database study. RESULTS We identified four cases of NC following a tick-borne encephalitis (TBE) vaccination with FSME Immun. Additional four cases could be detected in the database of the Paul-Ehrlich-Institut, Federal Institute for Vaccines and Biomedicines in Germany. CONCLUSIONS Our findings implicate TBE vaccination as a potential additional environmental factor for the development of NC and add additional evidence for an immunological mechanism in the pathogenesis of the disease

The Swiss Narcolepsy Scale (SNS) and its short form (sSNS) for the discrimination of narcolepsy in patients with hypersomnolence: a cohort study based on the Bern Sleep-Wake Database.

Previous studies reported high sensitivity and specificity of the Swiss Narcolepsy Scale (SNS) for the diagnosis of narcolepsy type 1. We used data from the Bern Sleep-Wake Database to investigate the discriminating capacity of both the SNS and the Epworth Sleepiness Scale (ESS) to identify narcolepsy type 1 and type 2 in patients with central disorders of hypersomnolence (CDH) or sleepy patients with obstructive sleep apnea (OSA). In addition, we aimed to develop a simplified version of the SNS. We created the two-item short-form SNS (sSNS), based on the discriminative capability of the models including all possible combinations of the five questions of the SNS. Using the previously published co-efficiencies, we confirmed the high capacity of the SNS in identifying narcolepsy type 1. The updated SNS (based on new co-efficiencies and cutoff) and the sSNS showed high capacity and were both superior to ESS in identifying narcolepsy type 1. The sSNS correlated significantly with the SNS (r = - 0.897, p < 0.001). No scale showed sufficient discrimination for narcolepsy type 2. This is the largest cohort study that confirms the discriminating power of SNS for narcolepsy type 1 in patients with hypersomnolence and the first study to assess its discriminative power for narcolepsy type 2. The easy-to-use and easy-to-calculate short-form scale has a high discriminating power for narcolepsy type 1 and may be used as screening tool, especially among general practitioners, to identify patients and accelerate their referral to a center of expertise