Restless legs syndrome in patients with high serum ferritin and normal iron levels

Trabajo presentado como póster en el 18.º Congreso de la European Sleep Research Society, celebrado en Innsbruck (Austria) en 2006Objetivo. Documentar la asociación entre síndrome de piernas inquietas (SPI) y concentraciones de ferritina elevadas en cinco pacientes. Pacientes y métodos. Estudiamos a cinco varones con una edad media de 59 años (rango: 36-73 años). Todos los pacientes fueron remitidos por SPI (dos de ellos donantes de sangre), en dos casos asociado a síndrome de apnea obstructiva del sueño. Se efectuaron registros videopolisomnográficos y se realizó una analítica para determinar los niveles de ferritina y hierro en plasma. Resultados. Los cinco pacientes presentaban criterios clínicos de SPI: parestesias en las pantorrillas asociadas a una necesidad imperiosa de mover las piernas, inquietud motora, agravamiento de los síntomas por la tarde y por la noche, mejoría con el movimiento, dificultad de conciliación del sueño y despertares nocturnos frecuentes. La exploración neurológica, el electroencefalograma, el electromiograma y la resonancia magnética cerebral fueron normales. Los registros videopolisomnográficos mostraron sueño nocturno fragmentado, reducción del tiempo total de sueño, escasa eficiencia, índice de apnea-hipopnea > 10/h en dos casos, y en los cinco casos, índice de movimientos periódicos de las piernas por hora de sueño > 5/h. En todos los casos los niveles de hierro sérico estaban dentro de los límites normales y la concentración de ferritina era elevada. Conclusiones. La asociación entre SPI con movimientos periódicos de las piernas durante el sueño, hierro sérico normal y ferritina elevada no se ha descrito previamente. El hallazgo de la disminución de concentración de ferritina en uno de los pacientes –meses más tarde del tratamiento con fármacos dopaminérgicos– apoya la implicación de un mecanismo dopaminérgico en la fisiopatología del SPIAim. To document the association between restless legs syndrome (RLS) and high ferritin levels in five patients. Patients and methods. The five patients were male, mean age: 59 years (range: 36-73 years). The patients were referred for RLS (two of them blood donors), in two cases associated with obstructive sleep apnea. Patients underwent a video-PSG recording. Serum iron and serum ferritin were determined. Results. All patients fulfilled the clinical criteria for RLS: leg paresthesias associated with an urge to move, motor restlessness, worsening of symptoms during the evening and night, and partial relief with activity, difficulty falling asleep, and presence of nocturnal awakenings due to RLS. Neurological examination, EEGs, EMGs and MRIs were normal. Video-PSGs recordings showed a disturbed and fragmented sleep with a reduction in total sleep time, low sleep efficiency, respiratory abnormalities with an apnea-hipopnea index > 10/h in two cases, and in all of them a periodic leg movements index > 5/h. The serum iron levels were within the normal range in all cases, whereas those in serum ferritin levels were high. Conclusions. To our knowledge the association of normal serum iron with high serum ferritin levels in patients diagnosed clinically and polygraphically as having RLS with periodic leg movements has not been described before. The notion of an involvement of a dopaminergic mechanism in the pathophysiology of RLS is supported by the decrease in the values of serum ferritin concentration observed in one patient during follow-up while being treated with dopaminergic agent

Data-Driven Phenotyping of Central Disorders of Hypersomnolence With Unsupervised Clustering.

BACKGROUND AND OBJECTIVES Recent studies fueled doubts as to whether all currently defined central disorders of hypersomnolence are stable entities, especially narcolepsy type 2 and idiopathic hypersomnia. New reliable biomarkers are needed and the question arises whether current diagnostic criteria of hypersomnolence disorders should be reassessed. The main aim of this data-driven observational study was to see if data-driven algorithms would segregate narcolepsy type 1 and identify more reliable subgrouping of individuals without cataplexy with new clinical biomarkers. METHODS We used agglomerative hierarchical clustering, an unsupervised machine learning algorithm, to identify distinct hypersomnolence clusters in the large-scale European Narcolepsy Network database. We included 97 variables, covering all aspects of central hypersomnolence disorders such as symptoms, demographics, objective and subjective sleep measures, and laboratory biomarkers. We specifically focused on subgrouping of patients without cataplexy. The number of clusters was chosen to be the minimal number for which patients without cataplexy were put in distinct groups. RESULTS We included 1078 unmedicated adolescents and adults. Seven clusters were identified, of which four clusters included predominantly individuals with cataplexy. The two most distinct clusters consisted of 158 and 157 patients respectively, were dominated by those without cataplexy and, amongst other variables, significantly differed in presence of sleep drunkenness, subjective difficulty awakening and weekend-week sleep length difference. Patients formally diagnosed as narcolepsy type 2 and idiopathic hypersomnia were evenly mixed in these two clusters. DISCUSSION Using a data-driven approach in the largest study on central disorders of hypersomnolence to date, our study identified distinct patient subgroups within the central disorders of hypersomnolence population. Our results contest inclusion of sleep-onset rapid eye moment periods (SOREMPs) in diagnostic criteria for people without cataplexy and provide promising new variables for reliable diagnostic categories that better resemble different patient phenotypes. Cluster-guided classification will result in a more solid hypersomnolence classification system that is less vulnerable to instability of single features

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)

Sueño normal y patológico.

En este artículo se realiza una revisión bibliográfica, del sueño normal y del sueño patológico en el sujeto adulto. Se ha pretendido ofrecer una visión panorámica, de las bases neurofisiológicas del sueño, del concepto de ritmo circadiano, de la semiología electrofisiológica del sueño, de las variaciones fisiológicas que entraña el hecho de dormir, y de las diversas patologías derivadas del sueñ

Sueño y Patología: Introducción: epidemiología, clasificación, y evaluación de los trastornos del sueño.

Los autores que participan en esta Mesa Redonda analizan algunos aspectos de los trastornos del sueño y de la vigilancia, e intentan ofrecer una panorámica actual acerca de la evaluación de estos trastornos; de los aspectos neurobiológicos fundamentales; de las hipersomnias con especial énfasis en el Síndrome de Apnea del Sueño y, finalmente, se revisan los aspectos sociales y legales de los trastornos de la vigilancia