Long-Term Outcome of a Series of Patients With Narcolepsy Type 1 and Comorbidity With Immunopathological and Autoimmune Diseases.

Background: The aim of this study was to evaluate the long-term outcome of our series of narcolepsy type 1 (NT1) patients with comorbid autoimmune diseases (ADs) and other immunopathological diseases (IDs), focusing on the incidence of new ADs and IDs in this sample. Methods: A longitudinal observational study was conducted over 6 years (2014 - 2020) in a series of 158 Caucasians NT1 patients (96 males; mean age: 50.1 ± 19.0 years) from the previous study. All but one case (familial case) were HLA-DQB1*06:02-positive. The diagnosis of narcolepsy was made according to the International Classification of Sleep Disorders (ICSD-3). Results: Twenty-one patients have been diagnosed with a new ID, 10 of them with an AD (autoimmune thyroid disease, psoriasis, rheumatoid arthritis, transverse myelitis, granuloma annulare, primary biliary cirrhosis, alopecia areata and antiphospholipid syndrome), and 11 with other IDs (allergic rhinitis, allergic asthma, atopic dermatitis, food allergy, contact dermatitis and drug allergy). One patient was diagnosed with two new ADs. We found IDs in 46 patients (24 females and 22 males) and the overall prevalence in this series is actually 29.11%; 22 of them (13.92%) had an AD, with a percentage higher than estimated in the general population. Conclusions: The prevalence of AD/ID is high in our series, suggesting that NT1 might arise on a background of generalized susceptibility to immune-mediated processes. The occurrence of an ID can in turn influence the development of others in genetically predisposed individuals, which explains the increased associations observed in this long-term study.

Hemocromatosis genética hepato-cerebral en un paciente con síndrome de piernas inquietas / Hepato-cerebral hereditary hemochromatosis in a patient with restless legs syndrome

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Aumento de los depósitos cerebrales de hierro en una familia de donantes de sangre con síndrome de piernas inquietas / Brain iron accumulation in a blood donor family with restless legs síndrome

Introducción. La fisiopatología del síndrome de piernas inquietas (SPI) es compleja. El mecanismo a través del cual la ferropenia favorece el desarrollo del SPI no está esclarecido, aunque se sugiere la presencia de una alteración en la homeostasis cerebral del hierro. Casos clínicos. Se presentan los hallazgos inusuales en una familia de donantes de sangre con SPI. Tres miembros de la misma familia fueron diagnosticados de SPI, cumpliendo los criterios definidos por el grupo internacional para el estudio del SPI (International Restless Legs Syndrome Study Group). Todos eran donantes de sangre habituales (rango de donación: 10-40 años) y los síntomas de SPI tenían un curso de 3-5 años. La exploración general y neurológica fue normal en todos los casos, así como los electromiogramas. El estudio fenotípico y genotípico descartó la presencia de hemocromatosis y otras causas genéticas de sobrecarga cerebral de hierro. Los estudios polisomnográficos mostraron sueño nocturno perturbado, con reducción de su eficiencia, y un aumento del índice de movimientos periódicos de las piernas. La resonancia magnética craneal evidenció un aumento de los depósitos cerebrales de hierro en los ganglios basales, la sustancia negra, el núcleo rojo y los dentados. Conclusión. Este aumento patológico de los depósitos cerebrales de hierro sugiere la presencia de un complejo trastorno del metabolismo cerebral del hierro en nuestros pacientes. Futuros estudios deben confirmar estos hallazgos y profundizar en el estudio de su relación con la fisiopatología del SPI

Introduction. The pathophysiology of restless legs syndrome (RLS) is complex. Secondary RLS with iron deficiency - which suggests disturbed iron homeostasis - remains to be elucidated. Case reports. We report the findings from a unique blood donor family with RLS. Three blood donors family members were diagnosed with RLS defined by the International RLS Study Group and without history of neurologic diseases and RLS symptoms in the last 3-5 years (range of blood donation: 10-40 years). The neurological examination and electromyographies were normal. A polisomnography showed disturbed nocturnal sleep with a reduction in sleep efficiency and an increased periodic limbs movement index. The cranial MRI showed brain iron deposits in basal ganglia, substantia nigra, red nuclei and dentate nuclei. Phenotypic and genotypic studies rule out genetic haemochromatosis or iron overload. Conclusion. The abnormal iron accumulation in the basal ganglia indicated a complex iron metabolism disorder of the central nervous system. Further studies are warranted to confirm our findings and its role in the pathophysiology of RLS

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.