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

Background and ObjectivesRecent 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 of whether current diagnostic criteria of hypersomnolence disorders should be reassessed. The main aim of this data-driven observational study was to see whether data-driven algorithms would segregate narcolepsy type 1 and identify more reliable subgrouping of individuals without cataplexy with new clinical biomarkers.MethodsWe 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.ResultsWe included 1,078 unmedicated adolescents and adults. Seven clusters were identified, of which 4 clusters included predominantly individuals with cataplexy. The 2 most distinct clusters consisted of 158 and 157 patients, were dominated by those without cataplexy, and among other variables, significantly differed in presence of sleep drunkenness, subjective difficulty awakening, and weekend-week sleep length difference. Patients formally diagnosed as having narcolepsy type 2 and idiopathic hypersomnia were evenly mixed in these 2 clusters.DiscussionUsing 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 REM periods 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

Positive airway pressure (PAP) treatment reduces glycated hemoglobin (HbA1c) levels in obstructive sleep apnea patients with concomitant weight loss: Longitudinal data from the ESADA

Patients with obstructive sleep apnea (OSA) are at increased risk of developing metabolic disease such as diabetes. The effects of positive airway pressure on glycemic control are contradictory. We therefore evaluated the change in glycated hemoglobin (HbA1c) in a large cohort of OSA patients after long-term treatment with positive airway pressure. HbA1c levels were assessed in a subsample of the European Sleep Apnea Database [n=1608] at baseline and at long-term follow up with positive airway pressure therapy (mean 378.9±423.0 days). In a regression analysis, treatment response was controlled for important confounders. Overall, HbA1c decreased from 5.98±1.01% to 5.93±0.98% (p=0.001). Patient subgroups with a more pronounced HbA1c response included patients with diabetes (−0.15±1.02, p=0.019), those with severe OSA baseline (−0.10±0.68, p=0.005), those with morbid obesity (−0.20±0.81, p<0.001). The strongest HbA1c reduction was observed in patients with a concomitant weight reduction >5 kilos (−0.38±0.99, p<0.001). In robust regression analysis, severe OSA (p=0.038) and morbid obesity (p=0.005) at baseline, and weight reduction >5 kilos (p<0.001) during follow up were independently associated with a reduction of HbA1c following PAP treatment. In contrast, PAP treatment alone without weight reduction was not associated with significant Hb1Ac reduction. In conclusion, positive airway pressure therapy is associated with HbA1c reduction in patients with severe OSA, in morbidly obese patients. and most obviously in those with significant weight lost during the follow-up. Our study underlines the importance to combine positive airway pressure use with adjustments in lifestyle to substantially modify metabolic complications in OSA

Arterial bicarbonate is associated with hypoxic burden and uncontrolled hypertension in obstructive sleep apnea - The ESADA cohort

Objective: Blood bicarbonate concentration plays an important role for obstructive sleep apnea (OSA) patients to maintain acid-base balance. We investigated the association between arterial standard bicarbonate ([HCO3-]) and nocturnal hypoxia as well as comorbid hypertension in OSA. Methods: A cross-sectional analysis of 3329 patients in the European Sleep Apnea Database (ESADA) was performed. Arterial blood gas analysis and lung function test were performed in conjunction with polysomnographic sleep studies. The 4% oxygen desaturation index (ODI), mean and minimum oxygen saturation (SpO2), and percentage of time with SpO2 below 90% (T90%) were used to reflect nocturnal hypoxic burden. Arterial hypertension was defined as a physician diagnosis of hypertension with ongoing antihypertensive medication. Hypertensive patients with SBP/DBP below or above 140/90 mmHg were classified as controlled-, uncontrolled hypertension, respectively. Results: The [HCO3-] level was normal in most patients (average 24.0 ± 2.5 mmol/L). ODI, T90% increased whereas mean and minimum SpO2 decreased across [HCO3-] tertiles (ANOVA, p = 0.030, <0.001, <0.001, and <0.001, respectively). [HCO3-] was independently associated with ODI, mean SpO2, minimum SpO2, and T90% after adjusting for confounders (β value [95%CI]: 1.21 [0.88–1.54], −0.16 [-0.20 to −0.11], −0.51 [-0.64 to −0.37], 1.76 [1.48–2.04], respectively, all p < 0.001). 1 mmol/L elevation of [HCO3-] was associated with a 4% increased odds of uncontrolled hypertension (OR: 1.04 [1.01–1.08], p = 0.013). Conclusion: We first demonstrated an independent association between [HCO3-] and nocturnal hypoxic burden as well as uncontrolled hypertension in OSA patients. Bicarbonate levels as an adjunctive measure provide insight into the pathophysiology of hypertension in OSA

Risk and predictors of dementia and parkinsonism in idiopathic REM sleep behaviour disorder: a multicentre study

Idiopathic REM sleep behaviour disorder (iRBD) is a powerful early sign of Parkinson's disease, dementia with Lewy bodies, and multiple system atrophy. This provides an unprecedented opportunity to directly observe prodromal neurodegenerative states, and potentially intervene with neuroprotective therapy. For future neuroprotective trials, it is essential to accurately estimate phenoconversion rate and identify potential predictors of phenoconversion. This study assessed the neurodegenerative disease risk and predictors of neurodegeneration in a large multicentre cohort of iRBD. We combined prospective follow-up data from 24 centres of the International RBD Study Group. At baseline, patients with polysomnographically-confirmed iRBD without parkinsonism or dementia underwent sleep, motor, cognitive, autonomic and special sensory testing. Patients were then prospectively followed, during which risk of dementia and parkinsonsim were assessed. The risk of dementia and parkinsonism was estimated with Kaplan-Meier analysis. Predictors of phenoconversion were assessed with Cox proportional hazards analysis, adjusting for age, sex, and centre. Sample size estimates for disease-modifying trials were calculated using a time-to-event analysis. Overall, 1280 patients were recruited. The average age was 66.3 \ub1 8.4 and 82.5% were male. Average follow-up was 4.6 years (range = 1-19 years). The overall conversion rate from iRBD to an overt neurodegenerative syndrome was 6.3% per year, with 73.5% converting after 12-year follow-up. The rate of phenoconversion was significantly increased with abnormal quantitative motor testing [hazard ratio (HR) = 3.16], objective motor examination (HR = 3.03), olfactory deficit (HR = 2.62), mild cognitive impairment (HR = 1.91-2.37), erectile dysfunction (HR = 2.13), motor symptoms (HR = 2.11), an abnormal DAT scan (HR = 1.98), colour vision abnormalities (HR = 1.69), constipation (HR = 1.67), REM atonia loss (HR = 1.54), and age (HR = 1.54). There was no significant predictive value of sex, daytime somnolence, insomnia, restless legs syndrome, sleep apnoea, urinary dysfunction, orthostatic symptoms, depression, anxiety, or hyperechogenicity on substantia nigra ultrasound. Among predictive markers, only cognitive variables were different at baseline between those converting to primary dementia versus parkinsonism. Sample size estimates for definitive neuroprotective trials ranged from 142 to 366 patients per arm. This large multicentre study documents the high phenoconversion rate from iRBD to an overt neurodegenerative syndrome. Our findings provide estimates of the relative predictive value of prodromal markers, which can be used to stratify patients for neuroprotective trials

What is that Thing Called Computer Science?

XXI century society, called Knowledge Society, has a direct dependency of the software products, considered by many as the most important development of modern technology. This dependence generates the need of scientists and professionals who research and develop products that meet social demands. This article describes the computer science area as one of the most demanded professions in this reality, and in order to make it known to more people

Apnea Detection in Polysomnographic Recordings Using Machine Learning Techniques

Sleep disorders are diagnosed in sleep laboratories by polysomnography, a multi-parameter examination that monitors biological signals during sleep. The subsequent evaluation of the obtained records is very time-consuming. The goal of this study was to create an automatic system for evaluation of the airflow and SpO2 channels of polysomnography records, through the use of machine learning techniques and a large database, for apnea and desaturation detection (which is unusual in other studies). To that end, a convolutional neural network (CNN) was designed using hyperparameter optimization. It was then trained and tested for apnea and desaturation. The proposed CNN was compared with the commonly used k-nearest neighbors (k-NN) method. The classifiers were designed based on nasal airflow and blood oxygen saturation signals. The final neural network accuracy for apnea detection reached 84%, and that for desaturation detection was 74%, while the k-NN classifier reached accuracies of 83% and 64% for apnea detection and desaturation detection, respectively