Orexin-A measurement in narcolepsy : A stability study and a comparison of LC-MS/MS and immunoassays

Background: Orexin-A and-B are neuropeptides involved in sleep-wake regulation. In human narcolepsy type 1, this cycle is disrupted due to loss of orexin-producing neurons in the hypothalamus. Cerebrospinal fluid (CSF) orexin-A measurement is used in the diagnosis of narcolepsy type 1. Currently available immunoassays may lack specificity for accurate orexin quantification. We developed and validated a liquid chromatography mass spectrometry assay (LC-MS/MS) for CSF orexin-A and B. Methods: We used CSF samples from narcolepsy type 1 (n = 22) and type 2 (n = 6) and non-narcoleptic controls (n = 44). Stable isotope-labeled orexin-A and-B internal standards were added to samples before solid-phase extraction and quantification by LC-MS/MS. The samples were also assayed by commercial radioimmunoassay (RIA, n = 42) and enzymatic immunoassay (EIA, n = 72) kits. Stability of orexins in CSF was studied for 12 months. Results: Our assay has a good sensitivity (10 pmol/L = 35 pg/mL) and a wide linear range (35-3500 pg/mL). Added orexin-A and-B were stable in CSF for 12 and 3 months, respectively, when frozen. The median orexin-A concentration in CSF from narcolepsy type 1 patients was <35 pg/mL (range <35-131 pg/mL), which was lower than that in CSF from control individuals (98 pg/mL, range <35-424 pg/mL). Orexin-A concentrations determined using our LC-MS/MS assay were five times lower than those measured with a commercial RIA. Orexin-B concentrations were undetectable Conclusions: Orexin-A concentrations measured by our LC-MS/MS assay were lower in narcolepsy type 1 patients as compared to controls. RIA yielded on average higher concentrations than LC-MS/MS.Peer reviewe

A prospective open label 2-8 year extension of the randomised controlled ICON trial on the long-term efficacy and safety of occipital nerve stimulation in medically intractable chronic cluster headache

BACKGROUND: We demonstrated in the randomised controlled ICON study that 48-week treatment of medically intractable chronic cluster headache (MICCH) with occipital nerve stimulation (ONS) is safe and effective. In L-ICON we prospectively evaluate its long-term effectiveness and safety. METHODS: ICON participants were enrolled in L-ICON immediately after completing ICON. Therefore, earlier ICON participants could be followed longer than later ones. L-ICON inclusion was stopped after the last ICON participant was enrolled in L-ICON and followed for ≥2 years by completing six-monthly questionnaires on attack frequency, side effects, subjective improvement and whether they would recommend ONS to others. Primary outcome was the change in mean weekly attack frequency 2 years after completion of the ICON study compared to baseline. Missing values for log-transformed attack-frequency were imputed for up to 5 years of follow-up. Descriptive analyses are presented as (pooled) geometric or arithmetic means and 95% confidence intervals. FINDINGS: Of 103 eligible participants, 88 (85%) gave informed consent and 73 (83%) were followed for ≥2 year, 61 (69%) ≥ 3 year, 33 (38%) ≥ 5 years and 3 (3%) ≥ 8.5 years. Mean (±SD) follow-up was 4.2 ± 2.2 years for a total of 370 person years (84% of potentially 442 years). The pooled geometric mean (95% CI) weekly attack frequency remained considerably lower after one (4.2; 2.8-6.3), two (5.1; 3.5-7.6) and five years (4.1; 3.0-5.5) compared to baseline (16.2; 14.4-18.3). Of the 49/88 (56%) ICON ≥50% responders, 35/49 (71%) retained this response and 15/39 (38%) ICON non-responders still became a ≥50% responder for at least half the follow-up period. Most participants (69/88; 78% [0.68-0.86]) reported a subjective improvement from baseline at last follow-up and 70/88 (81% [0.70-0.87]) would recommend ONS to others. Hardware-related surgery was required in 44/88 (50%) participants in 112/122 (92%) events (0.35 person-year-1 [0.28-0.41]). We didn't find predictive factors for effectiveness. INTERPRETATION: ONS is a safe, well-tolerated and long-term effective treatment for MICCH. FUNDING: The Netherlands Organisation for Scientific Research, the Dutch Ministry of Health, the NutsOhra Foundation from the Dutch Health Insurance Companies, and Medtronic.</p

A prospective open label 2-8 year extension of the randomised controlled ICON trial on the long-term efficacy and safety of occipital nerve stimulation in medically intractable chronic cluster headache

BACKGROUND: We demonstrated in the randomised controlled ICON study that 48-week treatment of medically intractable chronic cluster headache (MICCH) with occipital nerve stimulation (ONS) is safe and effective. In L-ICON we prospectively evaluate its long-term effectiveness and safety. METHODS: ICON participants were enrolled in L-ICON immediately after completing ICON. Therefore, earlier ICON participants could be followed longer than later ones. L-ICON inclusion was stopped after the last ICON participant was enrolled in L-ICON and followed for ≥2 years by completing six-monthly questionnaires on attack frequency, side effects, subjective improvement and whether they would recommend ONS to others. Primary outcome was the change in mean weekly attack frequency 2 years after completion of the ICON study compared to baseline. Missing values for log-transformed attack-frequency were imputed for up to 5 years of follow-up. Descriptive analyses are presented as (pooled) geometric or arithmetic means and 95% confidence intervals. FINDINGS: Of 103 eligible participants, 88 (85%) gave informed consent and 73 (83%) were followed for ≥2 year, 61 (69%) ≥ 3 year, 33 (38%) ≥ 5 years and 3 (3%) ≥ 8.5 years. Mean (±SD) follow-up was 4.2 ± 2.2 years for a total of 370 person years (84% of potentially 442 years). The pooled geometric mean (95% CI) weekly attack frequency remained considerably lower after one (4.2; 2.8-6.3), two (5.1; 3.5-7.6) and five years (4.1; 3.0-5.5) compared to baseline (16.2; 14.4-18.3). Of the 49/88 (56%) ICON ≥50% responders, 35/49 (71%) retained this response and 15/39 (38%) ICON non-responders still became a ≥50% responder for at least half the follow-up period. Most participants (69/88; 78% [0.68-0.86]) reported a subjective improvement from baseline at last follow-up and 70/88 (81% [0.70-0.87]) would recommend ONS to others. Hardware-related surgery was required in 44/88 (50%) participants in 112/122 (92%) events (0.35 person-year-1 [0.28-0.41]). We didn't find predictive factors for effectiveness. INTERPRETATION: ONS is a safe, well-tolerated and long-term effective treatment for MICCH. FUNDING: The Netherlands Organisation for Scientific Research, the Dutch Ministry of Health, the NutsOhra Foundation from the Dutch Health Insurance Companies, and Medtronic.</p

Guidelines of the International Headache Society for Controlled Clinical Trials in Cluster Headache

In 1995, a committee of the International Headache Society developed and published the first edition of the Guidelines for Controlled Trials of Drugs in Cluster Headache. These have not been revised. With the emergence of new medications, neuromodulation devices and trial designs, an updated version of the International Headache Society Guidelines for Controlled Clinical Trials in Cluster Headache is warranted. Given the scarcity of evidence-based data for cluster headache therapies, the update is largely consensus-based, but takes into account lessons learned from recent trials and demands by patients. It is intended to apply to both drug and neuromodulation treatments, with specific proposals for the latter when needed. The primary objective is to propose a template for designing high quality, state-of-the-art, controlled clinical trials of acute and preventive treatments in episodic and chronic cluster headache. The recommendations should not be regarded as dogma and alternative solutions to particular methodological problems should be explored in the future and scientifically validated

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

Sleep, vigilance, and thermosensitivity

The regulation of sleep and wakefulness is well modeled with two underlying processes: a circadian and a homeostatic one. So far, the parameters and mechanisms of additional sleep-permissive and wake-promoting conditions have been largely overlooked. The present overview focuses on one of these conditions: the effect of skin temperature on the onset and maintenance of sleep, and alertness. Skin temperature is quite well suited to provide the brain with information on sleep-permissive and wake-promoting conditions because it changes with most if not all of them. Skin temperature changes with environmental heat and cold, but also with posture, environmental light, danger, nutritional status, pain, and stress. Its effect on the brain may thus moderate the efficacy by which the clock and homeostat manage to initiate or maintain sleep or wakefulness. The review provides a brief overview of the neuroanatomical pathways and physiological mechanisms by which skin temperature can affect the regulation of sleep and vigilance. In addition, current pitfalls and possibilities of practical applications for sleep enhancement are discussed, including the recent finding of impaired thermal comfort perception in insomniacs

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