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

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®. 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®.

Heart Rate Variability in evaluation of autonomic dysfunction in idiopathic REM-sleep behaviour disorder

INTRODUCTION

Nearly 80% of people diagnosed with idiopathic REM sleep behaviour disorder (iRBD) via video-polysomnography (v-PSG) are expected to be in the prodromal stage of an alpha-synucleinopathy. Signs of autonomic dysfunction can appear earlier than motor or cognitive alpha-synucleinopathy symptoms. Heart Rate Variability (HRV) can potentially be an objective measurement of autonomic dysfunction, and furthermore can be obtained directly from v-PSG.

OBJECTIVES

The aim of this study was to evaluate dysautonomia in iRBD subjects using HRV obtained during different sleep stages and wakefulness from v-PSG.

MATERIAL AND METHODS

Subjects positively screened by an RBD screening questionnaire (RBD-SQ) underwent v-PSG to diagnose RBD. HRV obtained from v-PSG recordings was correlated to dysautonomia evaluated from a Non-Motor Symptoms Scale (NMSS) questionnaire. Optimal cut-off values of HRV parameters to predict dysautonomia were calculated using receiver operating characteristics (ROC) - area under the curve (AUC) analysis. The effect of confounder variables was predicted with binomial logistic regression and multiple regression analyses.

RESULTS

Out of 72 positively screened subjects, 29 subjects were diagnosed as iRBD (mean age 66 ± 7.7 years) by v-PSG. Eighty-three per cent of the iRBD subjects in our cohort were at the time of diagnosis classified as having possible or probable prodromal Parkinson's Disease (pPD) compared to zero subjects being positively screened in the control group. The iRBD-positive subjects showed significant inverse correlations of NMSS score, particularly to log low-frequency (LF) component of HRV during wakefulness: r = -0.59 (p = 0.001). Based on ROC analysis and correlation between NMSS score, log LF during wakefulness (AUC 0.74, cut-off 4.69, sensitivity 91.7%, specificity 64.7%, p = 0.028) was considered as the most accurate predictor of dysautonomia in the iRBD group. Apnoea-hypopnoea index (AHI) negatively predicted dysautonomia in the iRBD group. None of the HRV components was able to predict the presence of iRBD in the full cohort. Age, gender, and PSG variables were significant confounders of HRV prediction.

CONCLUSIONS

The presented study did not confirm the possibility of using HRV from v-PSG records of patients with iRBD to predict dysautonomia expressed by questionnaire methods. This is probably due to several confounding factors capable of influencing HRV in such a cohort.

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 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.

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 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.

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 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.

Idling for Decades: A European Study on Risk Factors Associated with the Delay Before a Narcolepsy Diagnosis

PURPOSE

Narcolepsy type-1 (NT1) is a rare chronic neurological sleep disorder with excessive daytime sleepiness (EDS) as usual first and cataplexy as pathognomonic symptom. Shortening the NT1 diagnostic delay is the key to reduce disease burden and related low quality of life. Here we investigated the changes of diagnostic delay over the diagnostic years (1990-2018) and the factors associated with the delay in Europe.

PATIENTS AND METHODS

We analyzed 580 NT1 patients (male: 325, female: 255) from 12 European countries using the European Narcolepsy Network database. We combined machine learning and linear mixed-effect regression to identify factors associated with the delay.

RESULTS

The mean age at EDS onset and diagnosis of our patients was 20.9±11.8 (mean ± standard deviation) and 30.5±14.9 years old, respectively. Their mean and median diagnostic delay was 9.7±11.5 and 5.3 (interquartile range: 1.7-13.2 years) years, respectively. We did not find significant differences in the diagnostic delay over years in either the whole dataset or in individual countries, although the delay showed significant differences in various countries. The number of patients with short (≤2-year) and long (≥13-year) diagnostic delay equally increased over decades, suggesting that subgroups of NT1 patients with variable disease progression may co-exist. Younger age at cataplexy onset, longer interval between EDS and cataplexy onsets, lower cataplexy frequency, shorter duration of irresistible daytime sleep, lower daytime REM sleep propensity, and being female are associated with longer diagnostic delay.

CONCLUSION

Our findings contrast the results of previous studies reporting shorter delay over time which is confounded by calendar year, because they characterized the changes in diagnostic delay over the symptom onset year. Our study indicates that new strategies such as increasing media attention/awareness and developing new biomarkers are needed to better detect EDS, cataplexy, and changes of nocturnal sleep in narcolepsy, in order to shorten the diagnostic interval.

Homocysteine Is a Marker of Increased Cardio-Cerebrovascular Disease Risk in Psoriatic Patients, but It Does Not Reflect the Effect of Biological Therapy in the Longitudinal Observation

BACKGROUND

Psoriasis is linked to atherosclerosis. Homocysteine (HCYS) has been identified as a marker of increased risk of cardio-cerebrovascular diseases (CCVD) in population.

OBJECTIVE

The aim of the study was to determine whether elevated HCYS serves as a marker of increased CCVD in psoriasis and whether biological therapy for long-term monitoring influences HCYS levels.

METHODS

Clinical data, laboratory tests, and comorbid diagnoses were summarized for the two groups of patients based on entrance HCYS levels. Patients (n = 76) were included in the follow-up gradually over a period of 5 years.

RESULTS

The psoriatic patients with normal (54%) and elevated (46%) HCYS before biological treatment did not vary in clinical data, laboratory tests, treatment, and comorbid diagnoses apart from CCVD. Elevated HCYS group showed a four-fold excess of CCVD (OR 4.2, 95%CI 1.21-4.86, p=0.024). HCYS levels in the longitudinal observation did not vary.

CONCLUSION

An increased CCVD risk, independent of other risk factors, is present in psoriatic patients with elevated HCYS. The HCYS level was not influenced by biological therapy in longitudinal observation. Further studies are needed to explore if elevated HCYS could serve as a marker of increased CCVD in any stage of psoriasis and if it should be included in classical screening strategies.

Comparison in detection of prodromal Parkinson's disease patients using original and updated MDS research criteria in two independent cohorts

INTRODUCTION

MDS research criteria for prodromal Parkinson's disease (pPD) were published in 2015 and updated in 2019. We aimed to determine the difference in pPD patient detection rates in two cohorts recruited via gastrointestinal symptoms (PARCAS study) and the presence of a probable REM sleep behaviour disorder (PDBIOM study) using the original and updated criteria.

METHODS

We evaluated all risk and prodromal markers, except genetic testing, plasma urate and physical inactivity, in both cohorts and DaT scan, diabetes mellitus type II and cognitive deficit in the PARCAS cohort. Thresholds of 50% probability for possible pPD and 80% for probable pPD were used.

RESULTS

PPD status as identified by the original/updated criteria showed differences for probable pPD (n = 8/9; original/updated criteria) and possible pPD (n = 9/13) in the PARCAS cohort (total n = 158), as well as for probable pPD (n = 19/21) and possible pPD (n = 6/3) in the PDBIOM cohort (total n = 48). A high concordance rate was found between the two criteria sets (p < 0.001 for all groups).

CONCLUSION

All probable pPD cases remained in the same category after evaluation with both criteria; three possible pPD cases based on the original criteria exceeded the threshold for probable pPD based on the updated criteria, and five possible new pPD cases were detected, with only one shift in the opposite direction. The updated MDS pPD research criteria tend to identify more patients as positive, yet their accuracy needs to be determined in prospective studies.

New 2013 incidence peak in childhood narcolepsy: more than vaccination?

Increased incidence rates of narcolepsy type-1 (NT1) have been reported worldwide after the 2009-2010 H1N1 influenza pandemic (pH1N1). While some European countries found an association between the NT1 incidence increase and the H1N1 vaccination Pandemrix, reports from Asian countries suggested the H1N1 virus itself to be linked to the increased NT1 incidence. Using robust data-driven modeling approaches, that is, locally estimated scatterplot smoothing methods, we analyzed the number of de novo NT1 cases (n = 508) in the last two decades using the European Narcolepsy Network database. We confirmed the peak of NT1 incidence in 2010, that is, 2.54-fold (95% confidence interval [CI]: [2.11, 3.19]) increase in NT1 onset following 2009-2010 pH1N1. This peak in 2010 was found in both childhood NT1 (2.75-fold increase, 95% CI: [1.95, 4.69]) and adulthood NT1 (2.43-fold increase, 95% CI: [2.05, 2.97]). In addition, we identified a new peak in 2013 that is age-specific for children/adolescents (i.e. 2.09-fold increase, 95% CI: [1.52, 3.32]). Most of these children/adolescents were HLA DQB1*06:02 positive and showed a subacute disease onset consistent with an immune-mediated type of narcolepsy. The new 2013 incidence peak is likely not related to Pandemrix as it was not used after 2010. Our results suggest that the increased NT1 incidence after 2009-2010 pH1N1 is not unique and our study provides an opportunity to develop new hypotheses, for example, considering other (influenza) viruses or epidemiological events to further investigate the pathophysiology of immune-mediated narcolepsy.

The insomnia phenotype in genetic Creutzfeldt-Jakob disease based on the E200K mutation

The aim of the presented study was to reveal the frequency of insomnia spells in E200K genetic Creutzfeldt-Jakob disease (gCJD) patients. Clinical records of 22 subjects diagnosed with E200K gCJD were retrospectively reviewed. The patients w/wo insomnia (n = 4, 18%/n = 18, 82%) did not differ in age, sex and the duration of the symptomatic phase. Analysis of the clinical features in the groups yielded differences in the clinical signs in the early phase of the disorder, proportion of homozygotes (Met/Met) at codon 129, MRI changes in the thalamus and the typical EEG abnormality. The study suggests that apart from traditional clinical features, the insomnia is not a rare early symptom in phenotype of E200K gCJD based on early thalamic involvement.

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.

Accuracy of Rating Scales and Clinical Measures for Screening of Rapid Eye Movement Sleep Behavior Disorder and for Predicting Conversion to Parkinson's Disease and Other Synucleinopathies

Rapid eye movement (REM) sleep behavior disorder (RBD) is characterized by repeated episodes of REM sleep-related vocalizations and/or complex motor behaviors. Definite diagnosis of RBD is based on history and polysomnography, both of which are less accessible due to the lack of trained specialists and high cost. While RBD may be associated with disorders like narcolepsy, focal brain lesions, and encephalitis, idiopathic RBD (iRBD) may convert to Parkinson's disease (PD) and other synucleinopathies in more than 80% of patients and it is to date the most specific clinical prodromal marker of PD. Identification of individuals at high risk for development of PD is becoming one of the most important topics for current PD-related research as well as for future treatment trials targeting prodromal PD. Furthermore, concomitant clinical symptoms, such as subtle motor impairment, hyposmia, autonomic dysfunction, or cognitive difficulties, in subjects with iRBD may herald its phenoconversion to clinically manifest parkinsonism. The assessment of these motor and non-motor symptoms in iRBD may increase the sensitivity and specificity in identifying prodromal PD subjects. This review evaluates the diagnostic accuracy of individual rating scales and validated single items for screening of RBD and the role and accuracy of available clinical, electrophysiological, imaging, and tissue biomarkers in predicting the phenoconversion from iRBD to clinically manifest synucleinopathies.

Narcolepsy-Associated HLA Class I Alleles Implicate Cell-Mediated Cytotoxicity

STUDY OBJECTIVES

Narcolepsy with cataplexy is tightly associated with the HLA class II allele DQB1*06:02. Evidence indicates a complex contribution of HLA class II genes to narcolepsy susceptibility with a recent independent association with HLA-DPB1. The cause of narcolepsy is supposed be an autoimmune attack against hypocretin-producing neurons. Despite the strong association with HLA class II, there is no evidence for CD4+ T-cell-mediated mechanism in narcolepsy. Since neurons express class I and not class II molecules, the final effector immune cells involved might include class I-restricted CD8+ T-cells.

METHODS

HLA class I (A, B, and C) and II (DQB1) genotypes were analyzed in 944 European narcolepsy with cataplexy patients and in 4,043 control subjects matched by country of origin. All patients and controls were DQB1*06:02 positive and class I associations were conditioned on DQB1 alleles.

RESULTS

HLA-A*11:01 (OR = 1.49 [1.18-1.87] P = 7.0*10(-4)), C*04:01 (OR = 1.34 [1.10-1.63] P = 3.23*10(-3)), and B*35:01 (OR = 1.46 [1.13-1.89] P = 3.64*10(-3)) were associated with susceptibility to narcolepsy. Analysis of polymorphic class I amino-acids revealed even stronger associations with key antigen-binding residues HLA-A-Tyr(9) (OR = 1.32 [1.15-1.52] P = 6.95*10(-5)) and HLA-C-Ser(11) (OR = 1.34 [1.15-1.57] P = 2.43*10(-4)).

CONCLUSIONS

Our findings provide a genetic basis for increased susceptibility to infectious factors or an immune cytotoxic mechanism in narcolepsy, potentially targeting hypocretin neurons.

DQB1 locus alone explains most of the risk and protection in narcolepsy with cataplexy in Europe

STUDY OBJECTIVE

Prior research has identified five common genetic variants associated with narcolepsy with cataplexy in Caucasian patients. To replicate and/or extend these findings, we have tested HLA-DQB1, the previously identified 5 variants, and 10 other potential variants in a large European sample of narcolepsy with cataplexy subjects.

DESIGN

Retrospective case-control study.

SETTING

A recent study showed that over 76% of significant genome-wide association variants lie within DNase I hypersensitive sites (DHSs). From our previous GWAS, we identified 30 single nucleotide polymorphisms (SNPs) with P < 10(-4) mapping to DHSs. Ten SNPs tagging these sites, HLADQB1, and all previously reported SNPs significantly associated with narcolepsy were tested for replication.

PATIENTS AND PARTICIPANTS

For GWAS, 1,261 narcolepsy patients and 1,422 HLA-DQB1*06:02-matched controls were included. For HLA study, 1,218 patients and 3,541 controls were included.

MEASUREMENTS AND RESULTS

None of the top variants within DHSs were replicated. Out of the five previously reported SNPs, only rs2858884 within the HLA region (P < 2x10(-9)) and rs1154155 within the TRA locus (P < 2x10(-8)) replicated. DQB1 typing confirmed that DQB1*06:02 confers an extraordinary risk (odds ratio 251). Four protective alleles (DQB1*06:03, odds ratio 0.17, DQB1*05:01, odds ratio 0.56, DQB1*06:09 odds ratio 0.21, DQB1*02 odds ratio 0.76) were also identified.

CONCLUSION

An overwhelming portion of genetic risk for narcolepsy with cataplexy is found at DQB1 locus. Since DQB1*06:02 positive subjects are at 251-fold increase in risk for narcolepsy, and all recent cases of narcolepsy after H1N1 vaccination are positive for this allele, DQB1 genotyping may be relevant to public health policy.

Meningeal carcinomatosis as the first manifestation of malignant carcinomatosis

Meningeal carcinomatosis (MC) is a malignant infiltration of the leptomeninges and subarachnoid space and can be a devastating complication of a systemic malignancy. Although often found in patients with known metastatic malignancies, MC can also be the initial manifestation of an underlying malignancy. We report four case studies where back pain, dizziness, cognitive decline, headache and headache with the cranial nerve VI palsy were the first signs of MC. In two cases, adenocarcinoma ventriculi was found, in other one, the markers of the gastrointestinal tract malignancy were highly positive but malignity was not found, and in the last one, there was a known breast carcinoma. The diagnosis of MC requires the finding of malignant cells in the cerebrospinal fluid, but sometimes several lumbar punctures are required to establish the diagnosis, and also MRI with gadolinium. Finally, we would like to highlight the fact that markedly decreased glycorrhachia in cerebrospinal fluid (CSF) can also be the first sign of MC (Fig. 6, Tab. 2, Ref. 23).

Salivary and plasma insulin levels in man

Changes in plasma and salivary immunoreactive insulin were studied in saliva and blood of 9 healthy subjects following either oral glucose or a test meal ingestion at 30 min intervals for up to 3h. The levels of plasma immunoreactive insulin reached maximum at 60 min with a value of 62.77 +/- 6.52 microU/ml after the glucose load and 62.62 +/- 6.52 microU/ml after the test meal. In the case of salivary immunoreactive insulin levels, the maximum was attained at 90 min with a value of 18.47 +/- 2.68 microU/min after the glucose load and 22.40 +/- 2.01 microU/min after the test meal. Positive linear correlation (r = 0.74) was found between the salivary and plasma immunoreactive insulin levels.