The hypocretin neurotransmission system in myotonic dystrophy type 1

BACKGROUND: Patients with myotonic dystrophy type 1 (DM1) frequently have symptoms of excessive daytime sleepiness (EDS). Some patients with DM1 show sleep-onset REM, similar to that observed in narcolepsy. Narcolepsy is characterized by impaired hypocretin (Hcrt) neurotransmission. OBJECTIVE: To test for dysregulation of Hcrt neurotransmission in a prospective cohort of patients with DM1. METHODS: Hcrt levels in CSF were measured by radioimmunoassay. Sleep physiology was assessed by overnight polysomnography (PSG) and a multiple sleep latency test (MSLT). Splicing of Hcrt receptor 1 and 2 (HcrtR1 and HcrtR2) mRNA was examined in postmortem samples of temporal cortex. RESULTS: Seventeen of 38 patients with DM1 reported symptoms of EDS. Among patients with DM1 with EDS who underwent PSG/MSLT, 7 of 13 showed reduced sleep latency, sleep-onset REM, or both. However, CSF Hcrt levels in DM1 (mean 277 pg/mL, n = 38) were not different from controls (mean 277 pg/mL, n = 33). Also, splicing of HcrtR1 and HcrtR2 mRNA in patients with DM1 was similar to controls. CONCLUSIONS: Excessive daytime sleepiness and dysregulation of REM sleep occur frequently in patients with myotonic dystrophy type 1 (DM1). However, the pathophysiologic basis is distinct from narcolepsy, as patients with DM1 do not have a consistent defect of Hcrt release or receptor splicing

Improving Genetic Prediction by Leveraging Genetic Correlations Among Human Diseases and Traits

Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7 for height to 47 for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait. © 2018 The Author(s)

Genome-wide association study identifies 30 Loci Associated with Bipolar Disorder

This paper is dedicated to the memory of Psychiatric Genomics Consortium (PGC) founding member and Bipolar disorder working group co-chair Pamela Sklar. We thank the participants who donated their time, experiences and DNA to this research, and to the clinical and scientific teams that worked with them. We are deeply indebted to the investigators who comprise the PGC. The views expressed are those of the authors and not necessarily those of any funding or regulatory body. Analyses were carried out on the NL Genetic Cluster Computer (http://www.geneticcluster.org ) hosted by SURFsara, and the Mount Sinai high performance computing cluster (http://hpc.mssm.edu).Bipolar disorder is a highly heritable psychiatric disorder. We performed a genome-wide association study including 20,352 cases and 31,358 controls of European descent, with follow-up analysis of 822 variants with P<1x10-4 in an additional 9,412 cases and 137,760 controls. Eight of the 19 variants that were genome-wide significant (GWS, p < 5x10-8) in the discovery GWAS were not GWS in the combined analysis, consistent with small effect sizes and limited power but also with genetic heterogeneity. In the combined analysis 30 loci were GWS including 20 novel loci. The significant loci contain genes encoding ion channels, neurotransmitter transporters and synaptic components. Pathway analysis revealed nine significantly enriched gene-sets including regulation of insulin secretion and endocannabinoid signaling. BDI is strongly genetically correlated with schizophrenia, driven by psychosis, whereas BDII is more strongly correlated with major depressive disorder. These findings address key clinical questions and provide potential new biological mechanisms for BD.This work was funded in part by the Brain and Behavior Research Foundation, Stanley Medical Research Institute, University of Michigan, Pritzker Neuropsychiatric Disorders Research Fund L.L.C., Marriot Foundation and the Mayo Clinic Center for Individualized Medicine, the NIMH Intramural Research Program; Canadian Institutes of Health Research; the UK Maudsley NHS Foundation Trust, NIHR, NRS, MRC, Wellcome Trust; European Research Council; German Ministry for Education and Research, German Research Foundation IZKF of Münster, Deutsche Forschungsgemeinschaft, ImmunoSensation, the Dr. Lisa-Oehler Foundation, University of Bonn; the Swiss National Science Foundation; French Foundation FondaMental and ANR; Spanish Ministerio de Economía, CIBERSAM, Industria y Competitividad, European Regional Development Fund (ERDF), Generalitat de Catalunya, EU Horizon 2020 Research and Innovation Programme; BBMRI-NL; South-East Norway Regional Health Authority and Mrs. Throne-Holst; Swedish Research Council, Stockholm County Council, Söderström Foundation; Lundbeck Foundation, Aarhus University; Australia NHMRC, NSW Ministry of Health, Janette M O'Neil and Betty C Lynch

Cognitive Behavioral Therapy for Insomnia (CBT-I): A Primer

Cognitive Behavioral Therapy for Insomnia (CBT-I) is a multi-component treatment for insomnia that targets difficulties with initiating and/or maintaining sleep and is delivered over the course of six to eight sessions. The primary focus of CBT-I is to address the perpetuating factors (according to the three-factor model of insomnia) that contribute to the development of chronic insomnia. Chronic insomnia is the most prevalent sleep disorder, occurring in approximately 6–10% of the population, and is a risk factor for multiple medical and psychiatric disorders. Despite its prevalence and morbidity, the widespread dissemination of CBT-I is not commensurate with insomnia’s overall public health impact. This is particularly surprising given its large evidence base and recent recommendation as the first line intervention for insomnia. The primary goal of this article is to provide a primer or brief introduction to CBT-I that is intended to be accessible to all clinicians and researchers, including non-sleep experts. Core components of CBT-I (i.e., Sleep Restriction Therapy, Stimulus Control Therapy, Sleep Hygiene, and Cognitive Therapy), relapse prevention strategies, multicultural considerations, adjuvants to traditional interventions, treatment adherence issues, efficacy, and further training options are described. A session-by-session outline is also provided

Acute insomnia: current conceptualizations and future directions

Despite significant contributions made in the area of persistent/chronic insomnia, especially with regard to the underlying mechanisms driving its maintenance, the area of acute insomnia has received comparatively little attention. The aim of this paper is to review the literature with regard to understanding the situational and personaological circumstances that surround the development of acute insomnia. The review begins by examining how the existing diagnostic systems conceptualise acute insomnia. Theoretical models that explain, or inferentially explain, the transition between normal sleep and acute insomnia are then explored and evaluated. The review then examines the current evidence base in terms of the pathogenesis of acute insomnia from naturalistic and experimental studies. Overall, the findings from the review confirm the paucity of evidence available but perhaps more importantly highlight the need for a structured diagnosis of acute insomnia as the first step in a research and treatment strategy. To this end a diagnostic system, drawing on the existing literature on stress and the systems used to diagnose depression, is forwarded and justified and a research agenda advanced