An evidence map of psychosocial interventions for the earliest stages of bipolar disorder.

Depression, schizophrenia, and bipolar disorder are three of the four most burdensome problems in people aged under 25 years. In psychosis and depression, psychological interventions are effective, low-risk, and high-benefit approaches for patients at high risk of first-episode or early-onset disorders. We review the use of psychological interventions for early-stage bipolar disorder in patients aged 15-25 years. Because previous systematic reviews had struggled to identify information about this emerging sphere of research, we used evidence mapping to help us identify the extent, distribution, and methodological quality of evidence because the gold standard approaches were only slightly informative or appropriate. This strategy identified 29 studies in three target groups: ten studies in populations at high risk for bipolar disorder, five studies in patients with a first episode, and 14 studies in patients with early-onset bipolar disorder. Of the 20 completed studies, eight studies were randomised trials, but only two had sample sizes of more than 100 individuals. The main interventions used were family, cognitive behavioural, and interpersonal therapies. Only behavioural family therapies were tested across all of our three target groups. Although the available interventions were well adapted to the level of maturity and social environment of young people, few interventions target specific developmental psychological or physiological processes (eg, ruminative response style or delayed sleep phase), or offer detailed strategies for the management of substance use or physical health

Collaborative meta-analysis finds no evidence of a strong interaction between stress and 5-HTTLPR genotype contributing to the development of depression

The hypothesis that the S allele of the 5-HTTLPR serotonin transporter promoter region is associated with increased risk of depression, but only in individuals exposed to stressful situations, has generated much interest, research and controversy since first proposed in 2003. Multiple meta-analyses combining results from heterogeneous analyses have not settled the issue. To determine the magnitude of the interaction and the conditions under which it might be observed, we performed new analyses on 31 data sets containing 38 802 European ancestry subjects genotyped for 5-HTTLPR and assessed for depression and childhood maltreatment or other stressful life events, and meta-analysed the results. Analyses targeted two stressors (narrow, broad) and two depression outcomes (current, lifetime). All groups that published on this topic prior to the initiation of our study and met the assessment and sample size criteria were invited to participate. Additional groups, identified by consortium members or self-identified in response to our protocol (published prior to the start of analysis) with qualifying unpublished data, were also invited to participate. A uniform data analysis script implementing the protocol was executed by each of the consortium members. Our findings do not support the interaction hypothesis. We found no subgroups or variable definitions for which an interaction between stress and 5-HTTLPR genotype was statistically significant. In contrast, our findings for the main effects of life stressors (strong risk factor) and 5-HTTLPR genotype (no impact on risk) are strikingly consistent across our contributing studies, the original study reporting the interaction and subsequent meta-analyses. Our conclusion is that if an interaction exists in which the S allele of 5-HTTLPR increases risk of depression only in stressed individuals, then it is not broadly generalisable, but must be of modest effect size and only observable in limited situations.Molecular Psychiatry advance online publication, 4 April 2017; doi:10.1038/mp.2017.44.ALSPAC: Grant 102215/2/13/2 from The Wellcome Trust and grant MC_UU_12013- /6 from the UK Medical Research Council. The University of Bristol also provides core support for ALSPAC. LB receives funding as an Early Career Research Fellow from the Leverhulme Trust. MRM is a member of the UK Centre for Tobacco and Alcohol Studies, a UK Clinical Research Council Public Health Research: Centre of Excellence. Funding from British Heart Foundation, Cancer Research UK, Economic and Social Research Council, Medical Research Council, and the National Institute for Health Research, under the auspices of the UK Clinical Research Collaboration, is gratefully acknowledged. ASPIS: EKBAN 97 from the General Secretariat of Research and Technology, Greek Ministry of Development. ATP: Grants DP130101459, DP160103160 and APP1082406 from the Australian Research Council and The National Health and Medical Research Council of Australia. CHDS: Grant HRC 11/792 from the Health Research Council of New Zealand. CoFaMS: Grant APP1060524 to BTB from the National Health and Medical Research Council of Australia. We acknowledge the University of Adelaide for the provision of seed funding in support of this project. COGA: Grant U10AA008401 from the National Institutes of Health, NIAAA and NIDA. COGEND: National Institutes of Health grants P01CA089392 from NCI and R01DA036583 from NIDA. DeCC: Grant G0701420 from the UK Medical Research Council, and a UK MRC Population Health Scientist fellowship (G1002366) and an MQ Fellows Award (MQ14F40) to Helen L Fisher. EPIC-Norfolk: Grants G9502233, G0300128, C865/A2883 from the UK Medical Research Council and Cancer Research UK. ESPRIT Montpellier: An unconditional grant from Novartis and from the National Research Agency (ANR Project 07 LVIE004). G1219: A project grant from the WT Grant Foundation and G120/635, a Career Development Award from the UK Medical Research Council to Thalia Eley. The GENESiS project was supported by Grant G9901258 from the UK Medical Research Council. This study presents independent research part- funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. GAN12-France: Research Protocol C0829 from INSERM; Research Protocol GAN12 from Assistance Publique des Hôpitaux de Paris; ANR-11-IDEX- 0004 from Investissements d’Avenir program managed by the ANR, and RTRS Sante Mentale from Fondation FondaMental. GENESIS: Grant PHRC UF 7653 & ANR NEURO 2007 ‘GENESIS’ from CHU Montpellier & Agence Nationale de la Recherche. Heart and Soul: Epidemiology Merit Review Program from the Department of Veterans Affairs; National Institutes of Health grant R01HL-079235 from NHLBI; Generalist Physician Faculty Scholars Program from the Robert Woods Johnson foundation; Paul Beeson Faculty Scholars Program from the American Federation for Aging Research; and a Young Investigator Award from the Bran and Behavior Research Foundation. MARS: Grant LA 733/2-1 from German Research Foundation (DFG) and the Federal Ministry for Education and Research as part of the 'National Genome Research Network'. MLS: National Institutes of Health grants R01 AA07065 and R37 AA07065 from NIAAA. MoodInFlame: Grant EU-FP7- HEALTH-F2-2008-222963 from the European Union. Muenster Neuroimaging Study: Grant FOR2107, DA1151/5-1 from the German Research Foundation (DFG). NEWMOOD: Grants LSHM-CT-2004-503474 from Sixth Framework Program of the European Union; KTIA_NAP_13-1-2013-0001, KTIA_13_NAP-A-II/14 from National Development Agency Hungarian Brain Research Program; KTIA_NAP_13-2-2015-0001 from MTA-SE-NAP B Genetic Brain Imaging Migraine Research Group, Hungarian Academy of Sciences, Semmelweis University; support from Hungarian Academy of Sciences, MTA-SE Neuropsychopharmacology and Neurochemistry Research Group; and support from the National Institute for Health Research Manchester Biomedical Research Centre. NESDA/NTR: The Netherlands Organization for Scientific Research (NWO) and MagW/ZonMW grants Middelgroot-911-09-032, Spinozapremie 56-464- 14192, Geestkracht program of the Netherlands Organization for Health Research and Development (ZonMW 10-000-1002), Center for Medical Systems Biology (CSMB, NWO Genomics), Genetic influences on stability and change in psychopathology from childhood to young adulthood (ZonMW 912-10-020), NBIC/BioAssist/RK (2008.024), Biobanking and Biomolecular Resources Research Infrastructure (BBMRI -NL, 184.021.007), VU University's Institute for Health and Care Research (EMGO+) and Neuroscience Campus Amsterdam (NCA); the European Science Council (ERC Advanced, 230374). Part of the genotyping and analyses were funded by the Genetic Association Information Network (GAIN) of the Foundation for the National Institutes of Health, Rutgers University Cell and DNA Repository (NIMH U24 MH068457-06), the Avera Institute, Sioux Falls, South Dakota (USA) and the National Institutes of Health (NIH R01 HD042157-01A1, MH081802, Grand Opportunity grants 1RC2 MH089951 and 1RC2 MH089995). PATH: Program Grant Number 179805 from the National Health and Medical Research Council of Australia. POUCH: Grants 20FY01-38 and 20-FY04-37 of the Perinatal Epidemiologic Research Initiative Program Grant from the March of Dimes Foundation; National Institutes of Health grant R01 HD34543 from NICHD and NINR; grant 02816-7 from the Thrasher Research Foundation; and grant U01 DP000143-01 from the Centers for Disease Control and Prevention. QIMRtwin: Grants 941177, 971232, 339450, 443011 from the National Health and Medical Research Council of Australia; AA07535, AA07728, AA10249 from US Public Health Service; National Institutes of Health grant K99DA023549-01A2 from NIDA. Additional support was provided by Beyond Blue. SALVe 2001 and SALVe 2006: Grants FO2012-0326, FO2013-0023, FO2014-0243 from The Brain Foundation (Hjärnfonden); SLS-559921 from Söderström-Königska Foundation; 2015-00897 from Swedish Council for Working Life and Social Research; and M15-0239 from Åke Wiberg's Foundation. Additional funding was provided by Systembolagets Råd för Alkoholforskning, SRA and Svenska Spel Research Council. SEBAS: National Institutes of Health grants R01 AG16790, R01 AG16661 and R56 AG01661 from NIA and grant P2CHD047879 from NICHD; and additional financial support from the Graduate School of Arts and Sciences at Georgetown University. SHIP/TREND: This work was supported by the German Federal Ministry of Education and Research within the framework of the e:Med research and funding concept (Integrament) Grant No. 01ZX1314E. Study of Health in Pomerania is part of the Community Medicine Research net of the University of Greifswald, Germany, which is funded by the Federal Ministry of Education and Research Grant Nos. 01ZZ9603, 01ZZ0103 and 01ZZ0403; the Ministry of Cultural Affairs; and the Social Ministry of the Federal State of Mecklenburg-West Pomerania. Genome-wide data were supported by the Federal Ministry of Education and Research Grant No. 03ZIK012 and a joint grant from Siemens Healthcare, Erlangen, Germany and the Federal State of Mecklenburg-West Pomerania. The Greifswald Approach to Individualised Medicine (GANI_MED) was funded by the Federal Ministry of Education and Research Grant No. 03IS2061A and the German Research Foundation Grant No. GR 1912/5-1. TRAILS: Grants GB-MW 940- 38-011, ZonMW Brainpower 100-001-004, Investment grant 175.010.2003.005, GBMaGW 480-07-001 and Longitudinal Survey and Panel Funding 481-08-013 from the Netherlands Organization for Scientific Research (NWO). Additional funding was provided by the Dutch Ministry of Justice, the European Science Foundation, BBMRINL and the participating centres (UMCG, RUG, Erasmus MC, UU, Radboud MC, Parnassia Bavo group): VAHCS: Grants APP1063091, 1008271 and 1019887 from Australia’s National Health and Medical Research Council of Australia (NHMRC)

Association of Impulsivity and Polymorphic MicroRNA-641 Target Sites in the SNAP-25 Gene.

Impulsivity is a personality trait of high impact and is connected with several types of maladaptive behavior and psychiatric diseases, such as attention deficit hyperactivity disorder, alcohol and drug abuse, as well as pathological gambling and mood disorders. Polymorphic variants of the SNAP-25 gene emerged as putative genetic components of impulsivity, as SNAP-25 protein plays an important role in the central nervous system, and its SNPs are associated with several psychiatric disorders. In this study we aimed to investigate if polymorphisms in the regulatory regions of the SNAP-25 gene are in association with normal variability of impulsivity. Genotypes and haplotypes of two polymorphisms in the promoter (rs6077690 and rs6039769) and two SNPs in the 3' UTR (rs3746544 and rs1051312) of the SNAP-25 gene were determined in a healthy Hungarian population (N = 901) using PCR-RFLP or real-time PCR in combination with sequence specific probes. Significant association was found between the T-T 3' UTR haplotype and impulsivity, whereas no association could be detected with genotypes or haplotypes of the promoter loci. According to sequence alignment, the polymorphisms in the 3' UTR of the gene alter the binding site of microRNA-641, which was analyzed by luciferase reporter system. It was observed that haplotypes altering one or two nucleotides in the binding site of the seed region of microRNA-641 significantly increased the amount of generated protein in vitro. These findings support the role of polymorphic SNAP-25 variants both at psychogenetic and molecular biological levels

Impact of a cis-associated gene expression SNP on chromosome 20q11.22 on bipolar disorder susceptibility, hippocampal structure and cognitive performance.

BackgroundBipolar disorder is a highly heritable polygenic disorder. Recent enrichment analyses suggest that there may be true risk variants for bipolar disorder in the expression quantitative trait loci (eQTL) in the brain.AimsWe sought to assess the impact of eQTL variants on bipolar disorder risk by combining data from both bipolar disorder genome-wide association studies (GWAS) and brain eQTL.MethodTo detect single nucleotide polymorphisms (SNPs) that influence expression levels of genes associated with bipolar disorder, we jointly analysed data from a bipolar disorder GWAS (7481 cases and 9250 controls) and a genome-wide brain (cortical) eQTL (193 healthy controls) using a Bayesian statistical method, with independent follow-up replications. The identified risk SNP was then further tested for association with hippocampal volume (n = 5775) and cognitive performance (n = 342) among healthy individuals.ResultsIntegrative analysis revealed a significant association between a brain eQTL rs6088662 on chromosome 20q11.22 and bipolar disorder (log Bayes factor = 5.48; bipolar disorder P = 5.85×10(-5)). Follow-up studies across multiple independent samples confirmed the association of the risk SNP (rs6088662) with gene expression and bipolar disorder susceptibility (P = 3.54×10(-8)). Further exploratory analysis revealed that rs6088662 is also associated with hippocampal volume and cognitive performance in healthy individuals.ConclusionsOur findings suggest that 20q11.22 is likely a risk region for bipolar disorder; they also highlight the informative value of integrating functional annotation of genetic variants for gene expression in advancing our understanding of the biological basis underlying complex disorders, such as bipolar disorder

Variability in phase and amplitude of diurnal rhythms is related to variation of mood in bipolar and borderline personality disorder

Abstract Variable mood is an important feature of psychiatric disorders. However, its measurement and relationship to objective measureas of physiology and behaviour have rarely been studied. Smart-phones facilitate continuous personalized prospective monitoring of subjective experience and behavioural and physiological signals can be measured through wearable devices. Such passive data streams allow novel estimates of diurnal variability. Phase and amplitude of diurnal rhythms were quantified using new techniques that fitted sinusoids to heart rate (HR) and acceleration signals. We investigated mood and diurnal variation for four days in 20 outpatients with bipolar disorder (BD), 14 with borderline personality disorder (BPD) and 20 healthy controls (HC) using a smart-phone app, portable electrocardiogram (ECG), and actigraphy. Variability in negative affect, positive affect, and irritability was elevated in patient groups compared with HC. The study demonstrated convincing associations between variability in subjective mood and objective variability in diurnal physiology. For BPD there was a pattern of positive correlations between mood variability and variation in activity, sleep and HR. The findings suggest BPD is linked more than currently believed with a disorder of diurnal rhythm; in both BPD and BD reducing the variability of sleep phase may be a way to reduce variability of subjective mood

Influence of birth cohort on age of onset cluster analysis in bipolar I disorder

PURPOSE: Two common approaches to identify subgroups of patients with bipolar disorder are clustering methodology (mixture analysis) based on the age of onset, and a birth cohort analysis. This study investigates if a birth cohort effect will influence the results of clustering on the age of onset, using a large, international database. METHODS: The database includes 4037 patients with a diagnosis of bipolar I disorder, previously collected at 36 collection sites in 23 countries. Generalized estimating equations (GEE) were used to adjust the data for country median age, and in some models, birth cohort. Model-based clustering (mixture analysis) was then performed on the age of onset data using the residuals. Clinical variables in subgroups were compared. RESULTS: There was a strong birth cohort effect. Without adjusting for the birth cohort, three subgroups were found by clustering. After adjusting for the birth cohort or when considering only those born after 1959, two subgroups were found. With results of either two or three subgroups, the youngest subgroup was more likely to have a family history of mood disorders and a first episode with depressed polarity. However, without adjusting for birth cohort (three subgroups), family history and polarity of the first episode could not be distinguished between the middle and oldest subgroups. CONCLUSION: These results using international data confirm prior findings using single country data, that there are subgroups of bipolar I disorder based on the age of onset, and that there is a birth cohort effect. Including the birth cohort adjustment altered the number and characteristics of subgroups detected when clustering by age of onset. Further investigation is needed to determine if combining both approaches will identify subgroups that are more useful for research