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Posttraumatic stress disorder (PTSD) is a psychiatric disorder that can develop upon exposure to a traumatic event. While most people are able to recover promptly, others are at increased risk of developing PTSD. However, the exact underlying biological mechanisms of differential susceptibility are unknown. Identifying biomarkers of PTSD could assist in its diagnosis and facilitate treatment planning. Here, we identified serum microRNAs (miRNAs) of subjects that underwent a traumatic event and aimed to assess their potential to serve as diagnostic biomarkers of PTSD. Next-generation sequencing was performed to examine circulating miRNA profiles of 24 members belonging to the Dutch military cohort Prospective Research in Stress-Related Military Operations (PRISMO). Three groups were selected: "susceptible" subjects who developed PTSD after combat exposure, "resilient" subjects without PTSD, and nonexposed control subjects (N = 8 per group). Differential expression analysis revealed 22 differentially expressed miRNAs in PTSD subjects compared to controls and 1 in PTSD subjects compared to resilient individuals (after multiple testing correction and a log2 fold-change cutoff of ≥|1|). Weighted Gene Coexpression Network Analysis (WGCNA) identified a module of coexpressed miRNAs which could distinguish between the three groups. In addition, receiver operating characteristic curve analyses suggest that the miRNAs with the highest module memberships could have a strong diagnostic accuracy as reflected by high areas under the curves. Overall, the results of our pilot study suggest that serum miRNAs could potentially serve as diagnostic biomarkers of PTSD, both individually or grouped within a cluster of coexpressed miRNAs. Larger studies are now needed to validate and build upon these preliminary findings.

AbstractThis short essay recounts the author's interactions with Nick Martin in the years they both worked with Lindon Eaves at Virginia Commonwealth University. Although coming from very different academic traditions, they became close colleagues building their young careers together. Nick generously shared his statistical genetics expertise and the author taught Nick a thing or two about psychiatric illness.

Intro -- Suicide From a Global Perspective: Vulnerable Populations and Controversies -- Suicide From a Global Perspective: Vulnerable Populations and Controversies -- Library of Congress Cataloging-in-Publication Data -- Contents -- Foreword -- Preface -- Contributors -- Editors -- Corresponding Authors -- Chapter 1: Suicide in Prepubertal Children -- Introduction -- Risk Factors -- Personal/Individual Characteristics -- Psychiatric Co-Morbidity -- Prior Suicide Attempts -- Gender -- Cognitive Development and Maturity -- Genetics -- Family Characteristics/Psychopathology -- Negative Life Stressors/Environmental Influences -- Problems of Reporting Prepubertal Suicides -- Methods of Suicide in Prepubertal Children -- Cross-Cultural Considerations -- Identification, Prevention and Intervention -- Identifying At-Risk Prepubertal Children -- Prevention -- Intervention and Treatment -- References -- Chapter 2: Youth and Student Suicide -- Introduction -- Risk Factors -- Personal/Individual Characteristics -- Psychiatric Co-Morbidity -- Personality Correlates -- Genetics -- Gender -- Sexual Orientation -- Prior Suicide Attempts -- Family Characteristics/Psychopathology -- Negative Life Stressors/Environmental Influences -- Developmental Issues -- Prevention and Intervention -- References -- Chapter 3: Suicide in Old Age -- Introduction -- Time Trends -- Epidemiology and Cross-National Variation -- Age, Period and Cohort Effects -- Cohort Effects -- Age Effects -- Period Effects -- Demographic, Social and Socio-Economic Factors -- Sex -- Social Factors -- Cultural Factors -- Socio-Economic Factors -- Psychiatric Morbidity -- Depressive Illness -- Alcohol and Substance Misuse and Dependence -- Schizophrenia -- Neurotic Disorders and Personality Disorder -- Dementia -- Absence of Mental Illness -- Physical Illness -- Influence of Hospitalization.

Attempting to predict future is dangerous. This is particularly true in medical science where change is a result of chance discoveries. Currently, practicing psychiatrists are aware of deficiencies in psychiatric practice. However, we have a number of genuine reasons for optimism and excitement. Genetics, novel treatment approaches, new investigative techniques, large-scale treatment trials, and research in general medicine and neurology will give better insights in psychiatric disorders and its management. Psychiatric services in rural India can be reached by telemedicine. There are some threat perceptions which require solving and remedying. Subspecialties in psychiatry are the need of the hour. There is also a requirement for common practice guidelines. Mental Health Care Bill, 2013, requires suitable amendments before it is passed in the Indian Parliament. Research in psychiatry is yet to be developed as adequate resources are not available.

ABSTRACT
ObjectivesWe have conducted a feasibility study linking clinically rich survey data to routine data to create a platform for psychosis research in Wales: K Lloyd et al (2015), A national population-based e-cohort of people with psychosis (PsyCymru) linking prospectively ascertained phenotypically rich and genetic data to routinely collected records: overview, recruitment and linkage, Schizophrenia Research. Now we expand upon this through the linkage of large clinically rich cohorts with a range of mental health diagnoses along with genetic data to conduct validation exercises, develop novel methodologies, assess genetic and environment interactions and outcomes and address hypothesis-driven research questions.
ApproachThrough collaborations between the Farr Institute, Cardiff University based MRC centre for Neuropsychiatric Genetics and Genomics and the National Centre for Mental Health (NCMH) clinically rich data and genetic (CNVs, SNPs & polygenic scores) data from around 6000+ participants recruited from a variety of mental health research studies including 'PsyCymru', 'Genetic susceptibility to cognitive deficits study and NCMH amongst others will be loaded and linked to the datasets within SAIL. The analysis plan would firstly include validation exercises to compare the data between sources. Methodologies would be developed using this data to determine illness onset, relapse, chronicity, severity and response to treatment applied to large population-based mental health e-cohorts.
ResultsBy pooling together health service data, genetic variants, environmental and lifestyle factors, phenotypic and endo-phenotypic (cognitive scores) along with the ability to ascertain temporal relationships afforded by the longitudinal perspective available in SAIL we may be able to evaluate potential risk factors, assess the complex GxE interactions that lead to disease progression, and assess outcomes such as prognosis, remission, relapse and premature mortality. The on-going routine updates provide us with the opportunity to follow-up these individuals across multiple health care settings in a cost effective and in-obtrusive manner and to carry out health services utilization/benefit and treatment surveillance in a naturalistic setting. This resource will continue to expand over the coming years in size, breadth and depth of data, with continued recruitment and additional measures planned.
ConclusionTo advance mental health research by developing our understanding of the causes, course and outcomes of mental illness that may lead to the development of better diagnostic classification, predictive, preventative strategies and therapeutic approaches.

Publisher's version (útgefin grein) ; Based on the discovery by the Resilience Project (Chen R. et al. Nat Biotechnol 34:531–538, 2016) of rare variants that confer resistance to Mendelian disease, and protective alleles for some complex diseases, we posited the existence of genetic variants that promote resilience to highly heritable polygenic disorders1,0 such as schizophrenia. Resilience has been traditionally viewed as a psychological construct, although our use of the term resilience refers to a different construct that directly relates to the Resilience Project, namely: heritable variation that promotes resistance to disease by reducing the penetrance of risk loci, wherein resilience and risk loci operate orthogonal to one another. In this study, we established a procedure to identify unaffected individuals with relatively high polygenic risk for schizophrenia, and contrasted them with risk-matched schizophrenia cases to generate the first known "polygenic resilience score" that represents the additive contributions to SZ resistance by variants that are distinct from risk loci. The resilience score was derived from data compiled by the Psychiatric Genomics Consortium, and replicated in three independent samples. This work establishes a generalizable framework for finding resilience variants for any complex, heritable disorder. ; SJG is supported by grants from the U.S. National Institutes of Health (5R01MH101519, 5R01AG054002), the Sidney R. Baer, Jr. Foundation, and NARSAD: The Brain & Behavior Research Foundation. SVF is supported by the K.G. Jebsen Centre for Research on Neuropsychiatric Disorders, University of Bergen, Bergen, Norway, the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement number 602805, the European Union's Horizon 2020 research and innovation programme under grant agreement number 667302 and NIMH grants 5R01MH101519 and U01 MH109536-01. HJE is supported by grants from the U.S. National Institutes of Health (U10 AA008401; U01 MH109532). Statistical analyses were conducted on the Genetic Cluster Computer, which is financially supported by the Netherlands Scientific Organization (NOW; 480-05-003) along with a supplement from the Dutch Brain Foundation and VU University. The Danish iPSYCH (The Lundbeck Foundation Initiative for Integrative Psychiatric Research) and GEMS2 teams acknowledge funding from The Lundbeck Foundation (grant no R102-A9118 and R155-2014-1724), the Stanley Medical Research Institute, an Advanced Grant from the European Research Council (project no: 294838), the Danish Strategic Research Council and grants from Aarhus University to the iSEQ and CIRRAU centers. The Danish National Biobank resource at Statens Serum Institut was supported by the Novo Nordisk Foundation. Computational resources for handling and statistical analysis of iPSYCH data on the GenomeDK HPC facility were provided by the iSEQ center, Aarhus University, Denmark (grant to ADB). ; Peer Reviewed

Neurophysiological measurements of the response to pre-pulse and startle stimuli have been suggested to represent an important endophenotype for both substance dependence and other select psychiatric disorders. We have previously shown, in young adult Mexican Americans (MA), that presentation of a short delay acoustic pre-pulse, prior to the startle stimuli can elicit a late negative component at about 400 msec (N4S), in the event-related potential (ERP), recorded from frontal cortical areas. In the present study, we investigated whether genetic factors associated with this endophenotype could be identified. The study included 420 (age 18–30 years) MA men (n= 170), and women (n= 250). DNA was genotyped using an Affymetrix Axiom Exome1A chip. An association analysis revealed that theCCKARandCCKBR(cholecystokinin A and B receptor) genes each had a nearby variant that showed suggestive significance with the amplitude of the N4S component to pre-pulse stimuli. The neurotransmitter cholecystokinin (CCK), along with its receptors,CCKARandCCKBR, have been previously associated with psychiatric disorders, suggesting that variants near these genes may play a role in the pre-pulse/startle response in this cohort.

One of Irving I. Gottesman's many contributions to behavior genetics, and part of his enduring legacy, was his introduction of the term 'endophenotype' to the field of psychiatry. Gottesman argued that focusing on endophenotypes, rather than complex heterogeneous clinical diagnoses, could help elucidate disease etiology. Although a different strategy for gene identification ultimately proved successful (that of amassing extremely large sample sizes in order to overcome the 'noise' of heterogeneity and have sufficient power to find genes of very small effect), the endophenotype concept continues to make a meaningful contribution to the field. The endophenotype concept forced the field to move beyond a simple disease model of finding genes 'for' psychiatric outcomes, and reminded us that genes are quite distal from complex behavioral outcomes and disorders. Endophenotypes called our attention to the steps along that pathway. In that process, the concept of endophenotypes evolved and expanded to include discussion of the role that other intermediary traits and psychological processes play in the development and genetic etiology of psychiatric and substance use disorders. As large-scale consortia continues to identify genes and generate genome-wide polygenic scores that are associated with behavioral outcomes, the next important step will be to characterize the pathways and mechanisms by which genetic risk unfolds. This essential step of mapping risk from genes to behavior is an evolution that follows naturally from the endophenotype concept, and could ultimately translate into improved prevention and intervention for individuals who are pre-disposed to mental health challenges.

AbstractAlcohol abuse and dependence are among the most common psychiatric conditions identified in epidemiological surveys of the general population. The aim of this article is to examine the psychometric properties of Diagnostic and Statistical Manual of Mental Disorders, (4th ed.; DSM-IV; American Psychiatric Association, 1994) criteria for alcohol abuse and dependence using latent class analysis (LCA). Six thousand two hundred and sixty-five young Australian twins (median age 30 years) were interviewed by telephone between 1996 and 2000 using a modified version of the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA). DSM-IV symptoms of alcohol abuse and dependence were collected by structured diagnostic interview and analyzed using methods of LCA. LCA revealed a 4-class solution for women that classified individuals according to the severity of their alcohol- related problems: no/few problems (66.5%), heavy drinking (23.9%), moderate dependence (7.6%) and severe dependence (2.0%). Among men the preferred solution included 5 classes corresponding to no/few problems (46.4%), heavy drinking (34.3%), moderate dependence (12.2%), severe dependence (3.0%) and abuse (4.0%). Evidence of a male-specific class of alcohol-related problems corresponding to abuse partially supports the DSM conceptualization of alcohol use disorders but suggests that this conceptualization — and measurement — may need to be refined for women. Identification of a male- specific abuse class also has important implications for interventions and treatment as these individuals experienced significant alcohol-related problems and comprised approximately 21% of all men classified with an alcohol use disorder.

Significant effort has been put forth to increase understanding regarding the role of the human microbiome in health- and disease-related processes. In turn, the United States (US) Veteran Microbiome Project (US-VMP) was conceptualized as a means by which to serially collect microbiome and health-related data from those seeking care within the Veterans Health Administration (VHA). In this manuscript, exposures related to military experiences, as well as conditions and health-related factors among patients seen in VHA clinical settings are discussed in relation to common psychological and physical outcomes. Upon enrollment in the study, Veterans complete psychometrically sound (i.e., reliable and valid) measures regarding their past and current medical history. Participants also provide skin, oral, and gut microbiome samples, and permission to track their health status via the VHA electronic medical record. To date, data collection efforts have been cross-diagnostic. Within this manuscript, we describe current data collection practices and procedures, as well as highlight demographic, military, and psychiatric characteristics of the first 188 Veterans enrolled in the study. Based on these findings, we assert that this cohort is unique as compared to those enrolled in recent large-scale studies of the microbiome. To increase understanding regarding disease and health among diverse cohorts, efforts such as the US-VMP are vital. Ongoing barriers and facilitators to data collection are discussed, as well as future research directions, with an emphasis on the importance of shifting current thinking regarding the microbiome from a focus on normalcy and dysbiosis to health promotion and disease prevention.

Posttraumatic stress disorder (PTSD) is a psychiatric disorder that can develop upon exposure to a traumatic event. While most people are able to recover promptly, others are at increased risk of developing PTSD. However, the exact underlying biological mechanisms of differential susceptibility are unknown. Identifying biomarkers of PTSD could assist in its diagnosis and facilitate treatment planning. Here, we identified serum microRNAs (miRNAs) of subjects that underwent a traumatic event and aimed to assess their potential to serve as diagnostic biomarkers of PTSD. Next-generation sequencing was performed to examine circulating miRNA profiles of 24 members belonging to the Dutch military cohort Prospective Research in Stress-Related Military Operations (PRISMO). Three groups were selected: "susceptible" subjects who developed PTSD after combat exposure, "resilient" subjects without PTSD, and nonexposed control subjects (N = 8 per group). Differential expression analysis revealed 22 differentially expressed miRNAs in PTSD subjects compared to controls and 1 in PTSD subjects compared to resilient individuals (after multiple testing correction and a log2 fold-change cutoff of ≥|1|). Weighted Gene Coexpression Network Analysis (WGCNA) identified a module of coexpressed miRNAs which could distinguish between the three groups. In addition, receiver operating characteristic curve analyses suggest that the miRNAs with the highest module memberships could have a strong diagnostic accuracy as reflected by high areas under the curves. Overall, the results of our pilot study suggest that serum miRNAs could potentially serve as diagnostic biomarkers of PTSD, both individually or grouped within a cluster of coexpressed miRNAs. Larger studies are now needed to validate and build upon these preliminary findings.