Handbook of Psychiatric Genetics KennethBlumErnest P.NobleHandbook of Psychiatric Genetics1996CRC PressBoca Raton, FL478119.00(cloth);119.00 (cloth); 79.95 (paper)

Unknow

Mental health resilience in the adolescent offspring of parents with depression:a prospective longitudinal study

Background Young people whose parents have depression have a greatly increased risk of developing a psychiatric disorder, but poor outcomes are not inevitable. Identification of the contributors to mental health resilience in young people at high familial risk is an internationally recognised priority. Our objectives were to identify protective factors that predict sustained good mental health in adolescents with a parent with depression and to test whether these contribute beyond what is explained by parent illness severity. Methods The Early Prediction of Adolescent Depression study (EPAD) is a prospective longitudinal study of offspring of parents with recurrent depression. Parents with recurrent major depressive disorder, co-parents, and offspring (aged 9–17 years at baseline) were assessed three times over 4 years in a community setting. Offspring outcomes were operationalised as absence of mental health disorder, subthreshold symptoms, or suicidality on all three study occasions (sustained good mental health); and better than expected mental health (mood and behavioural symptoms at follow-up lower than predicted given severity of parental depression). Family, social, cognitive, and health behaviour predictor variables were assessed using interview and questionnaire measures. Findings Between February and June, 2007, we screened 337 families at baseline, of which 331 were eligible. Of these, 262 completed the three assessments and were included in the data for sustained mental health. Adolescent mental health problems were common, but 53 (20%) of the 262 adolescents showed sustained good mental health. Index parent positive expressed emotion (odds ratio 1·91 [95% CI 1·31–2·79]; p=0·001), co-parent support (1·90 [1·38–2·62]; p<0·0001), good-quality social relationships (2·07 [1·35–3·18]; p=0·001), self-efficacy (1·49 [1·05–2·11]; p=0·03), and frequent exercise (2·96 [1·26–6·92]; p=0·01) were associated with sustained good mental health. Analyses accounting for parent depression severity were consistent, but frequent exercise only predicted better than expected mood-related mental health (β=–0·22; p=0·0004) not behavioural mental health, whereas index parents' expression of positive emotions predicted better than expected behavioural mental health (β=–0·16; p=0·01) not mood-related mental health. Multiple protective factors were required for offspring to be free of mental health problems (zero or one protective factor, 4% sustained good mental health; two protective factors, 10%; three protective factors, 13%, four protective factors, 38%; five protective factors, 48%). Interpretation Adolescent mental health problems are common, but not inevitable, even when parental depression is severe and recurrent. These findings suggest that prevention programmes will need to enhance multiple protective factors across different domains of functioning

The health informatics cohort enhancement project (HICE): using routinely collected primary care data to identify people with a lifetime diagnosis of psychotic disorder

Background: We have previously demonstrated that routinely collected primary care data can be used to identify potential participants for trials in depression [1]. Here we demonstrate how patients with psychotic disorders can be identified from primary care records for potential inclusion in a cohort study. We discuss the strengths and limitations of this approach; assess its potential value and report challenges encountered. Methods: We designed an algorithm with which we searched for patients with a lifetime diagnosis of psychotic disorders within the Secure Anonymised Information Linkage (SAIL) database of routinely collected health data. The algorithm was validated against the "gold standard" of a well established operational criteria checklist for psychotic and affective illness (OPCRIT). Case notes of 100 patients from a community mental health team (CMHT) in Swansea were studied of whom 80 had matched GP records. Results: The algorithm had favourable test characteristics, with a very good ability to detect patients with psychotic disorders (sensitivity > 0.7) and an excellent ability not to falsely identify patients with psychotic disorders (specificity > 0.9). Conclusions: With certain limitations our algorithm can be used to search the general practice data and reliably identify patients with psychotic disorders. This may be useful in identifying candidates for potential inclusion in cohort studies

'Sifting the significance from the data' - the impact of high-throughput genomic technologies on human genetics and health care.

This report is of a round-table discussion held in Cardiff in September 2009 for Cesagen, a research centre within the Genomics Network of the UK's Economic and Social Research Council. The meeting was arranged to explore ideas as to the likely future course of human genomics. The achievements of genomics research were reviewed, and the likely constraints on the pace of future progress were explored. New knowledge is transforming biology and our understanding of evolution and human disease. The difficulties we face now concern the interpretation rather than the generation of new sequence data. Our understanding of gene-environment interaction is held back by our current primitive tools for measuring environmental factors, and in addition, there may be fundamental constraints on what can be known about these complex interactions.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Reproductive outcomes and risk of subsequent illness in women diagnosed with postpartum psychosis

Women who experience postpartum psychosis (PP) seek guidance on further pregnancies and risk of illness; however empirical data are limited. This study describes reproductive and mental health outcomes in women diagnosed with PP and examines clinical risk factors as predictors of further illness

Opportunities to engage in positive activities during the COVID-19 pandemic: Perspectives of individuals with mood disorders

Background Despite cross-sectional population and clinical studies finding individuals with existing mood disorders being adversely impacted by the COVID-19 pandemic, longitudinal studies have not shown a worsening of psychiatric symptoms. In response to these findings, we explored opportunities to engage in positive activities during the pandemic from the perspectives of individuals with mood disorders. Methods A bespoke survey, containing closed and open questions, was sent to participants with mood disorders who were part of the UK Bipolar Disorder Research Network (BDRN). Questions related to experiences of positive impacts of the pandemic, levels of engagement in positive activities and coping strategies. Results Response rate was 46.4 % (N = 1688). 61.9 % reported positive life changes during the pandemic, with slower pace of life reported most frequently (52.8 %). 47.3 % reported no adverse impact of the pandemic on implementing their usual coping strategies. Activities that respondents most commonly reported the same or greater level of engagement in compared to before the pandemic were avoiding known mood triggers (82.3 %), relaxation techniques (78.8 %) and the ability to maintain set routines (69.4 %). Limitations Responder bias may be present and experiences during the pandemic are likely to differ among other clinical and research mood disorders cohorts. Conclusions Our findings may help to explain why longitudinal studies have not found a worsening of mental health symptoms during the COVID-19 pandemic. Identifying potential facilitators to maintaining mental health have wider applicability, and may help to inform future evidence-based psychoeducation and self-management programmes for mood disorders

Heaviness, health and happiness: a cross-sectional study of 163 066 UK Biobank participants

&lt;b&gt;Background&lt;/b&gt;&lt;p&gt;&lt;/p&gt; Obesity is known to increase the risk of many diseases and reduce overall quality of life. This study examines the relationship with self-reported health (SRH) and happiness.&lt;p&gt;&lt;/p&gt; &lt;b&gt;Methods&lt;/b&gt; &lt;p&gt;&lt;/p&gt;We conducted a cross-sectional study of the 163 066 UK Biobank participants who completed the happiness rating. The association between adiposity and SRH and happiness was examined using logistic regression. SRH was defined as good (excellent, good), or poor (fair, poor). Self-reported happiness was defined as happy (extremely, very, moderately) or unhappy (moderately, very, extremely). &lt;p&gt;&lt;/p&gt; &lt;b&gt;Results&lt;/b&gt; &lt;p&gt;&lt;/p&gt;Poor health was reported by 44 457 (27.3%) participants. The adjusted ORs for poor health were 3.86, 2.92, 2.60 and 6.41 for the highest, compared with lowest, deciles of Body Mass Index, waist circumference, waist to hip ratio and body fat percent, respectively. The associations were stronger in men (p&lt;0.001). Overall, 7511 (4.6%) participants felt unhappy, and only class III obese participants were more likely to feel unhappy (adjusted OR 1.33, 95% CI 1.15 to 1.53, p&lt;0.001) but the associations differed by sex (p&lt;0.001). Among women, there was a significant association between unhappiness and all levels of obesity. By contrast, only class III obese men had significantly increased risk and overweight and class I obese men were less likely to be unhappy. &lt;p&gt;&lt;/p&gt; &lt;b&gt;Conclusions&lt;/b&gt;&lt;p&gt;&lt;/p&gt;Obesity impacts adversely on happiness as well as health, but the association with unhappiness disappeared after adjustment for self-reported health, indicating this may be mediated by health. Compared with obese men, obese women are less likely to report poor health, but more likely to feel unhappy. &lt;p&gt;&lt;/p&gt

The Bipolar Affective Disorder Dimension Scale (BADDS) – a dimensional scale for rating lifetime psychopathology in Bipolar spectrum disorders

BACKGROUND: Current operational diagnostic systems have substantial limitations for lifetime diagnostic classification of bipolar spectrum disorders. Issues include: (1) It is difficult to operationalize the integration of diverse episodes of psychopathology, (2) Hierarchies lead to loss of information, (3) Boundaries between diagnostic categories are often arbitrary, (4) Boundaries between categories usually require a major element of subjective interpretation, (5) Available diagnostic categories are relatively unhelpful in distinguishing severity, (6) "Not Otherwise Specified (NOS)" categories are highly heterogeneous, (7) Subclinical cases are not accommodated usefully within the current diagnostic categories. This latter limitation is particularly pertinent in the context of the increasing evidence for the existence of a broader bipolar spectrum than has been acknowledged within existing classifications. METHOD: We have developed a numerical rating system, the Bipolar Affective Disorder Dimension Scale, BADDS, that can be used as an adjunct to conventional best-estimate lifetime diagnostic procedures. The scale definitions were informed by (a) the current concepts of mood syndrome recognized within DSMIV and ICD10, (b) the literature regarding severity of episodes, and (c) our own clinical experience. We undertook an iterative process in which we initially agreed scale definitions, piloted their use on sets of cases and made modifications to improve utility and reliability. RESULTS: BADDS has four dimensions, each rated as an integer on a 0 – 100 scale, that measure four key domains of lifetime psychopathology: Mania (M), Depression (D), Psychosis (P) and Incongruence (I). In our experience it is easy to learn, straightforward to use, has excellent inter-rater reliability and retains the key information required to make diagnoses according to DSMIV and ICD10. CONCLUSIONS: Use of BADDS as an adjunct to conventional categorical diagnosis provides a richer description of lifetime psychopathology that (a) can accommodate sub-clinical features, (b) discriminate between illness severity amongst individuals within a single conventional diagnostic category, and (c) demonstrate the similarity between the illness experience of individuals who have been classified into different disease categories but whose illnesses both fall near the boundaries between the two categories. BADDS may be useful for researchers and clinicians who are interested in description and classification of lifetime psychopathology of individuals with disorders lying on the bipolar spectrum

A National Population-Based E-cohort of People with Psychosis (PsyCymru) Linkage of Phenotypical and Genetic Data to Routinely Collected Records

Introduction PsyCymru was established to investigate the feasibility of linking a prospectively ascertained, well characterised (linked clinical cohort) of people with psychosis in Wales, UK with large amounts of anonymised routinely collected health record data. We are now additionally linking genetic data. Objectives and Approach PsyCymru aimed to create a research platform for psychosis research in Wales by establishing two cohorts. The first was a well-characterised clinically assessed cohort with genetic data. Consented individuals underwent structured interviews using well-validated questionnaires and gave blood sample for DNA extraction, sequencing, and candidate gene identification. This data was then linked to routinely collected health and social datasets with identity encryption. The second is a larger e-cohort of prevalent psychosis cases created using a validated algorithm applied to anonymised routine data. Both cohorts were tracked prospectively and retrospectively in the Secure Anonymised Information Linkage (SAIL) databank. Results In total, data from 958 individuals for the clinical cohort were imported to SAIL. Among these individuals, genetic data for 740 were analysed. The genetic data included robust loci for schizophrenia, pathogenic copy-number variations (CNVs) for various conditions (e.g., autism, intellectual disability, congenital malformations), polygenic risks scores for schizophrenia, as well as pathogenic/non-pathogenic duplications or deletions of chromosome spanning more than 500kb or 1Mb. For the e-cohort, 29,797 individuals were found having a psychosis diagnosis from primary and secondary care between 2004 to 2013. Social demographic data for both cohorts were also analysed based on sex, age, area deprivation, urbanicity, and employment status. Conclusion/Implications This unique platform pooled data together from multiple sources; linking clinical, psychological, biological, genetic, and health care factors to address assorted research questions. This resource will continue to expand over the coming years in size, breadth and depth of data, with continued recruitment and additional measures planned