Autistic and Schizotypal Traits and Global Functioning in Bipolar I Disorder

Objective: To determine the expression of autistic and positive schizotypal traits in a large sample of adults with bipolar I disorder (BD-I), and the effect of co-occurring autistic and positive schizotypal traits on global functioning in BD-I. Method: Autistic and positive schizotypal traits were self-assessed in 797 individuals with BD-I recruited by the Bipolar Disorder Research Network. Differences in global functioning (rated using the Global Assessment Scale) during lifetime worst depressive and manic episodes (GASD and GASM respectively) were calculated in groups with high/low autistic and positive schizotypal traits. Regression analyses assessed the interactive effect of autistic and positive schizotypal traits on global functioning. Results: 47.2% (CI=43.7-50.7%) showed clinically significant levels of autistic traits, and 23.22% (95% CI=20.29-26.14) showed clinically significant levels of positive schizotypal traits. In the worst episode of mania, the high autistic, high positive schizotypal group had better global functioning compared to the other groups. Individual differences analyses showed that high levels of co-occurring traits were associated with better global functioning in both mood states. Limitations: Autistic and schizotypal traits were assessed using self-rated questionnaires. Conclusions: Expression of autistic and schizotypal traits in adults with BD-I is prevalent, and may be important to predict illness aetiology, prognosis, and diagnostic practices in this population. Future work should focus on replicating these findings in independent samples, and on the biological and/or psychosocial mechanisms underlying better global functioning in those who have high levels of both autistic and positive schizotypal traits.

History of Premenstrual Mood Change and Postpartum Episodes are Associated with Perimenopausal Episodes in Women with Bipolar Disorder

Background and Aims: Reproductive life events are potential triggers of mood episodes in women with bipolar disorder. We aimed to establish whether a history of premenstrual mood change and postpartum episodes are associated with perimenopausal episodes in women who have bipolar disorder. Methods: Participants were 339 post-menopausal women with DSM-IV bipolar disorder recruited into the Bipolar Disorder Research Network (www.bdrn.org). Women self-reported presence (N = 200) or absence (N = 139) of an illness episode during the perimenopausal period. History of premenstrual mood change was measured using the self-report Premenstrual Symptoms Screening Tool (PSST), and history of postpartum episodes was measured via semi-structured interview (Schedules for Clinical Assessment in Neuropsychiatry, SCAN) and inspection of case-notes. Results: History of a postpartum episode within 6 months of delivery (OR = 2.13, p = 0.03) and history of moderate/severe premenstrual syndrome (OR = 6.33, p < 0.001) were significant predictors of the presence of a perimenopausal episode, even after controlling for demographic factors. When we narrowed the definition of premenstrual mood change to premenstrual dysphoric disorder, it remained significant (OR = 2.68, p = 0.007). Conclusions: Some women who have bipolar disorder may be particularly sensitive to reproductive life events. Previous mood episodes in relation to the female reproductive lifecycle may help clinicians predict individual risk for women with bipolar disorder approaching the menopause. There is a need for prospective longitudinal studies of women with bipolar disorder providing frequent contemporaneous ratings of their mood to overcome the limitations of retrospective self-report data.

Identifying Risk Factors for Postpartum Mood Episodes in Bipolar Disorder – A UK Prospective Study

Background and Aims: Women with bipolar disorder are vulnerable to episodes postpartum, but risk factors are poorly understood. We are exploring risk factors for postpartum mood episodes in women with bipolar disorder using a prospective longitudinal design. Methods: Pregnant women with lifetime DSM-IV bipolar disorder are being recruited into the Bipolar Disorder Research Network (www.BDRN.org). Baseline assessments during late pregnancy include lifetime psychopathology and potential risk factors for perinatal episodes such as medication use, sleep, obstetric factors, and psychosocial factors. Blood samples are taken for genetic analysis. Perinatal psychopathology is assessed via follow-up interview at 12-weeks postpartum. Interview data are supplemented by clinician questionnaires and case-note review. Potential risk factors will be compared between women who experience perinatal episodes and those who remain well. Results: 80 participants have been recruited to date. 32/61 (52%) women had a perinatal recurrence by follow-up. 16 (26%) had onset in pregnancy. 21 (34%) had postpartum onset, 19 (90%) within 6-weeks of delivery: 11 (18%) postpartum psychosis, 5 (8%) postpartum hypomania, 5 (8%) postpartum depression. Postpartum relapse was more frequent in women with bipolar-I than bipolar-II disorder (45% vs 17%). 62% women with postpartum relapse took prophylactic medication peripartum and almost all received care from secondary psychiatric services (95%). Conclusions: Rate of postpartum relapse is high, despite most women receiving specialist care and medication perinatally. A larger sample size will allow us to examine potential risk factors for postpartum episodes, which will assist in providing accurate and personalised advice to women with bipolar disorder who are considering pregnancy.

Postpartum Psychosis in Bipolar Disorder: No Association with Personality, Cognitive Style and Temperament

Background and Aims: Bipolar disorder has been associated with a number of personality traits, cognitive styles and affective temperaments. Women who have bipolar disorder are at increased risk of experiencing postpartum psychosis, however no previous research has investigated these traits in relationship to postpartum episodes. Our aim was to establish whether aspects of personality, cognitive style and affective temperament, that have been associated with bipolar disorder, confer vulnerability to postpartum psychosis over and above their known association with bipolar disorder. Methods: Participants were 552 parous women with DSM-IV bipolar I disorder recruited into the Bipolar Disorder Research Network (www.bdrn.org). Postpartum psychosis group: lifetime episode of postpartum psychosis within 6 weeks of delivery (N = 284). Non-postpartum psychosis group: no history of any perinatal mood episodes (N = 268). Bipolar disorder-associated personality traits (neuroticism, extraversion, schizotypy and impulsivity), cognitive styles (low self-esteem and dysfunctional attitudes) and affective temperaments were measured using well validated self-report questionnaire measures. Results: After controlling for key demographic, clinical and pregnancy-related variables, and measures of current mood state, there were no statistically significant differences between the postpartum psychosis group and non-postpartum psychosis group on any of the personality, cognitive style or affective temperament measures. Conclusions: Personality traits, cognitive styles and affective temperaments associated with the bipolar disorder diathesis in general were not associated with the onset of postpartum psychosis specifically. We have found no evidence that these traits should play a key role when evaluating risk of postpartum psychosis in women with bipolar I disorder considering pregnancy.

The Influence of Borderline Personality Traits on the Course of Bipolar Disorder

Background and Aims: Bipolar disorder and borderline personality disorder are commonly comorbid. Borderline personality disorder is diagnosed categorically, but personality pathology may be better characterised dimensionally. The impact of borderline personality traits (not diagnosis) on the course of bipolar disorder is unknown. We examined the presence and severity of borderline personality traits in a large UK sample of bipolar disorder, and the impact of these traits on illness course. Methods: Borderline Evaluation of Severity over Time (BEST) was used to measure presence and severity of borderline traits in 1447 individuals with DSM-IV bipolar I disorder (n = 1008) and bipolar II disorder (n = 439) recruited into the Bipolar Disorder Research Network (www.bdrn.org). Clinical course was measured via semi-structured interview (Schedules for Clinical Assessment in Neuropsychiatry) and case-notes. Results: BEST score was higher in bipolar II than bipolar I (36 v 27, p < 0.001) and 9/12 individual BEST traits were significantly more common in bipolar II than bipolar I. Within both bipolar I and bipolar II higher BEST score was associated with younger age of bipolar onset (p < 0.001), history of alcohol misuse (p < 0.010), and history of suicide attempt (p < 0.001). Conclusions: Borderline personality traits are common in bipolar disorder, and more severe in bipolar II than bipolar I disorder. Borderline trait severity was associated with more severe bipolar illness course; younger age of onset, alcohol misuse and suicidal behaviour. Clinicians should be vigilant for borderline personality traits irrespective of whether criteria for diagnosis are met, particularly in those with bipolar II disorder and younger age of bipolar onset.

Autistic and Schizotypal Traits and Global Functioning in Bipolar Disorder

Background and Aims To determine the expression of autistic and positive schizotypal traits in a large sample of adults with bipolar disorder (BD), and the effect of co-occurring autistic and positive schizotypal traits on global functioning in BD. Methods Autistic and positive schizotypal traits were assessed in 797 individuals with BD recruited by the Bipolar Disorder Research Network (BDRN), using the Autism-Spectrum Quotient and Kings Schizotypy Questionnaire (KSQ), respectively. Differences in global functioning (rated using the Global Assessment Scale) during lifetime worst depressive and manic episodes (GASD and GASM respectively) were calculated in groups with high/low autistic and positive schizotypal traits. Regression analyses assessed the interactive effect of autistic and positive schizotypal traits on global functioning. Results 47.2% (CI = 43.7–50.7%) showed clinically significant levels of autistic traits. Mean of sample on the KSQ-Positive scale was 11.98 (95% CI: 11.33–12.62). In the worst episode of mania, the high autistic, high positive schizotypal group had better global functioning than the low autistic, low positive schizotypal group (mean difference = 3.72, p = 0.004). High levels of co-occurring traits were associated with better global functioning in both mood states in individuals with a history of psychosis (GASM: p < 0.001; GASD: p = 0.055). Conclusions Expression of autistic and schizotypal traits in adults with BD is prevalent, and may be important to predict course of illness, prognosis, and in devising individualised therapies. Future work should focus on replicating these findings in independent samples, and on the biological and/or psychosocial mechanisms underlying better global functioning in those who have high levels of both autistic and positive schizotypal traits.

Validation of Electronic Health Record Phenotyping of Bipolar Disorder Cases and Controls

Objective: The study was designed to validate use of elec-tronic health records (EHRs) for diagnosing bipolar disorder and classifying control subjects. Method: EHR data were obtained from a health care system of more than 4.6 million patients spanning more than 20 years. Experienced clinicians reviewed charts to identify text features and coded data consistent or inconsistent with a diagnosis of bipolar disorder. Natural language processing was used to train a diagnostic algorithm with 95% specificity for classifying bipolar disorder. Filtered coded data were used to derive three additional classification rules for case subjects and one for control subjects. The positive predictive value (PPV) of EHR-based bipolar disorder and subphenotype di- agnoses was calculated against diagnoses from direct semi- structured interviews of 190 patients by trained clinicians blind to EHR diagnosis. Results: The PPV of bipolar disorder defined by natural language processing was 0.85. Coded classification based on strict filtering achieved a value of 0.79, but classifications based on less stringent criteria performed less well. No EHR- classified control subject received a diagnosis of bipolar dis- order on the basis of direct interview (PPV=1.0). For most subphenotypes, values exceeded 0.80. The EHR-based clas- sifications were used to accrue 4,500 bipolar disorder cases and 5,000 controls for genetic analyses. Conclusions: Semiautomated mining of EHRs can be used to ascertain bipolar disorder patients and control subjects with high specificity and predictive value compared with diagnostic interviews. EHRs provide a powerful resource for high-throughput phenotyping for genetic and clinical research.

Seasonality Shows Evidence for Polygenic Architecture and Genetic Correlation With Schizophrenia and Bipolar Disorder

OBJECTIVE: To test common genetic variants for association with seasonality (seasonal changes in mood and behavior) and to investigate whether there are shared genetic risk factors between psychiatric disorders and seasonality. METHOD: Genome-wide association studies (GWASs) were conducted in Australian (between 1988 and 1990 and between 2010 and 2013) and Amish (between May 2010 and December 2011) samples in whom the Seasonal Pattern Assessment Questionnaire (SPAQ) had been administered, and the results were meta-analyzed in a total sample of 4,156 individuals. Genetic risk scores based on results from prior large GWAS studies of bipolar disorder, major depressive disorder (MDD), and schizophrenia were calculated to test for overlap in risk between psychiatric disorders and seasonality. RESULTS: The most significant association was with rs11825064 (P = 1.7 × 10⁻⁶, β = 0.64, standard error = 0.13), an intergenic single nucleotide polymorphism (SNP) found on chromosome 11. The evidence for overlap in risk factors was strongest for schizophrenia and seasonality, with the schizophrenia genetic profile scores explaining 3% of the variance in log-transformed global seasonality scores. Bipolar disorder genetic profile scores were also associated with seasonality, although at much weaker levels (minimum P value = 3.4 × 10⁻³), and no evidence for overlap in risk was detected between MDD and seasonality. CONCLUSIONS: Common SNPs of large effect most likely do not exist for seasonality in the populations examined. As expected, there were overlapping genetic risk factors for bipolar disorder (but not MDD) with seasonality. Unexpectedly, the risk for schizophrenia and seasonality had the largest overlap, an unprecedented finding that requires replication in other populations and has potential clinical implications considering overlapping cognitive deficits in seasonal affective disorders and schizophrenia.

Doença bipolar de início tardio: caso clínico

Introdução: A perturbação bipolar afecta aproximadamente 1% da população, com o diagnóstico geralmente estabelecido durante a adolescência/início da idade adulta e sendo apenas feito em 0.1% da população geriátrica. A perturbação bipolar de início tardio é heterogénea e a sua etiopatogenia é complexa. A idade de início tem um impacto significativo na natureza e curso desta doença. Objectivos: As autoras apresentam um caso de perturbação bipolar de início tardio, aos 76 anos, sem que esteja identificada uma causa orgânica subjacente. Conclusão: Este caso demonstra a importância de um amplo diagnóstico diferencial e manejo farmacológico, quando se abordam sintomas maniformes/depressivos de novo em doentes geriátricos.

Calculable lepton masses, seesaw relations, and four neutrino mixings in a 3-3-1 model with an extra U(1) symmetry

We propose a scheme in which the masses of the heavier leptons obey seesaw type relations. The light lepton masses, except the electron and the electron neutrino ones, are generated by one loop level radiative corrections. We work in a version of the 3-3-1 electroweak model that predicts singlets (charged and neutral) of heavy leptons beyond the known ones. An extra U(1)(Omega) symmetry is introduced in order to avoid the light leptons getting masses at the tree level. The electron mass induces an explicit symmetry breaking at U(1)(Omega). We discuss also the mixing matrix among four neutrinos. The new energy scale required is not higher than a few TeV.

Pronóstico a largo plazo del trastorno bipolar con episodios mixtos.

122 p.

O tratamento do doente bipolar na consulta de medicina dentária: um caso clínico

Dissertação para obtenção do grau de Mestre no Instituto Superior de Ciências da Saúde Egas Moniz

CHARACTERIZATION OF THERMAL AND MECHANICAL PROPERTIES OF POLYPROPYLENE–BASED COMPOSITES FOR FUEL CELL BIPOLAR PLATES AND DEVELOPMENT OF EDUCATIONAL TOOLS IN HYDROGEN AND FUEL CELL TECHNOLOGIES

In this project we developed conductive thermoplastic resins by adding varying amounts of three different carbon fillers: carbon black (CB), synthetic graphite (SG) and multi–walled carbon nanotubes (CNT) to a polypropylene matrix for application as fuel cell bipolar plates. This component of fuel cells provides mechanical support to the stack, circulates the gases that participate in the electrochemical reaction within the fuel cell and allows for removal of the excess heat from the system. The materials fabricated in this work were tested to determine their mechanical and thermal properties. These materials were produced by adding varying amounts of single carbon fillers to a polypropylene matrix (2.5 to 15 wt.% Ketjenblack EC-600 JD carbon black, 10 to 80 wt.% Asbury Carbons’ Thermocarb TC-300 synthetic graphite, and 2.5 to 15 wt.% of Hyperion Catalysis International’s FIBRILTM multi-walled carbon nanotubes) In addition, composite materials containing combinations of these three fillers were produced. The thermal conductivity results showed an increase in both through–plane and in–plane thermal conductivities, with the largest increase observed for synthetic graphite. The Department of Energy (DOE) had previously set a thermal conductivity goal of 20 W/m·K, which was surpassed by formulations containing 75 wt.% and 80 wt.% SG, yielding in–plane thermal conductivity values of 24.4 W/m·K and 33.6 W/m·K, respectively. In addition, composites containing 2.5 wt.% CB, 65 wt.% SG, and 6 wt.% CNT in PP had an in–plane thermal conductivity of 37 W/m·K. Flexural and tensile tests were conducted. All composite formulations exceeded the flexural strength target of 25 MPa set by DOE. The tensile and flexural modulus of the composites increased with higher concentration of carbon fillers. Carbon black and synthetic graphite caused a decrease in the tensile and flexural strengths of the composites. However, carbon nanotubes increased the composite tensile and flexural strengths. Mathematical models were applied to estimate through–plane and in–plane thermal conductivities of single and multiple filler formulations, and tensile modulus of single–filler formulations. For thermal conductivity, Nielsen’s model yielded accurate thermal conductivity values when compared to experimental results obtained through the Flash method. For prediction of tensile modulus Nielsen’s model yielded the smallest error between the predicted and experimental values. The second part of this project consisted of the development of a curriculum in Fuel Cell and Hydrogen Technologies to address different educational barriers identified by the Department of Energy. By the creation of new courses and enterprise programs in the areas of fuel cells and the use of hydrogen as an energy carrier, we introduced engineering students to the new technologies, policies and challenges present with this alternative energy. Feedback provided by students participating in these courses and enterprise programs indicate positive acceptance of the different educational tools. Results obtained from a survey applied to students after participating in these courses showed an increase in the knowledge and awareness of energy fundamentals, which indicates the modules developed in this project are effective in introducing students to alternative energy sources.

Towards person-centered neuroimaging markers for resilience and vulnerability in Bipolar Disorder

Improved clinical care for Bipolar Disorder (BD) relies on the identification of diagnostic markers that can reliably detect disease-related signals in clinically heterogeneous populations. At the very least, diagnostic markers should be able to differentiate patients with BD from healthy individuals and from individuals at familial risk for BD who either remain well or develop other psychopathology, most commonly Major Depressive Disorder (MDD). These issues are particularly pertinent to the development of translational applications of neuroimaging as they represent challenges for which clinical observation alone is insufficient. We therefore applied pattern classification to task-based functional magnetic resonance imaging (fMRI) data of the n-back working memory task, to test their predictive value in differentiating patients with BD (n=30) from healthy individuals (n=30) and from patients' relatives who were either diagnosed with MDD (n=30) or were free of any personal lifetime history of psychopathology (n=30). Diagnostic stability in these groups was confirmed with 4-year prospective follow-up. Task-based activation patterns from the fMRI data were analyzed with Gaussian Process Classifiers (GPC), a machine learning approach to detecting multivariate patterns in neuroimaging datasets. Consistent significant classification results were only obtained using data from the 3-back versus 0-back contrast. Using contrast, patients with BD were correctly classified compared to unrelated healthy individuals with an accuracy of 83.5%, sensitivity of 84.6% and specificity of 92.3%. Classification accuracy, sensitivity and specificity when comparing patients with BD to their relatives with MDD, were respectively 73.1%, 53.9% and 94.5%. Classification accuracy, sensitivity and specificity when comparing patients with BD to their healthy relatives were respectively 81.8%, 72.7% and 90.9%. We show that significant individual classification can be achieved using whole brain pattern analysis of task-based working memory fMRI data. The high accuracy and specificity achieved by all three classifiers suggest that multivariate pattern recognition analyses can aid clinicians in the clinical care of BD in situations of true clinical uncertainty regarding the diagnosis and prognosis.