The study sought to explore nutrition graduates’ employability and role of employability capitals in supporting nutrition science graduate outcomes.

In-depth semi-structured, audio-recorded interviews were conducted with nutrition graduates who had completed a nutrition science degree between 2015 and 2021. Interpretivism guided this study, which endeavoured to co-construct meaning with participants. Transcribed interviews were thematically analysed, whereby data were coded, themes identified and discussed by all authors. The data were further mapped against the graduate capital model (GCM) by deductively coding against the five graduate capitals (human, identity, social, psychological and cultural).

Ireland and Australia.

Forty-two nutrition graduates from across nine universities in Ireland and twenty-two from a single university programme in Australia.

All elements of the GCM were identified with human, social and identity capital most dominant and identified as significantly influential on employability. Presence or absence of these capitals could be clearly identified within each graduates’ experience. Formation of professional identity and connection to the profession was strongest amongst Irish graduates. However, more than half of the Australian cohort perceived barriers to professional identity formation, including lack of regulation, imposter syndrome, presence of non-qualified individuals and comparison to dietetics. Both psychological and cultural capitals were rarely spoken about.

The development of human, social and identity capital is observed among nutrition science graduates. Further investigation is required to enhance the process of identity development and ascertain potential remedies for obstacles. The absence of psychological and cultural capital, therefore, poses a significant issue for the resilience and comprehension of prospective graduates.

Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.

To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.

This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.

The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.

Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.

Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.

To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.

Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.

Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.

AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.

Copy number variants (CNVs) play a significant role in disease pathogenesis in a small subset of individuals with schizophrenia (~2.5%). Chromosomal microarray testing is a first-tier genetic test for many neurodevelopmental disorders. Similar testing could be useful in schizophrenia.

To determine whether clinically identifiable phenotypic features could be used to successfully model schizophrenia-associated (SCZ-associated) CNV carrier status in a large schizophrenia cohort.

Logistic regression and receiver operating characteristic (ROC) curves tested the accuracy of readily identifiable phenotypic features in modelling SCZ-associated CNV status in a discovery data-set of 1215 individuals with psychosis. A replication analysis was undertaken in a second psychosis data-set (n = 479).

In the discovery cohort, specific learning disorder (OR = 8.12; 95% CI 1.16–34.88, P = 0.012), developmental delay (OR = 5.19; 95% CI 1.58–14.76, P = 0.003) and comorbid neurodevelopmental disorder (OR = 5.87; 95% CI 1.28–19.69, P = 0.009) were significant independent variables in modelling positive carrier status for a SCZ-associated CNV, with an area under the ROC (AUROC) of 74.2% (95% CI 61.9–86.4%). A model constructed from the discovery cohort including developmental delay and comorbid neurodevelopmental disorder variables resulted in an AUROC of 83% (95% CI 52.0–100.0%) for the replication cohort.

These findings suggest that careful clinical history taking to document specific neurodevelopmental features may be informative in screening for individuals with schizophrenia who are at higher risk of carrying known SCZ-associated CNVs. Identification of genomic disorders in these individuals is likely to have clinical benefits similar to those demonstrated for other neurodevelopmental disorders.

Residual symptoms and cognitive impairment are among important sources of disability in patients with bipolar disorder. Methylene blue could improve such symptoms because of its potential neuroprotective effects.

We conducted a double-blind crossover study of a low dose (15 mg, ‘placebo’) and an active dose (195 mg) of methylene blue in patients with bipolar disorder treated with lamotrigine.

Thirty-seven participants were enrolled in a 6-month trial (trial registration: NCT00214877). The outcome measures included severity of depression, mania and anxiety, and cognitive functioning.

The active dose of methylene blue significantly improved symptoms of depression both on the Montgomery–Åsberg Depression Rating Scale and Hamilton Rating Scale for Depression (P = 0.02 and 0.05 in last-observation-carried-forward analysis). It also reduced the symptoms of anxiety measured by the Hamilton Rating Scale for Anxiety (P = 0.02). The symptoms of mania remained low and stable throughout the study. The effects of methylene blue on cognitive symptoms were not significant. The medication was well tolerated with transient and mild side-effects.

Methylene blue used as an adjunctive medication improved residual symptoms of depression and anxiety in patients with bipolar disorder.

Despite an increasing prevalence of adults living with a CHD, little is known about the psychosocial impact of CHD. We sought to investigate the relative impact of disease severity and patients’ perceptions about their condition on depression, anxiety, and quality of life over a period of a year.

A total of 110 patients aged over 16 years completed an initial questionnaire containing measures for anxiety, depression, quality of life, and illness perceptions when they attended the Adult Congenital Heart Disease Clinic. Cardiologists rated the patients’ disease severity and illness course. A year later, patients were invited to complete the same measures. Regression analyses were performed to determine the relative impact of illness perceptions and disease severity on psychological outcomes a year later.

At baseline, 23% of the study population had depressive symptoms and 30% had elevated trait anxiety. After controlling for associations with disease-related variables, illness perceptions explained 28% of the variance in depression, 40% anxiety, and 27% overall quality of life at baseline. Baseline illness perceptions bivariately predicted quality of life, cardiac anxiety, and depression 1 year later, and regression analyses controlling for other factors showed that they were significant predictors of outcomes 1 year later.

Symptoms of depression and anxiety are common among adults with CHD. Patients’ illness perceptions are related to psychological outcomes, especially cross-sectionally. Future research could investigate whether an intervention to discuss patients’ perceptions about their CHD can improve mental health and quality of life.

Little is known about the impact of insulin resistance on bipolar disorder.

To examine the relationships between insulin resistance, type 2 diabetes and clinical course and treatment outcomes in bipolar disorder.

We measured fasting glucose and insulin in 121 adults with bipolar disorder. We diagnosed type 2 diabetes and determined insulin resistance. The National Institute of Mental Health Life Chart was used to record the course of bipolar disorder and the Alda scale to establish response to prophylactic lithium treatment.

Patients with bipolar disorder and type 2 diabetes or insulin resistance had three times higher odds of a chronic course of bipolar disorder compared with euglycaemic patients (50% and 48.7% respectively v. 27.3%, odds ratio (OR) = 3.07, P = 0.007), three times higher odds of rapid cycling (38.5% and 39.5% respectively v. 18.2%, OR = 3.13, P = 0.012) and were more likely to be refractory to lithium treatment (36.8% and 36.7% respectively v. 3.2%, OR = 8.40, P<0.0001). All associations remained significant after controlling for antipsychotic exposure and body mass index in sensitivity analyses.

Comorbid insulin resistance may be an important factor in resistance to treatment in bipolar disorder.

Recent data provide strong support for a substantial common polygenic contribution (i.e. many alleles each of small effect) to genetic susceptibility for schizophrenia and overlapping susceptibility for bipolar disorder.

To test hypotheses about the relationship between schizophrenia and psychotic types of bipolar disorder.

Using a polygenic score analysis to test whether schizophrenia polygenic risk alleles, en masse, significantly discriminate between individuals with bipolar disorder with and without psychotic features. The primary sample included 1829 participants with bipolar disorder and the replication sample comprised 506 people with bipolar disorder.

The subset of participants with Research Diagnostic Criteria schizoaffective bipolar disorder (n = 277) were significantly discriminated from the remaining participants with bipolar disorder (n = 1552) in both the primary (P = 0.00059) and the replication data-sets (P = 0.0070). In contrast, those with psychotic bipolar disorder as a whole were not significantly different from those with non-psychotic bipolar disorder in either data-set.

Genetic susceptibility influences at least two major domains of psychopathological variation in the schizophrenia–bipolar disorder clinical spectrum: one that relates to expression of a ‘bipolar disorder-like’ phenotype and one that is associated with expression of ‘schizophrenia-like’ psychotic symptoms.

Psychiatric phenotypes are currently defined according to sets of descriptive criteria. Although many of these phenotypes are heritable, it would be useful to know whether any of the various diagnostic categories in current use identify cases that are particularly helpful for biological–genetic research.

To use genome-wide genetic association data to explore the relative genetic utility of seven different descriptive operational diagnostic categories relevant to bipolar illness within a large UK case–control bipolar disorder sample.

We analysed our previously published Wellcome Trust Case Control Consortium (WTCCC) bipolar disorder genome-wide association data-set, comprising 1868 individuals with bipolar disorder and 2938 controls genotyped for 276 122 single nucleotide polymorphisms (SNPs) that met stringent criteria for genotype quality. For each SNP we performed a test of association (bipolar disorder group v. control group) and used the number of associated independent SNPs statistically significant at P<0.00001 as a metric for the overall genetic signal in the sample. We next compared this metric with that obtained using each of seven diagnostic subsets of the group with bipolar disorder: Research Diagnostic Criteria (RDC): bipolar I disorder; manic disorder; bipolar II disorder; schizoaffective disorder, bipolar type; DSM–IV: bipolar I disorder; bipolar II disorder; schizoaffective disorder, bipolar type.

The RDC schizoaffective disorder, bipolar type (v. controls) stood out from the other diagnostic subsets as having a significant excess of independent association signals (P<0.003) compared with that expected in samples of the same size selected randomly from the total bipolar disorder group data-set. The strongest association in this subset of participants with bipolar disorder was at rs4818065 (P = 2.42 × 10–7). Biological systems implicated included gamma amniobutyric acid (GABA)A receptors. Genes having at least one associated polymorphism at P<10–4 included B3GALTS, A2BP1, GABRB1, AUTS2, BSN, PTPRG, GIRK2 and CDH12.

Our findings show that individuals with broadly defined bipolar schizoaffective features have either a particularly strong genetic contribution or that, as a group, are genetically more homogeneous than the other phenotypes tested. The results point to the importance of using diagnostic approaches that recognise this group of individuals. Our approach can be applied to similar data-sets for other psychiatric and non-psychiatric phenotypes.