Accurate diagnosis of bipolar disorder (BPD) is difficult in clinical practice, with an average delay between symptom onset and diagnosis of about 7 years. A depressive episode often precedes the first manic episode, making it difficult to distinguish BPD from unipolar major depressive disorder (MDD).

We use genome-wide association analyses (GWAS) to identify differential genetic factors and to develop predictors based on polygenic risk scores (PRS) that may aid early differential diagnosis.

Based on individual genotypes from case–control cohorts of BPD and MDD shared through the Psychiatric Genomics Consortium, we compile case–case–control cohorts, applying a careful quality control procedure. In a resulting cohort of 51 149 individuals (15 532 BPD patients, 12 920 MDD patients and 22 697 controls), we perform a variety of GWAS and PRS analyses.

Although our GWAS is not well powered to identify genome-wide significant loci, we find significant chip heritability and demonstrate the ability of the resulting PRS to distinguish BPD from MDD, including BPD cases with depressive onset (BPD-D). We replicate our PRS findings in an independent Danish cohort (iPSYCH 2015, N = 25 966). We observe strong genetic correlation between our case–case GWAS and that of case–control BPD.

We find that MDD and BPD, including BPD-D are genetically distinct. Our findings support that controls, MDD and BPD patients primarily lie on a continuum of genetic risk. Future studies with larger and richer samples will likely yield a better understanding of these findings and enable the development of better genetic predictors distinguishing BPD and, importantly, BPD-D from MDD.

Information on the time spent completing cognitive testing is often collected, but such data are not typically considered when quantifying cognition in large-scale community-based surveys. We sought to evaluate the added value of timing data over and above traditional cognitive scores for the measurement of cognition in older adults.

We used data from the Longitudinal Aging Study in India-Diagnostic Assessment of Dementia (LASI-DAD) study (N = 4,091), to assess the added value of timing data over and above traditional cognitive scores, using item-specific regression models for 36 cognitive test items. Models were adjusted for age, gender, interviewer, and item score.

Compared to Quintile 3 (median time), taking longer to complete specific items was associated (p < 0.05) with lower cognitive performance for 67% (Quintile 5) and 28% (Quintile 4) of items. Responding quickly (Quintile 1) was associated with higher cognitive performance for 25% of simpler items (e.g., orientation for year), but with lower cognitive functioning for 63% of items requiring higher-order processing (e.g., digit span test). Results were consistent in a range of different analyses adjusting for factors including education, hearing impairment, and language of administration and in models using splines rather than quintiles.

Response times from cognitive testing may contain important information on cognition not captured in traditional scoring. Incorporation of this information has the potential to improve existing estimates of cognitive functioning.

This study explored the prospective use of the Ages and Stages Questionnaires-3 in follow-up after cardiac surgery.

For children undergoing cardiac surgery at 5 United Kingdom centres, the Ages and Stages Questionnaires-3 were administered 6 months and 2 years later, with an outcome based on pre-defined cut-points: Red = 1 or more domain scores >2 standard deviations below the normative mean, Amber = 1 or more domain scores 1–2 standard deviations below the normal range based on the manual, Green = scores within the normal range based on the manual.

From a cohort of 554 children <60 months old at surgery, 306 participated in the postoperative assessment: 117 (38.3%) were scored as Green, 57 (18.6%) as Amber, and 132 (43.1%) as Red. Children aged 6 months at first assessment (neonatal surgery) were likely to score Red (113/124, 85.6%) compared to older age groups (n = 32/182, 17.6%). Considering risk factors of congenital heart complexity, univentricular status, congenital comorbidity, and child age in a logistic regression model for the outcome of Ages and Stages score Red, only younger age was significant (p < 0.001). 87 children had surgery in infancy and were reassessed as toddlers. Of these, 43 (49.2%) improved, 30 (34.5%) stayed the same, and 13 (16.1%) worsened. Improved scores were predominantly in those who had a first assessment at 6 months old.

The Ages and Stages Questionnaires results are most challenging to interpret in young babies of 6 months old who are affected by complex CHD.

The association between cannabis and psychosis is established, but the role of underlying genetics is unclear. We used data from the EU-GEI case-control study and UK Biobank to examine the independent and combined effect of heavy cannabis use and schizophrenia polygenic risk score (PRS) on risk for psychosis.

Genome-wide association study summary statistics from the Psychiatric Genomics Consortium and the Genomic Psychiatry Cohort were used to calculate schizophrenia and cannabis use disorder (CUD) PRS for 1098 participants from the EU-GEI study and 143600 from the UK Biobank. Both datasets had information on cannabis use.

In both samples, schizophrenia PRS and cannabis use independently increased risk of psychosis. Schizophrenia PRS was not associated with patterns of cannabis use in the EU-GEI cases or controls or UK Biobank cases. It was associated with lifetime and daily cannabis use among UK Biobank participants without psychosis, but the effect was substantially reduced when CUD PRS was included in the model. In the EU-GEI sample, regular users of high-potency cannabis had the highest odds of being a case independently of schizophrenia PRS (OR daily use high-potency cannabis adjusted for PRS = 5.09, 95% CI 3.08–8.43, p = 3.21 × 10−10). We found no evidence of interaction between schizophrenia PRS and patterns of cannabis use.

Regular use of high-potency cannabis remains a strong predictor of psychotic disorder independently of schizophrenia PRS, which does not seem to be associated with heavy cannabis use. These are important findings at a time of increasing use and potency of cannabis worldwide.

Cannabis use and familial vulnerability to psychosis have been associated with social cognition deficits. This study examined the potential relationship between cannabis use and cognitive biases underlying social cognition and functioning in patients with first episode psychosis (FEP), their siblings, and controls.

We analyzed a sample of 543 participants with FEP, 203 siblings, and 1168 controls from the EU-GEI study using a correlational design. We used logistic regression analyses to examine the influence of clinical group, lifetime cannabis use frequency, and potency of cannabis use on cognitive biases, accounting for demographic and cognitive variables.

FEP patients showed increased odds of facial recognition processing (FRP) deficits (OR = 1.642, CI 1.123–2.402) relative to controls but not of speech illusions (SI) or jumping to conclusions (JTC) bias, with no statistically significant differences relative to siblings. Daily and occasional lifetime cannabis use were associated with decreased odds of SI (OR = 0.605, CI 0.368–0.997 and OR = 0.646, CI 0.457–0.913 respectively) and JTC bias (OR = 0.625, CI 0.422–0.925 and OR = 0.602, CI 0.460–0.787 respectively) compared with lifetime abstinence, but not with FRP deficits, in the whole sample. Within the cannabis user group, low-potency cannabis use was associated with increased odds of SI (OR = 1.829, CI 1.297–2.578, FRP deficits (OR = 1.393, CI 1.031–1.882, and JTC (OR = 1.661, CI 1.271–2.171) relative to high-potency cannabis use, with comparable effects in the three clinical groups.

Our findings suggest increased odds of cognitive biases in FEP patients who have never used cannabis and in low-potency users. Future studies should elucidate this association and its potential implications.

Seattle Children’s Research Institute is identifying the amount and type of health equity scholarship being conducted institution wide. However, methods for categorizing how scholarship is equity-focused are lacking. We developed and evaluated the reliability of a health equity scholarship coding schema applied to Seattle Children’s affiliated scholarship.

A 2021–2022 Ovid MEDLINE affiliation search yielded 3551 affiliated scholarship records, with 1079 records identified via an existing filter as scholarship addressing social determinants of health. Through reliability testing and examining concordance and discordance across three independent coders of these records, we developed a coding schema to classify health equity scholarship (yes/no). When health equity scholarship proved positive/Yes, the coders assigned a one through five maturity rating of the scholarship towards addressing inequities. Subsequent reliability testing including a new coder was conducted for 992 subsequent affiliated scholarship records (Oct 2022–June 2023), with additional testing of the sensitivity and specificity of the existing filter relative to the new coding schema.

Reliability for identifying health equity scholarship was consistently high (Fleiss kappas ≥ .78) and categorization of health equity scholarship into maturity levels was moderate (Fleiss kappas ≥ .47). The coding schema identified additional health equity scholarship not captured in an existing filter for social determinants of health scholarship. Based on the new schema, 23.3% of Seattle Childrens’ affiliated scholarship published October 2002–June 2023 was health equity focused.

This new coding schema can be used to identify and categorize health equity scholarship to help quantitate the health equity focus of portfolios of human-focused research.

Military Servicemembers and Veterans are at elevated risk for suicide, but rarely self-identify to their leaders or clinicians regarding their experience of suicidal thoughts. We developed an algorithm to identify posts containing suicide-related content on a military-specific social media platform.

Publicly-shared social media posts (n = 8449) from a military-specific social media platform were reviewed and labeled by our team for the presence/absence of suicidal thoughts and behaviors and used to train several machine learning models to identify such posts.

The best performing model was a deep learning (RoBERTa) model that incorporated post text and metadata and detected the presence of suicidal posts with relatively high sensitivity (0.85), specificity (0.96), precision (0.64), F1 score (0.73), and an area under the precision-recall curve of 0.84. Compared to non-suicidal posts, suicidal posts were more likely to contain explicit mentions of suicide, descriptions of risk factors (e.g. depression, PTSD) and help-seeking, and first-person singular pronouns.

Our results demonstrate the feasibility and potential promise of using social media posts to identify at-risk Servicemembers and Veterans. Future work will use this approach to deliver targeted interventions to social media users at risk for suicide.

Globally, mental disorders account for almost 20% of disease burden and there is growing evidence that mental disorders are associated with various social determinants. Tackling the United Nations Sustainable Development Goals (UN SDGs), which address known social determinants of mental disorders, may be an effective way to reduce the global burden of mental disorders.

To examine the evidence base for interventions that seek to improve mental health through targeting the social determinants of mental disorders.

We conducted a systematic review of reviews, using a five-domain conceptual framework which aligns with the UN SDGs (PROSPERO registration: CRD42022361534). PubMed, PsycInfo, and Scopus were searched from 01 January 2012 until 05 October 2022. Citation follow-up and expert consultation were used to identify additional studies. Systematic reviews including interventions seeking to change or improve a social determinant of mental disorders were eligible for inclusion. Study screening, selection, data extraction, and quality appraisal were conducted in accordance with PRISMA guidelines. The AMSTAR-2 was used to assess included reviews and results were narratively synthesised.

Over 20,000 records were screened, and 101 eligible reviews were included. Most reviews were of low, or critically low, quality. Reviews included interventions which targeted sociocultural (n = 31), economic (n = 24), environmental (n = 19), demographic (n = 15), and neighbourhood (n = 8) determinants of mental disorders. Interventions demonstrating the greatest promise for improved mental health from high and moderate quality reviews (n = 37) included: digital and brief advocacy interventions for female survivors of intimate partner violence; cash transfers for people in low-middle-income countries; improved work schedules, parenting programs, and job clubs in the work environment; psychosocial support programs for vulnerable individuals following environmental events; and social and emotional learning programs for school students. Few effective neighbourhood-level interventions were identified.

This review presents interventions with the strongest evidence base for the prevention of mental disorders and highlights synergies where addressing the UN SDGs can be beneficial for mental health. A range of issues across the literature were identified, including barriers to conducting randomised controlled trials and lack of follow-up limiting the ability to measure long-term mental health outcomes. Interdisciplinary and novel approaches to intervention design, implementation, and evaluation are required to improve the social circumstances and mental health experienced by individuals, communities, and populations.

None Declared