The differential associations of positive and negative symptoms with suicidality

BACKGROUND: Suicide is one of the leading causes of death in people with schizophrenia. Identifying risk factors for suicide in schizophrenia is therefore an important clinical and research priority. METHOD: A cross-sectional secondary analysis was conducted on the DNA Polymorphisms in Mental Illness Study (DPIM) data. Suicidality data was extracted, and the number of positive and negative symptoms were established for a total of 1494 participants. Logistic and negative binomial regression analyses were conducted to assess for associations between positive or negative symptoms and suicidal ideation, attempt, or number of attempts, whilst adjusting for potential confounders. RESULTS: Negative symptoms were associated with a reduction in the risk of suicidal ideation (odds ratio [OR]: 0.83; 95 % CI: 0.75-0.91) and suicide attempt (OR: 0.79; 95 % CI: 0.71-0.88) after adjusting for age and sex. Positive symptoms were associated with an increased risk of suicidal ideation (OR: 1.06; 95 % CI: 1.03-1.09), suicide attempt (OR: 1.04; 95 % CI: 1.00-1.07) and number of suicide attempts (incidence rate ratio [IRR]: 1.05; 95 % CI: 1.01-1.08). Further adjusting for depressive symptoms slightly increased the magnitude of associations with negative symptoms but attenuated associations between positive symptoms and suicidality to the null. CONCLUSIONS: Negative symptoms are associated with a reduced risk of suicidality, whilst positive symptoms are associated with an increased risk of suicidality. Depressive symptoms may confound or mediate these associations

The Communication of Metacognition for Social Strategy in Psychosis: An Exploratory Study

Sharing privately held information, for example, one’s confidence in the likelihood of future events, can greatly help others make better decisions as well as promoting one’s reputation and social influence. Differences in metacognition on the one hand, and difficulties in social functioning and social cognition on the other, have been reported in people diagnosed with schizophrenia and bipolar disorder. However, despite clear relevance few studies have investigated the link between these abilities and psychosis. In this exploratory study, we compared individuals diagnosed with schizophrenia, bipolar disorder, and a group of unselected general population controls, in an online competitive advice-giving task. Participants gave advice to a client by making a probabilistic perceptual judgment. They could strategically adapt the advice confidence to gain influence over the client. Crucially, participants competed with a rival adviser to attract the client’s endorsement. We observe that participants diagnosed with schizophrenia displayed an overall overconfidence in their advice compared with other, bipolar, and unselected control groups, but did not differ in metacognitive efficiency from controls. Symptom-based analysis revealed that the social-influence effect was associated with the presence of delusions but not hallucinations or mood symptoms. These results suggest that the social communication of uncertainty should be further investigated in psychosis

Multiple psychiatric polygenic risk scores predict associations between childhood adversity and bipolar disorder

BACKGROUND: It remains unclear how adverse childhood experiences (ACE) and increased genetic risk for bipolar disorder (BD) interact to influence BD symptom outcomes. Here we calculated multiple psychiatric polygenic risk scores (PRS) and used the measures of ACE to understand these gene-environment interactions. METHOD: 885 BD subjects were included for analyses. BD, ADHD, MDD and SCZ PRSs were calculated using the PRS-CS-auto method. ACEs were evaluated using the Children Life Event Questionnaire (CLEQ). Participants were divided into groups based on the presence of ACE and the total number of ACEs. The associations between total ACE number, PRSs and their interactions were evaluated using multiple linear and logistic regressions. Secondary analyses were performed to evaluate the influence of ACE and PRS on sub-phenotypes of BD. RESULTS: The number of ACEs increased with the ADHD PRS. BD participants who had ACEs showed an earlier age of BD onset and higher odds of having rapid cycling. Increased BD PRS was associated with increased odds of developing psychotic symptoms. Higher ADHD PRS was associated with increased odds of having rapid cycling. No prediction effect was observed from MDD and SCZ PRS. And, we found no significant interaction between ACE numbers and any of the PRSs in predicting any selected BD sub-phenotypes. LIMITATIONS: The study was limited by sample size, ACE definition, and cross-sectional data collection method. CONCLUSIONS: The findings consolidate the importance of considering multiple psychiatric PRSs in predicting symptom outcomes among BD patients

Mapping 2007-08 Tuition And Fees In Higher Education

Using data sets from US News & World Report and the Association to Advance Collegiate Schools of Business, this paper isolates 10 factors that account for 90 percent of the variation in tuition and fees across 523 institutions of higher learning in the United States.  It is hoped that the results will give guidance to schools by quantifying the costs and benefits of making a given change to their tuition and fee structure.&nbsp

Prediction of disease comorbidity using explainable artificial intelligence and machine learning techniques: A systematic review

OBJECTIVE: Disease comorbidity is a major challenge in healthcare affecting the patient's quality of life and costs. AI-based prediction of comorbidities can overcome this issue by improving precision medicine and providing holistic care. The objective of this systematic literature review was to identify and summarise existing machine learning (ML) methods for comorbidity prediction and evaluate the interpretability and explainability of the models. MATERIALS AND METHODS: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework was used to identify articles in three databases: Ovid Medline, Web of Science and PubMed. The literature search covered a broad range of terms for the prediction of disease comorbidity and ML, including traditional predictive modelling. RESULTS: Of 829 unique articles, 58 full-text papers were assessed for eligibility. A final set of 22 articles with 61 ML models was included in this review. Of the identified ML models, 33 models achieved relatively high accuracy (80-95%) and AUC (0.80-0.89). Overall, 72% of studies had high or unclear concerns regarding the risk of bias. DISCUSSION: This systematic review is the first to examine the use of ML and explainable artificial intelligence (XAI) methods for comorbidity prediction. The chosen studies focused on a limited scope of comorbidities ranging from 1 to 34 (mean = 6), and no novel comorbidities were found due to limited phenotypic and genetic data. The lack of standard evaluation for XAI hinders fair comparisons. CONCLUSION: A broad range of ML methods has been used to predict the comorbidities of various disorders. With further development of explainable ML capacity in the field of comorbidity prediction, there is a significant possibility of identifying unmet health needs by highlighting comorbidities in patient groups that were not previously recognised to be at risk for particular comorbidities

Investigating the association between schizophrenia and distance visual acuity: Mendelian randomisation study

BACKGROUND: Increased rates of visual impairment are observed in people with schizophrenia. AIMS: We assessed whether genetically predicted poor distance acuity is causally associated with schizophrenia, and whether genetically predicted schizophrenia is causally associated with poorer visual acuity. METHOD: We used bidirectional, two-sample Mendelian randomisation to assess the effect of poor distance acuity on schizophrenia risk, poorer visual acuity on schizophrenia risk and schizophrenia on visual acuity, in European and East Asian ancestry samples ranging from approximately 14 000 to 500 000 participants. Genetic instrumental variables were obtained from the largest available summary statistics: for schizophrenia, from the Psychiatric Genomics Consortium; for visual acuity, from the UK Biobank; and for poor distance acuity, from a meta-analysis of case-control samples. We used the inverse variance-weighted method and sensitivity analyses to test validity of results. RESULTS: We found little evidence that poor distance acuity was causally associated with schizophrenia (odds ratio 1.00, 95% CI 0.91-1.10). Genetically predicted schizophrenia was associated with poorer visual acuity (mean difference in logMAR score: 0.024, 95% CI 0.014-0.033) in European ancestry samples, with a similar but less precise effect that in smaller East Asian ancestry samples (mean difference: 0.186, 95% CI -0.008 to 0.379). CONCLUSIONS: Genetic evidence supports schizophrenia being a causal risk factor for poorer visual acuity, but not the converse. This highlights the importance of visual care for people with psychosis and refutes previous hypotheses that visual impairment is a potential target for prevention of schizophrenia

Evidence for the association of the DAOA (G72) gene with schizophrenia and bipolar disorder but not for the association of the DAO gene with schizophrenia

Background: Previous linkage and association studies have implicated the D-amino acid oxidase activator gene (DAOA)/G30 locus or neighbouring region of chromosome 13q33.2 in the genetic susceptibility to both schizophrenia and bipolar disorder. Four single nucleotide polymorphisms (SNPs) within the D-amino acid oxidase (DAO) gene located at 12q24.11 have also been found to show allelic association with schizophrenia.Methods: We used the case control method to test for genetic association with variants at these loci in a sample of 431 patients with schizophrenia, 303 patients with bipolar disorder and 442 ancestrally matched supernormal controls all selected from the UK population.Results: Ten SNPs spanning the DAOA locus were genotyped in these samples. In addition three SNPs were genotyped at the DAO locus in the schizophrenia sample. Allelic association was detected between the marker rs3918342 (M23), 3' to the DAOA gene and both schizophrenia (chi(2) = 5.824 p = 0.016) and bipolar disorder (chi(2) = 4.293 p = 0.038). A trend towards association with schizophrenia was observed for two other DAOA markers rs3916967 (M14, chi(2) = 3.675 p = 0.055) and rs1421292 (M24; chi(2) = 3.499 p = 0.062). A test of association between a three marker haplotype comprising of the SNPs rs778293 (M22), rs3918342 (M23) and rs1421292 (M24) and schizophrenia gave a global empirical significance of p = 0.015. No evidence was found to confirm the association of genetic markers at the DAO gene with schizophrenia.Conclusion: Our results provide some support for a role for DAOA in susceptibility to schizophrenia and bipolar disorder

Genome-wide association study identifies 30 Loci Associated with Bipolar Disorder

Bipolar disorder is a highly heritable psychiatric disorder. We performed a genome-wide association study including 20,352 cases and 31,358 controls of European descent, with follow-up analysis of 822 variants with P\u3c1×10−4 in an additional 9,412 cases and 137,760 controls. Eight of the 19 variants that were genome-wide significant (GWS, p \u3c 5×10−8) in the discovery GWAS were not GWS in the combined analysis, consistent with small effect sizes and limited power but also with genetic heterogeneity. In the combined analysis 30 loci were GWS including 20 novel loci. The significant loci contain genes encoding ion channels, neurotransmitter transporters and synaptic components. Pathway analysis revealed nine significantly enriched gene-sets including regulation of insulin secretion and endocannabinoid signaling. BDI is strongly genetically correlated with schizophrenia, driven by psychosis, whereas BDII is more strongly correlated with major depressive disorder. These findings address key clinical questions and provide potential new biological mechanisms for BD

Genome-wide association study of antisocial personality disorder diagnostic criteria provides evidence for shared risk factors across disorders

INTRODUCTION: While progress has been made in determining the genetic basis of antisocial behaviour, little progress has been made for antisocial personality disorder (ASPD), a condition that often co-occurs with other psychiatric conditions including substance use disorders, attention deficit hyperactivity disorder (ADHD), and anxiety disorders. This study aims to improve the understanding of the genetic risk for ASPD and its relationship with other disorders and traits. METHODS: We conducted a genome-wide association study (GWAS) of the number of ASPD diagnostic criteria data from 3217 alcohol-dependent participants recruited in the UK (UCL, N = 644) and the USA (Yale-Penn, N = 2573). RESULTS: We identified rs9806493, a chromosome 15 variant, that showed a genome-wide significant association (Z-score = -5.501, P = 3.77 × 10-8) with ASPD criteria. rs9806493 is an eQTL for SLCO3A1 (Solute Carrier Organic Anion Transporter Family Member 3A1), a ubiquitously expressed gene with strong expression in brain regions that include the anterior cingulate and frontal cortices. Polygenic risk score analysis identified positive correlations between ASPD and smoking, ADHD, depression traits, and posttraumatic stress disorder. Negative correlations were observed between ASPD PRS and alcohol intake frequency, reproductive traits, and level of educational attainment. CONCLUSION: This study provides evidence for an association between ASPD risk and SLCO3A1 and provides insight into the genetic architecture and pleiotropic associations of ASPD