Recent Stressful Life Events and Suicidal Ideation in Schizophrenia: A 1-Year Follow-up Study.

ABSTRACT: Half of patients with schizophrenia experience suicidal ideation. Only few studies have examined the effects of recent stress on both current and emergent suicidal ideation.A cohort of 85 patients with schizophrenia spectrum disorders was assessed. The study was divided into a cross-sectional and longitudinal arms to test the effect of recent stress on suicidal ideation. Analysis was done using logistic regression models.After correcting for covariates, recent stress had no significant effect on current suicidal ideation. However, increased total stress (odds ratio [OR] = 1.099 [1.032-1.170], p = 0.003) and health-related stress (OR = 1.331 [1.074-1.650], p = 0.009) at follow-up were predictive of emergent suicidal ideation.With this sample size, we were unable to draw firm conclusions regarding the effect of specific life events on suicidal ideation. Further studies involving larger samples that investigate the interplay between several risk factors are needed.

Meta-Analysis of Neuropsychological Studies in Panic Disorder Patients: Evidence of Impaired Performance during the Emotional Stroop Task.

BACKGROUND: Recent investigations have highlighted significant differences in verbal recall between patients with panic disorder (PD) and controls. These studies have highlighted that verbal memory and working memory could be impaired in PD. OBJECTIVES: The objective of the present meta-analysis is to confirm this hypothesis, reviewing the studies that have investigated neurocognitive testing in PD. METHODS: We performed a systematic literature search for studies published between 1980 and 2015 that reported cognitive measurements in PD patients and controls. Effect size estimates were computed using the restricted maximum likelihood model. Only case-control studies were selected for this meta-analysis. We included studies that made a direct comparison between PD subjects and healthy controls. The diagnostic group consisted of adult patients aged over 18 years diagnosed with PD. We excluded the studies that did not employ a case-control design. All statistical analyses were carried out on R using the "metafor" package version 1.9-8. The effect size for each study neuropsychological test was calculated using the mean and SD of performance results, and p values < 0.05 were considered significant. RESULTS: We identified few studies that tested verbal memory and executive functions in PD patients and controls, and this difference was not significant. On the other hand, there are several studies that have used the emotional Stroop task to assess cognitive functions in PD. There is no robust evidence of impairment of memory function in PD; however, when considering the emotional Stroop task, it was found that PD patients performed slower (p < 0.01) than healthy controls for all three types of stimuli (neutral, negative, positive). CONCLUSION: This meta-analysis included a small number of studies, which may have introduced bias into the analysis. However, there is some evidence of impairment of neurocognitive functions in PD when performing the emotional Stroop task. Furthermore, the paucity of studies evaluating neurocognition in PD suggests the need for further research in this field in order to draw meaningful conclusions.

Differential Methylation Analysis of Suicidal Ideation Severity in Schizophrenia with the Illumina MethylationEPIC Array.

There is a multitude of factors that makes difficult to identify those at risk for suicide, especially among schizophrenia patients. Suicide cannot be explained by genetics alone, therefore epigenetic mechanisms including DNA methylation are thought to play a role. DNA methylation could be a valuable tool in helping predict those at-risk individuals. This cross-sectional study comprised 112 subjects diagnosed with schizophrenia spectrum disorders, and were grouped according to the current suicidal ideation severity. DNA methylation across the genome was measured with the Infinium® MethylationEPIC BeadChip. We utilized the dmpFinder and bumphunter functions within the Bioconductor minfi package to identify differentially methylated positions (DMPs) and differentially methylated regions (DMRs), respectively. Following quality control, we removed one sample from the analysis and reported the most significant DMPs and DMRs associated with suicidal ideation severity. All positions and regions identified in this analysis were only found to have suggestive levels of significance at the genome-wide level. The present study was one of the first to investigate genome-wide methylation and suicidal ideation severity. While there were many strengths of our study, including investigating both differentially methylated positions and regions, further larger-scale studies are necessary to replicate, support, and validate our findings presented here.

Prediction of physical violence in schizophrenia with machine learning algorithms.

Patients with schizophrenia have been shown to have an increased risk for physical violence. While certain features have been identified as risk factors, it has been difficult to integrate these variables to identify violent patients. The present study thus attempts to develop a clinically-relevant predictive tool. In a population of 275 schizophrenia patients, we identified 103 participants as violent and 172 as non-violent through electronic medical documentation, and conducted cross-sectional assessments to identify demographic, clinical, and sociocultural variables. Using these predictors, we utilized seven machine learning classification algorithms to predict for past instances of physical violence. Our classification algorithms predicted with significant accuracy compared to random discrimination alone, and had varying degrees of predictive power, as described by various performance measures. We determined that the random forest model performed marginally better than other algorithms, with an accuracy of 62% and an area under the receiver operator characteristic curve (AUROC) of 0.63. To summarize, machine learning classification algorithms are becoming increasingly valuable, though, optimization of these models is needed to better complement diagnostic decisions regarding early interventional measures to predict instances of physical violence.

Early-life stressful events and suicide attempt in schizophrenia: Machine learning models.

Childhood abuse and neglect predicts suicide attempt. Furthermore, other early-life stressful events may predict lifetime suicide attempt in psychiatric disorders. We assessed 189 schizophrenics for suicide attempt and stressful life events. Early-life stressful events were used as predictors of lifetime suicide attempt in three machine learning models. In our sample, 38% of the schizophrenics had at least one suicide attempt lifetime. The machine learning models provided an overall significant prediction (accuracy range: 62-69%). Childhood sexual molestation and mental illness were important predictors of suicide attempt. Early-life stressful events should be included in models aiming at predicting suicide attempt in schizophrenia.

No interaction between polygenic scores and childhood trauma in predicting suicide attempt in schizophrenia.

BACKGROUND: Approximately 5% of patients with schizophrenia commit suicide, and 20% to 40% of them have at least one suicide attempt during their lifetime. Previous research has identified childhood trauma as a potential risk factor for suicide attempt in schizophrenia. The Psychiatric Genetics Consortium found 108 common genetic risk loci associated with schizophrenia. Moreover, familial, adoption, and twin studies suggested that suicidal behaviour is under genetic influence. OBJECTIVE: Our objective was to determine the effect of childhood trauma and schizophrenia polygenic risk in leading to suicide attempt, as well as to determine any interaction effect between the polygenic scores with childhood trauma. METHODS: The study design was cross-sectional and retrospective considering lifetime suicide attempt as the main dependent variable. Childhood trauma was assessed using the Childhood Trauma Questionnaire. Polygenic Risk Score calculation was done using the genome-analysis toolkit, PLINK. The suicide attempts were recorded using the Columbia Suicide Severity Rating Scale. RESULTS: We included 224 subjects in our sample and 93 attempted suicide at least once in their lifetime. When comparing the weighted scores in attempters and non-attempters, we found no association (p > .05). CONCLUSION: Although our results do not support our hypothesis, the interaction analysis of genetic risk for schizophrenia in combination with the history of childhood trauma requires larger samples with high-quality suicide risk assessment.

Epigenome-wide association study of suicide attempt in schizophrenia.

Schizophrenia is a major clinical problem and represents a major risk factor for suicide. The molecular mechanisms of suicidal behavior in psychosis remain poorly investigated, although it has been hypothesized that epigenetic processes are involved in the etiology of both psychosis and suicidality. In this study, epigenome-wide patterns of methylation were measured in schizophrenia suicide attempters (n = 54) and schizophrenia non-suicide attempters (n = 69) using DNA extracted from white blood cells (WBC). Analyses focused on identifying differentially methylated CpG sites and gene regions between the attempters and non-attempters. We identified the CpG site cg19647197 within the CCDC53 gene, which is characterized by hypomethylation of WBC in the attempters compared to the non-attempters. Our results suggest that there is variation in DNA methylation associated with suicide attempt that may offer novel highlights into the molecular mechanisms linked to suicide attempt associated with schizophrenia.