Assessment of suicide attempt and death in bipolar affective disorder: a combined clinical and genetic approach

Telomerase activity and telomere homeostasis in major depressive disorder, schizophrenia and bipolar disorder: a systematic review

INTRODUCTION

Life expectancy is decreased in individuals diagnosed with Major depressive disorder (MDD), Schizophrenia (SCZ) or Bipolar disorder (BD), which suggests cellular ageing. Telomere length (TL), a biomarker of cellular ageing, has been consistently reported as shorter in these individuals, but the mechanisms involved remain unknown.

METHOD

A literature search was conducted which included studies focusing on telomerase activity and markers involved in telomere homeostasis (Single nucleotide polymorphisms, gene expression, protein levels). We also searched for articles investigating the impact of psychotropic medications and other interventions on any of these markers.

RESULTS

We identified 25 articles among which individuals with MDD were overrepresented (n = 16). Telomerase was the most studied marker in terms of activity and gene expression, and few studies included analyses of other markers. Five studies out of seven reported an elevated telomerase activity in individuals with MDD. Seven of the twelve clinical trials identified, measured the impact of interventions in MDD (antidepressants, electroconvulsive therapy and yoga) with heterogeneous designs.

CONCLUSION

Literature suggests that telomerase activity is increased in MDD, with no major changes following exposure to antidepressants. Case-control and clinical intervention studies in BD and SCZ are too scarce to conclude. Furthermore, this review highlights that the complexity of telomere homeostasis has been overlooked in the literature thus limiting the understanding of the underlying molecular mechanisms. Future research should include larger and trans-nosographic samples, adopt more homogeneous designs for clinical trials, and perform more comprehensive analyses of the TL homeostasis markers.

Multimodal Machine Learning Prediction of 12-Month Suicide Attempts in Bipolar Disorder

INTRODUCTION

Bipolar disorder (BD) patients present an increased risk of suicide attempts. Most current machine learning (ML) studies predicting suicide attempts are cross-sectional, do not employ time-dependent variables, and do not assess more than one modality. Therefore, we aimed to predict 12-month suicide attempts in a sample of BD patients, using clinical and brain imaging data.

METHODS

A sample of 163 BD patients were recruited and followed up for 12 months. Gray matter volumes and cortical thickness were extracted from the T1-weighted images. Based on previous literature, we extracted 56 clinical and demographic features from digital health records. Support Vector Machine was used to differentiate BD subjects who attempted suicide. First, we explored single modality prediction (clinical features, GM, and thickness). Second, we implemented a multimodal stacking-based data fusion framework.

RESULTS

During the 12 months, 6.13% of patients attempted suicide. The unimodal classifier based on clinical data reached an area under the curve (AUC) of 0.83 and balanced accuracy (BAC) of 72.7%. The model based on GM reached an AUC of 0.86 and BAC of 76.4%. The multimodal classifier (clinical + GM) reached an AUC of 0.88 and BAC of 83.4%, significantly increasing the sensitivity. The most important features were related to suicide attempts history, medications, comorbidities, and depressive polarity. In the GM model, the most relevant features mapped in the frontal, temporal, and cerebellar regions.

CONCLUSIONS

By combining models, we increased the detection of suicide attempts, reaching a sensitivity of 80%. Combining more than one modality proved a valid method to overcome limitations from single-modality models and increasing overall accuracy.

SNP-Associated Differential Methylation in ARHGEF38: Insights into Genetic-Epigenetic Interactions

Objective

Associations have been seen between suicidal behavior and differential DNA methylation of certain genes, with one study showing significant hypomethylation of ARHGEF38 in postmortem brain samples from individuals with bipolar disorder who died by suicide. Our objective was to explore ARHGEF38 methylation in individuals with bipolar disorder and a history of suicide attempt.

Method

With pyrosequencing, we looked at the previously identified region of interest in ARHGEF38. We investigated the methylation levels of 3 CpG sites in 47 individuals with bipolar disorder and a history of suicide attempt, 47 individuals with bipolar disorder without a history of suicide attempt, and 47 non-bipolar disorder controls.

Results

None of the CpG sites measured had an association between groups, although there were distinct clusters of differential methylation in each group. Applying genotypes of SNPs found in the region of interest, rs2121558 and rs1447093, these clusters showed stepwise methylation at each CpG site, regardless of phenotype.

Conclusions

In this relatively small sample size study, differential methylation in ARHGEF38 was not associated with history of suicide attempt, failing to replicate findings from a related outcome, suicide death. However, we did provide evidence of SNP and DNA methylation interplay in this region. This highlights the potential relevance of considering genetics when interrogating epigenetic mechanisms.

Highlights

ARHGEF38 methylation is not associated with bipolar disorder and suicide attempt Methylation of ARHGEF38 is heavily influenced by the presence of SNPs Suicide phenotype, genetics, and sample type impact DNA methylation.

Association between a large change between the minimum and maximum monthly values of solar insolation and a history of suicide attempts in bipolar I disorder

BACKGROUND

The rate of suicide attempts by patients with bipolar disorder is high. In addition to patient and country specific factors, environmental factors may contribute to suicidal behavior. Sunlight has multiple diverse impacts on human physiology and behavior. Solar insolation is defined as the electromagnetic energy from the sun striking a surface area on earth. We previously found that a large change in solar insolation between the minimum and maximum monthly values was associated with an increased risk of suicide attempts in patients with bipolar I disorder.

METHODS

The association between solar insolation and a history of suicide attempts in bipolar disorder was again investigated using an international database with 15% more data and more sites at diverse locations and countries.

RESULTS

Data were available from 5641 patients with bipolar I disorder living at a wide range of latitudes in 41 countries in both hemispheres. A large change in solar insolation between the minimum and maximum monthly values was associated with a history of suicide attempts in patients with bipolar I disorder, a replication of our prior analysis. The estimated model also associated state sponsored religion in the onset country, female gender, a history of alcohol or substance abuse, and being part of a younger birth cohort with a history of suicide attempts.

CONCLUSIONS

A large change between the minimum and maximum monthly values of solar insolation was associated with a history of suicide attempts in bipolar I disorder, replicating our prior research. Physicians should be aware that daylight has wide ranging physiological and psychiatric impacts, and that living with large changes in solar insolation may be associated with an increased suicide risk.

Validation of a youth suicide risk calculator in an adult sample with bipolar disorder

BACKGROUND

Bipolar disorder (BD) conveys the highest risk of suicide of all mental disorders. We sought to externally validate a risk calculator (RC) of suicide attempts developed in youth with BD from the Course and Outcome of Bipolar Youth (COBY) study in an adult sample.

METHODS

A prospective cohort of adults with BD from the National Institute of Mental Health Collaborative Depression Study (CDS; N = 427; mean (+/- SD) age at intake (36 +/- 13 years)) was secondarily analyzed to validate the COBY RC for one-year risk of suicide attempts/deaths. Nine of the ten predictor variables from the COBY RC were available in the CDS and used: age, age of mood disorder onset, first and second (partial) degree family history of suicide, history of psychotic symptoms, substance use disorder, prior suicide attempt, socioeconomic status, and non-suicidal self-injury (prospectively, incompletely at baseline).

RESULTS

Over a mean (SD) follow-up of 19 (10) years, 29 % of the CDS sample attempted suicide. The RC predicted suicide attempts/deaths over one-year follow-up with an area under the receiver operating characteristic curve (AUC) of 0.78 (95 % CI 0.75-0.80). The RC performed slightly better in those with a younger age of mood disorder onset.

LIMITATIONS

Clinical samples may limit generalizability; the RC does not assess more acute suicide risk.

CONCLUSIONS

One-year risk of suicide attempts/deaths can be predicted with acceptable accuracy in youth and adults with BD, comparable to commonly used RCs to predict cardiovascular risk. This RC may help identify higher-risk individuals with BD for personalized treatment and research. https://cobysuicideattemptsrc.shinyapps.io/Shiny.

Polygenic Risk Scores for Bipolar Disorder: Progress and Perspectives

Bipolar disorder (BD) is a common and highly heritable psychiatric disorder, the study of BD genetic characteristics can help with early prevention and individualized treatment. At the same time, BD is a highly heterogeneous polygenic genetic disorder with significant genetic overlap with other psychiatric disorders. In recent years, polygenic risk scores (PRS) derived from genome-wide association studies (GWAS) data have been widely used in genetic studies of various complex diseases and can be used to explore the genetic susceptibility of diseases. This review discusses phenotypic associations and genetic correlations with other conditions of BD based on PRS, and provides ideas for genetic studies and prevention of BD.

Genetics and epigenetics of self-injurious thoughts and behaviors: Systematic review of the suicide literature and methodological considerations

Suicide is a multifaceted and poorly understood clinical outcome, and there is an urgent need to advance research on its phenomenology and etiology. Epidemiological studies have demonstrated that suicidal behavior is heritable, suggesting that genetic and epigenetic information may serve as biomarkers for suicide risk. Here we systematically review the literature on genetic and epigenetic alterations observed in phenotypes across the full range of self-injurious thoughts and behaviors (SITB). We included 577 studies focused on genome-wide and epigenome-wide associations, candidate genes (SNP and methylation), noncoding RNAs, and histones. Convergence of specific genes is limited across units of analysis, although pathway-based analyses do indicate nervous system development and function and immunity/inflammation as potential underlying mechanisms of SITB. We provide suggestions for future work on the genetic and epigenetic correlates of SITB with a specific focus on measurement issues.

Development of a Suicide Prediction Model for the Elderly Using Health Screening Data

Suicide poses a serious problem globally, especially among the elderly population. To tackle the issue, this study aimed to develop a model for predicting suicide by using machine learning based on the elderly population. To obtain a large sample, the study used the big data health screening cohort provided by the National Health Insurance Sharing Service. By applying a machine learning technique, a predictive model that comprehensively utilized various factors was developed to select the elderly aged > 65 years at risk of suicide. A total of 48,047 subjects were included in the analysis. Individuals who died by suicide were older, and the number of men was significantly greater. The suicide group had a more prominent history of depression, with the use of medicaments significantly higher. Specifically, the prescription of benzodiazepines alone was associated with a high suicide risk. Furthermore, body mass index, waist circumference, total cholesterol, and low-density lipoprotein level were lower in the suicide group. We developed a model for predicting suicide by using machine learning based on the elderly population. This suicide prediction model can satisfy the performance to some extent by employing only the medical service usage behavior without subjective reports.