Genetic evidence for a potential causal relationship between insomnia symptoms and suicidal behavior: a Mendelian randomization study

Empirical clustering to identify individuals for whom insomnia is more closely related to suicidal ideation

BACKGROUND

Although the effect sizes are modest, insomnia is consistently associated with suicidal thoughts and behaviors. Subgroup analyses can efficiently identify for whom insomnia is most relevant to suicidal ideation. To improve clinical case identification, the present study sought to identify subclusters of lifetime suicidal ideators for whom insomnia was most closely related to current suicidal ideation.

METHODS

Data on N = 4750 lifetime suicidal ideators were extracted from the Military Suicide Research Consortium's Common Data Elements. Data on sociodemographic characteristics, severity and history of suicidal thoughts and behaviors, and related clinical characteristics were clustered by unsupervised machine learning algorithms. Robust Poisson regression estimated cluster by insomnia associations with current suicidal ideation.

RESULTS

Three clusters were identified: a modest symptom severity cluster (N = 1757, 37.0 %), an elevated severity cluster (N = 1444 30.4 %), and a high severity cluster (N = 1549 32.6 %). In Cluster 1, insomnia was associated with current suicidal ideation (PRR 1.29 [1.13-1.46]) and remained significant after adjusting for sociodemographic and clinical covariates. In Cluster 2, insomnia was associated with current suicidal ideation (PRR 1.14 [1.01-1.30]), but not after adjusting for sociodemographic and clinical covariates. In Cluster 3, insomnia was associated with current suicidal ideation (PRR 1.12 [1.03-1.21]) and remained significant after adjusting for sociodemographic covariates, but not clinical covariates.

LIMITATIONS

Cross-sectional design, lack of diagnostic data, non-representative sample.

CONCLUSION

Insomnia appears more closely related to current suicidal ideation among modest severity individuals than other subgroups. Future work should use prospective designs and more comprehensive risk factor measures to confirm these findings.

Optimising the use of electronic medical records for large scale research in psychiatry

The explosion and abundance of digital data could facilitate large-scale research for psychiatry and mental health. Research using so-called "real world data"-such as electronic medical/health records-can be resource-efficient, facilitate rapid hypothesis generation and testing, complement existing evidence (e.g. from trials and evidence-synthesis) and may enable a route to translate evidence into clinically effective, outcomes-driven care for patient populations that may be under-represented. However, the interpretation and processing of real-world data sources is complex because the clinically important 'signal' is often contained in both structured and unstructured (narrative or "free-text") data. Techniques for extracting meaningful information (signal) from unstructured text exist and have advanced the re-use of routinely collected clinical data, but these techniques require cautious evaluation. In this paper, we survey the opportunities, risks and progress made in the use of electronic medical record (real-world) data for psychiatric research.

Estimating the direct effects of the genetic liabilities to bipolar disorder, schizophrenia, and behavioral traits on suicide attempt using a multivariable Mendelian randomization approach

Bipolar disorder (BD) and schizophrenia (SZ) are associated with higher odds of suicide attempt (SA). In this study, we aimed to explore the effect of BD and SZ genetic liabilities on SA, also considering the contribution of behavioral traits, socioeconomic factors, and substance use disorders. Leveraging large-scale genome-wide association data from the Psychiatric Genomics Consortium (PGC) and the UK Biobank (UKB), we conducted a two-sample Mendelian randomization (MR) analysis to evaluate the putative causal effect of BD (41,917 cases, 371,549 controls) and SZ (53,386 cases, 77,258 controls) on SA (26,590 cases, 492,022 controls). Then, we assessed the putative causal effect of BD and SZ on behavioral traits, socioeconomic factors, and substance use disorders. Considering the associations identified, we evaluated the direct causal effect of behavioral traits, socioeconomic factors, and substance use disorders on SA using a multivariable MR approach. The genetic liabilities to BD and SZ were associated with higher odds of SA (BD odds ratio (OR) = 1.24, p = 3.88 × 10-12; SZ OR = 1.09, p = 2.44 × 10-20). However, while the effect of mental distress (OR = 1.17, p = 1.02 × 10-4) and risk-taking (OR = 1.52, p = 0.028) on SA was independent of SZ genetic liability, the BD-SA relationship appeared to account for the effect of these risk factors. Similarly, the association with loneliness on SA was null after accounting for the effect of SZ genetic liability. These findings highlight the complex interplay between genetic risk of psychiatric disorders and behavioral traits in the context of SA, suggesting the need for a comprehensive mental health assessment for high-risk individuals.

Potential genetic and epigenetic mechanisms in insomnia: A systematic review

Insomnia is a stress-related sleep disorder conceptualised within a diathesis-stress framework, which it is thought to result from predisposing factors interacting with precipitating stressful events that trigger the development of insomnia. Among predisposing factors genetics and epigenetics may play a role. A systematic review of the current evidence for the genetic and epigenetic basis of insomnia was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) system. A total of 24 studies were collected for twins and family heritability, 55 for genome-wide association studies, 26 about candidate genes for insomnia, and eight for epigenetics. Data showed that insomnia is a complex polygenic stress-related disorder, and it is likely to be caused by a synergy of genetic and environmental factors, with stress-related sleep reactivity being the important trait. Even if few studies have been conducted to date on insomnia, epigenetics may be the framework to understand long-lasting consequences of the interaction between genetic and environmental factors and effects of stress on the brain in insomnia. Interestingly, polygenic risk for insomnia has been causally linked to different mental and medical disorders. Probably, by treating insomnia it would be possible to intervene on the effect of stress on the brain and prevent some medical and mental conditions.

Selection of genetic instruments in Mendelian randomisation studies of sleep traits

This review explores the criteria used for the selection of genetic instruments of sleep traits in the context of Mendelian randomisation studies. This work was motivated by the fact that instrument selection is the most important decision when designing a Mendelian randomisation study. As far as we are aware, no review has sought to address this to date, even though the number of these studies is growing rapidly. The review is divided into the following sections which are essential for genetic instrument selection: 1) Single-gene region vs polygenic analysis; 2) Polygenic analysis: biologically-vs statistically-driven approaches; 3) P-value; 4) Linkage disequilibrium clumping; 5) Sample overlap; 6) Type of exposure; 7) Total (R2) and average strength (F-statistic) metrics; 8) Number of single-nucleotide polymorphisms; 9) Minor allele frequency and palindromic variants; 10) Confounding. Our main aim is to discuss how instrumental choice impacts analysis and compare the strategies that Mendelian randomisation studies of sleep traits have used. We hope that our review will enable more researchers to take a more considered approach when selecting genetic instruments for sleep exposures.

Estimating the direct effects of the genetic liabilities to bipolar disorder, schizophrenia, and behavioral traits on suicide attempt using a multivariable Mendelian randomization approach

Bipolar disorder (BD) and schizophrenia (SZ) are associated with higher odds of suicide attempt (SA). In this study, we aimed to explore the effect of BD and SZ genetic liabilities on SA, also considering the contribution of behavioral traits, socioeconomic factors, and substance use disorders. Leveraging large-scale genome-wide association data from the Psychiatric Genomics Consortium (PGC) and the UK Biobank (UKB), we conducted a two-sample Mendelian randomization (MR) analysis to evaluate the putative causal effect of BD (41,917 cases, 371,549 controls) and SZ (53,386 cases, 77,258 controls) on SA (26,590 cases, 492,022 controls). Then, we assessed the putative causal effect of BD and SZ on behavioral traits, socioeconomic factors, and substance use disorders. Considering the associations identified, we evaluated the direct causal effect of behavioral traits, socioeconomic factors, and substance use disorders on SA using a multivariable MR approach. The genetic liabilities to BD and SZ were associated with higher odds of SA (BD odds ratio (OR)=1.24, p=3.88×10-12; SZ OR=1.09, p=2.44×10-20). However, while the effect of mental distress (OR=1.17, p=1.02×10-4) and risk-taking (OR=1.52, p=0.028) on SA was independent of SZ genetic liability, the BD-SA relationship appeared to account for the effect of these risk factors. Similarly, the association with loneliness on SA was null after accounting for the effect of SZ genetic liability. These findings highlight the complex interplay between genetic risk of psychiatric disorders and behavioral traits in the context of SA, suggesting the need for a comprehensive mental health assessment for high-risk individuals.

Meta-analysis of insomnia, suicide, and psychopathology in schizophrenia

PURPOSE OF REVIEW

Insomnia is common in schizophrenia. Insomnia has been associated with suicidal ideation and behavior, as well as greater severity of psychopathology, in schizophrenia. This review performs a meta-analysis of associations between insomnia, suicide, and psychopathology in patients with schizophrenia.

RECENT FINDINGS

We searched major electronic databases from inception until November 2022 for studies of insomnia, suicide, and psychopathology in patients with schizophrenia. Random effects meta-analysis calculating odds ratios (ORs, for suicide) and effect sizes (ESs, for psychopathology) and 95% confidence intervals (CIs) were performed. Ten studies met the inclusion criteria, comprising 3428 patients with schizophrenia. Insomnia was associated with a significant increased odds of suicidal ideation (OR = 1.84, 95% CI 1.28-2.65, P  < 0.01) and suicide attempt or death (OR = 5.83, 95% CI 1.61-2.96, P  < 0.01). Insomnia was also associated with total (ES = 0.16, 95% CI 0.09-0.23, P  < 0.01), positive (ES = 0.14, 95% CI 0.08-0.20, P  = 0.02), and general (ES = 0.17, 95% CI 0.08-0.27, P  < 0.01) psychopathology. In meta-regression analyses, BMI was negatively associated with suicidal ideation. Otherwise, age, sex, and study year were all unrelated to the associations.

SUMMARY

Insomnia is associated with suicide and psychopathology in schizophrenia. Formal assessment and treatment of insomnia appears relevant to the clinical care of schizophrenia.

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.

Leveraging genetic discoveries for sleep to determine causal relationships with common complex traits

Sleep occurs universally and is a biological necessity for human functioning. The consequences of diminished sleep quality impact physical and physiological systems such as neurological, cardiovascular, and metabolic processes. In fact, people impacted by common complex diseases experience a wide range of sleep disturbances. It is challenging to uncover the underlying molecular mechanisms responsible for decreased sleep quality in many disease systems owing to the lack of suitable sleep biomarkers. However, the discovery of a genetic component to sleep patterns has opened a new opportunity to examine and understand the involvement of sleep in many disease states. It is now possible to use major genomic resources and technologies to uncover genetic contributions to many common diseases. Large scale prospective studies such as the genome wide association studies (GWAS) have successfully revealed many robust genetic signals associated with sleep-related traits. With the discovery of these genetic variants, a major objective of the community has been to investigate whether sleep-related traits are associated with disease pathogenesis and other health complications. Mendelian Randomization (MR) represents an analytical method that leverages genetic loci as proxy indicators to establish causal effect between sleep traits and disease outcomes. Given such variants are randomly inherited at birth, confounding bias is eliminated with MR analysis, thus demonstrating evidence of causal relationships that can be used for drug development and to prioritize clinical trials. In this review, we outline the results of MR analyses performed to date on sleep traits in relation to a multitude of common complex diseases.