Dataset Information

The effect of sexual abuse and dissociation on suicide attempt.

ABSTRACT:

Background

Suicide attempt is the most predictive risk factor of suicide. Trauma - especially sexual abuse - is a risk factor for suicide attempt and suicide. A common reaction to sexual abuse is dissociation. Higher levels of dissociation are linked to self-harm, suicide ideation, and suicide attempt, but the role of dissociation in suicidal behavior is unclear.

Methods

In this naturalistic study, ninety-seven acute psychiatric patients with suicidal ideation, of whom 32 had experienced sexual abuse, were included. Suicidal behaviour was assessed with The Columbia suicide history form (CSHF). The Brief trauma questionnaire (BTQ) was used to identify sexual abuse. Dissociative symptoms were assessed with Dissociative experiences scale (DES).

Results

Patients who had experienced sexual abuse reported higher levels of dissociation and were younger at onset of suicidal thoughts, more likely to self-harm, and more likely to have attempted suicide; and they had made more suicide attempts. Mediation analysis found dissociative experiences to significantly mediate a substantive proportion of the relationship between sexual abuse and number of suicide attempts (indirect effects = 0.17, 95% CI = 0.05, 0.28, proportion mediated = 68%). Dissociative experiences significantly mediated the role of sexual abuse as a predictor of being in the patient group with more than four suicide attempts (indirect effects = 0.11, 95% CI = 0.02, 0.19, proportion mediated = 34%).

Conclusion

The results illustrate the importance of assessment and treatment of sexual abuse and trauma-related symptoms such as dissociation in suicide prevention. Dissociation can be a contributing factor to why some people act on their suicidal thoughts.

SUBMITTER: Brokke SS

PROVIDER: S-EPMC8751353 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background: Suicide is moderately heritable and also more common in those who report childhood abuse. Previously, it was found that allele A of GABRG2 (GABA A receptor subunit gamma2) polymorphism rs211034 was protective in a suicide attempt (SA). Hence, it was proposed that rs211034 may interact with childhood trauma to influence cognitive deficits related to SA or depression risk. Genetic variants may predict the benefits of certain cognitive treatments. Methods: A total of 52 individuals who had attempted suicide, 59 individuals with major depressive disorder (MDD) or bipolar depression who had not previously attempted suicide, and 90 healthy volunteers were subjected to the modified Suicide Stroop task and were clinically assessed using the Childhood Trauma Questionnaire (CTQ) and Hamilton Depression Scale-24 items (HAMD-24). rs211034 was genotyped using Sanger sequencing. Results: After correcting for covariates, depressed participants displayed longer reaction times for all emotional conditions, including suicide-related words, compared with healthy controls. Depressed suicide attempters displayed longer reaction times for negative words than depressed non-attempters. Depressed non-attempters displayed higher interference scores for negative words compared with healthy controls. There was an interaction between rs211034 risk allele and the effects of reported childhood sexual abuse (CSA) on reaction time for all emotional words and suicide-related words. Carriers of the rs211034 risk allele A exhibited shorter reaction times, but the protective effects of this allele were eliminated in those exposed to reported CSA. Conclusion: Only limited results were found regarding effects of a past suicide attempt on response times to emotional and suicide-related words, but there was an overall effect of major depression on slower response time. Protective genetic effects of the rs211034 A allele on this slowing were eliminated in those with a history of sexual abuse during childhood. Further research is needed to better characterize the mechanisms underlying the effects of childhood trauma on these genetic effects.

Project description:Suicide accounts for nearly 800,000 deaths per year worldwide with rates of both deaths and attempts rising. Family studies have estimated substantial heritability of suicidal behavior; however, collecting the sample sizes necessary for successful genetic studies has remained a challenge. We utilized two different approaches in independent datasets to characterize the contribution of common genetic variation to suicide attempt. The first is a patient reported suicide attempt phenotype asked as part of an online mental health survey taken by a subset of participants (n = 157,366) in the UK Biobank. After quality control, we leveraged a genotyped set of unrelated, white British ancestry participants including 2433 cases and 334,766 controls that included those that did not participate in the survey or were not explicitly asked about attempting suicide. The second leveraged electronic health record (EHR) data from the Vanderbilt University Medical Center (VUMC, 2.8 million patients, 3250 cases) and machine learning to derive probabilities of attempting suicide in 24,546 genotyped patients. We identified significant and comparable heritability estimates of suicide attempt from both the patient reported phenotype in the UK Biobank (h2SNP = 0.035, p = 7.12 × 10-4) and the clinically predicted phenotype from VUMC (h2SNP = 0.046, p = 1.51 × 10-2). A significant genetic overlap was demonstrated between the two measures of suicide attempt in these independent samples through polygenic risk score analysis (t = 4.02, p = 5.75 × 10-5) and genetic correlation (rg = 1.073, SE = 0.36, p = 0.003). Finally, we show significant but incomplete genetic correlation of suicide attempt with insomnia (rg = 0.34-0.81) as well as several psychiatric disorders (rg = 0.26-0.79). This work demonstrates the contribution of common genetic variation to suicide attempt. It points to a genetic underpinning to clinically predicted risk of attempting suicide that is similar to the genetic profile from a patient reported outcome. Lastly, it presents an approach for using EHR data and clinical prediction to generate quantitative measures from binary phenotypes that can improve power for genetic studies.