Influence of the GABA Receptor Subunit Gene Polymorphism and Childhood Sexual Abuse on Processing Speed in Major Depression and Suicide Attempt

Yin, Honglei; Guo, Jia; Xin, Qianqian; Zheng, Shuqiong; Xue, Xiang; Li, Enze; Liu, Ting; Yan, Na; Keilp, John G.; Mann, J. John

doi:10.3389/fpsyt.2021.712231

Cited by 7 publications

(5 citation statements)

References 51 publications

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“…Our previous study found slower processing speed in depressed patients, regardless of suicide attempt history. Furthermore, depressed attempters showed enhanced positive-word response time compared to depressed non-attempters [ 27 ]. Therefore, based on these previous findings, the performance of SST was no longer used for statistical analysis, but for ML in this study.…”

Section: Methodsmentioning

confidence: 99%

Can cognition help predict suicide risk in patients with major depressive disorder? A machine learning study

et al. 2022

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Background Previous studies suggest that deficits in cognition may increase the risk of suicide. Our study aims to develop a machine learning (ML) algorithm-based suicide risk prediction model using cognition in patients with major depressive disorder (MDD). Methods Participants comprised 52 depressed suicide attempters (DSA) and 61 depressed non-suicide attempters (DNS), and 98 healthy controls (HC). All participants were required to complete a series of questionnaires, the Suicide Stroop Task (SST) and the Iowa Gambling Task (IGT). The performance in IGT was analyzed using repeated measures ANOVA. ML with extreme gradient boosting (XGBoost) classification algorithm and locally explanatory techniques assessed performance and relative importance of characteristics for predicting suicide attempts. Prediction performances were compared with the area under the curve (AUC), decision curve analysis (DCA), and net reclassification improvement (NRI). Results DSA and DNS preferred to select the card from disadvantageous decks (decks "A" + "B") under risky situation (p = 0.023) and showed a significantly poorer learning effect during the IGT (F = 2.331, p = 0.019) compared with HC. Performance of XGBoost model based on demographic and clinical characteristics was compared with that of the model created after adding cognition data (AUC, 0.779 vs. 0.819, p > 0.05). The net benefit of model was improved and cognition resulted in continuous reclassification improvement with NRI of 5.3%. Several clinical dimensions were significant predictors in the XGBoost classification algorithm. Limitations A limited sample size and failure to include sufficient suicide risk factors in the predictive model. Conclusion This study demonstrate that cognitive deficits may serve as an important risk factor to predict suicide attempts in patients with MDD. Combined with other demographic characteristics and attributes drawn from clinical questionnaires, cognitive function can improve the predictive effectiveness of the ML model. Additionally, explanatory ML models can help clinicians detect specific risk factors for each suicide attempter within MDD patients. These findings may be helpful for clinicians to detect those at high risk of suicide attempts quickly and accurately, and help them make proactive treatment decisions.

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Section: Methodsmentioning

confidence: 99%

Can cognition help predict suicide risk in patients with major depressive disorder? A machine learning study

et al. 2022

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“…23 CT frequencies were found to be similar to those reported in previous studies using the CTQ in SA patients. [41][42][43] This study did not used a healthy control group and to our knowledge, there are not Mexican studies that analyzed the CT in a healthy group.…”

Section: Discussionmentioning

confidence: 99%

NR3C1 and NR3C2 Genes Increase the Risk of Suicide Attempt in Psychiatric Disorder Patients with History of Childhood Trauma

Sanabrais-Jiménez,

Esquivel-López,

Sotelo-Ramírez

et al. 2023

NDT

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Background Hypothalamic-pituitary-adrenal axis gene variants and childhood trauma (CT) are considered risk factors for suicide attempt (SA). The aim of the present study was analyzed gene x environment (GxE) interaction of NR3C1, NR3C2 , and CT, and NR3C1 and NR3C2 gene expression in the development of SA with CT. Participants and Methods A total of 516 psychiatric Mexican patients from Instituto Nacional de Psiquiatría Ramón de la Fuente Muñiz. Among them, 274 had SA at least once and 242 had not SA. Genetic variants of NR3C1 and NR3C2 were genotyped in all the patients, of which were obtained the CT information from medical records. Additionally, the gene expression of NR3C1 and NR3C2 was also analyzed for a subsample of 96 patients, obtaining the TC information from Childhood Trauma Questionnaire (CTQ). Results The analysis showed a GxE interaction of NR3C1, NR3C2 , and CT (OR=2.8, 95% CI [1.9–3.9], p <0.0001). Interactions were also observed with neglect (OR=2.1, 95% CI [1.4–3.1], p <0.0001), emotional abuse (OR=2.1, 95% CI [1.5–3], p <0.0001), and sexual abuse (OR=2.4, 95% CI [1.4–2.9], p <0.0001) in the prediction of SA. The analysis of gene expression identified an overexpression of NR3C1 in SA patients with high scores for physical and sexual abuse ( p <0.0001; p <0.0006, respectively) and emotional neglect ( p =0.014). An underexpression was observed of NR3C2 , associated with high scores of trauma subtypes (p<0.0001) except physical neglect. Additionally, we observed an overexpression of NR3C1 gene in patients with SA carriers of A allele of rs6191 (p=0.0015). Also, overexpression of NR3C1 gene in carriers of G allele of rs6198 and underexpression of NR3C2 gene in carriers of G allele of rs5522 (p<0.0001). Conclusion Our findings suggest that genetic variants of NR3C1 and NR3C2 differentially affect expression levels, increasing the susceptibility to SA in psychiatric patients with a history of CT.

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“…Yet, more recent studies indicate that the SST has poor concurrent validity and cannot effectively differentiate persons with versus without SA history (Niu et al, 2021;Wilson et al, 2019). Overall, attentional bias to suicide-related words is not robustly associated with SITB (Becker et al, 1999;Cha et al, 2017Cha et al, , 2018Chung & Jeglic, 2016Niu et al, 2021;Stewart et al, 2017;Tavakoli et al, 2021;Williams & Broadbent, 1986;Yin et al, 2021), and does not account for unique variance in SA history when controlling for depressive symptoms (Chung & Jeglic, 2016. Thus, SST is not a robust predictor of suicide risk.…”

Section: Stroop Testmentioning

confidence: 99%

The role of cognitive flexibility in self-injurious thoughts and behaviors: A systematic review.

Chung,

Heakes,

Kaufman

2024

Clinical Psychology: Science and Practice

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Cognitive inflexibility is theorized to be an etiological and maintenance factor for self-injurious thoughts and behaviors (SITBs). The vast literature on cognitive flexibility and SITB is rife with conflicting results, and thus, is challenging to interpret. We conducted a systematic review to synthesize the available evidence on cognitive flexibility and SITB (n = 126 studies). Results indicate most laboratory paradigms we reviewed have poor ecological validity, and many studies use inadequate measures of cognitive flexibility. Evidence suggests cognitive flexibility, as currently measured, is not robustly associated with SITB history above and beyond certain psychopathology presentations (e.g., depression). Cognitive reappraisal appears to be more cognitively demanding for persons with (vs. without) suicidal ideation (SI) and/or nonsuicidal self-injury history. Higher cognitive flexibility may have a distal protective influence on SITB by buffering the effects of perceived stress and psychological pain. Preliminary evidence indicates that state cognitive flexibility may have a proximal association with concurrent state SI. Nonetheless, cognitive flexibility does not appear to be a primary mechanism of change in many SITB interventions, however, further research is warranted. We discuss several notable limitations in the current literature and make suggestions to guide future research in this area.

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Influence of the GABA Receptor Subunit Gene Polymorphism and Childhood Sexual Abuse on Processing Speed in Major Depression and Suicide Attempt

Cited by 7 publications

References 51 publications

Can cognition help predict suicide risk in patients with major depressive disorder? A machine learning study

Can cognition help predict suicide risk in patients with major depressive disorder? A machine learning study

NR3C1 and NR3C2 Genes Increase the Risk of Suicide Attempt in Psychiatric Disorder Patients with History of Childhood Trauma

The role of cognitive flexibility in self-injurious thoughts and behaviors: A systematic review.

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