Association of adenosine triphosphate-related genes to major depression and suicidal behavior: Cognition as a potential mediator.

BACKGROUND: Soluble epoxide hydrolase (sEH, encoded by EPHX2) and P2X2 (a subtype of ATP receptors) may mediate the antidepressant-like effects of ATP. We sought to determine whether polymorphisms and mRNA expression of EPHX2 and P2X2 are associated with depression and suicidal behavior and how cognition may mediate such associations. METHOD: We examined 83 single nucleotide polymorphisms (SNPs) of EPHX2 and P2X2. Subjects were MDD suicide attempters (N = 143), MDD non-suicide attempters (N = 248), and healthy volunteers (HV, N = 110). Data on demographics, depression severity, and suicide attempts were collected. Participants completed a set of cognitive tasks. Polymorphisms were genotyped using MALDI-TOF MS within the MassARRAY system. The expression of mRNA was measured using real-time polymerase chain reaction (RT-PCR). RESULTS: Cognitive function was a significant mediator (p = 0.006) of the genetic effect on depression. Allele C of rs202059124 was associated with depression risk (OR = 11.57, 95%CI: 2.33-209.87, p = 0.0181). A significant relationship was found between P2X2 mRNA expression and depression (OR = 0.68, 95%CI: 0.49-0.94, p = 0.0199). One haploblock (rs9331942 and rs2279590) was associated with suicide attempts: subjects with haplotype GC (frequency = 19.8 %, p = 0.017) and AT (frequency = 35.2 %, p < 0.001) had a lower rate of suicide attempts. CONCLUSIONS: Our results confirmed that cognitive impairment plays a role in the effect of rs9331949 on depression. Moreover, we confirmed a relationship between P2X2, EPHX2, and MDD in humans and presented preliminary haplotype-based evidence that implicates EPHX2 in suicide. LIMITATIONS: The main limitation of this study is the limited sample size. More comprehensive and multi-domain cognition tasks and different assessment measures are required in further study.

Thematic trends and knowledge structure on cognitive behavior therapy for insomnia: A bibliometric and visualization analysis.

Objective: To find publications trend about cognitive behavior therapy for insomnia (CBTI) using bibliometric and visualization analysis. In this study, the authors sought to identify the publication trends of peer-reviewed articles about CBTI. Materials and methods: Analyses were focused on the past 18 years from 2004 to 2021. All searches were performed on the Web of Science Core Collection database. The search was repeated to include structural cognitive behavior therapy for insomnia. Quantitative analysis was assessed using the bibliometric tool. Visualization analysis was carried out using VOSviewer. Results: In the 736 articles reviewed, the number of publications has been increasing every year for the past 18 years. Behavioral sleep medicine and sleep were the most active journals published on CBTI. The United States and Canada had the highest scientific publications in the field. Morin CM and Espie CA were the most active authors. The study type mostly observed were randomized controlled trials, meta-analyses, and epidemiological. Publications on digital-based cognitive behavior therapy and accessibility to primary care settings represent the future trends of research on CBTI. Conclusion: Possible explanations for CBTI publication trends were discussed, including the emergence of the evidence-based therapy, feasibility, and scalability. Potential CBTI publications trends in the future and clinical implications were also discussed.

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

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.