Childhood adversity and depression in Chinese populations: A multilevel meta-analysis of studies using the Childhood Trauma Questionnaire (CTQ)

Childhood maltreatment and suicide attempts: A prospective cohort study in Chinese middle school students

BACKGROUND

Suicide is a global public health crisis, and little evidence has focused on associations between suicide attempts and childhood maltreatment (CM) in Chinese middle school students.

OBJECTIVE

This study aimed to explore the relationship between childhood maltreatment and suicide attempts and the sex difference in Chinese middle school students.

PARTICIPANTS AND SETTING

In this prospective cohort study, students in grades 7 and 8 filled out the questionnaire at baseline and 6-month follow-up.

METHODS

Demographic data, childhood maltreatment, and suicide attempts were surveyed. Depression, anxiety, and stress were measured at baseline as covariates. Logistic regression was employed to measure the impact of childhood maltreatment and its sex differences on suicide attempts.

RESULTS

Among 782 students, 39.6 % suffered from childhood maltreatment, and the incidence rate of suicide attempts in 6 months was 4.60 % (36/782). After controlling for covariates, childhood maltreatment (adjusted odds ratio, OR = 2.899, 95%CI = 1.349-6.227) and its subtypes, physical abuse (adjusted OR = 4.077, 95%CI = 1.593-10.505) and emotional neglect (adjusted OR = 2.179, 95%CI = 1.059-4.481) were independent risk factors of suicide attempts. The association remained significant in females but not males, while no interactions between sex and childhood maltreatment were found.

CONCLUSIONS

Childhood maltreatment was prevalent in Chinese middle school students and closely associated with the incidence of suicide attempts, which provided valuable evidence for suicide prevention.

Models for depression recognition and efficacy assessment based on clinical and sequencing data

Major depression is a complex psychiatric disorder that includes genetic, neurological, and cognitive factors. Early detection and intervention can prevent progression, and help select the best treatment. Traditional clinical diagnosis tends to be subjective and misdiagnosed. Based on this, this study leverages clinical scale assessments and sequencing data to construct disease prediction models. Firstly, data undergoes preprocessing involving normalization and other requisite procedures. Feature engineering is then applied to curate subsets of features, culminating in the construction of a model through the implementation of machine learning and deep learning algorithms. In this study, 18 features with significant differences between patients and healthy controls were selected. The depression recognition model was constructed by deep learning with an accuracy of 87.26 % and an AUC of 91.56 %, which can effectively distinguish patients with depression from healthy controls. In addition, 33 features selected by recursive feature elimination method were used to construct a prognostic effect model of patients after 2 weeks of treatment, with an accuracy of 75.94 % and an AUC of 83.33 %. The results show that the deep learning algorithm based on clinical and sequencing data has good accuracy and provides an objective and accurate method for the diagnosis and pharmacodynamic prediction of depression. Furthermore, the selected differential features can serve as candidate biomarkers to provide valuable clues for diagnosis and efficacy prediction.

Workplace trauma and professional quality of Life in clinical and forensic psychiatry: the CRITIC study

Background

Frontline staff in psychiatry need to perform at a very high professional level in order to ensure patient and community safety. At the same time they are exposed to high levels of stress and workplace trauma. This may have severe consequences for their professional quality of life. In addition, health care workers in general have higher incidence levels of childhood adversity than the general population. The CRITIC (CRITical Incidents and aggression in Caregivers) Study aims to improve increased understanding of the interaction between personal life history (childhood adversity and benevolence), individual capabilities, exposure to trauma and violence at work and Professional Quality of Life (ProQOL).

Method

The Critic Study is a cross-sectional survey of these aspects in frontline, treatment and administrative staff in clinical and forensic psychiatry. We aim to include 360 participants. Participants will be asked to complete questionnaires on childhood adversity and childhood benevolence (assessing personal life history), professional quality of life, current trauma and violence exposure, current mental health (depression, anxiety and stress), coping, social support, work engagement and resilience. In this study we will examine the moderating role of adverse and benevolent childhood experiences in the association between workplace trauma exposure and professional quality of life. Finally, a theoretical model on the relationships between trauma, stress and coping in the context of professional functioning will be tested using structural equation modelling.

Discussion

The CRITIC study examines which factors influence the complex relationship between childhood adversity and benevolence, and ProQOL in healthcare workers. It also aims to provide insight into the complex relationship between personal life history, individual characteristics, exposure to trauma and violence at work and ProQOL. The results can be used for designing interventions to increase resilience to trauma and to improve professional quality of life among health care professionals.

Trial registration

The CRITIC study has been approved by the Medical Ethical Committee of the Erasmus Medical Centre, under trial registration number NL73417.078.20.

The Importance of Adverse Childhood Experiences in Depressive Symptoms and Their Biological Consequences in Healthy Adults: Results of a Polish University Student Study

Adverse childhood experiences (ACEs) have a long-lasting effect on both physical and mental health. The aim of this study was to assess the consequences of ACEs and experienced stress on depression and the role of biological disturbances in this relationship in a student population. Potential participants filled out a screening questionnaire; 60 of 126 students met the inclusion criteria and were tested for the severity of stress and depressive symptoms, ACEs, dietary habits, and serum concentrations of biological markers. Depressive symptoms were related to a younger age (p = 0.012), a higher severity of stress (p = 0.001), ACEs (p = 0.007), and lower triglyceride (p = 0.01) and cortisol concentrations (p = 0.01). An inverse relationship between the triglyceride concentration and emotional abuse (R = -0.38) and emotional neglect (R = -0.33) was found. Occludin was positively associated with physical abuse (R = 0.31). Cortisol was inversely associated with emotional abuse (R = -0.35). Emotional neglect was associated with lipopolysaccharide binding protein (R = 0.38) and insulin levels (R = -0.31). The most promising multi-panel of biomarkers for recognizing mood symptoms included triglycerides, tight junction protein 1, and cortisol (cut-offs of ≤ 95.5 mg/dL, 0.72 ng/mL, and 134.63 ng/mL, respectively). This study confirmed the association between ACEs and depressive symptoms and the importance of psychological stress in developing mood disorders. ACEs could affect biological dysregulation. Some of the biological markers could be helpful in early detection of depression.

Childhood trauma and social support affect symptom profiles through cortical thickness abnormalities in major depressive disorder: A structural equation modeling analysis

BACKGROUND

Childhood trauma, low social support, and alexithymia are recognized as risk factors for major depressive disorder (MDD). However, the mechanisms of risk factors, symptoms, and corresponding structural brain abnormalities in MDD are not fully understood. Structural equation modeling (SEM) has advantages in studying multivariate interrelationships. We aim to illustrate their relationships using SEM.

METHODS

313 MDD patients (213 female; mean age 42.49 years) underwent magnetic resonance imaging and completed assessments. We integrated childhood trauma, alexithymia, social support, anhedonia, depression, anxiety, suicidal ideation and cortical thickness into a multivariate SEM.

RESULTS

We first established the risk factors-clinical phenotype SEM with an adequate fit. Cortical thickness results show a negative correlation of childhood trauma with the left middle temporal gyrus (MTG) (p = 0.012), and social support was negatively correlated with the left posterior cingulate cortex (PCC) (p < 0.001). The final good fit SEM (χ2 = 32.92, df = 21, χ2/df = 1.57, CFI = 0.962, GFI = 0.978, RMSEA = 0.043) suggested two pathways, with left PCC thickness mediating the relationship between social support and suicidal ideation, and left MTG thickness mediating between childhood trauma and anhedonia/anxiety.

CONCLUSION

Our findings provide evidence for the impact of risk factor variables on the brain structure and clinical phenotype of MDD patients. Insufficient social support and childhood trauma might lead to corresponding cortical abnormalities in PCC and MTG, affecting the patient's mood and suicidal ideation. Future interventions should aim at these nodes.