Adverse childhood experiences mediate the negative association between borderline personality disorder symptoms and plasma oxytocin

The quest for a biological phenotype of adolescent non-suicidal self-injury: a machine-learning approach

Non-suicidal self-injury (NSSI) is a transdiagnostic psychiatric symptom with high prevalence and relevance in child and adolescent psychiatry. Therefore, it is of great interest to identify a biological phenotype associated with NSSI. The aim of the present study was to cross-sectionally investigate patterns of biological markers underlying NSSI and associated psychopathology in a sample of female patients and healthy controls. Comprehensive clinical data, saliva and blood samples, heart rate variability and pain sensitivity, were collected in n = 149 patients with NSSI and n = 40 healthy participants. Using machine-based learning, we tested the extent to which oxytocin, dehydroepiandrosterone (DHEA), beta-endorphin, free triiodothyronine (fT3), leukocytes, heart rate variability and pain sensitivity were able to classify participants regarding their clinical outcomes in NSSI, depression and borderline personality disorder symptomatology. We evaluated the predictive performance of several models (linear and logistic regression, elastic net regression, random forests, gradient boosted trees) using repeated cross-validation. With NSSI as an outcome variable, both logistic regression and machine learning models showed moderate predictive performance (Area under the Receiver Operating Characteristic Curve between 0.67 and 0.69). Predictors with the highest predictive power were low oxytocin (OR = 0.55; p = 0.002), low pain sensitivity (OR = 1.15; p = 0.021), and high leukocytes (OR = 1.67; p = 0.015). For the psychopathological outcome variables, i.e., depression and borderline personality disorder symptomatology, models including the biological variables performed not better than the null model. A combination of hormonal and inflammatory markers, as well as pain sensitivity, were able to discriminate between participants with and without NSSI disorder. Based on this dataset, however, complex machine learning models were not able to detect non-linear patterns of associations between the biological markers. These findings need replication and future research will reveal the extent to which the respective biomarkers are useful for longitudinal prediction of clinical outcomes or treatment response.

Child maltreatment and resilience in adulthood: a protocol for a systematic review and meta-analysis

Background: Although child maltreatment (CM) has been linked to health problems and poor psychosocial functioning, not all individuals exposed to CM develop or experience negative consequences later in life. This suggests that some individuals show resilience after being exposed to CM. However, conclusions have been limited by inconsistent findings across different CM subtypes and resilience domains.Objective: To develop a protocol for conducting a systematic review and meta-analysis to quantify associations between CM (overall and its subtypes) and resilience (global and its multiple domains) in adulthood, and to examine moderators and mediators of these associations.Method: PubMed, PsycINFO, Embase, Scopus, and Web of Science will be searched to identify relevant studies on the association between CM (exposure) and resilience (outcome) in adults (≥ 18 years). Data will be screened and extracted by at least two independent reviewers. The methodological quality of the included studies will be independently assessed with a modified version of the Newcastle-Ottawa Scale (NOS). If deemed viable, a meta-analysis will be conducted using a random effects model. Heterogeneity of evidence will be estimated with the I2 statistic, and publication bias will be assessed. The effects of potential moderators (e.g. timing and severity of CM, age, sex, family cohesion, socio-economic status, country/region) will be analysed using meta-regression and subgroup analyses, and meta-analytical structural equation modelling will be employed to synthesise indirect mediation effects. Candidate moderators and mediators (e.g. genetic factors, brain functioning, attachment style, personality traits, physical activity, and social support) will be also examined qualitatively.Conclusions: This protocol will facilitate a systematic review and meta-analysis that has the potential to enhance our knowledge about the association between CM exposure in early life and resilience in adulthood. Understanding associations and underlying mechanisms between CM and resilience is potentially important in informing prevention and interventions to sustain health and improve outcomes among adults with a history of CM.PROSPERO registration: CRD42023394120.