Fear of cancer recurrence (FCR), defined as worry about cancer progression or return, is a common concern among patients and survivors. This study aims to identify factors influencing the onset of FCR and to evaluate its impact on emotional distress and quality of life (QoL) in patients undergoing systemic cancer treatment.

A sample of 175 patients undergoing systemic cancer treatment completed self-report questionnaires assessing socio-demographics, clinical factors and fear of cancer recurrence, anxiety, depression and QoL (FCRI, HADS, EORTC-QLQ-C30).

Data analysis using linear and generalized linear models revealed that FCR is inversely associated with QoL and positively correlated with anxiety and depression. These associations were independent of socio-demographic and clinical variables, with anxiety and depression fully mediating the FCR-QoL relationship. Gender and time since diagnosis emerged as significant predictors, with an increase in FCR at 12 months post-diagnosis.

Our results indicate that FCR, anxiety and depression linked, are present after several months post-diagnosis, suggesting that FCR tends to intensify over time. These findings carry important psychological implications, highlighting the need to support patients in recognizing and managing their fear and emotional distress. Implementing a psychoeducational counseling approximately six months after diagnosis – engaging oncologists, psycho-oncologists, and patients within a “stepped-care” framework – may be effective in mitigating FCR and its emotional consequences.

Childhood maltreatment is a robust predictor of aggression. Research indicates that both maltreatment experiences and aggression are moderately heritable. It has been hypothesized that gene–environment correlation may be at play, whereby genetic predispositions to aggression in parents and children may be confounded with family environments conducive to its expression. Building on this framework, we tested whether maltreatment mediates the association between a polygenic score for aggression (PGSAGG) and school-age aggression, and whether this varied for reactive and proactive aggression.

The sample comprised 721 participants (44.9% males; 99.0% White) with prospective assessments of maltreatment from 5 months to 12 years (10 assessments;1998–2010), and teachers-reported aggression from ages 6 to 13 (6 assessments; 2004–2011). The PGSAGG was derived using a Bayesian estimation method (PRS-CS).

PGSAGG was associated with most aggression measures across specific ages and trajectories. Maltreatment experiences partially mediated the association between PGSAGG and the Childhood-Limited trajectory of reactive – but not proactive – aggression.

Children with higher genetic propensities for aggression were more likely to experience maltreatment, which partly explained the association between PGSAGG and a Childhood-Limited trajectory of reactive aggression during elementary school. This finding reinforces the possibility of confounding influences between genetic liability for aggression and maltreatment experiences.

The macro-social and environmental conditions in which people live, such as the level of a country’s development or inequality, are associated with brain-related disorders. However, the relationship between these systemic environmental factors and the brain remains unclear. We aimed to determine the association between the level of development and inequality of a country and the brain structure of healthy adults.

We conducted a cross-sectional study pooling brain imaging (T1-based) data from 145 magnetic resonance imaging (MRI) studies in 7,962 healthy adults (4,110 women) in 29 different countries. We used a meta-regression approach to relate the brain structure to the country’s level of development and inequality.

Higher human development was consistently associated with larger hippocampi and more expanded global cortical surface area, particularly in frontal areas. Increased inequality was most consistently associated with smaller hippocampal volume and thinner cortical thickness across the brain.

Our results suggest that the macro-economic conditions of a country are reflected in its inhabitants’ brains and may explain the different incidence of brain disorders across the world. The observed variability of brain structure in health across countries should be considered when developing tools in the field of personalized or precision medicine that are intended to be used across the world.

Prolonged childhood and adolescent loneliness (CAL) is linked to various adverse mental health outcomes, yet its impact on schizophrenia spectrum disorders (SSD) has been understudied. While loneliness is associated with psychosis and worsens symptoms in SSD, few studies have explored the long-term effects of early loneliness on SSD risk. Understanding how CAL interacts with genetic liability to schizophrenia is essential for identification of high-risk individuals.

This study evaluated whether prolonged CAL is associated with increased SSD risk and examined the interaction between CAL and genetic liability for schizophrenia. Gender differences in these associations were also explored.

Data from the European Gene–Environment Interactions in Schizophrenia (EU-GEI) study were analysed, including 1261 individuals with SSD, 1282 unaffected siblings and 1525 healthy controls. CAL was retrospectively assessed for periods before age 12 years and age 12–16 years. Genetic risk was measured using polygenic risk scores for schizophrenia. Logistic regression models and the Relative Excess Risk due to Interaction (RERI) method were used to examine gene–environment interactions, with stratification by gender.

Prolonged CAL was associated with higher odds of SSD (odds ratio [95% CI] = 5.20 [3.85−7.01] for loneliness before age 12; odds ratio [95% CI] = 7.26 [5.63−9.38] for loneliness during adolescence). The interaction between CAL and genetic risk was strongest during adolescence (RERI [95% CI] = 23.46 [10.75−53.53]). Females showed a greater effect (odds ratio [95 %CI] = 10.04 [6.80−14.94]) than males (odds ratio [95% CI] = 5.50 [3.95−7.66]). Incorporating CAL and genetic interaction increased predictive values to 17% for SSD risk − rising to 22.5% in females − compared with 2.6 and 2.8%, respectively, for genetic risk alone.

Prolonged CAL significantly increases SSD risk, particularly in females. The inclusion of CAL alongside genetic risk substantially enhances predictive accuracy. Early identification of CAL could inform preventive strategies, especially in genetically vulnerable populations.

Binge-eating disorder (BED) is characterized by highly distressing episodes of loss-of-control over-eating. We have examined the use of repetitive transcranial magnetic stimulation (rTMS) for the treatment of people with BED and associated obesity. Such non-invasive brain stimulation (NIBS) techniques are used therapeutically in several psychiatric conditions and there is an associated scientific rationale.

Sixty participants were randomly allocated to receive 20 sessions of neuronavigated 10 Hz rTMS administered to the left dorsolateral prefrontal cortex (dlPFC) or sham treatment. Primary outcomes were the frequency of binge eating episodes (BEE) and the ‘urge to eat’ (craving) evaluated at baseline and end-of-treatment (8 weeks post-randomization). Secondary outcomes included body mass index (BMI), hunger, general and specific eating disorder psychopathology. Follow-up analyses were conducted for most outcomes at 16 weeks post-randomization. Multilevel models were used to evaluate group, time, and group-by-time interactions for the association between rTMS exposure and outcomes.

The real rTMS group (compared with sham treatment), showed a significantly greater decrease in the number of BEE at the end of treatment (Estimated Mean [EM]: 2.41 95% CI: 1.84–3.15 versus EM: 1.45 95% CI: 1.05–1.99, p = 0.02), and at follow-up (EM: 3.79 95% CI: 3–4.78 versus EM: 2.45 95% CI: 1.88–3.17, p = 0.02; group × time interaction analysis p = 0.02). No group differences were found for other comparisons.

rTMS was associated with reduced BEE during and after treatment: it suggests rTMS is a promising intervention for BED.

Studies conducted during the COVID-19 pandemic found high occurrence of suicidal thoughts and behaviours (STBs) among healthcare workers (HCWs). The current study aimed to (1) develop a machine learning-based prediction model for future STBs using data from a large prospective cohort of Spanish HCWs and (2) identify the most important variables in terms of contribution to the model’s predictive accuracy.

This is a prospective, multicentre cohort study of Spanish HCWs active during the COVID-19 pandemic. A total of 8,996 HCWs participated in the web-based baseline survey (May–July 2020) and 4,809 in the 4-month follow-up survey. A total of 219 predictor variables were derived from the baseline survey. The outcome variable was any STB at the 4-month follow-up. Variable selection was done using an L1 regularized linear Support Vector Classifier (SVC). A random forest model with 5-fold cross-validation was developed, in which the Synthetic Minority Oversampling Technique (SMOTE) and undersampling of the majority class balancing techniques were tested. The model was evaluated by the area under the Receiver Operating Characteristic (AUROC) curve and the area under the precision–recall curve. Shapley’s additive explanatory values (SHAP values) were used to evaluate the overall contribution of each variable to the prediction of future STBs. Results were obtained separately by gender.

The prevalence of STBs in HCWs at the 4-month follow-up was 7.9% (women = 7.8%, men = 8.2%). Thirty-four variables were selected by the L1 regularized linear SVC. The best results were obtained without data balancing techniques: AUROC = 0.87 (0.86 for women and 0.87 for men) and area under the precision–recall curve = 0.50 (0.55 for women and 0.45 for men). Based on SHAP values, the most important baseline predictors for any STB at the 4-month follow-up were the presence of passive suicidal ideation, the number of days in the past 30 days with passive or active suicidal ideation, the number of days in the past 30 days with binge eating episodes, the number of panic attacks (women only) and the frequency of intrusive thoughts (men only).

Machine learning-based prediction models for STBs in HCWs during the COVID-19 pandemic trained on web-based survey data present high discrimination and classification capacity. Future clinical implementations of this model could enable the early detection of HCWs at the highest risk for developing adverse mental health outcomes.

NCT04556565