Mental disorders are intergenerationally associated, particularly affecting adolescent offspring. However, the extent of such intergenerational associations among adult women in China remains unclear. This study aimed to examine the intergenerational associations of depression and anxiety between two adult female generations.

This cross-sectional study included 2,130 grandmother-mother dyads from the Grandmothers, Mothers, and Their Children’s Health study. Depression and anxiety of the grandmaternal (G0) and maternal (G1) generations were assessed using the 10-item version of the Center for Epidemiologic Studies Depression Scale and the 7-item Generalized Anxiety Disorder Scale, respectively, with scores of 10 or higher defined as depression and anxiety. Statistical analyses included logistic regression, negative binomial regression, and restricted cubic spline analyses.

A total of 11.4% G0 and 11.5% G1 participants reported depression, and 4.1% G0 and 3.1% G1 participants reported anxiety. Depression in G0 was associated with 4.29-fold (95% CI: 3.09–5.94) and 3.50-fold (95% CI: 1.98–6.01) higher odds of depression and anxiety in G1, respectively, while anxiety in G0 was associated with 3.95-fold (95% CI: 2.41–6.35) and 5.47-fold (95% CI: 2.64–10.60) higher odds of depression and anxiety in G1, respectively; dose–response relationships were also observed. In addition, the intergenerational association of depression was stronger among G0 participants residing in rural areas, whereas G0 depression and anxiety were more strongly associated with anxiety in G1 among those with higher household income.

Mental disorders were intergenerationally associated between mothers and their adult daughters. These findings emphasize the importance of family-based interventions for mental health.

Individual responses to stress are highly heterogeneous, resulting in diverse psychopathological outcomes. This variability poses challenges for traditional diagnostic frameworks and underscores the need for a transdiagnostic approach to guide interventions. This study aimed to identify distinct phenotypes within a stress-exposed population and to characterize their biological profiles using a multimodal machine learning framework.

A total of 809 stress-exposed adults (mean age 40.5 ± 8.74 years; 53.7% female) underwent clinical, laboratory, and structural MRI assessments. Data-driven clustering of clinical variables identified phenotypes, followed by machine learning classifiers trained on neuroimaging and laboratory data to predict phenotype membership. SHapley Additive exPlanations (SHAP) analysis was used to identify key biological features distinguishing each phenotype.

Three phenotypes were identified: a multi-risk group (n = 321) characterized by prominent depression, anxiety, and sleep disturbances; an alcohol-related risk group (n = 226) with high alcohol misuse and minimal comorbidity; and a resilient low-risk group (n = 262). Machine learning models accurately classified these phenotypes, indicating distinct biological profiles. SHAP analysis revealed phenotype-specific signatures: the multi-risk phenotype was associated with frontal-subcortical structural alterations and dysregulated cortisol, whereas the alcohol-related risk phenotype was characterized by frontal-insular structural alterations and metabolic abnormalities.

This study demonstrates the stratification of stress-exposed individuals into clinically and biologically distinct phenotypes. By integrating multimodal data with machine learning, we identified phenotype-specific neurobiological and metabolic profiles that extend beyond conventional diagnostic frameworks. These findings support a transdiagnostic, data-driven approach to improve risk stratification and inform personalized interventions in stress-exposed populations.

Forensic psychiatric services are expanding in many countries, and discharging patients from secure hospitals relies on accurate estimates of risk of adverse outcomes. Novel evidence-based tools for estimating one key risk, violent reoffending, have been developed in recent years. We aimed to externally validate one new tool, FoVOx, in forensic psychiatric patients sentenced to treatment and to develop an updated model (FoVOx2), incorporating additional clinical predictors.

Using Swedish national registers, we conducted a temporal external validation of FoVOx by examining 767 patients discharged between 2014 and 2023. For the FoVOx2 cohort, 906 patients discharged between 2008 and 2023 were followed up, and additional predictors tested. The outcome was violent reconviction within 12 or 24 months. Model performance was evaluated using Harrell’s C-index, area under the receiver operating characteristics curves (AUCs), calibration, and classification metrics at predefined thresholds.

In temporal validation, FoVOx showed moderate discrimination (AUCs 0.69 and 0.71; C-index = 0.69) and acceptable overall accuracy (Brier <0.11). Calibration was generally good, with mild overestimation at the highest predicted risks (>20%) at 12 months and slight underprediction at 24 months. The updated FoVOx2 model incorporated novel predictors, including clozapine treatment and additional diagnostic categories. It was associated with improved performance (AUCs 0.77; optimism-corrected C-index = 0.72; Brier 0.06 and 0.09) and demonstrated good calibration (intercept ≈ 0; slopes 1.03 and 1.05).

Updating risk assessment tools with additional clinical factors can lead to incremental improvement in model performance. Implementing tools should consider clinical utility and impact as next steps.

Cultural competence training has become increasingly common in psychiatric practice across Europe and worldwide, supported by evidence demonstrating improvements in quality of care, clinical effectiveness, and patient satisfaction. Since the publication of the original EPA guideline in 2015, the field has advanced considerably in its theoretical foundations, evidence base, and institutional support. This updated guideline addresses recent developments and challenges in research, policy, and clinical practice.

The guideline is based on a systematic meta-review of 15 systematic reviews and evidence-based guidelines on cultural competence training in psychiatric care and mental health. The evidence was synthesized to identify key concepts, educational components, implementation strategies, and current debates.

The review comprises four sections. First, it examines core elements of cultural competence training, emphasizing cultural humility, structural competence, and perspectives from the social and cultural sciences. Second, it provides practical recommendations for curriculum design, including the potential role of digital learning tools. Third, it addresses critiques of cultural competence, particularly concerns regarding essentializing and potentially racist interpretations. Finally, it explores strategies for integrating cultural competence training into existing psychiatric services, highlighting both individual and organizational factors that support implementation.

Cultural competence benefits not only migrant and ethnically diverse populations but all patients and healthcare professionals, including members of majority cultures. Successful implementation requires cultural sensitivity, critical self-reflection, and sustained commitment at individual, organizational, and system levels to achieve meaningful and lasting improvements in psychiatric care.

Cognitive reserve (CR) is considered a positive factor in the onset, progression, and prognosis of diseases. It may also help explain the clinical heterogeneity observed in schizophrenia (SZ).

This cross-sectional study included 70 patients with SZ and 64 healthy controls. Participants were assessed on CR, symptoms, cognition, and functional outcomes. Following PRISMA guidelines, we also searched PubMed, Scopus, Web of Science, Embase, the Cochrane Library, and PsycINFO for studies published up to September 25, 2025. Study quality was evaluated using the Newcastle-Ottawa Scale (NOS).

Patients with higher CR showed less severe negative and general psychopathological symptoms, along with better functional outcomes. Meta-analysis confirmed this relationship and further revealed positive correlations between CR and multiple cognitive domains, including speed of processing, working memory, verbal learning, visual learning, and reasoning and problem solving.

This study demonstrates a positive association between CR and symptoms severity, cognitive performance, and functional outcomes in SZ.

Multiple observational studies have reported associations between age at menarche (AAM) and mental health problems, yet their shared genetic architecture remains poorly characterized.

We leveraged genome-wide association study summary statistics for AAM and 15 mental health-related phenotypes. We conducted a multi-method integrative analysis encompassing linkage disequilibrium score regression, pleiotropic analysis under the composite null hypothesis, functional mapping and annotation, multi-marker analysis of genomic annotation, pathway enrichment, and bidirectional two-sample Mendelian randomization (MR) to explore shared genetic architecture and potential causal relationships.

Our study identified significant genetic correlations between AAM and eight mental health-related phenotypes (miserableness, fed-up feelings, nervous feelings, ever thought that life is not worth living, ever self-harmed, depression, ever smoker, and age started smoking in former smokers). A total of 155 pleiotropic loci, 18 colocalized loci (e.g., 6q16.3), and 203 pleiotropic genes (e.g., LIN28B) were identified. These genes are expressed in multiple regions, including the cerebral cortex and hypothalamus, and are involved in various biological processes and signaling pathways. Additionally, MR analysis revealed causal associations between AAM and 5 mental health-related phenotypes (mood swings, miserableness, fed-up feelings, and age at which smokers started smoking in former/current smokers).

Our study revealed extensive genetic associations between AAM and mental health-related phenotypes, and further explored the potential causal relationships between them. These findings enhance our understanding of the relationship from a genetic perspective and establish a foundation for future research to explore the biological pathways and environmental interactions contributing to these associations.

Schizophrenia-spectrum disorders (SSD) are characterized by structural and functional brain abnormalities, including disrupted functional connectivity within networks such as the salience network. The anterior cingulate cortex (ACC), a core hub of this network, has shown alterations in the major neurotransmitter systems comprising glutamate (Glu) and γ-aminobutyric acid (GABA). While dysfunctional glutamatergic signaling has been proposed as a key mechanism in SSDs, the relationship between anterior cingulate cortex (ACC) functional connectivity, glutamatergic neurotransmission, and clinical symptomatology remains poorly understood.

Here, we combined resting-state functional MRI to measure functional brain connectivity with proton magnetic resonance spectroscopy to measure Glu and GABA levels in the dorsal ACC of 26 patients with schizophrenia spectrum disorders and 38 healthy controls.

Patients showed reduced connectivity within the salience network compared to controls. Across the whole sample, a dorsal ACC-seed showed glutamate-dependent connectivity to several clusters, including right insula, thalamus, and cerebellum. In patients, the averaged connectivity of these clusters was associated with positive symptom severity.

These findings suggest that disrupted glutamatergic modulation of large-scale brain networks may underlie core clinical features in schizophrenia spectrum disorders.

Cohort studies of individuals with a suicide attempt are crucial for identifying risk and protective factors to prevent recurrence. The SURVIVE prospective cohort study aims to investigate the demographic and clinical profiles of individuals presenting with suicidal behaviour.

A total of 1,741 individuals (289 adolescents aged 12–17 and 1,443 adults aged 18 and older) were recruited from emergency departments at eight hospitals across five Spanish regions following a suicide attempt. Baseline data were collected using structured clinical interviews and validated self-report instruments. Sociodemographic and psychiatric variables were analysed.

Most participants were female, and approximately 20% were migrants. Religious affiliation was reported by 35.6% of adolescents and 48.4% of adults. Depression and anxiety were the most prevalent psychiatric diagnoses, while trauma-related and eating disorders were more frequent in adolescents, and substance use disorders in adults. Non-suicidal self-injury was reported by 76.6% of adolescents and 40.1% of adults. The most common method of attempt was self-poisoning in both groups. Psychotropic medication use was widespread, with both groups receiving a similar number of prescriptions.

The SURVIVE study provides a detailed characterization of suicide attempters in Spain, highlighting age-specific clinical patterns and contextual risk factors such as migration and religion. These findings underscore the need for tailored prevention strategies that consider developmental stage, psychiatric comorbidity, and social vulnerability.

Cognitive impairment associated with schizophrenia (CIAS) is a prevalent, meaningful feature of schizophrenia with limited real-world data on its recognition and care setting impact. The LUCIA initiative is an international multi-stakeholder study that explored awareness, assessment practices, and the burden of CIAS to inform future care pathways.

A three phase, Delphi-informed design was applied, comprising expert interviews to frame the enquiry, qualitative interviews with health and social care professionals (HCPs; n = 74) and caregiver advocates (n = 11), two waves of a Delphi survey among HCPs (n=449 and 343, respectively) and one round among 61 patients and 112 caregivers across 15 countries (n = 964).

The results showed poor awareness of CIAS across stakeholders. Structured cognitive assessment was infrequent, and clinicians largely relied on the dementia oriented Mini-Mental State Examination (MMSE) rather than schizophrenia specific tools, citing time, training, and unclear actionability as key barriers. CIAS imposed broad humanistic, clinical, societal, and economic burden – poorer quality of life, social isolation, higher comorbidities, increased hospital days and health care costs, and heavy informal care. Consensus actions prioritized the development of brief, validated screening instruments, improved psychoeducation, and accelerated research into effective pharmacological and non pharmacological interventions.

These results provide additional evidence for the under-recognition of CIAS worldwide, despite its substantial multidimensional societal burden. The use of dementia-oriented cognitive tests carries significant risks of misclassification and inappropriate management. Therefore, improving awareness, implementing assessment guidelines, and accelerating therapeutic innovation is critical to improve the quality of life of CIAS patients and the wider community.

Thought disorder (TD) is a core feature of severe mental illnesses such as schizophrenia, characterized by disruptions in speech, language, and communication. People with TD face unique barriers that hinder their involvement in research, both as participants and as partners. Their systematic underrepresentation in psychiatric research is driven by pervasive assumptions about their decisional capacity, willingness to participate, and ability to engage in research. This perpetuates a biased evidence base, likely hindering the therapeutic progress toward addressing this core problem.

This review, informed by professional (clinical and research) and lived (bottom-up and phenomenological) experience of TD, examines how flawed assumptions regarding capacity, engagement, and participatory abilities serve as active barriers to inclusion.

We argue for a shift toward supported inclusion through tailored capacity assessments, enhanced informed consent procedures, targeted training of research personnel, and systemic institutional practices. Incorporating lived experiences of those with TD as research partners is integral to this approach, fostering co-production of research that is more valid, inclusive, and applicable.

Without these inclusion-focused changes, the development of treatments for TD is likely to have very slow progress and a critical segment of the severely unwell population will continue to be underrepresented from the scientific process, undermining both the utility and generalizability of psychiatric research.

Improving quality of life (QoL) is a primary objective in the treatment of schizophrenia. The current analysis aimed to evaluate the impact of antipsychotic administration routes on QoL in early-phase schizophrenia (SZ) patients randomized to treatment with either long-acting injectable (LAI) or oral aripiprazole or paliperidone as part of the “European Long-acting Antipsychotics in Schizophrenia Trial” (EULAST).

A total of 492 patients were followed for up to 19 months. QoL was assessed using the EQ-5D-5L. In the primary analysis, the relationships between oral versus LAI treatment and between treatment with paliperidone versus aripiprazole were investigated by fitting generalized estimating equation models. In secondary analyses, we examined the individual dimensions of the EQ-5D-5L, including mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. In subgroup analyses, we estimated the treatment effect on the EQ-5D-5L across clinically relevant subgroups, including sex, age, symptomatology, and side effects.

Overall, EQ-5D-5L scores improved over the course of the study, with no significant differences between patients treated with LAI versus oral antipsychotics (p = 0.662) or between those treated with aripiprazole versus paliperidone (p = 0.266). Subgroup analyses based on sex, age, medication side effects, and psychopathology also did not reveal any significant differences in EQ-5D-5L outcomes between LAI and oral antipsychotic treatment.

These findings indicate comparable QoL in SZ patients starting LAI or oral antipsychotic treatment with aripiprazole or paliperidone. Further research with extended follow-up periods is required to gain deeper insights into potential subgroup-specific benefits and the long-term effects of different antipsychotic administration routes on QoL.

The vulnerability–stress framework guiding gene–environment interaction (GxE) research overlooks the role of positive experiences. The Differential Susceptibility (DS) model offers a broader perspective, suggesting that individuals vary in sensitivity to both negative and positive environments. This study aimed at replicating previous DS research by examining interactions between polygenic scores for environmental sensitivity (PGS-ES) and positive and negative early exposures on subclinical psychosis and internalizing psychopathology, functioning, and wellbeing.

The sample consisted of 638 twins from the first wave of the TwinssCan study, a general population twin cohort. PGS-ES and adversity, bullying and positive experiences in childhood were collected, along with assessments of psychotic, affective, functioning, and positive mental health. GxE interactions were tested under a competitive–confirmatory approach.

DS effects were found for the interactions between PGS-ES and all environmental exposures on schizotypic eccentricity and functioning. Adolescents with high genetic sensitivity were rated as more eccentric and less functional under childhood adversity but were rated as less eccentric and better adjusted under childhood favorable conditions. DS also resulted from the interaction between PGS-ES and positive childhood on social coping. No significant models emerged for internalizing or wellbeing.

Findings overall supported DS, indicating that genetic sensitivity to the environment operates in a “for better and for worse” manner depending on the quality of environmental exposures. It extends initial evidence that DS applies to nonclinical psychosis expression and highlights the importance of considering the full spectrum of environmental conditions to understand both risk and opportunity factors in GxE.

Late-life depression refers to a depression occurring in older adults, defined as individuals aged 65 years and older. It is common and often mismanaged due to complex clinical profiles, polypharmacy, and limited evidence-based guidance. Existing tools poorly address psychiatric nuances in older adults. This work aims to develop French recommendations based on experts’ consensus on the use of antidepressants in unipolar late-life depression, to guide safer prescribing practices.

This tool was developed using a Delphi survey, based on a review of literature published between 2014 and 2024 and focused on “antidepressants” and “late-life depression” Experts from various fields: geriatric psychiatry, clinical pharmacy and general practice, rated items using a 9-point Likert scale. Items with a median score ≥ 7 and at least 80% agreement were validated and included in the final version of the tool.

Twenty to 23 experts per round, different from the authors of the proposed items, participated in a four-round Delphi process. The resulting tool includes 57 validated items across 10 sections and a stepped-care algorithm for treating major depression in older adults. It addresses drug choice, dosing, monitoring, comorbidities, and treatment resistance, prioritizing safe first-line options like sertraline, citalopram, and escitalopram.

A Delphi survey involving multidisciplinary experts led to a French consensus tool for prescribing antidepressants in unipolar late-life depression. It integrates clinical evidence and expert judgment to address treatment complexity, drug safety, and resistance. The tool offers practical, stepwise recommendations tailored to primary care, aiming to optimize antidepressant use, reduce iatrogenesis, and improve patient outcomes.

The network theory of mental disorders posits that associations between symptoms activate other symptoms to maintain a disorder over time. Network analytic approaches therefore may inform treatment targets. In the present study, we compared baseline OCD symptom networks among treatment responders to non-responders and examined how network structure and connectivity changed from before to after exposure and response prevention (ERP) treatment.

Community adults with OCD (n = 712) who underwent intensive outpatient treatment were assessed using the Yale-Brown Obsessive Compulsive Scale (YBOCS) at admission and discharge. Network comparison tests were used to (a) examine differences in baseline symptom network structures between treatment responders versus non-responders and (b) examine changes in network structures from pre- to post-treatment.

Pre-treatment network structures and global connectivity did not differ significantly between treatment responders and non-responders. However, post-treatment networks exhibited greater global strength (i.e., stronger associations between OCD symptoms) and significantly different network structure (i.e., different patterns of associations between OCD symptoms) relative to the pre-treatment network.

Findings showed that network structure and connectivity in OCD may be more informative as a marker of therapeutic change than in discriminating treatment responders from nonresponders using baseline symptoms. After ERP treatment, associations between obsessions and compulsions demonstrated significantly greater global network strength and altered network structure, thus underscoring the potential for network approaches to identify mechanisms of change throughout OCD treatment. Future studies incorporating session-by-session data may clarify when and how these network shifts occur over the course of therapy to help identify treatment targets.

Registry-based studies can inform suicide prevention by identifying mental disorders with the highest risk. Previous studies focused on severe disorders and suicide, with limited data on non-lethal self-harm or population impact. We quantified individual- and population-level associations of 32 mental disorders with non-lethal intentional self-harm (NLISH) and suicide.

Registry-based cohort study representative for all residents of Catalonia (Spain) aged ≥10 years (2014–2019; n = 645,571). Cause-specific Cox models estimated individual (hazard ratios [HRs]) and population-level (population attributable fractions [PAFs]) associations with NLISH and suicide, stratified by sex and adjusted for age, socioeconomic status, and nationality.

Individual-level associations with NLISH were strongest for borderline personality disorder (BPD; females HR = 26.9 [95%CI 24.9–29.0]; males HR = 18.9 [95%CI 16.7–21.4]). Associations with suicide were strongest for BPD in females (HR = 40.9 [95%CI 28.5–58.8]) and obsessive-compulsive disorder in males (HR = 17.4 [95%CI 5.3–56.5]). Associations with suicide were stronger among females, and those aged 10–44 across mood, substance use, dissociative, borderline personality, and psychotic disorders. Substantial proportions of outcomes were associated with common disorders: depressive episodes (PAFs 29.8–49.8%), substance use disorders (PAFs 25.1–48.7%), mixed anxiety-depressive disorders (PAFs 19.7–53.2%), and adjustment disorders (PAFs 10.6–44.6%).

Depressive, anxiety, adjustment, and substance use disorders are associated with large shares of self-harm and suicide, whereas BPD confers particularly high individual risk. Our findings support multilevel prevention strategies, especially among young people, including improved risk assessment, collaborative care, and timely access to specialized interventions.

Bipolar disorder is a recurrent and disabling condition, with a critical clinical need to prevent transitions from euthymia or depression (normal or low activation states) to mania (a high activation state). This study investigates how disruptions in sleep–wake and circadian rhythms may trigger these high activation states, to inform more effective relapse prevention strategies.

We developed a computational agent-based model integrating empirical evidence, clinical expertise, and lived experience to simulate how 24-hour sleep–wake behaviors (SWBs) influence manic episodes. Individual characteristics were drawn from the Brain and Mind Youth Cohort (N = 2,330), and multiple scenarios were simulated to assess how SWB dynamics affect the emergence and course of mania.

In the absence of all irregularities, no individuals experienced a manic episode. Removing behavioral feedback loops resulted in a substantial reduction in manic episodes and delayed onset. In contrast, eliminating light–dark entrainment slightly increased the frequency of manic episodes, suggesting that seasonal adaptation plays a stabilizing role. When examining components of SWB separately, removing sleep irregularities alone had only a modest effect on mania rates, whereas reducing activity irregularities led to the largest benefit: a significant drop in mean manic episodes, a delay in onset, and preventing mania in 65% of the simulated agent population.

Our findings highlight the value of computational modeling for uncovering causal dynamics in mental health. These specific findings demonstrate how daily irregularities in sleep–wake behavior may be a necessary condition for mania. Targeting behavioral regularity may offer a powerful pathway for prevention and early intervention.

People with psychotic disorders have high cardiometabolic risk, yet prediction tools are rarely validated outside early-intervention settings. We externally validated and recalibrated the UK Psychosis Metabolic Risk Calculator (PsyMetRiC) in a Dutch cohort of young adults with psychotic disorders in long-term care.

We used data from the PHAMOUS registry. Individuals aged 16–35 years, without metabolic syndrome (MetS) at baseline (prevalence 21.2%), were included. MetS incidence over approximately 6 years (last assessment 1–6 years; cumulative incidence 29.1%) was defined using international criteria. Full (biochemical + clinical) and partial (clinical only) PsyMetRiC models were applied to 10 multiply imputed datasets. Discrimination, calibration, and decision-curve analysis (DCA) were assessed before and after logistic recalibration of intercept and slope.

In external validation, C-statistics were about 0.69 for the full and 0.67 for the partial model. Both systematically underpredicted MetS risk; recalibration yielded calibration intercepts near 0 and slopes near 1, while discrimination was unchanged. DCA suggested that, across risk thresholds of 0.10–0.35, using recalibrated PsyMetRiC could provide higher net benefit than “treat all” or “treat none.”

In this chronic-care cohort, PsyMetRiC showed moderate discrimination and improved calibration after logistic recalibration. The recalibrated models may support more targeted metabolic monitoring and prevention, but interpretation is limited by registry design, variable follow-up times, reliance on multiple imputation, and modest power for subgroup analyses.