IntroductionResearch shows that parents of children with intellectual disability experience more mental health problems than those of children with typical development, which has been related to several factors such as sociodemographic characteristics, parenting attitudes, practices and styles, the presence of behaviors that challenge and the level of supports required by their child. To understand the relationship of these variables is crucial for the development of interventions and supports for this group.MethodsOne hundred and twenty-three Chilean mothers and fathers of children with intellectual disability between 7 and 17 years of age participated in our study.ResultsParticipants reported higher levels of depression, anxiety, perceived stress, and parental stress compared to the general population. Cluster analysis identified two groups based on mental health symptoms (low vs. high), revealing differences in their child's behavior, parenting styles, parental age, and socioeconomic status. Parents in the low symptoms group are older, from higher socioeconomic status and reported better parental attitudes, less behaviors that challenge and more prosocial behaviors in their child, that the participants in the high symptoms group. A conditional classification tree analysis highlighted limit setting and parental involvement as key predictors of these clusters.ConclusionOur findings emphasize the need to further explore the relationship between mental health and contextual factors in parents of children with intellectual disability to identify risk of mental health problems in the parents and develop effective interventions for this group.
IntroductionRecent prevalence studies estimate that 5-10% of individuals with intellectual and developmental disabilities (IDD) experience depression. Despite these trends, few evidence-based mental health interventions have been designed specifically for this population. To address this gap, Jahoda et al. (2017) developed Beat It, a manualized therapeutic approach adapted from behavioral activation for individuals with IDD.MethodsThis single-arm feasibility study was designed to be conducted at two outpatient clinics; however, one site withdrew before recruitment commences, resulting in data collection at a single location. Pre- and post-intervention data were collected using the Glasgow Depression Scale for People with Learning Disabilities (GDS-LD).ResultsParticipants who completed the Glasgow Depression Scale at both pre- and post-intervention (n = 6) reported significantly improved depressive symptoms following the intervention. Glasgow Depression Scale scores decreased from pre-intervention (M = 23.50, SD = 3.70) to post-intervention (M = 14.83, SD = 6.96). A paired samples t-test indicated that this reduction was statistically significant, t(5) = 4.73, p = .005, reflecting a large within-participant effect (Cohen's dz = 1.93). Qualitative findings revealed that participants valued the structured, manualized format and the accompanying materials, which were perceived as helpful and accessible.ConclusionFeasibility outcomes suggest that with minimal modifications to the study protocols and intervention materials to enhance acceptability, a larger-scale study can be conducted to further evaluate the effectiveness of Beat It in U.S. outpatient settings.
BACKGROUND:Narcissistic Personality Disorder (NPD) involves disturbances in self-regulation, interpersonal functioning, and personality organization. Although traditionally characterized by grandiosity, contemporary models suggest that grandiose self-states coexist with vulnerable features such as shame, emotional dysregulation, and hypersensitivity to rejection. Recent evidence indicates that metacognitive impairments may underlie both grandiose and vulnerable narcissistic presentations; however, no study has examined how metacognition interacts with personality traits and interpersonal difficulties within an integrated system. METHODS:A cross-sectional network analysis was conducted on 287 patients with NPD. Measures included the Metacognition Assessment Interview, the Pathological Narcissism Inventory, the Personality Inventory for DSM-5 (PID-5), the Inventory of Interpersonal Problems, and SCL-90-R Depression. A Gaussian graphical model with LASSO regularization was estimated, and expected influence was used as the primary index of node centrality. Network accuracy and stability were assessed through bootstrapping procedures. RESULTS:Narcissistic vulnerability was the most central node, followed by interpersonal sensitivity and metacognitive integration. Narcissistic vulnerability showed strong associations with PID-5 Negative Affectivity and Detachment, whereas narcissistic grandiosity was related to PID-5 Antagonism. Metacognitive integration occupied a central position, linking maladaptive traits and interpersonal distress. Network stability indices indicated good reliability. CONCLUSIONS:Findings suggest that narcissistic vulnerability and interpersonal hypersensitivity are central aspects of dysfunction in NPD, whereas metacognitive integration appears closely associated with the organization of psychological processes within the network. Although causal inferences cannot be drawn, the results are consistent with theoretical models underlying Metacognitive Interpersonal Therapy (MIT), supporting the potential relevance of targeting integrative metacognitive capacities in NPD treatment.
Background 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.Methods 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.Results 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.Conclusions Mental disorders were intergenerationally associated between mothers and their adult daughters. These findings emphasize the importance of family-based interventions for mental health.
BACKGROUND: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. METHODS: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. RESULTS: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. CONCLUSIONS: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.
Neurology and psychiatry have operated as separate disciplines for over a century, yet this division reflects historical and institutional developments rather than the underlying biology of the brain. Contemporary neuroscience shows that brain and mental health disorders share genetic susceptibilities, inflammatory and metabolic pathways, environmental and social risk factors, and clinical features that cross diagnostic boundaries. Cognitive, emotional, sensory, and motor symptoms regularly appear across both neurological and psychiatric populations, and conditions such as seizures, psychosis, mood disorders, cognitive disorders, and sleep disorders are common to both. A brain health framework addresses this reality by treating the brain as a single biological organ whose function emerges from the interplay between genome and exposome - including stress, trauma, social context, existential meaning, pollution, and physical health - and which underlies perception, behaviour, cognition, emotion, resilience, and vulnerability. Translating this perspective into practice requires coordinated action across domains. Clinically, collaborative models such as joint neurology-psychiatry consultations and shared outpatient pathways can be implemented within existing resources to improve diagnostic clarity and continuity of care. In training, a more harmonised curriculum with shared foundations in neurobiology, joint seminars, and cross-rotations would equip clinicians with a common language while preserving specialist depth, and support the emerging fields of preventive neurology and preventive psychiatry. In research, organising studies around shared mechanisms and symptom dimensions, and launching joint funding calls, would enhance translational relevance and reduce duplication. To realise this vision, sustained leadership from European professional bodies is essential to establish collaboration as a shared professional standard.
BACKGROUND: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. METHODS: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. RESULTS: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). CONCLUSIONS: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.
Background 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).Methods 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).Results 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.Conclusions This study demonstrates a positive association between CR and symptoms severity, cognitive performance, and functional outcomes in SZ.
Background Multiple observational studies have reported associations between age at menarche (AAM) and mental health problems, yet their shared genetic architecture remains poorly characterized.Methods 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.Results 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).Conclusions 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.
BACKGROUND: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. METHODS: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. RESULTS: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. CONCLUSIONS:These findings suggest that disrupted glutamatergic modulation of large-scale brain networks may underlie core clinical features in schizophrenia spectrum disorders.
BACKGROUND: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. METHODS: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). RESULTS: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. CONCLUSIONS: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.
IntroductionIndividuals with mild intellectual disabilities or borderline intellectual functioning (MID-BIF) are at heightened risk of exposure to traumatic events, including sexual abuse. While such experiences are known to increase the risk of PTSD in the general population, it remains unclear whether sexual abuse, gender, or age at first trauma are similarly predictive of PTSD symptom severity in individuals with MID-BIF. This study investigates whether sexual abuse, gender, and age at first A-criterion trauma are associated with the frequency of PTSD symptoms in adults with MID-BIF.MethodFifty-eight adults with MID-BIF were categorized into sexual abuse (SA) and non-sexual trauma (non-SA) groups based on the Diagnostic Interview Trauma and Stressors - Intellectual Disability. PTSD symptoms were assessed using the Trauma Screener-ID.ResultsNo significant differences in PTSD symptom frequency were found between the SA and non-SA groups. Furthermore, sexual abuse, gender, and age at first trauma did not significantly predict PTSD symptom frequency.ConclusionsAlthough a high prevalence of sexual abuse was observed among individuals with MID-BIF, it was not associated with increased PTSD symptom frequency. These findings suggest that factors such as poly-victimization may play a more central role in PTSD symptomatology in this population, highlighting the importance of comprehensive, trauma-sensitive assessment and care.
Background 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.Methods 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.Results 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.Conclusions 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.
Background 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).Methods 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.Results 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.Conclusions 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.
Background 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.Methods 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.Results 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.Conclusion 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.
BACKGROUND: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. METHODS: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. RESULTS: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. CONCLUSIONS: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.
BACKGROUND: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. METHODS: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. RESULTS: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%). CONCLUSIONS: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.
BACKGROUND: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. METHODS: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. RESULTS: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." CONCLUSIONS: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.
