BACKGROUND:Bipolar disorder (BD) is associated with clinical and biological markers of premature aging. In this largest study of brain age in BD to date, with 2919 participants, we compared brain-predicted age difference (brain-PAD) in individuals with BD and healthy comparison (HC) participants. Brain-PAD is a machine learning-estimated metric that quantifies the difference between an individual's predicted brain age and their chronological age, a potential clinical bio-signature of premature brain aging. Within individuals with BD, we also examined how medication and clinical characteristics were related to brain-PAD. METHODS:Age was predicted from 77 MRI measures of regional subcortical and lateral ventricle volumes, cortical thickness, and surface area for 1342 BD and 1577 HC adult participants, aged 18-75 yrs. old (μ = 37.2; SD = 12.3), from the curated ENIGMA Bipolar Disorder working group (ENIGMA-BD) and leveraging an ENIGMA machine learning model previously trained and validated using independent samples. Chronological age was subtracted from predicted age to produce an individual-level estimate known as brain-PAD. Linear mixed models (adjusting for sex and age as fixed effects and site as a random effect) were used to examine group differences and clinical associations. RESULTS:BD was associated with higher brain-PAD, compared to HC, primarily among older patients, as demonstrated by a significant age by diagnosis interaction (+0.05 [SE: 0.02] years). Individuals with BD on antiepileptic (AED) medications only (+3.20 [SE: 0.78] years) or on both AED and second-generation antipsychotics (SGA) (+3.74 [SE: 0.89] years) demonstrated greater brain-PAD compared to individuals who were not on any of the examined medications. Those taking lithium, whether alone or with AED and SGA independently, showed no difference in brain-PAD compared to individuals not taking any of the examined medications. However, individuals who were taking lithium showed lower brain-PAD compared to those on AED (-4.48 [SE: 0.84] years) or AED and SGA (-5.01 [SE:0.92] years). Individuals with a BD I subtype diagnosis had a higher brain-PAD (+1.50 [SE:0.55] years) compared to those with BDII or subtypes that are not otherwise specified (NOS). CONCLUSIONS:Results from this study suggest compounding effects of BD diagnosis and older age on brain-PAD, an ML-derived summary metric of structural alterations. Within BD, brain-PAD was differentially related to medication use, consistent with prior findings from ENIGMA-BD. Notably, AED use was generally related to more advanced brain age. Lithium use, alone or in combination with other medications, was not associated with advanced brain age, suggesting a possible neuroprotective effect of lithium. Brain-PAD as an ML-derived summary metric of structural alterations of the brain may provide clinical utility in assessing long-term holistic brain health to monitor the effectiveness of lifestyle modifications or treatments over time. LIMITATIONS:The cross-sectional nature of the study design and the limited granularity of the clinical data limit interpretation. Longitudinal studies with detailed chronicity data, medications and clinical measures overtime will improve brain-PAD modeling in BD.

Importance Childhood trauma is associated with increased risk for bipolar disorder, but the biological mechanisms of this association remain incompletely defined. Gray matter differences observed after trauma exposure overlap with those reported in bipolar disorder, suggesting that the association of childhood trauma with bipolar disorder might be mediated through brain morphology. Objective To determine whether cortical thickness, cortical surface, or subcortical volume mediate the association of childhood trauma with bipolar disorder. Design, Setting, and Participants This case-control study conducted a cross-sectional analysis of individuals with bipolar disorder and healthy controls from 19 international cohorts (Enhancing NeuroImaging Genetics Through Meta-Analyses [ENIGMA] Bipolar Disorder Working Group) from January 2010 to December 2022. Data were analyzed from January 2025 to January 2026. Exposures The primary exposure was the severity of total childhood trauma assessed with the Childhood Trauma Questionnaire, with secondary analyses of 5 subscales (emotional neglect and abuse, physical neglect and abuse, and sexual abuse). Main Outcomes and Measures The primary outcome was bipolar disorder diagnosis (case vs control). The primary measure was the mediation effects of childhood trauma on diagnosis via gray matter (75 bilateral-averaged cortical thickness, surface, and subcortical volume measures). The mediation pathway from severity of childhood trauma to bipolar disorder through brain morphology was specified a priori. High-dimensional mediation analysis, with leave-one-site-out cross-validation and permutation testing for significance (false discovery rate [FDR]), was conducted. Results The final sample included 2221 healthy controls (mean [SD] age, 35.6 [13.2] years; 1274 female [57%]) and 1031 participants with bipolar disorder (mean [SD] age, 38.6 [13.7] years; 579 female [56%]). Severity of childhood trauma was directly associated with higher likelihood of having a bipolar disorder diagnosis (median coefficient, 0.841; 95% CI, 0.834-0.851; range, 0.776-0.893; FDR P < .001). Less than 1% of the association between childhood trauma and bipolar disorder was mediated by brain morphology. Statistically significant mediators were hippocampal volume (median coefficient, 0.004; 95% CI, 0.002-0.005; range, 0-0.008; FDR P < .001), medial orbitofrontal gray matter thickness (median coefficient, 0.002; 95% CI, 0.002-0.003; range, 0-0.004; FDR P < .001), and superior frontal gyrus gray matter thickness (median coefficient, 0.002; 95% CI, 0.002-0.003; range, 0-0.005; FDR P < .001). Conclusions and Relevance This study found that severity of childhood trauma exposure was associated with bipolar disorder diagnosis in part through a smaller hippocampus, thinner cortex in the medial orbitofrontal gyrus, and thinner cortex in the superior frontal gyrus. The identification of this mechanistic pathway improves understanding of the disorder, could help to identify those at risk, and enable the development of new interventions.

Voxel-based meta-analyses—also known as coordinate-based meta-analyses (CBMAs)—are powerful tools for synthesizing evidence from neuroimaging studies in human neuroscience, including investigations of psychological functions and differences in brain disorders. To achieve their full potential in accurately assessing the evidence, CBMAs should adhere to established best-practicmpe guidelines, such as the “Ten Simple Rules” published in 2018. Yet, even when studies report following these recommendations, the degree to which individual items are applicable or fully addressed is often unclear. To better support the evaluation of methodological rigor—which the 10 rules already promote but are not always consistently applied—, the developers of the most used CBMA methods followed a Delphi-style iterative process to create a reporting checklist focused on the methodological quality of CBMAs (Qual-CBMA). Qual-CBMA comprises criteria (e.g., preregistration, systematic search, homogeneous study characteristics, etc.) that authors should verify and comment on explicitly in the checklist (and, when unmet, also in the manuscript). The checklist encourages rigor and transparency by prompting authors to identify potential methodological limitations and to discuss their relevance—or irrelevance—in the context of their specific study. The checklist is designed as an aid to make reporting clearer and more transparent, not as a tool for evaluating whether authors have done something incorrectly. In this context, a high-quality CBMA is not defined by meeting every criterion, but by clearly commenting on the criteria—and explaining when unmet criteria are appropriately not applicable given the study’s objectives. We encourage authors to submit the Qual-CBMA checklist, together with their accompanying comments, when publishing new CBMAs, thereby reinforcing transparency and rigorous methodology and advancing understanding in cognitive neuroscience and clinical conditions.

Predicting symptom change is a key goal of machine learning in mental health. However, models can seem more accurate than they are due to regression to the mean, a common statistical effect often overlooked in machine learning. Here we outline its implications and a simple framework for separating genuine prediction from statistical artefact.

Working memory (WM) impairments occur across psychosis stages, but whether their neural correlates are shared or stage-specific is unknown. This meta-analysis examined WM-related brain activity across psychosis stages: familial and clinical high-risk for psychosis (at-risk), first-episode psychosis (early psychosis), and chronic schizophrenia (chronic psychosis). PubMed, Ovid, and Web of Science were searched up to February 2026 for functional magnetic resonance imaging (fMRI) studies comparing individuals in each stage and controls during WM. Statistical maps were requested from study authors. Seed-based d-mapping assessed WM-related fMRI correlates at each stage. Significance was set at family-wise error-corrected p < .05. Sixty-three studies were included, resulting in 68 datasets: 14 (1 map) in the at-risk stage, 9 (1 map) in the early psychosis and 45 (12 maps) in the chronic psychosis stage. In the at-risk stage, hyperactivations were found in the superior temporal gyri, right insula and putamen. At the early psychosis stage, lower activation relative to controls was identified in temporo-parietal regions, prefrontal cortex, striatum and thalamus. In chronic psychosis, higher activation relative to controls was observed in the medial prefrontal cortex, anterior cingulate, superior temporal gyri, right insula, posterior cingulate, and superior frontal gyrus, whereas lower activation was found in the cerebellum, bilateral precuneus and supplementary motor area. In combined early and chronic psychosis, anterior cingulate activation was positively associated with antipsychotic dose and illness duration. Overlaps in temporo-parietal hypoactivation in the early and chronic psychosis stages were found. These findings indicate that disruptions in WM circuitry may represent a potential biomarker of psychosis staging.

BACKGROUND:Obesity, which is common in bipolar disorder (BD), is associated with smaller hippocampal volumes. We do not know the role of weight/weight gain in relation to longitudinal hippocampal changes among individuals with BD. METHODS:In collaboration with the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis)-BD Working Group, we obtained T1-weighted magnetic resonance imaging and clinical data from 233 participants with BD and 701 healthy control participants (HCs) scanned twice, 2.84 ± 1.63 years apart on average. We estimated subcortical volumes using FreeSurfer longitudinal image processing stream and used linear mixed models to assess the bidirectional relationship between baseline body mass index (BMI) or BMI change and hippocampal volume or volume change. While the hippocampus was our a priori region of interest, we repeated these analyses in other subcortical regions. RESULTS:Baseline BMI predicted future hippocampal atrophy, but baseline brain structure did not predict future weight changes. BMI increased significantly over time (F1,1085 = 15.98, p < .001). Individuals with lower baseline BMI experienced greater weight gain (F1,922 = 105.12, p < .001). Greater weight gain was associated with greater hippocampal atrophy over time (F1,899 = 16.33, p = .001), more so in participants with BD than HCs (F1,898 = 6.91, p = .009). Consequently, lower baseline BMI predicted greater future hippocampal volume loss (F1,904 = 14.77, p < .001). These associations were not observed in other subcortical regions. CONCLUSIONS:Our findings suggest that weight gain is a modifiable risk factor for hippocampal atrophy, especially in individuals with lower BMI and those with BD. Prevention of weight gain in general, but especially in people with BD, could provide neuroprotective benefits.

People with severe mental illnesses (SMIs), including schizophrenia spectrum disorders (SSD), major depressive disorder (MDD), and bipolar disorder (BD), experience elevated rates of physical comorbidity and premature mortality. Dementia and ischemic stroke contribute substantially to this burden, yet age-specific patterns of these associations remain poorly characterized. To bridge this gap, we conducted a population-based cross-sectional study using the PADRIS-PRESTO cohort, integrating primary and specialized care records from the Catalan public health system (Spain). Adults with a lifetime SMI diagnosis were compared with controls without any psychiatric condition. Weighted age-stratified prevalence was calculated and logistic regression models estimated age-specific odds ratios (ORs) with 95% confidence intervals (CIs), adjusting for socioeconomic status and medical comorbidity burden. Among 694,086 participants, 176,870 had an SMI diagnosis. Dementia prevalence was 3.49% (range, 0.33% at ages 20-29 to 39.51% at 90-99 years) in SMIs and 0.36% (0.02%-17.7%) in controls. Ischemic stroke prevalence was 5.3% (0.22%-16.4%) in SMIs and 2.75% (0.08%-14.67%) in controls. The ORs between SMI and dementia were significant across all age groups, with the strongest association observed at younger ages (OR = 23.21, 95% CIs = 13.74-39.19 at 30-39 years). Elevated ORs for stroke persisted up to 70-79 years, peaking at 40-49 years (OR = 2.99, 95% CIs = 2.63-3.41). Patterns were consistent across SMI subtypes. These findings highlight the need for earlier and more systematic neurological and cardiometabolic screening and management within psychiatric care and support policies that recognize SMIs as risk factors for vascular diseases and dementia.

Background:Large-scale T1-weighted MRI studies have established grey-matter abnormalities in bipolar disorder (BD), with our group contributing to consensus findings. However, structural connectivity, particularly within emotion- and reward-related circuits, remains poorly understood. Diffusion-weighted MRI (dMRI) enables investigation of white-matter pathways, yet prior work is constrained by small samples, methodological heterogeneity, and unclear medication effects. We conducted the largest dMRI network analysis in BD, relating symptom burden and polypharmacy to tractography-derived connectivity and graph-theoretic metrics. Methods:Cross-sectional structural and diffusion MRI scans from 449 individuals with BD (35.7±12.6 years) and 510 controls (33.3±12.6 years), aged 18-65, were analyzed across 16 ENIGMA-BD sites. Standardized segmentation/parcellation and constrained spherical deconvolution tractography generated individual structural connectivity matrices. Graph-theoretic metrics of global and subnetwork organization were related to symptom severity and medications. Results:BD showed widespread network alterations (lower density and efficiency, longer path length, and higher betweenness centrality), altered microstructural organization in a limbic-basal ganglia circuit, and abnormal streamline counts in a default-mode/salience/fronto-limbic-basal ganglia network. Longer illness duration, later onset, and psychosis history were associated with greater abnormalities in network architecture, whereas more manic episodes were associated with greater fronto-limbic connectivity. Antidepressant (particularly SSRI), anticonvulsant, and antipsychotic use related to poorer global and fronto-limbic connectivity; no clear lithium effects emerged. Conclusions:As the largest structural connectivity study in BD, we reveal widespread disruption in reward and emotion-regulation networks influenced by illness severity and medication use. Results show that multisite harmonization is feasible and highlight ENIGMA-BD as a scalable framework for identifying reproducible neurobiological markers.

Abstract Elucidating the neurobiological basis of neurodevelopmental and psychiatric conditions (NDPCs) remains challenging because brain alterations vary within diagnoses and overlap across them. Whether diverse alterations follow a systematic organization that may reflect shared vulnerabilities remains unknown. Here, we assembled 10,135 individuals with schizophrenia, autism, bipolar, obsessive-compulsive, generalized anxiety, and major depressive disorders, and 11,998 reference participants across six continents through the ENIGMA consortium. Using normative modeling, we quantified individual deviations in cortical thickness, surface area, and subcortical volumes relative to lifespan reference trajectories (5 to 80 years). We show that structural deviations converged along cortical axes reflecting connectome organization, maturation, and cytoarchitectonic diversity. These axes mirrored typical population variation, but their expression differed across diagnoses and partly scaled with symptom severity. Even rare and highly individualized extreme deviations followed this organization, concentrating in densely connected regions. Finally, brain structural deviations overlapped substantially across diagnoses, while differences between them increased toward the association cortex. Together, we provide large-scale evidence that structural deviations across NDPCs are systematically constrained by the brain’s intrinsic architecture. This shared organization provides a framework for reconciling individual variability with transdiagnostic similarities and motivates an integrative, systems-level understanding of mental health.

Atypical peripheral blood cytokine concentrations have been shown in autism, but no clear pattern has been observed. This systematic review and meta-analysis summarised current state of findings, expanded the range of cytokines, accounted for study risk of bias, and examined relations between cytokines and autism traits. Literature comparing peripheral blood cytokine in autistic and non-autistic people was systematically searched in Ovid® Embase, MEDLINE and APA PsycINFO, Web of Science™ and Scopus, resulting in 98 studies and 54 cytokines (4236 autistic, 3333 non-autistic controls; age 2 to 65 years) in the meta-analysis. Study risk of bias was assessed using adapted Newcastle-Ottawa Scale. Compared to controls, autistic people had elevated levels of IL1-beta (Hedges' g = 0.620, 95%CI[0.32, 0.92]), IL4 (g = 0.245, 95%CI [0.07, 0.42]), IL6 (g = 0.365, 95%CI [0.011, 0.62]), IL8 (g = 0.384, 95%CI [0.15, 0.62]), IFN-gamma (g = 0.404, 95%CI [0.09, 0.72]), TNF-alpha (g = 0.31, 95%CI [0.11, 0.51]), CXCL1/GRO-α (g = 0.364, 95%CI [0.058, 0.670]) and MIF (g = 0.560, 95%CI [0.14, 0.98]). Over a third of studies were classified as having a high risk of bias; their removal revealed higher IL7 and IL1RA in autism relative to controls. Narrative synthesis produced no strong evidence for an association between cytokine and autism traits among autistic individuals. Altogether, our findings support a predominance of pro-inflammatory cytokines, while also indicating potential modulatory contributions from inhibitory cytokines, which reflect group-level differences between autistic and non-autistic individuals, but not variations of autism traits within the autistic population. However, higher-quality studies with low risk of bias are needed before firm conclusions can be drawn.

Importance:Childhood trauma is associated with increased risk for bipolar disorder, but the biological mechanisms of this association remain incompletely defined. Gray matter differences observed after trauma exposure overlap with those reported in bipolar disorder, suggesting that the association of childhood trauma with bipolar disorder might be mediated through brain morphology. Objective:To determine whether cortical thickness, cortical surface, or subcortical volume mediate the association of childhood trauma with bipolar disorder. Design, Setting, and Participants:This case-control study conducted a cross-sectional analysis of individuals with bipolar disorder and healthy controls from 19 international cohorts (Enhancing NeuroImaging Genetics Through Meta-Analyses [ENIGMA] Bipolar Disorder Working Group) from January 2010 to December 2022. Data were analyzed from January 2025 to January 2026. Exposures:The primary exposure was the severity of total childhood trauma assessed with the Childhood Trauma Questionnaire, with secondary analyses of 5 subscales (emotional neglect and abuse, physical neglect and abuse, and sexual abuse). Main Outcomes and Measures:The primary outcome was bipolar disorder diagnosis (case vs control). The primary measure was the mediation effects of childhood trauma on diagnosis via gray matter (75 bilateral-averaged cortical thickness, surface, and subcortical volume measures). The mediation pathway from severity of childhood trauma to bipolar disorder through brain morphology was specified a priori. High-dimensional mediation analysis, with leave-one-site-out cross-validation and permutation testing for significance (false discovery rate [FDR]), was conducted. Results:The final sample included 2221 healthy controls (mean [SD] age, 35.6 [13.2] years; 1274 female [57%]) and 1031 participants with bipolar disorder (mean [SD] age, 38.6 [13.7] years; 579 female [56%]). Severity of childhood trauma was directly associated with higher likelihood of having a bipolar disorder diagnosis (median coefficient, 0.841; 95% CI, 0.834-0.851; range, 0.776-0.893; FDR P < .001). Less than 1% of the association between childhood trauma and bipolar disorder was mediated by brain morphology. Statistically significant mediators were hippocampal volume (median coefficient, 0.004; 95% CI, 0.002-0.005; range, 0-0.008; FDR P < .001), medial orbitofrontal gray matter thickness (median coefficient, 0.002; 95% CI, 0.002-0.003; range, 0-0.004; FDR P < .001), and superior frontal gyrus gray matter thickness (median coefficient, 0.002; 95% CI, 0.002-0.003; range, 0-0.005; FDR P < .001). Conclusions and Relevance:This study found that severity of childhood trauma exposure was associated with bipolar disorder diagnosis in part through a smaller hippocampus, thinner cortex in the medial orbitofrontal gyrus, and thinner cortex in the superior frontal gyrus. The identification of this mechanistic pathway improves understanding of the disorder, could help to identify those at risk, and enable the development of new interventions.

BACKGROUND:Mixed states in bipolar disorder (BD) remain a diagnostic and nosological challenge, with current classifications differing in their ability to capture the overlap between manic and depressive symptomatology. METHODS:We used baseline data from 1384 patients in a manic or mixed episode of BD according to DSM-IV criteria across five randomized clinical trials. We modelled mood symptom networks using the Young Mania Rating Scale (YMRS) and Montgomery-Åsberg Depression Rating Scale (MADRS). We identified mood symptom clusters and compared symptom networks between DSM-IV manic and mixed episodes, and between DSM-IV mixed episodes and DSM-5 manic episodes with mixed features. RESULTS:The joint YMRS-MADRS mood symptom network revealed strong within-domain coherence (i.e., strong connectivity among manic symptoms and among depressive symptoms). There were positive and negative associations between manic and depressive nodes, indicating that these domains both co-occur and inhibit each other. Depressive symptoms, especially sadness and anhedonia items, showed greater overall connectivity and centrality than manic symptoms. Cluster analyses identified three main domains - manic, depressive, and a mixed activation-dysphoria cluster -plus a smaller insomnia-related cluster. DSM-IV manic and mixed episodes differed significantly in structure and global connectivity, whereas DSM-5 distinctions were subtler and reflected increased depressive burden within manic presentations. CONCLUSIONS:Manic and depressive symptoms formed distinct yet interconnected clusters within a shared affective network. An activation-dysphoria cluster linked both domains, capturing core elements of mixed presentations. DSM-IV groups showed clearly differentiated network structures, whereas DSM-5 mixed features reflected variations within the manic spectrum rather than a distinct configuration.

Psychiatric and neurological disorders often co-occur, complicating their assessment, management, and outcomes. No umbrella review (UR) has summarized the meta-analytic evidence on the co-occurrence of psychiatric and neurological disorders and assessed its credibility. Meta-analytic systematic reviews of observational studies documenting the prevalence and outcomes associated with the co-occurrence of neurological and psychiatric disorders, indexed from inception through August 25, 2025, and meeting established diagnostic criteria, were included. Meta-analytic prevalence and association estimates were recalculated and graded based on quantitative criteria. The AMSTAR-2 assessed the quality of the meta-analyses, while several subgroup analyses and meta-regressions aimed to explain the heterogeneity. We included 81 meta-analyses (N=42,464,788, k=2,273), yielding 166 meta-analytic estimates. Based on pre-existing meta-analytic evidence, among 90 prevalence estimates, 45 (50%) met moderate/strong credibility criteria. Strong credibility emerged in bipolar disorder for comorbid migraine (34.8%,95%CI=25.54-44.69) and chronic pain (28.9%,95%CI=16.4-43.4), in epilepsy for ADHD (22.3%,95%CI=20.3-24.4), depression (23.1%,95%CI=20.6-28.3), autism (11.1%,95%CI=9.8-12.1), and PTSD (7.7%,95%CI=5.2-11.2), in myasthenia gravis for anxiety (33%,95%CI=25.0-42.0 children), in multiple sclerosis for anxiety (36%,95%CI=30.0-42.0) and depression (27.01%,95%CI=22.80-31.68), in neuropathic chronic pain for depression (37.5%,95%CI=28.7-47.1) and anxiety (33.8%,95%CI=28.5-39.6), in neuromyelitis optica for depression (40%,95%CI=32.0-49.0), in stroke for anxiety (29.3%,95%CI=24.8-33.8) and depression (31%,95%CI=24.0-38.0 children), in dementia for depression (25.01%,95%CI=22.1-28.4). Several additional disorders were comorbid in >5%with lower credibility. No outcomes reached strong credibility criteria. The present study provides an atlas of neurological and psychiatric multimorbidity across varying levels of credibility, reinforcing the need for an integrated, multidisciplinary approach to patient care and for more research on actionable risk/protective factors and outcomes.

Background This umbrella review evaluates adverse childhood experiences (ACEs) as etiological risk factors for non-mental medical diseases. Methods We systematically searched PubMed, EMBASE and WoS up to 30 April 2025, following PRISMA, PRIOR and MOOSE guidelines. Eligible studies were meta-analyses or systematic reviews investigating associations between ACEs and non-mental medical disease, with control groups and adequate data for extraction. Studies on congenital disorders or without relevant data were excluded. Methodological quality was assessed using AMSTAR. The protocol was registered in PROSPERO (CRD42022384104). Meta-analytic data were synthesised with random-effects models to calculate odds ratios (OR) with confidence intervals and p-values. Heterogeneity was assessed with I2, small-study effects with the Egger test, and 95% prediction intervals were calculated. Evidence credibility was graded according to Ioannidis’ criteria. Findings We included 36 reviews/meta-analyses covering 250 non-duplicated studies with 6,064,006 participants (412,760 cases and 5,651,246 controls). There was highly suggestive evidence (Class II) linking any ACE to any non-mental medical disease (OR = 1.57; 95% CI: 1.49, 1.66). Strong associations were found between any non-mental medical disease and abuse of any type (OR = 1.61), physical abuse (OR = 1.59), sexual abuse (OR = 1.58) and bullying (OR = 2.04). Regarding specific diseases, highly suggestive associations were identified for headache (OR = 1.91), irritable bowel syndrome (OR = 1.79), diabetes (OR = 1.67), and cardiovascular disease (OR = 1.46). Convincing evidence emerged for a modest association between ACEs and cardiovascular disease (OR = 1.19) and endocrine/metabolic disorders (OR = 1.62) in prospective studies. Interpretation ACEs are significant risk factors for adult non-mental medical diseases, underscoring the importance of early intervention and prevention for vulnerable groups. Early stressors have lasting physical health impacts. Limitations include reliance on self-reported trauma and heterogeneous study designs; prospective data showed lower bias, reinforcing the need for rigorous research. Future studies should explore genetic, environmental, and resilience factors that may moderate medical risks among individuals exposed to ACEs. Funding No funding was received.

INTRODUCTIONS:Lithium remains the first-line maintenance treatment for bipolar disorder (BD), yet its safety in older-age BD (OABD) is uncertain due to limited large-scale data. OABD patients often experience more severe physical comorbidities compared to younger BD patients, but systematic evidence regarding lithium's association with these conditions is lacking. This study examined the association between lithium use and physical comorbidities across age groups using the international Global Aging and Geriatric Experiments in Bipolar Disorder (GAGE-BD) dataset. METHODS:A cross-sectional analysis was conducted using combined Wave 1 and 2 data from GAGE-BD project, encompassing 37 studies from 20 sites worldwide. Participants aged ≥ 18 years with BD were classified according to current lithium use. Physical comorbidities were harmonised into eight organ-system domains. Sociodemographic and clinical variables were compared between individuals receiving lithium treatment and those not prescribed lithium. Generalised linear mixed models adjusted for age, site and lithium × age interaction were applied to evaluate associations with physical comorbidities. RESULTS:Of the 2873 total participants included in the study, 1069 were currently receiving lithium treatment and 1804 were not prescribed lithium. Participants treated with lithium showed a lower overall prevalence of physical comorbidities compared with those not receiving lithium. Regression analyses revealed significant age-by-lithium interactions for cardiovascular (χ2 = 9.27, p = 0.002), respiratory (χ2 = 7.56, p = 0.006), genitourinary (χ2 = 8.66, p = 0.003) and endocrine (χ2 = 16.96, p < 0.001) comorbidities. Model-estimated curve crossings occurred at approximately 43 years (cardiovascular), 32 years (respiratory), 51 years (genitourinary) and 59 years (endocrine), above which predicted prevalence was lower among participants treated with lithium, whereas no differences were observed for gastrointestinal, hepatic, renal or musculoskeletal systems. CONCLUSIONS:Lithium use in OABD was associated with a lower burden of specific physical comorbidities, particularly cardiovascular, respiratory and endocrine conditions. Although prescription bias cannot be excluded, the findings challenge the perception that lithium exacerbates physical health risks in older adults. Instead, they support lithium's continued use-with appropriate monitoring-as a safe and effective treatment option for OABD, and highlight the need for prospective studies to further clarify the relationship between lithium exposure and physical health outcomes.

BACKGROUND:Bipolar disorder (BD) is associated with impairments in facial emotion recognition (FER), affecting social functioning and quality of life. Understanding FER deficits in BD is crucial for tailoring interventions and improving treatment outcomes. This systematic review and meta-analysis aims to evaluate FER differences among individuals with BD, unaffected first-degree relatives (FDRs), and healthy controls (HCs), exploring predictors related to patient and study characteristics. METHODS:We systematically searched PubMed/MEDLINE, Scopus, EMBASE, and PsycINFO databases from inception to March 28, 2024. Random-effects meta-analyses were conducted to explore differences in accuracy and reaction time during FER identification and discrimination tasks. RESULTS:A total of 100 studies were included, comprising 4920 individuals with BD (females = 56%, mean age = 34.1 ± 9.1), 676 FDRs (females = 55%, mean age = 36.1 ± 12), and 4909 HCs (females = 53.2%, mean age = 32.5 ± 9.5). Compared to HCs, adults with BD exhibited significantly lower accuracy (SMD = -0.47; 95% CIs = -0.56, -0.38) and higher reaction time (SMD = 0.57; 95%CIs = 0.33, 0.81) during facial emotion identification tasks. During facial emotion discrimination tasks, adults with BD had significantly lower accuracy than HCs (SMD = -0.59; 95%CIs = -0.78, -0.4), but similar speed. No significant differences were observed between BD and FDRs. Meta-regressions identified several predictors of FER performance, including manic symptom severity, stimulus duration, and presence of practice before task. CONCLUSIONS:FER deficits appear to be a core feature of BD and require specialized, systematic assessment. Identifying these deficits may help guide interventions aimed at improving affective cognition and social outcomes in individuals with BD.

Negative symptoms (avolition, anhedonia, asociality, blunted affect, and alogia) are among the most disabling features of schizophrenia spectrum disorders. In the absence of treatment consensus guidelines, this PRISMA-compliant meta-analysis (PROSPERO: CRD42024613967) evaluated efficacy and clinical significance of interventions targeting this dimension. Web of Science/PsycInfo databases were searched from inception to December 2024. Five categories (antipsychotics, other pharmacological agents, brain stimulation, psychosocial, and lifestyle interventions) were analyzed across short/middle/long follow-up times. Categories were divided into 27 subcategories (e.g., 'other pharmacological agents' divided in 14 subcategories including antidepressants, antibiotics, immunomodulators) regardless of follow-up, assessing evidence with GRADE criteria. The primary outcome was the change in negative symptom severity, measured with validated scales (PANSS/SANS/BPRS/CAINS/BNSS) as standardized mean differences (SMD). A clinically meaningful SMD threshold was estimated from the regression between SMD and one-point reductions on the Clinical Global Impression-Severity (CGI-S) scale. This study meta-analyzed 451 trials (n = 42566). The clinically meaningful threshold, obtained from 122 trials reporting CGI-S, was SMD ≥ 0.457. In 214 high-quality studies (n = 19746), 2 category-by-follow-up combinations and 16 subcategories showed significant improvements. Clinically meaningful SMDs for subcategories were antibiotics (0.95; CI: 0.18-1.71; moderate-GRADE), integrated psychosocial interventions (0.93; CI: 0.53-1.33; very-low-GRADE), antidepressants (0.76; CI: 0.33-1.19; moderate-GRADE), physical activity (0.68; CI: 0.39-0.96; very-low-GRADE), transcranial current stimulation (0.52; CI: 0.17-0.86; low-GRADE), and immunomodulators (0.47; CI: 0.26-0.67; high-GRADE), typically as adjuncts to antipsychotics. Heterogeneity was the main limitation. While selected interventions may yield meaningful improvements, more rigorous designs are needed to identify reliable, personalized and scalable treatment options.