D. C. Henderson, P. M. Copeland, D. D. Nguyen, C. P. Borba, C. Cather, A. Eden Evins, O. Freudenreich, L. Baer, and D. C. Goff, “Homocysteine Levels and Glucose Metabolism in Non-Obese, Non-Diabetic Chronic Schizophrenia,” Acta Psychiatrica Scandinavica 113 (2006): 121–125. https://doi.org/10.1111/j.1600-0447.2005.00621.x.
The author's name, A. E. Evins, has been corrected to A. E. Evins.
We apologize for this error.
D. C. Henderson, X. Fan, B. Sharma, P. M. Copeland, C. P. Borba, R. Boxill, O. Freudenreich, C. Cather, A. Eden Evins, and D. C. Goff, “A Double-Blind, Placebo-Controlled Trial of Rosiglitazone for Clozapine-Induced Glucose Metabolism Impairment in Patients with Schizophrenia,” Acta Psychiatrica Scandinavica 119 (2009): 457–465. https://doi.org/10.1111/j.1600-0447.2008.01325.x.
The author's name, A. Eden Evins, has been corrected to A. E. Evins.
We apologize for this error.
Major psychiatric disorder, including schizophrenia, bipolar disorder, and major depressive disorder, has been individually associated with increased risk of stroke. However, few studies have directly compared the stroke risk across these diagnostic groups within a unified cohort framework while accounting for stroke subtypes and relevant confounders.
Using Taiwan's National Health Insurance Research Database, we identified 30,945 patients with schizophrenia, 30,360 with bipolar disorder, 30,447 with major depressive disorder, and 91,752 age-matched controls without psychiatric illness between 2001 and 2009. Participants were followed until death or the end of 2011. Cox regression models were used to estimate the hazard ratio (HR) for ischemic and hemorrhagic stroke, adjusting for potential confounding factors. Sensitivity analyses were conducted by excluding stroke events occurring within the first 1 or 3 years of psychiatric diagnosis.
All three psychiatric groups exhibited significantly higher risks of ischemic and hemorrhagic stroke compared with controls. Stroke risk remained consistently elevated across age and sex strata for all psychiatric groups. Greater cumulative exposure to antidepressants was associated with reduced stroke risk across all three disorders; antipsychotics showed protective associations in schizophrenia and bipolar disorder, non-lithium mood stabilizers were protective only in bipolar disorder, and lithium showed no significant association with stroke risk.
Schizophrenia, bipolar disorder, and major depressive disorder are independently associated with increased stroke risk. These findings highlight the need for integrated vascular risk monitoring in psychiatric care.
Introduction: Major depressive disorder (MDD) and bipolar disorder (BD) are often associated with persistent cognitive deficits that impair psychosocial functioning. While pro-cognitive interventions show promise, trial findings are inconsistent, potentially due to baseline factors influencing treatment response. This systematic review summarizes evidence on pre-treatment characteristics associated with cognitive improvement and offers methodological recommendations.
Methods: A systematic search was conducted in PubMed/MEDLINE, EMBASE, PsycINFO, and Cochrane Library from inception to February 28, 2025. Eligible studies included primary or secondary analyses of randomized controlled trials (RCTs) investigating predictors of cognitive response to pro-cognitive interventions in MDD and/or BD. Two researchers independently conducted study selection and risk of bias assessments. Findings were synthesized qualitatively.
Results: Forty studies (N = 3864) were identified, covering pharmacological treatments (k = 20; N = 2299), psychological therapies (k = 16; N = 1165), brain stimulation (k = 2; N = 168), and physical activity (k = 2; N = 232). Poorer baseline cognitive performance was the most consistent predictor of greater cognitive improvement, though the direction of the effect was not entirely uniform across all studies. Baseline depression severity showed no significant association with cognitive outcomes. Age, education, sex, IQ, diagnosis, and medication status were similarly non-predictive. Risk of bias was high in 77% of studies, mainly due to deviations from specified outcomes, poor randomization processes, and inconsistent handling of missing data. Considerable heterogeneity in interventions, outcome measures, and sample characteristics limited replicability and precluded meta-analysis.
Conclusion: Poorer baseline cognition emerged as the most reliable predictor of greater cognitive improvement across interventions. More rigorous, well-powered studies are needed to replicate these findings and identify robust predictors to guide personalized pro-cognitive treatment approaches in mood disorders.
Maternal attention-deficit/hyperactivity disorder (ADHD) has been associated with various pregnancy outcomes, but the degree to which that association is explained by concomitant mental disorders and smoking during pregnancy remains unclear.
To investigate the association between maternal ADHD and pregnancy outcomes.
Through the Swedish Medical Birth Register, we identified 977,266 women who gave birth to a live singleton between January 1, 2006, and December 1, 2020 (1,617,121 pregnancies). Of these, 1.3% (12,553 women; 17,434 pregnancies) had an ADHD diagnosis prior to pregnancy. The primary outcome was preterm birth (< 37 weeks), with secondary outcomes being postterm birth (> 41 weeks), small for gestational age, large for gestational age, birth weight (≤ 2500, 2501–3500, > 4500 g), acute and planned cesarean section, assisted vaginal delivery, preeclampsia, and gestational diabetes. Generalized linear mixed-effects models adjusted for maternal age, year of childbirth, maternal education, comorbid mental disorders, and smoking during pregnancy.
There were 1089 (6.6%) preterm births among women with ADHD, and 73,423 (4.9%) preterm births among women without an ADHD diagnosis, corresponding to a crude OR of 1.33 (95% CI 1.25, 1.42). This association attenuated to nonsignificance after adjusting for maternal age, year of childbirth, maternal education, and comorbid mental disorders (adjOR = 1.06, 95% CI: 0.99, 1.13). Fully adjusted models revealed that ADHD was associated with an increased risk of having a large for gestational age baby (adjOR = 1.16, 95% CI: 1.06, 1.26) and undergoing a planned caesarean section (adjOR = 1.16, 95% CI: 1.06, 1.26). Sensitivity analyses using a broader ADHD definition suggested associations with preterm birth (adjOR = 1.09, 95% CI: 1.04, 1.15) and acute caesarean section (adjOR = 1.09, 95% CI: 1.04, 1.13).
After adjustments for comorbid mental disorders and smoking during pregnancy, maternal ADHD was not associated with preterm birth. An increased risk of delivering large for gestational age babies and undergoing planned caesarean sections was found in women with ADHD.
Mental disorders are associated with high costs—at the individual and societal level. But families also shoulder large costs in the form of caring for mentally ill family members. Yet, we do not know how common it is to have a family member with a mental disorder and whether this experience falls disproportionately on those who struggle with mental disorders themselves. To fill this gap, this study estimates the cumulative risk of having a family member with a diagnosed mental disorder.
The study uses full population Danish registry data to follow individuals born in 1970 and their family members (parents, siblings, partners, and children) between 1980 and 2018. The study uses nationwide records of outpatient and inpatient hospital treatment for psychiatric disorders and cumulative incidence functions to estimate the cumulative risk of having a family member diagnosed with a mental disorder.
Among this cohort (N = 69,811) the cumulative risk of having a family member with a diagnosed mental disorder by age 49 is 51.4% [95% CI: 51.0%–51.8%]. Individuals diagnosed with a mental disorder themselves by age 49 (N = 9899) were 1.42 times more likely to have a family member with a mental disorder, and this difference grows for the cumulative risk of experiencing multiple family members with diagnosed mental disorders.
This study provides novel nationwide estimates of how common it is to have a family member with a diagnosed mental disorder. The cumulative risk of having a mental disorder severe enough to result in secondary care treatment and diagnosis is estimated at 13.9% among the 1970 birth cohort, but many more (just above 50%) experience mental disorders through their family network. These results highlight how many individuals and families experience (severe) mental disorders at close hand and show the disproportionate potential caregiving burden carried by individuals who are themselves diagnosed with a mental disorder at some point.
Background: Cognitive deficits are common in individuals with bipolar disorder (BD), but there is considerable variability in cognitive functioning. Childhood maltreatment (CM), which is frequently reported in BD, has been linked to poorer cognitive performance, potentially through mechanisms such as inflammation. However, the relationship between CM and global cognition and the mediating role of inflammation in BD warrant further investigation.
Methods: The study sample consisted of 112 BD individuals and 83 healthy controls (HCs). Participants completed the MATRICS Consensus Cognitive Battery (MCCB), the Wisconsin Card Sorting Test, and the Childhood Trauma Questionnaire (CTQ). A composite inflammation index was created using blood levels of C-reactive protein (CRP), interleukin (IL)-6, and tumor necrosis factor (TNF)-α, and was used in primary analyses.
Results: The BD group, compared to HC, showed higher levels of inflammation and CM. Across the entire sample, higher total CM was associated with poorer global cognitive performance, with a medium effect size, even after accounting for diagnostic status. The associations were evident across all CM subscales. Specific cognitive domains affected included speed of processing, working memory, visual learning, and reasoning and problem solving. The association between CM and poorer global cognitive performance was partially mediated by inflammation (indirect effect: β = -0.048, CI = -0.10, -0.009). Within the BD group, higher total CM was similarly associated with worse global cognitive performance. The associations were evident across all CM subscales, except for physical neglect. Significant associations were observed between total CM and MCCB domains of speed of processing, attention and vigilance, working memory, visual learning, reasoning and problem solving, as well as cognitive flexibility. Within the HC group, only emotional neglect and physical neglect were associated with poorer global cognition.
Conclusions: This study provides evidence that total CM and its subscales are associated with poorer global cognitive performance in a sample of individuals with BD and HC, with stronger associations found within the BD group. In addition, inflammation partially mediated the relationship between CM and global cognition. These findings highlight the importance of trauma-informed and cognition-focused interventions aimed at enhancing cognitive outcomes and slowing cognitive decline in individuals with BD who have a history of CM. Furthermore, the results suggest that while inflammation plays a role in the CM-cognition link, its effects are complex and likely interact with other biological and environmental factors.
Prior studies have identified comorbidity between pediatric somatic and psychiatric diseases within specific diagnostic groups. However, population-level data on these associations and their impact on mortality and morbidity are limited. This study aimed to examine these associations in the Danish pediatric population.
We conducted a national cohort study using Danish register data, including 1,413,177 children and adolescents from 2019 to 2023. We assessed background factors, mortality, and hospital contacts across three patient groups: somatic-only, psychiatric-only, and somatic-psychiatric. A new-user design classified patients as somatic-only or psychiatric-only if they had no hospital contacts in the preceding 12 months. Patients with subsequent contacts for the other condition were reclassified into the somatic-psychiatric group.
Most individuals were included in the somatic-only group (n = 532,324), with fewer in the psychiatric-only group (n = 21,501) or somatic-psychiatric group (n = 23,108). Psychiatric patients were more often boys and from lower socioeconomic backgrounds. Somatic hospital contacts often involved less severe symptoms. In contrast, psychiatric contacts involved specific diagnoses, including suicide attempts. Pediatric patients with both conditions had a higher 3-year readmission risk (12.1%, 95% CI: 11.6%–12.6%) compared to somatic-only patients (9.4%, 95% CI: 9.3%–9.5%), and longer average hospital stays (6.32 vs. 1.97 h). Psychiatric patients also had significantly higher all-cause mortality.
Somatic hospital contacts were more common, but children with psychiatric conditions faced significantly higher mortality and morbidity. These findings are relevant amid rising pediatric psychiatric diagnoses and recent Danish policy to integrate psychiatric and somatic care. Further research is needed to replicate these findings and inform optimal resource allocation for pediatric psychiatric care.
Machine learning studies sometimes include a high number of predictors relative to the number of training cases. This increases the risk of overfitting and poor generalizability. A recent study hypothesized that between-trial heterogeneity precluded generalizable outcome prediction in schizophrenia from being achieved. However, an alternative explanation is that predictor overinclusion might explain the low generalizability in that analysis.
Positive and Negative Syndrome Scale (PANSS) item-data, age, sex, and treatment allocation (antipsychotic/placebo) from 18 placebo-controlled trials of risperidone and paliperidone, in schizophrenia or schizoaffective disorder, were used as predictors for training five supervised learning models to predict symptom remission after 4 weeks of treatment. Sensitivity analyses varying the number of training cases and including simulated uninformative predictors were conducted to assess model performance, as were analyses on simulated data.
Better-than-chance predictions could be achieved for all models using as few as 384 training cases (BAC 0.60, SD 0.035 for an ensemble model). Model performance increased with the number of training cases (n = 4384, BAC 0.63, SD 0.041) and was higher when validated on a set of unseen trials without placebo controls (n = 1508, BAC 0.68, SD 0.013). Predictive performance was substantially decreased by including simulated uninformative predictors. Analyses of simulated data suggest that considerably larger sample sizes than commonly used might be required to effectively separate weakly informative from uninformative predictors.
Supervised learning models can generate better-than-chance predictions in schizophrenia from small datasets, but this requires that not too many uninformative predictors are included. Since highly predictive models have not yet been established for schizophrenia—and since strong linear predictors are easy to identify—commonly collected clinical trial data likely do not contain predictors with strong linear relations to clinically relevant outcomes. If correct, future machine learning analyses should focus on maximizing the probability of identifying weakly predictive features.
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