To Örebro University

Cardiovascular disease (CVD) risk prediction models for the general population may not provide accurate predictions in individuals with bipolar disorder (BD) who have elevated risks of cardiometabolic conditions and premature mortality. Therefore, we aimed to: 1) develop a five-year CVD risk prediction model in this population by using nationwide register data from Sweden, 2) investigate whether the performance improved when we considered additional risk factors, including psychiatric comorbidity, psychotropic medication, and socio-demographic variables, compared to using established CVD risk factors only, and 3) whether machine learning approach provided improvements compared to standard logistic regression models. We followed 33,933 persons with BD aged 30-82 years old, without previous CVD, from the date of BD diagnosis registered between 2007-2014, for up to five years. The logistic regression model containing only established risk factors yielded an area under the receiver operating characteristic curve (AUC) of 0.76 (95% confidence interval 0.74-0.78) in the test dataset, while the logistic regression model and the best performing machine learning model including additional predictors yielded similar results (AUC was 0.77 (0.75, 0.79) in both models). The performance of logistic regression models slightly improved with additional predictors when continuous risk scores were used. In conclusion, standard logistic regression and established CVD risk factors may be sufficient to predict CVD in individuals with BD when using population register-based data from Sweden. External validation across diverse healthcare settings and rigorous assessment of clinical impact will be crucial next steps before implementing these models in clinical practice.

Background: Poor persistence and adherence to attention-deficit/hyperactivity disorder (ADHD) medication is a significant barrier to effective long-term care, particularly during adolescence, yet age-specific and sex-specific trajectories remain poorly characterised.

Objective: To characterise medication initiation, discontinuation and long-term adherence patterns for children and adolescents diagnosed with ADHD in a real-world setting.

Methods: A nationwide retrospective cohort study, including 8961 children and adolescents (aged <18 years) with a new ADHD diagnosis in child and adolescent mental health services between 1 January 2010 and 31 December 2012, with follow-up until 31 December 2021. Main outcomes were medication initiation rates; time to first medication discontinuation, analysed using Kaplan-Meier estimates and restricted mean survival time at 1 year and longitudinal adherence, measured by the proportion of days covered over 9 years.

Findings: Of the 8961 individuals in the cohort (mean age at diagnosis, 12 years; 69% male), 6661 (74.3%) initiated medication, with a median time from diagnosis to initiation of 106 days (IQR 17–231); 55% initiated within 90 days. Discontinuation increased significantly with age; adolescents aged 15–17 years remained on treatment for 31.9 fewer days (95% CI −40.8 to −23.1; p<0.001) in the first year compared with children aged 5–11 years. Females also discontinued significantly earlier than males (difference −13.2 days; 95% CI −19.8 to −6.5; p<0.001). Longitudinal analysis confirmed that older age at initiation and female sex were associated with a significantly steeper decline in medication coverage over time.Conclusions Discontinuation and low adherence to ADHD medication were common and increased substantially through adolescence, with females at higher risk of early cessation.

Clinical implications: Late adolescence warrants closer clinical monitoring and shared decision-making to support appropriate treatment continuation or well-informed discontinuation, particularly for older adolescents and females. Integrating structured transition planning and attention to sex-specific barriers may help reduce avoidable non-adherence during this high-risk period.

Mendelian Randomization (MR) has become an essential tool in psychiatric research offering valuable insights into the causal relationships underlying risks and consequences of psychiatric conditions. This method utilizes genetic data to infer causal effects, effectively reducing biases commonly encountered in traditional observational studies. By leveraging genetic information, MR helps to identify potential risk factors for psychiatric conditions, paving the way for more effective interventions. However, to draw reliable and meaningful conclusions from MR studies, several critical concepts must be carefully evaluated. These include instrument selection, the magnitude of effect, the strength of the causal evidence, generalizability across diverse populations, and the clinical relevance of findings. This review will explore these key concepts in depth with illustrative examples providing a comprehensive and accessible guide for clinicians and scientists to understand and interpret psychiatric MR findings. Additionally, we will discuss novel emerging techniques, such as advanced statistical methods and the integration of high-dimensional genomic data, highlighting their potential impact on the progression of MR studies. The overall aim of this review is to foster a deeper understanding of its application in psychiatric research, ultimately enhancing its ability to unravel the intricacies of psychiatric disorders and inform personalized treatment strategies.

More than 100 prediction models have been developed to support the diagnosis, prognosis, or treatment of ADHD, yet none has reached routine clinical practice. In this Personal View, we argue that an important reason for this implementation gap is the absence of clearly defined prediction-based decision rules—formalised mappings from a model's output to specific clinical actions. Most published models report discrimination metrics such as the area under the receiver-operating-characteristic curve, but do not specify what a clinician should do differently for a patient classified as at high risk versus low risk. Without this link to action, even an accurate model remains clinically inert. We describe how prediction-based decision rules, combined with target trial emulation of their clinical utility in large observational datasets, offer a feasible path forward. We illustrate the approach with two worked ADHD examples (treatment intensity guided by predicted persistence and medication selection guided by predicted treatment response) and propose four priorities to shift the field from model development towards decision-oriented evaluation and implementation.

The major anxiety disorders (ANX; including generalized anxiety disorder, panic disorder and phobias) are highly prevalent, often onset early and cause substantial global disability. Although distinct in their clinical presentations, they probably represent differential expressions of a dysregulated threat–response system. Here, we present a genome-wide association meta-analysis comprising 122,341 European ancestry ANX cases and 729,881 controls. We identified 58 independent genome-wide significant risk variants and 66 genes with robust biological support. In an independent sample of 1,175,012 self-report ANX cases and 1,956,379 controls, 51 out of the 58 associations replicated. As predicted by twin studies, we found substantial genetic correlation between ANX and depression, neuroticism and other internalizing phenotypes. Follow-up analyses demonstrated enrichment in all major brain regions and highlighted GABAergic signaling as one potential mechanism implicated in ANX genetic risk. These results advance our understanding of the genetic architecture of ANX and prioritize genes for functional follow-up studies.

Anxiety is heritable and exists on a continuum, with symptoms ranging from adaptive threat response to clinical disorder. Here we performed a genome-wide association meta-analysis of generalized anxiety symptom severity in 693,869 individuals of European ancestry from 14 cohorts. We identified 80 independent genome-wide significant variants within 74 loci, 39 of which were newly associated with anxiety. SNP-based heritability was 5.9% (posterior s.d. = 0.15%). Polygenic scores were significantly associated with anxiety symptom severity and disorder in European, African and South Asian ancestry samples (R2 = 1.2-2.9%). Significant genetic correlations (rg) were estimated with mental and physical health traits, including case-control anxiety, neuroticism and depression (rg = 0.71-0.85), irritable bowel syndrome (rg = 0.57), coronary artery disease, endometriosis and migraine (rg = 0.20-0.27). Gene-based and pathway analyses implicated synaptic and axonal processes, with enriched expression in the brain. These findings highlight the discovery power gained from analysing a quantitative trait rather than a case-control phenotype in anxiety genetics.

Psychiatric disorders display high levels of comorbidity and genetic overlap1,2, challenging current diagnostic boundaries. For disorders for which diagnostic separation has been most debated, such as schizophrenia and bipolar disorder3, genomic methods have revealed that the majority of genetic signal is shared4. While over a hundred pleiotropic loci have been identified by recent cross-disorder analyses5, the full scope of shared and disorder-specific genetic influences remains poorly defined. Here we addressed this gap by triangulating across a suite of cutting-edge statistical and functional genomic analyses applied to 14 childhood- and adult-onset psychiatric disorders (1,056,201 cases). Using genetic association data from common variants, we identified and characterized five underlying genomic factors that explained the majority of the genetic variance of the individual disorders (around 66% on average) and were associated with 238 pleiotropic loci. The two factors defined by (1) Schizophrenia and bipolar disorders (SB factor); and (2) major depression, PTSD and anxiety (Internalizing factor) showed high levels of polygenic overlap6 and local genetic correlation and very few disorder-specific loci. The genetic signal shared across all 14 disorders was enriched for broad biological processes (for example, transcriptional regulation), while more specific pathways were shared at the level of the individual factors. The shared genetic signal across the SB factor was substantially enriched in genes expressed in excitatory neurons, whereas the Internalizing factor was associated with oligodendrocyte biology. These observations may inform a more neurobiologically valid psychiatric nosology and implicate targets for therapeutic development designed to treat commonly occurring comorbid presentations.

Purpose: We investigated how multidimensional neighborhood types, capturing the complexity of neighborhood social structure, related to criminal convictions and substance use disorders (SUDs) in Sweden while accounting for residential mobility and shared familial influences.

Methods: Using nationwide Swedish register data (1991-2020), we followed 1.82 million individuals born 19852002 across five neighborhood types previously identified using latent class analysis. These were defined by socioeconomic and demographic indicators, with the most common type (rural low-diversity neighborhoods) serving as the reference category. Associations between neighborhood types, violent and nonviolent convictions, and SUDs were estimated using Cox proportional hazards models with hazard ratios (HRs) and 95% confidence intervals (CIs). Neighborhood type was modeled as a time-varying exposure, capturing residential changes over time. Sibling comparison models were applied to account for familial confounding.

Results: Risks of violent convictions, nonviolent convictions, and SUDs were highest in resource-limited neighborhoods (i.e., socioeconomically deprived areas with high residential turnover; violent: adjusted HR (aHR) = 4.70, 95% CI 4.26-5.19; nonviolent: aHR = 2.07, 95% CI 1.92-2.22; SUDs: aHR = 3.36, 95% CI 3.093.65). Moderate increases were observed in urban high-diversity neighborhoods (i.e., characterized by ethnic heterogeneity), whereas risks were lowest in socioeconomically advantaged urban areas. Associations were attenuated but remained statistically significant in sibling comparisons. Sex differences were modest and followed similar patterns across outcomes.

Conclusion: Neighborhoods marked by socioeconomic disadvantage and residential instability, rather than ethnic diversity, were linked to higher risks of criminal convictions and SUDs. Remaining associations after familial adjustment suggest modest but meaningful contextual effects.

BACKGROUND: 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.OBJECTIVE: To investigate the association between maternal ADHD and pregnancy outcomes.

METHODS: 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.

RESULTS: 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).

CONCLUSIONS: 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.