Theoretical and empirical contributions have identified insula as key in addiction. However, anatomical modifications of the insula in addictive states, and their variations across substance use disorders (SUDs), remain to be specifically explored. We therefore explored the specificities and commonalities of insula gray matter (GM) alterations in severe alcohol use disorder (sAUD) and severe cocaine use disorder (sCUD).

We explored insula GM volume through a refined parcellation in 12 subregions (six bilateral): anterior inferior cortex (AIC), anterior short gyrus, middle short gyrus, posterior short gyrus, anterior long gyrus (ALG), and posterior long gyrus (PLG). Using a linear mixed model analysis, we explored the insula volume profiles of 50 patients with sAUD, 61 patients with sCUD, and 36 healthy controls (HCs).

In both sAUD and sCUD, we showed overall insular lower volume with a right-sided lateralization effect, and a major volume deficit in bilateral ALG. Moreover, differences emerged across groups, with higher left AIC and PLG volume deficits in sCUD compared to sAUD and HC.

We offered the first joint exploration of GM insular volumes in two SUD through refined parcellation, thus unveiling the similarities and dissimilarities in volume deficit profiles. Our results bring evidence complementing prior ones suggesting the core role of the right and posterior insula in craving and interoception, two crucial processes in addiction. Left AIC and PLG group differences also show that, while insula is a region of interest in SUD, sCUD and sAUD generate distinct insular profiles, which might parallel clinical differences across SUD.

A large increase in the rate of hospitalizations for adolescents and children with anorexia nervosa (AN) was observed during the coronavirus disease (COVID-19) pandemic. It is still not clear whether this was a temporary effect or whether the increased admission rates persist.

Data were retrieved from the largest health insurance in Germany comprising 2.5 million children between 9 and 19 y. All patients of this age group with a discharge diagnosis of typical (AN) and atypical AN (AAN) according to the International Classification of Diseases, Tenth Revision (ICD-10), were included. Admission rates per 10,000 person-years were computed separately by sex, age and type of AN for entire years from 2019 to 2022 and the first half of 2023 in relation to the entire number of insured persons of the same sex and age per year.

Two years after the final lockdown admission rates were still significantly higher in adolescent and childhood AN than in the pre-COVID-19 time. While admission rates declined for adolescents in 2023, those for children remained high, with an increase for girls of more than 40% compared with the rate before the pandemic (1.42 (CI 1.26, 1.60); p < 0.0001). The highest admission risk for AAN relative to the pre-COVID-19 period was observed in adolescents in the first half of 2023 (1.6; CI 1.34; 1.90; p < 0.0001).

Children appear to be especially vulnerable to the pandemic-associated disruptions. Clinicians should try to determine the ongoing effects of the pandemic and support early detection and treatment of AN to prevent its often lifelong consequences.

Since its introduction in the diagnostic manuals DSM-5 and ICD-11, the construct of personality functioning has gained increasing attention. However, it remains unclear which factors might predict improvement in personality functioning.

We examined a sample of 648 completed psychodynamic psychotherapies conducted by 172 therapists at the Heidelberg Institute for Psychotherapy. A machine learning approach was used to filter for variables that are relevant for the prediction of the improvement of personality functioning from a broad data set of variables collected at the beginning of each psychodynamic psychotherapy.

On average, we found an improvement of 0.24 (SD = 0.48) in the OPD-SQ. This corresponds to a medium effect in the improvement of personality functioning. Patients with initially high impairment experienced particularly large improvements. Overall, we found a large number of variables that proved to be predictive for the improvement of personality functioning. Limitations in social activity due to physical and emotional problems proved to be one of the most important predictors of improvement. Most of the effect sizes were small.

Overall, the improvement in personality functioning during psychotherapy is determined more by the sum of a large number of small effects than by individual variables. In particular, variables that capture social areas of life proved to be robust predictors.

Quality of life is decreased in bipolar disorders (BD) and contributes to poor prognosis. However, little is known about the causal pathways that may affect it. This study aimed to explore health-related QoL (HRQoL) in BD and investigate its relationship with cognition and psychosocial functioning.

This multicenter cross-sectional study used a neuropsychological battery to assess five cognition domains. Functioning was evaluated using global and domain-based tools, and health-related HRQoL was assessed using the EQ-5D-3L. Structural equation modeling was used to test whether the association between cognition and HRQoL would be mediated by functioning in BD while controlling for covariates such as residual depression, anxiety, antipsychotic medication, and psychotic features.

We included 1 190 adults with euthymic BD. The model provided a good fit for the data. In this model, the direct effect of cognition on HRQoL was not significant (β = − 0.03, z = −0.78, p = 0.433). The total effect of cognition on HRQoL was weak, albeit significant (β = 0.05, z = 3.6, p < 0.001), thus suggesting that cognition affected HRQoL only indirectly through functioning. Anxiety was associated with decreased functioning (β = −0.27, z = −7.4, p < 0.001) and QoL (β = −0.39, z = −11.8, p < 0.001).

These findings suggest that improving cognition may not directly lead to a higher HRQoL. Cognitive remediation is expected to improve HRQoL only through functioning enhancement. They also reveal the potential importance of functional remediation and reduction of comorbid anxiety symptoms in improving HRQoL in BD.

The “suicidal transition” from ideation to an act has become a specific topic of research. However, rates in the general population, variations across time and risk factors are unclear.

Data were collected from the phone survey Baromètre Santé among 18–75-year-olds in France. Seven independent samples interviewed between 2000 and 2021 (total N = 133,827 people; 51.3% females) were questioned about suicidal ideation and attempts over the previous 12 months. Transition was calculated as the weighted ratio of attempt on ideation 12-month rates.

Mean 12-month rates of suicidal ideation, attempts and transition were 4.7% (95% Confidence Interval (CI) (4.6–4.8)), 0.5% (95% CI (0.4–0.5)) and 7.7% (95% CI (6.8–8.6)), respectively. Transition rates varied between 4.5 and 11.9% across surveys. In multivariable analyses, higher transitions rates were associated with a previous suicide attempt (adjusted Odds Ratio (aOR) = 11.1 95% CI (7.9–15.6)); 18–25 vs 26–55-year-olds (1.8 95% CI (1.2–2.8)); lower vs higher income (1.7 95% CI (1.0–2.7); and lower vs higher professional categories (aOR around 1.9). No significant association was found with gender, education level, employment status, living alone, urbanicity, current major depression, daily smoking, weekly heavy drinking, cannabis use, and body mass index.

Most people with suicidal ideation do not attempt suicide. These findings emphasize the need to avoid generic terms such as “suicidality”, and to increase research on suicidal transition to improve prevention and prediction. They may also inform the organization of suicide prevention in the general population.

Clinical decision support systems (CDSS) based on machine-learning (ML) models are emerging within psychiatry. If patients do not trust this technology, its implementation may disrupt the patient-clinician relationship. Therefore, the aim was to examine whether receiving basic information about ML-based CDSS increased trust in them.

We conducted an online randomized survey experiment in the Psychiatric Services of the Central Denmark Region. The participating patients were randomized into one of three arms: Intervention = information on clinical decision-making supported by an ML model; Active control = information on a standard clinical decision process, and Blank control = no information. The participants were unaware of the experiment. Subsequently, participants were asked about different aspects of trust and distrust regarding ML-based CDSS. The effect of the intervention was assessed by comparing scores of trust and distrust between the allocation arms.

Out of 5800 invitees, 992 completed the survey experiment. The intervention increased trust in ML-based CDSS when compared to the active control (mean increase in trust: 5% [95% CI: 1%; 9%], p = 0.0096) and the blank control arm (mean increase in trust: 4% [1%; 8%], p = 0.015). Similarly, the intervention reduced distrust in ML-based CDSS when compared to the active control (mean decrease in distrust: −3%[−1%; −5%], p = 0.021) and the blank control arm (mean decrease in distrust: −4% [−1%; −8%], p = 0.022). No statistically significant differences were observed between the active and the blank control arms.

Receiving basic information on ML-based CDSS in hospital psychiatry may increase patient trust in such systems.

Suicidal behavior constitutes a multi-cause phenomenon that may also be present in people without a mental disorder. This study aims to analyze suicidal behavior outcomes in a sample of attempters, from a symptom-based approach.

The sample comprised 673 patients (72% female; M = 40.9 years) who attended a hospital emergency department due to a suicide attempt. A wide range of clinical factors (e.g., psychopathology symptoms, psychiatric diagnoses, impulsivity, acquired capability), was administered within 15 days after the index attempt. Nine psychopathology domains were explored to identify the profile of symptoms, using latent profile analysis. The relationship between the profile membership and suicide outcome (i.e., intensity of suicidal ideation, number of suicide behaviors, and medical injury derived from index attempt) was also studied, using linear and logistic regression.

Three psychopathology profiles were identified: high-symptom profile (45.02% of participants), moderate-symptom profile (42.50%), and low-symptom profile (12.48%). High-symptom profile members were more likely to show higher risk of non-suicidal self-injury, acquired capability for suicide, and more severe suicide behavior and ideation. On the other hand, a more severe physical injury was associated with low-symptom profile membership in comparison to membership from the other profiles (OR < 0.45, p < .05).

A symptom-based approach may be useful to monitor patients and determine the risk of attempt repetition in the future and potential medical injury, and to optimize prevention and intervention strategies.

Nearly two-thirds of individuals with a mental disorder start experiencing symptoms during adolescence or early adulthood, and the onset of a mental disorder during this critical life stage strongly predicts adverse socioeconomic and health outcomes. Subthreshold manifestations of autism spectrum disorders (ASDs), also called autistic traits (ATs), are known to be associated with a higher vulnerability to the development of other psychiatric disorders. This study aimed to assess the presence of ATs in a population of young adults seeking specialist assistance and to evaluate the study population across various psychopathological domains in order to determine their links with ATs.

We recruited a sample of 263 adolescents and young adults referring to a specialized outpatient clinic, and we administered them several self-report questionnaires for the evaluation of various psychopathological domains. We conducted a cluster analysis based on the prevalence of ATs, empathy, and sensory sensitivity scores.

The cluster analysis identified three distinct groups in the sample: an AT cluster (22.43%), an intermediate cluster (45.25%), and a no-AT cluster (32.32%). Moreover, subjects with higher ATs exhibited greater symptomatology across multiple domains, including mood, anxiety, eating disorder severity, psychotic symptoms, and personality traits such as detachment and vulnerable narcissism.

This study highlights the importance of identifying ATs in young individuals struggling with mental health concerns. Additionally, our findings underscore the necessity of adopting a dimensional approach to psychopathology to better understand the complex interplay of symptoms and facilitate tailored interventions.

This study investigates the effects of accelerated high-frequency repetitive transcranial magnetic stimulation (aHF-rTMS), applied to the left dorsolateral prefrontal cortex (DLPFC), on locus coeruleus (LC) functional connectivity in the treatment of refractory medication-resistant major depression (MRD).

We studied 12 antidepressant-free refractory MRD patients, focusing on how aHF-rTMS affects the LC, a central component of the brain’s noradrenergic system and key to mood regulation and stress response.

A stronger decrease in LC functional connectivity following aHF-rTMS treatment resulted in better clinical improvement. We observed such LC functional connectivity decreases with several brain regions, including the superior frontal gyrus, precentral gyrus, middle occipital gyrus, and cerebellum. Moreover, our exploratory analyses hint at a possible role for E-field modeling in forecasting clinical outcomes. Additional analyses suggest potential genetic and dopaminergic factors influencing changes in LC functional connectivity in relation to clinical response.

The findings of this study underscore the pivotal role of the LC in orchestrating higher cognitive functions through its extensive connections with the prefrontal cortices, facilitating decision-making, influencing attention, and addressing depressive rumination. Additionally, the observed enhancement in LC-(posterior) cerebellar connectivity not only highlights the cerebellum’s role in moderating clinical outcomes through noradrenergic system modulation but also suggests its potential as a predictive marker for aHF-rTMS efficacy. These results reveal new insights into the neural mechanisms of refractory depression and suggest therapeutic targets for enhancing noradrenergic activity, thereby addressing both cognitive and psychomotor symptoms associated with the disorder.

The differential diagnosis of psychiatric disorders is relatively challenging for several reasons. In this context, we believe that task-based magnetic resonance imaging (MRI) can serve as a tool for differential diagnosis. The aim of this study was to explore the commonalities in brain activities among individuals with psychiatric disorders and to identify the key brain regions that can distinguish between these disorders.

The PubMed, MEDLINE, EMBASE, Web of Science, Scopus, PsycINFO, and Google Scholar databases were searched for whole-brain functional MRI studies that compared psychiatric patients and normal controls. The psychiatric disorders included schizophrenia (SCZ), bipolar disorder (BD), major depressive disorder (MDD), obsessive–compulsive disorder, attention-deficit/hyperactivity disorder (ADHD), and autism spectrum disorder (ASD). Studies using go–nogo paradigms were selected, we then conducted activation likelihood estimation (ALE) meta-analysis, factor analysis, and regression analysis on these studies subsequently.

A total of 152 studies (108 with patients) were selected and a consistent pattern was found, that is, decreased activities in the same brain regions across six disorders. Factor analysis clustered six disorders into three pairs: SCZ and ASD, MDD and BD, and ADHD and BD. Furthermore, the heterogeneity of SCZ and ASD was located in the left and right thalamus; and the heterogeneity of MDD and BD was located in the thalamus, insula, and superior frontal gyrus.

The results can lead to a new classification method for psychiatric disorders, benefit the differential diagnosis at an early stage, and help to understand the biobasis of psychiatric disorders.

The estimates of attention-deficit hyperactivity disorder (ADHD) prevalence across various studies are significantly variable, contributing to uncertainty in ADHD prevalence estimation. Previous systematic reviews and meta-analyses have attributed this variability primarily to the methodological characteristics of the studies, including the diagnostic criteria, source of information, and impairment requirement for the diagnosis.

Review identified studies reporting ADHD prevalence in representative samples of children and adults in Europe and worldwide. Studies that were conducted in the general population were included. We focused on studies that report ADHD prevalence based on clinical diagnosis (clinical diagnostic criteria based on the Diagnostic and Statistical Manual of Mental Disorders and International Classification of Diseases criteria, other diagnostic tools, such as various scales or interviews based on clinical diagnostic criteria). PubMed/Medline was searched to identify relevant articles published until 2024/2/01. The study was registered in PROSPERO (CRD42020200220) and followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines for systematic review and meta-analysis.

In total, 117 studies were subjected to full evaluation. In the meta-analysis, 103 studies representing 159 independent datapoints were included. The overall prevalence of ADHD in register studies was 1.6%, 95% CI [0.9; 3.0], in survey studies 5.0%, 95% CI [2.9; 8.6], in one-stage clinical studies 4.2%, 95% CI [2.9; 6.0], and in two-stage clinical studies 4.8%, 95% CI [4.0; 5.8].

Exact comparisons among studies with different diagnostic criteria and types of sampling can impact prevalence estimates. When comparing data from methodologically different studies, these factors need to be considered.

Adverse childhood events (ACEs) have been linked to widespread chronic pain (CP) in various cross-sectional studies, mainly in clinical populations. However, the independent role of different ACEs on the development of different types of CP remains elusive. Accordingly, we aimed to prospectively assess the associations between specific types of ACEs with the development of multisite CP in a large population-based cohort.

Data stemmed from the three first follow-up evaluations of CoLaus|PsyCoLaus, a prospective population-based cohort study of initially 6734 participants (age range: 35–75 years). The present sample included 1537 participants with 2161 analyzable intervals (49.7% men, mean age 57.3 years). Diagnostic criteria for ACEs were elicited using semi-structured interviews and CP was assessed by self-rating questionnaires. Multinomial logistic regressions with generalized estimating equations method analyzed the relationship between the different ACEs measured in the beginning of the interval and the risk of developing multisite CP during the follow-up. Sensitivity analyses were performed to assess the predictive value of ACEs on multisite CP with neuropathic features.

Participants with a history of parental divorce or separation had an increased risk of developing multisite CP at during follow-up in comparison to those without (RR1.98; 95% CI 1.13–3.47). A strong association was highlighted between parental divorce or separation and the risk of subsequent CP with neuropathic characteristics (RR 4.21, 95% CI 1.45–12.18).

These results highlight the importance of psychotherapeutic management of people experiencing parental separation to prevent CP in the future.

Music listening has been used as a sleep intervention among different populations. This systematic review and meta-analysis aimed to explore whether music is an effective sleep aid in adults with mental health problems.

We searched for studies investigating music interventions for sleep in adults with mental health problems. The primary outcome was subjective sleep quality; secondary outcomes were objective sleep outcomes, quality of life, and other mental health symptoms. Risk of bias assessment (RoB1) and random-effect model were used for the systematic review and meta-analyses.

The initial screening (n = 1492) resulted in 15 studies in the systematic review. Further qualified studies led to the meta-analysis of sleep quality (n = 7), depression (n = 5), and anxiety (n = 5). We found that the music listening intervention showed a potential effect on subjective sleep quality improvement compared to treatment-as-usual or no-intervention groups. When excluding an outlier study with an extreme effect, the meta-analysis showed a moderate effect on sleep quality (Hedges’ g = −0.66, 95% CI [−1.19, −0.13], t = −3.21, p = 0.0236). The highest risk of bias was the blinding of participants and researchers due to the nature of the music intervention.

Our results suggest that music interventions could have the potential to improve sleep quality among individuals with mental health problems, even though more high-quality studies are needed to establish the effect fully.

Stressors across the lifespan are associated with the onset of major depressive disorder (MDD) and increased severity of depressive symptoms. However, it is unclear how lifetime stressors are related to specific MDD subtypes. The present study aims to examine the relationships between MDD subtypes and stressors experienced across the lifespan while considering potential confounders.

Data analyzed were from the Zone d’Épidémiologie Psychiatrique du Sud-Ouest de Montréal (N = 1351). Lifetime stressors included childhood maltreatment, child–parent bonding, and stressful life events. Person-centered analyses were used to identify the clusters/profiles of the studied variables and multinomial logistic regression analyses were performed to examine the relationships between stressors and identified MDD subtypes. Intersectional analysis was applied to further examine how distal stressors interact with proximal stressors to impact the development of MDD subtypes.

There was a significant association between proximal stressors and melancholic depression, whereas severe atypical depression and moderate depression were only associated with some domains of stressful life events. Additionally, those with severe atypical depression and melancholic depression were more likely to be exposed to distal stressors such as childhood maltreatment. The combinations of distal and proximal stressors predicted a greater risk of all MDD subtypes except for moderate atypical depression.

MDD was characterized into four subtypes based on depressive symptoms and severity. Different stressor profiles were linked with various MDD subtypes. More specific interventions and clinical management are called to provide precision treatment for MDD patients with unique stressor profiles and MDD subtypes.

While several risk factors for schizophrenia have been identified, their individual impacts are rather small. The relative independent and cumulative impacts of multiple risk factors on disease risk and age of onset warrant further investigation.

We conducted a register-based case–control study including all individuals receiving a schizophrenia spectrum disorder in Denmark from 1973 to 2018 (N = 29,142), and a healthy control sample matched 5:1 on age, sex, and parental socioeconomic status (N = 136,387). Register data included parental history of psychiatric illness, birth weight, gestational age, season of birth, population density of birthplace, immigration, paternal age, and Apgar scores. Data were analysed using logistic regression and machine learning.

Parental history of psychiatric illness (OR = 2.32 [95%CI 2.21–2.43]), high paternal age (OR = 1.30 [1.16–1.45]), and low birth weight (OR = 1.28 [1.16–1.41]) increased the odds of belonging to the patient group. In contrast, being a second-generation immigrant (OR = 0.65 [0.61–0.69]) and high population density of the birthplace (OR = 0.92 [0.89–0.96]) decreased the odds. The findings were supported by a decision tree analysis where parental history, paternal age, and birth weight contributed most to diagnostic classification (ACCtest = 0.69, AUCtest = 0.59, p < 0.001). Twenty percent of patients were child-onset cases. Here, female sex (OR = 1.82 [1.69–1.97]) and parental psychiatric illness (OR = 1.62 [1.49–1.77]) increased the odds of receiving the diagnosis <18 years.

Multiple early factors contribute independently to a higher psychosis risk, suggesting cumulative effects leading to symptom onset. Routine assessments of the most influential risk factors could be incorporated into clinical practise. Being female increased the risk of diagnosis during childhood, suggesting sex differences in the developmental trajectories of the disorder.

Major depressive disorder (MDD) is a leading cause of disability and premature mortality. This study compared the overall survival (OS) between patients with MDD and non-MDD controls stratified by gender, age, and comorbidities.

This nationwide population-based cohort study utilized longitudinal patient data (01/01/2010 – 12/31/2020) from the Hungarian National Health Insurance Fund database, which contains healthcare service data for the Hungarian population. Patients with MDD were selected and matched 1:1 to those without MDD using exact matching. The rates of conversion from MDD to bipolar disorder (BD) or schizophrenia were also investigated.

Overall, 471,773 patients were included in each of the matched MDD and non-MDD groups. Patients with MDD had significantly worse OS than non-MDD controls (hazard ratio [HR] = 1.50; 95% CI: 1.48−1.51; males HR = 1.69, 95% CI: 1.66–1.72; females HR = 1.40, 95% CI: 1.38–1.42). The estimated life expectancy of patients with MDD was 7.8 and 6.0 years less than that of controls aged 20 and 45 years, respectively. Adjusted analyses based on the presence of baseline comorbidities also showed that patients with MDD had worse survival than non-MDD controls (adjusted HR = 1.29, 95% CI: 1.28–1.31). After 11 years of follow-up, the cumulative conversions from MDD to BD and schizophrenia were 6.8 and 3.4%, respectively. Converted patients had significantly worse OS than non-converted patients.

Compared with the non-MDD controls, a higher mortality rate in patients with MDD, especially in those with comorbidities and/or who have converted to BD or schizophrenia, suggests that early detection and personalized treatment of MDD may reduce the mortality in patients diagnosed with MDD.

Major depressive disorder (MDD) is one of the most prevalent medical conditions worldwide. Different factors were found to play a role in its etiology, including environmental ones (e.g., air pollution). The aim of this study was to evaluate the association between air pollution exposure and MDD severity.

Four hundred sixteen MDD subjects were recruited. Severity of MDD and functioning were evaluated through five rating scales: Montgomery–Asberg Depression Rating Scale (MADRS), Hamilton Depression Rating Scale (HAMD), Clinical Global Impression (CGI), Global Assessment of Functioning (GAF), and Sheehan Disability Scale (SDS). Daily mean estimates of particulate matter with diameter ≤10 (PM10) and 2.5 μm (PM2.5), nitrogen dioxide (NO2), and apparent temperature (AT) were estimated based on subjects’ residential addresses. Daily estimates of the 2 weeks preceding recruitment were averaged to obtain cumulative exposure. Multivariate linear and ordinal regression models were applied to assess the associations between air pollutants and MDD severity, overall and stratifying by hypersusceptibility and AT.

Two-thirds of subjects were women and one-third had a family history of depression. Most women had depression with symptoms of anxiety, while men had predominantly melancholic depression. NO2 exposure was associated with worsening of MDD severity (HAMD: β = 1.94, 95% confidence interval [CI], [0.41–3.47]; GAF: β = −1.93, 95% CI [−3.89 to 0.02]), especially when temperatures were low or among hypersusceptible subjects. PM exposure showed an association with MDD severity only in these subgroups.

Exposure to air pollution worsens MDD severity, with hypersusceptibility and lower temperatures being exacerbating factors.

Cognitive impairment (CI) is one of the most prevalent and burdensome consequences of COVID-19 infection, which can persist up to months or even years after remission of the infection. Current guidelines on post-COVID CI are based on available knowledge on treatments used for improving CI in other conditions. The current review aims to provide an updated overview of the existing evidence on the efficacy of treatments for post-COVID CI.

A systematic literature search was conducted for studies published up to December 2023 using three databases (PubMed–Scopus–ProQuest). Controlled and noncontrolled trials, cohort studies, case series, and reports testing interventions on subjects with CI following COVID-19 infection were included.

After screening 7790 articles, 29 studies were included. Multidisciplinary approaches, particularly those combining cognitive remediation interventions, physical exercise, and dietary and sleep support, may improve CI and address the different needs of individuals with post-COVID-19 condition. Cognitive remediation interventions can provide a safe, cost-effective option and may be tailored to deficits in specific cognitive domains. Noninvasive brain stimulation techniques and hyperbaric oxygen therapy showed mixed and preliminary results. Evidence for other interventions, including pharmacological ones, remains sparse. Challenges in interpreting existing evidence include heterogeneity in study designs, assessment tools, and recruitment criteria; lack of long-term follow-up; and under-characterization of samples in relation to confounding factors.

Further research, grounded on shared definitions of the post-COVID condition and on the accurate assessment of COVID-related CI, in well-defined study samples and with longer follow-ups, is crucial to address this significant unmet need.

We need to better understand the risk factors and predictors of medication-related weight gain to improve metabolic health of individuals with schizophrenia. This study explores how trajectories of antipsychotic medication (AP) use impact body weight early in the course of schizophrenia.

We recruited 92 participants with first-episode psychosis (FEP, n = 92) during their first psychiatric hospitalization. We prospectively collected weight, body mass index (BMI), metabolic markers, and exact daily medication exposure during 6-week hospitalization. We quantified the trajectory of AP medication changes and AP polypharmacy using a novel approach based on meta-analytical ranking of medications and tested it as a predictor of weight gain together with traditional risk factors.

Most people started treatment with risperidone (n = 57), followed by olanzapine (n = 29). Then, 48% of individuals remained on their first prescribed medication, while 33% of people remained on monotherapy. Almost half of the individuals (39/92) experienced escalation of medications, mostly switch to AP polypharmacy (90%). Only baseline BMI was a predictor of BMI change. Individuals in the top tercile of weight gain, compared to those in the bottom tercile, showed lower follow-up symptoms, a trend for longer prehospitalization antipsychotic treatment, and greater exposure to metabolically problematic medications.

Early in the course of illness, during inpatient treatment, baseline BMI is the strongest and earliest predictor of weight gain on APs and is a better predictor than type of medication, polypharmacy, or medication switches. Baseline BMI predicted weight change over a period of weeks, when other traditional predictors demonstrated a much smaller effect.

Cardiometabolic diseases (CMDs) including heart disease, stroke, and type 2 diabetes have been individually linked to depression. However, their combined impact on depression risk is unclear. We aimed to examine the association between cardiometabolic multimorbidity and depression and explore the role of genetic background in this association.

Within the Swedish Twin Registry, 40,080 depression-free individuals (mean age 60 years) were followed for 18 years. Cardiometabolic multimorbidity was defined as having ≥2 CMDs. CMDs and depression were ascertained based on the National Patient Register. Cox regression was used to estimate the CMD-depression association in a classical cohort study design and a matched co-twin design involving 176 twin pairs. By comparing the associations between monozygotic and dizygotic co-twins, the contribution of genetic background was estimated.

At baseline, 4809 (12.0%) participants had one CMD and 969 (2.4%) had ≥2 CMDs. Over the follow-up period, 1361 participants developed depression. In the classical cohort design, the multi-adjusted hazard ratios (95% confidence interval [CIs]) of depression were 1.52 (1.31–1.76) for those with one CMD and 1.83 (1.29–2.58) for those with ≥2 CMDs. CMDs had a greater risk effect on depression if they developed in mid-life (<60 years) as opposed to late life (≥60 years). In matched co-twin analysis, the CMD-depression association was significant among dizygotic twins (HR = 1.63, 95% CI, 1.02–2.59) but not monozygotic twins (HR = 0.90, 95% CI, 0.32–2.51).

Cardiometabolic multimorbidity is associated with an elevated risk of depression. Genetic factors may contribute to the association between CMDs and depression.