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The role of environmental stress and DNA methylation in the longitudinal course of bipolar disorder
(2020)
BACKGROUND
Optical coherence tomography (OCT) and electroretinography (ERG) studies have revealed structural and functional retinal alterations in individuals with schizophrenia spectrum disorders (SSD). However, it remains unclear which specific retinal layers are affected, how the retina, brain, and clinical symptomatology are connected, and how alterations of the visual system are related to genetic disease risk.
METHODS
OCT, ERG, and brain magnetic resonance imaging (MRI) were applied to comprehensively investigate the visual system in a cohort of 103 patients with SSD and 130 healthy control individuals. The sparse partial least squares (SPLS) algorithm was used to identify multivariate associations between clinical disease phenotype and biological alterations of the visual system. The association of the revealed patterns with the individual polygenetic disease risk for schizophrenia was explored in a post hoc analysis. In addition, covariate-adjusted case-control comparisons were performed for each individual OCT and ERG parameter.
RESULTS
The SPLS analysis yielded a phenotype-eye-brain signature of SSD in which greater disease severity, longer duration of illness, and impaired cognition were associated with electrophysiological alterations and microstructural thinning of most retinal layers. Higher individual loading onto this disease-relevant signature of the visual system was significantly associated with elevated polygenic risk for schizophrenia. In case-control comparisons, patients with SSD had lower macular thickness, thinner retinal nerve fiber and inner plexiform layers, less negative a-wave amplitude, and lower b-wave amplitude.
CONCLUSIONS
This study demonstrates multimodal microstructural and electrophysiological retinal alterations in individuals with SSD that are associated with disease severity and individual polygenetic burden.
The response variability to repetitive transcranial magnetic stimulation (rTMS) challenges the effective use of this treatment option in patients with schizophrenia. This variability may be deciphered by leveraging predictive information in structural MRI, clinical, sociodemographic, and genetic data using artificial intelligence. We developed and cross-validated rTMS response prediction models in patients with schizophrenia drawn from the multisite RESIS trial. The models incorporated pre-treatment sMRI, clinical, sociodemographic, and polygenic risk score (PRS) data. Patients were randomly assigned to receive active (N = 45) or sham (N = 47) rTMS treatment. The prediction target was individual response, defined as ≥20% reduction in pre-treatment negative symptom sum scores of the Positive and Negative Syndrome Scale. Our multimodal sequential prediction workflow achieved a balanced accuracy (BAC) of 94% (non-responders: 92%, responders: 95%) in the active-treated group and 50% in the sham-treated group. The clinical, clinical + PRS, and sMRI-based classifiers yielded BACs of 65%, 76%, and 80%, respectively. Apparent sadness, inability to feel, educational attainment PRS, and unemployment were most predictive of non-response in the clinical + PRS model, while grey matter density reductions in the default mode, limbic networks, and the cerebellum were most predictive in the sMRI model. Our sequential modelling approach provided superior predictive performance while minimising the diagnostic burden in the clinical setting. Predictive patterns suggest that rTMS responders may have higher levels of brain grey matter in the default mode and salience networks which increases their likelihood of profiting from plasticity-inducing brain stimulation methods, such as rTMS. The future clinical implementation of our models requires findings to be replicated at the international scale using stratified clinical trial designs.
Association between mitochondria-related genes and cognitive performance in the PsyCourse Study
(2023)
As core symptoms of schizophrenia, cognitive deficits contribute substantially to poor outcomes. Early life stress (ELS) can negatively affect cognition in patients with schizophrenia and healthy controls, but the exact nature of the mediating factors is unclear. Therefore, we investigated how ELS, education, and symptom burden are related to cognitive performance.
The sample comprised 215 patients with schizophrenia (age, 42.9 ± 12.0 years; 66.0 % male) and 197 healthy controls (age, 38.5 ± 16.4 years; 39.3 % male) from the PsyCourse Study. ELS was assessed with the Childhood Trauma Screener (CTS). We used analyses of covariance and correlation analyses to investigate the association of total ELS load and ELS subtypes with cognitive performance.
ELS was reported by 52.1 % of patients and 24.9 % of controls. Independent of ELS, cognitive performance on neuropsychological tests was lower in patients than controls (p < 0.001). ELS load was more closely associated with neurocognitive deficits (cognitive composite score) in controls (r = −0.305, p < 0.001) than in patients (r = −0.163, p = 0.033). Moreover, the higher the ELS load, the more cognitive deficits were found in controls (r = −0.200, p = 0.006), while in patients, this correlation was not significant after adjusting for PANSS.
ELS load was more strongly associated with cognitive deficits in healthy controls than in patients. In patients, disease-related positive and negative symptoms may mask the effects of ELS-related cognitive deficits. ELS subtypes were associated with impairments in various cognitive domains. Cognitive deficits appear to be mediated through higher symptom burden and lower educational level.
Older Adults with Bipolar Disorder (OABD) represent a heterogeneous group, including those with early and late onset of the disorder. Recent evidence shows both groups have distinct clinical, cognitive, and medical features, tied to different neurobiological profiles. This study explored the link between polygenic risk scores (PRS) for bipolar disorder (PRS-BD), schizophrenia (PRS-SCZ), and major depressive disorder (PRS-MDD) with age of onset in OABD. PRS-SCZ, PRS-BD, and PRS-MDD among early vs late onset were calculated. PRS was used to infer posterior SNP effect sizes using a fully Bayesian approach. Demographic, clinical, and cognitive variables were also analyzed. Logistic regression analysis was used to estimate the amount of variation of each group explained by standardized PRS-SCZ, PRS-MDD, and PRS-BD. A total of 207 OABD subjects were included (144 EOBD; 63 LOBD). EOBD showed higher PRS-BD compared to LOBD (p = 0.005), while no association was found between age of onset and PRS-SCZ or PRS-MDD. Compared to LOBD, EOBD individuals also showed a higher likelihood for suicide attempts (p = 0.01), higher presence of psychotic symptoms (p = 0.003), higher prevalence of BD-I (p = 0.002), higher rates of familiarity for any psychiatric disorder (p = 0.004), and lower processing speed measured with Trail-Making Test part A (p = 0.03). OABD subjects with an early onset showed a greater genetic burden for BD compared to subjects with a late onset. These findings contribute to the notion that EOBD and LOBD may represent different forms of OABD, particularly regarding the genetic predisposition to BD.
Background
Employment and relationship are crucial for social integration. However, individuals with major psychiatric disorders often face challenges in these domains.
Aims
We investigated employment and relationship status changes among patients across the affective and psychotic spectrum – in comparison with healthy controls, examining whether diagnostic groups or functional levels influence these transitions.
Method
The sample from the longitudinal multicentric PsyCourse Study comprised 1260 patients with affective and psychotic spectrum disorders and 441 controls (mean age ± s.d., 39.91 ± 12.65 years; 48.9% female). Multistate models (Markov) were used to analyse transitions in employment and relationship status, focusing on transition intensities. Analyses contained multiple multistate models adjusted for age, gender, job or partner, diagnostic group and Global Assessment of Functioning (GAF) in different combinations to analyse the impact of the covariates on the hazard ratio of changing employment or relationship status.
Results
The clinical group had a higher hazard ratio of losing partner (hazard ratio 1.46, P < 0.001) and job (hazard ratio 4.18, P < 0.001) than the control group (corrected for age/gender). Compared with controls, clinical groups had a higher hazard of losing partner (affective group, hazard ratio 2.69, P = 0.003; psychotic group, hazard ratio 3.06, P = 0.001) and job (affective group, hazard ratio 3.43, P < 0.001; psychotic group, hazard ratio 4.11, P < 0.001). Adjusting for GAF, the hazard ratio of losing partner and job decreased in both clinical groups compared with controls.
Conclusion
Patients face an increased hazard of job loss and relationship dissolution compared with healthy controls, and this is partially conditioned by the diagnosis and functional level. These findings underscore a high demand for destigmatisation and support for individuals in managing their functional limitations.
Abstract
Previous studies have suggested that choroid plexus (ChP) enlargement occurs in individuals with schizophrenia-spectrum disorders (SSD) and is associated with peripheral inflammation. However, it is unclear whether such an enlargement delineates a biologically defined subgroup of SSD. Moreover, it remains elusive how ChP is linked to brain regions associated with peripheral inflammation in SSD. A cross-sectional cohort of 132 individuals with SSD and 107 age-matched healthy controls (HC) underwent cerebral magnetic resonance imaging (MRI) and clinical phenotyping to investigate the ChP and associated regions. A case-control comparison of ChP volumes was conducted, and structural variance was analyzed by employing the variability ratio (VR). K-means clustering analysis was used to identify subgroups with distinct patterns of the ventricular system, and the clusters were compared in terms of demographic, clinical, and immunological measures. The relationship between ChP volumes and brain regions, previously associated with peripheral inflammation, was investigated. We did not find a significant enlargement of the ChP in SSD compared to HC but detected an increased VR of ChP and lateral ventricle volumes. Based on these regions, we identified 3 clusters with differences in cognitive measures and possibly inflammatory markers. Larger ChP volume was associated with higher volumes of hippocampus, putamen, and thalamus in SSD but not in HC. This study suggests that ChP variability, but not mean volume, is increased in individuals with SSD, compared to HC. Larger ChP volumes in SSD were associated with higher volumes of regions previously associated with peripheral inflammation.
Although lipid biology may play a key role in the pathophysiology of mental health disorders such as schizophrenia (SCZ) and bipolar disorder (BD), the nature of this interplay and how it could shape phenotypic presentation, including cognitive performance is still incompletely understood. To address this question, we analyzed the association of plasma level of different lipid species with cognitive performance in the transdiagnostic PsyCourse Study. Plasma lipidomic profiles of 623 individuals (188 SCZ, 243 BD, 192 healthy controls) belonging to the PsyCourse Study were assessed using liquid chromatography and untargeted mass spectrometry. The association between 364 annotated lipid species from 16 lipid classes and six cognitive tests was evaluated. Likewise, the association of polygenic risk scores (PRS) for SCZ, BD, executive function (EF), and educational attainment (EA) with lipid plasma levels were also investigated. In the regression analysis, three lipid species belonging to phosphatidylethanolamine plasmalogen and one belonging to ceramide class showed significant negative association with Digit-Symbol test scores. Lipid class-based enrichment analysis in LipidR replicated the significance of the phosphatidylethanolamines class for the Digit-Symbol test, which evaluates the processing speed in cognitive tasks. Polygenic load for SCZ, BD, EF, or EA was not associated with lipid levels. Our findings suggest a link between lipids and cognitive performance independent of mental health disorders. Still, independent replication is warranted to better understand if phosphatidylethanolamines could represent an actionable pharmacologic target to tackle cognitive dysfunction, an important unmet clinical need that affects long-term functional outcomes in individuals with severe mental health disorders.
Alterations in glial cell function and cytokine levels in the central nervous system may be influenced by neuroinflammatory processes, which have a pathogenic role in psychiatric disorders. Variability in genes that encode inflammatory mediators is associated with risk of developing mental disorders. Therefore, by analyzing data from the transdiagnostic PsyCourse Study, we aimed to investigate whether variations in inflammatory mediator genes are associated with current symptom severity.
We used cross-sectional data from 1320 individuals with a psychiatric disorder and 466 neurotypical individuals. Outcome variables were the psychopathological data from various rating scales and questionnaires that measured depressive, psychotic, and manic symptoms. Furthermore, from a whole-genome SNP array dataset, we extracted single nucleotide polymorphisms (SNPs) in the loci of genes related to inflammatory mediators, and we performed an association analysis by considering covariates. False discovery rate (FDR) was used to adjust the results for multiple comparisons.
A total of 1594 individuals and 1336 SNPs were included in the analyses. The results of regression analysis showed a significant positive association of six SNPs located on the interleukin (IL)-1 receptor type 1 (IL-1R1) gene locus with Altman Self-Rating Mania Scale scores (FDR-adjusted p value < 0.05).
Our findings show that genetic variations in IL-1R1 may influence the pathophysiology of psychiatric disorders by affecting brain cytokine profiles associated with manic episodes. IL-1R1 encodes a membrane-bound receptor for IL-1. Several physiological functions, including inflammation, are linked to the IL-1/IL-1R1 signaling pathway. Replication of our findings is warranted.
Adverse childhood experiences (ACE) contribute significantly to mental disorders. While existing research has primarily focused on specific diagnostic categories, a comprehensive understanding of how childhood trauma interacts with biological factors, symptom severity and functioning requires a broader perspective. Therefore, this study adopted a cross-diagnostic approach to examine the impact of ACE on quality of life (QoL), psychosocial functioning, and symptom burden by analyzing data from the PsyCourse Study, a longitudinal, multicenter research project conducted in Germany and Austria. We used multivariate linear regression models and cluster analysis to evaluate data from 725 participants with affective and psychotic disorders and healthy controls who completed the self-assessed Childhood Trauma Screener (CTS) during the course of the study. The results showed that across diagnoses, QoL was significantly impacted by ACE, particularly emotional neglect. An ablation study revealed that 2.3% to 6.2% of the variability in QoL domains could be attributed to ACE. Across diagnoses, symptoms of depression were significantly associated with ACE, especially emotional abuse, but psychotic and manic symptoms were not. Polygenic risk scores (PRS) did not emerge as significant predictors for any examined outcomes. Cluster analysis revealed distinct symptom profiles: Averaged over time, patients with less trauma exposure were rather in the subclinical than in the clinically ill clusters. We conclude that the pervasive influence of ACE on disease severity should be considered when evaluating and treating patients with affective and psychotic disorders.
Importance: Numerous studies indicate that the traditional categorical classification of severe mental disorders (SMD), such as schizophrenia, bipolar disorders, and major depressive disorders, does not align with the underlying biology of those disorders as they frequently overlap in terms of symptoms and risk factors.
Objective: This study aimed to identify transdiagnostic patient clusters based on disease severity and explore the underlying biological mechanisms independently of the traditional categorical classification.
Design: We utilized data from 443 participants diagnosed with SMD of the PsyCourse Study, a longitudinal study with deep phenotyping across up to four visits. We performed longitudinal clustering to group patients based on symptom trajectories and cognitive performance. The resulting clusters were compared on cross-sectional variables, including independent measures of severity as well as polygenic risk scores, serum protein quantification, miRNA expression, and DNA methylation.
Results: We identified two distinct clusters of patients that exhibited marked differences in illness severity but did not differ significantly in age, sex, or diagnostic proportions. We found 19 serum proteins significantly dysregulated between the two clusters. Functional enrichment pointed to a convergence of immune system dysregulation and neurodevelopmental processes.
Conclusion: The observed differences in serum protein expression suggest that disease severity is associated with the convergence of immune system dysregulation and neurodevelopmental alterations, particularly involving pathways related to inflammation and brain plasticity. The identification of pro-inflammatory proteins among the differentially expressed markers underscores the potential role of systemic inflammation in the pathophysiology of SMD. These results highlight the importance of considering illness severity as a core dimension in psychiatric research and clinical practice and suggest that targeting immune-related mechanisms may offer promising new therapeutic avenues for patients with SMD.
Background
Polygenic scores (PGSs) hold the potential to identify patients who respond favorably to specific psychiatric treatments. However, their biological interpretation remains unclear. In this study, we developed pathway-specific PGSs (PSPGSs) for lithium response and assessed their association with clinical lithium response in patients with bipolar disorder.
Methods
Using sets of genes involved in pathways affected by lithium, we developed 9 PSPGSs and evaluated their associations with lithium response in the International Consortium on Lithium Genetics (ConLi+Gen) (N = 2367), with validation in combined PsyCourse (Pathomechanisms and Signatures in the Longitudinal Course of Psychosis) (N = 105) and BipoLife (N = 102) cohorts. The association between each PSPGS and lithium response—defined both as a continuous ALDA score and a categorical outcome (good vs. poor responses)—was evaluated using regression models, with adjustment for confounders. The cutoff for a significant association was p < .05 after multiple testing correction.
Results
The PGSs for acetylcholine, GABA (gamma-aminobutyric acid), and mitochondria were associated with response to lithium in both categorical and continuous outcomes. However, the PGSs for calcium channel, circadian rhythm, and GSK (glycogen synthase kinase) were associated only with the continuous outcome. Each score explained 0.29% to 1.91% of the variance in the categorical and 0.30% to 1.54% of the variance in the continuous outcomes. A multivariate model combining PSPGSs that showed significant associations in the univariate analysis (combined PSPGS) increased the percentage of variance explained (R2) to 3.71% and 3.18% for the categorical and continuous outcomes, respectively. Associations for PGSs for GABA and circadian rhythm were replicated. Patients with the highest genetic loading (10th decile) for acetylcholine variants were 3.03 times more likely (95% CI, 1.95 to 4.69) to show a good lithium response (categorical outcome) than patients with the lowest genetic loading (1st decile).
Conclusions
PSPGSs achieved predictive performance comparable to the conventional genome-wide PGSs, with the added advantage of biological interpretability using a smaller list of genetic variants.
