Schizophrenia (Heidelberg, Germany)最新文献

Understanding the biological underpinning of relapse could improve the outcomes of patients with psychosis. Relapse is elicited by multiple reasons/triggers, but the consequence frequently accompanies deteriorations of brain function, leading to poor prognosis. Structural brain imaging studies have recently been pioneered to address this question, but a lack of molecular investigations is a knowledge gap. Following a criterion used for recent publications by others, we defined the experiences of relapse by hospitalization(s) due to psychotic exacerbation. We hypothesized that relapse-associated molecules might be underscored from the neurometabolites whose levels have been different between overall patients with early-stage psychosis and healthy subjects in our previous report. In the present study, we observed a significant decrease in the levels of N-acetyl aspartate in the anterior cingulate cortex and thalamus in patients who experienced relapse compared to patients who did not. Altogether, decreased N-acetyl aspartate levels may indicate relapse-associated deterioration of neuronal networks in patients.

Schizophrenia is often characterized by recurring relapses, which are associated with a substantial clinical and economic burden. Early identification of individuals at the highest risk for relapse in real-world treatment settings could help improve outcomes and reduce healthcare costs. Prior work has identified a few consistent predictors of relapse in schizophrenia, however, studies to date have been limited to insurance claims data or small patient populations. Thus, this study used a large sample of health systems electronic health record (EHR) data to analyze relationships between patient-level factors and relapse and model a set of factors that can be used to identify the increased prevalence of relapse, a severe and preventable reality of schizophrenia. This retrospective, observational cohort study utilized EHR data extracted from the largest Midwestern U.S. non-profit healthcare system to identify predictors of relapse. The study included patients with a diagnosis of schizophrenia (ICD-10 F20) or schizoaffective disorder (ICD-10 F25) who were treated within the system between October 15, 2016, and December 31, 2021, and received care for at least 12 months. A relapse episode was defined as an emergency room or inpatient encounter with a pre-determined behavioral health-related ICD code. Patients' baseline characteristics, comorbidities and healthcare utilization were described. Modified log-Poisson regression (i.e. log Poisson regression with a robust variance estimation) analyses were utilized to estimate the prevalence of relapse across patient characteristics, comorbidities and healthcare utilization and to ultimately identify an adjusted model predicting relapse. Among the 8119 unique patients included in the study, 2478 (30.52%) experienced relapse and 5641 (69.48%) experienced no relapse. Patients were primarily male (54.72%), White Non-Hispanic or Latino (54.23%), with Medicare insurance (51.40%), and had baseline diagnoses of substance use (19.24%), overweight/obesity/weight gain (13.06%), extrapyramidal symptoms (48.00%), lipid metabolism disorder (30.66%), hypertension (26.85%), and diabetes (19.08%). Many differences in patient characteristics, baseline comorbidities, and utilization were revealed between patients who relapsed and patients who did not relapse. Through model building, the final adjusted model with all significant predictors of relapse included the following variables: insurance, age, race/ethnicity, substance use diagnosis, extrapyramidal symptoms, number of emergency room encounters, behavioral health inpatient encounters, prior relapses episodes, and long-acting injectable prescriptions written. Prevention of relapse is a priority in schizophrenia care. Challenges related to historical health record data have limited the knowledge of real-world predictors of relapse. This study offers a set of variables that could conceivably be used to construct algorithms or models to proactively monitor demographic,

Genome-wide association studies suggest significant overlaps in Parkinson's disease (PD) and schizophrenia (SZ) risks, but the underlying mechanisms remain elusive. The protein-protein interaction network ('interactome') plays a crucial role in PD and SZ and can incorporate their spatiotemporal specificities. Therefore, to study the linked biology of PD and SZ, we compiled PD- and SZ-associated genes from the DisGeNET database, and constructed their interactomes using BioGRID and HPRD. We examined the interactomes using clustering and enrichment analyses, in conjunction with the transcriptomic data of 26 brain regions spanning foetal stages to adulthood available in the BrainSpan Atlas. PD and SZ interactomes formed four gene clusters with distinct temporal identities (Disease Gene Networks or 'DGNs'1-4). DGN1 had unique SZ interactome genes highly expressed across developmental stages, corresponding to a neurodevelopmental SZ subtype. DGN2, containing unique SZ interactome genes expressed from early infancy to adulthood, correlated with an inflammation-driven SZ subtype and adult SZ risk. DGN3 contained unique PD interactome genes expressed in late infancy, early and late childhood, and adulthood, and involved in mitochondrial pathways. DGN4, containing prenatally-expressed genes common to both the interactomes, involved in stem cell pluripotency and overlapping with the interactome of 22q11 deletion syndrome (comorbid psychosis and Parkinsonism), potentially regulates neurodevelopmental mechanisms in PD-SZ comorbidity. Our findings suggest that disrupted neurodevelopment (regulated by DGN4) could expose risk windows in PD and SZ, later elevating disease risk through inflammation (DGN2). Alternatively, variant clustering in DGNs may produce disease subtypes, e.g., PD-SZ comorbidity with DGN4, and early/late-onset SZ with DGN1/DGN2.

Childhood trauma has been linked to schizophrenia, but underlying biological mechanisms remain elusive. This study explored the potential role of plasma oxytocin as a mediator in the relationship between childhood trauma and the psychopathology of schizophrenia. 160 patients with schizophrenia and 80 age- and sex-matched healthy controls were assessed for childhood trauma experiences using the Childhood Trauma Questionnaire and structured interviews. Psychopathology was evaluated using the Positive and Negative Syndrome Scale and plasma oxytocin levels were measured. Results showed that patients with schizophrenia had lower oxytocin levels and higher childhood trauma scores than healthy controls. There was a significant correlation between childhood trauma scores and psychopathology, with plasma oxytocin levels being inversely associated with psychopathology, except for positive symptoms. Hierarchical regression analysis indicated that both childhood trauma scores and plasma oxytocin levels significantly predicted psychopathology. Plasma oxytocin levels partially mediated the relationship between childhood trauma and schizophrenia psychopathology. This study underscores the potential role of oxytocin in bridging the gap between childhood trauma and schizophrenia.

Deficit schizophrenia (DS) is a subtype of schizophrenia (SCZ). The polygenic effects on the neuroimaging alterations in DS still remain unknown. This study aims to calculate the polygenic risk scores for schizophrenia (PRS-SCZ) in DS, and further explores the potential associations with functional features of brain. PRS-SCZ was calculated according to the Whole Exome sequencing and Genome-wide association studies (GWAS). Resting-state fMRI, as well as biochemical features and neurocognitive data were obtained from 33 DS, 47 NDS and 41 HCs, and association studies of genetic risk with neuroimaging were performed in this sample. The analyses of amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo) and functional connectivity (FC) were performed to detect the functional alterations between DS and NDS. In addition, correlation analysis was used to investigate the relationships between functional features (ALFF, ReHo, FC) and PRS-SCZ. The PRS-SCZ of DS was significantly lower than that in NDS and HC. Compared to NDS, there was a significant increase in the ALFF of left inferior temporal gyrus (ITG.L) and left inferior frontal gyrus (IFG.L) and a significant decrease in the ALFF of right precuneus (PCUN.R) and ReHo of right middle frontal gyrus (MFG.R) in DS. FCs were widely changed between DS and NDS, mainly concentrated in default mode network, including ITG, PCUN and angular gyrus (ANG). Correlation analysis revealed that the ALFF of left ITG, the ReHo of right middle frontal gyrus, the FC value between insula and ANG, left ITG and right corpus callosum, left ITG and right PCUN, as well as the scores of Trail Making Test-B, were associated with PRS-SCZ in DS. The present study demonstrated the differential polygenic effects on functional changes of brain in DS and NDS, providing a potential neuroimaging-genetic perspective for the pathogenesis of schizophrenia.

A recent meta-epidemiological study did not reveal major differences between the results of blinded and open randomised-controlled trials (RCTs). Fewer patients may consent to double-blind RCTs than to open RCTs, compromising generalisability, making this question very important. However, the issue has not been addressed in schizophrenia. We used a database of randomised, acute-phase antipsychotic drug trials. Whenever at least one open and one blinded RCT was available for a comparison of two drugs, we contrasted the results by random-effects meta-analysis with subgroup tests. The primary outcome was overall symptoms as measured by the Positive and Negative Syndrome Scale, supplemented by seven secondary efficacy and side-effect outcomes. We also examined whether open RCTs were biased in favour of more recently introduced antipsychotics, less efficacious or more prone to side-effects antipsychotics, and pharmaceutical sponsors. 183 RCTs (155 blinded and 28 open) with 34715 participants comparing two active drugs were available. The results did not suggest general differences between open and blinded RCTs, which examined two active drugs. Only 12 out of 122 subgroup tests had a p-value below 0.1, four below 0.05, and if a Bonferroni correction for multiple tests had been applied, only one would have been significant. There were some exceptions which, however, did not always confirm the originally hypothesized direction of bias. Due to the relatively small number of open RCTs, our analysis is exploratory, but this fundamental question should be given more scientific attention. Currently, open RCTs should be excluded from meta-analyses, at least in sensitivity analyses.

Bipolar disorder (BD) showed the highest suicide rate of all psychiatric disorders, and its underlying causative genes and effective treatments remain unclear. During diagnosis, BD is often confused with schizophrenia (SC) and major depressive disorder (MDD), due to which patients may receive inadequate or inappropriate treatment, which is detrimental to their prognosis. This study aims to establish a diagnostic model to distinguish BD from SC and MDD in multiple public datasets through bioinformatics and machine learning and to provide new ideas for diagnosing BD in the future. Three brain tissue datasets containing BD, SC, and MDD were chosen from the Gene Expression Omnibus database (GEO), and two peripheral blood datasets were selected for validation. Linear Models for Microarray Data (Limma) analysis was carried out to identify differentially expressed genes (DEGs). Functional enrichment analysis and machine learning were utilized to identify. Least absolute shrinkage and selection operator (LASSO) regression was employed for identifying candidate immune-associated central genes, constructing protein-protein interaction networks (PPI), building artificial neural networks (ANN) for validation, and plotting receiver operating characteristic curve (ROC curve) for differentiating BD from SC and MDD and creating immune cell infiltration to study immune cell dysregulation in the three diseases. RBM10 was obtained as a candidate gene to distinguish BD from SC. Five candidate genes (LYPD1, HMBS, HEBP2, SETD3, and ECM2) were obtained to distinguish BD from MDD. The validation was performed by ANN, and ROC curves were plotted for diagnostic value assessment. The outcomes exhibited the prediction model to have a promising diagnostic value. In the immune infiltration analysis, Naive B, Resting NK, and Activated Mast Cells were found to be substantially different between BD and SC. Naive B and Memory B cells were prominently variant between BD and MDD. In this study, RBM10 was found as a candidate gene to distinguish BD from SC; LYPD1, HMBS, HEBP2, SETD3, and ECM2 serve as five candidate genes to distinguish BD from MDD. The results obtained from the ANN network showed that these candidate genes could perfectly distinguish BD from SC and MDD (76.923% and 81.538%, respectively).

We examined the association between causal attributions and self-reported motivational negative symptoms (amotivation) in a German online community sample (n = 251). Bivariate correlations revealed significant associations between amotivation and attribution of success to external, variable, and specific causes. No associations between amotivation and failure attributions were found. Our data suggest that demotivational causal attributions of success could be a feature of amotivation and a promising target for research and intervention.

Schizophrenia is a severe mental illness and a major risk factor for suicide, with approximately 50% of schizophrenia patients attempting and 10% dying from suicide. Although genetic components play a significant role in schizophrenia risk, the underlying genetic risk factors for suicide are poorly understood. The complement component C4 gene, an immune gene involved in the innate immune system and located in the major histocompatibility complex (MHC) region, has been identified to be strongly associated with schizophrenia risk. In addition, recent findings have also suggested that the MHC region has been associated with suicide risk across disorders, making C4 a potential candidate of interest for studying suicidality in schizophrenia patients. Despite growing interest in investigating the association between the C4 gene and schizophrenia, to our knowledge, no work has been done to examine the potential of C4 variants as suicide risk factors in patients with schizophrenia. In this study, we investigated the association between different C4 copy number variants and predicted C4 brain expression with suicidal outcomes (suicide attempts/suicidal ideation). We directly genotyped 434 schizophrenia patients to determine their C4A and C4B copy number variants. We found the C4AS copy number to be marginally and negatively associated with suicide risk, potentially being protective against suicide attempts (OR = 0.49; p = 0.05) and suicidal ideation (OR = 0.65; p = 0.07). Furthermore, sex-stratified analyses revealed that there are no significant differences between males and females. Our preliminary findings encourage additional studies of C4 and potential immune dysregulation in suicide.