Schizophrenia (Heidelberg, Germany)最新文献

Methamphetamine use can produce psychotic symptoms almost indistinguishable from schizophrenia (SCZ). Variation in DNA methylation may be closely implicated in the etiology and longitudinal development of psychiatric disorders. However, the relationship between psychotic symptoms, functional disability, and DNA methylation is still unclear. This study consists of three periods: discovery, validation, and follow-up. In the discovery stage, we employed genome-wide DNA methylation profiling (Illumina 450K) in peripheral blood mononuclear cells to test whether DNA methylation associates with psychotic symptoms and function state in representative SCZ and methamphetamine-induced psychotic disorder (MIP) patients. Then, we found seven differentially methylated regions/genes (DMRs, in UBA6, APOL3, KIF17, MLLT3, GRM8, CSNK1E, SETDB1) overlapping with genetic variants reported in previous studies of psychosis. In the validation stage, we compared the above-mentioned seven genes by MethLight qPCR method in Chinese Han males (N = 109 SCZ patients, N = 99 methamphetamine use disorder with MIP patients, N = 150 methamphetamine use disorder without MIP patients, N = 282 normal controls, age range: 18-50 years). GRM8 showed robustly altered methylation, which has passed rigorous filtration in subsequent validation, suggesting a remarkable contribution to SCZ and MIP. In addition, hypermethylation of GRM8 showed a significant association with the total scores of the Positive Negative Syndrome Scale and WHO disability assessment schedule II in both baseline and follow-up periods. Our findings suggest that increased CpG methylation in the promoter of GRM8 is a potential candidate epigenetic biomarker of psychotic symptoms in transdiagnostic samples of SCZ and MIP.

Hyperactivity of the human anterior hippocampus has been reported to spread from its CA1 subfield to the subiculum around the onset of first-episode psychosis and could be a cellular target for early therapeutic intervention in the schizophrenia prodrome. However, to what extent CA1 hyperactivity actually causes schizophrenia-related symptoms remains unknown. Here, we mimic this endophenotype by direct optogenetic activation of excitatory cells in the homologous mouse region, ventral CA1 (vCA1) and assess its consequence in multiple schizophrenia-related behavioural tests. We find that hyperactivity of vCA1 causes hyperlocomotion and impairments of spatial and object-related short-term habituation (spatial novelty-preference and novel-object recognition memory) and spatial working memory, whereas social interaction, spatial exploration, and anxiety remain unaltered. Stimulation of the ventral subiculum, in contrast, only increased locomotion and exploration. In conclusion, CA1 hyperactivity may be a direct driver of prodromal cognitive symptoms and of aberrant salience assignment leading to psychosis.

Several multivariate prognostic models have been published to predict outcomes in patients with first episode psychosis (FEP), but it remains unclear whether those predictions generalize to independent populations. Using a subset of demographic and clinical baseline predictors, we aimed to develop and externally validate different models predicting functional outcome after a FEP in the context of a schizophrenia-spectrum disorder (FES), based on a previously published cross-validation and machine learning pipeline. A crossover validation approach was adopted in two large, international cohorts (EUFEST, n = 338, and the PSYSCAN FES cohort, n = 226). Scores on the Global Assessment of Functioning scale (GAF) at 12 month follow-up were dichotomized to differentiate between poor (GAF current < 65) and good outcome (GAF current ≥ 65). Pooled non-linear support vector machine (SVM) classifiers trained on the separate cohorts identified patients with a poor outcome with cross-validated balanced accuracies (BAC) of 65-66%, but BAC dropped substantially when the models were applied to patients from a different FES cohort (BAC = 50-56%). A leave-site-out analysis on the merged sample yielded better performance (BAC = 72%), highlighting the effect of combining data from different study designs to overcome calibration issues and improve model transportability. In conclusion, our results indicate that validation of prediction models in an independent sample is essential in assessing the true value of the model. Future external validation studies, as well as attempts to harmonize data collection across studies, are recommended.

Social disconnection, including objective social isolation and subjective loneliness, is linked to substantial health risks. Yet, little is known about the predictors of social disconnection in individuals with mental illness. Here, we used machine learning to identify predictors of social isolation and loneliness in schizophrenia (N = 72), a psychiatric condition associated with social disconnection. For comparison, we also included two other groups: a psychiatric comparison sample of bipolar disorder (N = 48) and a community sample enriched for social isolation (N = 151). We fitted statistical models of social isolation and loneliness within and across groups. Each model included five candidate predictors: social avoidance motivation, depression, nonsocial cognition, social anhedonia, and social cognition. The results showed that social anhedonia explained unique variance in social isolation and loneliness in all samples, suggesting that it contributes to social isolation and loneliness broadly. However, nonsocial cognition explained unique variance in social isolation only within schizophrenia. Thus, social anhedonia could be a potential intervention target across populations, whereas nonsocial cognition may play a unique role in determining social disconnection in schizophrenia.

Schizophrenia and schizoaffective disorder present burdens to patients and health systems through elevated healthcare resource utilization (HCRU) and costs. However, there is a paucity of evidence describing these burdens across payor types. To identify unmet needs, this study characterized patients with schizophrenia or schizoaffective disorder by payor type. We identified patients aged 12-94 years with newly diagnosed schizophrenia or schizoaffective disorder (index date) between 01/01/2014 and 08/31/2020 with continuous enrollment for 12 months before and after index date from the Healthcare Integrated Research Database. After stratifying by post-index relapse frequency (0, 1, or ≥2) and payor type (commercial, Medicare Advantage/Supplemental (Medigap)/Part D, or managed Medicaid), we examined patient characteristics, treatment patterns, HCRU, costs, and relapse patterns and predictors. During follow-up, 25% of commercial patients, 29% of Medicare patients, and 37% of Medicaid patients experienced relapse. Atypical antipsychotic discontinuation was most common among Medicaid patients, with 65% of these patients discontinuing during follow-up. Compared to commercial patients, Medicare and Medicaid patients had approximately half as many psychotherapy visits during follow-up (12 vs. 5 vs. 7 visits, respectively). Relative to baseline, average unadjusted all-cause costs during follow-up increased by 105% for commercial patients, 66% for Medicare patients, and 77% for Medicaid patients. Patients with schizophrenia or schizoaffective disorder had high HCRU and costs but consistently low psychotherapy utilization, and they often discontinued pharmacologic therapy and experienced relapse. These findings illustrate the high burden and unmet need for managing these conditions and opportunities to improve care for underserved patients.

Distinguishing stable and fluctuating psychopathological features in young individuals at Ultra High Risk (UHR) for psychosis is challenging, but critical for building robust, accurate, early clinical detection and prevention capabilities. Over a 24-month period, 159 UHR individuals were assessed using the Positive and Negative Symptom Scale (PANSS). Generalisability Theory was used to validate the PANSS with this population and to investigate stable and fluctuating features, by estimating the reliability and generalisability of three factor (Positive, Negative, and General) and five factor (Positive, Negative, Cognitive, Depression, and Hostility) symptom models. Acceptable reliability and generalisability of scores across occasions and sample population were demonstrated by the total PANSS scale (Gr = 0.85). Fluctuating symptoms (delusions, hallucinatory behaviour, lack of spontaneity, flow in conversation, emotional withdrawal, and somatic concern) showed high variability over time, with 50-68% of the variance explained by individual transient states. In contrast, more stable symptoms included excitement, poor rapport, anxiety, guilt feeling, uncooperativeness, and poor impulse control. The 3-factor model of PANSS and its subscales showed robust reliability and generalisability of their assessment scores across the UHR population and evaluation periods (G = 0.77-0.93), offering a suitable means to assess psychosis risk. Certain subscales within the 5-factor PANSS model showed comparatively lower reliability and generalisability (G = 0.33-0.66). The identified and investigated fluctuating symptoms in UHR individuals are more amendable by means of intervention, which could have significant implications for preventing and addressing psychosis. Prioritising the treatment of fluctuating symptoms could enhance intervention efficacy, offering a sharper focus in clinical trials. At the same time, using more reliable total scale and 3 subscales can contribute to more accurate assessment of enduring psychosis patterns in clinical and experimental settings.

The treatment of cognitive impairment in schizophrenia is an unaddressed need due to the absence of novel treatments. Recent studies demonstrated that fingolimod and siponimod have neuroprotective effects in several neuropsychiatric disorders; however, their pharmacological mechanisms are unclear. The objective of this study was to identify potential molecular mechanisms of fingolimod and siponimod for improving cognition of schizophrenia through network pharmacology and molecular docking. The putative target genes of ingredients, schizophrenia, and impaired cognition were obtained from online databases, including SwissTargetPrediction, PharmMapper, GeneCards, CTD, DisGeNET, and OMIM. A protein-protein interaction network was constructed to identify core targets. The DAVID database was used for GO and KEGG pathway enrichment analyses. An ingredient-target-pathway-disease network was constructed using Cytoscape. Finally, the interactions between ingredients and core targets were assessed with molecular docking. The analysis revealed 260 targets shared by fingolimod and siponimod, 257 unique targets for fingolimod, and 88 unique targets for siponimod. Two signaling pathways were involved in fingolimod-mediated improvements in the cognition of schizophrenia, including the PI3K-Akt and MAPK signaling pathways. The core targets that regulated these two pathways included IL1B, AKT1, TNF, IL6, INS, BCL2, and BDNF. The MAPK signaling pathway was involved in siponimod-mediated improvement in the cognition of schizophrenia. The MAPK pathway was regulated by three core targets, namely TNF, AKT1, and CASP3. Docking scores ranged from -5.0 to -10.4 kcal/mol. Our analysis revealed that fingolimod regulates the PI3K-Akt and MAPK signaling pathways via the core targets IL1B, AKT1, TNF, IL6, INS, BCL2, and BDNF, and siponimod regulates the MAPK signaling pathways via the core targets AKT1, TNF, and CASP3 to improve the cognition of schizophrenia. Our results provide potential targets and a theoretical basis for the design of new drugs to treat the impaired cognition of schizophrenia.

Identification of youth presenting early risk factors for psychosis may facilitate preventive intervention. Through school-based screening, we recruited 112 children aged 9-12 years who presented multiple putative antecedents of schizophrenia (ASz), a family history of schizophrenia (FHx), or neither of these risk factors (typically-developing; TD). Clinical and functional outcomes were assessed at age 17-21 years (N = 93). Compared to the TD group, the ASz group had higher total Prodromal Questionnaire (PQ) scores (β = 10.59, 95% CI = 3.76, 17.42) and total psychopathology scores (β = 6.13, 95% CI: 1.03, 11.23), were more likely to score above-threshold on the PQ positive symptoms scale (OR = 4.00, 95% CI = 1.08, 14.83), and had lower scores on the Social and Occupational Functioning Scale (β = -9.43, 95% CI = -15.08, -3.77) at follow-up. The FHx and TD groups did not differ on any outcome. Findings suggest that population screening for putative antecedents of schizophrenia may identify children who would benefit from preventative intervention.

The Positive and Negative Syndrome Scale (PANSS) is the most widely used rating scale to assess psychotic symptoms in patients with schizophrenia and other primary psychoses. However, a definitive consensus regarding its dimensional structure remains elusive. The present work aims to determine the number of dimensions of the scale through a network analysis approach in a sample of individuals experiencing first-episode schizophrenia spectrum disorder (FE-SSD) with minimal or no prior exposure to antipsychotic treatment. Baseline data of 446 participants (age 25.96 ± 5.99 years, 70% males) enrolled in the OPTiMiSE trial were analysed. Exploratory Graph Analysis (EGA) was conducted to evaluate the dimensionality of the PANSS, and a bootstrap approach (bootEGA) was employed to assess model stability. The analysis was replicated, excluding unstable items with stability values below 0.75, until a stable model was achieved. The analysis of the 30 items of the PANSS revealed inadequate structural consistency, resulting in the exclusion of 9 unstable items. The final model comprised 21 symptoms distributed across four communities (Positive, Cognitive/Disorganised, Excited/Aggressive and Negative) but lacked a depressive domain. In conclusion, we propose a concise version of the PANSS, incorporating 21 items, to better assess the core symptoms of the first episode of SSD. This revised version provides clinicians with a robust psychometric tool with reduced administration time, but the complementary administration of a dedicated instrument for evaluating affective symptoms is advisable.