Schizophrenia: A Narrative Review of Etiopathogenetic, Diagnostic and Treatment Aspects

Role of different omics data in the diagnosis of schizophrenia disorder: A machine learning study

Schizophrenia is a serious mental disorder that affects millions of people worldwide. This disorder slowly disintegrates thinking ability and changes behaviours of patients. These patients will show some psychotic symptoms such as hallucinations, delusions, thought disorder and movement disorder. These symptoms are in common with some other psychiatric disorders such as bipolar disorder, major depressive disorder and mood spectrum disorder. As patients would require immediate treatment, an on-time diagnosis is critical. This study explores the use of omics data in diagnosis of schizophrenia. Transcriptome, miRNA and epigenome data are used in diagnosis of patients with schizophrenia with the aid of machine learning algorithms. As the data is in high dimension, mutual information and feature importance are independently used for selecting relevant features for the study. Selected sets of features (biomarkers) are individually used with different machine learning algorithms and their performances are compared to select the best-performing model. This study shows that the top 140 miRNA features selected using mutual information along with support vector machines give the highest accuracy (0.86 ± 0.07) in the diagnosis of schizophrenia. All reported accuracies are validated using 5-fold cross validation. They are further validated using leave one out cross validation and the accuracies are reported in the supplementary material.

New clues for the role of cerebellum in schizophrenia and the associated cognitive impairment

Schizophrenia (SZ) is a complex neuropsychiatric disorder associated with severe cognitive dysfunction. Although research has mainly focused on forebrain abnormalities, emerging results support the involvement of the cerebellum in SZ physiopathology, particularly in Cognitive Impairment Associated with SZ (CIAS). Besides its role in motor learning and control, the cerebellum is implicated in cognition and emotion. Recent research suggests that structural and functional changes in the cerebellum are linked to deficits in various cognitive domains including attention, working memory, and decision-making. Moreover, cerebellar dysfunction is related to altered cerebellar circuit activities and connectivity with brain regions associated with cognitive processing. This review delves into the role of the cerebellum in CIAS. We initially consider the major forebrain alterations in CIAS, addressing impairments in neurotransmitter systems, synaptic plasticity, and connectivity. We then focus on recent findings showing that several mechanisms are also altered in the cerebellum and that cerebellar communication with the forebrain is impaired. This evidence implicates the cerebellum as a key component of circuits underpinning CIAS physiopathology. Further studies addressing cerebellar involvement in SZ and CIAS are warranted and might open new perspectives toward understanding the physiopathology and effective treatment of these disorders.

Integrative metabolomics-genomics analysis identifies key networks in a stem cell-based model of schizophrenia

Schizophrenia (SCZ) is a neuropsychiatric disorder, caused by a combination of genetic and environmental factors. The etiology behind the disorder remains elusive although it is hypothesized to be associated with the aberrant response to neurotransmitters, such as dopamine and glutamate. Therefore, investigating the link between dysregulated metabolites and distorted neurodevelopment holds promise to offer valuable insights into the underlying mechanism of this complex disorder. In this study, we aimed to explore a presumed correlation between the transcriptome and the metabolome in a SCZ model based on patient-derived induced pluripotent stem cells (iPSCs). For this, iPSCs were differentiated towards cortical neurons and samples were collected longitudinally at various developmental stages, reflecting neuroepithelial-like cells, radial glia, young and mature neurons. The samples were analyzed by both RNA-sequencing and targeted metabolomics and the two modalities were used to construct integrative networks in silico. This multi-omics analysis revealed significant perturbations in the polyamine and gamma-aminobutyric acid (GABA) biosynthetic pathways during rosette maturation in SCZ lines. We particularly observed the downregulation of the glutamate decarboxylase encoding genes GAD1 and GAD2, as well as their protein product GAD65/67 and their biochemical product GABA in SCZ samples. Inhibition of ornithine decarboxylase resulted in further decrease of GABA levels suggesting a compensatory activation of the ornithine/putrescine pathway as an alternative route for GABA production. These findings indicate an imbalance of cortical excitatory/inhibitory dynamics occurring during early neurodevelopmental stages in SCZ. Our study supports the hypothesis of disruption of inhibitory circuits to be causative for SCZ and establishes a novel in silico approach that enables for integrative correlation of metabolic and transcriptomic data of psychiatric disease models.

Neuroinflammation and Schizophrenia: New Therapeutic Strategies through Psychobiotics, Nanotechnology, and Artificial Intelligence (AI)

The prevalence of schizophrenia, affecting approximately 1% of the global population, underscores the urgency for innovative therapeutic strategies. Recent insights into the role of neuroinflammation, the gut-brain axis, and the microbiota in schizophrenia pathogenesis have paved the way for the exploration of psychobiotics as a novel treatment avenue. These interventions, targeting the gut microbiome, offer a promising approach to ameliorating psychiatric symptoms. Furthermore, advancements in artificial intelligence and nanotechnology are set to revolutionize psychobiotic development and application, promising to enhance their production, precision, and effectiveness. This interdisciplinary approach heralds a new era in schizophrenia management, potentially transforming patient outcomes and offering a beacon of hope for those afflicted by this complex disorder.

Enhancing Predictive Power: Integrating a Linear Support Vector Classifier with Logistic Regression for Patient Outcome Prognosis in Virtual Reality Therapy for Treatment-Resistant Schizophrenia

(1) Background: Approximately 30% of schizophrenia patients are known to be treatment-resistant. For these cases, more personalized approaches must be developed. Virtual reality therapeutic approaches such as avatar therapy (AT) are currently undergoing investigations to address these patients' needs. To further tailor the therapeutic trajectory of patients presenting with this complex presentation of schizophrenia, quantitative insight about the therapeutic process is warranted. The aim of the study is to combine a classification model with a regression model with the aim of predicting the therapeutic outcomes of patients based on the interactions taking place during their first immersive session of virtual reality therapy. (2) Methods: A combination of a Linear Support Vector Classifier and logistic regression was conducted over a dataset comprising 162 verbatims of the immersive sessions of 18 patients who previously underwent AT. As a testing dataset, 17 participants, unknown to the dataset, had their first immersive session presented to the combinatory model to predict their clinical outcome. (3) Results: The model accurately predicted the clinical outcome for 15 out of the 17 participants. Classification of the therapeutic interactions achieved an accuracy of 63%. (4) Conclusion: To our knowledge, this is the first attempt to predict the outcome of psychotherapy patients based on the content of their interactions with their therapist. These results are important as they open the door to personalization of psychotherapy based on quantitative information about the interactions taking place during AT.

Reviewing the Potential Therapeutic Approaches Targeting the Modulation of Gastrointestinal Microflora in Schizophrenia

Schizophrenia (SCZ) is a severe brain disorder characterized by an intriguing clinical panel that has begun to gain interest due to its particular phenotype. Having considered the role of gut microflora in psychiatry, the latest discoveries might offer further insight into the underlying mechanisms. Thus, we aimed to offer an updated overview of the therapeutic potential of microorganism-derived supplements alongside dedicated protocols that target the re-establishment of the host's eubiosis. Based on combinations of specific keywords, we performed searches in four databases (PubMed/Medline, ISI Web of Knowledge, Scopus, and ScienceDirect) for the established interval (2018-2022) and identified twenty two eligible cases, restricted only to human patients' experiences. Up until the writing of this manuscript, it has been revealed that the administration of specific lactic acid bacteria strains (Lactobacillus and Bifidobacterium), or those combined with vitamin D and selenium, maintain the integrity of the gut flora, preventing antagonistic effects including inflammation, antipsychotic-related body weight gain (olanzapine) and other metabolic dysfunctionalities. However, there are multiple antipsychotics that exert a potent effect upon gut flora, influencing a plethora of pathways and creating a dysbalance ratio between beneficial and opportunistic pathogens. Risperidone, amisulpride, and clozapine are just a few examples, but the current literature is unfortunately inconsistent and reported data is contradictory, which is why we support additional studies in this context. Moreover, we further argue the utility of studying how distinct controlled substances influence microbial communities, considering that ketamine is proved to alleviate depressive-like behavior as opposed to amphetamine and phencyclidine, which are known substances to trigger SCZ-like symptoms in experimental models. Probiotics may be regarded as the most consequential vehicle through which the gut flora can be successfully influenced, in adequate doses exerting a beneficial role as an alternative approach to alleviate SCZ symptoms.