Systematic Review and Meta-Analysis of Mass Spectrometry Proteomics Applied to Human Peripheral Fluids to Assess Potential Biomarkers of Schizophrenia.

Mass spectrometry (MS)-based techniques can be a powerful tool to identify neuropsychiatric disorder biomarkers, improving prediction and diagnosis ability. Here, we evaluate the efficacy of MS proteomics applied to human peripheral fluids of schizophrenia (SCZ) patients to identify disease biomarkers and relevant networks of biological pathways. Following PRISMA guidelines, a search was performed for studies that used MS proteomics approaches to identify proteomic differences between SCZ patients and healthy control groups (PROSPERO database: CRD42021274183). Nineteen articles fulfilled the inclusion criteria, allowing the identification of 217 differentially expressed proteins. Gene ontology analysis identified lipid metabolism, complement and coagulation cascades, and immune response as the main enriched biological pathways. Meta-analysis results suggest the upregulation of FCN3 and downregulation of APO1, APOA2, APOC1, and APOC3 in SCZ patients. Despite the proven ability of MS proteomics to characterize SCZ, several confounding factors contribute to the heterogeneity of the findings. In the future, we encourage the scientific community to perform studies with more extensive sampling and validation cohorts, integrating omics with bioinformatics tools to provide additional comprehension of differentially expressed proteins. The produced information could harbor potential proteomic biomarkers of SCZ, contributing to individualized prognosis and stratification strategies, besides aiding in the differential diagnosis.

Serum uric acid as a predictor of bipolarity in individuals with a major depressive episode.

OBJECTIVES: There are no well-established biomarkers to predict the risk of conversion to bipolar disorder (BD) in patients with depression. Given the putative role of purinergic neurotransmission dysfunction in BD, the purpose of our study was to evaluate if higher serum uric acid (UA) levels could predict BD conversion in depressed inpatients. METHODS: We reviewed retrospectively the records of subjects hospitalized between June 2007 and June 2010 with a diagnosis of major depressive disorder (MDD) who had undergone routine UA levels testing at admission. At an approximate 10-year follow-up we identified subjects with a subsequent diagnosis of BD. We compared UA levels between the BD-converter and non-BD converter groups, performed Receiver operating characteristic curve analysis to evaluate the prognostic accuracy of serum UA levels and calculated the clinical utility index (CUI) as a risk biomarker for conversion to BD. RESULTS: The study included 250 subjects (55 "BD-converters" and 195 "No BD-converters"). "BD-converters" had significantly higher plasma UA levels compared to "No BD-converters" in their index hospitalization irrespective of gender (males: 403.84 ± 91.76 vs 270.81 ± 53.58 µmol/L; U = 94.5, P < 0.001 and females 302.19 ± 52.64 vs 202.69 ± 48.93 µmol/L; t = 10.75, P < 0.001). Serum UA levels showed a very good to excellent accuracy for predicting conversion to BD in inpatients with MDD (area under the curve [AUC]: 0.90; 95% CI: 0.86, 0.94) and had a good to excellent CUI- and a moderate to good CUI+ grading for discriminating BD-converter cases from non-BD converters. CONCLUSIONS: Our study suggests that depressed patients with higher levels of serum UA are at greater risk of a subsequent manic or hypomanic episode. The purinergic system could prove a promising path for the search of biomarkers in BD.

Treatment Options for Insomnia in Schizophrenia: A Systematic Review.

BACKGROUND: Insomnia is a common feature of schizophrenia. Although several studies have been published about the influence of certain drugs on schizophrenia patients' sleep, there are no well-grounded recommendations about insomnia treatment in this clinical setting. The present review aimed to identify relevant empirical evidence on available treatments of insomnia in patients with schizophrenia, assessing their safety and efficacy. METHODS: This is a systematic review of clinical trials investigating the effect of treatments for insomnia in patients with a diagnosis of schizophrenia. Data were obtained from Medline/PubMed, Embase, PsycInfo, and the Cochrane Library. Risk of bias was assessed in individual studies for selection, performance, detection, attrition, and reporting bias. RESULTS: Four studies met inclusion criteria; 2 using melatonin, 1 using paliperidone, and 1 with eszopiclone. All reported positive results: melatonin increased sleep efficiency and total duration of sleep; paliperidone decreased sleep latency onset and increased total sleep time and sleep efficiency; eszopiclone decreased insomnia severity index. CONCLUSIONS: Despite a very limited number of specific studies on this matter, all 4 studies have shown good benefit/risk ratios and reviewed options-melatonin, paliperidone, and eszopiclone-might represent valid options for residual insomnia in schizophrenia.

Proteomic analysis of peripheral blood mononuclear cells in first episode psychosis - Protein and peptide-centered approaches to elucidate potential diagnostic biomarkers.

Diagnosing patients suffering from psychotic disorders is far from being achieved with molecular support, despite all the efforts to study these disorders from different perspectives. Characterizing the proteome of easily obtainable blood specimens, such as the peripheral blood mononuclear cells (PBMCs), has particular interest in biomarker discovery and generating pathophysiological knowledge. This approach has been explored in psychiatry, and while generating valuable information, it has not translated into meaningful biomarker discovery. In this project, we report the proof-of-concept of a methodology that aims to explore further information obtained with classical proteomics approaches that is easily overlooked. PBMC samples from first-episode psychosis and control subjects were subjected to a SWATH-MS approach, and the classical protein relative quantification was performed, where 389 proteins were found to be important to distinguish the two groups. Individual analysis of the quantified peptides was also performed, highlighting peptides of unchanged proteins that were significantly altered. With the combination of protein- and peptide-centered proteomics approaches, it is possible to highlight that information about proteoforms, namely regulation at the peptide level possibly due to post-translational modifications, is routinely overlooked and that its diagnostic potential should be further explored. SIGNIFICANCE: Our exploratory findings highlight the potential of MS-based proteomics strategies, combining protein- and peptide-centered approaches, to aid clinical decision-making in first-episode psychosis, helping to establish early biomarkers for schizophrenia and other psychotic disorders. Particularly, the less popular peptide-centered approach allows the identification/measurement of overlooked modulated peptides that may have potential biomarker characteristics. The application in parallel of protein- and peptide-centered strategies is transversal to research of other diseases, potentially allowing a more comprehensive characterization of the metabolic/pathophysiological alterations related to a specific disease.

Metabolic parameters as possible diagnostic predictors in first-episode psychosis: An exploratory retrospective cohort study.

AIM: Patients in early phases of schizophrenia or mood disorders with psychotic symptoms have a wide array of metabolic abnormalities. We analysed the potential predictive value of uric acid (UA) levels and other metabolic parameters in first-episode psychosis patients to differentiate between non-affective and affective psychosis. METHODS: Retrospective chart review of all patients referenced to a first-episode psychosis unit (n = 149), between 2012 and 2017, with available UA levels. Patients included (n = 37) were compared according to the follow-up diagnosis of schizophrenia or mood disorder. RESULTS: Mood disorder patients presented higher UA levels (p = .030) and lower fasting blood glucose levels (p = .020) compared with schizophrenia patients. The remaining variables did not show significant intergroup differences. CONCLUSIONS: Findings in this first-episode psychosis cohort support previous evidence suggesting higher UA levels as a predictor of affective psychosis and glucose dysfunction as predictive of schizophrenia. Further studies are needed to explore metabolic parameters as possible diagnostic predictors in first-episode psychosis.

Risk Calculators in Bipolar Disorder: A Systematic Review.

INTRODUCTION: Early recognition of bipolar disorder improves the prognosis and decreases the burden of the disease. However, there is a significant delay in diagnosis. Multiple risk factors for bipolar disorder have been identified and a population at high-risk for the disorder has been more precisely defined. These advances have allowed the development of risk calculators to predict individual risk of conversion to bipolar disorder. This review aims to identify the risk calculators for bipolar disorder and assess their clinical applicability. METHODS: A systematic review of original studies on the development of risk calculators in bipolar disorder was performed. The studies' quality was evaluated with the Newcastle-Ottawa Quality Assessment Form for Cohort Studies and according to recommendations of the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis Initiative. RESULTS: Three studies met the inclusion criteria; one developed a risk calculator of conversion from major depressive episode to bipolar disorder; one of conversion to new-onset bipolar spectrum disorders in offspring of parents with bipolar disorder; and the last one of conversion in youths with bipolar disorder not-otherwise-specified. CONCLUSIONS: The calculators reviewed in this article present good discrimination power for bipolar disorder, although future replication and validation of the models is needed.