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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.

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.

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. info:eu-repo/semantics/publishedVersion

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.

Delirium is a common and serious acute neuropsychiatric syndrome and is known to increase the risk for subsequent functional decline and mortality. The etiologies of delirium are diverse and multifactorial and often reflect the pathophysiological consequences of an acute medical illness, medical complication or drug intoxication. Delirium can have a widely variable presentation and is often missed and underdiagnosed as a result. At this chapter we discuss the prediction, etiology, prevention, diagnosis, subtypes and treatment (pharmacological and non-pharmacological) of delirium in the elderly population.

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

Background: Delayed diagnosis of bipolar disorder (BD) is common in adolescents and young adults and the search for biomarkers to help in early diagnosis in BD at-risk populations is an important goal of neuroimaging research. Functional connectivity studies in BD patients suggests that anomalous connectivity between prefrontal and limbic regions could be risk biomarkers for BD. The aim of this review is to provide an overview of the neuroimaging literature that employed functional connectivity techniques in adolescents and young adults at-risk for BD. Methods: A literature search was conducted using PubMed and EMBASE databases, to identify fMRI studies that em- ployed a measure of functional or e ective connectivity or network based statistics and included individuals at-risk for BD who were in the age range of early-mid adolescence (13–18 years old) and/or young adulthood (19–25 years old). Results: Ten studies focusing on 4 functional imaging domains were identi ed, namely emotion processing, a ective cognition, reward processing and resting-state. Altered functional connectivity between amygdala and ventrolateral prefrontal cortex (PFC); amygdala and anterior cingulate cortex and between anterior cingulate cortex, ventrolateral PFC and dorsolateral PFC emerged as putative risk biomarkers. Heterogeneity in BD at-risk samples, tasks and connectivity analysis methods has been identi ed. Conclusions: Youth at-risk for BD have altered functional connectivity in prefrontal-limbic networks supporting emo- tion regulation that might underlie emotion lability and mood dysregulation predisposing to BD. Future longitudinal studies in adolescents and young adults with Bipolar At-Risk criteria are important to establish functional connectivity measures as risk biomarkers.

Schizophrenia is a severe mental disorder and one of the leading causes of disease burden worldwide. It represents a source of significant suffering and disability to the affected individuals, and is associated with substantial societal and economical costs. The diagnosis of schizophrenia still depends exclusively on the detection of symptoms that are also present in other mental disorders. This situation causes overlapping of the boundaries of the diagnostic categories and constitutes a source of diagnostic errors. Moreover, current treatment algorithms do not take into account the substantial interindividual variability in response to antipsychotic drugs. As a result, around one-third of patients are treatment-resistant to first line antipsychotic drugs. This deleterious consequence is associated with poor individual outcomes and elevated healthcare costs. Neuroimaging research in schizophrenia has shed some light in a vast array of structural and functional connectivity abnormalities and neurochemical (dopamine and glutamate) imbalances, which may constitute ‘organic surrogates’ of this disorder. However, the neuroimaging field, so far, has not been able to identify biomarkers that could facilitate early detection and allow individualised treatment management. This paper reviews neuroimaging studies from different modalities that may provide relevant biomarkers for schizophrenia. We discuss how the current application of novel Machine Learning methods to the analyses of imaging data is allowing the translation of such findings into potential biomarkers enabling the prediction of clinical outcomes at the individual level, towards the development of innovative and personalised treatment strategies. info:eu-repo/semantics/publishedVersion

ObjectivesThere are few published studies on the relationship between gender and psychiatric disorders. We assessed sex differences in a sample of first-admission psychiatric inpatients to identify possible risk factors and targets for sex-tailored treatment interventions.MethodsA retrospective study of admissions to the psychiatry department, Coimbra hospital Universitary Centre, Portugal, in 2015 was accomplished (n = 924). The two groups were compared for demographic features, psychiatric diagnoses and medical comorbidities.ResultsMale patients were significantly younger (age average = 47.7 vs. 53.3). Differences in employment, educational, and marital statuses were found between male and female psychiatric patients. Having a degree was a protective factor for males, whereas it was a risk factor for females. Being divorced and single were both risk factors for medical co-morbidity in females. A higher proportion of men among patients hospitalized for schizophrenia (14.9 vs. 5.5%) or substance use disorder (10.3 vs. 2.1%) and a higher proportion of women among those admitted for affective disorders (43.3 vs. 25.9%), including bipolar disorder (13.4 vs. 9.0%), were found. No significant differences in duration of hospitalization between the two groups (22.8 in male vs. 22.2 days in female)ConclusionsThe differences between the two groups of inpatients were very pronounced. A better understanding of these differences may help to establish more effective treatment strategies.Disclosure of interestThe authors have not supplied their declaration of competing interest.