Literature DB >> 25124519

Neuroimmune biomarkers in schizophrenia.

Jakub Tomasik1, Hassan Rahmoune2, Paul C Guest2, Sabine Bahn3.   

Abstract

Schizophrenia is a heterogeneous psychiatric disorder with a broad spectrum of clinical and biological manifestations. Due to the lack of objective tests, the accurate diagnosis and selection of effective treatments for schizophrenia remains challenging. Numerous technologies have been employed in search of schizophrenia biomarkers. These studies have suggested that neuroinflammatory processes may play a role in schizophrenia pathogenesis, at least in a subgroup of patients. The evidence indicates alterations in both pro- and anti-inflammatory molecules in the central nervous system, which have also been found in peripheral tissues and may correlate with schizophrenia symptoms. In line with these findings, certain immunomodulatory interventions have shown beneficial effects on psychotic symptoms in schizophrenia patients, in particular those with distinct immune signatures. In this review, we evaluate these findings and their potential for more targeted drug interventions and the development of companion diagnostics. Although currently no validated markers exist for schizophrenia patient stratification or the prediction of treatment efficacy, we propose that utilisation of inflammatory markers for diagnostic and theranostic purposes may lead to novel therapeutic approaches and deliver more effective care for schizophrenia patients.
Copyright © 2014 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Add-on treatment; Biomarker; Immune system; Personalised medicine; Schizophrenia

Mesh:

Substances:

Year:  2014        PMID: 25124519     DOI: 10.1016/j.schres.2014.07.025

Source DB:  PubMed          Journal:  Schizophr Res        ISSN: 0920-9964            Impact factor:   4.939


  33 in total

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Review 6.  Advantages and Limitations of Animal Schizophrenia Models.

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Review 9.  Emerging therapeutic targets for schizophrenia: a framework for novel treatment strategies for psychosis.

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10.  An integrated machine learning framework for a discriminative analysis of schizophrenia using multi-biological data.

Authors:  Peng-Fei Ke; Dong-Sheng Xiong; Jia-Hui Li; Zhi-Lin Pan; Jing Zhou; Shi-Jia Li; Jie Song; Xiao-Yi Chen; Gui-Xiang Li; Jun Chen; Xiao-Bo Li; Yu-Ping Ning; Feng-Chun Wu; Kai Wu
Journal:  Sci Rep       Date:  2021-07-19       Impact factor: 4.379

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