Serum S100B Is Related to Illness Duration and Clinical Symptoms in Schizophrenia-A Meta-Regression Analysis.


S100B has been linked to glial pathology in several psychiatric disorders. Previous studies found higher S100B serum levels in patients with schizophrenia compared to healthy controls, and a number of covariates influencing the size of this effect have been proposed in the literature. Here, we conducted a meta-analysis and meta-regression analysis on alterations of serum S100B in schizophrenia in comparison with healthy control subjects. The meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement to guarantee a high quality and reproducibility. With strict inclusion criteria 19 original studies could be included in the quantitative meta-analysis, comprising a total of 766 patients and 607 healthy control subjects. The meta-analysis confirmed higher values of the glial serum marker S100B in schizophrenia if compared with control subjects. Meta-regression analyses revealed significant effects of illness duration and clinical symptomatology, in particular the total score of the Positive and Negative Syndrome Scale (PANSS), on serum S100B levels in schizophrenia. In sum, results confirm glial pathology in schizophrenia that is modulated by illness duration and related to clinical symptomatology. Further studies are needed to investigate mechanisms and mediating factors related to these findings.

PubMed ID: 26941608

Projects: LIFE Adult

Publication type: Journal article

Journal: Front Cell Neurosci

Human Diseases: Schizophrenia

Citation: Front Cell Neurosci. 2016 Feb 25;10:46. doi: 10.3389/fncel.2016.00046. eCollection 2016.

Date Published: 5th Mar 2016

Registered Mode: by PubMed ID

Authors: K. Schumberg, M. Polyakova, J. Steiner, M. L. Schroeter

help Submitter

Views: 1881

Created: 13th May 2019 at 08:44

Last updated: 7th Dec 2021 at 17:58

help Attributions


Related items

Powered by
Copyright © 2008 - 2021 The University of Manchester and HITS gGmbH
Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig

By continuing to use this site you agree to the use of cookies