Systematic Review and Meta-Analysis of Circulating S100B Blood Levels in Schizophrenia
Author(s) -
Katina Aleksovska,
Emanuele Leoncini,
Stefano Bonassi,
Alfredo Cesario,
Stefania Boccia,
Alessandra Frustaci
Publication year - 2014
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0106342
Subject(s) - meta analysis , medicine , schizophrenia (object oriented programming) , biomarker , confidence interval , case control study , web of science , publication bias , oncology , psychiatry , bioinformatics , biology , biochemistry
S100B is a calcium-binding protein secreted in central nervous system from astrocytes and other glia cells. High blood S100B levels have been linked to brain damage and psychiatric disorders. S100B levels have been reported to be higher in schizophrenics than healthy controls. To quantify the relationship between S100B blood levels and schizophrenia a systematic literature review of case-control studies published on this topic within July 3 rd 2014 was carried out using three bibliographic databases: Medline, Scopus and Web of Science. Studies reporting mean and standard deviation of S100B blood levels both in cases and controls were included in the meta-analysis. The meta-Mean Ratio (mMR) of S100B blood levels in cases compared to controls was used as a measure of effect along with its 95% Confidence Intervals (CI). 20 studies were included totaling for 994 cases and 785 controls. Schizophrenia patients showed 76% higher S100B blood levels than controls with mMR = 1.76 95% CI: 1.44–2.15. No difference could be found between drug-free patients with mMR = 1.84 95%CI: 1.24–2.74 and patients on antipsychotic medication with mMR = 1.75 95% CI: 1.41–2.16). Similarly, ethnicity and stage of disease didn't affect results. Although S100B could be regarded as a possible biomarker of schizophrenia, limitations should be accounted when interpreting results, especially because of the high heterogeneity that remained >70%, even after carrying out subgroups analyses. These results point out that approaches based on traditional categorical diagnoses may be too restrictive and new approaches based on the characterization of new complex phenotypes should be considered.
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