Literature DB >> 26541453

Pretreatment levels of the fatty acid handling proteins H-FABP and CD36 predict response to olanzapine in recent-onset schizophrenia patients.

Jakub Tomasik1, Emanuel Schwarz2, Santiago G Lago3, Matthias Rothermundt4, F Markus Leweke5, Nico J M van Beveren6, Paul C Guest7, Hassan Rahmoune8, Johann Steiner9, Sabine Bahn10.   

Abstract

Traditional schizophrenia pharmacotherapy remains a subjective trial and error process involving administration, titration and switching of drugs multiple times until an adequate response is achieved. Despite this time-consuming and costly process, not all patients show an adequate response to treatment. As a consequence, relapse is a common occurrence and early intervention is hampered. Here, we have attempted to identify candidate blood biomarkers associated with drug response in 121 initially antipsychotic-free recent-onset schizophrenia patients treated with widely-used antipsychotics, namely olanzapine (n=40), quetiapine (n=23), risperidone (n=30) and a mixture of these drugs (n=28). Patients were recruited and investigated as two separate cohorts to allow biomarker validation. Data analysis showed the most significant relationship between pre-treatment levels of heart-type fatty acid binding protein (H-FABP) and response to olanzapine (p=0.008, F=8.6, β=70.4 in the discovery cohort and p=0.003, F=15.2, β=24.4 in the validation cohort, adjusted for relevant confounding variables). In a functional follow-up analysis of this finding, we tested an independent cohort of 10 patients treated with olanzapine and found that baseline levels of plasma H-FABP and expression of the binding partner for H-FABP, fatty acid translocase (CD36), on monocytes predicted the reduction of psychotic symptoms (p=0.040, F=6.0, β=116.3 and p=0.012, F=11.9, β=-0.0054, respectively). We also identified a set of serum molecules changed after treatment with antipsychotic medication, in particular olanzapine. These molecules are predominantly involved in cellular development and metabolism. Taken together, our findings suggest an association between biomarkers involved in fatty acid metabolism and response to olanzapine, while other proteins may serve as surrogate markers associated with drug efficacy and side effects.
Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Biomarker; CD36; FABP3; Olanzapine; Personalised medicine; Schizophrenia

Mesh:

Substances:

Year:  2015        PMID: 26541453     DOI: 10.1016/j.bbi.2015.10.019

Source DB:  PubMed          Journal:  Brain Behav Immun        ISSN: 0889-1591            Impact factor:   7.217


  6 in total

Review 1.  Making Sense of Blood-Based Proteomics and Metabolomics in Psychiatric Research.

Authors:  Paul C Guest; Francesca L Guest; Daniel Martins-de Souza
Journal:  Int J Neuropsychopharmacol       Date:  2015-12-30       Impact factor: 5.176

2.  Proteomic Differences in Blood Plasma Associated with Antidepressant Treatment Response.

Authors:  Christoph W Turck; Paul C Guest; Giuseppina Maccarrone; Marcus Ising; Stefan Kloiber; Susanne Lucae; Florian Holsboer; Daniel Martins-de-Souza
Journal:  Front Mol Neurosci       Date:  2017-08-31       Impact factor: 5.639

3.  Blood-Based Lipidomics Approach to Evaluate Biomarkers Associated With Response to Olanzapine, Risperidone, and Quetiapine Treatment in Schizophrenia Patients.

Authors:  Adriano Aquino; Guilherme L Alexandrino; Paul C Guest; Fabio Augusto; Alexandre F Gomes; Michael Murgu; Johann Steiner; Daniel Martins-de-Souza
Journal:  Front Psychiatry       Date:  2018-05-25       Impact factor: 4.157

4.  Drug discovery for psychiatric disorders using high-content single-cell screening of signaling network responses ex vivo.

Authors:  Santiago G Lago; Jakub Tomasik; Geertje F van Rees; Hannah Steeb; David A Cox; Nitin Rustogi; Jordan M Ramsey; Joshua A Bishop; Tracey Petryshen; Stephen J Haggarty; Javier Vázquez-Bourgon; Sergi Papiol; Paula Suarez-Pinilla; Benedicto Crespo-Facorro; Nico J van Beveren; Sabine Bahn
Journal:  Sci Adv       Date:  2019-05-08       Impact factor: 14.136

5.  Multimodel inference for biomarker development: an application to schizophrenia.

Authors:  Jason D Cooper; Sung Yeon Sarah Han; Jakub Tomasik; Sureyya Ozcan; Nitin Rustogi; Nico J M van Beveren; F Markus Leweke; Sabine Bahn
Journal:  Transl Psychiatry       Date:  2019-02-11       Impact factor: 6.222

Review 6.  The druggable schizophrenia genome: from repurposing opportunities to unexplored drug targets.

Authors:  Santiago G Lago; Sabine Bahn
Journal:  NPJ Genom Med       Date:  2022-03-25       Impact factor: 6.083

  6 in total

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