Literature DB >> 25929723

Discovery of serum biomarkers predicting development of a subsequent depressive episode in social anxiety disorder.

M G Gottschalk1, J D Cooper1, M K Chan1, M Bot2, B W J H Penninx3, S Bahn4.   

Abstract

Although social anxiety disorder (SAD) is strongly associated with the subsequent development of a depressive disorder (major depressive disorder or dysthymia), no underlying biological risk factors are known. We aimed to identify biomarkers which predict depressive episodes in SAD patients over a 2-year follow-up period. One hundred sixty-five multiplexed immunoassay analytes were investigated in blood serum of 143 SAD patients without co-morbid depressive disorders, recruited within the Netherlands Study of Depression and Anxiety (NESDA). Predictive performance of identified biomarkers, clinical variables and self-report inventories was assessed using receiver operating characteristics curves (ROC) and represented by the area under the ROC curve (AUC). Stepwise logistic regression resulted in the selection of four serum analytes (AXL receptor tyrosine kinase, vascular cell adhesion molecule 1, vitronectin, collagen IV) and four additional variables (Inventory of Depressive Symptomatology, Beck Anxiety Inventory somatic subscale, depressive disorder lifetime diagnosis, BMI) as optimal set of patient parameters. When combined, an AUC of 0.86 was achieved for the identification of SAD individuals who later developed a depressive disorder. Throughout our analyses, biomarkers yielded superior discriminative performance compared to clinical variables and self-report inventories alone. We report the discovery of a serum marker panel with good predictive performance to identify SAD individuals prone to develop subsequent depressive episodes in a naturalistic cohort design. Furthermore, we emphasise the importance to combine biological markers, clinical variables and self-report inventories for disease course predictions in psychiatry. Following replication in independent cohorts, validated biomarkers could help to identify SAD patients at risk of developing a depressive disorder, thus facilitating early intervention.
Copyright © 2015 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Affective disorder; Depression; Dysthymia; Major depressive disorder; Mood disorder; Prediction; Prognosis; Risk factors; SAD; Secondary depression; Social phobia

Mesh:

Substances:

Year:  2015        PMID: 25929723     DOI: 10.1016/j.bbi.2015.04.011

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


  4 in total

1.  Alterations in brain synaptic proteins and mRNAs in mood disorders: a systematic review and meta-analysis of postmortem brain studies.

Authors:  Edison Leung; Ethan W Lau; Andi Liang; Constanza de Dios; Robert Suchting; Linda Östlundh; Joseph C Masdeu; Masahiro Fujita; Marsal Sanches; Jair C Soares; Sudhakar Selvaraj
Journal:  Mol Psychiatry       Date:  2022-01-13       Impact factor: 13.437

Review 2.  The Current State and Validity of Digital Assessment Tools for Psychiatry: Systematic Review.

Authors:  Nayra A Martin-Key; Benedetta Spadaro; Erin Funnell; Eleanor Jane Barker; Thea Sofie Schei; Jakub Tomasik; Sabine Bahn
Journal:  JMIR Ment Health       Date:  2022-03-30

Review 3.  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

4.  Parallel changes in serum proteins and diffusion tensor imaging in methamphetamine-associated psychosis.

Authors:  Michael S Breen; Anne Uhlmann; Sureyya Ozcan; Man Chan; Dalila Pinto; Sabine Bahn; Dan J Stein
Journal:  Sci Rep       Date:  2017-03-02       Impact factor: 4.379

  4 in total

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