Literature DB >> 29374800

Blood-based biomarkers predicting response to antidepressants.

Yasmin Busch1, Andreas Menke2,3.   

Abstract

Major depressive disorder is a common, serious and in some cases, life-threatening condition and affects approximately 350 million people globally. Although there is effective treatment available for it, more than 50% of the patients fail to respond to the first antidepressant they receive. The selection of a distinct treatment is still exclusively based on clinical judgment without incorporating lab-derived objective measures. However, there is growing evidence of biomarkers that it helps to improve diagnostic processes and treatment algorithms. Here genetic markers and blood-based biomarkers of the monoamine pathways, inflammatory pathways and the hypothalamic-pituitary-adrenal (HPA) axis are reviewed. Promising findings arise from studies investigating inflammatory pathways and immune markers that may identify patients suitable for anti-inflammatory based treatment regimes. Next, an early normalization of a disturbed HPA axis or depleted neurotrophic factors may predict stable treatment response. Genetic markers within the serotonergic system may identify patients who are vulnerable because of stressful life events, but evidence for guiding treatment regimes still is inconsistent. Therefore, there is still a great need for studies investigating and validating biomarkers for the prediction of treatment response to facilitate the treatment selection and shorten the time to remission and thus provide personalized medicine in psychiatry.

Entities:  

Keywords:  5-HTT; Antidepressants; BDNF; Biomarkers; FKBP5; Genetic variants; HPA axis; Immune; Inflammation; Major depression; SNPs

Mesh:

Substances:

Year:  2018        PMID: 29374800     DOI: 10.1007/s00702-018-1844-x

Source DB:  PubMed          Journal:  J Neural Transm (Vienna)        ISSN: 0300-9564            Impact factor:   3.575


  190 in total

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2.  No association of TPH1 218A/C polymorphism with treatment response and intolerance to SSRIs in Japanese patients with major depression.

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Journal:  Neuropsychobiology       Date:  2008-03-07       Impact factor: 2.328

3.  SSRI response in depression may be influenced by SNPs in HTR1B and HTR1A.

Authors:  Sandra M Villafuerte; Kamala Vallabhaneni; Elzbieta Sliwerska; Francis J McMahon; Elizabeth A Young; Margit Burmeister
Journal:  Psychiatr Genet       Date:  2009-12       Impact factor: 2.458

4.  Genetic predictors of response to antidepressants in the GENDEP project.

Authors:  Rudolf Uher; Patricia Huezo-Diaz; Nader Perroud; Rebecca Smith; Marcella Rietschel; Ole Mors; Joanna Hauser; Wolfgang Maier; Dejan Kozel; Neven Henigsberg; Mara Barreto; Anna Placentino; Mojca Zvezdana Dernovsek; Thomas G Schulze; Petra Kalember; Astrid Zobel; Piotr M Czerski; Erik Roj Larsen; Daniel Souery; Caterina Giovannini; Joanna M Gray; Cathryn M Lewis; Anne Farmer; Katherine J Aitchison; Peter McGuffin; Ian Craig
Journal:  Pharmacogenomics J       Date:  2009-04-14       Impact factor: 3.550

5.  A randomized controlled trial of the tumor necrosis factor antagonist infliximab for treatment-resistant depression: the role of baseline inflammatory biomarkers.

Authors:  Charles L Raison; Robin E Rutherford; Bobbi J Woolwine; Chen Shuo; Pamela Schettler; Daniel F Drake; Ebrahim Haroon; Andrew H Miller
Journal:  JAMA Psychiatry       Date:  2013-01       Impact factor: 21.596

6.  Serotonin and interleukin-6: the role of genetic polymorphisms in IFN-induced neuropsychiatric symptoms.

Authors:  Marc Udina; José Moreno-España; Ricard Navinés; Dolors Giménez; Klaus Langohr; Mònica Gratacòs; Lucile Capuron; Rafael de la Torre; Ricard Solà; Rocío Martín-Santos
Journal:  Psychoneuroendocrinology       Date:  2013-04-06       Impact factor: 4.905

Review 7.  Blood-borne biomarkers of mortality risk: systematic review of cohort studies.

Authors:  Evelyn Barron; Jose Lara; Martin White; John C Mathers
Journal:  PLoS One       Date:  2015-06-03       Impact factor: 3.240

8.  Insufficient glucocorticoid signaling and elevated inflammation in coronary heart disease patients with comorbid depression.

Authors:  Naghmeh Nikkheslat; Patricia A Zunszain; Mark A Horowitz; Izabela G Barbosa; Jennie A Parker; Aye-Mu Myint; Markus J Schwarz; Andre T Tylee; Livia A Carvalho; Carmine M Pariante
Journal:  Brain Behav Immun       Date:  2015-02-12       Impact factor: 7.217

9.  Evidence for sustained elevation of IL-6 in the CNS as a key contributor of depressive-like phenotypes.

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Journal:  Transl Psychiatry       Date:  2012-12-04       Impact factor: 6.222

10.  Pharmacokinetic genes do not influence response or tolerance to citalopram in the STAR*D sample.

Authors:  Eric J Peters; Susan L Slager; Jeffrey B Kraft; Greg D Jenkins; Megan S Reinalda; Patrick J McGrath; Steven P Hamilton
Journal:  PLoS One       Date:  2008-04-02       Impact factor: 3.240

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  10 in total

Review 1.  Moving pharmacoepigenetics tools for depression toward clinical use.

Authors:  Laura M Hack; Gabriel R Fries; Harris A Eyre; Chad A Bousman; Ajeet B Singh; Joao Quevedo; Vineeth P John; Bernhard T Baune; Boadie W Dunlop
Journal:  J Affect Disord       Date:  2019-02-06       Impact factor: 4.839

Review 2.  Computational approaches and machine learning for individual-level treatment predictions.

Authors:  Martin P Paulus; Wesley K Thompson
Journal:  Psychopharmacology (Berl)       Date:  2019-05-27       Impact factor: 4.530

Review 3.  Precision pharmacotherapy: psychiatry's future direction in preventing, diagnosing, and treating mental disorders.

Authors:  Andreas Menke
Journal:  Pharmgenomics Pers Med       Date:  2018-11-19

4.  Identification of Key Genes and the Pathophysiology Associated With Major Depressive Disorder Patients Based on Integrated Bioinformatics Analysis.

Authors:  Guangyin Zhang; Shixin Xu; Zhenqing Zhang; Yu Zhang; Yankun Wu; Jing An; Jinyu Lin; Zhuo Yuan; Li Shen; Tianmei Si
Journal:  Front Psychiatry       Date:  2020-04-03       Impact factor: 4.157

5.  Ex vivo glucocorticoid receptor-mediated IL-10 response predicts the course of depression severity.

Authors:  Bernd Lenz; Christiane Mühle; Claudia von Zimmermann; Lea Böhm; Tanja Richter-Schmidinger; Johannes Kornhuber
Journal:  J Neural Transm (Vienna)       Date:  2021-01-15       Impact factor: 3.575

6.  Integrated Analysis of Methylomic and Transcriptomic Data to Identify Potential Diagnostic Biomarkers for Major Depressive Disorder.

Authors:  Yinping Xie; Ling Xiao; Lijuan Chen; Yage Zheng; Caixia Zhang; Gaohua Wang
Journal:  Genes (Basel)       Date:  2021-01-27       Impact factor: 4.096

7.  A novel joint index based on peripheral blood CD4+/CD8+ T cell ratio, albumin level, and monocyte count to determine the severity of major depressive disorder.

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Review 8.  Prognostic Significance of Blood-Based Baseline Biomarkers in Treatment-Resistant Depression: A Literature Review of Available Studies on Treatment Response.

Authors:  Theano Gkesoglou; Stavroula I Bargiota; Eleni Iordanidou; Miltiadis Vasiliadis; Vasilios-Panteleimon Bozikas; Agorastos Agorastos
Journal:  Brain Sci       Date:  2022-07-18

9.  Genistein Improves the Major Depression through Suppressing the Expression of miR-221/222 by Targeting Connexin 43.

Authors:  Fang Shen; Wan-Li Huang; Bao-Ping Xing; Xiang Fang; Mei Feng; Chun-Ming Jiang
Journal:  Psychiatry Investig       Date:  2018-09-13       Impact factor: 2.505

10.  Weighted Gene Coexpression Network Analysis Identifies Specific Modules and Hub Genes Related to Major Depression.

Authors:  Guangyin Zhang; Shixin Xu; Zhuo Yuan; Li Shen
Journal:  Neuropsychiatr Dis Treat       Date:  2020-03-12       Impact factor: 2.570

  10 in total

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