Blood transcriptomic markers for major depression: from animal models to clinical settings. (30th March 2015)

• Record Type: Journal Article
• Authors: Redei, Eva E.; Mehta, Neha S.
• Is Part Of: Annals of the New York Academy of Sciences. Volume 1344(2015)
• Page Start: 37
• Page End: 49
• Publication Date: 2015-03-30
• Subjects: animal models of depression -- selective breeding -- gene expression -- blood transcripts -- early‐onset major depression -- major depression; Medical sciences -- Periodicals; Medicine -- Periodicals; Science -- Periodicals; 610
• DOI: 10.1111/nyas.12748
• Languages: English
• ISSNs: 0077-8923

Abstract

Abstract : Depression is a heterogeneous disorder and, similar to other spectrum disorders, its manifestation varies by age of onset, severity, comorbidity, treatment responsiveness, and other factors. A laboratory blood test based on specific biomarkers for major depressive disorder (MDD) and its subgroups could increase diagnostic accuracy and expedite the initiation of treatment. We identified candidate blood biomarkers by examining genome‐wide expression differences in the blood of animal models representing both the genetic and environmental/stress etiologies of depression. Human orthologs of the resulting transcript panel were tested in pilot studies. Transcript abundance of 11 blood markers differentiated adolescent subjects with early‐onset MDD from adolescents with no disorder (ND). A set of partly overlapping transcripts distinguished adolescent patients who had comorbid anxiety disorders from those with only MDD. In adults, blood levels of nine transcripts discerned subjects with MDD from ND controls. Even though cognitive behavioral therapy (CBT) resulted in remission of some patients, the levels of three transcripts consistently signaled prior MDD status. A coexpression network of transcripts seems to predict responsiveness to CBT. Thus, our approach can be developed into clinically valid diagnostic panels of blood transcripts for different manifestations of MDD, potentially reducing diagnostic heterogeneity and advancing individualized treatment strategies.