Circulating microRNA associated with future relapse status in major depressive disorder

Abstract

Major depressive disorder (MDD) is an episodic condition with relapsing and remitting disease course. Elucidating biomarkers that can predict future relapse in individuals responding to an antidepressant treatment holds the potential to identify those patients who are prone to illness recurrence. The current study explored relationships between relapse risk in recurrent MDD and circulating microRNAs (miRNAs) that participate in RNA silencing and post-transcriptional regulation of gene expression. Serum samples were acquired from individuals with a history of recurrent MDD who were followed longitudinally in the observational study, OBSERVEMDD0001 (ClinicalTrials.gov Identifier: NCT02489305). Circulating miRNA data were obtained in 63 participants who relapsed (“relapsers”) and 154 participants who did not relapse (“non-relapsers”) during follow-up. The miRNA was quantified using the ID3EAL™ miRNA Discovery Platform from MiRXES measuring 575 circulating miRNAs using a patented qPCR technology and normalized with a standard curve from spike-in controls in each plate. The association between miRNAs and subsequent relapse was tested using a linear model, adjusting for age, gender, and plate. Four miRNAs were nominally associated with relapse status during the observational follow-up phase with a false discover rate adjusted p-value < 0.1. Enrichment analysis of experimentally validated targets revealed 112 significantly enriched pathways, including neurogenesis, response to cytokine, neurotrophin signaling, vascular endothelial growth factor signaling, relaxin signaling, and cellular senescence pathways. These data suggest these miRNAs putatively associated with relapse status may have the potential to regulate genes involved in multiple signaling pathways that have previously been associated with MDD. If shown to be significant in a larger, independent sample, these data may hold potential for developing a miRNA signature to identify patients likely to relapse, allowing for earlier intervention.