Absolute Measurements of Macrophage Migration Inhibitory Factor and Interleukin-1-β mRNA Levels Accurately Predict Treatment Response in Depressed Patients

Abstract

Background: Increased levels of inflammation have been associated with a poorer response to antidepressants in several clinical samples, but these findings have had been limited by low reproducibility of biomarker assays across laboratories, difficulty in predicting response probability on an individual basis, and unclear molecular mechanisms.

Methods: Here we measured absolute mRNA values (a reliable quantitation of number of molecules) of Macrophage Migration Inhibitory Factor and interleukin-1β in a previously published sample from a randomized controlled trial comparing escitalopram vs nortriptyline (GENDEP) as well as in an independent, naturalistic replication sample. We then used linear discriminant analysis to calculate mRNA values cutoffs that best discriminated between responders and nonresponders after 12 weeks of antidepressants. As Macrophage Migration Inhibitory Factor and interleukin-1β might be involved in different pathways, we constructed a protein-protein interaction network by the Search Tool for the Retrieval of Interacting Genes/Proteins.

Results: We identified cutoff values for the absolute mRNA measures that accurately predicted response probability on an individual basis, with positive predictive values and specificity for nonresponders of 100% in both samples (negative predictive value=82% to 85%, sensitivity=52% to 61%). Using network analysis, we identified different clusters of targets for these 2 cytokines, with Macrophage Migration Inhibitory Factor interacting predominantly with pathways involved in neurogenesis, neuroplasticity, and cell proliferation, and interleukin-1β interacting predominantly with pathways involved in the inflammasome complex, oxidative stress, and neurodegeneration.

Conclusion: We believe that these data provide a clinically suitable approach to the personalization of antidepressant therapy: patients who have absolute mRNA values above the suggested cutoffs could be directed toward earlier access to more assertive antidepressant strategies, including the addition of other antidepressants or antiinflammatory drugs.

Keywords: cytokine absolute blood levels; personalized medicine; predictors; treatment response.

© The Author 2016. Published by Oxford University Press on behalf of CINP.

Figures

Figure 1.

Representative interaction between Macrophage Migration Inhibitory Factor (MIF) (Figure 1a) or interleukin (IL)-1β (Figure 1b) and their neighbors’ targets, where nodes (genes) can be either colored (if they are directly linked to the input, in that case MIF) or white (nodes of a higher interaction/depth-this is not the case). Lines represent predicted functional edges of interaction between nodes, and they are represented with eight different colors according to the type of evidence and the predictive method used (neighborhood, gene fusion, co-occurrence, coexpression, experiments, databases, and textmining): green line,activation; red line, inhibition; blue line, binding; light blue line, phenotype; violet, catalyzes; pink, posttranslational mechanism; black, reaction; yellow, coexpression (http://string-db.org).