In The international journal of neuropsychopharmacology
BACKGROUND : There is a lack of reliable biomarkers for Major depressive disorder (MDD) in clinical practice. However, several studies have shown an association between alterations in microRNA levels and MDD, albeit none of them has taken advantage of machine learning (ML).
METHOD : Supervised and unsupervised ML were applied to blood microRNA expression profiles from a MDD case-control dataset (n=168) to distinguish between 1) case vs. control status, 2) MDD severity levels defined based on the Montgomery-Asberg Depression Rating Scale (MADRS) and 3) antidepressant responders vs. non-responders.
RESULTS : MDD cases were distinguishable from healthy controls with an area-under-the receiver-operating characteristic curve (AUC) of 0.97 on testing data. High vs. low severity cases were distinguishable with an AUC of 0.63. Unsupervised clustering of patients, before supervised ML analysis of each cluster for MDD severity, improved the performance of the classifiers (AUC of 0.70 for cluster 1 and 0.76 for cluster 2). Antidepressant responders could not be successfully separated from non-responders, even after patient stratification by unsupervised clustering. However, permutation testing of the top microRNA, identified by the ML model trained to distinguish responders vs. non-responders in each of the two clusters, showed an association with antidepressant response. Each of these microRNA markers was only significant when comparing responders vs. non-responders of the corresponding cluster, but not using the heterogeneous un-clustered patient set.
CONCLUSIONS : Supervised and unsupervised ML analysis of microRNA may lead to robust biomarkers for monitoring clinical evolution and for more timely assessment of treatment in MDD patients.
Qi Bill, Fiori Laura M, Turecki Gustavo, Trakadis Yannis J