Prediction of repeated-dose intravenous ketamine response in major depressive disorder using the GWAS-based machine learning approach

Abstract

Major depressive disorder (MDD) is one of the most common psychiatric disorders. Various clinical studies have shown that the N-methyl-D-aspartate (NMDA) receptor antagonist ketamine has rapid, robust, and sustained antidepressant effects. However, given the concerns about the adverse effects of ketamine on patients, it would be important to identify a set of biomarkers that could be used to predict clinical outcomes for its treatment. A total of 83 MDD patients received treatment with six ketamine infusions for up to 2 weeks and were classified into "responders" or "non-responders" based on an average change in the HAMD score >50% from baseline. A nested cross-validation approach was applied to prevent information leakage and overestimation of model performance. The initial dataset was divided randomly into training and test sets in a nested six-fold cross-validation. We first performed genome-wide logistic regression to find potentially significant variants related to treatment response and then selected the top SNPs based on the genetic association results using the random forests algorithm. Subsequently, six machine learning models were employed to construct prediction models by using ten-fold cross-validation. A series of model comparisons showed that the best performing fold was characterized by accuracy of 0.85, precision of 0.75, and a sensitivity of 1.00 with the support vector machine algorithm. Together, these findings demonstrated that the machine learning approach can predict the treatment outcomes of multiple ketamine infusions on the basis of the genotyping information of each participant.

Keywords: Antidepressant; Depression; Ketamine; Machine-learning; Single nucleotide polymorphisms.