Predictive diagnosis of major depression using NMR-based metabolomics and least-squares support vector machine

Background: Major depressive (MD) disorder is a serious psychiatric disorder that can result in suicidal behavior if not treated. The MD diagnosis using a standardized instrument instead of a structured interview will be advantageous for treatment and management of the MD, but so far no such technique exists. We developed an integrated analytical method of NMR-based metabolomics and least squares-support vector machine (LS-SVM) for predictive diagnosis of the MD. Methods: The metabolite profiles in clinical plasma samples obtained from 72 depressive patients and 54 healthy subjects were analyzed by NMR spectroscopy. Then, LS-SVM models with different kernels were trained and tested using 80% and 20% of samples, respectively. Results: We found that the best performance for the MD prediction was achieved by LS-SVM equipped with RBF kernel. Moreover, the predictive performance of the MD using multi-biomarkers was largely improved as compared with that using a single biomarker. In this study, the LS-SVM-RBF using glucose-lipid signaling can achieve the MD prediction with the AUC values of 0.94 (0.89-0.99) in the training set and 0.96 (0.92-1.00) in the test set. Conclusion: The LS-SVM-RBF using glucose-lipid signaling obtained from NMR spectroscopy can be used as an auxiliary diagnostic tool for the MD. (C) 2016 Elsevier B.V. All rights reserved.