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

• Published in: Clinica chimica acta Vol. 464; pp. 223 - 227
• Main Authors: Zheng, Hong, Zheng, Peng, Zhao, Liangcai, Jia, Jianmin, Tang, Shengli, Xu, Pengtao, Xie, Peng, Gao, Hongchang
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.01.2017
• Subjects: Adult; Artificial intelligence; Depression; Depressive Disorder, Major - blood; Depressive Disorder, Major - diagnosis; Depressive Disorder, Major - metabolism; Depressive Disorder, Major - pathology; Female; Glucose - metabolism; Humans; Least-Squares Analysis; Lipid Metabolism; Magnetic Resonance Spectroscopy; Male; Metabolome; Metabolomics - methods; Plasma; Prediction; Signal Transduction; Support Vector Machine; Metabolome; Plasma; Depression; Artificial intelligence; Prediction
• ISSN: 0009-8981; 1873-3492
• DOI: 10.1016/j.cca.2016.11.039

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. 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. 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. The LS-SVM-RBF using glucose-lipid signaling obtained from NMR spectroscopy can be used as an auxiliary diagnostic tool for the MD. •Major depression diagnosis can be achieved by a standardized instrument.•An integrated analytical method of metabolomics and LS-SVM was developed.•Multi-biomarkers outperform single-biomarker for depression diagnosis.•Plasma glucose-lipid signaling can be used for predictive diagnosis of depression.