title: Predictive diagnosis of major depression using NMR-based metabolomics and least-squares support vector machine.
abstract: 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.
  
  
  CONCLUSIONS
  The LS-SVM-RBF using glucose-lipid signaling obtained from NMR spectroscopy can be used as an auxiliary diagnostic tool for the MD.
authors.affiliation: School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou 325035, China.
authors.forename: Hong
authors.initials: H
authors.lastname: Zheng
authors.affiliation: Department of Neurology, The First Affiliated Hospital, Chongqing Medical University, Chongqing 400016, China.
authors.forename: Peng
authors.initials: P
authors.lastname: Zheng
authors.affiliation: School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou 325035, China.
authors.forename: Liangcai
authors.initials: L
authors.lastname: Zhao
authors.affiliation: School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou 325035, China.
authors.forename: Jianmin
authors.initials: J
authors.lastname: Jia
authors.affiliation: School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou 325035, China.
authors.forename: Shengli
authors.initials: S
authors.lastname: Tang
authors.affiliation: School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou 325035, China.
authors.forename: Pengtao
authors.initials: P
authors.lastname: Xu
authors.affiliation: Department of Neurology, The First Affiliated Hospital, Chongqing Medical University, Chongqing 400016, China.
authors.forename: Peng
authors.initials: P
authors.lastname: Xie
authors.affiliation: School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou 325035, China. Electronic address: gaohc27@wmu.edu.cn.
authors.forename: Hongchang
authors.initials: H
authors.lastname: Gao
mesh_terms: D000328:Adult; D003865:Major Depressive Disorder / Q000097:blood / Q000175:diagnosis* / Q000378:metabolism* / Q000473:pathology; D005260:Female; D005947:Glucose / Q000378:metabolism; D006801:Humans; D016018:Least-Squares Analysis; D050356:Lipid Metabolism; D009682:Magnetic Resonance Spectroscopy; D008297:Male; D055432:Metabolomics / Q000379:methods*; D015398:Signal Transduction; D060388:Support Vector Machine
chemical_list: D005947:Glucose
keywords: Artificial intelligence; Depression; Metabolome; Plasma; Prediction