Dataset Information

Blood and urinary metabolomic evidence validating traditional Chinese medicine diagnostic classification of major depressive disorder.

ABSTRACT: Background:Major depressive disorder (MDD) is a highly heterogeneous disease. Further classification may characterize its heterogeneity. The purpose of this study was to examine whether metabolomic variables could differentiate traditional Chinese medicine (TCM) diagnostic subtypes of MDD. Methods:Fifty medication-free patients who were experiencing a recurrent depressive episode were classified into Liver Qi Stagnation (LQS, n?=?30) and Heart and Spleen Deficiency (HSD, n?=?20) subtypes according to TCM diagnosis. Healthy volunteers (n?=?28) were included as controls. Gas chromatography-mass spectrometry (GC-MS) was used to examine serum and urinary metabolomic profiles. Results:Twenty-eight metabolites were identified for good separations between TCM subtypes and healthy controls in serum samples. Both TCM subtypes had similar profiles in proteinogenic branched-chain amino acids (BCAAs) (valine, leucine, and isoleucine) and energy metabolism-related metabolites that were differentiated from healthy controls. The LQS subtype additionally differed from healthy controls in multiple amino acid metabolites that are involved in biosynthesis of monoamine and amino acid neurotransmitters, including phenylalanine, 3-hydroxybutric acid, o-tyrosine, glycine, l-tryptophan, and N-acetyl-l-aspartic acid. Threonic acid, methionine, stearic acid, and isobutyric acid are differentially associated with the two subtypes. Conclusions:While both TCM subtypes are associated with aberrant BCAA and energy metabolism, the LQS subtype may represent an MDD subpopulation characterized by abnormalities in the biosynthesis of monoamine and amino acid neurotransmitters and closer associations with stress-related pathophysiology. The metabolites differentially associated with the two subtypes are promising biomarkers for predicting TCM subtype-specific antidepressant response [registered at http://www.clinicaltrials.gov (NCT02346682) on January 27, 2015].

SUBMITTER: Liu LY

PROVIDER: S-EPMC6203264 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Blood and urinary metabolomic evidence validating traditional Chinese medicine diagnostic classification of major depressive disorder.

Liu Lan-Ying LY Zhang Hong-Jian HJ Luo Li-Yuan LY Pu Jin-Bao JB Liang Wei-Qing WQ Zhu Chun-Qin CQ Li Ya-Ping YP Wang Pei-Rong PR Zhang Yuan-Yuan YY Yang Chun-Yu CY Zhang Zhang-Jin ZJ

Chinese medicine 20181025

<h4>Background</h4>Major depressive disorder (MDD) is a highly heterogeneous disease. Further classification may characterize its heterogeneity. The purpose of this study was to examine whether metabolomic variables could differentiate traditional Chinese medicine (TCM) diagnostic subtypes of MDD.<h4>Methods</h4>Fifty medication-free patients who were experiencing a recurrent depressive episode were classified into Liver Qi Stagnation (LQS, n = 30) and Heart and Spleen Deficiency (HSD, n = 20) s ...[more]

PMID: 30386416

Similar Datasets

Project description:Background: Major Depressive Disorder (MDD) is a moderately heritable disorder with a high lifetime prevalence. At present, laboratory blood tests to support MDD diagnosis are not available. Methods: We used a classifier approach on blood gene expression profiles of a unique set of non-medicated subjects (MDD patients and controls) to select genes of which expression is predictive for disease status. To reveal blood gene expression changes related to MDD disease, we applied a powerful ex vivo stimulus to the blood, i.e. incubation with lipopolysaccharide (LPS; 10 ng/ml blood). Results: Based on LPS-stimulated blood gene expression using whole-genome microarrays in 42 subjects (primary cohort; 21 MDD patients (mean age 42.3 years), 21 healthy controls (mean age 41.9 years)), we identified a set of genes (CAPRIN1, CLEC4A, KRT23, MLC1, PLSCR1, PROK2, ZBTB16) that serves as a molecular signature of MDD. These findings were validated for the primary cohort using an independent quantitative PCR method (P = 0.007). The difference between depressive patients and controls was confirmed (P = 0.019) in a replication cohort of 13 patients with MDD (mean age 42.8 years) and 14 controls (mean age 45.6 years). The MDD-signature score comprised of expression levels of 7 genes could discriminate depressive patients from controls with sensitivity of 76.9% and specificity of 71.8%. Conclusions: We show for the first time that molecular analysis of stimulated blood cells can be used as an endophenotype for MDD diagnosis, which is a milestone in establishing biomarkers for neuropsychiatric disorders with moderate heritability in general. Our results may provide a new entry point for following and predicting treatment outcome, as well as prediction of severity and recurrence of MDD. In total, 33 MDD patients and 34 healthy controls were analyzed using basal gene expression in whole blood, and gene expression from whole blood that was stimulated with LPS for 5-6 h, using microarrays. Patients were arbitrarily selected from all patients to serve as primary cohort (nMDD = 21 (MDD01-MDD21); nControls = 21 (Con01-Con21)), or replication cohort (nMDD = 12 (MDD22-MDD35); nControls = 13 (Con22-Con37)) using microarrays. This submission does not include Samples CON21_LPS or CON30_LPS.

Project description:BackgroundMajor depressive disorder (MDD) is a global health challenge that impacts the quality of patients' lives severely. The disorder can manifest in many forms with different combinations of symptoms, which makes its clinical diagnosis difficult. Robust biomarkers are greatly needed to improve diagnosis and to understand the etiology of the disease. The main purpose of this study was to create a predictive model for MDD diagnosis based on peripheral blood transcriptomes.Materials and methodsWe collected nine RNA expression datasets for MDD patients and healthy samples from the Gene Expression Omnibus database. After a series of quality control and heterogeneity tests, 302 samples from six studies were deemed suitable for the study. R package "MetaOmics" was applied for systematic meta-analysis of genome-wide expression data. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic effectiveness of individual genes. To obtain a better diagnostic model, we also adopted the support vector machine (SVM), random forest (RF), k-nearest neighbors (kNN), and naive Bayesian (NB) tools for modeling, with the RF method being used for feature selection.ResultsOur analysis revealed six differentially expressed genes (AKR1C3, ARG1, KLRB1, MAFG, TPST1, and WWC3) with a false discovery rate (FDR) < 0.05 between MDD patients and control subjects. We then evaluated the diagnostic ability of these genes individually. With single gene prediction, we achieved a corresponding area under the curve (AUC) value of 0.63 ± 0.04, 0.67 ± 0.07, 0.70 ± 0.11, 0.64 ± 0.08, 0.68 ± 0.07, and 0.62 ± 0.09, respectively, for these genes. Next, we constructed the classifiers of SVM, RF, kNN, and NB with an AUC of 0.84 ± 0.09, 0.81 ± 0.10, 0.73 ± 0.11, and 0.83 ± 0.09, respectively, in validation datasets, suggesting that the SVM classifier might be superior for constructing an MDD diagnostic model. The final SVM classifier including 70 feature genes was capable of distinguishing MDD samples from healthy controls and yielded an AUC of 0.78 in an independent dataset.ConclusionThis study provides new insights into potential biomarkers through meta-analysis of GEO data. Constructing different machine learning models based on these biomarkers could be a valuable approach for diagnosing MDD in clinical practice.

Dataset Information

Blood and urinary metabolomic evidence validating traditional Chinese medicine diagnostic classification of major depressive disorder.

Publications

Blood and urinary metabolomic evidence validating traditional Chinese medicine diagnostic classification of major depressive disorder.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets