Sex-Specific Urinary Biomarkers For Major Depressive Disorder Diagnosis:Identified By The Combined Application Of GC-MS-and NMR-Based Metabonomics

BackgroundsMajor depressive disorder(MDD) is a mental disorder caused by the interactions of environment and genetic factors. The main clinical symptoms of MDD are mood disorders and depressed symptoms. WHO predicts that MDD will be the second cause of human death and disability by 2020. Nowadays, the prevention and treatment of MDD is still very hard, because of the unclear pathogenesis and the lack of objective diagnostic method. Currently, the diagnosis of MDD is still mainly depended on the subjective identification of the depressive symptoms, but the high heterogeneity of clinical symptoms often results in misdiagnosis. Metabonomics could detect the changed level of metabolites in the biological samples under disease status, which has been widely used to identify the biomarkers of neuropsychiatric disorders. Previous studies have used metabonomics to identify the potential biomarkers for MDD diagnosis. But these studies had two limitations: only used one metabonomic platform; not taken the gender-based differences into consideration. These limitations limit the clinical application of those identified biomarkers. Therefore, here we used the combined application of Gas chromatography-mass spectrometry(GC-MS)- and Nuclear Magnetic Resonance(NMR)-based metabonomics platforms to identify the sex-specific urinary biomarkers for MDD diagnosis.Purposes· Check whether the sex-specific urinary biomarkers for neuropsychiatric disorders diagnosis exist or not.· Check whether differential metabolites identified by the combined application of GC-MS- and NMR-based metabonomics platforms are superior to those identify by the single one metabonomics platform.· Combined application of GC-MS- and NMR to obtain the sex-specific urinary biomarkers for MDD diagnosis.· Preliminary analyzing the function of the identified differential metabolites in the pathogenesis of MDDMethods First step: 86 bipolar disorder(BD)(42 men and 44 women) and 96 healthy controls(HC)(53 men and 43 women) were recruited. The morning urine was collected. NMR-based metabonomics platform was used to identify the sex-specific urinary biomarker for BD diagnosis.Second step: 71 BD and 126 HC were recruited. These subjects were allocated into two set: training set(43 BD and 78 HC) and testing set(28 BD and 48 HC). The morning urine was collected. The combined application of GC-MS- and NMR-based metabonomics platforms was used to identified the potential urinary biomarkers for BD diagnosis. Moreover, those biomarkers were use to compare with the biomarkers identified by single one metabonomics platform on the sensitivity.Third step: 93 MDD(50 men and 43 women) and 123 HC(75 men and 48 women) were recruited. The morning urine was collected. The combined application of GC-MS- and NMR-based metabonomics platforms was used to identify the potential urinary biomarkers for MDD diagnosis. Moreover, the treated MDD patients(12 men and 19 women) were used to analyze the relationship of these identified differential metabolites and MDD.Results 1. Sex-specific Urinary Biomarkers for BD DiagnosisTotally, nine urinary metabolites were responsible for the discrimination between men HC and men BD, and four potential biomarkers(α-hydroxybutyrate, choline, formate, and N-methylnicotinamide) were identified. This panel could effectively part men BD from men HC with 85.7% sensitivity and 90.6% specificity, but could not effectively part women BD from women HC. Eleven urinary metabolites were responsible for the discrimination between women HC and women BD, and four potential biomarkers(α-hydroxybutyrate, oxalacetate, acetone, and N-methylnicotinamide) were identified. This panel could effectively part women BD from women HC with 81.8% sensitivity and 83.7% specificity, but could not effectively part men BD from men HC.2. Combined of NMR and GC-MS for BD biomarkerTotally, 26 urinary metabolites responsible for the discrimination between HC and BD were identified in the training set, and five potential biomarkers(β-alanine, azelaic acid, pseudouridine, α-hydroxybutyrate and 2,4-dihydroxypyrimidine) were identified. This panel could effectively part BD from HC with 86.0% sensitivity and 92.3% specificity in the training set, and 96.4% sensitivity and 85.7% specificity in the testing set. Moreover, the accuracy of BD diagnosis(83.7%) by this panel was significantly higher than that of(71.8%) by the single one metabonomics platform.3. Combined of NMR and GC-MS for MDD Sex-specific BiomarkerTotally, 27 urinary metabolites responsible for the discrimination between men HC and men MDD were identified, and six potential biomarkers(tyrosine, N-acetyl-D-glucosamine, indoxyl sulphate, citrate, N-methylnicotinamide and succinate) were identified. This panel could effectively discriminate men MDD from men HC with 86.0% sensitivity and 90.7% specificity, but could not effectively part women MDD from women HC. 36 urinary metabolites responsible for the discrimination between women HC and women MDD were identified, and six potential biomarkers(m-Hydroxyphenylacetate, malonate, glycolate, hypoxanthine, isobutyrate and azelaic acid) were identified. This panel could effectively discriminate women MDD from women HC with 86.0% sensitivity and 93.8% specificity, but could not effectively part men MDD from men HC. Moreover, the levels of most of differential metabolites had the tendency to return the normal level after treatment.ConclusionBy single metabonomics platform, we found the sex-specific urinary biomarkers for BD diagnosis, which was one of the neuropsychiatric disorders. This result might indicate that there were sex-specific urinary biomarkers for another neuropsychiatric disorder-MDD, which had unbalanced incidence in men and women. By the combined application of NMR and GC-MS, the differential metabolites here responsible for the part of BD and HC were superior to the differential metabolites identified by the single metabonomics platform. As a result, we obtain some sex-specific differential metabolites by the combined application of NMR and GC-MS, and identified some sex-specific urinary biomarkers for MDD diagnosis. These results indicated that the development of sex-specific and objective diagnostic method for MDD was necessary. Our study also provides data for future studies to further explore the pathogenesis of BD and MDD.