Identification of Sex-Specific Plasma Biomarkers Using Metabolomics for Major Depressive Disorder in Children and Adolescents
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ABSTRACT: Background
Children and adolescents are at a high risk of major depressive disorder (MDD) with known sex differences in epidemiology. However, there are currently no objective laboratory-based sex-specific biomarkers available to support the diagnoses of male and female patients with MDD. Methods
Here, a male set of 42 cases and 27 healthy controls (HCs) and a female set of 42 cases and 22 HCs were recruited. This study investigated the sex differences of plasma metabolite biomarkers in young patients with MDD by the application of ultra-high-performance liquid chromatography equipped with quadrupole time-of-flight mass spectrometry. Results
The metabolic profiles showed clear separations in both male and female sets. In total, this study identified 57 male-related and 53 female-related differential metabolites. Compared with HCs, both male and female subjects with MDD displayed four significantly altered pathways. Notably, biliverdin was selected as an independent diagnostic male-specific biomarker with an area under the receiver operating characteristic curve of 0.966, and phosphatidylcholine (10:0/14:1) was selected as a female-specific biomarker, achieving an area under the curve (AUC) of 0.957. Conclusion
This metabolomics study may aid in the development of a plasma-based test for the diagnosis of male and female children and adolescents with MDD, as well as give new insight into the pathophysiology of sex differences in children and adolescents with MDD.
SUBMITTER: Jiang Y
PROVIDER: S-EPMC9329558 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Background: Major depressive disorder (MDD) is a common disease which is complicated by metabolic disorder. Although MDD has been studied relatively intensively, its metabolism is yet to be elucidated. Methods: To profile the global pathophysiological processes of MDD patients, we used metabolomics to identify differential metabolites and applied a new database Metabolite set enrichment analysis (MSEA) to discover dysfunctions of metabolic pathways of this disease. Hydrophilic metabolomics were applied to identify metabolites by profiling the plasma from 55 MDD patients and 100 sex-, gender-, BMI-matched healthy controls. The metabolites were then analyzed in MSEA in an attempt to discover different metabolic pathways. To investigate dysregulated pathways, we further divided MDD patients into two cohorts: (1) MDD patients with anxiety symptoms and (2) MDD patients without anxiety symptoms. Results: Metabolites which were hit in those pathways correlated with depressive and anxiety symptoms. Altogether, 17 metabolic pathways were enriched in MDD patients, and 23 metabolites were hit in those pathways. Three metabolic pathways were enriched in MDD patients without anxiety, including glycine and serine metabolism, arginine and proline metabolism, and phenylalanine and tyrosine metabolism. In addition, L-glutamic acid was positively correlated with the severity of depression and retardation if hit in MDD patients without anxiety symptoms. Conclusions: Different kinds of metabolic pathophysiological processes were found in MDD patients. Disorder of glycine and serine metabolism was observed in both MDD patients with anxiety and those without.
| S-EPMC8502978 | biostudies-literature
Project description:Purpose There are great demands for identifying markers of major depressive disorder (MDD), which is a common mental illness with a prevalence of approximately 6%. Finding potential markers to aid MDD diagnosis is in high demand. Experimental design In this study, combination of the salt-out assisted liquid-liquid extraction (SALLE) pretreatment method and a nontargeted peptidomics approach based on nano-LC-Orbitrap/MS is primarily employed to discover the candidate peptide biomarkers from the plasma of 238 subjects. Results A large number of peptides are enriched and identified from the plasma samples, of which 42 peptides show significant differences between MDD patients and controls by univariate statistical analysis. A diagnostic model combined four peptide markers (P1, P9, P17, P29) is established by binary logistic regression analysis, yielding an overall prediction accuracy of 91.7% and 82.2% in the discovery and validation sets, respectively. Conclusions and clinical relevance In conclusion, the good performance of diagnostic model in both discovery and validation sets demonstrates the robustness of peptide markers panel. It is very valuable for quantification the absolute content of four peptides and further verification.
2023-04-13 | PXD023009 | Pride
Project description:BackgroundMajor depressive disorder (MDD) is a neuropsychiatric disorder caused by multiple factors. Although there are clear guidelines for the diagnosis of MDD, the direct and objective diagnostic methods remain inadequate thus far.MethodsThis study aims to discover peripheral biomarkers in patients with MDD and promote the diagnosis of MDD. Plasma samples of healthy controls (HCs, n = 52) and patients with MDD (n = 38) were collected, and then, metabolism analysis was performed using ultrahigh-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS). Heatmap analysis was performed to identify the different metabolites. Meanwhile, receiver operating characteristic (ROC) curves of these differential metabolites were generated.ResultsSix differential metabolites were found by LC-MS/MS analysis. Three of these were increased, including L-aspartic acid (Asp), diethanolamine, and alanine. Three were decreased, including O-acetyl-L-carnitine (LAC), cystine, and fumarate. In addition, LAC, Asp, fumarate, and alanine showed large areas under the curve (AUCs) by ROC analysis.ConclusionThe study explored differences in peripheral blood between depressed patients and HCs. These results indicated that differential metabolites with large AUCs may have the potential to be promising biomarkers for the diagnosis of MDD.
| S-EPMC8899025 | biostudies-literature
Project description:IntroductionMajor depressive disorder (MDD) is a highly prevalent mental disorder affecting millions of people worldwide. However, a clear causative etiology of MDD remains unknown. In this study, we aimed to identify critical protein alterations in plasma from patients with MDD and integrate our proteomics and previous metabolomics data to reveal significantly perturbed pathways in MDD. An isobaric tag for relative and absolute quantification (iTRAQ)-based quantitative proteomics approach was conducted to compare plasma protein expression between patients with depression and healthy controls (CON).MethodsFor integrative analysis, Ingenuity Pathway Analysis software was used to analyze proteomics and metabolomics data and identify potential relationships among the differential proteins and metabolites.ResultsA total of 74 proteins were significantly changed in patients with depression compared with those in healthy CON. Bioinformatics analysis of differential proteins revealed significant alterations in lipid transport and metabolic function, including apolipoproteins (APOE, APOC4 and APOA5), and the serine protease inhibitor. According to canonical pathway analysis, the top five statistically significant pathways were related to lipid transport, inflammation and immunity.ConclusionCausal network analysis by integrating differential proteins and metabolites suggested that the disturbance of phospholipid metabolism might promote the inflammation in the central nervous system.
| S-EPMC5995410 | biostudies-literature
Project description:ObjectiveTo identify predictors and moderators of relapse during continuation treatment among depressed youth randomly assigned to fluoxetine or placebo.MethodsPotential predictors and moderators of relapse that were identified by a literature review were examined in 102 youth (aged 7-18 years), diagnosed with major depressive disorder as defined by DSM-IV criteria, who were considered responders after 12 weeks of fluoxetine treatment (acute phase). This randomized controlled trial was conducted from June 2000 through October 2005. Each candidate predictor and moderator was evaluated with a multiple logistic regression model to examine the main and interaction effects of 12 weeks of continuation treatment on relapse status (at week 24) while controlling for age, sex, and depression severity. Relapse was defined as a Children's Depression Rating Scale-Revised total score ≥ 40 with worsening of depressive symptoms for at least 2 weeks.ResultsYouth with comorbid dysthymia (adjusted odds ratio [OR] = 2.88, P = .03) and low levels of family leadership (adjusted OR = 1.39, P = .006) at baseline are more likely to relapse than their counterparts. Higher levels of depression (OR = 1.21, P = .003) and higher levels of residual sleep disturbance (insomnia) (OR = 6.74, P = .006) and irritability (OR = 7.40, P = .01) at the end of acute treatment (12 weeks) increased the odds of relapse. Higher levels of depressive symptoms at baseline in youth who remained on fluoxetine for continuation treatment were associated with increased odds of relapse (adjusted OR = 1.14, P = .03). Females who remained on fluoxetine for the duration of continuation treatment were almost 9 times more likely to relapse than males (adjusted OR = 8.86, P = .007).ConclusionsThis is the first large continuation study for treatment of depression in youth to examine predictors and moderators of relapse. Youth with greater improvement by the end of 3 months of treatment were less likely to relapse than those with continued depressive symptoms. In addition, youth with comorbid dysthymia had 3 times greater risk of relapse that those without. Targeting residual symptoms, particularly sleep disturbance and irritability, earlier in treatment may reduce relapse rates.Trial registrationClinicalTrials.gov identifier: NCT00332787.
| S-EPMC9590113 | biostudies-literature
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.
2011-10-04 | E-GEOD-19738 | biostudies-arrayexpress
Project description:ObjectiveTo date, the current diagnosis of major depressive disorder (MDD) still depends on clinical symptomatologic criteria, misdiagnosis and ineffective treatment are common. The study aimed to explore circulating biomarkers for MDD diagnosis.MethodsA high-throughput antibody array technology was utilized to detect 440 circulating cytokines in eight MDD patients and eight age-and gender-matched healthy controls. LASSO regression was conducted for MDD-related characteristic proteins selection. Enzyme-linked immunosorbent assay (ELISA) was used to validate the characteristic proteins in 40 MDD patients and 40 healthy controls. Receiver operating characteristic (ROC) curve was employed to evaluate the diagnostic values of characteristic proteins for discriminating MDD patients from healthy controls. Correlations between the levels of characteristic proteins and depression severity (HAMD-17 scores) were evaluated using linear regression.ResultsThe levels of 59 proteins were found aberrant in MDD patients compared with healthy controls. LASSO regression found six MDD-related characteristic proteins including insulin, CD40L, CD155, Lipocalin-2, HGF and LIGHT. ROC curve analysis showed that the area under curve (AUC) values of six characteristic proteins were more than 0.85 in discriminating patients with MDD from healthy controls. Furthermore, significant relationship was found between the levels of insulin, CD155, Lipocalin-2, HGF, LIGHT and HAMD-17 scores in MDD group.ConclusionThese results suggested that six characteristic proteins screened from 59 proteins differential in MDD may hold promise as diagnostic biomarkers in discriminating patients with MDD. Among six characteristic proteins, insulin, CD155, Lipocalin-2, HGF and LIGHT might be useful to estimate the severity of depressive symptoms.
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Project description:Women are more vulnerable to major depressive disorder (MDD) than men. However, molecular biomarkers of sex differences are limited. Here we combined gas chromatography-mass spectrometry (GC-MS)- and nuclear magnetic resonance (NMR)-based metabonomics to investigate sex differences of urinary metabolite markers in MDD, and further explore their potential of diagnosing MDD. Consequently, the metabolite signatures of women and men MDD subjects were significantly different from of that in their respective healthy controls (HCs). Twenty seven women and 36 men related differentially expressed metabolites were identified in MDD. Fourteen metabolites were changed in both women and men MDD subjects. Significantly, the women-specific (m-Hydroxyphenylacetate, malonate, glycolate, hypoxanthine, isobutyrate and azelaic acid) and men-specific (tyrosine, N-acetyl-d-glucosamine, N-methylnicotinamide, indoxyl sulfate, citrate and succinate) marker panels were further identified, which could differentiate men and women MDD patients from their respective HCs with higher accuracy than previously reported sex-nonspecific marker panels. Our findings demonstrate that men and women MDD patients have distinct metabonomic signatures and sex-specific biomarkers have promising values in diagnosing MDD.
| S-EPMC5314113 | biostudies-literature
Project description:Major depressive disorder (MDD) is a debilitating psychiatric illness. However, there is currently no objective laboratory-based diagnostic tests for this disorder. Although, perturbations in multiple neurotransmitter systems have been implicated in MDD, the biochemical changes underlying the disorder remain unclear, and a comprehensive global evaluation of neurotransmitters in MDD has not yet been performed. Here, using a GC-MS coupled with LC-MS/MS-based targeted metabolomics approach, we simultaneously quantified the levels of 19 plasma metabolites involved in GABAergic, catecholaminergic, and serotonergic neurotransmitter systems in 50 first-episode, antidepressant drug-naïve MDD subjects and 50 healthy controls to identify potential metabolite biomarkers for MDD (training set). Moreover, an independent sample cohort comprising 49 MDD patients, 30 bipolar disorder (BD) patients and 40 healthy controls (testing set) was further used to validate diagnostic generalizability and specificity of these candidate biomarkers. Among the 19 plasma neurotransmitter metabolites examined, nine were significantly changed in MDD subjects. These metabolites were mainly involved in GABAergic, catecholaminergic and serotonergic systems. The GABAergic and catecholaminergic had better diagnostic value than serotonergic pathway. A panel of four candidate plasma metabolite biomarkers (GABA, dopamine, tyramine, kynurenine) could distinguish MDD subjects from health controls with an AUC of 0.968 and 0.953 in the training and testing set, respectively. Furthermore, this panel distinguished MDD subjects from BD subjects with high accuracy. This study is the first to globally evaluate multiple neurotransmitters in MDD plasma. The altered plasma neurotransmitter metabolite profile has potential differential diagnostic value for MDD.
| S-EPMC6039504 | biostudies-literature
Project description:Major depressive disorder (MDD) is a heterogeneous disease. Efforts to identify biomarkers for sub-classifying MDD and antidepressant therapy by genome-wide association studies (GWAS) alone have generally yielded disappointing results. We applied a metabolomics-informed genomic research strategy to study the contribution of genetic variation to MDD pathophysiology by assaying 31 metabolites, including compounds from the tryptophan, tyrosine, and purine pathways, in plasma samples from 290 MDD patients. Associations of metabolite concentrations with depressive symptoms were determined, followed by GWAS for selected metabolites and functional validation studies of the genes identified. Kynurenine (KYN), the baseline plasma metabolite that was most highly associated with depressive symptoms, was negatively correlated with severity of those symptoms. GWAS for baseline plasma KYN concentrations identified SNPs across the beta-defensin 1 (DEFB1) and aryl hydrocarbon receptor (AHR) genes that were cis-expression quantitative trait loci (eQTLs) for DEFB1 and AHR mRNA expression, respectively. Furthermore, the DEFB1 locus was associated with severity of MDD symptoms in a larger cohort of 803 MDD patients. Functional studies demonstrated that DEFB1 could neutralize lipopolysaccharide-stimulated expression of KYN-biosynthesizing enzymes in monocytic cells, resulting in altered KYN concentrations in the culture media. In addition, we demonstrated that AHR was involved in regulating the expression of enzymes in the KYN pathway and altered KYN biosynthesis in cell lines of hepatocyte and astrocyte origin. In conclusion, these studies identified SNPs that were cis-eQTLs for DEFB1 and AHR and, which were associated with variation in plasma KYN concentrations that were related to severity of MDD symptoms.
| S-EPMC5802574 | biostudies-literature