A network-based approach to discover diagnostic metabolite markers associated with depressive features for major depressive disorder
BackgroundDespite the high prevalence of major depressive disorder (MDD), current diagnostic methods rely on subjective clinical assessments, highlighting the need for biomarkers. This study aimed to investigate plasma metabolite signatures in patients with MDD compared with healthy controls (HC) an...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-06-01
|
| Series: | Frontiers in Psychiatry |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1610520/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850130976288538624 |
|---|---|
| author | Yuzhen Zheng Duan Zeng Ying Tian Siyuan Li Shen He Huafang Li Huafang Li Huafang Li |
| author_facet | Yuzhen Zheng Duan Zeng Ying Tian Siyuan Li Shen He Huafang Li Huafang Li Huafang Li |
| author_sort | Yuzhen Zheng |
| collection | DOAJ |
| description | BackgroundDespite the high prevalence of major depressive disorder (MDD), current diagnostic methods rely on subjective clinical assessments, highlighting the need for biomarkers. This study aimed to investigate plasma metabolite signatures in patients with MDD compared with healthy controls (HC) and to identify diagnostic biomarkers associated with depressive features.MethodsA total of 99 patients with MDD and 50 HC were included in this study from a study cohort. Targeted plasma metabolomics was employed to quantify metabolites across diverse biochemical classes. Weighted gene co-expression network analysis (WGCNA) was performed to construct metabolite networks and identify modules and metabolites associated with depressive features. Diagnostic models were developed based on the identified hub metabolites, using six supervised machine-learning algorithms. Model interpretability was enhanced through the application of the SHapley Additive exPlanations (SHAP) algorithm.ResultsPathways such as biosynthesis of phenylalanine, tyrosine and tryptophan, glutathione metabolism, and arginine and proline metabolism were significantly enriched in the comparison of metabolic profiles between the MDD and HC groups. Seven hub metabolites were identified as the biomarker signatures that effectively discriminate the MDD and HC groups. Among these metabolites, one sphingomyelin (SM (OH) C16:1), one hexosylceramide (HexCer(d18:1/24:1)), one phosphatidylcholine (PC aa C40:6), and one cholesteryl ester (CE(20:4)) were positively associated with the depression severity, sadness/depressive mood, and other depressive features, while methionine, arginine, and tyrosine showed negative correlation. The deep neural network model incorporating these seven biomarkers achieved the highest diagnostic performance, with an area under the curve (AUC) of 0.803 (95% CI, 0.643–0.962).ConclusionWe identified a novel signature of seven biomarkers for constructing an explainable diagnostic model that effectively discriminates between the MDD and HC groups. These biomarkers were associated with depressive symptoms. The findings provide new insights into the biological diagnosis of MDD.Clinical Trial Registrationhttps://clinicaltrials.gov/search?cond=NCT04518592. |
| format | Article |
| id | doaj-art-d3f0876d08884ecf83bfb91b4c803dc6 |
| institution | OA Journals |
| issn | 1664-0640 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychiatry |
| spelling | doaj-art-d3f0876d08884ecf83bfb91b4c803dc62025-08-20T02:32:33ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402025-06-011610.3389/fpsyt.2025.16105201610520A network-based approach to discover diagnostic metabolite markers associated with depressive features for major depressive disorderYuzhen Zheng0Duan Zeng1Ying Tian2Siyuan Li3Shen He4Huafang Li5Huafang Li6Huafang Li7Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaShanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaClinical Research Center, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, ChinaShanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaShanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaShanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaShanghai Key Laboratory of Psychotic Disorders, Shanghai, ChinaBackgroundDespite the high prevalence of major depressive disorder (MDD), current diagnostic methods rely on subjective clinical assessments, highlighting the need for biomarkers. This study aimed to investigate plasma metabolite signatures in patients with MDD compared with healthy controls (HC) and to identify diagnostic biomarkers associated with depressive features.MethodsA total of 99 patients with MDD and 50 HC were included in this study from a study cohort. Targeted plasma metabolomics was employed to quantify metabolites across diverse biochemical classes. Weighted gene co-expression network analysis (WGCNA) was performed to construct metabolite networks and identify modules and metabolites associated with depressive features. Diagnostic models were developed based on the identified hub metabolites, using six supervised machine-learning algorithms. Model interpretability was enhanced through the application of the SHapley Additive exPlanations (SHAP) algorithm.ResultsPathways such as biosynthesis of phenylalanine, tyrosine and tryptophan, glutathione metabolism, and arginine and proline metabolism were significantly enriched in the comparison of metabolic profiles between the MDD and HC groups. Seven hub metabolites were identified as the biomarker signatures that effectively discriminate the MDD and HC groups. Among these metabolites, one sphingomyelin (SM (OH) C16:1), one hexosylceramide (HexCer(d18:1/24:1)), one phosphatidylcholine (PC aa C40:6), and one cholesteryl ester (CE(20:4)) were positively associated with the depression severity, sadness/depressive mood, and other depressive features, while methionine, arginine, and tyrosine showed negative correlation. The deep neural network model incorporating these seven biomarkers achieved the highest diagnostic performance, with an area under the curve (AUC) of 0.803 (95% CI, 0.643–0.962).ConclusionWe identified a novel signature of seven biomarkers for constructing an explainable diagnostic model that effectively discriminates between the MDD and HC groups. These biomarkers were associated with depressive symptoms. The findings provide new insights into the biological diagnosis of MDD.Clinical Trial Registrationhttps://clinicaltrials.gov/search?cond=NCT04518592.https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1610520/fullmajor depressive disordermetabolomicsbiomarkersmachine-learningWGCNA |
| spellingShingle | Yuzhen Zheng Duan Zeng Ying Tian Siyuan Li Shen He Huafang Li Huafang Li Huafang Li A network-based approach to discover diagnostic metabolite markers associated with depressive features for major depressive disorder Frontiers in Psychiatry major depressive disorder metabolomics biomarkers machine-learning WGCNA |
| title | A network-based approach to discover diagnostic metabolite markers associated with depressive features for major depressive disorder |
| title_full | A network-based approach to discover diagnostic metabolite markers associated with depressive features for major depressive disorder |
| title_fullStr | A network-based approach to discover diagnostic metabolite markers associated with depressive features for major depressive disorder |
| title_full_unstemmed | A network-based approach to discover diagnostic metabolite markers associated with depressive features for major depressive disorder |
| title_short | A network-based approach to discover diagnostic metabolite markers associated with depressive features for major depressive disorder |
| title_sort | network based approach to discover diagnostic metabolite markers associated with depressive features for major depressive disorder |
| topic | major depressive disorder metabolomics biomarkers machine-learning WGCNA |
| url | https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1610520/full |
| work_keys_str_mv | AT yuzhenzheng anetworkbasedapproachtodiscoverdiagnosticmetabolitemarkersassociatedwithdepressivefeaturesformajordepressivedisorder AT duanzeng anetworkbasedapproachtodiscoverdiagnosticmetabolitemarkersassociatedwithdepressivefeaturesformajordepressivedisorder AT yingtian anetworkbasedapproachtodiscoverdiagnosticmetabolitemarkersassociatedwithdepressivefeaturesformajordepressivedisorder AT siyuanli anetworkbasedapproachtodiscoverdiagnosticmetabolitemarkersassociatedwithdepressivefeaturesformajordepressivedisorder AT shenhe anetworkbasedapproachtodiscoverdiagnosticmetabolitemarkersassociatedwithdepressivefeaturesformajordepressivedisorder AT huafangli anetworkbasedapproachtodiscoverdiagnosticmetabolitemarkersassociatedwithdepressivefeaturesformajordepressivedisorder AT huafangli anetworkbasedapproachtodiscoverdiagnosticmetabolitemarkersassociatedwithdepressivefeaturesformajordepressivedisorder AT huafangli anetworkbasedapproachtodiscoverdiagnosticmetabolitemarkersassociatedwithdepressivefeaturesformajordepressivedisorder AT yuzhenzheng networkbasedapproachtodiscoverdiagnosticmetabolitemarkersassociatedwithdepressivefeaturesformajordepressivedisorder AT duanzeng networkbasedapproachtodiscoverdiagnosticmetabolitemarkersassociatedwithdepressivefeaturesformajordepressivedisorder AT yingtian networkbasedapproachtodiscoverdiagnosticmetabolitemarkersassociatedwithdepressivefeaturesformajordepressivedisorder AT siyuanli networkbasedapproachtodiscoverdiagnosticmetabolitemarkersassociatedwithdepressivefeaturesformajordepressivedisorder AT shenhe networkbasedapproachtodiscoverdiagnosticmetabolitemarkersassociatedwithdepressivefeaturesformajordepressivedisorder AT huafangli networkbasedapproachtodiscoverdiagnosticmetabolitemarkersassociatedwithdepressivefeaturesformajordepressivedisorder AT huafangli networkbasedapproachtodiscoverdiagnosticmetabolitemarkersassociatedwithdepressivefeaturesformajordepressivedisorder AT huafangli networkbasedapproachtodiscoverdiagnosticmetabolitemarkersassociatedwithdepressivefeaturesformajordepressivedisorder |