Utilizing metagenomic profiling and machine learning model to identify bacterial biomarkers for major depressive disorder
BackgroundMajor depressive disorder (MDD) is highly heterogeneous, which provides a significant challenge in the management of this disorder. However, the pathogenesis of major depressive disorder is not fully understood. Studies have shown that depression is highly correlated with gut flora. The ob...
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Psychiatry |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1539596/full |
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| author | Xuan Wang Di Cao Hanlin Zhang Wei Chen Jiaxin Sun Huimin Hu |
| author_facet | Xuan Wang Di Cao Hanlin Zhang Wei Chen Jiaxin Sun Huimin Hu |
| author_sort | Xuan Wang |
| collection | DOAJ |
| description | BackgroundMajor depressive disorder (MDD) is highly heterogeneous, which provides a significant challenge in the management of this disorder. However, the pathogenesis of major depressive disorder is not fully understood. Studies have shown that depression is highly correlated with gut flora. The objective of this study was to explore the potential of microbial biomarkers in the diagnosis of major depressive disorder.MethodsIn this study, we used a metagenomic approach to analyze the composition and differences of gut bacterial communities in 36 patients with major depressive disorder and 36 healthy individuals. We then applied a Support Vector Machine Recursive Feature Elimination (SVM-RFE) machine learning model to find potential microbial markers.ResultsOur results showed that the alpha diversity of the intestinal flora did not differ significantly in major depressive disorder compared to healthy populations. However, the beta diversity was significantly altered. Machine learning identified 8 MDD-specific bacterial biomarkers, with Alistipes, Dysosmobacter, Actinomyces, Ruthenibacterium, and Thomasclavelia being significantly enriched, while Faecalibacterium, Pseudobutyrivibrio, and Roseburia were significantly reduced, demonstrating superior diagnostic accuracy (area under the curve, AUC = 0.919). In addition, the gut bacteria performed satisfactorily in the validation cohort with an AUC of 0.800 (95% CI: 0.6334-0.9143).ConclusionThis study reveals the complex relationship between gut microbiota and major depressive disorder and provides a scientific basis for the development of a microbiota-based diagnostic tool for depression. |
| format | Article |
| id | doaj-art-2a257f3617b14554a1d072c102bf0ffa |
| institution | DOAJ |
| issn | 1664-0640 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychiatry |
| spelling | doaj-art-2a257f3617b14554a1d072c102bf0ffa2025-08-20T02:43:16ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402025-02-011610.3389/fpsyt.2025.15395961539596Utilizing metagenomic profiling and machine learning model to identify bacterial biomarkers for major depressive disorderXuan Wang0Di Cao1Hanlin Zhang2Wei Chen3Jiaxin Sun4Huimin Hu5Department of Dermatology, Lianyungang Municipal Oriental Hospital, Lianyungang, ChinaDepartment of Dermatology, The First People’s Hospital of Lianyungang, Lianyungang, ChinaDepartment of Dermatology, Jieshou City People’s Hospital, Fuyang, ChinaDepartment of Dermatology, Lianyungang Municipal Oriental Hospital, Lianyungang, ChinaDepartment of Dermatology, Lianyungang Municipal Oriental Hospital, Lianyungang, ChinaDepartment of Dermatology, The Affiliated Huai’an Hospital of Xuzhou Medical University and The Second People’s Hospital of Huai’an, Huaian, ChinaBackgroundMajor depressive disorder (MDD) is highly heterogeneous, which provides a significant challenge in the management of this disorder. However, the pathogenesis of major depressive disorder is not fully understood. Studies have shown that depression is highly correlated with gut flora. The objective of this study was to explore the potential of microbial biomarkers in the diagnosis of major depressive disorder.MethodsIn this study, we used a metagenomic approach to analyze the composition and differences of gut bacterial communities in 36 patients with major depressive disorder and 36 healthy individuals. We then applied a Support Vector Machine Recursive Feature Elimination (SVM-RFE) machine learning model to find potential microbial markers.ResultsOur results showed that the alpha diversity of the intestinal flora did not differ significantly in major depressive disorder compared to healthy populations. However, the beta diversity was significantly altered. Machine learning identified 8 MDD-specific bacterial biomarkers, with Alistipes, Dysosmobacter, Actinomyces, Ruthenibacterium, and Thomasclavelia being significantly enriched, while Faecalibacterium, Pseudobutyrivibrio, and Roseburia were significantly reduced, demonstrating superior diagnostic accuracy (area under the curve, AUC = 0.919). In addition, the gut bacteria performed satisfactorily in the validation cohort with an AUC of 0.800 (95% CI: 0.6334-0.9143).ConclusionThis study reveals the complex relationship between gut microbiota and major depressive disorder and provides a scientific basis for the development of a microbiota-based diagnostic tool for depression.https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1539596/fullmajor depressive disordergut bacteriametagenomemachine learningdiagnosing disease |
| spellingShingle | Xuan Wang Di Cao Hanlin Zhang Wei Chen Jiaxin Sun Huimin Hu Utilizing metagenomic profiling and machine learning model to identify bacterial biomarkers for major depressive disorder Frontiers in Psychiatry major depressive disorder gut bacteria metagenome machine learning diagnosing disease |
| title | Utilizing metagenomic profiling and machine learning model to identify bacterial biomarkers for major depressive disorder |
| title_full | Utilizing metagenomic profiling and machine learning model to identify bacterial biomarkers for major depressive disorder |
| title_fullStr | Utilizing metagenomic profiling and machine learning model to identify bacterial biomarkers for major depressive disorder |
| title_full_unstemmed | Utilizing metagenomic profiling and machine learning model to identify bacterial biomarkers for major depressive disorder |
| title_short | Utilizing metagenomic profiling and machine learning model to identify bacterial biomarkers for major depressive disorder |
| title_sort | utilizing metagenomic profiling and machine learning model to identify bacterial biomarkers for major depressive disorder |
| topic | major depressive disorder gut bacteria metagenome machine learning diagnosing disease |
| url | https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1539596/full |
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