Dissecting biological heterogeneity in major depressive disorder based on neuroimaging subtypes with multi-omics data
Abstract The heterogeneity of Major Depressive Disorder (MDD) has been increasingly recognized, challenging traditional symptom-based diagnostics and the development of mechanism-targeted therapies. This study aims to identify neuroimaging-based MDD subtypes and dissect their predominant biological...
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Nature Publishing Group
2025-03-01
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| Series: | Translational Psychiatry |
| Online Access: | https://doi.org/10.1038/s41398-025-03286-7 |
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| author | Lili Tang Rui Tang Junjie Zheng Pengfei Zhao Rongxin Zhu Yanqing Tang Xizhe Zhang Xiaohong Gong Fei Wang |
| author_facet | Lili Tang Rui Tang Junjie Zheng Pengfei Zhao Rongxin Zhu Yanqing Tang Xizhe Zhang Xiaohong Gong Fei Wang |
| author_sort | Lili Tang |
| collection | DOAJ |
| description | Abstract The heterogeneity of Major Depressive Disorder (MDD) has been increasingly recognized, challenging traditional symptom-based diagnostics and the development of mechanism-targeted therapies. This study aims to identify neuroimaging-based MDD subtypes and dissect their predominant biological characteristics using multi-omics data. A total of 807 participants were included in this study, comprising 327 individuals with MDD and 480 healthy controls (HC). The amplitude of low-frequency fluctuations (ALFF), a functional neuroimaging feature, was extracted for each participant and used to identify MDD subtypes through machine learning clustering. Multi-omics data, including profiles of genetic, epigenetics, metabolomics, and pro-inflammatory cytokines, were obtained. Comparative analyses of multi-omics data were conducted between each MDD subtype and HC to explore the molecular underpinnings involved in each subtype. We identified three neuroimaging-based MDD subtypes, each characterized by unique ALFF pattern alterations compared to HC. Multi-omics analysis showed a strong genetic predisposition for Subtype 1, primarily enriched in neuronal development and synaptic regulation pathways. This subtype also exhibited the most severe depressive symptoms and cognitive decline compared to the other subtypes. Subtype 2 is characterized by immuno-inflammation dysregulation, supported by elevated IL-1 beta levels, altered epigenetic inflammatory measures, and differential metabolites correlated with IL-1 beta levels. No significant biological markers were identified for Subtype 3. Our results identify neuroimaging-based MDD subtypes and delineate the distinct biological features of each subtype. This provides a proof of concept for mechanism-targeted therapy in MDD, highlighting the importance of personalized treatment approaches based on neurobiological and molecular profiles. |
| format | Article |
| id | doaj-art-c4e13dd1823f45d99a20912bc285faff |
| institution | DOAJ |
| issn | 2158-3188 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Publishing Group |
| record_format | Article |
| series | Translational Psychiatry |
| spelling | doaj-art-c4e13dd1823f45d99a20912bc285faff2025-08-20T03:05:49ZengNature Publishing GroupTranslational Psychiatry2158-31882025-03-0115111110.1038/s41398-025-03286-7Dissecting biological heterogeneity in major depressive disorder based on neuroimaging subtypes with multi-omics dataLili Tang0Rui Tang1Junjie Zheng2Pengfei Zhao3Rongxin Zhu4Yanqing Tang5Xizhe Zhang6Xiaohong Gong7Fei Wang8Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical UniversityEarly Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical UniversityEarly Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical UniversityEarly Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical UniversityEarly Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical UniversityDepartment of Psychiatry, Shengjing Hospital of China Medical UniversityEarly Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical UniversityState Key Laboratory of Genetic Engineering, MOE key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan UniversityEarly Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical UniversityAbstract The heterogeneity of Major Depressive Disorder (MDD) has been increasingly recognized, challenging traditional symptom-based diagnostics and the development of mechanism-targeted therapies. This study aims to identify neuroimaging-based MDD subtypes and dissect their predominant biological characteristics using multi-omics data. A total of 807 participants were included in this study, comprising 327 individuals with MDD and 480 healthy controls (HC). The amplitude of low-frequency fluctuations (ALFF), a functional neuroimaging feature, was extracted for each participant and used to identify MDD subtypes through machine learning clustering. Multi-omics data, including profiles of genetic, epigenetics, metabolomics, and pro-inflammatory cytokines, were obtained. Comparative analyses of multi-omics data were conducted between each MDD subtype and HC to explore the molecular underpinnings involved in each subtype. We identified three neuroimaging-based MDD subtypes, each characterized by unique ALFF pattern alterations compared to HC. Multi-omics analysis showed a strong genetic predisposition for Subtype 1, primarily enriched in neuronal development and synaptic regulation pathways. This subtype also exhibited the most severe depressive symptoms and cognitive decline compared to the other subtypes. Subtype 2 is characterized by immuno-inflammation dysregulation, supported by elevated IL-1 beta levels, altered epigenetic inflammatory measures, and differential metabolites correlated with IL-1 beta levels. No significant biological markers were identified for Subtype 3. Our results identify neuroimaging-based MDD subtypes and delineate the distinct biological features of each subtype. This provides a proof of concept for mechanism-targeted therapy in MDD, highlighting the importance of personalized treatment approaches based on neurobiological and molecular profiles.https://doi.org/10.1038/s41398-025-03286-7 |
| spellingShingle | Lili Tang Rui Tang Junjie Zheng Pengfei Zhao Rongxin Zhu Yanqing Tang Xizhe Zhang Xiaohong Gong Fei Wang Dissecting biological heterogeneity in major depressive disorder based on neuroimaging subtypes with multi-omics data Translational Psychiatry |
| title | Dissecting biological heterogeneity in major depressive disorder based on neuroimaging subtypes with multi-omics data |
| title_full | Dissecting biological heterogeneity in major depressive disorder based on neuroimaging subtypes with multi-omics data |
| title_fullStr | Dissecting biological heterogeneity in major depressive disorder based on neuroimaging subtypes with multi-omics data |
| title_full_unstemmed | Dissecting biological heterogeneity in major depressive disorder based on neuroimaging subtypes with multi-omics data |
| title_short | Dissecting biological heterogeneity in major depressive disorder based on neuroimaging subtypes with multi-omics data |
| title_sort | dissecting biological heterogeneity in major depressive disorder based on neuroimaging subtypes with multi omics data |
| url | https://doi.org/10.1038/s41398-025-03286-7 |
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