Dissecting biological heterogeneity in major depressive disorder based on neuroimaging subtypes with multi-omics data

作者：Lili Tang, Rui Tang, Junjie Zheng, Pengfei Zhao, Rongxin Zhu, Yanqing Tang, Xizhe Zhang, Xiaohong Gong, Fei Wang · 发表于：Translational Psychiatry · 年份：2025 · DOI：10.1038/s41398-025-03286-7 · 被引用次数：31 · 研究领域：Functional Brain Connectivity Studies、Mental Health Research Topics、Tryptophan and brain disorders

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 correla...