Background: Major Depressive Disorder (MDD) causes a significant disease burden around the world. However, the pathogenesis remains unclear and objective biomarkers lack; consequently, diagnosis and treatment of MDD still rely primarily on symptoms. The onset of MDD is considered to be caused due to interaction between biological characteristics and environmental stress, but current researches have not thoroughly explained deviations in clinical symptom presentations and treatment response. This barrier may be related to the multifaceted nature of MDD’s pathogenic mechanisms, insufficient representativeness of research samples, or due to the high heterogeneity, which makes it difficult to explain the role of different pathogenic mechanisms under the current diagnostic system with a single etiological hypothesis. Therefore, there may be different subtypes of MDD, with possible variations in the pathophysiological mechanisms. The high heterogeneity of MDD limits the clarification of pathogenesis and discovery of highly sensitive and specific biomarkers.
Methods:
1. Utilizing machine learning methods for clustering and classification to identify potential subtypes of MDD with more homogeneity.
2. Explore the environmental risk factors (i.e., childhood trauma, stressful events, etc.) and biological characteristics (i.e., neuroimaging, transcriptomics, etc.) of MDD (and its subtypes), elucidating the mechanisms and progression of MDD (and its subtypes).
3. Investigate the clinical features of different subtypes, discover biological markers associated with MDD (and its subtypes), and explore the potential of these indicators in diagnosis, treatment, and prognosis.
