重度抑郁症多组学研究与精准治疗

Major Depressive Disorder (MDD) is a highly heterogeneous mental illness, with significant individual differences in symptom presentation and pathophysiological mechanisms, posing substantial challenges to clinical diagnosis and treatment. Research on biological subtypes is considered an important approach to elucidate the heterogeneity of MDD, which is crucial for achieving personalized and precise diagnosis and treatment of depression. In our previous studies, we used semi-supervised machine learning algorithms based on brain gray matter structural MRI data to successfully identify two distinct subtypes of MDD in terms of gray matter structure. This finding provides important neuroimaging evidence for the precise classification of MDD; however, the multi-omics mechanisms underlying these two subtypes and their differential responses to pharmacological or physical treatments remain unclear, limiting their clinical application value. This study aims to validate the stability of the two MDD gray matter structural subtypes identified in our earlier research using multicenter large-sample data. We will integrate multi-omics data, including neuroimaging features, molecular mechanisms, and socio-psychological factors, to deeply analyze the multi-omics mechanisms of different gray matter structural subtypes. Additionally, the study will explore the differences in treatment response and response mechanisms between the two subtypes and construct a predictive model for MDD treatment efficacy. This will provide theoretical support and practical guidance for the precise classification and personalized treatment of depression.