Wang Xiang, Su Yingying, Liu Qian, Li Muzi, Zeighami Yashar, Fan Jie, Adams G Camelia, Tan Changlian, Zhu Xiongzhao, Meng Xiangfei
Medical Psychological Center, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China; Medical Psychological Institute of Central South University, Central South University, Changsha, Hunan, China; National Clinical Research Center on Mental Disorders (Xiangya), Changsha, Hunan, China; National Center for Mental Disorder, Changsha, Hunan, China; Department of Psychiatry, Faculty of Medicine and Health Sciences, McGill University, Montréal, QC, Canada; Douglas Research Centre, Montréal, QC, Canada.
Department of Psychiatry, Faculty of Medicine and Health Sciences, McGill University, Montréal, QC, Canada; Douglas Research Centre, Montréal, QC, Canada; School of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen, Guangdong, China.
EBioMedicine. 2025 Jun;116:105756. doi: 10.1016/j.ebiom.2025.105756. Epub 2025 May 14.
The heterogeneity of major depressive disorder (MDD) significantly hinders its effective and optimal clinical outcomes. This study aimed to identify MDD subtypes by adopting a data-driven approach and assessing validity based on symptomatology and neuroimaging.
A total of 259 patients with MDD and 92 healthy controls were enrolled in this cross-sectional study. Latent profile analysis (LPA) was used to identify MDD subtypes based on validated clinical symptoms. To examine whether there were differences between these identified MDD subtypes, network analysis was used to test any differences in symptom patterns between these subtypes. We also compared neural activity between these identified MDD subtypes and tested whether certain neural activities were related to individual subtypes. This MDD subtyping was further tested in an independent dataset that contains 86 patients with MDD.
Five MDD subtypes with distinct depressive symptom patterns were identified using the LPA model, with the 5-class model selected as the optimal classification solution based on its superior fit indices (AIC = 6656.296, aBIC = 6681.030, entropy = 0.917, LMR p = 0.3267, BLRT p < 0.001). The identified subtypes include atypical-like depression, two melancholic depression (moderate and severe) subtypes with distinct patterns on feeling anxious, and two anhedonic depression subtypes (moderate and severe) with different manifestations on weight/appetite loss. The reproducibility of the classification was also confirmed. Significant differences in symptom structures between melancholic and two anhedonic subtypes, and between anhedonic and atypical subtypes were observed (all p < 0.05). Furthermore, these identified subtypes had differential neural activities in both regional spontaneous neural activity (pFWE < 0.005) and functional connectivity between different brain regions (pFDR < 0.005), linked to different clinical symptoms (FDR q < 0.05).
The network analysis and neuroimaging tests support the existence and validity of the identified MDD subtypes, each exhibiting unique clinical manifestations and neural activity patterns. The categorisation of these subtypes sheds light on the heterogeneity of depression and suggest that personalised treatment and management strategies tailored to specific subtypes may enhance intervention strategies in clinical settings.
National Natural Science Foundation of China (NSFC) and China Scholarship Council (CSC).
重度抑郁症（MDD）的异质性严重阻碍了其有效和最佳的临床治疗效果。本研究旨在采用数据驱动的方法识别MDD亚型，并基于症状学和神经影像学评估其有效性。
本横断面研究共纳入259例MDD患者和92名健康对照。基于经过验证的临床症状，采用潜在类别分析（LPA）来识别MDD亚型。为了检验这些识别出的MDD亚型之间是否存在差异，使用网络分析来测试这些亚型之间症状模式的任何差异。我们还比较了这些识别出的MDD亚型之间的神经活动，并测试了某些神经活动是否与个体亚型相关。这种MDD亚型分类在一个包含86例MDD患者的独立数据集中进行了进一步验证。
使用LPA模型识别出五种具有不同抑郁症状模式的MDD亚型，基于其优越的拟合指数（AIC = 6656.296，aBIC = 6681.030，熵 = 0.917，LMR p = 0.3267，BLRT p < 0.001），选择五类模型作为最佳分类解决方案。识别出的亚型包括非典型性抑郁症、两种具有不同焦虑感受模式的 melancholic 抑郁症（中度和重度）亚型，以及两种在体重/食欲丧失方面有不同表现的快感缺失性抑郁症（中度和重度）亚型。分类的可重复性也得到了证实。观察到 melancholic 亚型与两种快感缺失性亚型之间以及快感缺失性亚型与非典型性亚型之间在症状结构上存在显著差异（所有p < 0.05）。此外，这些识别出的亚型在区域自发神经活动（pFWE < 0.005）和不同脑区之间的功能连接性（pFDR < 0.005）方面均有不同的神经活动，且与不同的临床症状相关（FDR q < 0.05）。
网络分析和神经影像学测试支持了所识别的MDD亚型的存在和有效性，每种亚型都表现出独特的临床表现和神经活动模式。这些亚型的分类揭示了抑郁症的异质性，并表明针对特定亚型量身定制的个性化治疗和管理策略可能会增强临床环境中的干预策略。
中国国家自然科学基金（NSFC）和中国留学基金委（CSC）。
