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抑郁症临床亚型相关研究综述
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Abstract:
抑郁症是一种高度异质性疾病，具有高发病率和高复发率等特点。抑郁症患者的症状特征、临床轨迹和治疗结果因人而异，表明存在不同的临床亚型。近年来，越来越多的研究尝试通过识别临床亚型，以提高抑郁症的诊断同质性并推动精准治疗的发展。文章总结了常用的抑郁症临床分型的方案，探讨分型面临的挑战和未来的方向。在今后的研究中，通过设计标准化的研究方案、比较不同亚型之间潜在的生物标志物的差异，将有助于更好地理解潜在的疾病机制、预测预后以及制定个体化精准治疗策略。未来应在大样本、多模态的研究框架下，结合遗传学、神经影像与机器学习方法，建立精准的抑郁症亚型划分体系。
Depression is a highly heterogeneous disorder characterized by high prevalence and recurrence rates. The symptom profiles, clinical trajectories, and treatment outcomes of patients with depression vary substantially among individuals, suggesting the existence of distinct clinical subtypes. In recent years, increasing efforts have been made to identify these subtypes to enhance diagnostic homogeneity and advance precision treatment. This paper summarizes the commonly used clinical classification schemes for depression, discusses the challenges faced in subtype identification, and outlines potential future directions. In future research, the design of standardized study protocols and the comparison of potential biological markers across subtypes will facilitate a better understanding of underlying disease mechanisms, improve prognostic prediction, and guide the development of individualized and precise therapeutic strategies. Future studies should adopt large-sample, multimodal research frameworks integrating genetics, neuroimaging, and machine learning approaches to establish a biologically informed and precise classification system for depressive disorders.
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