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抑郁症标志物研究的进展与挑战

Advances and Challenges in Depression Marker Research.

作者信息

Jin Weifeng, Chen Shuzi, Li Dan, Chen Qing, Zhu Mengyuan, Wang Mengxia, Fu Xiaomei, Lin Ping

机构信息

Department of Medical Laboratory, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.

出版信息

Neuropsychiatr Dis Treat. 2025 Jul 31;21:1549-1567. doi: 10.2147/NDT.S527897. eCollection 2025.

Abstract

Depressive disorders diagnosis relies on subjective clinical assessment due to the lack of validated biomarkers. This review synthesizes recent advances in depression biomarkers across genetic, epigenetic, neuroendocrine, neuroimaging, immune/inflammatory, and gut microbiota domains. Literature was systematically searched via PubMed/Web of Science.We analyze mechanisms, highlight challenges (eg, clinical heterogeneity, inadequate animal models), and propose future directions: multidimensional bioinformatics, AI-driven models, RDoC framework implementation, and interdisciplinary collaboration. Critically, our analysis reveals that multimodal integration of biomarkers-rather than single-domain approaches-holds the greatest promise for overcoming diagnostic heterogeneity and guiding personalized interventions. These strategies may revolutionize MDD management through early detection and tailored therapeutics.

摘要

由于缺乏经过验证的生物标志物,抑郁症的诊断依赖于主观临床评估。本综述综合了抑郁症生物标志物在遗传、表观遗传、神经内分泌、神经影像学、免疫/炎症和肠道微生物群领域的最新进展。通过PubMed/科学网系统检索文献。我们分析了机制,突出了挑战(如临床异质性、动物模型不足),并提出了未来方向:多维生物信息学、人工智能驱动模型、疾病分类与诊断标准框架的实施以及跨学科合作。至关重要的是,我们的分析表明,生物标志物的多模态整合——而非单领域方法——最有希望克服诊断异质性并指导个性化干预。这些策略可能通过早期检测和量身定制的治疗方法彻底改变重度抑郁症的管理。

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