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This is a Literature Review study.

September 10, 2025Open Access

Advances and Challenges in Depression Marker Research

Key Points

Integration of multimodal biomarkers shows promise for guiding tailored interventions in depression management.
The review identifies challenges, such as clinical heterogeneity and inadequate animal models affecting the understanding of biomarkers.
The study emphasizes the potential of AI-driven models and multidimensional bioinformatics to improve depression diagnosis.
Systematic literature search across genetic, neuroendocrine, and gut microbiota domains reveals gaps and future directions for research in depression.

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.

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