A Bibliometric Analysis of Research on Natural Medicines for Depression from 2004 to 2024

2004年至2024年抑郁症天然药物研究的文献计量分析

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Abstract

OBJECTIVE: Depression imposes a severe global health burden. Natural medicines offer multi-target therapeutic potential, but a comprehensive bibliometric assessment of this field is lacking. This study analyzes the research landscape from 2004 to 2024 to map global contributions, collaboration networks, and evolving trends, thereby providing an evidence-based roadmap for future research. METHODS: We systematically retrieved publications on natural product-based medicines for depression from the Web of Science Core Collection (WoSCC) between 2004 and 2024. Bibliometric analysis was performed using CiteSpace, VOSviewer, and the R package "bibliometrix" to examine publication trends, collaboration networks, co-citation patterns, and keyword hotspots. RESULTS: Our analysis of 3,610 publications from 92 countries/regions revealed China's dominant leadership in both productivity and collaborative influence. The Beijing University of Chinese Medicine was identified as the most productive institution, while the Journal of Ethnopharmacology and Phytomedicine were the most cited journals. Key prolific authors included Qin Xuemei and Zhang Yi, and foundational works by researchers such as Porsolt and Sarris Jerome were the most frequently cited. Keyword clustering analysis identified three major research themes: mechanistic investigations (eg, "neuroinflammation", "oxidative stress"), phytochemical analysis (eg, "flavonoids", "alkaloids"), and clinical applications (eg, "randomized controlled trial"). The field has evolved from foundational preclinical models toward a stronger focus on clinical translational research. CONCLUSION: This bibliometric study highlights China's central role in the research on natural medicines for depression and delineates the evolution of major research themes. The findings underscore the necessity for enhanced international collaboration and more rigorous clinical trials to validate the efficacy of bioactive compounds, which is crucial for advancing novel antidepressant drug development.

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