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The effect of ginsenosides on depression in preclinical studies: A systematic review and meta-analysis

  • Kim, Yunna (College of Korean Medicine, Kyung Hee University) ;
  • Cho, Seung-Hun (College of Korean Medicine, Kyung Hee University)
  • 투고 : 2020.03.16
  • 심사 : 2020.08.31
  • 발행 : 2021.05.01

초록

Background: Many ginsenosides have been shown to be efficacious for major depressive disorder (MDD), which is a highly recurrent disorder, through several preclinical studies. We aimed to review the literature assessing the antidepressant effects of ginsenosides on MDD animal models, to establish systematic scientific evidence in a rigorous manner. Methods: We performed a systematic review on the antidepressant effects of ginsenoside evaluated in in vivo studies. We searched for preclinical trials from inception to July 2019 in electronic databases such as Pubmed and Embase. In vivo studies examining the effect of a single ginsenoside on animal models of primary depression were included. Items of each study were evaluated by two independent reviewers. A meta-analysis was conducted to assess behavioral changes induced by ginsenoside Rg1, which was the most studied ginsenoside. Data were pooled using the random-effects models. Results: A total of 517 studies were identified, and 23 studies were included in the final analysis. They reported on many ginsenosides with different antidepressant effects and biological mechanisms of action. Of the 12 included articles assessing ginsenoside Rg1, pooled results of forced swimming test from 9 articles (mean difference (MD): 20.50, 95% CI: 16.13-24.87), and sucrose preference test from 11 articles (MD: 28.29, 95% CI: 22.90-33.69) showed significant differences compared with vehicle treatment. The risk of bias of each study was moderate, but there was significant heterogeneity across studies. Conclusion: These estimates suggest that ginsenosides, including ginsenoside Rg1, reduces symptoms of depression, modulates underlying mechanisms, and can be a promising antidepressant.

키워드

과제정보

This work was supported by a grant from Kyung Hee University in 2016. (KHU-20161702)

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