Bacterial Metabolites of Human Gut Microbiota Correlating with Depression.

Bacterial Metabolites of Human Gut Microbiota Correlating with Depression.
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DOI:
10.3390/ijms21239234
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发表时间:
2020-12-03
影响因子:
5.6
通讯作者:
Chekhonin VP
Chekhonin VP
中科院分区:
生物学2区
文献类型:
--
作者:
Averina OV;Zorkina YA;Yunes RA;Kovtun AS;Ushakova VM;Morozova AY;Kostyuk GP;Danilenko VN;Chekhonin VP

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抑郁症是一种全球性的精神健康威胁,影响着全球约2.64亿人。尽管我们对抑郁症的病理生理学的理解有了长足的进步,但目前还没有可靠的生物标志物对客观诊断和临床治疗做出贡献。微生物区系-肠道-脑轴的发现促使科学家研究肠道微生物区系(GM)在抑郁症发病机制中的作用。在过去的十年里,许多研究都是在这个领域进行的。在GM对大脑的中介作用等机制中,已鉴定出具有神经活性和免疫调节特性的代谢物和化合物的产生。这篇全面的综述集中在与抑郁症有关的低分子化合物作为转基因潜在产品的研究上。还介绍了GM参与抑郁症的其他可能机制,以及抑郁症患者微生物区系组成的变化。综上所述,功能性食品和心理生物药物在缓解抑郁方面的治疗潜力得到了考虑。所描述的与GM相关的生物标志物可能会在临床实践中提高抑郁障碍的诊断标准,并代表着一种潜在的基于元基因组学技术的未来诊断工具,用于评估抑郁障碍的发展。
Depression is a global threat to mental health that affects around 264 million people worldwide. Despite the considerable evolution in our understanding of the pathophysiology of depression, no reliable biomarkers that have contributed to objective diagnoses and clinical therapy currently exist. The discovery of the microbiota-gut-brain axis induced scientists to study the role of gut microbiota (GM) in the pathogenesis of depression. Over the last decade, many of studies were conducted in this field. The productions of metabolites and compounds with neuroactive and immunomodulatory properties among mechanisms such as the mediating effects of the GM on the brain, have been identified. This comprehensive review was focused on low molecular weight compounds implicated in depression as potential products of the GM. The other possible mechanisms of GM involvement in depression were presented, as well as changes in the composition of the microbiota of patients with depression. In conclusion, the therapeutic potential of functional foods and psychobiotics in relieving depression were considered. The described biomarkers associated with GM could potentially enhance the diagnostic criteria for depressive disorders in clinical practice and represent a potential future diagnostic tool based on metagenomic technologies for assessing the development of depressive disorders.
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