Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P. R. China.
Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P. R. China.
Trends Microbiol. 2021 Aug;29(8):736-746. doi: 10.1016/j.tim.2021.03.015. Epub 2021 Apr 21.
Microorganisms that colonize the mammalian skin and cavity play critical roles in various physiological functions of the host. Numerous studies have revealed strong associations between the microbiota and multiple diseases. However, association does not mean causation. To clarify the mechanisms underlying microbiota-mediated diseases, research is moving from associative analyses to causation studies. In this article, we first introduce the principles of the computational methods for causal inference, and then discuss the applications of these methods in microbiome medicine. Furthermore, we examine the reliability of theoretically inferred causality by the interventionist framework. Finally, we show the potential of confirmed causality in microbiota-targeted therapy, especially in personalized dietary intervention. We conclude that a comprehensive understanding of the causal relationships between diets, microbiota, host targets, and diseases is critical to future microbiome medicine.
定植于哺乳动物皮肤和腔道的微生物在宿主的各种生理功能中发挥着关键作用。大量研究表明，微生物群与多种疾病之间存在密切关联。然而，关联并不意味着因果关系。为了阐明微生物群介导疾病的机制，研究正在从关联分析转向因果关系研究。在本文中，我们首先介绍了计算因果推断方法的原理，然后讨论了这些方法在微生物组医学中的应用。此外，我们还通过干预框架检验了理论推断因果关系的可靠性。最后，我们展示了在靶向微生物组治疗中证实因果关系的潜力，特别是在个性化饮食干预中。我们得出的结论是，全面了解饮食、微生物群、宿主靶标和疾病之间的因果关系对于未来的微生物组医学至关重要。
