小柯机器人

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肠道菌群研究不可靠的原因终找到？
2020-11-08 15:28
来源：小柯机器人

美国国立卫生研究院Yasmine Belkaid、Ivan Vujkovic-Cvijin等研究人员合作发现，宿主变量混淆了人类疾病相关的肠道菌群研究。该研究于2020年11月4日在线发表于《自然》杂志。

研究人员推断了人类肠道微生物群谱最大、最普遍的异质性来源，并且还鉴定了人类的生活方式和生理特征，在这些因素中，如果病例与对照之间的差异不均等，则会混淆微生物群分析，从而产生与人类疾病相关的虚假微生物关联。研究人员将酒精摄入频率和排便质量确定为肠道菌群差异的强大来源，这些在健康参与者和患有疾病的参与者之间分布不同，并且可能混淆研究设计。
 
研究人员证明，对于许多流行的高负担人类疾病，实验组和对照组如果减少了这些混淆变量能够降低微生物群中观察到的差异和虚假关联的发生率。在此基础上，研究人员提供了一个人类变量列表，并建议在人类微生物群研究中获取这些宿主变量来匹配比较组，研究人员预计这些变量将在解析与人类疾病真正相关的肠道菌群成员时提高稳健性和可重复性。
 
微生物群在人类疾病中作用的研究存在很低的一致性，这限制了鉴定宿主相关微生物与病理之间因果关系的能力。微生物群组成中个体之间的广泛异质性加剧了获得假阳性的风险，这可能是由于人类生活方式和生理变量在人群中的差异造成了对微生物群的不同影响。
 
附：英文原文

Title: Host variables confound gut microbiota studies of human disease

Author: Ivan Vujkovic-Cvijin, Jack Sklar, Lingjing Jiang, Loki Natarajan, Rob Knight, Yasmine Belkaid

Issue&Volume: 2020-11-04

Abstract: Low concordance between studies that examine the role of microbiota in human diseases is a pervasive challenge that limits the capacity to identify causal relationships between host-associated microorganisms and pathology. The risk of obtaining false positives is exacerbated by wide interindividual heterogeneity in microbiota composition1, probably due to population-wide differences in human lifestyle and physiological variables2 that exert differential effects on the microbiota. Here we infer the greatest, generalized sources of heterogeneity in human gut microbiota profiles and also identify human lifestyle and physiological characteristics that, if not evenly matched between cases and controls, confound microbiota analyses to produce spurious microbial associations with human diseases. We identify alcohol consumption frequency and bowel movement quality as unexpectedly strong sources of gut microbiota variance that differ in distribution between healthy participants and participants with a disease and that can confound study designs. We demonstrate that for numerous prevalent, high-burden human diseases, matching cases and controls for confounding variables reduces observed differences in the microbiota and the incidence of spurious associations. On this basis, we present a list of host variables that we recommend should be captured in human microbiota studies for the purpose of matching comparison groups, which we anticipate will increase robustness and reproducibility in resolving the members of the gut microbiota that are truly associated with human disease. The authors use a machine-learning approach to uncover confounding variables in studies that seek to establish an association between the gut microbiota and human disease.

DOI: 10.1038/s41586-020-2881-9

Source: https://www.nature.com/articles/s41586-020-2881-9

Nature：《自然》，创刊于1869年。隶属于施普林格·自然出版集团，最新IF：69.504
官方网址：http://www.nature.com/
投稿链接：http://www.nature.com/authors/submit_manuscript.html


本期文章：《自然》：Online/在线发表

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