Metaorganism Immunity Section, Laboratory of Immune Systems Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.
National Institute of Allergy and Infectious Diseases Microbiome Program, National Institutes of Health, Bethesda, MD, USA.
Nature. 2020 Nov;587(7834):448-454. doi: 10.1038/s41586-020-2881-9. Epub 2020 Nov 4.
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 composition, probably due to population-wide differences in human lifestyle and physiological variables 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.
研究微生物组在人类疾病中的作用时，各研究之间的一致性较低，这是一个普遍存在的挑战，限制了确定宿主相关微生物与病理学之间因果关系的能力。由于微生物组组成的个体间广泛异质性，获得假阳性的风险加剧，这可能是由于人类生活方式和生理变量在人群中的差异对微生物组产生不同的影响。在这里，我们推断出人类肠道微生物组谱中最大的、普遍的异质来源，还确定了人类生活方式和生理特征，如果在病例和对照组之间不平均匹配，会混淆微生物组分析，从而产生与人类疾病虚假的微生物关联。我们发现，饮酒频率和排便质量是肠道微生物组方差的意外强来源，它们在健康参与者和患病参与者之间的分布不同，并且可能会干扰研究设计。我们证明，对于许多常见的、高负担的人类疾病，为混杂变量匹配病例和对照组可以减少微生物组中观察到的差异和虚假关联的发生率。在此基础上，我们提出了一份宿主变量清单，我们建议在人类微生物组研究中捕获这些变量，以便匹配比较组，我们预计这将提高解决与人类疾病真正相关的肠道微生物组成员的稳健性和可重复性。
