Habitual diet can influence health-related outcomes directly, but such effects may also be modulated indirectly by gut microbiota. We consider randomized trials and the question to what extent the effect of diet on an outcome of interest is mediated through the gut microbiome or whether there is a diet–microbiome interaction identifying subgroups of individuals who are more susceptible to specific dietary effects. The baseline microbiome by itself may be a modifier of the effects of diet on health. Yet, the high dimensionality of microbiome data requires innovative statistical approaches to identify potential mediating or moderating effects. To motivate our proposal for an appropriate analysis workflow, we consider a randomized trial that investigates the effect of a 4-week vegan diet on the diversity of gut microbiota and branched-chain amino acid metabolism in healthy omnivorous volunteers. To address the challenge of compositional microbiome data, we consider an adaptation of the lasso for penalized estimation of multivariable regression models with a large number of microbiotic taxa. This is plugged into a classical regression mediation effect analysis strategy. The interaction effects are obtained via an approach that can directly estimate them without having to deal with main effects. As a result we obtain signatures comprised of microbiotic taxa with potential mediating and moderating effects. Some taxa no longer show up as mediating, when taking moderating effects into account. Thus, the proposed analysis strategy allows to identify specific mediating effects, while avoiding potential erroneous conclusions, where moderating effects might have believed to be mediating effects.

中文翻译：

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评估高维微生物组测量在饮食干预研究中的中介作用

习惯性饮食可以直接影响健康相关结果，但这种影响也可能受到肠道微生物群的间接调节。我们考虑随机试验以及饮食对感兴趣结果的影响在多大程度上是通过肠道微生物组介导的问题，或者是否存在饮食-微生物组相互作用来识别更容易受到特定饮食影响的个体亚组。基线微生物组本身可能是饮食对健康影响的调节剂。然而，微生物组数据的高维度需要创新的统计方法来识别潜在的中介或调节作用。为了激发我们对适当分析工作流程的建议，我们考虑了一项随机试验，该试验调查了 4 周纯素饮食对健康杂食志愿者肠道微生物群多样性和支链氨基酸代谢的影响。为了解决组成微生物组数据的挑战，我们考虑对套索进行调整，以对具有大量微生物分类群的多变量回归模型进行惩罚估计。这被插入到经典的回归中介效应分析策略中。交互效应是通过一种可以直接估计它们而无需处理主效应的方法获得的。因此，我们获得了由具有潜在中介和调节作用的微生物类群组成的特征。当考虑调节效应时，一些分类群不再显示为中介。因此，