Department of Population Health, Division of Biostatistics, New York University Grossman School of Medicine, New York, NY, 10016, USA.
Department of Population Health, Division of Epidemiology, New York University Grossman School of Medicine, New York, NY, 10016, USA.
Microbiome. 2023 Jul 27;11(1):164. doi: 10.1186/s40168-023-01608-9.
Emerging evidence suggests the potential mediating role of microbiome in health disparities. However, no analytic framework can be directly used to analyze microbiome as a mediator between health disparity and clinical outcome, due to the non-manipulable nature of the exposure and the unique structure of microbiome data, including high dimensionality, sparsity, and compositionality.
Considering the modifiable and quantitative features of the microbiome, we propose a microbial causal mediation model framework, SparseMCMM_HD, to uncover the mediating role of microbiome in health disparities, by depicting a plausible path from a non-manipulable exposure (e.g., ethnicity or region) to the outcome through the microbiome. The proposed SparseMCMM_HD rigorously defines and quantifies the manipulable disparity measure that would be eliminated by equalizing microbiome profiles between comparison and reference groups and innovatively and successfully extends the existing microbial mediation methods, which are originally proposed under potential outcome or counterfactual outcome study design, to address health disparities.
Through three body mass index (BMI) studies selected from the curatedMetagenomicData 3.4.2 package and the American gut project: China vs. USA, China vs. UK, and Asian or Pacific Islander (API) vs. Caucasian, we exhibit the utility of the proposed SparseMCMM_HD framework for investigating the microbiome's contributions in health disparities. Specifically, BMI exhibits disparities and microbial community diversities are significantly distinctive between reference and comparison groups in all three applications. By employing SparseMCMM_HD, we illustrate that microbiome plays a crucial role in explaining the disparities in BMI between ethnicities or regions. 20.63%, 33.09%, and 25.71% of the overall disparity in BMI in China-USA, China-UK, and API-Caucasian comparisons, respectively, would be eliminated if the between-group microbiome profiles were equalized; and 15, 18, and 16 species are identified to play the mediating role respectively.
The proposed SparseMCMM_HD is an effective and validated tool to elucidate the mediating role of microbiome in health disparity. Three BMI applications shed light on the utility of microbiome in reducing BMI disparity by manipulating microbial profiles. Video Abstract.
新出现的证据表明，微生物组在健康差异中可能起到中介作用。然而，由于暴露的不可操作性和微生物组数据的独特结构，包括高维性、稀疏性和组成性，不能直接使用分析框架将微生物组分析为健康差异和临床结果之间的中介。
考虑到微生物组的可调节和定量特征，我们提出了一个微生物因果中介模型框架 SparseMCMM_HD，通过描绘一条从不可操作的暴露（例如，种族或地区）到通过微生物组到达结果的合理路径，来揭示微生物组在健康差异中的中介作用。所提出的 SparseMCMM_HD 严格定义和量化了可调节的差异度量，该度量可以通过在比较组和参考组之间使微生物组谱平衡来消除，并且创新和成功地扩展了现有的微生物中介方法，这些方法最初是在潜在结果或反事实结果研究设计下提出的，以解决健康差异问题。
通过从 curatedMetagenomicData 3.4.2 包和美国肠道项目中选择的三个体重指数（BMI）研究，即中国与美国、中国与英国以及亚洲或太平洋岛民（API）与高加索人，我们展示了所提出的 SparseMCMM_HD 框架用于研究微生物组在健康差异中的贡献的效用。具体来说，在所有三个应用中，BMI 表现出差异，并且参考组和比较组之间的微生物群落多样性明显不同。通过使用 SparseMCMM_HD，我们说明了微生物组在解释种族或地区之间 BMI 差异方面起着关键作用。如果平衡组间微生物组谱，中国与美国、中国与英国以及 API 与高加索人之间 BMI 差异的总体差异将分别消除 20.63%、33.09%和 25.71%；并分别鉴定出 15、18 和 16 种微生物起中介作用。
所提出的 SparseMCMM_HD 是一种有效的、经过验证的工具，可以阐明微生物组在健康差异中的中介作用。三个 BMI 应用说明了通过操纵微生物组谱来减少 BMI 差异的微生物组的效用。视频摘要。
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