ArticleMicrobiome2023
A microbial causal mediation analytic tool for health disparity and applications in body mass index.
Article in Microbiome, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.
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Who cites it
7 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- STROBE-causal machine learning for the human microbiome: systematic review on methodological innovations and validation frameworks.Frontiers in microbiology · 2026Pooled it
- Beyond just correlation: causal machine learning for the microbiome, from prediction to health policy with econometric tools.Frontiers in microbiology · 2025Pooled it
- Article
- Instrumental variable estimation for compositional treatments.Scientific reports · 2025Article
- Racial-Ethnic Disparities of Obesity Require Community Context-Specific Biomedical Research for Native Hawaiians and Other Pacific Islanders.Nutrients · 2024Review
- A unified web cloud computing platform MiMedSurv for microbiome causal mediation analysis with survival responses.Scientific reports · 2024Article
- The features of male breast cancer in China: A real-world study.Breast (Edinburgh, Scotland) · 2024Article
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6 authors.
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Abstract
backgroundEmerging 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.
methodsConsidering 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.
resultsThrough 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.
conclusionsThe 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.
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