ArticleFrontiers in genetics2020
An Information-Based Approach for Mediation Analysis on High-Dimensional Metagenomic Data.
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Diet, gut microbiome, and cognition in neurodegeneration: a review and methodological framework.Frontiers in aging neuroscience · 2026Review
- Microbiome mediation analysis: methods, assumptions, and practical considerations.Frontiers in cellular and infection microbiology · 2026Review
- Exploring the role of microbiome in cystic fibrosis clinical outcomes through a mediation analysis.mSystems · 2025Article
- Instrumental variable estimation for compositional treatments.Scientific reports · 2025Article
- Multimedia: multimodal mediation analysis of microbiome data.Microbiology spectrum · 2025Article
- multimedia: Multimodal Mediation Analysis of Microbiome Data.bioRxiv : the preprint server for biology · 2024Article
- Article
- Gut microbiota metabolically mediate intestinal helminth infection in Zebrafish.bioRxiv : the preprint server for biology · 2024Article
- A Guide to Dietary Pattern-Microbiome Data Integration.The Journal of nutrition · 2022Review
- The gut microbiome as mediator between diet and its impact on immune function.Scientific reports · 2022Article
- Improve the Colorectal Cancer Diagnosis Using Gut Microbiome Data.Frontiers in molecular biosciences · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The human microbiome plays a critical role in the development of gut-related illnesses such as inflammatory bowel disease and clinical pouchitis. A mediation model can be used to describe the interaction between host gene expression, the gut microbiome, and clinical/health situation (e.g., diseased or not, inflammation level) and may provide insights into underlying disease mechanisms. Current mediation regression methodology cannot adequately model high-dimensional exposures and mediators or mixed data types. Additionally, regression based mediation models require some assumptions for the model parameters, and the relationships are usually assumed to be linear and additive. With the microbiome being the mediators, these assumptions are violated. We propose two novel nonparametric procedures utilizing information theory to detect significant mediation effects with high-dimensional exposures and mediators and varying data types while avoiding standard regression assumptions. Compared with available methods through comprehensive simulation studies, the proposed method shows higher power and lower error. The innovative method is applied to clinical pouchitis data as well and interesting results are obtained.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.