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Updated: Aug 14, 2025
An In Vitro Batch-culture Model to Estimate the Effects of Interventional Regimens on Human Fecal Microbiota
Published on: July 31, 2019
Development of Microbiome Biomarkers in Intervention Studies
Olajumoke Evangelina Owokotomo1, Rudradev Sengupta1,2, Ziv Shkedy1
1Center for Statistics, Data Science Institute, Universiteit Hasselt, Diepenbeek 3590, Belgium.
Aims:
There has been an increased interest in studying the association between microbial communities and different diseases and in discovering microbiome biomarkers. This association is pivotal to discover such biomarkers. In this paper, we present a unified modelling approach that can be used to detect and develop microbiome biomarkers for different clinical responses of interest at different levels of the microbiome ecosystem.
Methods And Results:
We extended the methodology rooted in the information theory and joint modelling approaches for the evaluation of surrogate endpoints in randomized clinical trials to the high-dimensional microbiome setting. The unified modelling approach introduced in this paper allows for detecting biomarkers associated with a clinical response of interest, adjusting for the intervention applied to the subjects. For some microbiome features, the association is driven by the treatment, while for others, the association reflects the correlation between the microbiome biomarker and the clinical response of interest.
Conclusions:
The results have demonstrated that biomarkers can be identified at different levels of the microbiome phylogenetic tree using various measures as biomarkers.
