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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.
Journal of Applied Microbiology
|January 10, 2023
Summary
This study introduces a unified modeling approach to identify microbiome biomarkers associated with clinical responses. The method reveals biomarkers at various phylogenetic levels, aiding disease research.
Area of Science:
- Microbiome research
- Biomarker discovery
- Systems biology
Background:
- Growing interest in the human microbiome's role in disease.
- Need for robust methods to identify microbiome biomarkers.
- Current approaches may lack a unified framework.
Purpose of the Study:
- To present a unified modeling approach for detecting microbiome biomarkers.
- To identify biomarkers associated with clinical responses at different ecosystem levels.
- To leverage information theory and joint modeling for high-dimensional data.
Main Methods:
- Extended information theory and joint modeling from clinical trials.
- Applied to high-dimensional microbiome data.
- Accounted for treatment interventions in the analysis.
Main Results:
- Developed a unified approach to detect microbiome biomarkers.
- Identified biomarkers associated with clinical responses, adjusting for treatment.
- Distinguished between treatment-driven and correlative associations.
Conclusions:
- Demonstrated the identification of biomarkers across the microbiome phylogenetic tree.
- Showcased the utility of various measures for biomarker identification.
- Validated the unified approach for microbiome biomarker discovery.
