Article ; Online: Evaluation of gut microbiota predictive potential associated with phenotypic characteristics to identify multifactorial diseases.
2024 Volume 16, Issue 1, Page(s) 2297815
Abstract: Gut microbiota has been implicated in various clinical conditions, yet the substantial heterogeneity in gut microbiota research results necessitates a more sophisticated approach than merely identifying statistically different microbial taxa between ... ...
| Abstract | Gut microbiota has been implicated in various clinical conditions, yet the substantial heterogeneity in gut microbiota research results necessitates a more sophisticated approach than merely identifying statistically different microbial taxa between healthy and unhealthy individuals. Our study seeks to not only select microbial taxa but also explore their synergy with phenotypic host variables to develop novel predictive models for specific clinical conditions. Design: We assessed 50 healthy and 152 unhealthy individuals for phenotypic variables (PV) and gut microbiota (GM) composition by 16S rRNA gene sequencing. The entire modeling process was conducted in the R environment using the Random Forest algorithm. Model performance was assessed through ROC curve construction. Results: We evaluated 52 bacterial taxa and pre-selected PV ( Conclusion: Our findings underscore that the selection of bacterial taxa based solely on differences in relative abundance between groups is insufficient to serve as clinical markers. Machine learning techniques are essential for mitigating the considerable variability observed within gut microbiota. In our study, the use of microbial taxa alone exhibited limited predictive power for health outcomes, while the integration of phenotypic variables into predictive models substantially enhanced their predictive capabilities. |
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| MeSH term(s) | Humans ; Gastrointestinal Microbiome/genetics ; RNA, Ribosomal, 16S/genetics ; Diabetes Mellitus, Type 2 ; Biomarkers |
| Chemical Substances | RNA, Ribosomal, 16S ; Biomarkers |
| Language | English |
| Publishing date | 2024-01-18 |
| Publishing country | United States |
| Document type | Journal Article |
| ZDB-ID | 2575755-6 |
| ISSN | 1949-0984 ; 1949-0984 |
| ISSN (online) | 1949-0984 |
| ISSN | 1949-0984 |
| DOI | 10.1080/19490976.2023.2297815 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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