Evaluation of gut microbiota predictive potential associated with phenotypic characteristics to identify multifactorial diseases

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Citações na Scopus
0
Tipo de produção
article
Data de publicação
2024
DOI
Scopus
Web of Science
PubMed
Título da Revista
ISSN da Revista
Título do Volume
Editora
TAYLOR & FRANCIS INC
Autores
ALVES, Juliana Tepedin Martins o
FERNANDES, Gabriel da Rocha
Citação
GUT MICROBES, v.16, n.1, article ID 2297815, 18p, 2024
Projetos de Pesquisa
Unidades Organizacionais
Fascículo
Resumo
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 (p < 0.05) for their contribution to the final models. Across all diseases, the models achieved their best performance when GM and PV data were integrated. Notably, the integrated predictive models demonstrated exceptional performance for rheumatoid arthritis (AUC = 88.03%), type 2 diabetes (AUC = 96.96%), systemic lupus erythematosus (AUC = 98.4%), and type 1 diabetes (AUC = 86.19%). 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.
Palavras-chave
Gut microbiota, phenotypic variables, 16S rRNA, random forest, prediction models
URI
https://observatorio.fm.usp.br/handle/OPI/57939
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Coleções
Artigos e Materiais de Revistas Científicas - FM/MGT
Artigos e Materiais de Revistas Científicas - HC/ICHC
Artigos e Materiais de Revistas Científicas - LIM/35
Artigos e Materiais de Revistas Científicas - ODS/03