Machine learning model for predicting age in healthy individuals using age-related gut microbes and urine metabolites.
Gut Microbes
; 15(1): 2226915, 2023.
Article
en En
| MEDLINE
| ID: mdl-37351626
ABSTRACT
Age-related gut microbes and urine metabolites were investigated in 568 healthy individuals using metataxonomics and metabolomics. The richness and evenness of the fecal microbiota significantly increased with age, and the abundance of 16 genera differed between the young and old groups. Additionally, 17 urine metabolites contributed to the differences between the young and old groups. Among the microbes that differed by age, Bacteroides and Prevotella 9 were confirmed to be correlated with some urine metabolites. The machine learning algorithm eXtreme gradient boosting (XGBoost) was shown to produce the best performing age predictors, with a mean absolute error of 5.48 years. The accuracy of the model improved to 4.93 years with the inclusion of urine metabolite data. This study shows that the gut microbiota and urine metabolic profiles can be used to predict the age of healthy individuals with relatively good accuracy.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Microbiota
/
Microbioma Gastrointestinal
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Child, preschool
/
Humans
Idioma:
En
Revista:
Gut Microbes
Año:
2023
Tipo del documento:
Article