Machine learning model for predicting age in healthy individuals using age-related gut microbes and urine metabolites

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.

Automated summary

Age-related changes in gut microbes and urine metabolites were studied in 568 healthy individuals. The richness and evenness of fecal microbiota increased with age, and 16 genera showed significant abundance differences between young and old groups. Additionally, 17 urine metabolites contributed to the age-related differences. Bacteroides and Prevotella 9 were found to be correlated with some urine metabolites. The machine learning algorithm XGBoost achieved the best age prediction performance, with a mean absolute error of 5.48 years. Including urine metabolite data improved the accuracy to 4.93 years. This study highlights the potential of using gut microbiota and urine metabolic profiles for age prediction in healthy individuals.