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

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.

KEYWORDS:

• Age; prediction; urine
• fecal
• metabolomics
• metataxonomics

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data presented in this study are openly available in MetaboLights repository at www.ebi.ac.uk/metabolights/MTBLS6313.

Supplemental material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/19490976.2023.2226915

Additional information

Funding

This research was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (No. 2022R1A5A2029546), the Korea Innovation Foundation (INNOPOLIS) grant funded by the Korean government (Ministry of Science and ICT) through a science and technology project that opens the future of the region (grant number: 2021-DD-UP-0380), a Korea University Grant, and the Institute of Biomedical Science and Food Safety, CJ-Korea University Food Safety Hall at Korea University, Republic of Korea.