한빛사논문
Seung-Ho Seo 1#, Chang-Su Na 2#, Seong-Eun Park 3, Eun-Ju Kim 3, Woo-Seok Kim 4, ChunKyun Park 5, Seungmi Oh 5, Yanghee You 2, Mee-Hyun Lee 2, Kwang-Moon Cho 6, Sun Jae Kwon 6, Tae Woong Whon 7, Seong Woon Roh 8, Hong-Seok Son 3
1Research & Development Team, Sonlab Inc, Seoul, Republic of Korea.
2College of Korean Medicine, Dongshin University, Naju, Republic of Korea.
3Department of Biotechnology, College of Life Sciences and Biotechnology, Korea University, Seoul, Republic of Korea.
4Kyurim Korean Medical Clinic, Cheonan, Republic of Korea.
5Department of Applied Statistics, Yonsei University, Seoul, Republic of Korea.
6AccuGene Inc, Incheon, Republic of Korea.
7Kimchi Functionality Research Group, World Institute of Kimchi, Gwangju, Republic of Korea.
8Microbiome Research Institute, LISCure Biosciences Inc, Seongnam, Republic of Korea.
#These authors contributed equally to this work.
Correspondence : Hong-Seok Son
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.
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