Predicting age from binarized human oral microbial data combined with an ensemble of classifiers.

Abstract

It is well established that the composition of the human microbiome changes with age; however, limited research has explored the association between the oral microbiome and aging, as well as its potential for age prediction. In this study, we investigated the correlation between the oral microbiome and age by analyzing samples from 150 individuals across a wide age range (6-78 years). The observed species richness and Chao1 index significantly increased with age. Permutational multivariate analysis of variance (PERMANOVA), using both Bray-Curtis and Jaccard distances, identified age as a major factor influencing microbial variation. After a comprehensive comparison of four different oral microbiome data processing approaches, we then developed an ensemble model based on binarized oral microbial data, incorporating an eXtreme Gradient Boosting (XGBoost) algorithm with 32 classifiers. This ensemble model achieved a mean absolute error (MAE) of 7.20 years in the independent validation set (