ArticlemSystems2025
Predicting age from binarized human oral microbial data combined with an ensemble of classifiers.
Article in mSystems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Oral microbiome signatures predict biological age and host health.Nature communications · 2026Article
- Clinical translation of salivary MicroAge for understanding the oral-systemic connection in healthy longevity.Frontiers in microbiology · 2026Review
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Authors and funding
13 authors.
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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 (
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