Saliva MicroAge: A salivary microbiome based machine learning model for noninvasive aging assessment and health state prediction

Saliva MicroAge is a machine learning-based model designed to estimate biological age and assess health status using globally sourced salivary microbiome data. Trained on 4532 healthy samples, the model achieves high accuracy in predicting chronological age and captures health-related deviations (MicroAgeGap) in various diseases. Taxonomic and functional analyses of key microbial features reveal biological relevance to aging processes, offering a noninvasive and scalable approach for aging monitoring and precision health assessment.