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Saliva MicroAge: A salivary microbiome based machine learning model for noninvasive aging assessment and health state
Tiansong Xu1,2, Yuting Niu1, Chenyu Deng1,3
1Department of Geriatric Dentistry Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices Beijing China.
Abstract:
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.
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