A Machine Learning Framework for Assessing Gut Microbiota Similarity in Alzheimer's Disease: A Proof-of-Concept Application to a Jing Si Herbal Tea Intervention Cohort
This study investigates gut microbiome composition in Alzheimer's disease (AD) and its modulation following a 24-week Jing Si Herbal Tea (JSHT) intervention. A machine learning framework trained on multiple public datasets was used to distinguish AD from cognitively normal profiles and to generate a Microbiota Resemblance Score (MRS), quantifying similarity to a healthy microbiome. Gut microbiome samples from two JSHT intake groups (3 g and 6 g daily) were analyzed using high-throughput 16S rRNA gene sequencing to assess longitudinal microbial shifts, enabling evaluation of intervention-associated changes and identification of taxa linked to cognitive status.