Abstract
J Alzheimers Dis. 2026 Oct 29:13872877261500124. doi: 10.1177/13872877261500124. Online ahead of print.
ABSTRACT
Alterations in the gut microbiota (GM) are increasingly implicated in Alzheimer's disease (AD) pathogenesis through the microbiota-gut-brain axis (MGBA), influencing neuroinflammation, amyloid-β deposition, and tau aggregation. The inherent complexity of microbiota data necessitates artificial intelligence (AI) and machine learning (ML) to identify novel biomarkers and therapeutic targets. In this narrative review, we examined AI applications in AD-related GM research, including metataxonomic and metagenomic approaches. AI methods facilitate microbial signature identification and enterotype characterization associated with AD progression, enable multi-omics integration clarifying molecular pathways linking GM dysbiosis to neurodegeneration, support predictive modeling for early AD detection, and inform personalized MGBA-targeted interventions. Employed techniques include classical ML (e.g., random forests) for taxonomic classification, deep learning (e.g., neural networks) for complex pattern recognition, and explainable AI for identifying key bacterial taxa and metabolic functions. Significant challenges persist regarding methodological standardization, larger longitudinal cohorts, and ethical considerations in clinical applications. Future priorities include developing interpretable models, integrating longitudinal multi-omics data to establish causality, and validating AI-derived insights for precision AD prevention and treatment. AI uniquely enables high-dimensional, non-linear analyses that advance understanding of microbiome-brain interactions, though clinical translation requires rigorous validation and interdisciplinary collaboration.
PMID:42957639 | DOI:10.1177/13872877261500124
UK DRI Authors
