Journal of Alzheimer's disease : JADOct 30, 2026

Using Artificial Intelligence to Study Gut-Brain Interactions in Alzheimer's Disease

Gut-Brain Axis Weekly Brief ↗PubMed ↗DOI ↗

Updated Nov 2, 2026

Abstract

Alterations in gut microbiota are implicated in Alzheimer's disease pathogenesis through the microbiota-gut-brain axis.

Microbiota changes may influence neuroinflammation, amyloid-β deposition, and tau aggregation associated with Alzheimer's disease.
Artificial intelligence and machine learning are employed to identify biomarkers and therapeutic targets in gut microbiota research related to Alzheimer's.
AI methods facilitate the identification of microbial signatures and the characterization of gut microbiota enterotypes linked to Alzheimer's progression.
Integration of multi-omics data with AI could clarify molecular pathways connecting gut microbiota dysbiosis to neurodegeneration.
Predictive modeling supported by AI may assist in early detection of Alzheimer's disease and inform personalized interventions targeting the microbiota-gut-brain axis.

Simplified

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.

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Using Artificial Intelligence to Study Gut-Brain Interactions in Alzheimer's Disease

Abstract

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