@article{Gao2026, 
author = {Yan Gao and Can Zhang and Zhi-Wen Zhou and Hong-Shuo Sun and Zhong-Ping Feng and Shan-Shan Jie and Qian-Hang Shao and Song-Wei Yang and Qi-Di Ai and Jia-Qing Yan and Nai-Hong Chen and Shi-Feng Chu and Zhao Zhang and Wen-Bin He},
title = {Artificial intelligence in gut-brain axis research for Alzheimer’s disease: From associative insights toward causal inference and personalized interventions},
year = {2026},
journal = {Food & Medicine Homology Resources},
keywords = {Alzheimer’s disease, gut-brain axis, gut microbiota, artificial intelligence, precision medicine},
url = {https://www.sciopen.com/article/10.26599/FMHR.2026.9740006},
doi = {10.26599/FMHR.2026.9740006},
abstract = {Alzheimer’s disease (AD) research is shifting from a “brain-centered” model toward a “gut-brain axis system” perspective. However, the causal roles of gut microbiota in AD pathology remain elusive, as traditional association studies struggle to distinguish potential drivers from passengers. This review examines the potential applications and inherent limitations of artificial intelligence (AI) in dissecting this complex relationship. We discuss three emerging and conceptually promising directions: First, AI-frameworks for hypothesis-generating causal discovery that can prioritize microbiota-AD relationships for subsequent experimental validation, while recognizing that current approaches predominantly identify statistical associations rather than empirically supported causal conclusions; second, multi-modal fusion networks, when applied with appropriate regularization and validation, can assist in constructing cross-scale molecular maps across microbiota, metabolites, and the brain, though such pathway inferences require careful interpretation as they represent statistical associations rather than biochemically validated mechanisms; and third, the conceptual application of digital twin-based simulation as a long-term vision for personalized microbiota modulation, which currently lacks the ecological resolution and validation required for predictive modeling. By integrating multi-pathway mechanisms of the gut-brain axis and characteristic profiles of gut microbiota, we critically evaluate the technical advantages of machine learning in microbiome data analysis alongside major challenges including overfitting, batch effects, confounding factors, and reproducibility. Leveraging AI-driven microbiota analysis, we propose a phased translational roadmap, from near-term standardization and cross-cohort validation, through mid-term endophenotyping and trial enrichment strategies, to the long-term exploration of personalized interventions, toward the eventual goal of precision prevention. This article outlines a full-cycle framework from discovery to clinical translation and envisions a conceptual ecosystem for future intelligent microecological health management. Achieving the promise of AI-empowered gut-brain axis research will require deep integration across neuroscience, microbiology, computational science, and clinical medicine, alongside rigorous experimental validation, standardized protocols, and realistic appraisal of current technological boundaries.}
}