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Artificial Intelligence-Driven Food Safety: Decoding Gut Microbiota-Mediated Health Effects of Non-Microbial Contaminants
Summary
This review explores how food contaminants like heavy metals, pesticides, and micro- and nanoplastics can alter gut bacteria in ways that affect overall health, from immune function to metabolic regulation. The study highlights how artificial intelligence tools are helping researchers untangle the complex relationships between these contaminants, gut microbiome changes, and health outcomes that traditional methods struggle to decode.
A wide range of non-microbial contaminants-such as heavy metals, pesticide residues, antibiotics, as well emerging foodborne contaminants like micro- and nanoplastics and persistent organic pollutants-can enter the human body through daily diet and exert subtle yet chronic effects that are increasingly recognized to be gut microbiota-dependent. However, the relationships among multi-contaminant exposure profiles, dynamic microbial community structures, microbial metabolites, and diverse clinical or subclinical phenotypes are highly non-linear and multidimensional, posing major challenges to traditional analytical approaches. Artificial intelligence (AI) is emerging as a powerful tool to untangle the complex interactions between foodborne non-microbial contaminants, the gut microbiota, and host health. This review synthesizes current knowledge on how key classes of non-microbial food contaminants modulate gut microbial composition and function, and how these alterations, in turn, influence intestinal barrier integrity, immune homeostasis, metabolic regulation, and systemic disease risk. We then highlight recent advances in the application of AI techniques, including machine learning (ML), deep learning (DL), and network-based methods, to integrate multi-omics and exposure data, identify microbiota and metabolite signatures of specific contaminants, and infer potential causal pathways within "contaminant-microbiota-host" axes. Finally, we discuss current limitations, such as data heterogeneity, small-sample bias, and interpretability gaps, and propose future directions for building standardized datasets, explainable AI frameworks, and human-relevant experimental validation pipelines. Overall, AI-enabled analysis offers a promising avenue to refine food safety risk assessment, support precision nutrition strategies, and develop microbiota-targeted interventions against non-microbial food contaminants.