Artificial Intelligence-Driven Food Safety: Decoding Gut Microbiota-Mediated Health Effects of Non-Microbial...

Area of Science:

• Environmental Health
• Microbiology
• Computational Biology

Background:

• Non-microbial food contaminants (heavy metals, pesticides, plastics) affect gut microbiota and host health.
• Complex interactions between contaminants, gut microbes, and health phenotypes challenge traditional analysis.
• Artificial intelligence (AI) offers novel approaches to study these relationships.

Purpose of the Study:

• To review how non-microbial food contaminants alter gut microbial composition and function.
• To explore the impact of these alterations on host health, including immune and metabolic regulation.
• To highlight AI applications in analyzing contaminant-microbiota-host interactions.

Main Methods:

• Literature review synthesizing current knowledge on contaminant effects on gut microbiota.
• Exploration of AI techniques (machine learning, deep learning, network methods) for data integration.
• Analysis of AI's role in identifying contaminant signatures and causal pathways.

Main Results:

• Non-microbial contaminants modulate gut microbiota, affecting intestinal barrier, immunity, and metabolism.
• AI can integrate multi-omics and exposure data to reveal contaminant-specific microbial and metabolite signatures.
• AI facilitates inference of causal pathways in contaminant-microbiota-host axes.

Conclusions:

• AI-enabled analysis is crucial for understanding complex contaminant-microbiota-host interactions.
• Future work requires standardized datasets, explainable AI, and validation pipelines.
• AI offers potential for improved food safety, precision nutrition, and microbiota-targeted interventions.