Precision nutrition through diet-gut microbiome interactions: Emerging insights driven by artificial intelligence, microbiome health metrics, and mechanistic modeling.

Summary

This narrative review synthesises the emerging field of precision nutrition driven by diet-gut microbiome interactions, integrating advances in multi-omics profiling, machine learning, and mechanistic modelling. The authors outline computational workflows for managing high-dimensional multimodal data, summarise core ML applications including dietary pattern classification and metabolic response prediction, and highlight composite microbiome health metrics and digital twin approaches. The review emphasises that AI-guided personalisation shows clinical promise in randomised controlled trials, whilst identifying translational priorities including diverse longitudinal cohorts, standardised benchmarking, and clinically trustworthy AI systems.

Regional applicability

The review addresses global scientific challenges in personalised nutrition applicable across healthcare systems including the United Kingdom. However, as a narrative synthesis without primary research data, its implementation in UK clinical practice would require validation in UK populations and integration with existing NHS dietary guidance frameworks.

Key measures

Multi-omics profiling (microbiome, metabolomics, host phenotypes); microbiome health indices (GMWI2, DI-GM); postprandial metabolic response prediction; responder stratification; genome-scale metabolic models; machine learning classification accuracy

Outcomes reported

The review synthesises advances in AI/ML applications to multi-omics data for predicting inter-individual metabolic responses to diet based on gut microbiome composition and function. It examines microbiome health metrics, genome-scale metabolic models, and hybrid AI-mechanistic frameworks for enabling precision dietary recommendations.

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