Integrating Nuclear Techniques, Isotopic Tracing, Omics, and Artificial Intelligence in Nutritional Systems for Advancing Precision Public Health Nutrition

Jan 2, 2026The Journal of nutrition

Using Advanced Tracing, Biology Data, and AI to Improve Personalized Public Health Nutrition

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Circadian Biology on OpenScience ↗PubMed ↗DOI ↗

Abstract

Recent advances in nuclear techniques, particularly stable isotope tracing, combined with multi-omics technologies and artificial intelligence, provide a transformative framework for high-resolution nutritional analysis.

Stable isotope tracers offer precise in vivo measurements of nutrient absorption, bioavailability, and metabolic processes.
Omics platforms reveal molecular responses to dietary changes across various biological levels.
AI-driven data analytics improve data integration, causal understanding, and predictive modeling in nutrition.
This integrated approach supports the discovery of biomarkers and the development of personalized nutrition strategies.
The proposed Nutritional Systems Integration framework aims to connect research with public health policies to enhance food security and health equity.

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Understanding the complex interplay between diet, nutrient metabolism, and chronic disease risk is central to advancing precision public health nutrition. Conventional dietary assessments often fail to capture the biochemical and interindividual variability necessary for mechanistic interpretation and the development of personalized nutritional interventions. Recent advances in nuclear techniques, particularly stable isotope tracing, combined with multi-omics technologies and artificial intelligence (AI), provide a transformative framework for high-resolution nutritional analysis. This review critically synthesized these interdisciplinary innovations, highlighting how the integration of stable isotope methodologies, omics platforms-including genomics, transcriptomics, proteomics, metabolomics, and microbiomics-and AI-driven data analytics enables dynamic, quantitative mapping of nutrient metabolism and gene-diet interactions. Stable isotope tracers offer in vivo precision for measuring nutrient absorption, bioavailability, and metabolic fluxes; omics platforms elucidate molecular responses to dietary exposures; and AI enhances data harmonization, causal inference, and predictive modeling. Together, these approaches support biomarker discovery, personalized nutrition, and population-level dietary interventions targeting both malnutrition and non-communicable diseases. We propose the Nutritional Systems Integration framework, which operationalizes isotope-omics-AI convergence to inform evidence-based, adaptive nutrition policies. This systems-driven paradigm establishes feedback loops connecting research, clinical application, and public health strategy-advancing global goals in food security, health equity, and disease prevention. Embracing such integrative methodologies can transform nutrition science from descriptive epidemiology into predictive, precision-based solutions for sustainable and equitable health outcomes.

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Integrating Nuclear Techniques, Isotopic Tracing, Omics, and Artificial Intelligence in Nutritional Systems for Advancing Precision Public Health Nutrition

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