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Updated: May 22, 2026

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
Published on: November 29, 2024
Whole-body metabolic modelling and its prospects in precision nutrition
Bram Nap1,2, Cyrille C Thinnes1,2,3, Ines Thiele1,2,3,4,5
1School of Medicine, https://ror.org/03bea9k73University of Galway, Galway, Ireland.
Abstract:
Nutrition has long been investigated with respect to its influence on human health. With the availability of various omics data, such as metagenomics and metabolomics, novel insights have been obtained into the influence of nutrition, particularly concerning the gut microbiome. The gut microbiome plays an important role in the breakdown of food-derived compounds and in producing essential bioactive metabolites required for human health. However, this wealth of information made the interactions between nutrition and human health increasingly intricate, and unravelling these links is complex. This review covers the concepts of genome-scale metabolic modelling as a tool to understand the links between nutrition, the gut microbiome and human metabolism and its applications. Genome-scale metabolic modelling treats metabolism as a mathematical problem which was used to develop models of human metabolism that incorporate physiology and organ-specific metabolism, known as whole-body metabolic models (WBMs). WBMs can incorporate physiological data, such as sex, weight and body fat percentage, as well as nutrition in the form of its metabolite constituents. Finally, the gut microbiome can also be incorporated through a mathematical representation of the species present, based on stool metagenomics. WBMs have already been applied to understand gut microbiome-host co-metabolism in various non-communicable diseases. However, challenges remain, as metabolites measured in food items in public databases typically cover only common metabolites, and engagement with end-users such as nutritionists and policymakers is limited. Nevertheless, WBMs represent a promising step towards digital metabolic twins and thus personalised nutrition and medicine.
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