Habitual consumption of poor quality diets is linked directly to risk factors for many non-communicable diseases. This has resulted in the vast majority of countries and the World Health Organization developing policies for healthy eating to reduce the prevalence of non-communicable diseases in the population. However, there is mounting evidence of variability in individual metabolic responses to any dietary intervention. We have developed a method for applying a pipeline for understanding interindividual differences in response to diet, based on coupling data from highly controlled dietary studies with deep metabolic phenotyping. In this feasibility study, we create an individual Dietary Metabotype Score (DMS) that embodies interindividual variability in dietary response and captures consequent dynamic changes in concentrations of urinary metabolites. We find an inverse relationship between the DMS and blood glucose concentration. There is also a relationship between the DMS and urinary metabolic energy loss. Furthermore, we use a metabolic entropy approach to visualize individual and collective responses to dietary interventions. Potentially, the DMS offers a method to target and to enhance dietary response at the individual level, thereby reducing the burden of non-communicable diseases at the population level.
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All presented data are tabulated and detailed in the main text and the Supplementary Information. The study protocol availability is detailed in the Methods. Diets provided to participants are detailed in the Supplementary Information. Quantified NMR data, DMS, AUC glucose and calorific value for Diets 1 and 4 presented here are freely available (CC BY-NC 3.0) from Mendeley Data at https://doi.org/10.17632/6xvt7cnffd.1.
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This Article presents independent research funded by the UK National Institute for Health Research (NIHR) and Medical Research Council (MRC). The views expressed are those of the authors and not necessarily those of the UK National Health Service (NHS), the NIHR or the UK Department of Health. I.G.-P. is supported by a NIHR Career Development Research Fellowship (NIHR-CDF-2017-10-032). J.M.P. is supported by a Rutherford Fund Fellowship at Health Data Research (HDR) UK (MR/S004033/1). G.F. is supported by an NIHR Senior Investigator award. J.C.M., J.D., M.B., E.H. and G.F. are supported by an MRC grant entitled ‘Metabolomics for Monitoring Dietary Exposure’ (MR/J010308/1). E.H. is supported by the Department of Jobs, Tourism, Science and Innovation, Government of Western Australian through the Premier’s Science Fellowship Program. This study was supported by the NIHR/Wellcome Trust Imperial Clinical Research Facility, infrastructure support was provided by the NIHR Imperial Biomedical Research Centre (BRC) in line with the Gut Health research theme. The Section of Investigative Medicine is funded by grants from the MRC, UK Biotechnology and Biological Sciences Research Council, NIHR and an Integrative Mammalian Biology (IMB) Capacity Building Award. We also thank IKA Werke GmbH & Co for granting access to the C1-bomb calorimeter. We gratefully acknowledge the NutriTech consortium (European Union Framework 7 programme, grant agreement ID 289511) for sharing their data with us. The samples from the validation study are part of an ongoing clinical trial and we acknowledge the Egg Nutrition Center – American Egg Board for funding (to I.G.-P.), and J. Brignardello and J. McLean for conducting the clinical trial. We acknowledge funding from the Australian Medical Research Futures Fund for the Australian National Phenome Centre.
J.D. has worked on the Cook to Health project (of which Groupe SEB is a collaborator and partly funded by EIT-Health) and the FACET project (of which Abbott, Spain, is a collaborator and partly funded by EIT-Health), both outside the submitted work. G.F. is lead for the Imperial Nestlé Collaboration and reports personal fees from Unilever, both outside the submitted work. All other authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended Data Fig. 1 Diet information for each of the four dietary intervention provided to the 19 volunteers.
Detailed discretion of the meal structure for each diet. The coloured sections show the nutritional breakdown and the non-coloured give details of the foods making up the diet at each meal.
Extended Data Fig. 2 Dietary Metabotype Score predictions for each participant prior to admission to follow the four dietary interventions.
Dietary Metabotype Score (DMS) is derived from the MCCV-PLS predictions (see Methods) for each participant’s baseline sample (before starting each of the four diets). Diet 1 (dark green) is the most concordant with WHO guidelines, followed by Diet 2 (light green) and Diet 3 (orange) and Diet 4 (red) is the least concordant. Confidence regions are highlighted in green and indicate the probability of the median DMS for each sample. This shows that that the dietary habits in the home environment is highly variable between and within participants as evidenced by the initial DMS. Missing data: no fasting urine sample was available for participant 4 prior to starting Diet 4.
Extended Data Fig. 3 Urinary metabolic reaction network maps highlighting the expression of the dietary metabotype of individual participants in response to Diet 1 and Diet 4 ordered by participant number.
A, participant 1 (entropy = 90.50), B, participant 2 (entropy = 90.04), C, participant 3 (entropy = 101.76), D, participant 4 (entropy = 95.49), E, participant 5 (entropy = 96.55), F, participant 7 (entropy = 106.37), G, participant 8 (entropy = 84.46), H, participant 9 (entropy = 81.69). The size of coloured nodes is proportional to the fold-change of that metabolite for that person. The colours of the edges are related to the sum of fold-changes of each pair of metabolites connected via their shortest path (fewest number of chemical reactions – see Methods). The more disordered (high entropy), or metabolically flexible, a subnetwork is, the stronger the associated edge weights (yellow-orange) in contrast to less perturbed pathways (magenta-blue edge weights). Entropy is expressed in arbitrary units.
Extended Data Fig. 4 Urinary metabolic reaction network maps highlighting the expression of the dietary metabotype of individual participants in response to Diet 1 and Diet 4 ordered by participant number.
A, participant 10 (entropy = 81.92), B, participant 11 (entropy = 95.31), C, participant 12 (entropy = 80.13), D, participant 13 (entropy = 84.06), E, participant 14 (entropy = 78.81), F, participant 15 (entropy = 106.53), G, participant 17 (entropy = 76.12), H, participant 18 (entropy = 95.74), I, participant 19 (entropy = 92.33). The size of coloured nodes is proportional to the fold-change of that metabolite for that person. The colours of the edges are related to the sum of fold-changes of each pair of metabolites connected via their shortest path (fewest number of chemical reactions – see Methods). The more disordered (high entropy), or metabolically flexible, a subnetwork is, the stronger the associated edge weights (yellow-orange) in contrast to less perturbed pathways (magenta-blue edge weights). Entropy is expressed in arbitrary units.
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Garcia-Perez, I., Posma, J.M., Chambers, E.S. et al. Dietary metabotype modelling predicts individual responses to dietary interventions. Nat Food 1, 355–364 (2020). https://doi.org/10.1038/s43016-020-0092-z
Nature Food (2020)
Nature Food (2020)