Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

RETRACTED ARTICLE: Dietary metabotype modelling predicts individual responses to dietary interventions

This article was retracted on 13 January 2023

This article has been updated

Abstract

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.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Schematic representation of methodology.
Fig. 2: Interindividual variability in metabolite response to dietary intervention.
Fig. 3: Contribution of important 1H NMR variables in the model.
Fig. 4: Relationship between DMS and metabolic outcomes.
Fig. 5: Urinary metabolic reaction networks built using the MetaboNetworks software.

Similar content being viewed by others

Data availability

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.

Code availability

The codes for executing the MCCV-PLS (with repeated measures) algorithm can be obtained from https://bitbucket.org/jmp111/capls/src/. The code for executing the STORM algorithm can be obtained from https://bitbucket.org/jmp111/storm/src. These can be executed in a MATLAB environment.

Change history

  • 14 November 2022

    Editor’s Note: Readers are alerted that the statement of ethical approval for the semi-controlled four-day feeding study utilised in this Article, granted as subsidiary to the main study ‘Dietary biomarker discovery using metabonomics’ approved by the London–Brent Research Ethics (13/LO/0078), has been called into question. A further editorial response will follow the resolution of this issue for this Article.

  • 13 January 2023

    This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1038/s43016-023-00690-4

References

  1. Forouhi, N. G. & Unwin, N. Global diet and health: old questions, fresh evidence, and new horizons. Lancet393, 1916–1918 (2019).

    Article  Google Scholar 

  2. GBD 2017 Diet Collaborators Health effects of dietary risks in 195 countries, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet393, 1958–1972 (2019).

    Article  Google Scholar 

  3. Zeevi, D. et al. Personalized nutrition by prediction of glycemic responses. Cell163, 1079–1094 (2015).

    Article  CAS  Google Scholar 

  4. Price, N. D. et al. A wellness study of 108 individuals using personal, dense, dynamic data clouds. Nat. Biotechnol.35, 747–756 (2017).

    Article  CAS  Google Scholar 

  5. Minot, S. et al. The human gut virome: inter-individual variation and dynamic response to diet. Genome Res.21, 1616–1625 (2011).

    Article  CAS  Google Scholar 

  6. Holmes, E. et al. Human metabolic phenotype diversity and its association with diet and blood pressure. Nature453, 396–400 (2008).

    Article  ADS  CAS  Google Scholar 

  7. Stella, C. et al. Susceptibility of human metabolic phenotypes to dietary modulation. J. Proteome Res.5, 2780–2788 (2006).

    Article  CAS  Google Scholar 

  8. Loo, R. L., Zou, X., Appel, L. J., Nicholson, J. K. & Holmes, E. Characterization of metabolic responses to healthy diets and association with blood pressure: application to the Optimal Macronutrient Intake Trial for Heart Health (OmniHeart), a randomized controlled study. Am. J. Clin. Nutr.107, 323–334 (2018).

    Article  Google Scholar 

  9. Lefevre, M., Champagne, C. M., Tulley, R. T., Rood, J. C. & Most, M. M. Individual variability in cardiovascular disease risk factor responses to low-fat and low-saturated-fat diets in men: body mass index, adiposity, and insulin resistance predict changes in LDL cholesterol. Am. J. Clin. Nutr.82, 1145–1146 (2005). 957-963; quiz.

    Article  Google Scholar 

  10. Kirwan, L. et al. Phenotypic factors influencing the variation in response of circulating cholesterol level to personalised dietary advice in the Food4Me study. Br. J. Nutr.116, 2011–2019 (2016).

    Article  CAS  Google Scholar 

  11. Katan, M. B., Beynen, A. C., de Vries, J. H. & Nobels, A. Existence of consistent hypo- and hyperresponders to dietary cholesterol in man. Am. J. Epidemiol.123, 221–234 (1986).

    Article  CAS  Google Scholar 

  12. Heinzmann, S. S. et al. Stability and robustness of human metabolic phenotypes in response to sequential food challenges. J. Proteome Res.11, 643–655 (2012).

    Article  CAS  Google Scholar 

  13. Griffin, N. W. et al. Prior dietary practices and connections to a human gut microbial metacommunity alter responses to diet interventions. Cell Host Microbe21, 84–96 (2017).

    Article  CAS  Google Scholar 

  14. de Toro-Martin, J., Arsenault, B. J., Despres, J. P. & Vohl, M. C. Precision nutrition: a review of personalized nutritional approaches for the prevention and management of metabolic syndrome. Nutrients9, 913 (2017).

  15. Wang, D. D. & Hu, F. B. Precision nutrition for prevention and management of type 2 diabetes. Lancet Diabetes Endocrinol.6, 416–426 (2018).

    Article  Google Scholar 

  16. Poslusna, K., Ruprich, J., de Vries, J. H., Jakubikova, M. & van’t Veer, P. Misreporting of energy and micronutrient intake estimated by food records and 24 hour recalls, control and adjustment methods in practice. Br. J. Nutr.101 (Suppl. 2), S73–S85 (2009).

    Article  CAS  Google Scholar 

  17. Celis-Morales, C. et al. Effect of personalized nutrition on health-related behaviour change: evidence from the Food4Me European randomized controlled trial. Int. J. Epidemiol.46, 578–588 (2017).

    Google Scholar 

  18. Cheng, S. et al. Metabolite profiling identifies pathways associated with metabolic risk in humans. Circulation125, 2222–2231 (2012).

    Article  CAS  Google Scholar 

  19. Elliott, P. et al. Urinary metabolic signatures of human adiposity. Sci. Transl. Med. 7, 285ra6 (2015).

  20. Nicholson, J. K. et al. Host–gut microbiota metabolic interactions. Science336, 1262–1267 (2012).

    Article  ADS  CAS  Google Scholar 

  21. Garcia-Perez, I. et al. Objective assessment of dietary patterns by use of metabolic phenotyping: a randomised, controlled, crossover trial. Lancet Diabetes Endocrinol.5, 184–195 (2017).

    Article  Google Scholar 

  22. Global Strategy on Diet, Physical Activity and Health (WHO, 2004).

  23. Krupp, D., Doberstein, N., Shi, L. & Remer, T. Hippuric acid in 24-hour urine collections is a potential biomarker for fruit and vegetable consumption in healthy children and adolescents. J. Nutr.142, 1314–1320 (2012).

    Article  CAS  Google Scholar 

  24. Cheung, W. et al. A metabolomic study of biomarkers of meat and fish intake. Am. J. Clin. Nutr.105, 600–608 (2017).

    Article  CAS  Google Scholar 

  25. Slupsky, C. M. et al. Investigations of the effects of gender, diurnal variation, and age in human urinary metabolomic profiles. Anal. Chem.79, 6995–7004 (2007).

    Article  CAS  Google Scholar 

  26. Rose, C., Parker, A., Jefferson, B. & Cartmell, E. The characterization of feces and urine: a review of the literature to inform advanced treatment technology. Crit. Rev. Environ. Sci. Technol.45, 1827–1879 (2015).

    Article  CAS  Google Scholar 

  27. Posma, J. M., Robinette, S. L., Holmes, E. & Nicholson, J. K. MetaboNetworks, an interactive Matlab-based toolbox for creating, customizing and exploring sub-networks from KEGG. Bioinformatics30, 893–895 (2014).

    Article  CAS  Google Scholar 

  28. Rubner, M. The energy value of the human diet. Z. Biol.42, 261–305 (1901).

    CAS  Google Scholar 

  29. Degen, L. P. & Phillips, S. F. Variability of gastrointestinal transit in healthy women and men. Gut39, 299–305 (1996).

    Article  CAS  Google Scholar 

  30. Cummings, J. H., Beatty, E. R., Kingman, S. M., Bingham, S. A. & Englyst, H. N. Digestion and physiological properties of resistant starch in the human large bowel. Br. J. Nutr.75, 733–747 (1996).

    Article  CAS  Google Scholar 

  31. Jumpertz, R. et al. Energy-balance studies reveal associations between gut microbes, caloric load, and nutrient absorption in humans. Am. J. Clin. Nutr.94, 58–65 (2011).

    Article  CAS  Google Scholar 

  32. Gibbons, H. et al. Metabolomic-based identification of clusters that reflect dietary patterns. Mol. Nutr. Food Res.61, 1601050 (2017).

  33. Posma, J. M. et al. Subset Optimization by Reference Matching (STORM): an optimized statistical approach for recovery of metabolic biomarker structural information from 1H NMR spectra of biofluids. Anal. Chem.84, 10694–10701 (2012).

    Article  CAS  Google Scholar 

  34. Posma, J. M. et al. Integrated analytical and statistical two-dimensional spectroscopy strategy for metabolite identification: application to dietary biomarkers. Anal. Chem.89, 3300–3309 (2017).

    Article  CAS  Google Scholar 

  35. Posma, J. M. et al. Optimized phenotypic biomarker discovery and confounder elimination via covariate-adjusted projection to latent structures from metabolic spectroscopy data. J. Proteome Res.17, 1586–1595 (2018).

    Article  CAS  Google Scholar 

  36. Manach, C., Williamson, G., Morand, C., Scalbert, A. & Remesy, C. Bioavailability and bioefficacy of polyphenols in humans. I. Review of 97 bioavailability studies. Am. J. Clin. Nutr.81, 230S–242S (2005).

    Article  CAS  Google Scholar 

  37. Storey, J. D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl Acad. Sci. USA100, 9440–9445 (2003).

    Article  ADS  MathSciNet  CAS  MATH  Google Scholar 

  38. Eckburg, P. B. et al. Diversity of the human intestinal microbial flora. Science308, 1635–1638 (2005).

    Article  ADS  Google Scholar 

  39. Human Microbiome Project Consortium Structure, function and diversity of the healthy human microbiome. Nature486, 207–214 (2012).

    Article  ADS  Google Scholar 

  40. Veselkov, K. A. et al. A metabolic entropy approach for measurements of systemic metabolic disruptions in patho-physiological states. J. Proteome Res.9, 3537–3544 (2010).

    Article  CAS  Google Scholar 

  41. Garcia-Perez, I. et al. An analytical pipeline for quantitative characterization of dietary intake: application to assess grape intake. J. Agric. Food Chem.64, 2423–2431 (2016).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

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.

Author information

Authors and Affiliations

Authors

Contributions

I.G.-P., J.M.P., J.K.N., E.H. and G.F. conceptualized the study and wrote the manuscript. I.G.-P., J.M.P. and E.S.C. analysed the data. I.G.-P., E.S.C. and G.F. ran the clinical trial. I.G.-P., J.C.M., J.D., E.H. and G.F. designed the clinical trial. All authors read and approved the final manuscript, and approved the final submitted version. G.F. assumes responsibility for the completeness and accuracy of the data and analyses, and for adherence to the study protocol.

Corresponding authors

Correspondence to Jeremy K. Nicholson, Elaine Holmes or Gary Frost.

Ethics declarations

Competing interests

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.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

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.

Supplementary information

Supplementary Information

The Supplementary Information contains 19 figures and 4 tables.

Reporting Summary

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Garcia-Perez, I., Posma, J.M., Chambers, E.S. et al. RETRACTED ARTICLE: Dietary metabotype modelling predicts individual responses to dietary interventions. Nat Food 1, 355–364 (2020). https://doi.org/10.1038/s43016-020-0092-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43016-020-0092-z

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing