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Nutriome–metabolome relationships provide insights into dietary intake and metabolism

Nature Food volume 1, pages 426–436 (2020)Cite this article

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Abstract

Dietary assessment traditionally relies on self-reported data, which are often inaccurate and may result in erroneous diet–disease risk associations. We illustrate how urinary metabolic phenotyping can be used as an alternative approach to obtain information on dietary patterns. We used two multipass 24 h dietary recalls, obtained on two occasions on average 3 weeks apart, paired with two 24 h urine collections from 1,848 US individuals; 67 nutrients influenced the urinary metabotype (metabolic phenotype) of 46 structurally identified metabolites characterized by 1H NMR spectroscopy. We investigated the stability of each metabolite over time and showed that the urinary metabolic profile is more stable within individuals than reported dietary patterns. The 46 metabolites accurately predicted healthy and unhealthy dietary patterns in a free-living US cohort, and these predictions were replicated in an independent UK cohort. We mapped these metabolites into a host-microbial metabolic network to identify key pathways and functions related to diet. These data can be used in future studies to evaluate how this set of diet-derived, stable, measurable bioanalytical markers is associated with disease risk. This knowledge may give new insights into biological pathways that characterize the shift from a healthy to an unhealthy metabolic phenotype and hence indicate entry points for prevention and intervention strategies.

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Fig. 1: Schematic of the bidirectional metabolic modelling approach.
Fig. 2: Biclustered heatmap of partial correlations between nutrient intakes and urinary metabolites.
Fig. 3: (Partial) intraclass correlations across all data.
Fig. 4: Subgraphs from three metabolic pathways alongside partial correlations between metabolites.

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Data availability

The data reported in this manuscript are tabulated in the main paper and in the Supplementary Tables. The NutriomeXplorer software contains all nutrient–metabolite associations and can be obtained from two separate public repositories (Figshare: https://doi.org/10.35092/yhjc.12181938, Box: https://imperialcollegelondon.box.com/s/f1in5lsnoh1hej5b8bvqr14tt7uoaq2v). The data that support the findings of this study are available from the corresponding authors upon request. Applications for access to the INTERMAP data can be made to the access committee (led by L.V.H.).

Code availability

The codes for executing the PLS, covariate-adjusted (O)PLS and simple orthogonal PLS/PLS-DA 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. The codes for calculating the ACC, ICC and pICC can be obtained from https://bitbucket.org/jmp111/nutriome/src (this repository also contains a MATLAB version of the NutriomeXplorer). These can be executed in a MATLAB environment.

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Acknowledgements

We thank the staff at local, national and international centres for collecting the INTERMAP data and samples. A partial listing of colleagues can be found in ref. 21. J.M.P. is supported by a Rutherford Fund Fellowship at Health Data Research (HDR) UK (MR/S004033/1). I.G.-P. is supported by a National Institute for Health Research (NIHR) fellowship (NIHR-CDF-2017-10-032). G.F. is an NIHR Senior Investigator. E.H. is supported by a Premier’s Science Fellowship (Western Australia). INTERMAP is supported by the US National Heart, Lung and Blood Institute (grant numbers R01-HL050490, R01-HL084228 and R01-HL135486), and received funding from the Chicago Health Research Foundation, and national agencies in Japan (grant number [A] 090357003) and the United Kingdom (project grant from the West Midlands National Health Service Research and Development, and grant number R2019EPH from the Chest, Heart and Stroke Association, Northern Ireland). Infrastructure support was provided by the NIHR Imperial Biomedical Research Centre (BRC). P.E. and E.H. acknowledge support from the UK Dementia Research Institute at Imperial College London, which receives funding from UK DRI Ltd funded by the Medical Research Council, the Alzheimer’s Society and Alzheimer’s Research UK. The funders had no role in study design.

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Authors and Affiliations

  1. Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK

    Joram M. Posma

  2. Health Data Research UK London, London, UK

    Joram M. Posma & Paul Elliott

  3. Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK

    Isabel Garcia-Perez, Gary Frost & Elaine Holmes

  4. Department of Community Health Sciences, College of Applied Medical Sciences, King Saud University, Riyadh, Kingdom of Saudi Arabia

    Ghadeer S. Aljuraiban

  5. Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, UK

    Ghadeer S. Aljuraiban, Queenie Chan & Paul Elliott

  6. MRC Centre for Environment and Health, School of Public Health, Faculty of Medicine, Imperial College London, London, UK

    Queenie Chan & Paul Elliott

  7. Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA

    Linda Van Horn & Jeremiah Stamler

  8. Institute for Minority Health Research, University of Illinois at Chicago, Chicago, IL, USA

    Martha Daviglus

  9. UK Dementia Research Institute, Faculty of Medicine, Imperial College London, London, UK

    Elaine Holmes & Paul Elliott

  10. Division of Computational and Systems Medicine, Health Futures Institute, Murdoch University, Perth, Western Australia, Australia

    Elaine Holmes & Jeremy K. Nicholson

  11. The Australian National Phenome Center, Harry Perkins Institute, Murdoch University, Perth, Western Australia, Australia

    Elaine Holmes & Jeremy K. Nicholson

  12. National Institute for Health Research Imperial Biomedical Research Centre, Imperial College London, London, UK

    Paul Elliott

  13. British Heart Foundation Centre of Research Excellence at Imperial, Imperial College London, London, UK

    Paul Elliott

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Contributions

Conceptualization: J.M.P., P.E. and J.K.N. Methodology: J.M.P., E.H., P.E. and J.K.N. Software: J.M.P. Formal analysis: J.M.P. Investigation: J.M.P., I.G.-P. and G.A. Resources: L.V.H., M.D., J.S., E.H., P.E. and J.K.N. Writing—original draft: J.M.P., P.E. and J.K.N. Writing—review and editing: J.M.P., I.G.-P., G.F., G.S.A., Q.C., L.V.H., E.H., P.E. and J.K.N. Supervision: G.F., E.H., P.E. and J.K.N. Project administration: Q.C., L.V.H, M.D., J.S., E.H. and P.E. Funding acquisition: J.M.P., I.G.-P., L.V.H., M.D., J.S., E.H., P.E. and J.K.N.

Corresponding authors

Correspondence to Elaine Holmes, Paul Elliott or Jeremy K. Nicholson.

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Extended data

Extended Data Fig. 1 Representative partial 600 MHz 1H-NMR spectra of human urine showing selected assignments of some of the major metabolic signals.

Full 600 MHz 1H-NMR spectrum (mean of first visit data) is shown in 3 panels, top to bottom: δ 0.5–2.5 ppm, δ 2.5–4.5 ppm and δ 6.4–9.5 ppm. Metabolites found associated with intake of nutrients are labelled. Key: 1 – fatty acids (C5-C10), 2 – pantothenate, 3 – isoleucine, 4 – leucine, 5 – valine, 6 – 2-hydroxy-2-(4-methylcyclohex-3-en-1-yl)propoxyglucuronide, 7 – ethanol, 8 – ethyl glucuronide, 9 – 3-hydroxyisovalerate, 10 – alanine, 11 – unknown 1, 12 – acetate, 13 – phenylacetylglutamine, 14 – glutamine, 15 – O-acetylcarnitine, 16 – acetone, 17 – proline betaine, 18 – succinate, 19 – citrate, 20 – dimethylamine, 21 – S-methyl-cysteine-sulfoxide metabolite, 22 – N-acetyl-S-methyl-cysteine-sulfoxide, 23 – S-methyl-cysteine-sulfoxide metabolite, 24 – S-methyl-cysteine-sulfoxide, 25 – dimethylglycine, 26 – creatine, 27 – creatinine, 28 – N6,N6,N6-trimethyllysine, 29 – histidine, 30 – 1-methylhistidine, 31 – carnitine, 32 – taurine, 33 – trimethylamine-N-oxide, 34 – 4-hydroxyproline betaine, 35 – unknown 2, 36 – 4-hydroxyhippurate, 37 – hippurate, 38 – N-methylpyridinium, 39 – N-methylnicotinate, 40 – N-methylnicotinamide, 41 – N-methyl-2-pyridone-5-carboxamide, 42 – tyrosine, 43 – 3-hydroxymandelate, 44 – 2-furoylglycine, 45 – pseudouridine, 46 – formate.

Extended Data Fig. 2 Multicompartmental metabolic reaction network illustrating metabolic influence of 80 nutrients in the U.S. INTERMAP cohort (n = 1,848).

Grey nodes indicate the metabolites associated with one or more of the nutrients. Lines indicate reactions (mediated by Homo sapiens enzymes or by gut bacteria, see Methods). White nodes are intermediate metabolites connecting them (with 3 or more associated reactions) and white boxes are intermediate metabolites with two reactions. The background shading illustrates different types of metabolism based on closest affinity classification.

Extended Data Fig. 3 Schema of study design and exclusion criteria.

Scatter plots represent the expected/reported protein ratio (x-axis) and expected/reported energy ratio (y-axis) for both urine collections. The 95% confidence intervals (CI95) are indicated by the ellipses; red crosses indicate participants that mapped outside the CI95.

Supplementary information

Supplementary Information

Supplementary Figs. 1–17, Tables 1–7, Notes 1 and 2, Discussion, Methods and References.

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Posma, J.M., Garcia-Perez, I., Frost, G. et al. Nutriome–metabolome relationships provide insights into dietary intake and metabolism. Nat Food 1, 426–436 (2020). https://doi.org/10.1038/s43016-020-0093-y

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