Nutriome–metabolome relationships provide insights into dietary intake and metabolism


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.

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:, Box: 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 The code for executing the STORM algorithm can be obtained from The codes for calculating the ACC, ICC and pICC can be obtained from (this repository also contains a MATLAB version of the NutriomeXplorer). These can be executed in a MATLAB environment.


  1. 1.

    Rosell, M. S., Hellenius, M. L., de Faire, U. H. & Johansson, G. K. Associations between diet and the metabolic syndrome vary with the validity of dietary intake data. Am. J. Clin. Nutr. 78, 84–90 (2003).

    CAS  PubMed  Google Scholar 

  2. 2.

    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), 73–85 (2009).

    Google Scholar 

  3. 3.

    Freisling, H. et al. Dietary reporting errors on 24 h recalls and dietary questionnaires are associated with BMI across six European countries as evaluated with recovery biomarkers for protein and potassium intake. Br. J. Nutr. 107, 910–920 (2012).

    CAS  PubMed  Google Scholar 

  4. 4.

    Ioannidis, J. P. A. The challenge of reforming nutritional epidemiologic research. JAMA 320, 969–970 (2018).

    PubMed  Google Scholar 

  5. 5.

    Brennan, L. & Hu, F. B. Metabolomics-based dietary biomarkers in nutritional epidemiology—current status and future opportunities. Mol. Nutr. Food Res. 63, e1701064 (2019).

    PubMed  Google Scholar 

  6. 6.

    Guasch-Ferre, M., Bhupathiraju, S. N. & Hu, F. B. Use of metabolomics in improving assessment of dietary intake. Clin. Chem. 64, 82–98 (2018).

    CAS  PubMed  Google Scholar 

  7. 7.

    Ulaszewska, M. M. et al. Nutrimetabolomics: an integrative action for metabolomic analyses in human nutritional studies. Mol. Nutr. Food Res. 63, e1800384 (2019).

    PubMed  Google Scholar 

  8. 8.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Holmes, E. et al. Human metabolic phenotype diversity and its association with diet and blood pressure. Nature 453, 396–400 (2008).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Zeevi, D. et al. Personalized nutrition by prediction of glycemic responses. Cell 163, 1079–1094 (2015).

    CAS  PubMed  Google Scholar 

  11. 11.

    Nicholson, J. K. & Wilson, I. D. High-resolution proton magnetic-resonance spectroscopy of biological-fluids. Prog. Nucl. Magn. Reson. Spectrosc. 21, 449–501 (1989).

    CAS  Google Scholar 

  12. 12.

    Gavaghan, C. L., Holmes, E., Lenz, E., Wilson, I. D. & Nicholson, J. K. An NMR-based metabonomic approach to investigate the biochemical consequences of genetic strain differences: application to the C57BL10J and Alpk:ApfCD mouse. FEBS Lett. 484, 169–174 (2000).

    CAS  PubMed  Google Scholar 

  13. 13.

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

    CAS  PubMed  Google Scholar 

  14. 14.

    Nicholson, G. et al. Human metabolic profiles are stably controlled by genetic and environmental variation. Mol. Syst. Biol. 7, 525 (2011).

  15. 15.

    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).

    CAS  PubMed  Google Scholar 

  16. 16.

    Claesson, M. J. et al. Gut microbiota composition correlates with diet and health in the elderly. Nature 488, 178–184 (2012).

    ADS  CAS  PubMed  Google Scholar 

  17. 17.

    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).

    PubMed  PubMed Central  Google Scholar 

  18. 18.

    Gibbons, H. et al. Demonstration of the utility of biomarkers for dietary intake assessment; proline betaine as an example. Mol. Nutr. Food Res. 61, 1700037 (2017).

  19. 19.

    Scalbert, A. et al. The food metabolome: a window over dietary exposure. Am. J. Clin. Nutr. 99, 1286–1308 (2014).

    CAS  PubMed  Google Scholar 

  20. 20.

    Fenech, M. Nutrition and genome health. Forum Nutr. 60, 49–65 (2007).

    CAS  PubMed  Google Scholar 

  21. 21.

    Stamler, J. et al. INTERMAP: background, aims, design, methods, and descriptive statistics (nondietary). J. Hum. Hypertens. 17, 591–608 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Dennis, B. et al. INTERMAP: the dietary data—process and quality control. J. Hum. Hypertens. 17, 609–622 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    McLean, R. M. Measuring population sodium intake: a review of methods. Nutrients 6, 4651–4662 (2014).

    PubMed  PubMed Central  Google Scholar 

  25. 25.

    Yi, S. S. & Kansagra, S. M. Associations of sodium intake with obesity, body mass index, waist circumference, and weight. Am. J. Prev. Med. 46, 53–55 (2014).

    Google Scholar 

  26. 26.

    Elliott, P. et al. Urinary metabolic signatures of human adiposity. Sci. Transl. Med. 7, 285ra262 (2015).

    Google Scholar 

  27. 27.

    Aburto, N. J. et al. Effect of lower sodium intake on health: systematic review and meta-analyses. BMJ 346, f1326 (2013).

    PubMed  PubMed Central  Google Scholar 

  28. 28.

    Teague, C. et al. Ethyl glucoside in human urine following dietary exposure: detection by 1H NMR spectroscopy as a result of metabonomic screening of humans. Analyst 129, 259–264 (2004).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Dahl, H., Stephanson, N., Beck, O. & Helander, A. Comparison of urinary excretion characteristics of ethanol and ethyl glucuronide. J. Anal. Toxicol. 26, 201–204 (2002).

    CAS  PubMed  Google Scholar 

  30. 30.

    Svensson, B. G., Akesson, B., Nilsson, A. & Paulsson, K. Urinary-excretion of methylamines in men with varying intake of fish from the Baltic Sea. J. Toxicol. Environ. Health 41, 411–420 (1994).

    CAS  PubMed  Google Scholar 

  31. 31.

    Zhang, A. Q., Mitchell, S. C. & Smith, R. L. Dietary precursors of trimethylamine in man: a pilot study. Food Chem. Toxicol. 37, 515–520 (1999).

    CAS  PubMed  Google Scholar 

  32. 32.

    de Zwart, F. J. et al. Glycine betaine and glycine betaine analogues in common foods. Food Chem. 83, 197–204 (2003).

    Google Scholar 

  33. 33.

    Heinzmann, S. S. et al. Metabolic profiling strategy for discovery of nutritional biomarkers: proline betaine as a marker of citrus consumption. Am. J. Clin. Nutr. 92, 436–443 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Pujos-Guillot, E. et al. Mass spectrometry-based metabolomics for the discovery of biomarkers of fruit and vegetable intake: citrus fruit as a case study. J. Proteome Res. 12, 1645–1659 (2013).

    CAS  PubMed  Google Scholar 

  35. 35.

    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. Bioinformatics 30, 893–895 (2014).

    CAS  PubMed  Google Scholar 

  36. 36.

    Drewnowski, A. Defining nutrient density: development and validation of the nutrient rich foods index. J. Am. Coll. Nutr. 28, 421–426 (2009).

    Google Scholar 

  37. 37.

    Mellen, P. B., Gao, S. K., Vitolins, M. Z. & Goff, D. C. Deteriorating dietary habits among adults with hypertension. Arch. Intern. Med. 168, 308–314 (2008).

    PubMed  Google Scholar 

  38. 38.

    Molitor, J. et al. Blood pressure differences associated with Optimal Macronutrient Intake Trial for Heart Health (OMNIHEART)-like diet compared with a typical American diet. Hypertension 64, 1198–1204 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    World Health Organization & Food and Agriculture Organization Diet, Nutrition and the Prevention of Chronic Diseases Technical Report Series 916 (World Health Organization, 2003).

  41. 41.

    Appel, L. J. et al. A clinical trial of the effects of dietary patterns on blood pressure. DASH Collaborative Research Group. N. Engl. J. Med. 336, 1117–1124 (1997).

    CAS  PubMed  Google Scholar 

  42. 42.

    Tasevska, N., Runswick, S. A. & Bingham, S. A. Urinary potassium is as reliable as urinary nitrogen for use as a recovery biomarker in dietary studies of free living individuals. J. Nutr. 136, 1334–1340 (2006).

    PubMed  Google Scholar 

  43. 43.

    Mente, A., Irvine, E. J., Honey, R. J. D. & Logan, A. G. Urinary potassium is a clinically useful test to detect a poor quality diet. J. Nutr. 139, 743–749 (2009).

    CAS  PubMed  Google Scholar 

  44. 44.

    Kesteloot, H. et al. Relation of urinary calcium and magnesium excretion to blood pressure: the International Study of Macro- and Micro-nutrients and Blood Pressure and the International Cooperative Study on Salt, other Factors, and Blood Pressure. Am. J. Epidemiol. 174, 44–51 (2011).

    PubMed  Google Scholar 

  45. 45.

    Garcia-Perez, I. et al. Urinary metabolic phenotyping the slc26a6 (chloride–oxalate exchanger) null mouse model. J. Proteome Res. 11, 4425–4435 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Midttun, O., Ulvik, A., Nygard, O. & Ueland, P. M. Performance of plasma trigonelline as a marker of coffee consumption in an epidemiologic setting. Am. J. Clin. Nutr. 107, 941–947 (2018).

    PubMed  Google Scholar 

  47. 47.

    Whitton, C. et al. National Diet and Nutrition Survey: UK food consumption and nutrient intakes from the first year of the rolling programme and comparisons with previous surveys. Br. J. Nutr. 106, 1899–1914 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Iwahori, T. et al. Six random specimens of daytime casual urine on different days are sufficient to estimate daily sodium/potassium ratio in comparison to 7-day 24-h urine collections. Hypertens. Res. 37, 765–771 (2014).

    CAS  PubMed  Google Scholar 

  49. 49.

    Wilson, T. et al. Spot and cumulative urine samples are suitable replacements for 24-hour urine collections for objective measures of dietary exposure in adults using metabolite biomarkers. J. Nutr. 149, 1692–1700 (2019).

    PubMed  Google Scholar 

  50. 50.

    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).

    CAS  PubMed  Google Scholar 

  51. 51.

    Dumas, M. E. et al. Assessment of analytical reproducibility of 1H NMR spectroscopy based metabonomics for large-scale epidemiological research: the INTERMAP study. Anal. Chem. 78, 2199–2208 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Smith, L. M. et al. Large-scale human metabolic phenotyping and molecular epidemiological studies via 1H NMR spectroscopy of urine: investigation of borate preservation. Anal. Chem. 81, 4847–4856 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Keun, H. C. et al. Analytical reproducibility in 1H NMR-based metabonomic urinalysis. Chem. Res. Toxicol. 15, 1380–1386 (2002).

    CAS  PubMed  Google Scholar 

  54. 54.

    Nicholson, J. K. et al. Metabolic phenotyping in clinical and surgical environments. Nature 491, 384–392 (2012).

    ADS  CAS  PubMed  Google Scholar 

  55. 55.

    Garcia-Perez, I. et al. Dietary metabotype modelling predicts individual responses to dietary interventions. Nat. Food (2020).

  56. 56.

    Holmes, E. et al. Detection of urinary drug metabolite (Xenometabolome) signatures in molecular epidemiology studies via statistical total correlation (NMR) spectroscopy. Anal. Chem. 79, 2629–2640 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Dieterle, F., Ross, A., Schlotterbeck, G. & Senn, H. Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics. Anal. Chem. 78, 4281–4290 (2006).

    CAS  PubMed  Google Scholar 

  58. 58.

    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).

    CAS  PubMed  Google Scholar 

  59. 59.

    Garcia-Perez, I. et al. Identifying unknown metabolites using NMR-based metabolic profiling techniques. Nat. Protoc. (in the press).

  60. 60.

    Macdiarmid, J. & Blundell, J. Assessing dietary intake: who, what and why of under-reporting. Nutr. Res. Rev. 11, 231–253 (1998).

    CAS  PubMed  Google Scholar 

  61. 61.

    Maroni, B. J., Steinman, T. I. & Mitch, W. E. A method for estimating nitrogen intake of patients with chronic renal-failure. Kidney Int. 27, 58–65 (1985).

    CAS  PubMed  Google Scholar 

  62. 62.

    Mariotti, F., Tome, D. & Mirand, P. P. Converting nitrogen into protein—beyond 6.25 and Jones’ factors. Crit. Rev. Food Sci. Nutr. 48, 177–184 (2008).

    CAS  PubMed  Google Scholar 

  63. 63.

    Black, A. E. Critical evaluation of energy intake using the Goldberg cut-off for energy intake: basal metabolic rate. A practical guide to its calculation, use and limitations. Int. J. Obes. 24, 1119–1130 (2000).

    CAS  Google Scholar 

  64. 64.

    Schofield, W. N. Predicting basal metabolic rate, new standards and review of previous work. Hum. Nutr. Clin. Nutr. 39(Suppl. 1), 5–41 (1985).

    PubMed  Google Scholar 

  65. 65.

    Fulgoni, V. L. III, Keast, D. R. & Drewnowski, A. Development and validation of the nutrient-rich foods index: a tool to measure nutritional quality of foods. J. Nutr. 139, 1549–1554 (2009).

    PubMed  Google Scholar 

  66. 66.

    Storey, J. D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl Acad. Sci. USA 100, 9440–9445 (2003).

    ADS  MathSciNet  CAS  MATH  Google Scholar 

  67. 67.

    Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. 68.

    Eckburg, P. B. et al. Diversity of the human intestinal microbial flora. Science 308, 1635–1638 (2005).

    ADS  PubMed  PubMed Central  Google Scholar 

  69. 69.

    The Human Microbiome Project Consortium Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).

  70. 70.

    Hoffmann, C. et al. Archaea and fungi of the human gut microbiome: correlations with diet and bacterial residents. PLoS ONE 8, e66019 (2013).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Gaci, N., Borrel, G., Tottey, W., O’Toole, P. W. & Brugere, J. F. Archaea and the human gut: new beginning of an old story. World J. Gastroenterol. 20, 16062–16078 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. 72.

    Jaccard, P. Distribution de la flore alpine dans le bassin des Dranses et dans quelques régions voisines. Bull. Soc. Vaudoise Sci. Nat. 37, 241–272 (1901).

    Google Scholar 

  73. 73.

    Newman, M. E. J. & Girvan, M. Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113 (2004).

    ADS  CAS  Google Scholar 

  74. 74.

    Guimera, R. & Nunes Amaral, L. A. Functional cartography of complex metabolic networks. Nature 433, 895–900 (2005).

    ADS  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  75. 75.

    Wang, Z. & Zhang, J. Z. In search of the biological significance of modular structures in protein networks. PLoS Comput. Biol. 3, 1011–1021 (2007).

    MathSciNet  CAS  Google Scholar 

  76. 76.

    Maslov, S. & Sneppen, K. Specificity and stability in topology of protein networks. Science 296, 910–913 (2002).

    ADS  CAS  PubMed  Google Scholar 

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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.

Author information




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 or 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 (2020).

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