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Nutrient concentrations in food display universal behaviour

Abstract

Extensive programmes around the world endeavour to measure and catalogue the composition of food. Here we analyse the nutrient content of the full US food supply and show that the concentration of each nutrient follows a universal single-parameter scaling law that accurately captures the eight orders of magnitude in nutrient content variability. We show that the universality is rooted in the biochemical constraints obeyed by the metabolic pathways responsible for nutrient modulation, allowing us to confirm the empirically observed scaling law and to predict its variability in agreement with the data. We propose that the natural nutrient variability in food can be quantitatively formalized. This provides a mathematical rationale for imputing missing values in food composition databases and paves the way towards a quantitative understanding of the impact of food processing on nutrient balance and health effects.

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Fig. 1: Nutrient composition of food.
Fig. 2: The nutrient content across the food supply.
Fig. 3: Metabolic origins of nutrient scaling.

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Data availability

The raw data are available at https://github.com/menicgiulia/FoodLaws. Source data are provided with this paper.

Code availability

The processing codes are available at https://github.com/menicgiulia/FoodLaws.

References

  1. Kubo, R., Ichimura, H., Usui, T. & Hashitsume, N. Statistical Mechanics (North-Holland Personal Library, 1990).

  2. Barabasi, A.-L. & Pósfai, M. Network Science by Albert-László Barabási (Cambridge University Press, 2016).

  3. Barabási, A.-L., Menichetti, G. & Loscalzo, J. The nutritional dark matter: the unmapped chemical complexity of our diet. Nat. Food https://doi.org/10.1038/s43016-019-0005-1 (2019).

  4. Hooton, F., Menichetti, G. & Barabási, A.-L. Exploring food contents in scientific literature with FoodMine. Sci. Rep. 10, 16191 (2020).

    Article  CAS  Google Scholar 

  5. Milanlouei, S. et al. A systematic comprehensive lÿongitudinal evaluation of dietary factors associated with acute myocardial infarction and fatal coronary heart disease. Nat. Commun. 11, 6074 (2020).

    Article  ADS  CAS  Google Scholar 

  6. Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N. & Barabasi, A. L. The large-scale organization of metabolic networks. Nature 407, 651–654 (2000).

    Article  ADS  CAS  Google Scholar 

  7. List of EuroFIR Databases (EuroFIR, accessed 7 January 2021); https://www.eurofir.org/food-information/food-composition-databases/

  8. Placzek, S. et al. BRENDA in 2017: new perspectives and new tools in BRENDA. Nucleic Acids Res. 45, D380–D388 (2017).

    Article  CAS  Google Scholar 

  9. USDA Food and Nutrient Database for Dietary Studies Version 5.0 (USDA, 2012); http://www.ars.usda.gov/ba/bhnrc/fsrg

  10. Sebastian, R. et al. Flavonoid Values for USDA Survey Foods and Beverages 2007–2010 (USDA, 2016); www.ars.usda.gov/nea/bhnrc/fsrg

  11. Willett, W. Monographs in Epidemiology and Biostatistics: Nutritional Epidemiology Vol. 15 (Oxford Univ. Press, 1990).

  12. Hansen, A. The three extreme value distributions: an introductory review. Front. Phys. 8, 533 (2020).

    Google Scholar 

  13. Limpert, E., Stahel, W. A. & Abbt, M. Log-normal distributions across the sciences: keys and clues. Bioscience 51, 341–352 (2001).

    Article  Google Scholar 

  14. FoodData Central (US Department of Agriculture, Agricultural Research Service, 2019); https://fdc.nal.usda.gov/

  15. National Nutrient Database for Standard Reference, Release 28, Documentation and User Guide (USDA, 2015).

  16. Frida Fooddata Version 2 (DTU Food, 2016).

  17. Neveu, V. et al. Phenol-Explorer: an online comprehensive database on polyphenol contents in foods. Database (Oxford) 2010, bap024 (2010).

    Article  CAS  Google Scholar 

  18. WishartLab (FooDB, 2017); http://foodb.ca/

  19. FoodData Central: Foundation Foods (U.S. Department of Agriculture, A. R. S., 2019); https://fdc.nal.usda.gov/

  20. Bar-Even, A., Noor, E., Flamholz, A., Buescher, J. M. & Milo, R. Hydrophobicity and charge shape cellular metabolite concentrations. PLoS Comput. Biol. 7, e1002166 (2011).

    Article  ADS  CAS  Google Scholar 

  21. Muchowska, K.B., Varma, S.J. & Moran, J. Synthesis and breakdown of universal metabolic precursors promoted by iron. Nature 569, 104–107 (2019).

    Article  ADS  CAS  Google Scholar 

  22. Chae, L., Kim, T., Nilo-Poyanco, R. & Rhee, S. Y. Genomic signatures of specialized metabolism in plants. Science 344, 510–513 (2014).

    Article  ADS  CAS  Google Scholar 

  23. Park, J. O. et al. Metabolite concentrations, fluxes and free energies imply efficient enzyme usage. Nat. Chem. Biol. 12, 482–489 (2016).

    Article  CAS  Google Scholar 

  24. Michal, G. & Schomburg, D. Biochemical Pathways: An Atlas of Biochemistry and Molecular Biology 2nd edn (John Wiley & Sons, 2013); https://doi.org/10.1002/9781118657072

  25. Almaas, E., Kovács, B., Vicsek, T., Oltvai, Z. N. & Barabási, A.-L. Global organization of metabolic fluxes in the bacterium Escherichia coli. Nature 427, 839–843 (2004).

    Article  ADS  CAS  Google Scholar 

  26. Almaas, E., Oltvai, Z. N. & Barabási, A. L. The activity reaction core and plasticity of metabolic networks. PLoS Comput. Biol. 1, 0557–0563 (2005).

    Article  CAS  Google Scholar 

  27. Levine, E. & Hwa, T. Stochastic fluctuations in metabolic pathways. Proc. Natl Acad. Sci. USA 104, 9224–9229 (2007).

    Article  ADS  MathSciNet  CAS  Google Scholar 

  28. Stryer, L., Berg, M. J. & Tymoczko, L. J. Biochemistry (W. H. Freeman, 2002).

  29. Peregrín-Alvarez, J.M., Sanford, C. & Parkinson, J. The conservation and evolutionary modularity of metabolism. Genome Biol 10, R63 (2009).

  30. Khan, A. H., Zou, Z., Xiang, Y., Chen, S. & Tian, X. L. Conserved signaling pathways genetically associated with longevity across the species. Biochim. Biophys. Acta Mol. Basis Dis. 1865, 1745–1755 (2019).

    Article  CAS  Google Scholar 

  31. Plant Metabolic Network (PlantCyc Pathway: Choline Biosynthesis, 2019); https://pmn.plantcyc.org/PLANT/NEW-IMAGE?type=PATHWAY&object=PWY-3385

  32. Bulmer, A. M. G. On fitting the Poisson lognormal distribution to species-abundance data. Biometrics 30, 101–110 (1974).

    Article  Google Scholar 

  33. Küken, A., Eloundou-Mbebi, J. M. O., Basler, G. & Nikoloski, Z. Cellular determinants of metabolite concentration ranges. PLoS Comput. Biol. 15, e1006687 (2019).

    Article  ADS  Google Scholar 

  34. Bar-Even, A. et al. The moderately efficient enzyme: evolutionary and physicochemical trends shaping enzyme parameters. Biochemistry 50, 4402–4410 (2011).

    Article  CAS  Google Scholar 

  35. Dourado, H., Maurino, V. & Lercher, M. Enzymes and substrates are balanced at minimal combined mass concentration in vivo. Preprint at bioRxiv https://doi.org/10.1101/128009 (2017).

  36. Vazquez, A. et al. Impact of the solvent capacity constraint on E. coli metabolism. BMC Syst. Biol. 2, 7 (2008).

    Article  Google Scholar 

  37. Vazquez, A. Optimal cytoplasmatic density and flux balance model under macromolecular crowding effects. J. Theor. Biol. 264, 356–359 (2010).

    Article  ADS  CAS  Google Scholar 

  38. Furusawa, C., Suzuki, T., Kashiwagi, A., Yomo, T. & Kaneko, K. Ubiquity of log-normal distributions in intra-cellular reaction dynamic. Biophysics (Nagoya-shi) 1, 25–31 (2005).

  39. Beal, J. Biochemical complexity drives log-normal variation in genetic expression. Eng. Biol. 1, 55–60 (2017).

    Article  Google Scholar 

  40. Salman, H. et al. Universal protein fluctuations in populations of microorganisms. Phys. Rev. Lett. 108, 238105 (2012).

    Article  ADS  Google Scholar 

  41. Taniguchi, Y. et al. Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells. Science 329, 533–539 (2011).

    Article  ADS  Google Scholar 

  42. Kærn, M., Elston, T. C., Blake, W. J. & Collins, J. J. Stochasticity in gene expression: from theories to phenotypes. Nat. Rev. Genet. 6, 451–464 (2005).

    Article  Google Scholar 

  43. Banavar, J. R., Maritan, A. & Rinaldo, A. Size and form in efficient transportation networks. Nature 399, 130–132 (1999).

    Article  ADS  CAS  Google Scholar 

  44. Maritan, A., Rigon, R., Banavar, J. R. & Rinaldo, A. Network allometry. Geophys. Res. Lett. 29, 1508 (2002).

    Article  ADS  Google Scholar 

  45. Enquist, B. J., Brown, J. H. & West, G. B. Allometric scaling of plant energetics and population density. Nature 395, 163–165 (1998).

    Article  ADS  CAS  Google Scholar 

  46. Gallos, L. K., Song, C., Havlin, S. & Makse, H. A. Scaling theory of transport in complex biological networks. Proc. Natl Acad. Sci. USA 104, 7746–7751 (2007).

    Article  ADS  CAS  Google Scholar 

  47. Cordain, L. et al. Origins and evolution of the Western diet: health implications for the 21st century. Am. J. Clin. Nutr. 81, 341–354 (2005).

    Article  CAS  Google Scholar 

  48. Micha, R., Wallace, S. K. & Mozaffarian, D. Red and processed meat consumption and risk of incident coronary heart disease, stroke, and diabetes mellitus: a systematic review and meta-analysis. Circulation 121, 2271–2283 (2010).

    Article  Google Scholar 

  49. Fiolet, T. et al. Consumption of ultra-processed foods and cancer risk: results from NutriNet-Sant‚ prospective cohort. Brit. Med. J. 360, k322 (2018).

    Article  Google Scholar 

  50. Adjibade, M. et al. Prospective association between ultra-processed food consumption and incident depressive symptoms in the French NutriNet-Sant‚ cohort. BMC Med. 17, 78 (2019).

    Article  Google Scholar 

  51. Alonso-Pedrero, L. et al. Ultra-processed food consumption and the risk of short telomeres in an elderly population of the Seguimiento Universidad de Navarra (SUN) Project. Am. J. Clin. Nutr. 111, 1259–1266 (2020).

    Article  Google Scholar 

  52. Carrera-Bastos, P., Fontes-Villalba, M., O'Keefe, J. H., Lindeberg, S. & Cordain, L. The western diet and lifestyle and diseases of civilization. Res. Rep. Clin. Cardiol. 2, 15–35 (2011).

    Google Scholar 

  53. Bornholdt, S. & Sneppen, K. Robustness as an evolutionary principle. Proc. R. Soc. Lond. B 267, 2281–2286 (2000).

    Article  CAS  Google Scholar 

  54. Riehl, W. J., Krapivsky, P. L., Redner, S. & SegrŠ, D. Signatures of arithmetic simplicity in metabolic network architecture. PLoS Comput. Biol. 6, e1000725 (2010).

    Article  ADS  MathSciNet  Google Scholar 

  55. Segré, D., Shenhav, B., Kafri, R. & Lancet, D. The molecular roots of compositional inheritance. J. Theor. Biol. 213, 481–491 (2001).

    Article  ADS  Google Scholar 

  56. Palsson, B. Systems Biology: Properties of Reconstructed Networks (Cambridge Univ. Press, 2006); https://doi.org/10.1017/CBO9780511790515

  57. Gupta, S., Hawk, T., Aggarwal, A. & Drewnowski, A. Characterizing ultra-processed foods by energy density, nutrient density, and cost. Front. Nutr. 6 (2019).

  58. Menichetti, G., Ravandi, B., Mozaffarian, D. & Barabasi, A.-L. Machine learning prediction of food processing. Preprint at medRxiv https://doi.org/10.1101/2021.05.22.21257615 (2021).

  59. FNDDS Web Page (USDA, 2019); https://www.ars.usda.gov/northeast-area/beltsville-md-bhnrc/beltsville-human-nutrition-research-center/food-surveys-research-group/docs/fndds/

  60. Kapur, J. N. Maximum-entropy Models in Science and Engineering (India, Wiley, 1989).

  61. NCBI Taxonomy (National Center for Biotechnology Information, 2019); https://www.ncbi.nlm.nih.gov/taxonomy

  62. Huerta-Cepas, J., Serra, F. & Bork, P. ETE 3: reconstruction, analysis, and visualization of phylogenomic data. Mol. Biol. Evol. 33, 1635–1638 (2016).

    Article  CAS  Google Scholar 

  63. Yannai, S. Dictionary of Food Compounds with CD-ROM Choice Reviews Online Vol. 51 (Taylor & Francis, 2013).

Download references

Acknowledgements

This work was partially supported by NIH grant no. 1P01HL132825, American Heart Association grant no. 151708, ERC grant no. 810115-DYNASET and Rockefeller Foundation grant no. 2019 FOD 026. We thank J. Loscalzo for useful discussions and insights on enzyme kinetics, as well as M. Sebek and S. Ofaim for helping with the chemical classification and disambiguation.

Author information

Authors and Affiliations

Authors

Contributions

G.M. and A.-L.B. conceived the project and wrote the manuscript. G.M. performed the data query, data integration, statistical analysis and analytical calculations.

Corresponding author

Correspondence to Albert-László Barabási.

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Competing interests

A.-L.B. is the founder of Scipher Medicine and Naring Health, companies that explore the use of network-based tools in health, and Datapolis, which focuses on urban data.

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Nature Food thanks the anonymous reviewers for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Sections 1–11, Figs. 1–22 and Tables 1 and 2.

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Source data

Source Data Fig. 1

Nutrient values for PDFs and scaling laws.

Source Data Fig. 2

Extensive nutrient values for PDFs and scaling laws.

Source Data Fig. 3

KM values for PDFs and scaling laws.

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Menichetti, G., Barabási, AL. Nutrient concentrations in food display universal behaviour. Nat Food 3, 375–382 (2022). https://doi.org/10.1038/s43016-022-00511-0

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