Nutrient concentrations in food display universal behaviour.

Journal

Nature food

ISSN: 2662-1355

Titre abrégé: Nat Food

Pays: England

ID NLM: 101761102

Informations de publication

Date de publication:
05 2022

Historique:

received: 02 02 2021

accepted: 08 04 2022

medline: 1 5 2023

pubmed: 29 4 2023

entrez: 28 4 2023

Statut: ppublish

Résumé

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.

Identifiants

DOI: 10.1038/s43016-022-00511-0 PMID: 37117566

pubmed: 37117566

doi: 10.1038/s43016-022-00511-0

pii: 10.1038/s43016-022-00511-0

doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

Pagination

375-382

Subventions

Organisme : NHLBI NIH HHS

ID : P01 HL132825

Pays : United States

Informations de copyright

Références

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

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

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).

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

doi: 10.1038/s41598-020-73105-0

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).

doi: 10.1038/s41467-020-19888-2

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

doi: 10.1038/35036627

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

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

doi: 10.1093/nar/gkw952

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

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

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

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

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

doi: 10.1641/0006-3568(2001)051[0341:LNDATS]2.0.CO;2

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

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

Frida Fooddata Version 2 (DTU Food, 2016).

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

doi: 10.1093/database/bap024

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

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

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).

doi: 10.1371/journal.pcbi.1002166

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

doi: 10.1038/s41586-019-1151-1

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

doi: 10.1126/science.1252076

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

doi: 10.1038/nchembio.2077

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

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).

doi: 10.1038/nature02289

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

doi: 10.1371/journal.pcbi.0010068

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

doi: 10.1073/pnas.0610987104

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

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

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).

doi: 10.1016/j.bbadis.2018.09.001

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

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

doi: 10.2307/2529621

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

doi: 10.1371/journal.pcbi.1006687

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

doi: 10.1021/bi2002289

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).

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

doi: 10.1186/1752-0509-2-7

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

doi: 10.1016/j.jtbi.2010.02.024

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).

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

doi: 10.1049/enb.2017.0004

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

doi: 10.1103/PhysRevLett.108.238105

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

doi: 10.1126/science.1188308

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).

doi: 10.1038/nrg1615

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

doi: 10.1038/20144

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

doi: 10.1029/2001GL014533

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

doi: 10.1038/25977

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).

doi: 10.1073/pnas.0700250104

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).

doi: 10.1093/ajcn.81.2.341

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).

doi: 10.1161/CIRCULATIONAHA.109.924977

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

doi: 10.1136/bmj.k322

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).

doi: 10.1186/s12916-019-1312-y

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).

doi: 10.1093/ajcn/nqaa075

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).

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

doi: 10.1098/rspb.2000.1280

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

doi: 10.1371/journal.pcbi.1000725

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

doi: 10.1006/jtbi.2001.2440

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

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

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).

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/

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

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

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

doi: 10.1093/molbev/msw046

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