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The unmapped chemical complexity of our diet

A Publisher Correction to this article was published on 21 January 2020

Abstract

Our understanding of how diet affects health is limited to 150 key nutritional components that are tracked and catalogued by the United States Department of Agriculture and other national databases. Although this knowledge has been transformative for health sciences, helping unveil the role of calories, sugar, fat, vitamins and other nutritional factors in the emergence of common diseases, these nutritional components represent only a small fraction of the more than 26,000 distinct, definable biochemicals present in our food—many of which have documented effects on health but remain unquantified in any systematic fashion across different individual foods. Using new advances such as machine learning, a high-resolution library of these biochemicals could enable the systematic study of the full biochemical spectrum of our diets, opening new avenues for understanding the composition of what we eat, and how it affects health and disease.

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Fig. 1: Untracked biochemicals and their health implications.
Fig. 2: Linking the diet to the genome and disease.

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References

  1. Willett, W. Nutritional Epidemiology (Oxford Univ. Press, 1990).

  2. National Food Institute, Technical University of Denmark. Frida Food data version 2 (DTU, 2016); frida.fooddata.dk

  3. Composition of Foods Raw, Processed, Prepared. National Nutrient Database for Standard Reference, Release 28. Documentation and User Guide (USDA, 2015).

  4. USDA. FoodData Central. Garlic, raw https://fdc.nal.usda.gov/fdc-app.html#/food-details/169230/nutrients (2019).

  5. FooDB. Garlic http://foodb.ca/foods/FOOD00008 (data dump 06/29/2017, ID=8, 2017).

  6. FooDB. Soft-necked garlic http://foodb.ca/foods/FOOD00850 (data dump 06/29/2017, ID=880, 2017).

  7. Luo, Y., Shang, P. & Li, D. Luteolin: a flavonoid that has multiple cardio-protective effects and its molecular mechanisms. Front. Pharmacol. 8, 1–10 (2017).

    Google Scholar 

  8. Dagnino, S. & Macherone, A. (eds) Unraveling the Exposome: A Practical View (Springer, 2019).

  9. FooDB. Compounds http://foodb.ca/compounds (accessed 1 August 2019).

  10. Wink, M. (ed) in Annual Plant Reviews Volume 40: Biochemistry of Plant Secondary Metabolism 2nd edn Ch. 1 (Wiley, 2010).

  11. Yang, L. et al. Response of plant secondary metabolites to environmental factors. Molecules 23, 1–26 (2018).

    Google Scholar 

  12. Hooton, F., Menichetti, G. & Barabási, A.-L. FoodMine: exploring food contents in scientific literature. Preprint at https://doi.org/10.1101/2019.12.17.880062 (2019).

  13. Rao, P. et al. Diallyl sulfide: potential use in Novel therapeutic interventions in alcohol, drugs, and disease mediated cellular toxicity by targeting cytochrome P450 2E1. Curr. Drug Metab. 16, 486–503 (2015).

    Article  CAS  Google Scholar 

  14. Garcia-Abujeta, J. L. et al. Allergic contact dermatitis to diallyl disulphide in Spain. J. Allergy Clin. Immunol. 117, S130 (2006).

    Article  Google Scholar 

  15. Cho, C. E. et al. Trimethylamine-N-oxide (TMAO) response to animal source foods varies among healthy young men and is influenced by their gut microbiota composition: a randomized controlled trial. Mol. Nutr. Food Res. 61, 1–12 (2017).

    Google Scholar 

  16. Senthong, V. et al. Intestinal microbiota-generated metabolite trimethylamine-N-oxide and 5-year mortality risk in stable coronary artery disease: the contributory role of intestinal microbiota in a COURAGE-like patient cohort. J. Am. Heart Assoc. 5, 1–7 (2016).

    Google Scholar 

  17. Velasquez, M. T., Ramezani, A., Manal, A. & Raj, D. S. Trimethylamine N-oxide: the good, the bad and the unknown. Toxins 8, e326 (2016).

    Article  Google Scholar 

  18. Wang, Z. et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature 472, 57–65 (2011).

    Article  ADS  CAS  Google Scholar 

  19. Estruch, R. et al. Primary prevention of cardiovascular disease with a Mediterranean diet supplemented with extra-virgin olive oil or nuts. N. Engl. J. Med. 378, e34 (2018).

    Article  CAS  Google Scholar 

  20. Lawson, L. D. & Hunsaker, S. M. Allicin bioavailability and bioequivalence from garlic supplements and garlic foods. Nutrients 10, 4–6 (2018).

    Article  Google Scholar 

  21. Patterson, K. Y. et al. USDA Database for the Choline Content of Common Foods, Release 2 (USDA, 2008).

  22. Davis, A. P. et al. Comparative Toxicogenomics Database (NC State University, accessed: 25 March 2019); ctdbase.org

  23. King, B. L., Davis, A. P., Rosenstein, M. C., Wiegers, T. C. & Mattingly, C. J. Ranking transitive chemical-disease inferences using local network topology in the comparative toxicogenomics database. PLoS ONE 7, e46524 (2012).

    Article  ADS  CAS  Google Scholar 

  24. Varshney, R. & Budoff, M. J. Garlic and heart disease. J. Nutr. 146, 416S–421S (2016).

    Article  CAS  Google Scholar 

  25. Wu, W. K. et al. Dietary allicin reduces transformation of L-carnitine to TMAO through impact on gut microbiota. J. Funct. Foods 15, 408–417 (2015).

    Article  CAS  Google Scholar 

  26. 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 

  27. Goh, K.-I. et al. The human disease network. Proc. Natl Acad. Sci. USA 104, 8685–90 (2007).

    Article  ADS  CAS  Google Scholar 

  28. Barabási, A.-L., Gulbahce, N. & Loscalzo, J. Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12, 56–68 (2011).

    Article  Google Scholar 

  29. Panagiotou, G. & Nielsen, J. Nutritional systems biology: definitions and approaches. Annu. Rev. Nutr. 29, 329–339 (2009).

    Article  CAS  Google Scholar 

  30. Menche, J. et al. Uncovering disease-disease relationships through the incomplete interactome. Science 347, 1257601 (2015).

    Article  Google Scholar 

  31. Loscalzo, J., Barabási, A.-L. & Silverman, E. K. Network medicine: complex systems in human disease and therapeutics (Harvard Univ. Press, 2017).

  32. Szklarczyk, D. et al. STITCH 5: Augmenting protein-chemical interaction networks with tissue and affinity data. Nucleic Acids Res. 44, D380–D384 (2016).

    Article  CAS  Google Scholar 

  33. Faria do Valle I. et al. Network-based framework for understanding the health benefits of dietary polyphenols (in press).

  34. Greenberg, J. A., Axen, K. V., Schnoll, R. & Boozer, C. N. Coffee, tea and diabetes: the role of weight loss and caffeine. Int. J. Obes. 29, 1121–1129 (2005).

    Article  CAS  Google Scholar 

  35. Iso, H. et al. The relationship between green tea and total caffeine intake and risk for self-reported type 2 diabetes among Japanese adults. Ann. Intern. Med. 144, 554–562 (2006).

    Article  Google Scholar 

  36. Song, Y., Manson, J. E., Buring, J. E., Sesso, H. D. & Liu, S. Associations of dietary flavonoids with risk of type 2 diabetes, and markers of insulin resistance and systemic inflammation in women: a prospective study and cross-sectional analysis. J. Am. Coll. Nutr. 24, 376–84 (2005).

    Article  CAS  Google Scholar 

  37. Wolfram, S. et al. Epigallocatechin gallate supplementation alleviates diabetes in rodents. J. Nutr. 136, 2512–2518 (2006).

    Article  CAS  Google Scholar 

  38. Keske, M. A. et al. Vascular and metabolic actions of the green tea polyphenol epigallocatechin gallate. Curr. Med. Chem. 22, 59–69 (2015).

    Article  CAS  Google Scholar 

  39. Dodd, D. et al. A gut bacterial pathway metabolizes aromatic amino acids into nine circulating metabolites. Nature 551, 648–652 (2017).

    Article  ADS  CAS  Google Scholar 

  40. Schmidt, T. S. B., Raes, J. & Bork, P. The human gut microbiome: from association to modulation. Cell 172, 1198–1215 (2018).

    Article  CAS  Google Scholar 

  41. Sebastian, R. et al. Flavonoid Values for USDA Survey Foods and Beverages 2007–2010 (Food Surveys Research Group, 2016); www.ars.usda.gov/Services/docs.htm?docid=25102

  42. Duke, J. A. Database of Biologically Active Phytochemicals and Their Activity (CRC Press, 1992).

  43. FooDB (2017); http://foodb.ca/

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

    Article  CAS  Google Scholar 

  45. Plumb, J. et al. eBASIS (Bioactive substances in food information systems) and bioactive intakes: major updates of the bioactive compound composition and beneficial bioeffects database and the development of a probabilistic model to assess intakes in Europe. Nutrients 9, 1–15 (2017).

    Article  Google Scholar 

  46. NCBI. Taxonomy (US National Library of Medicine, 2018); https://www.ncbi.nlm.nih.gov/taxonomy

  47. Xing, E. P., Ng, A. Y., Jordan, M. I. & Russell, S. Distance metric learning, with application to clustering with side-information. in Proc. 15th International Conference on Neural Information Processing Systems 521–528 (MIT Press, 2002).

  48. Davis, J. V., Kulis, B., Jain, P., Sra, S. & Dhillon, I. S. Information-theoretic metric learning. in Proc. 24th International Conference on Machine Learning (ed. Ghahramani, Z.) 209–216 (ACM, 2007).

  49. Bao, J., Wang, W., Yang, T. & Wu, G. An incremental clustering method based on the boundary profile. PLoS ONE 13, e0196108 (2018).

    Article  Google Scholar 

  50. García-Cañas, V., Simó, C., Herrero, M., Ibáñez, E. & Cifuentes, A. Present and future challenges in food analysis: Foodomics. Anal. Chem. 84, 10150–10159 (2012).

    Article  Google Scholar 

  51. Capozzi, F. & Bordoni, A. Foodomics: a new comprehensive approach to food and nutrition. Genes Nutr. 8, 1–4 (2013).

    Article  CAS  Google Scholar 

  52. Jones, K. C. & de Voogt, P. Persistent organic pollutants (POPs): state of the science. Environ. Pollut. 100, 209–221 (1999).

    Article  CAS  Google Scholar 

  53. Espiñeira, M. & Santaclara, F. J. (eds) in Advances in Food Traceability Techniques and Technologies: Improving Quality Throughout the Food Chain. 3–8 (Elsevier, 2016).

  54. Patel, C. J. & Ioannidis, J. P. A. Studying the elusive environment in large scale. JAMA 311, 2173–2174 (2014).

    Article  CAS  Google Scholar 

  55. Milanlouei, S., et al. A systematic comprehensive longitudinal evaluation of dietary factors associated with coronary heart disease (in press).

  56. Khera, A. V. et al. Genetic risk, adherence to a healthy lifestyle, and coronary disease. N. Engl. J. Med. 375, 2349–2358 (2016).

    Article  CAS  Google Scholar 

  57. Schroeder, S. et al. We can do better-improving the health of the American people. N. Engl. J. Med. 357, 1221–1228 (2007).

    Article  CAS  Google Scholar 

  58. Mozaffarian, D., Rosenberg, I. & Uauy, R. History of modern nutrition science—implications for current research, dietary guidelines, and food policy. BMJ 361, k2392 (2018).

    Article  Google Scholar 

  59. Bennett, D. A., Landry, D., Little, J. & Minelli, C. Systematic review of statistical approaches to quantify, or correct for, measurement error in a continuous exposure in nutritional epidemiology. BMC Med. Res. Methodol. 17, 1–22 (2017).

    Article  Google Scholar 

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Acknowledgements

This work was supported by NIH grant no. 1P01HL132825 and American Heart Association grant no. 151708, and NIH grant no. HG007690, HL119145, GM107618 and American Heart Association grant D700382 to J.L.; A.L.B. is also supported by ERC 810115-DYNASET. We thank D. Mozaffarian (Tufts Friedman School of Nutrition Science and Policy), W.C. Willett (Harvard T.H. Chan School of Public Health), M. Sebek, F. Hooton, J. Cheng, I. do Valle, S. Milanlouei and P. Ruppert (Northeastern University) for help with data and useful discussions.

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A.L.B., G.M. and J.L. conceived the project and wrote the manuscript. G.M. performed the statistical analysis.

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Correspondence to Albert-László Barabási.

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A.L.B. is founder of Nomix and Foodome, and J.L. and A.L.B. are founders of Scipher Medicine, companies that explore the role of networks and food in health.

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Barabási, AL., Menichetti, G. & Loscalzo, J. The unmapped chemical complexity of our diet. Nat Food 1, 33–37 (2020). https://doi.org/10.1038/s43016-019-0005-1

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