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Network medicine framework shows that proximity of polyphenol targets and disease proteins predicts therapeutic effects of polyphenols

Nature Food volume 2, pages 143–155 (2021)Cite this article

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Abstract

Polyphenols, natural products present in plant-based foods, play a protective role against several complex diseases through their antioxidant activity and by diverse molecular mechanisms. Here we develop a network medicine framework to uncover mechanisms for the effects of polyphenols on health by considering the molecular interactions between polyphenol protein targets and proteins associated with diseases. We find that the protein targets of polyphenols cluster in specific neighbourhoods of the human interactome, whose network proximity to disease proteins is predictive of the molecule’s known therapeutic effects. The methodology recovers known associations, such as the effect of epigallocatechin-3-O-gallate on type 2 diabetes, and predicts that rosmarinic acid has a direct impact on platelet function, representing a novel mechanism through which it could affect cardiovascular health. We experimentally confirm that rosmarinic acid inhibits platelet aggregation and α-granule secretion through inhibition of protein tyrosine phosphorylation, offering direct support for the predicted molecular mechanism. Our framework represents a starting point for mechanistic interpretation of the health effects underlying food-related compounds, allowing us to integrate into a predictive framework knowledge on food metabolism, bioavailability and drug interaction.

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Fig. 1: Properties of polyphenol protein targets.
Fig. 2: Protein–protein interactions of polyphenol targets.
Fig. 3: Proximity between polyphenol targets and disease proteins is predictive of the therapeutic effects of the polyphenol.
Fig. 4: Relationships among gene expression perturbation, network proximity and the therapeutic effects of polyphenols on diseases.
Fig. 5: Diseases proximal to polyphenol targets have higher gene expression perturbation profiles.
Fig. 6: RA modulates platelet function.

Data availability

All data supporting the findings of this study are available at https://github.com/italodovalle/polyphenols and within the paper and its Supplementary Information files.

Code availability

Computer code is available at https://github.com/italodovalle/polyphenols.

References

  1. 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  PubMed  PubMed Central  Google Scholar 

  2. Arts, I. C. W. & Hollman, P. C. H. Polyphenols and disease risk in epidemiologic studies. Am. J. Clin. Nutr. 81, 317S–325S (2005).

    Article  CAS  PubMed  Google Scholar 

  3. Wang, X., Ouyang, Y. Y., Liu, J. & Zhao, G. Flavonoid intake and risk of CVD: a systematic review and meta-analysis of prospective cohort studies. Br. J. Nutr. 111, 1–11 (2014).

    Article  ADS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Pérez-Jiménez, J., Neveu, V., Vos, F. & Scalbert, A. Systematic analysis of the content of 502 polyphenols in 452 foods and beverages: an application of the phenol-explorer database. J. Agric. Food Chem. 58, 4959–4969 (2010).

    Article  PubMed  Google Scholar 

  6. Zhang, H. & Tsao, R. Dietary polyphenols, oxidative stress and antioxidant and anti-inflammatory effects. Curr. Opin. Food Sci. 8, 33–42 (2016).

    Article  Google Scholar 

  7. Boly, R. et al. Quercetin inhibits a large panel of kinases implicated in cancer cell biology. Int. J. Oncol. 38, 833–842 (2011).

    CAS  PubMed  Google Scholar 

  8. Lacroix, S. et al. A computationally driven analysis of the polyphenol-protein interactome. Sci. Rep. 8, 2232 (2018).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  9. Hanhineva, K. et al. Impact of dietary polyphenols on carbohydrate metabolism. Int. J. Mol. Sci. 11, 1365–1402 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Hervert-Hernández, D. & Goñi, I. Dietary polyphenols and human gut microbiota: a review. Food Rev. Int. 27, 154–169 (2011).

    Article  Google Scholar 

  11. Zhang, S. et al. Dietary pomegranate extract and inulin affect gut microbiome differentially in mice fed an obesogenic diet. Anaerobe 48, 184–193 (2017).

    Article  CAS  PubMed  Google Scholar 

  12. Thazhath, S. S. et al. Administration of resveratrol for 5 wk has no effect on glucagon-like peptide 1 secretion, gastric emptying, or glycemic control in type 2 diabetes: a randomized controlled trial. Am. J. Clin. Nutr. 103, 66–70 (2016).

    Article  CAS  PubMed  Google Scholar 

  13. Bhatt, J. K., Thomas, S. & Nanjan, M. J. Resveratrol supplementation improves glycemic control in type 2 diabetes mellitus. Nutr. Res. 32, 537–541 (2012).

    Article  CAS  PubMed  Google Scholar 

  14. Sharma, A. et al. A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in asthma. Hum. Mol. Genet. 24, 3005–3020 (2014).

    Article  Google Scholar 

  15. Menche, J. et al. Disease networks. Uncovering disease–disease relationships through the incomplete interactome. Science 347, 1257601 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Guney, E., Menche, J., Vidal, M. & Barabási, A.-L. Network-based in silico drug efficacy screening. Nat. Commun. 7, 10331 (2016).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  17. Cheng, F. et al. Network-based approach to prediction and population-based validation of in silico drug repurposing. Nat. Commun. 9, 2691 (2018).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  18. Kovács, I. A. et al. Network-based prediction of protein interactions. Nat. Commun. 10, 1240 (2019).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  19. Sarkar, F. H., Li, Y., Wang, Z. & Kong, D. Cellular signaling perturbation by natural products. Cell. Signal. 21, 1541–1547 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. 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  PubMed  Google Scholar 

  21. 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, 376–384 (2005).

    Article  CAS  PubMed  Google Scholar 

  22. 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  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  24. Davis, A. P. et al. The comparative toxicogenomics database: update 2019. Nucleic Acids Res. 47, D948–D954 (2019).

    Article  CAS  PubMed  Google Scholar 

  25. Muthu, R., Selvaraj, N. & Vaiyapuri, M. Anti-inflammatory and proapoptotic effects of umbelliferone in colon carcinogenesis. Hum. Exp. Toxicol. 35, 1041–1054 (2016).

    Article  CAS  PubMed  Google Scholar 

  26. Muthu, R. & Vaiyapuri, M. Synergistic and individual effects of umbelliferone with 5-fluorouracil on tumor markers and antioxidant status of rat treated with 1,2-dimethylhydrazine. Biomed. Aging Pathol. 3, 219–227 (2013).

    Article  CAS  Google Scholar 

  27. Subramanian, A. et al. A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell 171, 1437–1452.e17 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Grover, S. P., Bergmeier, W. & Mackman, N. Platelet signaling pathways and new inhibitors. Arterioscler. Thromb. Vasc. Biol. 38, e28–e35 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Moco, S., Martin, F. P. J. & Rezzi, S. Metabolomics view on gut microbiome modulation by polyphenol-rich foods. J. Proteome Res. 11, 4781–4790 (2012).

    Article  CAS  PubMed  Google Scholar 

  30. van Duynhoven, J. et al. Metabolic fate of polyphenols in the human superorganism. Proc. Natl Acad. Sci. USA 108, 4531–4538 (2011).

    Article  ADS  PubMed  Google Scholar 

  31. Ottaviani, J. I., Heiss, C., Spencer, J. P. E., Kelm, M. & Schroeter, H. Recommending flavanols and procyanidins for cardiovascular health: revisited. Mol. Aspects Med. 61, 63–75 (2018).

    Article  CAS  PubMed  Google Scholar 

  32. Stalmach, A., Troufflard, S., Serafini, M. & Crozier, A. Absorption, metabolism and excretion of Choladi green tea flavan-3-ols by humans. Mol. Nutr. Food Res. 53, S44–53 (2009).

  33. Meng, X. et al. Identification and characterization of methylated and ring-fission metabolites of tea catechins formed in humans, mice, and rats. Chem. Res. Toxicol. 15, 1042–1050 (2002).

  34. Perez-Vizcaino, F., Duarte, J. & Santos-Buelga, C. The flavonoid paradox: conjugation and deconjugation as key steps for the biological activity of flavonoids. J. Sci. Food Agric. 92, 1822–1825 (2012).

    Article  CAS  PubMed  Google Scholar 

  35. Shimoi, K. & Nakayama, T. Glucuronidase deconjugation in inflammation. Methods Enzymol. 400, 263–272 (2005).

    Article  CAS  PubMed  Google Scholar 

  36. Kaneko, A. et al. Glucuronides of phytoestrogen flavonoid enhance macrophage function via conversion to aglycones by β-glucuronidase in macrophages. Immun. Inflamm. Dis. 5, 265–279 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Cheng, F., Kovács, I. A. & Barabási, A.-L. Network-based prediction of drug combinations. Nat. Commun. 10, 1197 (2019).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  38. Smalley, J. L., Gant, T. W. & Zhang, S.-D. Application of connectivity mapping in predictive toxicology based on gene-expression similarity. Toxicology 268, 143–146 (2010).

    Article  CAS  PubMed  Google Scholar 

  39. Lamb, J. et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313, 1929–1935 (2006).

    Article  ADS  CAS  PubMed  Google Scholar 

  40. Amanzadeh, E. et al. Quercetin conjugated with superparamagnetic iron oxide nanoparticles improves learning and memory better than free quercetin via interacting with proteins involved in LTP. Sci. Rep. 9, 6876 (2019).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  41. Shaikh, J., Ankola, D. D., Beniwal, V., Singh, D. & Kumar, M. N. V. R. Nanoparticle encapsulation improves oral bioavailability of curcumin by at least 9-fold when compared to curcumin administered with piperine as absorption enhancer. Eur. J. Pharm. Sci. 37, 223–230 (2009).

    Article  CAS  PubMed  Google Scholar 

  42. Chao, E. C. & Henry, R. R. SGLT2 inhibition-A novel strategy for diabetes treatment. Nat. Rev. Drug Discov. 9, 551–559 (2010).

    Article  CAS  PubMed  Google Scholar 

  43. Caldera, M. et al. Mapping the perturbome network of cellular perturbations. Nat. Commun. 10, 5140 (2019).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  44. Jensen, K., Ni, Y., Panagiotou, G. & Kouskoumvekaki, I. Developing a molecular roadmap of drug–food interactions. PLoS Comput. Biol. 11, e1004048 (2015).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  45. Rolland, T. et al. A proteome-scale map of the human interactome network. Cell 159, 1212–1226 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Cheng, F., Jia, P., Wang, Q. & Zhao, Z. Quantitative network mapping of the human kinome interactome reveals new clues for rational kinase inhibitor discovery and individualized cancer therapy. Oncotarget 5, 3697–3710 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Calçada, D. et al. The role of low-grade inflammation and metabolic flexibility in aging and nutritional modulation thereof: a systems biology approach. Mech. Ageing Dev. http://dx.doi.org/10.1016/j.mad.2014.01.004 (2014).

  48. Hornbeck, P. V. et al. PhosphoSitePlus, 2014: mutations, PTMs and recalibrations. Nucleic Acids Res. 43, D512–D520 (2015).

    Article  CAS  PubMed  Google Scholar 

  49. Li, T. et al. A scored human protein–protein interaction network to catalyze genomic interpretation. Nat. Methods 14, 61–64 (2016).

    Article  MathSciNet  PubMed  PubMed Central  Google Scholar 

  50. Chatr-Aryamontri, A. et al. The BioGRID interaction database: 2017 update. Nucleic Acids Res. 45, D369–D379 (2017).

    Article  CAS  PubMed  Google Scholar 

  51. Cowley, M. J. et al. PINA v2.0: mining interactome modules. Nucleic Acids Res. 40, D862–D865 (2012).

    Article  CAS  PubMed  Google Scholar 

  52. Peri, S. et al. Human protein reference database as a discovery resource for proteomics. Nucleic Acids Res. 32, D497–D501 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Orchard, S. et al. The MIntAct project–IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res. 42, D358–D363 (2014).

    Article  CAS  PubMed  Google Scholar 

  54. Breuer, K. et al. InnateDB: systems biology of innate immunity and beyond–recent updates and continuing curation. Nucleic Acids Res. 41, D1228–D1233 (2013).

    Article  CAS  PubMed  Google Scholar 

  55. Meyer, M. J., Das, J., Wang, X. & Yu, H. INstruct: a database of high-quality 3D structurally resolved protein interactome networks. Bioinformatics 29, 1577–1579 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Mosca, R., Céol, A. & Aloy, P. Interactome3D: adding structural details to protein networks. Nat. Methods 10, 47–53 (2013).

    Article  CAS  PubMed  Google Scholar 

  57. Meyer, M. J. et al. Interactome INSIDER: a structural interactome browser for genomic studies. Nat. Methods 15, 107–114 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Fazekas, D. et al. SignaLink 2 – a signaling pathway resource with multi-layered regulatory networks. BMC Syst. Biol. https://doi.org/10.1186/1752-0509-7-7 (2013).

  59. Huttlin, E. L. et al. Architecture of the human interactome defines protein communities and disease networks. Nature 545, 505–509 (2017).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  60. Szklarczyk, D. et al. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43, D447–D452 (2015).

    Article  CAS  PubMed  Google Scholar 

  61. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  62. Roweth, H. G. et al. Two novel, putative mechanisms of action for citalopram-induced platelet inhibition. Sci. Rep. 8, 16677 (2018).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  63. Roweth, H. G. et al. Citalopram inhibits platelet function independently of SERT-mediated 5-HT transport. Sci. Rep. 8, 3494 (2018).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  64. Nath, S., Bachani, M., Harshavardhana, D. & Steiner, J. P. Catechins protect neurons against mitochondrial toxins and HIV proteins via activation of the BDNF pathway. J. Neurovirol. 18, 445–455 (2012).

    Article  CAS  PubMed  Google Scholar 

  65. Park, K.-S. et al. (−)-Epigallocatethin-3-O-gallate counteracts caffeine-induced hyperactivity: evidence of dopaminergic blockade. Behav. Pharmacol. 21, 572–575 (2010).

    Article  CAS  PubMed  Google Scholar 

  66. Ramesh, E., Geraldine, P. & Thomas, P. A. Regulatory effect of epigallocatechin gallate on the expression of C-reactive protein and other inflammatory markers in an experimental model of atherosclerosis. Chem. Biol. Interact. 183, 125–132 (2010).

    Article  CAS  PubMed  Google Scholar 

  67. Han, S. G., Han, S.-S., Toborek, M. & Hennig, B. EGCG protects endothelial cells against PCB 126-induced inflammation through inhibition of AhR and induction of Nrf2-regulated genes. Toxicol. Appl. Pharmacol. 261, 181–188 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Sheng, R., Gu, Z.-L. & Xie, M.-L. Epigallocatechin gallate, the major component of polyphenols in green tea, inhibits telomere attrition mediated cardiomyocyte apoptosis in cardiac hypertrophy. Int. J. Cardiol. 162, 199–209 (2013).

    Article  PubMed  Google Scholar 

  69. Devika, P. T. & Stanely Mainzen Prince, P. (−)-Epigallocatechin gallate protects the mitochondria against the deleterious effects of lipids, calcium and adenosine triphosphate in isoproterenol induced myocardial infarcted male Wistar rats. J. Appl. Toxicol. 28, 938–944 (2008).

    CAS  PubMed  Google Scholar 

  70. Yi, Q.-Y. et al. Chronic infusion of epigallocatechin-3-O-gallate into the hypothalamic paraventricular nucleus attenuates hypertension and sympathoexcitation by restoring neurotransmitters and cytokines. Toxicol. Lett. 262, 105–113 (2016).

    Article  CAS  PubMed  Google Scholar 

  71. Devika, P. T. & Prince, P. S. M. Preventive effect of (−)-epigallocatechin-gallate (EGCG) on lysosomal enzymes in heart and subcellular fractions in isoproterenol-induced myocardial infarcted Wistar rats. Chem. Biol. Interact. 172, 245–252 (2008).

    Article  CAS  PubMed  Google Scholar 

  72. Hushmendy, S. et al. Select phytochemicals suppress human T-lymphocytes and mouse splenocytes suggesting their use in autoimmunity and transplantation. Nutr. Res. 29, 568–578 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Shen, K. et al. Epigallocatechin 3-gallate ameliorates bile duct ligation induced liver injury in mice by modulation of mitochondrial oxidative stress and inflammation. PLoS ONE 10, e0126278 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  74. ZHEN, M. et al. Green tea polyphenol epigallocatechin-3-gallate inhibits oxidative damage and preventive effects on carbon tetrachloride–induced hepatic fibrosis. J. Nutr. Biochem. 18, 795–805 (2007).

    Article  CAS  PubMed  Google Scholar 

  75. Yasuda, Y. et al. (−)-Epigallocatechin gallate prevents carbon tetrachloride-induced rat hepatic fibrosis by inhibiting the expression of the PDGFRβ and IGF-1R. Chem. Biol. Interact. 182, 159–164 (2009).

    Article  CAS  PubMed  Google Scholar 

  76. Cao, W. et al. iTRAQ-based proteomic analysis of combination therapy with taurine, epigallocatechin gallate, and genistein on carbon tetrachloride-induced liver fibrosis in rats. Toxicol. Lett. 232, 233–245 (2015).

    Article  CAS  PubMed  Google Scholar 

  77. Kitamura, M. et al. Epigallocatechin gallate suppresses peritoneal fibrosis in mice. Chem. Biol. Interact. 195, 95–104 (2012).

    Article  CAS  PubMed  Google Scholar 

  78. Sakla, M. S. & Lorson, C. L. Induction of full-length survival motor neuron by polyphenol botanical compounds. Hum. Genet. 122, 635–643 (2008).

    Article  CAS  PubMed  Google Scholar 

  79. Shimizu, M. et al. (−)-Epigallocatechin gallate inhibits growth and activation of the VEGF/VEGFR axis in human colorectal cancer cells. Chem. Biol. Interact. 185, 247–252 (2010).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

This study was supported, in part, by NIH grants 1P01HL132825, HG007690, HL108630 and HL119145; American Heart Association grants 151708 and D700382 and ERC grant 810115-DYNASET. We would like to thank P. Ruppert, G. Menichetti and I. Kovacs for support in this study, F. Cheng for assembling the human interactome and A. Grishchenko for help with data visualization.

Author information

Authors and Affiliations

  1. Network Science Institute and Department of Physics, Northeastern University, Boston, MA, USA

    Italo F. do Valle & Albert-László Barabási

  2. Division of Hematology, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA

    Harvey G. Roweth, Michael W. Malloy & Elisabeth Battinelli

  3. Harvard Medical School, Boston, MA, USA

    Harvey G. Roweth, Michael W. Malloy, Elisabeth Battinelli & Joseph Loscalzo

  4. Nestlé Institute of Health Sciences, Lausanne, Switzerland

    Sofia Moco & Denis Barron

  5. Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA

    Joseph Loscalzo

  6. Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Albert-László Barabási

  7. Department of Network and Data Science, Central European University, Budapest, Hungary

    Albert-László Barabási

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  1. Italo F. do Valle
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Contributions

I.F.d.V. and A.-L.B. designed the study. I.F.d.V. performed all computational analyses. H.G.R., M.W.M., E.B. and J.L. designed and performed experimental validation. J.L. guided I.F.d.V. in validation case studies. S.M. and D.B. guided I.F.d.V. in data interpretation and curation of disease associations obtained from the literature. I.F.d.V. and A.-L.B. wrote the paper with input from all authors. All authors read and approved the manuscript.

Corresponding author

Correspondence to Albert-László Barabási.

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Competing interests

J.L. and A.-L.B. are co-scientific founders of Scipher Medicine, Inc., which applies network medicine strategies to biomarker development and personalized drug selection; A.-L.B. is the founder of Datapolis Inc., which explores mobility patterns in urban planning, and Foodome, Inc., which applies data science to health. I.F.d.V. is a scientific consultant for Foodome, Inc.

Additional information

Peer review information Nature Food thanks Dariush Mozaffarian and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–13 and Notes.

Reporting Summary

Supplementary Table 1

Summary of polyphenols evaluated in this study.

Supplementary Table 2

Predicted gastrointestinal (GI) absorption and bioavailability.

Supplementary Table 3

Polyphenols proximal to vascular diseases.

Supplementary Data 1

Human interactome assembled in this study.

Supplementary Data 2

Network proximity calculations between 65 polyphenols and 299 diseases.

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do Valle, I.F., Roweth, H.G., Malloy, M.W. et al. Network medicine framework shows that proximity of polyphenol targets and disease proteins predicts therapeutic effects of polyphenols. Nat Food 2, 143–155 (2021). https://doi.org/10.1038/s43016-021-00243-7

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