Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Potential value of nutrigenomics in Crohn's disease

Nature Reviews Gastroenterology & Hepatology volume 9, pages 260–270 (2012)Cite this article

Subjects

Abstract

Crohn's disease is a chronic relapsing condition that has no certain cure. Both genetic susceptibility and nutrition have key roles, but their level of involvement varies between patients. Interacting gene pathways influence the probability of disease development, but these are affected by stress and various environmental factors, including diet. In addition, the role of the gut microbiome must not be underestimated, as it is substantially altered in patients with Crohn's disease. Although an elemental diet might lead to disease remission, reintroducing real foods and sustainable diets in patients with Crohn's disease is currently difficult, and would benefit from the sensitivity and rapid feedback provided by the field of nutrigenomics. Nutrigenomics utilizes high-throughput genomics technologies to reveal changes in gene and protein expression that are modulated by the patient's nutrition. The most widely used technique thus far is transcriptomics, which permits measurement of changes in the expression of thousands of genes simultaneously in one sample. Given the volume of numbers generated in such studies, data-basing and bioinformatics are essential to ensure the correct application of nutrigenomics at the population level. These methods have been successfully applied to animal models of Crohn's disease, and the time is right to move them to human studies.

Key Points

  • Crohn's disease is an inflammatory condition that develops in genetically predisposed individuals who are exposed to stressors, including certain diets; however, the dietary response cannot currently be predicted with certainty

  • Nutrigenetic, metabonomic and proteomic technologies might enable the identification of patients who will or will not respond to a given diet or medicinal food, thereby increasing the likelihood of efficacy

  • Metabonomics enables the identification of early and sensitive biomarkers that might facilitate validation of novel diets or medicinal foods that could delay disease development or progression

  • Transcriptomics (gene-expression profiling), in combination with advanced bioinformatic methods, might facilitate nonhypothesis-based animal or human trials on the potential effects of certain diets or medicinal foods

  • These methods have proved informative in interpreting the effects of long-chain n-3 fatty acids on Crohn's disease in animal models

  • It is time to apply these methods to develop an improved rationale for dietary interventions in patients with Crohn's disease

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Distinguishing those patients whose symptoms will improve from those least likely to improve, or at least not benefit, from a given dietary regime.
Figure 2: Examples of the use of easily accessible tissues as surrogate markers in nutrigenomics studies.
Figure 3: An illustrative pathway of some of the key genes and nutrients involved in one-carbon methylation.
Figure 4: The intestine, its microbiome, their interactions among themselves, the hepatic system (and more standard biochemistry) and food items that are either directly allogenic or otherwise cause intolerance and tissue reactions.

Similar content being viewed by others

References

  1. Triggs, C. M. et al. Dietary factors in chronic inflammation: food tolerances and intolerances of a New Zealand Caucasian Crohn's disease population. Mutat. Res. 690, 123–138 (2010).

    Article  CAS  PubMed  Google Scholar 

  2. Ferguson, L. R. et al. Genetic factors in chronic inflammation: single nucleotide polymorphisms in the STAT-JAK pathway, susceptibility to DNA damage and Crohn's disease in a New Zealand population. Mutat. Res. 690, 108–115 (2010).

    Article  CAS  PubMed  Google Scholar 

  3. Franke, A. et al. Genome-wide meta-analysis increases to 71 the number of confirmed Crohn's disease susceptibility loci. Nat. Genet. 42, 1118–1125 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Rutgeerts, P. J. From aphthous ulcer to full-blown Crohn's disease. Dig. Dis. 29, 211–214 (2011).

    Article  PubMed  Google Scholar 

  5. Shamir, R. Nutrition and growth in inflammatory bowel disease. J. Pediatr. Gastroenterol. Nutr. 51 (Suppl. 3), S131–S132 (2010).

    Article  PubMed  Google Scholar 

  6. Petermann, I. et al. Mushroom intolerance: a novel diet-gene interaction in Crohn's disease. Br. J. Nutr. 102, 506–508 (2009).

    Article  CAS  PubMed  Google Scholar 

  7. Wittwer, J. et al. Nutrigenomics in human intervention studies: current status, lessons learned and future perspectives. Mol. Nutr. Food Res. 55, 341–358 (2011).

    Article  CAS  PubMed  Google Scholar 

  8. Afman, L. A. & Müller, M. Nutrigenomics: from molecular nutrition to prevention of disease. J. Am. Diet. Assoc. 106, 569–576 (2006).

    Article  CAS  PubMed  Google Scholar 

  9. Afman, L. A. & Müller, M. Human nutrigenomics of gene regulation by dietary fatty acids. Prog. Lipid Res. 51, 63–70 (2012).

    Article  CAS  PubMed  Google Scholar 

  10. Bakker, G. C. et al. An antiinflammatory dietary mix modulates inflammation and oxidative and metabolic stress in overweight men: a nutrigenomics approach. Am. J. Clin. Nutr. 91, 1044–1059 (2010).

    Article  CAS  PubMed  Google Scholar 

  11. Bouwens, M. et al. Fish-oil supplementation induces antiinflammatory gene expression profiles in human blood mononuclear cells. Am. J. Clin. Nutr. 90, 415–424 (2009).

    Article  CAS  PubMed  Google Scholar 

  12. Roy, N. C., Altermann, E., Park, Z. A. & McNabb, W. C. A comparison of analog and next-generation transcriptomic tools for mammalian studies. Brief. Funct. Genomics 10, 135–150 (2011).

    Article  CAS  PubMed  Google Scholar 

  13. Mesko, B. et al. Peripheral blood gene expression patterns discriminate among chronic inflammatory diseases and healthy controls and identify novel targets. BMC Med. Genomics 3, 15 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Burakoff, R. et al. Blood-based biomarkers can differentiate ulcerative colitis from Crohn's disease and noninflammatory diarrhea. Inflamm. Bowel Dis. 17, 1719–1725 (2011).

    Article  PubMed  Google Scholar 

  15. Burakoff, R. et al. Differential regulation of peripheral leukocyte genes in patients with active Crohn's disease and Crohn's disease in remission. J. Clin. Gastroenterol. 44, 120–126 (2010).

    Article  PubMed  Google Scholar 

  16. Lees, C. W., Barrett, J. C., Parkes, M. & Satsangi, J. New IBD genetics: common pathways with other diseases. Gut 60, 1739–1753 (2011).

    Article  CAS  PubMed  Google Scholar 

  17. Hamm, C. M. et al. NOD2 status and human ileal gene expression. Inflamm. Bowel Dis. 16, 1649–1657 (2010).

    Article  PubMed  Google Scholar 

  18. Lang, M. et al. Gene expression profiles of mucosal fibroblasts from strictured and nonstrictured areas of patients with Crohn's disease. Inflamm. Bowel Dis. 15, 212–223 (2009).

    Article  PubMed  Google Scholar 

  19. Bogaert, S. et al. Differential mucosal expression of Th17-related genes between the inflamed colon and ileum of patients with inflammatory bowel disease. BMC Immunol. 11, 61 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Ferguson, L. R., Tatham, A. L., Lin, Z. & Denny, W. A. Epigenetic regulation of gene expression as an anticancer drug target. Curr. Cancer Drug Targets 11, 199–212 (2011).

    Article  CAS  PubMed  Google Scholar 

  21. Konycheva, G. et al. Dietary methyl donor deficiency during pregnancy in rats shapes learning and anxiety in offspring. Nutr. Res. 31, 790–804 (2011).

    Article  CAS  PubMed  Google Scholar 

  22. McKay, J. A. & Mathers, J. C. Diet induced epigenetic changes and their implications for health. Acta Physiol. 202, 103–118 (2011).

    Article  CAS  Google Scholar 

  23. Niculescu, M. D. & Lupu, D. S. Nutritional influence on epigenetics and effects on longevity. Curr. Opin. Clin. Nutr. Metab. Care 14, 35–40 (2011).

    Article  CAS  PubMed  Google Scholar 

  24. Zeisel, S. H. Gene response elements, genetic polymorphisms and epigenetics influence the human dietary requirement for choline. IUBMB Life 59, 380–387 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Ulrich, C. M., Reed, M. C. & Nijhout, H. F. Modeling folate, one-carbon metabolism, and DNA methylation. Nutr. Rev. 66 (Suppl. 1), S27–S30 (2008).

    Article  PubMed  Google Scholar 

  26. Vujkovic, M. et al. The maternal homocysteine pathway is influenced by riboflavin intake and MTHFR polymorphisms without affecting the risk of orofacial clefts in the offspring. Eur. J. Clin. Nutr. 64, 266–273 (2010).

    Article  CAS  PubMed  Google Scholar 

  27. Carr, D. F., Whiteley, G., Alfirevic, A. & Pirmohamed, M. Investigation of inter-individual variability of the one-carbon folate pathway: a bioinformatic and genetic review. Pharmacogenomics J. 9, 291–305 (2009).

    Article  CAS  PubMed  Google Scholar 

  28. Ma, E. et al. Dietary intake of folate, vitamin B6, and vitamin B12, genetic polymorphism of related enzymes, and risk of breast cancer: a case-control study in Brazilian women. BMC Cancer 9, 122 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Fernández-Miranda, C. et al. Hyperhomocysteinemia and methylenetetrahydrofolate reductase 677C→T and 1298A→C mutations in patients with inflammatory bowel disease. Rev. Esp. Enferm. Dig. 97, 497–504 (2005).

    Article  PubMed  Google Scholar 

  30. Nakano, E., Taylor, C. J., Chada, L., McGaw, J. & Powers, H. J. Hyperhomocystinemia in children with inflammatory bowel disease. J. Pediatr. Gastroenterol. Nutr. 37, 586–590 (2003).

    Article  CAS  PubMed  Google Scholar 

  31. Mahmud, N. et al. Increased prevalence of methylenetetrahydrofolate reductase C677T variant in patients with inflammatory bowel disease, and its clinical implications. Gut 45, 389–394 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Oussalah, A., Guéant, J. L. & Peyrin-Biroulet, L. Meta-analysis: hyperhomocysteinaemia in inflammatory bowel diseases. Aliment. Pharmacol. Ther. 34, 1173–1184 (2011).

    Article  CAS  PubMed  Google Scholar 

  33. Stocco, G. et al. Prevalence of methylenetetrahydrofolate reductase polymorphisms in young patients with inflammatory bowel disease. Dig. Dis. Sci. 51, 474–479 (2006).

    Article  CAS  PubMed  Google Scholar 

  34. Zintzaras, E. Genetic variants of homocysteine/folate metabolism pathway and risk of inflammatory bowel disease: a synopsis and meta-analysis of genetic association studies. Biomarkers 15, 69–79 (2010).

    Article  CAS  PubMed  Google Scholar 

  35. Collin, S. M. et al. Association of folate-pathway gene polymorphisms with the risk of prostate cancer: a population-based nested case-control study, systematic review, and meta-analysis. Cancer Epidemiol. Biomarkers Prev. 18, 2528–2539 (2009).

    Article  CAS  PubMed  Google Scholar 

  36. Figueiredo, J. C. et al. Genes involved with folate uptake and distribution and their association with colorectal cancer risk. Cancer Causes Control 21, 597–608 (2010).

    Article  PubMed  Google Scholar 

  37. Kasperzyk, J. L. et al. Nutrients and genetic variation involved in one-carbon metabolism and Hodgkin lymphoma risk: a population-based case-control study. Am. J. Epidemiol. 174, 816–827 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Kominsky, D. J. et al. An endogenously anti-inflammatory role for methylation in mucosal inflammation identified through metabolite profiling. J. Immunol. 186, 6505–6514 (2011).

    Article  CAS  PubMed  Google Scholar 

  39. Mowat, C. et al. Guidelines for the management of inflammatory bowel disease in adults. Gut 60, 571–607 (2011).

    Article  PubMed  Google Scholar 

  40. Nimmo, E. R. et al. Genome-wide methylation profiling in Crohn's disease identifies altered epigenetic regulation of key host defense mechanisms including the Th17 pathway. Inflamm. Bowel Dis. http://dx.doi.org/10.1002/ibd.21912.

  41. Ferguson, L. R. RNA silencing: mechanism, biology and responses to environmental stress. Mutat. Res. 714, 93–94 (2011).

    Article  CAS  PubMed  Google Scholar 

  42. Halusková, J. Epigenetic studies in human diseases. Folia Biol. 56, 83–96 (2010).

    Google Scholar 

  43. Richardson, K., Lai, C.-Q., Parnell, L. D., Lee, Y.-C. & Ordovas, J. M. A genome-wide survey for SNPs altering microRNA seed sites identifies functional candidates in GWAS. BMC Genomics 12, 504 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Jin, G. et al. Human polymorphisms at long non-coding RNAs (lncRNAs) and association with prostate cancer risk. Carcinogenesis 32, 1655–1659 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Dimitrov, D. V. The human gutome: nutrigenomics of the host-microbiome interactions. OMICS 15, 419–430 (2011).

    Article  CAS  PubMed  Google Scholar 

  46. Jones, B. V. The human gut mobile metagenome: a metazoan perspective. Gut Microbes 1, 415–431 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Arumugam, M. et al. Enterotypes of the human gut microbiome. Nature 473, 174–180 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Gentschew, L. & Ferguson, L. R. Role of nutrition and microbiota in susceptibility to inflammatory bowel diseases. Mol. Nutr. Food Res. (in press).

  49. Kang, S. et al. Dysbiosis of fecal microbiota in Crohn's disease patients as revealed by a custom phylogenetic microarray. Inflamm. Bowel Dis. 16, 2034–2042 (2010).

    Article  PubMed  Google Scholar 

  50. Nagalingam, N. A., Kao, J. Y. & Young, V. B. Microbial ecology of the murine gut associated with the development of dextran sodium sulfate-induced colitis. Inflamm. Bowel Dis. 17, 917–926 (2011).

    Article  PubMed  Google Scholar 

  51. Han, D. Y., Fraser, A. G., Dryland, P. & Ferguson, L. R. Environmental factors in the development of chronic inflammation: a case-control study on risk factors for Crohn's disease within New Zealand. Mutat. Res. 690, 116–122 (2010).

    Article  CAS  PubMed  Google Scholar 

  52. Barnett, M. P. et al. Changes in colon gene expression associated with increased colon inflammation in interleukin-10 gene-deficient mice inoculated with Enterococcus species. BMC Immunol. 11, 39 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Schaible, T. D., Harris, R. A., Dowd, S. E., Smith, C. W. & Kellermayer, R. Maternal methyl-donor supplementation induces prolonged murine offspring colitis susceptibility in association with mucosal epigenetic and microbiomic changes. Hum. Mol. Genet. 20, 1687–1696 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. van Ommen, B. & Stierum, R. Nutrigenomics: exploiting systems biology in the nutrition and health arena. Curr. Opin. Biotechnol. 13, 517–521 (2002).

    Article  CAS  PubMed  Google Scholar 

  55. de Graaf, A. A. et al. Nutritional systems biology modeling: from molecular mechanisms to physiology. PLoS Comput. Biol. 5, e1000554 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Funke, B. Laser microdissection of intestinal epithelial cells and downstream analysis. Methods Mol. Biol. 755, 189–196 (2011).

    Article  CAS  PubMed  Google Scholar 

  57. DeBusk, R. The role of nutritional genomics in developing an optimal diet for humans. Nutr. Clin. Pract. 25, 627–633 (2010).

    Article  PubMed  Google Scholar 

  58. Hurd, P. J. & Nelson, C. J. Advantages of next-generation sequencing versus the microarray in epigenetic research. Brief. Funct. Genomic. Proteomic. 8, 174–183 (2009).

    Article  CAS  PubMed  Google Scholar 

  59. Papanicolaou, A., Stierli, R., Ffrench-Constant, R. H. & Heckel, D. G. Next generation transcriptomes for next generation genomes using est2assembly. BMC Bioinformatics 10, 447 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Kovatcheva-Datchary, P., Zoetendal, E. G., Venema, K., de Vos, W. M. & Smidt, H. Tools for the tract: understanding the functionality of the gastrointestinal tract. Therap. Adv. Gastroenterol. 2, 9–22 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Liu, G. E. Applications and case studies of the next-generation sequencing technologies in food, nutrition and agriculture. Recent Pat. Food Nutr. Agric. 1, 75–79 (2009).

    Article  CAS  PubMed  Google Scholar 

  62. Summerer, D. et al. Microarray-based multicycle-enrichment of genomic subsets for targeted next-generation sequencing. Genome Res. 19, 1616–1621 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Knoch, B. et al. Molecular characterization of the onset and progression of colitis in inoculated interleukin-10 gene-deficient mice: a role for PPARα. PPAR Res. 2010, 621069 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Dommels, Y. E. et al. Characterization of intestinal inflammation and identification of related gene expression changes in mdr1a−/− mice. Genes Nutr. 2, 209–223 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Knoch, B. et al. Genome-wide analysis of dietary eicosapentaenoic acid- and oleic acid-induced modulation of colon inflammation in interleukin-10 gene-deficient mice. J. Nutrigenet. Nutrigenomics 2, 9–28 (2009).

    Article  CAS  PubMed  Google Scholar 

  66. Rudkowska, I. et al. Validation of the use of peripheral blood mononuclear cells as surrogate model for skeletal muscle tissue in nutrigenomic studies. OMICS 15, 1–7 (2011).

    Article  CAS  PubMed  Google Scholar 

  67. Anderson, N. L. & Anderson, N. G. Proteome and proteomics: new technologies, new concepts, and new words. Electrophoresis 19, 1853–1861 (1998).

    Article  CAS  PubMed  Google Scholar 

  68. Parnell, L. D. & Schueller, C. M. Bioinformatics of the urinary proteome. Methods Mol. Biol. 641, 101–122 (2010).

    Article  CAS  PubMed  Google Scholar 

  69. Kussmann, M., Panchaud, A. & Affolter, M. Proteomics in nutrition: status quo and outlook for biomarkers and bioactives. J. Proteome Res. 9, 4876–4887 (2010).

    Article  CAS  PubMed  Google Scholar 

  70. Bictash, M. et al. Opening up the “Black Box”: metabolic phenotyping and metabolome-wide association studies in epidemiology. J. Clin. Epidemiol. 63, 970–979 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Chadeau-Hyam, M. et al. Metabolic profiling and the metabolome-wide association study: significance level for biomarker identification. J. Proteome Res. 9, 4620–4627 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Forsythe, I. J. & Wishart, D. S. Exploring human metabolites using the human metabolome database. Curr. Protoc. Bioinformatic 25, 14.8.1–148.45 (2009).

    Google Scholar 

  73. Nicholson, J. K. & Lindon, J. C. Systems biology: metabonomics. Nature 455, 1054–1056 (2008).

    Article  CAS  PubMed  Google Scholar 

  74. Bjerrum, J. T. et al. Metabonomics in ulcerative colitis: diagnostics, biomarker identification, and insight into the pathophysiology. J. Proteome Res. 9, 954–962 (2010).

    Article  CAS  PubMed  Google Scholar 

  75. Kussmann, M. & Blum, S. OMICS-derived targets for inflammatory gut disorders: opportunities for the development of nutrition related biomarkers. Endocr. Metab. Immune Disord. Drug Targets 7, 271–287 (2007).

    Article  CAS  PubMed  Google Scholar 

  76. Martin, F.-P. et al. Dietary modulation of gut functional ecology studied by fecal metabonomics. J. Proteome Res. 9, 5284–5295 (2010).

    Article  CAS  PubMed  Google Scholar 

  77. McNiven, E. M., German, J. B. & Slupsky, C. M. Analytical metabolomics: nutritional opportunities for personalized health. J. Nutr. Biochem. 22, 995–1002 (2011).

    Article  CAS  PubMed  Google Scholar 

  78. Fay, L. B. & German, J. B. Personalizing foods: is genotype necessary? Curr. Opin. Biotechnol. 19, 121–128 (2008).

    Article  CAS  PubMed  Google Scholar 

  79. Martin, F.-P. et al. Metabolic effects of dark chocolate consumption on energy, gut microbiota, and stress-related metabolism in free-living subjects. J. Proteome Res. 8, 5568–5579 (2009).

    Article  CAS  PubMed  Google Scholar 

  80. Davis, C. D. & Milner, J. A. Nutrigenomics, vitamin D and cancer prevention. J. Nutrigenet. Nutrigenomics 4, 1–11 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. O'Sullivan, A. et al. Biochemical and metabolomic phenotyping in the identification of a vitamin D responsive metabotype for markers of the metabolic syndrome. Mol. Nutr. Food Res. 55, 679–690 (2011).

    Article  CAS  PubMed  Google Scholar 

  82. Cross, H. S., Nittke, T. & Kallay, E. Colonic vitamin D metabolism: Implications for the pathogenesis of inflammatory bowel disease and colorectal cancer. Mol. Cell. Endocrinol. 347, 70–79 (2011).

    Article  CAS  PubMed  Google Scholar 

  83. Chan, E. C. et al. Metabolic profiling of human colorectal cancer using high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy and gas chromatography mass spectrometry (GC/MS). J. Proteome Res. 8, 352–361 (2009).

    Article  CAS  PubMed  Google Scholar 

  84. Bertini, I. et al. The metabonomic signature of celiac disease. J. Proteome Res. 8, 170–177 (2009).

    Article  CAS  PubMed  Google Scholar 

  85. Lauridsen, M. B. et al. 1H NMR spectroscopy-based interventional metabolic phenotyping: a cohort study of rheumatoid arthritis patients. J. Proteome Res. 9, 4545–4553 (2010).

    Article  CAS  PubMed  Google Scholar 

  86. Lin, H.-M. et al. Metabolomic analysis identifies inflammatory and noninflammatory metabolic effects of genetic modification in a mouse model of Crohn's disease. J. Proteome Res. 9, 1965–1975 (2010).

    Article  CAS  PubMed  Google Scholar 

  87. Lin, H.-M., Helsby, N. A., Rowan, D. D. & Ferguson, L. R. Using metabolomic analysis to understand inflammatory bowel diseases. Inflamm. Bowel Dis. 17, 1021–1029 (2011).

    Article  PubMed  Google Scholar 

  88. O'Sullivan, A., Gibney, M. J. & Brennan, L. Dietary intake patterns are reflected in metabolomic profiles: potential role in dietary assessment studies. Am. J. Clin. Nutr. 93, 314–321 (2011).

    Article  CAS  PubMed  Google Scholar 

  89. Hearty, A. P. & Gibney, M. J. Comparison of cluster and principal component analysis techniques to derive dietary patterns in Irish adults. Br. J. Nutr. 101, 598–608 (2009).

    Article  CAS  PubMed  Google Scholar 

  90. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Dragsted, L. O. Biomarkers of meat intake and the application of nutrigenomics. Meat Sci. 84, 301–307 (2010).

    Article  CAS  PubMed  Google Scholar 

  92. Ferguson, L. R. Meat and cancer. Meat Sci. 84, 308–313 (2010).

    Article  CAS  PubMed  Google Scholar 

  93. Omenn, G. S. Bioinformatics and systems biology of cancers. Prog. Mol. Biol. Transl. Sci. 95, 159–191 (2010).

    Article  CAS  PubMed  Google Scholar 

  94. Yan, Q. Bioinformatics for transporter pharmacogenomics and systems biology: data integration and modeling with UML. Methods Mol. Biol. 637, 23–45 (2010).

    Article  CAS  PubMed  Google Scholar 

  95. Banasik, K. et al. Bioinformatics-driven identification and examination of candidate genes for non-alcoholic fatty liver disease. PLoS ONE 6, e16542 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Coen, M. et al. Mechanistic aspects and novel biomarkers of responder and non-responder phenotypes in galactosamine-induced hepatitis. J. Proteome Res. 8, 5175–5187 (2009).

    Article  CAS  PubMed  Google Scholar 

  97. Holmes, E., Wilson, I. D. & Nicholson, J. K. Metabolic phenotyping in health and disease. Cell 134, 714–717 (2008).

    Article  CAS  PubMed  Google Scholar 

  98. Huebner, C. et al. Genetic analysis of MDR1 and inflammatory bowel disease reveals protective effect of heterozygous variants for ulcerative colitis. Inflamm. Bowel Dis. 15, 1784–1793 (2009).

    Article  PubMed  Google Scholar 

  99. Wang, A. H. et al. The effect of IL-10 genetic variation and interleukin 10 serum levels on Crohn's disease susceptibility in a New Zealand population. Hum. Immunol. 72, 431–435 (2011).

    Article  CAS  PubMed  Google Scholar 

  100. Bouwens, M., Grootte Bromhaar, M., Jansen, J., Müller, M. & Afman, L. A. Postprandial dietary lipid-specific effects on human peripheral blood mononuclear cell gene expression profiles. Am. J. Clin. Nutr. 91, 208–217 (2010).

    Article  CAS  PubMed  Google Scholar 

  101. Bouwens, M., Afman, L. A. & Müller, M. Activation of peroxisome proliferator-activated receptor alpha in human peripheral blood mononuclear cells reveals an individual gene expression profile response. BMC Genomics 9, 262 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Bouwens, M. et al. Fish-oil supplementation induces antiinflammatory gene expression profiles in human blood mononuclear cells. Am. J. Clin. Nutr. 90, 415–424 (2009).

    Article  CAS  PubMed  Google Scholar 

  103. Knoch, B. et al. Genome-wide analysis of dietary eicosapentaenoic acid- and oleic acid-induced modulation of colon inflammation in interleukin-10 gene-deficient mice. J. Nutrigenet. Nutrigenomics 2, 9–28 (2009).

    Article  CAS  PubMed  Google Scholar 

  104. Knoch, B., Nones, K., Barnett, M. P., McNabb, W. C. & Roy, N. C. Diversity of caecal bacteria is altered in interleukin-10 gene-deficient mice before and after colitis onset and when fed polyunsaturated fatty acids. Microbiology 156, 3306–3316 (2010).

    Article  CAS  PubMed  Google Scholar 

  105. Roy, N., Barnett, M., Dommels, Y. & McNabb, W. Nutrigenomics applied to an animal model of inflammatory bowel diseases: transcriptomic analysis of the effects of eicosapentaenoic acid- and arachidonic acid-enriched diets. Mutat. Res. 622, 103–116 (2007).

    Article  CAS  PubMed  Google Scholar 

  106. Knoch, B. et al. Dietary oleic acid as a control fatty acid for polyunsaturated fatty acid intervention studies: a transcriptomics and proteomics investigation using interleukin-10 gene-deficient mice. Biotechnol. J. 5, 1226–1240 (2010).

    Article  CAS  PubMed  Google Scholar 

  107. Cooney, J. M. et al. Proteomic analysis of colon tissue from interleukin-10 gene-deficient mice fed polyunsaturated fatty acids with comparison to transcriptomic analysis. J. Proteome Res. 11, 1065–1077 (2012).

    Article  CAS  PubMed  Google Scholar 

  108. Ferguson, L. R., Smith, B. G. & James, B. J. Combining nutrition, food science and engineering in developing solutions to Inflammatory bowel diseases—omega-3 polyunsaturated fatty acids as an example. Food Funct. 1, 60–72 (2010).

    Article  CAS  PubMed  Google Scholar 

  109. Belluzzi, A. et al. Effect of an enteric-coated fish-oil preparation on relapses in Crohn's disease. N. Engl. J. Med. 334, 1557–1560 (1996).

    Article  CAS  PubMed  Google Scholar 

  110. Feagan, B. G. et al. Omega-3 free fatty acids for the maintenance of remission in Crohn disease: the EPIC Randomized Controlled Trials. JAMA 299, 1690–1697 (2008).

    Article  CAS  PubMed  Google Scholar 

  111. Turner, D., Zlotkin, S. H., Shah, P. S. & Griffiths, A. M. Omega 3 fatty acids (fish oil) for maintenance of remission in Crohn's disease. Cochrane Database of Systematic Reviews, Issue 1. Art. No.: CD006320. http://dx.doi.org/10.1002/14651858.CD006320.pub3.

  112. Williams, C. M. et al. The challenges for molecular nutrition research 1: linking genotype to healthy nutrition. Genes Nutr. 3, 41–49 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  113. van Ommen, B. et al. The challenges for molecular nutrition research 2: quantification of the nutritional phenotype. Genes Nutr. 3, 51–59 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Daniel, H., Drevon, C. A., Klein, U. I., Kleemann, R. & van Ommen, B. The challenges for molecular nutrition research 3: comparative nutrigenomics research as a basis for entering the systems level. Genes Nutr. 3, 101–106 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  115. van Ommen, B., Cavallieri, D., Roche, H. M., Klein, U. I. & Daniel, H. The challenges for molecular nutrition research 4: the “nutritional systems biology level”. Genes Nutr. 3, 107–113 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. van Ommen, B. et al. Challenges of molecular nutrition research 6: the nutritional phenotype database to store, share and evaluate nutritional systems biology studies. Genes Nutr. 5, 189–203 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

I wish to thank Virginia Parslow, for editorial help, and Valerie Gray, for the illustrations. Nutrigenomics New Zealand (www.nutrigenomics.org.nz) is a collaboration among The University of Auckland, Plant and Food Research Ltd and AgResearch Ltd. It is funded by the New Zealand Ministry of Science and Innovation.

Author information

Authors and Affiliations

  1. Discipline of Nutrition, Faculty of Medical and Health Sciences, The University of Auckland, Private Bag 92019, Auckland, New Zealand

    Lynnette R. Ferguson

Authors
  1. Lynnette R. Ferguson
    View author publications

    You can also search for this author in PubMed Google Scholar

Ethics declarations

Competing interests

The author declares no competing financial interests.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ferguson, L. Potential value of nutrigenomics in Crohn's disease. Nat Rev Gastroenterol Hepatol 9, 260–270 (2012). https://doi.org/10.1038/nrgastro.2012.41

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrgastro.2012.41

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing