Skip to main content
    • Aa
    • Aa

Inter-individual differences in response to dietary intervention: integrating omics platforms towards personalised dietary recommendations

  • Johanna W. Lampe (a1), Sandi L. Navarro (a1), Meredith A. J. Hullar (a1) and Ali Shojaie (a2)

Technologic advances now make it possible to collect large amounts of genetic, epigenetic, metabolomic and gut microbiome data. These data have the potential to transform approaches towards nutrition counselling by allowing us to recognise and embrace the metabolic, physiologic and genetic differences among individuals. The ultimate goal is to be able to integrate these multi-dimensional data so as to characterise the health status and disease risk of an individual and to provide personalised recommendations to maximise health. To this end, accurate and predictive systems-based measures of health are needed that incorporate molecular signatures of genes, transcripts, proteins, metabolites and microbes. Although we are making progress within each of these omics arenas, we have yet to integrate effectively multiple sources of biologic data so as to provide comprehensive phenotypic profiles. Observational studies have provided some insights into associative interactions between genetic or phenotypic variation and diet and their impact on health; however, very few human experimental studies have addressed these relationships. Dietary interventions that test prescribed diets in well-characterised study populations and that monitor system-wide responses (ideally using several omics platforms) are needed to make correlation–causation connections and to characterise phenotypes under controlled conditions. Given the growth in our knowledge, there is the potential to develop personalised dietary recommendations. However, developing these recommendations assumes that an improved understanding of the phenotypic complexities of individuals and their responses to the complexities of their diets will lead to a sustainable, effective approach to promote health and prevent disease – therein lies our challenge.

  • View HTML
    • Send article to Kindle

      To send this article to your Kindle, first ensure is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle.

      Note you can select to send to either the or variations. ‘’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

      Find out more about the Kindle Personal Document Service.

      Inter-individual differences in response to dietary intervention: integrating omics platforms towards personalised dietary recommendations
      Available formats
      Send article to Dropbox

      To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your Dropbox account. Find out more about sending content to Dropbox.

      Inter-individual differences in response to dietary intervention: integrating omics platforms towards personalised dietary recommendations
      Available formats
      Send article to Google Drive

      To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your Google Drive account. Find out more about sending content to Google Drive.

      Inter-individual differences in response to dietary intervention: integrating omics platforms towards personalised dietary recommendations
      Available formats
Corresponding author
Corresponding author: Dr J. W. Lampe, fax +1 206 667 7850, email
Linked references
Hide All

This list contains references from the content that can be linked to their source. For a full set of references and notes please see the PDF or HTML where available.

1. DP Jones , Y Park & TR Ziegler (2012) Nutritional metabolomics: progress in addressing complexity in diet and health. Annu Rev Nutr 32, 183202.

3. JW Lampe & JD Potter (2006) Genetic variation, diet and disease susceptibility. In Gene-Environment Interactions: Fundamentals of Ecogenetics, pp. 321350 [ LG Costa and DL Eaton , editors]. Hoboken, NJ: John Wiley & Sons, Inc.

4. AD Bryan & KE Hutchison (2012) The role of genomics in health behavior change: challenges and opportunities. Public Health Genomics 15, 139145.

5. C Gieger , L Geistlinger , E Altmaier (2008) Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum. PLoS Genet 4, e1000282.

6. K Suhre , H Wallaschofski , J Raffler (2011) A genome-wide association study of metabolic traits in human urine. Nat Genet 43, 565569.

7. F Guarner & JR Malagelada (2003) Gut flora in health and disease. Lancet 361, 512519.

8. J Qin , R Li , J Raes (2010) A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 5965.

9. SR Gill , M Pop , RT Deboy (2006) Metagenomic analysis of the human distal gut microbiome. Science 312, 13551359.

10. M Arumugam , J Raes , E Pelletier (2011) Enterotypes of the human gut microbiome. Nature 473, 174180.

11. DR Donohoe & SJ Bultman (2012) Metaboloepigenetics: interrelationships between energy metabolism and epigenetic control of gene expression. J Cell Physiol 227, 31693177.

12. DR Donohoe , N Garge , XX Zhang (2011) The microbiome and butyrate regulate energy metabolism and autophagy in the mammalian colon. Cell Metab 13, 517526.

13. JM Ordovas & V Mooser (2006) Metagenomics: the role of the microbiome in cardiovascular diseases. Curr Opin Lipidol 17, 157161.

14. JJ Qin , YR Li , ZM Cai (2012) A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490, 5560.

15. JR Marchesi , BE Dutilh , N Hall (2011) Towards the human colorectal cancer microbiome. PLoS One 6, e20447.

16. CS Plottel & MJ Blaser (2011) Microbiome and malignancy. Cell Host Microbe 10, 324335.

17. BP Willing , J Dicksved , J Halfvarson (2010) A pyrosequencing study in twins shows that gastrointestinal microbial profiles vary with inflammatory bowel disease phenotypes. Gastroenterology 139, 18441854.

19. SL Navarro , J Chang , S Peterson (2009) Modulation of human serum glutathione S-transferase-A1/2 concentration by cruciferous vegetables in a controlled feeding study is influenced by GSTM1 and GSTT1 genotypes. Cancer Epidemiol Biomarkers Prev 18, 29742978.

20. AJ Cross , JM Major , R Sinha (2011) Urinary biomarkers of meat consumption. Cancer Epidemiol Biomarkers Prev 20, 11071111.

21. ML Neuhouser , Y Schwarz , C Wang (2012) A low-glycemic load diet reduces serum C-reactive protein and modestly increases adiponectin in overweight and obese adults. J Nutr 142, 369374.

22. SL Navarro , Y Chen , L Li (2011) UGT1A6 and UGT2B15 polymorphisms and acetaminophen conjugation in response to a randomized, controlled diet of select fruits and vegetables. Drug Metab Dispos 39, 16501657.

23. WR Russell , SW Gratz , SH Duncan (2011) High-protein, reduced-carbohydrate weight-loss diets promote metabolite profiles likely to be detrimental to colonic health. Am J Clin Nutr 93, 10621072.

24. F Li , MA Hullar , Y Schwarz (2009) Human gut bacterial communities are altered by addition of cruciferous vegetables to a controlled fruit- and vegetable-free diet. J Nutr 139, 16851691.

25. KM Tuohy , S Kolida , AM Lustenberger (2001) The prebiotic effects of biscuits containing partially hydrolysed guar gum and fructo-oligosaccharides–a human volunteer study. Br J Nutr 86, 341348.

26. K Faust & J Raes (2012) Microbial interactions: from networks to models. Nat Rev Microbiol 10, 538550.

27. K Faust , JF Sathirapongsasuti , J Izard (2012) Microbial co-occurrence relationships in the human microbiome. PLoS Comput Biol 8, e1002606.

28. C Lozupone , K Faust , J Raes (2012) Identifying genomic and metabolic features that can underline early successional and opportunistic lifestyles of human gut symbionts. Genome Res 22, 19741984.

30. MD Niculescu , EA Pop , LM Fischer (2007) Dietary isoflavones differentially induce gene expression changes in lymphocytes from postmenopausal women who form equol as compared with those who do not. J Nutr Biochem 18, 380390.

31. KS Solanky , NJ Bailey , BM Beckwith-Hall (2005) Biofluid 1H NMR-based metabonomic techniques in nutrition research - metabolic effects of dietary isoflavones in humans. J Nutr Biochem 16, 236244.

32. HA Brauer , TE Libby , BL Mitchell (2011) Cruciferous vegetable supplementation in a controlled diet study alters the serum peptidome in a GSTM1-genotype dependent manner. Nutr J 10, 11.

33. S Rezzi , Z Ramadan , FP Martin (2007) Human metabolic phenotypes link directly to specific dietary preferences in healthy individuals. J Proteome Res 6, 44694477.

34. SS Heinzmann , CA Merrifield , S Rezzi (2012) Stability and robustness of human metabolic phenotypes in response to sequential food challenges. J Proteome Res 11, 643655.

35. MY Hirai , M Yano , DB Goodenowe (2004) Integration of transcriptomics and metabolomics for understanding of global responses to nutritional stresses in Arabidopsis thaliana. Proc Natl Acad Sci USA 101, 1020510210.

36. MY Hirai , M Klein , Y Fujikawa (2005) Elucidation of gene-to-gene and metabolite-to-gene networks in arabidopsis by integration of metabolomics and transcriptomics. J Biol Chem 280, 2559025595.

37. KA Lê Cao , PG Martin , C Robert-Granié (2009) Sparse canonical methods for biological data integration: application to a cross-platform study. BMC Bioinformatics 10, 34.

38. SP Gygi , Y Rochon , BR Franza (1999) Correlation between protein and mRNA abundance in yeast. Mol Cell Biol 19, 17201730.

39. K Van Deun , AK Smilde , MJ van der Werf (2009) A structured overview of simultaneous component based data integration. BMC Bioinformatics 10, 246.

40. N Putluri , A Shojaie , VT Vasu (2011) Metabolomic profiling reveals a role for androgen in activating amino acid metabolism and methylation in prostate cancer cells. PLoS One 6, e21417.

41. N Putluri , A Shojaie , VT Vasu (2011) Metabolomic profiling reveals potential markers and bioprocesses altered in bladder cancer progression. Cancer Res 71, 73767386.

42. M Imielinski , S Cha , T Rejtar (2012) Integrated proteomic, transcriptomic, and biological network analysis of breast carcinoma reveals molecular features of tumorigenesis and clinical relapse. Mol Cell Proteomics 11, M111 014910.

43. LM Poisson , JM Taylor & D Ghosh (2011) Integrative set enrichment testing for multiple omics platforms. BMC Bioinformatics 12, 459.

44. A Jauhiainen , O Nerman , G Michailidis (2012) Transcriptional and metabolic data integration and modeling for identification of active pathways. Biostatistics 13, 748761.

46. AD Coviello , R Haring , M Wellons (2012) A genome-wide association meta-analysis of circulating sex hormone-binding globulin reveals multiple Loci implicated in sex steroid hormone regulation. PLoS Genet 8, e1002805.

47. R Qayyum , BM Snively , E Ziv (2012) A meta-analysis and genome-wide association study of platelet count and mean platelet volume in african americans. PLoS Genet 8, e1002491.

48. DR Nyholt , SK Low , CA Anderson (2012) Genome-wide association meta-analysis identifies new endometriosis risk loci. Nat Genet 44, 13551359.

49. RS Houlston , E Webb , P Broderick (2008) Meta-analysis of genome-wide association data identifies four new susceptibility loci for colorectal cancer. Nat Genet 40, 14261435.

50. E Zeggini , LJ Scott , R Saxena (2008) Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet 40, 638645.

51. JD Cooper , DJ Smyth , AM Smiles (2008) Meta-analysis of genome-wide association study data identifies additional type 1 diabetes risk loci. Nat Genet 40, 13991401.

52. P Wirapati , C Sotiriou , S Kunkel (2008) Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures. Breast Cancer Res 10, R65.

53. DR Rhodes , J Yu , K Shanker (2004) Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression. Proc Natl Acad Sci USA 101, 93099314.

55. SE Chiuve , TT Fung , EB Rimm (2012) Alternative dietary indices both strongly predict risk of chronic disease. J Nutr 142, 10091018.

56. S Hooda , BM Boler , MC Serao (2012) 454 pyrosequencing reveals a shift in fecal microbiota of healthy adult men consuming polydextrose or soluble corn fiber. J Nutr 142, 12591265.

57. AB Ross , SJ Bruce , A Blondel-Lubrano (2011) A whole-grain cereal-rich diet increases plasma betaine, and tends to decrease total and LDL-cholesterol compared with a refined-grain diet in healthy subjects. Br J Nutr 105, 14921502.

58. A Costabile , A Klinder , F Fava (2008) Whole-grain wheat breakfast cereal has a prebiotic effect on the human gut microbiota: a double-blind, placebo-controlled, crossover study. Br J Nutr 99, 110120.

60. SC Smith , R Choy , SK Johnson (2006) Lupin kernel fiber consumption modifies fecal microbiota in healthy men as determined by rRNA gene fluorescent in situ hybridization. Eur J Nutr 45, 335341.

61. SK Johnson , V Chua , RS Hall (2006) Lupin kernel fibre foods improve bowel function and beneficially modify some putative faecal risk factors for colon cancer in men. Br J Nutr 95, 372378.

63. MO Weickert , AM Arafat , M Blaut (2011) Changes in dominant groups of the gut microbiota do not explain cereal-fiber induced improvement of whole-body insulin sensitivity. Nutr Metab (Lond) 8, 90.

64. SH Duncan , A Belenguer , G Holtrop (2007) Reduced dietary intake of carbohydrates by obese subjects results in decreased concentrations of butyrate and butyrate-producing bacteria in feces. Appl Environ Microbiol 73, 10731078.

65. RE Ley , PJ Turnbaugh , S Klein (2006) Microbial ecology: human gut microbes associated with obesity. Nature 444, 10221023.

66. FA van Dorsten , S Peters , G Gross (2012) Gut microbial metabolism of polyphenols from black tea and red wine/grape juice is source-specific and colon-region dependent. J Agric Food Chem 60, 1133111342.

67. SS Heinzmann , IJ Brown , Q Chan (2010) Metabolic profiling strategy for discovery of nutritional biomarkers: proline betaine as a marker of citrus consumption. Am J Clin Nutr 92, 436443.

69. S Tulipani , R Llorach , O Jauregui (2011) Metabolomics unveils urinary changes in subjects with metabolic syndrome following 12-week nut consumption. J Proteome Res 10, 50475058.

70. R Llorach , I Garrido , M Monagas (2010) Metabolomics study of human urinary metabolome modifications after intake of almond (Prunus dulcis (Mill.) D.A. Webb) skin polyphenols. J Proteome Res 9, 58595867.

71. FA Van Dorsten , CA Daykin , TP Mulder (2006) Metabonomics approach to determine metabolic differences between green tea and black tea consumption. J Agric Food Chem 54, 69296938.

72. LG Rasmussen , H Winning , F Savorani (2012) Assessment of the effect of high or low protein diet on the human urine metabolome as measured by NMR. Nutrients 4, 112131.

73. AA Moazzami , JX Zhang , A Kamal-Eldin (2011) Nuclear magnetic resonance-based metabolomics enable detection of the effects of a whole grain rye and rye bran diet on the metabolic profile of plasma in prostate cancer patients. J Nutr 141, 21262132.

74. AM Zivkovic , MM Wiest , U Nguyen (2009) Assessing individual metabolic responsiveness to a lipid challenge using a targeted metabolomic approach. Metabolomics 5, 209218.

75. R Llorach , M Urpi-Sarda , O Jauregui (2009) An LC-MS-based metabolomics approach for exploring urinary metabolome modifications after cocoa consumption. J Proteome Res 8, 50605068.

76. HC Bertram , C Hoppe , BO Petersen (2007) An NMR-based metabonomic investigation on effects of milk and meat protein diets given to 8-year-old boys. Br J Nutr 97, 758763.

77. C Stella , B Beckwith-Hall , O Cloarec (2006) Susceptibility of human metabolic phenotypes to dietary modulation. J Proteome Res 5, 27802788.

Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Proceedings of the Nutrition Society
  • ISSN: 0029-6651
  • EISSN: 1475-2719
  • URL: /core/journals/proceedings-of-the-nutrition-society
Please enter your name
Please enter a valid email address
Who would you like to send this to? *



Full text views

Total number of HTML views: 20
Total number of PDF views: 114 *
Loading metrics...

Abstract views

Total abstract views: 251 *
Loading metrics...

* Views captured on Cambridge Core between September 2016 - 21st September 2017. This data will be updated every 24 hours.