Skip to main content Accessibility help

Lessons on dietary biomarkers from twin studies

  • Cristina Menni (a1)


Metabolomic and microbiome profiling are promising tools to identify biomarkers of food intake and health status. The individual's genetic makeup plays a significant role on health, metabolism, gut microbes and diet and twin studies provide unique opportunities to untangle gene–environment effects on complex phenotypes. This brief review discusses the value of twin studies in nutrition research with a particular focus on metabolomics and the gut microbiome. Although, the twin model is a powerful tool to segregate the genetic component, to date, very few studies combine the twin design and metabolomics/microbiome in nutritional sciences. Moreover, since the individual's diet has a strong influence on the microbiome composition and the gut microbiome is modifiable (60 % of microbiome diversity is due to the environment), future studies should target the microbiome via dietary interventions.

  • 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. 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.

      Lessons on dietary biomarkers from twin studies
      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 <service> account. Find out more about sending content to Dropbox.

      Lessons on dietary biomarkers from twin studies
      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 <service> account. Find out more about sending content to Google Drive.

      Lessons on dietary biomarkers from twin studies
      Available formats


This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (, which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.

Corresponding author

Corresponding author: C. Menni, email


Hide All
1. van Dongen, J, Slagboom, PE, Draisma, HH et al. (2012) The continuing value of twin studies in the omics era. Nat Rev Genet 13, 640653.
2. Neale, M & Cardon, L (1992) Methodology for Genetic Studies of Twins and Families. Dordrecht: Kluwer Academic Publishers.
3. Kyvic, K (2000) Generalisability and assumptions of twin studies. In Advances in Twin and Sib-pair Analysis, pp. 6777 [Spector, TD, Sneider, H and MacGregor, AJ editors]. London: Greenwich Medical Media.
4. Visscher, PM, Hill, WG & Wray, NR (2008) Heritability in the genomics era–concepts and misconceptions. Nat Rev Genet 9, 255266.
5. Rijsdijk, FV & Sham, PC (2002) Analytic approaches to twin data using structural equation models. Brief Bioinformatics 3, 913.
6. Packard, CJ, O'Reilly, DS, Caslake, MJ et al. (2000) Lipoprotein-associated phospholipase A2 as an independent predictor of coronary heart disease. West of Scotland Coronary Prevention Study Group. N Engl J Med 343, 11481155.
7. Pallister, T, Spector, TD & Menni, C (2014) Twin studies advance the understanding of gene-environment interplay in human nutrigenomics. Nutr Res Rev 27, 242251.
8. Teucher, B, Skinner, J, Skidmore, PM et al. (2007) Dietary patterns and heritability of food choice in a UK female twin cohort. Twin Res Hum Genet 10, 734748.
9. Pallister, T, Sharafi, M, Lachance, G et al. (2015) Food preference patterns in a UK Twin cohort. Twin Res Hum Genet 18, 793805.
10. Swinburn, BA, Caterson, I, Seidell, JC et al. (2004) Diet, nutrition and the prevention of excess weight gain and obesity. Public Health Nutr 7, 123146.
11. Ley, SH, Pan, A, Li, Y et al. (2016) Changes in overall diet quality and subsequent Type 2 diabetes risk: three U.S. prospective cohorts. Diab Care (Epublication ahead of print version).
12. Freedman, LS, Schatzkin, A & Wax, Y (1990) The impact of dietary measurement error on planning sample size required in a cohort study. Am J Epidemiol 132, 11851195.
13. Kaaks, RJ (1997) Biochemical markers as additional measurements in studies of the accuracy of dietary questionnaire measurements: conceptual issues. Am J Clin Nutr 65, 1232S1239S.
14. Oresic, M (2009) Metabolomics, a novel tool for studies of nutrition, metabolism and lipid dysfunction. Nutr Metab Cardiovasc Dis 19, 816824.
15. Nicholson, JK & Lindon, JC (2008) Systems biology: metabonomics. Nature 455, 10541056.
16. Suhre, K, Meisinger, C, Doring, A et al. (2010) Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting. PLoS ONE 5, e13953.
17. Menni, C, Fauman, E, Erte, I et al. (2013) Biomarkers for type 2 diabetes and impaired fasting glucose using a nontargeted metabolomics approach. Diabetes 62, 42704276.
18. Menni, C, Graham, D, Kastenmuller, G et al. (2015) Metabolomic identification of a novel pathway of blood pressure regulation involving hexadecanedioate. Hypertension 66, 422429.
19. Menni, C, Mangino, M, Cecelja, M et al. (2015) Metabolomic study of carotid-femoral pulse-wave velocity in women. J Hypertens 33, 791796.
20. Altmaier, E, Kastenmuller, G, Romisch-Margl, W et al. (2011) Questionnaire-based self-reported nutrition habits associate with serum metabolism as revealed by quantitative targeted metabolomics. Eur J Epidemiol 26, 145156.
21. O'Sullivan, A, Gibney, MJ & Brennan, L (2011) Dietary intake patterns are reflected in metabolomic profiles: potential role in dietary assessment studies. Am J Clin Nutr 93, 314321.
22. Menni, C, Zhai, G, Macgregor, A et al. (2013) Targeted metabolomics profiles are strongly correlated with nutritional patterns in women. Metabolomics 9, 506514.
23. Scalbert, A, Brennan, L, Manach, C et al. (2014) The food metabolome: a window over dietary exposure. Am J Clin Nutr 99, 12861308.
24. Illig, T, Gieger, C, Zhai, G et al. (2010) A genome-wide perspective of genetic variation in human metabolism. Nat Genet 42, 137141.
25. Suhre, K, Shin, SY, Petersen, AK et al. (2011) Human metabolic individuality in biomedical and pharmaceutical research. Nature 477, 5460.
26. Kettunen, J, Tukiainen, T, Sarin, AP et al. (2012) Genome-wide association study identifies multiple loci influencing human serum metabolite levels. Nat Genet 44, 269276.
27. Shin, SY, Fauman, EB, Petersen, AK et al. (2014) An atlas of genetic influences on human blood metabolites. Nat Genet 46, 543550.
28. Jaremek, M, Yu, Z, Mangino, M et al. (2013) Alcohol-induced metabolomic differences in humans. Transl Psychiatry 3, e276.
29. Pallister, T, Haller, T, Thorand, B et al. (2016) Metabolites of milk intake: a metabolomic approach in UK twins with findings replicated in two European cohorts. Eur J Nutr (Epublication ahead of print version).
30. Pallister, T, Jennings, A, Mohney, RP et al. (2016) Characterizing blood metabolomics profiles associated with self-reported food intakes in female twins. PLoS ONE 11, e0158568.
31. Marchesi, JR, Adams, DH, Fava, F et al. (2016) The gut microbiota and host health: a new clinical frontier. Gut 65, 330339.
32. Lepage, P, Leclerc, MC, Joossens, M et al. (2013) A metagenomic insight into our gut's microbiome. Gut 62, 146158.
33. Turnbaugh, PJ, Ley, RE, Hamady, M et al. (2007) The human microbiome project. Nature 449, 804810.
34. Ley, RE, Peterson, DA & Gordon, JI (2006) Ecological and evolutionary forces shaping microbial diversity in the human intestine. Cell 124, 837848.
35.(2012) Structure, function and diversity of the healthy human microbiome. Nature 486, 207214.
36. Jurasinski, G, Retzer, V & Beierkuhnlein, C (2009) Inventory, differentiation, and proportional diversity: a consistent terminology for quantifying species diversity. Oecologia 159, 1526.
37. Leprieur, F, Albouy, C, De Bortoli, J et al. (2012) Quantifying phylogenetic beta diversity: distinguishing between ‘true’ turnover of lineages and phylogenetic diversity gradients. PLoS ONE 7, e42760.
38. Turnbaugh, PJ, Hamady, M, Yatsunenko, T et al. (2009) A core gut microbiome in obese and lean twins. Nature 457, 480484.
39. Howitt, MR & Garrett, WS (2012) A complex microworld in the gut: gut microbiota and cardiovascular disease connectivity. Nat Med 18, 11881189.
40. Knights, D, Lassen, KG & Xavier, RJ (2013) Advances in inflammatory bowel disease pathogenesis: linking host genetics and the microbiome. Gut 62, 15051510.
41. Tang, WH, Wang, Z, Levison, BS et al. (2013) Intestinal microbial metabolism of phosphatidylcholine and cardiovascular risk. N Engl J Med 368, 15751584.
42. Huttenhower, C, Knight, R, Brown, CT et al. (2014) Advancing the microbiome research community. Cell 159, 227230.
43. Kostic, AD, Xavier, RJ & Gevers, D (2014) The microbiome in inflammatory bowel disease: current status and the future ahead. Gastroenterology 146, 14891499.
44. Qin, HY, Cheng, CW, Tang, XD et al. (2014) Impact of psychological stress on irritable bowel syndrome. World J Gastroenterol 20, 1412614131.
45. Hartstra, AV, Bouter, KE, Backhed, F et al. (2015) Insights into the role of the microbiome in obesity and type 2 diabetes. Diab Care 38, 159165.
46. Patterson, E, Ryan, PM, Cryan, JF et al. (2016) Gut microbiota, obesity and diabetes. Postgrad Med J 92, 286300.
47. Wu, GD, Chen, J, Hoffmann, C et al. (2011) Linking long-term dietary patterns with gut microbial enterotypes. Science 334, 105108.
48. Kasubuchi, M, Hasegawa, S, Hiramatsu, T et al. (2015) Dietary gut microbial metabolites, short-chain fatty acids, and host metabolic regulation. Nutrients 7, 28392849.
49. Buddington, RK, Williams, CH, Kostek, BM et al. (2010) Maternal-to-infant transmission of probiotics: concept validation in mice, rats, and pigs. Neonatology 97, 250256.
50. De Filippo, C, Cavalieri, D, Di Paola, M et al. (2010) Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa. Proc Natl Acad Sci USA 107, 1469114696.
51. David, LA, Maurice, CF, Carmody, RN et al. (2014) Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559563.
52. Hirakawa, M, Arase, Y, Amakawa, K et al. (2015) Relationship between alcohol intake and risk factors for metabolic syndrome in men. Intern Med 54, 21392145.
53. Sonnenburg, JL & Backhed, F (2016) Diet-microbiota interactions as moderators of human metabolism. Nature 535, 5664.
54. Ward, RE, Ninonuevo, M, Mills, DA et al. (2006) In vitro fermentation of breast milk oligosaccharides by bifidobacterium infantis and Lactobacillus gasseri . Appl Environ Microbiol 72, 44974499.
55. Sela, DA & Mills, DA (2010) Nursing our microbiota: molecular linkages between bifidobacteria and milk oligosaccharides. Trends Microbiol 18, 298307.
56. Goodrich, JK, Waters, JL, Poole, AC et al. (2014) Human genetics shape the gut microbiome. Cell 159, 789799.
57. Goodrich, JK, Davenport, ER, Beaumont, M et al. (2016) Genetic determinants of the gut microbiome in UK Twins. Cell Host Microbe 19, 731743.
58. Jackson, M, Jeffery, IB, Beaumont, M et al. (2016) Signatures of early frailty in the gut microbiota. Genome Med 8, 8.
59. Jackson, MA, Goodrich, JK, Maxan, ME et al. (2016) Proton pump inhibitors alter the composition of the gut microbiota. Gut 65, 749756.


Related content

Powered by UNSILO

Lessons on dietary biomarkers from twin studies

  • Cristina Menni (a1)


Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed.