Skip to main content Accessibility help
×
Home

Information:

  • Access
  • Open access
  • Cited by 1

Actions:

      • Send article to Kindle

        To send this article to your Kindle, first ensure no-reply@cambridge.org 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 @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ 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
        ×
Export citation

Abstract

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.

Twins history affords means of distinguishing between the effects of tendencies received at birth, and those that were imposed by the circumstances of their after lives; in other words between the effects of nature and of nurture’ Galton 1875

More than a century after Galton's observation, twin studies remain an invaluable tool to determine the role of genes and environment on human development and behaviour. Classical twin studies compare the trait concordance between monozygotic (MZ) and dizoygotic (DZ) twin pairs to estimate to what extent the observed phenotype difference is genetically determined( Reference van Dongen, Slagboom and Draisma 1 ). The assumption is that MZ twins share 100 % of their genes as they originate from a single zygote and are therefore genetic clones, whereas DZ twins originate from two separate eggs and thus share 50 % of their genes. If MZ twins are more alike than DZ twins with respect to the trait, then the trait is said to be influenced by genes, otherwise the trait is said to be determined by lifestyle and environment.

More formally, the twin phenotypic variance (i.e. individual differences of a trait) can be divided into three sources of variation: additive genetic variance (A), shared/common environmental variance (C) and non-shared/unique environmental variance (E)( Reference Neale and Cardon 2 ). Additive genetic influences represent the sum of the effects of the individual alleles at all loci that influence the trait. The common environmental component estimates the contribution of family environment, which is assumed to be equal in both MZ and DZ twin pairs( Reference Kyvic, Spector, Sneider and MacGregor 3 ), whereas the unique environmental component does not contribute to twin similarity, rather it estimates the effects that apply only to each individual including measurement error. Heritability (h 2) is defined as the proportion of the phenotypic variation attributable to genetic factors( Reference Visscher, Hill and Wray 4 ), and is given by the equation, h 2 = (A)/(A + C + E). A number of assumptions underlie the twin study. These include: (i) MZ and DZ twin pairs share 100 % of their common environment; (ii) twins are representative of the general population; (iii) gene–environment interactions are minimal for the trait under study; (iv) there is random assortment( Reference Rijsdijk and Sham 5 ).

Twin studies have shown that the genetic make-up of individuals plays a significant role in a multitude of dietary phenotypes, including energy, macronutrient intakes, dietary patterns and specific food groups as extensively reviewed( Reference Packard, O'Reilly and Caslake 6 , Reference Pallister, Spector and Menni 7 ). Heritability estimates of different energy and macronutrient intakes range between 8 and 70 %, while both ‘healthy’ and ‘unhealthy’ diet patterns are relatively stable in adults and have heritability ranging from 33 to 50–54 %( Reference Pallister, Spector and Menni 7 ). Specific foods intakes, including garlic (46 %), fruit and vegetables (49 %), and coffee (41 %) are also highly heritable in adults( Reference Teucher, Skinner and Skidmore 8 ). A recent study from our group identified four major food-liking patterns (fruit and vegetables, distinctive tastes, sweet and high carbohydrate, and meat) accounting for 26 % of the total variance with heritability estimates ranging from 36 to 58 % indicating genetic factors influence food liking–disliking( Reference Pallister, Sharafi and Lachance 9 ).

Besides estimating heritability of dietary assessment, using MZ twins discordant for dietary factor/nutrition status provides a naturally unique case-controlled experiment of assessing the links between diet and human biology. Due to their shared upbringing, including shared fetal exposure, matched genes and sex, MZ twins allow one to isolate the non-genetic contribution.

Diet is partially responsible for the dramatic rise in obesity( Reference Swinburn, Caterson and Seidell 10 ) and obesity-related diseases, such as type 2 diabetes( Reference Ley, Pan and Li 11 ); however, studies do not consistently support associations between dietary intakes and disease endpoints. Dietary intakes in epidemiological settings are generally measured via self-reported dietary assessment methods that are subject to recall bias and measurement error( Reference Freedman, Schatzkin and Wax 12 , Reference Kaaks 13 ). Nutritional epidemiological studies could be improved by the use of food intake biomarkers to better capture exposure.

This brief review discusses the value of twin studies in nutrition research with a particular focus on metabolomics and the gut microbiome.

Metabolomics

Metabolomic profiling has the potential to identify biomarkers of food intake( Reference Oresic 14 ). Metabolomics is a new high-throughput technology that measures endogenous metabolites in cells, tissues and other biosamples such as blood, urine and saliva( Reference Nicholson and Lindon 15 ). These metabolites provide a direct signature of the biochemical activities of the individual at a particular time, reflecting the metabolic effects of nutritional intake, physical activities and environmental exposures as well as the biological pathways associated with diet-related diseases including obesity, CVD and type 2 diabetes( Reference Suhre, Meisinger and Doring 16 Reference Menni, Mangino and Cecelja 19 ). Metabolites have been proven to be of use in nutritional research and novel dietary biomarkers within the metabolome have been identified( Reference Altmaier, Kastenmuller and Romisch-Margl 20 Reference Scalbert, Brennan and Manach 23 ). However, as the individual genetic makeup influences the levels of metabolites, explaining up to 81 % of the variation in their blood levels( Reference Illig, Gieger and Zhai 24 Reference Shin, Fauman and Petersen 27 ), the twin model can be employed to estimate the nutritional impact on metabolites, segregating the genetic component.

To date there are relatively few studies that combine a twin study design and metabolomics in nutritional sciences. Studies from TwinsUK have successfully identified potential biomarkers of alcohol intake( Reference Jaremek, Yu and Mangino 28 ) including lysophosphatidylcholines, diethyl ether lipids, diaclylphosphatidycholines and sphingolipids, as well as biomarkers of self-reported dietary patterns( Reference Menni, Zhai and Macgregor 22 ), food preference patterns( Reference Pallister, Sharafi and Lachance 9 ) and self-reported food intakes( Reference Pallister, Haller and Thorand 29 , Reference Pallister, Jennings and Mohney 30 ).

Taking advantage of the twin nature of the TwinsUK data, we first looked for association in the larger twin population excluding MZ twins discordant for a nutritional intake. Then, for each significant metabolite–dietary variable we run the same analysis on the discordant MZ twin pairs to replicate results in this set.

Using a targeted metabolomics approach (Bioacrates platform; 163 metabolites), we identified forty-two dietary pattern–metabolite significant associations in 1003 female twins and successfully replicated eleven associations in a subcohort of MZ twin discordant for dietary intake (between twenty-eight and forty discordant twins independent from the first analysis)( Reference Menni, Zhai and Macgregor 22 ). The strongest associations were observed for fruit and vegetables intake with the glycerophospholipid phosphatidylcholine diaclyl C38 : 6 and with the sphingolipid sphingomyeline C26 : 1. Significant associations were also found with coffee and garlic intake and for hypoenergetic dieting.

In a more recent study from our group, we looked for association between 601 blood metabolites (456 measured with the untargeted Metabolon platform and 145 measured with Biocrates) and seventy-one reported food intakes from FFQ in 3500 female twins to look for novel biomarkers of nutrition intake. We identified 180 associations with thirty-nine food group after meta-analysing the discovery and replication cohort (MZ discordant), overall consisting of 106 different metabolites, mainly lipids, including seventy-three novel associations( Reference Pallister, Jennings and Mohney 30 ). In particular, we identified the amino-acid trans-4-hydroxyproline, a component of collagen, as a novel biomarker for red meat intake; ergothioneine as a potential marker of mushroom intake and two metabolites derived from the gut bacteria transformation of phenolic compounds as biomarkers of fruit intake. All the findings are compiled into the open access DietMetab database (www.twinsuk.ac.uk/dietmetab-data).

Using a similar design and replicating in two independent European populations, we have also identified four novel biomarkers of milk intake: trimethyl-N-aminovalerate, uridine, hydroxysphingomyelin C14 : 1 and diacylphosphotidylcholine C28 : 1( Reference Pallister, Haller and Thorand 29 ).

Microbiome

The study of the gut microbiome is an exciting area of medicine because of its immediate potential for therapeutic interventions( Reference Marchesi, Adams and Fava 31 ). The term microbiome describes the DNA material of microbial communities within an animal and recent advances in technology are revealing its complexity( Reference Lepage, Leclerc and Joossens 32 ). Human subjects have about 100 trillion gut microbes that outnumber their cells ten to one( Reference Turnbaugh, Ley and Hamady 33 ). Human subjects and their microbiome have co-evolved and live intimately in a symbiotic relationship( Reference Ley, Peterson and Gordon 34 ) with the microbes producing a wide range of enzymes, chemicals, hormones and vitamins that can potentially interact with the host( Reference Marchesi, Adams and Fava 31 ). The composition of the microbiome varies by anatomical site, with the primary determinant of community composition being anatomical location( Reference Ley, Peterson and Gordon 34 ). Diversity measures the number of microbes present at a particular site( 35 ). The α diversity refers to the number of different species at a particular site( Reference Jurasinski, Retzer and Beierkuhnlein 36 ), while β diversity considers how many taxa are shared between populations, acting as a similarity score( Reference Leprieur, Albouy and De Bortoli 37 ). A low diversity of gut microbes (sometimes called dysbiosis) has been implicated in many human diseases including CVD, obesity, inflammatory bowel disease, colitis and type 2 diabetes( Reference Turnbaugh, Hamady and Yatsunenko 38 Reference Patterson, Ryan and Cryan 46 ). Only recently, thanks to genetic sequencing have we been able to study microbes properly and realise that the vast majority are not harmful and many are beneficial( Reference Marchesi, Adams and Fava 31 ). Research has shown that the largest influence on the gut microbiome comes from diet( Reference Wu, Chen and Hoffmann 47 ) and hence we could potentially alter many diseases with food and monitor the effects via microbes. For instance, dietary fibre is fermented in the intestine by the colonic microbiota resulting in increased production of SCFA( Reference Kasubuchi, Hasegawa and Hiramatsu 48 ). SCFA are involved in the energy metabolism as they regulate the balance between fatty acid synthesis, fatty acid oxidation, and lipolysis in the body. A reduction in the concentrations of free fatty acids in plasma results in a decrease in body weight( Reference Kasubuchi, Hasegawa and Hiramatsu 48 ).

Studies so far on food and the microbiome have mainly focused on meat-based/plant-based diet and highlighted differences in the microbiota of plant- and animal-based diets( Reference Wu, Chen and Hoffmann 47 , Reference Buddington, Williams and Kostek 49 , Reference De Filippo, Cavalieri and Di Paola 50 Reference Hirakawa, Arase and Amakawa 52 ). In particular, they have demonstrated that human gut responds rapidly to major dietary changes( Reference Sonnenburg and Backhed 53 ). A meat-based diet, rich in animal protein, fats, artificial additives, and lacking in fibre, has been shown to drive chronic conditions such as obesity, the metabolic syndrome and atherosclerosis by encouraging gut dysbiosis. Dietary interventional studies, however, have shown that the effects of meat-based diets on the gut microbiome could be rapidly reversed by adopting a plant-based, minimally processed diet( Reference Ward, Ninonuevo and Mills 54 , Reference Sela and Mills 55 ). Long-term dietary habits, however, tend to determine the composition of the gut microbiota with studies showing that long-term dietary habits associated with the individual microbiome signature( Reference Wu, Chen and Hoffmann 47 ). Also, changes in diet are dependent on the individual gut microbiota composition suggesting that not everyone reacts the same to the same dietary change( Reference David, Maurice and Carmody 51 ).

Moreover, the way in which individuals respond to dietary interventions may be complicated by host genetics and so the twin model could be a valuable tool to study the association of the gut microbiome with nutritional phenotypes. Novel twin data shows that although many microbes are driven by environment, a substantial proportion of microbiota with disease associations have a heritable component( Reference Goodrich, Waters and Poole 56 , Reference Goodrich, Davenport and Beaumont 57 ). The most heritable taxon, the Christensenella family was shown to be associated with leanness in human subjects and prevents weight gain under high-fat diet in mice( Reference Goodrich, Waters and Poole 56 ).

Using discordant twins, we have also successfully identified a striking negative association between frailty due to ageing and gut microbiota diversity( Reference Jackson, Jeffery and Beaumont 58 ) as well as a significant alteration in the gut microbiota in proton pump inhibitors users( Reference Jackson, Goodrich and Maxan 59 ). In general, therefore, a more diverse gut microbiome signals a healthier state and this is modified by age, diet and use of medication.

Conclusions

In conclusion, twin research is an invaluable tool to untangle gene–environment effects on complex phenotypes including metabolomics, the gut microbiome and complex phenotypes such as obesity and diet related diseases. Indeed, twins have provided valuable evidence that diet is influenced by genetics suggesting that future dietary counselling should target this domain.

Metabolomics and microbiomics have the potential to identify biomarkers of food intake and health status. Combining these omics with twin data allows the separation of features influenced by genetics from those influenced by the environment. This supports the efficient longitudinal monitoring of individuals at high genetic risk as they progress from health to disease making this model an invaluable means of testing personalised medicine strategies.

Finally, because the gut microbiome is modifiable (60 % of microbiome diversity is due to the environment) and since an individual's diet has a strong influence on the microbiome composition, future studies should target the microbiome via dietary interventions.

Financial Support

TwinsUK was funded by the Wellcome Trust; European Community's Seventh Framework Programme (FP7/2007-2013). The study also receives support from the National Institute for Health Research (NIHR) Clinical Research Facility at Guy's & St Thomas’ NHS Foundation Trust and NIHR Biomedical Research Centre based at Guy's and St. Thomas’ NHS Foundation Trust and King's College London. C. M. is funded by the MRC AimHY (MR/M016560/1) grant.

Conflicts of Interest

None.

Authorship

C. M. was solely responsible for all aspects of preparation of the present paper.

References

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.