1 O’Toole, P. W. & Claesson, M. J. Gut microbiota: changes throughout the lifespan from infancy to elderly. Int. Dairy J. 20, 281–291 (2010)

2 Frank, D. N. et al. Molecular-phylogenetic characterization of microbial community imbalances in human inflammatory bowel diseases. Proc. Natl Acad. Sci. USA 104, 13780–13785 (2007)

3 Qin, J. et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65 (2010)

4 Kassinen, A. et al. The fecal microbiota of irritable bowel syndrome patients differs significantly from that of healthy subjects. Gastroenterology 133, 24–33 (2007)

5 Jeffery, I. B. et al. An irritable bowel syndrome subtype defined by species-specific alterations in faecal microbiota. Gut 61, 997–1006 (2012)

6 Ley, R. E., Turnbaugh, P. J., Klein, S. & Gordon, J. I. Microbial ecology: human gut microbes associated with obesity. Nature 444, 1022–1023 (2006)

7 Rajilić-Stojanović, M. et al. Development and application of the human intestinal tract chip, a phylogenetic microarray: analysis of universally conserved phylotypes in the abundant microbiota of young and elderly adults. Environ. Microbiol. 11, 1736–1751 (2009)

8 Claesson, M. J. et al. Composition, variability, and temporal stability of the intestinal microbiota of the elderly. Proc. Natl Acad. Sci. USA 108 (Suppl 1). 4586–4591 (2011)

9 Biagi, E. et al. Through ageing, and beyond: gut microbiota and inflammatory status in seniors and centenarians. PLoS ONE 5, e10667 (2010)

10 Franceschi, C. et al. Inflamm-aging: an evolutionary perspective on immunosenescence. Ann. NY Acad. Sci. 908, 244–254 (2000)

11 Garrett, W. S., Gordon, J. I. & Glimcher, L. H. Homeostasis and inflammation in the intestine. Cell 140, 859–870 (2010)

12 Guigoz, Y., Dore, J. & Schiffrin, E. J. The inflammatory status of old age can be nurtured from the intestinal environment. Curr. Opin. Clin. Nutr. Metab. Care 11, 13–20 (2008)

13 van Tongeren, S. P., Slaets, J. P., Harmsen, H. J. & Welling, G. W. Fecal microbiota composition and frailty. Appl. Environ. Microbiol. 71, 6438–6442 (2005)

14 Lovat, L. B. Age related changes in gut physiology and nutritional status. Gut 38, 306–309 (1996)

15 Hildebrandt, M. A. et al. High-fat diet determines the composition of the murine gut microbiome independently of obesity. Gastroenterology 137, 1716–1724 (2009)

16 Mai, V., McCrary, Q. M., Sinha, R. & Glei, M. Associations between dietary habits and body mass index with gut microbiota composition and fecal water genotoxicity: an observational study in African American and Caucasian American volunteers. Nutr. J. 8, 49 (2009)

17 Muegge, B. D. et al. Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans. Science 332, 970–974 (2011)

18 De Filippo, C. et al. 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, 14691–14696 (2010)

19 Turnbaugh, P. J. et al. The effect of diet on the human gut microbiome: a metagenomic analysis in humanized gnotobiotic mice. Sci. Transl. Med. 1, 6ra14 (2009)

20 Faith, J. J., McNulty, N. P., Rey, F. E. & Gordon, J. I. Predicting a human gut microbiota’s response to diet in gnotobiotic mice. Science 333, 101–104 (2011)

21 Wu, G. D. et al. Linking long-term dietary patterns with gut microbial enterotypes. Science 334, 105–108 (2011)

22 Lozupone, C. & Knight, R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235 (2005)

23 Drescher, L. S., Thiele, S. & Mensink, G. B. A new index to measure healthy food diversity better reflects a healthy diet than traditional measures. J. Nutr. 137, 647–651 (2007)

24 Jansson, J. et al. Metabolomics reveals metabolic biomarkers of Crohn’s disease. PLoS ONE 4, e6386 (2009)

25 Pryde, S. E., Duncan, S. H., Hold, G. L., Stewart, C. S. & Flint, H. J. The microbiology of butyrate formation in the human colon. FEMS Microbiol. Lett. 217, 133–139 (2002)

26 de Groot, V., Beckerman, H., Lankhorst, G. J. & Bouter, L. M. How to measure comorbidity. a critical review of available methods. J. Clin. Epidemiol. 56, 221–229 (2003)

27 Mahoney, F. I. & Barthel, D. W. Functional evaluation: the Barthel index. Md. State Med. J. 14, 61–65 (1965)

28 Kidd, D. et al. The functional independence measure: a comparative validity and reliability study. Disabil. Rehabil. 17, 10–14 (1995)

29 Folstein, M. F., Folstein, S. E. & McHugh, P. R. “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician. J. Psychiatr. Res. 12, 189–198 (1975)

30 Bauer, J. M., Kaiser, M. J., Anthony, P., Guigoz, Y. & Sieber, C. C. The mini nutritional assessment–its history, today’s practice, and future perspectives. Nutr. Clin. Pract. 23, 388–396 (2008)

31 Cruz-Jentoft, A. J. et al. Sarcopenia: European consensus on definition and diagnosis: report of the European Working Group on Sarcopenia in Older People. Age Ageing 39, 412–423 (2010)

32 Nyamundanda, G., Brennan, L. & Gormley, I. C. Probabilistic principal component analysis for metabolomic data. BMC Bioinformatics 11, 571 (2010)

33 Wang, Z. et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature 472, 57–63 (2011)

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

35 European Commission . Population structure and ageing http://epp.eurostat.ec.europa.eu/statistics_explained/index.php/Population_structure_and_ageing (2011)

36 Kinsella, K. & He, W. An Aging World: 2008 (US Government Printing Office, 2009)

37 Kau, A. L., Ahern, P. P., Griffin, N. W., Goodman, A. L. & Gordon, J. I. Human nutrition, the gut microbiome and the immune system. Nature 474, 327–336 (2011)

38 Walker, A. W. et al. Dominant and diet-responsive groups of bacteria within the human colonic microbiota. ISME J. 5, 220–230 (2011)

39 Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nature Methods 7, 335–336 (2010)

40 Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007)

41 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)

42 Harrington, J. et al. Sociodemographic, health and lifestyle predictors of poor diets. Public Health Nutr. 14, 2166–2175 (2011)

43 McCance, R. A. & Widdowson, E. M. The composition of foods 6th edn (Royal Soc. Chemistry, 2002)

44 Claesson, M. J. et al. Comparative analysis of pyrosequencing and a phylogenetic microarray for exploring microbial community structures in the human distal intestine. PLoS ONE 4, e6669 (2009)

45 Lilburn, T. G. & Garrity, G. M. Exploring prokaryotic taxonomy. Int. J. Syst. Evol. Microbiol. 54, 7–13 (2004)

46 Caporaso, J. G. et al. PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics 26, 266–267 (2010)

47 Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2–approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010)

48 Hamady, M., Lozupone, C. & Knight, R. Fast UniFrac: facilitating high-throughput phylogenetic analyses of microbial communities including analysis of pyrosequencing and PhyloChip data. ISME J. 4, 17–27 (2010)

49 Namiki, T., Hachiya, T., Tanaka, H. & Sakakibara, Y. in ACM Conference on Bioinformatics Computational Biology and Biomedicine (Association for Computing Machinery, 2011)

50 Chevreux, B. et al. Using the miraEST assembler for reliable and automated mRNA transcript assembly and SNP detection in sequenced ESTs. Genome Res. 14, 1147–1159 (2004)

51 Noguchi, H., Park, J. & Takagi, T. MetaGene: prokaryotic gene finding from environmental genome shotgun sequences. Nucleic Acids Res. 34, 5623–5630 (2006)

52 Koenker, R. & Basset, G. Regression quantiles. Econometrica 46, 33–50 (1978)

53 Feng, X. D., He, X. M. & Hu, J. H. Wild bootstrap for quantile regression. Biometrika 98, 995–999 (2011)

54 Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. 57, 289–300 (1995)