Gut microbiota composition correlates with diet and health …

ARTICLE RESEARCH

Full Methods and any associated references are available in the online version of thepaper.

in Table 1, plus separate PCoAs for the community-only, and long- stay-only subjects. The community and whole-cohort analyses iden- tified an association of depression with axis 2—subjects in the lower path had higher GDT scores. IL-6 and IL-8 levels were higher in the upper path by whole-cohort analysis (Fig. 4 and Supplementary Fig. 14), whereas CRP levels were higher in the lower path in long- stay-only analysis. Furthermore, subjects in the lower path had higher systolic and diastolic blood pressure, except in the community-only analysis. This apparent inconsistency is explained by a highly signifi- cant change in diastolic blood pressure along the primary PCoA axis in the long-stay subjects, emphasizing the value of a stratified cohort. The subjects in the upper path were older but had higher Barthel and FIM scores than subjects of a similar age in the lower path (Supplementary Fig. 14), consistent with healthier ageing. Movement along PCoA axis 1 of the whole cohort (that is, from community to long-stay, left to right, Fig. 4) is associated with a reduction in abundance of Ruminococcus and Prevotella , and increased abundance of the Oscillibacter CAG, accompanied by calf circumference decrease and weight decrease (Table 1), and increase in IL-6 levels. Moving along axis 1 of the long- stay PCA (that is, between the two right-ward arms), there is a reduction inthe Oscillibacter CAG, increase in abundance of the Bacteroides CAG, reduced FIM and Barthel indices, and increased levels of CRP (Fig. 4). Consideration of the microbiota–health correlations in the long-stay cohort (Fig. 4), upwards along axis 2, highlights the association with increased frailty, reduced muscle mass, and poorer mental activity mov- ing away from community-type microbiota. Health–microbiota associations were statistically significant, even when regression models were adjusted for location. Although other factors undoubtedly contribute to health decline, and are difficult to completely adjust for in retrospective studies, the most plausible interpretation of our data is that diet shapes the microbiota, which then affects health in older people. Diet-determined differences in microbiota composition may have subtle impacts in young adults in developed countries. These would be difficult to correlate with health parameters, but become far more evident in the elderly who are immunophysiologically compromised. This is supported by the stronger microbiota–health associations evident in the long-stay cohort, and there is now a reasonable case for microbiota-related acceleration of ageing-related health deterioration. An ageing population is now a general feature of western countries 35,36 and an emerging phenomenon even among developing countries. The association of the intestinal microbiota of older people with inflammation 12 and the clear association between diet and microbiota outlined in this and previous studies 20,21,37,38 argue in favour of an approach of modulating the microbiota with dietary interventions designed to promote healthier ageing. Dietary supplements with defined food ingredients that promote particular components of the microbiota may prove useful for maintaining health in older people. On a community basis, microbiota profiling, potentially coupled with metabolomics, offers the potential for biomarker-based identification of individuals at risk for, or undergoing, less-healthy ageing. METHODS SUMMARY Amplicons of the 16S rRNA gene V4 region were sequenced on a 454 Genome Sequencer FLX Titanium platform. Sequencing reads were quality filtered, OTU clustered, ChimeraSlayer filtered and further analysed using the QIIME pipeline 39 and RDP-classifier 40 . Statistical analysis was performed using Stata and R software packages. Nuclear magnetic resonance (NMR) spectroscopy was performed on a 600 MHz Varian NMR Spectrometer as previously described 41 . Habitual dietary intake was assessed using a validated, semiquan- titative, FFQ, administered by personnel who received standardized training in dietary assessment. FFQ coding, data cleaning and data checks were conducted by a single, trained individual to ensure con- sistency of data.

Received 10 January; accepted 14 June 2012. Published online 13 July 2012.

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. etal. 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. etal. The fecal microbiota of irritable bowel syndrome patients differs significantly from that of healthy subjects. Gastroenterology 133, 24–33 (2007). 5. Jeffery, I. B. etal. 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. Rajilic´-Stojanovic´, 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. etal. 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. PLoSONE 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. etal. 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. PLoSONE 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. AgeAgeing 39, 412–423 (2010). 32. Nyamundanda, G., Brennan, L. & Gormley, I. C. Probabilistic principal component analysis for metabolomic data. BMC Bioinformatics 11, 571 (2010).

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