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Gut microbiome pattern reflects healthy ageing and predicts survival in humans

Nature Metabolism volume 3, pages 274–286 (2021)Cite this article

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An Author Correction to this article was published on 18 March 2021

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Abstract

The gut microbiome has important effects on human health, yet its importance in human ageing remains unclear. In the present study, we demonstrate that, starting in mid-to-late adulthood, gut microbiomes become increasingly unique to individuals with age. We leverage three independent cohorts comprising over 9,000 individuals and find that compositional uniqueness is strongly associated with microbially produced amino acid derivatives circulating in the bloodstream. In older age (over ~80 years), healthy individuals show continued microbial drift towards a unique compositional state, whereas this drift is absent in less healthy individuals. The identified microbiome pattern of healthy ageing is characterized by a depletion of core genera found across most humans, primarily Bacteroides. Retaining a high Bacteroides dominance into older age, or having a low gut microbiome uniqueness measure, predicts decreased survival in a 4-year follow-up. Our analysis identifies increasing compositional uniqueness of the gut microbiome as a component of healthy ageing, which is characterized by distinct microbial metabolic outputs in the blood.

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Fig. 1: Conceptual outline of study and analysis workflow.
Fig. 2: Associations between gut microbiome uniqueness and age across the Arivale cohort.
Fig. 3: Reflection of gut microbiome uniqueness in plasma metabolites.
Fig. 4: Increased dissimilarity of the gut microbiome as a function of healthy ageing in the MrOS cohort.
Fig. 5: Associations between identified gut microbial ageing patterns and survival in older adults.

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Data availability

Qualified researchers can access the full Arivale deidentified dataset supporting the findings in this study for research purposes. Requests should be sent to Andrew Magis (andrew.magis@isbscience.org). The MrOS dataset is available to researchers through the following website: https://mrosonline.ucsf.edu/. The data are available to qualified researchers on submission and approval of a research plan. The AGP biom table and the accompanying metadata are publicly available through figshare, reference nos. 6137192 (ref. 49) and 6137315 (ref. 50), respectively.

Code availability

Code used to process gut microbiome samples is available on the Gibbons lab GitHub page (https://github.com/gibbons-lab/mbtools) and code used for statistical analysis is available through the Hood-Price lab GitHub (https://github.com/PriceLab/AgingMicrobiome).

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References

  1. Yatsunenko, T. et al. Human gut microbiome viewed across age and geography. Nature 486, 222–227 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. O’Toole, P. W. & Jeffery, I. B. Gut microbiota and aging. Science 350, 1214–1215 (2015).

    Article  PubMed  Google Scholar 

  3. Castro-Mejía, J. L. et al. Physical fitness in community-dwelling older adults is linked to dietary intake, gut microbiota, and metabolomic signatures. Aging Cell https://doi.org/10.1111/acel.13105 (2020).

  4. Jackson, M. et al. Signatures of early frailty in the gut microbiota. Genome Med. https://doi.org/10.1186/s13073-016-0262-7 (2016).

  5. Claesson, M. J. et al. Gut microbiota composition correlates with diet and health in the elderly. Nature 488, 178–184 (2012).

    Article  CAS  PubMed  Google Scholar 

  6. Biagi, E. et al. Gut microbiota and extreme longevity. Curr. Biol. 26, 1480–1485 (2016).

    Article  CAS  PubMed  Google Scholar 

  7. Kim, B. S. et al. Comparison of the gut microbiota of centenarians in longevity villages of South Korea with those of other age groups. J. Microbiol. Biotechnol. https://doi.org/10.4014/jmb.1811.11023 (2019).

  8. Wu, L. et al. A cross-sectional study of compositional and functional profiles of gut microbiota in sardinian centenarians. mSystems 4, https://doi.org/10.1128/mSystems.00325-19 (2019).

  9. Kong, F. et al. Gut microbiota signatures of longevity. Curr. Biol. https://doi.org/10.1016/j.cub.2016.08.015 (2016).

  10. Kong, F., Deng, F., Li, Y. & Zhao, J. Identification of gut microbiome signatures associated with longevity provides a promising modulation target for healthy aging. Gut Microbes https://doi.org/10.1080/19490976.2018.1494102 (2019).

  11. Vangay, P. et al. US immigration westernizes the human gut microbiome. Cell https://doi.org/10.1016/j.cell.2018.10.029 (2018).

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

    Article  CAS  PubMed  Google Scholar 

  13. Jeffery, I. B., Lynch, D. B. & O’Toole, P. W. Composition and temporal stability of the gut microbiota in older persons. ISME J. https://doi.org/10.1038/ismej.2015.88 (2016).

  14. Ghosh, T. S., Das, M., Jeffery, I. B. & O’Toole, P. W. Adjusting for age improves identification of gut microbiome alterations in multiple diseases. eLife https://doi.org/10.7554/eLife.50240 (2020).

  15. Tierney, B. T. et al. The predictive power of the microbiome exceeds that of genome-wide association studies in the discrimination of complex human disease. Preprint at bioRxiv https://doi.org/10.1101/2019.12.31.891978 (2020).

  16. Salosensaari, A. et al. Taxonomic signatures of long-term mortality risk in human gut microbiota. medRxiv https://doi.org/10.1101/2019.12.30.19015842 (2020).

  17. Zubair, N. et al. Genetic predisposition impacts clinical changes in a lifestyle coaching program. Sci. Rep. https://doi.org/10.1038/s41598-019-43058-0 (2019).

  18. Blank, J. B. et al. Overview of recruitment for the osteoporotic fractures in men study (MrOS). Contemp. Clin. Trials https://doi.org/10.1016/j.cct.2005.05.005 (2005).

  19. Abrahamson, M., Hooker, E., Ajami, N. J., Petrosino, J. F. & Orwoll, E. S. Successful collection of stool samples for microbiome analyses from a large community-based population of elderly men. Contemp. Clin. Trials Commun. https://doi.org/10.1016/j.conctc.2017.07.002 (2017).

  20. McDonald, D. et al. American gut: an open platform for citizen science microbiome research. mSystems https://doi.org/10.1128/msystems.00031-18 (2018).

  21. Gibbons, S. M., Duvallet, C. & Alm, E. J. Correcting for batch effects in case-control microbiome studies. PLoS Comput. Biol. https://doi.org/10.1371/journal.pcbi.1006102 (2018).

  22. Wilmanski, T. et al. Blood metabolome predicts gut microbiome α-diversity in humans. Nat. Biotechnol. https://doi.org/10.1038/s41587-019-0233-9 (2019).

  23. Rampelli, S. et al. Functional metagenomic profiling of intestinal microbiome in extreme ageing. Aging https://doi.org/10.18632/aging.100623 (2013).

  24. Collino, S. et al. Metabolic signatures of extreme longevity in northern italian centenarians reveal a complex remodeling of lipids, amino acids, and gut microbiota metabolism. PLoS ONE https://doi.org/10.1371/journal.pone.0056564 (2013).

  25. Vich Vila, A. et al. Impact of commonly used drugs on the composition and metabolic function of the gut microbiota. Nat. Commun. https://doi.org/10.1038/s41467-019-14177-z (2020).

  26. Nemet, I. et al. A cardiovascular disease-linked gut microbial metabolite acts via adrenergic receptors. Cell https://doi.org/10.1016/j.cell.2020.02.016 (2020).

  27. Han, H. et al. p-Cresyl sulfate aggravates cardiac dysfunction associated with chronic kidney disease by enhancing apoptosis of cardiomyocytes. J. Am. Heart Assoc. https://doi.org/10.1161/JAHA.115.001852 (2015).

  28. Lewis, K. N., Rubinstein, N. D. & Buffenstein, R. A window into extreme longevity; the circulating metabolomic signature of the naked mole-rat, a mammal that shows negligible senescence. GeroScience https://doi.org/10.1007/s11357-018-0014-2 (2018).

  29. Roager, H. M. & Licht, T. R. Microbial tryptophan catabolites in health and disease. Nat. Commun. https://doi.org/10.1038/s41467-018-05470-4 (2018).

  30. Sonowal, R. et al. Indoles from commensal bacteria extend healthspan. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.1706464114 (2017).

  31. Krishnan, S. et al. Gut microbiota-derived tryptophan metabolites modulate inflammatory response in hepatocytes and macrophages. Cell Rep. https://doi.org/10.1016/j.celrep.2018.03.109 (2018).

  32. Beaumont, M. et al. The gut microbiota metabolite indole alleviates liver inflammation in mice. FASEB J. https://doi.org/10.1096/fj.201800544 (2018).

  33. Alexeev, E. E. et al. Microbiota-derived indole metabolites promote human and murine intestinal homeostasis through regulation of interleukin-10 receptor. Am. J. Pathol. https://doi.org/10.1016/j.ajpath.2018.01.011 (2018).

  34. Lustgarten, M. S., Price, L. L., Chalé, A. & Fielding, R. A. Metabolites related to gut bacterial metabolism, peroxisome proliferator-activated receptor-alpha activation, and insulin sensitivity are associated with physical function in functionally-limited older adults. Aging Cell https://doi.org/10.1111/acel.12251 (2014).

  35. Cussotto, S. et al. Tryptophan metabolic pathways are altered in obesity and are associated with systemic inflammation. Front. Immunol. https://doi.org/10.3389/fimmu.2020.00557 (2020).

  36. Márquez, E. J. et al. Sexual-dimorphism in human immune system aging. Nat. Commun. https://doi.org/10.1038/s41467-020-14396-9 (2020).

  37. Bárcena, C. et al. Healthspan and lifespan extension by fecal microbiota transplantation into progeroid mice. Nat. Med. https://doi.org/10.1038/s41591-019-0504-5 (2019).

  38. Kundu, P. et al. Neurogenesis and prolongevity signaling in young germ-free mice transplanted with the gut microbiota of old mice. Sci. Transl. Med. https://doi.org/10.1126/scitranslmed.aau4760 (2019).

  39. Zwielehner, J. et al. Combined PCR-DGGE fingerprinting and quantitative-PCR indicates shifts in fecal population sizes and diversity of Bacteroides, bifidobacteria and Clostridium cluster IV in institutionalized elderly. Exp. Gerontol. https://doi.org/10.1016/j.exger.2009.04.002 (2009).

  40. Earls, J. C. et al. Multi-omic biological age estimation and its correlation with wellness and disease phenotypes: a longitudinal study of 3,558 individuals. J. Gerontol. A Biol. Sci. Med. Sci. 74, S52–S60 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Cawthon, P. M., Shahnazari, M., Orwoll, E. S. & Lane, N. E. Osteoporosis in men: findings from the osteoporotic fractures in men study (MrOS). Therap. Adv. Musculoskel. Dis. https://doi.org/10.1177/1759720X15621227 (2016).

  42. Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Wright, E. S. DECIPHER: harnessing local sequence context to improve protein multiple sequence alignment. BMC Bioinf. https://doi.org/10.1186/s12859-015-0749-z (2015).

  44. Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2—approximately maximum-likelihood trees for large alignments. PLoS ONE https://doi.org/10.1371/journal.pone.0009490 (2010).

  45. McMurdie, P. J. & Holmes, S. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE https://doi.org/10.1371/journal.pone.0061217 (2013).

  46. Bray, J. R. & Curtis, J. T. An ordination of the upland forest communities of southern wisconsin. Ecol. Monogr. https://doi.org/10.2307/1942268 (1957).

  47. Lozupone, C. & Knight, R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. https://doi.org/10.1128/AEM.71.12.8228-8235.2005 (2005).

  48. Martin, B. D., Witten, D. & Willis, A. D. Modeling microbial abundances and dysbiosis with beta-binomial regression. Ann. Appl. Stat. https://doi.org/10.1214/19-AOAS1283 (2020).

  49. McDonald, D. et al. American Gut Project fecal sOTU counts table. figshare https://doi.org/10.6084/m9.figshare.6137192.v1 (2018).

  50. McDonald, D. et al. Full American Gut Project mapping file. figshare https://doi.org/10.6084/m9.figshare.6137315.v1 (2018).

  51. Manor, O. et al. A multi-omic association study of trimethylamine N-oxide. Cell Rep. https://doi.org/10.1016/j.celrep.2018.06.096 (2018).

  52. Mackey, D. C. et al. Life-space mobility and mortality in older men: a prospective cohort study. J. Am. Geriatr. Soc. https://doi.org/10.1111/jgs.12892 (2014).

  53. Maier, L. et al. Extensive impact of non-antibiotic drugs on human gut bacteria. Nature https://doi.org/10.1038/nature25979 (2018).

  54. Charlesworth, C. J., Smit, E., Lee, D. S. H., Alramadhan, F. & Odden, M. C. Polypharmacy among adults aged 65 years and older in the United States: 1988–2010. J. Gerontol. Ser. A Biol. Sci. Med. Sci. https://doi.org/10.1093/gerona/glv013 (2015).

  55. Machón, M., Vergara, I., Dorronsoro, M., Vrotsou, K. & Larrañaga, I. Self-perceived health in functionally independent older people: associated factors. BMC Geriatr. https://doi.org/10.1186/s12877-016-0239-9 (2016).

  56. Idler, E. L. & Benyamini, Y. Self-rated health and mortality: a review of twenty-seven community studies. J. Health Social Behav. https://doi.org/10.2307/2955359 (1997).

  57. Mossey, J. M. & Shapiro, E. Self-rated health: a predictor of mortality among the elderly. Am. J. Public Health https://doi.org/10.2105/AJPH.72.8.800 (1982).

  58. Peel, C. et al. Assessing mobility in older adults: the UAB study of aging life-space assessment. Phys. Ther. https://doi.org/10.1093/ptj/85.10.1008 (2005).

  59. Middleton, A., Fritz, S. L. & Lusardi, M. Walking speed: the functional vital sign. J. Aging Phys. Activity https://doi.org/10.1123/japa.2013-0236 (2015).

  60. Mielke, M. M. et al. Assessing the temporal relationship between cognition and gait: Slow gait predicts cognitive decline in the Mayo Clinic study of aging. J. Gerontol. Ser. A Biol. Sci. Med. Sci. https://doi.org/10.1093/gerona/gls256 (2013).

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Acknowledgements

We thank C. Funk for helpful discussions throughout the course of this project. We also thank J. Dougherty and M. Brunkow for their coordination efforts. This work was supported by the M.J. Murdock Charitable Trust (to L.H. and N.D.P.), Arivale and a generous gift from C. Ellison (T.W.). S.M.G., C.D. and S.P. were supported by a Washington Research Foundation Distinguished Investigator Award and by start-up funds from the Institute for Systems Biology. Further support came from the National Academy of Medicine Catalyst Award (to N.D.P., S.M.G., L.H. and E.S.O.) and a National Institutes of Health (NIH) grant (no. U19AG023122) awarded by the National Institute on Aging (NIA). The MrOS Study is supported by NIH funding. The following institutes provide support: the NIA, the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), the National Center for Advancing Translational Sciences (NCATS) and the NIH Roadmap for Medical Research under the following grant nos.: U01 AG027810, U01 AG042124, U01 AG042139, U01 AG042140, U01 AG042143, U01 AG042145, U01 AG042168, U01 AR066160 and UL1 TR000128. Partial support for MrOS microbiome genotyping was provided by NIAMS grant R01AR061445.

Author information

Author notes

  1. John C. Earls

    Present address: Onegevity Health, New York, NY, USA

  2. Jennifer C. Lovejoy

    Present address: Lifestyle Medicine Institute, Redlands, CA, USA

Authors and Affiliations

  1. Institute for Systems Biology, Seattle, WA, USA

    Tomasz Wilmanski, Christian Diener, Noa Rappaport, Sushmita Patwardhan, John C. Earls, Anat Zimmer, Gustavo Glusman, Max Robinson, James T. Yurkovich, Andrew T. Magis, Jennifer C. Lovejoy, Leroy Hood, Sean M. Gibbons & Nathan D. Price

  2. Oregon Health and Science University, Portland, OR, USA

    Jack Wiedrick, Jodi Lapidus & Eric S. Orwoll

  3. Herbert Wertheim School of Public Health and Human Longevity Science at UCSD and Department of Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA

    Deborah M. Kado

  4. Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA

    Jane A. Cauley & Joseph Zmuda

  5. Center for Musculoskeletal Health, Department of Internal Medicine, University of California Davis Medical Center, Sacramento, CA, USA

    Nancy E. Lane

  6. eScience Institute, University of Washington, Seattle, WA, USA

    Sean M. Gibbons

  7. Department of Bioengineering, University of Washington, Seattle, WA, USA

    Sean M. Gibbons

  8. Onegevity Health, New York, NY, USA

    Nathan D. Price

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Contributions

T.W., S.M.G., L.H., E.S.O. and N.D.P conceptualized the study. T.W., J.W., J.L., J.A.C., S.M.G., E.S.O and N.D.P. participated in the study design. T.W., C.D., N.R., S.P., J.W., J.L., J.C.E., A.Z. and J.T.Y. performed data analysis and figure generation. G.G. and M.R. aided in dissimilarity analysis. G.G., M.R., N.E.L., J.Z., J.A.C. and D.M.K. assisted in results interpretation. A.T.M. and J.C.L. managed the logistics of data collection and integration. T.W., S.M.G., E.S.O. and N.D.P. were the primary authors of the paper, with contributions from all others. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Sean M. Gibbons, Eric S. Orwoll or Nathan D. Price.

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The authors declare no competing interests.

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Peer review information Nature Metabolism thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editors: Christoph Schmitt; Pooja Jha.

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Extended data

Extended Data Fig. 1 Arivale cohort Demographics table.

For comparisons between males and females, χ2 tests were run for categorical variables and two-sided t-tests for continuous variables. Obese was defined as BMI ≥ 30. Abbreviations: BMI- body mass index; LDL-low-density lipoprotein cholesterol; HDL-high-density lipoprotein cholesterol, s.d.-standard deviation. P-values <0.05 (two-sided) are colored in red.

Extended Data Fig. 2 MrOS discovery cohort characteristics table stratified into composite healthy and remainder of cohort.

Statistical tests used to compare groups are as follows: independent samples t-tests were used for comparing age, body mass index (BMI), Shannon diversity and Observed Species; χ2 or Fisher’s exact (if assumptions of χ2 were not met) tests were used to compare ethnicity (percentage Hispanic), and prevalence of each of the specified diseases. P-values <0.05, two-sided are colored in red.

Extended Data Fig. 3 Associations between age and gut microbiome uniqueness across cohorts using different distance metrics.

a, Age ß-coefficients and corresponding P-values from OLS models predicting Bray-Curtis uniqueness at the ASV- and genus-level in the American Gut Project (AGP) and two vendors in the Arivale cohort. In the AGP dataset, the analysis was performed on all samples, and then repeated on the subset of samples who had available sex and BMI data for covariate adjustment. P-values reported are derived from OLS linear regression models and result from a two-sided hypothesis. b, Spearman correlations of different ß-diversity metrics with age on both the ASV- and genus-level independently in each vendor used for gut microbiome processing in the Arivale cohort.

Extended Data Fig. 4 Table with associations between Bray-Curtis gut microbiome uniqueness and clinical, demographic, and diet/lifestyle/health measures in the Arivale Cohort.

‘pvalue’ corresponds to the unadjusted P-Value of the ß-coefficient (B-coef column) for each analyte from an OLS model adjusted for gut microbiome vendor. ‘r_squared’ reflects the percent of variance explained beyond microbiome vendor for each analyte independently for the Genus-level Bray-Curtis measure. ‘age_adjusted_coeff’ and ‘age_adjusted_corr_pvalue’ correspond to the ß-coefficient and the Bonferroni corrected P-Value (two-sided) for each analyte predicting Genus-level Bray-Curtis Uniqueness, adjusting for gut microbiome vendor and age. The ‘age_adj_coeff (ASV-level)’ and the ‘age_adj_corr_pvalue (ASV-level)’ correspond to analysis done on the ASV-level Bray-Curtis Uniqueness measure, where models were adjusted for vendor and age. ‘Missing’ shows the number of missing observations for each analyte. Values highlighted in red are statistically significant after multiple-hypothesis correction (Bonferroni P-Value<0.05, two-sided).

Extended Data Fig. 5 Table of associations between Bray-Curtis gut microbiome uniqueness and plasma metabolites in the Arivale cohort.

‘pvalue’ corresponds to the unadjusted P-Value of the ß-coefficient (covariate_adj. Beta_coeff column) for each analyte from an OLS model adjusted for age, age2, sex, a sex*age interaction term, BMI, Shannon diversity, and vendor with Genus-level Bray-Curtis uniqueness as the dependent variable. ‘corr_pval’ corresponds to the Bonferoni corrected P-value. ‘SUPER_PATHWAY’ indicates what pathway the metabolite belongs to. The last three columns are the same as the first three, but for Bray-Curtis uniqueness calculated on the ASV level. All metabolites with an unadjusted P-Value<0.01 are shown. Values highlighted in red are statistically significant after multiple-hypothesis correction (Bonferroni P-Value<0.05, two-sided).

Extended Data Fig. 6 Associations between taxa and gut microbiome uniqueness across cohorts and sex.

a–d, Plots demonstrating the correlation coefficients between genus-level Bray-Curtis gut microbiome uniqueness and individual taxa in the (a) Discovery MrOS cohort, (b) Vendor A in the Arivale Cohort, (c) Validation MrOS cohort, (d) and vendor B of the Arivale cohort. Only correlations > |0.20|) are shown. (e) Plots demonstrating the strength of correlation between genus-level Bray-Curtis microbiome uniqueness and individual taxa in the in the AGP dataset. The strongest 20 associations are shown. (d-e) Plots demonstrating the strength of correlation between gut microbiome uniqueness and individual taxa in Vendor A of the Arivale cohort in (f) females and (g) males. (h) Scatter plot of correlation coefficients for each genus tested between males and females. The correlation of the coefficients for each genus between sexes is shown. Only genera that had less than 5% zero values and a mean greater than five counts were tested.

Extended Data Fig. 7 Table of Spearman correlation coefficients and Beta-coefficients testing associations between age and uniqueness in the MrOS cohort.

Uniqueness measures reported in this table were calculated at the genus level. ‘Health Stratification’ corresponds to the metric used to define healthy individuals. ‘ Spearman Rho’ reports the Spearman correlation coefficient between age and microbiome uniqueness for the specified group of participants, while the ‘pvalue’ column provides the corresponding p-value. ‘Beta_coeff’ is the BMI adjusted age beta-coefficient predicting uniqueness across the same stratifications as the ‘Spearman Rho’ column. ‘Coef_pvalue’ provides the p-value corresponding to the age Beta-coefficient from linear regression models. ‘Sample_size’ is the number of participants in each stratification while the last column ‘Healthy (yes = 1/no=0)’ specifies whether the group of participants is the healthy subgroup (yes(1)), or the remainder of the cohort (No(0)). Significant p-values (P < 0.05, two-sided) are highlighted in red. No multiple hypothesis correction was performed.

Extended Data Fig. 8 Associations between age and gut microbiome measures across health stratifications in the MrOS cohort.

a–e, Plots demonstrating the strength of Spearman correlation between age and gut microbiome measures at different taxonomic resolutions. a, The blue/red panel corresponds to the calculated Weighted UniFrac (ß-diversity) uniqueness score at the genus level, while (b) the grey/green and (c) grey/yellow panels correspond to Shannon diversity and Observed species (α-diversity measures) at the ASV level, respectively. Significant correlations (two-sided) are indicated with asterisks. Exact correlation coefficients and corresponding p-values for (a) are provided in Extended Data Fig. 7. d-e, The same plots as in (b-c), with α-diversity calculated at the genus level. f, Comparison of ASV level and genus-level analysis in healthy ageing in the MrOS cohort. Barplots represent correlation coefficients comparing age and uniqueness at the ASV level across composite healthy MrOS individuals, and the remainder of the cohort in both the discovery and validation groups. g, ß-coefficients for age from OLS regression models predicting genus-level Bray-Curtis uniqueness in healthy composite individuals and remainder of the cohort, adjusted individually for the most commonly reported supplements and medications in the MrOS cohort.

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Wilmanski, T., Diener, C., Rappaport, N. et al. Gut microbiome pattern reflects healthy ageing and predicts survival in humans. Nat Metab 3, 274–286 (2021). https://doi.org/10.1038/s42255-021-00348-0

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