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Gut microbiome pattern reflects healthy ageing and predicts survival in humans

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

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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.

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 ( The MrOS dataset is available to researchers through the following website: 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 ( and code used for statistical analysis is available through the Hood-Price lab GitHub (

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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.

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Authors and Affiliations



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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Competing interests

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.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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.

ad, 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.

ae, 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).

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