Gut microbiome pattern reflects healthy ageing and predicts survival in humans

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.

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, age, 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 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 aging 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.

Fig. 1.. Conceptual outline of study and analysis workflow.

(a) Two different study populations were used: the Arivale cohort and the Osteoporotic Fractures in Men (MrOS) cohort. (b) Each of these two study populations were further subdivided into two groups; the Arivale cohort was split based on the microbiome vendor used to collect and process samples while the MrOS cohort separated into Discovery and Validation groups based on the batch in which the samples were run (discovery samples were processed in the initial batch, validation samples were processed several years later). (c) We profiled the microbiomes from these four study populations beginning with the Arivale cohort and validating our findings across the additional populations. (d) Our analysis pipeline further explored associations between the identified gut microbial aging pattern, lifestyle factors, and host physiology in the combined Arivale cohort, as well as health metrics and mortality in the combined MrOS cohort.

Fig. 2.. Associations between gut microbial uniqueness and age across the Arivale cohort.

(a) Boxplots showing gut microbiome uniqueness measures calculated using the ASV-level (grey) and genus-level (blue) Bray-Curtis dissimilarity metric across the adult lifespan across the Arivale cohort, adjusted for vendor. Asterisks indicate significant differences relative to the youngest <30 group, from a linear regression model adjusted for vendor, sex, BMI, and Shannon diversity (ASV-level: (50–59) P=3.52e-02, (60–69) P=1.88e-05, (70–79) P=1.47e-09, (80+) P=1.12e-02, genus-level: (40–49) P=7.15e-02, (50–59) P=3.57e-03, (60–69) P=4.33e-07, (70–79) P=8.16e-09, (80+) P=7.90e-03, two-sided). Also shown is the distribution of uniqueness calculated using the Bray-Curtis metric on both the ASV and genus level. (b) Spearman correlation coefficients for measures of Bray-Curtis uniqueness with age in individuals whose stool samples were processed by vendor A or B, as well as an additional external dataset (The American Gut Project). (c) Boxplots showing gut microbiome uniqueness scores calculated using the ASV-level Bray-Curtis across early, mid and late adulthood in the American Gut Project dataset. Asterisks indicate significant differences relative to the youngest <30 group, from a linear regression model adjusted for sex and Shannon diversity ((50–59) P=2.77e-09, (80+) P=2.95e-02). In both (A) and (C), box plots represent the interquartile range (25th to 75th percentile, IQR), with the middle line demarking the median; whiskers span 1.5 × IQR, points beyond this range are shown individually. (d) Percent of variance explained in genus-level Bray-Curtis uniqueness by a diverse number of demographic and lifestyle factors, as well as a subset of clinical laboratory tests.

Fig. 3.. Reflection of gut microbiome uniqueness in plasma metabolites.

(a) A plot of -log10 p-values for each of the 653 plasma metabolites measured in the Arivale cohort, from OLS regression models predicting genus-level Bray-Curtis uniqueness adjusted for microbiome vendor, sex, age, age, a sex*age interaction term, BMI, and Shannon diversity. Metabolites are color-coded by their super-family. All metabolites above the light red line are significant after multiple-hypothesis correction (Bonferroni P<0.05, two-sided), while the blue line indicates the unadjusted P-value threshold. Asterisks (*) indicate metabolites that were confidently identified on the basis of mass spectrometry data, but for which no reference standards are available to verify the identity. (b) Spearman correlation coefficients for each of the metabolites significantly associated with genus-level Bray-Curtis uniqueness after adjusting for covariates and multiple-hypothesis correction (Bonferroni P<0.05 two-sided). (c) Spearman correlation coefficients for each of the metabolites significantly associated with the ASV-level Bray-Curtis uniqueness measure after adjusting for covariates and multiple-hypothesis correction (Bonferroni P<0.05 two-sided). For both subfigures b) and c), bars are color-coded as in a). (d) Scatter plot of genus-level Bray-Curtis Uniqueness and the strongest metabolite predictor, phenylacetylglutamine, adjusted for vendor. (e) Scatter plot of ASV-level Bray-Curtis uniqueness and the strongest metabolite predictor, phenylacetylglutamine, adjusted for vendor. The lines shown are the y∼x regression lines, and the shaded regions are 95% confidence intervals for the slope of the line. The p-values reported in (d) and (e) are a result of two-sided statistical tests.

Fig. 4.. Increased dissimilarity of the gut microbiome as a function of healthy aging in the MrOS cohort.

(a-c) PCoA of the MrOS discovery cohort color-coded by (a) genus-level Bray-Curtis uniqueness, (b) relative Bacteroides abundance, and (c) relative Prevotella abundance. (d) Barplot demonstrating the correlation of strongest taxa associated with genus-level gut microbiome uniqueness in the MrOS discovery cohort, color-coded by phylum. (e) Correlation of genus-level Bray-Curtis uniqueness scores with age across the MrOS discovery and validation cohorts under different health stratifications. Also shown are age β-coefficients (slopes) with 95% confidence intervals from (OLS) linear regression models predicting genus-level Bray-Curtis uniqueness adjusted for BMI across the same stratifications. ‘Excellent’ corresponds to individuals who self-reported their health to be excellent, while ‘<Excellent’ incorporates all individuals who self-reported their health being anything less than excellent (good, fair, poor, or very poor).’Composite Healthy’ refers to individuals who fell into the healthy sub-group in at least 3 of the 4 stratifications performed. LSC: Life-Space Score. Significance of association was tested using a two-sided hypothesis, and p-values have not been corrected for multiple hypothesis testing. Exact correlation coefficients, β-coefficients, and corresponding p-values can be found in Extended Data 7.

Figure 5.. Associations between identified gut microbial aging patterns and survival in older adults.

(a) Boxplots demonstrating the relative abundance of the genus Bacteroides across tertiles of age in community-dwelling individuals identified as healthy on 3+ criteria specified (composite healthy) and the remainder of the cohort. (b) Kaplan Meier Curve demonstrating the association between overall survival and relative Bacteroides abundance grouped into tertiles in community-dwelling MrOS participants 85+ years at time of sampling. (c) Kaplan Meier Curve demonstrating the association between overall survival and ASV-level Weighted UniFrac uniqueness grouped into tertiles in community-dwelling MrOS participants who were 85+ years at the time of sampling. P-values shown in (b) and (c) are a result of Log-rank tests (two-sided) comparing the two survival curves, and have not been corrected for multiple hypothesis testing.