Abstract
Insight into associations between the gut microbiome with metabolism and aging is crucial for tailoring interventions to promote healthy longevity. In a discovery cohort of 10,207 individuals aged 40–93 years, we used 21 metabolic parameters to classify individuals into five clusters, termed metabolic multimorbidity clusters (MCs), that represent different metabolic subphenotypes. Compared to the cluster classified as metabolically healthy (MC1), clusters classified as ‘obesity-related mixed’ (MC4) and ‘hyperglycemia’ (MC5) exhibited an increased 11.1-year cardiovascular disease (CVD) risk by 75% (multivariable-adjusted hazard ratio (HR): 1.75, 95% confidence interval (CI): 1.43–2.14) and by 117% (2.17, 1.72–2.74), respectively. These associations were replicated in a second cohort of 9,061 individuals with a 10.0-year follow-up. Based on analysis of 4,491 shotgun fecal metagenomes from the discovery cohort, we found that gut microbial composition was associated with both MCs and age. Next, using 55 age-specific microbial species to capture biological age, we developed a gut microbial age (MA) metric, which was validated in four external cohorts comprising 4,425 metagenomic samples. Among individuals aged 60 years or older, the increased CVD risk associated with MC4 or MC5, as compared to MC1, MC2 or MC3, was exacerbated in individuals with high MA but diminished in individuals with low MA, independent of age, sex and other lifestyle and dietary factors. This pattern, in which younger MA appears to counteract the CVD risk attributable to metabolic dysfunction, implies a modulating role of MA in cardiovascular health for metabolically unhealthy older people.
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Data availability
In accordance with the Medical Ethics Committee of Ruijin Hospital and the institutional review board of BGI-Shenzhen related to protecting individual privacy, metagenomic sequencing data of the 4,491 fecal DNA samples from the JD_2014 cohort have been deposited at the China Nucleotide Sequence Archive under accession number CNP0004479 (https://doi.org/10.26036/CNP0004479) for controlled access. The controlled-access sequencing data are available upon reasonable request from the corresponding Data Access Committee (tiange.wang@shsmu.edu.cn). All phenotypic data that support the findings of this study are available from the corresponding author upon reasonable request.
Code availability
Computational analyses for microbiota were conducted using the bioBakery suite of tools. Species-level microbial abundances were determined using MetaPhlAn version 3.0.7 (https://github.com/biobakery/MetaPhlAn), and functional potential profiling was performed with HUMAnN version 3.0.0 (https://github.com/biobakery/humann).
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Acknowledgements
This work was supported by the grants from the National Key R&D Program of China (2021YFA1301103), the National Natural Science Foundation of China (82088102, 91857205, 82370820 and 81930021), the ‘Shanghai Municipal Education Commission–Gaofeng Clinical Medicine Grant Support’ from Shanghai Jiao Tong University School of Medicine (20171901 Round 2) and the Innovative Research Team of High-Level Local Universities in Shanghai. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. The authors are grateful to K. Kristiansen from the University of Copenhagen for his valuable suggestions in interpreting the findings of this study.
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T.W., G.N., J. Li, H.Z., Y.B. and W.W. contributed to the conception and design of the study. T.W., Z.S., H.R., F.Y. and H.Z. performed bioinformatic analyses. T.W., H.Z., Z.S. and H.R. drafted the manuscript. T.W., H.Z., G.N., J. Li, Y.B. and W.W. critically revised the manuscript. T.W., G.N., Y.B. and W.W. obtained funding. T.W., M.X., J. Lu, C.Y., K.W., M.C., X.X., D.L., L.K., R.Z., J.Z., M.L., Y.X., Z.Z., Y.C., H.Y. and J.W. contributed to the acquisition or interpretation of data. All authors performed proofreading of the manuscript for important intellectual content and gave final approval of the version to be published. G.N., Y.B. and W.W. are the guarantors of this work and take responsibility for the integrity of the data and the accuracy of the data analysis.
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This study was approved by the Medical Ethics Committee of Ruijin Hospital (no. 2020 Lin-Lun-Shen 359), Shanghai Jiao Tong University School of Medicine. All study participants provided written informed consent before study participation. The metagenomic analysis on the JD_2014 cohort was also approved by the institutional review board of BGI Research (no. BGI-IRB 21144).
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Extended data
Extended Data Fig. 1 Validation of the five metabolic MCs in JD_2014.
a. In JD_2014 (N = 4,491), k-means-based unsupervised clustering was applied to 21 Z-score-transformed metabolic variables (including BMI, WC, SBP, DBP, FPG, PPG, HbA1c, Fins, Pins, HOMA-IR, TC, HDL-C, LDL-C, TG, ApoA-1, ApoB, ALT, AST, GGT, eGFR, and UA), revealing five robust MCs with distinctive subphenotypes. The heatmap displays the transformed values of 21 metabolic variables in all participants. b. Radar charts show the mean standardized Z-scores of metabolic variables of the JD_2014 cohort. Each MC exhibited a unique metabolic subphenotype, with MC1 characterized by a relatively healthy metabolic profile and other MCs by feature variables highlighted in colors. Colored feature variables for MCs2-5 were identified using the criteria of P < 0.05 and Cliff’s delta effect size>0.30 (two-sided Wilcoxon rank-sum test). c. Point plots show the average Jaccard similarity for the five MCs obtained using k-means clustering and random assignments in JD_2010, CM_2010, and JD_2014 (100 bootstrap resampling). d. Feature variables for each MC (MCs2-5 versus MC1) were identified using the criteria of P < 0.05 and Cliff’s delta effect size>0.3 (two-sided Wilcoxon rank-sum test) and are highlighted with an asterisk. e. The prevalence of metabolic disturbances closely aligned with the feature variables in each respective MC in JD_2010, CM_2010, and JD_2014.
Extended Data Fig. 2 Stability assessment of the five metabolic MCs in men and women.
a. Heatmaps depict the distribution of all clustering metabolic variables in JD_2010, CM_2010, and JD_2014, stratified by sex. b. Point plots show the Jaccard similarity index for each MC among men and women in JD_2010, CM_2010, and JD_2014. c. PCoA based on Euclidean distance shows the distributions of metabolic variables across the five MCs between men and women. Data are presented as mean values with standard errors. d. Bar plots show the distribution of MCs between men and women. The percentages of each MC within each sex group are presented. Percentages might not sum to 100% because of rounding.
Extended Data Fig. 3 Associations between metabolic MCs, age, and the risk of incident CVD in JD_2010 and CM_2010.
a. The distributions of five metabolic MCs varied by age stratifications in JD_2010 and CM_2010 cohorts. Percentages might not sum to 100% because of rounding. b. Forest plots display the multivariable-adjusted HRs with 95% CIs for incident CVD associated with older versus younger age groups in the overall, low-risk (MCs123) and high-risk (MCs45) participants in JD_2010 and CM_2010. Cox proportional hazards regression models were adjusted for MCs (not for MCs-stratified models), sex, educational attainment, smoking status, alcohol drinking status, and physical activity. The error bars represent the two-sided 95% CIs determined by multivariable analysis in Cox regression.
Extended Data Fig. 4 Distinct gut microbiome signatures associated with age and metabolism in JD_2014.
a. Box plots display the log 10-transformed relative abundances of the four MCs-related core genera (Fig. 3b), including Faecalibacterium, Alistipes, Ruminococcus, and Megamonas, among five MCs (Top) and between age groups (Bottom). b. Box plots display the levels of microbial richness, two uniqueness indices (Kendall and Aitchison), and inter-individual beta diversity (Bray-Curtis dissimilarity at the genus level) across five MCs and between age groups. The central line represents the median value, the box represents the interquartile range (IQR), and the whiskers extend to the minimum and maximum values within 1.5 times the IQR from the lower and upper quartiles, respectively. Statistical significance was determined using the Kruskal-Wallis test followed by Dunn’s test (two-sided) for MCs groups, and the two-sided Wilcoxon rank-sum test for age groups. P < 0.05 is considered statistical significance. c. Spearman’s rank correlation analysis (two-sided) between uniqueness indices and age or microbial richness. The four uniqueness indices were calculated using different distance metrics to capture different aspects of within-sample microbial variations: Aitchison distance, Bray-Curtis dissimilarity, Jaccard distance, and Kendall’s tau coefficient (details see Methods). Spearman’s rho and P-values are presented. d. The heatmap shows the associations between two uniqueness indices (Aitchison and Kendall) and age-related microbial species, with colors indicating respective phyla. Statistical significance was determined using Spearman’s correlation (two-sided), with a BH-adjusted P < 0.05 denoted by an asterisk. e. The heatmap shows the associations between two uniqueness indices (Aitchison and Kendall) and MCs-related microbial species, with colors indicating respective phyla. Statistical significance was determined using Spearman’s correlation (two-sided), with a BH-adjusted P < 0.05 denoted by an asterisk.
Extended Data Fig. 5 Estimation of the impact of commonly used oral medications on gut microbiota in JD_2014.
a. Bar plot shows the explained variance (R2, one-sided) of ten types of commonly used oral medications (adjusted R2, one-sided) on gut microbiota determined by PERMANOVA with Bray-Curtis dissimilarities at the genus level. Adjusted R2 for each type of medication was estimated with adjustment for other medicines. Five out of ten oral medications showed significant associations with gut microbiota, including AGIs, biguanides, ARBs, TZDs, and calcium antagonists. Asterisks denote a BH-adjusted P < 0.05. b. The bar plot shows the distribution of drug-negative and drug-positive participants (without or with the use of any of the five types of medications or statins) in each MC. The number and percentage of drug-negative and drug-positive participants in each MC are presented. c. Top: Heatmap displays the significantly differential species between drug-negative MC1 (metabolically healthy cluster) and drug-negative MCs2-5 (metabolically unhealthy clusters), and between drug-negative MC1 and drug-positive unhealthy clusters (MCs2-5) (two-sided Wilcoxon rank-sum test, BH-adjusted P < 0.05). Bottom: Heatmap shows the significantly differential species between metabolically unhealthy participants (MCs2-5) with and without a certain type of medication (two-sided Wilcoxon rank-sum test, BH-adjusted P < 0.05). Asterisks denote a BH-adjusted P < 0.05.
Extended Data Fig. 6 Identification of age-related gut microbial pathways in JD_2014.
Left: Bar plot shows the gut microbial pathways significantly associated with age in MC1 using the partial Spearman’s correlation (two-sided) after adjusting for sex, educational attainment, smoking status, alcohol drinking status, physical activity, fruit and vegetable intake, and medication use. Red indicates significant positive associations and blue indicates significant negative associations. Right: Heatmap shows associations between relative abundances of age-related species and pathways in JD_2014 using Spearman’s correlation analysis (two-sided). A BH-adjusted P < 0.05 is considered as statistical significance. Asterisks denote a BH-adjusted P < 0.05.
Extended Data Fig. 7 Validation of the age-related gut microbial species and pathways in four external datasets.
Heatmaps show the associations of age with age-related gut microbial species and pathways in JD_2014 and four external validation cohorts obtained from the curatedMetagenomicData (details see Methods). For JD_2014, models were adjusted for sex, educational attainment, smoking status, alcohol drinking status, physical activity, fruit and vegetable intake, and medication use; for external datasets, models were adjusted for sex, using partial Spearman’s correlation analysis (two-sided). The ‘*’ denotes a BH-adjusted P < 0.05 and the ‘#’ denotes a P < 0.05. Purple indicates significant positive associations, green indicates significant negative associations, and gray indicates non-significant associations.
Extended Data Fig. 8 Associations between MA and the risk of incident CVD in JD_2014.
a. Cox proportional hazards regression models were applied to estimate the associations between MA groups and incident CVD during a 6.8-year follow-up period among the entire JD_2014 cohort (45–91 years), the younger age group (45- < 60 years), and the older age group (≥60 years), as well as the narrower age subgroups stratified by 10-year age spans (for example, 60–70 years, 61–71 years, and 62–72 years). Forest plots display the unadjusted HRs (95% CIs) (Left) and multivariable-adjusted HRs (95% CIs) (Right) for incident CVD associated with the HMA group compared with the LMA group. b. Forest plots display the multivariable-adjusted HRs (95% CIs) for incident CVD associated with the MCs45-HMA group compared with the overall MCs123 group among the older age group and the narrower age subgroups stratified by 10-year age spans. c. Forest plots display the multivariable-adjusted HRs (95% CIs) for incident CVD associated with the MCs45-LMA group compared with the overall MCs123 group among the older age group and the narrower age subgroups stratified by 10-year age spans. For a, b, and c, multivariable-adjusted models were adjusted for age, sex, educational attainment, smoking status, alcohol drinking status, physical activity, fruit and vegetable intake, and medication use. BH-adjusted P < 0.05 is considered statistical significance. The error bars represent the two-sided 95% CIs determined by multivariable analysis in Cox regression.
Extended Data Fig. 9 Distinct association patterns of Prevotella with metabolically healthy enriched microbial species among three enterotypes in JD_2014.
a. Histograms are the mean of relative abundance or prevalence (%) of Prevotella and the annotated species in three enterotypes (ETs) in older (aged≥60 years) MCs45 participants. b. Spearman’s rank correlation analysis (two-sided) reveals distinct association patterns between Prevotella and the other top abundant genera within the three ETs in older MCs45 participants. These include Megamonas, Bacteroides, Faecalibacterium, Eubacterium, Roseburia, Ruminococcus, Blautia, Klebsiella, and Escherichia. Asterisks denote a BH-adjusted P < 0.05. c. Spearman’s rank correlation analysis (two-sided) reveals distinct association patterns between Prevotella, P. copri, and the metabolically healthy enriched species within the three ETs in older MCs45 participants. The ‘*’ denotes a BH-adjusted P < 0.05, and the ‘#’ denotes a P < 0.05.
Extended Data Fig. 10 Interaction between host age and metabolic MCs on gut microbial composition in JD_2014.
a. Bar plot shows the significant impact of host age on gut microbiota in MCs123 and MCs45 groups, separately, determined by PERMANOVA (one-sided) with Bray-Curtis dissimilarities at the genus level. Asterisks denote a P < 0.05. b. The relationships between age and a set of age-related gut microbial variables, including 55 age-related species, MA, and microbial richness, in MCs subgroups, separately (MCs123 and MCs45, MC1 to MC5). Boxplot shows the absolute Spearman’s rho (two-sided) between MCs123 and MCs45. Boxes represent the interquartile ranges (IQRs) between the first and third quartiles, the center line represents the median, and the whiskers extend 1.5 times the IQR from the top and bottom of the box. Asterisks denote a BH-adjusted P < 0.05. c. Detailed information on health metrics used for stratifying healthy and unhealthy groups in JD_2014. d. The point plot shows the partial Spearman’s correlations (two-sided) between age and age-related gut microbial signatures, including MA, richness, four uniqueness indices, and the relative abundances of genera Bacteroides and Prevotella, within healthy and unhealthy groups. All models were adjusted for sex, educational attainment, smoking status, alcohol drinking status, physical activity, fruit and vegetable intake, and medication use (not for medication-stratified groups). P < 0.05 is considered as statistical significance. Red indicates significant associations in unhealthy groups, green indicates significant associations in healthy groups, and gray indicates non-significant associations. e. PERMANOVA analysis (one-sided) indicates the interaction effect between age and MCs (MCs1 and MCs45) on gut microbial composition. The interaction P values from the unadjusted model (P = 0.01), the model including sex and all environmental factors excluding medications (adjusted Model 1, P = 0.04), and the fully adjusted Model (adjusted Model 1 + medications, P = 0.07) are shown.
Supplementary information
Supplementary Tables 1–16
Supplementary Table 1a. Baseline characteristics of participants in the JD_2010, CM_2010 and JD_2014 cohorts according to five metabolic MCs. Supplementary Table 1b. Characteristics of participants in the JD_2010 and JD_2014 cohorts in 2010. Supplementary Table 2. Distribution of the clustering metabolic variables for each metabolic MC. Supplementary Table 3. Prevalence of related metabolic disorders in each metabolic MC. Supplementary Table 4. Associations of age, MC, MA and ET with incident CVD risk. Supplementary Table 5. Associations of gut microbiota with host and environmental factors (PERMANOVA, Bray–Curtis dissimilarity at the genus level, one-sided F-test). Supplementary Table 6. Summary of drug-negative and drug-positive individuals within five MCs. Supplementary Table 7. Partial Spearman’s correlations between species abundances and metabolic variables. Supplementary Table 8. Identification of gut microbial species associated with metabolic health. Supplementary Table 9. Identification of gut microbial features (richness, uniqueness indices and species abundances) associated with host age. Supplementary Table 10. Spearman’s correlations between species abundances and two uniqueness indices. Supplementary Table 11. Identification of gut microbial pathways associated with host age. Supplementary Table 12. List of gut metagenomic datasets used in this study from curatedMetagenomicData. Supplementary Table 13. Partial Spearman’s correlations between abundances of microbial features (species and pathways) and host age in four external cohorts. Supplementary Table 14. Partial Spearman’s correlations between MA and age-related species in the JD_2014 and validation cohorts. Supplementary Table 15. Distribution of metabolic variables between low-MA and high-MA groups within each metabolic cluster. Supplementary Table 16. Spearman’s correlations between Prevotella/P. copri and MC1-enriched species within each ET.
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Wang, T., Shi, Z., Ren, H. et al. Divergent age-associated and metabolism-associated gut microbiome signatures modulate cardiovascular disease risk. Nat Med 30, 1722–1731 (2024). https://doi.org/10.1038/s41591-024-03038-y
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DOI: https://doi.org/10.1038/s41591-024-03038-y
