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Longevity of centenarians is reflected by the gut microbiome with youth-associated signatures - Nature.com

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Abstract

Centenarians are an excellent model to study the relationship between the gut microbiome and longevity. To characterize the gut microbiome signatures of aging, we conducted a cross-sectional investigation of 1,575 individuals (20–117 years) from Guangxi province of China, including 297 centenarians (n = 45 with longitudinal sampling). Compared to their old adult counterparts, centenarians displayed youth-associated features in the gut microbiome characterized by an over-representation of a Bacteroides-dominated enterotype, increase in species evenness, enrichment of potentially beneficial Bacteroidetes and depletion of potential pathobionts. Health status stratification in older individuals did not alter the directional trends for these signature comparisons but revealed more apparent associations in less healthy individuals. Importantly, longitudinal analysis of centenarians across a 1.5-year period indicated that the youth-associated gut microbial signatures were enhanced with regard to increased evenness, reduction in interindividual variation and stability of Bacteroides, and that centenarians with low microbial evenness were prone to large microbiome instability during aging. These results together highlight a youth-related aging pattern of the gut microbiome for long-lived individuals.

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Fig. 1: Enterotypes of the gut microbiome in the cohorts.
Fig. 2: High level of species evenness in the gut microbiome of centenarians.
Fig. 3: Characteristics of the gut microbial community in different age groups.
Fig. 4: Differential microbial compositions in each age group.
Fig. 5: Long-lived individuals show a unique aging pattern of the gut microbiome.
Fig. 6: The gut microbiome markers for centenarians.

Data availability

Raw sequencing data were deposited in GenBank under accession no. PRJNA830660. Metadata information is available in Supplementary Tables 9 and 10. All other data are available from the corresponding authors upon reasonable request.

Code availability

The custom script for the prediction analysis is available at https://github.com/AIAGE-PROJECT/The-gut-microbiome-and-longevity-project-GMLP.

References

  1. Dato, S. et al. The genetics of human longevity: an intricacy of genes, environment, culture and microbiome. Mech. Ageing Dev. 165, 147–155 (2017).

    Article  CAS  PubMed  Google Scholar 

  2. Cani, P. D. Human gut microbiome: hopes, threats and promises. Gut 67, 1716–1725 (2018).

    Article  CAS  PubMed  Google Scholar 

  3. de Vos, W. M., Tilg, H., Van Hul, M. & Cani, P. D. Gut microbiome and health: mechanistic insights. Gut 71, 1020–1032 (2022).

    Article  CAS  PubMed  Google Scholar 

  4. DeJong, E. N., Surette, M. G. & Bowdish, D. M. E. The gut microbiota and unhealthy aging: disentangling cause from consequence. Cell Host Microbe 28, 180–189 (2020).

    Article  CAS  PubMed  Google Scholar 

  5. Kim, S. & Jazwinski, S. M. The gut microbiota and healthy aging: a mini-review. Gerontology 64, 513–520 (2018).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. 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 

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

    Article  CAS  PubMed  Google Scholar 

  9. 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. 29, 429–440 (2019).

    Article  PubMed  Google Scholar 

  10. Wilmanski, T. et al. Gut microbiome pattern reflects healthy ageing and predicts survival in humans. Nat. Metab. 3, 274–286 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Ghosh, T. S., Shanahan, F. & O’Toole, P. W. The gut microbiome as a modulator of healthy ageing. Nat. Rev. Gastroenterol. Hepatol. 19, 565–584 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Larson, P. J. et al. Associations of the skin, oral and gut microbiome with aging, frailty and infection risk reservoirs in older adults. Nat. Aging 2, 941–955 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Puca, A. A., Spinelli, C., Accardi, G., Villa, F. & Caruso, C. Centenarians as a model to discover genetic and epigenetic signatures of healthy ageing. Mech. Ageing Dev. 174, 95–102 (2018).

    Article  PubMed  Google Scholar 

  14. Tavella, T. et al. Elevated gut microbiome abundance of Christensenellaceae, Porphyromonadaceae and Rikenellaceae is associated with reduced visceral adipose tissue and healthier metabolic profile in Italian elderly. Gut Microbes 13, 1–19 (2021).

    Article  PubMed  Google Scholar 

  15. Zhang, S. et al. Gut microbiota in healthy and unhealthy long-living people. Gene 779, 145510 (2021).

    Article  CAS  PubMed  Google Scholar 

  16. Rampelli, S. et al. Shotgun metagenomics of gut microbiota in humans with up to extreme longevity and the increasing role of xenobiotic degradation. mSystems 5, e00124-20 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Sato, Y. et al. Novel bile acid biosynthetic pathways are enriched in the microbiome of centenarians. Nature 599, 458–464 (2021).

    Article  CAS  PubMed  Google Scholar 

  18. Ren, M., Li, H., Fu, Z. & Li, Q. Succession analysis of gut microbiota structure of participants from long-lived families in Hechi, Guangxi, China. Microorganisms 9, 2524 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Wu, L. et al. A cross-sectional study of compositional and functional profiles of gut microbiota in Sardinian centenarians. mSystems 4, e00325-19. (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Chen, L. et al. The long-term genetic stability and individual specificity of the human gut microbiome. Cell 184, 2302–2315 (2021).

    Article  CAS  PubMed  Google Scholar 

  21. Costea, P. I. et al. Enterotypes in the landscape of gut microbial community composition. Nat. Microbiol. 3, 8–16 (2018).

    Article  CAS  PubMed  Google Scholar 

  22. McCoubrey, L. E., Gaisford, S., Orlu, M. & Basit, A. W. Predicting drug–microbiome interactions with machine learning. Biotechnol. Adv. 54, 107797 (2022).

    Article  CAS  PubMed  Google Scholar 

  23. Arumugam, M. et al. Enterotypes of the human gut microbiome. Nature 473, 174–180 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Gauffin Cano, P., Santacruz, A., Moya, Á. & Sanz, Y. Bacteroides uniformis CECT 7771 ameliorates metabolic and immunological dysfunction in mice with high-fat-diet induced obesity. PLoS ONE 7, e41079 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Brown, E. M. et al. Bacteroides-derived sphingolipids are critical for maintaining intestinal homeostasis and symbiosis. Cell Host Microbe 25, 668–680 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Kelly, D. et al. Commensal anaerobic gut bacteria attenuate inflammation by regulating nuclear-cytoplasmic shuttling of PPAR-γ and RelA. Nat. Immunol. 5, 104–112 (2004).

    Article  CAS  PubMed  Google Scholar 

  27. Tan, H., Zhai, Q. & Chen, W. Investigations of Bacteroides spp. towards next-generation probiotics. Food Res. Int. 116, 637–644 (2019).

    Article  CAS  PubMed  Google Scholar 

  28. Tan, H., Zhao, J., Zhang, H., Zhai, Q. & Chen, W. Novel strains of Bacteroides fragilis and Bacteroides ovatus alleviate the LPS-induced inflammation in mice. Appl. Microbiol. Biotechnol. 103, 2353–2365 (2019).

    Article  CAS  PubMed  Google Scholar 

  29. Wang, K. et al. Parabacteroides distasonis alleviates obesity and metabolic dysfunctions via production of succinate and secondary bile acids. Cell Rep. 26, 222–235 (2019).

    Article  PubMed  Google Scholar 

  30. Parker, B. J., Wearsch, P. A., Veloo, A. C. M. & Rodriguez-Palacios, A. The genus Alistipes: gut bacteria with emerging implications to inflammation, cancer, and mental health. Front. Immunol. 11, 906 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Huber-Ruano, I. et al. Orally administered Odoribacter laneus improves glucose control and inflammatory profile in obese mice by depleting circulating succinate. Microbiome 10, 135 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. 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 10, 210–215 (2019).

    Article  PubMed  Google Scholar 

  33. Biagi, E. et al. The gut microbiota of centenarians: signatures of longevity in the gut microbiota profile. Mech. Ageing Dev. 165, 180–184 (2017).

    Article  PubMed  Google Scholar 

  34. Costea, P. I. et al. Enterotypes in the landscape of gut microbial community composition. Nat. Microbiol. 3, 8–16 (2018).

    Article  CAS  PubMed  Google Scholar 

  35. Cheng, M. & Ning, K. Stereotypes about enterotype: the old and new ideas. Genomics Proteomics Bioinformatics 17, 4–12 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Zhong, H. et al. Impact of early events and lifestyle on the gut microbiota and metabolic phenotypes in young school-age children. Microbiome 7, 2 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Xiao, L., Wang, J., Zheng, J., Li, X. & Zhao, F. Deterministic transition of enterotypes shapes the infant gut microbiome at an early age. Genome Biol. 22, 243 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Jeffery, I. B., Lynch, D. B. & O’Toole, P. W. Composition and temporal stability of the gut microbiota in older persons. ISME J. 10, 170–182 (2016).

    Article  CAS  PubMed  Google Scholar 

  39. Biagi, E. et al. Through ageing, and beyond: gut microbiota and inflammatory status in seniors and centenarians. PLoS ONE 5, e10667 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Li, C. et al. Deep insights into the gut microbial community of extreme longevity in south Chinese centenarians by ultra-deep metagenomics and large-scale culturomics. NPJ Biofilms Microbiomes 8, 28 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  Google Scholar 

  42. Jeffery, I. B. & O’Toole, P. W. Diet-microbiota interactions and their implications for healthy living. Nutrients 5, 234–252 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Hiippala, K. et al. Isolation of anti-inflammatory and epithelium reinforcing Bacteroides and Parabacteroides spp. from a healthy fecal donor. Nutrients 12, 935 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Raftar, S. K. A. et al. The anti-inflammatory effects of Akkermansia muciniphila and its derivates in HFD/CCL4-induced murine model of liver injury. Sci. Rep. 12, 2453 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Deng, F., Li, Y. & Zhao, J. The gut microbiome of healthy long-living people. Aging 11, 289–290 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Zafar, H. & Saier, M. H. Jr. Gut species in health and disease. Gut Microbes 13, 1–20 (2021).

    Article  PubMed  Google Scholar 

  47. Cani, P. D. & Knauf, C. A newly identified protein from Akkermansia muciniphila stimulates GLP-1 secretion. Cell Metab. 33, 1073–1075 (2021).

    Article  CAS  PubMed  Google Scholar 

  48. Hirata, T. Associations of cardiovascular biomarkers and plasma albumin with exceptional survival to the highest ages. Nat. Commun. 11, 3820 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Anderson, E. L. et al. A robust ambient temperature collection and stabilization strategy: enabling worldwide functional studies of the human microbiome. Sci. Rep. 6, 31731 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Peel, C. et al. Assessing mobility in older adults: the UAB Study of Aging Life-Space Assessment. Phys. Ther. 85, 1008–1119 (2005).

    Article  PubMed  Google Scholar 

  51. Mackey, D. C. et al. Life-space mobility and mortality in older men: a prospective cohort study. J. Am. Geriatr. Soc. 62, 1288–1296 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Chitalu, P., Tsui, A., Searle, S. D. & Davis, D. Life–space, frailty, and health-related quality of life. BMC Geriatr. 22, 646 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. 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 

  55. Bokulich, N. A. et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 6, 90 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Allard, G., Ryan, F. J., Jeffery, I. B. & Claesson, M. J. SPINGO: a rapid species-classifier for microbial amplicon sequences. BMC Bioinformatics 16, 324 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  57. McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Shi, Y., Zhang, L., Do, K.-A., Peterson, C. B. & Jenq, R. R. aPCoA: covariate adjusted principal coordinates analysis. Bioinformatics 36, 4099–4101 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Holmes, I., Harris, K. & Quince, C. Dirichlet multinomial mixtures: generative models for microbial metagenomics. PLoS ONE 7, e30126 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Thioulouse, J. et al. Multivariate Analysis of Ecological Data with ade4 (Springer, 2018).

  62. Feng, Q. et al. Gut microbiome development along the colorectal adenoma-carcinoma sequence. Nat. Commun. 6, 6528 (2015).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

This work was supported by funding and grants for Xiaochun Chen and Xiaodong Chen from the Nanning New Industry Research Institute for Longevity Technology and from the Science and Technology Base Special Project (no. 20201080) and for W.L. from the Special Project to Guide Technological Innovation of the Science and Technology Bureau of Nanning City (no. RC20212301).

Author information

Authors and Affiliations

Authors

Contributions

S.W. and W.L. conceived and supervised the project. S.W., W.L., S.P., P.Y., Xiaochun Chen and Z.L. designed and performed the data analysis. L.M., X.Y., L.H. and W.L. performed the library construction and 16S rRNA sequencing. Xiaodong Chen, Y.H., S.W. and W.L. collected the fecal samples and health information from each participant. S.W., W.L. and S.P. wrote the manuscript; J.L. and T.P. participated in study design, safety review and discussion. All authors discussed the results and read the manuscript.

Corresponding authors

Correspondence to Weifei Luo or Shuai Wang.

Ethics declarations

Competing interests

W.L., S.P., Xiaodong Chen, L.M., Y.H., X.Y., L.H., P.Y. and Xiaochun Chen are employees of the AIage Life Science Corporation. The AIage Life Science Corporation partially funded this research. The Guangxi Free Trade Zone Aisheng Biotechnology Corporation is a subsidiary of the AIage Life Science Corporation. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. The other authors declare no competing interests.

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Nature Aging thanks the anonymous reviewers for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 There was no significant difference in terms of microbial community structure between males and females in the centenarian group.

Comparison of the microbial α-diversity, including the Chao1 index (a), Pielou’s index (b) and Shannon index (c), between female (n = 229) and male (n = 68) centenarians (two-sided Wilcoxon rank-sum test). (d) β-Diversity was calculated using Bray‒Curtis distance and is shown by PCoA plot (Adonis test, P = 0.487, R2 = 0.0033). Data in boxes with no common letters are significantly different in one-way ANOVA with Tukey post hoc test (two-sided, P < 0.05). Boxes in boxplots represent the 25th percentile, median, and 75th percentile and whiskers stretch to 1.5 times the interquartile range from the corresponding hinge.

Extended Data Fig. 2 PCoA for the gut microbiome based on Bray‒Curtis distance.

The gut microbiota compositions of centenarians who had/did not have a recorded high blood pressure (a) (Yes, n = 110; No, n = 186), body mass index (b) (BMI ≥ 24, n = 6; 18.5 ≤ BMI ≤ 23.9, n = 168; BMI ˂ 18.5, n = 123), alcohol drinking (c) (Yes, n = 25; No, n = 266) or tea drinking (d) (Yes, n = 18; No, n = 279) were analyzed. These variations among individuals were not associated with any clear separation of the gut microbiome. The Adonis test and one-way ANOVA with Tukey’s post hoc test were used for all panels. Data in boxes with no common letters are significantly different in one-way ANOVA with Tukey post hoc test (two-sided, P < 0.05). Boxes in boxplots represent the 25th percentile, median, and 75th percentile and whiskers stretch to 1.5 times the interquartile range from the corresponding hinge.

Extended Data Fig. 3 The identified enterotypes for all the samples.

(a) Laplace approximation for the Dirichlet multinomial mixtures (DMM) protocol indicated that the optimal number of clusters was 4 in the cohorts. The top 20 drivers of enterotype 1 (b), enterotype 2 (c), enterotype 3 (d) and enterotype 4 (e) in the cohorts are listed. The values of the x-coordinate indicate the contribution of each variable to the enterotype in between-class analysis (BCA).

Extended Data Fig. 4 PCoA of weighted or unweighted UniFrac distances.

Variation in gut microbial community structure among groups (n = 1575) represented by PCoA plots based on unweighted UniFrac distance (a) and weighted UniFrac distance (b). The Adonis test and one-way ANOVA with Tukey’s post hoc test were used for all panels. Data in boxes with no common letters are significantly different in one-way ANOVA with Tukey post hoc test (two-sided, P < 0.05). Boxes in boxplots represent the 25th percentile, median, and 75th percentile and whiskers stretch to 1.5 times the interquartile range from the corresponding hinge.

Extended Data Fig. 5 LEfSe results for different comparisons.

(a) Differentially abundant taxa between the old adult group and the combined non-old-adult group (20-44 and 100-117 years, including the young group and the centenarian group as subclasses in LEfSe; Kruskal‒Wallis test between classes, Wilcoxon rank-sum test between subclasses, P < 0.05, LDA > 2). (b) Differentially abundant taxa between the young group and the centenarian group (Kruskal‒Wallis test between classes; P < 0.05, LDA > 2). (c) Differentially abundant species determined by SPINGO analysis between the old adult group and the combined non-old-adult group (young group and centenarian group; Kruskal‒Wallis test between classes, Wilcoxon rank-sum test between subclasses, P < 0.05, LDA > 2). All statistical testes used were two-sided.

Extended Data Fig. 6 LEfSe results between the old adult group and the centenarian group under health status stratifications.

(a) LEfSe results in healthy (H; n = 63) centenarians and healthy old adults (n = 60) (P < 0.05, LDA > 2). (b) LEfSe results in less healthy (LH) centenarians (n = 60) and less healthy old adults (n = 59) (P < 0.05, LDA > 2). Kruskal‒Wallis test between classes and Wilcoxon rank-sum test between subclasses were used (two-sided).

Extended Data Fig. 7 Comparisons of microbial compositions between baseline and follow-up.

(a) aPCoA of the Bray‒Curtis distance plot for baseline and follow-up after adjusting for hypertension, medication use, and alcohol and tea drinking. (b) α-Diversity differences between baseline and follow-up for stratified health statuses (HB, n = 21; HF, n = 15; LHB, n = 19; LHF, n = 27). (c) Composition of the gut microbiome at the phylum level between baseline and follow-up for stratified health statuses. (d) LEfSe between baseline and follow-up stratified by health status (two-sided Kruskal‒Wallis test between classes; P < 0.05, LDA > 2). Healthy baseline (HB, n = 21), healthy follow-up (HF, n = 15), less healthy baseline (LHB, n = 19) and less healthy follow-up (LHF, n = 27) samples were examined. The Wilcoxon rank-sum test (two-sided) was used for panel b. Boxes in boxplots represent the 25th percentile, median, and 75th percentile and whiskers stretch to 1.5 times the interquartile range from the corresponding hinge.

Extended Data Fig. 8 Cross-sectional comparison of the taxa with differential abundances in the longitudinal (follow-up) analysis.

In old adults (age 66-85 years, n = 386), Weissella, Streptococcus and Clostridium sensu stricto 1 were enriched, but Lachnoclostridium, Phascolarctobacterium and Blautia were less abundant than those in young adults (age 20-44 years, n = 314). The Wilcoxon rank-sum test (two-sided) was used. Box plots show the median and upper and lower quartiles, and whiskers extend to 1.5 times the interquartile range.

Extended Data Fig. 9 The gut microbial markers identified by random forest analysis to differentiate centenarian individuals (n = 297) and non-centenarian individuals (n = 700).

The Wilcoxon rank-sum test (two-sided) was used. Boxes in boxplots represent the 25th percentile, median, and 75th percentile and whiskers stretch to 1.5 times the interquartile range from the corresponding hinge.

Extended Data Fig. 10 Rarefaction curves for 16S rRNA sequencing.

The estimated ASV richness approached saturation in the samples of cross-sectional cohorts (a) and longitudinal cohorts (follow-up analysis) (b).

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Pang, S., Chen, X., Lu, Z. et al. Longevity of centenarians is reflected by the gut microbiome with youth-associated signatures. Nat Aging (2023). https://ift.tt/W6MOGrk

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