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

Nature Aging volumeĀ 3,Ā pages 436ā€“449 (2023)Cite this article

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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 (20ā€“117 years) from Guangxi province of China, including 297 centenarians (nā€‰=ā€‰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.

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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.

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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).

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Author notes

  1. These authors contributed equally: Shifu Pang, Xiaodong Chen.

Authors and Affiliations

  1. AIage Life Science Corporation Ltd., Guangxi Free Trade Zone Aisheng Biotechnology Corporation Ltd., Nanning, China

    Shifu Pang,Ā Xiaodong Chen,Ā Lili Meng,Ā Yu Huang,Ā Xiuqi Yu,Ā Lianfei Huang,Ā Pengpeng Ye,Ā Xiaochun ChenĀ &Ā Weifei Luo

  2. The Grand Health Industry Research Institute, Guangxi Academy of Sciences, Nanning, China

    Xiaodong Chen,Ā Weifei LuoĀ &Ā Shuai Wang

  3. State Key Laboratory of Non-food Biomass and Enzyme Technology, Guangxi Academy of Sciences, Nanning, China

    Zhilong LuĀ &Ā Weifei Luo

  4. State Key Laboratory of Veterinary Etiological Biology, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China

    Lianfei HuangĀ &Ā Shuai Wang

  5. Medical College, Guangxi University, Nanning, China

    Jian Liang

  6. Guangxi Key Laboratory of Enhanced Recovery after Surgery for Gastrointestinal Cancer, Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China

    Tao Peng

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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.

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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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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 Ī±-diversity, including the Chao1 index (a), Pielouā€™s index (b) and Shannon index (c), between female (nā€‰=ā€‰229) and male (nā€‰=ā€‰68) centenarians (two-sided Wilcoxon rank-sum test). (d) Ī²-Diversity was calculated using Brayā€’Curtis distance and is shown by PCoA plot (Adonis test, Pā€‰=ā€‰0.487, R2ā€‰=ā€‰0.0033). Data in boxes with no common letters are significantly different in one-way ANOVA with Tukey post hoc test (two-sided, Pā€‰<ā€‰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 Brayā€’Curtis distance.

The gut microbiota compositions of centenarians who had/did not have a recorded high blood pressure (a) (Yes, nā€‰=ā€‰110; No, nā€‰=ā€‰186), body mass index (b) (BMIā€‰ā‰„ā€‰24, nā€‰=ā€‰6; 18.5 ā‰¤ BMIā€‰ā‰¤ā€‰23.9, nā€‰=ā€‰168; BMI Ė‚ 18.5, nā€‰=ā€‰123), alcohol drinking (c) (Yes, nā€‰=ā€‰25; No, nā€‰=ā€‰266) or tea drinking (d) (Yes, nā€‰=ā€‰18; No, nā€‰=ā€‰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 Tukeyā€™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, Pā€‰<ā€‰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 (nā€‰=ā€‰1575) represented by PCoA plots based on unweighted UniFrac distance (a) and weighted UniFrac distance (b). The Adonis test and one-way ANOVA with Tukeyā€™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, Pā€‰<ā€‰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; Kruskalā€’Wallis test between classes, Wilcoxon rank-sum test between subclasses, Pā€‰<ā€‰0.05, LDAā€‰>ā€‰2). (b) Differentially abundant taxa between the young group and the centenarian group (Kruskalā€’Wallis test between classes; Pā€‰<ā€‰0.05, LDAā€‰>ā€‰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; Kruskalā€’Wallis test between classes, Wilcoxon rank-sum test between subclasses, Pā€‰<ā€‰0.05, LDAā€‰>ā€‰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; nā€‰=ā€‰63) centenarians and healthy old adults (nā€‰=ā€‰60) (Pā€‰<ā€‰0.05, LDAā€‰>ā€‰2). (b) LEfSe results in less healthy (LH) centenarians (nā€‰=ā€‰60) and less healthy old adults (nā€‰=ā€‰59) (Pā€‰<ā€‰0.05, LDAā€‰>ā€‰2). Kruskalā€’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 Brayā€’Curtis distance plot for baseline and follow-up after adjusting for hypertension, medication use, and alcohol and tea drinking. (b) Ī±-Diversity differences between baseline and follow-up for stratified health statuses (HB, nā€‰=ā€‰21; HF, nā€‰=ā€‰15; LHB, nā€‰=ā€‰19; LHF, nā€‰=ā€‰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 Kruskalā€’Wallis test between classes; Pā€‰<ā€‰0.05, LDAā€‰>ā€‰2). Healthy baseline (HB, nā€‰=ā€‰21), healthy follow-up (HF, nā€‰=ā€‰15), less healthy baseline (LHB, nā€‰=ā€‰19) and less healthy follow-up (LHF, nā€‰=ā€‰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, nā€‰=ā€‰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, nā€‰=ā€‰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 (nā€‰=ā€‰297) and non-centenarian individuals (nā€‰=ā€‰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 3, 436ā€“449 (2023). https://doi.org/10.1038/s43587-023-00389-y

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  • DOI: https://doi.org/10.1038/s43587-023-00389-y

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