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Sex- and age-related trajectories of the adult human gut microbiota shared across populations of different ethnicities

Nature Aging volume 1, pages 87–100 (2021)Cite this article

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Abstract

Lifelong sex- and age-related trajectories of the human gut microbiota remain largely unexplored. Using metagenomics, we derived the gut microbial composition of 2,338 adults (26–76 years) from a Han Chinese population-based cohort where metabolic health, hormone levels and aspects of their lifestyles were also recorded. In this cohort, and in three independent cohorts distributed across China, Israel and the Netherlands, we observed sex differences in the gut microbial composition and a shared age-related decrease in sex-dependent differences in gut microbiota. Compared to men, the gut microbiota of premenopausal women exhibited higher microbial diversity and higher abundances of multiple species known to have beneficial effects on host metabolism. We also found consistent sex-independent, age-related gut microbial characteristics across all populations, with the presence of members of the oral microbiota being the strongest indicator of older chronological age. Our findings highlight the existence of sex- and age-related trajectories in the human gut microbiota that are shared between populations of different ethnicities and emphasize the pivotal links between sex hormones, gut microbiota and host metabolism.

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Fig. 1: Overview of the Pinggu cohort.
Fig. 2: Identification of the gut microbial covariates in the PG cohort.
Fig. 3: Identification of sex-associated core gut microbial species among populations.
Fig. 4: Age-related trajectories of sex differences in the gut microbiota.
Fig. 5: Identification of common age-related gut microbial features in three populations.

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Data availability

According to the Human Genetic Resources Administration of China regulation and ethical restrictions by the ethics committee of Peking University Health Science Center and the institutional review board of BGI-Shenzhen related to protecting individual privacy, metagenomic sequencing data of the 2,338 fecal DNA samples from the current PG cohort have been deposited at the China Nucleotide Sequence Archive under accession no. CNP0000381 and the European Genome-phenome Archive (EGA) under accession no. EGAS00001004820 for controlled access. The controlled-access sequencing data are available upon request from the corresponding Data Access Committee (datasubs@cngb.org, zhonghuanzi@genomics.cn or shizhun@genomics.cn). All metagenomic sequencing data of validation cohorts are from publicly available repositories. The metagenomic dataset from 876 Chinese adults can be accessed at the NCBI Sequence Read Archive (accession nos. SRA045646 and SRA050230) and European Nucleotide Archive (ENA; accession nos. ERP006678, ERP005860, ERP013562 and ERP013563). The metagenomic dataset of 875 Israeli adults can be accessed at the ENA under project no. PRJEB11532, and data of 1,135 Dutch adults from the LLD cohort can be accessed at the EGA under accession no. EGAS00001001704. The metagenomic dataset of 281 Dutch children from the KOALA cohort can be accessed at the ENA under project no. PRJEB26795.

Code availability

Software and parameters to perform analyses in this paper are available as Supplementary Software.

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Acknowledgements

We thank all the participants for agreeing to join this study. We are grateful to the research teams from the Department of Endocrinology and Metabolism of Beijing PG Hospital and Peking University People’s Hospital for their contribution to field survey and data collection. We acknowledge the China National Gene Bank for the support of fecal DNA extraction, library preparation and shotgun sequencing. This work was supported by grants from the National Key Research and Development Program (2016YFC1304901), Beijing Science and Technology Committee (D131100005313008) and Shenzhen Municipal Government of China (JCYJ20170817145809215).

Author information

Author notes

  1. These authors contributed equally: Xiuying Zhang, Huanzi Zhong, Yufeng Li, Zhun Shi.

Authors and Affiliations

  1. Department of Endocrinology and Metabolism, Peking University People’s Hospital, Peking University Diabetes Centre, Beijing, China

    Xiuying Zhang, Xianghai Zhou, Xueyao Han & Linong Ji

  2. BGI-Shenzhen, Shenzhen, China

    Huanzi Zhong, Zhun Shi, Huahui Ren, Zhe Zhang, Shanmei Tang, Yuxiang Lin, Fangming Yang, Dan Wang, Chao Fang, Shida Zhu, Yong Hou, Xun Xu, Huanming Yang, Jian Wang, Karsten Kristiansen & Junhua Li

  3. Laboratory of Genomics and Molecular Biomedicine, Department of Biology, University of Copenhagen, Copenhagen, Denmark

    Huanzi Zhong, Huahui Ren, Chao Fang & Karsten Kristiansen

  4. Department of Endocrinology, Beijing Friendship Hospital Pinggu Campus, Capital Medical University, Beijing, China

    Yufeng Li, Zuodi Fu & Lianying Wang

  5. School of Future Technology, University of Chinese Academy of Sciences, Beijing, China

    Fangming Yang

  6. James D. Watson Institute of Genome Sciences, Hangzhou, China

    Huanming Yang & Jian Wang

  7. School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China

    Junhua Li

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  1. Xiuying Zhang
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Contributions

L.J., X. Zhang and J.L. designed and coordinated the study. X. Zhang, Y. Li, X. Zhou and X.H. guided the establishment of the PG cohort. Y. Li, Z.F. and L.W. were responsible for the collection of biological samples and data through field surveys. H.Z., Z.S., H.R., Z.Z., S.T., Y. Lin, F.Y., D.W. and C.F. collected all phenotypic and metagenomic datasets and carried out bioinformatic analyses. Z.S., H.Z, and H.R. contributed to the design and revisions of data figures. H.Z. contributed to data interpretation and wrote the manuscript based on discussions with X. Zhang, Y. Li, Z.S., H.R., Z.Z., K.K. and J.L. K.K., J.L., S.Z., Y.H., X.X., H.Y., J.W. and L.J. provided key advice. K.K., J.L. and L.J. substantially revised the manuscript. K.K. fully complied with the regulations of the Human Genetic Resources Administration of China in the project. K.K did not have access to the fecal samples and phenotypic and metagenomic raw datasets in the project. All authors participated in discussions and contributed important advice to the revisions of the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Karsten Kristiansen, Junhua Li or Linong Ji.

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The authors declare no competing interests.

Additional information

Peer review information Nature Aging thanks Alex Zhavoronkov and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Extended data

Extended Data Fig. 1 Correlation between host factors in the PG cohort.

a-c, Heatmap showing Spearman’ s rank correlation between selected host factors in all PG adults (a), in all women (b) and in all men (c). *, adjusted P < 0.05 and absolute Rho value ≥0.3. Detailed information of adjusted P values and Rho values between each two factors is shown in the Source Data.

Source data

Extended Data Fig. 2 Age-independent sex differences in lifestyle and dietary patterns.

a-b, Bar plot showing the distribution of averaged intake of alcohol per week (a) and smoking (b) among individuals in each sex. c, Principal components analysis (PCA) of dietary habits (18 food items) of PG women (red) and men (blue). Primary contributors for PC1 and PC2 are shown, including frequency of consumption of dairy products, nut, legumes and soy products, fried food, red meat and tea. d-u, Distribution curves showing consumption frequency of 18 recorded food items in women (red) and men (blue). For each kind of food, the Y-axis indicates the average consumption frequency per week. The summary statistics of all factors is shown in Supplementary Table 1.

Source data

Extended Data Fig. 3 Sex-associated gut microbial features in the PG cohort.

a-c, Box plot showing a significantly higher microbial Shannon index in all women (n = 892) than in all men (n = 849) (a), men with (smokers, n = 574) or without smoking (non-smokers, n = 275) (b) and men with different intake of alcohol (c). Intake of alcohol: L0, men with no alcohol intake (n = 164); L1 + L2: men with averaged intake of alcohol ≤ 210 g per week (n = 381) (L1: 0~140 g, n = 321; L2: 140~210 g, n = 60); L3: men with averaged intake of alcohol >210 g per week (n = 304). The P-value (a, Pmaaslin=9.9E-5) indicates a significant positive association between women and Shannon index of common species, after adjustment for 14 covariates (age, the first five principal components (PCs) of dietary habits, ‘daily sedentary time’, ‘hours of sleep per day’, BMI, TG, DBP, HOMA-IR, FT4 and hs-CRP) using MaAsLin (see details in Methods). P values from two-sided Wilcoxon rank-sum test are shown in panel a (Pwilcoxon), b, c. d, Dot plot showing the associations of species with sex (Module ‘Sex’ in four cohorts), with the five PCs of dietary data (Module ‘Dietary habits’ in the PG cohort); alcohol intake and smoking (Module ‘Lifestyle’ in the PG men) and four metabolic parameters (Module ‘Metabolic parameters’ in the PG cohort) identified by MaAsLin. Colored dots indicate the directions of associations. Red/dark red, significant associations with women/positive associations with diet, lifestyle or metabolic parameters (adjusted P < 0.05); blue/dark blue, significant associations with men/negative associations with diet, lifestyle or metabolic parameters (adjusted P < 0.05); gray, no significant associations (adjusted P ≥ 0.05); gray dots with red-colored edges, moderate associations with women (adjusted P < 0.1). The sizes of dots represent the adjusted P values from MaAsLin (adjusted P ≥ 0.05, < 0.05, 0.01 and 0.001). The greater the size, the more significant the association. PC1-PC5, indicate the first five principal components (PC) from PCA of dietary habits. Detailed information of MaAsLin models including the regression coefficient, the coefficient standard error, and the number of samples with measured taxon abundance greater than 0 is shown in Supplementary Table 8. e-h, Boxplot showing significantly higher relative abundances of Eubacterium eligens (e), Bacteroides pectinophilus (f), Haemophilus haemolyticus (g) and Haemophilus parainfluenzae (h) in women (n = 892) and non-smoker men (n = 275) than that in smoker men (n = 574). Adjusted P values from two-sided Wilcoxon rank-sum test are shown. i, Boxplot showing a significant higher relative abundance of Ruminococcus gnavus in men of with averaged intake of alcohol >210 g per week (L3) (n = 304) than that in women (n = 892) and men with less (L1 + 2: n = 381) or no (L0, n = 164) alcohol intake per week. j, Boxplot showing no significant association between the relative abundance of Streptococcus thermophilus and sex after adjusting for 14 covariates (age, the first five PCs of dietary habits, ‘daily sedentary time’, ‘hours of sleep per day’, BMI, TG, DBP, HOMA-IR, FT4 and hs-CRP) using MaAsLin (see details in Methods). BH adjusted P values from two-sided Wilcoxon rank-sum test (Pwilcox = 5.79E-14) and MaAsLin (Pmaaslin = 0.12) are shown. All box plots (a-c, e-j) show medians (bold black lines), upper and lower quartiles, error bars extend to 1.5 interquartile range (IQR) below the first quartile and above the third quartile. k, Dot plot showing the associations of Streptococcus thermophilus with the five main food contributors for principal component 2 (the averaged consumption frequency of tea, dairy products, red meat, rice and nut), after adjusting for sex, age, ‘daily sedentary time’, ‘hours of sleep per day’, BMI, TG, DBP, HOMA-IR, FT4 and hs-CRP using MaAsLin.

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Extended Data Fig. 4 Comparison of the gut microbial composition among Chinese, Israeli and Dutch adults.

a-d, Density curves showing the age distributions of women (red) and men (blue) in the PG cohort (a), the public metagenomic data of Chinese adults (b), the Israeli (c) and the Dutch (d) cohorts. e-h, Box plot showing relative abundances of enterotype driving genera in Chinese (left, n = 3,214, including 2,338 samples from the PG cohort and 876 public metagenomic data of Chinese adults), Israeli (middle, n = 876) and Dutch (right, n = 1,133) adults. Panel e for Bacteroides; f for Prevotella, g for Ruminococcus and h for Bifidobacterium. i-k, Box plot showing gene-level based microbial alpha diversity in the two enterotypes of Chinese adults (i), the three enterotypes of Israeli adults (j) and of the three enterotypes of LLD adults (k). ET denotes enterotype. B, P, and R represent Bacteroides, Prevotella, and Ruminococcus for short, respectively. Comparisons (e-k) were performed using two-sided Wilcoxon rank-sum test. All box plots (e-k) show medians (bold black lines), upper and lower quartiles, error bars extend to 1.5 interquartile range (IQR) below the first quartile and above the third quartile. l, Optimal number of enterotypes identified in 100 times randomly sampled subsets of the metagenomic datasets of Chinese, Israeli and Dutch adults, respectively. The sample number on the X-axis represents the sample size of each subset.

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Extended Data Fig. 5 Sex-associated gut microbial KOs identified using MaAsLin.

a-c, Number of gut microbial KEGG Orthology (KOs) significantly associated with women (a, red), men (b, blue) and sex (c, green, total) in the four adult cohorts. Dark red, percentage of women-associated KOs in the PDoC, the Israeli and the Dutch cohort overlapping with the PG cohort; dark blue, percentage of men-associated KOs in the PDoC, the Israeli and the Dutch cohort overlapping with the PG cohort; dark green, percentage of sex-associated KOs in the PDoC, the Israeli and the Dutch cohort overlapping with the PG cohort. For the PG cohort, MaAsLin models between sex and KOs were applied by adjusting for 14 covariates (age, the first five principal components (PCs) of dietary habits, ‘daily sedentary time’, ‘hours of sleep per day’, BMI, TG, DBP, HOMA-IR, FT4 and hs-CRP), and for other cohorts, MaAsLin models between sex and KOs were applied by adjusting for age. The detailed information of MaAsLin models including the 379 sex-associated core gut microbial KOs, the regression coefficient and the number of samples with measured taxon abundance greater than 0 is shown is shown in Supplementary Table 9. d, Dot plot showing significant associations between sex and relative abundances of five selected gut microbial KOs (MaAsLin, adjusted P < 0.05). e, Bar plot showing the genus Akkermansia as the main contributor for the relative abundance of women-enriched K10800 (single-strand selective monofunctional uracil DNA glycosylase, panel d).

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Extended Data Fig. 6 Testosterone-associated gut microbial species identified using MaAsLin.

a-i, Scatterplot showing significant positive associations between testosterone level and nine bacterial species in the PG men after adjusting for 16 covariates (intake of alcohol, smoking, age, the first five principal components (PCs) of dietary habits, ‘daily sedentary time’, ‘hours of sleep per day’, BMI, TG, DBP, HOMA-IR, FT4 and hs-CRP) using MaAsLin (see details in Methods). BH adjusted P values from MaAsLin models (Pmaaslin) and Spearman’s correlation analysis (Pscc) are shown in each panel. BH adjusted P < 0.05 is considered as statistically significant. Blue dots, men. The detailed information of MaAsLin models including the BH adjusted P values, regression coefficient and the number of samples with measured taxon abundance greater than 0 is shown is shown in Supplementary Table 10.

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Extended Data Fig. 7 Sliding window analysis of sex-explained variances in the abundance of the 45 sex-associated species in the PG cohort.

a-c, Sliding window analysis revealing sex-explained variances in the abundance of women-enriched species (partial eta2, red lines, a) and men-enriched species (partial eta2, blue lines, b) in the PG cohort (in relation to Fig. 3b). Sliding 20-years age windows in increments of 1 year from young to old are used to smoothen the curves. A total of 31 age windows are generated and used for analysis. A curve showing sample number (black dot) in each sliding age window, ranging from 464 to 1042 individuals (c). For instance, ‘35’ (in the X axis) indicates the age window from 26 to 45 years (35 ± 10 years; n = 713); ‘65’ indicates the age window from 55 to 76 years (65 ± 10 years; n = 464). d, Boxplot showing the distribution of the sex-explained variances (partial eta2) of selected sex-associated species across different age windows (n = 31). Species are shown if the delta value of effect sizes (partial eta2) (mean value of younger subgroups; left side of the dashed lines, a-b) minus mean value of older subgroups; right side of the dashed lines, a-b) was greater than 0.01. Red, species enriched in women, blue, species enriched in men. All box plots show medians (bold black lines), upper and lower quartiles, error bars extend to 1.5 interquartile range (IQR) below the first quartile and above the third quartile.

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Extended Data Fig. 8 Age-related sex differences in alpha diversity and abundances of sex-associated core species.

Scatterplots showing age-related sex differences in alpha diversity (a, b) and abundances of the ten sex-associated core species characterizing women (c-l). Red dots, women; blue dots, men. Dashed lines indicate age-related trajectories of the gut microbial Shannon index (a, b) and the log10-transformed relative abundance of each species (c-l) in adults with BMI ≥ 25.52; solid lines indicate age-related trajectories in adults with BMI < 25.52 (the median BMI value of the PG cohort).

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Extended Data Fig. 9 Age-related gut microbial functional characteristics.

a-c, Scatter plots showing significant positive associations between age and gut microbial alpha diversity at the KO level in the PG (a), the Israeli(b) and the Dutch cohorts (c). P values from MaAsLin models (Pmaaslin) and Spearman’s correlation analysis (Pscc) are shown. Red dots, women; blue dots, men. d-e, Venn diagrams showing the number of KOs significantly associated with increased age (d) and decreased age (e) in the PG, Israeli and Dutch adults (MaAsLin, BH adjusted P <0.05). f, Bar plot showing the KEGG functional categories of shared age-related KOs among three cohorts. Dark and light brown indicate positive (n = 628, d) and negative (n = 40, e) associations with age, respectively (MaAsLin, BH adjusted P <0.05). The detailed information of 1,760 age-related gut microbial KOs is shown in Supplementary Table 13.

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Extended Data Fig. 10 External-validation of the age prediction models among the PG, PDoC, Israeli, Dutch cohorts.

a-b, Scatter plots showing the performance of the PG age prediction model on the external validation datasets of Israeli (a) and Dutch (b) adults. c-d, Scatter plots showing the performance of the Israeli age prediction model on the external validation datasets of PG (c) and Dutch (d) adults. e-f, Scatter plots showing the performance of the Dutch age prediction model on the external validation datasets of PG (e) and Israeli (f) adults. g, Density curves showing the age distributions of women (red) and men (blue) in each of the five published cohorts of the PDoC dataset. Detailed information of each cohort is shown in Supplementary Table 15. h-j, Scatter plots showing the predictive performance of age prediction models on the five Chinese validation cohorts using random-forest models built on the PG (h), Israeli (i) and Dutch (j) individuals (Supplementary Table 16). For each validation dataset, Spearman’s rho values between predicted age and actual age, mean absolute error (MAE), and root-mean-square error (RMSE) are shown. Shaded area indicates age predictions corresponding to the trend line ± 5 years. Each dot represents an individual. All prediction models of each cohort were trained using the random forest algorithm. For each validation dataset, Spearman’s rho values between predicted age and actual age, mean absolute error (MAE), and root-mean-square error (RMSE) are shown to indicate the model performance. Shaded area indicates age predictions corresponding to the trend line ± 5 years. Each dot represents an individual: red for women, blue for men.

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Supplementary information

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Supplementary Tables

Supplementary Tables 1–17.

Supplementary Software

The details of all relevant software used in this study.

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Zhang, X., Zhong, H., Li, Y. et al. Sex- and age-related trajectories of the adult human gut microbiota shared across populations of different ethnicities. Nat Aging 1, 87–100 (2021). https://doi.org/10.1038/s43587-020-00014-2

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