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Gut microbial features and circulating metabolomic signatures of frailty in older adults

Nature Aging (2024)Cite this article

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Abstract

Frailty, a multidimensional indicator of suboptimal aging, reflects cumulative declines across multiple physiological systems. Although age-related changes have been reported in gut microbiota, their role in healthy aging remains unclear. In this study, we calculated frailty index (FI) from 33 health-related items to reflect the overall health status of 1,821 older adults (62–96 years, 55% female) and conducted multi-omics analysis using gut metagenomic sequencing data and plasma metabolomic data. We identified 18 microbial species and 17 metabolites shifted along with frailty severity, with stronger links observed in females. The associations of nine species, including various Clostridium species and Faecalibacterium prausnitzii, with FI were reproducible in two external populations. Plasma glycerol levels, white blood cell count and kidney function partially mediated these associations. A composite microbial score derived from FI significantly predicted 2-year mortality (adjusted hazard ratio across extreme quartiles, 2.86; 95% confidence interval, 1.38–5.93), highlighting the potential of microbiota-based strategies for risk stratification in older adults.

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Fig. 1: Study population, measurements and analysis strategy.
Fig. 2: Associations of frailty severity with overall gut microbiome composition and individual gut microbial species.
Fig. 3: Plasma metabolites differ across participants with various frailty severity.
Fig. 4: Mediation of clinical biomarkers and circulating metabolites between gut microbial species and frailty severity.
Fig. 5: Performances of four models based on the traditional risk factors with and without gut microbial features and plasma metabolomic features using the random forest algorithm.
Fig. 6: Traditional, omics-based and combined FIs as predictors of 2-year all-cause mortality.

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Data availability

The datasets generated and analyzed during the current study from the current RLAS cohort have been deposited to the National Center for National Omics Data Encyclopedia, accessible via the following accession numbers: sequence data: OEP001391; metabolite data and associated metadata: OEZ014274. All metagenomic sequencing data of the validation cohort are from publicly available repositories. The metagenomic dataset from 342 Chinese adults can be accessed at the National Center for Biotechnology Information (NCBI) Sequence Read Archive (accession number PRJNA613947). The metagenomic dataset from 42 American adults can be accessed via NCBI PRJNA699281. Source data are provided with this paper.

Code availability

Code used for statistical analysis is available at https://github.com/RLASFI/RLAS-Frailty.

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Acknowledgements

We are grateful to the participants for their participation in this research and to the research staff in the Rugao Longitudinal Ageing Study. The computations in this research were performed using the CFFF platform of Fudan University; the data analysis server is supported by the Human Phenome Data Center of Fudan University; and we thank the center staff for their support. This work was jointly supported by the National Key R&D Program of China (2021YFA1301000), the National Natural Science Foundation of China (81973032) and the Shanghai Municipal Science and Technology Major Project (grant no. 2023SHZDZX02).

Author information

Author notes

  1. These authors contributed equally: Yanni Pu, Zhonghan Sun, Hui Zhang, Qingxia Huang.

Authors and Affiliations

  1. State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, China

    Yanni Pu, Zhonghan Sun, Qingxia Huang, Zhendong Mei, Mengmeng Kong, Wenjun Yang, Chenhao Lin, Xiaofeng Zhou, Shuchun Lin, Qiumin Huang, Lili Huang, Huiru Tang & Yan Zheng

  2. State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences, Fudan University, Shanghai, China

    Hui Zhang & Xiaofeng Wang

  3. School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Hui Zhang

  4. Department of Gastroenterology, Rugao People’s Hospital, Rugao, China

    Zhengdong Wang

  5. Ministry of Education Key Laboratory of Public Health Safety, School of Public Health, Institute of Nutrition, Fudan University, Shanghai, China

    Peilu Wang, Liang Sun & Yan Zheng

  6. School of Public Health, Zhejiang University School of Medicine, Hangzhou, China

    Changzheng Yuan

  7. Institute of Gut Microbiota Research and Engineering Development, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China

    Qian Xu

  8. National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China

    Xiaofeng Wang & Yan Zheng

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  1. Yanni Pu
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Contributions

Y.P., Z.S., H.Z. and Qingxia Huang contributed equally to this work. H.T., X.W. and Y.Z. contributed to the design and interpretation of the study. Y.P. and Z.S. conducted the data analyses and drafted the manuscript. X.W., H.Z. and Z.W. contributed to medical data collection. Qingxia Huang and H.T. contributed to the generation of metabolomics data. Y.P., M.K., X.Z., S.L. and Y.Z. contributed to the generation of metagenomics data. H.Z., Qingxia Huang, Z.M., P.W., C.L., W.Y., Qiuming Huang, L.H., L.S., C.Y., Q.X. and Y.Z. critically revised and edited the manuscript. H.T., X.W. and Y.Z. obtained funding. All authors read and approved the final version of the manuscript.

Corresponding authors

Correspondence to Huiru Tang, Xiaofeng Wang or Yan Zheng.

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Nature Aging thanks Kenneth Rockwood and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Flowchart of participants included in the main analysis.

The diagram illustrates the sample selection process. First, participants with missing complete frailty or age information were excluded. The remaining participants were further selected based on the availability of metagenomic (n = 1,448) and metabolomic profiles (n = 1,771), resulting in a final sample of 1,415 participants for integrated analysis.

Extended Data Fig. 2 Frailty index and its individual components.

a, Distribution of the frailty index (FI) in the study population. Each participant’s FI was evaluated by 33-item deficits in 5 domains (Methods). The total FI ranged from 0 (no frailty) to 1 (severe frailty). Higher FI values indicated a greater presence of disabilities, diseases, symptoms, signs, cognitive and psychological abnormalities among participants. b, Correlations between FI and indices of its individual domains. Values in the figure are spearman correlation coefficients. As expected, the composite FI scores were not dominated by any single component and the disability domain exhibited the strongest correlation with the FI, as evidenced by the highest correlation coefficient. c, Association of FI with 2-year mortality risk in all participants. P-value was estimated by long-rank test.

Source data

Extended Data Fig. 3 Overview of the microbiome composition and function of RLAS cohort.

a, Phylum-level relative abundance composition of 1,448 samples in the cohort, sorted by abundance of Phylum Bacteroidetes. Each vertical line indicates one sample. b, Relative abundance of the top 10 MetaCyc pathways of 1,448 samples (sorted to match panel a). Each vertical line indicates one sample.

Source data

Extended Data Fig. 4 Microbial diversity and composition among participants with different frailty severity and sex.

a, Boxplot showing the association of Shannon alpha-diversity with frailty in an unadjusted model (P-trend = 0.68, 0.31, and 0.18 among total population (n = 1,448), women (n = 775), and men (n = 673), respectively). In boxplots, central band of the boxplot represents the median of the group, the lower and upper hinges correspond to the first and third quartiles, and the whiskers represent the 1.5× IQR from the hinge, whatever is lower. P for linear trend was from a general linear model with the Shannon diversity index as the dependent variable and the ordinal values of each frailty group as independent variables. b, Alterations of gut microbial constructions in frailty in female (left) and male (right). PCA based on the Aitchison dissimilarity metric showed a significant difference in the gut microbial composition among four frailty groups in female, but not in male. Statistical significance and variance were assessed using PERMANOVA/Adonis analysis. Results of pairwise adonis analysis are shown in the table below with the no-frailty group as the reference.

Source data

Extended Data Fig. 5 Alteration in gut microbiome composition of frail participants across two replication cohorts.

a-b, Assessment of Shannon diversity index across four frailty groups in the Chinese cohort (n = 342) (a) and the American cohort (n = 42) (b). P values for linear trend were derived from general linear models with the Shannon diversity index as the dependent variable and the ordinal values of each frailty group as independent variable. c-d, PCA illustrating the gut microbial compositions of groups based on species-level Aitchison dissimilarity with a 95% ellipse for each group for the Chinese cohort (c) and the American cohort (d). The marginal boxplots describe the overall distribution of PC1 and PC2 values within each group. Boxes in all boxplots represent the 25th percentile, median, and 75th percentile and whiskers stretch to 1.5 times interquartile range from corresponding hinge. Statistical P values in box plots were calculated from Wilcoxon rank-sum tests (two-sided). The PERMANOVA P-value was calculated model for covariate adjustment with 999 permutations. PERMANOVA significance for pairwise comparison are reported in the bottom.

Source data

Extended Data Fig. 6 Significant associations between frailty-related microbial species and clinical biomarkers.

Colors of the heatmap are in correspondence to the beta coefficient for frailty-related clinical biomarkers from linear regression models in MaAsLin with frailty-related species (AST-transformed relative abundance) as outcomes. The biomarkers levels were standardized into Z-scores of in before including them in the MaAsLin models. All models corrected for age, sex, BMI, smoking, drinking status, educational level, and physical activity level. The Benjamini-Hochberg method is used to calculate FDR-adjusted P values to address the multiple comparison issue. These analyses were based on 1,448 samples. All the statical tests were two-sided.

Source data

Extended Data Fig. 7 Associations between gut microbiota dysbiosis and frailty severity.

Statistically significant species with frailty severity through linear regression model for covariate adjustment for age, sex, BMI, smoking status, drinking status, educational level, and physical activity level. The significant P values are indicated: * FDR-adjusted P < 0.2, ** FDR-adjusted P < 0.1, *** FDR-adjusted P < 0.05.

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Extended Data Fig. 8 Associations between gut microbiota functional dysbiosis and frailty severity.

a, Statistically significant pathways in community-level with frailty severity through linear regression model for covariate adjustment for age, sex, BMI, smoking status, drinking status, educational level, and physical activity level (left panel). b, Boxplot showing the distribution of functional pathways across four frailty groups (n = 222, 778, 310 and 138 for no, mild, moderate, and severe frailty, respectively). In boxplots, central band of the boxplot represents the median of the group, the lower and upper hinges correspond to the first and third quartiles, and the whiskers stretch to 1.5 times the interquartile ranged from the corresponding hinge.

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Extended Data Fig. 9 Associations between gut microbiota functional dysbiosis and frailty severity.

Statistically significant MetaCyc pathways with frailty severity through linear regression models for covariate adjustment for age, sex, BMI, smoking status, drinking status, educational level, and physical activity level. The significant P values are indicated: * FDR-adjusted P < 0.2, ** FDR-adjusted P < 0.1, *** FDR-adjusted P < 0.05.

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Extended Data Fig. 10 The characteristic of developed microbiota-based frailty index (FI-microbiota) and metabolite-based frailty index (FI-metabolite).

a-c, Violin plots showing the distribution of FI-microbiota across four frailty groups (n = 222, 778, 310 and 138 for no, mild, moderate, and severe frailty, respectively) (a) and FI-metabolite across four frailty groups (n = 251, 929, 404 and 187 for no, mild, moderate, and severe, respectively) (c). The upper and lower edges of the boxplot indicate the 75th and 25th percentiles, respectively, and the middle line indicates the median. P values were calculated using two-sided Wilcoxon rank-sum tests. b-d, Heatmap of Spearman correlations between FI-microbiota and its component (b), and between FI-metabolite and its components. The number on each cell is the significant correlation coefficient and the blank cell represents no significant.

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Pu, Y., Sun, Z., Zhang, H. et al. Gut microbial features and circulating metabolomic signatures of frailty in older adults. Nat Aging (2024). https://doi.org/10.1038/s43587-024-00678-0

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