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Personal aging markers and ageotypes revealed by deep longitudinal profiling

Nature Medicine volume 26, pages 83–90 (2020)Cite this article

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Abstract

The molecular changes that occur with aging are not well understood1,2,3,4. Here, we performed longitudinal and deep multiomics profiling of 106 healthy individuals from 29 to 75 years of age and examined how different types of ‘omic’ measurements, including transcripts, proteins, metabolites, cytokines, microbes and clinical laboratory values, correlate with age. We identified both known and new markers that associated with age, as well as distinct molecular patterns of aging in insulin-resistant as compared to insulin-sensitive individuals. In a longitudinal setting, we identified personal aging markers whose levels changed over a short time frame of 2–3 years. Further, we defined different types of aging patterns in different individuals, termed ‘ageotypes’, on the basis of the types of molecular pathways that changed over time in a given individual. Ageotypes may provide a molecular assessment of personal aging, reflective of personal lifestyle and medical history, that may ultimately be useful in monitoring and intervening in the aging process.

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Fig. 1: Integrative Personal Omics Profiling (iPOP) cohort sampling and data collection for aging analyses.
Fig. 2: Aging molecules and pathways revealed from cross-sectional analyses.
Fig. 3: Personal aging markers show personalized aging patterns that are distinct from those of cross-sectional aging markers.
Fig. 4: Personal ageotypes defined from four major groups of pathways.

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Data availability

Raw data included in this study are hosted on the NIH Human Microbiome 2 project site (https://portal.hmpdacc.org) with no restrictions on their use. Exome sequencing data are also available at dbGaP under study accession phs001719.v1.p1. Both raw and processed data are also hosted on the Stanford iPOP site (http://med.stanford.edu/ipop.html). For additional information regarding the study, please contact the corresponding author.

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Acknowledgements

We thank numerous colleagues who contributed to this study, including all the authors of the Zhou et al. study13. We thank the Stanford Center for Genomics and Personalized Medicine (SCGPM) for providing the computing and data storage resources. We also thank the Jackson Laboratory for Genomic Medicine for contributing to this project. This work was supported by grants from the National Institutes of Health (NIH) Common Fund Human Microbiome Project (U54DK102556), by a grant award to the SCGPM Genome Sequencing Service Center (NIH, S10OD020141), by the Stanford Clinical and Translational Science Award to Spectrum (NIH, UL1TR001085) and by the Diabetes Genomics and Analysis Core of the Stanford Diabetes Research Center (P30DK116074). S.M.S.-F.R. was supported by NIH grant K08 ES028825.

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Author notes

  1. These authors contributed equally: Sara Ahadi, Wenyu Zhou.

Authors and Affiliations

  1. Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA

    Sara Ahadi, Wenyu Zhou, Sophia Miryam Schüssler-Fiorenza Rose, M. Reza Sailani, Kévin Contrepois, Monika Avina, Melanie Ashland, Anne Brunet & Michael Snyder

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  1. Sara Ahadi
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Contributions

S.A., W.Z., M.R.S., K.C. and M. Avina performed experimental bench work and collected data. W.Z. and S.A. analyzed the data and generated results, with contributions from S.M.S.-F.R. M. Avina managed and coordinated the biobank sample inventory. M. Ashland and W.Z. managed the cohort and coordinated clinical visits. A.B. and M.S. obtained funding and provided additional study resources. S.A., W.Z., A.B., S.M.S.-F.R. and M.S. wrote and revised the manuscript. M.S. supervised the overall study.

Corresponding author

Correspondence to Michael Snyder.

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Competing interests

M.S. is a cofounder of Personalis, Qbio, Sensomics, Mirvie, Filtricine, Protos and January. He is on the scientific advisory board of Jungla, Jupiter and Genapsys.

Additional information

Peer review information Michael Basson was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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

Extended data

Extended Data Fig. 1 Significant analytes associated with aging in the cross-section cohort (n = 106).

Left: Number of significant multi-omics molecules correlating with age based on p-value threshold (before multiple hypothesis correction). Right: The categories and their corresponding percentage (frequency) of metabolites significantly associated with the age. Significance is based on the Spearman rank tests.

Extended Data Fig. 2 Scatter plot of number of significant molecules with the age in longitudinal data of 43 individuals.

(Intentionally blank).

Extended Data Fig. 3 Scatter plot showing associations of the magnitude (cumulative trend values of contributing molecules) of each ageotpes with BMI based on 43 individuals.

Associations are not significant. Significance is calculated in the linear regression model.

Extended Data Fig. 4 Scatter plot showing associations of the magnitude (cumulative trend values of contributing molecules) of each ageotpes with Age based on 43 individuals.

Associations are not significant. Significance is calculated in the linear regression model.

Extended Data Fig. 5 Scatter plot showing associations of the magnitude (cumulative trend values of contributing molecules) of each ageotpes with insulin-resistant/sensitive status based on 43 individuals.

Associations are not significant. Significance is calculated in the linear regression model.

Supplementary information

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Supplementary Tables

Supplementary Tables 1–18

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Ahadi, S., Zhou, W., Schüssler-Fiorenza Rose, S.M. et al. Personal aging markers and ageotypes revealed by deep longitudinal profiling. Nat Med 26, 83–90 (2020). https://doi.org/10.1038/s41591-019-0719-5

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