Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Personal aging markers and ageotypes revealed by deep longitudinal profiling


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.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type



Prices may be subject to local taxes which are calculated during checkout

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.

Data availability

Raw data included in this study are hosted on the NIH Human Microbiome 2 project site ( 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 ( For additional information regarding the study, please contact the corresponding author.


  1. Lopez-Otin, C., Blasco, M. A., Partridge, L., Serrano, M. & Kroemer, G. The hallmarks of aging. Cell 153, 1194–1217 (2013).

    Article  CAS  Google Scholar 

  2. Aunan, J. R., Cho, W. C. & Soreide, K. The biology of aging and cancer: a brief overview of shared and divergent molecular hallmarks. Aging Dis. 8, 628–642 (2017).

    Article  Google Scholar 

  3. Hung, C. W., Chen, Y. C., Hsieh, W. L., Chiou, S. H. & Kao, C. L. Ageing and neurodegenerative diseases. Ageing Res. Rev. 9(Suppl. 1), S36–S46 (2010).

    Article  Google Scholar 

  4. Kalyani, R. R. & Egan, J. M. Diabetes and altered glucose metabolism with aging. Endocrinol. Metab. Clin. North Am. 42, 333–347 (2013).

    Article  CAS  Google Scholar 

  5. Steenman, M. & Lande, G. Cardiac aging and heart disease in humans. Biophys. Rev. 9, 131–137 (2017).

    Article  Google Scholar 

  6. Field, A. E. et al. DNA methylation clocks in aging: categories, causes, and consequences. Mol. Cell 71, 882–895 (2018).

    Article  CAS  Google Scholar 

  7. Horvath, S. DNA methylation age of human tissues and cell types. Genome Biol. 14, R115 (2013).

    Article  Google Scholar 

  8. Lu, A. T. et al. DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging 11, 303–327 (2019).

    Article  CAS  Google Scholar 

  9. Pani, L. N. et al. Effect of aging on A1C levels in individuals without diabetes: evidence from the Framingham Offspring Study and the National Health and Nutrition Examination Survey 2001–2004. Diabetes Care 31, 1991–1996 (2008).

    Article  Google Scholar 

  10. Bonomini, F., Rodella, L. F. & Rezzani, R. Metabolic syndrome, aging and involvement of oxidative stress. Aging Dis. 6, 109–120 (2015).

    Article  Google Scholar 

  11. Gutch, M., Kumar, S., Razi, S. M., Gupta, K. K. & Gupta, A. Assessment of insulin sensitivity/resistance. Indian J. Endocrinol. Metab. 19, 160–164 (2015).

    Article  CAS  Google Scholar 

  12. Knowles, J. W. et al. Measurement of insulin-mediated glucose uptake: direct comparison of the modified insulin suppression test and the euglycemic, hyperinsulinemic clamp. Metabolism 62, 548–553 (2013).

    Article  CAS  Google Scholar 

  13. Zhou, W. et al. Longitudinal of host–microbe dynamics in prediabetes. Nature 569, 663–671 (2019).

    Article  CAS  Google Scholar 

  14. Schüssler-Fiorenza Rose, S. M. et al. A longitudinal big data approach for precision health. Nat. Med. 25, 792–804 (2019).

    Article  Google Scholar 

  15. Wetzels, J. F., Kiemeney, L. A., Swinkels, D. W., Willems, H. L. & den Heijer, M. Age- and gender-specific reference values of estimated GFR in Caucasians: the Nijmegen Biomedical Study. Kidney Int. 72, 632–637 (2007).

    Article  CAS  Google Scholar 

  16. Edelberg, J. M., Cai, D. & Xaymardan, M. Translation of PDGF cardioprotective pathways. Cardiovasc. Toxicol. 3, 27–35 (2003).

    Article  CAS  Google Scholar 

  17. Pola, R. et al. Age-dependent VEGF expression and intraneural neovascularization during regeneration of peripheral nerves. Neurobiol. Aging 25, 1361–1368 (2004).

    Article  CAS  Google Scholar 

  18. Kolovou, G. et al. Ageing mechanisms and associated lipid changes. Curr. Vasc. Pharmacol. 12, 682–689 (2014).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  20. Galkin, F. et al. Human microbiome aging clocks based on deep learning and tandem of permutation feature importance and accumulated local effects. Preprint at bioRxiv (2018).

  21. Bonafe, M. et al. Polymorphic variants of insulin-like growth factor I (IGF-I) receptor and phosphoinositide 3-kinase genes affect IGF-I plasma levels and human longevity: cues for an evolutionarily conserved mechanism of life span control. J. Clin. Endocrinol. Metab. 88, 3299–3304 (2003).

    Article  CAS  Google Scholar 

  22. Mori, K. et al. Fetuin-A is associated with calcified coronary artery disease. Coron. Artery Dis. 21, 281–285 (2010).

    Article  Google Scholar 

  23. Reiner, A. P. et al. PROC, PROCR and PROS1 polymorphisms, plasma anticoagulant phenotypes, and risk of cardiovascular disease and mortality in older adults: the Cardiovascular Health Study. J. Thromb. Haemost. 6, 1625–1632 (2008).

    Article  CAS  Google Scholar 

  24. Franceschi, C. & Campisi, J. Chronic inflammation (inflammaging) and its potential contribution to age-associated diseases. J. Gerontol. A Biol. Sci. Med. Sci. 69(Suppl. 1), S4–S9 (2014).

    Article  Google Scholar 

  25. Peters, M. J. et al. The transcriptional landscape of age in human peripheral blood. Nat. Commun. 6, 8570 (2015).

    Article  CAS  Google Scholar 

  26. Bae, E., Kim, H. E., Koh, E. & Kim, K. S. Phosphoglucomutase1 is necessary for sustained cell growth under repetitive glucose depletion. FEBS Lett. 588, 3074–3080 (2014).

    Article  CAS  Google Scholar 

  27. Liu, Z. et al. A new aging measure captures morbidity and mortality risk across diverse subpopulations from NHANES IV: a cohort study. PLoS Med. 15, e1002718 (2018).

    Article  Google Scholar 

  28. Liu, Z. et al. Correction: a new aging measure captures morbidity and mortality risk across diverse subpopulations from NHANES IV: a cohort study. PLoS Med. 16, e1002760 (2019).

    Article  Google Scholar 

  29. Carter, T. A. et al. Mechanisms of aging in senescence-accelerated mice. Genome Biol. 6, R48 (2005).

    Article  Google Scholar 

  30. Ross, G. R. et al. Enhanced store-operated Ca2+ influx and ORAI1 expression in ventricular fibroblasts from human failing heart. Biol. Open 6, 326–332 (2017).

    Article  CAS  Google Scholar 

  31. Kreienkamp, R. et al. A cell-intrinsic interferon-like response links replication stress to cellular aging caused by progerin. Cell Rep. 22, 2006–2015 (2018).

    Article  CAS  Google Scholar 

  32. Ma, X. et al. The nuclear receptor RXRA controls cellular senescence by regulating calcium signaling. Aging Cell 17, e12831 (2018).

    Article  Google Scholar 

  33. Cevenini, E. et al. Human models of aging and longevity. Expert Opin. Biol. Ther. 8, 1393–1405 (2008).

    Article  CAS  Google Scholar 

  34. Lean, M. E. J., Anderson, A. S., Morrison, C. & Currall, J. Evaluation of a dietary targets monitor. Eur. J. Clin. Nutr. 57, 667–673 (2003).

    Article  CAS  Google Scholar 

  35. Hagströmer, M., Oja, P. & Sjöström, M. The International Physical Activity Questionnaire (IPAQ): a study of concurrent and construct validity. Public Health Nutr. 9, 755–762 (2006).

    Article  Google Scholar 

Download references


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.

Author information

Authors and Affiliations



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.

Ethics declarations

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

Reporting Summary

Supplementary Tables

Supplementary Tables 1–18

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing