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.

A clinically meaningful metric of immune age derived from high-dimensional longitudinal monitoring


Immune responses generally decline with age. However, the dynamics of this process at the individual level have not been characterized, hindering quantification of an individual’s immune age. Here, we use multiple ‘omics’ technologies to capture population- and individual-level changes in the human immune system of 135 healthy adult individuals of different ages sampled longitudinally over a nine-year period. We observed high inter-individual variability in the rates of change of cellular frequencies that was dictated by their baseline values, allowing identification of steady-state levels toward which a cell subset converged and the ordered convergence of multiple cell subsets toward an older adult homeostasis. These data form a high-dimensional trajectory of immune aging (IMM-AGE) that describes a person’s immune status better than chronological age. We show that the IMM-AGE score predicted all-cause mortality beyond well-established risk factors in the Framingham Heart Study, establishing its potential use in clinics for identification of patients at risk.

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: Study design.
Fig. 2: Individuals’ cellular immune profiles exhibit inter-individual variability both at baseline and in their rate of change.
Fig. 3: Annual change of immune cell–subset frequency depends on baseline levels rather than on age.
Fig. 4: Immune cell dynamics in older adults yield a convergence on an attractor point.
Fig. 5: A linear trajectory explains dynamics of cellular frequencies in healthy aging.
Fig. 6: IMM-AGE score predicts all-cause mortality risk beyond well-established risk factors.

Code availability

Source code is available from the corresponding author upon reasonable request.

Data availability

Frequencies of gated cellular populations for the snapshot and ongoing data sets are in Supplementary Tables 3 and 5, respectively. Adjusted frequencies for the ongoing data set are in Supplementary Table 6. Subjects’ identities were shuffled to protect their privacy. Raw fcs files of the ongoing data set used for both cellular phenotyping and phospho-flow analysis were deposited into the Immport website ( and can be downloaded using the following accession codes stratified by year: SDY212 (2008), SDY312 (2009), SDY311 (2010), SDY112 (2011), SDY315 (2012) and SDY478 (2013). Raw gene expression .CEL files for years 2011–2012 were deposited into the Immport website and can be downloaded using the following accession codes stratified by year: SDY112 (2011) and SDY315 (2012). Raw and processed gene expression data for years 2013–2015 are available in the GEO website ( through the following accession numbers: GSE123696 (2013), GSE123687 (2014) and GSE123698 (2015). Longitudinal clinical data of the participants are available in Supplementary Table 13, with the same shuffling of subjects’ identities used for cellular frequency data. DMAP gene expression data are publicly available on the GEO website (accession number GSE24759). Gene expression and methylation data from the Framingham Heart Study are available through dbGaP (study identifier phs000007). Phenotypic data are similarly available using the following accession codes: gender and age at exam 8 (pht003099); smoking status, blood pressure and blood pressure treatment (pht000747); HDL, total cholesterol and fasting glucose (pht000742); diabetes treatment (pht000041). Cardiovascular disease status at exam 8 and all-cause mortality during follow-up time were derived from the files ‘survival and follow-up status for cardiovascular events' (pht003316) and ‘survival – all cause mortality' (pht003317), respectively.


  1. Goronzy, J. J. & Weyand, C. M. Understanding immunosenescence to improve responses to vaccines. Nat. Immunol. 14, 428–436 (2013).

    Article  CAS  Google Scholar 

  2. Deleidi, M., Jggle, M. & Rubino, G. Immune ageing, dysmetabolism and inflammation in neurological diseases. Front. Neurosci. 9, 172 (2015).

  3. Dorshkind, K., Montecino-Rodriguez, E. & Signer, R. A. J. The ageing immune system: is it ever too old to become young again? Nat. Rev. Immunol. 9, 57–62 (2009).

    Article  CAS  Google Scholar 

  4. Gruver, A. L., Hudson, L. L. & Sempowski, G. D. Immunosenescence of ageing. J. Pathol. 211, 144–156 (2007).

    Article  CAS  Google Scholar 

  5. McElhaney, J. E. Influenza vaccine responses in older adults. Ageing Res. Rev. 10, 379–388 (2011).

    Article  CAS  Google Scholar 

  6. Carr, E. J. et al. The cellular composition of the human immune system is shaped by age and cohabitation. Nat. Immunol. 17, 461–468 (2016).

  7. Nikolich-Žugich, J. The twilight of immunity: emerging concepts in aging of the immune system. Nat. Immunol. 19, 10–19 (2018).

  8. Ló Pez-Otín, C., Blasco, M. A., Partridge, L., Serrano, M. & Kroemer, G. The hallmarks of aging. Cell 153, 1194–1217 (2013).

    Article  Google Scholar 

  9. Franceschi, C. et al. Inflamm-aging. An evolutionary perspective on immunosenescence. Ann. N. Y. Acad. Sci. 908, 244–254 (2000).

    Article  CAS  Google Scholar 

  10. Shen-Orr, S. S. et al. Defective signaling in the JAK-STAT pathway tracks with chronic inflammation and cardiovascular risk in aging humans. Cell Syst. 3, 374–384.e4 (2016).

  11. Furman, D. et al. Expression of specific inflammasome gene modules stratifies older individuals into two extreme clinical and immunological states. Nat. Med. 23, 174–184 (2017).

  12. Orrù, V. et al. Genetic variants regulating immune cell levels in health and disease. Cell 155, 242–256 (2013).

    Article  Google Scholar 

  13. Patin, E. et al. Natural variation in the parameters of innate immune cells is preferentially driven by genetic factors. Nat. Immunol. 19, 302–314 (2018).

    Article  CAS  Google Scholar 

  14. Roederer, M. et al.The genetic architecture of the human immune system: a bioresource for autoimmunity and disease pathogenesis. Cell 161, 387–403 (2015).

    Article  CAS  Google Scholar 

  15. Brodin, P. et al. Variation in the human immune system is largely driven by non-heritable Influences. Cell 160, 37–47 (2015).

    Article  CAS  Google Scholar 

  16. Tsang, J. S. Utilizing population variation, vaccination, and systems biology to study human immunology. Trends Immunol. 36, 479–493 (2015).

    Article  CAS  Google Scholar 

  17. Shen-Orr, S. S. & Furman, D. Variability in the immune system: Of vaccine responses and immune states. Curr. Opin. Immunol. 25, 542–547 (2013).

    Article  CAS  Google Scholar 

  18. Brodin, P. & Davis, M. M. Human immune system variation. Nat. Rev. Immunol. 17, 21–29 (2017).

  19. Kaczorowski, K. J. et al. Continuous immunotypes describe human immune variation and predict diverse responses. Proc. Natl Acad. Sci. USA 114, E6097–E6106 (2017).

  20. Horvath, S. et al. An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease. Genome Biol. 17, 171 (2016).

  21. Clegg, A., Young, J., Iliffe, S., Rikkert, M. O. & Rockwood, K. Frailty in elderly people. Lancet 381, 62167-62169 (2013).

  22. Nakaya, H. I. et al. Systems analysis of immunity to influenza vaccination across multiple years and in diverse populations reveals shared molecular signatures. Immun ity 43, 1186–1198 (2015).

  23. Bektas, A., Schurman, S. H., Sen, R. & Ferrucci, L. Aging, inflammation and the environment. Exp. Gerontol. 105, 10–18 (2018).

  24. Pawelec, G. Immune parameters associated with mortality in the elderly are context-dependent: lessons from Sweden, Holland and Belgium. Biogerontology 19, 537–545 (2018).

  25. Lin, Y. et al. Changes in blood lymphocyte numbers with age in vivo and their association with the levels of cytokines/cytokine receptors. Immun. Ageing 13, 24 (2016).

    Article  Google Scholar 

  26. Strogatz, S. H. Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering (Studies in Nonlinearity) 1st edn (Addison-Wesley, Reading, Mass., 1994).

  27. Furman, D. et al. Apoptosis and other immune biomarkers predict influenza vaccine responsiveness. Mol. Syst. Biol. 9, 659 (2013).

    Article  Google Scholar 

  28. Merino, J. et al. Progressive decrease of CD8high+ CD28+ CD57– cells with ageing. Clin. Exp. Immunol. 112, 48–51 (1998).

    Article  CAS  Google Scholar 

  29. Afzali, B. et al. CD161 expression characterizes a subpopulation of human regulatory T cells that produces IL-17 in a STAT3-dependent manner. Eur. J. Immunol. 43, 2043–2054 (2013).

    Article  CAS  Google Scholar 

  30. Haghverdi, L., Büttner, M., Wolf, F. A., Buettner, F. & Theis, F. J. Diffusion pseudotime robustly reconstructs lineage branching. Nat. Methods 13, 845–848 (2016).

    Article  CAS  Google Scholar 

  31. Shen-Orr, S. S. et al. Defective signaling in the JAK-STAT pathway tracks with chronic inflammation and cardiovascular risk in aging humans. Cell Syst. 3, 374–384.e4 (2016).

    Article  CAS  Google Scholar 

  32. D’Agostino, R. B. et al. General cardiovascular risk profile for use in primary care: The Framingham Heart Study. Circulation 117, 743–753 (2008).

  33. Barbie, D. A. et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature 462, 108–112 (2009).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  35. Marioni, R. E. et al. DNA methylation age of blood predicts all-cause mortality in later life. Genome Biol. 16, 25 (2015).

    Article  Google Scholar 

  36. Waddington, C. H. The Strategy of the Genes. A Discussion of Some Aspects of Theoretical Biology. With an Appendix by H. Kacser (George Allen & Unwin, London, 1957).

  37. Pawelec, G. Immune parameters associated with mortality in the elderly are context-dependent: lessons from Sweden, Holland and Belgium. Biogerontology 19, 537–545 (2018).

  38. Ridker, P. M. et al. Antiinflammatory therapy with canakinumab for atherosclerotic disease. N. Engl. J. Med. 377, 1119–1131 (2017).

    Article  CAS  Google Scholar 

  39. Blazkova, J. et al. Multicenter systems analysis of human blood reveals immature neutrophils in males and during pregnancy. J. Immunol. 198, 2479–2488 (2017).

    Article  CAS  Google Scholar 

  40. Furman, D. et al. Systems analysis of sex differences reveals an immunosuppressive role for testosterone in the response to influenza vaccination. Proc. Natl Acad. Sci. USA 111, 869–874 (2014).

    Article  CAS  Google Scholar 

  41. Furman, D. et al. Cytomegalovirus infection enhances the immune response to influenza. Sci. Transl. Med. 7, 281ra43 (2015).

    Article  Google Scholar 

  42. Dai, H., Leeder, J. S. & Cui, Y. A modified generalized Fisher method for combining probabilities from dependent tests. Front. Genet. 5, 32 (2014).

    Article  Google Scholar 

  43. Novershtern, N. Densely interconnected transcriptional circuits control cell states in human hematopoiesis. Cell 144, 296–309 (2011).

    Article  CAS  Google Scholar 

Download references


We thank M. G’Sell for statistical analysis advice, O. Barak, B. Kidd, F. Haddad and members of the Shen-Orr lab for illuminating discussions; the clinical and administrative staff at the Stanford-Lucille Packard Children’s Hospital Vaccine Program; members of the Stanford Human Immune Monitoring Center for help with assay measurements; and D. Cohen for computational support. This work was supported in part by grants from the Ellison Medical Foundation, Howard Hughes Medical Institute, and National Institute of Allergy and Infectious Diseases (U19 AI057229 and U19 AI090019) to M.M.D.; and from the Israeli Science Foundation (grant 1365/12), Rappaport Institute and Kolk family awards to S.S.S.-O. The project was supported by National Institutes of Health (NIH)/National Center for Research Resources Clinical and Translational Science Awards to Stanford University (UL1 RR025744). This trial is registered at as NCT01827462. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations



M.M.D. and S.S.S.-O. conceived the experiment; C.L.D. carried out and supervised subject enrollment, follow-up and sample collection; M.L., X.J., Y.R.-H., D.F. and H.T.M. carried out and supervised the data measurement; Y.P., A.A., D.F., M.M.D. and S.S.S.-O. designed the overall data analysis strategy; Y.P. and A.A. performed the overall data analysis; Y.P., A.A., M.L., E.S., H.T.M. and S.S.S.-O. analyzed the cell subset data; Y.P., A.A., R.G., S.S., P.K. and S.S.S.-O. analyzed gene expression data; A.A., Y.P., D.F., H.R. and O.C. analyzed the clinical data; O.C. and U.R. provided input on cardiovascular analysis; Y.P., A.A. and K.K. performed integrative analysis; Y.P., A.A., M.M.D. and S.S.S.-O. co-wrote the paper.

Corresponding authors

Correspondence to Mark M. Davis or Shai S. Shen-Orr.

Ethics declarations

Competing interests

The authors declare competing interests: S.S.S.-O., Y.P. and A.A. are co-inventors of a patent application filed by the Technion and related to this work (provisional application number 62/667,698). S.S.S.-O., D.F. and M.M.D. are co-inventors of US patent US10119959B2 filed by Stanford and related to this work. R.G. holds equity in and is an employee of CytoReason. S.S.S-O, E.S. and K.K. hold equity in and consult for CytoReason. The authors have no other competing interests to disclose.

Additional information

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

Extended data

Extended Data Figure 1 Study demographics and experimental platforms.

a, Histogram summarizes number of visits (years in which immune profiling was conducted) across individuals stratified by age group. b, Histogram summarizes total number of individuals profiled per year. c, Age and gender distributions of individuals profiled per year. d, Data types and platforms used for immune profiling in each year for snapshot (right column) and ongoing data sets (left columns). e, Data types and relevant years of the Stanford University’s longitudinal study of aging and vaccination that were analyzed in our study (bottom row) and other studies (top rows). CBC, complete blood count.

Extended Data Figure 2 Snapshot cohort analysis.

a, Interindividual (pink, n = 18 individuals) and intraindividual (blue, n = 6 years) variation distributions calculated using coefficient of variation per cell subset. Boxes represent 25th and 75th percentiles around the median (line). Whiskers, 1.5× interquantile range. b, Percentage of total variance per cell subset explained by interindividual variation (total sum of squares attributed to subject) and intraindividual variation (residual sum of squares). c, Lengths of within-individual trajectories in young adults versus older adults as measured in the PCA two-dimensional space (P = 0.03, two-tailed t-test, n = 3 young adults and n = 15 older adults). Boxes represent 25th and 75th percentiles around the median (line). Whiskers, 1.5× interquantile range. d, Spearman correlations of individual-level slopes of cell subsets highly correlated with the first two principal components with age and the individual’s baseline position along the PCA two-dimensional axes (right and left boxplots, respectively, P = 1.7 × 104, two-tailed paired t-test, n = 39). Boxes represent 25th and 75th percentiles around the median (line). Whiskers, 1.5× interquantile range. *P < 0.05, ***P = 0.001.

Extended Data Figure 3 Adjustment of the ongoing dataset using young individuals improves its correlation with the snapshot dataset.

a,, Boxplots of representative cell-subset frequencies in 72 old (pink) and 63 young (light blue) adults before (upper panels) and after (lower panels) adjustment for young individuals. Boxes represent 25th and 75th percentiles around the median (line). Whiskers, 1.5× interquantile range. b, Individual slopes of frequencies of 30 immune cell subsets identified as associated with age calculated based on the snapshot (left), adjusted ongoing (middle) and nonadjusted ongoing (right) data sets. Adjustment of the ongoing data set improved the correlation between the slopes measured in the snapshot and ongoing data sets from 0.29 (linear regression P = 0.12, n = 30) to 0.54 (linear regression P = 0.004, n = 30). Boxes represent 25th and 75th percentiles around the median (line). Whiskers, 1.5× interquantile range.

Extended Data Figure 4 Age poorly affects longitudinal dynamics in cellular frequencies.

Scatter plots of annual change versus individual age for each cell subset identified as significantly age dependent. Blue lines denote linear regression lines.

Extended Data Figure 5 Classification scheme of cell subsets based on their dynamics.

Cell subsets identified as significantly age dependent in a combinatorial analysis across years were filtered based on annual-change data quality. Cell subsets exhibiting an annual change not significantly different from 0 were classified as fluctuating, whereas those exhibiting a significant nonzero annual change were subjected to an additional analysis testing the relationship of their annual change with baseline frequencies. Cell subsets with a nonsignificant association were classified as linear whereas those exhibiting a significant association were classified as asymptotic and were subjected to three additional tests analyzing the significance of their identified attractor point locations.

Extended Data Figure 6 Three stages of longitudinal dynamics of cell subsets’ frequencies are captured in the ongoing dataset.

a, Scatter plots of annual change versus baseline frequencies for cell subsets classified as asymptotic. Blue, red, green and purple lines correspond to linear regression lines, attractor point frequencies and median frequency in young and older adults, respectively. Confidence intervals for attractor points are delimited by grey dashed lines. b, Scatter plots of annual change versus the baseline frequencies for cell subsets classified as slow linear. Green and purple lines denote median frequency in young and older adults, respectively. c, Scatter plots of annual change versus the baseline frequencies for cell subsets classified as fluctuating. Green and purple lines denote median frequency in young and older adults, respectively.

Extended Data Figure 7 Inherent correlations between cell subsets.

Boxplots denote ages at which cell subsets reached their corresponding attractor point frequencies stratified by either cell subset (median interquartile range of 14.75 years; naive CD8+ T cells were excluded as most individuals did not reach the attractor point frequencies; n = 72 individuals) (a) or individuals (median interquartile range of 2 years; n = 10 cell subsets) (b). Boxes represent 25th and 75th percentiles around the median (line). Whiskers, 1.5× interquantile range. c, Scheme describing longitudinal correlations calculated between every pair of cell subsets. d, Longitudinal pairwise Spearman correlations between cell subsets are indicated by circle color whereas circle size corresponds to P value (calculated by permutations; only correlations (P < 0.05) are shown; n = 69 individuals).

Extended Data Figure 8 A linear trajectory explains the dynamics of cellular frequencies in healthy aging.

a, Annual change in pseudotime calculated for young (two-tailed binomial test P = 0.648, n = 77) and older (two-tailed binomial test P = 0.0018, n = 210) individuals. Boxes represent 25th and 75th percentiles around the median (line). Whiskers, 1.5× interquantile range. b, Linear-model-derived significance levels (n = 294 samples, calculated as –log[P]) of cellular frequencies that were regressed either versus age (x-axis) or pseudotime (y-axis). Each dot denotes a cell subset with shape corresponding to the direction of young-old differences and color corresponding to the cell subset classification. Dashed line is the y = x line. c, Scaled frequencies of cell subsets classified as asymptotic, slow linear or fluctuating (left bar) along the age axis. Median values of scaled frequencies calculated using young individuals are in the left bar. d, Cytokine response score of year 2012 samples colored by age and median regression line versus pseudotime (quantile regression P = 0.021, 0.77 (n = 17) for pseudotime and age, respectively). Quantile regression lines of quantiles 0.25 and 0.75 are shown as dashed lines. e, Scaled individual phospho-flow cytokine responses measured in years 2011–2012 in individuals positioned in either half of the trajectory as divided by the median (n = 28 and n = 6 in each group for 2011 and 2012, respectively). Dots and error bars correspond to median and s.d., respectively.

Extended Data Figure 9 Identification of a gene-set whose expression correlates with IMM-AGE.

a, Scaled gene expression by individuals ordered based on their IMM-AGE scores for genes identified as consistently changing along the trajectory. Information about individuals’ IMM-AGE, age and year appears on top. b, Difference between number of up- and downregulated genes of the identified gene set expressed by different cell types in the DMAP data set. Only cell types exhibiting significant enrichment either for up- or downregulated genes are displayed.

Extended Data Figure 10 Clinical associations of IMM-AGE scores in the Framingham Heart Study dataset.

a, Expression levels of genes used for IMM-AGE approximation in gene expression samples of Framingham Heart Study participants ordered by approximated IMM-AGE scores. b, Correlation of estimated IMM-AGE scores with age and gender (linear regression P = 7 × 10–63 and 7.22 × 10–27 (n = 2,292) for age and gender, respectively). Points correspond to individuals; color denotes gender. c, Age- and gender-adjusted IMM-AGE score of individuals stratified based on cardiovascular disease (dots); bold lines denote mean values (P = 0.0023, n = 2,292, two-tailed t-test). d, IMM-AGE score association with cardiovascular risk factors as obtained by linear regression. Bar colors denote positive (light blue) or negative (dark blue) associations. e, Linear regression of IMM-AGE versus DNA methylation age, where both variables were adjusted for cardiovascular risk factors and cardiovascular disease (n = 2,139 individuals). **P < 0.01; CVD, cardiovascular disease.

Supplementary information

Supplementary Information

Supplementary Figures 1–4, Supplementary Note.

Reporting Summary

Supplementary Tables 1–14

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alpert, A., Pickman, Y., Leipold, M. et al. A clinically meaningful metric of immune age derived from high-dimensional longitudinal monitoring. Nat Med 25, 487–495 (2019).

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