A clinically meaningful metric of immune age derived from high-dimensional longitudinal monitoring
Open accessAbstract
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.
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An IMM-AGE score constructed from proteins, transcripts, and immune cell counting in adults explained a significant portion of interindividual cytokine response variability.
“A recent study of adults used proteins, transcripts, and immune cell counting to construct an immune age (IMM-AGE) score that explained a significant portion of the interindividual cytokine response variability (114).”
From Connecting aging biology and inflammation in the omics era (2022) · cites this paper as [114]
The IMM-AGE score was a stronger predictor of mortality than a DNA methylation clock in the same cohort.
“The IMM-AGE score was a stronger predictor of mortality than a DNA methylation clock in the same cohort.”
From Connecting aging biology and inflammation in the omics era (2022) · cites this paper as [114]
A multi-omics IMM-AGE metric in 135 adults described immune functioning better than chronological age and accurately predicted all-cause mortality.
“This enabled the researchers to construct a high-dimensional trajectory of immune aging (IMM-AGE) that described individuals’ immune functioning better than their chronological age. This new metric in turn accurately predicted all-cause mortality”
From Chronic inflammation in the etiology of disease across the life span (2019) · cites this paper as [52]
Differences among older adults in the extent of systemic chronic inflammation are thought to reflect inter-individual differences in exposure to pro-inflammatory factors, although within-person studies are limited.
“The fact that differences exist in the extent to which older adults exhibit SCI52,64 is thought to be indicative of inter-individual differences in exposure to these and other related pro-inflammatory factors, although studies documenting within-person associations between these risk factors and SCI are limited.”
From Chronic inflammation in the etiology of disease across the life span (2019) · cites this paper as [52]
The authors' earlier cellular composite metric for immune aging, IMM-AGE, was strongly associated with all-cause mortality.
“Recent work from our group identified a cellular composite metric for immune aging (IMM-AGE), which was strongly associated with all-cause mortality16.”
From An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging (2021) · cites this paper as [16]
The nine established hallmarks of aging have all been shown to be connected with sustained systemic inflammation.
“have all been shown to be linked to sustained systemic inflammation7–16.”
From An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging (2021) · cites this paper as [16]
The definition of the corresponding age between humans and mice is still under discussion.
“Furthermore, the definition of the corresponding age between humans and mice is still under discussion [210, 211]”
From Immunosenescence: a key player in cancer development (2020) · cites this paper as [211]
A marked change in aging relates to the composition and functionality of CD4 T cells.
“A marked change observed in aging relates to the composition and functionality of CD4 T cells, the main orchestrators of adaptive immune responses (1–3).”
From Aging promotes reorganization of the CD4 T cell landscape toward extreme regulatory and effector phenotypes (2019) · cites this paper as [3]
A recent set of publications profiled immune cell and inflammatory markers to develop an immune ageing clock that predicted mortality, cardiovascular outcomes and immunosenescence.
“A recent set of publications did this by profiling immune cell and inflammatory markers to develop an immune ageing clock that predicted mortality, cardiovascular outcomes and immunosenescence171,172.”
From Measuring biological age using omics data (2022) · cites this paper as [172]
The authors used information from the clock to identify CXCL9 as a potential causal regulator of immune ageing and validated this result through functional follow-up in cellular models.
“The authors used information from the clock to identify CXCL9 as a potential causal regulator of immune ageing and validated this result through functional follow-up in cellular models, which further supports the ability of these hybrid approaches to discover causal regulators of ageing.”
From Measuring biological age using omics data (2022) · cites this paper as [172]
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- Authors
- Alpert, A., Pickman, Y., Leipold, M., Rosenberg-Hasson, Y., Ji, X., Gaujoux, R., Rabani, H., Starosvetsky, E., Kveler, K., Schaffert, S., Furman, D., Caspi, O., Rosenschein, U., Khatri, P., Dekker, C. L., Maecker, H. T., Davis, M. M., & Shen-Orr, S. S.
- Affiliations
- Technion – Israel Institute of Technology (IL)
- Stanford University (US)
- Stanford Medicine (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Davis, Mark M.
- Shen-Orr, Shai S.
- ORCID
- Venue
- Nature Medicine
- Published
- 2019-03
- Volume
- 25
- Issue
- 3
- Pages
- 487-495
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- PMC
- PMC6686855
- OpenAlex
- W2919822707
- Cited by
- 513
- References
- 40
- Citations by year
- 2026: 62 · 2025: 81 · 2024: 72 · 2023: 55 · 2022: 55 · 2021: 80 · 2020: 79 · 2019: 27 · 2017: 1 · 2013: 1
- FWCI
- 20.59
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Cancer Institute (P30CA124435)
- National Institute of Allergy and Infectious Diseases (U19AI090019, U19AI057229)
- National Center for Research Resources (UL1RR025744)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Life Sciences › Immunology and Microbiology › Immunology › T-cell and B-cell Immunology
- Keywords
- immunosenescence, interindividual variability, immune homeostasis, longitudinal immune monitoring, all-cause mortality prediction, aging biomarkers
- MeSH terms
- Immunosenescence, Adult, Aged, Aged, 80 and over, Aging, Female, Humans, Individuality, Longitudinal Studies, Lymphocytes, Male, Middle Aged, Mortality, Phenotype, Multivariate Analysis, Proportional Hazards Models, Cytokines, Young Adult, Healthy Volunteers
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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