The protein inputs of an ultra-predictive aging clock represent viable anti-aging drug targets
Abstract
Machine learning models capable of predicting age given a set of inputs are referred to as aging clocks. We recently developed an aging clock that utilizes 491 plasma protein inputs, has an exceptional accuracy, and is capable of measuring biological age. Here, we demonstrate that this clock is extremely predictive (r = 0.95) when used to measure age in a novel plasma proteomic dataset derived from 370 human subjects aged 18-69 years. Over-representation analyses of the proteins that make up this clock in the Gene Ontology and Reactome databases predominantly implicated innate and adaptive immune system processes. Immunological drugs and various age-related diseases were enriched in the DrugBank and GLAD4U databases. By performing an extensive literature review, we find that at least 269 (54.8 %) of these inputs regulate lifespan and/or induce changes relevant to age-related disease when manipulated in an animal model. We also show that, in a large plasma proteomic dataset, the majority (57.2 %) of measurable clock proteins significantly change their expression level with human age. Different subsets of proteins were overlapped with distinct epigenetic, transcriptomic, and proteomic aging clocks. These findings indicate that the inputs of this age predictor likely represent a rich source of anti-aging drug targets.
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Follow-up studies have shown that dozens of proteins identified in plasma proteomic ageing clocks directly regulate lifespan, and hundreds have biological connections to the health status of different organs.
“Follow-up studies have shown that dozens of proteins identified in plasma proteomic ageing clocks directly regulate lifespan, and hundreds have biological connections to the health status of different organs98,99.”
From Measuring biological age using omics data (2022) · cites this paper as [99]
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Plasma proteomic profile of frailty
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Secretory phospholipase A2 group IIA modulates insulin sensitivity and metabolism
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Using the drug-protein interactome to identify anti-ageing compounds for humans
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Sclerostin antibody stimulates periodontal regeneration in large alveolar bone defects
Yao, Y., Kauffmann, F., Maekawa, S., Sarment, L., Sugai, J. V., Schmiedeler, C., Doherty, E. J., Holdsworth, G., Kostenuik, P. J., & Giannobile, W. V. (2020) · Scientific Reports · doi:10.1038/s41598-020-73026-y · Cited by 44 · see citing works
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Molecular Mechanisms of Aging and Immune System Regulation in Drosophila
Eleftherianos, I., & Castillo, J. C. (2012) · International Journal of Molecular Sciences · doi:10.3390/ijms13089826 · Cited by 40 · see citing works
Clinical value of R-spondins in triple-negative and metaplastic breast cancers
Coussy, F., Lallemand, F., Vacher, S., Schnitzler, A., Chemlali, W., Caly, M., Nicolas, A., Richon, S., Meseure, D., Botty, R. E., et al. (2017) · British Journal of Cancer · doi:10.1038/bjc.2017.131 · Cited by 40 · see citing works
Muscle stem cell renewal suppressed by GAS1 can be reversed by GDNF in mice
Li, L., Rozo, M., Yue, S., Zheng, X., Tan, F. J., Lepper, C., & Fan, C. (2019) · Nature Metabolism · doi:10.1038/s42255-019-0110-3 · Cited by 40 · see citing works
Lee, S., Lee, S. K., Paik, D., & Min, K. (2012) · Oxidative Medicine and Cellular Longevity · doi:10.1155/2012/854502 · Cited by 39 · see citing works
Yang, C. X., Schön, E., Obeidat, M., Kobor, M. S., McEwen, L. M., MacIsaac, J. L., Lin, D. T. S., Novak, R. M., Hudson, F., Klinker, H., et al. (2020) · The Journal of Infectious Diseases · doi:10.1093/infdis/jiaa599 · Cited by 38 · see citing works
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Systemic milieu and age-related deterioration
Zhang, H., Cherian, R., & Jin, K. (2019) · GeroScience · doi:10.1007/s11357-019-00075-1 · Cited by 32 · see citing works
CSNK2A1 Promotes Gastric Cancer Invasion Through the PI3K-Akt-mTOR Signaling Pathway
Jiang, C., Ma, Z., Zhang, G., Xigui, Y., Du, Q., & Wang, W. (2019) · Cancer Management and Research · doi:10.2147/cmar.s222620 · Cited by 32 · see citing works
Ngo, K., Pohl, P. H. I., Wang, D., Leme, A. S., Lee, J., Di, Y. P., Roughley, P. J., Robbins, P. D., Niedernhofer, L. J., Sowa, G., et al. (2017) · Spine · doi:10.1097/brs.0000000000002258 · Cited by 31 · see citing works
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Bradford, E. M., Miller, M. L., Prasad, V., Nieman, M. L., Gawenis, L. R., Berryman, M. A., Lorenz, J. N., Tso, P., & Shull, G. E. (2010) · American Journal of Physiology-Regulatory, Integrative and Comparative Physiology · doi:10.1152/ajpregu.00849.2009 · Cited by 21 · see citing works
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Functional role of ADAMTS5 in adiposity and metabolic health
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Tax1BP1 limits hepatic inflammation and reduces experimental hepatocarcinogenesis
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Convergence of longevity and immunity: lessons from animal models
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Systematic meta-analyses of gene-specific genetic association studies in prostate cancer
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Flagged first, then by citations
4 of the 117 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Measuring biological age using omics data
Rutledge, J., Oh, H., & Wyss-Coray, T. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-022-00511-7 · Cited by 570 · see citing works
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Advances and Utility of the Human Plasma Proteome
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Human age reversal: Fact or fiction?
Johnson, A. A., English, B. W., Shokhirev, M. N., Sinclair, D. A., & Cuellar, T. L. (2022) · Aging Cell · doi:10.1111/acel.13664 · Cited by 81 · see citing works
Self-citationOrgan-specific biological clocks: Ageotyping for personalized anti-aging medicine
Prattichizzo, F., Frigé, C., Pellegrini, V., Scisciola, L., Santoro, A., Monti, D., Rippo, M. R., Ivanchenko, M., Olivieri, F., & Franceschi, C. (2024) · Ageing Research Reviews · doi:10.1016/j.arr.2024.102253 · Cited by 75 · see citing works
Glycine and aging: Evidence and mechanisms
Johnson, A. A., & Cuellar, T. L. (2023) · Ageing Research Reviews · doi:10.1016/j.arr.2023.101922 · Cited by 60 · see citing works
Self-citationBiomarkers of ageing: Current state‐of‐art, challenges, and opportunities
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Current Trends in Anti-Aging Strategies
Rosén, R., & Yarmush, M. L. (2023) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-120122-123054 · Cited by 54 · see citing works
Shokhirev, M. N., & Johnson, A. A. (2022) · Ageing Research Reviews · doi:10.1016/j.arr.2022.101721 · Cited by 42 · see citing works
Self-citationImmunity and lifespan: answering long-standing questions with comparative genomics
O’Connor, E., & Cornwallis, C. K. (2022) · Trends in Genetics · doi:10.1016/j.tig.2022.02.014 · Cited by 19 · see citing works
A set of common buccal CpGs that predict epigenetic age and associate with lifespan-regulating genes
Johnson, A. A., Torosin, N. S., Shokhirev, M. N., & Cuellar, T. L. (2022) · iScience · doi:10.1016/j.isci.2022.105304 · Cited by 16 · see citing works
Self-citationHealthy Aging Metabolomic and Proteomic Signatures Across Multiple Physiological Compartments
Moaddel, R., Candia, J., Ubaida‐Mohien, C., Tanaka, T., Moore, A. Z., Zhu, M., Fantoni, G., Church, S. J., D'Agostino, J., Fan, J., et al. (2025) · Aging Cell · doi:10.1111/acel.70014 · Cited by 14 · see citing works
DoliClock: a lipid-based aging clock reveals accelerated aging in neurological disorders
Latumalea, D., Unfried, M., Barardo, D., Gruber, J., & Kennedy, B. K. (2025) · Aging · doi:10.18632/aging.206266 · Cited by 8 · see citing works
Insights into the therapeutic strategies for aging and aging-associated diseases
Dong, R., Wu, Q., Kan, J., Fu, C., Sorrentino, V., Chow, A., Lei, Y., Wu, D., Xu, Z., Du, J., et al. (2026) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-026-02662-z · Cited by 9 · see citing works
The secretome atlas of two mouse models of progeria
Quintana‐Torres, D., Valle‐Cao, A., Bousquets‐Muñoz, P., Freitas‐Rodríguez, S., Rodríguez, F. J., Lucía, A., López-Otı́n, C., López‐Soto, A., & Folgueras, A. R. (2023) · Aging Cell · doi:10.1111/acel.13952 · Cited by 4 · see citing works
Predicting lung aging using scRNA-Seq data
Song, Q., Singh, A., McDonough, J. E., Adams, T. S., Vos, R., Man, R. D., Myers, G., Ceulemans, L. J., Vanaudenaerde, B. M., Wuyts, W. A., et al. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012632 · Cited by 4 · see citing works
Proteomic aging clocks in epidemiological studies: advances, applications and prospects
Xiao, H., Lau, C. E., Dehghan, A., & Robinson, O. (2026) · Nature Aging · doi:10.1038/s43587-026-01118-x · Cited by 4 · see citing works
Improving our understanding of the biology of aging: findings from the Age-It Research Program
Chiti, F., Conti, F., Corda, D., Giorgino, F., Graziani, A., Passarino, G., Sandri, M., & di Fagagna, F. D. (2025) · The Journals of Gerontology Series B · doi:10.1093/geronb/gbaf197 · Cited by 4 · see citing works
Epigenetic Insights Into Aging: Emerging Roles of Natural Products in Therapeutic Interventions
Pang, Y., Zhang, H., Li, H., Wang, X., Wei, P., Yi, L., & Lin, S. (2025) · Phytotherapy Research · doi:10.1002/ptr.70003 · Cited by 3 · see citing works
Aging Biomarkers in Assessing the Efficacy of Geroprotective Therapy: Problems and Prospects
Rybina, O. Y., & PASYUKOVA, E. G. (2024) · Nanobiotechnology Reports · doi:10.1134/s2635167624601104 · Cited by 2 · see citing works
- Authors
- Johnson, A. A., Shokhirev, M. N., & Lehallier, B.
- Affiliations
- Salk Institute for Biological Studies (US)
- Alkahest, Inc. (United States) (US)
- Corresponding author
- Johnson, Adiv A.
- Venue
- Ageing Research Reviews
- Publisher
- Elsevier BV
- Published
- 2021-09
- Volume
- 70
- Pages
- 101404
- ISSN
- 1568-1637 (ISSN-L)
- OpenAlex
- W3182318090
- Cited by
- 22
- References
- 117
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 3 · 2023: 5 · 2022: 7 · 2021: 1
- FWCI
- 0.33
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Ageing Research Reviews (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (R01 GM102491-07)
- National Institute on Aging (1RF1AG064049-01, P30AG068635)
- National Cancer Institute (P30 CA014195-46)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Health Sciences › Medicine › Physiology › Telomeres, Telomerase, and Senescence
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Keywords
- aging clocks, plasma proteomics, biological age, innate immune system, adaptive immunity, age-related diseases, lifespan regulation, age-related protein expression, epigenetic clocks, drug repurposing
- MeSH terms
- Aged, Aging, Animals, Pharmaceutical Preparations, Humans, Longevity, DNA Methylation, Proteomics, Epigenesis, Genetic, Epigenomics
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.
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