Data mining of human plasma proteins generates a multitude of highly predictive aging clocks that reflect different aspects of aging
Open accessAbstract
We previously identified 529 proteins that had been reported by multiple different studies to change their expression level with age in human plasma. In the present study, we measured the q‐value and age coefficient of these proteins in a plasma proteomic dataset derived from 4263 individuals. A bioinformatics enrichment analysis of proteins that significantly trend toward increased expression with age strongly implicated diverse inflammatory processes. A literature search revealed that at least 64 of these 529 proteins are capable of regulating life span in an animal model. Nine of these proteins (AKT2, GDF11, GDF15, GHR, NAMPT, PAPPA, PLAU, PTEN, and SHC1) significantly extend life span when manipulated in mice or fish. By performing machine‐learning modeling in a plasma proteomic dataset derived from 3301 individuals, we discover an ultra‐predictive aging clock comprised of 491 protein entries. The Pearson correlation for this clock was 0.98 in the learning set and 0.96 in the test set while the median absolute error was 1.84 years in the learning set and 2.44 years in the test set. Using this clock, we demonstrate that aerobic‐exercised trained individuals have a younger predicted age than physically sedentary subjects. By testing clocks associated with 1565 different Reactome pathways, we also show that proteins associated with signal transduction or the immune system are especially capable of predicting human age. We additionally generate a multitude of age predictors that reflect different aspects of aging. For example, a clock comprised of proteins that regulate life span in animal models accurately predicts age.
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Lehallier et al. demonstrated that many proteins in the plasma ageing clock were modulated by parabiosis in mice and exercise in humans.
“Lehallier et al. also demonstrated that many proteins in the plasma ageing clock were modulated by parabiosis in mice and exercise in humans97, two rejuvenation paradigms.”
From Measuring biological age using omics data (2022) · cites this paper as [97]
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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
Self-citationDNA repair‐deficient premature aging models display accelerated epigenetic age
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The protein inputs of an ultra-predictive aging clock represent viable anti-aging drug targets
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Self-citation
- Authors
- Lehallier, B., Shokhirev, M. N., Wyss‐Coray, T., & Johnson, A. A.
- Affiliations
- Stanford University (US)
- Salk Institute for Biological Studies (US)
- VA Palo Alto Health Care System (US)
- Tucson Arizona USA
- Corresponding author
- Lehallier, Benoit
- Johnson, Adiv A.
- Venue
- Aging Cell
- Publisher
- Wiley
- Published
- 2020-10-08
- Volume
- 19
- Issue
- 11
- Pages
- e13256-e13256
- ISSN
- 1474-9718 (ISSN-L)
- 1474-9726
- PMC
- PMC7681068
- OpenAlex
- W3092598205
- Cited by
- 106
- References
- 133
- Citations by year
- 2026: 19 · 2025: 15 · 2024: 26 · 2023: 19 · 2022: 16 · 2021: 9 · 2020: 2
- FWCI
- 3.80
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Aging Cell (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- British Heart Foundation (RG/13/13/30194)
- Medical Research Council (MR/L003120/1)
- National Institute on Aging (RF1AG064049)
- National Cancer Institute (P30CA014195)
- National Institute of General Medical Sciences (R01GM102491)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Health Sciences › Medicine › Physiology › Telomeres, Telomerase, and Senescence
- Social Sciences › Psychology › Neuropsychology and Physiological Psychology › Aging and Gerontology Research
- Keywords
- aging clocks, human plasma proteins, plasma proteomics, age prediction, lifespan regulation, aging biomarkers, inflammation, aerobic exercise, signal transduction, immune system, GDF11, GDF15, visfatin, AKT2, PTEN, SHC1, growth hormone receptor, PAPP-A, PLAU, Reactome pathways
- MeSH terms
- Machine Learning, Adolescent, Adult, Aged, Aging, Animals, Blood Proteins, Disease Models, Animal, Humans, Longevity, Middle Aged, Mice, Young Adult, Data Mining
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