Undulating changes in human plasma proteome profiles across the lifespan
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Abstract
Aging is a predominant risk factor for several chronic diseases that limit healthspan 1. Mechanisms of aging are thus increasingly recognized as potential therapeutic targets. Blood from young mice reverses aspects of aging and disease across multiple tissues 2-10, which supports a hypothesis that age-related molecular changes in blood could provide new insights into age-related disease biology. We measured 2,925 plasma proteins from 4,263 young adults to nonagenarians (18-95 years old) and developed a new bioinformatics approach that uncovered marked non-linear alterations in the human plasma proteome with age. Waves of changes in the proteome in the fourth, seventh and eighth decades of life reflected distinct biological pathways and revealed differential associations with the genome and proteome of age-related diseases and phenotypic traits. This new approach to the study of aging led to the identification of unexpected signatures and pathways that might offer potential targets for age-related diseases.
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Lehallier et al. demonstrated robust and highly accurate chronological age prediction across multiple independent cohorts.
“Lehallier et al.95 further demonstrated robust and highly accurate chronological age prediction across multiple independent cohorts.”
From Measuring biological age using omics data (2022) · cites this paper as [95]
Both Tanaka et al. and Lehallier et al. built proteomic clocks with standard methods and observed associations with many physiological and clinical ageing phenotypes, including physical functioning, cognitive test scores and clinical chemistry markers of health.
“Both studies built proteomic clocks with standard methods and observed associations with many physiological and clinical ageing phenotypes, including physical functioning, cognitive test scores and clinical chemistry markers of health.”
From Measuring biological age using omics data (2022) · cites this paper as [95]
Lehallier et al. developed a highly predictive plasma proteomic ageing clock.
“This study developed a highly predictive plasma proteomic ageing clock”
From Measuring biological age using omics data (2022) · cites this paper as [95]
Lehallier et al. demonstrated that plasma proteins change in three non-linear 'waves' of ageing.
“For example, Lehallier et al.95 demonstrated that plasma proteins change in three non-linear ‘waves’ of ageing;”
From Measuring biological age using omics data (2022) · cites this paper as [95]
Lehallier et al. showed relationships between the proteomic age gap and many ageing traits such as cognitive function and motor function.
“and showed relationships between the proteomic age gap and many ageing traits such as cognitive function and motor function.”
From Measuring biological age using omics data (2022) · cites this paper as [95]
Lehallier et al. describe the non-linear patterns of proteomic ageing, which have implications for future ageing clock models.
“It also describes the non-linear patterns of proteomic ageing, which have implications for future ageing clock models.”
From Measuring biological age using omics data (2022) · cites this paper as [95]
A recent study of some 3,000 proteins in blood of over 4,000 healthy people concluded that markers of aging do not suggest a monotonic aging process, but instead show periods of accelerated change and periods of slower change.
“A recent study of some 3,000 proteins in blood of over 4,000 healthy people concluded that markers of aging do not in fact suggest at monotonic aging process, but instead show periods of accelerated change and periods of slower change.28”
From Asynchronous, contagious and digital aging (2021) · cites this paper as [28]
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- Authors
- Lehallier, B., Gate, D., Schaum, N., Nanasi, T., Lee, S. E., Yousef, H., Moran Losada, P., Berdnik, D., Keller, A., Verghese, J., Sathyan, S., Franceschi, C., Milman, S., Barzilai, N., & Wyss-Coray, T.
- Affiliations
- Stanford Medicine (US)
- VA Palo Alto Health Care System (US)
- Institute for Stem Cell Biology and Regenerative Medicine (US)
- Saarland University (DE)
- Albert Einstein College of Medicine (US)
- N. I. Lobachevsky State University of Nizhny Novgorod (RU)
- Corresponding author
- Lehallier, Benoit
- ORCID
- Venue
- Nature Medicine
- Published
- 2019-12
- Volume
- 25
- Issue
- 12
- Pages
- 1843-1850
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- PMC
- PMC7062043
- OpenAlex
- W2994160326
- Cited by
- 981
- References
- 69
- Citations by year
- 2026: 186 · 2025: 200 · 2024: 163 · 2023: 141 · 2022: 108 · 2021: 86 · 2020: 93 · 2019: 4
- FWCI
- 27.01
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Nature Medicine (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Cure Alzheimer's Fund
- European Federation of Pharmaceutical Industries and Associations
- National Institute for Health and Care Research (NF-SI-0617-10113)
- British Heart Foundation (RG/18/13/33946, RG/13/13/30194)
- European Commission (FP6-2004-LIFESCIHEALTH-5)
- NOMIS Stiftung
- National Institutes of Health
- Wu Tsai Neurosciences Institute, Stanford University
- Medical Research Council (MR/L003120/1)
- National Institute on Aging (F32AG055255, AG053015, P50AG047366, AG045034, R01AG061155, R01AG045034, DP1AG053015, P30AG038072, R01AG057909, T32AG047126, K23AG051148, R01AG044829)
- National Institute of Neurological Disorders and Stroke (R00NS112458, K99NS112458)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- plasma proteome, aging, lifespan, age-related diseases, nonagenarians, aptamer-based proteomics, aging biomarkers, healthspan
- MeSH terms
- Adolescent, Adult, Aged, Aged, 80 and over, Aging, Animals, Blood Proteins, Chronic Disease, Female, Humans, Longevity, Male, Middle Aged, Risk Factors, Proteome, Mice, Young Adult
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