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Personal aging markers and ageotypes revealed by deep longitudinal profiling
Open accessAbstract
The molecular changes that occur with aging are not well understood 1-4. Here, we performed longitudinal and deep multiomics profiling of 106 healthy individuals from 29 to 75 years of age and examined how different types of 'omic' measurements, including transcripts, proteins, metabolites, cytokines, microbes and clinical laboratory values, correlate with age. We identified both known and new markers that associated with age, as well as distinct molecular patterns of aging in insulin-resistant as compared to insulin-sensitive individuals. In a longitudinal setting, we identified personal aging markers whose levels changed over a short time frame of 2-3 years. Further, we defined different types of aging patterns in different individuals, termed 'ageotypes', on the basis of the types of molecular pathways that changed over time in a given individual. Ageotypes may provide a molecular assessment of personal aging, reflective of personal lifestyle and medical history, that may ultimately be useful in monitoring and intervening in the aging process.
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“However, as tissues age at different rates41,42,75,76, there is likely a limit to how well ageing of blood cells can measure and explain mechanisms of ageing of the rest of the body; perhaps this is evidenced by the small (albeit statistically significant) associations between second-generation ageing clocks and organ-specific measures of age-related dysfunction.”
From Measuring biological age using omics data (2022) · cites this paper as [76]
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“The measures we studied summarize biological aging in general and do not isolate system-specific aging processes41.”
From Effect of long-term caloric restriction on DNA methylation measures of biological aging in healthy adults from the CALERIE trial (2023) · cites this paper as [41]
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“Different senescent cells with unique features have distinct functions in tissue ageing and various chronic diseases.”
From Immune Clearance of Senescent Cells to Combat Ageing and Chronic Diseases (2020) · cites this paper as [38]
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“Most of the time, however, the subsequent samples are years apart and hence time between the measurements greatly exceeds the organism state autocorrelation time reported here.”
From Longitudinal Analysis of Blood Markers Reveals Progressive Loss of Resilience and Predicts Human Lifespan Limit (2021) · cites this paper as [56]
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“A recent longitudinal multi-omics study in humans identified four population-level ageing pathways seen across individuals who were enriched for biological pathways related to liver, kidney, metabolic and immune dysfunction76.”
From Measuring biological age using omics data (2022) · cites this paper as [76]
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“The authors also observed considerable individual deviation from population-level ageing trends and suggest the results imply individuals are ageing at different rates and through different mechanisms.”
From Measuring biological age using omics data (2022) · cites this paper as [76]
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“In one pioneering human study, a multi-omics analysis of aging biomarkers suggested that different individuals manifest different “ageotypes”, meaning that the tissue that ages most rapidly differs from one person to another.44”
From Asynchronous, contagious and digital aging (2021) · cites this paper as [44]
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Metadata
- Authors
- Ahadi, S., Zhou, W., Schüssler-Fiorenza Rose, S. M., Sailani, M. R., Contrepois, K., Avina, M., Ashland, M., Brunet, A., & Snyder, M.
- Affiliations
- Stanford Medicine (US)
- Corresponding author
- Snyder, Michael
- ORCID
- Venue
- Nature Medicine
- Published
- 2020-01
- Volume
- 26
- Issue
- 1
- Pages
- 83-90
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- PMC
- PMC7301912
- OpenAlex
- W3000365770
- Cited by
- 408
- References
- 37
- Citations by year
- 2026: 78 · 2025: 73 · 2024: 69 · 2023: 58 · 2022: 38 · 2021: 57 · 2020: 34
- FWCI
- 15.47
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- U.S. Department of Health & Human Services | National Institutes of Health (U54DK102556, S10OD020141, UL1 TR001085, P30DK116074, K08 ES028825)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Keywords
- aging, multi-omics profiling, longitudinal profiling, insulin resistance, transcriptomics, proteomics, metabolomics, cytokines, microbiome, laboratory parameters
- MeSH terms
- Adult, Aged, Aging, Female, Humans, Longitudinal Studies, Male, Middle Aged, Cohort Studies, Biomarkers, Statistics, Nonparametric, Genomics, Young Adult
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