Cited by 414Genetics, Aging, and Longevity in Model OrganismsSingle-cell and spatial transcriptomicsMetabolomics and Mass Spectrometry Studies

Personal aging markers and ageotypes revealed by deep longitudinal profiling

Open access
Ahadi, Sara, Zhou, Wenyu, Schüssler-Fiorenza Rose, Sophia Miryam, Sailani, M. Reza, Contrepois, Kévin, Avina, Monika, Ashland, Melanie, Brunet, Anne +1 more·2020·Nature Medicine

Abstract

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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Crossref · 10.1038/s41591-019-0719-5

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  • Different senescent cells with unique features have distinct functions in tissue ageing and various chronic diseases.

    “Different senescent cells with unique features have distinct functions in tissue ageing and various chronic diseases.”
  • As tissues age at different rates, 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.

    “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]

  • A recent longitudinal multi-omics study in humans identified four population-level ageing pathways enriched for biological pathways related to liver, kidney, metabolic and immune dysfunction.

    “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]

  • The authors of that study observed considerable individual deviation from population-level ageing trends.

    “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]

  • 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.

    “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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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
Venue
Nature Medicine
Published
2020-01
Volume
26
Issue
1
Pages
83-90
ISSN
  • 1078-8956 (ISSN-L)
  • 1546-170X
OpenAlex
W3000365770
Cited by
408
References
37
Citations by year
2026: 78 · 2025: 73 · 2024: 69 · 2023: 58 · 2022: 38 · 2021: 57 · 2020: 34 · 2019: 1
FWCI
15.47
Type
journal article
Language
en
Open access
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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