Cited by 121Genetics, Aging, and Longevity in Model OrganismsTelomeres, Telomerase, and SenescenceSingle-cell and spatial transcriptomics

The DOI matches its Crossref record; 86 of the 86 references it cites were found in a registry or an index; 8 of the 70 sources its claims cite were checked for support.

Asynchronous, contagious and digital aging

Open access
Rando, Thomas A., Wyss-Coray, Tony·2021·Nature Aging

Abstract

Aging has largely been defined by analog measures of organ and organismal dysfunction. This has led to the characterization of aging processes at the molecular and cellular levels that underlie these gradual changes. However, current knowledge does not fully explain the growing body of data emerging from large epidemiological, systems biology, and single cell studies of entire organisms pointing to varied rates of aging between individuals (different functionality and lifespan), across lifespan (asynchronous aging), and within an organism at the tissue and organ levels (aging mosaicism). Here we consider these inhomogeneities in the broader context of the rate of aging and from the perspective of underlying cellular changes. These changes reflect genetic, environmental, and stochastic factors that cells integrate to adopt new homeostatic, albeit less functional, states, such as cellular senescence. In this sense, cellular aging can be viewed, at least in part, as a quantal process we refer to as "digital aging". Nevertheless, analog declines of tissue dysfunction and organ failure with age could be the sum of underlying digital events. Importantly, cellular aging, digital or otherwise, is not uniform across time or space within the organism or between organisms of the same species. Certain tissues may exhibit earliest signs of cellular aging, acting as drivers for organismal aging as signals from those driver cells within those tissues may accelerate the aging of other cells locally or even systemically. Advanced methodologies at the systems level and at the single cell level are likely to continue to refine our understanding to the processes of how cells and tissues age and how the integration of those processes leads to the complexities of individual, organismal aging.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This Perspective argues that aging is asynchronous across individuals, across the lifespan, and among tissues, with discrete cellular state transitions (“digital aging”) and local/systemic spread (“contagious aging”) contributing to organismal decline. It calls for biomarkers and single-cell/systems approaches to measure rates of aging rather than relying on lifespan alone.

Key findings

  • Aging is non-uniform over time: cited studies report non-linear trajectories, including DNA methylation changing 24 times faster in the first few years of life than after puberty, inflection points around ages 70 and 85 in an 845-person Swedish cohort, two rapid epigenomic-change peaks around ages 40 and 60–70 in peripheral blood mononuclear cells, four “phases of aging” in skin from 121 humans aged 21–76, and brain-connectivity transitions around ages 40 and 70–75.
  • Aging is non-uniform across tissues and individuals: transcriptomic changes in 17 mouse organs across 10 ages show different trajectories; a human multi-omics study suggested different individuals manifest different “ageotypes”; single-cell studies show mosaicism, and in old versus young mice the proportion of cells expressing the senescence marker p16 more than doubled in multiple tissues.
  • Cellular aging can be viewed partly as “digital” or quantal: cell death and cellular senescence are discrete states, and analog tissue decline may be the sum of underlying digital events; senescent-cell secretome can induce senescence in neighboring cells.
  • Aging may be “contagious” locally and systemically: parabiosis and plasma-infusion studies show blood-borne factors can promote or reverse aging phenotypes, with aged plasma accelerating and young plasma reversing brain endothelial cell transcriptome aging; chronic diseases such as cancer, diabetes, and HIV/AIDS are linked to later co-morbidities, consistent with “aging begets aging.”

Methods

  • Design: Perspective/review article; no new experiments, primary data, or formal meta-analysis reported.
  • Evidence base: synthesizes cited epidemiological, omics, single-cell, parabiosis, and plasma-infusion studies across model organisms and humans.
  • Analysis: conceptual synthesis proposing “asynchronous,” “digital,” and “contagious” aging frameworks; no statistical methods or systematic search strategy are described.

Limitations

  • Stated: No unified theory of the molecular basis of cellular aging has emerged; the authors explicitly do not attempt to define aging, its causes, or when underlying changes begin, and identifying non-genetic/non-environmental determinants of individual aging rates remains a major challenge.
  • Stated: The relative contribution of analog versus digital cellular aging as drivers of tissue aging, and whether lifespan/healthspan interventions shift digital states, remain to be determined by further single-cell studies.
  • Evident: As a Perspective, it presents a selective conceptual synthesis rather than a systematic review or primary analysis; many cited examples come from model organisms, limiting direct generalizability to humans.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1038/s43587-020-00015-1

Cites

86 of 86 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Verification

Claims the paper makes

46 Not checked yet · 37 Own interpretation · 3 Partly supported · 1 Supported · accuracy not yet measured

Verify all 62 references — free account

Show all 63 claims

  • Probably not a substantial claim. 14
  • It may state two separate claims as one. 10
24 of 24
  • The paper relies on this without support.

    Aging has largely been defined by analog measures of organ and organismal dysfunction.

    “Aging has largely been defined by analog measures of organ and organismal dysfunction.”
  • The paper relies on this without support.

    Current knowledge does not fully explain the growing body of data emerging from large epidemiological, systems biology, and single cell studies of entire organisms pointing to varied rates of aging between individuals, across lifespan, and within an organism at the tissue and organ levels.

    “However, current knowledge does not fully explain the growing body of data emerging from large epidemiological, systems biology, and single cell studies of entire organisms pointing to varied rates of aging between individuals (different functionality and lifespan), across lifespan (asynchronous aging), and within an organism at the tissue and organ levels (aging mosaicism).”
  • The paper relies on this without support.

    Within any given species, there will be individuals that age more rapidly and those that age more slowly.

    “Within any given species, there will be individuals that age more rapidly and those that age more slowly.”
  • The authors state this as an assumption.

    Nearly all animals in a protected environment will age before dying.

    “Assuming that nearly all animals in a protected environment will age before dying, it is almost tautological that members of a species whose maximal lifespan is 100 days age more rapidly than those from a species whose maximal lifespan is 100 years.”
  • The paper relies on this without support.

    Identifying which factors determine individual rates of aging remains a major challenge to the field.

    “Identifying which factors (especially if not genetic and not environmental) determine individual rates of aging remains a major challenge to the field.”
  • The paper relies on this without support.

    In many diseases, including Alzheimer’s disease, Huntington’s disease, and Parkinson’s disease, intracellular aggregation of proteins is a cardinal feature of the pathology.

    “In many diseases, including Alzheimer’s disease, Huntington’s disease, and Parkinson’s disease, a cardinal feature of the pathology is intracellular aggregation of proteins.”
  • The paper relies on this without support.

    Some prions are truly contagious, in the sense of being transmissible between individuals or across species.

    “Of course, some prions are truly contagious, in the sense of being transmissible between individuals or across species”
  • The paper relies on this without support.

    Protein aggregation can be self-propagating.

    “since protein aggregation can be self-propagating”
  • The paper relies on this without support.

    Protein aggregation is one of the key features of cellular aging.

    “As protein aggregation is one of the key features of cellular aging”
9 of 9

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Citations

Cites

Research works printed but not linked: 1

  1. Pearl, R. The Rate of Living (University of London Press, 1928).

Cited by

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Metadata

Authors
Rando, T. A., & Wyss-Coray, T.
Affiliations
VA Palo Alto Health Care System (US)
Corresponding author
Rando, Thomas A.
Venue
Nature Aging
Published
2021-01-14
Volume
1
Issue
1
Pages
29-35
ISSN
2662-8465 (ISSN-L)
OpenAlex
W3119607554
Cited by
121
References
86
Citations by year
2026: 20 · 2025: 21 · 2024: 24 · 2023: 19 · 2022: 29 · 2021: 8
FWCI
7.15
Type
journal article
Language
en
Open access
Locations
  • Nature Aging (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • NOMIS Stiftung (n/a)
  • Nan Fung Life Sciences
  • The Glenn Foundation for Medical Research
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Telomeres, Telomerase, and Senescence
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
Keywords
digital aging, cellular senescence, cellular aging, aging rate, tissue-specific aging, organ dysfunction, lifespan variation, stochastic aging
MeSH terms
Aging, Humans, Longevity, Cellular Senescence

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