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Cited by 570Genetics, Aging, and Longevity in Model OrganismsTelomeres, Telomerase, and SenescenceEpigenetics and DNA Methylation

Measuring biological age using omics data

Open access
Rutledge, Jarod, Oh, Hamilton, Wyss-Coray, Tony·2022·Nature Reviews Genetics
DOI 10.1038/s41576-022-00511-7PMC PMC10048602
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Abstract

Age is the key risk factor for diseases and disabilities of the elderly. Efforts to tackle age-related diseases and increase healthspan have suggested targeting the ageing process itself to 'rejuvenate' physiological functioning. However, achieving this aim requires measures of biological age and rates of ageing at the molecular level. Spurred by recent advances in high-throughput omics technologies, a new generation of tools to measure biological ageing now enables the quantitative characterization of ageing at molecular resolution. Epigenomic, transcriptomic, proteomic and metabolomic data can be harnessed with machine learning to build 'ageing clocks' with demonstrated capacity to identify new biomarkers of biological ageing.

AI snapshot

Written from the paper’s full text.

TL;DR

This review surveys machine-learning ‘ageing clocks’ built from epigenomic, transcriptomic, proteomic and metabolomic data, arguing that they can estimate biological age but often capture different, partly non-causal ageing signals. It calls for clocks tailored to specific tissues/applications, multi-omic and longitudinal designs, and training beyond chronological age.

Key findings

  • DNA methylation clocks: the first, in 2011, used about 100 saliva samples and predicted chronological age within ~5 years; the Hannum clock used 71 CpGs in 656 whole-blood samples, and the Horvath pan-tissue clock used 353 CpGs in 8,000 samples across 51 tissue/cell types. First-generation epigenetic age gaps correlate only mildly with each other (r = 0.1–0.5), and a meta-analysis in 14 cohorts found that improving chronological-age accuracy to very high levels reduced associations with mortality risk.
  • Second-generation methylation clocks (PhenoAge, GrimAge, DunedinPoAm) were designed to associate with mortality or ageing rate. DunedinPoAm used a 1-year birth cohort (n = 810) tracking 18 clinical chemistry and physiological biomarkers at ages 26, 32 and 38, and showed stronger associations with physical functioning, cognition, self-rated health and mortality than PhenoAge.
  • Transcriptomic, proteomic and metabolomic clocks: transcriptomic clocks are noisy; Meyer et al. reported that binarized transcriptomic data improved human fibroblast chronological age prediction to r² = 0.92 and mean error of 6.63 years. SomaLogic aptamer proteomics can reliably quantify over 7,000 proteins, and a metabolomic clock from 56 plasma metabolites was associated with cardiovascular risk factors, cardiovascular disease risk and all-cause mortality.
  • Cross-omics comparisons show limited concordance: Tanaka et al.’s plasma proteomic age gap had no correlation with Horvath and low correlation with GrimAge/PhenoAge; Robinson et al.’s metabolomic age gap had no correlation with Horvath, Hannum and PhenoAge; a multi-omics study found mild correlations between epigenetic and transcriptomic age gaps and between proteomic and metabolomic age gaps, but no correlation between epigenetic and proteomic/metabolomic age gaps.

Methods

  • Design: critical narrative review of ageing clocks built from omics data; no new experimental data, cohort, or primary statistical analysis.
  • Data: published studies using epigenomic, transcriptomic, proteomic, metabolomic and other omics layers, plus selected multi-omic and longitudinal studies.
  • Analysis: qualitative synthesis and comparison of clock construction, accuracy, age-gap associations with ageing phenotypes, and limitations; the review explicitly focuses on what biological ageing phenotypes age gaps measure rather than chronological-age prediction accuracy.

Limitations

  • Stated in the paper: current clocks are correlative statistical models, not causal; most are trained on cross-sectional data and do not directly predict future ageing rates; first-generation methylation clocks have weak/variable mortality associations and can overfit chronological-age noise; blood-based clocks may not capture ageing of other tissues because tissues age at different rates.
  • Stated in the paper: omics-specific limitations include transcriptomic noise/small samples and older microarray technology; proteomic confounding by kidney function and less validation than methylation clocks; metabolomic orphan compounds, sensitivity issues, and lower age-prediction accuracy despite large samples.
  • Evident from the text: as a review, it reports no new validation or head-to-head benchmark; comparisons across clocks are constrained by few meta-analyses and low concordance, so relative utility remains unresolved.

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

Integrity signals

Registry

Crossref · 10.1038/s41576-022-00511-7

Cites

201 of 201 references verified0 citation errors · 0 not checkable

Statistics

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Claims the paper makes

0 of 237 cited sources checked · 0 supported · 0 flagged

  • Premisecitation

    Attempts to both remove and exploit confounding by cell composition changes have not altered the weak associations with mortality and other health outcomes.

    “However, attempts to both remove and exploit the confounding by cell composition changes have not altered the weak associations with mortality and other health outcomes60.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [60] Horvath, S. et al. Decreased epigenetic age of PBMCs from Italian semi-supercentenarians and their offspring. Aging 7, 1159–1170 (2015). source

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  • Premisecitation

    Mortality risk has shown an overall weak and variable association with DNA methylation clocks.

    “However, mortality risk, which is widely used as a measure of ‘global’ biological age, has shown an overall weak and variable association with these clocks46,58, which raises questions as to what biology they actually measure.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [46] Chen, B. H. et al. DNA methylation-based measures of biological age: meta-analysis predicting time to death. Aging 8, 1844–1865 (2016). source

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    • cites [58] Fransquet, P. D., Wrigglesworth, J., Woods, R. L., Ernst, M. E. & Ryan, J. The epigenetic clock as a predictor of disease and mortality risk: a systematic review and meta-analysis. Clin. Epigenetics 11, 62 (2019). source

      Not checked in the free run.

  • Premisecitation

    Across many cancers and in multiple cohorts, cancer tissues showed marked age acceleration on the Horvath clock.

    “Moreover, across many cancers and in multiple cohorts, cancer tissues showed marked age acceleration on the Horvath clock44,57, suggesting it measures epigenetic dysregulation that is shared between ageing and many cancers.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [44] Horvath, S. DNA methylation age of human tissues and cell types. Genome Biol. 14, 3156 (2013). This study comprehensively investigated CpG methylation ageing clocks across multiple human tissues and cell lines and discovered that there are some widely conserved methylation changes that occur throughout the body with ageing, which may be related to cancer and other diseases of ageing. source

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    • cites [57] Perna, L. et al. Epigenetic age acceleration predicts cancer, cardiovascular, and all-cause mortality in a German case cohort. Clin. Epigenetics 8, 64 (2016). source

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  • Interpretationcitation

    It seems possible to overfit methylation clock models on molecular ageing noise that is unrelated to ageing phenotypes.

    “It seems possible to ‘overfit’ these models on molecular ageing ‘noise’ that is unrelated to ageing phenotypes, suggesting that training on chronological age alone may be a suboptimal approach.”
    Quote in the paperSubstantial claim
  • Interpretationcitation

    Training methylation clocks on chronological age alone may be a suboptimal approach.

    “It seems possible to ‘overfit’ these models on molecular ageing ‘noise’ that is unrelated to ageing phenotypes, suggesting that training on chronological age alone may be a suboptimal approach.”
    Quote in the paperSubstantial claim
  • Classified as probably not a substantial claim. 34
  • May combine two claims. 50

  • Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

    Premisecitation

    Biological ageing is enormously complex and is thought to be driven by the interaction of multiple dysregulated cellular and biochemical processes.

    “Biological ageing is enormously complex and is thought to be driven by the interaction of multiple dysregulated cellular and biochemical processes3,4.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [3] Kennedy, B. K. et al. Geroscience: linking aging to chronic disease. Cell 159, 709–713 (2014). source

      Not checked in the free run.

    • cites [4] López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M. & Kroemer, G. The hallmarks of aging. Cell 153, 1194–1217 (2013). This landmark review organized a framework to think about ageing through the lens of multiple conserved cellular and molecular processes and has become highly influential in the field of ageing research. source

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    Interpretationreasoning

    Molecular biomarkers that reflect the biological age of a cell type, tissue, organ or whole organism are needed to develop drugs that target ageing.

    “As such, molecular biomarkers that reflect the biological age of a cell type, tissue, organ (such as the heart or brain) or whole organism are needed to develop drugs that target ageing.”
    Quote in the paperNot a substantial claim

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    Premisecitation

    Chronological age predictors, termed ageing clocks, are one framework to interpret omics data in the context of ageing and have shown considerable promise.

    “Chronological age predictors, which the field has colloquially termed ageing clocks43,44, are one framework to interpret omics data in the context of ageing and has shown considerable promise45.”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [43] Hannum, G. et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol. Cell 49, 359–367 (2013). source

      Not checked in the free run.

    • cites [44] Horvath, S. DNA methylation age of human tissues and cell types. Genome Biol. 14, 3156 (2013). This study comprehensively investigated CpG methylation ageing clocks across multiple human tissues and cell lines and discovered that there are some widely conserved methylation changes that occur throughout the body with ageing, which may be related to cancer and other diseases of ageing. source

      Not checked in the free run.

    • cites [45] Galkin, F. et al. Biohorology and biomarkers of aging: current state-of-the-art, challenges and opportunities. Ageing Res. Rev. 60, 101050 (2020). source

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    Premisecitation

    Globally, the human population is rapidly ageing but healthspan has not increased in kind.

    “Globally, the human population is rapidly ageing but our healthspan — the period of life free from disease — has not increased in kind1.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [1] Beard, J. R. et al. The World report on ageing and health: a policy framework for healthy ageing. Lancet 387, 2145–2154 (2016). source

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    Interpretationreasoning

    The availability of large-scale omics data has posed new challenges for the analysis and interpretation of molecular ageing.

    “However, the availability of large-scale omics data has posed new challenges for the analysis and interpretation of molecular ageing.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Horvath built the pan-tissue Horvath clock (353-CpG clock) using 8,000 samples encompassing 51 different tissue and cell types.

    “In 2013, this concept of the ‘epigenetic ageing clock’ was explicitly termed and popularized, first by Hannum et al.43, who built the Hannum clock (71-CpG clock) using 656 whole-blood samples, and then further by Horvath44, who built the pan-tissue Horvath clock (353-CpG clock) using 8,000 samples encompassing 51 different tissue and cell types.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [44] Horvath, S. DNA methylation age of human tissues and cell types. Genome Biol. 14, 3156 (2013). This study comprehensively investigated CpG methylation ageing clocks across multiple human tissues and cell lines and discovered that there are some widely conserved methylation changes that occur throughout the body with ageing, which may be related to cancer and other diseases of ageing. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Recent studies have demonstrated cellular and functional rejuvenation by transient epigenetic reprogramming.

    “Epigenetic alteration is a hallmark of ageing4, and recent studies have demonstrated cellular and functional rejuvenation by transient epigenetic reprogramming17,51,52.”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [17] Lu, Y. et al. Reprogramming to recover youthful epigenetic information and restore vision. Nature 588, 124–129 (2020). This study experimentally demonstrated a direct link between alterations in DNA methylation in epigenetic injury response and regeneration, and a potential causal role for DNA demethylation enzymes in cellular ageing. source

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    • cites [51] Ocampo, A. et al. In vivo amelioration of age-associated hallmarks by partial reprogramming. Cell 167, 1719–1733.e12 (2016). source

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    • cites [52] Sarkar, T. J. et al. Transient non-integrative expression of nuclear reprogramming factors promotes multifaceted amelioration of aging in human cells. Nat. Commun. 11, 1545 (2020). source

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  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation

    Ageing contributes to many diseases that affect all organ systems and is the greatest risk factor for heart disease, neurodegeneration and cancer.

    “Ageing contributes to many diseases that affect all organ systems and is the greatest risk factor for heart disease, neurodegeneration and cancer2.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [2] Niccoli, T. & Partridge, L. Ageing as a risk factor for disease. Curr. Biol. 22, R741–R752 (2012). source

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    Premisecitation

    Hannum et al. built the Hannum clock (71-CpG clock) using 656 whole-blood samples.

    “In 2013, this concept of the ‘epigenetic ageing clock’ was explicitly termed and popularized, first by Hannum et al.43, who built the Hannum clock (71-CpG clock) using 656 whole-blood samples, and then further by Horvath44, who built the pan-tissue Horvath clock (353-CpG clock) using 8,000 samples encompassing 51 different tissue and cell types.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [43] Hannum, G. et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol. Cell 49, 359–367 (2013). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Bocklandt et al. demonstrated highly accurate chronological age prediction (within about 5 years) using only about 100 saliva samples.

    “The first DNA methylation clock was an elastic net model (BOX 1) built in 2011 by Bocklandt et al.53, who demonstrated highly accurate chronological age prediction (within ~5 years) using only about 100 saliva samples.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [53] Bocklandt, S. et al. Epigenetic predictor of age. PLoS One 6, e14821 (2011). source

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    Premisecitation

    High-throughput genomic, proteomic and metabolomic methods are enabling the characterization and quantification of thousands of epigenetic marks, transcripts, proteins and metabolites, and are beginning to reveal how complex organisms change globally with age at the molecular level.

    “High-throughput genomic, proteomic and metabolomic methods are enabling the characterization and quantification of thousands of epigenetic marks, transcripts, proteins and metabolites, and are beginning to reveal how complex organisms change globally with age at the molecular level41,42.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [41] Almanzar, N. et al. A single-cell transcriptomic atlas characterizes ageing tissues in the mouse. Nature 583, 590–595 (2020). This study demonstrates that, in mice, different organs experience different patterns and rates of molecular and cellular ageing, which highlights the need for more sophisticated ageing clocks to account for intra-individual ageing variation. source

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    • cites [42] Schaum, N. et al. Ageing hallmarks exhibit organ-specific temporal signatures. Nature 583, 596–602 (2020). source

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  • May combine two claims.

    Premisereasoning

    By design, second-generation clocks specifically designed to associate with mortality risk have robust associations with mortality risk, and some show associations with heart disease risk, physical functioning and several blood chemistry markers of health.

    “By design, all these clocks have robust associations with mortality risk, and some show associations with heart disease risk, physical functioning (balance, grip strength, walking speed) and several blood chemistry markers of health.”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [47] Levine, M. E. et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging 10, 573–591 (2018). This study developed an innovative composite ageing score that incorporated chronological and biomarker measurements of age to train an improved second-generation DNA methylation ageing clock. The method has been influential in improving the performance and biological relevance of ageing clock models. source

      Not checked in the free run.

    • cites [48] Lu, A. T. et al. DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging 11, 303–327 (2019). source

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    • cites [50] Zhang, Y. et al. DNA methylation signatures in peripheral blood strongly predict all-cause mortality. Nat. Commun. 8, 14617 (2017). source

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  • May combine two claims.

    Premisecitation

    The transcriptomic clock was found to have associations with some biomarkers and risk factors, including smoking status, and a unique association with systolic blood pressure that was not detected by the Horvath and Hannum clocks.

    “However, the transcriptomic clock was found to have associations with some biomarkers and risk factors, including smoking status, and a unique association with systolic blood pressure that was not detected by the Horvath and Hannum clocks.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [81] Peters, M. J. et al. The transcriptional landscape of age in human peripheral blood. Nat. Commun. 6, 8570 (2015). source

      Not checked in the free run.

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    Premisecitation

    Baird et al. and Menni et al. developed the first ageing clock models based on the SomaLogic aptamer-based proteomic platform using human cerebrospinal fluid and plasma samples, respectively, although associations with any ageing phenotypes or organ function were not examined.

    “Pioneering studies by Baird et al.94 and Menni et al.91 developed the first ageing clock models based on the SomaLogic aptamer-based proteomic platform using human cerebrospinal fluid and plasma samples, respectively, although associations with any ageing phenotypes or organ function were not examined.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [94] Baird, G. S. et al. Age-dependent changes in the cerebrospinal fluid proteome by slow off-rate modified aptamer array. Am. J. Pathol. 180, 446–456 (2012). source

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    • cites [91] Menni, C. et al. Circulating proteomic signatures of chronological. Age. J. Gerontol. Ser. A 70, 809–816 (2015). source

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    Premisecitation

    The Peters et al. transcriptomic clock had highly variable age prediction accuracy across cohorts and was significantly less accurate in chronological age prediction than epigenetic clocks in all tested cohorts.

    “The transcriptomic clock had highly variable age prediction accuracy across cohorts and was significantly less accurate in chronological age prediction than epigenetic clocks in all tested cohorts.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [81] Peters, M. J. et al. The transcriptional landscape of age in human peripheral blood. Nat. Commun. 6, 8570 (2015). source

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  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

    Interpretationnone given

    The general lack of understanding of the molecular and cellular causes and consequences of genomic CpG methylation remains a barrier to realizing the potential of epigenetic clocks.

    “Additionally, while epigenetic clocks have been shown to be highly reproducible, the general lack of understanding of the molecular and cellular causes and consequences of genomic CpG methylation remains a barrier to realizing the potential of these models.”
    Quote in the paperNot a substantial claim

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    Study design and conductcitation

    Most transcriptomic clock studies have been conducted on small sample sizes and not tested on independent cohorts, or used older microarray technology, which is less accurate and reproducible than modern RNA sequencing.

    “Most studies have been conducted on small sample sizes and not tested on independent cohorts, or used older microarray technology, which is less accurate and reproducible than modern RNA sequencing85,86.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [85] Wang, Z., Gerstein, M. & Snyder, M. RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10, 57–63 (2009). source

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    • cites [86] Zhang, W. et al. Comparison of RNA-seq and microarray-based models for clinical endpoint prediction. Genome Biol. 16, 133 (2015). source

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  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

    Interpretationnone given

    Using RNA gene expression levels to develop ageing clocks links ageing more directly to genes, increasing both the interpretability and experimental testability of these models.

    “Using RNA gene expression levels to develop ageing clocks links ageing more directly to genes, increasing both the interpretability and experimental testability of these models.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    The Meyer et al. clock could detect expected changes in biological age for long-lived daf2 mutants as well as irradiation and caloric restriction and performed well on diverse lifespan-affecting treatments in independent datasets.

    “Moreover, this clock could detect expected changes in biological age for long-lived daf2 mutants as well as irradiation and caloric restriction and performed well on diverse lifespan-affecting treatments in independent datasets.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [83] Meyer, D. H. & Schumacher, B. BiT age: a transcriptome-based aging clock near the theoretical limit of accuracy. Aging Cell 20, e13320 (2021). This study developed multiple methodological innovations for training accurate and reproducible transcriptomic ageing clocks, and developed clocks with striking generalizable performance across multiple ageing treatment conditions in worms. source

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    Premisecitation

    An elastic net-based clock derived from binarized transcriptomic data improved chronological age prediction to an r2 of 0.92 and mean error of 6.63 years and could detect accelerated ageing in progeria samples.

    “Additionally, they demonstrated, using human fibroblast data from Fleischer et al.82, that an elastic net-based clock derived from binarized transcriptomic data improved chronological age prediction to an r2 of 0.92 and mean error of 6.63 years and could detect accelerated ageing in progeria samples.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [82] Fleischer, J. G. et al. Predicting age from the transcriptome of human dermal fibroblasts. Genome Biol. 19, 221 (2018). source

      Not checked in the free run.

    • cites [83] Meyer, D. H. & Schumacher, B. BiT age: a transcriptome-based aging clock near the theoretical limit of accuracy. Aging Cell 20, e13320 (2021). This study developed multiple methodological innovations for training accurate and reproducible transcriptomic ageing clocks, and developed clocks with striking generalizable performance across multiple ageing treatment conditions in worms. source

      Not checked in the free run.

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    Premisecitation

    The Fleischer et al. method outperformed standard penalized linear regression-based clocks at chronological age prediction and uniquely detected accelerated ageing in progeria samples.

    “The method outperformed standard penalized linear regression-based clocks at chronological age prediction and uniquely detected accelerated ageing in progeria samples, suggesting that methods designed to be robust to transcriptional noise improve performance.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [82] Fleischer, J. G. et al. Predicting age from the transcriptome of human dermal fibroblasts. Genome Biol. 19, 221 (2018). source

      Not checked in the free run.

  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation

    Holzscheck et al. reported that their clock was associated with multiple measures of biological skin ageing and that their model responded in expected ways to known perturbations of biological age in silico.

    “They reported that their clock was associated with multiple measures of biological skin ageing and that their model responded in expected ways to known perturbations of biological age in silico.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [84] Holzscheck, N. et al. Modeling transcriptomic age using knowledge-primed artificial neural networks. NPJ Aging Mech. Dis. 7, 15 (2021). This study aimed to design a more transparent and biologically interpretable neural network ageing clock, showcasing some of the methods available to make inferences about ageing with these more complex models. source

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    Premisecitation

    Another large 1H-NMR biobank study developed a metabolomic clock from 56 reliably measurable metabolites in plasma and evaluated relationships between the metabolomic age gap, cardiovascular phenotypes and mortality.

    “Another large 1H-NMR biobank study subsequently developed a metabolomic clock from 56 reliably measurable metabolites in plasma and evaluated relationships between the metabolomic age gap, cardiovascular phenotypes and mortality119.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [119] van den Akker, E. B. et al. Metabolic age based on the BBMRI-NL 1H-NMR metabolomics repository as biomarker of age-related disease. Circ. Genom. Precis. Med. 13, 541–547 (2020). This study developed a biobank-scale targeted metabolomic ageing clock that is predictive of mortality and cardiovascular outcomes. source

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    Premisecitation

    Follow-up studies have shown that dozens of proteins identified in plasma proteomic ageing clocks directly regulate lifespan, and hundreds have biological connections to the health status of different organs.

    “Follow-up studies have shown that dozens of proteins identified in plasma proteomic ageing clocks directly regulate lifespan, and hundreds have biological connections to the health status of different organs98,99.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [98] Johnson, A. A., Shokhirev, M. N., Wyss-Coray, T. & Lehallier, B. Systematic review and analysis of human proteomics aging studies unveils a novel proteomic aging clock and identifies key processes that change with age. Ageing Res. Rev. 60, 101070 (2020). source

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    • cites [99] Johnson, A. A., Shokhirev, M. N. & Lehallier, B. The protein inputs of an ultra-predictive aging clock represent viable anti-aging drug targets. Ageing Res. Rev. 70, 101404 (2021). source

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  • May combine two claims.

    Interpretationreasoning

    Blood plasma contains proteins from nearly all organs and cell types, making it feasible to develop clocks focused on the ageing biology of specific tissues.

    “Blood plasma contains proteins from nearly all organs and cell types, making it feasible to develop clocks focused on the ageing biology of specific tissues.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Interpretationreasoning

    Proteomic technologies are newer and less developed than DNA quantification technologies and therefore proteomic clocks have been less extensively validated than methylation clocks.

    “Additionally, proteomic technologies are newer and less developed than DNA quantification technologies and therefore proteomic clocks have been less extensively validated than methylation clocks.”
    Quote in the paperNot a substantial claim

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    Premisecitation

    Metabolomic age acceleration correlated with triglyceride levels, obesity, heavy drinking, diabetes mellitus, depressive symptoms, depression, anxiety and post-traumatic stress disorder.

    “Metabolomic age acceleration correlated with triglyceride levels, obesity, heavy drinking, diabetes mellitus, depressive symptoms, depression, anxiety and post-traumatic stress disorder.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [120] Robinson, O. et al. Determinants of accelerated metabolomic and epigenetic aging in a UK cohort. Aging Cell 19, e13149 (2020). This study compared multiple untargeted metabolomic methods for developing ageing clocks and additionally assessed their relationship to health traits and to the Horvath, Hannum and PhenoAge epigenetic ageing clocks. source

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    • cites [121] Hertel, J. et al. Measuring biological age via metabonomics: the metabolic age score. J. Proteome Res. 15, 400–410 (2016). source

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    Premisecitation

    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.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [49] Tanaka, T. et al. Plasma proteomic signature of age in healthy humans. Aging Cell 17, e12799 (2018). source

      Not checked in the free run.

    • cites [95] Lehallier, B. et al. Undulating changes in human plasma proteome profiles across the lifespan. Nat. Med. 25, 1843–1850 (2019). This study developed a highly predictive plasma proteomic ageing clock and showed relationships between the proteomic age gap and many ageing traits such as cognitive function and motor function. It also describes the non-linear patterns of proteomic ageing, which have implications for future ageing clock models. source

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    Premisecitation

    In independent prospective cohorts, accelerated metabolomic age was found to be associated with cardiovascular risk factors, cardiovascular disease risk and all-cause mortality risk.

    “In independent prospective cohorts, accelerated metabolomic age was found to be associated with cardiovascular risk factors, cardiovascular disease risk and all-cause mortality risk.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [119] van den Akker, E. B. et al. Metabolic age based on the BBMRI-NL 1H-NMR metabolomics repository as biomarker of age-related disease. Circ. Genom. Precis. Med. 13, 541–547 (2020). This study developed a biobank-scale targeted metabolomic ageing clock that is predictive of mortality and cardiovascular outcomes. source

      Not checked in the free run.

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    Premisecitation

    Tanaka et al. described the first plasma protein-based ageing clock, which investigated the relationship between proteomic age gap and biological ageing.

    “In 2018, Tanaka et al.49 described the first plasma protein-based ageing clock, which investigated the relationship between proteomic age gap and biological ageing.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [49] Tanaka, T. et al. Plasma proteomic signature of age in healthy humans. Aging Cell 17, e12799 (2018). source

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  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Robinson et al. assessed mass spectrometry metabolomic ageing clocks for enrichment of biological pathways and identified enrichments for metabolism of several vitamins, amino acids and xenobiotics.

    “Robinson et al.120 additionally assessed mass spectrometry metabolomic ageing clocks for the enrichment of biological pathways and identified enrichments for the metabolism of several vitamins, amino acids and xenobiotics.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [120] Robinson, O. et al. Determinants of accelerated metabolomic and epigenetic aging in a UK cohort. Aging Cell 19, e13149 (2020). This study compared multiple untargeted metabolomic methods for developing ageing clocks and additionally assessed their relationship to health traits and to the Horvath, Hannum and PhenoAge epigenetic ageing clocks. source

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  • May combine two claims.

    Premisecitation

    Targeted methods have better sensitivity but are limited by predefining a set of metabolites to look for, greatly reducing the number of features detected and precluding the discovery of new metabolites.

    “Targeted methods have better sensitivity but are limited by predefining a set of metabolites to look for in the experiment, greatly reducing the number of features detected and precluding the discovery of new metabolites122–124.”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [122] Bingol, K. Recent advances in targeted and untargeted metabolomics by NMR and MS/NMR methods. High Throughput 7, 9 (2018). source

      Not checked in the free run.

    • cites [123] Gertsman, I. & Barshop, B. A. Promises and pitfalls of untargeted metabolomics. J. Inherit. Metab. Dis. 41, 355–366 (2018). source

      Not checked in the free run.

    • cites [124] Gorrochategui, E., Jaumot, J., Lacorte, S. & Tauler, R. Data analysis strategies for targeted and untargeted LC-MS metabolomic studies: overview and workflow. TrAC. Trends Anal. Chem. 82, 425–442 (2016). source

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  • May combine two claims.

    Premisecitation

    Untargeted metabolomics methods have the advantage of detecting many thousands of metabolite features; however, most compounds detected via mass spectrometry and NMR are orphan compounds whose structure has not been identified.

    “Untargeted metabolomics methods have the advantage of being able to detect many thousands of metabolite features; however, most compounds detected via mass spectrometry and NMR are orphan compounds, that is, their structure has not been identified122–124.”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [122] Bingol, K. Recent advances in targeted and untargeted metabolomics by NMR and MS/NMR methods. High Throughput 7, 9 (2018). source

      Not checked in the free run.

    • cites [123] Gertsman, I. & Barshop, B. A. Promises and pitfalls of untargeted metabolomics. J. Inherit. Metab. Dis. 41, 355–366 (2018). source

      Not checked in the free run.

    • cites [124] Gorrochategui, E., Jaumot, J., Lacorte, S. & Tauler, R. Data analysis strategies for targeted and untargeted LC-MS metabolomic studies: overview and workflow. TrAC. Trends Anal. Chem. 82, 425–442 (2016). source

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    Premisecitation

    Loss of proteostasis is a hallmark of ageing, and other hallmarks of ageing imply alterations in the proteome such as differential levels of insulin and peptide hormones, signalling proteins, and inflammatory cytokines.

    “Additionally, loss of proteostasis is a hallmark of ageing4,100, and other hallmarks of ageing, such as dysregulated nutrient sensing, altered intercellular communication and cellular senescence, also imply alterations in the proteome such as differential levels of insulin and peptide hormones, signalling proteins, and inflammatory cytokines.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [4] López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M. & Kroemer, G. The hallmarks of aging. Cell 153, 1194–1217 (2013). This landmark review organized a framework to think about ageing through the lens of multiple conserved cellular and molecular processes and has become highly influential in the field of ageing research. source

      Not checked in the free run.

    • cites [100] Hipp, M. S., Kasturi, P. & Hartl, F. U. The proteostasis network and its decline in ageing. Nat. Rev. Mol. Cell Biol. 20, 421–435 (2019). source

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    Premisecitation

    Kidney function has an effect on plasma protein concentrations that is not fully understood but can confound ageing analysis.

    “Kidney function has an effect on plasma protein concentrations that is not fully understood but can confound ageing analysis101,102.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [101] Williams, S. A. et al. Plasma protein patterns as comprehensive indicators of health. Nat. Med. 25, 1851–1857 (2019). source

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    • cites [102] Tryggvason, K. & Wartiovaara, J. How does the kidney filter plasma? Physiology 20, 96–101 (2005). source

      Not checked in the free run.

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    Premisecitation

    Sensitivity is a challenge in untargeted methods, and many metabolites are detected in some but not other samples, which limits the utility of many analytes for modelling.

    “Sensitivity is another challenge in untargeted methods, and many metabolites are detected in some but not other samples122–124, which limits the utility of many analytes for modelling.”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [122] Bingol, K. Recent advances in targeted and untargeted metabolomics by NMR and MS/NMR methods. High Throughput 7, 9 (2018). source

      Not checked in the free run.

    • cites [123] Gertsman, I. & Barshop, B. A. Promises and pitfalls of untargeted metabolomics. J. Inherit. Metab. Dis. 41, 355–366 (2018). source

      Not checked in the free run.

    • cites [124] Gorrochategui, E., Jaumot, J., Lacorte, S. & Tauler, R. Data analysis strategies for targeted and untargeted LC-MS metabolomic studies: overview and workflow. TrAC. Trends Anal. Chem. 82, 425–442 (2016). source

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  • May combine two claims.

    Premisecitation

    Tanaka et al. showed associations with mortality, multi-morbidity, healthspan and lifespan.

    “In a follow-up study, Tanaka et al. showed associations with mortality, multi-morbidity, healthspan and lifespan96.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [96] Tanaka, T. et al. Plasma proteomic biomarker signature of age predicts health and life span. eLife 9, e61073 (2020). source

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  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Changes in chromatin state have been strongly implicated in ageing by progeroid syndromes, which disrupt the nuclear lamina and its associated regulation of heterochromatin structure.

    “Nonetheless, changes in chromatin state have been strongly implicated in ageing by progeroid syndromes, which disrupt the nuclear lamina and its associated regulation of heterochromatin structure142,143.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [142] Kudlow, B. A., Kennedy, B. K. & Monnat, R. J. Werner and Hutchinson–Gilford progeria syndromes: mechanistic basis of human progeroid diseases. Nat. Rev. Mol. Cell Biol. 8, 394–404 (2007). source

      Not checked in the free run.

    • cites [143] Zhang, W. et al. A Werner syndrome stem cell model unveils heterochromatin alterations as a driver of human aging. Science https://doi.org/10.1126/science.aaa1356 (2015). source

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  • May combine two claims.

    Premisecitation

    Training first-generation methylation clocks on increasing sample sizes can increase age prediction accuracy to virtually perfect, which erases associations between age gap and biological ageing.

    “Supporting noise as a large factor in these models, a recent analysis showed that training first-generation methylation clocks on increasing sample sizes can increase the age prediction accuracy to virtually perfect, which in turn erases the associations between age gap and biological ageing61.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [61] Zhang, Q. et al. Improved precision of epigenetic clock estimates across tissues and its implication for biological ageing. Genome Med. 11, 54 (2019). This study demonstrates an important paradox in the training of first-generation ageing clocks: increasingly perfect prediction of chronological age by a clock trained only on chronological age reduces its ability to discover drivers of variation in biological ageing between people. source

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  • May combine two claims.

    Premisecitation

    The Galkin et al. microbiome clock had similar accuracy and variance to clocks trained on other omics modalities, and in a separate cohort, it predicted individuals with type 1 diabetes mellitus to be significantly older.

    “Their clock had similar accuracy and variance to clocks trained on other omics modalities, and in a separate cohort, the microbiome clock predicted individuals with type 1 diabetes mellitus to be significantly older.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [137] Galkin, F. et al. Human gut microbiome aging clock based on taxonomic profiling and deep learning. iScience 23, 101199 (2020). The first attempt to develop an ageing clock based on microbiome sequencing data; the authors compared multiple methods, settling on a simple neural network architecture to predict age and detect accelerated microbiome ageing in patients with diabetes mellitus. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Tanaka et al.'s plasma proteomic age gap had no correlation with the Horvath clock and low correlation with the GrimAge and PhenoAge clocks.

    “Tanaka et al.’s plasma proteomic age gap had no correlation with the Horvath clock and low correlation with the GrimAge and PhenoAge clocks96;”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [96] Tanaka, T. et al. Plasma proteomic biomarker signature of age predicts health and life span. eLife 9, e61073 (2020). source

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  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Studies in the past decade have demonstrated that gut microbiome composition changes with age, that exceptionally long-lived individuals have a different microbiome composition from average older adults, and that certain microorganisms and the metabolites they produce are beneficial to human health.

    “Studies in the past decade have demonstrated that gut microbiome composition changes with age, that exceptionally long-lived individuals have a different microbiome composition from average older adults131–133, and that certain microorganisms131,134,135 and the metabolites they produce132,134,136 are beneficial to human health.”
    Quote in the paperNot a substantial claim0 of 6 sources checked
    • cites [131] Badal, V. D. et al. The gut microbiome, aging, and longevity: a systematic review. Nutrients 12, 3759 (2020). source

      Not checked in the free run.

    • cites [132] Collino, S. et al. Metabolic signatures of extreme longevity in Northern Italian centenarians reveal a complex remodeling of lipids, amino acids, and gut microbiota metabolism. PLoS One 8, e56564 (2013). source

      Not checked in the free run.

    • cites [133] Rampelli, S. et al. Functional metagenomic profiling of intestinal microbiome in extreme ageing. Aging 5, 902–912 (2013). source

      Not checked in the free run.

    • cites [134] Bárcena, C. et al. Healthspan and lifespan extension by fecal microbiota transplantation into progeroid mice. Nat. Med. 25, 1234–1242 (2019). source

      Not checked in the free run.

    • cites [135] Cani, P. D. & de Vos, W. M. Next-generation beneficial microbes: the case of Akkermansia muciniphila. Front. Microbiol. 8, 1765 (2017). source

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    • cites [136] Sato, Y. et al. Novel bile acid biosynthetic pathways are enriched in the microbiome of centenarians. Nature 599, 458–464 (2021). source

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  • May combine two claims.

    Interpretationreasoning

    Plasma and urine metabolomics carry information from multiple tissues across the body, increasing the potential ageing information of metabolomics relative to methylation and transcriptomic clocks of blood cells.

    “Similar to plasma proteomics, plasma and urine metabolomics also carry information from multiple tissues across the body, increasing the potential ageing information of metabolomics relative to methylation and transcriptomic clocks of blood cells.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Interpretationnone given

    There are few meta-analyses comparing biological age estimates between different clocks, and the few comparisons that exist show a general lack of concordance both within and across omics layers.

    “There are few meta-analyses that compare biological age estimates between different clocks but the few comparisons that do exist show a general lack of concordance both within and across omics layers.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Robinson et al.'s metabolomic age gap had no correlation with the Horvath, Hannum and PhenoAge clocks in the same cohort, yet there was some overlap in the correlation between age gap and phenotypes tested.

    “and Robinson et al.’s metabolomic age gap had no correlation with the Horvath, Hannum and PhenoAge clocks in the same cohort, yet there was some overlap in the correlation between age gap and phenotypes tested120.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [120] Robinson, O. et al. Determinants of accelerated metabolomic and epigenetic aging in a UK cohort. Aging Cell 19, e13149 (2020). This study compared multiple untargeted metabolomic methods for developing ageing clocks and additionally assessed their relationship to health traits and to the Horvath, Hannum and PhenoAge epigenetic ageing clocks. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    In one multi-omics study, epigenetic and transcriptomic clock age gaps were mildly correlated, and proteomic and metabolomic clock age gaps were mildly correlated.

    “One multi-omics study wherein the authors built their own telomere, epigenetic, proteomic and metabolomics clocks within a single cohort found mild correlations between epigenetic and transcriptomic clock age gaps and between proteomic and metabolomic clock age gaps155.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [155] Jansen, R. et al. An integrative study of five biological clocks in somatic and mental health. eLife 10, e59479 (2021). source

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    First-generation epigenetic clocks seem to capture signals mostly related to chronological time, whereas second-generation epigenetic clocks and other omics ageing clocks capture more physiologically relevant ageing signals.

    “Overall, it is clear from the above studies that first-generation epigenetic clocks seem to capture signals mostly related to chronological time, whereas second-generation epigenetic clocks and other omics ageing clocks capture more physiologically relevant ageing signals.”
    Quote in the paperSubstantial claim

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    Premisecitation

    Metabolomic clocks have demonstrated lower age prediction accuracy than other omics data types despite training on extremely large sample sizes, and effect sizes for ageing traits have been modest.

    “Furthermore, metabolomic clocks have demonstrated lower age prediction accuracy than other omics data types despite training on extremely large sample sizes, and effect sizes for ageing traits have been modest113,116–121.”
    Quote in the paperSubstantial claim0 of 7 sources checked
    • cites [113] Rist, M. J. et al. Metabolite patterns predicting sex and age in participants of the Karlsruhe Metabolomics and Nutrition (KarMeN) study. PLoS One 12, e0183228 (2017). source

      Not checked in the free run.

    • cites [116] Menni, C. et al. Metabolomic markers reveal novel pathways of ageing and early development in human populations. Int. J. Epidemiol. 42, 1111–1119 (2013). source

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    • cites [117] Fischer, K. et al. Biomarker profiling by nuclear magnetic resonance spectroscopy for the prediction of all-cause mortality: an observational study of 17,345 Persons. PLoS Med. 11, e1001606 (2014). source

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    • cites [118] Deelen, J. et al. A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals. Nat. Commun. 10, 3346 (2019). source

      Not checked in the free run.

    • cites [119] van den Akker, E. B. et al. Metabolic age based on the BBMRI-NL 1H-NMR metabolomics repository as biomarker of age-related disease. Circ. Genom. Precis. Med. 13, 541–547 (2020). This study developed a biobank-scale targeted metabolomic ageing clock that is predictive of mortality and cardiovascular outcomes. source

      Not checked in the free run.

    • cites [120] Robinson, O. et al. Determinants of accelerated metabolomic and epigenetic aging in a UK cohort. Aging Cell 19, e13149 (2020). This study compared multiple untargeted metabolomic methods for developing ageing clocks and additionally assessed their relationship to health traits and to the Horvath, Hannum and PhenoAge epigenetic ageing clocks. source

      Not checked in the free run.

    • cites [121] Hertel, J. et al. Measuring biological age via metabonomics: the metabolic age score. J. Proteome Res. 15, 400–410 (2016). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Merleev et al. used mass spectrometry to examine the concentration of 159 glycans on 17 common glycoproteins in plasma, which showed promise as an ageing clock, although results were not validated in an independent cohort nor associated with any ageing phenotypes.

    “Merleev et al.130 used mass spectrometry methods to examine the concentration of 159 glycans on 17 common glycoproteins in plasma, which also showed promise as an ageing clock, although results were not validated in an independent cohort nor associated with any ageing phenotypes.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [130] Merleev, A. A. et al. A site-specific map of the human plasma glycome and its age and gender-associated alterations. Sci. Rep. 10, 17505 (2020). source

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  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Belsky et al. developed a DNA methylation clock surrogate for longitudinal health and biomarker measurements to more directly predict an individual's ageing rate instead of relying on cross-sectional measures of relative biological age.

    “Here, the authors developed a DNA methylation clock surrogate for longitudinal health and biomarker measurements to more directly predict an individual’s ageing rate instead of relying on cross-sectional measures of relative biological age.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [73] Belsky, D. W. et al. Quantification of the pace of biological aging in humans through a blood test, the DunedinPoAm DNA methylation algorithm. eLife 9, e54870 (2020). Here, the authors developed a DNA methylation clock surrogate for longitudinal health and biomarker measurements to more directly predict an individual’s ageing rate instead of relying on cross-sectional measures of relative biological age. source

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  • May combine two claims.

    Interpretationreasoning

    Current ageing clocks do not provide causal insights into ageing but can illuminate testable hypotheses and support or contradict other observations in molecular geroscience.

    “They do not provide causal insights into ageing but can illuminate testable hypotheses and support or contradict other observations in molecular geroscience.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Current ageing clocks are all correlative statistical models.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Modern human genetics methods, such as genetic colocalization and Mendelian randomization, can be used to test causality in ageing clock models.

    “Modern human genetics methods, such as genetic colocalization163 and Mendelian randomization164, can also be used to test causality in ageing clock models.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [163] Hormozdiari, F. et al. Colocalization of GWAS and eQTL signals detects target genes. Am. J. Hum. Genet. 99, 1245–1260 (2016). source

      Not checked in the free run.

    • cites [164] Emdin, C. A., Khera, A. V. & Kathiresan, S. Mendelian randomization. JAMA 318, 1925–1926 (2017). source

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  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Meyer and Schumacher developed multiple methodological innovations for training accurate and reproducible transcriptomic ageing clocks.

    “This study developed multiple methodological innovations for training accurate and reproducible transcriptomic ageing clocks”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [83] Meyer, D. H. & Schumacher, B. BiT age: a transcriptome-based aging clock near the theoretical limit of accuracy. Aging Cell 20, e13320 (2021). This study developed multiple methodological innovations for training accurate and reproducible transcriptomic ageing clocks, and developed clocks with striking generalizable performance across multiple ageing treatment conditions in worms. source

      Not checked in the free run.

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    Premisecitation

    The authors used information from the clock to identify CXCL9 as a potential causal regulator of immune ageing and validated this result through functional follow-up in cellular models.

    “The authors used information from the clock to identify CXCL9 as a potential causal regulator of immune ageing and validated this result through functional follow-up in cellular models, which further supports the ability of these hybrid approaches to discover causal regulators of ageing.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [171] Sayed, N. et al. An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging. Nat. Aging 1, 598–615 (2021). source

      Not checked in the free run.

    • cites [172] Alpert, A. et al. A clinically meaningful metric of immune age derived from high-dimensional longitudinal monitoring. Nat. Med. 25, 487–495 (2019). source

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  • May combine two claims.

    Interpretationreasoning

    Ageing clocks built from DNA methylation data, transcriptomics, proteomics and metabolomics have all demonstrated a capacity to identify new biomarkers of biological ageing.

    “Ageing clocks built from DNA methylation data, transcriptomics, proteomics and metabolomics have all demonstrated a capacity to identify new biomarkers of biological ageing, and additional omics modalities are likely to do the same in the near future.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    Ultrastructural studies of human tissues have shown similar results, and different human brain regions exhibit different gene expression trajectories with age.

    “Ultrastructural studies of human tissues157 have shown similar results, and even different human brain regions exhibit different gene expression trajectories with age158.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [157] Arrojo e Drigo, R. et al. Age Mosaicism across multiple scales in adult tissues. Cell Metab. 30, 343–351.e3 (2019). source

      Not checked in the free run.

    • cites [158] Işıldak, U., Somel, M., Thornton, J. M. & Dönertaş, H. M. Temporal changes in the gene expression heterogeneity during brain development and aging. Sci. Rep. 10, 4080 (2020). source

      Not checked in the free run.

  • May combine two claims.

    Premisenone given

    The vast majority of ageing clocks built so far are not trained on longitudinal data and do not directly predict future rates of ageing.

    “Lastly, it is important to note that the vast majority of ageing clocks built so far are not trained on longitudinal data and do not directly predict future rates of ageing — a shortcoming that may limit their current clinical utility and explanatory power.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Lu et al. found that DNA methylation enzymes were required for epigenetic reprogramming of mouse retinal cells.

    “Lu et al.17 found that DNA methylation enzymes were required for epigenetic reprogramming of mouse retinal cells but much more work is needed.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [17] Lu, Y. et al. Reprogramming to recover youthful epigenetic information and restore vision. Nature 588, 124–129 (2020). This study experimentally demonstrated a direct link between alterations in DNA methylation in epigenetic injury response and regeneration, and a potential causal role for DNA demethylation enzymes in cellular ageing. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Meyer and Schumacher developed clocks with striking generalizable performance across multiple ageing treatment conditions in worms.

    “and developed clocks with striking generalizable performance across multiple ageing treatment conditions in worms.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [83] Meyer, D. H. & Schumacher, B. BiT age: a transcriptome-based aging clock near the theoretical limit of accuracy. Aging Cell 20, e13320 (2021). This study developed multiple methodological innovations for training accurate and reproducible transcriptomic ageing clocks, and developed clocks with striking generalizable performance across multiple ageing treatment conditions in worms. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    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;”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [95] Lehallier, B. et al. Undulating changes in human plasma proteome profiles across the lifespan. Nat. Med. 25, 1843–1850 (2019). This study developed a highly predictive plasma proteomic ageing clock and showed relationships between the proteomic age gap and many ageing traits such as cognitive function and motor function. It also describes the non-linear patterns of proteomic ageing, which have implications for future ageing clock models. source

      Not checked in the free run.

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    Premisecitation

    The study of cellular senescence has shown that some cells in a tissue age and senesce long before others, and their secreted factors may spur the ageing of other cells locally or in other tissues through blood-mediated communication.

    “At the cellular level, the study of cellular senescence has shown that some cells in a tissue age and senesce long before others, and their secreted factors may even spur the ageing of other cells locally or in other tissues through blood-mediated communication159.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [159] Tuttle, C. S. L. et al. Cellular senescence and chronological age in various human tissues: a systematic review and meta-analysis. Aging Cell 19, e13083 (2020). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S4 (based).

    Premisenone given

    The omics revolution has illuminated the complexity of ageing biology by showing that many tens of thousands of molecular features change with age.

    “The omics revolution has illuminated the sheer complexity of ageing biology by showing that many tens of thousands of molecular features change with age.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    Together, these observations support the notion that ageing clocks need to be more targeted to harness the organismal complexity of ageing.

    “Together, these observations support the notion that ageing clocks need to be more targeted to harness the organismal complexity of ageing.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Both PhenoAge and GrimAge models trained estimators of plasma proteins and metabolites with known disease biology.

    “Interestingly, both models trained estimators of plasma proteins and metabolites with known disease biology, again pointing to the plasma as fertile ground for future study.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [47] Levine, M. E. et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging 10, 573–591 (2018). This study developed an innovative composite ageing score that incorporated chronological and biomarker measurements of age to train an improved second-generation DNA methylation ageing clock. The method has been influential in improving the performance and biological relevance of ageing clock models. source

      Not checked in the free run.

    • cites [48] Lu, A. T. et al. DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging 11, 303–327 (2019). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Proteins are most often the direct mediators of biological processes and form the vast majority of currently druggable targets in disease.

    “Proteins have several additional advantages as they are most often the direct mediators of biological processes and form the vast majority of currently druggable targets in disease160.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [160] Hopkins, A. L. & Groom, C. R. The druggable genome. Nat. Rev. Drug Discov. 1, 727–730 (2002). source

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    Transcriptomic and proteomic clocks have a potential advantage because they are more amenable to genetic screening methods and some causal paths are known.

    “Here, transcriptomic and proteomic clocks have a potential advantage because they are more amenable to genetic screening methods and some causal paths are known.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Robinson et al. assessed their relationship to health traits and to the Horvath, Hannum and PhenoAge epigenetic ageing clocks.

    “and additionally assessed their relationship to health traits and to the Horvath, Hannum and PhenoAge epigenetic ageing clocks.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [120] Robinson, O. et al. Determinants of accelerated metabolomic and epigenetic aging in a UK cohort. Aging Cell 19, e13149 (2020). This study compared multiple untargeted metabolomic methods for developing ageing clocks and additionally assessed their relationship to health traits and to the Horvath, Hannum and PhenoAge epigenetic ageing clocks. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Galkin et al. compared multiple methods, settling on a simple neural network architecture to predict age and detect accelerated microbiome ageing in patients with diabetes mellitus.

    “the authors compared multiple methods, settling on a simple neural network architecture to predict age and detect accelerated microbiome ageing in patients with diabetes mellitus.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [137] Galkin, F. et al. Human gut microbiome aging clock based on taxonomic profiling and deep learning. iScience 23, 101199 (2020). The first attempt to develop an ageing clock based on microbiome sequencing data; the authors compared multiple methods, settling on a simple neural network architecture to predict age and detect accelerated microbiome ageing in patients with diabetes mellitus. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Li et al. found that the age gaps for the different clocks tested are largely uncorrelated.

    “this study found that the age gaps for the different clocks tested are largely uncorrelated.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [151] Li, X. et al. Longitudinal trajectories, correlations and mortality associations of nine biological ages across 20-years follow-up. eLife 9, e51507 (2020). One of the few current papers that compares multiple omics ageing-clock measurements longitudinally; this study found that the age gaps for the different clocks tested are largely uncorrelated. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Galkin et al. made the first attempt to develop an ageing clock based on microbiome sequencing data.

    “The first attempt to develop an ageing clock based on microbiome sequencing data”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [137] Galkin, F. et al. Human gut microbiome aging clock based on taxonomic profiling and deep learning. iScience 23, 101199 (2020). The first attempt to develop an ageing clock based on microbiome sequencing data; the authors compared multiple methods, settling on a simple neural network architecture to predict age and detect accelerated microbiome ageing in patients with diabetes mellitus. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    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.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [95] Lehallier, B. et al. Undulating changes in human plasma proteome profiles across the lifespan. Nat. Med. 25, 1843–1850 (2019). This study developed a highly predictive plasma proteomic ageing clock and showed relationships between the proteomic age gap and many ageing traits such as cognitive function and motor function. It also describes the non-linear patterns of proteomic ageing, which have implications for future ageing clock models. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Li et al. is one of the few current papers that compares multiple omics ageing-clock measurements longitudinally.

    “One of the few current papers that compares multiple omics ageing-clock measurements longitudinally”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [151] Li, X. et al. Longitudinal trajectories, correlations and mortality associations of nine biological ages across 20-years follow-up. eLife 9, e51507 (2020). One of the few current papers that compares multiple omics ageing-clock measurements longitudinally; this study found that the age gaps for the different clocks tested are largely uncorrelated. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    van den Akker et al. developed a biobank-scale targeted metabolomic ageing clock that is predictive of mortality and cardiovascular outcomes.

    “This study developed a biobank-scale targeted metabolomic ageing clock that is predictive of mortality and cardiovascular outcomes.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [119] van den Akker, E. B. et al. Metabolic age based on the BBMRI-NL 1H-NMR metabolomics repository as biomarker of age-related disease. Circ. Genom. Precis. Med. 13, 541–547 (2020). This study developed a biobank-scale targeted metabolomic ageing clock that is predictive of mortality and cardiovascular outcomes. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Robinson et al. compared multiple untargeted metabolomic methods for developing ageing clocks.

    “This study compared multiple untargeted metabolomic methods for developing ageing clocks”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [120] Robinson, O. et al. Determinants of accelerated metabolomic and epigenetic aging in a UK cohort. Aging Cell 19, e13149 (2020). This study compared multiple untargeted metabolomic methods for developing ageing clocks and additionally assessed their relationship to health traits and to the Horvath, Hannum and PhenoAge epigenetic ageing clocks. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Nelson et al. used simulation studies to show that the use of more complex second-generation ageing score training objectives overcomes some of these limitations.

    “and used simulation studies to show that the use of more complex second-generation ageing score training objectives overcomes some of these limitations.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [168] Nelson, P. G., Promislow, D. E. L. & Masel, J. Biomarkers for aging identified in cross-sectional studies tend to be non-causative. J. Gerontol. Ser. A 75, 466–472 (2020). This study demonstrated important limitations of ageing-clock models that are built with cross-sectional cohort studies and used simulation studies to show that the use of more complex second-generation ageing score training objectives overcomes some of these limitations. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Nelson et al. demonstrated important limitations of ageing-clock models that are built with cross-sectional cohort studies.

    “This study demonstrated important limitations of ageing-clock models that are built with cross-sectional cohort studies”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [168] Nelson, P. G., Promislow, D. E. L. & Masel, J. Biomarkers for aging identified in cross-sectional studies tend to be non-causative. J. Gerontol. Ser. A 75, 466–472 (2020). This study demonstrated important limitations of ageing-clock models that are built with cross-sectional cohort studies and used simulation studies to show that the use of more complex second-generation ageing score training objectives overcomes some of these limitations. source

      Not checked in the free run.

76 of 76

  • The paper relies on this without support.

    Current ageing clocks are all correlative statistical models.

    “Ultimately, current ageing clocks are all correlative statistical models.”
1 of 1

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • A recent paper summarizes the development and application of aging clocks with the help of machine learning analysis of histological data, illustrating the ability of machine learning to identify novel biomarkers of biological aging.

    “A recent paper summarizes the development and application of aging clocks with the help of machine learning analysis of histological data. This illustrates the ability of machine learning to identify novel biomarkers of biological aging and provides a boost to early warning and intervention in aging and precision medicine strategies.372”

    From Inflammation and aging: signaling pathways and intervention therapies (2023) · cites this paper as [372]

    The Omic clocks section says this review surveys ageing clocks built from omics data using machine-learning models, and the Conclusions discuss identifying ageing signatures and advancing personalized medicine. The cited review does not establish the claim's specific description of clocks developed through machine-learning analysis of histological data; it describes molecular omics data instead. No page numbers are available in our copy. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Conflicting claims about the first plasma-protein ageing clock

    Minor

    The paper calls Tanaka et al.'s 2018 model the first plasma protein-based ageing clock, but elsewhere says Menni et al. developed an ageing-clock model using plasma samples. The reference list dates Menni et al. to 2015.

1 of 1

Cites

Flagged first, then by citations

Research works printed but not linked: 3

  1. Elsevier. Tietz Textbook of Clinical Chemistry and Molecular Diagnostics-6th Edition https://www.elsevier.com/books/tietz-textbook-of-clinical-chemistry-and-molecular-diagnostics/rifai/978-0-323-35921-4 (2017).
  2. Platt, J. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv. Large Margin Classif. 10, 61–74 (1999).
  3. Shrikumar, A., Greenside, P. & Kundaje, A. In Proceedings of the 34th International Conference on Machine Learning 3145–3153 (PMLR, 2017).

Cited by

Sorted by citations

Related

Authors
Rutledge, J., Oh, H., & Wyss-Coray, T.
Affiliations
Stanford Medicine (US)
ORCID
Venue
Nature Reviews Genetics
Publisher
Springer Science and Business Media LLC
Published
2022-06-17
Volume
23
Issue
12
Pages
715-727
ISSN
  • 1471-0056 (ISSN-L)
  • 1471-0064
DOI
10.1038/s41576-022-00511-7
PMC
PMC10048602
OpenAlex
W4283016888
Cited by
570
References
201
Citations by year
2026: 145 · 2025: 178 · 2024: 148 · 2023: 82 · 2022: 16
FWCI
9.05
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Reviews Genetics (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • National Science Foundation
  • National Institute on Aging (P50AG047366, R01AG072255, P30AG066515)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
  • Health Sciences › Medicine › Physiology › Telomeres, Telomerase, and Senescence
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
Keywords
biological age, biological aging, age-related diseases, healthspan, epigenomics, transcriptomics, proteomics, metabolomics, aging biomarkers, machine learning
MeSH terms
Aged, Aging, Humans, Biomarkers, Proteomics, Metabolomics, Epigenomics

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Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-10.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-10.

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