The DOI matches its Crossref record; 201 of the 201 references it cites were found in a registry or an index; 16 of the 237 sources its claims cite were checked for support.
Measuring biological age using omics data
Open accessAbstract
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.
Overview
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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.
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Claims the paper makes
115 Not checked yet · 23 Own interpretation · 4 Partly supported · 3 No basis given · 2 Supported · 1 Supported so far · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“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.”
§ Measuring biological age using omics data > Omic clocks > DNA methylation
What the sources say
- Partly supportedcites [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. sourceExperimental — accuracy not yet measured
The Abstract (page unavailable) reports age-related changes in the cellular composition of different mouse organs, supporting the premise that ageing varies across tissues. It does not establish that those tissues age at different rates. The proposed limit of blood-cell ageing as a measure of whole-body ageing is the citing authors’ inference, not the part directly cited to this source. (LLM, conf=0.78)
- Supportedcites [42] Schaum, N. et al. Ageing hallmarks exhibit organ-specific temporal signatures. Nature 583, 596–602 (2020). source
The citation is attached to the statement that tissues age at different rates. In “Pairwise differential gene expression with age,” the paper describes differences in the onset and rate of ageing between organs and reports that some adipose-tissue changes arise before those in other organs. The subsequent inference about blood-cell ageing is not directly attributed to this source. (LLM, conf=0.92)
- Supportedcites [75] Rando, T. A. & Wyss-Coray, T. Asynchronous, contagious and digital aging. Nat. Aging 1, 29–35 (2021). source
The citation is attached to the statement that tissues age at different rates. The section ‘Variations in the rate of aging in an individual across time and space’ explicitly reports heterogeneity in aging rates among tissues. The proposed limit of blood-cell measures is the citing authors’ inference, not a claim directly attributed to this source. (LLM, conf=0.93)
- Partly supportedcites [76] Ahadi, S. et al. Personal aging markers and ageotypes revealed by deep longitudinal profiling. Nat. Med. 26, 83–90 (2020). sourceExperimental — accuracy not yet measured
The ageotypes passage (section labeled Abstract; page unavailable) reports that some individuals showed strong aging associations in kidney-related pathways but minor changes in other pathways. This supports differing organ-related aging patterns, but the paper does not directly measure the rates at which different tissues age. The proposed limit of blood-cell measures is the citing authors’ inference, not a finding attributed to this source. (LLM, conf=0.78)
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- Partly supported
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In the paper
“This could be due to the large degree of technical noise in DNA methylation arrays152–154, which limits the robustness of applying these models across datasets.”
§ Measuring biological age using omics data > Comparing different clocks
What the sources say
- Partly supportedcites [152] Bose, M. et al. Evaluation of microarray-based DNA methylation measurement using technical replicates: the Atherosclerosis Risk In Communities (ARIC) study. BMC Bioinformatics 15, 312–312 (2014). sourceExperimental — accuracy not yet measured
The Background and Discussion describe technical variation and batch effects in DNA methylation arrays, including CpG sites whose measurements have low reliability. The paper does not assess epigenetic clock age gaps or show that technical noise limits those models’ robustness across datasets. The cited explanation is therefore supported only as a general possibility, not as a finding about the clocks. (LLM, conf=0.86)
- Partly supportedcites [153] Logue, M. W. et al. The correlation of methylation levels measured using Illumina 450K and EPIC BeadChips in blood samples. Epigenomics 9, 1363–1371 (2017). sourceExperimental — accuracy not yet measured
The Discussion (page not specified) reports measurement noise and low cross-chip correlation at many low-variability CpG sites, which can make findings difficult to replicate. It does not link that noise to mild correlations among first-generation clock age gaps or establish that the age models lack robustness across datasets. The Results instead report similar methylation-age performance on the EPIC and 450K chips. (LLM, conf=0.87)
- Supportedcites [154] Sugden, K. et al. Patterns of reliability: assessing the reproducibility and integrity of DNA methylation measurement. Patterns 1, 100014 (2020). source
The Results section reports that the Hannum and Horvath clocks contain many unreliable methylation probes, despite being enriched for reliable ones. It also hypothesizes that algorithms with unreliable probes may perform poorly under varied testing conditions, supporting the proposed limitation across datasets. The cited paper does not need to establish the preceding observation about correlations between clock age gaps to support this sentence’s proposed explanation. (LLM, conf=0.87)
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- Partly supported
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In the paper
“Large mouse studies have observed that the transcriptomes of different organs and cell types have starkly different ageing trajectories41,42,156.”
§ Measuring biological age using omics data > Future perspectives > Defining the application of ageing clocks
What the sources say
- Supportedcites [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
The paper’s Abstract (page not available in our copy) describes a large mouse single-cell study across 23 tissues and organs and reports cell-specific ageing changes across multiple cell types and organs. Its examples of ageing microglia and kidney macrophages show different age-associated gene-expression patterns, supporting the paraphrase that their transcriptomic ageing trajectories differ. (LLM, conf=0.84)
- Partly supportedcites [42] Schaum, N. et al. Ageing hallmarks exhibit organ-specific temporal signatures. Nature 583, 596–602 (2020). sourceExperimental — accuracy not yet measured
The Gene expression dynamics with age section reports organ-specific differences in the timing and magnitude of expression changes, supporting different ageing trajectories across organs. But the paper also says many trajectory groups are shared across tissues. Its single-cell analysis identifies age-related genes specific to certain cell types, without establishing that different cell types have starkly different transcriptomic trajectories. (LLM, conf=0.86)
- Partly supportedcites [156] Zahn, J. M. et al. AGEMAP: a gene expression database for aging in mice. PLoS Genet. 3, e201 (2007). sourceExperimental — accuracy not yet measured
The Introduction reports markedly different age-related expression changes across 16 mouse tissues, and the Discussion identifies three distinct tissue-level transcriptional patterns. The paper does not establish different ageing trajectories for cell types: the Discussion says it cannot distinguish cell-intrinsic expression changes from changes in cell populations within tissues. The supplied passages have section labels but no page numbers. (LLM, conf=0.87)
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In the paper
“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.”
§ Measuring biological age using omics data > References
What the sources say
- Partly supportedcites [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. sourceExperimental — accuracy not yet measured
The Background says that a perfect chronological-age predictor would yield no information about inter-individual variation in biological age, and the Discussion reports that improved age prediction weakens the clock’s association with mortality. The paper does not show that these clocks discover drivers of biological ageing, or test whether greater accuracy reduces that ability specifically. (LLM, conf=0.84)
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In the paper
“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.”
§ Measuring biological age using omics data > Omic clocks > DNA methylation
What the sources say
- Not checked yetcites [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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In the paper
“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.”
§ Measuring biological age using omics data > Omic clocks > DNA methylation
What the sources say
- Not checked yetcites [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
- Not checked yetcites [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
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In the paper
“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.”
§ Measuring biological age using omics data > Omic clocks > DNA methylation
What the sources say
- Not checked yetcites [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 yetcites [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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In the paper
“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.”
§ Measuring biological age using omics data > Omic clocks > DNA methylation
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Measuring biological age using omics data > Omic clocks > DNA methylation
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In the paper
“Genes that regulate lifespan in animal models6–9 and human studies10–14 as well as multiple experimental paradigms, such as caloric restriction, heterochronic parabiosis and partial epigenetic reprogramming, have demonstrated that it is possible to manipulate ageing biology to ‘rejuvenate’ physiological functioning in complex model organisms15–28.”
§ Measuring biological age using omics data > Abstract
What the sources say
- Not checked yetcites [6] Vijg, J. & Suh, Y. Genetics of longevity and aging. Annu. Rev. Med. 56, 193–212 (2005). source
- Not checked yetcites [7] Helfand, S. L. & Rogina, B. Genetics of aging in the fruit fly, Drosophila melanogaster. Annu. Rev. Genet. 37, 329–348 (2003). source
- Not checked yetcites [8] Singh, P. P., Demmitt, B. A., Nath, R. D. & Brunet, A. The genetics of aging: a vertebrate perspective. Cell 177, 200–220 (2019). source
- Not checked yetcites [9] Kenyon, C., Chang, J., Gensch, E., Rudner, A. & Tabtiang, R. A C. elegans mutant that lives twice as long as wild type. Nature 366, 461–464 (1993). source
- Not checked yetcites [10] Kaplanis, J. et al. Quantitative analysis of population-scale family trees with millions of relatives. Science https://doi.org/10.1126/science.aam9309 (2018). source
- Not checked yetcites [11] Zenin, A. et al. Identification of 12 genetic loci associated with human healthspan. Commun. Biol. 2, 41 (2019). source
- Not checked yetcites [12] Deelen, J. et al. A meta-analysis of genome-wide association studies identifies multiple longevity genes. Nat. Commun. 10, 3669 (2019). source
- Not checked yetcites [13] Khan, S. S. et al. A null mutation in SERPINE1 protects against biological aging in humans. Sci. Adv. https://doi.org/10.1126/sciadv.aao1617 (2017). source
- Not checked yetcites [14] Martin, G. M., Bergman, A. & Barzilai, N. Genetic determinants of human health span and life span: progress and new opportunities. PLoS Genet. 3, e125 (2007). source
- Not checked yetcites [15] Villeda, S. A. et al. Young blood reverses age-related impairments in cognitive function and synaptic plasticity in mice. Nat. Med. 20, 659–663 (2014). source
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In the paper
“Epigenetic alteration is a hallmark of ageing4, and recent studies have demonstrated cellular and functional rejuvenation by transient epigenetic reprogramming17,51,52.”
§ Measuring biological age using omics data > Omic clocks > DNA methylation
What the sources say
- Not checked yetcites [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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In the paper
“However, translating the aforementioned interventions to the clinic requires the measurement of an individual’s biological age and rates of biological ageing.”
§ Measuring biological age using omics data > Abstract
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In the paper
“These hypotheses have been supported by observations that individuals with positive age gaps, termed age acceleration, are at greater risk of mortality and some diseases of ageing such as heart disease, metabolic syndrome and certain cancers46–50.”
§ Measuring biological age using omics data > Abstract
What the sources say
- Not checked yetcites [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
- Not checked yetcites [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 yetcites [48] Lu, A. T. et al. DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging 11, 303–327 (2019). source
- Not checked yetcites [49] Tanaka, T. et al. Plasma proteomic signature of age in healthy humans. Aging Cell 17, e12799 (2018). source
- Not checked yetcites [50] Zhang, Y. et al. DNA methylation signatures in peripheral blood strongly predict all-cause mortality. Nat. Commun. 8, 14617 (2017). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“One especially interesting aspect of DNA methylation clocks is their ability to accurately predict age across a wide range of different tissue types, regardless of the tissue type they were trained on, suggesting that they measure ageing signals that are shared between cell types43,44.”
§ Measuring biological age using omics data > Omic clocks > DNA methylation
What the sources say
- Not checked yetcites [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 yetcites [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
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“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.”
§ Measuring biological age using omics data > Omic clocks > DNA methylation
What the sources say
- Not checked yetcites [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 yetcites [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
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“In support of this notion, a large meta-analysis in 14 cohorts demonstrated that, taken to the extreme, improving a methylation clock’s accuracy to extremely high levels actually reduced its associations with mortality risk61.”
§ Measuring biological age using omics data > Omic clocks > DNA methylation
What the sources say
- Not checked yetcites [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
boosteddirect- Asserted: passes — The authors put it forward as true.
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In the paper
“One especially interesting aspect of DNA methylation clocks is their ability to accurately predict age across a wide range of different tissue types, regardless of the tissue type they were trained on, suggesting that they measure ageing signals that are shared between cell types43,44.”
§ Measuring biological age using omics data > Omic clocks > DNA methylation
What the sources say
- Not checked yetcites [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 yetcites [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
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“The DNAm GrimAge clock shows consistently stronger associations with various measures of age-related dysfunction, including heart disease, frailty, and cognitive and physical decline, compared to other clocks47,48,50,64–72.”
§ Measuring biological age using omics data > Omic clocks > DNA methylation
What the sources say
- Not checked yetcites [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 yetcites [48] Lu, A. T. et al. DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging 11, 303–327 (2019). source
- Not checked yetcites [50] Zhang, Y. et al. DNA methylation signatures in peripheral blood strongly predict all-cause mortality. Nat. Commun. 8, 14617 (2017). source
- Not checked yetcites [64] Verschoor, C. P. et al. Epigenetic age is associated with baseline and 3-year change in frailty in the Canadian longitudinal study on aging. Clin. Epigenetics 13, 163 (2021). source
- Not checked yetcites [65] Maddock, J. et al. DNA methylation age and physical and cognitive aging. J. Gerontol. Ser. A 75, 504–511 (2020). source
- Not checked yetcites [66] Sibbett, R. A. et al. DNA methylation-based measures of accelerated biological ageing and the risk of dementia in the oldest-old: a study of the Lothian Birth Cohort 1921. BMC Psychiatry 20, 91 (2020). source
- Not checked yetcites [67] Kuo, P.-L., Moore, A. Z., Lin, F. R. & Ferrucci, L. Epigenetic age acceleration and hearing: observations from the Baltimore Longitudinal Study of Aging. Front. Aging Neurosci. 13, 790926 (2021). source
- Not checked yetcites [68] McCrory, C. et al. GrimAge outperforms other epigenetic clocks in the prediction of age-related clinical phenotypes and all-cause mortality. J. Gerontol. Ser. A 76, 741–749 (2021). source
- Not checked yetcites [69] Crimmins, E. M., Thyagarajan, B., Levine, M. E., Weir, D. R. & Faul, J. Associations of age, sex, race/ethnicity, and education with 13 epigenetic clocks in a nationally representative U.S. sample: the health and retirement study. J. Gerontol. Ser. A 76, 1117–1123 (2021). source
- Not checked yetcites [70] Hillary, R. F. et al. An epigenetic predictor of death captures multi-modal measures of brain health. Mol. Psychiatry 26, 3806–3816 (2021). source
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In the paper
“An intriguing advance came in 2021 from Meyer et al.83, who demonstrated that a simple binarization and relative age scaling of transcriptomic data denoised the data and improved age prediction in Caenorhabditis elegans to the theoretical upper limit of accuracy (as accurately as the ages of the worms were tracked in increments of 1 day).”
§ Measuring biological age using omics data > Omic clocks > Transcriptomics
What the sources say
- Not checked yetcites [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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In the paper
“This idea is further supported by studies that show that epigenetic clocks trained in specific tissues have stronger associations with the functional states of those tissues77–80.”
§ Measuring biological age using omics data > Omic clocks > DNA methylation
What the sources say
- Not checked yetcites [77] Levine, M. E., Lu, A. T., Bennett, D. A. & Horvath, S. Epigenetic age of the pre-frontal cortex is associated with neuritic plaques, amyloid load, and Alzheimer’s disease related cognitive functioning. Aging 7, 1198–1211 (2015). source
- Not checked yetcites [78] Horvath, S. et al. Epigenetic clock for skin and blood cells applied to Hutchinson Gilford progeria syndrome and ex vivo studies. Aging 10, 1758–1775 (2018). source
- Not checked yetcites [79] Choukrallah, M.-A., Hoeng, J., Peitsch, M. C. & Martin, F. Lung transcriptomic clock predicts premature aging in cigarette smoke-exposed mice. BMC Genomics 21, 291 (2020). source
- Not checked yetcites [80] Sillanpää, E. et al. Blood and skeletal muscle ageing determined by epigenetic clocks and their associations with physical activity and functioning. Clin. Epigenetics 13, 110 (2021). source
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In the paper
“Studies using multiple proteomic technologies have shown that thousands of proteins change with age in human plasma91–93 and cerebrospinal fluid94, which has recently led to the development of multiple proteomic ageing clocks.”
§ Measuring biological age using omics data > Omic clocks > Proteomics
What the sources say
- Not checked yetcites [91] Menni, C. et al. Circulating proteomic signatures of chronological. Age. J. Gerontol. Ser. A 70, 809–816 (2015). source
- Not checked yetcites [92] Ignjatovic, V. et al. Age-related differences in plasma proteins: how plasma proteins change from neonates to adults. PLoS One 6, e17213 (2011). source
- Not checked yetcites [93] Lu, J. et al. Profiling plasma peptides for the identification of potential ageing biomarkers in Chinese Han adults. PLoS One 7, e39726 (2012). source
- Not checked yetcites [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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In the paper
“They implemented an artificial neural network that restricts neuron inputs and connectivity to known biological pathways.”
§ Measuring biological age using omics data > Omic clocks > Transcriptomics
What the sources say
- Not checked yetcites [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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In the paper
“More recently, Belsky et al.73 built the DunedinPoAm DNAm clock, an exciting development that, unlike many previous clocks that were trained to predict an individual’s current ageing state from cross-sectional data, was trained to more directly predict an individual’s ageing rate using longitudinal data.”
§ Measuring biological age using omics data > Omic clocks > DNA methylation
What the sources say
- Not checked yetcites [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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In the paper
“Strikingly, this clock uniquely detected precancerous tissue samples to be age accelerated.”
§ Measuring biological age using omics data > Omic clocks > DNA methylation
What the sources say
- Not checked yetcites [62] Yang, Z. et al. Correlation of an epigenetic mitotic clock with cancer risk. Genome Biol. 17, 205 (2016). source
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In the paper
“Major developments in mass spectrometry-based, antibody-based and aptamer-based proteomics over the past decade have enabled the robust quantification of thousands of proteins in single samples87–90.”
§ Measuring biological age using omics data > Omic clocks > Proteomics
What the sources say
- Not checked yetcites [87] Pappireddi, N., Martin, L. & Wühr, M. A review on quantitative multiplexed proteomics. ChemBioChem 20, 1210–1224 (2019). source
- Not checked yetcites [88] Suhre, K., McCarthy, M. I. & Schwenk, J. M. Genetics meets proteomics: perspectives for large population-based studies. Nat. Rev. Genet. 22, 19–37 (2021). source
- Not checked yetcites [89] Gold, L. et al. Aptamer-based multiplexed proteomic technology for biomarker discovery. PLoS One 5, e15004 (2010). source
- Not checked yetcites [90] Lundberg, M., Eriksson, A., Tran, B., Assarsson, E. & Fredriksson, S. Homogeneous antibody-based proximity extension assays provide sensitive and specific detection of low-abundant proteins in human blood. Nucleic Acids Res. 39, e102 (2011). source
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In the paper
“In 2016, Yang et al.62 built an epigenetic clock associated with the number of cell divisions by pre-selecting CpG sites that had low levels of methylation in fetal tissue and increased methylation levels throughout adulthood and that mapped to Polycomb group target (PCGT) promoters, before training a sparse linear model.”
§ Measuring biological age using omics data > Omic clocks > DNA methylation
What the sources say
- Not checked yetcites [62] Yang, Z. et al. Correlation of an epigenetic mitotic clock with cancer risk. Genome Biol. 17, 205 (2016). source
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In the paper
“This clock had stronger, more significant associations with age-related phenotypes, including physical functioning, cognition, self-rated health and mortality, than the DNAm PhenoAge clock.”
§ Measuring biological age using omics data > Omic clocks > DNA methylation
What the sources say
- Not checked yetcites [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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In the paper
“The authors showed that, in C. elegans, it is indeed possible to generate an accurate and biologically meaningful transcriptomic ageing clock.”
§ Measuring biological age using omics data > Omic clocks > Transcriptomics
What the sources say
- Not checked yetcites [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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In the paper
“However, the model was not evaluated on an independent test dataset, so it remains to be seen whether the method was a true improvement.”
§ Measuring biological age using omics data > Omic clocks > Transcriptomics
What the sources say
- Not checked yetcites [82] Fleischer, J. G. et al. Predicting age from the transcriptome of human dermal fibroblasts. Genome Biol. 19, 221 (2018). source
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In the paper
“The authors identified well-studied metabolites, such as albumin, very low-density lipoprotein particles and amino acids, that were associated with mortality due to multiple causes.”
§ Measuring biological age using omics data > Omic clocks > Metabolomics
What the sources say
- Not checked yetcites [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
- Not checked yetcites [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
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In the paper
“A large-scale biobank study used 1H-NMR of blood plasma to identify metabolites predictive of mortality risk in an Estonian and Finnish cohort117,118.”
§ Measuring biological age using omics data > Omic clocks > Metabolomics
What the sources say
- Not checked yetcites [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
- Not checked yetcites [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
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Lehallier et al. also demonstrated that many proteins in the plasma ageing clock were modulated by parabiosis in mice and exercise in humans97, two rejuvenation paradigms.”
§ Measuring biological age using omics data > Omic clocks > Proteomics
What the sources say
- Not checked yetcites [97] Lehallier, B., Shokhirev, M. N., Wyss‐Coray, T. & Johnson, A. A. Data mining of human plasma proteins generates a multitude of highly predictive aging clocks that reflect different aspects of aging. Aging Cell 19, e13256 (2020). source
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In the paper
“In both targeted and untargeted methods, even for confidently identified compounds, the biological processes that generated them are often poorly understood122–125.”
§ Measuring biological age using omics data > Omic clocks > Metabolomics
What the sources say
- Not checked yetcites [122] Bingol, K. Recent advances in targeted and untargeted metabolomics by NMR and MS/NMR methods. High Throughput 7, 9 (2018). source
- Not checked yetcites [123] Gertsman, I. & Barshop, B. A. Promises and pitfalls of untargeted metabolomics. J. Inherit. Metab. Dis. 41, 355–366 (2018). source
- Not checked yetcites [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
- Not checked yetcites [125] Krumsiek, J. et al. Mining the unknown: a systems approach to metabolite identification combining genetic and metabolic information. PLoS Genet. 8, e1003005 (2012). source
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Although the SomaLogic aptamer-based platform is especially powerful and can reliably quantify over 7,000 proteins in a variety of biofluids and cellular extracts103–106, it is not yet possible to reliably quantify the entire proteome.”
§ Measuring biological age using omics data > Omic clocks > Proteomics
What the sources say
- Not checked yetcites [103] Ferkingstad, E. et al. Large-scale integration of the plasma proteome with genetics and disease. Nat. Genet. 53, 1712–1721 (2021). source
- Not checked yetcites [104] Md Dom, Z. I. et al. Effect of TNFα stimulation on expression of kidney risk inflammatory proteins in human umbilical vein endothelial cells cultured in hyperglycemia. Sci. Rep. 11, 11133 (2021). source
- Not checked yetcites [105] Thanasupawat, T. et al. Slow off-rate modified aptamer (SOMAmer) proteomic analysis of patient-derived malignant glioma identifies distinct cellular proteomes. Int. J. Mol. Sci. 22, 9566 (2021). source
- Not checked yetcites [106] Yang, C. et al. Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disorders. Nat. Neurosci. 24, 1302–1312 (2021). source
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Lehallier et al.95 further demonstrated robust and highly accurate chronological age prediction across multiple independent cohorts.”
§ Measuring biological age using omics data > Omic clocks > Proteomics
What the sources say
- Not checked yetcites [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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In the paper
“State-of-the-art mass spectrometry and NMR methods can identify hundreds to thousands of metabolites in human plasma, and multiple studies have attempted to understand how they interact with ageing113–116.”
§ Measuring biological age using omics data > Omic clocks > Metabolomics
What the sources say
- Not checked yetcites [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 yetcites [114] Yu, Z. et al. Human serum metabolic profiles are age dependent. Aging Cell 11, 960–967 (2012). source
- Not checked yetcites [115] Lawton, K. A. et al. Analysis of the adult human plasma metabolome. Pharmacogenomics 9, 383–397 (2008). source
- Not checked yetcites [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
plain- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Although the SomaLogic aptamer-based platform is especially powerful and can reliably quantify over 7,000 proteins in a variety of biofluids and cellular extracts103–106, it is not yet possible to reliably quantify the entire proteome.”
§ Measuring biological age using omics data > Omic clocks > Proteomics
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Studies on heterochronic parabiosis and plasma exchange in animals have demonstrated that circulating proteins can have a causal influence on ageing phenotypes across the body, including in skeletal muscle107,108, heart109 and brain15,110–112.”
§ Measuring biological age using omics data > Omic clocks > Proteomics
What the sources say
- Not checked yetcites [107] Conboy, I. M. et al. Rejuvenation of aged progenitor cells by exposure to a young systemic environment. Nature 433, 760–764 (2005). source
- Not checked yetcites [108] Sinha, M. et al. Restoring systemic GDF11 levels reverses age-related dysfunction in mouse skeletal muscle. Science 344, 649–652 (2014). source
- Not checked yetcites [109] Loffredo, F. S. et al. Growth differentiation factor 11 is a circulating factor that reverses age-related cardiac hypertrophy. Cell 153, 828–839 (2013). source
- Not checked yetcites [15] Villeda, S. A. et al. Young blood reverses age-related impairments in cognitive function and synaptic plasticity in mice. Nat. Med. 20, 659–663 (2014). source
- Not checked yetcites [110] Eggel, A. & Wyss-Coray, T. A revival of parabiosis in biomedical research. Swiss Med. Wkly. https://doi.org/10.4414/smw.2014.13914 (2014). source
- Not checked yetcites [111] Katsimpardi, L. et al. Vascular and neurogenic rejuvenation of the aging mouse brain by young systemic factors. Science 344, 630–634 (2014). source
- Not checked yetcites [112] Alkahest, Inc. A Randomized, Double-Blind, Placebo-Controlled Study to Assess the Safety and Tolerability of Pulsed GRF6019 Infusions in Subjects With Severe Alzheimer’s Disease https://clinicaltrials.gov/ct2/show/results/NCT03765762 (2021).
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In the paper
“Age gaps in second-generation mortality-optimized clocks (Zhang, PhenoAge, GrimAge) also correlate only mildly with each other69,151.”
§ Measuring biological age using omics data > Comparing different clocks
What the sources say
- Not checked yetcites [69] Crimmins, E. M., Thyagarajan, B., Levine, M. E., Weir, D. R. & Faul, J. Associations of age, sex, race/ethnicity, and education with 13 epigenetic clocks in a nationally representative U.S. sample: the health and retirement study. J. Gerontol. Ser. A 76, 1117–1123 (2021). source
- Not checked yetcites [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
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In the paper
“Galkin et al.137 predicted chronological age from gut microbiome taxonomic composition using deep neural network models.”
§ Measuring biological age using omics data > Omic clocks > Other omics
What the sources say
- Not checked yetcites [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
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In the paper
“Interestingly, epigenetic age gaps showed no correlations with proteomic and metabolomic age gaps, emphasizing that different omics technologies may capture different ageing signals.”
§ Measuring biological age using omics data > Comparing different clocks
What the sources say
- Not checked yetcites [155] Jansen, R. et al. An integrative study of five biological clocks in somatic and mental health. eLife 10, e59479 (2021). source
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“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.”
§ Measuring biological age using omics data > Future perspectives > Defining the application of ageing clocks
What the sources say
- Not checked yetcites [76] Ahadi, S. et al. Personal aging markers and ageotypes revealed by deep longitudinal profiling. Nat. Med. 26, 83–90 (2020). source
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, they generally show more robust associations with physiological ageing and mortality across cohorts, and more work needs to be done to understand what specific features of ageing biology they capture.”
§ Measuring biological age using omics data > Comparing different clocks
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Krištić et al.129 observed changes in the N-glycosylation pattern of serum IgG protein, which predicted age in multiple European cohorts.”
§ Measuring biological age using omics data > Omic clocks > Other omics
What the sources say
- Not checked yetcites [129] Krištić, J. et al. Glycans are a novel biomarker of chronological and biological ages. J. Gerontol. Ser. A 69, 779–789 (2014). source
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In the paper
“Peters et al.’s transcriptomic age gap correlated with different biological ageing measures to those of the Horvath and Hannum clocks81;”
§ Measuring biological age using omics data > Comparing different clocks
What the sources say
- Not checked yetcites [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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In the paper
“Although it is not yet possible to comprehensively survey the glycome, recent mass spectrometry studies examining the concentrations of just a few well-studied glycans have been able to accurately predict chronological age and ageing phenotypes.”
§ Measuring biological age using omics data > Omic clocks > Other omics
What the sources say
- Not checked yetcites [129] Krištić, J. et al. Glycans are a novel biomarker of chronological and biological ages. J. Gerontol. Ser. A 69, 779–789 (2014). source
- Not checked yetcites [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
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“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.”
§ Measuring biological age using omics data > Future perspectives > Defining the application of ageing clocks
What the sources say
- Not checked yetcites [76] Ahadi, S. et al. Personal aging markers and ageotypes revealed by deep longitudinal profiling. Nat. Med. 26, 83–90 (2020). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This finding suggests that age gaps in these clocks are not strongly driven by biological ageing signals.”
§ Measuring biological age using omics data > Comparing different clocks
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In the paper
“Chromatin accessibility studies in ageing immune cells have demonstrated shifts in chromatin accessibility with age in CD8+ T cells144–147.”
§ Measuring biological age using omics data > Omic clocks > Other omics
What the sources say
- Not checked yetcites [144] Moskowitz, D. M. et al. Epigenomics of human CD8 T cell differentiation and aging. Sci. Immunol. https://doi.org/10.1126/sciimmunol.aag0192 (2017). source
- Not checked yetcites [145] Pulko, V. et al. Human memory T cells with a naive phenotype accumulate with aging and respond to persistent viruses. Nat. Immunol. 17, 966–975 (2016). source
- Not checked yetcites [146] Ucar, D. et al. The chromatin accessibility signature of human immune aging stems from CD8+ T cells. J. Exp. Med. 214, 3123–3144 (2017). source
- Not checked yetcites [147] Goronzy, J. J., Hu, B., Kim, C., Jadhav, R. R. & Weyand, C. M. Epigenetics of T cell aging. J. Leukoc. Biol. 104, 691–699 (2018). source
boostedcorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Despite all being modelled and trained on the chronological age of individuals, mostly from blood cells, age gaps from first-generation epigenetic clocks (Hannum, Horvath, Lin, Weidner, Vidal-Bralo) seem to only mildly correlate with each other (r = 0.1–0.5)69,151.”
§ Measuring biological age using omics data > Comparing different clocks
What the sources say
- Not checked yetcites [69] Crimmins, E. M., Thyagarajan, B., Levine, M. E., Weir, D. R. & Faul, J. Associations of age, sex, race/ethnicity, and education with 13 epigenetic clocks in a nationally representative U.S. sample: the health and retirement study. J. Gerontol. Ser. A 76, 1117–1123 (2021). source
- Not checked yetcites [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
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Still, the correlations between clocks based on different molecular features are fairly low, and differences are further emphasized by varying sensitivities to aspects of biological ageing (FIG. 4).”
§ Measuring biological age using omics data > Comparing different clocks
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In the paper
“Methylation levels in blood cells have been observed to correlate strongly with cell composition, which may be a result of changes in chromatin state with age148.”
§ Measuring biological age using omics data > Omic clocks > Other omics
What the sources say
- Not checked yetcites [148] Bell, C. G. et al. DNA methylation aging clocks: challenges and recommendations. Genome Biol. 20, 249 (2019). source
boostedcorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Moreover, IgG glycan age correlated with clinical markers of metabolic health129.”
§ Measuring biological age using omics data > Omic clocks > Other omics
What the sources say
- Not checked yetcites [129] Krištić, J. et al. Glycans are a novel biomarker of chronological and biological ages. J. Gerontol. Ser. A 69, 779–789 (2014). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Noise in metabolomic data may thus limit their current utility for ageing research.”
§ Measuring biological age using omics data > Omic clocks > Metabolomics
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In the paper
“They also observed that the abundance of certain well-studied beneficial microorganisms, such as Akkermansia muciniphila, had a larger impact on the model age predictions than the majority of other microorganisms detected.”
§ Measuring biological age using omics data > Omic clocks > Other omics
What the sources say
- Not checked yetcites [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
plaincorrelational- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“Additionally, they have shown great clinical utility and promise as prognostic biomarkers in many settings relevant to ageing, prominently in heart, kidney, liver, metabolic, inflammatory and neurodegenerative disease risk101,161,162.”
§ Measuring biological age using omics data > Future perspectives > Ideal molecular features to measure ageing
What the sources say
- Not checked yetcites [101] Williams, S. A. et al. Plasma protein patterns as comprehensive indicators of health. Nat. Med. 25, 1851–1857 (2019). source
- Not checked yetcites [161] Ganz, P. et al. Development and validation of a protein-based risk score for cardiovascular outcomes among patients with stable coronary heart disease. JAMA 315, 2532–2541 (2016). source
- Not checked yetcites [162] 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).
boostedcorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“A recent simulation study by Nelson et al.168 bolsters the rationale for training these composite models, showing that first-generation methylation ageing clocks do worse than random chance at identifying causal ageing loci due to cohort selection effects that occur in cross-sectional ageing cohorts169,170.”
§ Measuring biological age using omics data > Future perspectives > Moving beyond chronological age
What the sources say
- Not checked yetcites [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 yetcites [169] Delgado-Rodríguez, M. & Llorca, J. Bias. J. Epidemiol. Commun. Health 58, 635–641 (2004). source
- Not checked yetcites [170] Zhou, Z., Rahme, E., Abrahamowicz, M. & Pilote, L. Survival bias associated with time-to-treatment initiation in drug effectiveness evaluation: a comparison of methods. Am. J. Epidemiol. 162, 1016–1023 (2005). source
boosteddirect- Asserted: passes — The authors put it forward as true.
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In the paper
“similarly, epigenetic and other molecular features in blood cells177 or skin178 change in distinct undulating patterns across the lifespan.”
§ Measuring biological age using omics data > Conclusions
What the sources say
- Not checked yetcites [177] Márquez, E. J. et al. Sexual-dimorphism in human immune system aging. Nat. Commun. 11, 751 (2020). source
- Not checked yetcites [178] Holzscheck, N. et al. Multi-omics network analysis reveals distinct stages in the human aging progression in epidermal tissue. Aging 12, 12393–12409 (2020). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“However, methods that incorporate additional biological information on mortality, as in the PhenoAge clock approach, are able to correct for this effect and select causal loci with much greater frequency.”
§ Measuring biological age using omics data > Future perspectives > Moving beyond chronological age
What the sources say
- Not checked yetcites [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
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“The GrimAge clock48 took a similar approach but trained methylation clocks directly on clinical protein markers and smoking pack-years, whose outputs were then combined into a Cox proportional hazards model with age and sex to predict mortality.”
§ Measuring biological age using omics data > Future perspectives > Moving beyond chronological age
What the sources say
- Not checked yetcites [48] Lu, A. T. et al. DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging 11, 303–327 (2019). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“The primacy of disrupted proteostasis in neurodegenerative disease pathology additionally suggests that proteomic ageing clocks may offer unique insights into brain ageing100, for example.”
§ Measuring biological age using omics data > Future perspectives > Ideal molecular features to measure ageing
What the sources say
- Not checked yetcites [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
hedgedconditional- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Equally important, cells and tissues age at different rates and future clocks could benefit from honing in on what aspects of ageing they intend to measure: the overall, organismal age of an individual to predict, for example, mortality, or the age of the heart, lung or brain to gain deeper insight into specific diseases of ageing.”
§ Measuring biological age using omics data > Conclusions
plaincorrelational- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“A recent set of publications did this by profiling immune cell and inflammatory markers to develop an immune ageing clock that predicted mortality, cardiovascular outcomes and immunosenescence171,172.”
§ Measuring biological age using omics data > Future perspectives > Moving beyond chronological age
What the sources say
- Not checked yetcites [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 yetcites [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
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“The PhenoAge clock47 trained on a clinical chemistry panel of mortality risk factors and age.”
§ Measuring biological age using omics data > Future perspectives > Moving beyond chronological age
What the sources say
- Not checked yetcites [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
plain- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“Indeed, it could be argued that, as much of the basic biology of ageing seems largely conserved among many species with very different lifespans3,4,8, clocks that measure a universal cause or rate of ageing should function ubiquitously.”
§ Measuring biological age using omics data > Future perspectives > Moving beyond chronological age
What the sources say
- Not checked yetcites [3] Kennedy, B. K. et al. Geroscience: linking aging to chronic disease. Cell 159, 709–713 (2014). source
- Not checked yetcites [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 yetcites [8] Singh, P. P., Demmitt, B. A., Nath, R. D. & Brunet, A. The genetics of aging: a vertebrate perspective. Cell 177, 200–220 (2019). source
hedged- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In fact, using chronological age as the only guide can be quite misleading as highlighted in the methylation clocks section.”
§ Measuring biological age using omics data > Future perspectives > Understanding correlation and causation
plain- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“This concept has been demonstrated by principal component analysis-based biological age estimators, which involve unsupervised dimensionality reduction to unbiasedly identify ageing signals, seemingly even better than clocks that train on chronological age173–175.”
§ Measuring biological age using omics data > Future perspectives > Moving beyond chronological age
What the sources say
- Not checked yetcites [173] Nakamura, E., Miyao, K. & Ozeki, T. Assessment of biological age by principal component analysis. Mech. Ageing Dev. 46, 1–18 (1988). source
- Not checked yetcites [174] Pyrkov, T. V. et al. Extracting biological age from biomedical data via deep learning: too much of a good thing? Sci. Rep. 8, 5210 (2018). source
- Not checked yetcites [175] Pyrkov, T. V. et al. Quantitative characterization of biological age and frailty based on locomotor activity records. Aging 10, 2973–2990 (2022). source
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Further, they show that causal ageing features tend to become less represented with increased age as unhealthy agers become less likely to be included in studies due to death or health challenges that preclude participation in research.”
§ Measuring biological age using omics data > Future perspectives > Moving beyond chronological age
What the sources say
- Not checked yetcites [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
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“This study developed a highly predictive plasma proteomic ageing clock”
§ Measuring biological age using omics data > References
What the sources say
- Not checked yetcites [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
plain- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“Yang et al.62 and Lu et al.167 took a different approach and trained clocks on cellular features — mitotic cell division and telomere length, respectively — to develop models with specific sensitivity for these aspects of cellular ageing.”
§ Measuring biological age using omics data > Future perspectives > Moving beyond chronological age
What the sources say
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As ageing clocks based on different omics modalities do not typically agree and there are no clear ground-truth metrics of biological ageing to evaluate them by, it is too early to say which molecular category is likely to produce the best predictor of biological age and rate of ageing.”
§ Measuring biological age using omics data > Future perspectives > Ideal molecular features to measure ageing
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“and showed relationships between the proteomic age gap and many ageing traits such as cognitive function and motor function.”
§ Measuring biological age using omics data > References
What the sources say
- Not checked yetcites [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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 50
- Probably not a substantial claim. 34
Every source the paper cites for this claim was checked and supports it.
In the paper
“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.”
§ Measuring biological age using omics data > Abstract
What the sources say
- Supportedcites [43] Hannum, G. et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol. Cell 49, 359–367 (2013). source
The Conclusions describe a quantitative model based on methylation profiles that predicts age and suggest it may represent a common molecular clock. The paper is therefore an example of the age predictors identified by the citation; its Conclusions also describe potential applications for interpreting aging from molecular profiles. (LLM, conf=0.89)
- Supportedcites [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
The Background describes the DNA methylation age predictor as a biomarker for questions in aging research, and the multi-tissue predictor section calls its CpG-based predictor an epigenetic clock. The paper reports strong performance across tissues and describes potential research uses, supporting the claim that this kind of age predictor is a promising way to interpret omics data in aging. (LLM, conf=0.87)
- Supportedcites [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
Section 2.4 describes DNA-methylation clocks as useful for connecting ageing with diseases, phenotypes and lifestyle factors. Sections 2.5 and 3 describe transcriptomic clocks and report significant predictive power for ageing clocks, supporting their promise as a way to interpret omics data in ageing. Page numbers are unavailable in our copy. (LLM, conf=0.90)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“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.”
§ Measuring biological age using omics data > Omic clocks > Metabolomics
What the sources say
- Supportedcites [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
The Results section, “Metabolomic age predictors,” says the authors performed pathway enrichment analysis on UPLC-MS-derived features selected in at least 75% of their age-prediction models. It identifies enriched pathways for vitamin E and D3, several amino acids, and xenobiotic metabolism; Table 2 lists these pathways. (LLM, conf=0.94)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“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.”
§ Measuring biological age using omics data > Omic clocks > Other omics
What the sources say
- Not checked yetcites [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
- Supportedcites [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
The paper describes Werner syndrome as a premature-aging disorder and reports heterochromatin changes in WRN-deficient stem cells (Abstract; no page given). It also reports loss of the inner-nuclear-membrane proteins LAP2β and LBR, reduced heterochromatin beneath the nuclear envelope, and an association between WRN and the lamina–heterochromatin anchoring protein LAP2β. Together, these findings support the cited link between progeroid aging, chromatin state, and nuclear-envelope-associated heterochromatin regulation. (LLM, conf=0.86)
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Biological ageing is enormously complex and is thought to be driven by the interaction of multiple dysregulated cellular and biochemical processes3,4.”
§ Measuring biological age using omics data > Abstract
What the sources say
- Not checked yetcites [3] Kennedy, B. K. et al. Geroscience: linking aging to chronic disease. Cell 159, 709–713 (2014). source
- Not checked yetcites [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
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Measuring biological age using omics data > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
In the paper
“Globally, the human population is rapidly ageing but our healthspan — the period of life free from disease — has not increased in kind1.”
§ Measuring biological age using omics data > Abstract
What the sources say
- Not checked yetcites [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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, the availability of large-scale omics data has posed new challenges for the analysis and interpretation of molecular ageing.”
§ Measuring biological age using omics data > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“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.”
§ Measuring biological age using omics data > Omic clocks > DNA methylation
What the sources say
- Not checked yetcites [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
plainn = 8,000- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Epigenetic alteration is a hallmark of ageing4, and recent studies have demonstrated cellular and functional rejuvenation by transient epigenetic reprogramming17,51,52.”
§ Measuring biological age using omics data > Omic clocks > DNA methylation
What the sources say
- Not checked yetcites [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 yetcites [51] Ocampo, A. et al. In vivo amelioration of age-associated hallmarks by partial reprogramming. Cell 167, 1719–1733.e12 (2016). source
- Not checked yetcites [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
boosteddirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Ageing contributes to many diseases that affect all organ systems and is the greatest risk factor for heart disease, neurodegeneration and cancer2.”
§ Measuring biological age using omics data > Abstract
What the sources say
- Not checked yetcites [2] Niccoli, T. & Partridge, L. Ageing as a risk factor for disease. Curr. Biol. 22, R741–R752 (2012). source
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
In the paper
“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.”
§ Measuring biological age using omics data > Omic clocks > DNA methylation
What the sources say
- Not checked yetcites [43] Hannum, G. et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol. Cell 49, 359–367 (2013). source
plainn = 656- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“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.”
§ Measuring biological age using omics data > Omic clocks > DNA methylation
What the sources say
- Not checked yetcites [53] Bocklandt, S. et al. Epigenetic predictor of age. PLoS One 6, e14821 (2011). source
boostedcorrelationaln = 100- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“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.”
§ Measuring biological age using omics data > Abstract
What the sources say
- Not checked yetcites [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
- Not checked yetcites [42] Schaum, N. et al. Ageing hallmarks exhibit organ-specific temporal signatures. Nature 583, 596–602 (2020). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“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.”
§ Measuring biological age using omics data > Omic clocks > DNA methylation
What the sources say
- Not checked yetcites [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 yetcites [48] Lu, A. T. et al. DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging 11, 303–327 (2019). source
- Not checked yetcites [50] Zhang, Y. et al. DNA methylation signatures in peripheral blood strongly predict all-cause mortality. Nat. Commun. 8, 14617 (2017). source
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“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.”
§ Measuring biological age using omics data > Omic clocks > Transcriptomics
What the sources say
- Not checked yetcites [81] Peters, M. J. et al. The transcriptional landscape of age in human peripheral blood. Nat. Commun. 6, 8570 (2015). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“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.”
§ Measuring biological age using omics data > Omic clocks > Proteomics
What the sources say
- Not checked yetcites [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
- Not checked yetcites [91] Menni, C. et al. Circulating proteomic signatures of chronological. Age. J. Gerontol. Ser. A 70, 809–816 (2015). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“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.”
§ Measuring biological age using omics data > Omic clocks > Transcriptomics
What the sources say
- Not checked yetcites [81] Peters, M. J. et al. The transcriptional landscape of age in human peripheral blood. Nat. Commun. 6, 8570 (2015). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Measuring biological age using omics data > Omic clocks > DNA methylation
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“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.”
§ Measuring biological age using omics data > Omic clocks > Transcriptomics
What the sources say
- Not checked yetcites [85] Wang, Z., Gerstein, M. & Snyder, M. RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10, 57–63 (2009). source
- Not checked yetcites [86] Zhang, W. et al. Comparison of RNA-seq and microarray-based models for clinical endpoint prediction. Genome Biol. 16, 133 (2015). source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Measuring biological age using omics data > Omic clocks > Transcriptomics
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“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.”
§ Measuring biological age using omics data > Omic clocks > Transcriptomics
What the sources say
- Not checked yetcites [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
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“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.”
§ Measuring biological age using omics data > Omic clocks > Transcriptomics
What the sources say
- Not checked yetcites [82] Fleischer, J. G. et al. Predicting age from the transcriptome of human dermal fibroblasts. Genome Biol. 19, 221 (2018). source
- Not checked yetcites [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
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“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.”
§ Measuring biological age using omics data > Omic clocks > Transcriptomics
What the sources say
- Not checked yetcites [82] Fleischer, J. G. et al. Predicting age from the transcriptome of human dermal fibroblasts. Genome Biol. 19, 221 (2018). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“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.”
§ Measuring biological age using omics data > Omic clocks > Transcriptomics
What the sources say
- Not checked yetcites [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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
In the paper
“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.”
§ Measuring biological age using omics data > Omic clocks > Metabolomics
What the sources say
- Not checked yetcites [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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“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.”
§ Measuring biological age using omics data > Omic clocks > Proteomics
What the sources say
- Not checked yetcites [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
- Not checked yetcites [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
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Measuring biological age using omics data > Omic clocks > Proteomics
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Additionally, proteomic technologies are newer and less developed than DNA quantification technologies and therefore proteomic clocks have been less extensively validated than methylation clocks.”
§ Measuring biological age using omics data > Omic clocks > Proteomics
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Metabolomic age acceleration correlated with triglyceride levels, obesity, heavy drinking, diabetes mellitus, depressive symptoms, depression, anxiety and post-traumatic stress disorder.”
§ Measuring biological age using omics data > Omic clocks > Metabolomics
What the sources say
- Not checked yetcites [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 yetcites [121] Hertel, J. et al. Measuring biological age via metabonomics: the metabolic age score. J. Proteome Res. 15, 400–410 (2016). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“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.”
§ Measuring biological age using omics data > Omic clocks > Proteomics
What the sources say
- Not checked yetcites [49] Tanaka, T. et al. Plasma proteomic signature of age in healthy humans. Aging Cell 17, e12799 (2018). source
- Not checked yetcites [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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“In independent prospective cohorts, accelerated metabolomic age was found to be associated with cardiovascular risk factors, cardiovascular disease risk and all-cause mortality risk.”
§ Measuring biological age using omics data > Omic clocks > Metabolomics
What the sources say
- Not checked yetcites [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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“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.”
§ Measuring biological age using omics data > Omic clocks > Proteomics
What the sources say
- Not checked yetcites [49] Tanaka, T. et al. Plasma proteomic signature of age in healthy humans. Aging Cell 17, e12799 (2018). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“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.”
§ Measuring biological age using omics data > Omic clocks > Metabolomics
What the sources say
- Not checked yetcites [122] Bingol, K. Recent advances in targeted and untargeted metabolomics by NMR and MS/NMR methods. High Throughput 7, 9 (2018). source
- Not checked yetcites [123] Gertsman, I. & Barshop, B. A. Promises and pitfalls of untargeted metabolomics. J. Inherit. Metab. Dis. 41, 355–366 (2018). source
- Not checked yetcites [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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“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.”
§ Measuring biological age using omics data > Omic clocks > Metabolomics
What the sources say
- Not checked yetcites [122] Bingol, K. Recent advances in targeted and untargeted metabolomics by NMR and MS/NMR methods. High Throughput 7, 9 (2018). source
- Not checked yetcites [123] Gertsman, I. & Barshop, B. A. Promises and pitfalls of untargeted metabolomics. J. Inherit. Metab. Dis. 41, 355–366 (2018). source
- Not checked yetcites [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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“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.”
§ Measuring biological age using omics data > Omic clocks > Proteomics
What the sources say
- Not checked yetcites [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 yetcites [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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Kidney function has an effect on plasma protein concentrations that is not fully understood but can confound ageing analysis101,102.”
§ Measuring biological age using omics data > Omic clocks > Proteomics
What the sources say
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“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.”
§ Measuring biological age using omics data > Omic clocks > Metabolomics
What the sources say
- Not checked yetcites [122] Bingol, K. Recent advances in targeted and untargeted metabolomics by NMR and MS/NMR methods. High Throughput 7, 9 (2018). source
- Not checked yetcites [123] Gertsman, I. & Barshop, B. A. Promises and pitfalls of untargeted metabolomics. J. Inherit. Metab. Dis. 41, 355–366 (2018). source
- Not checked yetcites [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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“In a follow-up study, Tanaka et al. showed associations with mortality, multi-morbidity, healthspan and lifespan96.”
§ Measuring biological age using omics data > Omic clocks > Proteomics
What the sources say
- Not checked yetcites [96] Tanaka, T. et al. Plasma proteomic biomarker signature of age predicts health and life span. eLife 9, e61073 (2020). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“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.”
§ Measuring biological age using omics data > Comparing different clocks
What the sources say
- Not checked yetcites [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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“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.”
§ Measuring biological age using omics data > Omic clocks > Other omics
What the sources say
- Not checked yetcites [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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Tanaka et al.’s plasma proteomic age gap had no correlation with the Horvath clock and low correlation with the GrimAge and PhenoAge clocks96;”
§ Measuring biological age using omics data > Comparing different clocks
What the sources say
- Not checked yetcites [96] Tanaka, T. et al. Plasma proteomic biomarker signature of age predicts health and life span. eLife 9, e61073 (2020). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“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.”
§ Measuring biological age using omics data > Omic clocks > Other omics
What the sources say
- Not checked yetcites [131] Badal, V. D. et al. The gut microbiome, aging, and longevity: a systematic review. Nutrients 12, 3759 (2020). source
- Not checked yetcites [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 yetcites [133] Rampelli, S. et al. Functional metagenomic profiling of intestinal microbiome in extreme ageing. Aging 5, 902–912 (2013). source
- Not checked yetcites [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 yetcites [135] Cani, P. D. & de Vos, W. M. Next-generation beneficial microbes: the case of Akkermansia muciniphila. Front. Microbiol. 8, 1765 (2017). source
- Not checked yetcites [136] Sato, Y. et al. Novel bile acid biosynthetic pathways are enriched in the microbiome of centenarians. Nature 599, 458–464 (2021). source
boostedcorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“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.”
§ Measuring biological age using omics data > Omic clocks > Metabolomics
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In the paper
“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.”
§ Measuring biological age using omics data > Comparing different clocks
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In the paper
“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.”
§ Measuring biological age using omics data > Comparing different clocks
What the sources say
- Not checked yetcites [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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In the paper
“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.”
§ Measuring biological age using omics data > Comparing different clocks
What the sources say
- Not checked yetcites [155] Jansen, R. et al. An integrative study of five biological clocks in somatic and mental health. eLife 10, e59479 (2021). source
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In the paper
“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.”
§ Measuring biological age using omics data > Comparing different clocks
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In the paper
“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.”
§ Measuring biological age using omics data > Omic clocks > Metabolomics
What the sources say
- Not checked yetcites [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 yetcites [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
- Not checked yetcites [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
- Not checked yetcites [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 yetcites [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 yetcites [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 yetcites [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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In the paper
“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.”
§ Measuring biological age using omics data > Omic clocks > Other omics
What the sources say
- Not checked yetcites [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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In the paper
“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.”
§ Measuring biological age using omics data > References
What the sources say
- Not checked yetcites [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
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“They do not provide causal insights into ageing but can illuminate testable hypotheses and support or contradict other observations in molecular geroscience.”
§ Measuring biological age using omics data > Future perspectives > Understanding correlation and causation
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- Rests on an assumption: Current ageing clocks are all correlative statistical models.
None of the sources cited for this claim have been checked yet.
In the paper
“Modern human genetics methods, such as genetic colocalization163 and Mendelian randomization164, can also be used to test causality in ageing clock models.”
§ Measuring biological age using omics data > Future perspectives > Understanding correlation and causation
What the sources say
plainconditional- Asserted: passes — The authors put it forward as true.
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In the paper
“This study developed multiple methodological innovations for training accurate and reproducible transcriptomic ageing clocks”
§ Measuring biological age using omics data > References
What the sources say
- Not checked yetcites [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
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
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In the paper
“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.”
§ Measuring biological age using omics data > Future perspectives > Moving beyond chronological age
What the sources say
- Not checked yetcites [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 yetcites [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
plainconditional- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Measuring biological age using omics data > Conclusions
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In the paper
“Ultrastructural studies of human tissues157 have shown similar results, and even different human brain regions exhibit different gene expression trajectories with age158.”
§ Measuring biological age using omics data > Future perspectives > Defining the application of ageing clocks
What the sources say
- Not checked yetcites [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 yetcites [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
plaincorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“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.”
§ Measuring biological age using omics data > Future perspectives > Moving beyond chronological age
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In the paper
“Lu et al.17 found that DNA methylation enzymes were required for epigenetic reprogramming of mouse retinal cells but much more work is needed.”
§ Measuring biological age using omics data > Future perspectives > Understanding correlation and causation
What the sources say
- Not checked yetcites [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
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“and developed clocks with striking generalizable performance across multiple ageing treatment conditions in worms.”
§ Measuring biological age using omics data > References
What the sources say
- Not checked yetcites [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
boosted- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“For example, Lehallier et al.95 demonstrated that plasma proteins change in three non-linear ‘waves’ of ageing;”
§ Measuring biological age using omics data > Conclusions
What the sources say
- Not checked yetcites [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
boostedcorrelational- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“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.”
§ Measuring biological age using omics data > Future perspectives > Defining the application of ageing clocks
What the sources say
- Not checked yetcites [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
hedgedconditional- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The omics revolution has illuminated the sheer complexity of ageing biology by showing that many tens of thousands of molecular features change with age.”
§ Measuring biological age using omics data > Conclusions
plaincorrelational- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Together, these observations support the notion that ageing clocks need to be more targeted to harness the organismal complexity of ageing.”
§ Measuring biological age using omics data > Future perspectives > Defining the application of ageing clocks
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence.
None of the sources cited for this claim have been checked yet.
In the paper
“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.”
§ Measuring biological age using omics data > Future perspectives > Moving beyond chronological age
What the sources say
- Not checked yetcites [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 yetcites [48] Lu, A. T. et al. DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging 11, 303–327 (2019). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“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.”
§ Measuring biological age using omics data > Future perspectives > Ideal molecular features to measure ageing
What the sources say
- Not checked yetcites [160] Hopkins, A. L. & Groom, C. R. The druggable genome. Nat. Rev. Drug Discov. 1, 727–730 (2002). source
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Here, transcriptomic and proteomic clocks have a potential advantage because they are more amenable to genetic screening methods and some causal paths are known.”
§ Measuring biological age using omics data > Future perspectives > Understanding correlation and causation
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“and additionally assessed their relationship to health traits and to the Horvath, Hannum and PhenoAge epigenetic ageing clocks.”
§ Measuring biological age using omics data > References
What the sources say
- Not checked yetcites [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
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“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.”
§ Measuring biological age using omics data > References
What the sources say
- Not checked yetcites [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
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“this study found that the age gaps for the different clocks tested are largely uncorrelated.”
§ Measuring biological age using omics data > References
What the sources say
- Not checked yetcites [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
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The first attempt to develop an ageing clock based on microbiome sequencing data”
§ Measuring biological age using omics data > References
What the sources say
- Not checked yetcites [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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“It also describes the non-linear patterns of proteomic ageing, which have implications for future ageing clock models.”
§ Measuring biological age using omics data > References
What the sources say
- Not checked yetcites [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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“One of the few current papers that compares multiple omics ageing-clock measurements longitudinally”
§ Measuring biological age using omics data > References
What the sources say
- Not checked yetcites [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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“This study developed a biobank-scale targeted metabolomic ageing clock that is predictive of mortality and cardiovascular outcomes.”
§ Measuring biological age using omics data > References
What the sources say
- Not checked yetcites [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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“This study compared multiple untargeted metabolomic methods for developing ageing clocks”
§ Measuring biological age using omics data > References
What the sources say
- Not checked yetcites [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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“and used simulation studies to show that the use of more complex second-generation ageing score training objectives overcomes some of these limitations.”
§ Measuring biological age using omics data > References
What the sources say
- Not checked yetcites [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
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“This study demonstrated important limitations of ageing-clock models that are built with cross-sectional cohort studies”
§ Measuring biological age using omics data > References
What the sources say
- Not checked yetcites [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
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper relies on this without support.
Current ageing clocks are all correlative statistical models.
“Ultimately, current ageing clocks are all correlative statistical models.”
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
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.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Measuring biological age using omics data > Omic clocks > Proteomics
“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.”
§ Measuring biological age using omics data > Omic clocks > Proteomics
“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.”
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
Regularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928
López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M., & Kroemer, G. (2013) · Cell · doi:10.1016/j.cell.2013.05.039 · Cited by 14,982
RNA-Seq: a revolutionary tool for transcriptomics
Wang, Z., Gerstein, M., & Snyder, M. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2484 · Cited by 13,174
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00444-8 · Cited by 7,906
DNA methylation age of human tissues and cell types
Horvath, S. (2013) · Genome Biology · doi:10.1186/gb-2013-14-10-r115 · Cited by 7,075
The senescence-associated secretory phenotype: the dark side of tumor suppression
Coppé, J.-P., Desprez, P.-Y., Krtolica, A., & Campisi, J. (2010) · Annual Review of Pathology: Mechanisms of Disease · doi:10.1146/annurev-pathol-121808-102144 · Cited by 5,206
Resveratrol improves health and survival of mice on a high-calorie diet
Baur, J. A., Pearson, K. J., Price, N. L., Jamieson, H. A., Lerin, C., Kalra, A., Prabhu, V. V., Allard, J. S., Lopez-Lluch, G., Lewis, K., et al. (2006) · Nature · doi:10.1038/nature05354 · Cited by 4,290
Research works printed but not linked: 3
- 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).
- Platt, J. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv. Large Margin Classif. 10, 61–74 (1999).
- 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
Inflammation and aging: signaling pathways and intervention therapies
Li, X., Li, C., Zhang, W., Wang, Y., Qian, P., & Huang, H. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01502-8 · Cited by 1,443
“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) In 2022, Li et al.”
BackgroundBiomarkers of aging for the identification and evaluation of longevity interventions
Moqri, M., Herzog, C., Poganik, J. R., Justice, J., Belsky, D. W., Higgins-Chen, A., Moskalev, A., Fuellen, G., Cohen, A. A., Bautmans, I., et al. (2023) · Cell · doi:10.1016/j.cell.2023.08.003 · Cited by 736
Organ aging signatures in the plasma proteome track health and disease
Oh, H. S.-H., Rutledge, J., Nachun, D., Pálovics, R., Abiose, O., Moran-Losada, P., Channappa, D., Urey, D. Y., Kim, K., Sung, Y. J., et al. (2023) · Nature · doi:10.1038/s41586-023-06802-1 · Cited by 634
Self-citationNonlinear dynamics of multi-omics profiles during human aging
Shen, X., Wang, C., Zhou, X., Zhou, W., Hornburg, D., Wu, S., & Snyder, M. P. (2024) · Nature Aging · doi:10.1038/s43587-024-00692-2 · Cited by 500
From geroscience to precision geromedicine: Understanding and managing aging
Kroemer, G., Maier, A. B., Cuervo, A. M., Gladyshev, V. N., Ferrucci, L., Gorbunova, V., Kennedy, B. K., Rando, T. A., Seluanov, A., Sierra, F., et al. (2025) · Cell · doi:10.1016/j.cell.2025.03.011 · Cited by 395
Metadata
- Authors
- Rutledge, J., Oh, H., & Wyss-Coray, T.
- Affiliations
- Stanford Medicine (US)
- Venue
- Nature Reviews Genetics
- Published
- 2022-06-17
- Volume
- 23
- Issue
- 12
- Pages
- 715-727
- ISSN
- 1471-0056 (ISSN-L)
- 1471-0064
- 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
- green
- www.ncbi.nlm.nih.gov
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-10, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-10 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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