The DOI matches its Crossref record; 201 of the 201 references it cites were found in a registry or an index; 70 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
AI snapshot
Written from the paper’s full text.
TL;DR
This review surveys machine-learning ‘ageing clocks’ built from epigenomic, transcriptomic, proteomic and metabolomic data, arguing that they can estimate biological age but often capture different, partly non-causal ageing signals. It calls for clocks tailored to specific tissues/applications, multi-omic and longitudinal designs, and training beyond chronological age.
Key findings
- DNA methylation clocks: the first, in 2011, used about 100 saliva samples and predicted chronological age within ~5 years; the Hannum clock used 71 CpGs in 656 whole-blood samples, and the Horvath pan-tissue clock used 353 CpGs in 8,000 samples across 51 tissue/cell types. First-generation epigenetic age gaps correlate only mildly with each other (r = 0.1–0.5), and a meta-analysis in 14 cohorts found that improving chronological-age accuracy to very high levels reduced associations with mortality risk.
- Second-generation methylation clocks (PhenoAge, GrimAge, DunedinPoAm) were designed to associate with mortality or ageing rate. DunedinPoAm used a 1-year birth cohort (n = 810) tracking 18 clinical chemistry and physiological biomarkers at ages 26, 32 and 38, and showed stronger associations with physical functioning, cognition, self-rated health and mortality than PhenoAge.
- Transcriptomic, proteomic and metabolomic clocks: transcriptomic clocks are noisy; Meyer et al. reported that binarized transcriptomic data improved human fibroblast chronological age prediction to r² = 0.92 and mean error of 6.63 years. SomaLogic aptamer proteomics can reliably quantify over 7,000 proteins, and a metabolomic clock from 56 plasma metabolites was associated with cardiovascular risk factors, cardiovascular disease risk and all-cause mortality.
- Cross-omics comparisons show limited concordance: Tanaka et al.’s plasma proteomic age gap had no correlation with Horvath and low correlation with GrimAge/PhenoAge; Robinson et al.’s metabolomic age gap had no correlation with Horvath, Hannum and PhenoAge; a multi-omics study found mild correlations between epigenetic and transcriptomic age gaps and between proteomic and metabolomic age gaps, but no correlation between epigenetic and proteomic/metabolomic age gaps.
Methods
- Design: critical narrative review of ageing clocks built from omics data; no new experimental data, cohort, or primary statistical analysis.
- Data: published studies using epigenomic, transcriptomic, proteomic, metabolomic and other omics layers, plus selected multi-omic and longitudinal studies.
- Analysis: qualitative synthesis and comparison of clock construction, accuracy, age-gap associations with ageing phenotypes, and limitations; the review explicitly focuses on what biological ageing phenotypes age gaps measure rather than chronological-age prediction accuracy.
Limitations
- Stated in the paper: current clocks are correlative statistical models, not causal; most are trained on cross-sectional data and do not directly predict future ageing rates; first-generation methylation clocks have weak/variable mortality associations and can overfit chronological-age noise; blood-based clocks may not capture ageing of other tissues because tissues age at different rates.
- Stated in the paper: omics-specific limitations include transcriptomic noise/small samples and older microarray technology; proteomic confounding by kidney function and less validation than methylation clocks; metabolomic orphan compounds, sensitivity issues, and lower age-prediction accuracy despite large samples.
- Evident from the text: as a review, it reports no new validation or head-to-head benchmark; comparisons across clocks are constrained by few meta-analyses and low concordance, so relative utility remains unresolved.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
99 Not checked yet · 23 Own interpretation · 15 Partly supported · 5 Supported · 3 No basis given · 1 Supported so far · 1 Mixed support · 1 Not supported · accuracy not yet measured
Some cited sources support this claim and some don’t — read each source’s verdict below.
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
- Partly supportedcites [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). sourceExperimental — accuracy not yet measured
The Abstract and Discussion report that epigenetic age measured in prefrontal cortex tissue is associated with Alzheimer’s-related neuropathology and cognitive functioning. But the Methods describe a clock developed for multiple tissues, not one trained specifically in the prefrontal cortex, and the paper does not compare its associations with those of other clocks. It therefore supports the tissue-function association, not the claim that tissue-specific training produces stronger associations. (LLM, conf=0.88)
- Partly supportedcites [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). sourceExperimental — accuracy not yet measured
The Results (Ex vivo studies of anti-aging interventions) report that the skin-and-blood clock tracked cell proliferation with better resolution and accuracy than the pan-tissue clock. The Results (Epigenetic age of fibroblasts from Hutchinson Gilford Progeria fibroblasts) also report that it detected age acceleration in HGPS fibroblasts that the pan-tissue clock did not. These findings support specific examples, but not the broader claim that tissue-trained clocks generally have stronger associations with their tissues’ functional states. (LLM, conf=0.84)
- Could not verifycites [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). sourceExperimental — accuracy not yet measured
The Results section (‘Building a transcriptomic age predictor’; page unavailable) describes a lung-specific transcriptomic clock and compares its age-prediction accuracy with mouse epigenetic clocks. The Discussion describes gene expression as reflecting cellular responses, but neither section reports that tissue-trained epigenetic clocks have stronger associations with those tissues’ functional states. Searches for tissue-specific epigenetic clocks and functional-state comparisons found no passage addressing that claim. (LLM, conf=0.72)
- Disputedcites [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). sourceExperimental — accuracy not yet measured
The Discussion (Physical functioning; page unavailable) reports that the muscle-specific MEAT clock was not associated with physical functioning, physical activity, or body composition. The Conclusions (page unavailable) say the clocks studied have rather low value for estimating muscle ageing in relation to physiological adaptations, contrary to the claimed stronger functional associations. (LLM, conf=0.88)
Checked by a reasoning model — may be wrong.
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.
- Partly supported
At least one cited source doesn’t support this claim or points to a different paper, and none confirmed it — see the verdicts below.
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
- Disputedcites [82] Fleischer, J. G. et al. Predicting age from the transcriptome of human dermal fibroblasts. Genome Biol. 19, 221 (2018). sourceExperimental — accuracy not yet measured
The Results and discussion section says the authors tested their method on another publicly available fibroblast RNA-seq dataset, E-MTAB-3037, and compared its performance with other methods there. The Methods section clarifies that they fitted models separately on that dataset, so this was not a test of an unchanged model trained on their original dataset. (LLM, conf=0.83)
Checked by a reasoning model — may be wrong.
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.
- Disputed
The cited sources support only part of this claim — read each source’s verdict below.
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
- 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 cancer analysis (page unavailable) reports significant Horvath-clock age acceleration in each cancer or affected tissue examined across 32 datasets. In the epigenetic maintenance system model section, Horvath proposes that DNAm age reflects an age-related maintenance process and that cancer perturbs it. This supports the citing sentence’s hedged interpretation, rather than establishing the mechanism as fact. (LLM, conf=0.87)
- Partly supportedcites [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). sourceExperimental — accuracy not yet measured
The Discussion links age-related aberrant methylation to tumor processes and suggests that epigenetic age acceleration may indicate a more aggressive course of cancer. But this study measured epigenetic age in a cohort of older people; it does not report marked Horvath-clock acceleration in cancer tissues across many cancers or cohorts. (LLM, conf=0.79)
hedgedcorrelational- 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.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
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
- Could not verifycites [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. sourceExperimental — accuracy not yet measured
The Results and Discussion discuss DNAm PhenoAge, including its associations with coronary heart disease and physical functioning problems and its comparison with earlier clocks. Searches for “GrimAge” and the claimed outcomes found no passage about DNAm GrimAge, so this copy cannot verify the claim about that clock. (LLM, conf=0.78)
- Partly supportedcites [48] Lu, A. T. et al. DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging 11, 303–327 (2019). sourceExperimental — accuracy not yet measured
The Discussion says GrimAge outperforms other DNAm biomarkers on a variety of health-related metrics, including prediction of coronary heart disease. The Results (Age-related conditions) also associate AgeAccelGrim with lower physical functioning. Our copy does not establish stronger associations with frailty or cognitive decline, or show longitudinal physical decline, so the full list is not supported by this source. (LLM, conf=0.87)
- Could not verifycites [50] Zhang, Y. et al. DNA methylation signatures in peripheral blood strongly predict all-cause mortality. Nat. Commun. 8, 14617 (2017). sourceExperimental — accuracy not yet measured
The Discussion describes a ten-CpG mortality risk score distinct from established epigenetic clocks, not DNAm GrimAge. A search for GrimAge, frailty, cognitive or physical decline, and comparisons with other clocks found no passage assessing the claimed associations. Page numbers were not available in our copy. (LLM, conf=0.76)
- Partly supportedcites [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). sourceExperimental — accuracy not yet measured
The Results and Discussion report that GrimAge had the strongest association with baseline frailty among the clocks studied and was associated with three-year change in frailty. The passages do not establish stronger associations with heart disease or cognitive and physical decline, so this paper supports the frailty example but not the full list of outcomes. (LLM, conf=0.88)
- Could not verifycites [65] Maddock, J. et al. DNA methylation age and physical and cognitive aging. J. Gerontol. Ser. A 75, 504–511 (2020). sourceExperimental — accuracy not yet measured
The Results section examines age-based stereotype threat and memory recall in Chinese older adults, not epigenetic clocks. Searches for DNAm GrimAge and DNA-methylation clocks found no passage addressing their associations with heart disease, frailty, or cognitive and physical decline relative to other clocks. Page numbers were unavailable in the indexed passages. (LLM, conf=0.78)
- Partly supportedcites [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). sourceExperimental — accuracy not yet measured
The Background (page unavailable) says the newer measures, including AgeAccelGrim, have been shown to predict mortality and other morbidities more accurately than earlier measures. It does not establish that GrimAge consistently has stronger associations with heart disease, frailty, or cognitive and physical decline. The Discussion (page unavailable) also reports no consistent relationship between the measures studied and incident dementia. (LLM, conf=0.84)
- Partly supportedcites [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). sourceExperimental — accuracy not yet measured
The Discussion (page not provided) says GrimAge was associated with hearing when three other clocks were not, and suggests it may capture deviations from healthy aging better than those clocks. It does not establish consistently stronger GrimAge associations with heart disease, frailty, or cognitive and physical decline; its statement about physical and cognitive function concerns epigenetic clocks collectively. (LLM, conf=0.82)
- Partly supportedcites [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). sourceExperimental — accuracy not yet measured
The Discussion (page unavailable) says GrimAge outperformed the other clocks on many functional-health and cognitive measures, including frailty and walking speed. The Results (page unavailable) show cognitive associations in minimally adjusted models, but not after full adjustment. The paper does not report heart disease as an outcome, so it does not support that example. (LLM, conf=0.88)
- Partly supportedcites [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). sourceExperimental — accuracy not yet measured
The Results (page unavailable) report that GrimAge acceleration had the most variance explained by demographic and behavioral factors among the clocks. The Discussion (page unavailable) suggests that second-generation clocks, including GrimAge, may be more useful for aging-health research. But this paper does not show that GrimAge has consistently stronger associations than other clocks with heart disease, frailty, or cognitive and physical decline. (LLM, conf=0.84)
- Partly supportedcites [70] Hillary, R. F. et al. An epigenetic predictor of death captures multi-modal measures of brain health. Mol. Psychiatry 26, 3806–3816 (2021). sourceExperimental — accuracy not yet measured
The Results report associations of DNAm GrimAge with cognitive ability and frailty-associated physical traits, while the Discussion says it outperformed earlier clocks in predicting mortality. The paper does not establish consistently stronger associations than other clocks across all the listed outcomes, including heart disease and cognitive or physical decline; its evidence for longitudinal cognitive decline is described as tentative. The indexed copy provides section labels but no page numbers. (LLM, conf=0.84)
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.
- Could not verify
The cited sources support only part of this claim — read each source’s verdict below.
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
- Partly supportedcites [91] Menni, C. et al. Circulating proteomic signatures of chronological. Age. J. Gerontol. Ser. A 70, 809–816 (2015). sourceExperimental — accuracy not yet measured
The Abstract and Discussion report age-related changes in plasma proteins, and the Abstract mentions a separate CSF study. But the Methods say this study measured 1,129 plasma proteins, while the Discussion identifies only 11 with replicated age associations; it does not show that thousands of proteins change with age. (LLM, conf=0.90)
- Partly supportedcites [92] Ignjatovic, V. et al. Age-related differences in plasma proteins: how plasma proteins change from neonates to adults. PLoS One 6, e17213 (2011). sourceExperimental — accuracy not yet measured
The Abstract reports age-related changes in the human plasma proteome from neonates to adults, supporting the plasma portion of the claim. But it reports changes in up to 100 protein spots using 2D-DIGE, not thousands of proteins; the Discussion also describes the chosen method as one of several available. Cerebrospinal fluid is attached to a different citation in the citing sentence, so it is not attributed to this source. (LLM, conf=0.88)
- Partly supportedcites [93] Lu, J. et al. Profiling plasma peptides for the identification of potential ageing biomarkers in Chinese Han adults. PLoS One 7, e39726 (2012). sourceExperimental — accuracy not yet measured
The Results section reports age-related changes in human plasma peptide profiles, supporting the general point that plasma proteomic measurements vary with age. But it reports 44 significantly different peptide profiles out of 84 examined—not thousands of proteins. The cerebrospinal-fluid finding has a separate citation in the citing sentence. (LLM, conf=0.88)
- Partly supportedcites [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). sourceExperimental — accuracy not yet measured
For the CSF portion attributed to this source, the Abstract reports age-related protein correlations in human cerebrospinal fluid, and the paper discusses both its aptamer method and an earlier mass-spectrometry study. But the Abstract says this study detected 248 proteins and found several age correlations—not that thousands of CSF proteins change with age. The plasma citations and the ageing-clocks clause are not attributed to this source. (LLM, conf=0.91)
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.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
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
- Partly supportedcites [87] Pappireddi, N., Martin, L. & Wühr, M. A review on quantitative multiplexed proteomics. ChemBioChem 20, 1210–1224 (2019). sourceExperimental — accuracy not yet measured
The Conclusion states that mass spectrometry-based, isobaric-tag proteomics can accurately quantify thousands of proteins simultaneously across up to 11 samples. The paper does not establish the same claim for antibody-based or aptamer-based proteomics, so it supports only the mass spectrometry portion of the cited sentence. (LLM, conf=0.88)
- 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
- Partly supportedcites [89] Gold, L. et al. Aptamer-based multiplexed proteomic technology for biomarker discovery. PLoS One 5, e15004 (2010). sourceExperimental — accuracy not yet measured
The Discussion (page unavailable) describes an aptamer-based assay capable of measuring thousands of proteins in small samples, with a median coefficient of variation of 5%. But the assay reported here measured 813 proteins, so the paper supports the platform’s potential rather than demonstrated robust quantification of thousands in a sample. The Introduction also describes limits of antibody-based multiplexing, not comparable thousand-protein measurement by that method. (LLM, conf=0.88)
- Partly supportedcites [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). sourceExperimental — accuracy not yet measured
The Results and Discussion section reports antibody-based proximity extension assays run in multiplex panels of 23 analytes, supporting the claim that antibody-based methods advanced protein measurement. It does not establish robust quantification of thousands of proteins in a single sample or address mass spectrometry-based and aptamer-based proteomics. No page numbers are available in our copy. (LLM, conf=0.88)
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.
- Partly supported
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)
hedgedconditional- 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.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
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
- Could not verifycites [122] Bingol, K. Recent advances in targeted and untargeted metabolomics by NMR and MS/NMR methods. High Throughput 7, 9 (2018). sourceExperimental — accuracy not yet measured
The Untargeted Metabolomics section describes difficulty identifying unknown compounds, not difficulty understanding the biological processes that produced compounds already identified. The Introduction discusses using metabolites to investigate biological pathways, but does not say those pathways are often poorly understood in either targeted or untargeted methods. Searches for the biological origin of identified metabolites found no supporting passage; page numbers were not available in our copy. (LLM, conf=0.72)
- Partly supportedcites [123] Gertsman, I. & Barshop, B. A. Promises and pitfalls of untargeted metabolomics. J. Inherit. Metab. Dis. 41, 355–366 (2018). sourceExperimental — accuracy not yet measured
The Introduction and Experimental Planning/Conceptualization sections say that metabolic measurements may not be biologically informative and that observed intermediate levels may fail to reveal the underlying regulatory change. This supports a difficulty in inferring biological processes from metabolite measurements, but the paper does not establish that the processes generating confidently identified compounds are often poorly understood in both targeted and untargeted methods. Our copy provides no page numbers for these passages. (LLM, conf=0.78)
- 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
- Partly supportedcites [125] Krumsiek, J. et al. Mining the unknown: a systems approach to metabolite identification combining genetic and metabolic information. PLoS Genet. 8, e1003005 (2012). sourceExperimental — accuracy not yet measured
The Introduction says untargeted methods detect metabolites whose chemical identities remain unknown, limiting their use as functional biomarkers. The Discussion describes linking those unknown metabolites to biological processes. These passages support a gap in biological understanding for unidentified metabolites, but do not establish that the processes generating confidently identified compounds are often poorly understood in both targeted and untargeted methods. The passages in our copy have no page numbers. (LLM, conf=0.78)
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.
- Could not verify
The cited sources support only part of this claim — read each source’s verdict below.
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
- Partly supportedcites [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). sourceExperimental — accuracy not yet measured
The Methods section (The SOMAscan proteomic assay; page unavailable) describes an aptamer-based platform whose readout is proportional to target protein amount. The Introduction and Results (Proteomic data assessment; pages unavailable) report measuring 1,305 proteins in HUVEC cell lysate and supernatant. The paper does not establish reliable quantification of over 7,000 proteins across a variety of biofluids and cellular extracts. (LLM, conf=0.88)
- Partly supportedcites [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). sourceExperimental — accuracy not yet measured
The Introduction describes sensitive, aptamer-based relative quantification of 1,307 proteins, and Methods §4.2 applies the assay to glioma cell extracts (page numbers unavailable). The Discussion mentions 8K assays as anticipated tools, but this paper does not establish reliable quantification of over 7,000 proteins across a variety of biofluids and cellular extracts. (LLM, conf=0.88)
- Partly supportedcites [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). sourceExperimental — accuracy not yet measured
The Methods (Proteomic data QC process; page unavailable) describe aptamer-based measurements in CSF, plasma and brain tissue, and the reproducibility investigation reports high correlations between repeat measurements. But this study used 1,305 aptamers and does not establish reliable quantification of over 7,000 proteins or performance across cellular extracts generally. (LLM, conf=0.88)
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.
The cited sources support only part of this claim — read each source’s verdict below.
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)
hedgedconditional- 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.
- Partly supported
- 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
The cited sources support only part of this claim — read each source’s verdict below.
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
- Partly supportedcites [53] Bocklandt, S. et al. Epigenetic predictor of age. PLoS One 6, e14821 (2011). sourceExperimental — accuracy not yet measured
The Abstract and leave-one-out Results report age prediction from saliva DNA methylation with an average error of 5.2 years. The Abstract describes 68 twins and 60 additional participants, broadly consistent with ‘about 100’ samples, although the reported 5.2-year result used 66 subjects. The cited paper describes multivariate regression and lasso, not an elastic-net model, and does not establish that its model was the first DNA methylation clock. (LLM, conf=0.87)
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.
- Partly 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 cited sources support only part of this claim — read each source’s verdict below.
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
- Partly supportedcites [122] Bingol, K. Recent advances in targeted and untargeted metabolomics by NMR and MS/NMR methods. High Throughput 7, 9 (2018). sourceExperimental — accuracy not yet measured
Section 2.1 says targeted studies quantify a defined, typically predetermined set of metabolites; Section 3 contrasts this with untargeted studies that measure as many signals as possible and seek unknown metabolites. This supports the narrower scope of targeted analysis, but the paper does not say targeted methods have better sensitivity or quantify a reduction in detected features. It also does not explicitly say targeted methods preclude discovery of new metabolites. (LLM, conf=0.85)
- Partly supportedcites [123] Gertsman, I. & Barshop, B. A. Promises and pitfalls of untargeted metabolomics. J. Inherit. Metab. Dis. 41, 355–366 (2018). sourceExperimental — accuracy not yet measured
The Introduction’s “Targeted Versus Untargeted Metabolomics” section says targeted studies monitor selected analytes, whereas untargeted studies seek known and unknown metabolites for discovery. The Conclusion also says targeting a particular analyte risks missing an association. Our copy does not establish that targeted methods have better sensitivity or quantify a reduction in detected features; it also describes sophisticated targeted methods as having expanded their breadth. (LLM, conf=0.83)
- 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.
- Partly 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.
The cited sources support only part of this claim — read each source’s verdict below.
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
- Supportedcites [131] Badal, V. D. et al. The gut microbiome, aging, and longevity: a systematic review. Nutrients 12, 3759 (2020). source
Split into 3 atoms — 3 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
- Supportedcites [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
Split into 2 atoms — 2 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
- Supportedcites [133] Rampelli, S. et al. Functional metagenomic profiling of intestinal microbiome in extreme ageing. Aging 5, 902–912 (2013). source
Split into 2 atoms — 2 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
- Partly supportedcites [134] Bárcena, C. et al. Healthspan and lifespan extension by fecal microbiota transplantation into progeroid mice. Nat. Med. 25, 1234–1242 (2019). sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
- Partly supportedcites [135] Cani, P. D. & de Vos, W. M. Next-generation beneficial microbes: the case of Akkermansia muciniphila. Front. Microbiol. 8, 1765 (2017). sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 1 unverifiable. Expand the claim to see per-atom verdicts.
- Partly supportedcites [136] Sato, Y. et al. Novel bile acid biosynthetic pathways are enriched in the microbiome of centenarians. Nature 599, 458–464 (2021). sourceExperimental — accuracy not yet measured
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
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. 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
“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
- Supportedcites [155] Jansen, R. et al. An integrative study of five biological clocks in somatic and mental health. eLife 10, e59479 (2021). source
The Results (‘Computing biological clocks’) report small, significant correlations between transcriptomic and epigenetic aging (r = 0.15) and between proteomic and metabolomic aging (r = 0.19). The same section defines these aging indicators as biological-age estimates residualized for chronological age, matching the claim’s ‘clock age gaps.’ (LLM, conf=0.94)
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.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
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
- Partly supportedcites [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). sourceExperimental — accuracy not yet measured
The Results (Prediction of age; page unavailable) show that metabolite profiles predicted chronological age, with accuracy varying by sex and sample type. But the Methods (Data analysis; page unavailable) describe 301 samples, not an extremely large training cohort. Our copy does not compare metabolomic clocks with other omics data types or characterize effect sizes for ageing traits as modest. (LLM, conf=0.88)
- Partly supportedcites [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). sourceExperimental — accuracy not yet measured
The Results report a metabolite-derived age measure developed from 6,055 participants and small gains in explained variance for several ageing-related traits (2–5%), but much larger gains for DHEAS and cholesterol. The paper does not compare its age-prediction accuracy with other omics data types, so it does not support that central comparison or an unqualified claim that effects were modest. (LLM, conf=0.84)
- Could not verifycites [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). sourceExperimental — accuracy not yet measured
The Methods and Results describe a biomarker score developed to predict all-cause mortality; the Results also report its moderate correlation with age (r = 0.38). Our copy does not evaluate a metabolomic clock’s age-prediction accuracy against other omics data types or report effect sizes for ageing traits. Searches for those comparisons and terms found no supporting passage. (LLM, conf=0.76)
- Could not verifycites [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). sourceExperimental — accuracy not yet measured
The Results and Discussion describe a metabolic biomarker score developed in 44,168 people to predict all-cause mortality, not a clock trained to predict age. Searches for comparisons with other omics data types and modest effect sizes for ageing traits found no passage addressing those claims. The paper’s large sample size alone does not establish the claim about metabolomic clocks. (LLM, conf=0.76)
- Partly supportedcites [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. sourceExperimental — accuracy not yet measured
The BBMRI-NL resource section (page unavailable) reports training a metabolomic age predictor on 18,716 samples, supporting the large-sample part of the claim. The Discussion reports associations with cardio-metabolic outcomes, but our copy does not establish that its age-prediction accuracy was lower than that of other omics clocks or characterize effect sizes for ageing traits as modest. (LLM, conf=0.82)
- Partly 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. sourceExperimental — accuracy not yet measured
The Discussion (page unavailable) reports that the metabolomic clock predicted age less accurately than the Hannum epigenetic clock, but more accurately than the cited transcriptomic clock and other aging markers; it does not support a blanket comparison with other omics data types. The Results (Study population; Risk factors of age acceleration, pages unavailable) report 2,238 participants with metabolomic data and associations of 0.32–0.87 years for the listed risk factors, consistent with modest effects. The claim that the clock was trained on an extremely large sample is not established by those passages. (LLM, conf=0.88)
- 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
boosted- 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.
- Partly supported
Every source the paper cites for this claim was checked and supports 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
- Supportedcites [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
The BBMRI-NL resource section describes a metabolomic age predictor trained using selected NMR metabolomic variables from biobank samples. The Discussion reports that its age-difference score, ΔmetaboAge, was associated with increased risk of future cardiovascular disease and mortality. (LLM, conf=0.93)
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.
- Supported
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.
- 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
“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.
- 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
“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.
- 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
“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.
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.
- 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
“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.
- 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
“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.
- 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.
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
“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.
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
“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
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
“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
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: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
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
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
“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
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
“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
hedgedcorrelational- 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
“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.
- 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.
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
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.
- 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.
- 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 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.
- 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
“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.
- 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
“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
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.
None of the sources cited for this claim have been checked yet.
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.
- 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.
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
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.
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.
- 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 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.
- 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
“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.
- 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
“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.
- 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.
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.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
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.
- 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.
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.
- 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
“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.
- 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.
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.
- 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
“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.
- 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 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.
- 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.
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.
- 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 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 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.
- 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 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
Items 501–593 of 593
METRON: Metabolic Dynamic Perception Kolmogorov-Arnold Network for Biological Age Estimation
Li, Z., Yu, J., You, S., Liu, H., Cai, L., Deng, Y., Wei, L., Ji, J., Lin, Q., Li, X., et al. (2026) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbbio.2026.3669919
Integrative Proteomic and Metabolomic Signatures of Accelerated PhenoAge in the UK BiobankPreprint
Bloch, K., & de Magalhães, J. P. (2026) · medRxiv · doi:10.64898/2026.03.02.26347435
Új biológiai és klinikai öregedéstudományi eredmények az egészséges életkor meghosszabbítására
Holczbauer, A., Kalmár, A., Valcz, G., Takács, I., & Molnar, B. A. (2026) · Magyar Belorvosi Archívum · doi:10.59063/mba.2026.79.1.4
Molecular clues from RNA: the current landscape and future potential in forensic investigation
Wang, Q., Wang, J., Qi, Q., Li, S., Wen, D., & Cong, B. (2026) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2026.118803
Cao, K., Xu, Y., Gao, Q., Cao, L., & Wang, T. (2026) · BMC Public Health · doi:10.1186/s12889-026-27057-1
Ke, L., Li, Y., Xing, W., Jiang, S., & Zhao, L. (2026) · Neurological Sciences · doi:10.1007/s10072-026-08969-7
Yusupov, N., Fietz, J., Karlbauer, V. N., Maurus, I., Falkai, P., & Binder, E. B. (2026) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2026.100726
Proteomic-based Aging Clocks and MRI Markers of Cerebral Small Vessel Disease: ARIC and MESAPreprint
Park, S., Wang, S., Liu, J., Hughes, T. M., Raven, E. P., Veraart, J., Habes, M., Dubin, R. F., Deo, R., Post, W. S., et al. (2026) · medRxiv · doi:10.64898/2026.04.02.26350087
Staging concept for aging management: Definition, mechanism, and coping strategies
Wang, Z., Liu, S. S., Du, S., Cui, X., Chao, B., Liu, M., & Wu, M. (2026) · Open Research (University of Surrey) · doi:10.1002/viw.20250201
Lee, G., Haugg, F., Bontempi, D., He, J., Bitterman, D. S., Pai, S., Guthier, C. V., Fitzgerald, K. J., Kozono, D., Kann, B. H., et al. (2026) · JAMA Network Open · doi:10.1001/jamanetworkopen.2026.4872
Strumia, M., Bongard, V., Thuriot, S., Cestac, P., Sallerin, B., Lemaı̂tre, J.-M., Furman, D., Guyonnet, S., Vellas, B. J., Andrieu, S., et al. (2026) · GeroScience · doi:10.1007/s11357-026-02251-6
Huang, W., Zhang, S., Chen, W., Wang, W., Lin, Y., Zhang, S., & Huang, F. (2026) · The journal of nutrition health & aging · doi:10.1016/j.jnha.2026.100846
Hu, I. M., Gao, A. W., Hootkooper, R., & Janssens, G. E. (2026) · doi:10.63751/i9ge
Kefala, V., Grech, V.-S., Tertipi, Ν., Sfyri, Ε., Beloukas, A., & Rallis, E. A. (2026) · Genes · doi:10.3390/genes17050542
Modifiable lifestyle factors and 4.9-Year changes in phenotypic age in the Taiwan biobank
Lin, W. (2026) · Frontiers in Aging · doi:10.3389/fragi.2026.1802176
Building a framework for integrative longevity science: from rediscovery to innovation
González‐Freire, M. (2026) · Frontiers in Aging · doi:10.3389/fragi.2026.1742582
Alzyood, L., Gao, T., Sathyan, S., Aleksić, S., Milman, S., Barzilai, N., Melamed, M. L., & Chen, W. (2026) · Journal of Nephrology · doi:10.1093/joneph/aajaf058
InferAging: A non-invasive aging clock for quantifying individual differences in agingPreprint
Ando, Y., Yada, Y., Kashima, M., Bessho, Y., Hirata, H., Naoki, H., & Matsui, T. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.22.727332
Lee, H., Ye, Z., Yang, Y., Pan, Y., Maron, B., Wang, Z., Kochunov, P., Thompson, P. M., Hong, L. E., Ma, T., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.27.728155
Proteomic clocks combined with deep learning phenotypes track eye aging and diseases
Yang, S., Xin, Z., Li, H., Zhu, Z., Zhu, Z., Chen, Y., Zhang, Y., Xiong, R., Zhou, M., He, M., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02805-0
Using epigenetic aging data to drive health behavior change
Williford, P. A., Howell, D., & Dupont‐Versteegden, E. E. (2026) · PEC Innovation · doi:10.1016/j.pecinn.2026.100482
Liu, B.-X., Zhou, H., Ge, X., Zhang, Z., Jiang, W., Liu, W. T., Yu, P., Liu, J., Li, Y., Wu, Q., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-9884549/v1
Meikle, P., Wang, T., Beyene, H. B., Yi, C., Duong, T., Mellett, N. A., Meikle, T. G., Wu, J., Dakic, A., Cinel, M. A., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-9239578/v1
Ruutu, K., Verbeeren, J., Mikkola, T. M., Laine, M. K., Eriksson, J. G., & Wasenius, N. (2026) · The Journals of Gerontology Series A · doi:10.1093/gerona/glag159
Associations of Multiomics Biological Aging With Diabetic Retinopathy and Life Expectancy
Yu, J., Gao, Y., Zhang, Y. S., Xu, J., Chen, H., Ho, M., Kam, K. W., Young, A. L., Pang, C. P., Tham, C. C. Y., et al. (2026) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.67.6.36
Using a Biomarker of Biological Aging to Predict Incident Cardiometabolic Disease
Bourassa, K. J., Crowley, M. J., Edelman, D. E., Garrett, M. E., Serier, K. N., Naylor, J. C., Ashley‐Koch, A. E., Beckham, J. C., Kimbrel, N. A., Calhoun, P. S., et al. (2026) · Journal of the American Heart Association · doi:10.1161/jaha.125.048750
Goryanin, I., Popov, I., Tarakanov, A. V., Vesnin, S. G., Galazis, C., Osmonov, B., Damms, B., Losev, A., Mogy, S., & Goryanin, I. (2026) · Diagnostics · doi:10.3390/diagnostics16131994
Precision Gerontometry: Introduction, Fundamentals and Areas of Application
Lokhov, P. G., & Balashova, E. E. (2026) · Metabolites · doi:10.3390/metabo16070463
Yan, Y., Wang, M., Tao, Y., Wei, J., Zhao, L. N., Ni, R., Xiao, Y., & Ma, X. (2026) · Psychological Medicine · doi:10.1017/s003329172610498x
Wu, Y., Xia, X., Jing, P., Ye, F., & Wang, Q. (2026) · Journal of Affective Disorders · doi:10.1016/j.jad.2026.122255
Fernandez-Villa, J. M., González-Lara, M., Bello‐Chavolla, O. Y., Carrillo-Vega, M. F., García‐Peña, C., & Sánchez‐García, S. (2026) · Archives of Medical Research · doi:10.1016/j.arcmed.2026.103491
Arpawong, T. E., Cole, S., Badhesha, H., Kim, J. K., Beam, C. R., Klopack, E. T., Siegmund, K. D., Thyagarajan, B., & Crimmins, E. M. (2026) · npj Aging · doi:10.1038/s41514-026-00446-x
Biomarkers of aging. Part 1. General state of the problem and approaches to assessing biological age
Зуева, М. В., Kotelin, V. I., Neroeva, N. V., Chesnokova, N. B., Zhuravleva, A. K., Balatskaya, N. V., & Ryabina, M. V. (2026) · Russian Ophthalmological Journal · doi:10.21516/2072-0076-2026-19-2-203-211
Feng, J. (2026) · Research Square · doi:10.21203/rs.3.rs-10435801/v1
Pre-Transformer Models for Longevity Science and Deep Ageing Clocks: An Empirical AnalysisPreprint
Feng, J. (2026) · Research Square · doi:10.21203/rs.3.rs-10435689/v1
Renard, T., Boseret, M., & Aron, S. (2026) · BMC Ecology and Evolution · doi:10.1186/s12862-026-02564-2
Limitations of Deep Ageing Clocks and Strategies to Circumvent themPreprint
Feng, J. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6967918
Proteomic ageing clocks: methods, mechanisms and the muscle connection
Reisman, E. G., Jacques, M., Grolaux, R., Gladyshev, V. N., & Eynon, N. (2026) · Genome biology · doi:10.1186/s13059-026-04220-w
Chistyakov, D. V., Samokhin, A. S., Gorbatenko, V. O., Васильев, В. Г., Donnikov, M. Y., Morozkina, A., Sinyukova, T., Kovalenko, L., & Sergeeva, M. G. (2026) · Analytica—A Journal of Analytical Chemistry and Chemical Analysis · doi:10.3390/analytica7030050
Embracing non-linearity in human ageing
Grolaux, R., Teschendorff, A. E., & Eynon, N. (2026) · Nature Reviews Genetics · doi:10.1038/s41576-026-00993-9
Wang, C., Wu, H., Namba, S., Park, J. Y., The Biobank Japan Project, Matsuda, K., Okada, Y., He, Z., & Ionita‐Laza, I. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.05.742349
Feng, J. (2026) · Research Square · doi:10.21203/rs.3.rs-10599297/v1
Proteomic aging clock and cancer risk and mortality
Wang, S. (2022) · University of Minnesota Digital Conservancy (University of Minnesota)
Gillman, M. G., Chen, H., Howard, A. G., Mi, M., Chen, Z., Clish, C. B., Cruz, D. E., Durda, P., Johnson, C., Manichaikul, A. W., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.03.742409
Pavuluri, A., Gould, B., Indap, A. R., Salakh, N., Lacob, K., Dantas, A., Sazonova, O., & Ching, J. (2026) · medRxiv · doi:10.64898/2026.08.07.26359971
Quantitative Insights into Common and Organ-Specific Aging Gaps via Integrated Multi-Omics AnalysisPreprint
Wang, S., Huang, H., Li, Y., Song, Q., Yuan, L., Yang, Y., Sun, R., Hu, Y., Xiong, C., Ni, T., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-6294979/v1
A modular architecture of human cellular aging across organs and diseasesPreprint
Guo, J., Liu, C.-C., Yang, X., Feng, J., Wang, J.-H., Shi, W., Xian-lin, Y., Huang, D., Dong, S., Guo, Y., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.12.744367
Sun, R., Hang, W., Zhou, L., Chen, J., Zhao, C., Chen, C., & Wang, F. (2026) · International Journal of Drug Discovery and Pharmacology · doi:10.53941/ijddp.2026.100020
Gero-immunology: The critical role of aging when defining immune phenotypes
Thompson, L., & Phillip, J. M. (2026) · Current Opinion in Biomedical Engineering · doi:10.1016/j.cobme.2026.100679
Epigenetic aging and diabetes mellitus in a cohort of older German adultsPreprint
Yin, Q., Stevenson‐Hoare, J. O., Holleczek, B., Schöttker, B., & Brenner, H. (2026) · Research Square · doi:10.21203/rs.3.rs-10508706/v1
Tuminello, S., Anika, R., Turner, W. M., Joyama, Y., Lazaro, L., & Taioli, E. (2026) · GeroScience · doi:10.1007/s11357-026-02484-5
Homeostatic dysregulation and clocks: a distance-based perspective
Hao, M., Zhang, S., Li, X., Zhang, H., Li, Y., Wang, X., Rutenberg, A. D., & Cohen, A. A. (2026) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2026.112239
Guvatova, Z. G., Kobelyatskaya, A. A., Gorbunov, A., Pudova, E. A., & Moskalev, A. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27177580
Grycan, M., Zyśk, R., Grycan, G., Jakiel, G., Dudek, A., & Gromadzka, G. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27177521
Abdelhady, G., Su, Q., Wang, A. Z., Ganesan, R., Phan, B. N., Sestili, H. H., Cherupally, V. K., & Pfenning, A. R. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.20.745782
Ageing Prediction Models from Blood Gene Expression
Duarte, M. C. (2026) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)
Fermín‐Martínez, C. A., Garcia-Gonzalez, S. B., Castañeda, E. G. L., Ramirez-Rivera, D. L., Carrillo-Herrera, K. B., Cabrera-Quintana, L. A., López-Teros, M. T., Antonio‐Villa, N. E., Gonçalves, N. G., García‐Peña, C., et al. (2026) · Revista de investigaci�n Cl�nica · doi:10.1016/j.ric.2026.100053
A systematic evaluation of SIRT6 as a transcriptomic biomarker of agingPreprint
Kashuk, E., Tarakanova, A., Malygina, A., Kuzovkina, N., Ponomareva, A., Toiber, D., Khrameeva, E. E., & Smirnov, D. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.03.749075
Cao, J., Liu, Y., Jiang, L., 公丕民, Li, Z., & Yi, H. (2026) · Research Square · doi:10.21203/rs.3.rs-10583890/v1
Woo, J. P., Vincent, A., Kushwaha, A., Gan, S., Ichimura, S., Egorova, A. D., VELDTMAN, G. R., Wyss‐Coray, T., Guen, Y. L., & Reddy, S. (2026) · Journal of the American Heart Association · doi:10.1161/jaha.125.048352
Self-citationp16INK4a and DNA methylation clocks in women treated with chemotherapy for early breast cancer
Muss, H. B., Sun, C., Ding, Y. C., Ji, J., White, T. A., Nyrop, K. A., LeBrasseur, N. K., Neuhausen, S. L., Mitin, N., & Sedrak, M. S. (2026) · GeroScience · doi:10.1007/s11357-026-02521-3
The Hallmarks of Aging: From Molecular Mechanisms to Clinical Translation
Chmielewski, P. P., & Strzelec, B. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27188080
Sound of aging: large-scale evidence for a voice-based biological clock
Krongauz, D. L., Marmor, Y., Zulti, A., Godneva, A., Weinberger, A., & Segal, E. (2026) · npj Aging · doi:10.1038/s41514-026-00519-x
Li, X., Yang, M.-Y., Gai, S.-R., Wei, P., Gu, Z.-H., Han, M., Wu, Y.-Z., Yu, J., Chen, W.-J., Liao, Z.-R., et al. (2026) · Aging Cell · doi:10.1111/acel.70812
DNA methylation correlates with lifespan and predicts remaining lifespan in the zebra finchPreprint
Tangili, M., Palsbøll, P. J., & Verhulst, S. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.10.750555
Mao, Y., Guo, Z., Wang, J., Yang, L., Chen, J., Lu, Y., Zhang, Y., & Jin, J. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1904998
Wang, Z., Zhong, X., Cai, Y., Wang, J., Lin, B., Liu, L., Li, S., He, H., Yao, D., Yan, C., et al. (2026) · Cardiovascular Diabetology – Endocrinology Reports · doi:10.1186/s40842-026-00331-2
Nanoparticle-Based Tools to Study Hallmarks of Aging at the Molecular Level
Yao, S., Shen, G., & Zhao, J. (2026) · International Journal of Nanomedicine · doi:10.2147/ijn.s632817
Longitudinal proteomic organ-specific ageing in heart failure
Qu, K., Benavente, E. D., ünlü, E. H., van Ommen, A. M. L. N., Handoko, M. L., Teske, A. J., Cramer, M. J., Asselbergs, F. W., Rutten, F. H., Boersma, E. H., et al. (2026) · European Journal of Heart Failure · doi:10.1093/ejhf/xuag298
Rodrigues, G. D. S., Neto, I. V. D. S., Kawamura, T., Carolino, R. O. G., Pinto, A. P., dos Santos, J. D. M., Cintra, D. E., Ropelle, E. R., Pauli, J. R., de Freitas, E. C., et al. (2026) · Physiological Genomics · doi:10.1152/physiolgenomics.00089.2026
Metabolomic profiling identifies plasma protein biomarkers of biological agingPreprint
Luo, X., Liu, C., Xie, Y., Zhao, Y., Zhao, H., Huang, Y., Shuo, G., Wang, Y., Jian, X., Zhao, G., et al. (2026) · medRxiv · doi:10.64898/2026.09.27.26364110
Biological age uncertainty reveals discordant aging and predicts human healthspanPreprint
Li, Y., Feng, G., Yan, S., Huang, Q., Jin, J., Wang, Y., Pei, Z., Tay, L. X., Li, K., Yuan, Z., et al. (2026) · medRxiv · doi:10.64898/2026.09.26.26364091
Mostafaei, S., Gustavsson, K., Hagelin, H., Mak, J. K. L., Vũ, T. N., Karlsson, I. K., & Hägg, S. (2026) · Alzheimer s & Dementia · doi:10.1002/alz.71869
Ronca, D. B., Li, J., Lima, R. M., Wen, J., de Carvalho, K. M. B., & Wang, W. (2026) · Advances in predictive, preventive and personalised medicine · doi:10.1007/978-3-032-32838-0_3
Ando, T., Terashima, H., Shiozaki, N., Ando, K., Yoshida, H., Nozaki, Y., Fukuchi, T., & Akagi, T. (2026) · Japanese Journal of Ophthalmology · doi:10.1007/s10384-026-01408-2
Biological aging acceleration associated with electronic cigarette use in the United States
Ni, Y., Argos, M., & Rule, A. M. (2026) · Environmental Epidemiology · doi:10.1097/ee9.0000000000000536
Chen, T., Wang, D., Ma, Y., Ye, Y., Wang, X., Lei, Y., Zhou, X., & Zhao, J. (2026) · GeroScience · doi:10.1007/s11357-026-02572-6
Cellular senescence in multiple sclerosis: hallmarks, immunosenescence, and therapeutic implications
Yousef, A., Seubert, J. M., Power, C., & Nathoo, N. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1944657
Cao, J., Liu, Y., Jiang, L., & Yi, H. (2026) · Food & Function · doi:10.1039/d6fo02444k
Chmielewski, P. P., & Strzelec, B. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27208987
Zhang, J., Su, W., Duan, J., Sun, Y., & Liu, B. (2026) · The Innovation Life · doi:10.59717/j.xinn-life.2026.100262
Saliev, T. M., & Singh, P. B. (2026) · International Journal of Translational Medicine · doi:10.3390/ijtm6040045
Torres-Rivera, P. N., Nagamatsu, S. T., Martínez‐Magaña, J. J., Zillich, L., Núñez-Ríos, D. L., Figueroa, A., Kranzler, H. R., Polimanti, R., Levey, D. F., Gelernter, J. E., et al. (2026) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2026.100849
Khandani, B., & Bagheri, F. (2026) · Archives of Gerontology and Geriatrics · doi:10.1016/j.archger.2026.106459
Liu, H., Aranda, A. A. P., Au, A., Wu, D., Zhou, H., Wong, W. T., Liu, T., Beni, M. S., & Tse, G. (2026) · JMIR Formative Research · doi:10.2196/95321
Beyond age prediction: a task-oriented framework for evaluating molecular aging clocks
Simayijiang, H., Ye, P., 张流利, Wu, J., Li, Z., & Long, Y. (2026) · Biogerontology · doi:10.1007/s10522-026-10535-9
A blood aging clock defines disease-induced deviation from healthy molecular aging
Li, H., Liu, S., Zhou, Z., Lu, Q., Wang, G., Zhu, Z., & Li, X. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag605
Wang, Z., Xu, X., Shang, S., Fu, Y., Wang, J., Ren, Y., Jin, Z., 岳桂东, Guo, Y., Yuan, X., et al. (2026) · Metabolism · doi:10.1016/j.metabol.2026.156817
Ageing and Platelet-Rich Therapies: Biological Determinants of Regenerative Potential
Anitua, E., Tierno, R., & Alkhraisat, M. H. (2026) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2026.112270
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 ›
Are you an author, or do you see a problem with this page? Request a review
