Measuring biological age using omics data

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.”

What the sources say

• Supported

  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)

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

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.”

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

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.”

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.”

What the sources say

• Not checked yetcites [43] Hannum, G. et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol. Cell 49, 359–367 (2013). source
• Not checked yetcites [44] Horvath, S. DNA methylation age of human tissues and cell types. Genome Biol. 14, 3156 (2013). This study comprehensively investigated CpG methylation ageing clocks across multiple human tissues and cell lines and discovered that there are some widely conserved methylation changes that occur throughout the body with ageing, which may be related to cancer and other diseases of ageing. source
• Not checked yetcites [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

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.”

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

In the paper

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

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.”

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

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.”

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

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.”

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

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.”

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

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.”

What the sources say

• Not checked yetcites [53] Bocklandt, S. et al. Epigenetic predictor of age. PLoS One 6, e14821 (2011). source

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.”

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

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.”

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

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.”

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

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.”

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

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.”

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

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.”

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.”

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

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.”

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.”

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

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.”

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

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.”

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

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.”

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

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.”

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

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.”

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

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.”

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.”

In the paper

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

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

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.”

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

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.”

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

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.”

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

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.”

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

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.”

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

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.”

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

In the paper

“Kidney function has an effect on plasma protein concentrations that is not fully understood but can confound ageing analysis101,102.”

What the sources say

• Not checked yetcites [101] Williams, S. A. et al. Plasma protein patterns as comprehensive indicators of health. Nat. Med. 25, 1851–1857 (2019). source
• Not checked yetcites [102] Tryggvason, K. & Wartiovaara, J. How does the kidney filter plasma? Physiology 20, 96–101 (2005). source

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.”

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

In the paper

“In a follow-up study, Tanaka et al. showed associations with mortality, multi-morbidity, healthspan and lifespan96.”

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

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.”

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
• Not checked yetcites [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

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.”

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

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.”

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

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;”

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

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.”

What the sources say

• Not checked yetcites [131] Badal, V. D. et al. The gut microbiome, aging, and longevity: a systematic review. Nutrients 12, 3759 (2020). source
• Not checked yetcites [132] Collino, S. et al. Metabolic signatures of extreme longevity in Northern Italian centenarians reveal a complex remodeling of lipids, amino acids, and gut microbiota metabolism. PLoS One 8, e56564 (2013). source
• Not checked yetcites [133] Rampelli, S. et al. Functional metagenomic profiling of intestinal microbiome in extreme ageing. Aging 5, 902–912 (2013). source
• Not checked yetcites [134] Bárcena, C. et al. Healthspan and lifespan extension by fecal microbiota transplantation into progeroid mice. Nat. Med. 25, 1234–1242 (2019). source
• Not checked yetcites [135] Cani, P. D. & de Vos, W. M. Next-generation beneficial microbes: the case of Akkermansia muciniphila. Front. Microbiol. 8, 1765 (2017). source
• Not checked yetcites [136] Sato, Y. et al. Novel bile acid biosynthetic pathways are enriched in the microbiome of centenarians. Nature 599, 458–464 (2021). source

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.”

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.”

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.”

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

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.”

What the sources say

• Not checked yetcites [155] Jansen, R. et al. An integrative study of five biological clocks in somatic and mental health. eLife 10, e59479 (2021). source

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.”

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.”

What the sources say

• Not checked yetcites [113] Rist, M. J. et al. Metabolite patterns predicting sex and age in participants of the Karlsruhe Metabolomics and Nutrition (KarMeN) study. PLoS One 12, e0183228 (2017). source
• Not checked yetcites [116] Menni, C. et al. Metabolomic markers reveal novel pathways of ageing and early development in human populations. Int. J. Epidemiol. 42, 1111–1119 (2013). source
• Not checked yetcites [117] Fischer, K. et al. Biomarker profiling by nuclear magnetic resonance spectroscopy for the prediction of all-cause mortality: an observational study of 17,345 Persons. PLoS Med. 11, e1001606 (2014). source
• Not checked yetcites [118] Deelen, J. et al. A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals. Nat. Commun. 10, 3346 (2019). source
• Not checked yetcites [119] van den Akker, E. B. et al. Metabolic age based on the BBMRI-NL 1H-NMR metabolomics repository as biomarker of age-related disease. Circ. Genom. Precis. Med. 13, 541–547 (2020). This study developed a biobank-scale targeted metabolomic ageing clock that is predictive of mortality and cardiovascular outcomes. source
• Not checked yetcites [120] Robinson, O. et al. Determinants of accelerated metabolomic and epigenetic aging in a UK cohort. Aging Cell 19, e13149 (2020). This study compared multiple untargeted metabolomic methods for developing ageing clocks and additionally assessed their relationship to health traits and to the Horvath, Hannum and PhenoAge epigenetic ageing clocks. source
• Not checked yetcites [121] Hertel, J. et al. Measuring biological age via metabonomics: the metabolic age score. J. Proteome Res. 15, 400–410 (2016). source

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.”

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

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.”

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

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.”

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.”

What the sources say

• Not checked yetcites [163] Hormozdiari, F. et al. Colocalization of GWAS and eQTL signals detects target genes. Am. J. Hum. Genet. 99, 1245–1260 (2016). source
• Not checked yetcites [164] Emdin, C. A., Khera, A. V. & Kathiresan, S. Mendelian randomization. JAMA 318, 1925–1926 (2017). source

In the paper

“This study developed multiple methodological innovations for training accurate and reproducible transcriptomic ageing clocks”

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

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.”

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

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.”

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.”

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

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.”

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.”

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

In the paper

“and developed clocks with striking generalizable performance across multiple ageing treatment conditions in worms.”

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

In the paper

“For example, Lehallier et al.95 demonstrated that plasma proteins change in three non-linear ‘waves’ of ageing;”

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

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.”

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

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.”

In the paper

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

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.”

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

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.”

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

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.”

In the paper

“and additionally assessed their relationship to health traits and to the Horvath, Hannum and PhenoAge epigenetic ageing 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

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.”

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

In the paper

“this study found that the age gaps for the different clocks tested are largely uncorrelated.”

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

In the paper

“The first attempt to develop an ageing clock based on microbiome sequencing data”

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

In the paper

“It also describes the non-linear patterns of proteomic ageing, which have implications for future ageing clock models.”

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

In the paper

“One of the few current papers that compares multiple omics ageing-clock measurements longitudinally”

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

In the paper

“This study developed a biobank-scale targeted metabolomic ageing clock that is predictive of mortality and cardiovascular outcomes.”

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

In the paper

“This study compared multiple untargeted metabolomic methods for developing ageing 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

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.”

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

In the paper

“This study demonstrated important limitations of ageing-clock models that are built with cross-sectional cohort studies”

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