Epigenetic clock - Revision history

Dmitry Dzhagarov: /* Epigenetic clocks and aging */

2024-08-09T04:01:00Z

Epigenetic clocks and aging

← Older revision		Revision as of 04:01, 9 August 2024
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	== Epigenetic clocks and aging ==		== Epigenetic clocks and aging ==

			=== Retroelement-based epigenetic clocks ===
			Emerging evidence suggests a correlation between aging, chronic diseases, and the reactivation of specific retroelements, primarily LINEs and HERV-K-derived retrovirus like particles (RVLPs). These findings highlight the potential of DNA methylation states of specific retroelements as reliable predictors of chronical and potentially biological aging, complementing existing epigenetic clocks and offering an additional mechanism to consider in epigenetic clock signals. This permits the construction of retroelement-based epigenetic clocks to support the hypothesis of dysregulation of endogenous retroelements as a potential contributor to the biological hallmarks of aging and suggest that therapeutic interventions modifying the epigenetic states of specific retroelements in the human genome could have beneficial effects against a root cause of aging and disease.<ref>Ndhlovu, L. C., Bendall, M. L., Dwaraka, V., Pang, A. P., Dopkins, N., Carreras, N., ... & Corley, M. J. (2024). Retro‐age: A unique epigenetic biomarker of aging captured by DNA methylation states of retroelements. Aging Cell, e14288. https://doi.org/10.1111/acel.14288</ref><ref>Ndhlovu, L. C., Bendall, M. L., Dwaraka, V., Pang, A. P., Dopkins, N., Carreras, N., ... & Corley, M. J. (2023). Retroelement-Age Clocks: Epigenetic Age Captured by Human Endogenous Retrovirus and LINE-1 DNA methylation states. bioRxiv. PMID: 38106164 [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10723416/ PMC10723416] DOI: 10.1101/2023.12.06.570422</ref>

	It is not yet known whether epigenetic changes are a cause or consequence of other biological aging mechanisms. Epigenetic clocks have been used in some clinical trials of longevity drugs in an attempt to measure biological age. However, to enable its use as a surrogate marker, validation of various epigenetic clocks will require large-scale randomized clinical trials.		It is not yet known whether epigenetic changes are a cause or consequence of other biological aging mechanisms. Epigenetic clocks have been used in some clinical trials of longevity drugs in an attempt to measure biological age. However, to enable its use as a surrogate marker, validation of various epigenetic clocks will require large-scale randomized clinical trials.

Dmitry Dzhagarov: /* Relevance for longevity research */

2024-04-27T14:57:53Z

Relevance for longevity research

← Older revision		Revision as of 14:57, 27 April 2024
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	== Relevance for longevity research ==		== Relevance for longevity research ==
			For discussion see Fig. 2 from.<ref>Noroozi, R., Rudnicka, J., Pisarek, A., Wysocka, B., Masny, A., Boroń, M., ... & Pośpiech, E. (2024). Analysis of epigenetic clocks links yoga, sleep, education, reduced meat intake, coffee, and a SOCS2 gene variant to slower epigenetic aging. GeroScience, 46(2), 2583-2604. PMID: 38103096 [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10828238/ PMC10828238] DOI: 10.1007/s11357-023-01029-4</ref>
	=== Smoking ===		=== Smoking ===
	Research shows that smoking increases epigenetic age of buccal cells, airway cells, esophagus tissue, and lung tissue. Quitting smoking causes the epigenetic age acceleration in airway cells (but not in lung tissue) to revert to the level of non-smokers.<ref>Wu, X., Huang, Q., Javed, R., Zhong, J., Gao, H., & Liang, H. (2019). Effect of tobacco smoking on the epigenetic age of human respiratory organs. ''Clinical Epigenetics'', ''11''(1), 183. https://doi.org/10.1186/s13148-019-0777-z</ref>		Research shows that smoking increases epigenetic age of buccal cells, airway cells, esophagus tissue, and lung tissue. Quitting smoking causes the epigenetic age acceleration in airway cells (but not in lung tissue) to revert to the level of non-smokers.<ref>Wu, X., Huang, Q., Javed, R., Zhong, J., Gao, H., & Liang, H. (2019). Effect of tobacco smoking on the epigenetic age of human respiratory organs. ''Clinical Epigenetics'', ''11''(1), 183. https://doi.org/10.1186/s13148-019-0777-z</ref>
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	=== Partial epigenetic reprogramming ===		=== Partial epigenetic reprogramming ===
	YuanCheng Lu and colleagues were able to reverse loss of sight in age-related and glaucoma induced retinal ganglion cell loss, and even regenerate a mechanically damaged eye nerve. This was achieved by manipulating methylation patterns in mice, using partial epigenetic reprogramming delivered via viral gene therapy.<ref name=":1" /> Epigenetic clocks in this study demonstrated an apparent reversal of epigenetic age in mice treated with epigenetic reprogramming.<ref name=":1" />		YuanCheng Lu and colleagues were able to reverse loss of sight in age-related and glaucoma induced retinal ganglion cell loss, and even regenerate a mechanically damaged eye nerve. This was achieved by manipulating methylation patterns in mice, using partial epigenetic reprogramming delivered via viral gene therapy.<ref name=":1" /> Epigenetic clocks in this study demonstrated an apparent reversal of epigenetic age in mice treated with epigenetic reprogramming.<ref name=":1" />

	== Other uses ==		== Other uses ==
	[[File:CAGE.jpg\|thumb\|Predictors (cAge, ZhangAge, HannumAge, and HorvathAge) performance in the GSE55763 dataset in accordance with Bernabeu et al. 2022.<ref>Bernabeu, E., McCartney, D. L., Gadd, D. A., Hillary, R. F., Lu, A. T., Murphy, L., ... & Marioni, R. E. (2023). Refining epigenetic prediction of chronological and biological age. Genome Med 15, 12 https://doi.org/10.1186/s13073-023-01161-y</ref> Pearson correlation (r), root mean squared error (RMSE), and median absolute error (MAE)]]		[[File:CAGE.jpg\|thumb\|Predictors (cAge, ZhangAge, HannumAge, and HorvathAge) performance in the GSE55763 dataset in accordance with Bernabeu et al. 2022.<ref>Bernabeu, E., McCartney, D. L., Gadd, D. A., Hillary, R. F., Lu, A. T., Murphy, L., ... & Marioni, R. E. (2023). Refining epigenetic prediction of chronological and biological age. Genome Med 15, 12 https://doi.org/10.1186/s13073-023-01161-y</ref> Pearson correlation (r), root mean squared error (RMSE), and median absolute error (MAE)]]

Dmitry Dzhagarov: /* Discovery */

2023-04-21T12:45:46Z

Discovery

← Older revision		Revision as of 12:45, 21 April 2023
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	== Discovery ==		== Discovery ==
	Changes in methylation levels with aging have been observed for some time. The first work using epigenetic changes as a basis for biological clocks was published in 2009 by Schumacher.<ref>Schumacher, A. (2009). ''An epigenetic clock: Anticorrelation & DNA methylation as biomarker for aging.'' https://doi.org/10.13140/RG.2.2.12457.83042</ref> In 2013, the labs of Trey Ideker and Kang Zhang at the University of California, San Diego published the Hannum epigenetic clock, which consisted of 71 markers which accurately estimate age based on blood methylation levels.<ref>Hannum, G., Guinney, J., Zhao, L., Zhang, L., Hughes, G., Sadda, S., Klotzle, B., Bibikova, M., Fan, J.-B., Gao, Y., Deconde, R., Chen, M., Rajapakse, I., Friend, S., Ideker, T., & Zhang, K. (2013). Genome-wide methylation profiles reveal quantitative views of human aging rates. ''Molecular Cell'', ''49''(2), 359–367. https://doi.org/10.1016/j.molcel.2012.10.016</ref> In the same year, the first multi-tissue epigenetic clock was developed by Steve Horvath, a professor of human genetics and of biostatistics at UCLA.<ref name=":3">Horvath, S. (2013). DNA methylation age of human tissues and cell types. ''Genome Biology'', ''14''(10), 3156. https://doi.org/10.1186/gb-2013-14-10-r115</ref> Horvath’s clock allows the measurement of the age of different tissues of the same organism with the same clock, so it is the most widely used in aging research today.		Changes in methylation levels with aging have been observed for some time.<ref>Romanov, G. A., & Vaniushin, B. F. (1980). Intragenomic specificity of DNA methylation in animals. Qualitative differences in tissues and '''changes in methylation of repeating sequences during aging''', carcinogenesis and hormonal induction. [Article in Russian]. Molekuliarnaia Biologiia, 14(2), 357-368. PMID: 7383031</ref> The first work using epigenetic changes as a basis for biological clocks was published in 2009 by Schumacher.<ref>Schumacher, A. (2009). ''An epigenetic clock: Anticorrelation & DNA methylation as biomarker for aging.'' https://doi.org/10.13140/RG.2.2.12457.83042</ref> In 2013, the labs of Trey Ideker and Kang Zhang at the University of California, San Diego published the Hannum epigenetic clock, which consisted of 71 markers which accurately estimate age based on blood methylation levels.<ref>Hannum, G., Guinney, J., Zhao, L., Zhang, L., Hughes, G., Sadda, S., Klotzle, B., Bibikova, M., Fan, J.-B., Gao, Y., Deconde, R., Chen, M., Rajapakse, I., Friend, S., Ideker, T., & Zhang, K. (2013). Genome-wide methylation profiles reveal quantitative views of human aging rates. ''Molecular Cell'', ''49''(2), 359–367. https://doi.org/10.1016/j.molcel.2012.10.016</ref> In the same year, the first multi-tissue epigenetic clock was developed by Steve Horvath, a professor of human genetics and of biostatistics at UCLA.<ref name=":3">Horvath, S. (2013). DNA methylation age of human tissues and cell types. ''Genome Biology'', ''14''(10), 3156. https://doi.org/10.1186/gb-2013-14-10-r115</ref> Horvath’s clock allows the measurement of the age of different tissues of the same organism with the same clock, so it is the most widely used in aging research today.

	== Epigenetic clocks and aging ==		== Epigenetic clocks and aging ==

Dmitry Dzhagarov: /* ELOVL2 */

2023-04-21T12:37:53Z

ELOVL2

← Older revision		Revision as of 12:37, 21 April 2023
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	==== ELOVL2 ====		==== ELOVL2 ====
	Elongase of very long chain fatty acids 2 (ELOVL2) represents a robust candidate gene as (i) its epigenetic variability is highly correlated with age predictions, (ii) it is included in most current age prediction models, and (iii) it does not show tissue-specificity, as observed for most of the epigenetic markers identified so far.<ref>Slieker, R. C., Relton, C. L., Gaunt, T. R., Slagboom, P. E., & Heijmans, B. T. (2018). Age-related DNA methylation changes are tissue-specific with ELOVL2 promoter methylation as exception. Epigenetics & chromatin, 11, 1-11. PMID: 29848354 PMCID: PMC5975493 DOI: 10.1186/s13072-018-0191-3</ref><ref>Paparazzo, E., Lagani, V., Geracitano, S., Citrigno, L., Aceto, M. A., Malvaso, A., ... & Montesanto, A. (2023). An ELOVL2-Based Epigenetic Clock for Forensic Age Prediction: A Systematic Review. International Journal of Molecular Sciences, 24(3), 2254. PMID: 36768576 PMCID: PMC9916975 DOI: 10.3390/ijms24032254</ref><ref>Sukawutthiya, P., Sathirapatya, T., & Vongpaisarnsin, K. (2021). A minimal number CpGs of ELOVL2 gene for a chronological age estimation using pyrosequencing. Forensic Science International, 318, 110631. PMID: 33279766 DOI: 10.1016/j.forsciint.2020.110631</ref>		Elongase of very long chain fatty acids 2 (ELOVL2) represents a robust candidate gene as (i) its epigenetic variability is highly correlated with age predictions, (ii) it is included in most current age prediction models, and (iii) it does not show tissue-specificity, as observed for most of the epigenetic markers identified so far.<ref>Slieker, R. C., Relton, C. L., Gaunt, T. R., Slagboom, P. E., & Heijmans, B. T. (2018). Age-related DNA methylation changes are tissue-specific with ELOVL2 promoter methylation as exception. Epigenetics & chromatin, 11, 1-11. PMID: 29848354 PMCID: PMC5975493 DOI: 10.1186/s13072-018-0191-3</ref><ref>Paparazzo, E., Lagani, V., Geracitano, S., Citrigno, L., Aceto, M. A., Malvaso, A., ... & Montesanto, A. (2023). An ELOVL2-Based Epigenetic Clock for Forensic Age Prediction: A Systematic Review. International Journal of Molecular Sciences, 24(3), 2254. PMID: 36768576 PMCID: PMC9916975 DOI: 10.3390/ijms24032254</ref><ref>Sukawutthiya, P., Sathirapatya, T., & Vongpaisarnsin, K. (2021). A minimal number CpGs of ELOVL2 gene for a chronological age estimation using pyrosequencing. Forensic Science International, 318, 110631. PMID: 33279766 DOI: 10.1016/j.forsciint.2020.110631</ref>
			Functionally, Elovl2 plays an irreplaceable role in the synthesis of poly unsaturated fatty acids (PUFA)s, which are critical for a range of biological processes. Impaired Elovl2 function disturbs lipid synthesis with increased endoplasmic reticulum (ER) stress and mitochondrial dysfunction, leading to key aging phenotypes at both cellular and physiological level. Elovl2 deficiency induced a switch in metabolism from the tri-carboxylic acid cycle to glycolysis, an effect which produces more reactive oxidative species (ROS), causes oxidative stress in cells, tissues, and organs, and also act as a messenger for inflammatory responses. In addition, PUFAs are essential in the resolution of inflammation. In addition to that, there was a dramatic accumulation of fatty acids upon Elovl2 knockout, including arachidonic acid. As accumulation of arachidonic acid might also contribute to inflammation for its being used for Prostaglandin E<sub>2</sub> (PGE2) generation, PGE2 may be involved in inflammation upon Elovl2 knockout.<ref>Li, X., Wang, J., Wang, L., Gao, Y., Feng, G., Li, G., ... & Zhang, K. (2022). Lipid metabolism dysfunction induced by age-dependent DNA methylation accelerates aging. Signal Transduction and Targeted Therapy, 7(1), 162. PMID: 35610223 PMC9130224 DOI: 10.1038/s41392-022-00964-6</ref> The accumulation of free fatty acids in the ER would damage ER function, resulting in an increased incidence of unfolded or misfolded protein load and chronic ER stress.<ref>Siddiqui, A. J., Jahan, S., Chaturvedi, S., Siddiqui, M. A., Alshahrani, M. M., Abdelgadir, A., ... & Adnan, M. (2023). Therapeutic Role of ELOVL in Neurological Diseases. ACS omega. 8(11), 9764–9774 PMID: 36969404 PMC10034982 DOI: 10.1021/acsomega.3c00056</ref>

	== Discovery ==		== Discovery ==

Dmitry Dzhagarov: /* General purpose of epigenetic clocks */

2023-04-21T11:53:07Z

General purpose of epigenetic clocks

← Older revision		Revision as of 11:53, 21 April 2023
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	A variety of other aging clocks exist based on parameters other than the methylation status. These include transcriptomics clocks,<ref>Holzscheck, N., Falckenhayn, C., Söhle, J., Kristof, B., Siegner, R., & Werner, A. et al. (2021). Modeling transcriptomic age using knowledge-primed artificial neural networks. ''Npj Aging And Mechanisms Of Disease'', ''7''(1). doi: 10.1038/s41514-021-00068-5</ref><ref>Mamoshina, P., Volosnikova, M., Ozerov, I., Putin, E., Skibina, E., Cortese, F., & Zhavoronkov, A. (2018). Machine Learning on Human Muscle Transcriptomic Data for Biomarker Discovery and Tissue-Specific Drug Target Identification. ''Frontiers In Genetics'', ''9''. doi: 10.3389/fgene.2018.00242</ref> glycation clocks,<ref>Severin, F., Feniouk, B., & Skulachev, V. (2013). Advanced glycation of cellular proteins as a possible basic component of the “master biological clock”. ''Biochemistry (Moscow)'', ''78''(9), 1043-1047. doi: 10.1134/s0006297913090101</ref> telomere clocks,<ref>Harley, C. (1991). Telomere loss: mitotic clock or genetic time bomb?. ''Mutation Research/Dnaging'', ''256''(2-6), 271-282. doi: 10.1016/0921-8734(91)90018-7</ref> microbiome clocks,<ref>Galkin, F., Mamoshina, P., Aliper, A., Putin, E., Moskalev, V., Gladyshev, V., & Zhavoronkov, A. (2020). Human Gut Microbiome Aging Clock Based on Taxonomic Profiling and Deep Learning. ''Iscience'', ''23''(6), 101199. doi: 10.1016/j.isci.2020.101199</ref><ref>Gopu, V., Cai, Y., Krishnan, S., Rajagopal, S., Camacho, F., & Toma, R. et al. (2020). An accurate aging clock developed from the largest dataset of microbial and human gene expression reveals molecular mechanisms of aging. doi: 10.1101/2020.09.17.301887</ref> or more recently the DNAm PhenoAge,<ref>Levine, M., Lu, A., Quach, A., Chen, B., Assimes, T., & Bandinelli, S. et al. (2018). An epigenetic biomarker of aging for lifespan and healthspan. ''Aging'', ''10''(4), 573-591. doi: 10.18632/aging.101414</ref> which combines epigenetic clocks with several measurements of functional performance.		A variety of other aging clocks exist based on parameters other than the methylation status. These include transcriptomics clocks,<ref>Holzscheck, N., Falckenhayn, C., Söhle, J., Kristof, B., Siegner, R., & Werner, A. et al. (2021). Modeling transcriptomic age using knowledge-primed artificial neural networks. ''Npj Aging And Mechanisms Of Disease'', ''7''(1). doi: 10.1038/s41514-021-00068-5</ref><ref>Mamoshina, P., Volosnikova, M., Ozerov, I., Putin, E., Skibina, E., Cortese, F., & Zhavoronkov, A. (2018). Machine Learning on Human Muscle Transcriptomic Data for Biomarker Discovery and Tissue-Specific Drug Target Identification. ''Frontiers In Genetics'', ''9''. doi: 10.3389/fgene.2018.00242</ref> glycation clocks,<ref>Severin, F., Feniouk, B., & Skulachev, V. (2013). Advanced glycation of cellular proteins as a possible basic component of the “master biological clock”. ''Biochemistry (Moscow)'', ''78''(9), 1043-1047. doi: 10.1134/s0006297913090101</ref> telomere clocks,<ref>Harley, C. (1991). Telomere loss: mitotic clock or genetic time bomb?. ''Mutation Research/Dnaging'', ''256''(2-6), 271-282. doi: 10.1016/0921-8734(91)90018-7</ref> microbiome clocks,<ref>Galkin, F., Mamoshina, P., Aliper, A., Putin, E., Moskalev, V., Gladyshev, V., & Zhavoronkov, A. (2020). Human Gut Microbiome Aging Clock Based on Taxonomic Profiling and Deep Learning. ''Iscience'', ''23''(6), 101199. doi: 10.1016/j.isci.2020.101199</ref><ref>Gopu, V., Cai, Y., Krishnan, S., Rajagopal, S., Camacho, F., & Toma, R. et al. (2020). An accurate aging clock developed from the largest dataset of microbial and human gene expression reveals molecular mechanisms of aging. doi: 10.1101/2020.09.17.301887</ref> or more recently the DNAm PhenoAge,<ref>Levine, M., Lu, A., Quach, A., Chen, B., Assimes, T., & Bandinelli, S. et al. (2018). An epigenetic biomarker of aging for lifespan and healthspan. ''Aging'', ''10''(4), 573-591. doi: 10.18632/aging.101414</ref> which combines epigenetic clocks with several measurements of functional performance.
	== General purpose of epigenetic clocks ==		== General purpose of epigenetic clocks ==
	People vary significantly in how they age, with various factors leading to accelerated aging. Some examples include depression, stress, poverty, HIV/AIDs, diabetes, smoking, Down Syndrome, accelerated aging syndromes (e.g. progerias) and in childhood cancer survivors.<ref name=":2">[https://jamanetwork.com/journals/jamapsychiatry/article-abstract/2776612 Wertz, J., Caspi, A., Ambler, A., Broadbent, J., Hancox, R. J., Harrington, H., ... & Moffitt, T. E. (2021). Association of History of Psychopathology With Accelerated Aging at Midlife. JAMA psychiatry.]</ref><ref>[[Bersani, F. S., Mellon, S. H., Reus, V. I., & Wolkowitz, O. M. (2019). Accelerated aging in serious mental disorders. Current opinion in psychiatry, 32(5), 381.]]</ref><ref>[[Yegorov, Y. E., Poznyak, A. V., Nikiforov, N. G., Sobenin, I. A., & Orekhov, A. N. (2020). The link between chronic stress and accelerated aging. Biomedicines, 8(7), 198.]]</ref><ref>[[Crimmins, E. M., Kim, J. K., & Seeman, T. E. (2009). Poverty and biological risk: the earlier “aging” of the poor. Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences, 64(2), 286-292.\|Crimmins, E. M., Kim, J. K., & Seeman, T. E. (2009). Poverty and biological risk: the earlier “aging” of the poor. ''Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences'', ''64''(2), 286-292.]]</ref><ref>[./Https://clinicalepigeneticsjournal.biomedcentral.com/articles/10.1186/s13148-019-0777-z Wu, X., Huang, Q., Javed, R., Zhong, J., Gao, H., & Liang, H. (2019). Effect of tobacco smoking on the epigenetic age of human respiratory organs. Clinical epigenetics, 11(1), 1-9.]</ref><ref>Aung, H. L., Aghvinian, M., Gouse, H., Robbins, R. N., Brew, B. J., Mao, L., & Cysique, L. A. (2020). Is There Any Evidence of Premature, Accentuated and Accelerated Aging Effects on Neurocognition in People Living with HIV? A Systematic Review. AIDS and Behavior, 1-44.</ref><ref>[https://www.sciencedirect.com/science/article/pii/S1550413119302463 Aguayo-Mazzucato, C., Andle, J., Lee Jr, T. B., Midha, A., Talemal, L., Chipashvili, V., ... & Bonner-Weir, S. (2019). Acceleration of β cell aging determines diabetes and senolysis improves disease outcomes. Cell metabolism, 30(1), 129-142.]</ref><ref>[[Gensous, N., Bacalini, M. G., Franceschi, C., & Garagnani, P. (2020, July). Down syndrome, accelerated aging and immunosenescence. In Seminars in Immunopathology (pp. 1-11). Springer Berlin Heidelberg.]]</ref><ref>[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5559172/ Yamaga, M., Takemoto, M., Shoji, M., Sakamoto, K., Yamamoto, M., Ishikawa, T., ... & Yokote, K. (2017). Werner syndrome: a model for sarcopenia due to accelerated aging. Aging (Albany NY), 9(7), 1738.]</ref><ref name=":4">[https://academic.oup.com/jnci/article/113/2/112/5827003?login=true Guida, J. L., Agurs-Collins, T., Ahles, T. A., Campisi, J., Dale, W., Demark-Wahnefried, W., ... & Ness, K. K. (2020). Strategies to Prevent or Remediate Cancer and Treatment-Related Aging. ''JNCI: Journal of the National Cancer Institute'']</ref><ref>Kohanski, R. A., Deeks, S. G., Gravekamp, C., Halter, J. B., High, K., Hurria, A., ... & Sierra, F. (2016). Reverse geroscience: how does exposure to early diseases accelerate the age‐related decline in health? ''Annals of the New York Academy of Sciences, 1386,'' 30-44</ref> By measuring biological age, researchers could identify people who exhibit accelerated aging. This would determine who might benefit the most from an anti-aging drug, and perhaps be used as a surrogate marker for more quickly identifying if an aging intervention slows or even reverses aging.<ref>Ferrucci, L., Gonzalez-Freire, M., Fabbri, E., Simonsick, E., Tanaka, T., Moore, Z., Salimi, S., Sierra, F., & Cabo, R. de. (2020). Measuring biological aging in humans: A quest. ''Aging Cell'', ''19''(2), e13080. https://doi.org/https://doi.org/10.1111/acel.13080</ref>		People vary significantly in how they age, with various factors leading to accelerated aging. Some examples include depression, stress, poverty, HIV/AIDs, diabetes, smoking, Down Syndrome, accelerated aging syndromes (e.g. progerias) and in childhood cancer survivors.<ref name=":2">[https://jamanetwork.com/journals/jamapsychiatry/article-abstract/2776612 Wertz, J., Caspi, A., Ambler, A., Broadbent, J., Hancox, R. J., Harrington, H., ... & Moffitt, T. E. (2021). Association of History of Psychopathology With Accelerated Aging at Midlife. JAMA psychiatry.]</ref><ref>[[Bersani, F. S., Mellon, S. H., Reus, V. I., & Wolkowitz, O. M. (2019). Accelerated aging in serious mental disorders. Current opinion in psychiatry, 32(5), 381.]]</ref><ref>[[Yegorov, Y. E., Poznyak, A. V., Nikiforov, N. G., Sobenin, I. A., & Orekhov, A. N. (2020). The link between chronic stress and accelerated aging. Biomedicines, 8(7), 198.]]</ref><ref>[[Crimmins, E. M., Kim, J. K., & Seeman, T. E. (2009). Poverty and biological risk: the earlier “aging” of the poor. Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences, 64(2), 286-292.\|Crimmins, E. M., Kim, J. K., & Seeman, T. E. (2009). Poverty and biological risk: the earlier “aging” of the poor. ''Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences'', ''64''(2), 286-292.]]</ref><ref>[./Https://clinicalepigeneticsjournal.biomedcentral.com/articles/10.1186/s13148-019-0777-z Wu, X., Huang, Q., Javed, R., Zhong, J., Gao, H., & Liang, H. (2019). Effect of tobacco smoking on the epigenetic age of human respiratory organs. Clinical epigenetics, 11(1), 1-9.]</ref><ref>Aung, H. L., Aghvinian, M., Gouse, H., Robbins, R. N., Brew, B. J., Mao, L., & Cysique, L. A. (2020). Is There Any Evidence of Premature, Accentuated and Accelerated Aging Effects on Neurocognition in People Living with HIV? A Systematic Review. AIDS and Behavior, 1-44.</ref><ref>[https://www.sciencedirect.com/science/article/pii/S1550413119302463 Aguayo-Mazzucato, C., Andle, J., Lee Jr, T. B., Midha, A., Talemal, L., Chipashvili, V., ... & Bonner-Weir, S. (2019). Acceleration of β cell aging determines diabetes and senolysis improves disease outcomes. Cell metabolism, 30(1), 129-142.]</ref><ref>[[Gensous, N., Bacalini, M. G., Franceschi, C., & Garagnani, P. (2020, July). Down syndrome, accelerated aging and immunosenescence. In Seminars in Immunopathology (pp. 1-11). Springer Berlin Heidelberg.]]</ref><ref>[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5559172/ Yamaga, M., Takemoto, M., Shoji, M., Sakamoto, K., Yamamoto, M., Ishikawa, T., ... & Yokote, K. (2017). Werner syndrome: a model for sarcopenia due to accelerated aging. Aging (Albany NY), 9(7), 1738.]</ref><ref name=":4">[https://academic.oup.com/jnci/article/113/2/112/5827003?login=true Guida, J. L., Agurs-Collins, T., Ahles, T. A., Campisi, J., Dale, W., Demark-Wahnefried, W., ... & Ness, K. K. (2020). Strategies to Prevent or Remediate Cancer and Treatment-Related Aging. ''JNCI: Journal of the National Cancer Institute'']</ref><ref>Kohanski, R. A., Deeks, S. G., Gravekamp, C., Halter, J. B., High, K., Hurria, A., ... & Sierra, F. (2016). Reverse geroscience: how does exposure to early diseases accelerate the age‐related decline in health? ''Annals of the New York Academy of Sciences, 1386,'' 30-44</ref> By measuring biological age, researchers could identify people who exhibit accelerated aging or vice versa slow aging.<ref>Dec, E., Clement, J., Cheng, K., Church, G. M., Fossel, M. B., Rehkopf, D. H., ... & Horvath, S. (2023). Centenarian clocks: epigenetic clocks for validating claims of exceptional longevity. GeroScience, 1-19. PMID: 36964402 DOI: 10.1007/s11357-023-00731-7</ref><ref>Daunay, A., Hardy, L., Bouyacoub, Y., Sahbatou, M., Touvier, M., Blanché, H., ... & How-Kit, A. (2022). Centenarians consistently present a younger epigenetic age than their chronological age with four epigenetic clocks based on a small number of CpG sites. Aging, 14(19), 7718-7733. PMID: 36202132 PMC9596211 DOI: 10.18632/aging.204316</ref> This would determine who might benefit the most from an anti-aging drug, and perhaps be used as a surrogate marker for more quickly identifying if an aging intervention slows or even reverses aging.<ref>Ferrucci, L., Gonzalez-Freire, M., Fabbri, E., Simonsick, E., Tanaka, T., Moore, Z., Salimi, S., Sierra, F., & Cabo, R. de. (2020). Measuring biological aging in humans: A quest. ''Aging Cell'', ''19''(2), e13080. https://doi.org/https://doi.org/10.1111/acel.13080</ref>

	Quantifying biological age is considered important for longevity research, as running clinical trials over several decades to show whether human life has been extended is unrealistic. Instead, it might be more practical to use biological aging clocks to predict if a therapy is likely to extend healthspan and lifespan within a shorter timeframe.		Quantifying biological age is considered important for longevity research, as running clinical trials over several decades to show whether human life has been extended is unrealistic. Instead, it might be more practical to use biological aging clocks to predict if a therapy is likely to extend healthspan and lifespan within a shorter timeframe.

Dmitry Dzhagarov: /* Mechanism */

2023-04-21T11:32:35Z

Mechanism

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	These changes can be used to estimate the biological age of the organism, and there are a [[wikipedia:Epigenetic_clock#Other_age_estimators_based_on_DNA_methylation_levels\|number of approaches]] to achieving this measurement, the most common being the Horvath’s clock, developed by Horvath et al. in 2013.<ref>Bocklandt, S., Lin, W., Sehl, M. E., Sánchez, F. J., Sinsheimer, J. S., Horvath, S., & Vilain, E. (2011). Epigenetic predictor of age. ''PLOS ONE'', ''6''(6), e14821. https://doi.org/10.1371/journal.pone.0014821</ref> They used publicly available datasets of methylation data collected on [[wikipedia:Illumina,_Inc.\|Illumina]] chips, and analyzed 21,369 CpG sites available on both 27k and 450k chips (the number referring to the total number of sites that the chip analyzes). The team then used a penalized regression model (elastic net regularization, which is essentially a linear combination of lasso and ridge regularization penalties, which thus drives the model to have both smaller coefficients and fewer of them) to identify 353 sites providing the most signals, of which 193 correlated with age positively, and the remaining 160 negatively. The clock then applies a calibration function to the weighted average of these 353 sites methylation levels to determine the biological age.		These changes can be used to estimate the biological age of the organism, and there are a [[wikipedia:Epigenetic_clock#Other_age_estimators_based_on_DNA_methylation_levels\|number of approaches]] to achieving this measurement, the most common being the Horvath’s clock, developed by Horvath et al. in 2013.<ref>Bocklandt, S., Lin, W., Sehl, M. E., Sánchez, F. J., Sinsheimer, J. S., Horvath, S., & Vilain, E. (2011). Epigenetic predictor of age. ''PLOS ONE'', ''6''(6), e14821. https://doi.org/10.1371/journal.pone.0014821</ref> They used publicly available datasets of methylation data collected on [[wikipedia:Illumina,_Inc.\|Illumina]] chips, and analyzed 21,369 CpG sites available on both 27k and 450k chips (the number referring to the total number of sites that the chip analyzes). The team then used a penalized regression model (elastic net regularization, which is essentially a linear combination of lasso and ridge regularization penalties, which thus drives the model to have both smaller coefficients and fewer of them) to identify 353 sites providing the most signals, of which 193 correlated with age positively, and the remaining 160 negatively. The clock then applies a calibration function to the weighted average of these 353 sites methylation levels to determine the biological age.

			=== Methylation marker genes associated with aging ===
			Regarding the definition of the markers, many candidate loci have been proposed, such as ELOVL2 (cg16867657),<ref>Manco, L., & Dias, H. C. (2022). DNA methylation analysis of ELOVL2 gene using droplet digital PCR for age estimation purposes. Forensic Science International, 333, 111206. PMID 35131731 doi:10.1016/j.forsciint.2022.111206</ref> EDARADD,<ref>Ni, X. L., Yuan, H. P., Jiao, J., Wang, Z. P., Su, H. B., Lyu, Y., ... & Yang, Z. (2022). An epigenetic clock model for assessing the human biological age of healthy aging. Zhonghua yi xue za zhi, 102(2), 119-124. PMID 35012300 doi:10.3760/cma.j.cn112137-20210817-01862</ref><ref>Daunay, A., Hardy, L. M., Bouyacoub, Y., Sahbatou, M., Touvier, M., Blanché, H., ... & How-Kit, A. (2022). Centenarians consistently present a younger epigenetic age than their chronological age with four epigenetic clocks based on a small number of CpG sites. Aging, 14(19), 7718—7733. PMID 36202132 doi:10.18632/aging.204316</ref> C1orf132 (cg10501210),<ref>Spólnicka, M., Pośpiech, E., Pepłońska, B., Zbieć-Piekarska, R., Makowska, Ż., Pięta, A., ... & Branicki, W. (2018). DNA methylation in ELOVL2 and C1orf132 correctly predicted chronological age of individuals from three disease groups. International journal of legal medicine, 132(1), 1-11. PMID 28725932 PMC 5748441 doi:10.1007/s00414-017-1636-0</ref> TRIM59, FHL2, KLF14, PDE4C, FHL2 (cg22454769), OTUD7A (cg04875128), CCDC102B (cg19283806),<ref>Fleckhaus, J., & Schneider, P. M. (2020). Novel multiplex strategy for DNA methylation-based age prediction from small amounts of DNA via Pyrosequencing. Forensic Science International: Genetics, 44, 102189. PMID: 31648151 DOI: 10.1016/j.fsigen.2019.102189</ref> ASPA, and PENK.<ref>Fan, H., Xie, Q., Zhang, Z., Wang, J., Chen, X., & Qiu, P. (2022). Chronological age prediction: developmental evaluation of DNA methylation-based machine learning models. Frontiers in Bioengineering and Biotechnology, 9, 1462. PMID: 35141217 PMCID: PMC8819006 DOI: 10.3389/fbioe.2021.819991</ref><ref>Varshavsky, M., Harari, G., Glaser, B., Dor, Y., Shemer, R., & Kaplan, T. (2023). Accurate age prediction from blood using of small set of DNA methylation sites and a cohort-based machine learning algorithm. bioRxiv, 2023-01. https://doi.org/10.1101/2023.01.20.524874</ref><ref>Jung, S. E., Lim, S. M., Hong, S. R., Lee, E. H., Shin, K. J., & Lee, H. Y. (2019). DNA methylation of the ELOVL2, FHL2, KLF14, C1orf132/MIR29B2C, and TRIM59 genes for age prediction from blood, saliva, and buccal swab samples. Forensic Science International: Genetics, 38, 1-8. PMID: 30300865 DOI: 10.1016/j.fsigen.2018.09.010</ref>

			==== ELOVL2 ====
			Elongase of very long chain fatty acids 2 (ELOVL2) represents a robust candidate gene as (i) its epigenetic variability is highly correlated with age predictions, (ii) it is included in most current age prediction models, and (iii) it does not show tissue-specificity, as observed for most of the epigenetic markers identified so far.<ref>Slieker, R. C., Relton, C. L., Gaunt, T. R., Slagboom, P. E., & Heijmans, B. T. (2018). Age-related DNA methylation changes are tissue-specific with ELOVL2 promoter methylation as exception. Epigenetics & chromatin, 11, 1-11. PMID: 29848354 PMCID: PMC5975493 DOI: 10.1186/s13072-018-0191-3</ref><ref>Paparazzo, E., Lagani, V., Geracitano, S., Citrigno, L., Aceto, M. A., Malvaso, A., ... & Montesanto, A. (2023). An ELOVL2-Based Epigenetic Clock for Forensic Age Prediction: A Systematic Review. International Journal of Molecular Sciences, 24(3), 2254. PMID: 36768576 PMCID: PMC9916975 DOI: 10.3390/ijms24032254</ref><ref>Sukawutthiya, P., Sathirapatya, T., & Vongpaisarnsin, K. (2021). A minimal number CpGs of ELOVL2 gene for a chronological age estimation using pyrosequencing. Forensic Science International, 318, 110631. PMID: 33279766 DOI: 10.1016/j.forsciint.2020.110631</ref>

	== Discovery ==		== Discovery ==

Dmitry Dzhagarov: /* Other uses */

2023-04-19T20:42:54Z

Other uses

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	YuanCheng Lu and colleagues were able to reverse loss of sight in age-related and glaucoma induced retinal ganglion cell loss, and even regenerate a mechanically damaged eye nerve. This was achieved by manipulating methylation patterns in mice, using partial epigenetic reprogramming delivered via viral gene therapy.<ref name=":1" /> Epigenetic clocks in this study demonstrated an apparent reversal of epigenetic age in mice treated with epigenetic reprogramming.<ref name=":1" />		YuanCheng Lu and colleagues were able to reverse loss of sight in age-related and glaucoma induced retinal ganglion cell loss, and even regenerate a mechanically damaged eye nerve. This was achieved by manipulating methylation patterns in mice, using partial epigenetic reprogramming delivered via viral gene therapy.<ref name=":1" /> Epigenetic clocks in this study demonstrated an apparent reversal of epigenetic age in mice treated with epigenetic reprogramming.<ref name=":1" />
	== Other uses ==		== Other uses ==
			[[File:CAGE.jpg\|thumb\|Predictors (cAge, ZhangAge, HannumAge, and HorvathAge) performance in the GSE55763 dataset in accordance with Bernabeu et al. 2022.<ref>Bernabeu, E., McCartney, D. L., Gadd, D. A., Hillary, R. F., Lu, A. T., Murphy, L., ... & Marioni, R. E. (2023). Refining epigenetic prediction of chronological and biological age. Genome Med 15, 12 https://doi.org/10.1186/s13073-023-01161-y</ref> Pearson correlation (r), root mean squared error (RMSE), and median absolute error (MAE)]]
	Epigenetic clock also has many other [[wikipedia:Epigenetic_clock#Motivation_for_biological_clocks\|applications]]:		Epigenetic clock also has many other [[wikipedia:Epigenetic_clock#Motivation_for_biological_clocks\|applications]]:

Andrea: category change

2022-12-27T12:23:49Z

category change

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	== References ==		== References ==
	<references />		<references />
	~~[[Category:Longevity]]~~
	{{DEFAULTSORT:Epigenetic_clocks}}		{{DEFAULTSORT:Epigenetic_clocks}}
			[[Category:Main list]]
			[[Category:Longevity concepts]]

Andrea: minor title change +s

2022-12-27T09:43:34Z

minor title change +s

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			{{DISPLAYTITLE:Epigenetic clocks}}

	Epigenetic clocks are based on an individuals's DNA methylation (DNAm) status, referred to as ''DNAm age'' or ''epigenetic age''. They can be used to estimate chronological age and might potentially measure some aspects of [[biological age]]. Some studies argue that epigenetic clocks can predict all-cause mortality better than chronological age and other traditional risk factors.<ref name=":22">[https://jamanetwork.com/journals/jamapsychiatry/article-abstract/2776612 Wertz, J., Caspi, A., Ambler, A., Broadbent, J., Hancox, R. J., Harrington, H., ... & Moffitt, T. E. (2021). Association of History of Psychopathology With Accelerated Aging at Midlife. JAMA psychiatry.]</ref> However, there is currently no definitive evidence that epigenetic clocks can predict remaining lifespan and future health status at the individual level (but can be useful at the population level).		Epigenetic clocks are based on an individuals's DNA methylation (DNAm) status, referred to as ''DNAm age'' or ''epigenetic age''. They can be used to estimate chronological age and might potentially measure some aspects of [[biological age]]. Some studies argue that epigenetic clocks can predict all-cause mortality better than chronological age and other traditional risk factors.<ref name=":22">[https://jamanetwork.com/journals/jamapsychiatry/article-abstract/2776612 Wertz, J., Caspi, A., Ambler, A., Broadbent, J., Hancox, R. J., Harrington, H., ... & Moffitt, T. E. (2021). Association of History of Psychopathology With Accelerated Aging at Midlife. JAMA psychiatry.]</ref> However, there is currently no definitive evidence that epigenetic clocks can predict remaining lifespan and future health status at the individual level (but can be useful at the population level).
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	<references />		<references />
	[[Category:Longevity]]		[[Category:Longevity]]
			{{DEFAULTSORT:Epigenetic_clocks}}

Andrea at 19:57, 20 December 2022

2022-12-20T19:57:09Z

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	~~Biological~~ age ~~had~~, ~~in the past~~, ~~been thought~~ of ~~as synonymous~~ of ~~chronological age~~. However, ~~we now know that while~~ there is ~~a strong association between both, biological age may significantly differ between individuals of identical chronological age, possibly due to reasons such as dissimilar~~ health status.		Epigenetic clocks are based on an individuals's DNA methylation (DNAm) status, referred to as ''DNAm age'' or ''epigenetic age''. They can be used to estimate chronological age and might potentially measure some aspects of [[biological age]]. Some studies argue that epigenetic clocks can predict all-cause mortality better than chronological age and other traditional risk factors.<ref name=":22">[https://jamanetwork.com/journals/jamapsychiatry/article-abstract/2776612 Wertz, J., Caspi, A., Ambler, A., Broadbent, J., Hancox, R. J., Harrington, H., ... & Moffitt, T. E. (2021). Association of History of Psychopathology With Accelerated Aging at Midlife. JAMA psychiatry.]</ref> However, there is currently no definitive evidence that epigenetic clocks can predict remaining lifespan and future health status at the individual level (but can be useful at the population level).

	The definition of '''chronological age''' is very straight forward: it is the number of years an individual has been alive. However, biological age is somewhat a more loosely defined concept. '''Biological age''' most often refers to the epigenetic and DNA methylation marks observed in an individual. These marks aim to provide a proxy of physiological function and the extent of known age-related traits. It can also be used to infer remaining years of life lived in good health (ie. remaining ''healthspan'') and life expectancy (total remaining ''lifespan,'' including periods of poor health).		It is worth noting that epigenetic clocks have not offered a causal explanation of aging. They represent a tool to measure biological age status and, in a way, they show us what we already knew: there is a change in tissue composition over time across different cell types, with cells accumulating a number of [[Hallmarks of Aging\|hallmarks of aging]], specially “inflammaging”.<ref name=":32">Horvath, S. (2013). DNA methylation age of human tissues and cell types. ''Genome Biology'', ''14''(10), 3156. https://doi.org/10.1186/gb-2013-14-10-r115</ref><ref>López-Otín, C., Blasco, M., Partridge, L., Serrano, M., & Kroemer, G. (2013). The Hallmarks of Aging. ''Cell'', ''153''(6), 1194-1217. doi: 10.1016/j.cell.2013.05.039</ref> Nonetheless, epigenetic clocks might be useful in providing a solid framework to test rejuvenating interventions, such as [[epigenetic reprogramming]].<ref>Lu, Y., Brommer, B., Tian, X., Krishnan, A., Meer, M., & Wang, C. et al. (2020). Reprogramming to recover youthful epigenetic information and restore vision. ''Nature'', ''588''(7836), 124-129. doi: 10.1038/s41586-020-2975-4</ref>

	'''Epigenetic clocks''' are a way to measure chronological age and potentially some aspects of biological age. They are based on a subject's DNA methylation (DNAm) status, referred to as ''DNAm age'' or ''epigenetic age''. Some studies argue that epigenetic clocks can predict all-cause mortality better than chronological age and other traditional risk factors.<ref name=":2" /> However, there is currently no definitive evidence that epigenetic clocks can predict remaining lifespan and future health status at the individual level.

	It is worth noting that~~, whilst useful in certain settings,~~ epigenetic clocks have not ~~proved to offer~~ a causal explanation of aging. They ~~are merely~~ a ~~way of measuring~~ biological age status and, in a way, they show us what we already knew: there is a change in tissue composition over time across different cell types, with cells accumulating a number of [[Hallmarks of Aging\|hallmarks of aging]], specially “inflammaging”.<ref name=":3" /><ref>López-Otín, C., Blasco, M., Partridge, L., Serrano, M., & Kroemer, G. (2013). The Hallmarks of Aging. ''Cell'', ''153''(6), 1194-1217. doi: 10.1016/j.cell.2013.05.039</ref> Nonetheless, epigenetic clocks might be useful in providing a solid framework to test rejuvenating interventions, such as [[epigenetic reprogramming]].<ref>Lu, Y., Brommer, B., Tian, X., Krishnan, A., Meer, M., & Wang, C. et al. (2020). Reprogramming to recover youthful epigenetic information and restore vision. ''Nature'', ''588''(7836), 124-129. doi: 10.1038/s41586-020-2975-4</ref>

	A variety of other aging clocks exist based on parameters other than the methylation status. These include transcriptomics clocks,<ref>Holzscheck, N., Falckenhayn, C., Söhle, J., Kristof, B., Siegner, R., & Werner, A. et al. (2021). Modeling transcriptomic age using knowledge-primed artificial neural networks. ''Npj Aging And Mechanisms Of Disease'', ''7''(1). doi: 10.1038/s41514-021-00068-5</ref><ref>Mamoshina, P., Volosnikova, M., Ozerov, I., Putin, E., Skibina, E., Cortese, F., & Zhavoronkov, A. (2018). Machine Learning on Human Muscle Transcriptomic Data for Biomarker Discovery and Tissue-Specific Drug Target Identification. ''Frontiers In Genetics'', ''9''. doi: 10.3389/fgene.2018.00242</ref> glycation clocks,<ref>Severin, F., Feniouk, B., & Skulachev, V. (2013). Advanced glycation of cellular proteins as a possible basic component of the “master biological clock”. ''Biochemistry (Moscow)'', ''78''(9), 1043-1047. doi: 10.1134/s0006297913090101</ref> telomere clocks,<ref>Harley, C. (1991). Telomere loss: mitotic clock or genetic time bomb?. ''Mutation Research/Dnaging'', ''256''(2-6), 271-282. doi: 10.1016/0921-8734(91)90018-7</ref> microbiome clocks,<ref>Galkin, F., Mamoshina, P., Aliper, A., Putin, E., Moskalev, V., Gladyshev, V., & Zhavoronkov, A. (2020). Human Gut Microbiome Aging Clock Based on Taxonomic Profiling and Deep Learning. ''Iscience'', ''23''(6), 101199. doi: 10.1016/j.isci.2020.101199</ref><ref>Gopu, V., Cai, Y., Krishnan, S., Rajagopal, S., Camacho, F., & Toma, R. et al. (2020). An accurate aging clock developed from the largest dataset of microbial and human gene expression reveals molecular mechanisms of aging. doi: 10.1101/2020.09.17.301887</ref> or more recently the DNAm PhenoAge,<ref>Levine, M., Lu, A., Quach, A., Chen, B., Assimes, T., & Bandinelli, S. et al. (2018). An epigenetic biomarker of aging for lifespan and healthspan. ''Aging'', ''10''(4), 573-591. doi: 10.18632/aging.101414</ref> considered as an epigenetic biomarker of aging for lifespan and healthspan.

			A variety of other aging clocks exist based on parameters other than the methylation status. These include transcriptomics clocks,<ref>Holzscheck, N., Falckenhayn, C., Söhle, J., Kristof, B., Siegner, R., & Werner, A. et al. (2021). Modeling transcriptomic age using knowledge-primed artificial neural networks. ''Npj Aging And Mechanisms Of Disease'', ''7''(1). doi: 10.1038/s41514-021-00068-5</ref><ref>Mamoshina, P., Volosnikova, M., Ozerov, I., Putin, E., Skibina, E., Cortese, F., & Zhavoronkov, A. (2018). Machine Learning on Human Muscle Transcriptomic Data for Biomarker Discovery and Tissue-Specific Drug Target Identification. ''Frontiers In Genetics'', ''9''. doi: 10.3389/fgene.2018.00242</ref> glycation clocks,<ref>Severin, F., Feniouk, B., & Skulachev, V. (2013). Advanced glycation of cellular proteins as a possible basic component of the “master biological clock”. ''Biochemistry (Moscow)'', ''78''(9), 1043-1047. doi: 10.1134/s0006297913090101</ref> telomere clocks,<ref>Harley, C. (1991). Telomere loss: mitotic clock or genetic time bomb?. ''Mutation Research/Dnaging'', ''256''(2-6), 271-282. doi: 10.1016/0921-8734(91)90018-7</ref> microbiome clocks,<ref>Galkin, F., Mamoshina, P., Aliper, A., Putin, E., Moskalev, V., Gladyshev, V., & Zhavoronkov, A. (2020). Human Gut Microbiome Aging Clock Based on Taxonomic Profiling and Deep Learning. ''Iscience'', ''23''(6), 101199. doi: 10.1016/j.isci.2020.101199</ref><ref>Gopu, V., Cai, Y., Krishnan, S., Rajagopal, S., Camacho, F., & Toma, R. et al. (2020). An accurate aging clock developed from the largest dataset of microbial and human gene expression reveals molecular mechanisms of aging. doi: 10.1101/2020.09.17.301887</ref> or more recently the DNAm PhenoAge,<ref>Levine, M., Lu, A., Quach, A., Chen, B., Assimes, T., & Bandinelli, S. et al. (2018). An epigenetic biomarker of aging for lifespan and healthspan. ''Aging'', ''10''(4), 573-591. doi: 10.18632/aging.101414</ref> which combines epigenetic clocks with several measurements of functional performance.
	== General purpose of epigenetic clocks ==		== General purpose of epigenetic clocks ==
	People vary significantly in how they age, with various factors leading to accelerated aging. Some examples include depression, stress, poverty, HIV/AIDs, diabetes, smoking, Down Syndrome, accelerated aging syndromes (e.g. progerias) and in childhood cancer survivors.<ref name=":2">[https://jamanetwork.com/journals/jamapsychiatry/article-abstract/2776612 Wertz, J., Caspi, A., Ambler, A., Broadbent, J., Hancox, R. J., Harrington, H., ... & Moffitt, T. E. (2021). Association of History of Psychopathology With Accelerated Aging at Midlife. JAMA psychiatry.]</ref><ref>[[Bersani, F. S., Mellon, S. H., Reus, V. I., & Wolkowitz, O. M. (2019). Accelerated aging in serious mental disorders. Current opinion in psychiatry, 32(5), 381.]]</ref><ref>[[Yegorov, Y. E., Poznyak, A. V., Nikiforov, N. G., Sobenin, I. A., & Orekhov, A. N. (2020). The link between chronic stress and accelerated aging. Biomedicines, 8(7), 198.]]</ref><ref>[[Crimmins, E. M., Kim, J. K., & Seeman, T. E. (2009). Poverty and biological risk: the earlier “aging” of the poor. Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences, 64(2), 286-292.\|Crimmins, E. M., Kim, J. K., & Seeman, T. E. (2009). Poverty and biological risk: the earlier “aging” of the poor. ''Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences'', ''64''(2), 286-292.]]</ref><ref>[./Https://clinicalepigeneticsjournal.biomedcentral.com/articles/10.1186/s13148-019-0777-z Wu, X., Huang, Q., Javed, R., Zhong, J., Gao, H., & Liang, H. (2019). Effect of tobacco smoking on the epigenetic age of human respiratory organs. Clinical epigenetics, 11(1), 1-9.]</ref><ref>Aung, H. L., Aghvinian, M., Gouse, H., Robbins, R. N., Brew, B. J., Mao, L., & Cysique, L. A. (2020). Is There Any Evidence of Premature, Accentuated and Accelerated Aging Effects on Neurocognition in People Living with HIV? A Systematic Review. AIDS and Behavior, 1-44.</ref><ref>[https://www.sciencedirect.com/science/article/pii/S1550413119302463 Aguayo-Mazzucato, C., Andle, J., Lee Jr, T. B., Midha, A., Talemal, L., Chipashvili, V., ... & Bonner-Weir, S. (2019). Acceleration of β cell aging determines diabetes and senolysis improves disease outcomes. Cell metabolism, 30(1), 129-142.]</ref><ref>[[Gensous, N., Bacalini, M. G., Franceschi, C., & Garagnani, P. (2020, July). Down syndrome, accelerated aging and immunosenescence. In Seminars in Immunopathology (pp. 1-11). Springer Berlin Heidelberg.]]</ref><ref>[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5559172/ Yamaga, M., Takemoto, M., Shoji, M., Sakamoto, K., Yamamoto, M., Ishikawa, T., ... & Yokote, K. (2017). Werner syndrome: a model for sarcopenia due to accelerated aging. Aging (Albany NY), 9(7), 1738.]</ref><ref name=":4">[https://academic.oup.com/jnci/article/113/2/112/5827003?login=true Guida, J. L., Agurs-Collins, T., Ahles, T. A., Campisi, J., Dale, W., Demark-Wahnefried, W., ... & Ness, K. K. (2020). Strategies to Prevent or Remediate Cancer and Treatment-Related Aging. ''JNCI: Journal of the National Cancer Institute'']</ref><ref>Kohanski, R. A., Deeks, S. G., Gravekamp, C., Halter, J. B., High, K., Hurria, A., ... & Sierra, F. (2016). Reverse geroscience: how does exposure to early diseases accelerate the age‐related decline in health? ''Annals of the New York Academy of Sciences, 1386,'' 30-44</ref> By measuring biological age, researchers could identify people who exhibit accelerated aging. This would determine who might benefit the most from an anti-aging drug, and perhaps be used as a surrogate marker for more quickly identifying if an aging intervention slows or even reverses aging.<ref>Ferrucci, L., Gonzalez-Freire, M., Fabbri, E., Simonsick, E., Tanaka, T., Moore, Z., Salimi, S., Sierra, F., & Cabo, R. de. (2020). Measuring biological aging in humans: A quest. ''Aging Cell'', ''19''(2), e13080. https://doi.org/https://doi.org/10.1111/acel.13080</ref>		People vary significantly in how they age, with various factors leading to accelerated aging. Some examples include depression, stress, poverty, HIV/AIDs, diabetes, smoking, Down Syndrome, accelerated aging syndromes (e.g. progerias) and in childhood cancer survivors.<ref name=":2">[https://jamanetwork.com/journals/jamapsychiatry/article-abstract/2776612 Wertz, J., Caspi, A., Ambler, A., Broadbent, J., Hancox, R. J., Harrington, H., ... & Moffitt, T. E. (2021). Association of History of Psychopathology With Accelerated Aging at Midlife. JAMA psychiatry.]</ref><ref>[[Bersani, F. S., Mellon, S. H., Reus, V. I., & Wolkowitz, O. M. (2019). Accelerated aging in serious mental disorders. Current opinion in psychiatry, 32(5), 381.]]</ref><ref>[[Yegorov, Y. E., Poznyak, A. V., Nikiforov, N. G., Sobenin, I. A., & Orekhov, A. N. (2020). The link between chronic stress and accelerated aging. Biomedicines, 8(7), 198.]]</ref><ref>[[Crimmins, E. M., Kim, J. K., & Seeman, T. E. (2009). Poverty and biological risk: the earlier “aging” of the poor. Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences, 64(2), 286-292.\|Crimmins, E. M., Kim, J. K., & Seeman, T. E. (2009). Poverty and biological risk: the earlier “aging” of the poor. ''Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences'', ''64''(2), 286-292.]]</ref><ref>[./Https://clinicalepigeneticsjournal.biomedcentral.com/articles/10.1186/s13148-019-0777-z Wu, X., Huang, Q., Javed, R., Zhong, J., Gao, H., & Liang, H. (2019). Effect of tobacco smoking on the epigenetic age of human respiratory organs. Clinical epigenetics, 11(1), 1-9.]</ref><ref>Aung, H. L., Aghvinian, M., Gouse, H., Robbins, R. N., Brew, B. J., Mao, L., & Cysique, L. A. (2020). Is There Any Evidence of Premature, Accentuated and Accelerated Aging Effects on Neurocognition in People Living with HIV? A Systematic Review. AIDS and Behavior, 1-44.</ref><ref>[https://www.sciencedirect.com/science/article/pii/S1550413119302463 Aguayo-Mazzucato, C., Andle, J., Lee Jr, T. B., Midha, A., Talemal, L., Chipashvili, V., ... & Bonner-Weir, S. (2019). Acceleration of β cell aging determines diabetes and senolysis improves disease outcomes. Cell metabolism, 30(1), 129-142.]</ref><ref>[[Gensous, N., Bacalini, M. G., Franceschi, C., & Garagnani, P. (2020, July). Down syndrome, accelerated aging and immunosenescence. In Seminars in Immunopathology (pp. 1-11). Springer Berlin Heidelberg.]]</ref><ref>[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5559172/ Yamaga, M., Takemoto, M., Shoji, M., Sakamoto, K., Yamamoto, M., Ishikawa, T., ... & Yokote, K. (2017). Werner syndrome: a model for sarcopenia due to accelerated aging. Aging (Albany NY), 9(7), 1738.]</ref><ref name=":4">[https://academic.oup.com/jnci/article/113/2/112/5827003?login=true Guida, J. L., Agurs-Collins, T., Ahles, T. A., Campisi, J., Dale, W., Demark-Wahnefried, W., ... & Ness, K. K. (2020). Strategies to Prevent or Remediate Cancer and Treatment-Related Aging. ''JNCI: Journal of the National Cancer Institute'']</ref><ref>Kohanski, R. A., Deeks, S. G., Gravekamp, C., Halter, J. B., High, K., Hurria, A., ... & Sierra, F. (2016). Reverse geroscience: how does exposure to early diseases accelerate the age‐related decline in health? ''Annals of the New York Academy of Sciences, 1386,'' 30-44</ref> By measuring biological age, researchers could identify people who exhibit accelerated aging. This would determine who might benefit the most from an anti-aging drug, and perhaps be used as a surrogate marker for more quickly identifying if an aging intervention slows or even reverses aging.<ref>Ferrucci, L., Gonzalez-Freire, M., Fabbri, E., Simonsick, E., Tanaka, T., Moore, Z., Salimi, S., Sierra, F., & Cabo, R. de. (2020). Measuring biological aging in humans: A quest. ''Aging Cell'', ''19''(2), e13080. https://doi.org/https://doi.org/10.1111/acel.13080</ref>