The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Epigenetic Predictor of Age
Open accessAbstract
From the moment of conception, we begin to age. A decay of cellular structures, gene regulation, and DNA sequence ages cells and organisms. DNA methylation patterns change with increasing age and contribute to age related disease. Here we identify 88 sites in or near 80 genes for which the degree of cytosine methylation is significantly correlated with age in saliva of 34 male identical twin pairs between 21 and 55 years of age. Furthermore, we validated sites in the promoters of three genes and replicated our results in a general population sample of 31 males and 29 females between 18 and 70 years of age. The methylation of three sites--in the promoters of the EDARADD, TOM1L1, and NPTX2 genes--is linear with age over a range of five decades. Using just two cytosines from these loci, we built a regression model that explained 73% of the variance in age, and is able to predict the age of an individual with an average accuracy of 5.2 years. In forensic science, such a model could estimate the age of a person, based on a biological sample alone. Furthermore, a measurement of relevant sites in the genome could be a tool in routine medical screening to predict the risk of age-related diseases and to tailor interventions based on the epigenetic bio-age instead of the chronological age.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Figures



Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing 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.”
From Measuring biological age using omics data (2022) · cites this paper as [53]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
WGCNA: an R package for weighted correlation network analysis
Langfelder, P., & Horvath, S. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-559 · Cited by 27,252
Self-citationStatistical significance for genomewide studies
Storey, J. D., & Tibshirani, R. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1530509100 · Cited by 9,692
Adjusting batch effects in microarray expression data using empirical Bayes methods
Johnson, W. E., Li, C., & Rabinovic, A. (2007) · Biostatistics · doi:10.1093/biostatistics/kxj037 · Cited by 8,658
A General Framework for Weighted Gene Co-Expression Network Analysis
Zhang, B., & Horvath, S. (2005) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1128 · Cited by 5,684
Self-citationEpigenetic differences arise during the lifetime of monozygotic twins
Fraga, M. F., Ballestar, E., Paz, M. F., Ropero, S., Setien, F., Ballestar, M. L., Heine-Suñer, D., Cigudosa, J. C., Urioste, M., Benitez, J., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0500398102 · Cited by 3,602
Marks, P. A., & Breslow, R. (2007) · Nature Biotechnology · doi:10.1038/nbt1272 · Cited by 1,198
Geometric Interpretation of Gene Coexpression Network Analysis
Horvath, S., & Dong, J. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000117 · Cited by 867
Self-citationAge-dependent DNA methylation of genes that are suppressed in stem cells is a hallmark of cancer
Teschendorff, A. E., Menon, U., Gentry-Maharaj, A., Ramus, S. J., Weisenberger, D. J., Shen, H., Campan, M., Noushmehr, H., Bell, C. G., Maxwell, A. P., et al. (2010) · Genome Research · doi:10.1101/gr.103606.109 · Cited by 855
Genome-wide association analysis by lasso penalized logistic regression
Wu, T. T., Chen, Y. F., Hastie, T., Sobel, E., & Lange, K. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp041 · Cited by 785
Human aging-associated DNA hypermethylation occurs preferentially at bivalent chromatin domainsPreprint
Rakyan, V. K., Down, T. A., Maslau, S., Andrew, T., Yang, T.-P., Beyan, H., Whittaker, P., McCann, O. T., Finer, S., Valdes, A. M., et al. (2010) · Genome Research · doi:10.1101/gr.103101.109 · Cited by 746
Cited by
Sorted by citations
Items 301–400 of 999
Co-expressed miRNAs in gastric adenocarcinoma
Yepes, S., López, R., Andrade, R. E., Rodríguez-Urrego, P. A., López-Kleine, L., & Torres, M. M. (2016) · Genomics · doi:10.1016/j.ygeno.2016.07.002 · Cited by 49
Starnawska, A., Tan, Q., Lenart, A. G., McGue, M. K., Mors, O. N., Børglum, A. D., Christensen, K., Nyegaard, M., & Christiansen, L. (2016) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2016.10.025 · Cited by 49
DNA Methylation Signatures as Biomarkers of Prior Environmental Exposures
Ladd‐Acosta, C., & Fallin, M. D. (2019) · Current Epidemiology Reports · doi:10.1007/s40471-019-0178-z · Cited by 51
Excess brain age in the sleep electroencephalogram predicts reduced life expectancy
Paixão, L., Sikka, P., Sun, H., Jain, A., Hogan, J. S., THOMAS, R. J., & Westover, M. B. (2019) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2019.12.015 · Cited by 49
Evaluation of DNA methylation markers and their potential to predict human aging
Silva, D. S. B. S., Antunes, J., Balamurugan, K., Duncan, G. T., Alho, C. S., & McCord, B. R. (2015) · Electrophoresis · doi:10.1002/elps.201500137 · Cited by 48
Genetic Effects on DNA Methylation and Its Potential Relevance for Obesity in Mexican Americans
Carless, M. A., Kulkarni, H. R., Kos, M. Z., Charlesworth, J., Peralta, J. M., Göring, H. H. H., Curran, J. E., Almasy, L. A., Dyer, T. D., Comuzzie, A. G., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0073950 · Cited by 51
Wylde, Z., Spagopoulou, F., Hooper, A. K., Maklakov, A. A., & Bonduriansky, R. (2019) · PLoS Biology · doi:10.1371/journal.pbio.3000556 · Cited by 48
The evaluation of seven age-related CpGs for forensic purpose in blood from Chinese Han population
Pan, C., Yi, S., Xiao, C., Huang, Y., Chen, X., & Huang, D. (2020) · Forensic Science International Genetics · doi:10.1016/j.fsigen.2020.102251 · Cited by 48
Coninx, E., Chew, Y. C., Yang, X., Guo, W., Coolkens, A., Baatout, S., Moons, L., Verslegers, M., & Quintens, R. (2020) · Aging · doi:10.18632/aging.104056 · Cited by 46
Extremely Low Birth Weight and Accelerated Biological Aging
Lieshout, R. J. V., McGowan, P. O., de Vega, W. C., Savoy, C. D., Morrison, K. M., Saigal, S., Mathewson, K. J., & Schmidt, L. A. (2021) · PEDIATRICS · doi:10.1542/peds.2020-001230 · Cited by 47
A Targeted Epigenetic Clock for the Prediction of Biological Age
Gensous, N., Sala, C., Pirazzini, C., Ravaioli, F., Milazzo, M., Kwiatkowska, K. M., Marasco, E., Fanti, S. D., Giuliani, C., Pellegrini, C., et al. (2022) · Cells · doi:10.3390/cells11244044 · Cited by 47
A validation study of DNA methylation-based age prediction using semen in forensic casework samples
Lee, J. W., Choung, C. M., Jung, J. Y., Lee, H. Y., & Lim, S.-K. (2018) · Legal Medicine · doi:10.1016/j.legalmed.2018.01.005 · Cited by 47
Multi-omics approaches to human biological age estimation
Solovev, I. A., Shaposhnikov, M. V., & Moskalev, A. (2019) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2019.111192 · Cited by 49
Uncovering Forensic Evidence: A Path to Age Estimation through DNA Methylation
Castagnola, M. J., Medina-Paz, F., & Zapico, S. C. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25094917 · Cited by 53
Simpkin, A. J., Cooper, R., Howe, L. D., Relton, C. L., Smith, G. D., Teschendorff, A. E., Widschwendter, M., Wong, A., Kuh, D. J., & Hardy, R. (2017) · BMJ Open · doi:10.1136/bmjopen-2017-016708 · Cited by 45
Epigenetic clocks in the pediatric population: when and why they tick?
Wang, J., & Zhou, W. (2021) · Chinese Medical Journal · doi:10.1097/cm9.0000000000001723 · Cited by 51
Immune Memory in Aging: a Wide Perspective Covering Microbiota, Brain, Metabolism, and Epigenetics
Bulut, O. P., Kılıç, G., & Domínguez‐Andrés, J. (2021) · Clinical Reviews in Allergy & Immunology · doi:10.1007/s12016-021-08905-x · Cited by 50
DNA methylation age calculators reveal association with diabetic neuropathy in type 1 diabetes
Roshandel, D., Chen, Z., Canty, A. J., Bull, S. B., Natarajan, R. D., & Paterson, A. D. (2020) · Clinical Epigenetics · doi:10.1186/s13148-020-00840-6 · Cited by 48
Spartano, N. L., Wang, R., Yang, Q., Chernofsky, A., Murabito, J. M., Vasan, R. S., Levy, D. A., Beiser, A., & Seshadri, S. S. (2022) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000003041 · Cited by 44
Rentscher, K. E., Bethea, T. N., Zhai, W., Small, B. J., Zhou, X., Ahles, T. A., Ahn, J., Breen, E. C., Cohen, H. J., Extermann, M., et al. (2023) · Cancer · doi:10.1002/cncr.34818 · Cited by 49
Understanding the health effects of caregiving stress: New directions in molecular aging
Christian, L. M., Wilson, S. J., Madison, A. A., Prakash, R. S., Burd, C. E., Rosko, A. E., & Kiecolt‐Glaser, J. K. (2023) · Ageing Research Reviews · doi:10.1016/j.arr.2023.102096 · Cited by 50
Current State of Saliva Biomarkers for Aging and Alzheimer’s Disease
François, M., Bull, C. F., Fenech, M. F., & Leifert, W. R. (2018) · Current Alzheimer Research · doi:10.2174/1567205015666181022094924 · Cited by 46
Optimal sample size for calibrating DNA methylation age estimators
Mayne, B. T., Berry, O., & Jarman, S. (2021) · Molecular Ecology Resources · doi:10.1111/1755-0998.13437 · Cited by 47
A collaborative exercise on DNA methylation-based age prediction and body fluid typing
Lee, J. E., Lee, J. M., Naue, J., Fleckhaus, J., Freire-Aradas, A., Neubauer, J., Pośpiech, E., McCord, B. R., Kalamara, V., Gauthier, Q. T., et al. (2021) · Forensic Science International Genetics · doi:10.1016/j.fsigen.2021.102656 · Cited by 44
Association between depression and epigenetic age acceleration: A co‐twin control study
Liu, C., Wang, Z., Hui, Q., Goldberg, J. H., Smith, N. L., Shah, A. J., Murrah, N. V., Shallenberger, L. H., Diggers, E., Bremner, J. D., et al. (2022) · Depression and Anxiety · doi:10.1002/da.23279 · Cited by 42
Towards AI-driven longevity research: An overview
Marino, N., Putignano, G., Cappilli, S., Chersoni, E., Chadha, A. S., Calabrese, G., Bischof, E. Y., Vanhaelen, Q., Zhavoronkov, A. A., Scarano, B., et al. (2023) · Frontiers in Aging · doi:10.3389/fragi.2023.1057204 · Cited by 48
Varshavsky, M., Harari, G., Gläser, B., Dor, Y., Shemer, R., & Kaplan, T. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100567 · Cited by 46
Assessing the rate of aging to monitor aging itself
Xia, X., Wang, Y., Yu, Z., Chen, J., & Han, J. J. (2021) · Ageing Research Reviews · doi:10.1016/j.arr.2021.101350 · Cited by 46
Age-associated changes in DNA methylation across multiple tissues in an inbred mouse model
Spiers, H., Hannon, E., Wells, S., Williams, B., Fernandes, C., & Mill, J. (2016) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2016.02.001 · Cited by 43
Targeted methods for epigenetic age predictions in mice
Han, Y., Nikolić, M., Gobs, M., Franzen, J., de Haan, G., Geiger, H., & Wagner, W. (2020) · Scientific Reports · doi:10.1038/s41598-020-79509-2 · Cited by 41
Han, J. J. (2023) · Trends in Endocrinology and Metabolism · doi:10.1016/j.tem.2023.09.007 · Cited by 45
Allele-Specific Methylation Occurs at Genetic Variants Associated with Complex Disease
Hutchinson, J. N., Raj, T., Fagerness, J., Stahl, E. A., Viloria, F. T., Gimelbrant, A. A., Seddon, J. M., Daly, M. J., Chess, A. J., & Plenge, R. M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0098464 · Cited by 44
Evaluation of massively parallel sequencing for forensic DNA methylation profiling
Richards, R., Patel, J., Stevenson, K. E., & Harbison, S. (2018) · Electrophoresis · doi:10.1002/elps.201800086 · Cited by 48
Thrush, K., Higgins‐Chen, A., Liu, Z., & Levine, M. E. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.13.499978 · Cited by 46
Schwender, K., Holländer, O., Klopfleisch, S., Eveslage, M., Danzer, M. F., Pfeiffer, H., & Vennemann, M. (2021) · Forensic Science International Genetics · doi:10.1016/j.fsigen.2021.102521 · Cited by 42
Epigenetic age estimation in saliva and in buccal cells
Ambroa-Conde, A., Girón-Santamaría, L., Mosquera‐Miguel, A., Phillips, C., de Cal, M. C., Gómez‐Tato, A., Álvarez-Dios, J., de la Puente, M., Ruiz-Ramírez, J., Lareu, M. V., et al. (2022) · Forensic Science International Genetics · doi:10.1016/j.fsigen.2022.102770 · Cited by 51
Applications of massively parallel sequencing in forensic genetics
Carratto, T. M. T., Moraes, V. M. S., Recalde, T. S. F., de Oliveira, M. L. G., & Mendes‐Junior, C. T. (2022) · Genetics and Molecular Biology · doi:10.1590/1678-4685-gmb-2022-0077 · Cited by 54
Tracking single-cell evolution using clock-like chromatin accessibility loci
Xiao, Y., Jin, W., Ju, L., Fu, J., Wang, G., Yu, M., Chen, F., Qian, K., Wang, X., & Zhang, Y. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02241-z · Cited by 46
Analysis of Age-Related Global DNA Methylation in Chicken
Gryzińska, M., Błaszczak, E., Strachecka, A., & Jeżewska-Witkowska, G. (2013) · Biochemical Genetics · doi:10.1007/s10528-013-9586-9 · Cited by 45
Ma, Y., Smith, C. E., Lai, C., Irvin, M. R., Parnell, L. D., Lee, Y., Pham, L. D., Aslibekyan, S. W., Claas, S. A., Tsai, M. Y., et al. (2014) · Aging Cell · doi:10.1111/acel.12293 · Cited by 42
DNA Methylation Predicts Progression of Human Gastric Lesions
Schneider, B., Mera, R. M., Piazuelo, M. B., Bravo, J. C., Zabaleta, J., Delgado, A. G., Bravo, L. E., Wilson, K. T., El-Rifai, W. M., Peek, R. M., et al. (2015) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-15-0388 · Cited by 41
Zhang, Y., Hapala, J., Brenner, H., & Wagner, W. (2017) · Clinical Epigenetics · doi:10.1186/s13148-017-0315-9 · Cited by 45
Landscape of genome-wide age-related DNA methylation in breast tissue
Song, M., Brasky, T. M., Weng, D. Y., McElroy, J. P., Marian, C., Higgins, M. J., Ambrosone, C. B., Spear, S. L., Llanos, A. A. M., Kallakury, B., et al. (2017) · Oncotarget · doi:10.18632/oncotarget.22754 · Cited by 41
Epigenetic age signatures in bones
Lee, H. Y., Hong, S. R., Lee, J., Hwang, I. K., Kim, N. Y., Lee, J. M., Fleckhaus, J., Jung, S.-E., & Lee, Y. H. (2020) · Forensic Science International Genetics · doi:10.1016/j.fsigen.2020.102261 · Cited by 46
A New Robust Epigenetic Model for Forensic Age Prediction
Montesanto, A., D’Aquila, P., Lagani, V., Paparazzo, E., Geracitano, S., Formentini, L., Giacconi, R., Cardelli, M., Provinciali, M., Bellizzi, D., et al. (2020) · Journal of Forensic Sciences · doi:10.1111/1556-4029.14460 · Cited by 42
Biomarkers and ageing: The clock-watcher
Gibbs, W. W. (2014) · Nature · doi:10.1038/508168a · Cited by 42
Lei, M., Gibbons, F. X., Simons, R. L., Philibert, R. A., & Beach, S. R. H. (2020) · Genes · doi:10.3390/genes11030311 · Cited by 41
Biological Age Predictors: The Status Quo and Future Trends
Erema, V. V., Yakovchik, A., Kashtanova, D. A., Bochkaeva, Z. V., Ivanov, M. V., Сосин, Д. В., Matkava, L. R., Yudin, V. S., Makarov, V. V., Keskinov, A. A., et al. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232315103 · Cited by 43
Centenarian clocks: epigenetic clocks for validating claims of exceptional longevity
Dec, E., Clement, J., Cheng, K., Church, G. M., Fossel, M., Rehkopf, D. H., Rosero‐Bixby, L., Kobor, M. S., Lin, D. T. S., Lu, A. T.-H., et al. (2023) · GeroScience · doi:10.1007/s11357-023-00731-7 · Cited by 41
Self-citationUsing Epigenome-Wide Association Scans of DNA Methylation in Age-Related Complex Human Traits
Tsai, P.-C., Spector, T. D., & Bell, J. T. (2012) · Epigenomics · doi:10.2217/epi.12.45 · Cited by 40
Age-related DNA methylation changes for forensic age-prediction
Yi, S.-H., Jia, Y.-S., Mei, K., Yang, R.-Z., & Huang, D.-X. (2015) · Zeitschrift für Rechtsmedizin · doi:10.1007/s00414-014-1100-3 · Cited by 41
Li, C., Wang, Z., Hardy, T. M., Huang, Y., Hui, Q., Crusto, C. A., Wright, M. L., Taylor, J. Y., & Sun, Y. V. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20174273 · Cited by 39
DNA Methylation, Aging, and Cancer Risk: A Mini-Review
Chen, L., Ganz, P. A., & Sehl, M. E. (2022) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2022.847629 · Cited by 39
Self-citationReversing age: dual species measurement of epigenetic age with a single clockPreprint
Horvath, S., Singh, K., Raj, K., Khairnar, S. I., Sanghavi, A., Shrivastava, A., Zoller, J. A., Li, C. Z., Hereñú, C. B., Canatelli-Mallat, M., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.07.082917 · Cited by 43
Self-citationHolzscheck, N., Söhle, J., Kristof, B., Grönniger, E., Gallinat, S., Wenck, H., Winnefeld, M., Falckenhayn, C., & Kaderali, L. (2020) · Aging · doi:10.18632/aging.103499 · Cited by 40
An epigenetic toolbox for conservation biologists
Balard, A., Baltazar‐Soares, M., Eizaguirre, C., & Heckwolf, M. J. (2024) · Evolutionary Applications · doi:10.1111/eva.13699 · Cited by 41
Epigenetic Regulation of Aging and its Rejuvenation
An, Y., Wang, Q., Gao, K., Zhang, C., Ouyang, Y., Li, R., Zhou, M., Wu, T., Zhou, L., Xie, Z., et al. (2025) · MedComm · doi:10.1002/mco2.70369 · Cited by 47
Eckmann-Scholz, C., Bens, S., Kolarova, J., Schneppenheim, S., Caliebe, A., Heidemann, S., von Kaisenberg, C., Kautza, M., Jonat, W., Siebert, R., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0039014 · Cited by 42
Saliva as a source of genomic DNA for genetic studies: review of current methods and applications.
Sun, F., & Reichenberger, E. (2014) · PubMed · Cited by 40
Association of adult lung function with accelerated biological aging
Rezwan, F. I., Imboden, M., Amaral, A. F. S., Wielscher, M., Jeong, A., Triebner, K., Real, F. G., Järvelin, M., Jarvis, D., Probst-Hensch, N. M., et al. (2020) · Aging · doi:10.18632/aging.102639 · Cited by 38
Testing evolutionary models of senescence: traditional approaches and future directions
Robins, C., & Conneely, K. N. (2014) · Human Genetics · doi:10.1007/s00439-014-1492-7 · Cited by 39
Epigenetic clocks provide clues to the mystery of uterine ageing
Deryabin, P. I., & Borodkina, A. V. (2022) · Human Reproduction Update · doi:10.1093/humupd/dmac042 · Cited by 40
Next generation sequencing (NGS): a golden tool in forensic toolkit
Aly, S. M., & Sabri, D. M. (2015) · Archives of Forensic Medicine and Criminology · doi:10.5114/amsik.2015.61029 · Cited by 39
Thong, Z., Tan, J. Y. Y., Loo, E. S., Phua, Y. W., Chan, X. L. S., & Syn, C. K. (2021) · Scientific Reports · doi:10.1038/s41598-021-81556-2 · Cited by 39
A Blood–Bone–Tooth Model for Age Prediction in Forensic Contexts
Dias, M. H., Manco, L., Real, F. C., & Cunha, E. M. (2021) · Biology · doi:10.3390/biology10121312 · Cited by 38
Beyond youth: Understanding CAR T cell fitness in the context of immunological aging
Noll, J. H., Levine, B. L., June, C. H., & Fraietta, J. A. (2023) · Seminars in Immunology · doi:10.1016/j.smim.2023.101840 · Cited by 41
Longitudinal study of DNA methylation during the first 5 years of life
Urdinguio, R. G., Torró, M. I., Bayón, G. F., Pitti, J. Á., Fernández, A. F., Redón, P., Fraga, M. F., & Lurbe, E. (2016) · Journal of Translational Medicine · doi:10.1186/s12967-016-0913-x · Cited by 39
Neuroepigenetics of Aging and Age-Related Neurodegenerative Disorders
Lardenoije, R., Pishva, E., Lunnon, K., & van den Hove, D. (2018) · Progress in molecular biology and translational science · doi:10.1016/bs.pmbts.2018.04.008 · Cited by 37
Neurodegenerative movement disorders: An epigenetics perspective and promise for the future
Murthy, M. N., Cheng, Y. Y., Holton, J. L., & Bettencourt, C. (2021) · Neuropathology and Applied Neurobiology · doi:10.1111/nan.12757 · Cited by 38
Hosking, M., Flynn, C. T., & Whitton, J. L. (2014) · The Journal of Immunology · doi:10.4049/jimmunol.1400348 · Cited by 38
Multimodal Omics Approaches to Aging and Age-Related Diseases
Ji, Q., Jiang, X., Wang, M., Xin, Z., Zhang, W., Qu, J. T., & Liu, G. (2024) · Phenomics · doi:10.1007/s43657-023-00125-x · Cited by 39
A roadmap to the molecular human linking multiomics with population traits and diabetes subtypes
Halama, A., Zaghlool, S. B., Thareja, G., Kader, S. A., Al‐Muftah, W., Mook-Kanamori, M. J., Sarwath, H., Mohamoud, Y. A., Stephan, N., Ameling, S., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-51134-x · Cited by 43
Epigenetic Clock: Just a Convenient Marker or an Active Driver of Aging?
Ashapkin, V. V., Kutueva, L. I., & Vanyushin, B. F. (2019) · Advances in experimental medicine and biology · doi:10.1007/978-3-030-25650-0_10 · Cited by 41
The use of DNA methylation clock in aging research
He, X., Liu, J., Liu, B., & Shi, J. (2020) · Experimental Biology and Medicine · doi:10.1177/1535370220968802 · Cited by 38
How to Translate DNA Methylation Biomarkers Into Clinical Practice
Wagner, W. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.854797 · Cited by 39
Epigenetic clocks in neurodegenerative diseases: a systematic review
Yang, T., Xiao, Y., Cheng, Y., Huang, J., Wei, Q., Li, C., & Shang, H. (2023) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp-2022-330931 · Cited by 40
Aging and the Biological Response to Liver Injury
Marzioni, M., Pinto, C., Ninfole, E., Gaggiano, L., Benedetti, A., & Maroni, L. (2020) · Seminars in Liver Disease · doi:10.1055/s-0039-3402033 · Cited by 36
Watkins, S., Testa, C., Chen, J. T., Vivo, I. D., Simpkin, A. J., Tilling, K. M., Roux, A. V. D., Smith, G. D., Waterman, P. D., Suderman, M. J., et al. (2023) · Current Zoology · doi:10.1093/eep/dvad005 · Cited by 39
Roh, B.-Y., Lee, W. J., Ryu, J.-W., Ahn, J.-M., Yoon, C.-L., & Lee, S. (2017) · International Journal of Legal Medicine · doi:10.1007/s00414-017-1762-8 · Cited by 36
Variably methylated retrotransposons are refractory to a range of environmental perturbations
Bertozzi, T. M., Becker, J. L., Blake, G. E. T., Bansal, A., Nguyen, D. K., Fernandez‐Twinn, D. S., Ozanne, S. E., Bartolomei, M. S., Simmons, R. A., Watson, E. D., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00898-9 · Cited by 38
HIV, pathology and epigenetic age acceleration in different human tissues
Horvath, S., Lin, D. T. S., Kobor, M. S., Zoller, J. A., SAID, J. W., Morgello, S., Singer, E. J., Yong, W. H., Jamieson, B. D., & Levine, A. J. (2022) · GeroScience · doi:10.1007/s11357-022-00560-0 · Cited by 38
Self-citationCounteracting aged DNA methylation states to combat ageing and age-related diseases
Reale, A., Tagliatesta, S., Zardo, G., & Zampieri, M. (2022) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2022.111695 · Cited by 42
Effects of the UK Biobank collection protocol on potential biomarkers in saliva
Pramanik, R., Thompson, H., Kistler, J. O., Wade, W. G., Galloway, J. W., Peakman, T. C., & Proctor, G. (2012) · International Journal of Epidemiology · doi:10.1093/ije/dys166 · Cited by 36
Meng, G., Zhong, X., & Mei, H. (2016) · PLoS ONE · doi:10.1371/journal.pone.0150624 · Cited by 34
Differentially methylated gene patterns between age‐matched sarcopenic and non‐sarcopenic women
He, L., Khanal, P., Morse, C. I., Williams, A. G., & Thomis, M. A. (2019) · Journal of Cachexia Sarcopenia and Muscle · doi:10.1002/jcsm.12478 · Cited by 35
Dawes, K., Andersen, A. M., Reimer, R. A., Mills, J. A., Hoffman, E. A., Long, J. D., Miller, S., & Philibert, R. A. (2021) · Scientific Reports · doi:10.1038/s41598-021-01088-7 · Cited by 35
DNA methylation biomarkers in asthma and rhinitis: Are we there yet?
Legaki, E., Arsenis, C., Τaka, S., & Papadopoulos, N. G. (2022) · Clinical and Translational Allergy · doi:10.1002/clt2.12131 · Cited by 39
McKinney, B. C., Lin, C.-W., Oh, H., Tseng, G. C., Lewis, D. A., & Sibille, E. (2015) · Neuropsychopharmacology · doi:10.1038/npp.2015.107 · Cited by 34
Utility of DNA Methylation as a Biomarker in Aging and Alzheimer’s Disease
Milicic, L., Porter, T., Vacher, M. A., & Laws, S. M. (2023) · Journal of Alzheimer s Disease Reports · doi:10.3233/adr-220109 · Cited by 36
Epigenetic analyses in forensic medicine: future and challenges
Gerra, M. C., Dallabona, C., & Cecchi, R. (2024) · International Journal of Legal Medicine · doi:10.1007/s00414-024-03165-8 · Cited by 40
Application of AI in biological age prediction
Meng, D., Zhang, S., Huang, Y., Mao, K., & Han, J. J. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102777 · Cited by 35
López-Jiménez, F. A., Kapa, S., Friedman, P. A., LeBrasseur, N. K., Klavetter, E. W., Mangold, K. E., & Attia, Z. I. (2024) · JACC. Clinical electrophysiology · doi:10.1016/j.jacep.2024.02.011 · Cited by 38
Probabilistic inference of epigenetic age acceleration from cellular dynamics
Dabrowski, J., Yang, E. J., Crofts, S., Hillary, R. F., Simpson, D. J., McCartney, D. L., Marioni, R. E., Kirschner, K., Crespo, E. L., & Chandra, T. (2024) · Nature Aging · doi:10.1038/s43587-024-00700-5 · Cited by 35
DNA methylation‐based biomarkers for ageing long‐lived cetaceans
Parsons, K. M., Haghani, A., Zoller, J. A., Lu, A. T.-H., Fei, Z., Ferguson, S. H., Garde, E., Hanson, M. B., Emmons, C. K., Matkin, C. O., et al. (2023) · Molecular Ecology Resources · doi:10.1111/1755-0998.13791 · Cited by 35
Self-citationXiong, J., Jiang, H.-P., Peng, C., Deng, Q., Lan, M., Zeng, H., Zheng, F., Feng, Y., & Yuan, B. (2015) · Clinical Epigenetics · doi:10.1186/s13148-015-0109-x · Cited by 33
The integration of epigenetics and genetics in nutrition research for CVD risk factors
Ma, Y., & Ordovás, J. M. (2016) · Proceedings of The Nutrition Society · doi:10.1017/s0029665116000823 · Cited by 34
LINE-1 Hypermethylation in Serum Cell-Free DNA of Relapsing Remitting Multiple Sclerosis Patients
Dunaeva, M., Derksen, M., & Pruijn, G. J. M. (2017) · Molecular Neurobiology · doi:10.1007/s12035-017-0679-z · Cited by 33
Reversal of biological age in multiple rat organs by young porcine plasma fraction
Horvath, S., Singh, K., Raj, K., Khairnar, S. I., Sanghavi, A., Shrivastava, A., Zoller, J. A., Li, C. Z., Hereñú, C. B., Canatelli-Mallat, M., et al. (2023) · GeroScience · doi:10.1007/s11357-023-00980-6 · Cited by 36
Self-citationBarratclough, A., Smith, C. R., Gomez, F. M., Photopoulou, T., Takeshita, R., Pirotta, E., Thomas, L., McClain, A. M., Parry, C. B., Zoller, J. A., et al. (2021) · Journal of Zoological and Botanical Gardens · doi:10.3390/jzbg2030030 · Cited by 34
Self-citationDutta, S., Goodrich, J. M., Dolinoy, D. C., & Ruden, D. M. (2023) · Genes · doi:10.3390/genes15010016 · Cited by 37
Metadata
- Authors
- Bocklandt, S., Lin, W., Sehl, M. E., Sánchez, F. J., Sinsheimer, J. S., Horvath, S., & Vilain, E.
- Affiliations
- University of California, Los Angeles (US)
- Corresponding author
- Vilain, Eric
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2011-06-22
- Volume
- 6
- Issue
- 6
- Pages
- e14821
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC3120753
- OpenAlex
- W2080644309
- Cited by
- 1,012
- References
- 22
- Citations by year
- 2026: 56 · 2025: 71 · 2024: 83 · 2023: 92 · 2022: 95 · 2021: 108 · 2020: 87 · 2019: 64 · 2018: 52 · 2017: 65 · 2016: 58 · 2015: 63 · 2014: 43 · 2013: 40 · 2012: 30
- FWCI
- 14.97
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Telomeres, Telomerase, and Senescence
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Syndromes and Imprinting
- Keywords
- DNA methylation, age prediction, epigenetic clocks, saliva, EDARADD, NPTX2, promoter methylation, forensic age estimation, age-related disease risk
- MeSH terms
- Adolescent, Adult, Aged, Aging, Female, Humans, Male, Middle Aged, Models, Genetic, Saliva, Twins, Reproducibility of Results, DNA Probes, DNA Methylation, Oligonucleotide Array Sequence Analysis, Epigenesis, Genetic, Young Adult, Genetic Loci
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review