The DOI matches its Crossref record; 124 of the 124 references it cites were found in a registry or an index.
Biohorology and biomarkers of aging: Current state-of-the-art, challenges and opportunities
Open accessAbstract
The aging process results in multiple traceable footprints, which can be quantified and used to estimate an organism's age. Examples of such aging biomarkers include epigenetic changes, telomere attrition, and alterations in gene expression and metabolite concentrations. More than a dozen aging clocks use molecular features to predict an organism's age, each of them utilizing different data types and training procedures. Here, we offer a detailed comparison of existing mouse and human aging clocks, discuss their technological limitations and the underlying machine learning algorithms. We also discuss promising future directions of research in biohorology - the science of measuring the passage of time in living systems. Overall, we expect deep learning, deep neural networks and generative approaches to be the next power tools in this timely and actively developing field.
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
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?
The cited source says what the claim attributes to it.
In the citing 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.”
From Measuring biological age using omics data (2022) · cites this paper as [45]
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
A Unified Approach to Interpreting Model Predictions
Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707
Advances in Neural Information Processing Systems 29
Teymur, O., Zygalakis, K., & Calderhead, B. (2016) · Cited by 13,881
DNA methylation age of human tissues and cell types
Horvath, S. (2013) · Genome Biology · doi:10.1186/gb-2013-14-10-r115 · Cited by 7,075
“Why Should I Trust You?”: Explaining the Predictions of Any Classifier
Ribeiro, M., Singh, S., & Guestrin, C. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations · doi:10.18653/v1/n16-3020 · Cited by 6,682
Adversarial Discriminative Domain Adaptation
Tzeng, E., Hoffman, J., Saenko, K., & Darrell, T. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.316 · Cited by 5,154
Pleiotropy, natural selection, and the evolution of senescence.
Williams, G. C. (1957) · Evolution · doi:10.1111/j.1558-5646.1957.tb02911.x · Cited by 4,279
Genome-wide methylation profiles reveal quantitative views of human aging rates
Hannum, G., Guinney, J., Zhao, L., Zhang, L., Hughes, G., Sadda, S., Klotzle, B., Bibikova, M., Fan, J.-B., Gao, Y., et al. (2013) · Molecular Cell · doi:10.1016/j.molcel.2012.10.016 · Cited by 4,270
An epigenetic biomarker of aging for lifespan and healthspan
Levine, M. E., Lu, A. T., Quach, A., Chen, B. H., Assimes, T. L., Bandinelli, S., Hou, L., Baccarelli, A. A., Stewart, J. D., Li, Y., et al. (2018) · Aging · doi:10.18632/aging.101414 · Cited by 3,675
CpG islands and the regulation of transcription
Deaton, A. M., & Bird, A. (2011) · Genes & Development · doi:10.1101/gad.2037511 · Cited by 3,187
DNA methylation-based biomarkers and the epigenetic clock theory of ageing
Horvath, S., & Raj, K. (2018) · Nature Reviews Genetics · doi:10.1038/s41576-018-0004-3 · Cited by 3,095
Research works printed but not linked: 1
- Putin, 2017, Deep biomarkers of human aging: application of deep neural networks to biomarker development, Aging (Albany NY), 8, 1021, 10.18632/aging.100968 · DOI
Cited by
Sorted by citations
Biomarkers of aging for the identification and evaluation of longevity interventions
Moqri, M., Herzog, C., Poganik, J. R., Justice, J., Belsky, D. W., Higgins-Chen, A., Moskalev, A., Fuellen, G., Cohen, A. A., Bautmans, I., et al. (2023) · Cell · doi:10.1016/j.cell.2023.08.003 · Cited by 736
Self-citationMeasuring biological age using omics data
Rutledge, J., Oh, H., & Wyss-Coray, T. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-022-00511-7 · Cited by 570
Bao, H., Cao, J., Chen, M., Min, C., Chen, W., Chen, X., Chen, Y., Chen, Y., Chen, Y., Chen, Z., et al. (2023) · Science China Life Sciences · doi:10.1007/s11427-023-2305-0 · Cited by 402
BiT age: A transcriptome‐based aging clock near the theoretical limit of accuracy
Meyer, D. H., & Schumacher, B. (2021) · Aging Cell · doi:10.1111/acel.13320 · Cited by 165
DeepMAge: A Methylation Aging Clock Developed with Deep Learning
Galkin, F., Mamoshina, P., Kochetov, K., Sidorenko, D., & Zhavoronkov, A. A. (2021) · Aging and Disease · doi:10.14336/ad.2020.1202 · Cited by 156
Self-citationWolf, J., Rasmussen, D. K., Sun, Y. J., Vu, J. T., Wang, E., Espinosa, C. A., Bigini, F., Chang, R. T., Montague, A. A., Tang, P. H., et al. (2023) · Cell · doi:10.1016/j.cell.2023.09.012 · Cited by 130
Epigenetic clocks reveal a rejuvenation event during embryogenesis followed by aging
Kerepesi, C., Zhang, B., Lee, S.-G., Trapp, A., & Gladyshev, V. N. (2021) · Science Advances · doi:10.1126/sciadv.abg6082 · Cited by 130
Self-citationStress, diet, exercise: Common environmental factors and their impact on epigenetic age
Galkin, F., Kovalchuk, O., Koldasbayeva, D., Zhavoronkov, A. A., & Bischof, E. Y. (2023) · Ageing Research Reviews · doi:10.1016/j.arr.2023.101956 · Cited by 112
Self-citationLehallier, B., Shokhirev, M. N., Wyss‐Coray, T., & Johnson, A. A. (2020) · Aging Cell · doi:10.1111/acel.13256 · Cited by 106
Decoding aging clocks: New insights from metabolomics
Huang, H., Chen, Y., Xu, W., Cao, L., Qian, K., Bischof, E. Y., Kennedy, B. K., & Pu, J. (2024) · Cell Metabolism · doi:10.1016/j.cmet.2024.11.007 · Cited by 93
Pun, F. W., Leung, G. H. D., Leung, H.-W., Liu, B. H. M., Long, X., Ozerov, I. V., Wang, J., Ren, F., Aliper, A. M., Izumchenko, E., et al. (2022) · Aging · doi:10.18632/aging.203960 · Cited by 86
Self-citationHuman age reversal: Fact or fiction?
Johnson, A. A., English, B. W., Shokhirev, M. N., Sinclair, D. A., & Cuellar, T. L. (2022) · Aging Cell · doi:10.1111/acel.13664 · Cited by 85
Psychological aging, depression, and well-being
Mitina, M., Young, S., & Zhavoronkov, A. A. (2020) · Aging · doi:10.18632/aging.103880 · Cited by 81
Self-citationModeling transcriptomic age using knowledge-primed artificial neural networks
Holzscheck, N., Falckenhayn, C., Söhle, J., Kristof, B., Siegner, R., Werner, A., Schössow, J., Jürgens, C., Völzke, H., Wenck, H., et al. (2021) · npj Aging and Mechanisms of Disease · doi:10.1038/s41514-021-00068-5 · Cited by 71
Ageing as a software design flaw
de Magalhães, J. P. (2023) · Genome biology · doi:10.1186/s13059-023-02888-y · Cited by 71
Modeling the human aging transcriptome across tissues, health status, and sex
Shokhirev, M. N., & Johnson, A. A. (2020) · Aging Cell · doi:10.1111/acel.13280 · Cited by 65
Aging atlas reveals cell-type-specific effects of pro-longevity strategies
Gao, S. M., Qi, Y., Zhang, Q., Guan, Y., Lee, Y.-T., Ding, L., Wang, L., Mohammed, A., Li, H., Fu, Y., et al. (2024) · Nature Aging · doi:10.1038/s43587-024-00631-1 · Cited by 62
Biomarkers of ageing: Current state‐of‐art, challenges, and opportunities
Chen, R., Wang, Y., Zhang, S., Bulloch, G., Zhang, J., Liao, H., Shang, X., Clark, M., Peng, Q., Ge, Z., et al. (2023) · MedComm – Future Medicine · doi:10.1002/mef2.50 · Cited by 63
COVID-19 mortality as a fingerprint of biological age
Polidori, M. C., Sies, H., Ferrucci, L., & Benzing, T. R. (2021) · Ageing Research Reviews · doi:10.1016/j.arr.2021.101308 · Cited by 58
A unified framework for systematic curation and evaluation of aging biomarkers
Ying, K., Paulson, S., Eames, A., Tyshkovskiy, A., Li, S., Eynon, N., Jacques, M., Grolaux, R., Seale, K., Jacques, E., et al. (2025) · Nature Aging · doi:10.1038/s43587-025-00987-y · Cited by 65
Self-citationGalkin, F., Kochetov, K., Koldasbayeva, D., Faria, M., Fung, H. H.-L., Chen, A. X., & Zhavoronkov, A. A. (2022) · Aging · doi:10.18632/aging.204264 · Cited by 59
Self-citationHu, L., Li, J., Tang, Z., Gong, P., Chang, Z., Chen, Y., Ma, T., Jiang, S., Yang, C., & Zhang, T. (2025) · Cardiovascular Diabetology · doi:10.1186/s12933-025-02770-0 · Cited by 53
Wang, Y., Grant, O. A., Zhai, X., McDonald-Maier, K. D., & Schalkwyk, L. C. (2023) · GeroScience · doi:10.1007/s11357-023-00871-w · Cited by 53
Epigenetic clocks and programmatic aging
Gems, D., Virk, R. S., & de Magalhães, J. P. (2024) · Ageing Research Reviews · doi:10.1016/j.arr.2024.102546 · Cited by 50
Emerging rejuvenation strategies—Reducing the biological age
Zhang, B., Trapp, A., Kerepesi, C., & Gladyshev, V. N. (2021) · Aging Cell · doi:10.1111/acel.13538 · Cited by 53
Self-citationTowards 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
Self-citationAssessing 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
Histone mark age of human tissues and cell types
Camillo, L. P. D. L., Asif, M. H., Horvath, S., Larschan, E., & Singh, R. (2025) · Science Advances · doi:10.1126/sciadv.adk9373 · Cited by 49
An overview of contemporary theories of ageing
de Magalhães, J. P. (2025) · Nature Cell Biology · doi:10.1038/s41556-025-01698-7 · Cited by 47
CheekAge: a next-generation buccal epigenetic aging clock associated with lifestyle and health
Shokhirev, M. N., Torosin, N. S., Kramer, D. J., Johnson, A. A., & Cuellar, T. L. (2024) · GeroScience · doi:10.1007/s11357-024-01094-3 · Cited by 42
Ding, Z., Wei, Y., Peng, J., Wang, S., Chen, G., & Sun, J. (2023) · Biomedicines · doi:10.3390/biomedicines11102711 · Cited by 40
IgG N-glycome changes during the course of severe COVID-19: An observational study
Petrović, T., Vijay, A., Vučković, F., Trbojević‐Akmačić, I., Ollivere, B. J., Marjanović, D., Bego, T., Prnjavorac, B., Đerek, L., Markotić, A., et al. (2022) · EBioMedicine · doi:10.1016/j.ebiom.2022.104101 · Cited by 37
Targeting epigenetics: A novel promise for Alzheimer’s disease treatment
Jeremic, D., Jiménez‐Díaz, L., & Navarro‐López, J. D. (2023) · Ageing Research Reviews · doi:10.1016/j.arr.2023.102003 · Cited by 36
Programmed ageing: decline of stem cell renewal, immunosenescence, and Alzheimer's disease
Lathe, R., & Clair, D. S. (2023) · Biological reviews/Biological reviews of the Cambridge Philosophical Society · doi:10.1111/brv.12959 · Cited by 37
U‐shaped association between sleep duration and biological aging: Evidence from the UK Biobank study
Wang, X., Yan, X., Li, M., Cheng, L., Qi, X., Zhang, J., Pan, S., Xu, X., Wei, W., & Li, Y. (2024) · Aging Cell · doi:10.1111/acel.14159 · Cited by 35
Esposito, S., Gialluisi, A., Costanzo, S., Castelnuovo, A. F. D., Ruggiero, E., Curtis, A. D., Persichillo, M., Cerletti, C., Donati, M. B., de Gaetano, G., et al. (2022) · Clinical Nutrition · doi:10.1016/j.clnu.2022.02.023 · Cited by 33
Wang, S., Jurdi, N. E., Thyagarajan, B., Prizment, A. E., & Blaes, A. H. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25063319 · Cited by 37
Wang, C., Guan, X., Bai, Y., Feng, Y., Wei, W., Li, H., Li, G., Meng, H., Li, M., Jie, J., et al. (2021) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.14685 · Cited by 34
Longevity pharmacology comes of age
de Magalhães, J. P. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2021.02.015 · Cited by 32
Zagirova, D., Pushkov, S., Leung, G. H. D., Liu, B. H. M., Urban, A., Sidorenko, D., Kalashnikov, A., Kozlova, E., Naumov, V., Pun, F. W., et al. (2023) · Aging · doi:10.18632/aging.205055 · Cited by 33
Self-citationMao, S., Su, J., Wang, L., Bo, X., Li, C., & Chen, H. (2023) · Genome Research · doi:10.1101/gr.277491.122 · Cited by 32
Aging as a loss of morphostatic information: A developmental bioelectricity perspective
Pio‐Lopez, L., & Levin, M. G. (2024) · Ageing Research Reviews · doi:10.1016/j.arr.2024.102310 · Cited by 36
Contextualizing aging clocks and properly describing biological age
Johnson, A. A., & Shokhirev, M. N. (2024) · Aging Cell · doi:10.1111/acel.14377 · Cited by 30
Johnson, A. A., & Shokhirev, M. N. (2025) · Biogerontology · doi:10.1007/s10522-025-10265-4 · Cited by 30
Zhavoronkov, A. A., Kochetov, K., Diamandis, P., & Mitina, M. (2020) · Aging · doi:10.18632/aging.202344 · Cited by 30
Self-citationBae, C., Im, Y., Lee, J., Park, C.-S., Kim, M.-Y., Kwon, H., Kim, B., Park, H. R., Lee, C.-K., Kim, I., et al. (2021) · Frontiers in Analytical Science · doi:10.3389/frans.2021.709589 · Cited by 31
Strategic outline of interventions targeting extracellular matrix for promoting healthy longevity
Park, J. Y. C., King, A., Björk, V., English, B. W., Fedintsev, A., & Ewald, C. Y. (2023) · American Journal of Physiology-Cell Physiology · doi:10.1152/ajpcell.00060.2023 · Cited by 32
Wilczok, D. (2025) · Aging · doi:10.18632/aging.206190 · Cited by 32
Urban, A., Sidorenko, D., Zagirova, D., Kozlova, E., Kalashnikov, A., Pushkov, S., Naumov, V., Sarkisova, V. A., Leung, G. H. D., Leung, H.-W., et al. (2023) · Aging · doi:10.18632/aging.204788 · Cited by 30
Self-citationUtilization of Host and Microbiome Features in Determination of Biological Aging
Ratiner, K., Abdeen, S. K., Goldenberg, K., & Elinav, E. (2022) · Microorganisms · doi:10.3390/microorganisms10030668 · Cited by 27
What can we learn from physical capacity about biological age? A systematic review
Tzemah-Shahar, R., Hochner, H., Iktilat, K., & Agmon, M. (2022) · Ageing Research Reviews · doi:10.1016/j.arr.2022.101609 · Cited by 32
Wang, S., Rao, Z., Cao, R., Blaes, A. H., Coresh, J., Deo, R., Dubin, R. F., Joshu, C. E., Lehallier, B., Lutsey, P. L., et al. (2024) · PLoS Medicine · doi:10.1371/journal.pmed.1004464 · Cited by 26
Green, A. P., Klimm, F., Marshall, A. S., Leetmaa, R., Aryaman, J., Gómez-Durán, A., Chinnery, P. F., & Jones, N. S. (2025) · Nature Communications · doi:10.1038/s41467-025-57286-8 · Cited by 27
DNA methylation markers of age(ing) in non‐model animals
Tangili, M., Slettenhaar, A. J., Sudyka, J., Dugdale, H. L., Pen, I. R., Palsbøll, P. J., & Verhulst, S. (2023) · Molecular Ecology · doi:10.1111/mec.17065 · Cited by 24
Reprogramming cellular senescence and aging clocks for advanced cancer immunotherapy
Yang, H., Liu, D., Qiu, L., Wang, R., Zhang, C., Yu, D., Zhong, Q., Yuki, N., Song, Z., Zhu, Τ., et al. (2025) · Molecular Cancer · doi:10.1186/s12943-025-02459-8 · Cited by 30
McIntyre, R. L., Rahman, M., Vanapalli, S. A., Houtkooper, R. H., & Janssens, G. E. (2021) · Frontiers in Aging · doi:10.3389/fragi.2021.708680 · Cited by 23
The protein inputs of an ultra-predictive aging clock represent viable anti-aging drug targets
Johnson, A. A., Shokhirev, M. N., & Lehallier, B. (2021) · Ageing Research Reviews · doi:10.1016/j.arr.2021.101404 · Cited by 22
Yu, S., Kim, T. H., Han, Y.-H., Kang, Y., Jeong, D.-U., Lee, D., Kang, J., & Park, S. (2021) · Experimental and Therapeutic Medicine · doi:10.3892/etm.2021.10092 · Cited by 22
Yin, Z., Guo, X., Yang, Q., Li, P., Liang, S., Xu, X. Z. S., & Shang, X. (2022) · Genes · doi:10.3390/genes13040699 · Cited by 22
Tan, L., Zheng, Z.-Y., Huang, L., Jin, Z., Sulian, L., Wu, G.-S., & Luo, H. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.931886 · Cited by 21
Intersection clock reveals a rejuvenation event during human embryogenesis
Kerepesi, C., & Gladyshev, V. N. (2023) · Aging Cell · doi:10.1111/acel.13922 · Cited by 21
Self-citationDai, G.-C., Wang, H., Ming, Z., Lu, P., Li, Y., Gao, Y., Shi, L., Cheng, Z., Liu, X., & Rui, Y. (2024) · Ageing Research Reviews · doi:10.1016/j.arr.2024.102215 · Cited by 22
Yusri, K., Kumar, S., Fong, S., Gruber, J., & Sorrentino, V. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25126793 · Cited by 23
Zhu, Z., Lyu, J., Hao, X., Guo, H., Zhang, X., He, M., Cheng, X., Cheng, S., & Wang, C. (2024) · BMC Medicine · doi:10.1186/s12916-024-03769-2 · Cited by 20
The road ahead of dietary restriction on anti-aging: focusing on personalized nutrition
Duan, H., Li, J., Yu, L., & Fan, L. (2022) · Critical Reviews in Food Science and Nutrition · doi:10.1080/10408398.2022.2110034 · Cited by 20
Epistemic uncertainty challenges aging clock reliability in predicting rejuvenation effects
Kriukov, D. V., Kuzmina, E., Efimov, E., Dylov, D. V., & Khrameeva, E. E. (2024) · Aging Cell · doi:10.1111/acel.14283 · Cited by 20
Okada, D., Cheng, J. H., Zheng, C., Kumaki, T., & Yamada, R. (2023) · Human Genomics · doi:10.1186/s40246-023-00453-z · Cited by 18
Association between telomere length, frailty and death in older adults
Assar, M. E., Angulo, J., Carnicero, J. A. C., Walter, S., García‐García, F. J., Rodríguez‐Artalejo, F., & Rodríguez‐Mañas, L. (2020) · GeroScience · doi:10.1007/s11357-020-00291-0 · Cited by 17
Ageing and cancer: a research gap to fill
Solary, É., Abou‐Zeid, N., & Calvo, F. M. (2022) · Molecular Oncology · doi:10.1002/1878-0261.13222 · Cited by 18
Gao, Y., Shang, S., Guo, S., Wang, X., Zhou, H., Sun, Y., Gan, J., Zhang, Y., Li, X., Ning, S., et al. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac438 · Cited by 17
Predicting healthspan and disease risks through biological age
Li, G., Cheng, L., Wong, I. N., Yin, Y., Chen, J., Liu, L., & Zhang, K. (2025) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2025.10.006 · Cited by 16
Zhang, X., Wang, D.-S., Li, X., Miao, Y.-C., Gao, J., & Zhang, Q. (2023) · Industrial Crops and Products · doi:10.1016/j.indcrop.2023.117207 · Cited by 16
Steurer, B., Vanhaelen, Q., & Zhavoronkov, A. A. (2024) · The Journals of Gerontology Series A · doi:10.1093/gerona/glae006 · Cited by 16
Self-citationDiagnostic and Therapeutic Roles of Extracellular Vesicles in Aging‐Related Diseases
Sun, Z., Hou, X., Zhang, J., Li, J., Wu, P., Yan, L., & Qian, H. (2022) · Oxidative Medicine and Cellular Longevity · doi:10.1155/2022/6742792 · Cited by 16
Salekeen, R., Diaconeasa, A. G., Billah, M. M., & Islam, K. M. D. (2021) · Mitochondrion · doi:10.1016/j.mito.2021.07.007 · Cited by 16
Study on the relationship between selenium and cadmium in diseased human lungs
Smith, M. R., Hu, X. Y., Jarrell, Z. R., He, X., Orr, M. L., Fernandes, J., Chandler, J. D., Walker, D. I., Esper, A. M., Marts, L. T., et al. (2023) · Advances in Redox Research · doi:10.1016/j.arres.2023.100065 · Cited by 15
A metabolomic signature of decelerated physiological aging in human plasma
Janssens, G. E., Grevendonk, L., Schomakers, B. V., Perez, R. Z., van Weeghel, M., Schrauwen, P., Hoeks, J., & Houtkooper, R. H. (2023) · GeroScience · doi:10.1007/s11357-023-00827-0 · Cited by 15
Age prediction from human blood plasma using proteomic and small RNA data: a comparative analysis
Salignon, J., Faridani, O. R., Miliotis, T., Janssens, G. E., Chen, P., Zarrouki, B., Sandberg, R., Davidsson, P., & Riedel, C. G. (2023) · Aging · doi:10.18632/aging.204787 · Cited by 17
Increased Pace of Aging in COVID-Related Mortality
Galkin, F., Parish, A. J., Bischof, E. Y., Zhang, J. H., Mamoshina, P., & Zhavoronkov, A. A. (2021) · Life · doi:10.3390/life11080730 · Cited by 15
Self-citationRibaudo, G., & Gianoncelli, A. (2023) · Antioxidants · doi:10.3390/antiox12020288 · Cited by 14
Liu, Y., Kang, M., Wei, W., Hui, J., Gou, Y., Liu, C. Y., Zhou, R., Wang, B., Shi, P., Liu, H., et al. (2024) · The journal of nutrition health & aging · doi:10.1016/j.jnha.2024.100271 · Cited by 14
Liu, N., Peng, S., Wei, K., Chen, Q., Chen, X., He, L., Wu, B., & Lin, Y. Q. (2025) · BMC Public Health · doi:10.1186/s12889-025-22053-3 · Cited by 15
Magalhães, S., Almeida, I., Martins, F., Camões, F., Soares, A. R., Goodfellow, B. J., Rebelo, S., & Nunes, A. (2021) · Molecules · doi:10.3390/molecules26216410 · Cited by 15
Towards a Novel Frontier in the Use of Epigenetic Clocks in Epidemiology
Martínez‐Magaña, J. J., Hurtado-Soriano, J., Rivero-Segura, N. A., Montalvo‐Ortiz, J. L., Garcia-delaTorre, P., Becerril-Rojas, K., & Gómez-Verján, J. C. (2024) · Archives of Medical Research · doi:10.1016/j.arcmed.2024.103033 · Cited by 13
Wang, X., & Ji, J. (2025) · Scientific Reports · doi:10.1038/s41598-025-98948-3 · Cited by 12
Galkin, F., Kochetov, K., Keller, M., Zhavoronkov, A. A., & Etcoff, N. L. (2022) · Aging · doi:10.18632/aging.204061 · Cited by 12
Self-citationNatural products as geroprotectors: An autophagy perspective
Raj, S. D., Fann, D. Y., Wong, E. S. P., & Kennedy, B. K. (2021) · Medicinal Research Reviews · doi:10.1002/med.21815 · Cited by 12
Changes in accelerated aging and risk of cardiovascular disease and mortality: three cohort studies
Zhang, J., Yu, H., Geng, T., Chen, S., Wang, Y., Guo, H., Zhang, X.-M., He, M., Gao, J.-L., Liu, G., et al. (2025) · BMC Medicine · doi:10.1186/s12916-025-04365-8 · Cited by 12
Secci, R., Hartmann, A., Walter, M., Grabe, H. J., der Auwera, S. V., Kowald, A., Palmer, D. H., Rimbach, G. H., Fuellen, G., & Barrantes, I. (2021) · Critical Reviews in Food Science and Nutrition · doi:10.1080/10408398.2021.1975638 · Cited by 11
Shokhirev, M. N., & Johnson, A. A. (2025) · GeroScience · doi:10.1007/s11357-025-01824-1 · Cited by 10
Song, Y., Liu, Y. S., Talarico, F., Zhang, Y., Hayward, J., Wang, M., Stroulia, E., Dixon, R. A., Greiner, R., Li, X., et al. (2023) · Gerontology · doi:10.1159/000534015 · Cited by 10
Novel Epigenetic Clock Biomarkers of Age-Related Macular Degeneration
Mallik, S., Grodstein, F., Bennett, D. A. A., Vavvas, D. G., & Lemos, B. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.856853 · Cited by 10
Harinath, G., Zalzala, S., Nyquist, A., Wouters, M. C. A., Isman, A., Moel, M., Verdin, E. M., Kaeberlein, M. R., Kennedy, B. K., & Bischof, E. Y. (2024) · Ageing Research Reviews · doi:10.1016/j.arr.2024.102293 · Cited by 9
From clock to clock: Therapeutic target discovery for aging and age-related diseases
Chen, J., Leung, G. H. D., Leung, H., Ustiugova, A. S., Shneyderman, A., Korzinkin, M., Gennert, D. G., Pun, F. W., Aliper, A. M., Ren, F., et al. (2025) · Ageing Research Reviews · doi:10.1016/j.arr.2025.102871 · Cited by 10
Self-citationSandalova, E., & Maier, A. B. (2023) · Biogerontology · doi:10.1007/s10522-023-10077-4 · Cited by 9
Kang, S., Baker, S. A., Hayhoe, B. W. J., Price, G., Novak, G., Wong, J., Middleton, L. T., & Robinson, O. (2025) · The Journal of Prevention of Alzheimer s Disease · doi:10.1016/j.tjpad.2025.100274 · Cited by 9
Adapting Blood DNA Methylation Aging Clocks for Use in Saliva Samples With Cell-type Deconvolution
Galkin, F., Kochetov, K., Mamoshina, P., & Zhavoronkov, A. A. (2021) · Frontiers in Aging · doi:10.3389/fragi.2021.697254 · Cited by 8
Self-citationPlasma Proteomics Reveals Biomarkers and Undulating Changes in Metabolic Aging
Zhang, J., Yu, H., Xiong, Y., Xue, D., Chen, S., Li, J., Li, X., Xie, J., Wang, Y., Xu, K., et al. (2025) · Research · doi:10.34133/research.1004 · Cited by 7
Scale bar of aging trajectories for screening personal rejuvenation treatments
Shen, X., Wu, B., Jiang, W. M., Li, Y., Zhang, Y., Zhao, K., Nie, N., Gong, L., Liu, Y., Zou, X., et al. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.10.021 · Cited by 7
Fingelkurts, A. A., & Fingelkurts, A. A. (2023) · Brain Sciences · doi:10.3390/brainsci13030520 · Cited by 7
Metadata
- Authors
- Galkin, F., Mamoshina, P., Aliper, A., de Magalhães, J. P., Gladyshev, V. N., & Zhavoronkov, A.
- Affiliations
- University of Liverpool (GB)
- Brigham and Women's Hospital (US)
- Buck Institute for Research on Aging (US)
- Corresponding author
- Zhavoronkov, Alex
- Venue
- Ageing Research Reviews
- Publisher
- Elsevier BV
- Published
- 2020-07
- Volume
- 60
- Pages
- 101050
- ISSN
- 1568-1637 (ISSN-L)
- OpenAlex
- W3015295307
- Cited by
- 189
- References
- 123
- Citations by year
- 2026: 15 · 2025: 36 · 2024: 45 · 2023: 40 · 2022: 26 · 2021: 22 · 2020: 5
- FWCI
- 2.97
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Ageing Research Reviews (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Telomeres, Telomerase, and Senescence
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Keywords
- aging biomarkers, aging clocks, epigenetic aging, telomere shortening, gene expression, metabolites, machine learning, deep learning, generative models, age estimation
- MeSH terms
- Machine Learning, Aging, Algorithms, Animals, Humans, Biomarkers, Neural Networks, Computer, Mice
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
