The use of biological age calculators in clinical practice
https://doi.org/10.37586/2949-4745-2-2025-60-66
Abstract
Biological age (BA) is defined as an integrative indicator reflecting the degree of organismal aging and biological wear of physiological systems. In contrast to chronological age, BA is a potentially modifiable variable and may serve as a biomarker of geroprotective intervention efficacy. Recent advances have enabled the development ofBA calculators based onclinical and laboratory data, epigenetic modifications, immune signatures, microbiome, and multi-omics profiles. This article reviews various approaches to BA assessment, including epigenetic clocks (Horvath, GrimAge), phenotypic indices (PhenoAge, frailty index), immune aging models (iAge), and calculators derived from standard blood tests. The present review was prepared by conducting a comprehensive literature search utilising the PubMed and Scopus databases. A comprehensive search was conducted of original and review papers published primarily between 2010 and 2024, the focus of which was the description of BA estimation methods, their predictive utility, and clinical applicability. The review discusses the potential for integrating BA assessment into clinical practice and personalised medicine, as well as the need for further validation and standardisation of these tools across populations.
About the Authors
A. K. IlyushchenkoРоссия
Ilyushchenko Anna Konstantinovna
Moscow
A. A. Melnitskaya
Россия
Moscow
A. E. Veriaskina
Россия
Moscow
L. V. Matchekhina
Россия
Moscow
References
1. López-Otín C., Blasco MA, Partridge L., Serrano M., Kroemer G. Hallmarks of aging: An expanding universe. Cell. 2023; 186 (2): 243–278. DOI: 10.1016/j.cell.2022.11.001.
2. Klemera P., Doubal S. A new approach to the concept and computation of biological age. Mech Ageing Dev. 2006; 127 (3): 240–248. DOI: 10.1016/j.mad.2005.10.004.
3. Cawthon R .M., Smith K. R., O'Brien E., Sivatchenko A., Kerber R. A. Association between telomere length in blood and mortality in people aged 60 years or older. Lancet. 2003; 361(9355): 393–395. DOI: 10.1016/S0140-6736(03)12384-7.
4. Horvath S. DNA methylation age of human tissues and cell types [published correction appears in Genome Biol. 2015 May 13; 16: 96. DOI: 10.1186/s13059-015-0649-6. Genome Biol. 2013; 14 (10): R115. DOI: 10.1186/gb-2013-14-10-r115.
5. Hannum G., Guinney J., Zhao L., et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell. 2013; 49 (2): 359–367. DOI: 10.1016/j.molcel.2012.10.016.
6. Belsky D. W., Caspi A., Corcoran D. L., et al. DunedinPACE, a DNA methylation biomarker of the pace of aging. Elife. 2022; 11: e73420. Published 2022 Jan 14. DOI: 10.7554/eLife.73420.
7. Levine M. E. Modeling the rate of senescence: can estimated biological age predict mortality more accurately than chronological age? J Gerontol A Biol Sci Med Sci. 2013; 68 (6): 667–674. DOI: 10.1093/gerona/gls233.
8. Marioni R. E., Shah S., McRae A. F., et al. DNA methylation age of blood predicts all-cause mortality in later life. Genome Biol. 2015; 16 (1): 25. Published 2015 Jan 30. DOI: 10.1186/s13059-015-0584-6.
9. Krištić J., Vučković F., Menni C., et al. Glycans are a novel biomarker of chronological and biological ages. J Gerontol A Biol Sci Med Sci. 2014; 69 (7): 779–789. DOI: 10.1093/gerona/glt190.
10. Rode L., Nordestgaard B. G., Bojesen S. E. Peripheral blood leukocyte telomere length and mortality among 64, 637 individuals from the general population. J Natl Cancer Inst. 2015; 10 7(6): djv074. Published 2015 Apr 10. DOI: 10.1093/jnci/djv074.
11. Jylhävä J., Pedersen N. L., Hägg S. Biological Age Predictors. EBioMedicine. 2017; 21: 29–36. DOI: 10.1016/j.ebiom.2017.03.046.
12. Horvath S., Raj K. DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat Rev Genet. 2018; 19 (6): 371–384. DOI: 10.1038/s41576-018-0004-3.
13. Yamada H. Epigenetic Clocks and EpiScore for Preventive Medicine: Risk Stratification and Intervention Models for Age-Related Diseases. J Clin Med. 2025; 14 (10): 3604. Published 2025 May 21. DOI: 10.3390/jcm14103604.
14. Sayed N., Huang Y., Nguyen K., et al. An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging [published correction appears in Nat Aging. 2021 Aug; 1 (8): 748. DOI: 10.1038/s43587-021-00102-x. Nat Aging. 2021; 1: 598–615. DOI: 10.1038/s43587-021-00082-y.
15. Galkin F., Mamoshina P., Aliper A., et al. Human Gut Microbiome Aging Clock Based on Taxonomic Profiling and Deep Learning. iScience. 2020; 23 (6): 101199. DOI: 10.1016/j.isci.2020.101199.
16. Tanaka T., Biancotto A., Moaddel R., et al. Plasma proteomic signature of age in healthy humans. Aging Cell. 2018; 17 (5): e12799. DOI: 10.1111/acel.12799.
17. Huh J. Y., Ross G. W., Chen R., et al. Total and differential white blood cell counts in late life predict 8-year incident stroke: the Honolulu Heart Program. J Am Geriatr Soc. 2015; 63 (3): 439–446. DOI: 10.1111/jgs.13298.
18. Wang Q., Zhan Y., Pedersen N. L., Fang F., Hägg S. Telomere Length and All-Cause Mortality: A Meta-analysis. Ageing Res Rev. 2018; 48: 11–20. DOI: 10.1016/j.arr.2018.09.002.
19. Vetter V. M., Meyer A., Karbasiyan M., Steinhagen-Thiessen E., Hopfenmüller W., Demuth I. Epigenetic Clock and Relative Telomere Length Represent Largely Different Aspects of Aging in the Berlin Aging Study II (BASE-II). J Gerontol A Biol Sci Med Sci. 2019; 74 (1): 27–32. DOI: 10.1093/gerona/gly184.
20. Lu A. T., Quach A., Wilson J. G., et al. DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging (Albany NY). 2019; 11 (2): 303–327. DOI: 10.18632/aging.101684.
21. Lu A. T., Binder A. M., Zhang J., et al. DNA methylation GrimAge version 2. Aging (Albany NY). 2022; 14 (23): 9484–9549. DOI: 10.18632/aging.204434.
22. Lehallier B., Gate D., Schaum N., et al. Undulating changes in human plasma proteome profiles across the lifespan. Nat Med. 2019; 25 (12): 1843–1850. DOI: 10.1038/s41591-019-0673-2.
23. Gialluisi A., Santoro A., Tirozzi A., et al. Epidemiological and genetic overlap among biological aging clocks: New challenges in biogerontology. Ageing Res Rev. 2021; 72: 101502. DOI: 10.1016/j.arr.2021.101502.
24. Fahy G. M., Brooke R. T., Watson J. P., et al. Reversal of epigenetic aging and immunosenescent trends in humans. Aging Cell. 2019; 18 (6): e13028. DOI: 10.1111/acel.13028.
25. Zhavoronkov A., Mamoshina P. Deep Aging Clocks: The Emergence of AI-Based Biomarkers of Aging and Longevity. Trends Pharmacol Sci. 2019; 40 (8): 546–549. DOI: 10.1016/j.tips.2019.05.004.
26. Prattichizzo F., Frigé C., Pellegrini V., et al. Organ-specific biological clocks: Ageotyping for personalized anti-aging medicine. Ageing Res Rev. 2024; 96: 102253. DOI: 10.1016/j.arr.2024.102253.
27. Forrester S. N., Baek J., Hou L., Roger V., Kiefe C. I. A Comparison of 5 Measures of Accelerated Biological Aging and Their Association With Incident Cardiovascular Disease: The CARDIA Study. J Am Heart Assoc. 2024; 13 (8): e032847. DOI: 10.1161/JAHA.123.032847.
28. Furrer R., Handschin C. Biomarkers of aging: from molecules and surrogates to physiology and function. Physiol Rev. 2025; 105 (3): 1609–1694. DOI: 10.1152/physrev.00045.2024.
Review
For citations:
Ilyushchenko A.K., Melnitskaya A.A., Veriaskina A.E., Matchekhina L.V. The use of biological age calculators in clinical practice. Problems of Geroscience. 2025;(2):60-66. (In Russ.) https://doi.org/10.37586/2949-4745-2-2025-60-66
JATS XML














.jpg)