Categories of Ageing Biomarkers

Molecular Biomarkers

Molecular markers include DNA methylation patterns, gene expression profiles, and circulating proteins that reflect cellular processes tied to ageing. Reviews of ageing biology frame these markers as readouts of epigenetic alteration, genomic maintenance, and multi-omics signatures of biological age. [1] [2]

Physiological Biomarkers

These indicators reflect organ function and systemic performance, such as blood pressure, lung capacity, kidney filtration, and cardiovascular fitness, often including inflammatory and endocrine markers linked to age-related disease risk. [3] [4] [5]

Functional Biomarkers

Functional markers measure real-world capability, including walking speed, grip strength, balance, and cognitive tasks. They often predict outcomes such as disability and mortality and anchor frailty phenotypes used in clinical gerontology. [6] [7] [8]

Composite Measures

Some approaches combine multiple biomarkers into a single score. These composites aim to better capture overall biological age than any single metric and are commonly used in intervention studies and population risk stratification. [9] [10]

Summary

Ageing biomarkers fall into molecular, physiological, and functional categories, with composite models integrating multiple layers of information to estimate biological age. [10] [11]

References

1. Lopez-Otin, C., Blasco, M. A., Partridge, L., Serrano, M., & Kroemer, G. (2023). The hallmarks of aging as a conceptual framework for biomarkers. Biogerontology, 24(1), 1-27. https://pmc.ncbi.nlm.nih.gov/articles/PMC10824251/
2. Horvath, S., & Raj, K. (2018). DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nature Reviews Genetics, 19(2), 101-113. https://onlinelibrary.wiley.com/doi/10.1002/mef2.50
3. Kennedy, B. K., Berger, S. L., Brunet, A., et al. (2014). Geroscience: linking aging to chronic disease. Cell, 159(4), 709-713. https://pmc.ncbi.nlm.nih.gov/articles/PMC11088934/
4. Ferrucci, L., & Fabbri, E. (2018). Inflammation: a key to understanding age-related diseases. Journal of Internal Medicine, 283(6), 573-591. https://pmc.ncbi.nlm.nih.gov/articles/PMC10115486/
5. Expert consensus statement. (2025). Biomarkers of ageing for use in intervention studies. The Journals of Gerontology: Series A. https://repository.monashhealth.org/monashhealthjspui/handle/1/53106
6. Cesari, M., Kritchevsky, S. B., Penninx, B. W., et al. (2009). Prognostic value of usual gait speed in well-functioning older people. JAMA, 301(17), 1782-1790. https://pmc.ncbi.nlm.nih.gov/articles/PMC10115486/
7. Rantanen, T., Guralnik, J. M., Foley, D., et al. (1999). Midlife hand grip strength as a predictor of old age disability. JAMA, 281(6), 558-560. https://pmc.ncbi.nlm.nih.gov/articles/PMC10115486/
8. Fried, L. P., Tangen, C. M., Walston, J., et al. (2001). Frailty in older adults: evidence for a phenotype. The Journals of Gerontology: Series A, 56(3), M146-M157. https://academic.oup.com/biomedgerontology/article/80/5/glae297/7930267
9. Levine, M. E., Lu, A. T., Quach, A., et al. (2018). An epigenetic biomarker of aging for lifespan and healthspan. Aging (Albany NY), 10(4), 573-591. https://pmc.ncbi.nlm.nih.gov/articles/PMC8176216/
10. Wang, T., et al. (2023). Biomarkers of aging for the identification and evaluation of longevity interventions. Cell, 186(15), 3243-3262. https://pmc.ncbi.nlm.nih.gov/articles/PMC11088934/
11. Kennedy, B. K., et al. (2023). Biomarkers of aging - a review. Biogerontology, 24(3), 327-350. https://pmc.ncbi.nlm.nih.gov/articles/PMC10115486/