Biological Age Acceleration, Cancer Risk and Post-Diagnosis Multimorbidity

Primary Supervisor: Professor Nora Pashayan

Supervisory Team: Professor Antonis Antoniou

Supervisor's email: np275@cam.ac.uk 

Project Outline

Ageing and cancer share common biological processes, yet the mechanisms linking them remain poorly understood. Biological age acceleration, where an individual’s biological age exceeds their chronological age, predicts mortality, frailty and major chronic diseases (1–4), but it is still unclear whether accelerated ageing causally increases cancer risk or instead reflects accumulated lifestyle, environmental and socioeconomic exposures. Distinguishing between these explanations is essential for determining whether biological ageing represents a modifiable pathway or simply a risk indicator.

Biological age aims to quantify an individual’s physiological or molecular state relative to their chronological age.  Multiple classes of ageing clocks have been developed to capture different facets of biological decline, including epigenetic clocks based on DNA methylation (1,2), proteomic clocks using circulating proteins (5), transcriptomic clocks based on gene expression (6), and multidomain constructs built from clinical biomarkers, such as PhenoAge and Klemera–Doubal Biological Age (3,4).  Molecular hallmarks of ageing, such as genomic instability, mitochondrial dysfunction and chronic inflammation are implicated in both carcinogenesis and age-related multimorbidity (7,8), suggesting shared upstream biology.

Studies have shown that cancer survivors exhibit elevated biological age and ageing-related metabolic abnormalities compared with cancer-free populations (9, 10), reflecting increased vulnerability to age-related diseases.  Furthermore, a bidirectional relationship has been proposed: accelerated biological ageing may predispose individuals to cancer, while cancer and its treatments may induce therapy-related acceleration of ageing processes (11).  

Yet it remains unclear whether measuring biological ageing meaningfully improves prediction of who will develop cancer earlier, and who will experience faster health decline after diagnosis. Addressing this gap is essential for informing tailored cancer prevention, early detection strategies and survivorship care.

Project Plan

This project will use UK Biobank biomarker data linked to cancer registry, hospital and mortality data, together with lifestyle and socioeconomic measures required to construct and analyse biological ageing metrics.

The project will address three research areas:

1. 1. Association between biological age acceleration and incident cancer

To assess whether biological age acceleration is associated with incident cancer independently of chronological age, lifestyle factors and socioeconomic position.

1. 2. Association between biological age acceleration and post-diagnosis comorbidity accumulation

To evaluate whether individuals with higher biological age acceleration experience faster accumulation of age-related comorbidities after a cancer diagnosis.

1. 3. Evaluate the added clinical or public-health relevance of biological ageing measures

To explore whether incorporating biological age acceleration into cancer-related analyses meaningfully improves risk stratification, early identification of vulnerable subgroups, or understanding of inequalities in cancer outcomes.

Main Methods to be Used

• Literature review to summarise current evidence on biological ageing measures, cancer risk, and multimorbidity.
• Computation of biological age and biological age acceleration using multiple established metrics.
• Survival analysis  to estimate associations between biological age acceleration and incident cancer.
• Recurrent-event analysis  to examine the relationship between biological age acceleration and the accumulation of comorbidities after cancer diagnosis.
• Scenario analysis to illustrate the potential clinical or population impact of biological ageing by modelling hypothetical risk profiles (e.g., individuals with high vs. low biological age acceleration) and projecting differences in cancer risk or post-diagnosis health trajectories.

Key References

1. Horvath S, Raj K. DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat Rev Genet. 2018;19:371–84.
2. Levine ME, Lu AT, Quach A, Chen BH, Assimes TL, Bandinelli S, et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging. 2018;10:573–91.
3. Liu Z, Kuo P-L, Horvath S, Crimmins E, Ferrucci L, Levine ME. A new aging measure captures morbidity and mortality risk across diverse subpopulations. Aging. 2018;10:1924–41.
4. Levine ME. Modeling the rate of senescence: PhenoAge and KDM Biological Age. Aging. 2018;10:3244–65.
5. Lehallier B, Gate RE, Schaum N, Nanasi T, Lee SE, Yousef H, et al. Undulating changes in human plasma proteome across lifespan. Nat Med. 2019;25:1843–50.
6. Peters MJ, Joehanes R, Pilling LC, Schurmann C, Conneely KN, Powell J, et al. The transcriptional landscape of age in human peripheral blood. Nat Commun. 2015;6:8570.
7. López-Otín C, Blasco MA, Partridge L, Serrano M, Kroemer G. Hallmarks of aging: An expanding universe. Cell. 2023;186:22–55.
8. Yoshida R, Sasaki Y, Inoue K, Maeda T. Biological mechanisms linking aging and cancer. Clin Sci. 2020;134:1669–88.
9. Ness KK, Krull KR, Jones KE, Mulrooney DA, Armstrong GT, Green DM, et al. Physiologic frailty as a sign of accelerated aging among adult survivors of childhood cancer. J Clin Oncol. 2013;31:4496–503.
10. Wang Z, Xu J, Peng X, Zeng X, Wang W, Xiang Y, et al. Epigenetic age acceleration and adverse health outcomes among adult cancer survivors. J Natl Cancer Inst. 2021;113:1383–91.
11. Zaujan NAM, Shahril MR, Shahar S, Hanif EAM, Ab Muin NF, et al. Comparative analysis of ageing-related metabolic and biological markers between cancer survivors and healthy populations: systematic review. J Cancer Prev. 2025; 30(3): 121-137.