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PubMed · 42600665

Identifying biomarkers of accelerated ageing in cancer patients from routine clinical data.

Clodagh Bottomley·Evelyne Liuu·Beatriz Echarte Archanco·Danielle Harari·Tania Kalsi·Carlos de Pablo Gallega·Carly Welch
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Abstract

INTRODUCTION: Cancer and ageing have a bidirectional relationship: age is the strongest risk factor for cancer, and cancer and treatments can accelerate ageing. Therefore, biological age can differ from chronological age; biomarkers are needed to stratify interventions to minimise accelerated ageing. METHODS: PhenoAge was calculated from routine blood test results of patients attending a Geriatric Oncology clinic. PhenoAgeAccel was the residual from a regression of PhenoAge against age. RESULTS: Data were available for 173 patients (62% male). Mean PhenoAge was higher than age (84.3 (12.6) vs 76.2 (7.24), p&#x202f;<&#x202f;0.001), though the two were correlated (r&#x202f;=&#x202f;0.579, p&#x202f;<&#x202f;0.001). Unlike age, PhenoAge and PhenoAgeAccel were associated with one-year mortality (PhenoAge OR=1.083, 95% CI: 1.038-1.136; PhenoAgeAccel OR=1.096, 95% CI: 1.047-1.155). PhenoAge correlated with Clinical Frailty Score and Timed Up and Go (CFS: Rs=0.31, p&#x202f;<&#x202f;0.001; TUG: Rs=0.25, p&#x202f;<&#x202f;0.005); there were no correlations with age. PhenoAgeAccel correlated with the number of CGA interventions made (Rs=0.17, p&#x202f;<&#x202f;0.05), unlike age and PhenoAge. Patients with diabetes mellitus had a higher PhenoAgeAccel compared to those without (3.40 vs -1.71, p&#x202f;=&#x202f;0.002). In patients receiving systemic anti-cancer treatment, patients with PhenoAgeAccel calculated pre-treatment had less age acceleration than those with PhenoAgeAccel calculated post-treatment, both overall (2.18 vs -2.87; p&#x202f;=&#x202f;0.048) and in matched samples (n&#x202f;=&#x202f;21, 7.76 vs -2.87, p&#x202f;<&#x202f;0.001). CONCLUSIONS: PhenoAgeAccel is a greater predictor of risk than chronological age in older people with cancer. This makes it a promising biomarker to stratify patients for holistic geriatric assessment, dose reductions, or future geroprotective measures which could be integrated within electronic healthcare record systems.

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HumansNeoplasmsMaleFemaleAgingAgedAged, 80 and overBiomarkersGeriatric AssessmentRisk FactorsFrailtyAgeingBiological AgeCancerGeriatric oncologyPhenotypic ageJournal Article

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Clodagh Bottomley, Evelyne Liuu, Beatriz Echarte Archanco, Danielle Harari, Tania Kalsi, Carlos de Pablo Gallega, Carly Welch. 2026-08-14. Identifying biomarkers of accelerated ageing in cancer patients from routine clinical data.. https://doi.org/10.1016/j.mad.2026.112234

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Journal Article resourceAged subjectAged, 80 and over subjectAgeing subjectAging subjectBiomarkers subjectCancer subjectFemale subjectHumans subjectJournal Article subjectMale subjectNeoplasms subject

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