{
  "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 < 0.001), though the two were correlated (r = 0.579, p < 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 < 0.001; TUG: Rs=0.25, p < 0.005); there were no correlations with age. PhenoAgeAccel correlated with the number of CGA interventions made (Rs=0.17, p < 0.05), unlike age and PhenoAge. Patients with diabetes mellitus had a higher PhenoAgeAccel compared to those without (3.40 vs -1.71, p = 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 = 0.048) and in matched samples (n = 21, 7.76 vs -2.87, p < 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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "name": "Clodagh Bottomley"
    },
    {
      "name": "Evelyne Liuu"
    },
    {
      "name": "Beatriz Echarte Archanco"
    },
    {
      "name": "Danielle Harari"
    },
    {
      "name": "Tania Kalsi"
    },
    {
      "name": "Carlos de Pablo Gallega"
    },
    {
      "name": "Carly Welch"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fc18t0h08vx20.bib",
  "cited_by": 0,
  "doi": "10.1016/j.mad.2026.112234",
  "fulltext_url": null,
  "id": "fc18t0h08vx20",
  "issns": [
    "0047-6374",
    "1872-6216"
  ],
  "landing_url": "https://hal.science/hal-05773712v1",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.sdv"
    ],
    [
      "hal",
      "1.sdv.can"
    ],
    [
      "openalex",
      "2717|Frailty in Older Adults"
    ]
  ],
  "openalex": "W7127930771",
  "pdf_url": "https://www.sciencedirect.com/science/article/pii/S0047637426000862/pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/fc18t0h08vx20.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:45Z",
      "native_id": "hal-05773712",
      "source": "hal",
      "url": "https://hal.science/hal-05773712v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "hal:0.sdv",
      "subfield": "general"
    },
    {
      "field": "medicine",
      "origin": "hal:1.sdv.can",
      "subfield": "oncology"
    }
  ],
  "title": "Identifying biomarkers of accelerated ageing in cancer patients from routine clinical data",
  "title_text": "Identifying biomarkers of accelerated ageing in cancer patients from routine clinical data",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:45Z",
  "url": "https://research.draconitic.com/paper/fc18t0h08vx20",
  "venue": "Mechanisms of Ageing and Development",
  "versions": []
}