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September 10, 2025npj Digital MedicineOpen Access

From ageing clocks to human digital twins in personalising healthcare through biological age analysis

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Authors

MPMurih PusparumOTOlivier ThasSBStephan Beck

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Overview

Observation reveals biological age predicts health outcomes in a longitudinal cohort, suggesting applications in digital twin models.

Key Points

  • Biological age, derived from various biomarkers, can effectively predict health outcomes over time.
  • The study analyzed 29 epigenetics, 4 clinical-biochemistry, 2 proteomics, and 3 metabolomics clocks to derive biological age.
  • Continuous monitoring via digital twin frameworks supports individualized treatment plans and health trajectory predictions.
  • Findings indicate that biological age is stable within individuals but varies significantly between them, underlining its biomarker potential.

Cite This Study

Pusparum et al. (2025) studied this question.

synapsesocial.com/papers/68c1ce5d54b1d3bfb60f51c2https://doi.org/10.1038/s41746-025-01911-9
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Making Biological Ageing Clocks Personal2024
  2. 2Biological aging clocks as biomarkers of human aging: Biological basis, methodological design, and epidemiological implications2026
  3. 3Machine Learning Approaches for Biological Age Estimation: Narrative Review of Non-Invasive and Cost-Effective Methodologies2025
  4. 4Artificial intelligence approaches in biological age prediction: current status and challenges2026
  5. 5Tracking DNA methylation-based biological age over 8 years and its association with mortality in community-dwelling older adults2026