September 10, 2025npj Digital MedicineOpen Access

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

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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.

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

Pusparum et al. (2025) studied this question.

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