C-G.42: From ageing clocks to human digital twins in personalising healthcare through biological age analysis
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Age is the most important risk factor for the majority human diseases, yet chronological age alone poorly captures the biological diversity shaping individual health trajectories. Biological age (BA) predictors derived from epigenomic, proteomic, metabolomic, and clinical biochemistry data offer a promising way to quantify ageing as a dynamic, measurable process. In this study, we demonstrate the value of BA analysis within the IAM Frontier cohort, a deeply phenotyped 13-month longitudinal study of 30 healthy adults aged 45–59 years. Across repeated time points, we computed BA and health-related predictions using 29 epigenetic, 4 clinical-biochemistry, 2 proteomic, and 3 metabolomic clocks, alongside gold-standard clinical risk indicators.
Our findings show that ageing signatures differ markedly between individuals while remaining relatively stable within individuals, with epigenetic clocks providing the most consistent long-term signals and clinical/proteomic clocks showing greater sensitivity to short-term physiological change. Subject-level analyses revealed that multi-omics BA profiles could highlight subtle deviations, including smoking-related risk, abnormal lipid profiles, shortened telomere predictions, and immune-cell composition changes, several of which aligned with clinical or self-reported health indicators.
These results position BA predictors not merely as retrospective ageing measures, but as actionable biomarkers for preventive and personalised medicine. Integrated into human digital twin frameworks, longitudinal BA measurements could anchor real-time models of individual health, detect early departures from expected trajectories, and support simulation of lifestyle or therapeutic interventions. This work underscores the potential of multi-omics ageing clocks to complement routine clinical testing and advance scalable, adaptive, and biologically informed digital twins for precision healthcare.
Co-authors: Gokhan Ertaylan, Olivier Thas, Simone Ecker, Stephan Beck
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