What if aging could be measured more meaningfully than by simply counting birthdays?
The Core AI & Digital Biomarker, Acoustic and Inflammatory Biomarkers (ADAMMA) is excited to announce a new research collaboration with CARE Preventive AG, focused on advancing biological age estimation and personalized approaches to healthy aging: Biological Age Prediction based on Blood Biomarkers – A Comparative Study of Linear and Non-linear Predictive Models (BABBI)
Chronological age captures only part of the picture. Individuals of the same age can follow very different health trajectories and face very different risks. By combining longitudinal blood biomarker data, real-world intervention data, and advanced predictive models – ranging from classical linear approaches to machine-learning methods – this project aims to better understand biological aging and how it changes over time.
The BABBI project focuses on improving how biological age (BA) is estimated using routinely collected blood biomarkers, moving beyond chronological age as a proxy for health and aging. Because individuals of the same chronological age can have very different health trajectories and mortality risks, BA has emerged as a more informative indicator of physiological aging. BABBI builds on established blood-based aging measures such as Klemera–Doubal Age and PhenoAge, while addressing their limitations by systematically comparing traditional linear models with more flexible non-linear and machine-learning approaches trained on large public datasets and applied to real-world data from Care Preventive AG (CARE), a Swiss preventive healthcare platform.
