Longevity & AgingDigital Twins Could Predict Your Aging Trajectory and Test Interventions Before You Try Them
Wearables and biomarker testing generate enormous personal health data, but interpreting what that data actually means for aging is notoriously difficult. A new review proposes a formal digital twin framework that models aging as a dynamic, evolving system rather than a snapshot of static biomarkers. By integrating longitudinal data on cardiometabolic markers, inflammatory signals, functional performance, and epigenetic age, the framework builds a personalized computational model — a digital twin — that can simulate aging trajectories, test hypothetical interventions, and distinguish short-lived biomarker improvements from genuine, durable changes in how a person ages. The authors outline the model architecture, identify key biological state variables, and describe how different physiological systems interact within the model. This approach could eventually allow clinicians and individuals to run virtual experiments on their own biology before committing to real-world interventions.
