A machine learning-derived biological age model for liver grafts provides a superior assessment of aging compared to chronological age in transplantation

Liver transplantation is severely restricted by shortages of donors, yet relying on chronological age (CA) for donor selection often results in potentially viable grafts being discarded. This study developed a machine learning-based framework to predict the biological age (BA) of liver grafts and evaluate its clinical utility. Data from 247 donors were analyzed to create BA models using conventional linear methods and four genetic algorithm-optimized machine learning models. These models were then validated in an independent temporal cohort of 82 donor-recipient pairs. While multiple linear regression (MLR) showed a higher correlation (r = 0.82), the gradient boosting (GB) model captured unique nonlinear biological signals. Importantly, BA derived from the GB model demonstrated superior clinical relevance, serving as an independent predictor of biliary complications (AUC = 0.80) and graft survival, whereas CA failed to predict these outcomes. Multivariable Cox regression analysis revealed that accelerated ageing, defined as a BA exceeding CA, was an independent risk factor for graft loss (adjusted hazard ratio = 4.188). This exploratory study suggests that BA captures the aging heterogeneity of liver grafts which cannot be identified by the CA of donors. Furthermore, the genetic algorithm-optimized model shows potential in predicting biliary complications and survival outcomes.