Biological Age Acceleration Predicts Graft Failure and Mortality in Kidney Transplant Recipients: Evidence From the Transplant Lines Study

Background. Chronological age is widely used in kidney transplantation but inadequately captures interindividual differences in physiological aging. Biological age, reflecting multisystem functional decline, may better identify vulnerability and long-term risk; however, its prognostic value in kidney transplant recipients (KTRs) remains insufficiently defined. Methods. Clinically stable KTRs were recruited from the TransplantLines Study; biological age was estimated using phenotypic age, Klemera-Doubal Method Age, and Generalized Optimal Linear Differentiation biological age. Prognostic value for graft failure and all-cause mortality was evaluated using time-dependent integrated area under the curve (iAUC), multivariable Cox models, spline analyses, and optimal cutpoint selection. Results. A total of 688 KTRs were included. The median age was 59.3 y (interquartile range [IQR], 47.9–68.2 y), and 62.6% were male. Over a median follow-up of 36 mo (IQR, 20–63 mo), 72 graft failures and 73 deaths occurred. Generalized Optimal Linear Differentiation biological age showed the strongest correlation with chronological age ( r = 0.963) and the highest discriminative performance for graft failure (iAUC = 0.819) and mortality (iAUC = 0.861). Biological age acceleration, with a median of 4.4 y (IQR, 1.7–8.0 y), was significantly associated with graft failure (hazard ratio, 1.07; 95% confidence interval, 1.03-1.12) and patient survival (hazard ratio, 1.10; 95% confidence interval, 1.05-1.14) with each 1-y increase after multivariable adjustment. Spline analyses demonstrated graded dose-response relationships. Optimal biological age difference thresholds were 6.68 y for graft failure and 3.98 y for mortality (log-rank P < 0.0001). Conclusions. Biological age acceleration is strongly associated with graft failure and mortality, providing prognostic information beyond chronological age and supporting its potential role in individualized posttransplant risk stratification.