Progressive high-risk biological aging linked to increased urolithiasis risk in Chinese cohort.

Biological aging varies significantly between individuals, yet how its longitudinal dynamics influence urolithiasis risk remains poorly understood. This investigation sought to delineate distinct biological aging trajectories and quantify the mediating role of metabolic dysfunction in incident stone formation. We analyzed longitudinal physiological data from 27, 948 participants who underwent routine health screenings at the Affiliated Hospital of Yangzhou University (HMC-AHYU) between 2022 and 2024. The kml3d unsupervised machine learning algorithm was employed to identify latent trajectories of biological aging based on the Klemera-Doubal method (KDM) and Homeostatic Dysregulation (HD). We used Cox proportional hazards models and causal mediation analyses to evaluate the association between aging patterns and incident urolithiasis. Trajectory modeling delineated four distinct patterns of biological aging: Stable Low-Risk (Class A), Stable Moderate-Risk (Class B), Remissive High-Risk (Class C), and Progressive High-Risk (Class D). Compared to the Stable Low-Risk group, the Progressive High-Risk group was associated with an increased risk of lithogenesis in the unadjusted model (HR = 1.21, 95% CI: 1.00–1.45, P = 0.047), which was fully attenuated after adjusting for metabolic factors (HR = 1.14, 95% CI: 0.95–1.37, P = 0.170). Causal mediation analysis confirmed that this association was predominantly driven by BMI (22.9%, P Longitudinal biological aging trajectories are robust early predictors of urolithiasis. While progressive aging increases risk via metabolic factors (obesity and hypertension), the remissive trajectory suggests that physiological recovery is possible. Targeting these metabolic mediators may offer an effective strategy for stone prevention.