Background Accelerated biological aging (BioAgeAccel) has been implicated in type II diabetes (T2D) mellitus development; however, its dynamic changes and their links to T2D incidence, mortality and glycemic traits remain unclear.

Methods Leveraging repeated measures from the UK Biobank, we first calculated two BioAgeAccel metrics (KDMAccel and PhenoAgeAccel) and derived three burdens (slope, cumulative, and relative cumulative change). We then assessed associations of BioAgeAccel transitions and these burdens with incident T2D and mortality. Secondary analyses extended the two primary outcomes by incorporating glucose, HbA1c, and six IR surrogates, which were also evaluated as potential mediators.

Results Among 13,751 included participants, 412 (3.0%) new T2D cases and 609 (4.4%) all-cause deaths were identified within a median follow-up of 9.5 years. Dynamic transition from non-accelerated to accelerated aging was markedly related to elevated T2D risk (KDMAccel: HR=1.65 [1.24∼2.20]; PhenoAgeAccel: HR=1.50 [1.12∼2.00]) and all-cause mortality risk (KDMAccel: HR=1.32 [1.06∼1.64]; PhenoAgeAccel: HR=2.17 [1.73∼2.71]). BioAgeAccel burdens demonstrated dose-response effects, with cumulative BioAgeAccel showing the greatest influence on T2D (KDMAccel: HR=1.25 [1.03∼1.51]; PhenoAgeAccel: HR=1.26 [1.06∼1.49]) and all-cause mortality (KDMAccel: HR=1.25 [1.07∼1.47]; PhenoAgeAccel: HR=1.51 [1.31∼1.74]). Similar association patterns were observed for all the eight glycemic traits. Mediation analyses revealed that these glycemic traits on average mediated 19∼32% of the KDMAccel burden-T2D effect and 16∼24% of the PhenoAgeAccel burden-T2D effect. Incorporating BioAgeAccel burden into FINDRISC significantly enhanced prediction accuracy, reaching up to 10.9% improvement in some specific aging transition statuses.

Conclusion Dynamic biological aging trajectories and BioAgeAccel burdens are independently related to elevated risks of T2D and all-cause mortality, partly via glycemic dysregulation, highlighting biological aging as a potential intervention target.

The authors have declared no competing interest.

The research of Ping Zeng was supported in part by the National Natural Science Foundation of China (82173630 and 81402765), the Natural Science Foundation of Jiangsu Province of China (BK20241952), the QingLan Research Project of Jiangsu Province for Young and Middle-aged Academic Leaders, the Six-Talent Peaks Project in Jiangsu Province of China (WSN-087), and the Training Project for Youth Teams of Science and Technology Innovation at Xuzhou Medical University (TD202008). The research of Yu Yan was supported by the Students' Research and Innovation Project of National Experimental Teaching Demonstration Center of Basic Medicine at Xuzhou Medical University (2024BMS26).

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All data generated or analyzed during this study are included in this published article and its supplementary information files. This study used the UK Biobank resource with the application ID 88159. Researchers can access to the UK Biobank by submitting an application to the UK Biobank official website (https://www.ukbiobank.ac.uk/).