Development of a biomarker-enhanced arterial age model for young-to-middle-aged adults with type 2 diabetes

Direct vascular aging assessment is not always feasible in routine diabetes care. We aimed to derive a control-based arterial age reference using estimated pulse wave velocity, develop a biomarker-enhanced model for predicting arterial age gap, and evaluate its ability to identify accelerated arterial aging in young-to-middle-aged adults with type 2 diabetes mellitus. This study included 300 participants (150 T2DM, 150 age- and sex-matched controls). Arterial age was derived from a control-group ePWV-age regression. Age gap was defined as estimated arterial age minus chronological age. Candidate predictors were evaluated using multivariable linear regression, and the final model was internally validated by 10-fold cross-validation. Compared with controls, participants with T2DM had higher ePWV, older estimated arterial age, larger age gap, lower adropin, and higher oxLDL (all p < 0.001). Accelerated arterial aging was more frequent in T2DM than controls (76.0% vs. 20.0%). The final model integrating HbA1c, adropin, and oxLDL explained 42% of the variance in age gap (adjusted R²=0.418), showed good discrimination for accelerated arterial aging (AUC 0.889; 95% CI 0.828–0.910), and retained acceptable internal calibration (slope 0.943). A biomarker-enhanced arterial age model integrating HbA1c, adropin, and oxLDL provided an interpretable framework for identifying accelerated arterial aging in young-to-middle-aged adults with T2DM. Although promising for translational implementation, external validation and direct pulse wave velocity benchmarking are required before clinical application. The model also enabled a three-level arterial aging classification and web-based implementation for research use, supporting its potential as a practical risk-communication tool prototype.