SGLT2 inhibition, uric acid and inflammation in glycemic burden-related progression of cardiac autonomic neuropathy in type 2 diabetes mellitus: insights from machine learning and mediation analysis

Abstract

Objective To identify independent predictors of diabetic cardiac autonomic neuropathy (DCAN) deterioration in patients with type 2 diabetes mellitus (T2DM), develop machine learning (ML)-based risk prediction models, and explore the underlying mediation of glycemic burden. Methods This prospective cohort study included 293 T2DM patients who underwent standardized Ewing testing at baseline and follow-up. Univariable and multivariable logistic regression were utilized to identify predictors. Restricted cubic splines (RCS) were applied to evaluate non-linear relationships. Nine ML algorithms were developed and evaluated using AUC and calibration metrics, with SHAP values illustrating feature importance. Mediation analysis was performed to investigate whether longitudinal biomarker changes accounted for the association between HbA1c and DCAN deterioration. Results During follow-up, 80 patients (27.3%) experienced DCAN deterioration. Multivariable logistic regression identified elevated HbA1c at baseline as an independent risk factor, while SGLT2 inhibitor use was significantly associated with a lower risk of deterioration. RCS analysis revealed a continuous linear risk increase for HbA1c at baseline, whereas the platelet-to-lymphocyte ratio (PLR) at baseline exhibited an inverted U-shaped relationship. Among the ML algorithms, KNN achieved the highest AUC among models meeting the prespecified calibration criterion (AUC = 0.913; Brier score = 0.084; calibration slope = 0.701; calibration intercept = −0.053). SHAP analysis confirmed SGLT2 inhibitor use, HbA1c, and PLR as the top three predictors. Mediation analysis demonstrated that longitudinal increases in uric acid (ΔUA) and white blood cell count (ΔWBC) accounted for 20.6% and 12.9% of the association between HbA1c and DCAN progression, respectively. Conclusions SGLT2 inhibitor use is significantly associated with a lower risk of DCAN deterioration in T2DM patients, whereas elevated HbA1c levels correlate with disease progression, potentially involving pathways of exacerbated uric acid metabolism and systemic inflammation. Furthermore, the KNN-based ML model serves as a promising proof-of-concept tool for clinical risk stratification that warrants future external validation.