New biomarker in nomogram model for predicting diabetic neuropathy : a cross-sectional study

Background The number of patients with diabetic peripheral neuropathy (DPN) is increasing steadily. Currently, no reliable method exists to detect or prevent DPN at an early stage. This study aimed to develop a practical clinical model to help clinicians identify T2DM patients who are at risk of developing DPN and to facilitate early intervention. Methods We conducted a cross‑sectional study of 122 T2DM patients, with and without DPN, between 1 June and 31 December 2022. Indices of glucose and lipid metabolism, UDP‑glucose ceramide glucosyltransferase (Ugcg), fasting C‑peptide and demographic variables were compared and screened with logistic regression analysis. A nomogram was then constructed. Model discrimination, calibration and clinical utility were assessed with the area under the receiver operating characteristic curve (AUC), calibration plots and decision curve analysis (DCA). Main results: Multivariate analysis yielded a nomogram comprising age (OR 1.076, 95% CI 1.028–1.126; P = 0.002), alcohol consumption (OR 4.073, 95% CI 1.349–12.297; P = 0.013), HDL‑C (OR 6.024, 95% CI 1.072–33.847; P = 0.041), Ugcg (OR 0.979, 95% CI 0.963–0.994; P = 0.007) and fasting C‑peptide (OR 0.591, 95% CI 0.365–0.956; P = 0.032). The nomogram achieved an AUC of 0.85 (95% CI 0.78–0.91; P < 0.001). Calibration curves demonstrated good agreement between predicted and observed values (mean absolute error = 0.03). Decision curve analysis indicated maximal clinical benefit at a threshold probability of approximately 50%. Conclusion A Ugcg‑inclusive nomogram can identify diabetic patients who are at high risk of developing DPN. It also provides clinicians with a theoretical basis for early intervention.