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| A prediction model for diabetic peripheral neuropathy among patients with type 2 diabetes mellitus |
| LIU Mingkun1, ZHANG Fengxiang1, HAN Caijing1, WANG Xia1, CHEN Shikun1, JIN Mei1,2, SUN Jinyue1
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1. School of Public Health, Shandong Second Medical University, Weifang, Shandong 261053, China;
2. Affiliated Hospital of Shandong Second Medical University, Weifang, Shandong 261000, China |
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Abstract Objective To establish a risk prediction model for diabetic peripheral neuropathy (DPN) among patients with type 2 diabetes mellitus (T2DM), so as to provide a basis for DPN prevention and control. Methods T2DM inpatients aged 18-65 years admitted to the department of endocrinology and metabolism at Affiliated Hospital Shandong Second Medical University from April to December 2024 were selected as study subjects. Age, T2DM duration, hypertension history, 25-hydroxyvitamin D, serum C-peptide, and high density lipoprotein cholesterol (HDL-C) were collected through electronic medical records. Risk predictors of DPN among T2DM patients were screened using multivariable logistic regression model, and a nomogram was established. The receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis were employed to evaluate the discrimination, calibration and clinical utility of the nomogram, respectively. Results A total of 598 T2DM patients were enrolled, including 359 (60.03%) males and 239 (39.97%) females. The median age was 54.50 (interquartile range, 15.00) years, the median T2DM duration was 6.00 (interquartile range, 9.00) years. There were 262 cases of T2DM patients with DPN, accounting for 43.81%. Multivariable logistic regression identified hypertension history (OR=3.260, 95%CI: 2.220-4.790), alcohol use history (OR=2.150, 95%CI: 1.390-3.310), diabetes complications (OR=0.430, 95%CI: 0.270-0.680), T2DM duration (OR=1.040, 95%CI: 1.010-1.070), body mass index (OR=1.130, 95%CI: 1.070-1.200), 25-hydroxyvitamin D (OR=0.930, 95%CI: 0.910-0.960), and HDL-C (OR=0.400, 95%CI: 0.230-0.720) as risk predictors for DPN among T2DM patients. The area under the ROC curve of the established risk prediction model was 0.774 (95%CI: 0.737-0.812), with a sensitivity of 0.710 and a specificity of 0.723. The calibration curve after repeated sampling calibration approached the standard curve. Decision curve analysis showed that when the risk threshold probability was 0.2 to 0.4, the model demonstrates favorable clinical applicability. Conclusion The risk prediction model established in this study has favorable discrimination, calibration, and clinical utility, can effectively predict the risk of DPN among T2DM patients aged 18-65 years.
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Received: 04 March 2025
Revised: 16 June 2025
Published: 23 July 2025
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