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
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
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.
刘明坤, 张丰香, 韩彩静, 王霞, 陈世坤, 金梅, 孙金月. 2型糖尿病患者周围神经病变风险预测模型研究[J]. 预防医学, 2025, 37(7): 692-696.
LIU Mingkun, ZHANG Fengxiang, HAN Caijing, WANG Xia, CHEN Shikun, JIN Mei, SUN Jinyue. A prediction model for diabetic peripheral neuropathy among patients with type 2 diabetes mellitus. Preventive Medicine, 2025, 37(7): 692-696.
[1] ZAINO B,GOEL R,DEVARAGUDI S,et al.Diabetic neuropathy:pathogenesis and evolving principles of management[J/OL].Dis Mon,2023,69(9)[2025-06-16].https://doi.org/10.1016/j.disamonth.2023.101582. [2] GAO Y,CHEN S C,PENG M M,et al.Correlation between thioredoxin-interacting protein and nerve conduction velocity in patients with type 2 diabetes mellitus[J/OL].Front Neurol,2020,11[2025-06-16].https://doi.org/10.3389/fneur.2020.00733. [3] LI Z L,GAO Y,JIA Y J,et al.Correlation between hemoglobin glycosylation index and nerve conduction velocity in patients with type 2 diabetes mellitus[J].Diabetes Metab Syndr Obes,2021,14:4757-4765. [4] 李婉玲,冯晓芳,王玫.“360”体医融合在老年糖尿病周围神经病变病人全程管理中的应用[J].护理研究,2021,35(21):3913-3916. LI W L,FENG X F,WANG M.Application of“360”body-medicine integration in the whole process management of senile diabetic peripheral neuropathy patients[J].Chin Nurs Res,2021,35(21):3913-3916.(in Chinese) [5] 王英杰,孙高峰.2型糖尿病预测模型研究进展[J].预防医学,2025,37(4):369-372,377. WANG Y J,SUN G F.Research progress on prediction models for type 2 diabetes mellitus[J].China Prev Med J,2025,37(4):369-372,377.(in Chinese) [6] 苏银霞,卢耀勤,田翔华,等.基于常规体检指标的2型糖尿病风险预测研究进展[J].预防医学,2022,34(12):1230-1234. SUN Y X,LU Y Q,TIAN X H,et al.Research progress on risk prediction of type 2 diabetes mellitus based on routine physical examination indicators[J].China Prev Med J,2022,34(12):1230-1234.(in Chinese) [7] 中华医学会糖尿病学分会.中国2型糖尿病防治指南(2020年版)[J].中华糖尿病杂志,2021,13(4):315-409. Chinese Diabetes Society.Guideline for the prevention and treatment of type 2 diabetes mellitus in China(2020 edition)[J].Chin J Diabetes Mellitus,2021,13(4):315-409.(in Chinese) [8] LIU X X,CHEN D,FU H M,et al.Development and validation of a risk prediction model for early diabetic peripheral neuropathy based on a systematic review and meta-analysis[J/OL].Front Public Health,2023,11[2025-06-16].https://doi.org/10.3389/fpubh.2023.1128069. [9] WANG W M,JI Q H,RAN X W,et al.Prevalence and risk factors of diabetic peripheral neuropathy:a population-based cross-sectional study in China[J/OL].Diabetes Metab Res Rev,2023,39(8)[2025-06-16].https://doi.org/10.1002/dmrr.3702. [10] KARTHIKSARAVANAN K,MERITON A S.A study on prevalence of diabetic peripheral neuropathy in diabetic patients attending a rural health and training centre[J].J Family Med Prim Care,2024,13(2):726-729. [11] KHAWAJA N,ABU-SHENNAR J,SALEH M,et al.The prevalenceKHAWAJA N,ABU-SHENNAR J,SALEH M,The prevalence and risk factors of peripheral neuropathy among patients with type 2 diabetes mellitus;the case of Jordan[J/OL].Diabetol Metab Syndr,2018,10[2025-06-16].https://doi.org/10.1186/s13098-018-0309-6. [12] LAWAL Y,MSHELIA-RENG R,OMONUA S O,et al.Comparison of waist-height ratio and other obesity indices in the prediction of diabetic peripheral neuropathy[J/OL].Front Nutr,2022,9[2025-06-16].https://doi.org/10.3389/fnut.2022.949315. [13] WANG W M,JI Q H,RAN X W,et al.Prevalence and risk factors of diabetic peripheral neuropathy:a population-based cross-sectional study in China[J/OL].Diabetes Metab Res Rev,2023,39(8)[2025-06-16].https://doi.org/10.1002/dmrr.3702. [14] HE R,HU Y Y,ZENG H,et al.Vitamin D deficiency increases the risk of peripheral neuropathy in Chinese patients with type 2 diabetes[J/OL].Diabetes Metab Res Rev,2017,33(2)[2025-06-16].https://doi.org/10.1002/dmrr.2820. [15] 欧阳碧露,王国强,王萌萌,等.2型糖尿病周围神经病变临床预测模型的构建[J].基础医学与临床,2024,44(12):1685-1690. OUYANG B L,WANG G Q,WANG M M,et al.Development of a clinical prediction model for diabetic peripheral neuropathy with type 2 diabetes mellitus[J].Basic Clin Med,2024,44(12):1685-1690.(in Chinese)
