神经损伤与功能重建2025，Vol.20Issue(10)：569-574,6.DOI:10.16780/j.cnki.sjssgncj.20250003
2型糖尿病患者远端对称性多发性神经病变发生风险的列线图预测模型的建立与验证
Development and Validation of a Risk Nomogram Model for Predicting Distal Symmetric Polyneuropathy in Patients with Type 2 Diabetes Mellitus
摘要
Abstract
Objective:This study aims to identify the risk factors associated with distal symmetric polyneuropathy(DSPN)in patients with type 2 diabetes mellitus(T2DM)and to develop a predictive model for assessing the risk of DSPN.Methods:A total of 1,658 eligible T2DM patients were selected from Xiaogan Hospital,affiliated with Wuhan University of Science and Technology,between July 2021 and July 2024.General and clinical data were collected and then split into a training set and a validation set in a 7∶3 ratio.LASSO regression and binary logistic regression were employed to develop a nomogram model using the training set data,which was subsequently validated with the validation set data.The model's accuracy,discrimination,and clinical applicability were assessed using calibration curves,the area under the receiver operating characteristic(ROC)curve,and decision curve analysis(DCA).Results:The analysis identified duration of diabetes(OR=1.195,95%CI:1.116-1.280),fasting blood glucose(FPG)(OR=1.614,95%CI:1.435-1.816),neutrophil-to-lymphocyte ratio(NLR)(OR=1.388,95%CI:1.042-1.849)and urinary microalbumin(mALB)(OR=1.536,95%CI:1.113-2.120)as independent risk factors for DSPN.Conversely,high-density lipoprotein cholesterol(HDL-C)(OR=0.252,95%CI:0.160-0.397)and 25-hydroxyvitamin D[25(OH)D](OR=0.845,95%CI:0.825-0.864)were identified as independent protective factors.The nomogram model's predicted DSPN risk probability closely aligned with the actual probability,as demonstrated by the calibration curve.The area under the curve(AUC)for DSPN prediction was 0.896(95%CI:0.878-0.913)in the training group and 0.888(95%CI:0.860-0.917)in the validation group.The decision curve analysis(DCA)indicated that the model holds significant clinical value across a wide range of thresholds.Conclusion:This study successfully developed a highly accurate nomogram prediction model based on key predictors,including diabetes duration,FPG,NLR,mALB,HDL-C,and 25(OH)D.关键词
糖尿病周围神经病变/列线图/预测模型/影响因素Key words
diabetic peripheral neuropathy/nomogram/prediction/influencing factor分类
医药卫生引用本文复制引用
程凯倩,张方辉,刘培欣,梅靖宇,孙喆,朱钊,吴敏..2型糖尿病患者远端对称性多发性神经病变发生风险的列线图预测模型的建立与验证[J].神经损伤与功能重建,2025,20(10):569-574,6.基金项目
职业危害识别与控制湖北省重点实验室联合基金项目(医务人员维生素D水平与代谢综合征发生风险及其相关代谢组分聚集性分析,No.JF2023-K04) （医务人员维生素D水平与代谢综合征发生风险及其相关代谢组分聚集性分析,No.JF2023-K04）
职业危害识别与控制湖北省重点实验室开放基金(后疫情时代基于贝叶斯网络的医务人员健康风险评估研究,No.OHIC2022G09) （后疫情时代基于贝叶斯网络的医务人员健康风险评估研究,No.OHIC2022G09）
