吉林大学学报(医学版) ›› 2026, Vol. 52 ›› Issue (3): 806-812.doi: 10.13481/j.1671-587X.20260322

• 临床研究 • 上一篇    下一篇

2型糖尿病患者并发下肢动脉粥样硬化的影响因素分析及列线图模型构建

于晶1,黄胤倬,周中伟2,谢雨欣1,毛骞1()   

  1. 1.北华大学附属医院内分泌科,吉林 吉林 132012
    2.北华大学附属医院普通外科,吉林 吉林 132012
  • 收稿日期:2025-10-13 接受日期:2025-11-23 出版日期:2026-05-28 发布日期:2026-06-08
  • 通讯作者: 毛骞 E-mail:maoqian@beihua.edu.cn
  • 作者简介:于 晶(1997-),女,吉林省松原市人,在读硕士研究生,主要从事临床内分泌和代谢方面的研究。
  • 基金资助:
    吉林省科技厅自然科学基金项目(20200201529JC);吉林省卫健委科技项目(2017J076)

Analysis on influencing factors in type 2 diabetes mellitus patients complicated with lower limb atherosclerosis and construction of nomogram model

Jing YU1,Yinzhuo HUANG,Zhongwei ZHOU2,Yuxin XIE1,Qian MAO1()   

  1. 1.Department of Endocrinology,Affiliated Hospital,Beihua University,Jilin 132012,China
    2.Department of General Surgery,Affilicated Hospital,Beihua University,Jilin 132012,China
  • Received:2025-10-13 Accepted:2025-11-23 Online:2026-05-28 Published:2026-06-08
  • Contact: Qian MAO E-mail:maoqian@beihua.edu.cn

摘要:

目的 探讨2型糖尿病(T2DM)患者并发下肢动脉粥样硬化(LEAD)患者的危险因素,并建立列线图预测模型,为早期识别高危人群和制定干预策略提供临床依据。 方法 收集269例T2DM患者的临床资料,根据是否并发LEAD分为LEAD组(n=197)和非LEAD组(n=72)。采用单因素和多因素Logistic回归分析T2DM患者并发LEAD的危险因素,并利用列线图构建其风险预测模型,使用Boostrap法(B=1 000)进行内部验证,采用受试者工作特征(ROC)曲线、校准曲线和决策曲线(DCA)综合评估模型的预测效能。 结果 单因素分析,与非LEAD组比较,LEAD组患者年龄、病程、空腹血糖(FBG)、体质量指数(BMI)、甘油三酯(TG)、吸烟史和高血压病史差异有统计学意义(P<0.05);多因素Logistic回归分析,患者年龄[比值比(OR)=1.082,95%置信区间(CI):1.039~1.127,P<0.001]、BMI(OR=1.287,95%CI:1.154~1.436,P<0.001)和FBG(OR=1.159,95%CI:1.043~1.288,P=0.006)是T2DM并发LEAD的独立危险因素。Bootstrap内部一致性指数(C-index)为0.841(95%CI:0.793~0.890),表明该模型具有良好的判别效能。校准曲线分析,Brier评分=0.138,预测概率与实际观察概率高度一致,模型校准度良好。DCA标准化净获益值在0.2~0.8范围内,表明该模型具备显著的净获益优势。 结论 高BMI水平是T2DM患者并发LEAD的重要影响因素,基于危险因素构建的列线图风险模型,具有良好的预测效能。

关键词: 体质量指数, 2型糖尿病, 下肢动脉粥样硬化, 预测模型, 危险因素

Abstract:

Objective To discuss the risk factors in the type 2 diabetes mellitus (T2DM) patients complicated with low extremity atherosclerosis (LEAD), and to establish a nomogram prediction model, so as to provide clinical basis for early identification of high-risk populations and formulation of intervention strategies. Methods The clinical data of 269 patients with T2DM were collected. The patients were divided into LEAD group (n=197) and non-LEAD group (n=72) according to whether they had concurrent LEAD. Univariate and multivariate Logistic regression analyses were used to analyze the risk factors of T2DM patients complicated with LEAD; the nomogram was used to construct a risk prediction model; the Bootstrap method (B=1 000) was used for internal validation; the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to comprehensively evaluate the predictive performance of the model. Results The univariate analysis results showed that compared with non-LEAD group, the age, disease duration, fasting plasma glucose (FBG), body mass index(BMI), triglyceride (TG), smoking history, and history of hypertension in the patients in LEAD group had statistically significant differences (P<0.05). The multivariate Logistic regression analysis results showed that patients’ age [odds ratio (OR)=1.082, 95% confidence interval (CI): 1.039-1.127, P<0.001], BMI(OR=1.287, 95%CI: 1.154-1.436, P<0.001), and FBG (OR=1.159, 95%CI: 1.043-1.288, P=0.006) were the independent risk factors for T2DM complicated with LEAD. The Bootstrap internal consistency index (C-index) was 0.841 (95%CI: 0.793-0.890), indicating that the model had good discriminative performance. The calibration curve analysis results showed that the Brier score was 0.138, and the predicted probability was highly consistent with the actual observed probability, indicating good calibration of the model. The DCA standardized net benefit value ranged from 0.2 to 0.8, indicating that the model had a significant net benefit advantage. Conclusion High BMI level is an important influencing factor for the T2DM patients complicated with LEAD. The constructed nomogram risk model based on the risk factors has good predictive performance.

Key words: Body mass index, Type 2 diabetes mellitus, Lower extremity atherosclerosis, Prediction model, Risk factors

中图分类号: 

  • R587.1