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在中国新疆开发和验证了一种糖尿病外围神经病变与2型糖尿病的预测模型
Asiguli Adili1,2, Maihesumu Aikemu3, Karanvir Singh Rana3
1Department of Endocrinology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
Scientific reports
|October 16, 2025
概括
这项研究确定了2型糖尿病 (T2DM) 患者糖尿病外围神经病变 (DPN) 的关键风险因素. 开发了一种机器学习模型,用于准确的DPN预测和早期临床管理.
科学领域:
- 内分泌学和新陈代谢学
- 计算生物学和生物信息学
- 神经学 神经学
背景情况:
- 糖尿病外围神经病变 (DPN) 是2型糖尿病 (T2DM) 的常见并发症.
- 早期识别风险因素和开发预测模型对于临床决策和DPN管理至关重要.
- 现有的预测工具可能缺乏准确性或通用性.
研究的目的:
- 在T2DM患者中确定与DPN相关的显著风险因素.
- 开发和验证基于机器学习的DPN预测模型.
- 支持早期检测和临床管理DPN.
主要方法:
- 对1001名T2DM患者进行了回顾性队列研究.
- 统计分析包括t测试和千平方测试.
- 机器学习算法 (SVM-RFE, LASSO, RF, GBM, GLM) 用于风险因素识别和模型开发.
主要成果:
- 包括年龄,糖尿病持续时间,BMI,血压,葡萄糖,脂质,功能和尿路标志物在内的15个因素与DPN显著相关 (P <0.05).
- 通过SVM-RFE和LASSO回归,确定了七个核心风险因素.
- 随机森林 (RF) 模型表现出高预测性能,AUC为1.000 (培训),0.904 (验证) 和0.953 (外部数据集).
结论:
- 机器学习技术有效地确定了DPN的显著预测因素.
- 一个经过验证的射频模型显示了DPN预测的强大泛化性能.
- 开发的模型有可能有助于在T2DM患者中早期检测和临床管理DPN.
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