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在2型糖尿病中开发使用角膜神经参数用于糖尿病外周神经病变的诊断名图

Qincheng Qiao^1,2, Juan Cao³, Xinguo Hou^1,4,5,6,7

¹Department of Endocrinology and Metabolism, Qilu Hospital of Shandong University, Jinan, Shandong, People's Republic of China.

Translational vision science & technology

|September 22, 2025

概括

一种使用角膜神经参数的新型诺米图有助于预测2型糖尿病 (T2DM) 患者的糖尿病外围神经病变 (DPN) 风险. 这种工具可以改善个性化风险分层和早期诊断.

科学领域:

眼科医生眼科眼科
神经学神经学
糖尿病学糖尿病学

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.
目前缺乏有效的DPN诊断工具.
角膜神经成像为评估DPN提供了一个潜在的非侵入性方法.

研究的目的:

开发和验证一个纳米图集角膜神经参数,用于T2DM患者个性化DPN风险预测.
评估角膜神经参数在区分DPN的诊断性能.
探索角膜共聚焦显微镜 (CCM) 在DPN风险分层中的潜力.

主要方法:

角膜共聚焦显微镜 (CCM) 对111名T2DM患者和110名对照进行了检查.
视角神经纤维长度 (CNFL),神经分支密度 (CNBD) 和神经纤维密度 (CNFD) 被量化.
开发并使用ROC分析和校准曲线验证了结合CNFL,CNBD,CNFD和年龄的nomogram.

主要成果:

与单个参数模型相比,结合角膜神经参数的多变量模型显示出更好的诊断性能 (AUC 0.737-0.782).
鉴定出CNFL和CNFD是对DPN的保护因素,而CNBD则与风险增加具有矛盾的关联.
开发的名图表显示出良好的区分,校准,临床实用性和稳定性.

结论:

与单个参数模型相比,结合多个角膜神经参数的诺米图在T2DM中提供了优越的DPN风险分层.
这种基于角膜神经的诺米图表代表了个性化DPN风险预测的有希望的工具.
这些发现对管理T2DM并发症具有潜在的翻译价值.

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