Development of Diagnostic Nomograms Using Corneal Nerve Parameters for Diabetic Peripheral Neuropathy in Type 2 Diabetes Mellitus.

IF 2.6 3区医学 Q2 OPHTHALMOLOGY

Translational Vision Science & Technology Pub Date : 2025-09-02 DOI:10.1167/tvst.14.9.29

Qincheng Qiao, Juan Cao, Xinguo Hou

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引用次数: 0

Abstract

Purpose: Diabetic peripheral neuropathy (DPN), a common complication of type 2 diabetes mellitus (T2DM), lacks effective diagnostic tools. This study aimed to develop a nomogram that integrates corneal nerve parameters for individualized DPN risk prediction.

Methods: A total of 111 patients with T2DM and 110 healthy controls were enrolled. All participants underwent bilateral corneal confocal microscopy (CCM). High-quality images were selected by four blinded investigators. Corneal nerve fiber length (CNFL), corneal nerve branch density (CNBD), and corneal nerve fiber density (CNFD) were quantified using ACCMetrics and AiCCMetrics software. Diagnostic models-including single- and multi-parameter models-and a nomogram incorporating CNFL, CNBD, CNFD, and age were developed. Model performance was evaluated using receiver operating characteristic analysis with 500 bootstrap resamples, calibration curves, decision curve analysis, and clinical impact curves. Sensitivity analyses assessed robustness.

Results: Patients with DPN were significantly older (P = 0.005). CNFL and CNFD were higher in the DPN- group (P < 0.05), whereas CNBD showed no group difference. Single-parameter models yielded area under the curve (AUC) values ranging from 0.495 to 0.727, whereas multivariate models demonstrated improved performance with AUCs between 0.737 and 0.782. In the nomogram, CNFL and CNFD were protective factors, whereas CNBD paradoxically increased DPN risk. The model demonstrated good discrimination, calibration, clinical utility, and robustness.

Conclusions: A nomogram combining multiple corneal nerve parameters may outperform single-parameter models, thereby representing a potential tool for DPN risk stratification in T2DM.

Translational relevance: The corneal nerve-based nomogram may assist in personalized DPN risk prediction and holds potential translational value for individuals with T2DM.

Abstract Image

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应用角膜神经参数诊断2型糖尿病周围神经病变的影像学研究。

目的：糖尿病周围神经病变（DPN）是2型糖尿病（T2DM）的常见并发症，缺乏有效的诊断工具。本研究旨在开发一种整合角膜神经参数的图，用于个体化DPN风险预测。方法：选取T2DM患者111例，健康对照110例。所有参与者都接受了双侧角膜共聚焦显微镜检查（CCM）。高质量的图像由四名盲法研究者选择。采用ACCMetrics和AiCCMetrics软件对角膜神经纤维长度（CNFL）、角膜神经分支密度（CNBD）和角膜神经纤维密度（CNFD）进行量化。诊断模型-包括单参数和多参数模型-以及包含CNFL， CNBD， CNFD和年龄的nomogram。采用500个bootstrap样本的受试者工作特征分析、校准曲线、决策曲线分析和临床影响曲线来评估模型的性能。敏感性分析评估稳健性。结果：DPN患者年龄差异有统计学意义（P = 0.005）。DPN-组CNFL和CNFD明显高于DPN-组（P < 0.05），而CNBD组差异无统计学意义。单参数模型的曲线下面积（AUC）值在0.495至0.727之间，而多参数模型的AUC值在0.737至0.782之间，表现出更好的性能。在图中，CNFL和CNFD是保护因素，而CNBD反而增加了DPN的风险。该模型具有良好的识别、校准、临床实用性和鲁棒性。结论：结合多个角膜神经参数的nomogram方法可能优于单参数模型，因此可以作为T2DM患者DPN风险分层的潜在工具。翻译相关性：角膜神经图可能有助于个性化DPN风险预测，并对T2DM患者具有潜在的翻译价值。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Translational Vision Science & Technology Engineering-Biomedical Engineering

CiteScore

5.70

自引率

3.30%

发文量

346

审稿时长

25 weeks

期刊介绍： Translational Vision Science & Technology (TVST), an official journal of the Association for Research in Vision and Ophthalmology (ARVO), an international organization whose purpose is to advance research worldwide into understanding the visual system and preventing, treating and curing its disorders, is an online, open access, peer-reviewed journal emphasizing multidisciplinary research that bridges the gap between basic research and clinical care. A highly qualified and diverse group of Associate Editors and Editorial Board Members is led by Editor-in-Chief Marco Zarbin, MD, PhD, FARVO. The journal covers a broad spectrum of work, including but not limited to: Applications of stem cell technology for regenerative medicine, Development of new animal models of human diseases, Tissue bioengineering, Chemical engineering to improve virus-based gene delivery, Nanotechnology for drug delivery, Design and synthesis of artificial extracellular matrices, Development of a true microsurgical operating environment, Refining data analysis algorithms to improve in vivo imaging technology, Results of Phase 1 clinical trials, Reverse translational ("bedside to bench") research. TVST seeks manuscripts from scientists and clinicians with diverse backgrounds ranging from basic chemistry to ophthalmic surgery that will advance or change the way we understand and/or treat vision-threatening diseases. TVST encourages the use of color, multimedia, hyperlinks, program code and other digital enhancements.