Associating retinal nerve fiber layer thickness with glucose metabolism and diabetic retinopathy

视网膜神经纤维层厚度与葡萄糖代谢和糖尿病视网膜病变的关联

• 批准号: 10002287
• 负责人: Tobias Elze
• 金额: $ 24.03万
• 依托单位: SCHEPENS EYE RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10002287
• 关键词: Affect; Anatomy; Area; Bayesian Modeling; Blindness; Blood; Blood Glucose; Blood Tests; Clinical; Complement; Data; Development; Diabetes Mellitus; Diabetic Retinopathy; Diagnosis; Diagnostic; Dimensions; Disease; Disease Progression; Early treatment; Eye; Foundations; Future; Glycosylated hemoglobin A; Goals; Health; Heart Diseases; Kidney Failure; Linear Models; Linear Regressions; Location; Maps; Measurement; Measures; Metabolic; Metabolic Diseases; Modeling; Monitor; Non-Insulin-Dependent Diabetes Mellitus; OGTT; Optic Disk; Optical Coherence Tomography; Participant; Patients; Pattern; Phase; Population; Population Study; Procedures; Public Health; Research; Retina; Scanning; Selection Criteria; Severities; Severity of illness; Stroke; Sum; Techniques; Test Result; Testing; Thick; Thinness; Time; Validation; age group; archetypal analysis; base; clinical Diagnosis; diabetic; fasting plasma glucose; follow-up; fundus imaging; glucose metabolism; glucose tolerance; insight; machine learning method; macula; neglect; novel; predictive modeling; proliferative diabetic retinopathy; public health relevance; retinal nerve fiber layer; unsupervised learning

Project Summary/Abstract Type 2 diabetes mellitus (T2DM), a metabolic disease that affects over 300 million people worldwide and that can be accompanied by serious health complications such as heart disease, kidney failure, stroke, and damage to the eyes, in particular diabetic retinopathy (DR), which is diagnosed in a third of people with diabetes and which is the leading cause of blindness within the age group between 20 and 64 years. T2DM is clinically diagnosed by parameters related to glucose metabolism obtained by blood tests. Due to its long pre- symptomatic phase, an estimate of 25% of diabetics in the US are undiagnosed. In this project, the relationship between spatial patterns of retinal nerve fiber layer (RNFL) thickness (RNFLT), measured by spectral-domain optical coherence tomography (OCT), and blood test levels as well as levels of DR severity is investigated in 9,261 participants of a population based study. In a first step, OCT RNFLT measurements of the macular and the circumpapillary area around optic nerve head are segmented into spatial sectors, and representative spatial patterns of RNFLT are calculated by an unsupervised machine learning method. Afterwards, a multivariate linear model comparison is performed with the coefficients of the spatial RNFLT patterns as regressors and diagnostic blood test results as dependent variable. The optimal combination of the RNFLT patterns, determined by an established model selection criterion (Bayes Factor), is expected to reveal insight into the association between the specific retinal locations of RNFL thinning accompanying the change in parameters related glucose metabolism during the development and progression of T2DM. Furthermore, fundus images are graded by DR severity following a nine-step scale derived from the Early Treatment Diabetic Retinopathy Study from no DR to severe proliferative DR. The spatial RNFLT patterns and metabolic blood test scores are then compared with respect to modeling DR severity by linear regression. An optimal model of DR severity combining glucose metabolism parameters and RNFLT patterns is developed. Finally, in an analogous procedure, DR severity of the follow-up measurement, five years after baseline, is statistically predicted from RNFLT and metabolic blood parameters and from their change over time. To summarize, the proposed research identifies spatial patterns of RNFLT associated with parameters of glucose metabolism and their development over DR severity. Once accomplished, the proposed project would provide the details to establish RNFLT as an alternative manifestation of T2DM that complements diagnostic blood tests and thereby, for instance, lay the foundations for the development of novel and more accurate T2DM progression monitoring or the prediction of the onset of DR.

项目概要/摘要 2 型糖尿病 (T2DM) 是一种影响全球 3 亿多人的代谢性疾病， 可能伴有严重的健康并发症，如心脏病、肾衰竭、中风和 对眼睛的损害，特别是糖尿病视网膜病变 (DR)，三分之一的糖尿病视网膜病变患者被诊断出患有此病 糖尿病是 20 至 64 岁年龄段人群失明的主要原因。 T2DM 是 临床上通过血液检查获得的与葡萄糖代谢相关的参数进行诊断。由于其长期预 据估计，美国有 25% 的糖尿病患者在症状阶段未被诊断出来。在这个项目中，关系 视网膜神经纤维层 (RNFL) 厚度 (RNFLT) 的空间模式之间，通过谱域测量 光学相干断层扫描 (OCT)、血液检测水平以及 DR 严重程度的研究 一项基于人口的研究有 9,261 名参与者。 第一步，OCT RNFLT 测量黄斑和视神经周围视乳头周围区域 神经头被分割成空间扇区，并且 RNFLT 的代表性空间模式计算如下 一种无监督的机器学习方法。然后，进行多元线性模型比较 将空间 RNFLT 模式的系数作为回归量，诊断性血液测试结果作为 因变量。由已建立的模型确定的 RNFLT 模式的最佳组合 选择标准（贝叶斯因子），有望揭示特定视网膜之间的关联 RNFL 变薄的位置伴随着葡萄糖代谢相关参数的变化 T2DM 的发生和进展。此外，眼底图像根据 DR 严重程度进行分级 源自早期治疗糖尿病视网膜病变研究的九步量表，从无 DR 到严重 增殖性DR。然后将空间 RNFLT 模式和代谢血液测试分数与以下方面进行比较： 通过线性回归对 DR 严重程度进行建模。结合葡萄糖代谢的 DR 严重程度的最佳模型 开发了参数和 RNFLT 模式。最后，在类似的过程中，后续的 DR 严重程度 基线五年后的测量是根据 RNFLT 和代谢血液参数进行统计预测的 以及它们随时间的变化。 总而言之，所提出的研究确定了与参数相关的 RNFLT 空间模式 葡萄糖代谢及其随 DR 严重程度的发展。一旦完成，拟议的项目将 提供详细信息以将 RNFLT 作为 T2DM 的替代表现形式，以补充诊断 例如，血液测试，从而为开发新颖且更准确的方法奠定基础 T2DM 进展监测或 DR 发病预测。