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.

项目摘要/摘要 2型糖尿病（T2DM），一种代谢性疾病，影响了全球超过3亿人 可能伴随严重的健康并发症，例如心脏病，肾衰竭，中风和 对眼睛的损害，特别是糖尿病性视网膜病（DR），这是三分之一的人 糖尿病是20至64岁年龄段内失明的主要原因。 T2DM是 通过与血液测试获得的葡萄糖代谢有关的参数诊断。由于它的长期 有症状的阶段，在美国估计25％的糖尿病患者未被诊断。在这个项目中，关系 通过光谱域测量的视网膜神经纤维层（RNFL）厚度（RNFL）的空间模式 在研究中研究了光学相干断层扫描（OCT），血液测试水平以及DR的水平。 一项基于人群的研究的9,261名参与者。 在第一步，OptiC周围的黄斑和圆形乳头区域的OCT RNFLT测量值 神经头分为空间扇区，RNFLT的代表空间模式由 一种无监督的机器学习方法。之后，进行多元线性模型比较 将空间RNFLT模式的系数作为回归变量和诊断血液检查结果作为 因变量。由建立模型决定的RNFLT模式的最佳组合 选择标准（贝叶斯因子）有望揭示对特定视网膜之间关联的见解 伴随参数变化相关的葡萄糖代谢的RNFL变薄的位置 T2DM的发展和进展。此外，眼底图像是由Dr严重性在 九步量表从早期治疗糖尿病性视网膜病变研究中得出 增殖DR。然后将空间RNFLT模式和代谢血液测试评分与尊重进行比较 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，在类似过程中，随访的DR严重程度 在基线五年后的测量是从RNFLT和代谢血液参数统计预测的 以及随着时间的变化。 总而言之，拟议的研究确定了与参数相关的RNFLT的空间模式 葡萄糖代谢及其对DR严重性的发展。一旦完成，建议的项目将 提供详细信息以建立RNFLT作为T2DM的替代表现，以补充诊断 例如，血液检查，因此为开发新颖，更准确而奠定了基础 T2DM进程监测或DR的发作的预测。