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: 9809589
• 负责人: Tobias Elze
• 金额: $ 31.38万
• 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 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9809589
• 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; Retinal; Scanning; Selection Criteria; Severities; Severity of illness; Stroke; Sum; Techniques; Test Result; Testing; Thick; Thinness; Time; Validation; age group; base; clinical Diagnosis; diabetic; fasting plasma glucose; follow-up; fundus imaging; glucose metabolism; glucose tolerance; insight; learning strategy; 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.

项目总结/摘要 2型糖尿病（T2 DM）是一种代谢疾病，影响全球3亿多人， 可伴有严重的健康并发症，如心脏病、肾衰竭、中风， 对眼睛的损害，特别是糖尿病视网膜病变（DR），三分之一的人被诊断为糖尿病视网膜病变。 糖尿病是20至64岁年龄组失明的主要原因。t2 dm是 通过血液检查获得的与葡萄糖代谢相关的参数进行临床诊断。由于其长期的... 在症状阶段，估计美国有25%的糖尿病患者未被诊断。在这个项目中， 视网膜神经纤维层（RNFL）厚度（RNFLT）的空间模式之间， 光学相干断层扫描（OCT）和血液检查水平以及DR严重程度的水平进行了研究， 9，261名参与者参与了一项基于人口的研究。 在第一步中，OCT RNFLT测量黄斑和视神经周围的视乳头周围区域， 将神经头分割成空间扇区，并通过以下计算RNFLT的代表性空间模式： 一种无监督的机器学习方法。然后，进行多元线性模型比较 空间RNFLT模式的系数作为回归量，诊断血液测试结果作为 因变量RNFLT模式的最佳组合，由已建立的模型确定 选择标准（贝叶斯因子），预计将揭示洞察特定视网膜病变之间的关联， RNFL变薄的位置伴随着糖代谢相关参数的变化， T2 DM的发展和进展。此外，眼底图像按照DR严重程度进行分级， 从早期治疗糖尿病视网膜病变研究中得出的九步量表，从无DR到重度 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。结合糖代谢的糖尿病视网膜病变严重程度优化模型 参数和RNFLT模式。最后，在类似的过程中，随访的DR严重程度 基线后5年的测量结果，根据RNFLT和代谢血液参数进行统计学预测 以及它们随时间的变化。 总之，所提出的研究确定了与参数相关的RNFLT的空间模式， 葡萄糖代谢及其随DR严重程度的发展。一旦完成，拟议项目将 提供详细信息，将RNFLT确定为T2 DM的替代表现，以补充诊断 血液测试，从而，例如，奠定了基础，为发展新的和更准确的 T2 DM进展监测或DR发作预测。