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

项目摘要/摘要 2型糖尿病(T2 DM)，一种影响全球超过3亿人的代谢性疾病， 可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)，三分之一的人被诊断为 糖尿病是导致20岁至年龄段失明的主要原因。T2 DM是 通过血液检测获得的与糖代谢有关的参数进行临床诊断。因为它有很长的历史 在症状阶段，美国估计有25%的糖尿病患者没有得到诊断。在这个项目中，关系 光谱域测量视网膜神经纤维层(RNFL)厚度(RNFLT)的空间模式 光学相干断层扫描(OCT)和血液检测水平以及DR的严重程度在 9261名参与者参加了一项基于人群的研究。 在第一步中，OCT RNFLT测量黄斑和视神经周围的乳头周围区域 将神经头分割成空间扇区，计算出RNFLT的典型空间模式 一种无监督的机器学习方法。然后，进行多变量线性模型比较 以空间RNFLT模式的系数为回归变量，诊断血液检测结果为 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与 RNFL变薄的位置伴随着糖代谢相关参数的变化 2型糖尿病的发生发展。此外，根据DR严重程度对眼底图像进行分级 从无DR到重度糖尿病视网膜病变早期治疗研究的九个步骤量表 然后将空间RNFLT模式和代谢性血液测试分数与REPORT进行比较 对DR严重程度进行线性回归建模。结合糖代谢的糖尿病视网膜病变严重程度的优化模型 开发了参数和RNFLT模式。最后，在一个类似的程序中，对DR的严重程度进行随访 基线五年后的测量是根据RNFLT和代谢性血液参数进行统计预测的 以及它们随时间的变化。 综上所述，拟议的研究确定了RNFLT的空间模式与 糖尿病视网膜病变严重程度下的糖代谢及其发展。一旦完成，拟议的项目将 提供详细信息，将RNFLT确立为T2 DM的替代表现，以补充诊断 血液测试，因此，例如，奠定了新的和更准确的发展基础 T2 DM进展监测或DR发病的预测

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.