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.

项目总结/文摘