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

> **NIH NIH R21** · SCHEPENS EYE RESEARCH INSTITUTE · 2020 · $240,285

## Abstract

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.

## Key facts

- **NIH application ID:** 10002287
- **Project number:** 5R21EY030631-02
- **Recipient organization:** SCHEPENS EYE RESEARCH INSTITUTE
- **Principal Investigator:** Tobias Elze
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $240,285
- **Award type:** 5
- **Project period:** 2019-09-01 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10002287

## Citation

> US National Institutes of Health, RePORTER application 10002287, Associating retinal nerve fiber layer thickness with glucose metabolism and diabetic retinopathy (5R21EY030631-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10002287. Licensed CC0.

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