June 2013
Volume 54, Issue 15
Free
ARVO Annual Meeting Abstract  |   June 2013
Subretinal layer thickness ratio changes for early detection of diabetes
Author Affiliations & Notes
  • Ryan Shelton
    Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL
  • Jessica Taibl
    Anterior Segment & Vitreoretinal Surgury, The Eye Center/The Retina Center, Champaign, IL
    University of Illinois at Urbana-Champaign, Urbana, IL
  • Nathan Shemonski
    Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL
  • Samir Sayegh
    Anterior Segment & Vitreoretinal Surgury, The Eye Center/The Retina Center, Champaign, IL
  • Stephen Boppart
    Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL
  • Footnotes
    Commercial Relationships Ryan Shelton, None; Jessica Taibl, None; Nathan Shemonski, None; Samir Sayegh, None; Stephen Boppart, Diagnostic Photonics, Inc. (F), Welch Allyn, Inc. (C), Texas Instruments, Inc. (R)
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2013, Vol.54, 2428. doi:
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      Ryan Shelton, Jessica Taibl, Nathan Shemonski, Samir Sayegh, Stephen Boppart; Subretinal layer thickness ratio changes for early detection of diabetes. Invest. Ophthalmol. Vis. Sci. 2013;54(15):2428.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract
 
Purpose
 

To investigate the use of ratiometric analysis of retinal layer thicknesses as an early indicator of diabetes.

 
Methods
 

Eight patients were chosen for this study. Four of the patients were clinically diagnosed to have diabetes (DM+) and four were used as control subjects (DM-). All patients’ retinas were imaged using optical coherence tomography (RTVue, Optovue, Inc.) and showed no morphological abnormalities. Retinal images were then manually segmented at selected layers (see fig. 1) and ratios between various layer thicknesses were compared between DM- and DM+ cases. After analysis, these ratios were correlated to the presence or absence of diabetes.

 
Results
 

It was found that a ratiometric analysis of retinal layer thicknesses in the foveal region highlighted many ratios in which the mean of the DM+ group differed from the mean of the DM- group by at least one standard deviation. Figure 2 shows 12 ratios taken from the foveal region in which both means lie outside of their respective standard deviations, indicating a statistically significant difference. While there is some interdependence between these ratios, they include contributions from every segmented layer. A physiologically significant correlation is the fact that all of these ratios involve the inner segment and almost half of them involve the retinal pigment epithelium, two layers that would be directly affected by vascular changes associated with diabetic retinopathy.

 
Conclusions
 

A ratiometric analysis on layer thicknesses in the retina of diabetic and control patients reveals a statistically different set of thickness ratio values for diabetic patients. These results are encouraging for future efforts to diagnose or screen for diabetes at an early stage, before symptoms or gross retinal abnormalities occur. Previous studies have investigated layer thicknesses in the retina to draw conclusions about the presence of diabetes; however, an analysis of the ratios of these layer thicknesses is a more robust measure. Variations in overall retinal thickness between patients will not affect the accuracy of ratio measurements.

 
 
Figure 1: Representative OCT image of the retina of a DM+ patient with manual layer segmentation.
 
Figure 1: Representative OCT image of the retina of a DM+ patient with manual layer segmentation.
 
 
Figure 2: Statistical analysis of 12 retinal layer thickness ratios. Red squares: control subjects, blue circles: diabetic subjects.
 
Figure 2: Statistical analysis of 12 retinal layer thickness ratios. Red squares: control subjects, blue circles: diabetic subjects.
 
Keywords: 499 diabetic retinopathy • 552 imaging methods (CT, FA, ICG, MRI, OCT, RTA, SLO, ultrasound) • 550 imaging/image analysis: clinical  
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