Cone Photoreceptors in Diabetic Patients

Abstract

Purpose: Cones in diabetic patients are at risk due to metabolic and vascular changes. By imaging retinal vessel modeling at high magnification, we reduced its impact on cone distribution measurements. The retinal vessel images and retinal thickness measurements provided information about cone microenvironment.

Methods: We compared cone data in 10 diabetic subjects (28-78 yr) to our published norms from 36 younger and 10 older controls. All subjects were consented and tested in a manner approved by the Indiana University Institutional Review Board, which adhered to the Declaration of Helsinki. Custom adaptive optics scanning laser ophthalmoscopy (AOSLO) was used to image cones and retinal microcirculation. We counted cones in a montage of foveal and temporal retina, using four non-contiguous samples within 0.9-7 deg that were selected for best visibility of cones and least pathology. The data were fit with a two parameter exponential model: ln(cone density) = a ^* microns eccentricity + b. These results were compared to retinal thickness measurements from SDOCT.

Results: Diabetic cone maps were more variable than in controls and included patches, or unusually bright and dark cones, centrally and more peripherally. Model parameters and total cones within the central 14 deg of the macula differed across diabetic patients. Total cones fell into two groups: similar to normal for 5 vs. less than normal for 2 of 2 younger diabetic subjects and 3 older subjects, low but not outside the confidence limits. Diabetic subjects had all retinal vascular remodeling to varying degrees: microaneurysms; capillary thickening, thinning, or bends; and vessel elongation including capillary loops, tangles, and collaterals. Yet SD-OCT showed that no diabetic subject had a Total Retinal Thickness in any quadrant that fell outside the confidence limits for controls.

Conclusions: AOSLO images pinpointed widespread retinal vascular remodeling in all diabetic eyes, but the SDOCT showed no increased retinal thickness. Cone reflectivity changes were found in all diabetic patients, but significantly low cone density in only some. These results are consistent with early changes to neural, glial, or vascular components of the retinal without significant retinal thickening due to exudation.

Keywords: cone distribution; cones; diabetic patients; fovea; macula; risk to photoreceptors; vascular changes.

Figure 1

Cone distribution for a 25 yr old female control subject, shown in a montage of individual images (A) of the right eye collected with the confocal mode of the AOSLO. The fovea, which is on the right, has the highest cone density. There is smoothly decreasing cone density farther from the fovea, with the temporal meridian shown. Small white scale bar at lower left = 100 microns. (B–F) Individual cones shown at increased magnification in regions of 100 × 100 microns with the upper far corner at 8.7 (B), 8.4 (C), 7.0 (D), 4.8 (E), 2.8 (F) temporal and 1.1 deg temporal inferior (G) deg temporal with respect to the fovea.

Figure 2

Cone distribution for a 59 yr old female diabetic subject, collected with the confocal mode of the AOSLO (A), shown in a montage of cone images with the foveal region on the left. The retinal microvasculature layer montage (B) shows a strong response to ischemia, with numerous retinal vascular changes shadowing the cone layer: multiple microaneurysms with vascular remodeling surrounding them, capillary bends or loops, and doubled or collateral vessels, particularly near microaneurysms or complex tangles. The montage of individual images shows that cone density is highest in the fovea, then decreases with increasing eccentricity, with single cones or patches of cones being unusually bright (white arrows), as well as patches of dark cones. There are regions in which the overlying retinal vessel remodeling interferes with measurements of cones, by obscuring them (top black arrow). Sometimes the contrast across an entire individual image sequence of cone images was decreased, either due to the tear film or a decreased the wavefront correction across in a large area of edema (bottom left black arrow). Elsewhere, the contrast is decreased for regions smaller than an entire image, which rules out anterior segment artifact as the sole source of image degradation (bottom right arrow). Small white scale bars at lower left = 100 microns. A magnified region of 100 × 100 microns (C) shows patches of bright cones and areas of decreased contrast. An enlargement of the retinal microvascular layer (D) shows microaneurysms and extensive vessel remodeling. The cone parameters were a = −0.00034, and b = 10.28, outside of normal limits. RMS fit = 0.851.

Figure 3

Cone distribution for a 61 yr old female diabetic subject (A), shown in a montage of cone images with the foveal region on the right, with a more moderate response to ischemia than in Figure 2. Small white scale bar at lower left = 100 microns. There are microaneurysms, but less frequent capillary bends or loops, and few doubled or collateral vessels or the more complex tangles. (B–D) Magnified view of cones, showing the phenomena of both extra bright and dark cones in unusually large numbers in 100 × 100 micron samples of cones, with the upper far corner at 7.6, 5.6, and 1.0 deg temporal with respect to the fovea. (E,F) Enlarged views of the retinal microvascular layer, showing 500 × 500 microns samples of microaneurysms, capillary bends, and elongated and collateral vessels, but smaller or less frequent than in Figure 2. Cone parameters, were a = −0.00068, and b = 10.83, within normal limits. RMS fit = 0.791.

Figure 4

Total density plotted as a function of retinal eccentricity for the diabetic subjects, showing differences among subjects at all eccentricities.

Figure 5

Parameters for cone distribution from the exponential model ln (cone density)=a*microns eccentricity+b, with a plotted as a*100. (A) younger control subjects, (B) older control subjects, and (C) diabetic subjects.

Figure 6

Total cones plotted as a function of parameter b of the exponential model for (A) younger control subjects, (B) older control subjects, and (C) diabetic subjects. Younger subjects have smaller symbols.

Figure 7

Retinal thicknesses of the diabetic subjects from the ETDRS grid from the SD-OCT for (A) Central Macular Thickness, (B) Inner Temporal Thickness, and (C) Outer Temporal Thickness. No subject fell outside the confidence limits from the age-similar controls, although subject 209 is missing the outer nasal thickness measurement due to fixation error. The Total Retinal Thickness is significantly correlated with age for the diabetic subjects, with P < 0.02, 0.02, and 0.05 for the Central Macular Thickness, Inner Temporal Thickness, and Outer Temporal Thickness.

Figure 8

Retinal thickness of the diabetic subjects for all quadrants of the ETDRS grid, plotted as mean and standard deviation.

Figure 9

(A) Cone distribution for a 55 yr old female subject from our control population, shown in a montage of cone images with the foveal region on the right. This subject was selected because she had the second lowest cone densities in the sample. Small white scale bar at lower left = 100 microns. White line on the right indicates the foveal center, where the cones are not readily distinguished with this setting on the AOSLO. (B) Cone distribution for a 58 yr old male subject from our control population, shown in a montage of cone images with the foveal region on the right. This subject was selected because he had the highest cone densities in the sample. This subject lacks a foveal avascular zone, having readily imaged capillaries through the foveal center. Small white scale bar at lower left = 100 microns. White line on the right indicates the foveal center, where the cones are not readily distinguished with this setting on the AOSLO.