Disruption of the retinal parafoveal capillary network in type 2 diabetes before the onset of diabetic retinopathy

Abstract

Purpose: To establish, using adaptive optics scanning laser ophthalmoscopy (AOSLO), that the retinal parafoveal capillary network is altered before the onset of diabetic retinopathy in adult patients with type 2 diabetes.

Methods: AOSLO videos were acquired in the parafoveal region of one eye from control subjects and from patients with type 2 diabetes and no retinopathy. Detailed images of the parafoveal capillary network were generated with custom motion contrast enhancement algorithms. The combination of AOSLO images and videos enabled the simultaneous assessment of several features of the parafoveal capillary network. Arteriovenous (AV) channels were identified by finding the least tortuous capillary channels connecting terminal arterioles to postcapillary venules. Measures of capillary dropout and capillary hemodynamics were also quantified.

Results: The average tortuosity of AV channels was 26% higher in patients with type 2 diabetes when compared with controls, even though there were no signs of diabetic retinopathy in any of the eyes that were assessed (P < 0.05). In addition, the metrics of capillary dropout showed small changes (between 3% and 7%), leukocyte speed 14% lower, and pulsatility 25% higher, but none of these differences was statistically significant.

Conclusions: It is often difficult to find consistent changes in the retinal microvasculature due to large intersubject variability. However, with a novel application of AOSLO imaging, it is possible to visualize parafoveal capillaries and identify AV channels noninvasively. AV channels are disrupted in type 2 diabetes, even before the onset of diabetic retinopathy.

Figure 1.

Example of AOSLO imaging for one control subject. In this example, overlapping videos were taken in 21 different locations on the retina, processed to generate capillary images, and then compiled to generate a montage of the parafoveal capillary network. (A, B) A 45° fundus photograph, with and without AOSLO images. (C, D) Higher magnification of the white box in (B), showing a portion of the fundus photograph, with and without AOSLO images. (E, F) Higher magnification of the white box in (D), showing the fundus photograph, with and without one AOSLO image generated from a single video. Scale bar: (C, D) 500 μm; (E, F) 100 μm.

Figure 2.

Identification of AV channels on AOSLO images. The steps are: (A) identify locations of arterioles (red) and venules (blue), (B) identify candidate AV channels satisfying the branch selection rule, and (C) select the three least tortuous AV channels.

Figure 3.

Examples of parafoveal capillary montages generated using custom motion contrast enhancement algorithms. Higher intensities denote areas of greater intensity fluctuations due to blood flow as seen on unprocessed AOSLO videos. Subtle variations in intensity in noncapillary areas are artifacts due to the use of multiple overlapping videos. There were no obvious qualitative differences in appearance between the two groups. Shown are examples: (A) four control subjects and (B) four T2DM_NoDR subjects. Arrows: denote examples of peculiar capillary bends (shown in more detail in Fig. 4). (B, ) A rather large, avascular region outside of the FAZ, which may be indicative of early capillary dropout. Scale bar, 500 μm.

Figure 4.

Examples of capillary abnormalities in (A) control and (B) T2DM_NoDR subjects. There were capillary bends and dead-end capillaries present in both groups (top two rows), as well as objects of various sizes that were similar in appearance to microaneurysms (bottom row, arrows), despite the absence of microaneurysms on color fundus photographs. Scale bar, 100 μm.

Figure 5.

The three least tortuous AV channels from the (A) control and (B) T2DM_NoDR subjects. The capillary segments are arranged to best fill the space in each panel of the figure. The color map shows the range of tortuosities (arbitrary units, generated by calculating TSC/L · 10⁵; equation 1). Scale bar, 500 μm.

Figure 6.

Extracted FAZs for (A) control and (B) T2DM_NoDR subjects. Three FAZs could not be extracted due to poor data quality. Of the extracted FAZs, three FAZs were not used for quantification of FAZ shape because of poor quality data in one or more videos showing the edge of the FAZ (). For these FAZs, the extracted FAZ was estimated from the AOSLO image and quantified for size but not shape. Scale bar, 500 μm.

Figure 7.

Extracted capillaries for (A) control and (B) T2DM_NoDR subjects. Some subjects could not be analyzed because of data quality in one or more portions within the ROI. Scale bar, 500 μm.

Figure 8.

Results from statistical analyses, for control (A) and T2DM_NoDR (B) groups. AV channel tortuosity was significantly higher in the T2DM_NoDR group compared to the control group (P < 0.05). For FAZ shape, which had one outlier, a Wilcoxon rank sum test confirmed that the difference between groups was not statistically significant (P = 0.26).

Figure 9.

The proposed mechanism for progression from AV channel disruption to NPDR. EC, endothelial cell; MAs, microaneurysms; IRMAs, intraretinal microvascular abnormalities; CWS, cotton wool spots.