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Relating Retinal Vascular Oxygen Saturation and Microvasculature Morphology at Progressive Stages of Diabetic
Selin L Auvazian1, Jennifer Cano1, Sophie Leahy1
1Department of Ophthalmology, University of Southern California, Los Angeles, CA, USA.
Purpose:
Diabetic retinopathy (DR) is a common cause of vision loss in working age adults and presents changes in retinal vessel oxygenation and morphology. The purpose of this study was to test the hypothesis that there is an association of retinal vessel oxygen saturation with vessel density (VD) and tortuosity in DR.
Methods:
Ninety-five subjects were classified in the following groups: nondiabetic control (N = 25), no DR (N = 28), mild nonproliferative DR (NPDR; N = 21), moderate to severe NPDR (N = 14), or treated proliferative DR (PDR; N = 7). Retinal oximetry was performed to measure arterial and venous oxygen saturation (SO2A and SO2V) and calculate oxygen extraction fraction (OEF). Optical coherence tomography angiography (OCTA) was performed for measurements of VD and vessel tortuosity index (VTI).
Results:
There were statistically significant differences in SO2A and SO2V among groups (P ≤ 0.004). SO2A and SO2V were higher in the PDR group compared to the control group and SO2V was also higher in the moderate to severe NPDR group. VD differed significantly among groups (P = 0.003), whereas VTI was not significantly different (P = 0.22). Compared to the control group, VD was lower in moderate to severe NPDR and PDR groups. VD was also lower in the PDR group than that in the no DR group (P = 0.03). There was a significant correlation of VTI with SO2V (r = 0.32, P = 0.002) and OEF (r = -0.35, P = 0.001).
Conclusions:
Retinal vessel morphology, oxygenation, and tissue oxygen extraction were associated with each other in a cohort of subjects with and without DR.
Translational Relevance:
The findings of this study have the potential to improve clinical management of DR by providing better understanding of human disease pathophysiology and propelling future studies to identify multiple image-based biomarkers for improved disease diagnosis and monitoring.
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