Relationships Between Retinal Vascular Characteristics and Renal Function in Patients With Type 2 Diabetes Mellitus

Abstract

Purpose: To develop a deep learning-based method to achieve vessel segmentation and measurement on fundus images, and explore the quantitative relationships between retinal vascular characteristics and the clinical indicators of renal function.

Methods: We recruited patients with type 2 diabetes mellitus with different stages of diabetic retinopathy (DR), collecting their fundus photographs and results of renal function tests. A deep learning framework for retinal vessel segmentation and measurement was developed. The correlation between the renal function indicators and the severity of DR were explored, then the correlation coefficients between indicators of renal function and retinal vascular characteristics were analyzed.

Results: We included 418 patients (eyes) with type 2 diabetes mellitus. The albumin to creatinine ratio, blood uric acid, blood creatinine, blood albumin, and estimated glomerular filtration rate were significantly correlated with the progression of DR (P < 0.05); no correlation existed in other metrics (P > 0.05). The fractal dimension was found to significantly correlate with most of the clinical parameters of renal function (P < 0.05).

Conclusions: The albumin to creatinine ratio, blood uric acid, blood creatinine, blood albumin, and estimated glomerular filtration rate have significant correlation with the progression of moderate to proliferative DR. Through deep learning-based vessel segmentation and measurement, the fractal dimension was found to significantly correlate with most clinical parameters of renal function.

Translational relevance: Deep learning-based vessel segmentation and measurement on color fundus photographs could explore the relationships between retinal characteristics and renal function, facilitating earlier detection and intervention of type 2 diabetes mellitus complications.

Figure 1.

Automatic segmentation and quantitative measurement of retinal vessels. (a) The retinal vessel segmentation results in healthy eyes, (b) mild DR, (c) moderate DR, (d) severe NPDR, (e) proliferative retinopathy, and (f) the quantitative calculation of Df. Each segmented mask was divided into a series of squares by sliding windows, s represents the relative value of the window width to the image width, N(s) is the number of squares that contain blood vessels. Adjust the value of s to get multiple groups (s, N(s)), Df was then defined as the gradient of logarithms of the number of squares and the size of those squares; (g) is the measurement principle diagram of vessel caliber biomarkers. Zone B is defined as an area of 0.50 to 0.75 disc diameter surrounding the optic disc, all arterioles (red lines) and venules (blue lines) coursing through zone B were measured, 6 vessel diameters were obtained at different locations for each vessel and then the Knudtson–Hubbard formula was used to calculate average retinal arteriolar and venular caliber.

Figure 2.

The vessel segmentation results of 2 example fundus images. The first row is an example of mild NPDR, and the second row is another example of PDR. (a) The original fundus images, (b) the annotated ground truth of retinal vessels, (c) the outputs of the NFN+ model without vignetting mask augmentation, and (d) the outputs of the NFN+ model with vignetting mask augmentation.

Figure 3.

The correlations of renal function indicators and retinal vascular metrics with the severity of DR: (a) UMAlb, (b) UCr, (c) ACR, (d) blood urea, (e) blood UA, (f) blood Alb, (g) blood Cr, (h) eGFR, (i) Df, (j) AVR, and (k) CRAE and CRVE. ACR, urinary albumin-to-creatinine ratio; Alb, albumin; Cr, creatinine. *P < 0.05; **P < 0.01.

Figure 4.

The distribution of the eGFR in different stage of DR.

Figure 5.

The correlation coefficients between eGFR and Df (P < 0.05, r = 0.24).