Purpose: To investigate the urine protein (UP) and urine creatinine (UC) ratio in diabetes mellitus and report its influence as a risk factor for the presence and severity of diabetic retinopathy (DR).
Methods: In total, 150 diabetic patients presenting to the outpatient department were included. Detailed history with informed consent and ophthalmic examination, including visual assessment, external ocular examination, anterior segment evaluation, dilated fundus examination by slit-lamp biomicroscopy, and indirect ophthalmoscopy, was done. The early morning spot urine sample was used to determine spot urine protein creatinine ratio. Association with hypertension, fasting blood sugar (FBS), and HBA1C (glycosylated Hb) were also noted.
Results: Urinary PCR increased with the severity of the diabetic retinopathy (P < 0.001). HbA1c, FBS, and duration of diabetes had a direct correlation with urine PCR. ROC curve analysis showed that the optimal PCR cut-off value for predicting the risk of onset DR was 0.65. Retinopathy progressed with increasing urine PCR. Spot urine PCR strongly correlates with stages of diabetic retinopathy and proteinuria measured in 24-h urine samples.
Conclusion: The study showed that urine PCR can be a marker for risk and progression of diabetic retinopathy.
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http://dx.doi.org/10.4103/ijo.IJO_1269_21 | DOI Listing |
Taiwan J Ophthalmol
November 2024
Sirindhorn International Institute of Technology, Thammasat University, Bangkok, Thailand.
Recent advances of artificial intelligence (AI) in retinal imaging found its application in two major categories: discriminative and generative AI. For discriminative tasks, conventional convolutional neural networks (CNNs) are still major AI techniques. Vision transformers (ViT), inspired by the transformer architecture in natural language processing, has emerged as useful techniques for discriminating retinal images.
View Article and Find Full Text PDFTaiwan J Ophthalmol
December 2024
Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.
Wide field retinal imaging has emerged as a transformative technology over the last few decades, revolutionizing our ability to visualize the intricate landscape of the retina. By capturing expansive retinal areas, these techniques offer a panoramic view going beyond traditional imaging methods. In this review, we explore the significance of retinal imaging-based biomarkers to help diagnose ocular and systemic conditions.
View Article and Find Full Text PDFTaiwan J Ophthalmol
November 2024
Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Tamil Nadu, India.
Purpose: This study aimed to evaluate serum cystatin C as a potential biomarker for diabetic retinopathy (DR) in a rural Indian population, addressing the urgent need for effective screening tools amidst rising diabetes prevalence.
Materials And Methods: A cross-sectional study recruited 112 patients with diabetes mellitus from Sambalpur, Odisha, India, categorized into groups with and without DR. Serum cystatin C levels were measured alongside clinical and demographic parameters, using established diagnostic methods.
Taiwan J Ophthalmol
January 2024
Smt. Kanuri Santhamma Center for Vitreoretinal Diseases, Anant Bajaj Retina Institute, LV Prasad Eye Institute, Hyderabad, Telangana, India.
Diabetic retinopathy is one of the most severe forms of retinopathy and a leading cause of blindness all over the world. Of a greater concern is proliferative diabetic retinopathy which leads to vitreous haemorrhage and tractional retinal detachment in such cases. A majority of these cases require a surgical intervention to improve vision and prevent further vision loss.
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June 2025
Assistant Professor, Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Tamil Nadu, 600062, India.
Glaucoma, a severe eye disease leading to irreversible vision loss if untreated, remains a significant challenge in healthcare due to the complexity of its detection. Traditional methods rely on clinical examinations of fundus images, assessing features like optic cup and disc sizes, rim thickness, and other ocular deformities. Recent advancements in artificial intelligence have introduced new opportunities for enhancing glaucoma detection.
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