Visual contrast sensitivity could be an early marker of diabetic retinopathy

Abstract

The present study aimed to explore the early predictive marker of diabetic retinopathy (DR) and to elucidate the associated demographic, metabolic, and ocular factors. We enrolled 43 type 2 diabetic subjects with mild non-proliferative retinopathy (MNPDR), 30 diabetic subjects with no retinopathy (DNR), and 35 healthy controls (HC). The study groups showed no significant alteration in central macular thickness (CMT) and visual acuity (VA). The contrast sensitivity (CS) score was found to be significantly lower among DNR and MNPDR subjects compared to HCs (p < 0.0001). Between MNPDR and DNR subjects, the CS score was significantly lower in the former (p = 0.0036). CS score discriminated DNR subjects from HC, with 74% accuracy for the optimal threshold 0.71. The associated area under the ROC curve (AUC) is 0.82 (p < 0.0001) while the discrimination rule has 66% sensitivity and 80% specificity. The CS score also discriminated MNPDR subjects from DNR with 64% accuracy for the optimal threshold 0.53. The associated AUC is 0.65 (p < 0.023) and the rule has 86% sensitivity and 33% specificity. According to best subset regression analysis, not only glycaemic parameters but also lipid parameters [low-density lipoprotein cholesterol (LDL-C) (p = 0.045) and triglycerides (TG) (p = 0.0005)] were found to be significant predictors of CS. CMT (p = 0.058) was another marginally significant predictor of CS. CS may be used as an early predictive marker for DR. So, not only hyperglycemia, but also hyperlipidemia seems to significantly affect retinal CS function in diabetes.

Decreased Levels of miR-126 and miR-132 in Plasma and Vitreous Humor of Non-Proliferative Diabetic Retinopathy Among Subjects with Type-2 Diabetes Mellitus

“…Increased levels of TC, LDL-C, TG, MDA, and VEGF among NPDR subjects compared to DNR are in agreement with similar studies conducted earlier. 65 , 66 However, significant negative correlations of those conventional lipid parameters, MDA, an indicator of oxidative stress, 63 and VEGF with levels of miRs in our study could be due to up-regulation of target genes of miR-126 and miR-132 in NPDR. For example, the voltage-dependent anion-selective channel protein 2 ( VDAC2 ) and low-density lipoprotein receptor ( LDLR ), validated targets of miR-132 are associated with cholesterol metabolism pathway (KEGG ID: hsa04979 ).…”

Do different lipid components accelerate the pathogenesis and severity of Diabetic Retinopathy?

“…In the present study, plasma conventional lipid components of diabetic subjects like TCH, LDL-C showed a tendency of gradually increasing towards the pathogenesis of DR, and TG was found to be increased towards pathogenesis with the severity of DR. The findings are partially supported by Cowdhuri et al, 2017 and Pramanik et al, 2020 who have demonstrated a significant increment of TCH, LDL-C, and TG levels in MNPDR compared to HC and DNR [ 1 , 22 ]. Another prospective study by Prajapati et al, 2017 showed a stepwise increment of TCH, LDL-C, and TG from NPDR to PDR [ 35 ].…”

“…Blood level of HbA1C, plasma glucose (fasting and postprandial), and conventional lipid profile components were measured using commercial kits by standard manufacturer instructed protocols as discussed in our previous paper [ 22 ].…”

The Amelioration of Detrimental Biochemical Anomalies by Supplementing B, C, and E Vitamins in Subjects with Type 2 Diabetes Mellitus May Reduce the Rate of Development of Diabetic Retinopathy

“…Among the different detrimental pathways regarding enhanced glycation, oxidative stress, and lipid peroxidation derived from chronic hyperglycemia, cells attempt to create a barrier or defense system to maintain normal physiology. Superoxide dismutase (SOD), glutathione reductase (GR), glutathione peroxidase (GPX), and reduced glutathione (GSH), which are the normal cellular protectors against the development of damaging toxicity of free radicals and lipid peroxides, are severely affected by advanced AGEs, MDA, and ROS [ 3 , 4 , 6 ].…”