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Diabetic retinopathy (DR), a serious microvascular complication of diabetes, is a leading cause of blindness in adults. The pathogenesis of DR involves a variety of tissues and complex mechanisms, such as inflammation, oxidative stress, optic neurodegeneration, and autophagy. Nowadays, microRNAs (miRNAs), a novel group of non-coding small RNAs, have been extensively studied and recognized to play a key role in the pathogenesis of DR through aforementioned pathways. Furthermore, some miRNAs have been proposed as biomarkers that may be utilized to screen for DR. Also, miRNAs are a new therapy for DR. In this review, we summarize several miRNAs and, their roles in the pathogenesis of DR. miRNAs, as potential pharmacological targets for the diabetic retinopathy, may provide new insights for the treatment of DR.

Microglia, the main immune cell of the central nervous system (CNS), categorized into M1-like phenotype and M2-like phenotype, play important roles in phagocytosis, cell migration, antigen presentation, and cytokine production. As a part of CNS, retinal microglial cells (RMC) play an important role in retinal diseases. Diabetic retinopathy (DR) is one of the most common complications of diabetes. Recent studies have demonstrated that DR is not only a microvascular disease but also retinal neurodegeneration. RMC was regarded as a central role in neurodegeneration and neuroinflammation. Therefore, in this review, we will discuss RMC polarization and its possible regulatory factors in early DR, which will provide new targets and insights for early intervention of DR.

Background The triglyceride glucose (TyG) index reflects insulin resistance; the latter being associated with mild cognitive impairment (MCI). Objective To investigate the clinical value of the TyG index to identify MCI in patients living with type 2 diabetes (T2D) using a cross-sectional study. Methods This cross-sectional study was performed on 517 patients with T2D. The diagnosis of MCI was based on criteria established by the National Institute on Aging-Alzheimer’s Association workgroup, and patients were divided into the MCI group and the normal cognitive function (NCF) group. The logistic regression analysis determines whether the TyG index is related to MCI. Subsequently, we constructed the receiver operating characteristic curve (ROC) and calculated the area under the curve (AUC). The nomogram model of the influence factor was established and verified. Results Compared to the type 2 diabetes-normal cognitive function (T2D-NCF) group, the MCI subjects were olderand had higher TyG indexes, lower cognitive scores, and lower education levels (p < 0.01). After adjusting for the confounders, the TyG index was associated with MCI (OR = 7.37, 95% CI = 4.72–11.50, p < 0.01), and TyG-BMI was also associated with MCI (OR = 1.02, 95% CI = 1.01–1.02, p<0.01). The TyG index AUC was 0.79 (95% CI = 0.76–0.83). The consistency index of the nomogram was 0. 83[95% CI (0. 79, 0. 86)]. Conclusion Our results indicate that the TyG index and TyG-BMI are associated with MCI in T2D patients, and the TyG index is an excellent indicator of the risk of MCI in T2D patients. The nomogram incorporating the TyG index is useful to predict MCI risk in patients with T2D.