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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.

Background and Aim Obesity often coexists with diabetes, especially abdominal obesity, recognized as a risk factor for diabetic complications. Diabetic retinopathy (DR), as one of the most common microvascular complications of diabetes, may be associated with these indices. Lipid accumulation product (LAP) and Chinese visceral obesity index (CVAI) are novel visceral obesity indicators, which have been proven to be an influential factor predicting type 2 diabetes (T2DM). However, the correlation among LAP, CVAI, and DR still lacks systematic research in T2DM. The study aimed to explore the relationship among LAP, CVAI levels in different DR stages of T2DM patients and the diagnostic efficacy of LAP and CVAI for DR. Methods A total of 263 participants were recruited in this cross-sectional study. We enrolled 169 patients with T2DM, divided into the non-DR group (NDR, n = 61), non-proliferative DR group (NPDR, n = 55), and proliferative DR group (PDR, n = 53). And we also enrolled 94 healthy control participants. We collected demographic, anthropometric, and biochemical data on each subject. LAP and CVAI are calculated according to different formulas for men and women. Results Compared with the control group, LAP and CVAI were significantly higher (P < 0.05). After adjusting for confounding factors, LAP (OR: 1.029, 95CI%: 1.010–1.049, P < 0.05), WC (OR: 1.073, 95CI%: 1.009–1.141, P < 0.05) and CVAI (OR: 1.017, 95CI%: 1.000–1.033, P < 0.05) were all associated with an increased risk of DR. Furthermore, increased LAP (OR: 1.020, 95% CI: 0.100–0.290) is associated with DR severity (P < 0.001). Moreover, the LAP had the most significant area under the receiver operating characteristics (ROC) curve (AUC) (AUC = 0.728, 95% CI: 0.653–0.804). Conclusion A high LAP is associated with an increased risk of DR in T2DM patients, and the LAP index appears to be a good predictor of DR risk and severity in patients with T2DM, compared with BMI, WC, and CVAI.

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Diabetic retinopathy (DR) can increase the risk of mild cognitive impairment (MCI), which has been confirmed by previous researches. With the frequent occurrence of MCI in patients with DR, the early detection of MCI has become a research hot-spot. The aim of this study was to investigate the relationship between neuron-specific enolase (NSE) and MCI in patients with DR. Patients and Methods: A total of 124 patients with DR, including 56 MCI patients and 68 normal cognition patients, were recruited in this cross-sectional study. The demographic and clinical data of patients were collected through questionnaires. Serum NSE was measured using electrochemiluminescence immunoassay. The Minimum Mental State Examination (MMSE) scale was used to evaluate the cognitive function of the participants. Results: Compared with the normal cognition group, serum NSE levels and HbA1c levels in the MCI group were higher, while MMSE scores and educational level were lower (P<0.05). Serum NSE levels were significantly negatively correlated with MMSE total score, attention and calculation score, and language score (P<0.05). After adjusting for confounding factors, serum NSE still increased the MCI risk in DR patients (OR:1.606, 95CI%:1.264-2.041, P<0.001). The areas under the receiver operating characteristics (ROC) curves (AUC) of the crude model and the adjusted model were 0.75 and 0.73, respectively. Conclusion: A high serum NSE level is an independent risk factor for MCI in DR patients. In addition, serum NSE is expected to be a potential biomarker in DR patients with MCI.