- 作者列表："Jung KI","Woo JE","Park CK
BACKGROUND AND PURPOSE:Early retinal neurodegeneration occurs as one of the complications of diabetes even before clinically detectable diabetic vascular retinopathy. The pathogenesis of retinal diabetic neuropathy is still not well understood. We investigated the serial changes or fluctuations in intraocular pressure (IOP) and examined their roles in the pathogenesis of neuronal degeneration in diabetic retina. EXPERIMENTAL APPROACH:The streptozotocin-induced diabetic rats were given brinzolamide ophthalmic suspension, latanoprost ophthalmic solution or saline for 8 weeks. The combined treatment group was treated with both brinzolamide and latanoprost. KEY RESULTS:The fluctuation of IOP was higher in the diabetes group than in the normal control or the combined treatment group. The diabetes-induced apoptosis of retinal ganglion cells (RGCs) was decreased by combined treatment. The increased glial fibrillary acidic protein or Iba-1 expression in the retina or optic nerve head induced by diabetes was attenuated only by the combined treatment. Intercellular adhesion molecule 1 was increased in diabetic rats but not in the combined treatment group. CONCLUSIONS AND IMPLICATIONS:Elevated IOP fluctuations seemed to be associated with the gliosis, neuroinflammation, and neurodegeneration induced by diabetes. The loss of RGCs might be relieved by IOP-lowering medication. The improvement of unstable perfusion pressure could play a role in neuroprotection in the diabetic retina.
背景和目的: 早期视网膜神经退行性病变是糖尿病的并发症之一，甚至在临床发现糖尿病血管视网膜病变之前就已发生。视网膜糖尿病神经病变的发病机制尚不十分清楚。我们研究了眼压 (IOP) 的系列变化或波动，并检测了它们在糖尿病视网膜神经元变性发病机制中的作用。 实验方法: 链脲佐菌素诱导的糖尿病大鼠给予布林佐胺眼用混悬液、拉坦前列素眼用溶液或生理盐水灌胃 8 周。联合治疗组采用布林佐胺和拉坦前列素治疗。 主要结果: 糖尿病组眼压波动高于正常对照组或联合治疗组。联合治疗可减少糖尿病诱导的视网膜神经节细胞 (RGCs) 凋亡。糖尿病引起的视网膜或视神经头部胶质纤维酸性蛋白或 Iba-1 表达增加仅在联合治疗下减弱。糖尿病大鼠细胞间粘附分子 1 升高，而联合治疗组无升高。 结论和意义: 眼压波动升高似乎与糖尿病诱导的胶质细胞增生、神经炎症和神经变性有关。RGCs 的丢失可能通过降眼压药物来缓解。不稳定灌注压的改善对糖尿病视网膜具有神经保护作用。
METHODS:Aims We aimed to develop a prediction model based on clinical and biochemical variables for gestational diabetes mellitus (GDM) based on the 2013 World Health Organization (WHO) criteria. Methods A total of 1843 women from a Belgian multi-centric prospective cohort study underwent universal screening for GDM. Using multivariable logistic regression analysis, a model to predict GDM was developed based on variables from early pregnancy. The performance of the model was assessed by receiver-operating characteristic (AUC) analysis. To account for over-optimism, an eightfold cross-validation was performed. The accuracy was compared with two validated models (van Leeuwen and Teede). Results A history with a first degree relative with diabetes, a history of smoking before pregnancy, a history of GDM, Asian origin, age, height and BMI were independent predictors for GDM with an AUC of 0.72 [95% confidence interval (CI) 0.69–0.76)]; after cross-validation, the AUC was 0.68 (95% CI 0.64–0.72). Adding biochemical variables, a history of a first degree relative with diabetes, a history of GDM, non-Caucasian origin, age, height, weight, fasting plasma glucose, triglycerides and HbA_1c were independent predictors for GDM, with an AUC of the model of 0.76 (95% CI 0.72–0.79); after cross-validation, the AUC was 0.72 (95% CI 0.66–0.78), compared to an AUC of 0.67 (95% CI 0.63–0.71) using the van Leeuwen model and an AUC of 0.66 (95% CI 0.62–0.70) using the Teede model. Conclusions A model based on easy to use variables in early pregnancy has a moderate accuracy to predict GDM based on the 2013 WHO criteria.
METHODS:Leveraging the availability of nationwide electronic health records from over 500,000 pregnancies in Israel, a machine-learning approach offers an alternative means of predicting gestational diabetes at high accuracy in the early stages of pregnancy. Gestational diabetes mellitus (GDM) poses increased risk of short- and long-term complications for mother and offspring^ 1 – 4 . GDM is typically diagnosed at 24–28 weeks of gestation, but earlier detection is desirable as this may prevent or considerably reduce the risk of adverse pregnancy outcomes^ 5 , 6 . Here we used a machine-learning approach to predict GDM on retrospective data of 588,622 pregnancies in Israel for which comprehensive electronic health records were available. Our models predict GDM with high accuracy even at pregnancy initiation (area under the receiver operating curve (auROC) = 0.85), substantially outperforming a baseline risk score (auROC = 0.68). We validated our results on both a future validation set and a geographical validation set from the most populated city in Israel, Jerusalem, thereby emulating real-world performance. Interrogating our model, we uncovered previously unreported risk factors, including results of previous pregnancy glucose challenge tests. Finally, we devised a simpler model based on just nine questions that a patient could answer, with only a modest reduction in accuracy (auROC = 0.80). Overall, our models may allow early-stage intervention in high-risk women, as well as a cost-effective screening approach that could avoid the need for glucose tolerance tests by identifying low-risk women. Future prospective studies and studies on additional populations are needed to assess the real-world clinical utility of the model.
METHODS::Repurposing of currently approved medications is an attractive option for the development of novel treatment strategies against physiological and infectious diseases. The antidiabetic sulfonylurea glyburide has demonstrated off-target capacity to inhibit activation of the NLRP3 inflammasome in a variety of disease models, including vaginal candidiasis, caused primarily by the fungal pathogen Candida albicans Therefore, we sought to determine which of the currently approved sulfonylurea drugs prevent the release of interleukin 1β (IL-1β), a major inflammasome effector, during C. albicans challenge of the human macrophage-like THP1 cell line. Findings revealed that the second-generation antidiabetics (glyburide, glisoxepide, gliquidone, and glimepiride), which exhibit greater antidiabetic efficacy than prior iterations, demonstrated anti-inflammatory effects with various degrees of potency as determined by calculation of 50% inhibitory concentrations (IC50s). These same compounds were also effective in reducing IL-1β release during noninfectious inflammasome activation (e.g., induced by lipopolysaccharide [LPS] plus ATP), suggesting that their anti-inflammatory activity is not specific to C. albicans challenge. Moreover, treatment with sulfonylurea drugs did not impact C. albicans growth and filamentation or THP1 viability. Finally, the use of ECE1 and Candidalysin deletion mutants, along with isogenic NLRP3-/- cells, demonstrated that both Candidalysin and NLRP3 are required for IL-1β secretion, further confirming that sulfonylureas suppress inflammasome signaling. Moreover, challenge of THP1 cells with synthetic Candidalysin peptide demonstrated that this toxin is sufficient to activate the inflammasome. Treatment with the experimental inflammasome inhibitor MCC950 led to similar blockade of IL-1β release, suggesting that Candidalysin-mediated inflammasome activation can be inhibited independently of potassium efflux. Together, these results demonstrate that the second-generation antidiabetic sulfonylureas retain anti-inflammatory activity and may be considered for repurposing against immunopathological diseases, including vaginal candidiasis.