Interleukin-38 增加 2 型糖尿病患儿的胰岛素敏感性。
- 作者列表："Liu Y","Chen T","Zhou F","Mu D","Liu S
:The prevalence of type 2 diabetes mellitus (DM) is increasing in the children population. It is well known that inflammation contributes to the type 2 DM pathogenesis. Interleukin 38 (IL-38) is one newly identified anti-inflammatory factor. Therefore, we investigated whether the expression level of IL-38 is associated with type 2 DM in the children and the underlying mechanism. Children with recently diagnosed type 2 diabetes mellitus were recruited and studied. The healthy subjects without glucose metabolism abnormalities were used as controls. The IL-38 expression level was determined by quantitative PCR and ELISA (Enzyme-linked immunoassay). Statistic analysis showed that the IL-38 level was significantly associated with type 2 DM and insulin resistance in the children. The patients were then divided into two groups, one group sensitive to insulin therapy while the other resistant to insulin therapy. Data showed that the IL-38 was highly expressed in the group sensitive to insulin therapy. In the mice model, overexpressing the IL-38 could suppress the expression of IL-36, a pro-inflammatory factor, and also the diabetes development. Thus our results showed that higher IL-38 was associated with the increased insulin sensitive in children with type 2 DM and inhibited T2DM development in the mouse model through suppressing the function of IL-36.
: 儿童人群中 2 型糖尿病 (DM) 的患病率正在增加。众所周知，炎症参与了 2 型糖尿病的发病。白细胞介素 38 (IL-38) 是新近发现的抗炎因子。因此，我们研究了 IL-38 的表达水平是否与儿童 2 型糖尿病相关及其机制。招募并研究了近期诊断为 2 型糖尿病的儿童。以无糖代谢异常的健康受试者作为对照。通过定量 PCR 和 ELISA (酶联免疫分析) 测定 IL-38 表达水平。统计分析显示，IL-38 水平与 2 型糖尿病及胰岛素抵抗显著相关。然后将患者分为两组，一组对胰岛素治疗敏感，另一组对胰岛素治疗抵抗。数据显示，IL-38 在胰岛素治疗敏感组中高表达。在小鼠模型中，过表达 IL-38 可以抑制促炎因子 IL-36 的表达，也可以抑制糖尿病的发生。因此，我们的结果表明，较高的 IL-38 与 2 型糖尿病儿童胰岛素敏感性增加有关，并通过抑制 IL-36 功能抑制小鼠模型的 T2DM 发展。
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.