Association of insulin regimens with severe hypoglycemia in people with Type 1 diabetes - a Danish case-control study.
胰岛素方案与 1 型糖尿病患者严重低血糖的相关性-丹麦病例对照研究。
- 作者列表："Jensen MH","Hejlesen O","Vestergaard P
AIMS:To evaluate the risk of severe hypoglycemia for people with Type 1 diabetes (T1D) when exposed to insulin regimens including human insulin only or insulin analogues. METHODS:A total of 19,896 people with T1D were extracted from the Danish National Patient Register. 6,379 T1D people experiencing one of more severe hypoglycemic episodes (total of 17,242 episodes) were matched 1:1 with T1D people without severe hypoglycemia. A logistic regression model with last insulin regimen used as exposure was constructed to analyse the effect on severe hypoglycemia. RESULTS:People on a basal-bolus regimen with insulin analogues had a reduced risk of severe hypoglycemia of 39% (OR: 0.61, 95% CI: 0.54-0.68) compared to people on a basal-bolus human insulin only regimen. Furthermore, people on a pre-mixed regimen containing an insulin analogue had a 58% (OR: 0.42, 95% CI: 0.36-0.49) reduced risk of severe hypoglycemia compared to people on a pre-mixed human insulin only. CONCLUSIONS:This study indicates that use of a basal-bolus insulin regimen with an insulin analogue is safer with respect to severe hypoglycemia in people with T1D than the use of a basal-bolus human insulin only regimen.
目的: 评估 1 型糖尿病 (T1D) 患者暴露于胰岛素治疗方案 (包括仅人胰岛素或胰岛素类似物) 时发生严重低血糖的风险。 方法: 从丹麦国家患者登记中抽取 19,896 例 T1D 患者。6,379 例经历较严重低血糖发作之一 (共 17,242 次) 的 T1D 人群 1:1 与无严重低血糖的 T1D 人群相匹配。构建以末次胰岛素方案为暴露的 logistic 回归模型，分析对严重低血糖的影响。 结果: 使用胰岛素类似物的基础推注方案的患者发生严重低血糖的风险降低 39% (OR: 0.61，95% CI: 0.54-0.68) 与仅使用基础推注人胰岛素方案的人相比。此外，使用含有胰岛素类似物的预混合方案的人的比例为 58% (OR: 0.42，95% CI: 0.36-0.49) 与仅使用预混合人胰岛素的人相比，严重低血糖的风险降低。 结论: 本研究表明，对于 T1D 患者的严重低血糖，使用胰岛素类似物的基础推注胰岛素方案比仅使用基础推注人胰岛素方案更安全。
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.