Long-term outcomes after coronary artery bypass surgery in patients with diabetes.
- 作者列表："Axelsson TA","Adalsteinsson JA","Arnadottir LO","Helgason D","Johannesdottir H","Helgadottir S","Orrason AW","Andersen K","Gudbjartsson T
OBJECTIVES:Our aim was to investigate the outcome of patients with diabetes undergoing coronary artery bypass grafting (CABG) surgery in a whole population with main focus on long-term mortality and complications. METHODS:This was a nationwide retrospective analysis of all patients who underwent isolated primary CABG in Iceland between 2001 and 2016. Overall survival together with the composite end point of major adverse cardiac and cerebrovascular events was compared between patients with diabetes and patients without diabetes during a median follow-up of 8.5 years. Multivariable regression analyses were used to evaluate the impact of diabetes on both short- and long-term outcomes. RESULTS:Of a total of 2060 patients, 356 (17%) patients had diabetes. Patients with diabetes had a higher body mass index (29.9 vs 27.9 kg/m2) and more often had hypertension (83% vs 62%) and chronic kidney disease (estimated glomerular filtration rate ≤60 ml/min/1.73 m2, 21% vs 14%). Patients with diabetes had an increased risk of operative mortality [odds ratio 2.52, 95% confidence interval (CI) 1.27-4.80] when adjusted for confounders. 5-Year overall survival (85% vs 91%, P < 0.001) and 5-year freedom from major adverse cardiac and cerebrovascular events were also inferior for patients with diabetes (77% vs 82%, P < 0.001). Cox regression analysis adjusting for potential confounders showed that the diagnosis of diabetes significantly predicted all-cause mortality [hazard ratio (HR) 1.87, 95% CI 1.53-2.29] and increased risk of major adverse cardiac and cerebrovascular events (HR 1.47, 95% CI 1.23-1.75). CONCLUSIONS:Patients with diabetes have significantly lower survival after CABG, both within 30 days and during long-term follow-up.
目的: 我们的目的是调查接受冠状动脉旁路移植术 (CABG) 手术的糖尿病患者在整个人群中的结局，主要关注长期死亡率和并发症。 方法: 这是对 2016 和 2001年在冰岛接受单纯原发性 CABG 的所有患者的全国性回顾性分析。在中位随访 8.5 年期间，比较了糖尿病患者和非糖尿病患者的总生存率以及主要不良心脑血管事件的复合终点。采用多变量回归分析评价糖尿病对短期和长期结局的影响。 结果: 在 2060年份的患者中，356 (17%) 的患者患有糖尿病。糖尿病患者的体重指数较高 (29.9 vs 27.9 kg/m2)，更经常患有高血压 (83% vs 62%) 和慢性肾脏病 (估计肾小球滤过率 ≤ 60 ml/min/1.73平方米，21% vs 14%)。校正混杂因素后，糖尿病患者手术死亡风险增加 [比值比 2.52，95% 置信区间 (CI) 1.27-4.80]。糖尿病患者的 5 年总生存率 (85% vs 91%，p <0.001) 和 5 年无主要心脏和脑血管不良事件发生率也较低 (77% vs 82%, P <0.001)。校正潜在混杂因素后的 Cox 回归分析显示，糖尿病的诊断显著预测全因死亡率 [危险比 (HR) 1.87, 95% CI 1.53-2.29] 和主要不良心脑血管事件风险增加 (HR 1.47，95% CI 1.23-1.75)。 结论: 无论是在 30 天内还是在长期随访期间，糖尿病患者 CABG 术后的生存率都显著降低。
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.