Impairment in Baroreflex Sensitivity in Recent-Onset Type 2 Diabetes Without Progression Over 5 Years.
5 年内无进展的近期发病的 2 型糖尿病患者压力反射敏感性受损。
- 作者列表："Kück JL","Bönhof GJ","Strom A","Zaharia OP","Müssig K","Szendroedi J","Roden M","Ziegler D","GDS group.
:Impaired baroreflex sensitivity (BRS) predicts cardiovascular mortality and is prevalent in long-term diabetes. We determined spontaneous BRS in recent-onset diabetes patients and its temporal sequence over 5 years by recording beat-to-beat blood pressure and R-R intervals over 10 minutes. Four time domain and four frequency domain BRS indices were computed in participants from the German Diabetes Study baseline cohort with recent-onset type 1/type 2 diabetes (n=206/381) and age-matched glucose-tolerant controls (Control 1/Control 2: n=65/83) and subsets of consecutive type 1/type 2 diabetes participants who reached the 5-year follow-up (n=84/137). Insulin sensitivity (M-value) was determined using a hyperinsulinemic-euglycemic clamp. After appropriate adjustment, three frequency domain BRS indices were reduced in type 2 diabetes compared to Control 2 and were positively associated with M-value and inversely associated with fasting glucose and HbA1c (P<0.05), whereas BRS was preserved in type 1 diabetes. After 5 years, a decrease in one and four BRS indices was observed in type 1 and type 2 diabetes patients, respectively (P<0.05), which was explained by the physiologic age-dependent decline. Unlike well-controlled recent-onset type 1 diabetes patients, those with type 2 diabetes show early baroreflex dysfunction, likely due to insulin resistance and hyperglycemia, albeit without progression over 5 years.
: 压力反射敏感性受损 (BRS) 可预测心血管死亡率，在长期糖尿病中普遍存在。我们通过记录 10 min 内的逐搏血压和 R-R 间期，确定了新近发病的糖尿病患者的自发性 BRS 及其 5 年以上的时间序列。在来自德国糖尿病研究基线队列的近期发病的 1 型/2 型糖尿病 (n = 206/381) 参与者中计算了 4 个时域和 4 个频域 BRS 指数和年龄匹配的糖耐量对照 (对照 1/对照 2: n = 65/83) 和达到 5 年随访的连续 1 型/2 型糖尿病参与者的子集 (n = 84/137)。使用高胰岛素-正葡萄糖钳夹测定胰岛素敏感性 (M 值)。经过适当调整后，与对照组 2 相比，2 型糖尿病患者的三个频域 BRS 指数均降低，且与 M 值呈正相关，与空腹血糖和 HbA1c 呈负相关 (P
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.