Time-varying risk of microvascular complications in latent autoimmune diabetes of adulthood compared with type 2 diabetes in adults: a post-hoc analysis of the UK Prospective Diabetes Study 30-year follow-up data (UKPDS 86).
成人隐匿性自身免疫性糖尿病与 2 型糖尿病微血管并发症的时变风险比较: 英国前瞻性糖尿病研究 30 年随访数据的事后分析 (UKPDS 86)。
- 作者列表："Maddaloni E","Coleman RL","Agbaje O","Buzzetti R","Holman RR
BACKGROUND:Latent autoimmune diabetes of adulthood (LADA) differs in clinical features from type 2 diabetes. Whether this difference translates into different risks of complications remains controversial. We examined the long-term risk of microvascular complications in people enrolled in the UK Prospective Diabetes Study (UKPDS), according to their diabetes autoimmunity status. METHODS:We did a post-hoc analysis of 30-year follow-up data from UKPDS (UKPDS 86). UKPDS participants with diabetes autoantibody measurements available and without previous microvascular events were included. Participants with at least one detectable autoantibody were identified as having latent autoimmune diabetes, and those who tested negative for all autoantibodies were identified as having type 2 diabetes. The incidence of the primary composite microvascular outcome (first occurrence of renal failure, renal death, blindness, vitreous haemorrhage, or retinal photocoagulation) was compared between adults with latent autoimmune diabetes and those with type 2 diabetes. The follow-up ended on Sept 30, 2007. Baseline and updated 9-year mean values of potential confounders were tested in Cox models to adjust hazard ratios (HRs). UKPDS is registered at the ISRCTN registry, 75451837. FINDINGS:Among the 5028 participants included, 564 had latent autoimmune diabetes and 4464 had type 2 diabetes. After median 17·3 years (IQR 12·6-20·7) of follow-up, the composite microvascular outcome occurred in 1041 (21%) participants. The incidence for the composite microvascular outcome was 15·8 (95% CI 13·4-18·7) per 1000 person-years in latent autoimmune diabetes and 14·2 (13·3-15·2) per 1000 person-years in type 2 diabetes. Adults with latent autoimmune diabetes had a lower risk of the composite outcome during the first 9 years of follow-up than those with type 2 diabetes (adjusted HR 0·45 [95% CI 0·30-0·68], p<0·0001), whereas in subsequent years their risk was higher than for those with type 2 diabetes (1·25 [1·01-1·54], p=0·047). Correcting for the higher updated 9-year mean HbA1c seen in adults with latent autoimmune diabetes than in those with type 2 diabetes explained entirely their subsequent increased risk for the composite microvascular outcome (adjusted HR 0·99 [95% CI 0·80-1·23], p=0·93). INTERPRETATION:At diabetes onset, adults with latent autoimmune diabetes have a lower risk of microvascular complications followed by a later higher risk of complications than do adults with type 2 diabetes, secondary to worse glycaemic control. Implementing strict glycaemic control from the time of diagnosis could reduce the later risk of microvascular complications in adults with latent autoimmune diabetes. FUNDING:European Foundation for the Study of Diabetes Mentorship Programme (AstraZeneca).
背景: 成年期隐匿性自身免疫性糖尿病 (LADA) 的临床特征与 2 型糖尿病不同。这种差异是否转化为并发症的不同风险仍存在争议。我们根据他们的糖尿病自身免疫状况，检查了英国前瞻性糖尿病研究 (UKPDS) 入组人群微血管并发症的长期风险。 方法: 我们对 UKPDS (UKPDS 86) 的 30 年随访数据进行了事后分析。包括有糖尿病自身抗体测量可用且既往无微血管事件的 UKPDS 参与者。至少有一种可检测到的自身抗体的参与者被确定为患有隐匿性自身免疫性糖尿病，所有自身抗体检测阴性的参与者被确定为患有 2 型糖尿病。主要复合微血管结局的发生率 (首次发生肾衰竭、肾性死亡、失明、玻璃体出血或视网膜光凝) 比较了成人隐匿性自身免疫性糖尿病患者和 2 型糖尿病患者。随访于 2007 年 9 月 30 日结束。在 Cox 模型中检测潜在混杂因素的基线和更新的 9 年平均值，以调整风险比 (HRs)。UKPDS 在 ISRCTN 登记处注册，75451837。 结果: 在纳入的 5028 名参与者中，564 有隐匿性自身免疫性糖尿病，4464 有 2 型糖尿病。中位随访 17 · 3 年 (IQR 12 · 6-20 · 7) 后，1041 例 (21%) 参与者出现复合微血管结局。复合微血管结局的发生率为 15 · 8 (95% CI 13 · 4-18 · 7) 隐匿性自身免疫性糖尿病患者每 1000 人年，2 型糖尿病患者每 1000 人年为 14 · 2 (13 · 3-15 · 2)。患有隐匿性自身免疫性糖尿病的成人患者在随访的前 9 年复合结果的风险低于 2 型糖尿病患者 (校正 HR 0 · 45 [95% CI 0 · 30 -0 · 68], 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.