Adherence to multiple medications in the TODAY (Treatment Options for type 2 Diabetes in Adolescents and Youth) cohort: effect of additional medications on adherence to primary diabetes medication.

当今 (青少年 2 型糖尿病治疗方案) 队列中多种药物的依从性: 额外药物对原发性糖尿病药物依从性的影响。

  • 影响因子:1.22
  • DOI:10.1515/jpem-2019-0315
  • 作者列表:"Shah R","McKay SV","Levitt Katz LE","El Ghormli L","Anderson BJ","Casey TL","Higgins L","Izquierdo R","Wauters AD","Chang N","TODAY Study Group.
  • 发表时间:2020-02-25

:Background Non-adherence to diabetes medication leads to poor outcomes and increased healthcare costs. Multiple factors affecting adherence in adults with type 2 diabetes (T2D) have been identified, but pediatric data is sparse. We aimed to determine whether initiation of additional oral medications or insulin affects adherence to primary study medication (PSM) in the Treatment Options for type 2 Diabetes in Adolescents and Youth (TODAY) study. Methods Six hundred and ninety-nine youth (aged 10-17 years) with recent-onset T2D were randomized in the TODAY study. Participants were categorized as adherent (≥80% taken by pill count) or non-adherent (<80%), and adherence was compared between those on additional medications or not. Subgroup analyses to assess influence of race/ethnicity, gender, medication type, or depression were performed. Results At 36 months, 46.3% of participants were taking additional oral medications and 31.9% were on insulin. There was no difference in study medication adherence with additional oral medications (55.1%, 67.1%, and 56.7% at month 36 in those prescribed 0, 1, or 2+  additional medications; p = 0.16). Girls on oral contraceptives (OC) had higher adherence (65.2% vs. 55.8% at month 36; p = 0.0054). Participants on insulin had lower adherence (39.7% vs. 59.3% at 36 months; p < 0.0001). There was decreased adherence in participants with baseline depression (p = 0.008). Conclusions Additional oral medications did not influence adherence to diabetes medications in TODAY. Addition of insulin led to reduced adherence. In subgroup analyses, OC use was associated with higher adherence in girls, while baseline depression was associated with lower adherence overall. Further studies examining potentially modifiable risk factors of adherence in pediatric T2D are needed.


: 背景不坚持糖尿病药物治疗导致不良结局和医疗费用增加。影响成人 2 型糖尿病 (T2D) 依从性的多个因素已经确定,但儿科数据很少。我们旨在确定在青少年和青年 2 型糖尿病治疗方案 (今天) 研究中,开始额外的口服药物或胰岛素是否影响对主要研究药物 (PSM) 的依从性。方法在 TODAY 研究中,对 698 例新近发病的 T2D 青年 (年龄 10 ~ 17 岁) 进行随机分组。将参与者分类为粘附 (按药丸计数服用 ≥ 80%) 或未粘附 (<80%),并比较额外药物治疗与否的依从性。进行亚组分析,以评估种族/种族、性别、药物类型或抑郁的影响。结果在 36 个月时,46.3% 的参与者服用额外的口服药物,31.9% 的参与者使用胰岛素。在处方 0 、 1 或 2 + 额外药物治疗的 36 个月时,研究药物依从性与额外口服药物无差异 (55.1% 、 67.1% 和 56.7%; P = 0.16)。服用口服避孕药 (OC) 的女孩依从性较高 (65.2% vs. 55.8% 在 36 个月; P = 0.0054)。接受胰岛素治疗的受试者依从性较低 (36 个月时 39.7% vs. 59.3%; P <0.0001)。基线抑郁的参与者依从性降低 (p = 0.008)。结论额外的口服药物不影响当今糖尿病药物的依从性。添加胰岛素导致依从性降低。在亚组分析中,OC 使用与女孩较高的依从性相关,而基线抑郁与总体依从性较低相关。需要进一步研究检查儿科 T2D 中依从性的潜在可改变风险因素。



来源期刊:Acta Diabetologica
作者列表:["Benhalima, Katrien","Crombrugge, Paul","Moyson, Carolien","Verhaeghe, Johan","Vandeginste, Sofie","Verlaenen, Hilde","Vercammen, Chris","Maes, Toon","Dufraimont, Els","Block, Christophe","Jacquemyn, Yves","Mekahli, Farah","Clippel, Katrien","Den Bruel, Annick","Loccufier, Anne","Laenen, Annouschka","Minschart, Caro","Devlieger, Roland","Mathieu, Chantal"]

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.

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来源期刊:Nature Medicine
作者列表:["Artzi, Nitzan Shalom","Shilo, Smadar","Hadar, Eran","Rossman, Hagai","Barbash-Hazan, Shiri","Ben-Haroush, Avi","Balicer, Ran D.","Feldman, Becca","Wiznitzer, Arnon","Segal, Eran"]

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.

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作者列表:["Lowes DJ","Hevener KE","Peters BM"]

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.