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Gestational diabetes risk in migrants. A nation-wide register-based study of all births in Denmark 2004-2015.

流动人口妊娠糖尿病风险。丹麦 2004-2015 年所有出生的全国注册研究。

  • 影响因子:5.19
  • DOI:10.1210/clinem/dgaa024
  • 作者列表:"Kragelund Nielsen K","Andersen GS","Damm P","Andersen AN
  • 发表时间:2020-01-17

BACKGROUND:Much remains to be understood about socioeconomic position and body mass index (BMI) in the pathways linking ethnicity, migration, and gestational diabetes mellitus (GDM). We investigated differences in GDM prevalence according to maternal country of origin and the role played by socioeconomic position and BMI on this relationship. Finally, we examined how length of residency was associated with GDM. METHODS:A register-based cohort study of the 725 482 pregnancies that resulted in a birth in Denmark, 2004 to 2015. Of these, 14.4% were by women who had migrated to Denmark. A GDM diagnosis was registered in 19 386 (2.7%) pregnancies, of which 4464 (23.0%) were in immigrant women. The crude risk of GDM according to maternal country of origin compared to Danish-born women ranged from an odds ratio (OR) of 0.50 (95% CI 0.34-0.71) for women from Sweden to an OR of 5.11 (95% CI 4.28-6.11) for women from Sri Lanka. Adjustment for socioeconomic position slightly attenuated the risks. Adjusting for BMI resulted in increased ORs for women, especially from Asian countries. The separate and joint effects of migration and overweight on GDM risk differed substantially between the countries of origin (P value interaction term < .001). Immigrants with 10 or more years of residency had a 56% increased risk of GDM (OR 1.56, 95% CI 1.44-1.68) compared to immigrants with less than 5 years in Denmark. This risk was somewhat diluted when adjusting for age and BMI. CONCLUSIONS:This study demonstrates substantial variation in the risk of GDM according to country of origin. The risk associations are only slightly affected by socioeconomic position and BMI.


背景: 在种族、移民和妊娠期糖尿病 (GDM) 的联系途径中,社会经济地位和体重指数 (BMI) 还有很多待了解。我们根据母亲原籍国调查了 GDM 患病率的差异,以及社会经济地位和 BMI 对这种关系的作用。最后,我们检测了住院时间与 GDM 的相关性。 方法: 一项基于登记的队列研究,研究对象为 2015年在丹麦 (725) 分娩的 482 2004 例妊娠。其中,14.4% 是移民到丹麦的妇女。19 386 例 (2.7%) 妊娠登记了 GDM 诊断,其中 4464 例 (23.0%) 为移民妇女。与丹麦出生的妇女相比,根据母亲原籍国,GDM 的粗风险范围为 0.50 的比值比 (OR) (95% CI 0.34-0.71) 瑞典女性为斯里兰卡女性的 OR 为 5.11 (95% CI 4.28-6.11)。社会经济地位的调整稍微减弱了风险。调整 BMI 导致女性的 or 增加,尤其是来自亚洲国家的女性。移民和超重对 GDM 风险的单独和联合影响在原籍国之间存在显著差异 (P 值交互作用术语 <.001)。与丹麦居住时间小于 5 年的移民相比,居住时间在 10 年或 10 年以上的移民 GDM 风险增加 56% (or 1.56,95% CI 1.44-1.68)。当调整年龄和 BMI 时,这种风险有所稀释。 结论: 本研究显示 GDM 的风险根据原产国存在实质性差异。风险相关性仅受社会经济地位和 BMI 的轻微影响。



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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翻译标题与摘要 下载文献
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.