Maternal racial and ethnic disparities in glycemic threshold for pharmacotherapy initiation for gestational diabetes.


  • 影响因子:1.44
  • DOI:10.1080/14767058.2020.1711728
  • 作者列表:"Palatnik A","Harrison RK","Walker RJ","Thakkar MY","Egede LE
  • 发表时间:2020-01-13

To compare the glycemic threshold for pharmacotherapy initiation in women with gestational diabetes (GDM) based on maternal race/ethnicity. A retrospective cohort study of women with GDM who received pharmacotherapy during pregnancy, in addition to diet and exercise, between 2015 and 2019 in a university center. The primary outcome was percent of elevated capillary blood glucoses (CBGs) prior to pharmacotherapy initiation. This was compared between four maternal racial and ethnic groups: non-Hispanic white (NHW), non-Hispanic black (NHB), Hispanic and other race and ethnicity group that included Asian, American Indian and Alaskan Native. Univariable and multivariable analyses were done to estimate whether there was an independent association between maternal race and ethnicity and the percent of elevated CBGs prior to pharmacotherapy initiation. A total of 440 women met inclusion criteria. In univariable analysis, NHB women, Hispanic, and women of other race and ethnicity had higher percent of elevated CBGs prior to pharmacotherapy initiation, compared to NHW women (45.5 ± 22.5% for NHW, 65.2 ± 25.4% for NHB, 58.3 ± 21.7% for Hispanic and 51.6 ± 26.8% for other race and ethnicity, respectively,  < .001). After the adjustment for maternal demographic and clinical factors, maternal race and ethnicity remained to be significantly associated with timing of pharmacotherapy initiation, with women of racial and ethnic minority having a higher percent of elevated CBGs prior to pharmacotherapy initiation (adjusted linear regression coefficient 18.1, 95% CI 11.3-25.0 for NHB, adjusted linear regression coefficient 13.2, 95% CI 5.0-21.4 for Hispanic, and adjusted linear regression coefficient 9.8, 95% CI 2.6-16.9 for women of other race and ethnicity). A significant variation was identified in glycemic threshold for pharmacotherapy initiation in women with GDM across different maternal racial and ethnic groups with minority women starting pharmacotherapy at higher percent of elevated CBGs.


比较基于母亲种族/种族的妊娠期糖尿病 (GDM) 妇女开始药物治疗的血糖阈值。2019 和 2015年在一所大学中心对妊娠期接受药物治疗的 GDM 妇女进行的回顾性队列研究。主要结局是开始药物治疗前毛细血管血糖升高 (cbg) 的百分比。这在四个母亲种族和族裔群体之间进行了比较: 非西班牙裔白人 (NHW),非西班牙裔黑人 (NHB),西班牙裔和其他种族和族裔群体,包括亚洲人,美洲印第安人和阿拉斯加本地人。进行单变量和多变量分析,以估计在开始药物治疗前,母亲种族和种族与 cbg 升高的百分比之间是否存在独立关联。共有 440 名妇女符合纳入标准。在单变量分析中,NHB 女性、西班牙裔和其他种族和民族的女性在药物治疗开始前 cbg 升高的百分比高于 NHW 女性 (NHW 为 45.5 ± 22.5%,NHB 分别为 65.2 ± 25.4%,西班牙裔为 58.3 ± 21.7%,其他种族和民族分别为 51.6 ± 26.8%,  <.001)。在对孕产妇人口和临床因素进行调整后,孕产妇种族和族裔仍然与药物治疗开始的时间显著相关,随着种族和少数民族妇女在开始药物治疗前 cbg 升高的百分比较高 (调整后 NHB 的线性回归系数 18.1,95% CI 11.3-25.0,调整线性回归系数 13.2,西班牙裔为 95% CI 5.0-21.4,其他种族和民族女性为调整线性回归系数 9.8,95% CI 2.6-16.9)。不同母亲种族和民族的 GDM 妇女开始药物治疗的血糖阈值存在显著差异,少数民族妇女开始药物治疗的 cbg 升高百分比较高。



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.