Impact of gestational diabetes diagnosis on concurrent depression in pregnancy.
- 作者列表："Miller NE","Curry E","Laabs SB","Manhas M","Angstman K
:Background: Gestational diabetes mellitus (GDM) affects nearly 5% of US pregnancies and is associated with poor outcomes. Perinatal depression is also associated with substantial risks to both the fetus and mother. There is limited data about the relationship between GDM and antenatal depression. Therefore, we looked at whether a GDM diagnosis would be associated with an increased risk of depression during pregnancy.Methods: We studied 562 pregnant women from 1 July 2013 to 30 June 2015, in a prospective multi-part survey on clinical obstetrical outcomes.Results: Of the 562 patients, 46 patients (8.0%) were diagnosed with GDM. There was no statistical difference between the groups for either history of prior or post-partum depression. Diagnosis of depression was present in 15.2% of the GDM group but only 6.2% of the control group. Regression modeling demonstrated an adjusted odds ratio (AOR) of 2.46 for a diagnosis of depression when the patient had a diagnosis of GDM (95% CI 1.01-6.03, p=.049).Conclusions: The diagnosis of GDM was associated with an elevated risk of concomitant pregnancy diagnosis of depression. Given the elevated risk to patients diagnosed with GDM, a more frequent depression screening interval could be considered during the remainder of the pregnancy, such as each prenatal visit.
背景: 妊娠期糖尿病 (GDM) 影响了近 5% 的美国孕妇，并且与不良结局相关。围产期抑郁症也与胎儿和母亲的实质性风险有关。关于 GDM 与产前抑郁关系的资料有限。因此，我们研究了 GDM 诊断是否与怀孕期间抑郁症风险增加相关。方法: 我们研究了 2013年7月1日至 2015年6月30日的 562 例孕妇，在一项关于临床产科结局的前瞻性多部分调查中。结果: 562 例患者中，46 例 (8.0%) 诊断为 GDM。两组间既往或产后抑郁史均无统计学差异。15.2% 的 GDM 组存在抑郁症的诊断，而对照组只有 6.2%。回归模型显示，当患者诊断为 GDM 时，抑郁诊断的校正比值比 (AOR) 为 2.46 (95% CI 1.01-6.03，p =.049)。结论: GDM 的诊断与合并妊娠诊断抑郁症的风险升高相关。鉴于被诊断为 GDM 的患者风险升高，在妊娠剩余时间内，如每次产前访视，可以考虑更频繁的抑郁筛查间隔。
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.