Implementing a healthy postpartum lifestyle after gestational diabetes or preeclampsia: a qualitative study of the partner’s role
- 作者列表："Ingfrid Almli","Hege S. Haugdahl","Heidi L. Sandsæter","Janet W. Rich-Edwards","Julie Horn
Abstract Background Women with preeclampsia (PE) and gestational diabetes mellitus (GDM) are at increased risk for later cardiovascular disease, and lifestyle measures are recommended to prevent subsequent disease. Partner support has been shown to be important in lifestyle modification in other diseases, but there is a lack of knowledge of partner involvement in PE and GDM. The aim of this study was to explore the partner’s experiences and knowledge of gestational diseases, and how the partner wishes to contribute to lifestyle change. Methods A qualitative study with one focus group interview and seven in-depth individual interviews, involving eleven partners of women with a pregnancy complicated by GDM or PE. The interview data were inductively analysed using four-step systematic text condensation, supported by interdependence theory. Results Partners experienced a strong “we-feeling” and wanted to support the woman in lifestyle changes. At the same time, they felt insecure, worried, foolish and left out and they missed information from clinicians. The partners felt that their involvement was crucial to lasting lifestyle changes and expected that the clinicians would routinely invite them to discuss lifestyle change. Conclusions Partners considered themselves an important resource for lifestyle changes for women with PE and GDM, but missed being more directly invited, informed and included in maternity care and wanted to participate in the care that followed the gestational disease. This study can help health professionals to realize that partners are an overlooked resource that can make important contributions to improve the health of the whole family if they are involved and supported by health services.
文摘背景子痫前期 (PE) 和妊娠期糖尿病 (GDM) 妇女发生后期心血管疾病的风险增加，建议采取生活方式措施以预防后续疾病。伴侣支持已被证明在其他疾病的生活方式改变中很重要，但缺乏伴侣参与 PE 和 GDM 的知识。本研究的目的是探索伴侣对妊娠疾病的经验和知识，以及伴侣希望如何为生活方式的改变做出贡献。方法采用 1 次焦点小组访谈和 7 次深入个人访谈的质性研究，包括 11 名妊娠合并 GDM 或 PE 的女性伴侣。采用四步系统文本缩合法对访谈数据进行归纳分析，并得到相互依存理论的支持。结果伴侣经历了强烈的 “我们感觉”，希望支持女性改变生活方式。与此同时，他们感到不安全、担心、愚蠢和被忽略，错过了临床医生的信息。合作伙伴认为他们的参与对持久的生活方式改变至关重要，并期望临床医生会常规邀请他们讨论生活方式的改变。结论伴侣认为自己是 PE 和 GDM 女性生活方式改变的重要资源，但错过了更直接的邀请, 知情并纳入产妇护理，并希望参与妊娠期疾病后的护理。这项研究可以帮助卫生专业人员认识到，合作伙伴是一个被忽视的资源，如果他们参与并得到卫生服务的支持，可以为改善整个家庭的健康做出重要贡献。
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.