Transgenerational effects of maternal obesity and gestational diabetes on offspring body composition and left ventricle mass: the Finnish Gestational Diabetes Prevention Study (RADIEL) 6-year follow-up.
母体肥胖和妊娠期糖尿病对后代身体成分和左心室质量的跨代影响: 芬兰妊娠期糖尿病预防研究 (RADIEL) 6 年随访。
- 作者列表："Litwin L","Sundholm JKM","Rönö K","Koivusalo SB","Eriksson JG","Sarkola T
AIM:To investigate the influence of maternal adiposity and gestational diabetes on offspring body composition and left ventricle mass in early childhood. METHODS:The observational follow-up study included 201 mother-child pairs, a sub-cohort from the Finnish Gestational Diabetes Prevention Study, who were recruited 6.1 ± 0.5 (mean ± SD) years postpartum, aiming for an equal number of mothers with and without gestational diabetes. RESULTS:Maternal pre-pregnancy BMI (mean ± SD; 30.5 ± 5.6 kg/m2 ) was associated with child body fat percentage [0.26 (95% CI; 0.08, 0.44)% increase in child body fat per 1 kg/m2 increase in pre-pregnancy BMI of mothers with obesity] and was reflected in child BMI Z-score (mean ± SD; 0.45 ± 0.93). Left ventricle mass, left ventricle mass index and left ventricle mass Z-score were not associated with gestational diabetes, pre-pregnancy BMI or child body fat percentage. After adjusting for child sex, body fat percentage, systolic blood pressure, pre-pregnancy BMI and maternal lean body mass, left ventricle mass increased by 3.08 (95% CI; 2.25, 3.91) g for each 1 kg in child lean body mass. CONCLUSIONS:Left ventricle mass at 6 years of age is determined predominantly by lean body mass. Maternal pre-gestational adiposity is reflected in child, but no direct association between left ventricle mass and child adiposity or evidence of left ventricle mass foetal programming related to gestational diabetes and maternal adiposity was observed in early childhood.
目的: 探讨母亲肥胖和妊娠期糖尿病对子代早期体成分和左心室质量的影响。 方法: 观察性随访研究包括来自芬兰妊娠糖尿病预防研究的子队列 201 对母子对，招募 6.1 ± 0.5 (平均值 ± SD) 产后 2 年，目标是有和无妊娠糖尿病的母亲数量相等。 结果: 母亲孕前 BMI (平均值 ± SD; 30.5 ± 5.6千克/m2) 与孩子体脂百分比相关 [0.26 (95% CI; 0.08，0.44) 肥胖母亲孕前 BMI 每增加 1千克 kg/m2 儿童体脂增加 %]，反映在儿童 BMI Z 评分中 (平均值 ± SD;0.45 ± 0.93)。左心室质量、左心室质量指数和左心室质量 Z 评分与妊娠糖尿病、孕前 BMI 或儿童体脂百分比无关。校正儿童性别、体脂百分比、收缩压、孕前 BMI 和母亲瘦体重后，左心室质量增加 3.08 (95% CI; 2.25，3.91) 儿童瘦体质量每 1千克 g。 结论: 6 岁时左心室质量主要由瘦体重决定。母亲孕前肥胖反映在儿童, 但在幼儿期未观察到左心室质量与儿童肥胖之间的直接关联，也未观察到与妊娠糖尿病和母亲肥胖相关的左心室质量胎儿编程的证据。
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.