Gestational Diabetes Mellitus and Future Cardiovascular Risk: An Update

Burlina, Silvia; Dalfra, Mg; Chilelli, Nino Cristiano; Lapolla, Annunziata

doi:10.1155/2016/2070926

Cited by 57 publications

(39 citation statements)

References 47 publications

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“…GDM has been reported to increase the risk of adverse fetal outcomes such as fetal macrosomia, and subsequent maternal consequences like metabolic syndrome and cardiovascular morbidity. However, early identification of identifying women at risk of GDM could aid in averting such risks, through early interventions [6,7]. Various groups have attempted to develop models that can predict an abnormal OGTT occurrence at 24-28 weeks, based on risk factors identified in the first trimester.…”

Section: Introductionmentioning

confidence: 99%

Nomogram for prediction of gestational diabetes mellitus in urban, Chinese, pregnant women

Guo

Zhang

et al. 2020

BMC Pregnancy Childbirth

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Background: This study sought to develop and validate a nomogram for prediction of gestational diabetes mellitus (GDM) in an urban, Chinese, antenatal population. Methods: Age, pre-pregnancy body mass index (BMI), fasting plasma glucose (FPG) in the first trimester and diabetes in first degree relatives were incorporated as validated risk factors. A prediction model (nomogram) for GDM was developed using multiple logistic regression analysis, from a retrospective study conducted on 3956 women who underwent their first antenatal visit during 2015 in Shanghai. Performance of the nomogram was assessed through discrimination and calibration. We refined the predicting model with t-distributed stochastic neighbor embedding (t-SNE) to distinguish GDM from non-GDM. The results were validated using bootstrap resampling and a prospective cohort of 6572 women during 2016 at the same institution. Results: Advanced age, pre-pregnancy BMI, high first-trimester, fasting, plasma glucose, and, a family history of diabetes were positively correlated with the development of GDM. This model had an area under the receiver operating characteristic (ROC) curve of 0.69 [95% CI:0.67-0.72, p < 0.0001]. The calibration curve for probability of GDM showed good consistency between nomogram prediction and actual observation. In the validation cohort, the ROC curve was 0.70 [95% CI: 0.68-0.72, p < 0.0001] and the calibration plot was well calibrated. In exploratory and validation cohorts, the distinct regions of GDM and non-GDM were distinctly separated in the t-SNE, generating transitional boundaries in the image by color difference. Decision curve analysis showed that the model had a positive net benefit at threshold between 0.05 and 0.78. Conclusions: This study demonstrates the ability of our model to predict the development of GDM in women, during early stage of pregnancy.

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Section: Introductionmentioning

confidence: 99%

Nomogram for prediction of gestational diabetes mellitus in urban, Chinese, pregnant women

Guo

Zhang

et al. 2020

BMC Pregnancy Childbirth

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“…Gestational diabetes occurs in 3%-4% of pregnancies 31 and is associated with maternal postpartum diabetes, metabolic syndrome and CVD. 35 Women with gestational diabetes had a 15-fold higher rate of subsequent type 2 diabetes over 8.5 years followup, with a median age at onset of 37 years. 36 Long-term 30-year follow-up of these women showed higher prevalence of cardiovascular disease (15.5% v. 12.4%; adjusted OR 1.85, 95% CI 1.21-2.82), with presentation at a younger age (45.5 ± 2.2 v. 52.5 ± 11.9 years) independent of development of postpartum diabetes or metabolic syndrome.…”

Section: Maternal Dysglycemiamentioning

confidence: 97%

“…These maternal cardiometabolic disorders have been identified as risk factors for longterm cardiovascular disease. [31][32][33][34][35][36][37][38][39]…”

Section: What Do Pregnancy Complications Indicate About Future Risk Omentioning

confidence: 99%

Identifying and managing younger women at high risk of cardiovascular disease

Srivaratharajah

Abramson

2019

CMAJ

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C ardiovascular disease (CVD), which is largely preventable, is a leading cause of morbidity and mortality among Canadian women. For this article, we use CVD to refer to ischemic heart disease and stroke. Data for peripheral arterial disease in women are limited and are addressed elsewhere. 1 Until now, risk reduction has focused largely on postmenopausal woman with traditional risk factors: diabetes, smoking, hypertension and hyperlipidemia. Consequently, CVD mortality has declined, largely driven by those aged 50 years and older. 2 A recent study of 20-year temporal trends in admissions to hospital and deaths caused by atherosclerotic cardiovascular disease in Ontario reported that mortality rates for circulatory diseases in women declined 52.8% between 1994 and 2012. 3 However, annual rates of decline were least evident in individuals younger than 50 years of age, suggesting that CVD among younger adults remains a cause for concern. 3 The lowest rate of decline in CVD-related mortality and, in some cases, an increase in CVD-related admissions to hospital and mortality have been observed in younger women. 3,4 Contemporary Canadian data suggest the gap in cardiovascular mortality between men and women may be closing. 5 Yet young women with ST-segment elevation myocardial infarction (MI) have 15%-20% higher rates of death than men of similar age. 6 Whether this is related to systematic differences in care or true biological differences, or a combination, is unclear. What is clear is that addressing cardiovascular health in women younger than 50 years of age requires thinking beyond traditional risk factors in primordial prevention. We present a brief overview of sex differences in traditional cardiovascular risk factors and a focused review of key nontraditional risk factors in younger women (i.e., ovarian dysfunction, infertility, reproductive therapies and pregnancy complications). Our approach to gathering evidence is outlined in Box 1.

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“…Clearly, identifying women at risk of GDM as early as possible in pregnancy enables interventions that reduce the risk of adverse fetal outcomes including those associated with fetal macrosomia, and, later maternal consequences including the metabolic syndrome and cardiovascular morbidity 6,7 . Many groups have tried to develop predictive models based on risk factors identified in the first trimester, to identify an abnormal OGTT at 24-28 weeks.…”

Section: Introductionmentioning

confidence: 99%

Screening for gestational diabetes mellitus in women with low BMI in an urban, Chinese, population: two retrospective cohorts study

Guo

Yang

Zhang

et al. 2019

Preprint

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Background We design this study to assess the utility of baseline characteristics in first trimester in the prediction of gestational diabetes mellitus (GDM) in non-obese, women in an urban, Chinese, antenatal population. Methods Multiple, logistic regression analysis was used to develop a predictive model for GDM, in a retrospective cohort of 3956 women (mean age 30.61, years, mean pre-pregnant BMI= 21.45 kg/m2) during 2015 in Shanghai. We refined the predictive model with t-distributed stochastic neighbor embedding (t-SNE) to attempt to distinguish GDM from non-GDM. Results GDM prevalence in the retrospective cohort was 16.7%. Advanced age, prepregnant obesity, high first-trimester, fasting, plasma glucose, and, a family history of diabetes were positively-related to the development of GDM. The simplified point scoring system yielded an AUC of 0.69. When the model was applied to a prospective cohort of 6572 women recruited in 2016 (mean age 30.80 years, mean prepregnant BMI = 21.18 kg/m2, incidence of GDM 10.8%), the AUC was 0.70. A score of ≥3 points was 91.3% specific and 25.4% sensitive for GDM. In both cohorts, the distinct regions of GDM and non-GDM were ideally separated in the t-SNE, generating transitional boundaries in the image found by different color. Conclusions Our low BMI, antenatal population now over-consumes a Western, diabetogenic diet during pregnancy though we attribute the declining incidence (2015, 16.7% vs 2016, 10.8%) to an improved antenatal education program. We propose a score system to screen for GDM in first trimester, and also, we should concentrate our efforts on prevention through antenatal education.

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Gestational Diabetes Mellitus and Future Cardiovascular Risk: An Update

Cited by 57 publications

References 47 publications

Nomogram for prediction of gestational diabetes mellitus in urban, Chinese, pregnant women

Nomogram for prediction of gestational diabetes mellitus in urban, Chinese, pregnant women

Identifying and managing younger women at high risk of cardiovascular disease

Screening for gestational diabetes mellitus in women with low BMI in an urban, Chinese, population: two retrospective cohorts study

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