Unmet needs of women with GDM: a health needs assessment in Sandwell, West Midlands

The Role of Placental Non-Coding RNAs in Adverse Pregnancy Outcomes

Non-coding RNAs (ncRNAs) are transcribed from the genome and do not encode proteins. In recent years, ncRNAs have attracted increasing attention as critical participants in gene regulation and disease pathogenesis. Different categories of ncRNAs, which mainly include microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), are involved in the progression of pregnancy, while abnormal expression of placental ncRNAs impacts the onset and development of adverse pregnancy outcomes (APOs). Therefore, we reviewed the current status of research on placental ncRNAs and APOs to further understand the regulatory mechanisms of placental ncRNAs, which provides a new perspective for treating and preventing related diseases.

Pregnancy as an opportunity to prevent type 2 diabetes mellitus: FIGO Best Practice Advice

Gestational diabetes (GDM) impacts approximately 17 million pregnancies worldwide. Women with a history of GDM have an 8-10-fold higher risk of developing type 2 diabetes and a 2-fold higher risk of developing cardiovascular disease (CVD) compared with women without prior GDM. Although it is possible to prevent and/or delay progression of GDM to type 2 diabetes, this is not widely undertaken. Considering the increasing global rates of type 2 diabetes and CVD in women, it is essential to utilize pregnancy as an opportunity to identify women at risk and initiate preventive intervention. This article reviews existing clinical guidelines for postpartum identification and management of women with previous GDM and identifies key recommendations for the prevention and/or delayed progression to type 2 diabetes for global clinical practice.

Evolution of Gestational Diabetes Mellitus across Continents in 21st Century

Over the last few decades, several definitions of gestational diabetes mellitus (GDM) have been described. There is currently not enough research to show which way is the best to diagnose GDM. Opinions differ in terms of the optimal screening and diagnostic measures, in part due to the differences in the population risks, the cost-effectiveness considerations, and the lack of an evidence base to support large national screening programs. The basic method for identifying the disease is the measurement of glucose plasma levels which may be determined when fasting, two hours after a meal, or simply at any random time. The currently increasing incidence of diabetes in the whole population, the altering demographics and the presence of lifestyle changes still require better methods of screening for hyperglycemia, especially during pregnancy. The main aim of this review is to focus on the prevalence and modifications to the screening criteria for GDM across all continents in the 21st century. We would like to show the differences in the above issues and correlate them with the geographical situation. Looking at the history of diabetes, we are sure that more than one evolution in GDM diagnosis will occur, due to the development of medicine, appearance of modern technologies, and the dynamic continuation of research.

Machine learning prediction of non-attendance to postpartum glucose screening and subsequent risk of type 2 diabetes following gestational diabetes

OBJECTIVE

The aim of the present study was to identify the factors associated with non-attendance of immediate postpartum glucose test using a machine learning algorithm following gestational diabetes mellitus (GDM) pregnancy.

METHOD

A retrospective cohort study of all GDM women (n = 607) for postpartum glucose test due between January 2016 and December 2019 at the George Eliot Hospital NHS Trust, UK.

RESULTS

Sixty-five percent of women attended postpartum glucose test. Type 2 diabetes was diagnosed in 2.8% and 21.6% had persistent dysglycaemia at 6-13 weeks post-delivery. Those who did not attend postpartum glucose test seem to be younger, multiparous, obese, and continued to smoke during pregnancy. They also had higher fasting glucose at antenatal oral glucose tolerance test. Our machine learning algorithm predicted postpartum glucose non-attendance with an area under the receiver operating characteristic curve of 0.72. The model could achieve a sensitivity of 70% with 66% specificity at a risk score threshold of 0.46. A total of 233 (38.4%) women attended subsequent glucose test at least once within the first two years of delivery and 24% had dysglycaemia. Compared to women who attended postpartum glucose test, those who did not attend had higher conversion rate to type 2 diabetes (2.5% vs 11.4%; p = 0.005).

CONCLUSION

Postpartum screening following GDM is still poor. Women who did not attend postpartum screening appear to have higher metabolic risk and higher conversion to type 2 diabetes by two years post-delivery. Machine learning model can predict women who are unlikely to attend postpartum glucose test using simple antenatal factors. Enhanced, personalised education of these women may improve postpartum glucose screening.