Effectiveness of pre-pregnancy lifestyle in preventing gestational diabetes mellitus-a systematic review and meta-analysis of 257,876 pregnancies.

BACKGROUND: Gestational Diabetes Mellitus (GDM) is hyperglycaemia first detected during pregnancy. Globally, GDM affects around 1 in 6 live births (up to 1 in 4 in low- and middle-income countries- LMICs), thus, urgent measures are needed to prevent this public health threat. OBJECTIVE: To determine the effectiveness of pre-pregnancy lifestyle in preventing GDM. METHODS: We searched MEDLINE, Web of science, Embase and Cochrane central register of controlled trials. Randomized control trials (RCTs), case-control studies, and cohort studies that assessed the effect of pre-pregnancy lifestyle (diet and/or physical activity based) in preventing GDM were included. Random effects model was used to calculate odds ratio (OR) with 95% confidence interval. The Cochrane ROB-2 and the Newcastle-Ottawa Scale were used for assessing the risk of bias. The protocol was registered in PROSPERO (ID: CRD42020189574) RESULTS: Database search identified 7935 studies, of which 30 studies with 257,876 pregnancies were included. Meta-analysis of the RCTs (N = 5; n = 2471) in women who received pre-pregnancy lifestyle intervention showed non-significant reduction of the risk of developing GDM (OR 0.76, 95% CI: 0.50-1.17, p = 0.21). Meta-analysis of cohort studies showed that women who were physically active pre-pregnancy (N = 4; n = 23263), those who followed a low carbohydrate/low sugar diet (N = 4; n = 25739) and those women with higher quality diet scores were 29%, 14% and 28% less likely to develop GDM respectively (OR 0.71, 95% CI: 0.57, 0.88, p = 0.002, OR 0.86, 95% CI: 0.68, 1.09, p = 0.22 and OR 0.72, 95% CI 0.60-0.87, p = 0.0006). CONCLUSION: This study highlights that some components of pre-pregnancy lifestyle interventions/exposures such as diet/physical activity-based preparation/counseling, intake of vegetables, fruits, low carbohydrate/low sugar diet, higher quality diet scores and high physical activity can reduce the risk of developing gestational diabetes. Evidence from RCTs globally and the number of studies in LMICs are limited, highlighting the need for carefully designed RCTs that combine the different aspects of the lifestyle and are personalized to achieve better clinical and cost effectiveness.

Prediction of postpartum prediabetes by machine learning methods in women with gestational diabetes mellitus.

Early onset of type 2 diabetes and cardiovascular disease are common complications for women diagnosed with gestational diabetes. Prediabetes refers to a condition in which blood glucose levels are higher than normal, but not yet high enough to be diagnosed as type 2 diabetes. Currently, there is no accurate way of knowing which women with gestational diabetes are likely to develop postpartum prediabetes. This study aims to predict the risk of postpartum prediabetes in women diagnosed with gestational diabetes. Our sparse logistic regression approach selects only two variables - antenatal fasting glucose at OGTT and HbA1c soon after the diagnosis of GDM - as relevant, but gives an area under the receiver operating characteristic curve of 0.72, outperforming all other methods. We envision this to be a practical solution, which coupled with a targeted follow-up of high-risk women, could yield better cardiometabolic outcomes in women with a history of GDM.

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.

A Minimal Model Approach for Analyzing Continuous Glucose Monitoring in Type 2 Diabetes.

Continuous glucose monitoring (CGM), a technique that records blood glucose at a regular intervals. While CGM is more commonly used in type 1 diabetes, it is increasingly becoming attractive for treating type 2 diabetic patients. The time series obtained from a CGM provides a rich picture of the glycemic state of the subjects and may help have tighter control on blood sugar by revealing patterns in their physiological responses to food. However, despite its importance, the biophysical understanding of CGM is far from complete. CGM data series is complex not only because it depends on the composition of the food but also varies with individual physiology. All of these make a full modeling of CGM data a difficult task. Here we propose a simple model to explain CGM data in type 2 diabetes. The model combines a relatively simple glucose-insulin dynamics with a two-compartment food model. Using CGM data of a healthy and a diabetic individual we show that this model can capture liquid meals well. The model also allows us to estimate the parameters in a relatively straightforward manner. This opens up the possibility of personalizing the CGM data. The model also predicts insulin time series from the model, and the rate of appearance of glucose due to food. Our methodology thus paves the way for novel analyses of CGM which have not been possible before.