Maternal obesity and degree of glucose intolerance on neonatal hypoglycaemia and birth weight: a retrospective observational cohort study in women with gestational diabetes mellitus

Early prediction of gestational diabetes mellitus using maternal demographic and clinical risk factors

OBJECTIVE

To build and validate an early risk prediction model for gestational diabetes mellitus (GDM) based on first-trimester electronic medical records including maternal demographic and clinical risk factors.

METHODS

To develop and validate a GDM prediction model, two datasets were used in this retrospective study. One included data of 14,015 pregnant women from Máxima Medical Center (MMC) in the Netherlands. The other was from an open-source database nuMoM2b including data of 10,038 nulliparous pregnant women, collected in the USA. Widely used maternal demographic and clinical risk factors were considered for modeling. A GDM prediction model based on elastic net logistic regression was trained from a subset of the MMC data. Internal validation was performed on the remaining MMC data to evaluate the model performance. For external validation, the prediction model was tested on an external test set from the nuMoM2b dataset.

RESULTS

An area under the receiver-operating-characteristic curve (AUC) of 0.81 was achieved for early prediction of GDM on the MMC test data, comparable to the performance reported in previous studies. While the performance markedly decreased to an AUC of 0.69 when testing the MMC-based model on the external nuMoM2b test data, close to the performance trained and tested on the nuMoM2b dataset only (AUC = 0.70).

Development and internal validation of a clinical score to predict neonatal hypoglycaemia in women with gestational diabetes

INTRODUCTION

Gestational diabetes (GD) is a risk factor for neonatal hypoglycaemia (NH), but other factors can increase this risk.

OBJECTIVES

To create a score to predict NH in women with GD.

METHODS

Retrospective study of women with GD with a live singleton birth between 2012 and 2017 from the Portuguese GD registry. Pregnancies with and without NH were compared. A logistic regression was used to study NH predictors. Variables independently associated with NH were used to score derivation. The model's internal validation was performed by a bootstrapping. The association between the score and NH was assessed by logistic regression.

RESULTS

We studied 10216 pregnancies, 410 (4.0%) with NH. The model's AUC was 0.628 (95%CI: 0.599-0.657). Optimism-corrected c-index: 0.626. Points were assigned to variables associated with NH in proportion to the model's lowest regression coefficient: insulin-treatment 1, preeclampsia 3, preterm delivery 2, male sex 1, and small-for-gestational-age 2, or large-for-gestational-age 3. NH prevalence by score category 0-1, 2, 3, 4, and ≥5 was 2.3%, 3.0%, 4.5%, 6.0%, 7.4%, and 11.5%, respectively. Per point, the OR for NH was 1.35 (95% CI: 1.27-1.42). A score of 2, 3, 4, 5 or ≥6 (versus ≤1) had a OR for NH of 1.67 (1.29-2.15), 2.24 (1.65-3.04), 2.83 (2.02-3.98), 3.08 (1.83-5.16), and 6.84 (4.34-10.77), respectively.

CONCLUSION

Per each score point, women with GD had 35% higher risk of NH. Those with ≥6 points had 6.8-fold higher risk of NH compared to a score ≤1. Our score may be useful for identifying women at a higher risk of NH.

Refining the diagnosis of gestational diabetes mellitus: a systematic review and meta-analysis

BACKGROUND

Perinatal outcomes vary for women with gestational diabetes mellitus (GDM). The precise factors beyond glycemic status that may refine GDM diagnosis remain unclear. We conducted a systematic review and meta-analysis of potential precision markers for GDM.

METHODS

Systematic literature searches were performed in PubMed and EMBASE from inception to March 2022 for studies comparing perinatal outcomes among women with GDM. We searched for precision markers in the following categories: maternal anthropometrics, clinical/sociocultural factors, non-glycemic biochemical markers, genetics/genomics or other -omics, and fetal biometry. We conducted post-hoc meta-analyses of a subset of studies with data on the association of maternal body mass index (BMI, kg/m2) with offspring macrosomia or large-for-gestational age (LGA).

RESULTS

A total of 5905 titles/abstracts were screened, 775 full-texts reviewed, and 137 studies synthesized. Maternal anthropometrics were the most frequent risk marker. Meta-analysis demonstrated that women with GDM and overweight/obesity vs. GDM with normal range BMI are at higher risk of offspring macrosomia (13 studies [n = 28,763]; odds ratio [OR] 2.65; 95% Confidence Interval [CI] 1.91, 3.68), and LGA (10 studies [n = 20,070]; OR 2.23; 95% CI 2.00, 2.49). Lipids and insulin resistance/secretion indices were the most studied non-glycemic biochemical markers, with increased triglycerides and insulin resistance generally associated with greater risk of offspring macrosomia or LGA. Studies evaluating other markers had inconsistent findings as to whether they could be used as precision markers.

CONCLUSIONS

Maternal overweight/obesity is associated with greater risk of offspring macrosomia or LGA in women with GDM. Pregnancy insulin resistance or hypertriglyceridemia may be useful in GDM risk stratification. Future studies examining non-glycemic biochemical, genetic, other -omic, or sociocultural precision markers among women with GDM are warranted.

Development of a prediction model for neonatal hypoglycemia risk factors: a retrospective study

Background

It's challenging for healthcare workers to detect neonatal hypoglycemia due to its rapid progression and lack of aura symptoms. This may lead to brain function impairment for the newborn, placing a significant care burden on the family and creating an economic burden for society. Tools for early diagnosis of neonatal hypoglycemia are lacking. This study aimed to identify newborns at high risk of developing neonatal hypoglycemia early by developing a risk prediction model.

Methods

Using a retrospective design, pairs (470) of women and their newborns in a tertiary hospital from December 2021 to September 2022 were included in this study. Socio-demographic data and clinical data of mothers and newborns were collected. Univariate and multivariate logistic regression were used to screen optimized factors. A neonatal hypoglycemia risk nomogram was constructed using R software, and the calibration curve and receiver operator characteristic curve (ROC) was utilized to evaluate model performance.

Results

Factors integrated into the prediction risk nomogram were maternal age (odds ratio [OR] =1.10, 95% CI: 1.04, 1.17), fasting period (OR=1.07, 95% CI: 1.03, 1.12), ritodrine use (OR=2.00, 95% CI: 1.05, 3.88), gestational diabetes mellitus (OR=2.13, 95% CI: 1.30, 3.50), gestational week (OR=0.80, 95% CI: 0.66, 0.96), fetal distress (OR=1.76, 95% CI: 1.11, 2.79) and neonatal body mass index (OR=1.50, 95% CI: 1.24, 1.84). The area under the curve (AUC) was 0.79 (95% confidence interval [CI]: 0.75, 0.82), specificity was 0.82, and sensitivity was 0.62.

Conclusion

The prediction model of this study demonstrated good predictive performance. The development of the model identifies advancing maternal age, an extended fasting period before delivery, ritodrine use, gestational diabetes mellitus diagnosis, fetal distress diagnosis and an increase in neonatal body mass index increase the probability of developing neonatal hypoglycemia, while an extended gestational week reduces the probability of developing neonatal hypoglycemia.

Facilitators and barriers to attending postpartum screening in women with a recent pregnancy complicated by gestational diabetes mellitus: a qualitative study

OBJECTIVE

Pregnant women with gestational diabetes mellitus (GDM) are 50% more likely to develop type II diabetes (T2D) within 6 months to 2 years after giving birth. Therefore, international guidelines recommend it is best practice for women diagnosed with GDM to attend screening for T2D 6-12 weeks postpartum and every 1-3 years thereafter for life. However, uptake of postpartum screening is suboptimal. This study will explore the facilitators of and barriers to attending postpartum screening for T2D that women experience.

STUDY DESIGN

This was a prospective qualitative cohort study using thematic analysis.

METHODS

A total of 27 in-depth, semistructured interviews were conducted over the telephone with women who had recent GDM. Interviews were recorded and transcribed, and data were analysed using thematic analysis.

RESULTS

Facilitators of and barriers to attending postpartum screening were identified at three different levels: personal, intervention, and healthcare systems level. The most common facilitators identified were concern for their own health and having the importance of screening explained to them by a health professional. The most common barriers identified were confusion over the test and COVID-19.

CONCLUSION

This study identified several facilitators of and barriers to attending postpartum screening. These findings will help to inform research and interventions for improving rates of attendance at postpartum screening to reduce the subsequent risk of developing T2D.

The effect of diet quality on the risk of developing gestational diabetes mellitus: A systematic review and meta-analysis

Objective

To examine the effect of diet quality on the risk of gestational diabetes mellitus.

Methods

This review included cohort and case-control studies reporting an association between diet quality and gestational diabetes mellitus. We searched PubMed, Cochrane Library, Web of Science, Embase, PsycINFO, CINAHL Complete, Chinese Periodical Full-text Database, China National Knowledge Infrastructure, Chinese Biomedical Literature Database, and China Wanfang Database for studies published from inception to November 18, 2022. The Newcastle-Ottawa Scale was used for quality assessment, and the overall quality of evidence was assessed using the GRADEpro GDT.

Results

A total of 19 studies (15 cohort, four case-control) with 108,084 participants were included. We found that better higher diet quality before or during pregnancy reduced the risk of developing gestational diabetes mellitus, including a higher Mediterranean diet (OR: 0.51; 95% CI: 0.30-0.86), dietary approaches to stop hypertension (OR: 0.66; 95% CI: 0.44-0.97), Alternate Healthy Eating Index (OR: 0.61; 95% CI: 0.44-0.83), overall plant-based diet index (OR: 0.57; 95% CI: 0.41-0.78), and adherence to national dietary guidelines (OR: 0.39; 95% CI:0.31-0.48). However, poorer diet quality increased the risk of gestational diabetes mellitus, including a higher dietary inflammatory index (OR: 1.37; 95% CI: 1.21-1.57) and overall low-carbohydrate diets (OR: 1.41; 95% CI: 1.22-1.64). After meta-regression, subgroup, and sensitivity analyses, the results remained statistically significant.

Conclusions

Before and during pregnancy, higher diet quality reduced the risk of developing gestational diabetes mellitus, whereas poorer diet quality increased this risk.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42022372488.

Effect of Evidence-Based Diet Nursing on Intestinal Flora and Maternal and Infant Prognosis in Patients with Gestational Diabetes

Background. Gestational diabetes mellitus (GDM) refers to the diabetes first discovered or occurring during pregnancy. The incidence of gestational diabetes in China is about 1%–5%, with an increasing trend in recent years. Objective. To observe the effect of evidence-based diet nursing on intestinal flora and maternal and infant prognosis in patients with gestational diabetes. Methods. One hundred and thirty patients with GDM admitted to our hospital from January 2020 to January 2022 were selected and divided into two groups according to the intervention method, with 65 cases in each group. The control group was given routine nursing plus diet nursing, while the observation group was given evidence-based nursing plus diet nursing. The changes of blood glucose index and intestinal flora before and after intervention in the two groups were detected, and the compliance behavior, pregnancy outcome, and perinatal outcome in the two groups were statistically analyzed. Results. After the intervention, the fasting blood glucose, 2 h postprandial blood glucose, and HbA1c in the two groups gradually decreased ( $P<0.05$ ). Further comparison between the groups showed that the fasting blood glucose, 2 h postprandial blood glucose, and HbA1c in the observation group were lower than those in the control group ( $P<0.05$ ). After intervention, the ratios of Bifidobacterium, Lactobacillus, and Bifidobacterium to E. coli in the two groups gradually increased ( $P<0.05$ ). Furthermore, comparison between the groups showed that the ratios of Bifidobacterium, Lactobacillus, and Bifidobacterium to E. coli in the observation group were higher than those in the control group ( $P<0.05$ ). The blood glucose rate, regular prenatal examination rate, and diet control rate of the observation group were 100.00%, 100.00%, and 95.38%, respectively, which were higher than 89.23%, 92.31%, and 84.62% of the control group, and the difference was significant ( $P<0.05$ ). The pregnancy infection rate and cesarean section rate in the observation group were 0.00% and 33.85%, respectively, which were lower than 6.15% and 60.00% in the control group, and the difference was significant ( $P<0.05$ ).The premature delivery rate and polyhydramnios rate in the observation group were 3.08% and 1.54%, respectively, which were not significantly different from 6.15% to 7.69% in the control group ( $P>0.05$ ). The rates of macrosomia, neonatal hypoglycemia, and neonatal hyperbilirubinemia in the observation group were 1.54%, 3.08%, and 9.23%, respectively, which were lower than those in the control group (10.77%, 13.85%, and 23.08%), and the differences were significant ( $P<0.05$ ). The fetal malformation rate and neonatal asphyxia rate in the observation group were 0.00% and 1.54%, respectively, which were not significantly different from 1.54% to 7.69% in the control group ( $P>0.05$ ). Conclusion. The application of evidence-based care combined with dietary care in GDM patients can improve intestinal flora, control blood glucose, improve patient compliance behavior, and improve maternal and infant outcomes.

A risk-prediction model using parameters of maternal body composition to identify gestational diabetes mellitus in early pregnancy

BACKGROUND

Accurate early risk-prediction for gestational diabetes mellitus (GDM) would target intervention and prevention in women at the highest risk. We evaluated maternal risk-factors and parameters of body-composition to develop a prediction model for GDM in early gestation.

METHODS

A prospective observational study was undertaken. Pregnant women aged between 18 and 50 y of age with gestational age between 10 and 16 weeks were included in the study. Women aged ≤18 y, twin-pregnancies, known foetal anomaly or pre-existing condition affecting oedema status were excluded. 8-point-skinfold thickness (SFT), mid-upper-arm-circumference (MUAC), waist, hip, weight and ultrasound measurements of subcutaneous (SAT) and visceral abdominal-adipose (VAT) were measured. Oral-glucose-tolerance-test (OGTT) for GDM diagnosis was undertaken at 28 weeks gestation. Binomial logistic-regression models were used to predict GDM. ROC-analysis determined discrimination and concordance of model and individual variables.

RESULTS

188 women underwent OGTT at ~28 weeks gestation. 20 women developed GDM. BMI (24.7 kg m^-2 (±6.1), 29.9 kg m^-2 (±7.8), p = 0.022), abdominal SAT(1.32 cm (CI 1.31, 1.53), 1.99 cm (CI 1.64, 2.31), p = 0.027), abdominal VAT(0.78 cm (CI 0.8, 0.96), 1.41 cm (CI 1.11, 1.65), p = 0.002), truncal SFT (84.8 mm (CI 88.2, 101.6), 130.4 mm (CI 105.1, 140.1), p = 0.010), waist (79.8 cm (CI 80.3, 84.1), 90.3 cm (CI 85.9, 96.2), p = 0.006) and gluteal hip (94.3 cm (CI 93.9, 98.0), 108.6 cm (CI 99.9, 111.6), p = 0.023) were higher in GDM vs. non-GDM. After screening variables for inclusion into the multivariate model, family history of diabetes, previous perinatal death, overall insulin resistant condition, abdominal SAT and VAT, 8-point SFT, MUAC and weight were included. The combined multivariate prediction model achieved an excellent level of discrimination, with an AUC of 0.860 (CI 0.774, 0.945) for GDM.

CONCLUSIONS

An early gestation risk prediction model, incorporating known risk-factors, and parameters of body-composition, accurately identify pregnant women in their first-trimester who developed GDM later on in gestation. This methodology could be used clinically to identify at-risk pregnancies, and target specific treatment through referred services to those mothers who would most benefit.

Probiotic Supplements Improve Blood Glucose and Insulin Resistance/Sensitivity among Healthy and GDM Pregnant Women: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Background

Probiotic supplements may be seen as a promising way to improve glucose metabolism. This study aimed to evaluate the effects of probiotic supplements on blood glucose, insulin resistance/sensitivity, and prevention of gestational diabetes mellitus (GDM) among pregnant women.

Methods

Eleven electronic databases were searched from inception to May 2020. Two authors independently identified randomized controlled trials (RCTs), assessed the eligibility and quality of the included studies, and then extracted data. The primary outcomes were fasting plasma glucose (FPG), 1 h and 2 h plasma glucose after 75 g oral glucose tolerance test (OGTT), HbA1c, fasting plasma insulin, insulin resistance, and insulin sensitivity. Fixed and random effect models were used to pool the results.

Results

A total of 20 RCTs involving 2972 participants were included according to the inclusion and exclusion criteria. The pooled results of this research showed that probiotic supplements could reduce the level of FPG (mean difference (MD) = −0.11; 95% CI = −0.15 to −0.04; P=0.0007), serum insulin (MD = −1.68; 95% CI = −2.44 to −0.92; P < 0.00001), insulin resistance (MD = −0.36; 95% CI = −0.53 to −0.20; P < 0.00001), and insulin sensitivity (MD = −21.80; 95% CI = −31.92 to −11.67; P < 0.00001). Regarding the subgroup analysis of different pregnant women, the effects of probiotics on FPG, insulin, and insulin resistance were more obvious among GDM and healthy women than among overweight/obese women. Furthermore, the differences were not significant in HbA1c (MD = −0.05; 95% CI = −0.12 to 0.03; P=0.23), 1 h OGTT (MD = −0.07; 95% CI = −0.25 to 0.10; P=0.42), and 2 h OGTT (MD = −0.03; 95% CI = −0.17 to 0.12; P=0.72).

Conclusion

This review found that probiotic supplements had certain functions to reduce the level of FPG and improve insulin, insulin resistance, and insulin sensitivity, especially for GDM and healthy pregnant women.

Sonographic and other nonglycemic factors can predict large-for-gestational-age infants in diet-managed gestational diabetes mellitus: A retrospective cohort study

BACKGROUND

Gestational diabetes mellitus (GDM) is one of the most common complications of pregnancy. Left untreated or poorly controlled, GDM results in adverse infant outcomes such as large for gestational age (LGA). This study aims to identify nonglycemic maternal and fetal factors predictive of LGA outcomes in pregnancies complicated by diet-managed GDM.

METHODS

This was a retrospective cohort study of singleton pregnancies complicated by diet-managed GDM from 2004 to 2015. Multiple logistic regression analysis was performed on maternal and perinatal factors to identify risk factors for LGA. In addition, a subset univariate analysis was conducted for pregnancies in which fetal ultrasound abdominal circumference measurements were available at gestational weeks 18 to 22, 24 to 28, and 29 to 33.

RESULTS

A total of 1064 women were included, delivering 123 LGA infants. Women with higher parity (odds ratio [OR] 1.44; CI, 1.23-1.68; P < .001) and higher prepregnancy body mass index (BMI) (OR 1.09; CI, 1.06-1.12; P < .001) were more likely to have LGA infants. Maternal smoking (OR 0.30; CI, 0.14-0.62; P = .001) and higher gestational age at birth (OR 0.91; CI, 0.84-0.99; P = .018) were associated with reduced risk. Subset univariate analysis showed that fetal abdominal circumference measurements at weeks 24 to 28 and 29 to 33 beyond the 75th percentile (OR 5.92 and 13.74, respectively) and 90th percentile (OR 4.57 and 15.89, respectively) were highly predictive of LGA.

CONCLUSIONS

Parity, smoking status, maternal BMI, gestational age, and ultrasound fetal abdominal circumference measurements were identified as useful predictors of LGA. Presence of these predictors may prompt closer monitoring of pregnancy and early therapeutic intervention to improve management and reduce the risk of adverse fetal and maternal outcomes.