Screening for gestational diabetes: usefulness of clinical risk factors

Identification of Primary Organophosphate Esters Contributing to Enhanced Risk of Gestational Diabetes Mellitus Based on a Case-Control Study

Epidemiological studies on associations of organophosphate ester (OPE) exposure and gestational diabetes mellitus (GDM) risk, which remain rare and inconclusive, were carried out with a case-control population comprising 287 GDM and 313 non-GDM pregnant women recruited from Tianjin. The GDM group suffered distinctly higher serum concentrations of tri-n-butyl phosphate (TNBP), tri(2-butoxyethyl) phosphate (TBOEP), triphenyl phosphate (TPHP), tri-iso-propyl phosphate (TIPP), and tri(1-chloro-2-propyl) phosphate (TCIPP) than the healthy control group (p < 0.001). Traditional analysis methods employed for either individual or mixture effects found positive correlations (p < 0.05) between the concentrations of five OPEs (i.e., TNBP, TBOEP, TPHP, TIPP, and TCIPP) and the incidence of GDM, while 2-ethylhexyl diphenyl phosphate, tri(1-chloro-2-propyl) phosphate, and bis(2-ethylhexyl) phosphate exhibited opposite effects. Three machine learning methods considering the concurrence of OPE mixture exposure and population characteristics were applied to clarify their relative importance to GDM risk, among which random forest performed the best. Several OPEs, particularly TNBP and TBOEP ranking at the top, made greater contributions than some demographical characteristics, such as prepregnancy body mass index and family history of diabetes, to the occurrence of GDM. This was further validated by another independent case-control population obtained from Hangzhou.

Understanding the Genetic Landscape of Gestational Diabetes: Insights into the Causes and Consequences of Elevated Glucose Levels in Pregnancy

Background/Objectives: During pregnancy, physiological changes in maternal circulating glucose levels and its metabolism are essential to meet maternal and fetal energy demands. Major changes in glucose metabolism occur throughout pregnancy and consist of higher insulin resistance and a compensatory increase in insulin secretion to maintain glucose homeostasis. For some women, this change is insufficient to maintain normoglycemia, leading to gestational diabetes mellitus (GDM), a condition characterized by maternal glucose intolerance and hyperglycaemia first diagnosed during the second or third trimester of pregnancy. GDM is diagnosed in approximately 14.0% of pregnancies globally, and it is often associated with short- and long-term adverse health outcomes in both mothers and offspring. Although recent studies have highlighted the role of genetic determinants in the development of GDM, research in this area is still lacking, hindering the development of prevention and treatment strategies. Methods: In this paper, we review recent advances in the understanding of genetic determinants of GDM and glycaemic traits during pregnancy. Results/Conclusions: Our review highlights the need for further collaborative efforts as well as larger and more diverse genotyped pregnancy cohorts to deepen our understanding of the genetic aetiology of GDM, address research gaps, and further improve diagnostic and treatment strategies.

Gestational diabetes mellitus in Cameroon: prevalence, risk factors and screening strategies

Background

The burden of gestational diabetes (GDM) and the optimal screening strategies in African populations are yet to be determined. We assessed the prevalence of GDM and the performance of various screening tests in a Cameroonian population.

Methods

We carried out a cross-sectional study involving the screening of 983 women at 24-28 weeks of pregnancy for GDM using serial tests, including fasting plasma (FPG), random blood glucose (RBG), a 1-hour 50g glucose challenge test (GCT), and standard 2-hour oral glucose tolerance test (OGTT). GDM was defined using the World Health Organization (WHO 1999), International Association of Diabetes and Pregnancy Special Group (IADPSG 2010), and National Institute for Health Care Excellence (NICE 2015) criteria. GDM correlates were assessed using logistic regressions, and c-statistics were used to assess the performance of screening strategies.

Findings

GDM prevalence was 5·9%, 17·7%, and 11·0% using WHO, IADPSG, and NICE criteria, respectively. Previous stillbirth [odds ratio: 3·14, 95%CI: 1·27-7·76)] was the main correlate of GDM. The optimal cut-points to diagnose WHO-defined GDM were 5·9 mmol/L for RPG (c-statistic 0·62) and 7·1 mmol/L for 1-hour 50g GCT (c-statistic 0·76). The same cut-off value for RPG was applicable for IADPSG-diagnosed GDM while the threshold was 6·5 mmol/L (c-statistic 0·61) for NICE-diagnosed GDM. The optimal cut-off of 1-hour 50g GCT was similar for IADPSG and NICE-diagnosed GDM. WHO-defined GDM was always confirmed by another diagnosis strategy while IADPSG and GCT independently identified at least 66·9 and 41·0% of the cases.

Interpretation

GDM is common among Cameroonian women. Effective detection of GDM in under-resourced settings may require simpler algorithms including the initial use of FPG, which could substantially increase screening yield.

A Prediction Model of Gestational Diabetes Mellitus Based on OGTT in Early Pregnancy: A Prospective Cohort Study

CONTEXT

Gestational diabetes mellitus (GDM) is a common obstetric complication. Although early intervention could prevent the development of GDM, there was no consensus on early identification for women at high risk of GDM.

OBJECTIVE

To develop a reliable prediction model of GDM in early pregnancy.

METHODS

In this prospective cohort study, between May 30, 2021, and August 13, 2022, a total of 721 women were included from Women's Hospital, Zhejiang University School of Medicine. Participants were asked to complete an oral glucose tolerance test (OGTT) during gestational weeks 7 through 14 for early prediction of GDM, and at weeks 24 through 28 for GDM diagnosis. Using OGTT results and baseline characteristics, logistic regression analysis was used to construct the prediction model. Receiver operating characteristic (ROC) curve, Hosmer-Lemeshow test, decision clinical analysis, and a nomogram were used for model performances assessment and visualization. Internal and external validation was performed to testify the stability of this model.

RESULTS

According to the International Association of Diabetes and Pregnancy Study Groups criteria in early OGTT, the mean (SD) age was 30.5 ± 3.7 years in low-risk participants and 31.0 ± 3.9 years in high-risk participants. The area under ROC curve (AUC) of the existing criteria at weeks 7 through 14 varied from 0.705 to 0.724. Based on maternal age, prepregnancy body mass index, and results of early OGTT, the AUC of our prediction model was 0.8720, which was validated by both internal (AUC 0.8541) and external (AUC 0.8241) confirmation.

CONCLUSIONS

The existing diagnostic criteria were unsatisfactory for early prediction of GDM. By combining early OGTT, we provided an effective prediction model of GDM in the first trimester.

Clinical First-Trimester Prediction Models for Gestational Diabetes Mellitus: A Systematic Review and Meta-Analysis

BACKGROUND

Gestational diabetes mellitus (GDM) is a common pregnancy complication that negatively impacts the health of both the mother and child. Early prediction of the risk of GDM may permit prompt and effective interventions. This systematic review and meta-analysis aimed to summarize the study characteristics, methodological quality, and model performance of first-trimester prediction model studies for GDM.

METHODS

Five electronic databases, one clinical trial register, and gray literature were searched from the inception date to March 19, 2022. Studies developing or validating a first-trimester prediction model for GDM were included. Two reviewers independently extracted data according to an established checklist and assessed the risk of bias by the Prediction Model Risk of Bias Assessment Tool (PROBAST). We used a random-effects model to perform a quantitative meta-analysis of the predictive power of models that were externally validated at least three times.

RESULTS

We identified 43 model development studies, six model development and external validation studies, and five external validation-only studies. Body mass index, maternal age, and fasting plasma glucose were the most commonly included predictors across all models. Multiple estimates of performance measures were available for eight of the models. Summary estimates range from 0.68 to 0.78 (I² ranged from 0% to 97%).

CONCLUSION

Most studies were assessed as having a high overall risk of bias. Only eight prediction models for GDM have been externally validated at least three times. Future research needs to focus on updating and externally validating existing models.

Second trimester cytokine profiles associated with gestational diabetes and hypertensive disorders of pregnancy

Healthy pregnancy requires a coordinated immune response, yet complications can arise, putting both the mother's and child's health at risk. Hypertensive disorders of pregnancy (HDP) and gestational diabetes mellitus (GDM) are pregnancy-related complications that account for most maternal morbidity and mortality. Cytokines are proteins released as part of the immune response to disease or infection and regulate inflammation. Certain pregnancy complications cause localized and systemic inflammation; however, cytokine profiles specific to such complications are not well understood. This study aims to examine associations between pregnancy complications of HDP and GDM and cytokine profiles in the second trimester of pregnancy. Data was obtained from the All Our Families birth cohort in Calgary, Alberta, Canada. The cohort collected questionnaires at the time of participant enrollment and maternal blood samples at 17-23 weeks gestation. Cases of HDP (n = 27) and GDM (n = 31) were matched to controls on BMI, maternal age, and smoking status in the preconception period at a 1:3 ratio. Cytokine levels were measured in blood samples using Luminex xMAP technology using a panel of 42 cytokines. Using R software, a Classification and Regression Tree (CART) analysis was conducted to identify cytokine profiles and levels associated with each complication. Four cytokines were identified in the HDP CART (in descending order of importance): Monocyte Chemoattractant Protein-1 (cut-off: <480pg/mL), Macrophage Inflammatory Protein-1β (cut-off: ≥26pg/mL), Eotaxin (cut-off: <27/≥27&<36/≥36pg/mL), and Soluble Cluster of Differentiation 40 Ligand (cut-off: <1342pg/mL). Six cytokine levels were identified in the GDM CART: Interleukin-1 Receptor Antagonist (IL-1Ra; cut-off: <25pg/mL), Interleukin-5 (cut-off: ≥0.4pg/mL), Interferon-γ (cut-off: <4.9pg/mL), IL-1Ra (cut-off: ≥111pg/mL), Eotaxin (cut-off: ≥21pg/mL), and Interleukin-18 (cut-off: ≥155pg/mL). By examining the complex inter-relationships between cytokines, findings of cytokine profiles guide further research in identifying biomarkers of pregnancy complications relevant to the design of the future management or prevention of these conditions.

Performance of early risk assessment tools to predict the later development of gestational diabetes

BACKGROUND

Several prognostic models for gestational diabetes mellitus (GDM) are provided in the literature; however, their clinical significance has not been thoroughly evaluated, especially with regard to application at early gestation and in accordance with the most recent diagnostic criteria. This external validation study aimed to assess the predictive accuracy of published risk estimation models for the later development of GDM at early pregnancy.

METHODS

In this cohort study, we prospectively included 1132 pregnant women. Risk evaluation was performed before 16 + 0 weeks of gestation including a routine laboratory examination. Study participants were followed-up until delivery to assess GDM status according to the IADPSG 2010 diagnostic criteria. Fifteen clinical prediction models were calculated according to the published literature.

RESULTS

Gestational diabetes mellitus was diagnosed in 239 women, that is 21.1% of the study participants. Discrimination was assessed by the area under the ROC curve and ranged between 60.7% and 76.9%, corresponding to an acceptable accuracy. With some exceptions, calibration performance was poor as most models were developed based on older diagnostic criteria with lower prevalence and therefore tended to underestimate the risk of GDM. The highest variable importance scores were observed for history of GDM and routine laboratory parameters.

CONCLUSIONS

Most prediction models showed acceptable accuracy in terms of discrimination but lacked in calibration, which was strongly dependent on study settings. Simple biochemical variables such as fasting glucose, HbA1c and triglycerides can improve risk prediction. One model consisting of clinical and laboratory parameters showed satisfactory accuracy and could be used for further investigations.

Oestrogen receptor α in T cells controls the T cell immune profile and glucose metabolism in mouse models of gestational diabetes mellitus

AIMS/HYPOTHESIS

The imbalance between maternal insulin resistance and a relative lack of insulin secretion underlies the pathogenesis of gestational diabetes mellitus (GDM). Alterations in T cell subtypes and increased levels of circulating proinflammatory cytokines have been proposed as potential mechanisms underlying the pathophysiology of insulin resistance in GDM. Since oestrogen modulates T cell immunity, we hypothesised that oestrogen plays a homeostatic role in visceral adipose tissue by coordinating T cell immunity through oestrogen receptor α (ERα) in T cells to prevent GDM.

METHODS

Female CD4-cre ERα^fl/fl (KO) mice on a C57BL/6 background with ERα ablation specifically in T cells, and ERα^fl/fl (ERα-floxed [FL]) mice were fed 60 kJ% high-fat diet (HFD) for 4 weeks. Female mice mated with male BALB/c mice to achieve allogenic pregnancy and were maintained on an HFD to generate the GDM model. Mice were divided into four experimental groups: non-pregnant FL, non-pregnant KO, pregnant FL (FL-GDM) and pregnant KO (KO-GDM). GTTs and ITTs were performed on day 12.5 or 13.5 and 16.5 after breeding, respectively. On day 18.5 after breeding, mice were killed and T cell subsets in the gonadal white adipose tissue (gWAT) and spleen were analysed using flow cytometry. Histological examination was also conducted and proinflammatory gene expression in gWAT and the liver was evaluated.

RESULTS

KO mice that mated with BALB/c mice showed normal fertility rates and fetal weights as compared with FL mice. Body and tissue weights were similar between FL and KO mice. When compared with FL-GDM mice, KO-GDM mice showed decreased insulin secretion (serum insulin concentration 15 min after glucose loading: 137.3 ± 18.3 pmol/l and 40.1 ± 36.5 pmol/l, respectively; p < 0.05), impaired glucose tolerance (glucose AUC in GTT: 2308.3 ± 54.0 mmol/l × min and 2620.9 ± 122.1 mmol/l × min, respectively; p < 0.05) and increased numbers of T helper (Th)17 cells in gWAT (0.4 ± 0.0% vs 0.8 ± 0.1%; p < 0.05). However, the contents of Th1 and regulatory T cells (Tregs) in gWAT remained similar between FL-GDM and KO-GDM. Glucose-stimulated insulin secretion was similar between isolated islets derived from FL and KO mice, but was reduced by IL-17A treatment. Moreover, the levels of proinflammatory gene expression, including expression of Emr1 and Tnfa in gWAT, were significantly higher in KO-GDM mice than in FL-GDM mice (5.1-fold and 2.7-fold, respectively; p < 0.01 for both). Furthermore, KO-GDM mice showed increased expression of genes encoding hepatokines, Ahsg and Fgf21 (both were 2.4-fold higher vs FL-GDM mice; p < 0.05 and p = 0.09, respectively), with no changes in inflammatory gene expression (e.g., Tnfa and Ifng) in the liver compared with FL-GDM mice.

CONCLUSIONS/INTERPRETATION

Deletion of ERα in T cells caused impaired maternal adaptation of insulin secretion, changes in hepatokine profiles, and enhanced chronic inflammation in gWAT alongside an abnormal increase in Th17 cells. These results suggest that the ERα-mediated oestrogen signalling effects in T cells regulate T cell immunity and contribute to glucose homeostasis in pregnancy.

Prevalence and associated factors of gestational diabetes mellitus among rural pregnant women in southern Tanzania

INTRODUCTION

Gestational diabetes mellitus (GDM) is a potential risk factor for both maternal and foetal complications during pregnancy. This study aimed to determine the prevalence and factors associated with GDM among pregnant women in Southern Tanzania.

METHODS

A cross-sectional study was conducted among 612 randomly selected pregnant women attending routine antenatal clinics in Southern Tanzania from September to October 2017. Detailed medical and gynaecological history was taken using pre-tested questionnaires. Blood samples were collected for fasting and oral glucose tolerance tests. We diagnosed GDM using the World Health Organization 2013 diagnostic criteria for diabetes mellitus. We performed statistical analysis using SPSS v24.0. Possible associations and statistical significance were measured using odds ratio at 95% confidence interval, and p-values of <0.05 were considered statistically significant.

RESULTS

The mean age and standard deviation of the study subjects was 24.5±6.9 years. The prevalence of GDM was 4.3%. GDM was significantly associated with: being overweight or obese (p<0.001), past history of pre-term delivery (p<0.001), past history of stillbirths (p<0.001), history of macrosmia (p<0.001), alcohol consumption (p=0.001), and having a first degree relative with diabetes mellitus (p<0.001).

CONCLUSION

Prevalence of Gestational Diabetes Mellitus is low in this study setting. We recommend close attention to at risk women to prevent development of GDM.

FUNDING

None declared.

External validation and clinical utility of prognostic prediction models for gestational diabetes mellitus: A prospective cohort study

Introduction

We performed an independent validation study of all published first trimester prediction models, containing non‐invasive predictors, for the risk of gestational diabetes mellitus. Furthermore, the clinical potential of the best performing models was evaluated.

Material and methods

Systemically selected prediction models from the literature were validated in a Dutch prospective cohort using data from Expect Study I and PRIDE Study. The predictive performance of the models was evaluated by discrimination and calibration. Clinical utility was assessed using decision curve analysis. Screening performance measures were calculated at different risk thresholds for the best model and compared with current selective screening strategies.

Results

The validation cohort included 5260 women. Gestational diabetes mellitus was diagnosed in 127 women (2.4%). The discriminative performance of the 12 included models ranged from 68% to 75%. Nearly all models overestimated the risk. After recalibration, agreement between the observed outcomes and predicted probabilities improved for most models.

Conclusions

The best performing prediction models showed acceptable performance measures and may enable more personalized medicine‐based antenatal care for women at risk of developing gestational diabetes mellitus compared with current applied strategies.

The predictive value of weight gain and waist circumference for gestational diabetes mellitus

Objective:

The first objective of this study was to investigate the relationship between gestational diabetes mellitus (GDM) and gestational weight gain (WG), waist circumference (WC), prepregnancy, and gestational body mass index (BMI). The second aim of our study was to assess the ability of WG, WC, prepregnancy, and gestational BMI with special reference to their cut-off points on predicting the risk of GDM in pregnant women in Turkey.

Materials and Methods:

A total of 261 women who underwent screening for GDM with the 75-g glucose tolerance test (GTT) between 24^th and 28^th gestational weeks were included. According to the 75-g oral GTT results, women were classified into two groups: the GDM group and non-GDM group. The data collected included age, parity, plasma glucose level for fasting, 1- and 2-h tests, WC, prepregnancy and gestational BMI, prepregnancy weight, WG during pregnancy, gestational age at birth, and birth weight.

Results:

WC at 20-24 weeks of gestation, prepregnancy BMI, and gestational BMI had a predictive capacity for GDM. According to our results, optimal cut-off points for the best predictive value of GDM were WC of 100 cm with a sensitivity of 84% and specificity of 70%, prepregnancy BMI of 25 kg/m² with a sensitivity of 81.8% and specificity of 76%, and gestational BMI of 28.3 kg/m² with a sensitivity of 75% and specificity of 77.4%.

Conclusion:

The measurement of prepregnancy BMI, gestational BMI, and WC may be useful in predicting the risk of GDM. Pregnant women with increased prepregnancy BMI, gestational BMI, and WC measurements may be susceptible to the development of GDM.

Prevalence of gestational diabetes mellitus in Eastern Mediterranean region: a systematic review and meta-analysis

PURPOSE

Gestational diabetes mellitus (GDM) is one of the costly challenges in the health field. Despite the individual studies in the Eastern Mediterranean, there is no comprehensive study in this regard. The aim of this study was to determine the prevalence of GDM in the Eastern Mediterranean region.

METHODS

In this meta-analysis and systematic review, three international databases (PubMed, Web of science and Scopus) were searched from inception until 30 December 2018. The Hui tool was used to assess the quality of the included studies.

RESULTS

Thirty-three studies performed on 887166 participants were included in the meta-analysis. Based on the results of random effect method, the overall prevalence of GDM was 11.7%. Between six country with have three or more study, pooled prevalence for Saudi Arabi it was 3.6 times more than Israel (17.6 vs. 4.9%), and for Pakistan, Qatar, Bahrain and Iran were 15.3%, 14.7%, 12.2%, and 8.6%, respectively.

CONCLUSION

Despite the high diversity of methods, the results of the present study indicate a high prevalence of GDM in the Eastern Mediterranean region, indicating more policymakers' interest in timely screening and proper management.

Development and validation of a clinical model for preconception and early pregnancy risk prediction of gestational diabetes mellitus in nulliparous women

Implementation of dietary and lifestyle interventions prior to and early in pregnancy in high risk women has been shown to reduce the risk of gestational diabetes mellitus (GDM) development later in pregnancy. Although numerous risk factors for GDM have been identified, the ability to accurately identify women before or early in pregnancy who could benefit most from these interventions remains limited. As nulliparous women are an under-screened population with risk profiles that differ from their multiparous counterparts, development of a prediction model tailored to nulliparous women may facilitate timely preventive intervention and improve maternal and infant outcomes. We aimed to develop and validate a model for preconception and early pregnancy prediction of gestational diabetes mellitus based on clinical risk factors for nulliparous women. A risk prediction model was built within a large California birth cohort including singleton live birth records from 2007-2012. Model accuracy was assessed both internally and externally, within a cohort of women who delivered at University of Iowa Hospitals and Clinics between 2009-2017, using discrimination and calibration. Differences in predictive accuracy of the model were assessed within specific racial/ethnic groups. The prediction model included five risk factors: race/ethnicity, age at delivery, pre-pregnancy body mass index, family history of diabetes, and pre-existing hypertension. The area under the curve (AUC) for the California internal validation cohort was 0.732 (95% confidence interval (CI) 0.728, 0.735), and 0.710 (95% CI 0.672, 0.749) for the Iowa external validation cohort. The model performed particularly well in Hispanic (AUC 0.739) and Black women (AUC 0.719). Our findings suggest that estimation of a woman's risk for GDM through model-based incorporation of risk factors accurately identifies those at high risk (i.e., predicted risk >6%) who could benefit from preventive intervention encouraging prompt incorporation of this tool into preconception and prenatal care.

Gestational diabetes risk factors and long-term consequences for both mother and offspring: a literature review

INTRODUCTION

Established risk factors for gestational diabetes mellitus (GDM) include ethnicity, obesity, and family history of diabetes. Untreated GDM patients have higher rates of maternal and perinatal morbidity. GDM is an independent risk factor for future longer-term risk of type 2 diabetes mellitus (T2DM), metabolic syndrome, cardiovascular morbidity, malignancies, ophthalmic, psychiatric, and renal disease in the mother. Offspring risk long-term adverse health outcomes, including T2DM, subsequent obesity, impacted neurodevelopmental outcome, increased neuropsychiatric morbidity, and ophthalmic disease.

AREAS COVERED

We critically review data from retrospective, prospective, and meta-analysis studies pertaining to established GDM risk factors, complications during pregnancy and birth (both mother and offspring), and long-term consequences (both mother and offspring).

EXPERT COMMENTARY

Many of the adverse consequences of GDM might be avoided with proper management and treatment. Patients belonging to high-risk ethnic groups, and/or with body mass index ≥ 25 kg/m², and/or known history of diabetes in first-degree relatives may benefit from universal screening and diagnostic criteria proposed by the International Association of Diabetes and Pregnancy Study Group (IADPSG). The IADPSG one-step method has several advantages, including simplicity of execution, greater patient-friendliness, and higher diagnostic accuracy. Additionally, evidence suggests that the recent increased popularity of bariatric surgery will help to decrease GDM rates over next 5 years. Similarly, metformin may be useful for treating and preventing obstetrical complications in confirmed GDM patients.

Gestational diabetes mellitus risk score: A practical tool to predict gestational diabetes mellitus risk in Tanzania

BACKGROUND

Universal screening for hyperglycemia during pregnancy may be in-practical in resource constrained countries. Therefore, the aim of this study was to develop a simple, non-invasive practical tool to predict undiagnosed Gestational diabetes mellitus (GDM) in Tanzania.

METHODS

We used cross-sectional data of 609 pregnant women, without known diabetes, collected in six health facilities from Dar es Salaam city (urban). Women underwent screening for GDM during ante-natal clinics visit. Smoking habit, alcohol consumption, pre-existing hypertension, birth weight of the previous child, high parity, gravida, previous caesarean section, age, MUAC ≥ 28 cm, previous stillbirth, haemoglobin level, gestational age (weeks), family history of type 2 diabetes, intake of sweetened drinks (soda), physical activity, vegetables and fruits consumption were considered as important predictors for GDM. Multivariate logistic regression modelling was used to create the prediction model, using a cut-off value of 2.5 to minimise the number of undiagnosed GDM (false negatives).

RESULTS

Mid-upper arm circumference (MUAC) ≥ 28 cm, previous stillbirth, and family history of type 2 diabetes were identified as significant risk factors of GDM with a sensitivity, specificity, positive predictive value, and negative predictive value of 69%, 53%, 12% and 95%, respectively. Moreover, the inclusion of these three predictors resulted in an area under the curve (AUC) of 0.64 (0.56-0.72), indicating that the current tool correctly classifies 64% of high risk individuals.

CONCLUSION

The findings of this study indicate that MUAC, previous stillbirth, and family history of type 2 diabetes significantly predict GDM development in this Tanzanian population. However, the developed non-invasive practical tool to predict undiagnosed GDM only identified 6 out of 10 individuals at risk of developing GDM. Thus, further development of the tool is warranted, for instance by testing the impact of other known risk factors such as maternal age, pre-pregnancy BMI, hypertension during or before pregnancy and pregnancy weight gain.

Risk Factors for Gestational Diabetes Mellitus: A Case-Control Study

Background: The underlying causes of gestational diabetes mellitus (GDM) are important because they are effective for the diagnosis and prevention of this condition. The aim of this study was to identify the risk factors for GDM and the possible etiological agents. Materials and Methods: This case-control study was conducted with 100 women with GDM and 100 healthy pregnant women at a tertiary care hospital, affiliated to Babol University. GDM was identified by impaired oral glucose tolerance test based on the Carpenter and Coustan criteria. Healthy women were randomly selected from the prenatal health care clinic of the same center and were matched to gestational age of 24 to 28 weeks. Descriptive and inferential statistics were used for data analysis via the SPSS software. Results: After adjusting variables, multivariate analysis identified 4 significant risk factors of GDM, including prepregnancy low physical activity (odds ratio [OR] = 2.85, 95% confidence interval [CI]= 0.97-8.34), advanced maternal age (OR = 1.24, 95% CI = 1.13-1.35), body mass index ⩾ 30 kg/m² (OR = 1.10, 95% CI = 1.02-1.20), and family history of diabetes mellitus (OR = 5.62, 95% CI = 2.26-13.96). Conclusion: We observed significant associations between low prepregnancy physical activity and obesity with GDM risk. Thus the finding of this study can help devise strategies for the prevention of GDM.

Prediction of gestational diabetes mellitus in the Born in Guangzhou Cohort Study, China

OBJECTIVE

To assess potential risk factors in identifying women at risk for gestational diabetes mellitus (GDM).

METHODS

The present study included data collected as part of a prospective cohort study, and included women with singleton pregnancies who underwent initial prenatal examination at a tertiary women and children's hospital in Guangzhou, China between February 1, 2012, and December 31, 2015. Maternal characteristics and medical history were investigated to evaluate associations with GDM. A risk factor scoring system for the prediction of GDM was generated using logistic regression.

RESULTS

Overall, 1129 (13.5%) of 8381 women were diagnosed with GDM. Women older than 35 years had a 3.95-fold increased risk of GDM (95% confidence interval 2.80-5.58) compared with women aged 16-25 years; obese women had a 6.54-fold higher risk (95% confidence interval 3.50-12.23) compared with underweight women. A risk scoring system was established based on age, body mass index, family history of diabetes, weight gain, and history of GDM. Screening for women with a score of 12 or more would have reduced the number undergoing oral glucose tolerance testing by 2131 (25.4%) patients with a sensitivity of 87% for GDM detection.

CONCLUSION

The assessment of risk factors for GDM could provide a foundation for improving risk-based screening strategies in this and similar populations.

A prospective cohort study of early-pregnancy risk factors for gestational diabetes in polycystic ovarian syndrome

BACKGROUND

Polycystic ovarian syndrome (PCOS) is a strong risk factor for gestational diabetes (GDM). However, the association between features of PCOS during early pregnancy and the risk of GDM is not clearly characterized. In this prospective cohort study, we seek to identify early-pregnancy risk factors for GDM in PCOS women.

METHODS

Between 2011 and 2013, 248 women with PCOS were followed from their first prenatal visit to delivery. Multiple early-pregnancy metabolic factors were evaluated for their association with the risk of GDM.

RESULTS

Among 248 subjects, 75 (30.2%) developed GDM. Single factor analysis identified a number of metabolic risk factors for GDM, including higher body mass index, fasting plasma glucose (FPG) and insulin resistance; abnormal cholesterol; elevated blood pressure and free androgen index; lower level of sex-hormone binding globulin (SHBG); and less gestational weight gain. Multivariate analysis showed that FPG, non-high-density lipoprotein-cholesterol and SHBG are independent predictive factors for GDM.

CONCLUSIONS

Our study established strong association of multiple early-pregnancy risk factors with development of GDM in PCOS women. These risk factors are predominantly related to the regulation of glucose, lipid, and androgen metabolism. Among these factors, FPG, non-high-density lipoprotein-cholesterol, and SHBG, predict incident GDM.

The Emerging Role of Biomarkers in the Diagnosis of Gestational Diabetes Mellitus

Gestational diabetes mellitus (GDM) is a common complication of pregnancy; its rising incidence is a result of increased maternal obesity and older maternal age together with altered diagnostic criteria identifying a greater proportion of pregnant women with GDM. Its consequences are far-reaching, associated with poorer maternal and neonatal outcomes compared to non-GDM pregnancies, and GDM has implications for metabolic health in both mother and offspring. Objective markers to identify women at high risk for the development of GDM are useful to target therapy and potentially prevent its development. Established clinical risk factors for GDM include overweight/obesity, age, ethnicity, and family history of diabetes, though they lack specificity for its development. The addition of biomarkers to predictive models of GDM may improve the ability to identify women at risk of GDM prior to its development. These biomarkers reflect the pathophysiologic mechanisms of GDM involving insulin resistance, chronic inflammation, and altered placental function. In addition, the role of epigenetic changes in GDM pathogenesis highlights the complex interplay between genetic and environmental factors, potentially offering further refinement of the prediction of GDM risk. In this review, we will discuss the clinical challenges associated with the diagnosis of GDM and its current pathophysiologic basis, giving rise to potential biomarkers that may aid in its identification. While not yet validated for clinical use, we explore the possible clinical role of biomarkers in the future. We also explore novel diagnostic tools, including high throughput methodologies, that may have potential future application in the identification of women with GDM.

The identification and treatment of women with hyperglycaemia in pregnancy: an analysis of individual participant data, systematic reviews, meta-analyses and an economic evaluation

BACKGROUND

Gestational diabetes mellitus (GDM) is associated with a higher risk of important adverse outcomes. Practice varies and the best strategy for identifying and treating GDM is unclear.

AIM

To estimate the clinical effectiveness and cost-effectiveness of strategies for identifying and treating women with GDM.

METHODS

We analysed individual participant data (IPD) from birth cohorts and conducted systematic reviews to estimate the association of maternal glucose levels with adverse perinatal outcomes; GDM prevalence; maternal characteristics/risk factors for GDM; and the effectiveness and costs of treatments. The cost-effectiveness of various strategies was estimated using a decision tree model, along with a value of information analysis to assess where future research might be worthwhile. Detailed systematic searches of MEDLINE^® and MEDLINE In-Process & Other Non-Indexed Citations^®, EMBASE, Cumulative Index to Nursing and Allied Health Literature Plus, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects, Health Technology Assessment database, NHS Economic Evaluation Database, Maternity and Infant Care database and the Cochrane Methodology Register were undertaken from inception up to October 2014.

RESULTS

We identified 58 studies examining maternal glucose levels and outcome associations. Analyses using IPD alone and the systematic review demonstrated continuous linear associations of fasting and post-load glucose levels with adverse perinatal outcomes, with no clear threshold below which there is no increased risk. Using IPD, we estimated glucose thresholds to identify infants at high risk of being born large for gestational age or with high adiposity; for South Asian (SA) women these thresholds were fasting and post-load glucose levels of 5.2 mmol/l and 7.2 mmol/l, respectively and for white British (WB) women they were 5.4 and 7.5 mmol/l, respectively. Prevalence using IPD and published data varied from 1.2% to 24.2% (depending on criteria and population) and was consistently two to three times higher in SA women than in WB women. Lowering thresholds to identify GDM, particularly in women of SA origin, identifies more women at risk, but increases costs. Maternal characteristics did not accurately identify women with GDM; there was limited evidence that in some populations risk factors may be useful for identifying low-risk women. Dietary modification additional to routine care reduced the risk of most adverse perinatal outcomes. Metformin (Glucophage,^® Teva UK Ltd, Eastbourne, UK) and insulin were more effective than glibenclamide (Aurobindo Pharma - Milpharm Ltd, South Ruislip, Middlesex, UK). For all strategies to identify and treat GDM, the costs exceeded the health benefits. A policy of no screening/testing or treatment offered the maximum expected net monetary benefit (NMB) of £1184 at a cost-effectiveness threshold of £20,000 per quality-adjusted life-year (QALY). The NMB for the three best-performing strategies in each category (screen only, then treat; screen, test, then treat; and test all, then treat) ranged between -£1197 and -£1210. Further research to reduce uncertainty around potential longer-term benefits for the mothers and offspring, find ways of improving the accuracy of identifying women with GDM, and reduce costs of identification and treatment would be worthwhile.

LIMITATIONS

We did not have access to IPD from populations in the UK outside of England. Few observational studies reported longer-term associations, and treatment trials have generally reported only perinatal outcomes.

CONCLUSIONS

Using the national standard cost-effectiveness threshold of £20,000 per QALY it is not cost-effective to routinely identify pregnant women for treatment of hyperglycaemia. Further research to provide evidence on longer-term outcomes, and more cost-effective ways to detect and treat GDM, would be valuable.

STUDY REGISTRATION

This study is registered as PROSPERO CRD42013004608.

FUNDING

The National Institute for Health Research Health Technology Assessment programme.

Family history of diabetes and the risk of gestational diabetes mellitus in Iran: A systematic review and meta-analysis

OBJECTIVE

Gestational diabetes is the most prevalent metabolic disorder being firstly diagnosed during pregnancy. The relationship between the family history of diabetes and the gestational diabetes mellitus (GDM) has been investigated in several primary studies with a number of contradictions in the results. Hence, the purpose of the present study is to determine the relationship between the GDM and the family history of diabetes using the meta-analysis method.

METHOD

All published papers in main national and international databases were systematically searched with some specific keywords to find the related studies between 2000 and 2016. We calculated the odds ratio (OR) with 95% confidence interval (CI) in analysis for each study using a random-effect and Mantel-Haenzel method. We also determined heterogeneity among these 33 articles and their publication bias.

RESULTS

We entered 33 relevant studies of 2516 articles into the meta-analysis process including 2697 women with family history of diabetes mellitus as well as 29134 women without. Of them, 954 and 4372 subjects developed GDM respectively. Combining the results of the primary studies using the meta-analysis method, the overall odds ratio of family history for developing GDM was estimated as of 3.46 (95% CI: 2.80-4.27).

CONCLUSION

This meta-analysis study revealed that the family history of diabetes is an important risk factor for the gestational diabetes mellitus.

Risk factor screening to identify women requiring oral glucose tolerance testing to diagnose gestational diabetes: A systematic review and meta-analysis and analysis of two pregnancy cohorts

BACKGROUND

Easily identifiable risk factors including: obesity and ethnicity at high risk of diabetes are commonly used to indicate which women should be offered the oral glucose tolerance test (OGTT) to diagnose gestational diabetes (GDM). Evidence regarding these risk factors is limited however. We conducted a systematic review (SR) and meta-analysis and individual participant data (IPD) analysis to evaluate the performance of risk factors in identifying women with GDM.

METHODS

We searched MEDLINE, Medline in Process, Embase, Maternity and Infant Care and the Cochrane Central Register of Controlled Trials (CENTRAL) up to August 2016 and conducted additional reference checking. We included observational, cohort, case-control and cross-sectional studies reporting the performance characteristics of risk factors used to identify women at high risk of GDM. We had access to IPD from the Born in Bradford and Atlantic Diabetes in Pregnancy cohorts, all pregnant women in the two cohorts with data on risk factors and OGTT results were included.

RESULTS

Twenty nine published studies with 211,698 women for the SR and a further 14,103 women from two birth cohorts (Born in Bradford and the Atlantic Diabetes in Pregnancy study) for the IPD analysis were included. Six studies assessed the screening performance of guidelines; six examined combinations of risk factors; eight evaluated the number of risk factors and nine examined prediction models or scores. Meta-analysis using data from published studies suggests that irrespective of the method used, risk factors do not identify women with GDM well. Using IPD and combining risk factors to produce the highest sensitivities, results in low specificities (and so higher false positives). Strategies that use the risk factors of age (>25 or >30) and BMI (>25 or 30) perform as well as other strategies with additional risk factors included.

CONCLUSIONS

Risk factor screening methods are poor predictors of which pregnant women will be diagnosed with GDM. A simple approach of offering an OGTT to women 25 years or older and/or with a BMI of 25kg/m2 or more is as good as more complex risk prediction models. Research to identify more accurate (bio)markers is needed. Systematic Review Registration: PROSPERO CRD42013004608.

Prediction models for the risk of gestational diabetes: a systematic review

BACKGROUND

Numerous prediction models for gestational diabetes mellitus (GDM) have been developed, but their methodological quality is unknown. The objective is to systematically review all studies describing first-trimester prediction models for GDM and to assess their methodological quality.

METHODS

MEDLINE and EMBASE were searched until December 2014. Key words for GDM, first trimester of pregnancy, and prediction modeling studies were combined. Prediction models for GDM performed up to 14 weeks of gestation that only include routinely measured predictors were eligible.Data was extracted by the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). Data on risk predictors and performance measures were also extracted. Each study was scored for risk of bias.

RESULTS

Our search yielded 7761 articles, of which 17 were eligible for review (14 development studies and 3 external validation studies). The definition and prevalence of GDM varied widely across studies. Maternal age and body mass index were the most common predictors. Discrimination was acceptable for all studies. Calibration was reported for four studies. Risk of bias for participant selection, predictor assessment, and outcome assessment was low in general. Moderate to high risk of bias was seen for the number of events, attrition, and analysis.

CONCLUSIONS

Most studies showed moderate to low methodological quality, and few prediction models for GDM have been externally validated. External validation is recommended to enhance generalizability and assess their true value in clinical practice.

Prevalence of Gestational Diabetes and Risk of Progression to Type 2 Diabetes: a Global Perspective

Despite the increasing epidemic of diabetes mellitus affecting populations at different life stages, the global burden of gestational diabetes mellitus (GDM) is not well assessed. Systematically synthesized data on global prevalence estimates of GDM are lacking, particularly among developing countries. The hyperglycemic intrauterine environment as exemplified in pregnancies complicated by GDM might not only reflect but also fuel the epidemic of type 2 diabetes mellitus (T2DM). We comprehensively reviewed available data in the past decade in an attempt to estimate the contemporary global prevalence of GDM by country and region. We reviewed the risk of progression from GDM to T2DM as well. Synthesized data demonstrate wide variations in both prevalence estimates of GDM and the risk of progression from GDM to T2DM. Direct comparisons of GDM burden across countries or regions are challenging given the great heterogeneity in screening approaches, diagnostic criteria, and underlying population characteristics. In this regard, collaborative efforts to estimate global GDM prevalence would be a large but important leap forward. Such efforts may have substantial public health implications in terms of informing health policy makers and healthcare providers for disease burden and for developing more targeted and effective diabetes prevention and management strategies globally.

Prevalence of gestational diabetes mellitus and associated risk factors in Turkish women: the Trabzon GDM Study

INTRODUCTION

The aim of this study was to investigate the prevalence of gestational diabetes mellitus (GDM) in Turkish pregnant women in the Trabzon Region and further to identify population-specific risk factors for GDM.

MATERIAL AND METHODS

In this prospective cross-sectional survey, universal screening for GDM was performed in 815 pregnant women. Screening was done with a 50-g oral glucose challenge test (GCT) with a 140 mg/dl cut-off point, then a diagnostic 100 g oral glucose tolerance test (OGTT) was performed according to Carpenter and Coustan (CC) criteria.

RESULTS

The GCT was positive in 182 (22.3%) cases. The OGTT was performed on the 182 screen-positive pregnant women. Thirty-five were diagnosed with GDM on the basis of their results for a prevalence of 4.3% (35/815). Of the pregnancies with negative GCT but having high risk factors for GDM (n = 31), 4 were diagnosed with GDM (0.5%). Prevalence of GDM was found to be 4.8% (n = 39) for all pregnant women. Gestational diabetes mellitus was positively associated with advanced maternal age (p < 0.001), prepregnancy body mass index (p < 0.001), cessation of cigarette smoking (p < 0.001), excessive weight gain during pregnancy (p = 0.003), previous history of GDM (p < 0.001), history of selected medical conditions (p = 0.018), family history of diabetes (FHD) (p < 0.001), and existence of at least one high risk factor for GDM (p < 0.001). In multiple logistic regression analysis, independent predictors for GDM were maternal age, cessation of cigarette smoking, increasing prepregnancy body mass index, weight gain of more than 8 kg during pregnancy, GDM history in previous pregnancies and a history of diabetes in first-degree relatives of pregnant women.

CONCLUSIONS

The prevalence of GDM in Trabzon province was found as moderate. Commonly recognized risk factors including older age, prepregnancy obesity, FHD and past history of GDM, are valid for our urban Turkish population. Also, excessive weight gain in pregnancy and cigarette cessation were observed to be nontradional risk factors of GDM. It was concluded that all pregnant women should be screened for GDM if prevalence was not low.

Circulating retinol-binding protein 4 levels in gestational diabetes mellitus: a meta-analysis of observational studies

Retinol-binding protein 4 (RBP4) is a novel adipocyte-derived cytokine playing an important role in the regulation of energy metabolism and insulin sensitivity. Although the association between RBP4 and metabolic dysfunction is well established, studies on the relationship between circulating RBP4 levels and the risk of gestational diabetes mellitus (GDM) have yielded inconclusive results. We performed a meta-analysis to investigate whether women with GDM had higher circulating RBP4 levels than the normglycemic pregnant women. PubMed, Web of Science and EMBASE were searched up to 1 August 2014. A total of 14 studies comprised of 884 women with GDM and 1251 normglycemic pregnant women were included. The overall results suggested that maternal circulating RBP4 levels were significantly higher in GDM than their normal controls (SMD: 0.49 μg/ml, 95% CI: 0.23-0.75 μg/ml, p < 0.001, random effect model). However, stratified results indicated that this significant difference only existed in the second/third trimester and was limited to Asian populations. Furthermore, subgroup analysis according to matched maternal age and BMI still demonstrated that GDM had higher circulating RBP4 levels than the normal controls. Our findings suggested that Asian women with GDM had increased circulating RBP4 levels in their second/third trimester.

Global estimates of the prevalence of hyperglycaemia in pregnancy

AIMS

We estimated the number of live births worldwide and by IDF Region who developed hyperglycaemia in pregnancy in 2013, including total diabetes in pregnancy (known and previously undiagnosed diabetes) and gestational diabetes.

METHODS

Studies reporting prevalence of hyperglycaemia first-detected in pregnancy (formerly termed gestational diabetes) were identified using PubMed and through a review of cited literature. A simple scoring system was developed to characterise studies on diagnostic criteria, year study was conducted, study design, and representation. The highest scoring studies by country with sufficient detail on methodology for characterisation and reporting at least three age-groups were selected for inclusion. Forty-seven studies from 34 countries were used to calculate age-specific prevalence of hyperglycaemia first-detected in pregnancy in women 20-49 years. Adjustments were then made to account for heterogeneity in screening method and blood glucose diagnostic threshold in studies and also to align with recently published diagnostic criteria as defined by the WHO for hyperglycaemia first detected in pregnancy. Prevalence rates were applied to fertility and population estimates to determine regional and global prevalence of hyperglycaemia in pregnancy for 2013. An estimate of the proportion of cases of hyperglycaemia in pregnancy due to total diabetes in pregnancy was calculated using age- and sex-specific estimates of diabetes from the IDF Diabetes Atlas and applied to age-specific fertility rates.

RESULTS

The global prevalence of hyperglycaemia in pregnancy in women (20-49 years) is 16.9%, or 21.4 million live births in 2013. An estimated 16.0% of those cases may be due to total diabetes in pregnancy. The highest prevalence was found in the South-East Asia Region at 25.0% compared with 10.4% in the North America and Caribbean Region. More than 90% of cases of hyperglycaemia in pregnancy are estimated to occur in low- and middle-income countries.

CONCLUSION

These are the first global estimates of hyperglycaemia in pregnancy and conform to the new WHO recommendations regarding diagnosis and also include estimates of live births in women with known diabetes. They indicate the importance of the disease from a public health and maternal and child health perspective, particularly in developing countries.

The IDF Diabetes Atlas methodology for estimating global prevalence of hyperglycaemia in pregnancy

INTRODUCTION

Hyperglycaemia is one of the most prevalent metabolic disorders occurring during pregnancy. Limited data are available on the global prevalence of hyperglycaemia in pregnancy. The International Diabetes Federation (IDF) has developed a methodology for generating estimates of the prevalence of hyperglycaemia in pregnancy, including hyperglycaemia first detected in pregnancy and live births to women with known diabetes, among women of childbearing age (20-49 years).

METHODS

A systematic review of the literature for studies reporting the prevalence of gestational diabetes was conducted. Studies were evaluated and scored to favour those that were representative of a large population, conducted recently, reported age-specific estimates, and case identification was based on blood test. Age-specific prevalence data from studies were entered to produce estimates for five-year age groups using logistic regression to smooth curves, with age as the independent variable. The derived age-specific prevalence was adjusted for differences in diagnostic criteria in the underlying data. Cases of hyperglycaemia in pregnancy were derived from age-specific estimates of fertility and age-specific population estimates. Country-specific estimates were generated for countries with available data. Regional and global estimates were generated based on aggregation and extrapolation for 219 countries and territories. Available fertility rates and diabetes prevalence estimates were used to estimate the proportion of hyperglycaemia in pregnancy that may be due to total diabetes in pregnancy - pregnancy in women with known diabetes and diabetes first detected in pregnancy.

RESULTS

The literature review identified 199 studies that were eligible for characterisation and selection. After scoring and exclusion requirements, 46 studies were selected representing 34 countries. More than 50% of selected studies came from Europe and North America and Caribbean. The smallest number of identified studies came from sub-Saharan Africa. The majority of studies were for high-income countries, although low- and middle-income countries were also represented.

CONCLUSION

Prevalence estimates of hyperglycaemia in pregnancy are sensitive to the data from which they are derived. The IDF methodology is a transparent, reproducible, and modifiable method for estimating the burden of hyperglycaemia in pregnancy. More data are needed, in particular from developing countries, to strengthen the methodology.

Improvement of selective screening strategy for gestational diabetes through a more accurate definition of high-risk groups

OBJECTIVE

This study aimed to assess the predictive value of risk factors (RFs) for gestational diabetes mellitus (GDM) established by selective screening (SS) and to identify subgroups of women at a higher risk of developing GDM.

DESIGN

A retrospective, single-center study design was employed.

METHODS

Data of 1015 women screened for GDM at 24-28 weeks of gestation and diagnosed according to the International Association of Diabetes and Pregnancy Study Groups criteria were evaluated. Information on RFs established by SS was also collected and their association with GDM was determined. To identify distinct and homogeneous subgroups of patients at a higher risk, the RECursive Partitioning and AMalgamation (RECPAM) method was used.

RESULTS

Overall, 113 (11.1%) women were diagnosed as having GDM. The application of the SS criteria would result in the execution of an oral glucose tolerance test (OGTT) in 58.3% of women and 26 (23.0%) cases of GDM would not be detected due to the absence of any RF. The RECPAM analysis identified high-risk subgroups characterized by fasting plasma glucose values >5.1 mmol/l (odds ratio (OR)=26.5; 95% CI 14.3-49.0) and pre-pregnancy BMI (OR=7.0; 95% CI 3.9-12.8 for overweight women). In a final logistic model including RECPAM classes, previous macrosomia (OR=3.6; 95% CI 1.1-11.6), and family history of diabetes (OR=1.8; 95% CI 1.1-2.8), but not maternal age, were also found to be associated with an increased risk of developing GDM. A screening approach based on the RECPAM model would reduce by over 50% (23.0 vs 10.6%) the number of undiagnosed GDM cases when compared with the current SS approach, at the expense of 50 additional OGTTs required.

CONCLUSIONS

A screening approach based on our RECPAM model results in a significant reduction in the number of undetected GDM cases compared with the current SS procedure.

Prevalence of gestational diabetes mellitus in urban and rural Tanzania

AIM

To estimate prevalence of gestational diabetes mellitus (GDM) and associated determinants in urban and rural Tanzania.

METHODS

A cross-sectional study was conducted from 2011 through 2012 in selected urban and rural communities. Pregnant women (609 urban, 301 rural), who were not previously known to have diabetes, participated during usual ante-natal clinic visits. Capillary blood samples were collected at fasting and 2h after 75 g glucose load and were measured using HemoCue. Diagnosis of GDM was made using 1999 World Health Organization (WHO) criteria.

RESULTS

Women in rural areas were younger (26.6 years) than in urban areas (27.5 years). Mean gestational age, height, and mid-upper arm circumference (MUAC) were similar for the two areas. Overall prevalence of GDM averaged 5.9%, with 8.4% in urban area and 1.0% in rural area. Prevalence of GDM was higher for women who had a previous stillbirth (OR 2.8, 95% CI 1.5-5.4), family history of type 2 diabetes (OR 2.1, 95% CI 1.1-4.2), and MUAC above 28 cm (OR 1.9, 95% CI 1.1-3.3), and lower for women with normal hemoglobin compared with anemia (OR 0.45, 95% CI 0.22-0.93).

CONCLUSIONS

Prevalence of GDM is higher than expected in urban areas in Tanzania, indicating an increasing population who are at risk for delivery complications and type 2 diabetes in Sub-Saharan Africa.

Waist circumference in predicting gestational diabetes mellitus

BACKGROUND

To evaluate waist circumference (WC) measured at 20-24 weeks of gestation as a predictor of gestational diabetes mellitus (GDM).

METHODS

This cross-sectional study included 240 women at 20-24 weeks of gestation. At enrollment, WC was measured, and both prepregnancy and gestational body mass index (BMI) were estimated. According to the results of 75-g oral glucose tolerance test (OGTT) performed at 24-28 weeks, subjects were allocated into two groups, non-GDM and GDM. WC sensitivity and specificity, and odds ratios (OR) and 95% confidence intervals for BMI and WC were estimated, and a receiver operating characteristics curve was generated.

RESULTS

Of the 240 pregnant women enrolled, 31 (13%) had GDM. Prepregnancy BMI (OR = 4.21), gestational BMI (OR = 3.17) and WC at 20-24 weeks (OR = 4.02) correlated with GDM risk. At 20-24 weeks, a WC of 85.5-88.5 cm was the optimal cutoff point for predicting GDM (Sens/Spec balance between 87.1/41.1% and 77.4/56.9%).

CONCLUSION

At 20-24 weeks of gestation, WC values in the range of 86-88 cm showed to be a good performance in predicting GDM.

Early prediction of gestational diabetes mellitus in Vietnam: clinical impact of currently recommended diagnostic criteria

OBJECTIVE

We aimed to compare the discriminative power of prognostic models for early prediction of women at risk for the development of gestational diabetes mellitus (GDM) using four currently recommended diagnostic criteria based on the 75-g oral glucose tolerance test (OGTT). We also described the potential effect of application of the models into clinical practice.

RESEARCH DESIGN AND METHODS

A prospective cross-sectional study of 2,772 pregnant women was conducted at a referral maternity center in Vietnam. GDM was determined by the American Diabetes Association (ADA), International Association of the Diabetes and Pregnancy Study Groups (IADPSG), Australasian Diabetes in Pregnancy Society (ADIPS), and World Health Organization (WHO) criteria. Prognostic models were developed using the Bayesian model averaging approach, and discriminative power was assessed by area under the curve. Different thresholds of predicted risk of developing GDM were applied to describe the clinical impact of the diagnostic criteria.

RESULTS

The magnitude of GDM varied substantially by the diagnostic criteria: 5.9% (ADA), 20.4% (IADPSG), 20.8% (ADIPS), and 24.3% (WHO). The ADA prognostic model, consisting of age and BMI at booking, had the best discriminative power (area under the curve of 0.71) and the most favorable cost-effective ratio if implemented in clinical practice. Selective screening of women for GDM using the ADA model with a risk threshold of 3% gave 93% sensitivity for identification of women with GDM with a 27% reduction in the number of OGTTs required.

CONCLUSIONS

A simple prognostic model using age and BMI at booking could be used for selective screening of GDM in Vietnam and in other low- and middle-income settings.

Definition, epidemiology, risk factors

Gestational diabetes mellitus is defined as glucose intolerance that begins or is first recognized during pregnancy. Its prevalence, generally situated between 2-6%, may reach 10-20% in high-risk populations, with an increasing trend across most racial/ethnic groups studied. Among traditional risk factors, previous gestational diabetes, advanced maternal age and obesity have the highest impact on gestational diabetes risk. Racial/ethnic origin and family history of type 2 diabetes have a significant but moderate impact (except for type 2 diabetes in siblings). Several non traditional factors have been recently characterized, either physiological (low birthweight and short maternal height) or pathological (polycystic ovaries). The multiplicity of risk factors and their interactions results in a low reliability of risk prediction on an individual basis.