Screening for Gestational Diabetes: Updated Evidence Report and Systematic Review for the US Preventive Services Task Force

Exploring Structural Uncertainty in Cost-Effectiveness Modeling of Gestational Diabetes Screening: An Application Example from Norway

BACKGROUND

Screening pregnant women for gestational diabetes mellitus (GDM) has recently been expanded in Norway, although screening eligibility criteria continue to be debated. We aimed to compare the cost-effectiveness of alternative GDM screening strategies and explored structural uncertainty and the value of future research in determining the most cost-effective eligibility criteria for GDM screening in Norway.

DESIGN

We developed a probabilistic decision tree to estimate the total costs and health benefits (i.e., quality-adjusted life-years; QALYs) associated with 4 GDM screening strategies (universal, current guidelines, high-risk, and no screening). We identified the most cost-effective strategy as the strategy with the highest incremental cost-effectiveness ratio below a Norwegian benchmark for cost-effectiveness ($28,400/QALY). We excluded inconclusive evidence on the effects of screening on later maternal type 2 diabetes mellitus (T2DM) in the primary analysis but included this outcome in a secondary analysis using 2 different sources of evidence (i.e., Cochrane or US Preventive Services Task Force). To quantify decision uncertainty, we conducted scenario analysis and value-of-information analyses.

RESULTS

Current screening recommendations were considered inefficient in all analyses, while universal screening was most cost-effective in our primary analysis ($26,014/QALY gained) and remained most cost-effective when we assumed a preventive effect of GDM treatment on T2DM. When we assumed no preventive effect, high-risk screening was preferred ($19,115/QALY gained). When we assumed GDM screening does not prevent perinatal death in scenario analysis, all strategies except no screening exceeded the cost-effectiveness benchmark. In most analyses, decision uncertainty was high.

CONCLUSIONS

The most cost-effective screening strategy, ranging from no screening to universal screening, depended on the source and inclusion of GDM treatment effects on perinatal death and T2DM. Further research on these long-term outcomes could reduce decision uncertainty.

HIGHLIGHTS

This article analyses the cost-effectiveness of 4 alternative gestational diabetes mellitus (GDM) screening strategies in Norway: universal screening, current (broad) screening, high-risk screening, and no screening.The current Norwegian screening recommendations were considered inefficient under all analyses.The most cost-effective screening strategy ranged from no screening to universal screening depending on the source and inclusion of GDM treatment effects on later maternal diabetes and perinatal death.The parameters related to later maternal diabetes and perinatal death accounted for most of the decision uncertainty.

Can System Inflammation Response Index or Systemic Immune Inflammation Index predict gestational diabetes mellitus in the first trimester? A prospective observational study

OBJECTIVE

To evaluate System Inflammation Response Index (SIRI) and Systemic Immune Inflammation Index (SII), which are the inflammatory indices, for the prediction of gestational diabetes mellitus (GDM) in the first trimester.

METHODS

This was a prospective observational study conducted in a tertiary center from April 2023 to September 2023. Ninety-four pregnant women with gestational diabetes and 107 healthy pregnant women were included. The two groups were compared according to first-trimester SIRI and SII values. A receiver operating characteristic (ROC) curve analysis was performed to determine the optimal cut-off levels of SII and SIRI in predicting GDM.

RESULTS

Significantly higher first-trimester SII and SIRI values were present in the gestational diabetes group (P < 0.001). Optimal cut-off values in the prediction of gestational diabetes were found to be 1.58 (area under the curve [AUC] 0.71, 67% sensitivity, 65% specificity, 95% confidence interval [CI] 0.64-0.78, P < 0.001) and 875 (AUC 0.70, 66% sensitivity, 65% specificity, 95% CI 0.63-0.77, P < 0.001) for SIRI and SII, respectively. Neutrophil counts, mean platelet volume (MPW), neutrophil to lymphocyte ratio (NLR), and red cell distribution width (RDW) were significantly higher in the GDM group (P < 0.001, P = 0.02, P = 0.01, P < 0.01, respectively).

CONCLUSION

Novel inflammatory indices SII and SIRI may be useful in the prediction of GDM in the first trimester, but their utility in the prediction of insulin requirement is questionable. They may be used as additional tools in routine clinical practice.

Maternal birth weight as an indicator of early and late gestational diabetes mellitus: The Japan Environment and Children's Study

AIMS

This study aimed to investigate the association of maternal birth weight (MBW) with early and late gestational diabetes mellitus (GDM).

METHODS

A total of 69318 pregnant Japanese women were included in this birth cohort study. The associations between maternal birth weight and early gestational diabetes mellitus (diagnosed at <24 gestational weeks) and late GDM (diagnosed at ≥24 gestational weeks) were investigated using a multinomial logistic regression model, with an maternal birth weight of 3000-3499 g as the reference category.

RESULTS

Lower maternal birth weight was associated with higher odds of developing early and late gestational diabetes mellitus (P < 0.0001 and P < 0.0001, respectively). The adjusted odds ratios (aORs) for early gestational diabetes mellitus in participants with a MBW of <2500 g and 2500-2999 g were 1.345 (95% confidence interval [CI]: 0.912-1.984) and 1.338 (95% CI: 1.098-1.629), respectively. The aORs for late gestational diabetes mellitus in participants with a MBW of <2500 g and 2500-2999 g were, 1.657 (95% CI: 1.298-2.115) and 1.218 (95% CI: 1.058-1.402), respectively.

CONCLUSIONS

Regardless of the gestational age when gestational diabetes mellitus was diagnosed, a lower maternal birth weight was associated with an increased risk of gestational diabetes mellitus. Furthermore, the association of a MBW <2500 g with late gestational diabetes mellitus tended to be stronger than that with early gestational diabetes mellitus.

Maternal fasting serum C-peptide concentrations in the first and second trimesters and subsequent risk of gestational diabetes mellitus: A nested case-control study among Chinese women

OBJECTIVE

To examine the association of serum connecting peptide (C-peptide) concentrations with gestational diabetes mellitus (GDM) risk among Chinese women.

METHODS

A nested case-control study was conducted on 436 reproductive-aged women, involving 218 GDM cases and 218 controls matched at 1:1 by maternal age, in Beijing, China between January 2016 and December 2017. Fasting serum C-peptide were successively determined at 10-14 and 15-20 weeks of gestation. Restricted cubic spline and logistic regression analyses were utilized, and receiver operating characteristic (ROC) curves were generated to evaluate the predictive capacity of C-peptide for GDM.

RESULTS

Fasting serum C-peptide concentrations exhibited a significant decrease from the initial to the subsequent trimester in females with normal glucose tolerance (NGT). For each 1 log ng/mL increase of fasting serum C-peptide during the first and second trimesters, GDM risk increased by 2.38-fold [odds ratio (OR): 2.38, 95% confidence intervals (95%CI): 1.33-4.40] and 3.07-fold (OR: 3.07, 95%CI: 1.49-6.62), respectively. The areas under the ROC curves for the first- and second-trimester C-peptide were 80.4% and 82.4%.

CONCLUSION

Our findings revealed a positive correlation between fasting serum C-peptide during the first and second trimesters and the risk of GDM or its subtypes, underscoring the potential of C-peptide as a predictor for GDM development.

Antepartum Fetal Surveillance and Optimal Timing of Delivery in Diabetic Women: A Narrative Review

Antepartum fetal surveillance (AFS) is essential for pregnant women with diabetes to mitigate the risk of stillbirth. However, there is still no universal consensus on the optimal testing method, testing frequency, and delivery timing. This review aims to comprehensively analyze the evidence concerning AFS and the most advantageous timing for delivery in both gestational and pregestational diabetes mellitus cases. This review's methodology involved an extensive literature search encompassing international diabetes guidelines and scientific databases, including PubMed, MEDLINE, Google Scholar, and Scopus. The review process meticulously identified and utilized pertinent articles for analysis. Within the scope of this review, a thorough examination revealed five prominent international guidelines predominantly addressing gestational diabetes. These guidelines discuss the utility and timing of fetal well-being assessments and recommendations for optimal pregnancy resolution timing. However, the scarcity of clinical trials directly focused on this subject led to a reliance on observational studies as the basis for most recommendations. Glucose control, maternal comorbidities, and the medical management received are crucial in making decisions regarding AFS and determining the appropriate delivery timing.

Predicting adverse perinatal outcomes among gestational diabetes complicated pregnancies using neural network algorithm

OBJECTIVE

The primary aim of this study is to utilize a neural network model to predict adverse neonatal outcomes in pregnancies complicated by gestational diabetes (GDM).

DESIGN

Our model, based on XGBoost, was implemented using Python 3.6 with the Keras framework built on TensorFlow by Google. We sourced data from medical records of GDM-diagnosed individuals who delivered at our tertiary medical center between 2012 and 2016. The model included simple pregnancy parameters, maternal age, body mass index (BMI), parity, gravity, results of oral glucose tests, treatment modality, and glycemic control. The composite neonatal adverse outcomes defined as one of the following: large or small for gestational age, shoulder dystocia, fetal umbilical pH less than 7.2, neonatal intensive care unit (NICU) admission, respiratory distress syndrome (RDS), hyperbilirubinemia, or polycythemia. For the machine training phase, 70% of the cohort was randomly chosen. Each sample in this set consisted of baseline parameters and the composite outcome. The remaining samples were then employed to assess the accuracy of our model.

RESULTS

The study encompassed a total of 452 participants. The composite adverse outcome occurred in 29% of cases. Our model exhibited prediction accuracies of 82% at the time of GDM diagnosis and 91% at delivery. The factors most contributing to the prediction model were maternal age, pre-pregnancy BMI, and the results of the single 3-h 100 g oral glucose tolerance test.

CONCLUSION

Our advanced neural network algorithm has significant potential in predicting adverse neonatal outcomes in GDM-diagnosed individuals.

Regional Disparities in the Infant Mortality Rate in Korea Between 2001 and 2021

BACKGROUND

The infant mortality rate (IMR) has been considered an important indicator of the overall public health level. Despite improvements in recent decades, regional inequalities in the IMR have been reported worldwide. However, there are no Korean epidemiological studies on regional disparities in the IMR.

METHODS

We extracted causes of death data from the Statistics Korea through the Korean Statistical Information Service database between 2001 and 2021. The total and regional IMRs were calculated to determine regional disparities. Based on causes of death and using Seoul as a reference, the excess infant deaths and population attributable fractions (PAFs) were calculated for 15 other metropolitan cities and provinces. The average annual percent changes by region from 2001 to 2021 were obtained using a joinpoint regression program. To assess inequities in IMR trends, the rate ratios (RRs) and rate differences (RDs) of the 15 regions were calculated by dividing the study period into period 1 (2001-2007), period 2 (2008-2014), and period 3 (2015-2021).

RESULTS

The overall IMR in Korea was 3.64 per 1,000 live births, and the IMRs in the 14 regions were relatively higher than that in Seoul, with RRs ranging from 1.15 (95% confidence interval [CI], 1.04, 1.27) in Jeju-do to 1.62 (95% CI, 1.54, 1.71) in Daegu, over the total study period. Significant differences in infant deaths by region were observed for all causes of death, with PAFs ranging from 2.2% (95% CI, 1.7, 2.6) in Gyeonggi-do to 38.4% (95% CI, 38.1, 38.6) in Daegu. The leading cause of excess infant deaths was perinatal problems. The IMR disparities in the relative and absolute measures decreased from 1.44 (1.34, 1.54) to 1.21 (1.10, 1.31) for RRs and from 0.79 (0.63, 0.96) to 0.30 (0.15, 0.45) for RDs between periods 1 and 2, followed by an increase from 1.21 (1.10, 1.31) to 1.36 (1.21, 1.53) for RRs and from 0.30 (0.15, 0.45) to 0.51(0.36, 0.67) for RDs between period 2 and 3.

CONCLUSION

Infant death is associated with place of residence and regional gaps have recently widened again in Korea. An in-depth investigation of the causes of regional disparities in infant mortality is required for effective governmental policies to achieve equality in infant health.

Development of machine learning models to predict gestational diabetes risk in the first half of pregnancy

BACKGROUND

Early prediction of Gestational Diabetes Mellitus (GDM) risk is of particular importance as it may enable more efficacious interventions and reduce cumulative injury to mother and fetus. The aim of this study is to develop machine learning (ML) models, for the early prediction of GDM using widely available variables, facilitating early intervention, and making possible to apply the prediction models in places where there is no access to more complex examinations.

METHODS

The dataset used in this study includes registries from 1,611 pregnancies. Twelve different ML models and their hyperparameters were optimized to achieve early and high prediction performance of GDM. A data augmentation method was used in training to improve prediction results. Three methods were used to select the most relevant variables for GDM prediction. After training, the models ranked with the highest Area under the Receiver Operating Characteristic Curve (AUCROC), were assessed on the validation set. Models with the best results were assessed in the test set as a measure of generalization performance.

RESULTS

Our method allows identifying many possible models for various levels of sensitivity and specificity. Four models achieved a high sensitivity of 0.82, a specificity in the range 0.72-0.74, accuracy between 0.73-0.75, and AUCROC of 0.81. These models required between 7 and 12 input variables. Another possible choice could be a model with sensitivity of 0.89 that requires just 5 variables reaching an accuracy of 0.65, a specificity of 0.62, and AUCROC of 0.82.

CONCLUSIONS

The principal findings of our study are: Early prediction of GDM within early stages of pregnancy using regular examinations/exams; the development and optimization of twelve different ML models and their hyperparameters to achieve the highest prediction performance; a novel data augmentation method is proposed to allow reaching excellent GDM prediction results with various models.

An early prediction model for gestational diabetes mellitus based on metabolomic biomarkers

BACKGROUND

Gestational diabetes mellitus (GDM) represents the main metabolic alteration during pregnancy. The available methods for diagnosing GDM identify women when the disease is established, and pancreatic beta-cell insufficiency has occurred.The present study aimed to generate an early prediction model (under 18 weeks of gestation) to identify those women who will later be diagnosed with GDM.

METHODS

A cohort of 75 pregnant women was followed during gestation, of which 62 underwent normal term pregnancy and 13 were diagnosed with GDM. Targeted metabolomics was used to select serum biomarkers with predictive power to identify women who will later be diagnosed with GDM.

RESULTS

Candidate metabolites were selected to generate an early identification model employing a criterion used when performing Random Forest decision tree analysis. A model composed of two short-chain acylcarnitines was generated: isovalerylcarnitine (C5) and tiglylcarnitine (C5:1). An analysis by ROC curves was performed to determine the classification performance of the acylcarnitines identified in the study, obtaining an area under the curve (AUC) of 0.934 (0.873-0.995, 95% CI). The model correctly classified all cases with GDM, while it misclassified ten controls as in the GDM group. An analysis was also carried out to establish the concentrations of the acylcarnitines for the identification of the GDM group, obtaining concentrations of C5 in a range of 0.015-0.25 μmol/L and of C5:1 with a range of 0.015-0.19 μmol/L.

CONCLUSION

Early pregnancy maternal metabolites can be used to screen and identify pregnant women who will later develop GDM. Regardless of their gestational body mass index, lipid metabolism is impaired even in the early stages of pregnancy in women who develop GDM.

Reproductive health after thoracic transplantation: An ISHLT expert consensus statement

Pregnancy after thoracic organ transplantation is feasible for select individuals but requires multidisciplinary subspecialty care. Key components for a successful pregnancy after lung or heart transplantation include preconception and contraceptive planning, thorough risk stratification, optimization of maternal comorbidities and fetal health through careful monitoring, and open communication with shared decision-making. The goal of this consensus statement is to summarize the current evidence and provide guidance surrounding preconception counseling, patient risk assessment, medical management, maternal and fetal outcomes, obstetric management, and pharmacologic considerations.

Partially Oxidized Carbon Nanomaterials with Ni/NiO Heterostructures as Durable Glucose Sensors

Conventional enzyme-based glucose biosensors have limited extensive applications in daily life because glucose oxidase is easily inactivated and is expensive. In this paper, we propose a strategy to prepare a new type of cost-effective, efficient, and robust nonenzymatic Ni-CNT-O for electrochemical glucose sensing. It is first followed by the pyrolysis of Ni-ABDC nanostrips using melamine to grow carbon nanotubes (CNTs) to give an intermediate product of Ni-CNT, which is further accompanied by partial oxidation to enable the facile formation of hierarchical carbon nanomaterials with improved hydrophilicity. A series of physicochemical characterizations have fully proved that Ni-CNT-O is a carbon-coated heterostructure of Ni and NiO nanoparticles embedded into coordination polymer-derived porous carbons. The obtained Ni-CNT-O exhibits a better electrocatalytic activity for glucose oxidation stemming from the synergistic effect of a metal element and a metal oxide than unoxidized Ni-CNT, which also shows high performance with a wide linear range from 1 to 3000 μM. It also offers a high sensitivity of 79.4 μA mM^-1 cm^-2, a low detection limit of 500 nM (S/N = 3), and a satisfactory long-term durability. Finally, this glucose sensor exhibits good reproducibility, high selectivity, as well as satisfactory results by comparing the current response of simulated serum within egg albumen.

Trends in Gestational Diabetes Screening Practices in British Columbia from 2005-2019

OBJECTIVES

To examine trends in the frequency and method (one-step vs. two-step) of gestational diabetes mellitus (GDM) screening in British Columbia (BC), Canada, across subgroups of pregnant individuals in the context of changing local and national clinical practice guidelines.

METHODS

We conducted a retrospective cohort study using de-identified linked perinatal and laboratory billing data. We included all pregnancies delivered in BC after 28 weeks gestation, with screening dates between June 2004 and May 2019. We calculated the prevalence of each screening method with 95% CI overall and over time, and we examined screening practices in subgroups and different geographic regions. In October 2010, BC began recommending a one-step method; therefore, we examined time periods relative to this and other Canadian guideline changes.

RESULTS

Screening completion increased over the study period, from 88% in 2004 to 96% in 2019. After a guideline change in 2010, use of one-step screening increased sharply from 2.0% (95% CI 1.9-2.0) to 45.2% (95% CI 44.9-45.6). Following the 2013 Diabetes Canada guideline change, one-step screening decreased to 42.8% (95% CI 42.5-43.1). Of those receiving one-step screening, 18% were diagnosed with GDM compared to 9% with two-step screening. Use of one-step screening was higher in pregnant people with risk factors and in larger urban centres.

CONCLUSION

GDM screening in BC demonstrated higher use of one-step screening among people with risk factors; however, there were strong regional disparities and considerable variation in screening practices over time and across subgroups.

[Diabetes and Pregnancy]

The quality of metabolic control at the beginning of pregnancy already determines the course and outcome of pregnanies with type 1 and type 2 diabetes mellitus. The preconceptional counseling and support provided by experienced teams is more important than modern technical equipment with insulin pumps and sensors for continuous glucose measurement. The incidence of congenital malformations is significantly reduced by a periconceptional HbA1c level < 6.5 % and folic acid supplementation started preconceptionally. To prevent preeclampsia, all women with type 1 and type 2 diabetes mellitus should be offered low-dose ASA, starting before 16 weeks of pregnancy. If the pregnant woman has a BMI < 25 kg/m² and persistently elevated fasting blood glucose levels, a GCK-MODY should be considered. For the diagnosis of asymptomatic gestational diabetes mellitus, all women in Germany with 24 + 0 to 27 + 6 weeks of pregnancy are offered a two-stage screening. Structured follow-up care is required after gestational diabetes mellitus, because these women have an increased risk of developing type 2 diabetes mellitus and cardiovascular complications. Pregnant women with COVID-19 and hyperglycemia have an increased risk of a severe course of the infection, which is further increased by obesity - they are an important target group for vaccination with an mRNA vaccine.

Impact of gestational diabetes mellitus treatment on medium/long-term outcomes after pregnancy: A systematic review and meta-analysis

AIM

We aimed to evaluate the effect of gestational diabetes mellitus (GDM) treatment on medium/long-term outcomes both the mother and offspring.

METHODS

We performed a systematic review on randomized clinical trials addressing specific treatment of women with GDM versus usual care and its impact on maternal and offspring outcomes at medium/long-term. MEDLINE, EMBASE and CENTRAL were searched from inception to 8 October 2021.

OUTCOME VARIABLES

maternal (diabetes, metabolic syndrome, 12 secondary); offspring (diabetes, impaired fasting glucose, impaired glucose tolerance, high body mass index, 15 secondary). Risk of bias was assessed with Cochrane tool and aggregation performed with Revman 5.4.

RESULTS

We included five studies (1140 women, 767 offspring) with follow-up ranging 4-16 years after delivery. GDM treatment likely does not reduce risk of maternal diabetes (RR 1.00; [95% CI 0.82-1.23]) and may not reduce that of metabolic syndrome (RR 0.93; [95% CI 0.71-1.22]). We obtained very uncertain evidence that treatment may increase maternal HDL-cholesterol. Findings showed that GDM treatment may not have an impact on infants' outcomes (RRs 0.79; [95% CI 0.39-1.69] for impaired fasting glucose; RR 0.91; [95% CI 0.74-1.12] for body mass index >85th centile and 0.89; [95% CI 0.65-1.22] for body mass index >95th centile respectively).

CONCLUSIONS

With current evidence is uncertain if specific treatment of women with GDM has an impact on medium/long-term metabolic outcomes either in the mother or in the offspring. These results add evidence to the recommendation of systematically reevaluating mother and offspring after delivery.

REGISTRATION

OSF, DOI 10.17605/OSF.IO/KFN79.

Gestational diabetes and other adverse pregnancy outcomes in polycystic ovary syndrome

PURPOSE OF REVIEW

This review provides an update on gestational diabetes (GDM) and other adverse pregnancy outcomes in individuals with polycystic ovary syndrome (PCOS), one of the most common metabolic disorders and causes of infertility.

RECENT FINDINGS

Recent studies using Rotterdam diagnostic criteria for PCOS have supported prior literature suggesting that pregnant individuals with PCOS are at an increased risk of GDM. Risk factors for GDM in this population include overweight/obesity, insulin resistance, hyperandrogenism, amenorrhea, and history of miscarriage. It is possible that subtypes of PCOS (metabolic vs. lean/reproductive) pose differential risk. There are no current screening or treatment guidelines specifically for individuals with PCOS for GDM prevention. Although metformin has been studied for GDM prevention in PCOS, there has been no proven benefit. For infertility treatment, assisted reproductive technology and in-vitro fertilization do not appear to increase the risk of GDM in individuals with PCOS desiring pregnancy.

SUMMARY

Recent studies of pregnant individuals with PCOS suggest an increased risk of adverse pregnancy outcomes, including GDM. Larger, prospective studies using standardized diagnostic criteria are warranted to determine if the risk is from PCOS generally, or if there are subtypes of PCOS (metabolic vs. lean/reproductive) at a higher risk of GDM than others.

Association of ambient fine particulate matter exposure with gestational diabetes mellitus and blood glucose levels during pregnancy

BACKGROUND

Previous studies have examined the associations between ambient fine particulate matter (PM_2.5) exposure and gestational diabetes mellitus (GDM). However, limited studies explored the relationships between PM_2.5 exposure and blood glucose levels during pregnancy, especially in highly polluted areas.

OBJECTIVES

To examine the associations of prenatal ambient PM_2.5 exposure with GDM and blood glucose levels, and to identify the sensitive exposure windows in a highly air-polluted area.

METHODS

From July 2016 to October 2017, a birth cohort study was conducted in Beijing, China. Participants were interviewed in each trimester regarding demographics, lifestyle, living and working environment, and medical conditions. Participant's daily ambient PM_2.5 levels from 3 m before last menstrual period (LMP) to the third trimester was estimated by a hybrid spatiotemporal model. Indoor air quality index was calculated based on environmental tobacco smoke, ventilation, cooking, painting, pesticide, and herbicide use. Distributed lag non-linear model was applied to explore the sensitive weeks of PM_2.5 exposure.

RESULTS

Of 165 pregnant women, 23 (13.94%) developed GDM. After adjusting for potential confounders, PM_2.5 exposure during the 1^st trimester was associated with higher odds of GDM (10 μg/m³ increase: OR = 1.89, 95% CI: 1.04-3.49). Each 10 μg/m³ increase in PM_2.5 during the 2^nd trimester was associated with 17.70% (2.21-33.20), 15.99% (2.96-29.01), 18.82% (4.11-33.52), and 17.10% (3.28-30.92) increase in 1-h, 2-h, Δ1h-fasting (1-h minus fasting), and Δ2h-fasting (2-h minus fasting) blood glucose levels, respectively. PM_2.5 exposure at 24^th-27^th weeks after LMP was associated with increased GDM risk. We identified sensitive exposure windows of 21^st-24^th weeks for higher 1-h and 2-h blood glucose levels and of 20^th-22^nd weeks for increased Δ1h-fasting and Δ2h-fasting.

CONCLUSIONS

Ambient PM_2.5 exposure during the second trimester was associated with higher odds of GDM and higher blood glucose levels. Avoiding exposure to high air pollution levels during the sensitive windows might prevent women from developing GDM.

Screening for Gestational Diabetes Mellitus and Pregnancy Outcomes: Results from a Multicentric Study in Tajikistan

The prevalence of gestational diabetes parallels the prevalence of type 2 diabetes mellitus and is associated with adverse pregnancy outcomes. However, these data are not available for many parts of the world. We assessed the prevalence of gestational diabetes and pregnancy outcomes in Tajikistan. This cohort study included 2438 consecutively recruited representative pregnant women from 8 locations in two cities in Tajikistan, in whom an oral glucose tolerance test (75 g, fasting, 1 h, 2 h) was performed during gestational weeks 24-28. Women with known diabetes and twin pregnancies were excluded. Associations between glucose tolerance test results and pregnancy outcomes were examined. According to the WHO 2013 thresholds, 32.4% of women qualified as having gestational diabetes, the vast majority (29.7%) based on an elevated fasting glucose level (5.1-5.6 mmol/L), while only 2.8% had elevated 1- or 2-hour values or met more than one threshold. Women with only elevated fasting glucose (impaired gestational fasting glycemia) had no evidence of adverse pregnancy outcomes, while those with elevated 1- and/or 2-hour values (impaired gestational glucose tolerance) had more pregnancy complications (infection of urinary tract 1.8 vs. 8.8% p<0.001; preeclampsia 0.7 vs. 10.3% p<0.001) and emergency cesarean sections (4.4 vs. 13.2% p=0.002). Neonates from pregnancies with impaired gestational glucose tolerance had lower APGARs, lower birth weights, lower 30 min glucose levels, and a lower probability of being discharged alive (all p<0.05). In conclusion, the formal prevalence of gestational diabetes is high in Tajikistan; however, this does not translate into adverse pregnancy outcomes for women with impaired gestational fasting glycemia.

Diabetes und Schwangerschaft – Update 2022

Gestationsdiabetes mellitus (GDM) ist mit einem Anteil von 80 % die häufigste Form der Hyperglykämie in der Schwangerschaft. Stillen reduziert das Risiko für Typ-2-Diabetes, je länger die Stilldauer, umso größer ist der Effekt. Bei Frauen, die eine Früh- oder eine Totgeburt durchgemacht haben, besteht ein erhöhtes Risiko, selbst frühzeitig zu versterben. Das Potenzial einer Präeklampsieprävention durch niedrig dosierte Azetylsalizylsäuregabe bei Frauen mit Diabetes mellitus wird bei weitem nicht ausgeschöpft. Schwangere reduzieren durch eine COVID-19-Impfung (COVID-19: „coronavirus disease 2019“) ihr Risiko für einen schweren Krankheitsverlauf. Die nach wie vor hohe Rate an perinataler Mortalität bei Schwangerschaften mit Typ-1- und Typ-2-Diabetes kann in erster Linie durch eine Verbesserung von Basisfaktoren bei der perikonzeptionellen Betreuung reduziert werden. Zweizeitiges Screening auf GDM reduziert im Vergleich zum einzeitigen Screening die GDM-Prävalenz und die Medikalisierung ohne Nachteile für die Mütter und ihre Neugeborenen. Die Zulassung von Metformin für Schwangere seit Februar 2022 bedeutet nicht, dass dieses Arzneimittel als primäre Pharmakotherapie für schwangere Frauen empfohlen wird.

Screening auf Gestationsdiabetes mellitus (GDM) – Welchen Weg weisen die neuen Studien? Übersicht und Kommentar

Das Screening auf Gestationsdiabetes mellitus (GDM) wird international nicht einheitlich durchgeführt. In Deutschland wird allen Schwangeren ein zweizeitiges Screening angeboten, zunächst ein 50-g Vortest (nicht nüchtern), im positiven Fall gefolgt von einem 75-g oralen Gluksetoleranztest (oGTT) über 2 h (nüchtern). Im Jahr 2021 sind zwei randomisierte Screeningstudien publiziert worden, in denen einzeitiges Screenen ohne Vortest mit dem zweizeitigen Screenen verglichen wurde. Einzeitiges Screenen erhöht die GDM-Prävalenz um das 2,3-fache ohne Vorteile für klinisch relevante Endpunkte von Müttern und Neugeborenen und um den Preis unnötiger Pharmakotherapie sowie Belastung der Ressourcen bei den Betreuern. Beim zweizeitigen Screening benötigen nur 20% der Frauen einen oGTT. Darüber hinaus wird die zweizeitige Strategie von den Frauen präferiert.

A novel methodological framework was described for detecting and quantifying overdiagnosis

OBJECTIVES

Methods to quantify overdiagnosis of screen detected cancer have been developed, but methods for quantifying overdiagnosis of non-cancer conditions (whether symptomatic or asymptomatic) have been lacking. We aimed to develop a methodological framework for quantifying overdiagnosis that may be used for asymptomatic or symptomatic conditions, and used Gestational Diabetes Mellitus as an example of how it may be applied.

STUDY DESIGN AND SETTING

We identify two earlier definitions for overdiagnosis, a narrower prognosis-based definition, and a wider utility-based definition. Building on the central importance of the concepts of prognostic information and clinical utility of a diagnosis, we consider the following questions: within a target population, do people found to have a disease using one diagnostic strategy but found not to have the disease using another diagnostic strategy (so called 'additional diagnoses'), have an increased risk of adverse clinical outcomes without treatment (prognosis evidence), and/or a decreased risk of adverse outcomes with treatment (utility evidence)?

RESULTS

Using Causal Directed Acyclic Graphs and Fair Umpires, we illuminate the relationships between diagnostics strategies and the frequency of overdiagnosis. We then use the example of Gestational Diabetes Mellitus to demonstrate how the Fair Umpire framework may be applied to estimate overdiagnosis.

CONCLUSION

Our framework may be used to quantify overdiagnosis in non-cancer conditions (and in cancer conditions), as well as to guide further studies on this topic.

Risk of Adverse Pregnancy Outcomes Among Pregnant Individuals With Gestational Diabetes by Race and Ethnicity in the United States, 2014-2020

IMPORTANCE

Gestational diabetes, which increases the risk of adverse pregnancy outcomes, has been increasing in frequency across all racial and ethnic subgroups in the US.

OBJECTIVE

To assess whether the frequency of adverse pregnancy outcomes among those in the US with gestational diabetes changed over time and whether the risk of these outcomes differed by maternal race and ethnicity.

DESIGN, SETTING, AND PARTICIPANTS

Exploratory serial, cross-sectional, descriptive study using US National Center for Health Statistics natality data for 1 560 822 individuals with gestational diabetes aged 15 to 44 years with singleton nonanomalous live births from 2014 to 2020 in the US.

EXPOSURES

Year of delivery and race and ethnicity, as reported on the birth certificate, stratified as non-Hispanic American Indian, non-Hispanic Asian/Pacific Islander, non-Hispanic Black, Hispanic/Latina, and non-Hispanic White (reference group).

MAIN OUTCOMES AND MEASURES

Maternal outcomes of interest included cesarean delivery, primary cesarean delivery, preeclampsia or gestational hypertension, intensive care unit (ICU) admission, and transfusion; neonatal outcomes included large for gestational age (LGA), macrosomia (>4000 g at birth), small for gestational age (SGA), preterm birth, and neonatal ICU (NICU) admission, as measured by the frequency (per 1000 live births) with estimation of mean annual percentage change (APC), disparity ratios, and adjusted risk ratios.

RESULTS

Of 1 560 822 included pregnant individuals with gestational diabetes (mean [SD] age, 31 [5.5] years), 1% were American Indian, 13% were Asian/Pacific Islander, 12% were Black, 27% were Hispanic/Latina, and 48% were White. From 2014 to 2020, there was a statistically significant increase in the overall frequency (mean APC per year) of preeclampsia or gestational hypertension (4.2% [95% CI, 3.3% to 5.2%]), transfusion (8.0% [95% CI, 3.8% to 12.4%]), preterm birth at less than 37 weeks (0.9% [95% CI, 0.3% to 1.5%]), and NICU admission (1.0% [95% CI, 0.3% to 1.7%]). There was a significant decrease in cesarean delivery (-1.4% [95% CI, -1.7% to -1.1%]), primary cesarean delivery (-1.2% [95% CI, -1.5% to -0.9%]), LGA (-2.3% [95% CI, -2.8% to -1.8%]), and macrosomia (-4.7% [95% CI, -5.3% to -4.0%]). There was no significant change in maternal ICU admission and SGA. In comparison with White individuals, Black individuals were at significantly increased risk of all assessed outcomes, except LGA and macrosomia; American Indian individuals were at significantly increased risk of all assessed outcomes except cesarean delivery and SGA; and Hispanic/Latina and Asian/Pacific Islander individuals were at significantly increased risk of maternal ICU admission, preterm birth, NICU admission, and SGA. Differences in adverse outcomes by race and ethnicity persisted through these years.

CONCLUSIONS AND RELEVANCE

From 2014 through 2020, the frequency of multiple adverse pregnancy outcomes in the US increased among pregnant individuals with gestational diabetes. Differences in adverse outcomes by race and ethnicity persisted.

The life course perspective of gestational diabetes: An opportunity for the prevention of diabetes and heart disease in women

Gestational diabetes mellitus (GDM), which has traditionally been defined as glucose intolerance of varying severity with first onset in pregnancy, is rising in prevalence with maternal hyperglycemia currently affecting one in every six pregnancies worldwide. Although often perceived as a medical complication of pregnancy, GDM is actually a chronic cardiometabolic disorder that identifies women who have an elevated lifetime risk of ultimately developing type 2 diabetes and cardiovascular disease. In identifying high-risk women early in the natural history of these conditions, the diagnosis of GDM raises the tantalizing possibility of early intervention and risk modification. However, before such promise can be realized in practice, a series of clinical challenges/obstacles (reviewed herein) must be overcome. Ultimately, the coupling of this life course perspective of GDM with concerted efforts to overcome these challenges may enable fulfilment of this unique opportunity for the primary prevention of diabetes and heart disease in women.

Extreme prematurity: Risk and resiliency

Individuals born extremely preterm (before 28 weeks of gestation) comprise only about 0.7% of births in the United States and an even lower proportion in other high resource countries. However, these individuals account for a disproportionate number of children with cerebral palsy, intellectual deficit, autism spectrum disorder, attention deficit hyperactivity disorder, and epilepsy. This review describes two large multiple center cohorts comprised of individuals born extremely preterm: the EPICURE cohort, recruited 1995 in the United Kingdom and the Republic of Ireland, and the Extremely Low Gestational Age Newborn (ELGAN), recruited 2002-2004 in five states in the United States. The primary focus of these studies has been neurodevelopmental disorders, but also of interest are growth, respiratory illness, and parent- and self-reported global health and well-being. Both of these studies indicate that among individuals born extremely preterm the risks of most neurodevelopmental disorders are increased. Early life factors that contribute to this risk include perinatal brain damage, some of which can be identified using neonatal head ultrasound, bronchopulmonary dysplasia, and neonatal systemic inflammation. Prenatal factors, particularly the family's socioeconomic position, also appear to contribute to risk. For most adverse outcomes, the risk is higher in males. Young adults born extremely preterm who have neurodevelopmental impairment, as compared to those without such impairment, rate their quality of life lower. However, young adults born extremely preterm who do not have neurodevelopmental impairments rate their quality of life as being similar to that of young adults born at term. Finally, we summarize the current state of interventions designed to improve the life course of extremely premature infants, with particular focus on efforts to prevent premature birth and on postnatal efforts to prevent adverse neurodevelopmental outcomes.

Adolescent body mass index and changes in pre-pregnancy body mass index in relation to risk of gestational diabetes

BACKGROUND

Pregestational excessive body mass index (BMI) is linked to an increased risk for gestational diabetes mellitus (GDM), but less is known on the effect of adolescent BMI on GDM occurrence. The study aimed to investigate possible associations of adolescent BMI and changes in BMI experienced before first pregnancy, with gestational diabetes risk.

METHODS

This retrospective study was based on linkage of a military screening database of adolescent health status (Israel Defence Forces) including measured height and weight, with medical records (Maccabi Healthcare Services, MHS) of a state-mandated health provider. The latter covers about 25% of the Israeli population; about 90% of pregnant women undergo screening by the two-step Carpenter-Coustan method. Adolescent BMI was categorized according to Center of Disease Control and Prevention percentiles. Only first documented pregnanies were analyzed and GDM was the outcome.

FINDINGS

Of 190,905 nulliparous women, 10,265 (5.4%) developed GDM. Incidence proportions of GDM were 5.1%, 6.1%, 7.3%, and 8.9% among women with adolescent normal BMI, underweight, overweight, and obesity (p<0.001), respectively. In models that accounted for age at pregnancy, birth year, and sociodemographic variables, the adjusted odd ratios (aORs) for developing GDM were: 1.2 (95%CI, 1.1-1.3), 1.5 (1.4-1.6), and 1.9 (1.7-2.1) for adolescent underweight, overweight, and obesity (reference group, normal BMI). Adolescent BMI tracked with BMI notes in the pre-pregnancy period (r=63%). Resuming normal pre-pregnancy BMI from overweight or obesity in adolescence diminished GDM risk, but this diminished risk was not observed among those who returned to a normal per-pre-pregnancy BMI from being underweight in adolescence. Sustained overweight or obesity conferred an aOR for developing GDM of 2.5 (2.2-2.7); weight gain from adolescent underweight and normal BMI to pre-pregnancy excessive BMI conferred aORs of 3.1 (1.6-6.2) and 2.6 (2.2-2.7), respectively.

INTERPRETATION

Change in BMI status from adolescence to pre-pregnancy may contribute to GDM risk. Identifying at-risk populations is important for early preventive interventions.

FUNDING

None.

Current Resources for Evidence-Based Practice, November 2021

An extensive review of new resources to support the provision of evidence-based care for women and infants. The current column includes a discussion of autonomy and respect in maternity care and commentaries on reviews focused on whether to induce women who present with mild preeclampsia in the late preterm period and the extent to which urinary incontinence symptoms prevent women from participating in exercise. It also includes a brief update about the USPSTF guidelines on screening for gestational diabetes.

Screening for Gestational Diabetes: US Preventive Services Task Force Recommendation Statement

IMPORTANCE

Gestational diabetes is diabetes that develops during pregnancy. Prevalence of gestational diabetes in the US has been estimated at 5.8% to 9.2%, based on traditional diagnostic criteria, although it may be higher if more inclusive criteria are used. Pregnant persons with gestational diabetes are at increased risk for maternal and fetal complications, including preeclampsia, fetal macrosomia (which can cause shoulder dystocia and birth injury), and neonatal hypoglycemia. Gestational diabetes has also been associated with an increased risk of several long-term health outcomes in pregnant persons and intermediate outcomes in their offspring.

OBJECTIVE

The USPSTF commissioned a systematic review to evaluate the accuracy, benefits, and harms of screening for gestational diabetes and the benefits and harms of treatment for the pregnant person and infant.

POPULATION

Pregnant persons who have not been previously diagnosed with type 1 or type 2 diabetes.

EVIDENCE ASSESSMENT

The USPSTF concludes with moderate certainty that there is a moderate net benefit to screening for gestational diabetes at 24 weeks of gestation or after to improve maternal and fetal outcomes. The USPSTF concludes that the evidence on screening for gestational diabetes before 24 weeks of gestation is insufficient, and the balance of benefits and harms of screening cannot be determined.

RECOMMENDATION

The USPSTF recommends screening for gestational diabetes in asymptomatic pregnant persons at 24 weeks of gestation or after. (B recommendation) The USPSTF concludes that the current evidence is insufficient to assess the balance of benefits and harms of screening for gestational diabetes in asymptomatic pregnant persons before 24 weeks of gestation. (I statement).

Performance of guidelines for the screening and diagnosis of gestational diabetes mellitus during the COVID-19 pandemic: A scoping review of the guidelines and diagnostic studies evaluating the recommended testing strategies

Aim

The COVID-19 pandemic has necessitated less resource-intensive testing guidelines to identify gestational diabetes mellitus (GDM). We performed a scoping review of the international evidence reporting the ability of diagnostic tests recommended during the pandemic to accurately identify patients with GDM, compared to pre-pandemic reference standards, and associated test and clinical outcomes.

Methods

A comprehensive search of the literature was carried out in Embase, LitCovid, Cochrane Covid-19 study register, and medRxiv on 14^th June 2021.

Results

145 unique citations were returned; after screening according to pre-specified inclusion criteria by title and abstract and then full text, 13 studies involving 40,836 pregnant people and an additional 52,884 instances of OGTT were included. Thresholds defined in the Australian pandemic guideline appear adequate to identify most GDM cases; false negative cases appeared at lower risk of hyperglycaemia-in-pregnancy(HIP)-related events. For UK and Canadian guidelines, a larger proportion would be misdiagnosed as non-GDM; these false negative cases had broadly equivalent HIP-related event rates as true positives.

Conclusions

The OGTT remains the most effective test to identify abnormal glucose processing in pregnancy, supporting the prompt return to standard guidelines post-pandemic. Cohort studies investigating the impact of the change in guidelines on GDM pregnancies and associated outcomes are needed.