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

Maternity Dietitians' Perspectives on Delivering Gestational Diabetes Care, and Postpartum Follow-Up in Ireland: A Qualitative Study

BACKGROUND

Medical nutrition therapy is recognized as the cornerstone to optimal glycemic management of gestational diabetes (GDM) during pregnancy and registered dietitian nutritionists (RDNs) are central to delivery of this, yet data are lacking on their views of the barriers and enablers to performing this role.

OBJECTIVE

This qualitative study aimed to explore the perspectives of RDNs providing medical nutrition therapy to people with GDM across the Republic of Ireland.

DESIGN

Focus groups and semistructured interviews were conducted with RDNs from maternity settings whose roles included providing dietetics services to people with GDM.

PARTICIPANTS AND SETTING

Five focus groups and 2 semistructured interviews were conducted with 16 RDNs from 13 maternity sites, whose roles included medical nutrition therapy to people with GDM, in Ireland in February to March 2021. A topic guide addressing GDM management, postpartum care, and future type 2 diabetes prevention was followed.

DATA ANALYSIS

Data from focus groups and interviews were analyzed using reflexive thematic analysis.

RESULTS

RDNs collectively viewed their role as central to supporting people with GDM. Three themes were generated: Theme 1-a complex layered role-addressed complexities in dietetics practice. Theme 2 highlighted challenges in providing optimum care with sub themes of huge changes with COVID-19, care is not equal, and no defined postpartum pathway. Theme three reported on framing future health needs through sub themes of the 6-week check is a missed opportunity, people need to understand their risk, and more integrated care and type 2 diabetes prevention is needed.

CONCLUSIONS

RDNs recognize the need for continuity of care in pregnancy and postpartum for people diagnosed with GDM. However, perceived disparities in allocation of dietetics services for GDM and an absence of agreed care pathways were seen as barriers to optimum care. Collaboration and communication across primary and secondary care settings are essential and require improvement.

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.

Relation between possible under-diagnosed/treated glucose dysmetabolism, delayed villous maturation, and lethal fetal pneumonia

BACKGROUND

Gestational diabetes mellitus (GDM) is a known risk factor for stillbirth (Rosenstein et al., 2012) [1]. Delayed villous maturation (DVM), predominantly seen in term placentas in pregnancies complicated with glucose dysmetabolism, may in part be a consequence of excessive maternal glucose leading to release of fetal insulin and other growth factors that promote excessive placental growth at the expense of villous maturation (Redline, 2012) [2].

CASES

We present three cases of under-diagnosed/treated glucose dysmetabolism in women in their first pregnancies cared for in other hospitals in the United Kingdom (UK) with the fatal fetal/neonatal outcomes and confirmed DVM in the placenta and congenital pneumonia on post-mortem examination in all three cases.

CONCLUSION

This cluster supports a hypothesis that DVM and glucose dysmentabolism may make babies more susceptible to severe perinatal infection. All three cases received the antenatal care in their subsequent pregnancies in our unit and had confirmed glucose dysmetabolism which was treated and resulted in healthy babies.

To neutrally offer or strongly recommend? General practitioners' perspectives on screening for gestational diabetes according to the national guideline in Norway

OBJECTIVE

To explore general practitioners' experiences and reflections on how the current Norwegian guideline for screening for gestational diabetes affects their clinical practice.

DESIGN

A qualitive study in which data were collected through semi-structured focus group interviews and analyzed thematically.

SETTING AND SUBJECTS

Five focus groups conducted in 2020 among GPs in Norway; three interviews took place face-to-face and two were held digitally. The total number of participants was 31.

RESULTS

GPs acknowledged the potential benefits of more extensive screening, but had concerns about the medicalization of pregnancy, stating that some women experienced considerable anxiety. The GPs expressed doubts about the guideline's evidence base but differed in how they interpreted what the guideline was asking them to do. Some offered eligible women the opportunity to be screened, while other set up a screening appointment without consulting the women first. For some, fear of incrimination made them recommend screening without being convinced that it was the right thing for the patient.

CONCLUSIONS

It is unclear whether the guideline for gestational diabetes requires GPs to recommend screening to pregnant women or if they should provide neutral information about the availability of screening. This ambiguity should be addressed, and the guideline evaluated against the core principles of general practice.

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.

Retinol-binding protein 4 (RBP4) circulating levels and gestational diabetes mellitus: a systematic review and meta-analysis

Background

Gestational diabetes mellitus (GDM) is a prevalent condition where diabetes is diagnosed during pregnancy, affecting both maternal and fetal outcomes. Retinol-binding protein 4 (RBP4) is a circulating adipokine which belongs to the lipocalin family and acts as a specific carrier protein that delivers retinol (vitamin A) from the liver to the peripheral tissues. Growing data indicate that circulating RBP4 levels may positively correlate with GDM. Thus, this systematic review and meta-analysis aimed to investigate the potential relationship between circulating RBP4 levels and GDM when measured at various stages of pregnancy.

Methods

MEDLINE, CINAHL, EMCARE, EMBASE, Scopus, and Web of Science databases were searched to identify studies comparing pregnant women with and without GDM, whose circulating RBP4 levels were measured in at least one pregnancy trimester. Findings were reported using standardized mean difference (SMD) and random-effects models were used to account for variability among studies. Furthermore, the risk of bias was assessed using the RoBANS tool.

Results

Out of the 34 studies identified, 32 were included in the meta-analysis (seven with circulating RBP4 levels measured in the first trimester, 19 at 24-28 weeks, and 14 at >28 weeks of pregnancy). RBP4 levels were statistically higher in the GDM group than in controls when measured during all these pregnancy stages, with the noted RBP4 SMD being 0.322 in the first trimester (95% CI: 0.126-0.517; p < 0.001; 946 GDM cases vs. 1701 non-GDM controls); 0.628 at 24-28 weeks of gestation (95% CI: 0.290-0.966; p < 0.001; 1776 GDM cases vs. 1942 controls); and 0.875 at >28 weeks of gestation (95% CI: 0.252-1.498; p = 0.006; 870 GDM cases vs. 1942 non-GDM controls). Significant study heterogeneity was noted for all three pregnancy timepoints.

Conclusion

The present findings indicate consistently higher circulating RBP4 levels in GDM cases compared to non-GDM controls, suggesting the potential relevance of RBP4 as a biomarker for GDM. However, the documented substantial study heterogeneity, alongside imprecision in effect estimates, underscores the need for further research and standardization of measurement methods to elucidate whether RBP4 can be utilized in clinical practice as a potential GDM biomarker.

Systematic review registration

PROSPERO (CRD42022340097: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022340097).

Review of Genetic and Artificial Intelligence approaches to improving Gestational Diabetes Mellitus Screening and Diagnosis in sub-Saharan Africa

Background: Adverse outcomes from gestational diabetes mellitus (GDM) in the mother and newborn are well established. Genetic variants may predict GDM and Artificial Intelligence (AI) can potentially assist with improved screening and early identification in lower resource settings. There is limited information on genetic variants associated with GDM in sub-Saharan Africa and the implementation of AI in GDM screening in sub-Saharan Africa is largely unknown. Methods: We reviewed the literature on what is known about genetic predictors of GDM in sub-Saharan African women. We searched PubMed and Google Scholar for single nucleotide polymorphisms (SNPs) involved in GDM predisposition in a sub-Saharan African population. We report on barriers that limit the implementation of AI that could assist with GDM screening and offer possible solutions. Results: In a Black South African cohort, the minor allele of the SNP rs4581569 existing in the PDX1 gene was significantly associated with GDM. We were not able to find any published literature on the implementation of AI to identify women at risk of GDM before second trimester of pregnancy in sub-Saharan Africa. Barriers to successful integration of AI into healthcare systems are broad but solutions exist. Conclusions: More research is needed to identify SNPs associated with GDM in sub-Saharan Africa. The implementation of AI and its applications in the field of healthcare in the sub-Saharan African region is a significant opportunity to positively impact early identification of GDM.

Prediction of Gestational Diabetes Mellitus in the First Trimester of Pregnancy Based on Maternal Variables and Pregnancy Biomarkers

Gestational diabetes mellitus (GDM) is a significant health concern with adverse outcomes for both pregnant women and their offspring. Recognizing the need for early intervention, this study aimed to develop an early prediction model for GDM risk assessment during the first trimester. Utilizing a prospective cohort of 4917 pregnant women from the Third Department of Obstetrics and Gynecology, Aristotle University of Thessaloniki, Greece, the study sought to combine maternal characteristics, obstetric and medical history, and early pregnancy-specific biomarker concentrations into a predictive tool. The primary objective was to create a series of predictive models that could accurately identify women at high risk for developing GDM, thereby facilitating early and targeted interventions. To this end, maternal age, body mass index (BMI), obstetric and medical history, and biomarker concentrations were analyzed and incorporated into five distinct prediction models. The study's findings revealed that the models varied in effectiveness, with the most comprehensive model combining maternal characteristics, obstetric and medical history, and biomarkers showing the highest potential for early GDM prediction. The current research provides a foundation for future studies to refine and expand upon the predictive models, aiming for even earlier and more accurate detection methods.

Antenatal and intrapartum care for women with gestational diabetes: a best practice implementation project

OBJECTIVES

This project aimed to improve the quality of antenatal and intrapartum care for women with gestational diabetes (GD) by increasing nursing and midwifery care compliance with best practice recommendations.

INTRODUCTION

GD is one of the most common diseases that can lead to several important maternal and fetal complications.

METHODS

This project was based on JBI's evidence implementation approach and included a baseline audit, the implementation of strategies, and a follow-up audit. The project was conducted in an obstetrics-gynecology department of a hospital in Bucharest, and the sample included 30 pregnant women with GD from that ward.

RESULTS

Regarding antenatal care, the baseline audit revealed low compliance (63%-87%) for criterion 2 (specific education), criterion 3 (multidisciplinary team), criterion 4 (individualized care plan), criterion 5 (training on self-monitoring), criterion 8 (dietician consultation), and criterion 10 (physical exercise program). Compliance with criterion 9 (physiotherapist consultation) was nil. Higher compliance (90%-100%) was reported for criterion 1 (screening for GD), criterion 6 (self-monitoring), and criterion 7 (maintenance of glycemic values). The intrapartum care audit criteria regarding maintaining blood glucose levels had compliance rates of 97% (criteria 13 and 14), 73% (criterion 11), and 67% (criterion 12). As a result of implementing the most appropriate strategies, maximum improvement was observed for all 12 audit criteria found to be deficient in the baseline audit.

CONCLUSIONS

Strategies were identified and applied to successfully implement the best practices (educational programs and improved procedures). However, specific actions, such as regular targeted audits and continuous monitoring, are needed to maintain long-term results.

Risk Estimation of Gestational Diabetes Mellitus in the First Trimester

CONTEXT

There is no early, first-trimester risk estimation available to predict later (gestational week 24-28) gestational diabetes mellitus (GDM); however, it would be beneficial to start an early treatment to prevent the development of complications.

OBJECTIVE

We aimed to identify early, first-trimester prediction markers for GDM.

METHODS

The present case-control study is based on the study cohort of a Hungarian biobank containing biological samples and follow-up data from 2545 pregnant women. Oxidative-nitrative stress-related parameters, steroid hormone, and metabolite levels were measured in the serum/plasma samples collected at the end of the first trimester from 55 randomly selected control and 55 women who developed GDM later.

RESULTS

Pregnant women who developed GDM later during the pregnancy were older and had higher body mass index. The following parameters showed higher concentration in their serum/plasma samples: fructosamine, total antioxidant capacity, testosterone, cortisone, 21-deoxycortisol; soluble urokinase plasminogen activator receptor, dehydroepiandrosterone sulfate, dihydrotestosterone, cortisol, and 11-deoxycorticosterone levels were lower. Analyzing these variables using a forward stepwise multivariate logistic regression model, we established a GDM prediction model with a specificity of 96.6% and sensitivity of 97.5% (included variables: fructosamine, cortisol, cortisone, 11-deoxycorticosterone, SuPAR).

CONCLUSION

Based on these measurements, we accurately predict the development of later-onset GDM (24th-28th weeks of pregnancy). Early risk estimation provides the opportunity for targeted prevention and the timely treatment of GDM. Prevention and slowing the progression of GDM result in a lower lifelong metabolic risk for both mother and offspring.

Association between sex steroid hormones and subsequent hyperglycemia during pregnancy

Objective

Sex steroid hormones may play a role in insulin resistance and glucose dysregulation. However, evidence regarding associations between early-pregnancy sex steroid hormones and hyperglycemia during pregnancy is limited. The primary objective of this study was to assess the relationships between first trimester sex steroid hormones and the subsequent development of hyperglycemia during pregnancy; with secondary evaluation of sex steroid hormones levels in mid-late pregnancy, concurrent with and subsequent to diagnosis of gestational diabetes.

Methods

Retrospective analysis of a prospective pregnancy cohort study was conducted. Medically low-risk participants with no known major endocrine disorders were recruited in the first trimester of pregnancy (n=319). Sex steroid hormones in each trimester, including total testosterone, free testosterone, estrone, estradiol, and estriol, were assessed using high-performance liquid chromatography and tandem mass spectrometry. Glucose levels of the 1-hour oral glucose tolerance test and gestational diabetes diagnosis were abstracted from medical records. Multivariable linear regression models were fitted to assess the associations of individual first trimester sex steroids and glucose levels.

Results

In adjusted models, first trimester total testosterone (β=5.24, 95% CI: 0.01, 10.46, p=0.05) and free testosterone (β=5.98, 95% CI: 0.97, 10.98, p=0.02) were positively associated with subsequent glucose concentrations and gestational diabetes diagnosis (total testosterone: OR=3.63, 95% CI: 1.50, 8.78; free testosterone: OR=3.69; 95% CI: 1.56, 8.73). First trimester estrone was also positively associated with gestational diabetes (OR=3.66, 95% CI: 1.56, 8.55). In mid-late pregnancy, pregnant people with gestational diabetes had lower total testosterone levels (β=-0.19, 95% CI: -0.36, -0.02) after adjustment for first trimester total testosterone.

Conclusion

Early-pregnancy sex steroid hormones, including total testosterone, free testosterone, and estrone, were positively associated with glucose levels and gestational diabetes in mid-late pregnancy. These hormones may serve as early predictors of gestational diabetes in combination with other risk factors.

Prediction of gestational diabetes using deep learning and Bayesian optimization and traditional machine learning techniques

The study aimed to develop a clinical diagnosis system to identify patients in the GD risk group and reduce unnecessary oral glucose tolerance test (OGTT) applications for pregnant women who are not in the GD risk group using deep learning algorithms. With this aim, a prospective study was designed and the data was taken from 489 patients between the years 2019 and 2021, and informed consent was obtained. The clinical decision support system for the diagnosis of GD was developed using the generated dataset with deep learning algorithms and Bayesian optimization. As a result, a novel successful decision support model was developed using RNN-LSTM with Bayesian optimization that gave 95% sensitivity and 99% specificity on the dataset for the diagnosis of patients in the GD risk group by obtaining 98% AUC (95% CI (0.95-1.00) and p < 0.001). Thus, with the clinical diagnosis system developed to assist physicians, it is planned to save both cost and time, and reduce possible adverse effects by preventing unnecessary OGTT for patients who are not in the GD risk group.

Obstetric and neonatal complications in large for gestational age pregnancy with late gestational diabetes

Objective

Gestational diabetes (GDM) is increasing in prevalence with effects starting in-utero, leading to excessive fetal growth. It is the leading cause of many perinatal complications. The aim was to determine the rate of obstetric and neonatal complications in pregnant women with high fetal weight and a recent diagnosis of GDM during the third trimester, despite normal earlier glycemic control.

Material and Methods

Prospective cohort study over four years involving pregnant women regularly visiting a single center who had normal glycemic index at 24-28 gestational weeks and ultrasonography (US) suggested high fetal weight during the third trimester. Oral glucose tolerance test was given, dividing the sample into the late GDM (LGDM) and the non-LGDM group.

Results

Of 176 women, 24 (13.64%) had LGDM, and 152 (86.36%) had non-LGDM. After exclusions these groups' sizes were (n=21) in LGDM and (n=132) in non-LGDM. Hemoglobin A1c level was significantly higher in LGDM than non-LGDM (5.9% versus 5.1%). However, obstetric and neonatal complications were largely comparable (p≥0.05) but higher in LGDM than non-LGDM women. Exceptions to this were birth weight (3219 g versus 3326 g), large for gestational age at delivery (85.72% versus 88.64%), and gestational age at delivery (37.9 versus 38.2 weeks) in the LGDM vs. non-LGDM groups, respectively. There was a significantly higher cesarean section (CS) rate (76.19% versus 51.52%; p<0.05) in the LGDM group.

Conclusion

The rate of newly diagnosed LGDM in pregnant women with high fetal weight during the third trimester by US was 13.64%. They had comparable obstetric and neonatal complications with non-GDM women, except for the rate of CS that was significantly higher in LGDM women.

Evaluation of first and second trimester maternal thyroid profile on the prediction of gestational diabetes mellitus and post load glycemia

Maternal thyroid alterations have been widely associated with the risk of gestational diabetes mellitus (GDM). This study aims to 1) test the first and the second trimester full maternal thyroid profile on the prediction of GDM, both alone and combined with non-thyroid data; and 2) make that prediction independent of the diagnostic criteria, by evaluating the effectiveness of the different maternal variables on the prediction of oral glucose tolerance test (OGTT) post load glycemia. Pregnant women were recruited in Concepción, Chile. GDM diagnosis was performed at 24-28 weeks of pregnancy by an OGTT (n = 54 for normal glucose tolerance, n = 12 for GDM). 75 maternal thyroid and non-thyroid parameters were recorded in the first and the second trimester of pregnancy. Various combinations of variables were assessed for GDM and post load glycemia prediction through different classification and regression machine learning techniques. The best predictive models were simplified by variable selection. Every model was subjected to leave-one-out cross-validation. Our results indicate that thyroid markers are useful for the prediction of GDM and post load glycemia, especially at the second trimester of pregnancy. Thus, they could be used as an alternative screening tool for GDM, independently of the diagnostic criteria used. The final classification models predict GDM with cross-validation areas under the receiver operating characteristic curve of 0.867 (p<0.001) and 0.920 (p<0.001) in the first and the second trimester of pregnancy, respectively. The final regression models predict post load glycemia with cross-validation Spearman r correlation coefficients of 0.259 (p = 0.036) and 0.457 (p<0.001) in the first and the second trimester of pregnancy, respectively. This investigation constitutes the first attempt to test the performance of the whole maternal thyroid profile on GDM and OGTT post load glycemia prediction. Future external validation studies are needed to confirm these findings in larger cohorts and different populations.

Gestational Diabetes and International Migration

In many countries, immigrant women constitute a substantial proportion of women giving birth. With gestational diabetes being one of the most common complications in pregnancy, understanding gestational diabetes in the context of international migration becomes increasingly relevant. Here, we examine the current evidence related to international migration and gestational diabetes, including short- and long-term adverse outcomes and the experiences of immigrant women with gestational diabetes care and long-term follow-up. Existing evidence focuses on immigrants in high-income countries, and has mainly examined differences in the risk of developing gestational diabetes, or on the experiences of immigrant women diagnosed with gestational diabetes. Studies suggest that the risk of gestational diabetes may be influenced by migration and that immigrant women likely experience particular barriers to care and follow-up. Current research on perinatal outcomes is inconclusive and studies on long-term outcomes are practically absent. Future research should include data on country of origin and examine the role of premigration and postmigration factors in developing gestational diabetes and its associated short- and long-term outcomes. Understanding these factors will provide useful insights into improving the health and health care needs of migrating populations and enable inclusion of culturally appropriate health care practices, thereby improving the health of our current and future generations.

[Preventive activities in women. PAPPS update 2022]

In the 2022 PAPPS update we present those specific preventive activities for women's health, except those related to cancer prevention (which are included in another document) and those aspects related to differential gender morbidity, a cross-cutting aspect for all working groups. Contraception is an essential preventive activity, considering basic the right to decide both the number of children and the time to have them. We must inform about the possible contraceptive methods, guaranteeing the monitoring of their safety, efficacy and effectiveness (tables are included on changing from one method to another to preserve contraceptive protection). We must inform about emergency contraception and propose it in the event of unprotected intercourse. All this will be done through opportunistic screening without requiring screening for thrombophilia or dyslipidemia, but for arterial hypertension. Pregnancy is an important life experience and the family doctor should not remain oblivious. We must be competent both in the preconception consultation (recommending the intake of folic acid, avoiding exposure to occupational and environmental risks, screening for certain pathologies and assessing the intake of drugs not indicated during pregnancy) and in the monitoring of pregnancy. Whether or not we monitor the pregnancy, we must not disregard its control, taking advantage of this period to promote healthy lifestyles and participating in the intercurrent processes that may occur. Menopause in general and osteoporosis in particular exemplify the strategy of medicalization of vital processes that has been followed from different instances and organizations. In our update we address the prevention and treatment of symptoms secondary to estrogen deprivation. We also propose the prevention of osteoporosis, including carrying out densitometry based on the risk of fracture in the next 10 years, and therefore densitometric screening is not recommended in women under 60 years of age. In risk assessment we recommend the use of the frax tool or better, the calibration of the risk of hip fracture with prevalence data from our setting. We linked the indication for treatment with the Z-Score (bone mineral density compared with women of the same age), as it is a condition associated with aging.

Metabolomic Identification of a Novel, Externally Validated Predictive Test for Gestational Diabetes Mellitus

CONTEXT

Undiagnosed gestational diabetes mellitus (GDM) is a major preventable cause of stillbirth. In the United Kingdom, women are selected for diagnostic testing for GDM based on risk factors, including body mass index (BMI) > 30 kg/m2.

OBJECTIVE

To improve the prediction of GDM using metabolomics.

METHODS

We performed metabolomics on maternal serum from the Pregnancy Outcome Prediction (POP) study at 12 and 20 weeks of gestational age (wkGA; 185 GDM cases and 314 noncases). Predictive metabolites were internally validated using the 28 wkGA POP study serum sample and externally validated using 24- to 28-wkGA fasting plasma from the Born in Bradford (BiB) cohort (349 GDM cases and 2347 noncases). The predictive ability of a model including the metabolites was compared with BMI > 30 kg/m2 in the POP study.

RESULTS

Forty-seven predictive metabolites were identified using the 12- and 20-wkGA samples. At 28 wkGA, 4 of these [mannose, 4-hydroxyglutamate, 1,5-anhydroglucitol, and lactosyl-N-palmitoyl-sphingosine (d18:1/16:0)] independently increased the bootstrapped area under the receiver operating characteristic curve (AUC) by >0.01. All 4 were externally validated in the BiB samples (P = 2.6 × 10-12, 2.2 × 10-13, 6.9 × 10-28, and 2.6 × 10-17, respectively). In the POP study, BMI > 30 kg/m2 had a sensitivity of 28.7% (95% CI 22.3-36.0%) and a specificity of 85.4% whereas at the same level of specificity, a predictive model using age, BMI, and the 4 metabolites had a sensitivity of 60.2% (95% CI 52.6-67.4%) and an AUC of 0.82 (95% CI 0.78-0.86).

CONCLUSIONS

We identified 4 strongly and independently predictive metabolites for GDM that could have clinical utility in screening for GDM.

Simple method for identification of women at risk of gestational diabetes mellitus in Arusha urban, Tanzania

BACKGROUND

Screening for gestational diabetes mellitus in Tanzania is challenged by limited resources. Therefore, this study aimed to develop a simple method for identification of women at risk of gestational diabetes mellitus in Arusha urban, Tanzania.

METHODS

This study used data from a cross sectional study, that was conducted between March and December 2018 in Arusha District involving 468 pregnant women who were not known to have diabetes before pregnancy. Urine glucose was tested using urine multistics and blood glucose levels by Gluco-Plus™ and diagnosed in accordance with the World Health Organization's criteria. Anthropometrics were measured using standard procedures and maternal characteristics were collected through face-to-face interviews using a questionnaire with structured questions. Univariate analysis assessed individual variables association with gestational diabetes mellitus where variables with p-value of < 0.05 were included in multivariable analysis and predictors with p-value < 0.1 remained in the final model. Each variable was scored based on its estimated coefficients and risk scores were calculated by multiplying the corresponding coefficients by ten to get integers. The model's performance was assessed using c-statistic. Data were analyzed using Statistical Package for Social Science™.

RESULTS

The risk score included body fat ≥ 38%, delivery to macrosomic babies, mid-upper arm circumference ≥ 28 cm, and family history of type 2 diabetes mellitus. The score correctly identified 98% of women with gestational diabetes with an area under the receiver operating characteristic curve of 0.97 (95% CI 0.96-0.99, p < 0.001), sensitivity of 0.98, and specificity of 0.46.

CONCLUSION

The developed screening tool is highly sensitive and correctly differentiates women with and without gestational diabetes mellitus in a Tanzanian sub-population.

Changed definition of disease and broader screening criteria had little impact on prevalence of gestational diabetes mellitus

INTRODUCTION

There are major controversies in screening for gestational diabetes mellitus (GDM). The present study evaluates the impact of the 2017 revised guidelines for GDM screening and a changed definition of GDM in Norway.

MATERIAL AND METHODS

We used a case-series design and included women with no pre-pregnancy diabetes mellitus, who gave birth after gestational week 29 to a singleton fetus at the University Hospital of North Norway, Tromsø, or at a local maternity ward in Troms county, during the first 6 months of 2013 (before group, n = 676) and 2018 (after group, n = 673). Data were collected from antenatal records, maternal health information sheets, and electronic medical records (Partus). We assessed the screening criteria age, parity, pre-pregnancy BMI, and ethnicity. Primary outcomes were change in size of the population eligible for GDM screening, screening adherence, and prevalence of GDM, and follow up of GDM (treatment and obstetric risk assessment at gestational week 36). Statistical analyses were done using IBM SPSS with chi-squared test. A p value less than 0.05 was considered statistically significant.

RESULTS

The proportion of women eligible for GDM screening increased from 46.4% in the before group to 67.6% in the after group (+45%) (p < 0.01). However, screening adherence among eligible women was only 28.3% and 49.2% in the before and after groups, respectively (p < 0.01). Among screened women, 16.9% (15/89) and 10.7% (24/224), respectively, were diagnosed with GDM, resulting in an overall estimated prevalence of 2.2% (15/676) and 3.6% (24/673). Among women diagnosed with GDM, 13.3% received no follow up in 2013 and this proportion was 20.8% in 2018. The remaining women underwent obstetric risk assessment at gestational week 36 as advised in the guidelines.

CONCLUSIONS

The introduction of broader screening criteria and a more liberal case definition increased the population eligible for GDM screening by 45%. The higher proportion of women screened resulted in an insignificant higher prevalence of GDM. Screening adherence was poor in both study groups. Stakeholders for obstetric care need to consolidate quality measures and revisit the screening algorithm.

Evolution of maternal and neonatal outcomes before and after the adoption of the IADPSG/WHO guidelines in Belgium: A descriptive study of 444,228 pregnancies

Objectives

To appraise adverse pregnancy outcomes after the adoption of IADPSG/WHO guidelines in Belgium.

Methods

A retrospective study of the Center for Perinatal Epidemiology registry was conducted. Demographic changes and adverse pregnancy outcomes were compared between a pre- and post-guideline period in women with and without hyperglycemia in pregnancy (HIP). Adjusted odds ratios with a 95% confidence interval (CI) were used to compare maternal and neonatal outcomes controlling for potential confounders (maternal age, body mass index (BMI), hypertension, parity, and multiple births).

Results

The prevalence of HIP increased (6.0%-9.2%). In the overall population regardless of glycemic status, gestational weight gain (12.3 ± 5.7 vs 11.9 ± 5.8; p < 0.001), hypertension (0.92; 95% CI, 0.89-0.94; p < 0.001), and neonatal intensive care unit/special care nursery (0.89; 95% CI, 0.87-0.91; p < 0.001) decreased despite increasing maternal age and pre-pregnancy BMI. Emergency cesarean section rates (1.07; 95% CI, 1.05-1.09; p < 0.001) increased, but not in the HIP population (1.02; 95% CI, 0.95-1.10; ns). The overall incidence of preterm birth (1.09; 95% CI, 1.06-1.12; p < 0.001), stillbirth (1.10; 95% CI, 1.01-1.21; p < 0.05), and perinatal mortality (1.10; 95% CI, 1.01-1.19; p < 0.05) increased, except in the HIP population (1.03; 95% CI, 0.95-1.11; ns), (1.04; 95% CI, 0.74-1.47; ns) and (1.09; 95% CI, 0.80-1.49; ns), respectively. The overall incidence of small- for-gestational-age remained unchanged (0.99; 95%CI, 0.97-1.01; ns) regardless of glycemic status. In the HIP population, large-for-gestational age (0.90; 95% CI, 0.84-0.95; p < 0.001) and macrosomia (0.84; 95% CI, 0.78-0.92; p < 0.001) decreased.

Conclusion

After the implementation of IADPSG/WHO guidelines, the prevalence of HIP increased by 53.7% and the incidence of major HIP-related pregnancy complications appears to be lower. However, we cannot conclude that the reduction of LGA-macrosomia is due to a better management of diabetes or due to greater recruitment of women with mild HIP associated with a lower risk of obstetrical complications.

Inter-ankle Systolic Blood Pressure Difference Is a Marker of Increased Fasting Blood-Glucose in Asian Pregnant Women

OBJECTIVE

This cross-sectional study aimed to determine the relationship between clinical blood pressures and blood pressures measured using Doppler with blood glucose in pregnancy by ethnicity.

METHODS

We recruited 179 (52% White European, 48% Asian) pregnant women at 24-28 weeks of gestation who underwent a glucose tolerance test in an antenatal clinic in Bradford Royal Infirmary, the UK, from 2012 to 2013. Systolic blood pressures in the arm (left and right brachial) and ankle [left and right posterior tibial (PT) and dorsalis pedalis (DP)] blood pressures were measured using a Doppler probe. The inter-arm (brachial) and inter-ankle (PT and DP) systolic blood pressure differences were obtained. A multivariate linear regression model adjusted for age, body mass index, and diabetes risk was used to assess the relationship between blood pressures and blood glucose.

RESULTS

Asian pregnant women had higher blood glucose but lower ankle blood pressures than White Europeans. In White Europeans, brachial blood pressures and clinical blood pressures were positively associated with fasting blood glucose (FBG), but brachial blood pressures did not perform better as an indicator of FBG than clinical blood pressures. In Asians, increased inter-ankle blood pressure difference was associated with increased FBG. For each 10 mmHg increase in the inter-ankle blood pressure difference, FBG increased by 0.12 mmol/L (Beta=0.12, 95%CI: 0.01-0.23).

CONCLUSION

The relationship between blood pressures with blood glucose differed by ethnicity. In Asians, inter-ankle systolic blood pressure difference was positively associated with blood glucose. This is first ever report on ankle blood pressures with blood glucose in pregnancy which suggests future potential as a non-invasive gestational diabetes risk screening tool.

Early Identification of the Maternal, Placental and Fetal Dialog in Gestational Diabetes and Its Prevention

Gestational diabetes mellitus (GDM) complicates between 5 and 12% of pregnancies, with associated maternal, fetal, and neonatal complications. The ideal screening and diagnostic criteria to diagnose and treat GDM have not been established and, currently, diagnostic use with an oral glucose tolerance test occurs late in pregnancy and produces poor reproducibility. Therefore, in recent years, significant research has been undertaken to identify a first-trimester biomarker that can predict GDM later in pregnancy, enable early intervention, and reduce GDM-related adverse pregnancy outcomes. Possible biomarkers include glycemic markers (fasting glucose and hemoglobin A1c), adipocyte-derived markers (adiponectin and leptin), pregnancy-related markers (pregnancy-associated plasma protein-A and the placental growth factor), inflammatory markers (C-reactive protein and tumor necrosis factor-α), insulin resistance markers (sex hormone-binding globulin), and others. This review summarizes current data on first-trimester biomarkers, the advantages, and the limitations. Large multi-ethnic clinical trials and cost-effectiveness analyses are needed not only to build effective prediction models but also to validate their clinical use.

Midwifery awareness of diabetes in pregnancy screening guidelines in Aotearoa New Zealand

OBJECTIVE

Effective and timely management of gestational diabetes mellitus (GDM) requires early detection. However, screening rates have been shown to be relatively low in New Zealand, despite the introduction of national screening guidelines in 2014 which indicate that all pregnant women should be screened. Thus, the aim of this study was to explore the awareness of the New Zealand Ministry of Health Diabetes in Pregnancy screening guidelines by New Zealand midwives.

DESIGN

A 24-question online survey based upon the New Zealand screening guidelines was distributed via New Zealand midwifery social media groups to explore the awareness of New Zealand midwives with regard to screening for diabetes in pregnancy. Free text comments were also allowed, these were broadly categorized and reviewed.

PARTICIPANTS

174 registered midwives in Aotearoa New Zealand completed the survey.

MEASUREMENTS AND FINDINGS

All participants responded that they routinely offer glycated haemoglobin screening for detection of undiagnosed pre-gestational diabetes, and 92.9% identified that this should occur prior to 20 weeks gestation (as per the national guidelines). However, less than two thirds of midwives thought that all women should be screened for GDM, with 18.2% indicating they would only do this if immediate risk factors were present. There also appeared to be some confusion over the time period for screening for GDM with 22.9% indicating that this should occur later than the guideline-recommended timepoint of 24-28 weeks gestation. Participants who identified as Māori and community-based midwives were most likely to screen for GDM 'only if risk factors were present'. Participants practicing for more than 6 years, those aged 45-54 years, and midwives identifying as Māori were most likely to screen for GDM after 28 weeks (though these did not reach statistical significance).

KEY CONCLUSIONS

The New Zealand Diabetes in Pregnancy screening guidelines do not appear to be well implemented in our sample group, particularly with regard to screening for GDM. This needs to be evaluated in a larger group of midwives, as education around the timeliness and importance of screening for all women may be required.

IMPLICATIONS FOR PRACTICE

A lack of appropriate or timely screening for GDM may mean that women are not being diagnosed or managed appropriately, which in turn may have implications for both mother and child.

The potential role of plantains, moringa, plantain-moringa combined diets, and other plant-based dietary patterns in controlling glycaemia among T2DM persons, a hospital based cross sectional survey in Ghana

BACKGROUND

Diabetes mellitus (DM) is an independent risk-factor for cardiovascular diseases. Plant-based dietary-patterns have been shown to reverse the effects of these cardiovascular risk-factors. Our study therefore aimed to investigate the potential roles moringa, moringa-plantain combined, and other plant based dietary-patterns could play in controlling glycaemia among persons with type-2-diabetes Mellitus (T2DM).

METHODS

Facility-based cross-sectional-study was conducted among 530 T2DM patients from August 2018 to September 2019 in Ghana. Structured-questionnaires were used to collect demographic, anthropometric, and clinical variables. Adherence to plant-based dietary-patterns were assessed using 3-day food record. SPSS version-20 was used to analyse the data.

RESULTS

BMI, HbA1c%, HDL-cholesterol and LDL-cholesterol were significantly correlated with adherence to plant-based dietary-patterns (p-value < 0.05). After adjusting for physical activity, adherence to plantain diets Standardized regression coefficient β (95%CI): -0.098 (-0.321, -0.022), yam β (95%CI): 0.148 (0.066, 0.496), moringa diets β (95%CI): -0.095 (-0.325,-0.011) and bean-diets β (95%CI): -0.112 (-0.577, -0.007) were significantly associated with glycemic control. Adherence to plantain-moriga combined diets β (95%CI): -0.406 (-0.413, -0.049) and plantain-beans combined diets β (95%CI): -0.128 (-0.188, -0.038) were also significantly associated with glycemic control.

CONCLUSION

Adherence to plantain, yam, beans, plantain-moriga combined diets, and plantain-beans combined diets could be associated with glycemic-control. Health care workers should prioritize these plant-based dietary-patterns for disease prevention and health promotion.

Identifying women with gestational diabetes based on maternal characteristics: an analysis of four Norwegian prospective studies

BACKGROUND

There is still no worldwide agreement on the best diagnostic thresholds to define gestational diabetes (GDM) or the optimal approach for identifying women with GDM. Should all pregnant women perform an oral glucose tolerance test (OGTT) or can easily available maternal characteristics, such as age, BMI and ethnicity, indicate which women to test? The aim of this study was to assess the prevalence of GDM by three diagnostic criteria and the predictive accuracy of commonly used risk factors.

METHODS

We merged data from four Norwegian cohorts (2002-2013), encompassing 2981 women with complete results from a universally offered OGTT. Prevalences were estimated based on the following diagnostic criteria: 1999WHO (fasting plasma glucose (FPG) ≥7.0 or 2-h glucose ≥7.8 mmol/L), 2013WHO (FPG ≥5.1 or 2-h glucose ≥8.5 mmol/L), and 2017Norwegian (FPG ≥5.3 or 2-h glucose ≥9 mmol/L). Multiple logistic regression models examined associations between GDM and maternal factors. We applied the 2013WHO and 2017Norwegian criteria to evaluate the performance of different thresholds of age and BMI.

RESULTS

The prevalence of GDM was 10.7, 16.9 and 10.3%, applying the 1999WHO, 2013WHO, and the 2017Norwegian criteria, respectively, but was higher for women with non-European background when compared to European women (14.5 vs 10.2%, 37.7 vs 13.8% and 27.0 vs 7.8%). While advancing age and elevated BMI increased the risk of GDM, no risk factors, isolated or in combination, could identify more than 80% of women with GDM by the latter two diagnostic criteria, unless at least 70-80% of women were offered an OGTT. Using the 2017Norwegian criteria, the combination "age≥25 years or BMI≥25 kg/m2" achieved the highest sensitivity (96.5%) with an OGTT required for 93% of European women. The predictive accuracy of risk factors for identifying GDM was even lower for non-European women.

CONCLUSIONS

The prevalence of GDM was similar using the 1999WHO and 2017Norwegian criteria, but substantially higher with the 2013WHO criteria, in particular for ethnic non-European women. Using clinical risk factors such as age and BMI is a poor pre-diagnostic screening method, as this approach failed to identify a substantial proportion of women with GDM unless at least 70-80% were tested.

The association of general obesity, central obesity and visceral body fat with the risk of gestational diabetes mellitus: Evidence from a systematic review and meta-analysis

BACKGROUND

Studies consistently reported that general obesity predicts gestational diabetes mellitus (GDM). However, whether other phenotypes of obesity such as central obesity and visceral adiposity might have differential associations as risk factors of GDM are less known. The objective of this study was to investigate the association of all these obesity phenotypes in developing GDM.

METHODS

PubMed, CINHAL, Embase, Scopus, Google Scholar and Web of Science were searched. Full-text research articles published in English from 1985 to February 2020 with cohort and cross-sectional studies design and reported an association between obesity and GDM were included. Case-control studies, case reports, conference abstract, women with previous metabolic disorders and articles considered general obesity only were excluded. A bias adjusted-quality effect meta-analysis was conducted to evaluate the association of these obesity phenotypes and GDM risk.

RESULTS

Twenty studies met the inclusion criteria representing data of ∼50 thousand women at the reproductive age with ∼7% prevalence of GDM. Meta-analysis of 14 datasets revealed that the three types of obesity were significantly associated with an increased risk of GDM. In addition, visceral adiposity was a stronger risk factor for GDM than other obesity phenotypes (odd ratio = 3.25, 95% confidence interval = 2.01-5.26) versus (odd ratio = 2.73, 95% confidence interval = 2.20-3.38) for general obesity and (odd ratio = 2.53, 95% confidence interval = 2.04-3.14) for central obesity.

CONCLUSION

The findings of this study suggest that general obesity, central obesity and visceral body fat were associated with an increased risk of GDM. Furthermore, the association with maternal visceral adiposity was more robust compared to general obesity and central obesity.

Do Mass Spectrometry-Derived Metabolomics Improve the Prediction of Pregnancy-Related Disorders? Findings from a UK Birth Cohort with Independent Validation

Many women who experience gestational diabetes (GDM), gestational hypertension (GHT), pre-eclampsia (PE), have a spontaneous preterm birth (sPTB) or have an offspring born small/large for gestational age (SGA/LGA) do not meet the criteria for high-risk pregnancies based upon certain maternal risk factors. Tools that better predict these outcomes are needed to tailor antenatal care to risk. Recent studies have suggested that metabolomics may improve the prediction of these pregnancy-related disorders. These have largely been based on targeted platforms or focused on a single pregnancy outcome. The aim of this study was to assess the predictive ability of an untargeted platform of over 700 metabolites to predict the above pregnancy-related disorders in two cohorts. We used data collected from women in the Born in Bradford study (BiB; two sub-samples, n = 2000 and n = 1000) and the Pregnancy Outcome Prediction study (POPs; n = 827) to train, test and validate prediction models for GDM, PE, GHT, SGA, LGA and sPTB. We compared the predictive performance of three models: (1) risk factors (maternal age, pregnancy smoking, BMI, ethnicity and parity) (2) mass spectrometry (MS)-derived metabolites (n = 718 quantified metabolites, collected at 26-28 weeks' gestation) and (3) combined risk factors and metabolites. We used BiB for the training and testing of the models and POPs for independent validation. In both cohorts, discrimination for GDM, PE, LGA and SGA improved with the addition of metabolites to the risk factor model. The models' area under the curve (AUC) were similar for both cohorts, with good discrimination for GDM (AUC (95% CI) BiB 0.76 (0.71, 0.81) and POPs 0.76 (0.72, 0.81)) and LGA (BiB 0.86 (0.80, 0.91) and POPs 0.76 (0.60, 0.92)). Discrimination was improved for the combined models (compared to the risk factors models) for PE and SGA, with modest discrimination in both studies (PE-BiB 0.68 (0.58, 0.78) and POPs 0.66 (0.60, 0.71); SGA-BiB 0.68 (0.63, 0.74) and POPs 0.64 (0.59, 0.69)). Prediction for sPTB was poor in BiB and POPs for all models. In BiB, calibration for the combined models was good for GDM, LGA and SGA. Retained predictors include 4-hydroxyglutamate for GDM, LGA and PE and glycerol for GDM and PE. MS-derived metabolomics combined with maternal risk factors improves the prediction of GDM, PE, LGA and SGA, with good discrimination for GDM and LGA. Validation across two very different cohorts supports further investigation on whether the metabolites reflect novel causal paths to GDM and LGA.

Association of household socioeconomic status, neighborhood support system and adherence to dietary recommendation among persons with T2DM, a facility-based cross-sectional study in Ghana

BACKGROUND

Dietary recommendation help persons with diabetes adopt to healthy eating habits to achieve optimal glycemic control. Socioeconomic-status and neighborhood support system can influence adherence to dietary recommendation. The purpose of our study is to assess the association of household-socioeconomic status and neighborhood-support system with adherence to dietary recommendation among persons with type 2 diabetes mellitus (T2DM).

METHODS

Facility-based cross-sectional-survey was conducted in Brong Ahafo region, Ghana. Six hospitals were randomly selected and 530 individuals with T2DM consecutively recruited from the selected hospitals for the study. Structured-questionnaires were used to collect socio-demographic variables. Adherence to dietary-recommendation was the outcome-variable, and was assessed using perceived dietary-adherence questionnaire.

RESULTS

Age (years) (P-value = 0.005), Physical-Activity level (P-value = 0.024) Receive-moderate Social-Support (P-value = 0.004) and High-Socioeconomic status (P-value = 0.046) were significantly correlated with adherence to dietary-recommendation. Age (years) regression coefficient (β) -0.089, 95%CI (- 0.12, - 0.001), Being married β0.103, 95%CI (0.002, 0.02), moderate and low-social support system β 0.309, 95%CI (0.17, 0.38) and β-0.192, 95%CI (- 0.26, - 0.06) respectively, and high-socioeconomic status β 0.197, 95%CI (0.06, 0.25) were significantly associated with adherence to dietary-recommendation.

CONCLUSION

Social-support system and socioeconomic-status could be associated with adherence to dietary-recommendation. Therefore, health workers should consider patients' social support system and socioeconomic status as modifiable factors for optimum adherence.

Effect of the IADPSG screening strategy for gestational diabetes on perinatal outcomes in Switzerland

AIMS

To evaluate the impact adoption of the International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria on prevalence of gestational diabetes mellitus (GDM) and risks of perinatal outcomes.

METHODS

Retrospectively, 155,103 women screened with selective two step criteria in Switzerland in period 1 (2005-2010) were compared to 170,427 women screened with IADPSG criteria in period 2 (2012-2017). GDM prevalence over time was established and multivariable regression used to assess variation in risks for GDM related events and perinatal outcomes.

RESULTS

GDM prevalence increased steadily over both study periods from 1.8% to 9.0%. A risk reduction of GDM-related events was shown only for women with one or two risk factors for GDM present (relative risk (95% confidence interval)): (0.93 (0.90,0.97), 0.90 (0.83,0.96)). The comparison of perinatal outcomes between the two study periods revealed a significant lower risk for newborns large for gestational age (LGA) (0.93 (0.91-0.95)), pre-term delivery (0.94 (0.92-0.97)) and neonatal hypoglycemia (0.83 (0.77-0.90)) in period 2.

CONCLUSION

The introduction of the IADPSG criteria for the screening of GDM increased prevalence by threefold with no substantial improvements in GDM related events for women without risk factors but reduced the risks for LGA, neonatal hypoglycemia and preterm birth.

Screening for Gestational Diabetes during the COVID-19 Pandemic-Current Recommendations and Their Consequences

Gestational diabetes mellitus (GDM) is recognized as one of the most common medical complications of pregnancy that can lead to significant short-term and long-term risks for the mother and the fetus if not detected early and treated appropriately. Current evidence suggests that, with the use of appropriate screening programs for GDM, those women diagnosed and treated have reduced perinatal morbidity. It has been implied that, when screening for GDM, there should be uniformity in the testing used and in further management. This paper summarizes and compares current screening strategies proposed by international bodies and discusses application in the context of the COVID-19 pandemic.

Implementation of a first-trimester prognostic model to improve screening for gestational diabetes mellitus

Background

Improvement in the accuracy of identifying women who are at risk to develop gestational diabetes mellitus (GDM) is warranted, since timely diagnosis and treatment improves the outcomes of this common pregnancy disorder. Although prognostic models for GDM are externally validated and outperform current risk factor based selective approaches, there is little known about the impact of such models in day-to-day obstetric care.

Methods

A prognostic model was implemented as a directive clinical prediction rule, classifying women as low- or high-risk for GDM, with subsequent distinctive care pathways including selective midpregnancy testing for GDM in high-risk women in a prospective multicenter birth cohort comprising 1073 pregnant women without pre-existing diabetes and 60 obstetric healthcare professionals included in nine independent midwifery practices and three hospitals in the Netherlands (effectiveness-implementation hybrid type 2 study). Model performance (c-statistic) and implementation outcomes (acceptability, adoption, appropriateness, feasibility, fidelity, penetration, sustainability) were evaluated after 6 months by indicators and implementation instruments (NoMAD; MIDI).

Results

The adherence to the prognostic model (c-statistic 0.85 (95%CI 0.81–0.90)) was 95% (n = 1021). Healthcare professionals scored 3.7 (IQR 3.3–4.0) on implementation instruments on a 5-point Likert scale. Important facilitators were knowledge, willingness and confidence to use the model, client cooperation and opportunities for reconfiguration. Identified barriers mostly related to operational and organizational issues. Regardless of risk-status, pregnant women appreciated first-trimester information on GDM risk-status and lifestyle advice to achieve risk reduction, respectively 89% (n = 556) and 90% (n = 564)).

Conclusions

The prognostic model was successfully implemented and well received by healthcare professionals and pregnant women. Prognostic models should be recommended for adoption in guidelines.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12884-021-03749-x.

Recurrent Gestational Diabetes Mellitus: A Narrative Review and Single-Center Experience

Gestational diabetes mellitus (GDM) is a frequently observed complication of pregnancy and is associated with an elevated risk of adverse maternal and neonatal outcomes. Many women with GDM will go on to have future pregnancies, and these pregnancies may or may not be affected by GDM. We conducted a literature search, and based on data from key studies retrieved during the search, we describe the epidemiology of GDM recurrence. This includes a summary of the observed clinical risk factors of increasing maternal age, weight, ethnicity, and requirement for insulin in the index pregnancy. We then present our data from Mayo Clinic (January 2013-December 2017) which identifies a GDM recurrence rate of 47.6%, and illustrates the relevance of population-based studies to clinical practice. Lastly, we examine the available evidence on strategies to prevent GDM recurrence, and note that more research is needed to evaluate the effect of interventions before, during and after pregnancy.

COVID-19 pandemic gestational diabetes screening guidelines: A retrospective study in Australian women

BACKGROUND AND AIMS

Gestational diabetes (GDM) is one of the most common medical complications of pregnancy and associated with significant perinatal and long-term morbidity. Temporary changes to the diagnostic testing for GDM have been recommended for the COVID-19 pandemic. This study aims to identify what proportion of women with GDM would be missed by the COVID-19 GDM screening criteria. Secondly an analysis of the relationship between HbA1c, fasting blood glucose (FBG) and pregnancy outcomes will be completed.

METHODS

This was a retrospective analysis of all GDM patients at an Australian secondary hospital between January 2019 and February 2020. The proportion of women with GDM who would have been missed using the COVID-19 guidelines was assessed. Patients were divided into groups according to how their GDM was managed during the pregnancy: Diet, Metformin (MF), Insulin and MF + Insulin groups. Differences between the groups were compared using one-way ANOVA and post-hoc analysis was completed using the Bonferroni test. Logistic regression was employed to further compare the differences between the groups.

RESULTS

The study group comprised 237 patients. Sixty patients (25.3%) would not have had GDM detected in their pregnancy using the COVID-19 guidelines. FBG was the most significant predictor for intervention with medication for GDM (p = 0.001).

CONCLUSION

HbA1c and FBG are poor screening tests for GDM. During the COVID-19 pandemic, the OGTT should be given clinical priority in high risk patients. Elevated FBG is a significant predictor for needing medical management and could be used in the future to better enable individualised treatment.

Metabolic syndrome markers and risk of hyperglycemia in pregnancy: a cross-sectional cohort study

The aim was to assess the role of Metabolic Syndrome (MetS) diagnostic markers, recommended by three different guidelines, in the prediction of hyperglycemia in pregnancy. This cross-sectional cohort study included 506 non-diabetic women, with a singleton pregnancy, who underwent a diagnostic test for hyperglycemia at 24-28 weeks. Clinical, anthropometric, and laboratory data were obtained. The relationship between MetS markers and the risk of hyperglycemia was evaluated by backward stepwise logistic regression analysis (OR, 95% CI). The limit of statistical significance was 95% (p < 0.05). Triglycerides (TG) ≥ 150 mg/dL, blood pressure (BP) ≥ 130/85 mmHg, fasting glucose (FG) ≥ 100 mg/dL, and waist circumference (WC) > 88 cm were identified as independent risk factors for hyperglycemia in pregnancy. These results might help the selective screening of hyperglycemia in pregnancy.

Do nuclear magnetic resonance (NMR)-based metabolomics improve the prediction of pregnancy-related disorders? Findings from a UK birth cohort with independent validation

BACKGROUND

Prediction of pregnancy-related disorders is usually done based on established and easily measured risk factors. Recent advances in metabolomics may provide earlier and more accurate prediction of women at risk of pregnancy-related disorders.

METHODS

We used data collected from women in the Born in Bradford (BiB; n = 8212) and UK Pregnancies Better Eating and Activity Trial (UPBEAT; n = 859) studies to create and validate prediction models for pregnancy-related disorders. These were gestational diabetes mellitus (GDM), hypertensive disorders of pregnancy (HDP), small for gestational age (SGA), large for gestational age (LGA) and preterm birth (PTB). We used ten-fold cross-validation and penalised regression to create prediction models. We compared the predictive performance of (1) risk factors (maternal age, pregnancy smoking, body mass index (BMI), ethnicity and parity) to (2) nuclear magnetic resonance-derived metabolites (N = 156 quantified metabolites, collected at 24-28 weeks gestation) and (3) combined risk factors and metabolites. The multi-ethnic BiB cohort was used for training and testing the models, with independent validation conducted in UPBEAT, a multi-ethnic study of obese pregnant women.

RESULTS

Maternal age, pregnancy smoking, BMI, ethnicity and parity were retained in the combined risk factor and metabolite models for all outcomes apart from PTB, which did not include maternal age. In addition, 147, 33, 96, 51 and 14 of the 156 metabolite traits were retained in the combined risk factor and metabolite model for GDM, HDP, SGA, LGA and PTB, respectively. These include cholesterol and triglycerides in very low-density lipoproteins (VLDL) in the models predicting GDM, HDP, SGA and LGA, and monounsaturated fatty acids (MUFA), ratios of MUFA to omega 3 fatty acids and total fatty acids, and a ratio of apolipoprotein B to apolipoprotein A-1 (APOA:APOB1) were retained predictors for GDM and LGA. In BiB, discrimination for GDM, HDP, LGA and SGA was improved in the combined risk factors and metabolites models. Risk factor area under the curve (AUC 95% confidence interval (CI)): GDM (0.69 (0.64, 0.73)), HDP (0.74 (0.70, 0.78)) and LGA (0.71 (0.66, 0.75)), and SGA (0.59 (0.56, 0.63)). Combined risk factor and metabolite models AUC 95% (CI): GDM (0.78 (0.74, 0.81)), HDP (0.76 (0.73, 0.79)) and LGA (0.75 (0.70, 0.79)), and SGA (0.66 (0.63, 0.70)). For GDM, HDP and LGA, but not SGA, calibration was good for a combined risk factor and metabolite model. Prediction of PTB was poor for all models. Independent validation in UPBEAT at 24-28 weeks and 15-18 weeks gestation confirmed similar patterns of results, but AUCs were attenuated.

CONCLUSIONS

Our results suggest a combined risk factor and metabolite model improves prediction of GDM, HDP and LGA, and SGA, when compared to risk factors alone. They also highlight the difficulty of predicting PTB, with all models performing poorly.

[Preventive activities in women's care]

A review is presented of the scientific evidence on preventive activities in women's care in relation to pregnancy follow-up, preventive activities in the planning and follow-up of contraceptive methods, preventive activities in menopause, and the prevention of osteoporotic fractures.

A Comparison of Predictive Performances between Old versus New Criteria in a Risk-Based Screening Strategy for Gestational Diabetes Mellitus

Background

The definition of the high-risk group for gestational diabetes mellitus (GDM) defined by the American College of Obstetricians and Gynecologists was changed from the criteria composed of five historic/demographic factors (old criteria) to the criteria consisting of 11 factors (new criteria) in 2017. To compare the predictive performances between these two sets of criteria.

Methods

This is a secondary analysis of a large prospective cohort study of non-diabetic Korean women with singleton pregnancies designed to examine the risk of GDM in women with nonalcoholic fatty liver disease. Maternal fasting blood was taken at 10 to 14 weeks of gestation and measured for glucose and lipid parameters. GDM was diagnosed by the two-step approach.

Results

Among 820 women, 42 (5.1%) were diagnosed with GDM. Using the old criteria, 29.8% (n=244) of women would have been identified as high risk versus 16.0% (n=131) using the new criteria. Of the 42 women who developed GDM, 45.2% (n=19) would have been mislabeled as not high risk by the old criteria versus 50.0% (n=21) using the new criteria (1-sensitivity, 45.2% vs. 50.0%, P>0.05). Among the 778 patients who did not develop GDM, 28.4% (n=221) would have been identified as high risk using the old criteria versus 14.1% (n=110) using the new criteria (1-specificity, 28.4% vs. 14.1%, P<0.001).

Conclusion

Compared with the old criteria, use of the new criteria would have decreased the number of patients identified as high risk and thus requiring early GDM screening by half (from 244 [29.8%] to 131 [16.0%]).

Interpregnancy weight change and recurrence of gestational diabetes mellitus: a population-based cohort study

OBJECTIVE

To estimate recurrence risk of gestational diabetes mellitus (GDM) by interpregnancy weight change.

DESIGN

Population-based cohort study.

SETTING AND POPULATION

Data from the Swedish (1992-2010) and the Norwegian (2006-2014) Medical Birth Registries on 2763 women with GDM in first pregnancy, registered with their first two singleton births and available information on height and weight.

METHODS

Interpregnancy weight change (BMI in second pregnancy minus BMI in first pregnancy) was categorised in six groups by BMI units. Relative risks (RRs) of GDM recurrence were obtained by general linear models for the binary family and adjusted for confounders. Analyses were stratified by BMI in first pregnancy (<25 and ≥25 kg/m² ).

MAIN OUTCOME MEASURE

GDM in second pregnancy.

RESULTS

Among overweight/obese women (BMI ≥25), recurrence risk of GDM decreased in women who reduced their BMI by 1-2 units (relative risk [RR] 0.80, 95% CI 0.65-0.99) and >2 units (RR 0.72, 95% CI 0.59-0.89) and increased if BMI increased by ≥4 units (RR 1.26, 95% CI 1.05-1.51) compared wth women with stable BMI (-1 to 1 units). In normal weight women (BMI <25), risk of GDM recurrence increased if BMI increased by 2-4 units (RR 1.32, 95% CI 1.08-1.60) and ≥4 units (RR 1.61, 95% CI 1.28-2.02) compared with women with stable BMI.

CONCLUSION

Interpregnancy weight loss reduced risk of GDM recurrence in overweight/obese women. Weight gain between pregnancies increased recurrence risk for GDM in both normal and overweight/obese women. Our findings highlight the importance of weight management in the interconception window in women with a history of GDM.

TWEETABLE ABSTRACT

Interpregnancy weight loss reduces recurrence of gestational diabetes mellitus in overweight/obese women.

Maternal factors associated with hyperglycemia in pregnancy and perinatal outcomes: a Brazilian reference center cohort study

BACKGROUND

While sufficient evidence supporting universal screening is not available, it is justifiable to look for specific risk factors for gestational diabetes mellitus (GDM) or hyperglycemia in pregnancy (HIP). The objective of this study is to identify independent risk factors for HIP and its adverse perinatal outcomes in a Brazilian public referral center.

METHODS

We included 569 singleton pregnant women who were split into three groups by glucose status: GDM (n = 207), mild gestational hyperglycemia (MGH; n = 133), and control (n = 229). Women who used corticosteroids or had a history of DM were excluded. HIP comprised both GDM and MGH, diagnosed by a 100 g- or 75 g-oral glucose tolerance test (OGTT) and a glucose profile at 24-28 weeks. Maternal characteristics were tested for their ability to predict HIP and its outcomes. Bivariate analysis (RR; 95% CI) was used to identify potential associations. Logistic regression (RRadj; 95% CI) was used to confirm the independent risk factors for HIP and its perinatal outcomes (p < 0.05).

RESULTS

Age ≥ 25 years [1.83, 1.12-2.99], prepregnancy BMI ≥ 25 kg/m2 [2.88, 1.89-4.39], family history of DM [2.12, 1.42-3.17] and multiparity [2.07, 1.27-3.37] were independent risk factors for HIP. Family history of DM [169, 1.16-2.16] and hypertension [2.00, 1.36-2.98] were independent risk factors for C-section. HbA1c ≥ 6.0% at birth was an independent risk factor for LGA [1.99, 1.05-3.80], macrosomia [2.43, 1.27-4.63], and birthweight Z-score > 2.0 [4.17, 1.57-11.10].

CONCLUSIONS

MGH presents adverse pregnancy outcomes similar to those observed in the GDM group but distinct from those observed in the control (no diabetes) group. In our cohort, age ≥ 25 years, prepregnancy BMI ≥ 25 kg/m2, family history of DM, and multiparity were independent risk factors for HIP, supporting the use of selective screening for this condition. These results should be validated in populations with similar characteristics in Brazil or other low- and middle-income countries.

Estimating the risk of gestational diabetes mellitus based on the 2013 WHO criteria: a prediction model based on clinical and biochemical variables in early pregnancy

AIMS

We aimed to develop a prediction model based on clinical and biochemical variables for gestational diabetes mellitus (GDM) based on the 2013 World Health Organization (WHO) criteria.

METHODS

A total of 1843 women from a Belgian multi-centric prospective cohort study underwent universal screening for GDM. Using multivariable logistic regression analysis, a model to predict GDM was developed based on variables from early pregnancy. The performance of the model was assessed by receiver-operating characteristic (AUC) analysis. To account for over-optimism, an eightfold cross-validation was performed. The accuracy was compared with two validated models (van Leeuwen and Teede).

RESULTS

A history with a first degree relative with diabetes, a history of smoking before pregnancy, a history of GDM, Asian origin, age, height and BMI were independent predictors for GDM with an AUC of 0.72 [95% confidence interval (CI) 0.69-0.76)]; after cross-validation, the AUC was 0.68 (95% CI 0.64-0.72). Adding biochemical variables, a history of a first degree relative with diabetes, a history of GDM, non-Caucasian origin, age, height, weight, fasting plasma glucose, triglycerides and HbA_1c were independent predictors for GDM, with an AUC of the model of 0.76 (95% CI 0.72-0.79); after cross-validation, the AUC was 0.72 (95% CI 0.66-0.78), compared to an AUC of 0.67 (95% CI 0.63-0.71) using the van Leeuwen model and an AUC of 0.66 (95% CI 0.62-0.70) using the Teede model.

CONCLUSIONS

A model based on easy to use variables in early pregnancy has a moderate accuracy to predict GDM based on the 2013 WHO criteria.

Investigating mitochondrial dysfunction in gestational diabetes mellitus and elucidating if BMI is a causative mediator

OBJECTIVE

Gestational diabetes mellitus (GDM) is defined as any degree of glucose intolerance which is diagnosed during pregnancy and poses considerable health risks for mother and child. Maternal body mass index (BMI) correlates with GDM diagnosis and the pathophysiology of this link may be explained through oxidative stress and mitochondrial dysfunction. In this study we investigate if mitochondrial dysfunction is evident in GDM by measuring cell free mitochondrial DNA concentration and determine if a potential relationship exists between maternal mitochondrial function and GDM diagnosis.

STUDY DESIGN

Plasma samples were taken at 20 weeks' gestation from women who subsequently developed GDM (n = 44) and matched with women with uncomplicated pregnancies (n = 85) as controls. Control group 1 was matched by maternal age and BMI (n = 41) to GDM cases, while control group 2 was matched by maternal age alone (n = 44). Prediction potential was determined by binary regression analysis. Statistical analysis was performed on SPSS Statistics v25.

RESULTS

Binary regression analysis showed a statistically significant association between mtDNA concentration and GDM diagnosis (p = 0.032) in GDM cases versus control group 2, indicating that GDM patients have higher circulating mtDNA concentrations relative to healthy control patients. The lack of statistical significance in control group 1 suggests that BMI may be linked to mitochondrial function in GDM patients.

CONCLUSION

These results demonstrate a potential pathogenic role for mitochondrial dysfunction in GDM, with BMI presenting as a likely physiological mediator.

Incidence and risk factors for hyperglycemia in pregnancy among nulliparous women: A Brazilian multicenter cohort study

OBJECTIVE

To assess the incidence and risk factors for hyperglycemia in pregnancy in a cohort of Brazilian nulliparous pregnant women.

MATERIALS AND METHODS

This is a secondary analysis of a multicenter cohort study that enrolled 1,008 nulliparous pregnant women at 19-21 weeks. Exclusion criteria included chronic exposure to corticosteroids and previous diabetes. Bivariate and multivariate analyses by Poisson regression were used to identify associated factors.

RESULTS

The incidence of hyperglycemia in pregnancy was 14.9% (150/1,008), and 94.7% of these cases were gestational diabetes mellitus (142/150). Significant associated factors included a family history of diabetes mellitus, maternal overweight or obesity at enrollment, and previous maternal conditions (polycystic ovarian syndrome, thyroid dysfunctions and hypertensive disorders). A BMI ≥ 26.3Kg/m2 (RRadj 1.87 [1.66-2.10]) and a family history of diabetes mellitus (RRadj 1.71 [1.37-2.15]) at enrollment were independent risk factors for HIP.

CONCLUSIONS

A family history of diabetes mellitus and overweight or obesity (until 19-21 weeks of gestation) may be used as selective markers for HIP in Brazilian nulliparous women. Given the scarcity of results in nulliparous women, our findings may contribute to determine the optimal diagnostic approach in populations of similar socioeconomic characteristics.

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.

Diagnostic Strategies for Gestational Diabetes Mellitus: Review of Current Evidence

PURPOSE OF REVIEW

Currently, the diagnosis of gestational diabetes mellitus (GDM) lacks uniformity. Several controversies are still under debate, especially on the method of screening and diagnosis. This review focuses on recent literature and provides current evidence for the screening and diagnosis of GDM.

RECENT FINDINGS

Selective screening would miss a significant number of women with GDM. In contrast, universal screening has been shown to be cost-effective, compared with selective screening, and is recommended by many medical societies. For the diagnostic methods for GDM, most observational cohort studies reported that the one-step method is associated with improved pregnancy outcomes and is cost-saving or cost-effective, compared with the two-step method, although these findings should be confirmed in the upcoming randomized controlled trials which compare the performance of one-step and two-step methods. On the other hand, the methods of early screening or diagnosis of GDM are varied, and current evidence does not justify their use during early pregnancy. In conclusion, current evidence favors universal screening for GDM using the one-step method. Early screening for GDM is not favorably supported by the literature.

Epigenetics and gestational diabetes: a review of epigenetic epidemiology studies and their use to explore epigenetic mediation and improve prediction

Epigenetics encapsulates a group of molecular mechanisms including DNA methylation, histone modification and microRNAs (miRNAs). Gestational diabetes (GDM) increases the risk of adverse perinatal outcomes and is associated with future offspring risk of obesity and type 2 diabetes. It has been hypothesised that epigenetic mechanisms mediate an effect of GDM on offspring adiposity and type 2 diabetes and this could provide a modifiable mechanism to reduce type 2 diabetes in the next generation. Evidence for this hypothesis is lacking. Epigenetic epidemiology could also contribute to reducing type 2 diabetes by identifying biomarkers that accurately predict risk of GDM and its associated future adverse outcomes. We reviewed published human studies that explored associations between any of maternal GDM, type 2 diabetes, gestational fasting or post-load glucose and any epigenetic marker (DNA methylation, histone modification or miRNA). Of the 81 relevant studies we identified, most focused on the potential role of epigenetic mechanisms in mediating intrauterine effects of GDM on offspring outcomes. Studies were small (median total number of participants 58; median number of GDM cases 27) and most did not attempt replication. The most common epigenetic measure analysed was DNA methylation. Most studies that aimed to explore epigenetic mediation examined associations of in utero exposure to GDM with offspring cord or infant blood/placenta DNA methylation. Exploration of any causal effect, or effect on downstream offspring outcomes, was lacking. There is a need for more robust methods to explore the role of epigenetic mechanisms as possible mediators of effects of exposure to GDM on future risk of obesity and type 2 diabetes. Research to identify epigenetic biomarkers to improve identification of women at risk of GDM and its associated adverse (maternal and offspring) outcomes is currently rare but could contribute to future tools for accurate risk stratification.

Risk factor screening for gestational diabetes mellitus based on the 2013 WHO criteria

OBJECTIVE

Since many European countries use risk factor screening for gestational diabetes mellitus (GDM), we aimed to determine the performance of selective screening for GDM based on the 2013 WHO criteria.

DESIGN AND METHODS

Overall, 1811 women received universal screening with a 75 g oral glucose tolerance test (OGTT) with GDM in 12.5% (n = 231) women based on the 2013 WHO criteria. We retrospectively applied different European selective screening guidelines to this cohort and evaluated the performance of different clinical risk factors to screen for GDM.

RESULTS

By retrospectively applying the English, Irish, French and Dutch guidelines for selective screening, respectively 28.5% (n = 526), 49.7% (n = 916), 48.5% (n = 894) and 50.7% (n = 935) had at least one risk factor, with GDM prevalence of respectively 6.5% (n = 120), 7.9% (n = 146), 8.0% (n = 147) and 8.4% (n = 154). Using maternal age ≥30 and/or BMI ≥25 for screening, positive rate was 69.9% (n = 1288), GDM prevalence 10.2% (n = 188), sensitivity 81.4% (CI: 75.8–86.2%) and specificity 31.8% (CI: 29.5–34.1%). Adding other clinical risk factors did not improve detection. GDM women without risk factors had more neonatal hypoglycemia (14.4 vs 4.0%, P = 0.001) and labor inductions (39.7 vs 25.9%, P = 0.020) than normal-glucose tolerant women, and less cesarean sections than GDM women with risk factors (13.8 vs 31.0%, P = 0.010).

CONCLUSIONS

By applying selective screening by European guidelines, about 50% of women would need an OGTT with the lowest number of missed cases (33%) by the Dutch guidelines. Screening with age ≥30 years and/or BMI ≥25, reduced the number of missed cases to 18.6% but 70% would need an OGTT.

Serum levels of adipokines in gestational diabetes: a systematic review

OBJECTIVE

To determine the difference of serum levels of 10 adipokines (apelin, chemerin, fatty acid-binding protein-4, fibroblast growth factor-21, monocyte chemoattractant protein-1, nesfatin-1, omentin-1, resistin, vaspin, and visfatin) among women with gestational diabetes and healthy pregnant controls.

MATERIALS AND METHODS

Literature search was conducted using the Medline (1966-2018), Scopus (2004-2018), Cochrane Central Register of Controlled Trials (CENTRAL) (1999-2018), Clinicaltrials.gov (2008-2018) and Google Scholar (2004-2018) databases, along with the reference list of the included studies.

RESULTS

Ninety-one studies were included in the present review, with a total number of 11,074 pregnant women. A meta-analysis was not conducted due to the high inter-study heterogeneity. Current evidence suggests that fatty acid-binding protein-4 levels are significantly increased in pregnancies complicated with gestational diabetes, while no association of serum apelin and monocyte chemoattractant protein-1 with the disease can be supported. Data regarding the rest adipokines are conflicting, since the available studies did not unanimously indicate a significant change of their levels in gestational diabetes.

CONCLUSIONS

The findings of the present systematic review suggest the promising role of fatty acid-binding protein-4 in the prediction of gestational diabetes, while inconsistent evidence exists regarding the rest novel adipokines. Future cohorts are needed to assess their predictive efficacy and fully elucidate their contribution in the disease.

Exploration of predictive metabolic factors for gestational diabetes mellitus in Japanese women using metabolomic analysis

AIMS/INTRODUCTION

We aimed to explore novel predictive markers for gestational diabetes mellitus using metabolomic analysis in pregnant Japanese women.

MATERIALS AND METHODS

We carried out a case-control study with a cohort of participants enrolled during the first or early second trimester in the Center of Chiba Unit of the Japan Environment and Children's Study. Participants were classified as either gestational diabetes mellitus cases or matched controls based on age, body mass index and parity. Metabolite levels of their serum and urine obtained randomly before the diagnosis of gestational diabetes mellitus were analyzed using hydrophilic interaction chromatography tandem mass spectrometry. Orthogonal projections to latent structures discriminant analysis was carried out to investigate metabolome profiles for the different groups. Metabolites with a variable importance in projection value of >1.5 were identified as potential markers.

RESULTS

In total, 242 participants were enrolled in the study, of which 121 were cases. The R2X, R2Y and Q2 parameters for the discrimination ability of the resulting models were 0.388, 0.492 and 0.45 for serum, and 0.454, 0.674 and 0.483 for urine, respectively. We finally identified three metabolites in serum and 20 in urine as potential biomarkers. Glutamine in serum and ethanolamine and 1,3-diphosphoglycerate in urine showed >0.8 area under the receiver operating characteristic curves.

CONCLUSIONS

The present study identified serum and urine metabolites that are possible predictive markers of subsequent gestational diabetes mellitus in Japanese women. Further studies are required to elucidate their efficacy.