Early prediction of gestational diabetes: a practical model combining clinical and biochemical markers

Integrating polygenic risk scores in the prediction of gestational diabetes risk in China

Background

Polygenic risk scores (PRS) serve as valuable tools for connecting initial genetic discoveries with clinical applications in disease risk estimation. However, limited studies have explored the association between PRS and gestational diabetes mellitus (GDM), particularly in predicting GDM risk among Chinese populations.

Aim

To evaluate the relationship between PRS and GDM and explore the predictive capability of PRS for GDM risk in a Chinese population.

Methods

A prospective cohort study was conducted, which included 283 GDM and 2,258 non-GDM cases based on demographic information on pregnancies. GDM was diagnosed using the oral glucose tolerance test (OGTT) at 24-28 weeks. The strength of the association between PRS and GDM odds was assessed employing odds ratios (ORs) with 95% confidence intervals (CIs) derived from logistic regression. Receiver operating characteristic curves, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were employed to evaluate the improvement in prediction achieved by the new model.

Results

Women who developed GDM exhibited significantly higher PRS compared to control individuals (OR = 2.01, 95% CI = 1.33-3.07). The PRS value remained positively associated with fasting plasma glucose (FPG), 1-hour post-glucose load (1-h OGTT), and 2-hour post-glucose load (2-h OGTT) (all p < 0.05). The incorporation of PRS led to a statistically significant improvement in the area under the curve (0.71, 95% CI: 0.66-0.75, p = 0.024) and improved discrimination and classification (IDI: 0.007, 95% CI: 0.003-0.012, p < 0.001; NRI: 0.258, 95% CI: 0.135-0.382, p < 0.001).

Conclusions

This study highlights the increased odds of GDM associated with higher PRS values and modest improvements in predictive capability for GDM.

A Clinical Update on Gestational Diabetes Mellitus

Gestational diabetes mellitus (GDM) traditionally refers to abnormal glucose tolerance with onset or first recognition during pregnancy. GDM has long been associated with obstetric and neonatal complications primarily relating to higher infant birthweight and is increasingly recognized as a risk factor for future maternal and offspring cardiometabolic disease. The prevalence of GDM continues to rise internationally due to epidemiological factors including the increase in background rates of obesity in women of reproductive age and rising maternal age and the implementation of the revised International Association of the Diabetes and Pregnancy Study Groups' criteria and diagnostic procedures for GDM. The current lack of international consensus for the diagnosis of GDM reflects its complex historical evolution and pragmatic antenatal resource considerations given GDM is now 1 of the most common complications of pregnancy. Regardless, the contemporary clinical approach to GDM should be informed not only by its short-term complications but also by its longer term prognosis. Recent data demonstrate the effect of early in utero exposure to maternal hyperglycemia, with evidence for fetal overgrowth present prior to the traditional diagnosis of GDM from 24 weeks' gestation, as well as the durable adverse impact of maternal hyperglycemia on child and adolescent metabolism. The major contribution of GDM to the global epidemic of intergenerational cardiometabolic disease highlights the importance of identifying GDM as an early risk factor for type 2 diabetes and cardiovascular disease, broadening the prevailing clinical approach to address longer term maternal and offspring complications following a diagnosis of GDM.

Proteomics Studies in Gestational Diabetes Mellitus: A Systematic Review and Meta-Analysis

Gestational Diabetes Mellitus (GDM) is the most common metabolic complication during pregnancy and is associated with serious maternal and fetal complications such as pre-eclampsia and stillbirth. Further, women with GDM have approximately 10 times higher risk of diabetes later in life. Children born to mothers with GDM also face a higher risk of childhood obesity and diabetes later in life. Early prediction/diagnosis of GDM leads to early interventions such as diet and lifestyle, which could mitigate the maternal and fetal complications associated with GDM. However, no biomarkers identified to date have been proven to be effective in the prediction/diagnosis of GDM. Proteomic approaches based on mass spectrometry have been applied in various fields of biomedical research to identify novel biomarkers. Although a number of proteomic studies in GDM now exist, a lack of a comprehensive and up-to-date meta-analysis makes it difficult for researchers to interpret the data in the existing literature. Thus, we undertook a systematic review and meta-analysis on proteomic studies and GDM. We searched MEDLINE, EMBASE, Web of Science and Scopus from inception to January 2022. We searched Medline, Embase, CINHAL and the Cochrane Library, which were searched from inception to February 2021. We included cohort, case-control and observational studies reporting original data investigating the development of GDM compared to a control group. Two independent reviewers selected eligible studies for meta-analysis. Data collection and analyses were performed by two independent reviewers. The PROSPERO registration number is CRD42020185951. Of 120 articles retrieved, 24 studies met the eligibility criteria, comparing a total of 1779 pregnant women (904 GDM and 875 controls). A total of 262 GDM candidate biomarkers (CBs) were identified, with 49 CBs reported in at least two studies. We found 22 highly replicable CBs that were significantly different (nine CBs were upregulated and 12 CBs downregulated) between women with GDM and controls across various proteomic platforms, sample types, blood fractions and time of blood collection and continents. We performed further analyses on blood (plasma/serum) CBs in early pregnancy (first and/or early second trimester) and included studies with more than nine samples (nine studies in total). We found that 11 CBs were significantly upregulated, and 13 CBs significantly downregulated in women with GDM compared to controls. Subsequent pathway analysis using Database for Annotation, Visualization and Integrated Discovery (DAVID) bioinformatics resources found that these CBs were most strongly linked to pathways related to complement and coagulation cascades. Our findings provide important insights and form a strong foundation for future validation studies to establish reliable biomarkers for GDM.

Sex-specific association of high maternal psychological stress during pregnancy on newborn birthweight

Birthweight is an important predictor of newborn health and has been linked to maternal psychological stress during pregnancy. However, it is unclear whether prenatal stress affects birthweight similarly for both male and female infants. We used a well-established pregnancy cohort to investigate the impact of high maternal psychological stress during pregnancy on birthweight as a function of infant sex. Overall, 5702 mother-newborn pairs were analysed. Of these, 198 mothers reported high levels of stress using the Psychological Stress Measure (nine-items version; PSM-9). Maternal psychological stress was assessed between the 24^th and 28^th week of gestation and analyses were performed jointly and independently as a function of neonatal sex (separate analyses for male and female infants). Newborns exposed to high maternal psychological stress during pregnancy (a score above 26 measured using the PSM-9 questionnaire, corresponding to >97.5^th percentile) were compared to newborns of mothers who reported lower stress. ANCOVAs revealed that high levels of maternal stress during pregnancy were linked to infant birthweight as a function of infant sex. Male infants of mothers who reported high levels of stress had a greater birthweight whereas female infants had a lower birthweight under the same conditions, in comparison to mothers who did not report greater levels of stress. Although the effect size is small, these results underline the possibility that male and female fetuses may use different strategies when adapting to maternal adversity and highlight the need to consider infant sex as a moderator of the association between maternal psychological stress during pregnancy and infant birthweight.

Optimising Cardiometabolic Risk Factors in Pregnancy: A Review of Risk Prediction Models Targeting Gestational Diabetes and Hypertensive Disorders

Cardiovascular disease, especially coronary heart disease and cerebrovascular disease, is a leading cause of mortality and morbidity in women globally. The development of cardiometabolic conditions in pregnancy, such as gestational diabetes mellitus and hypertensive disorders of pregnancy, portend an increased risk of future cardiovascular disease in women. Pregnancy therefore represents a unique opportunity to detect and manage risk factors, prior to the development of cardiovascular sequelae. Risk prediction models for gestational diabetes mellitus and hypertensive disorders of pregnancy can help identify at-risk women in early pregnancy, allowing timely intervention to mitigate both short- and long-term adverse outcomes. In this narrative review, we outline the shared pathophysiological pathways for gestational diabetes mellitus and hypertensive disorders of pregnancy, summarise contemporary risk prediction models and candidate predictors for these conditions, and discuss the utility of these models in clinical application.

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.

The diagnostic indicators of gestational diabetes mellitus from second trimester to birth: a systematic review

BACKGROUND

Gestational diabetes mellitus (GDM) is glucose intolerance first recognised during pregnancy. Both modalities and thresholds of the GDM diagnostic test, the Oral Glucose Tolerance Test (OGTT), have varied widely over time and among countries. Additionally, OGTT limitations include inconsistency, poor patient tolerability, and questionable diagnostic reliability. Many biological parameters have been reported to be modified by GDM and could potentially be used as diagnostic indicators. This study aimed to 1) systematically explore biomarkers reported in the literature as differentiating GDM from healthy pregnancies 2) screen those indicators assessed against OGTT to propose OGTT alternatives.

MAIN BODY

A systematic review of GDM diagnostic indicators was performed according to PRISMA guidelines (PROSPERO registration CRD42020145499). Inclusion criteria were full-text, comprehensible English-language articles published January 2009-January 2021, where a biomarker (from blood, ultrasound, amniotic fluid, placenta) was compared between GDM and normal glucose tolerance (NGT) women from the second trimester onward to immediately postpartum. GDM diagnostic method had to be clearly specified, and the number of patients per study higher than 30 in total or 15 per group. Results were synthesised by biomarkers.

RESULTS

Of 13,133 studies identified in initial screening, 174 studies (135,801 participants) were included. One hundred and twenty-nine studies described blood analytes, one amniotic fluid analytes, 27 ultrasound features, 17 post-natal features. Among the biomarkers evaluated in exploratory studies, Adiponectin, AFABP, Betatrophin, CRP, Cystatin-C, Delta-Neutrophil Index, GGT, TNF-A were those demonstrating statistically and clinically significant differences in substantial cohorts of patients (> 500). Regarding biomarkers assessed versus OGTT (i.e. potential OGTT alternatives) most promising were Leptin > 48.5 ng/ml, Ficolin3/adiponectin ratio ≥ 1.06, Chemerin/FABP > 0.71, and Ultrasound Gestational Diabetes Score > 4. These all demonstrated sensitivity and specificity > 80% in adequate sample sizes (> / = 100).

CONCLUSIONS

Numerous biomarkers may differentiate GDM from normoglycaemic pregnancy. Given the limitations of the OGTT and the lack of a gold standard for GDM diagnosis, advanced phase studies are needed to triangulate the most promising biomarkers. Further studies are also recommended to assess the sensitivity and specificity of promising biomarkers not yet assessed against OGTT.

TRIAL REGISTRATION

PROSPERO registration number CRD42020145499.

Extracellular vesicle-associated miRNAs are an adaptive response to gestational diabetes mellitus

BACKGROUND

Gestational diabetes mellitus (GDM) is a serious public health issue affecting 9-15% of all pregnancies worldwide. Recently, it has been suggested that extracellular vesicles (EVs) play a role throughout gestation, including mediating a placental response to hyperglycaemia. Here, we investigated the EV-associated miRNA profile across gestation in GDM, assessed their utility in developing accurate, multivariate classification models, and determined the signaling pathways in skeletal muscle proteome associated with the changes in the EV miRNA profile.

METHODS

Discovery: A retrospective, case-control study design was used to identify EV-associated miRNAs that vary across pregnancy and clinical status (i.e. GDM or Normal Glucose Tolerance, NGT). EVs were isolated from maternal plasma obtained at early, mid and late gestation (n = 29) and small RNA sequencing was performed. Validation: A longitudinal study design was used to quantify expression of selected miRNAs. EV miRNAs were quantified by real-time PCR (cases = 8, control = 14, samples at three times during pregnancy) and their individual and combined classification efficiencies were evaluated. Quantitative, data-independent acquisition mass spectrometry was use to establish the protein profile in skeletal muscle biopsies from normal and GDM.

RESULTS

A total of 2822 miRNAs were analyzed using a small RNA library, and a total of 563 miRNAs that significantly changed (p < 0.05) across gestation and 101 miRNAs were significantly changed between NGT and GDM. Analysis of the miRNA changes in NGT and GDM separately identified a total of 256 (NGT-group), and 302 (GDM-group) miRNAs that change across gestation. A multivariate classification model was developed, based on the quantitative expression of EV-associated miRNAs, and the accuracy to correctly assign samples was > 90%. We identified a set of proteins in skeletal muscle biopsies from women with GDM associated with JAK-STAT signaling which could be targeted by the miRNA-92a-3p within circulating EVs. Interestingly, overexpression of miRNA-92a-3p in primary skeletal muscle cells increase insulin-stimulated glucose uptake.

CONCLUSIONS

During early pregnancy, differently-expressed, EV-associated miRNAs may be of clinical utility in identifying presymptomatic women who will subsequently develop GDM later in gestation. We suggest that miRNA-92a-3p within EVs might be a protected mechanism to increase skeletal muscle insulin sensitivity in GDM.

The relationship between fasting plasma glucose in the first trimester of pregnancy and the incidence of gestational diabetes in Iran

AIMS

This study aimed to investigate the relationship between fasting plasma glucose in the first trimester of pregnancy and the incidence of gestational diabetes.

METHODS

This descriptive-analytical study was conducted using the merging data from two prospective cohort studies that were carried out from 2015 to 2018 in Ahvaz, southwest of Iran. Logistic regression and ROC curves were used to evaluate the predictive power of risk factors.

RESULTS

Of all 1270 pregnant women studied, 454 (35.75%) had gestational diabetes who were compared with the other 816 (64.25%) healthy women. The area under the curve (AUC) of fasting glucose for the first pregnancy visit was 0.80 (95% CI: 0.76-83), and the optimum fasting glucose cut-off point was 85.5 mg/dL with a sensitivity of 71% and a specificity of 69%. AUC of the combination of fasting glucose at the first visit with body mass index ≥25 kg/m² and family history of diabetes was calculated to be 0.85 (CI, 0.82-0.88) and 0.84 (CI, 0.79-0.89) respectively.

CONCLUSION

Fasting plasma glucose in the first trimester of pregnancy is a relatively good predictor of gestational diabetes. If this factor is combined with other risks, its diagnostic value will increase.

Risk Prediction Model of Gestational Diabetes Mellitus in a Chinese Population Based on a Risk Scoring System

INTRODUCTION

Gestational diabetes mellitus (GDM) is associated with adverse perinatal outcomes. Accurate models for early prediction of GDM are lacking. This study aimed to explore an early risk prediction model to identify women at high risk of GDM through a risk scoring system.

METHODS

This was a retrospective cohort study of 785 control pregnancies and 855 women with GDM. Maternal clinical characteristics and biochemical measures were extracted from the medical records. Logistic regression analysis was used to obtain coefficients of selected predictors for GDM in the training cohort. The discrimination and calibration of the risk scores were evaluated by the receiver-operating characteristic (ROC) curve and a Hosmer-Lemeshow test in the internal and external validation cohort, respectively.

RESULTS

In the training cohort (total = 1640), two risk scores were developed, one including predictors collected at the first antenatal care visit for early prediction of GDM, such as age, height, pre-pregnancy body mass index, educational background, family history of diabetes, menstrual history, history of cesarean delivery, GDM, polycystic ovary syndrome, hypertension, and fasting blood glucose (FBG), and the total risk score also including FBG and triglyceride values during 14-20 gestational weeks. Our total risk score yielded an area under the curve (AUC) of 0.845 (95% CI = 0.805-0.884). This performed better in an external validation cohort, with an AUC of 0.886 (95% CI = 0.856-0.916).

CONCLUSION

The GDM risk score, which incorporates several potential clinical features with routine biochemical measures of GDM, appears to be a sensitive and reliable screening tool for earlier detection of GDM risk.

Prediction Method of Gestational Diabetes Based on Electronic Medical Record Data

At present, the secondary application of electronic medical records is focused on auxiliary medical diagnosis to improve the accuracy of clinical diagnosis. The main research in this article is the prediction method of gestational diabetes based on electronic medical record data. In the original data, the ID number of the medical examiner did not match the medical examination record. In order to ensure the accuracy of the data, this part of the record was removed. First, the preparation stage before building the model is to determine the baseline accuracy of the original data, test the effectiveness of the machine learning algorithm, and then balance the target data set to solve the bias caused by the imbalance between data classes and the illusion of excessive model prediction results. Then, the disease prediction model is constructed by dividing the data set, selecting parameters and algorithms, and visualizing the model. Finally, the effect of predictive model construction is comprehensively judged based on multiple evaluation indicators and control experimental models. In this paper, the RF model can be used to rank the importance of the feature importance of the output feature on the importance of the classification result of the input feature. In order to test the accuracy of regression prediction, the experiment uses absolute mean error and root mean square error to evaluate the accuracy of fasting blood glucose prediction. A logistic regression model is constructed through the training set, and the test set data are brought into the prediction model for prediction. Experimental data show that when the features filtered by WBFS are used, the accuracy, F1 value, and AUC value of logistic regression are 0.809, 0.881, and 0.825, respectively, which is an increase of about 12% compared with when the feature is not used. The results show that the electronic medical record data drive can effectively improve the accuracy of predicting gestational diabetes.

lncRNA SNHG17 is Downregulated in Gestational Diabetes Mellitus (GDM) and Has Predictive Values

AIM

Long non-coding RNA (lncRNA) SNHG17 has been shown to participate in type 2 diabetes mellitus, while its role in gestational diabetes mellitus (GDM) is unknown.

METHODS

Quantitative real-time PCR (qRT-PCR) assays were conducted to compare the differential expression of SNHG17 among 60 GDM patients and 60 healthy pregnant female controls. In addition, peripheral blood samples from 240 pregnant females were collected to evaluate the predictive value of SNHG17 for GDM patients. All females were followed-up until delivery to record the occurrence of GDM and perinatal outcomes. GDM-free curves were plotted to compare the occurrence of GDM between high- and low- SNHG17 expression groups. The diagnostic value of plasma SNHG17 for GDM was analyzed by ROC curve analysis. Moreover, the cell counting kit (CCK-8) assay was performed to evaluate the impact of SNHG17 on cell viability of INS-1, and the level of insulin secretion was detected by enzyme linked immunosorbent assay (ELISA) after overexpression or knockdown of SNHG17.

RESULTS

SNHG17 was downregulated in GDM patients compared to normal pregnant females. Low plasma expression levels of SNHG17 were closely correlated with the high incidence rate of GDM (GDM-free curve). Remarkably, plasma expression levels of SNHG17 at 4 weeks before the diagnosis of GDM (diagnosed by standard method) can be used to distinguish (ROC curve) GDM patients (diagnosed during follow-up) from normal pregnant females (GDM was not diagnosed during follow-up).

CONCLUSION

Plasma circulating SNHG17 is downregulated in GDM and has predictive values.

Machine Learning Prediction Models for Gestational Diabetes Mellitus: A meta- analysis (Preprint)

Background

Gestational diabetes mellitus (GDM) is a common endocrine metabolic disease, involving a carbohydrate intolerance of variable severity during pregnancy. The incidence of GDM-related complications and adverse pregnancy outcomes has declined, in part, due to early screening. Machine learning (ML) models are increasingly used to identify risk factors and enable the early prediction of GDM.

Objective

The aim of this study was to perform a meta-analysis and comparison of published prognostic models for predicting the risk of GDM and identify predictors applicable to the models.

Methods

Four reliable electronic databases were searched for studies that developed ML prediction models for GDM in the general population instead of among high-risk groups only. The novel Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias of the ML models. The Meta-DiSc software program (version 1.4) was used to perform the meta-analysis and determination of heterogeneity. To limit the influence of heterogeneity, we also performed sensitivity analyses, a meta-regression, and subgroup analysis.

Results

A total of 25 studies that included women older than 18 years without a history of vital disease were analyzed. The pooled area under the receiver operating characteristic curve (AUROC) for ML models predicting GDM was 0.8492; the pooled sensitivity was 0.69 (95% CI 0.68-0.69; P<.001; I²=99.6%) and the pooled specificity was 0.75 (95% CI 0.75-0.75; P<.001; I²=100%). As one of the most commonly employed ML methods, logistic regression achieved an overall pooled AUROC of 0.8151, while non–logistic regression models performed better, with an overall pooled AUROC of 0.8891. Additionally, maternal age, family history of diabetes, BMI, and fasting blood glucose were the four most commonly used features of models established by the various feature selection methods.

Conclusions

Compared to current screening strategies, ML methods are attractive for predicting GDM. To expand their use, the importance of quality assessments and unified diagnostic criteria should be further emphasized.

Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis

BACKGROUND

Predictions in pregnancy care are complex because of interactions among multiple factors. Hence, pregnancy outcomes are not easily predicted by a single predictor using only one algorithm or modeling method.

OBJECTIVE

This study aims to review and compare the predictive performances between logistic regression (LR) and other machine learning algorithms for developing or validating a multivariable prognostic prediction model for pregnancy care to inform clinicians' decision making.

METHODS

Research articles from MEDLINE, Scopus, Web of Science, and Google Scholar were reviewed following several guidelines for a prognostic prediction study, including a risk of bias (ROB) assessment. We report the results based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies were primarily framed as PICOTS (population, index, comparator, outcomes, timing, and setting): Population: men or women in procreative management, pregnant women, and fetuses or newborns; Index: multivariable prognostic prediction models using non-LR algorithms for risk classification to inform clinicians' decision making; Comparator: the models applying an LR; Outcomes: pregnancy-related outcomes of procreation or pregnancy outcomes for pregnant women and fetuses or newborns; Timing: pre-, inter-, and peripregnancy periods (predictors), at the pregnancy, delivery, and either puerperal or neonatal period (outcome), and either short- or long-term prognoses (time interval); and Setting: primary care or hospital. The results were synthesized by reporting study characteristics and ROBs and by random effects modeling of the difference of the logit area under the receiver operating characteristic curve of each non-LR model compared with the LR model for the same pregnancy outcomes. We also reported between-study heterogeneity by using τ2 and I2.

RESULTS

Of the 2093 records, we included 142 studies for the systematic review and 62 studies for a meta-analysis. Most prediction models used LR (92/142, 64.8%) and artificial neural networks (20/142, 14.1%) among non-LR algorithms. Only 16.9% (24/142) of studies had a low ROB. A total of 2 non-LR algorithms from low ROB studies significantly outperformed LR. The first algorithm was a random forest for preterm delivery (logit AUROC 2.51, 95% CI 1.49-3.53; I2=86%; τ2=0.77) and pre-eclampsia (logit AUROC 1.2, 95% CI 0.72-1.67; I2=75%; τ2=0.09). The second algorithm was gradient boosting for cesarean section (logit AUROC 2.26, 95% CI 1.39-3.13; I2=75%; τ2=0.43) and gestational diabetes (logit AUROC 1.03, 95% CI 0.69-1.37; I2=83%; τ2=0.07).

CONCLUSIONS

Prediction models with the best performances across studies were not necessarily those that used LR but also used random forest and gradient boosting that also performed well. We recommend a reanalysis of existing LR models for several pregnancy outcomes by comparing them with those algorithms that apply standard guidelines.

TRIAL REGISTRATION

PROSPERO (International Prospective Register of Systematic Reviews) CRD42019136106; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=136106.

Prepregnancy smoking and the risk of gestational diabetes requiring insulin therapy

The relationship between maternal smoking and gestational diabetes mellitus (GDM) is inconclusive. We investigated whether prepregnancy smoking is a risk factor for insulin-requiring GDM in Korean women. Using the National Health Insurance Service database, 325,297 women who delivered between 2011 and 2015 and who received a health examination within 52 weeks before pregnancy were included. Insulin-requiring GDM was defined as no claims for diabetes mellitus and a fasting blood glucose level of < 126 mg/dL before pregnancy, and initiation of insulin treatment during pregnancy. Smoking status was identified in a self-reported questionnaire completed during the health examination. There were 2,114 women (0.65%) with GDM who required insulin therapy. Compared with nonsmokers, the fully adjusted odd ratios (ORs) of former smokers and current smokers for insulin-requiring GDM were 1.55 (95% confidence interval [CI] 1.27–1.90) and 1.73 (1.42–2.09), respectively. The ORs (95% CIs) of insulin-requiring GDM among women who reported ≤ 2, 2–≤ 4, 4–≤ 6, 6–≤ 8, 8–≤ 10, and > 10 pack-years of smoking were 1.50 (1.22–1.84), 1.71 (1.31–2.22), 1.60 (1.13–2.26), 1.97 (1.14–3.40), 2.34 (1.22–4.51), and 2.29 (1.25–4.22), respectively, compared with nonsmokers (P for trend < 0.001). This association was similar in women with or without obesity and abdominal obesity. In conclusions, women who smoke have a significantly higher risk of GDM requiring insulin therapy, which may be proportional to the cumulative exposure to smoking. Cessation of smoking should be emphasized in women of childbearing age for the prevention of GDM.

Role of C-reactive Protein(CRP) or high-sensitivity CRP in predicting gestational diabetes Mellitus:Systematic review

BACKGROUND AND AIMS

Gestational Diabetes Mellitus (GDM) is the most common disorder during pregnancy in 8-18% of pregnancies. Due to maternal and neonatal morbidity and mortality, early diagnosis and appropriate treatment have always been of interest to researchers. One of the recent cases for early diagnosis of GDM is the size of the C-reactive protein (CRP). The purpose of this review study was to investigate the role of CRP or its high sensitivity type in predicting GDM.

METHODS

Systematic searching of MEDLINE, ISI Web of Science, PubMed, Scopus, Google Scholar, and ProQuest databases between 2009 and 2019 using keywords 'Gestational Diabetes Mellitus','Screening', 'C-reactive protein',' High sensitivity CRP'was performed. The quality of articles was also assessed using the STROBE checklist.

RESULTS

After a thorough search of the mentioned databases, 31 articles with the desired quality were finally selected. Most of studies showed significant relationship between CRP or high-sensitivity CRP(hs-CRP) level with GDMbutthe relationship was not significant in fewstudies.

CONCLUSIONS

Blood levels of CRP or hs-CRP could be used as a potential indicator for GDM, but more studies are needed.

Development and validation of an early pregnancy risk score for the prediction of gestational diabetes mellitus in Chinese pregnant women

OBJECTIVE

To develop and validate a set of risk scores for the prediction of gestational diabetes mellitus (GDM) before the 15th gestational week using an established population-based prospective cohort.

METHODS

From October 2010 to August 2012, 19 331 eligible pregnant women were registered in the three-tiered antenatal care network in Tianjin, China, to receive their antenatal care and a two-step GDM screening. The whole dataset was randomly divided into a training dataset (for development of the risk score) and a test dataset (for validation of performance of the risk score). Logistic regression was performed to obtain coefficients of selected predictors for GDM in the training dataset. Calibration was estimated using Hosmer-Lemeshow test, while discrimination was checked using area under the receiver operating characteristic curve (AUC) in the test dataset.

RESULTS

In the training dataset (total=12 887, GDM=979 or 7.6%), two risk scores were developed, one only including predictors collected at the first antenatal care visit for early prediction of GDM, like maternal age, body mass index, height, family history of diabetes, systolic blood pressure, and alanine aminotransferase; and the other also including predictors collected during pregnancy, that is, at the time of GDM screening, like physical activity, sitting time at home, passive smoking, and weight gain, for maximum performance. In the test dataset (total=6444, GDM=506 or 7.9%), the calibrations of both risk scores were acceptable (both p for Hosmer-Lemeshow test >0.25). The AUCs of the first and second risk scores were 0.710 (95% CI: 0.680 to 0.741) and 0.712 (95% CI: 0.682 to 0.743), respectively (p for difference: 0.9273).

CONCLUSION

Both developed risk scores had adequate performance for the prediction of GDM in Chinese pregnant women in Tianjin, China. Further validations are needed to evaluate their performance in other populations and using different methods to identify GDM cases.

Sex Hormone-binding Globulin, Cardiometabolic Biomarkers, and Gestational Diabetes: A Longitudinal Study and Meta-analysis

Objective

This study investigated the prospective associations of circulating levels of sex hormone-binding globulin (SHBG) levels with cardiometabolic biomarkers and risk of gestational diabetes (GDM) during pregnancy. It also examines the longitudinal trajectory of SHBG in women with and without GDM.

Methods

We conducted a nested case-control study of 107 incident GDM cases and 214 matched controls within the Eunice Kennedy Shriver National Institute of Child Health and Human Development Fetal Growth Studies-Singleton Cohort. The cohort enrolled non-obese and obese women aged 18-40 years with a singleton pregnancy between 8 and 13 weeks of gestation from 2009 to 2013. GDM was ascertained via medical records review. Blood samples were drawn four times at gestational weeks 10-14, 15-26, 23-31, and 33-39. The prospective associations between SHBG levels and cardiometabolic biomarkers were examined using the Spearman partial correlation among the controls. The longitudinal trajectories of SHBG levels were examined among the cases and the controls. Meta-analysis of prospective studies were performed to examine the association between SHBG levels and GDM risk.

Results

SHBG levels at gestational weeks 10-14 were significantly inversely associated with fasting insulin (r = -0.17, P = 0.01) and insulin resistance as measured by HOMA-IR (r = -0.17, P = 0.01) at gestational week 15-26. SHBG at gestational weeks 10-14 and 15-26 was lower in cases than controls (mean ± standard deviation: (204.0 ± 97.6) vs. (220.9 ± 102.5) nmol/L, P = 0.16 and (305.6 ± 124.3) vs. (322.7 ± 105.1) nmol/L, P = 0.14, respectively), yet the differences were not significant. In the meta-analysis, SHBG was 41.5 nmol/L (95% confidence interval: 23.9, 59.1, P < 0.01) significantly lower among women with GDM than without, and each 50 nmol/L increase in SHBG was significantly associated with an odds ratio of 0.85 (95% confidence interval: 0.76-0.95, P = 0.01) for GDM.

Conclusion

Lower SHBG levels in early pregnancy were prospectively associated with higher high insulin levels and insulin resistance in mid-pregnancy and subsequent risk of GDM, independent of adiposity. SHBG may serve as a marker for the identification of high-risk pregnancies during early pregnancy.

Nomogram for prediction of gestational diabetes mellitus in urban, Chinese, pregnant women

Background

This study sought to develop and validate a nomogram for prediction of gestational diabetes mellitus (GDM) in an urban, Chinese, antenatal population.

Methods

Age, pre-pregnancy body mass index (BMI), fasting plasma glucose (FPG) in the first trimester and diabetes in first degree relatives were incorporated as validated risk factors. A prediction model (nomogram) for GDM was developed using multiple logistic regression analysis, from a retrospective study conducted on 3956 women who underwent their first antenatal visit during 2015 in Shanghai. Performance of the nomogram was assessed through discrimination and calibration. We refined the predicting model with t-distributed stochastic neighbor embedding (t-SNE) to distinguish GDM from non-GDM. The results were validated using bootstrap resampling and a prospective cohort of 6572 women during 2016 at the same institution.

Results

Advanced age, pre-pregnancy BMI, high first-trimester, fasting, plasma glucose, and, a family history of diabetes were positively correlated with the development of GDM. This model had an area under the receiver operating characteristic (ROC) curve of 0.69 [95% CI:0.67–0.72, p < 0.0001]. The calibration curve for probability of GDM showed good consistency between nomogram prediction and actual observation. In the validation cohort, the ROC curve was 0.70 [95% CI: 0.68–0.72, p < 0.0001] and the calibration plot was well calibrated. In exploratory and validation cohorts, the distinct regions of GDM and non-GDM were distinctly separated in the t-SNE, generating transitional boundaries in the image by color difference. Decision curve analysis showed that the model had a positive net benefit at threshold between 0.05 and 0.78.

Conclusions

This study demonstrates the ability of our model to predict the development of GDM in women, during early stage of pregnancy.

Clinical significance of serum melatonin in predicting the severity of oral squamous cell carcinoma

Melatonin, the primary hormone produced by the pineal gland, is intensely assessed for its anticancer properties. This study aimed to reveal the clinical significance of serum melatonin levels in predicting the severity of oral squamous cell carcinoma (OSCC). For this purpose, 40 male patients with OSCC and 30 healthy subjects were enrolled in this study. The serum levels of melatonin were determined by ELISA. The results revealed that the melatonin concentrations were significantly lower in the patients with OSCC compared with the controls (18.2 vs. 47.6 pg/ml, P<0.001). In addition, the serum melatonin levels had a high predictive accuracy for discriminating patients with OSCC with T-depth of invasion (DOI) II from the healthy controls (89.1%), as well as in discriminating patients with OSCC with nodal metastasis from those without nodal metastasis (83.8%). On the whole, the findings of this study suggest that the serum melatonin concentrations are closely related to the severity of OSCC and may thus be used to assess the different stages of oral cancer objectively and accurately. The present study also supports the conclusion that melatonin may be a potential therapeutic agent for use in the treatment of patients with OSCC.

Association of Maternal Weight and Gestational Weight Gain with Maternal and Neonate Outcomes: A Prospective Cohort Study

We investigated the association of outcomes with pre-pregnancy body mass index (ppBMI), Institute of Medicine (IOM) recommendations about gestational weight gain, and weight gain trajectories during pregnancy. A prospective cohort of 7866 pregnant women was recruited. ppBMI and weight gain at each follow up visit were collected. The outcomes were gestational diabetes mellitus (GDM), hypertensive disorders of pregnancy (HDP), caesarean delivery, macrosomia, small (SGA) and large (LGA) for gestational age, neonatal hypoglycemia. Group-based multi-trajectory modelling was used for weight kinetics during pregnancy. In the third trimester, 53.8% of women were above IOM recommendations, with an increased relative risk (RR) of HDP (1.91 (1.40-2.61)), caesarean (1.34 (1.15-1.56)), macrosomia (2.17 (1.77-2.67)), LGA (2.26 (1.83-2.80)), and hypoglycemia (1.89 (1.12-3.18)). Women with a weight gain above IOM recommendations in the second trimester who normalized their weight gain in third trimester had, compared to those who remained above IOM recommendations, fewer events of HDP (2.8% versus 5.3%, p = 0.008), caesarean delivery (16.9% versus 22%, p = 0.006), macrosomia (8.3% versus 14.2%, p < 0.001), and LGA (7% versus 13.2%, p < 0.001). Multi-trajectory modelling identified three profiles with continued variation in RR of complications, including GDM. Weight gain above IOM recommendations increased the risk of perinatal complications. A correction of excessive weight gain in the second trimester reduces these risks.

Sex hormone binding globulin for prediction of gestational diabetes mellitus in pre-conception and pregnancy: A systematic review

AIM

The purpose of the present study was to assess the relationship of sex hormone binding globulin (SHBG) and gestational diabetes mellitus (GDM).

METHODS

The Cochrane Library, Medline, ScienceDirect, and Web of Science were searched for studies published from the inception of the databases up to February 2019. Our inclusion criteria were published observational full-text articles. All data were analyzed using Review Manager 5.3. Of 208 papers reviewed, 26 studies (n = 6668) were considered for meta-analysis.

RESULTS

The SHBG level was significantly lower in women with GDM compared to healthy women (MD = -11.86; 95% CI: [-13.02, -10.71]). Also, SHBG in women with PCOS and GDM and obesity was significantly lower than women with PCOS without GDM (MD = -38.14; 95% CI: [-56.79, -19.48]) and normal weight women (MD: -58.96; 95% CI: [-79.32, -38.59]). SHBG in the second trimester was lower than that in the first trimester and pre-conception.

CONCLUSIONS

This systematic review showed that the level of SHBG is significantly lower in GDM pregnant women than that in healthy women. The results of this systematic review about the relationship of GDM and SHBG and suggestion to assess this marker in early pregnancy should be considered with caution.

Use of antidepressants and anxiolytics in early pregnancy and the risk of preeclampsia and gestational hypertension: a prospective study

Background

We investigated the association between antidepressant and anxiolytic exposure during the first and early second trimester of pregnancy (< 16 weeks), and hypertensive disorders of pregnancy (including preeclampsia and gestational hypertension) in women with singleton pregnancy.

Methods

This study is based on a large prospective cohort of 7866 pregnant women. We included pregnant women aged 18 years or older without chronic hepatic or renal disease at the time of recruitment. Participants lost to the follow-up, with multiple pregnancies and pregnancy terminations, miscarriages or fetal deaths before 20 weeks of gestation were excluded from the study, as well as women with no data on the antidepressant/anxiolytic medication use during pregnancy. Information concerning antidepressant or anxiolytic medication use was extracted from hospital records after delivery. The associations between their use and the risk of gestational hypertension or preeclampsia were calculated.

Results

The final sample for analysis included 6761 participants including 218 (3.2%) women who were exposed to antidepressant and/or anxiolytic medication before the 16th week of gestation. Forty-one women had a non-medicated depression or anxiety during the pregnancy. Moreover, 195 (2.9%) and 122 (1.8%) women developed gestational hypertension and preeclampsia respectively. When compared to women unexposed to antidepressant/anxiolytic medication, depression and anxiety, those using antidepressant and/or anxiolytic drugs before the 16th week of gestation were at increased risk of preeclampsia (adjusted odd ratio (aOR) 3.09 [CI_95% 1.56–6.12]), especially if they continued their medication after the 16th week (aOR 3.41 [CI_95% 1.66–7.02]) compared to those who did not (1.60 [CI_95% 0.21–12.34]).

Conclusions

Women exposed to antidepressant and/or anxiolytic medication before the 16th week of pregnancy have a 3-fold increased risk for preeclampsia when compared to women unexposed to antidepressant/anxiolytic medication, depression and anxiety. Also, our results suggested that women who stopped their medication before the 16th week of pregnancy could be benefit from reduced preeclampsia risk.

First-trimester proteomic profiling identifies novel predictors of gestational diabetes mellitus

Background

Gestational diabetes mellitus (GDM) is a common pregnancy complication associated with adverse outcomes including preeclampsia, caesarean section, macrosomia, neonatal morbidity and future development of type 2 diabetes in both mother and child. Current selective screening strategies rely on clinical risk factors such as age, family history of diabetes, macrosomia or GDM in a previous pregnancy, and they possess a relatively low specificity. Here we hypothesize that novel first trimester protein predictors of GDM can contribute to the current selective screening strategies for early and accurate prediction of GDM, thus allowing for timely interventions.

Methods

A proteomics discovery approach was applied to first trimester sera from obese (BMI ≥27 kg/m²) women (n = 60) in a nested case-control study design, utilizing tandem mass tag labelling and tandem mass spectrometry. A subset of the identified protein markers was further validated in a second set of serum samples (n = 210) and evaluated for their contribution as predictors of GDM in relation to the maternal risk factors, by use of logistic regression and receiver operating characteristic analysis.

Results

Serum proteomic profiling identified 25 proteins with significantly different levels between cases and controls. Three proteins; afamin, serum amyloid P-component and vitronectin could be further confirmed as predictors of GDM in a validation set. Vitronectin was shown to contribute significantly to the predictive power of the maternal risk factors, indicating it as a novel independent predictor of GDM.

Conclusions

Current selective screening strategies can potentially be improved by addition of protein predictors.

Heterogeneity of gestational diabetes (GDM) and challenges in developing a GDM risk score

AIMS

Gestational diabetes (GDM) affects a growing number of women and identification of individuals at risk, e.g., with risk prediction models, would be important. However, the performance of GDM risk scores has not been optimal. Here, we assess the impact of GDM heterogeneity on the performance of two top-rated GDM risk scores.

METHODS

This is a substudy of the RADIEL trial-a lifestyle intervention study including women at high GDM risk. We assessed the GDM risk score by Teede and that developed by Van Leeuwen in our high-risk cohort of 510 women. To investigate the heterogeneity of GDM, we further divided the women according to GDM history, BMI, and parity. With the goal of identifying novel predictors of GDM, we further analyzed 319 women with normal glucose tolerance in the first trimester.

RESULTS

Both risk scores underestimated GDM incidence in our high-risk cohort. Among women with a BMI ≥ 30 kg/m² and/or previous GDM, 49.4% developed GDM and 37.4% received the diagnosis already in the first trimester. Van Leeuwen score estimated a 19% probability of GDM and Teede succeeded in risk identification in 61%. The lowest performance of the risk scores was seen among the non-obese women. Fasting plasma glucose, HbA_1c, and family history of diabetes were predictors of GDM in the total study population. Analysis of subgroups did not provide any further information.

CONCLUSIONS

Our findings suggest that the marked heterogeneity of GDM challenges the development of risk scores for detection of GDM.

HbA1c Measured in the First Trimester of Pregnancy and the Association with Gestational Diabetes

We aimed to examine the prospective association between first trimester HbA1c and gestational diabetes (GDM) and explore the utility of HbA1c for prediction of GDM. We used data from a case-control study within the prospective NICHD Fetal Growth Studies-Singleton Cohort (2009-2013), which enrolled 2,802 women at 12 U.S. clinical centers. HbA1c was measured in GDM cases (n = 107) and matched controls (n = 214) targeted at 8-13, 16-22, 24-29, and 34-37 gestational weeks. We excluded women with HbA1c ≥ 6.5% (48 mmol/mol) at enrollment (n = 3) or who had a hemoglobin variant (n = 6). At 8-13 gestational weeks, women who later developed GDM had significantly higher HbA1c (5.3[standard deviation 0.3]%; 34[4]mmol/mol) than women without GDM (5.1[0.3]%; 32[3] mmol/mol) (P ≤ 0.001); this difference remained significant throughout pregnancy. Each 0.1% (1 mmol/mol) HbA1c increase at 8-13 weeks was associated with an adjusted 22% increased GDM risk (95% confidence interval 1.09-1.36). First trimester HbA1c significantly improved GDM prediction over conventional risk factors (AUC 0.59 vs 0.65; P = 0.04). In conclusion, women who develop GDM may have impaired glucose homeostasis early in or prior to pregnancy, as indicated by their elevated first trimester HbA1c. First trimester HbA1c may aid in early identification of at risk women.

A Novel Early Pregnancy Risk Prediction Model for Gestational Diabetes Mellitus

INTRODUCTION

Accurate early risk prediction for gestational diabetes mellitus (GDM) would target intervention and prevention in women at the highest risk. We evaluated novel biomarker predictors to develop a first-trimester risk prediction model in a large multiethnic cohort.

METHODS

Maternal clinical, aneuploidy and pre-eclampsia screening markers (PAPP-A, free hCGβ, mean arterial pressure, uterine artery pulsatility index) were measured prospectively at 11-13+6 weeks' gestation in 980 women (248 with GDM; 732 controls). Nonfasting glucose, lipids, adiponectin, leptin, lipocalin-2, and plasminogen activator inhibitor-2 were measured on banked serum. The relationship between marker multiples-of-the-median and GDM was examined with multivariate regression. Model predictive performance for early (< 24 weeks' gestation) and overall GDM diagnosis was evaluated by receiver operating characteristic curves.

RESULTS

Glucose, triglycerides, leptin, and lipocalin-2 were higher, while adiponectin was lower, in GDM (p < 0.05). Lipocalin-2 performed best in Caucasians, and triglycerides in South Asians with GDM. Family history of diabetes, previous GDM, South/East Asian ethnicity, parity, BMI, PAPP-A, triglycerides, and lipocalin-2 were significant independent GDM predictors (all p < 0.01), achieving an area under the curve of 0.91 (95% confidence interval [CI] 0.89-0.94) overall, and 0.93 (95% CI 0.89-0.96) for early GDM, in a combined multivariate prediction model.

CONCLUSIONS

A first-trimester risk prediction model, which incorporates novel maternal lipid markers, accurately identifies women at high risk of GDM, including early GDM.

Is Afamin a novel biomarker for gestational diabetes mellitus? A pilot study

BACKGROUND

In search of potential early biomarkers for timely prediction of gestational diabetes mellitus (GDM), we focused on afamin, a vitamin E-binding protein in human plasma.. Afamin plays a role in anti-apoptotic cellular processes related to oxidative stress and is associated with insulin resistance and other features of metabolic syndrome. During uncomplicated pregnancy its serum concentrations increase linearly. The aim of this study was to investigate the suitability of afamin as early marker for predicting GDM.

METHODS

In a first-trimester cohort from a prospective observational study of adverse pregnancy outcomes we secondarily analyzed afamin concentrations in 59 patients diagnosed with GDM and 51 controls. Additionally, afamin concentrations were cross-sectionally examined in a mid-trimester cohort of 105 women and compared with results from a simultaneously performed oral glucose tolerance test (OGTT). Subgroup analysis comparing patients treated with either insulin (iGDM) or dietary intervention (dGDM) was performed in both cohorts. Patients were recruited at the University Hospital Essen, Germany, between 2003 and 2016.

RESULTS

Results were adjusted for body-mass-index (BMI) and gestational age. First and mid-trimester cohorts yielded significantly elevated afamin concentrations in patients with pathological OGTT compared to patients without GDM (first trimester cohort: mean, 113.4 mg/l; 95% CI, 106.4-120.5 mg/l and 87.2 mg/l; 95% CI, 79.7-94.7 mg/l; mid-trimester cohort: mean, 182.9 mg/l; 95% CI, 169.6-196.2 mg/l and 157.3 mg/l; 95% CI, 149.1-165.4 mg/l, respectively). In the first-trimester cohort, patients developing iGDM later in pregnancy presented with significantly higher afamin concentrations compared to patients developing dGDM and compared to patients without GDM. In the mid-trimester cohort, mean concentrations of afamin differed significantly between patients with dGDM compared to controls and between patients with iGDM and controls. Patients with iGDM showed only slightly higher afamin levels compared to patients with dGDM.

CONCLUSION

Afamin may serve as a new early biomarker for pathological glucose metabolism during pregnancy. Further research is needed to determine afamin's concentrations during pregnancy, its predictive value for early detection of pregnancies at high risk to develop GDM and its diagnostic role during the second trimester.

First trimester prediction of gestational diabetes mellitus: A clinical model based on maternal demographic parameters

AIM

Develop a first trimester risk prediction model for GDM based on maternal clinical characteristics in a large metropolitan multi-ethnic population and compare its performance to that of other recently published GDM prediction models and clinical risk scoring systems.

METHODS

A retrospective case control study of 248 women who developed GDM and 732 controls who did not. Maternal clinical parameters were prospectively obtained at 11-13⁺⁶ weeks' gestation. A predictive multivariate regression model for GDM was developed, evaluated using areas under the receiver-operating characteristic (AUC) curve. The performance of this model was then compared with other published GDM prediction models applied to our cohort and our existing clinical risk scoring system.

RESULTS

Previous GDM, family history of diabetes, age, south/east Asian ethnicity, parity and body mass index (BMI) were significant predictors for GDM. The AUC of our multivariate regression model was 0.88 (95% Confidence Interval 0.85-0.92). This performed better than other predictive models applied to our cohort (AUCs 0.77-0.82).

CONCLUSION

A multivariate model based on weighted maternal clinical risk factors accurately predicts GDM in early pregnancy and performs better than other proposed multivariate and clinical risk scoring models in a multiethnic cohort.

Early Pregnancy Biochemical Predictors of Gestational Diabetes Mellitus

PURPOSE OF REVIEW

Universal oral glucose tolerance-based screening is employed to identify pregnant women with gestational diabetes mellitus (GDM), as treatment of this condition decreases the risk of associated complications. A simple and accurate blood test which identifies women at low or high risk for GDM in the first trimester would have the potential to decrease costs and improve outcomes through prevention or treatment. This review summarizes published data on early pregnancy biomarkers which have been tested as predictors of GDM.

RECENT FINDINGS

A large number of first-trimester biochemical predictors of GDM have been reported, mostly in small case-control studies. These include glycemic markers (fasting glucose, post-load glucose, hemoglobin A1C), inflammatory markers (C-reactive protein, tumor necrosis factor-alpha), insulin resistance markers (fasting insulin, sex hormone-binding globulin), adipocyte-derived markers (adiponectin, leptin), placenta-derived markers (follistatin-like-3, placental growth factor, placental exosomes), and others (e.g., glycosylated fibronectin, soluble (pro)renin receptor, alanine aminotransferase, ferritin). A few large studies suggest that first-trimester fasting glucose or hemoglobin A1C may be useful for identifying women who would benefit from early GDM treatment. To translate the findings from observational studies of first-trimester biomarkers for GDM to clinical practice, trials or cost-effectiveness analyses of screening and treatment strategies based on these novel biomarkers are needed.

Early Antenatal Prediction of Gestational Diabetes in Obese Women: Development of Prediction Tools for Targeted Intervention

All obese women are categorised as being of equally high risk of gestational diabetes (GDM) whereas the majority do not develop the disorder. Lifestyle and pharmacological interventions in unselected obese pregnant women have been unsuccessful in preventing GDM. Our aim was to develop a prediction tool for early identification of obese women at high risk of GDM to facilitate targeted interventions in those most likely to benefit. Clinical and anthropometric data and non-fasting blood samples were obtained at 15+0-18+6 weeks' gestation in 1303 obese pregnant women from UPBEAT, a randomised controlled trial of a behavioural intervention. Twenty one candidate biomarkers associated with insulin resistance, and a targeted nuclear magnetic resonance (NMR) metabolome were measured. Prediction models were constructed using stepwise logistic regression. Twenty six percent of women (n = 337) developed GDM (International Association of Diabetes and Pregnancy Study Groups criteria). A model based on clinical and anthropometric variables (age, previous GDM, family history of type 2 diabetes, systolic blood pressure, sum of skinfold thicknesses, waist:height and neck:thigh ratios) provided an area under the curve of 0.71 (95%CI 0.68-0.74). This increased to 0.77 (95%CI 0.73-0.80) with addition of candidate biomarkers (random glucose, haemoglobin A1c (HbA1c), fructosamine, adiponectin, sex hormone binding globulin, triglycerides), but was not improved by addition of NMR metabolites (0.77; 95%CI 0.74-0.81). Clinically translatable models for GDM prediction including readily measurable variables e.g. mid-arm circumference, age, systolic blood pressure, HbA1c and adiponectin are described. Using a ≥35% risk threshold, all models identified a group of high risk obese women of whom approximately 50% (positive predictive value) later developed GDM, with a negative predictive value of 80%. Tools for early pregnancy identification of obese women at risk of GDM are described which could enable targeted interventions for GDM prevention in women who will benefit the most.

First Trimester Maternal Glycated Hemoglobin and Sex Hormone-Binding Globulin Do Not Predict Third Trimester Glucose Intolerance of Pregnancy

Early pregnancy prediction of third trimester glucose intolerance may identify a population of women whose trajectory toward gestational diabetes mellitus (GDM) is modifiable. We assessed whether first trimester glycated hemoglobin (HbA1c) and sex hormone-binding globulin (SHBG), markers of insulin resistance, predicted third trimester glucose intolerance. Nondiabetic women with singleton pregnancies enrolled in a prospective observational study, 11 0/7 to 14 6/7 weeks. At enrollment, maternal characteristics, medical history, and blood samples were collected for HbA1c and SHBG. Two-step GDM screening was performed, 22 0/7 to 33 6/7 weeks. A 50 g oral glucose tolerance test ≥130 mg/dL defined screen positive, or glucose intolerance. Carpenter-Coustan criteria diagnosed GDM. Means HbA1c and SHBG were compared between glucose-intolerant versus normoglycemic women, and GDM versus no GDM women. We report unadjusted and adjusted odds ratios (OR; 95% confidence interval [CI]) of regression analyses. Adjusted models include race, enrollment body mass index, and history of GDM. Among 250 women, 29% were glucose intolerant and 6% had GDM. Among glucose-intolerant women, HbA1c was higher (5.3 ± 0.3 vs. 5.1 ± 0.3, P = .01) and associated with glucose intolerance in unadjusted, but not adjusted, models (OR: 2.9, 95% CI: 1.2-7.1; adjusted odds ratio [aOR]: 2.0, 95% CI: 0.7-5.4). Among GDM women, HbA1c was higher (5.4 ± 0.4 vs 5.2 ± 0.3, P = .002) and SHBG was lower (228 ± 72 vs 288 ± 93 mmol/L, P = .02). The HbA1c predicted GDM in unadjusted (OR: 13.2, 95% CI: 2.6-68.0) but not adjusted (aOR: 6.7, 95% CI: 0.8-55.2) models. Although metabolic alterations may well precede third trimester glucose intolerance, neither HbA1c of SHBG remained an independent predictor of glucose intolerance or GDM in adjusted models.