Gestational diabetes mellitus: including serum pregnancy-associated plasma protein-A testing in the clinical management of primiparous women? A case-control study

Enhancing gestational diabetes mellitus risk assessment and treatment through GDMPredictor: a machine learning approach

BACKGROUND

Gestational diabetes mellitus (GDM) is a serious health concern that affects pregnant women worldwide and can lead to adverse pregnancy outcomes. Early detection of high-risk individuals and the implementation of appropriate treatment can enhance these outcomes.

METHODS

We conducted a study on a cohort of 3467 pregnant women during their pregnancy, with a total of 5649 clinical and biochemical records collected. We utilized this dataset as our training dataset to develop a web server called GDMPredictor. The GDMPredictor utilizes advanced machine learning techniques to predict the risk of GDM in pregnant women. We also personalize treatment recommendations based on essential biochemical indicators, such as A1MG, BMG, CysC, CO2, TBA, FPG, and CREA. Our assessment of GDMPredictor's effectiveness involved training it on the dataset of 3467 pregnant women and measuring its ability to predict GDM risk using an AUC and auPRC.

RESULTS

GDMPredictor demonstrated an impressive level of precision by achieving an AUC score of 0.967. To tailor our treatment recommendations, we use the GDM risk level to identify higher risk candidates who require more intensive care. The GDMPredictor can accept biochemical indicators for predicting the risk of GDM at any period from 1 to 24 weeks, providing healthcare professionals with an intuitive interface to identify high-risk patients and give optimal treatment recommendations.

CONCLUSIONS

The GDMPredictor presents a valuable asset for clinical practice, with the potential to change the management of GDM in pregnant women. Its high accuracy and efficiency make it a reliable tool for doctors to improve patient outcomes. Early identification of high-risk individuals and tailored treatment can improve maternal and fetal health outcomes http://www.bioinfogenetics.info/GDM/ .

The association between human chorionic gonadotropin and adverse pregnancy outcomes: a systematic review and meta-analysis

OBJECTIVE

This study aimed to evaluate the association between human chorionic gonadotropin and adverse pregnancy outcomes.

DATA SOURCES

Medline, Embase, PubMed, and Cochrane were searched in November 2021 using Medical Subject Headings (MeSH) and relevant key words.

STUDY ELIGIBILITY CRITERIA

This analysis included published full-text studies of pregnant women with serum human chorionic gonadotropin testing between 8 and 28 weeks of gestation, investigating fetal outcomes (fetal death in utero, small for gestational age, preterm birth) or maternal factors (hypertension in pregnancy: preeclampsia, pregnancy-induced hypertension, placental abruption, HELLP syndrome, gestational diabetes mellitus).

METHODS

Studies were extracted using REDCap software. The Newcastle-Ottawa scale was used to assess for risk of bias. Final meta-analyses underwent further quality assessment using the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) method.

RESULTS

A total of 185 studies were included in the final review, including the outcomes of fetal death in utero (45), small for gestational age (79), preterm delivery (62), hypertension in pregnancy (107), gestational diabetes mellitus (29), placental abruption (17), and HELLP syndrome (2). Data were analyzed separately on the basis of categorical measurement of human chorionic gonadotropin and human chorionic gonadotropin measured on a continuous scale. Eligible studies underwent meta-analysis to generate a pooled odds ratio (categorical human chorionic gonadotropin level) or difference in medians (human chorionic gonadotropin continuous scale) between outcome groups. First-trimester low human chorionic gonadotropin levels were associated with preeclampsia and fetal death in utero, whereas high human chorionic gonadotropin levels were associated with preeclampsia. Second-trimester high human chorionic gonadotropin levels were associated with fetal death in utero and preeclampsia.

CONCLUSION

Human chorionic gonadotropin levels are associated with placenta-mediated adverse pregnancy outcomes. Both high and low human chorionic gonadotropin levels in the first trimester of pregnancy can be early warning signs of adverse outcomes. Further analysis of human chorionic gonadotropin subtypes and pregnancy outcomes is required to determine the diagnostic utility of these findings in reference to specific cutoff values.

Evaluating the Adipose Tissue Depth as a Predictor Factor for Gestational Diabetes in Later Pregnancy-A Systematic Review

The increasing prevalence of gestational diabetes mellitus (GDM) requires non-invasive and precise techniques for evaluating the predisposing risk factors such as visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT). According to PRISMA, we developed a systematic review and searched after "visceral adipose tissue AND gestational diabetes" and identified 221 articles on the MEDLINE and Word of Science databases. After assessing them for inclusion criteria and two researchers screened them, 11 relevant articles were included. Although evidence is conflicting, more studies favor using US-determined VAT in GDM prediction. VAT may be more valuable than body mass index or SAT in predicting GDM. VAT can represent an additive factor to the prediction tool of the risk of developing GDM when used in conjunction with other anthropometric or biological parameters or maternal risk factors. US measurements are heterogeneous given different evaluation techniques, cut-off values and inter-operator variation. A significant limitation is the lack of a gold standard to identify GDM confidently. Pregnant women may benefit from early monitoring and preventive care if classified as high risk for GDM early in the gestational period. US-measured VAT during the first trimester of pregnancy seems a valuable and inexpensive screening approach to predict GDM development later in pregnancy, either by itself or if used in conjunction with other clinical and biological parameters.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Placental Growth Factor and Pregnancy-Associated Plasma Protein-A as Potential Early Predictors of Gestational Diabetes Mellitus

Gestational diabetes mellitus (GDM) is one of the most common pregnancy complications and one of the main causes of adverse pregnancy outcomes. An early diagnosis of GDM is of fundamental importance in clinical practice. However, the major professional organizations recommend universal screening for GDM, using a 75 g oral glucose tolerance test at 24-28 weeks of gestation. A selective screening at an early stage of pregnancy is recommended only if there are maternal risk factors for diabetes. As a result, the GDM diagnosis is often delayed and established after the appearance of complications. The manifestation of GDM is directly related to insulin resistance, which is closely associated with endothelial dysfunction. The placenta, the placental peptides and hormones play a pivotal role in the manifestation and progression of insulin resistance during pregnancy. Recently, the placental growth factor (PlGF) and plasma-associated protein-A (PAPP-A), have been shown to significantly affect both insulin sensitivity and endothelial function. The principal function of PAPP-A appears to be the cleavage of circulating insulin-like growth factor binding protein-4 while PlGF has been shown to play a central role in the development and maturation of the placental vascular system and circulation. On one hand, these factors are widely used as early predictors (11-13 weeks of gestation) of complications during pregnancy, such as preeclampsia and fetal aneuploidies, in most countries. On the other hand, there is increasing evidence for their predictive role in the development of carbohydrate disorders, but some studies are rather controversial. Therefore, this review aims to summarize the available literature about the potential of serum levels of PlGF and PAPP-A as early predictors in the diagnosis of GDM.

Study on the Relationship and Predictive Value of First-Trimester Pregnancy-Associated Plasma Protein-A, Maternal Factors, and Biochemical Parameters in Gestational Diabetes Mellitus: A Large Case-Control Study in Southern China Mothers

OBJECTIVE

To investigate the relationship and predictive value of first-trimester pregnancy-associated plasma protein A (PAPP-A), maternal factors, and biochemical parameters with gestational diabetes mellitus (GDM) in southern China mothers.

METHODS

This study recruited 4872 pregnant women. PAPP-A, the free beta subunit of human chorionic gonadotropin (free β-HCG), fasting plasma glucose (FPG), total cholesterol (TC), triglycerides (TG), and high- and low-density lipoproteins (HDL, LDL) were measured at 11-13⁺ weeks of gestation. GDM was diagnosed based on a 75 g oral glucose tolerance test at 24-28 weeks of gestation. We performed stepwise logistic regression analysis to determine the odds ratio (OR) and the 95% confidence interval (CI) of GDM. We used Receiver Operating Characteristic (ROC) curves with the area under the curve (AUC) to evaluate the predictive value of PAPP-A, maternal factors, and biochemical markers. The significance of the differences between the AUC values was assessed using the DeLong test.

RESULTS

GDM was diagnosed in 750 (15.39%) women. Independent factors for GDM were age, pre-gestational BMI, GWG before a diagnosis of GDM, previous history of GDM, family history of diabetes, FPG, TG, LDL, PAPP-A, and TC. The AUC of PAPP-A was 0.56 (95% CI 0.53-0.58). The AUC of a model based on combined maternal factors, biochemical markers, and PAPP-A was 0.70 (95% CI 0.68-0.72). Differences in AUC values between PAPP-A alone and the model based on combined maternal factors, biochemical markers, and PAPP-A were statistically significant (Z= 9.983, P<0.001).

CONCLUSION

A Low serum PAPP-A level in the first trimester is an independent risk factor for developing GDM later in pregnancy. However, it is not a good independent predictor although the predictive value of a low serum PAPP-A level increases when combined with maternal factors and biochemical markers.

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.

First trimester screening for gestational diabetes mellitus with maternal factors and biomarkers

Introduction: This study aimed to identify risk factors among maternal characteristics, obstetric history and first trimester preeclampsia-specific biomarkers that were associated with subsequent development of gestational diabetes mellitus (GDM) and evaluate the performance of the prediction models. Methods: Secondary analysis of a prospective cohort study. The performance of the prediction models was assessed by area under receiver operating characteristic curve (AUROC). Results: A total of 837 (8.9%) cases of GDM and 8535 (91.1%) unaffected cases were included. The AUROC of the prediction model combining maternal characteristics and obstetric history (0.735) was better than that of the model utilizing maternal characteristics (AUROC 0.708) and preeclampsia-specific biomarkers (AUROC 0.566). Amongst the preeclampsia-specific biomarkers, the mean arterial pressure (MAP) contributed to the increasing risk of GDM, however, its addition did not improve the AUROC of the model combining maternal characteristics and obstetric history (0.738). Conclusion: The first trimester prediction model for GDM with maternal characteristics and obstetric history achieves moderate predictability. The inclusion of MAP in the model combining maternal characteristics and obstetric history does not improve the screening performance for GDM. Future studies are needed to explore the effect of blood pressure control from early pregnancy on preventing GDM.

Relationship between extreme values of first trimester maternal pregnancy associated plasma Protein-A, free-β-human chorionic gonadotropin, nuchal translucency and adverse pregnancy outcomes

OBJECTIVE

The aim of our study was to investigate the relationship between extreme values of first trimester screening markers and adverse obstetric outcomes.

MATERIALS AND METHODS

Our study was conducted by examining the prenatal and postnatal perinatal records of 786 singleton gestations between the ages of 18-40, who applied to Prof. Dr. Cemil Taşçıoğlu City Hospital outpatient clinics for first-trimester screening for aneuploidy, between January 1, 2017 and December 31, 2019.

RESULTS

The presence of small for gestational age (SGA) was found to be statistically significant for the <5 percentile (<0.37) pregnancy-associated plasma protein A (PAPP-A) group (p = 0.016). For <5 percentile β-hCG group, the presence of gestational diabetes mellitus (GDM), premature rupture of membrane (PROM) and preterm premature rupture of membrane (PPROM) was determined as a statistically significant risk (p = 0.015, p = 0.005, p = 0.02 respectively) In the univariate test, fetal death rate was found to be high for ≥90 percentile at nuchal translucency (NT), but the presence of fetal death was found to be statistically insignificant in logistic regression analysis. (p: 0.057).

CONCLUSION

First trimester screening test can be used in predicting pregnancy complications. In this study we found that serum levels of PAPP-A are associated with developing SGA, while GDM, PROM and PPROM are more common in low serum free β-hCG.

Emerging Protein Biomarkers for the Diagnosis or Prediction of Gestational Diabetes-A Scoping Review

Introduction: Gestational diabetes (GDM), defined as hyperglycemia with onset or initial recognition during pregnancy, has a rising prevalence paralleling the rise in type 2 diabetes (T2DM) and obesity. GDM is associated with short-term and long-term consequences for both mother and child. Therefore, it is crucial we efficiently identify all cases and initiate early treatment, reducing fetal exposure to hyperglycemia and reducing GDM-related adverse pregnancy outcomes. For this reason, GDM screening is recommended as part of routine pregnancy care. The current screening method, the oral glucose tolerance test (OGTT), is a lengthy, cumbersome and inconvenient test with poor reproducibility. Newer biomarkers that do not necessitate a fasting sample are needed for the prompt diagnosis of GDM. The aim of this scoping review is to highlight and describe emerging protein biomarkers that fulfill these requirements for the diagnosis of GDM. Materials and Methods: This scoping review was conducted according to preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines for scoping reviews using Cochrane Central Register of Controlled Trials (CENTRAL), the Cumulative Index to Nursing & Allied Health Literature (CINAHL), PubMed, Embase and Web of Science with a double screening and extraction process. The search included all articles published in the literature to July 2020. Results: Of the 3519 original database citations identified, 385 were eligible for full-text review. Of these, 332 (86.2%) were included in the scoping review providing a total of 589 biomarkers studied in relation to GDM diagnosis. Given the high number of biomarkers identified, three post hoc criteria were introduced to reduce the items set for discussion: we chose only protein biomarkers with at least five citations in the articles identified by our search and published in the years 2017-2020. When applied, these criteria identified a total of 15 biomarkers, which went forward for review and discussion. Conclusions: This review details protein biomarkers that have been studied to find a suitable test for GDM diagnosis with the potential to replace the OGTT used in current GDM screening protocols. Ongoing research efforts will continue to identify more accurate and practical biomarkers to take GDM screening and diagnosis into the 21st century.

Gestational diabetes - modern management and therapeutic approach (Review)

Gestational diabetes mellitus is an important healthcare problem with serious implications both to the mother and to the foetus. The necessity of clear screening criteria for the pregnant woman and also identifying from an early stage the risk groups can be beneficial instruments for better management of gestational diabetes. The present report identify the main screening criteria for patients at risk for gestational diabetes and the therapeutic-nutritional therapy for women that have gestational diabetes. The different diagnostic criteria, as well as the new instruments through which these criteria can be applied, are still heterogeneous, and it is necessary to unify and promote them. The prevalence of gestational diabetes has significantly increased in recent years, and this has led to an increase in the direct and indirect costs of healthcare. Establishing the optimal time and initiating the correct treatment is critical to achieving glycemic control and to minimize the impact on fetal development and perinatal complications.

PAPPA-mediated adipose tissue remodeling mitigates insulin resistance and protects against gestational diabetes in mice and humans

Pregnancy is a physiological state of continuous adaptation to changing maternal and fetal nutritional needs, including a reduction of maternal insulin sensitivity allowing for appropriately enhanced glucose availability to the fetus. However, excessive insulin resistance in conjunction with insufficient insulin secretion results in gestational diabetes mellitus (GDM), greatly increasing the risk for pregnancy complications and predisposing both mothers and offspring to future metabolic disease. Here, we report a signaling pathway connecting pregnancy-associated plasma protein A (PAPPA) with adipose tissue expansion in pregnancy. Adipose tissue plays a central role in the regulation of insulin sensitivity, and we show that, in both mice and humans, pregnancy caused remodeling of adipose tissue evidenced by altered adipocyte size, vascularization, and in vitro expansion capacity. PAPPA is known to be a metalloprotease secreted by human placenta that modulates insulin-like growth factor (IGF) bioavailability through prolteolysis of IGF binding proteins (IGFBPs) 2, 4, and 5. We demonstrate that recombinant PAPPA can stimulate ex vivo human adipose tissue expansion in an IGFBP-5- and IGF-1-dependent manner. Moreover, mice lacking PAPPA displayed impaired adipose tissue remodeling, pregnancy-induced insulin resistance, and hepatic steatosis, recapitulating multiple aspects of human GDM. In a cohort of 6361 pregnant women, concentrations of circulating PAPPA are inversely correlated with glycemia and odds of developing GDM. These data identify PAPPA and the IGF signaling pathway as necessary for the regulation of maternal adipose tissue physiology and systemic glucose homeostasis, with consequences for long-term metabolic risk and potential for therapeutic use.

First trimester biomarkers for prediction of gestational diabetes mellitus

PURPOSE

To develop a first trimester prediction model for gestational diabetes mellitus (GDM) using obesity, placental, and inflammatory biomarkers.

METHODS

We used a first trimester dataset of the ASPRE study to evaluate clinical and biochemical biomarkers. All biomarkers levels (except insulin) were transformed to gestational week-specific medians (MoMs), adjusted for maternal body mass index (BMI), maternal age, and parity. The MoM values of each biomarker in the GDM and normal groups were compared and used for the development of a prediction model assessed by area under the curve (AUC).

RESULTS

The study included 185 normal and 20 GDM cases. In the GDM group, compared to the normal group BMI and insulin (P = 0.003) were higher (both P < 0.003). The MoM values of uterine artery pulsatility index (UtA-PI) and soluble (s)CD163 were higher (both P < 0.01) while pregnancy associated plasma protein A (PAPP-A), placental protein 13 (PP13), and tumor-necrosis factor alpha (TNFα) were lower (all P < 0.005). There was no significant difference between the groups in placental growth factor, interleukin 6, leptin, peptide YY, or soluble mannose receptor (sMR/CD206). In screening for GDM in obese women the combination of high BMI, insulin, sCD163, and TNFα yielded an AUC of 0.95, with detection rate of 89% at 10% false positive rate (FPR). In non-obese women, the combination of sCD163, TNFα, PP13 and PAPP-A yielded an AUC of 0.94 with detection rate of 83% at 10% FPR.

CONCLUSION

A new model for first trimester prediction of the risk to develop GDM was developed that warrants further validation.

Early pregnancy prediction of gestational diabetes mellitus risk using prenatal screening biomarkers in nulliparous women

AIMS

To evaluate the clinical utility of first and second trimester prenatal screening biomarkers for early pregnancy prediction of gestational diabetes mellitus (GDM) risk in nulliparous women.

METHODS

We conducted a population-based cohort study of nulliparous women participating in the California Prenatal Screening Program from 2009 to 2011 (n = 105,379). GDM was ascertained from hospital discharge records or birth certificates. Models including maternal characteristics and prenatal screening biomarkers were developed and validated. Risk stratification and reclassification were performed to assess clinical utility of the biomarkers.

RESULTS

Decreased levels of first trimester pregnancy-associated plasma protein A (PAPP-A) and increased levels of second trimester unconjugated estriol (uE₃) and dimeric inhibin A (INH) were associated with GDM. The addition of PAPP-A only and PAPP-A, uE₃, and INH to maternal characteristics resulted in small, yet significant, increases in area under the receiver operating characteristic curve (AUC) (maternal characteristics only: AUC 0.714 (95% CI 0.703-0.724), maternal characteristics + PAPP-A: AUC 0.718 (95% CI 0.707-0.728), maternal characteristics + PAPP-A, uE₃, and INH: AUC 0.722 (0.712-0.733)); however, no net improvement in classification was observed.

CONCLUSIONS

PAPP-A, uE₃, and INH have limited clinical utility for prediction of GDM risk in nulliparous women. Utility of other readily accessible clinical biomarkers in predicting GDM risk warrants further investigation.

Are the first-trimester levels of PAPP-A and fb-hCG predictors for obstetrical complications in diabetic pregnancy?

Objective: To assess the levels of pregnancy-associated plasma protein-A (PAPP-A) and β-human chorionic gonadotropin (fb-hCG) in cases of diabetic pregnancy, to determine whether these biomarkers can be considered significant predictors for macrosomia, preeclampsia (PE), intrauterine growth restriction (IUGR), and preterm birth in mothers with different types of pregestational diabetes mellitus (DM).Methods: It was a retrospective cohort study. Study groups were presented: type 1 DM (n = 100), type 2 DM (n = 50), and controls (n = 25). At 11 + 0 to 13 + 6 week's gestation, we recorded maternal characteristics and medical history, and performed a combined test for the detection of risk of chromosomal abnormalities. To assess the performance of the markers in the prediction of the main obstetrical complications (PE, IUGR, preterm birth, and macrosomia), receiver-operating characteristic (ROC) curves were produced and area under the curves was calculated.Results: The study has shown that DM is associated with a high rate of perinatal complications: PE, IUGR, macrosomia, and preterm birth. The median level of PAPP-A was significantly lower in case of type 1 DM- 0.89 (inter quartile range (IQR), 0.51-1.1), and type 2 DM-0.88 (IQR, 0.42-1.15) compared to the unaffected group 1.03 (IQR, 0.96-1.12; p = .025). There were no significant differences in the fb-hCG multiples of the normal median (MoM; p = .14) between the diabetic and unaffected groups. More significant results were obtained when calculated by percentile: in diabetic pregnancies, PAPP-A and fb-hCG MoMs values were lower in the 5-10% ranges and higher in the 95% range, compared to the control group. ROC-analysis did not show any significant data that first-trimester PAPP-A and fb-hCG serum levels are predictors for PE, IUGR, macrosomia, and preterm birth.Conclusion: The routine first-trimester serum screening of fetal Down syndrome cannot be used as a tool of risk identification for PE, IUGR, macrosomia, and preterm birth in case of diabetic pregnancy.

Study on the correlation and predictive value of serum pregnancy-associated plasma protein A, triglyceride and serum 25-hydroxyvitamin D levels with gestational diabetes mellitus

BACKGROUND

Gestational diabetes mellitus (GDM) is a concern due to its rapid increase in incidence in recent years.

AIM

To investigate the correlation and predictive value of serum pregnancy-associated plasma protein A (PAPP-A), triglyceride (TG), and 25-hydroxyvitamin D [25-(OH)D] with GDM in early pregnancy.

METHODS

A total of 99 patients in early pregnancy admitted to Peking University International Hospital from November 2015 to September 2017 were included, and underwent a fasting glucose test and oral glucose tolerance test screening at 24-28 wk of pregnancy. Of these cases with GDM, 51 were assigned to group A and the remaining 48 cases without GDM were enrolled in group B. Serum PAPP-A, TG and 25-(OH)D in the two groups were compared and their correlation with blood sugar was analyzed. In addition, their diagnostic value in GDM was determined using receiver operating characteristic (ROC) curve analysis.

RESULTS

Group A had markedly lower serum PAPP-A and 25-(OH)D levels and a significantly higher serum TG level than group B, with statistical significance (P < 0.05). Furthermore, Pearson analysis identified that PAPP-A and 25-(OH)D levels were negatively correlated with fasting blood glucose (FBG) levels (r = -0.605, P < 0.001), (r = -0.597, P < 0.001), while TG and FBG levels were positively correlated (r = 0.628, P < 0.001). The sensitivity, specificity, area under the curve (AUC) and optimal cut-off value of serum PAPP-A level in the diagnosis of GDM were 72.55%, 82.35%, 0.861 and 16.340, respectively, while the sensitivity of TG in diagnosing GDM was 86.27%, the specificity was 66.67%, the AUC was 0.813, with an optimal cut-off value of 1.796. The corresponding sensitivity, specificity, AUC and optimal cut-off value of serum 25-(OH)D were 64.71%, 70.59%, 0.721 and 23.140, respectively. Moreover, multivariate logistic regression analysis revealed that FBG, vascular endothelial growth factor, Flt-1, serum PAPP-A, TG, and 25-(OH)D were related risk factors leading to GDM in patients.

CONCLUSION

Serum PAPP-A, TG, and 25-(OH)D levels are all correlated with blood glucose changes in GDM, and are independent factors affecting the occurrence of GDM and have certain value in the diagnosis of GDM.

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.

Visceral Adipose Tissue Depth in Early Pregnancy and Gestational Diabetes Mellitus - a Cohort Study

Some studies have suggested that abdominal visceral adipose tissue depth (VAD) measured by ultrasound in early pregnancy, may predict the future onset of gestational diabetes mellitus (GDM). Wheter this is true, independent of pre-pregnancy body mass index (BMI), has been debated, leading the current study. A prospective cohort study was completed, in which VAD was measured at around 14 weeks’ gestation. GDM was later assessed by an oral glucose tolerance test at 24 to 28 weeks, according to the IADPSG criteria. Logistic regression analysis and receiver operating curve (ROC) analysis were used to estimate the predictive value of VAD, above and beyond pre-pregnancy BMI. 627 pregnant women were enrolled, and 518 completed the study. VAD was measured at a mean of 14.4 weeks’ gestation. 87 women (16.8%) subsequently developed GDM. The unadjusted odds ratio (OR) for developing GDM was 1.99 (95% CI 1.59–2.46) per 1-cm increase in VAD. After adjusting for maternal BMI and age, the OR was 2.00 (95% CI 1.61 to 2.50). The ROC under the curve for developing GDM was higher for VAD (0.70, 95% CI 0.63 to 0.75) than for pre-pregnancy BMI (0.57 95% CI 0.50 to 0.64) (p < 0.001). In conclusion, higher VAD may better predict GDM than pre-pregnancy BMI.

Biosensors for Detection of Human Placental Pathologies: A Review of Emerging Technologies and Current Trends

Substantial growth in the biosensor research has enabled novel, sensitive and point-of-care diagnosis of human diseases in the last decade. This paper presents an overview of the research in the field of biosensors that can potentially predict and diagnosis of common placental pathologies. A survey of biomarkers in maternal circulation and their characterization methods is presented, including markers of oxidative stress, angiogenic factors, placental debris, and inflammatory biomarkers that are associated with various pathophysiological processes in the context of pregnancy complications. Novel biosensors enabled by microfluidics technology and nanomaterials is then reviewed. Representative designs of plasmonic and electrochemical biosensors for highly sensitive and multiplexed detection of biomarkers, as well as on-chip sample preparation and sensing for automatic biomarker detection are illustrated. New trends in organ-on-a-chip based placental disease models are highlighted to illustrate the capability of these in vitro disease models in better understanding the complex pathophysiological processes, including mass transfer across the placental barrier, oxidative stress, inflammation, and malaria infection. Biosensor technologies that can be potentially embedded in the placental models for real time, label-free monitoring of these processes and events are suggested. Merger of cell culture in microfluidics and biosensing can provide significant potential for new developments in advanced placental models, and tools for diagnosis, drug screening and efficacy testing.

PAPP-A concentrations change in patients with gestational diabetes

Our aim was to assess the relationship between gestational diabetes and glucose intolerance regarding maternal serum PAPP-A and free β-hCG concentrations in first trimester pregnancies. This study was conducted on 278 women between 18-45 years old with singleton pregnancies. The subjects were divided into four groups, according to their 50 and 100 g OGTT results. Group 1 was the Control Group, Group 2 with positive 50 g OGTT results, but negative 100 g, Group 3 had gestational diabetes after testing with 50 g OGTT (≥180 mg/dl) or with 100 g OGTT. Finally Group 4 was made of women with a one single high glucose level after testing with 100 g OGTT. These groups were analysed in terms of OGTT results. In the GDM group, serum PAPP-A concentrations were significantly lower when compared with the Control Group's (p = 0.015). There was either no significant differences regarding free β-hCG concentrations among the groups. GDM rate is 21.1%, the patients with GDM had significantly low concentrations of serum PAPP-A but their f β-hCG concentrations did not change. Our results are supported by several studies. However, we need greater numbered studies for exact results.IMPACT STATEMENTWhat is already known on this subject? Pregnancy associated plasma protein A (PAPP-A) is produced by the placenta in pregnancy. PAPP-A cleaves insulin-like growth factor (IGF) binding proteins. It would appear to have a role in regulating IGF bioavailability in pregnancy. This is important as the IGF axis plays a critical role in fetal growth, and placental growth and function during pregnancy. Some studies have reported that PAPP-A levels were impaired among women who subsequently developed GDM.What do the results of this study add? The patients with GDM had significantly low concentrations of serum PAPP-A but their free β-hCG levels did not change.What are the implications of these findings for clinical practice and/or further research? By looking at PAPP-A concentrations, we can predict patients that will be gestational diabetic and take precautions to protect the babies health, such as their diet or exercise.

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

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

Relationship between elevated serum level of placental growth factor and status of gestational diabetes mellitus

Objective: As only limited and confusing evidence about serum placental growth factor (PlGF) level in gestational diabetes mellitus (GDM) exist in the known literature, the aim of this study was to evaluate the association of maternal serum PlGF level with GDM status.Methods: The pregnant women attending the Obstetrics Outpatient Unit of Hitit University Hospital were screened at 24 and 28 weeks of gestation for GDM according to the suggestions of the American College of Obstetricians and Gynecologists (ACOG). Concisely, all of the low-risk pregnant women were evaluated with a 50 g glucose challenge test (GCT). Women with serum glucose ≥140 mg/dL at 1 h after GCT were subjected to a 100 g oral glucose tolerance test (OGTT). According to the criteria of Carpenter and Coustan, the GDM diagnosis was confirmed. Consequently, a total of 158 pregnant women eligible for inclusion criteria were categorized into two main groups; 76 of the GDM group, 82 of the control group. The demographic characteristic and biochemical parameters of the study population including age, body mass index (BMI), gestational age were recorded at the second trimester. The assays for glucose, insulin, and PlGF were carried out.Results: The mean maternal age of control and GDM groups were 27.9 and 30.5 years, respectively. The parameters such as age, BMI, and gestational age were statistically similar in both groups (p>.05, for all). As expected, serum insulin level and homeostasis model assessment-insulin resistance (HOMA-IR) value were significantly elevated in women with GDM (p<.001, for both). Moreover, maternal PlGF concentration was found to be higher in the GDM group compared to the control group (p=.029). Pearson's correlation analysis of PlGF with other study parameters revealed that there was a negative moderate and significant correlation in only control group (r= -0.416, p<.05). However, this correlation was not detected in the GDM group (r = 0.099, p>.05). None of the variables including maternal age, BMI, insulin, and HOMA-IR showed significant correlations in GDM and control groups.Conclusion: Our findings revealed that maternal serum PlGF level is increased in pregnant women complicated with GDM. Early identification of pregnant women who subsequently will pose GDM risk could improve the pregnancy outcomes.

First trimesters Pregnancy-Associated Plasma Protein-A levels value to Predict Gestational diabetes Mellitus: A systematic review and meta-analysis of the literature

Detecting pregnant women at risk of diabetes in first months can help them by early intervention for delaying or preventing onset of GDM. In this study, we aimed to assess the Predictive value of first trimester Pregnancy related plasma protein-A (PAPP-A) levels for detecting Gestational diabetes Mellitus (GDM). This systematic review and meta-analysis was conducted through probing in databases. PubMed, Scopus, Medline and Google scholar citations were searched to find the published papers from 1974 to 2017. Studies were considered eligible if they were cohorts, case-control studies, reported GDM result, not other types, conducted on singleton pregnancy, measured Serum pregnancy associated plasma protein A in the first trimester and evaluated the relation of first trimester pregnancy associated plasma protein-A and GDM. Two reviewers independently assessed the quality with Newcastle-Ottawa and extracted data in the Pre-defined checklist. Analysis of the data was carried out by "Comprehensive Meta-analysis Version 2 (CAM)" and Metadisc software. 17 articles have our inclusion criteria and were considered in our systematic review, 5 studies included in Meta-analysis. Meta-analysis of these articles showed that the predictive value of PAPP-A for GDM has 55% sensitivity (53-58), 90% (89-90) specificity, LR + 2.48 (0.83-7.36) and LR - 0.70 (0.45-1.09) with 95% confidence intervals. In our study PAPP-A has low predictive accuracy overall, but it may be useful when combined with other tests, and this is an active part for future research. One limitation of our study is significant heterogeneity because of different adjusted variables and varied diagnostic criteria.

First trimester prenatal screening biomarkers and gestational diabetes mellitus: A systematic review and meta-analysis

Biomarkers commonly assessed in prenatal screening have been associated with a number of adverse perinatal and birth outcomes. However, it is not clear whether first trimester measurements of prenatal screening biomarkers are associated with subsequent risk of gestational diabetes mellitus (GDM). We aimed to systematically review and statistically summarize studies assessing the relationship between first trimester prenatal screening biomarker levels and GDM development. We comprehensively searched PubMed/MEDLINE, EMBASE, CINAHL, and Scopus (from inception through January 2018) and manually searched the reference lists of all relevant articles. We included original, published, observational studies examining the association of first trimester pregnancy associated plasma protein-A (PAPP-A) and/or free β-human chorionic gonadotropin (free β-hCG) levels with GDM diagnosis. Mean differences were calculated comparing PAPP-A and free β-hCG multiples of median (MoM) levels between women who developed GDM and those who did not and were subsequently pooled using two-sided random-effects models. Our meta-analysis of 13 studies on PAPP-A and nine studies on free β-hCG indicated that first trimester MoM levels for both biomarkers were lower in women who later developed GDM compared to women who remained normoglycemic throughout pregnancy (MD -0.17; 95% CI -0.24, -0.10; MD -0.04; 95% CI -0.07–0.01). There was no evidence for between-study heterogeneity among studies on free β-hCG (I² = 0%). A high level of between-study heterogeneity was detected among the studies reporting on PAPP-A (I² = 90%), but was reduced after stratifying by geographic location, biomarker assay method, and timing of GDM diagnosis. Our meta-analysis indicates that women who are diagnosed with GDM have lower first trimester levels of both PAPP-A and free β-hCG than women who remain normoglycemic throughout pregnancy. Further assessment of the predictive capacity of these biomarkers within large, diverse populations is needed.

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.

Controversies in Screening and Diagnostic Criteria for Gestational Diabetes in Early and Late Pregnancy

This review serves to evaluate the screening and diagnostic strategies for gestational diabetes and overt diabetes in pregnancy. We focus on the different early screening and diagnostic approaches in first trimester including fasting plasma glucose, random plasma glucose, oral glucose tolerance test, hemoglobin A1c, risk prediction models and biomarkers. Early screening for gestational diabetes is currently not recommended since the potential benefits and harms of early detection and subsequent treatment need to be further evaluated in randomized controlled trials.

Gestational diabetes mellitus and first trimester pregnancy-associated plasma protein A: A case-control study in a Chinese population

AIMS/INTRODUCTION

To investigate the relationship between pregnancy-associated plasma protein A (PAPP-A) and gestational diabetes mellitus (GDM), and to determine whether PAPP-A has improved value for predicting GDM in a Chinese population.

MATERIALS AND METHODS

Clinical data for 599 GDM patients and 986 unaffected pregnant women undergoing both antenatal examinations and delivery were retrospectively analyzed. First-trimester serum PAPP-A levels were compared between the groups. Binary logistic regression analysis was used to explore the risk factors for GDM, and the area under the receiver operating characteristic curve was used to determine the value of PAPP-A for predicting GDM.

RESULTS

GDM-affected and unaffected pregnant women were significantly different in terms of age (P < 0.001), BMI (P < 0.001), family history of diabetes (P = 0.002), α-thalassemia trait (P < 0.01), parity (P < 0.001), conception methods (P < 0.001), gestational weeks at the time of labor (P < 0.001) and corrected PAPP-A multiples of the median values (P < 0.001). Binary logistic regression analysis showed that PAPP-A levels were negatively related to the subsequent development of GDM (odds ratio 0.798, 95% confidence interval 0.647-0.984). The area under the receiver operating characteristic curve for maternal factors was 0.684 (95% CI: 0.657-0.711), and did not significantly differ from that for the combination of maternal factors and serum PAPP-A levels, which was 0.686 (95% CI: 0.660-0.713; χ² = 0.625, P = 0.429).

CONCLUSIONS

Serum PAPP-A was an independent factor for the development of GDM in pregnant Chinese women. Serum-PAPP-A does not have improved value with respect to predicting GDM when combined with other maternal factors.

Early Pregnancy-Associated Plasma Protein A Concentrations Are Associated With Third Trimester Insulin Sensitivity

CONTEXT

First or early second trimester pregnancy-associated plasma protein A (PAPP-A) concentrations have previously been shown to be lower in women who subsequently develop gestational diabetes mellitus (GDM) and gestational hypertension.

OBJECTIVE

We therefore sought to investigate why circulating PAPP-A concentrations are related to the subsequent risk of GDM and gestational hypertension.

PATIENTS, DESIGN, AND SETTING

We measured serum PAPP-A concentrations around week 15 of pregnancy and related these to indices derived from week 28 oral glucose tolerance tests and blood pressures across pregnancy in the Cambridge Baby Growth Study cohort.

RESULTS

Increased PAPP-A concentrations were associated with reduced GDM risk [odds ratio 0.623 (0.453, 0.856), P = 3.5 × 10-3, n = 777] and reduced mean arterial blood pressures (β = -0.202 to -0.177, P = 1.7 to 6.9 × 10-3, n = 347 to 355). They were also negatively associated with week 28 fasting (β = -0.149, P = 6.6 × 10-4, n = 777) and 60-minute (β = -0.188, P = 1.5 × 10-5, n = 777) oral glucose tolerance test glucose concentrations. These associations were underpinned by the strong associations between increased week 15 PAPP-A concentrations and decreased week 28 insulin resistance (homeostasis model assessment of insulin resistance: β = -0.319, P = 1.7 × 10-13, n = 768), as well as increased insulin secretion relative to insulin sensitivity (insulin disposition index: β = 0.202, P = 6.5 × 10-6, n = 731).

CONCLUSIONS

These results suggest that links between PAPP-A concentrations in early pregnancy and subsequent glucose concentrations and blood pressures may be mediated by changes in insulin sensitivity (and secretion).

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

BACKGROUND

Gestational diabetes (GDM) is usually diagnosed late in pregnancy, precluding early preventive interventions. This study aims to develop a predictive model based on clinical factors and selected biochemical markers for the early risk assessment of GDM.

METHODS

Based on a prospective cohort of 7929 pregnant women from the Quebec City metropolitan area, a nested case-control study was performed including 264 women who developed GDM. Each woman who developed GDM was matched with two women with normal glycemic profile. Risk prediction models for GDM and GDM requiring insulin therapy were developed using multivariable logistic regression analyses, based on clinical characteristics and the measurement of three clinically validated biomarkers: glycated hemoglobin (HbA1c), sex hormone binding globulin (SHBG) and high-sensitivity C-reactive protein (hsCRP) measured between 14 and 17 weeks of gestation.

RESULTS

HbA1c and hsCRP were higher and SHBG was lower in women who developed GDM (p<0.001). The selected model for the prediction of GDM, based on HbA1c, SHBG, BMI, past history of GDM, family history of diabetes and soft drink intake before pregnancy yielded an area under the ROC curve (AUC) of 0.79 (0.75-0.83). For the prediction of GDM requiring insulin therapy, the selected model including the same six variables yielded an AUC of 0.88 (0.84-0.92) and a sensitivity of 68.9% at a false-positive rate of 10%.

CONCLUSIONS

A simple model based on clinical characteristics and biomarkers available early in pregnancy could allow the identification of women at risk of developing GDM, especially GDM requiring insulin therapy.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

First-trimester biochemical markers of placentation in screening for gestational diabetes mellitus

OBJECTIVE

To investigate whether first-trimester biochemical markers of placentation, including pregnancy-associated plasma protein-A (PAPP-A) and placental growth factor (PLGF), are altered in women that subsequently develop gestational diabetes mellitus (GDM) and to examine their potential value in improving the performance of screening for GDM by maternal characteristics and medical history.

METHODS

The study population of 31,225 singleton pregnancies, including 787 cases that developed GDM, was drawn from women undergoing routine prospective screening for pregnancy complications at 11-13 weeks' gestation. Maternal serum PAPP-A and PLGF were measured and the levels were expressed as multiples of the median (MoM) after adjustment for maternal characteristics and medical history. The performance of screening for GDM by maternal factors and MoM values of PAPP-A and PLGF was evaluated by receiver operating characteristic (ROC) curves.

RESULTS

In the GDM group, compared to the unaffected group, the median PAPP-A was reduced (0.949, 95% CI 0.913-0.987 MoM) (p=0.0009) and median PLGF was increased (1.053, 95% CI 1.023-1.083 MoM) (p=0.004). The performance of screening for GDM by maternal factors was not improved by the addition of PAPP-A and/or PLGF.

CONCLUSIONS

First trimester maternal serum PAPP-A and PLGF are not useful in screening for GDM.

The endocrine function of human placenta: an overview

During pregnancy, several tightly coordinated and regulated processes take place to enable proper fetal development and gestational success. The formation and development of the placenta is one of these critical pregnancy events. This organ plays essential roles during gestation, including fetal nourishment, support and protection, gas exchange and production of several hormones and other mediators. Placental hormones are mainly secreted by the syncytiotrophoblast, in a highly and tightly regulated way. These hormones are important for pregnancy establishment and maintenance, exerting autocrine and paracrine effects that regulate decidualization, placental development, angiogenesis, endometrial receptivity, embryo implantation, immunotolerance and fetal development. In addition, because they are released into maternal circulation, the profile of their blood levels throughout pregnancy has been the target of intense research towards finding potential robust and reliable biomarkers to predict and diagnose pregnancy-associated complications. In fact, altered levels of these hormones have been associated with some pathologies, such as chromosomal anomalies or pre-eclampsia. This review proposes to revise and update the main pregnancy-related hormones, addressing their major characteristics, molecular targets, function throughout pregnancy, regulators of their expression and their potential clinical interest.

Maternal serum osteocalcin at 11-14 weeks of gestation in gestational diabetes mellitus

BACKGROUND

Recent studies support that osteocalcin (OC), apart from its skeletal role, is implicated in glucose homoeostasis. Aims of this study were to examine the first-trimester maternal serum concentrations of OC in pregnancies that developed gestational diabetes mellitus (GDM) and to create a first-trimester prediction model for GDM.

DESIGN

Case-control study in a prospective cohort of pregnant women. Maternal serum levels of OC were measured in 40 cases that developed GDM and 94 unaffected controls. First-trimester biophysical parameters, biochemical indices, maternal-pregnancy characteristics, and OC concentrations were assessed in relation to GDM occurrence.

RESULTS

In the GDM group, first-trimester OC serum levels were increased compared to the control group (mean = 8·81 ng/mL, SD = 2·59 vs. mean = 7·34 ng/ml, SD = 3·04, P = 0·0058). Osteocalcin was independent of first-trimester biophysical and biochemical indices. Osteocalcin alone (OR = 1·21, CI: 1·02-1·43, P = 0·023) was a significant predictor of GDM [Model R(2) = 0·04, area under the curve (AUC) = 0·61, CI: 0·55-0·72, P < 0·001]. The combination of maternal and pregnancy characteristics with OC resulted in an improved prediction model for GDM (Model R(2) = 0·21, AUC = 0·80, CI: 0·71-0·88, P < 0·001). The combined model yields a sensitivity of 72·2% for 25% false-positive rate.

CONCLUSIONS

First-trimester maternal serum levels of OC are increased in GDM pregnancies. Osteocalcin combined with maternal and pregnancy characteristics provides an effective screening for GDM at 11-14 weeks.

Incorporating first trimester analytes to predict delivery of a large for gestational infant in women with impaired glucose tolerance

AIM

To investigate first trimester maternal characteristics of women with impaired glucose tolerance that deliver large for gestational age (LGA) infants.

METHODS

Nested analysis from a prospective study of singleton pregnancies enrolled at first trimester. We studied women with an abnormal 1 h glucose challenge test that had normal follow-up oral glucose tolerance testing. Maternal characteristics, ultrasound parameters and serum analytes were stratified by subsequent delivery of an LGA infant. Parameters identified as significant on univariate analysis were used for a derivation of prediction by logistic regression. Odds ratio and prediction performance was determined using receiver operator curve (ROC) statistics.

RESULTS

A total of 33/114 (28.9%) women meeting the criteria delivered LGA infants. Maternal height (cm), and first trimester pregnancy-associated protein-A and free β-hCG (MoM) predicted delivery of an LGA infant (ROC area under curve 0.73; 95% CI 0.63-0.83). At a cutoff value of 0.172 the prediction rule achieved 91% sensitivity, 44% specificity, 41% positive predictive and 92% negative predictive value.

CONCLUSION

Maternal height and first trimester high free β-hCG and PAPP-A levels may be used as predictors for delivery of LGA infants in women with impaired glucose tolerance.

Elevated placental growth factor concentrations at 11-14 weeks of gestation to predict gestational diabetes mellitus

OBJECTIVE

To examine maternal serum concentrations of placental growth factor (PlGF) at 11-14 gestational weeks in pregnancies that developed gestational diabetes mellitus (GDM) and to create first trimester prediction models for GDM.

METHODS

Case control study including 40 GDM cases and 94 controls. PlGF, biophysical and biochemical markers and maternal-pregnancy characteristics were analyzed.

RESULTS

Log10 transformed PlGF (log10 PlGF) was not related to maternal factors. Log10 PlGF was increased (p=0.008) in the GDM group compared to the control group. Log10 PlGF was associated with fasting glucose levels (p=0.04) in the oral glucose tolerance test. Log10 PlGF had a strong relation with birth weight adjusted for gestational age in the control but not in the GDM group. Maternal weight and maternal age were the only predictors of GDM among the maternal factors [area under the curve (AUC)=0.73, p<0.001]. Log10 PlGF alone was a significant predictor of GDM (AUC=0.63, p<0.001). Combination of maternal weight, maternal age and log10 PlGF resulted in an improved prediction (DR=71.4%, for 25% FPR, AUC=0.78, Model R(2)=0.17, p<0.001).

CONCLUSION

At 11-14weeks in pregnancies that develop GDM, the maternal serum levels of PlGF are increased. Measurement of serum PlGF at 11-14weeks improves the performance of early screening for GDM provided by maternal factors alone.

The first trimester: prediction and prevention of the great obstetrical syndromes

A number of groups are currently examining the potential of screening for pre-eclampsia and gestational diabetes at 12 weeks' gestation. This can be performed at the time of combined first-trimester screening for aneuploidy using a similar method of regression analysis to combine multiple demographic and investigative factors. At present, research into the prediction of pre-eclampsia is more robust and is associated with the potential for therapeutic intervention that can reduce the prevalence of early-onset pre-eclampsia and improve maternal and neonatal outcomes.

First trimester screening can predict adverse pregnancy outcomes

There has been an increasing drive over the last two decades to push the detection of women at risk of adverse pregnancy outcomes into the first trimester. This has led to a plethora of techniques, risk assessments and biomarkers, both fascinating and bewildering in its breadth. Despite the vast amount of knowledge available, it is often difficult to determine what is practicable and valuable for clinical practice. This is especially true as earlier diagnosis does not necessarily equate to improved outcomes for mother and child. We suggest that, at least for preeclampsia, fetal growth restriction, spontaneous preterm birth and gestational diabetes, there are effective first trimester tests available to identify the women at risk of subsequently developing complications. Unfortunately, there are no currently reliable first trimester tests available for identifying women at risk of stillbirth. It is likely that this field will continue to develop over time, and we hope that new and better strategies will continue to emerge to target these clinically important pathologies.

Validation of early risk-prediction models for gestational diabetes based on clinical characteristics

AIMS

Gestational diabetes (GDM) is generally diagnosed late in pregnancy, precluding early preventive interventions. This study aims to validate, in a large Caucasian population of pregnant women, models based on clinical characteristics proposed in the literature to identify, early in pregnancy, those at high risk of developing GDM in order to facilitate follow up and prevention.

METHODS

This is a cohort study including 7929 pregnant women recruited prospectively at their first prenatal visit. Clinical information was obtained by a self-administered questionnaire and extraction of data from the medical records. The performance of four proposed clinical risk-prediction models was evaluated for identifying women who developed GDM and those who required insulin therapy.

RESULTS

The four models yielded areas under the receiver operating characteristic curve (AUC) between 0.668 and 0.756 for the identification of women who developed GDM, a performance similar to those obtained in the original studies. The best performing model, based on ethnicity, body-mass index, family history of diabetes and past history of GDM, resulted in sensitivity, specificity and AUC of 73% (66-79), 81% (80-82) and 0.824 (0.793-0.855), respectively, for the identification of GDM cases requiring insulin therapy.

CONCLUSIONS

External validation of four risk-prediction models based exclusively on clinical characteristics yielded a performance similar to those observed in the original studies. In our cohort, the strategy seems particularly promising for the early prediction of GDM requiring insulin therapy. Addition of recently proposed biochemical markers to such models has the potential to reach a performance justifying clinical utilization.