Early Pregnancy Biochemical Predictors of Gestational Diabetes Mellitus

Future clinical prospects of C-peptide testing in the early diagnosis of gestational diabetes

Gestational diabetes is typically diagnosed in the late second or third trimester of pregnancy. It is one of the most common metabolic disorders among expectant mothers, with potential serious short- and long-term complications for both maternal and offspring health. C-peptide is secreted from pancreatic beta-cells into circulation in equimolar amounts with insulin. It is a useful biomarker to estimate the beta-cell function because it undergoes negligible hepatic clearance and consequently it has a longer half-life compared to insulin. Pregnancy induces increased insulin resistance due to physiological changes in hormonal and metabolic homeostasis. Inadequate compensation by islet beta-cells results in hyperglycemia. The standard oral glucose tolerance test at 24-28 wk of gestation sets the diagnosis. Accumulated evidence from prospective studies indicates a link between early pregnancy C-peptide levels and the risk of subsequent gestational diabetes. Elevated C-peptide levels and surrogate glycemic indices at the beginning of pregnancy could prompt appropriate strategies for secondary prevention.

Early Diagnosis of Prediabetes among Pregnant Women that Develop Gestational Diabetes Mellitus and Its Influence on Perinatal Outcomes

OBJECTIVE

 Purpose of this study was to determine whether early identification of impaired glucose tolerance consistent with prediabetes among pregnant women with gestational diabetes mellitus (GDM) in the first trimester impacts maternal and neonatal outcomes.

STUDY DESIGN

 This was a retrospective cohort study of patients who were screened for pregestational diabetes in early pregnancy at a large academic tertiary care center from October 1, 2017, to January 31, 2021, and who subsequently developed GDM. Demographic and perinatal outcomes were compared among women with GDM with a positive early diabetes screen consistent with prediabetes to women who screened negative in the first trimester. Multivariable logistic regression was performed to adjust for baseline demographic differences.

RESULTS

 During the study period, 260 women screened had negative first trimester diabetes screening and subsequently developed GDM, while 696 screened positive for prediabetes and developed GDM. Women with prediabetes were more likely to require insulin treatment for their GDM compared with those that screened negative (79.5 vs. 45.4%, p < 0.001), while those who screened negative were more likely to take an oral medication of metformin or glyburide for GDM management than those with prediabetes (41.5 vs. 16.4%, p < 0.001). Infants born to mothers who screened positive for prediabetes were more likely to require neonatal intensive care unit (NICU) admission compared with those who screened negative even when adjusted for type of GDM treatment used (adjusted odds ratio [aOR] = 8.5, 95% confidence interval [CI]: 1.5-49.9).

CONCLUSION

 Women identified as having early impaired glucose tolerance consistent with prediabetes that subsequently develop GDM are more likely to be prescribed insulin treatment and may be at increased risk of adverse neonatal outcomes leading to NICU admission than women with negative first trimester diabetes screening. Future studies should focus on whether different methods of early treatment and/or intervention improve perinatal outcomes.

KEY POINTS

· Prediabetes in early pregnancy is associated with higher rates of insulin treatment for GDM.. · Prediabetes in pregnancy increases the risk of developing GDM.. · Prediabetes in early pregnancy is associated with higher rates of NICU admission..

Refining the diagnosis of gestational diabetes mellitus: a systematic review and meta-analysis

BACKGROUND

Perinatal outcomes vary for women with gestational diabetes mellitus (GDM). The precise factors beyond glycemic status that may refine GDM diagnosis remain unclear. We conducted a systematic review and meta-analysis of potential precision markers for GDM.

METHODS

Systematic literature searches were performed in PubMed and EMBASE from inception to March 2022 for studies comparing perinatal outcomes among women with GDM. We searched for precision markers in the following categories: maternal anthropometrics, clinical/sociocultural factors, non-glycemic biochemical markers, genetics/genomics or other -omics, and fetal biometry. We conducted post-hoc meta-analyses of a subset of studies with data on the association of maternal body mass index (BMI, kg/m2) with offspring macrosomia or large-for-gestational age (LGA).

RESULTS

A total of 5905 titles/abstracts were screened, 775 full-texts reviewed, and 137 studies synthesized. Maternal anthropometrics were the most frequent risk marker. Meta-analysis demonstrated that women with GDM and overweight/obesity vs. GDM with normal range BMI are at higher risk of offspring macrosomia (13 studies [n = 28,763]; odds ratio [OR] 2.65; 95% Confidence Interval [CI] 1.91, 3.68), and LGA (10 studies [n = 20,070]; OR 2.23; 95% CI 2.00, 2.49). Lipids and insulin resistance/secretion indices were the most studied non-glycemic biochemical markers, with increased triglycerides and insulin resistance generally associated with greater risk of offspring macrosomia or LGA. Studies evaluating other markers had inconsistent findings as to whether they could be used as precision markers.

CONCLUSIONS

Maternal overweight/obesity is associated with greater risk of offspring macrosomia or LGA in women with GDM. Pregnancy insulin resistance or hypertriglyceridemia may be useful in GDM risk stratification. Future studies examining non-glycemic biochemical, genetic, other -omic, or sociocultural precision markers among women with GDM are warranted.

Comparison of machine learning and conventional logistic regression-based prediction models for gestational diabetes in an ethnically diverse population; the Monash GDM Machine learning model

BACKGROUND

Early identification of pregnant women at high risk of developing gestational diabetes (GDM) is desirable as effective lifestyle interventions are available to prevent GDM and to reduce associated adverse outcomes. Personalised probability of developing GDM during pregnancy can be determined using a risk prediction model. These models extend from traditional statistics to machine learning methods; however, accuracy remains sub-optimal.

OBJECTIVE

We aimed to compare multiple machine learning algorithms to develop GDM risk prediction models, then to determine the optimal model for predicting GDM.

METHODS

A supervised machine learning predictive analysis was performed on data from routine antenatal care at a large health service network from January 2016 to June 2021. Predictor set 1 were sourced from the existing, internationally validated Monash GDM model: GDM history, body mass index, ethnicity, age, family history of diabetes, and past poor obstetric history. New models with different predictors were developed, considering statistical principles with inclusion of more robust continuous and derivative variables. A randomly selected 80% dataset was used for model development, with 20% for validation. Performance measures, including calibration and discrimination metrics, were assessed. Decision curve analysis was performed.

RESULTS

Upon internal validation, the machine learning and logistic regression model's area under the curve (AUC) ranged from 71% to 93% across the different algorithms, with the best being the CatBoost Classifier (CBC). Based on the default cut-off point of 0.32, the performance of CBC on predictor set 4 was: Accuracy (85%), Precision (90%), Recall (78%), F1-score (84%), Sensitivity (81%), Specificity (90%), positive predictive value (92%), negative predictive value (78%), and Brier Score (0.39).

CONCLUSIONS

In this study, machine learning approaches achieved the best predictive performance over traditional statistical methods, increasing from 75 to 93%. The CatBoost classifier method achieved the best with the model including continuous variables.

Risk Estimation of Gestational Diabetes Mellitus in the First Trimester

CONTEXT

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus

BACKGROUND

Numerous laboratory tests are used in the diagnosis and management of diabetes mellitus. The quality of the scientific evidence supporting the use of these assays varies substantially.

APPROACH

An expert committee compiled evidence-based recommendations for laboratory analysis in screening, diagnosis, or monitoring of diabetes. The overall quality of the evidence and the strength of the recommendations were evaluated. The draft consensus recommendations were evaluated by invited reviewers and presented for public comment. Suggestions were incorporated as deemed appropriate by the authors (see Acknowledgments). The guidelines were reviewed by the Evidence Based Laboratory Medicine Committee and the Board of Directors of the American Association for Clinical Chemistry and by the Professional Practice Committee of the American Diabetes Association.

CONTENT

Diabetes can be diagnosed by demonstrating increased concentrations of glucose in venous plasma or increased hemoglobin A1c (HbA1c) in the blood. Glycemic control is monitored by the people with diabetes measuring their own blood glucose with meters and/or with continuous interstitial glucose monitoring (CGM) devices and also by laboratory analysis of HbA1c. The potential roles of noninvasive glucose monitoring, genetic testing, and measurement of ketones, autoantibodies, urine albumin, insulin, proinsulin, and C-peptide are addressed.

SUMMARY

The guidelines provide specific recommendations based on published data or derived from expert consensus. Several analytes are found to have minimal clinical value at the present time, and measurement of them is not recommended.

C-Reactive Protein: Pathophysiology, Diagnosis, False Test Results and a Novel Diagnostic Algorithm for Clinicians

The current literature provides a body of evidence on C-Reactive Protein (CRP) and its potential role in inflammation. However, most pieces of evidence are sparse and controversial. This critical state-of-the-art monography provides all the crucial data on the potential biochemical properties of the protein, along with further evidence on its potential pathobiology, both for its pentameric and monomeric forms, including information for its ligands as well as the possible function of autoantibodies against the protein. Furthermore, the current evidence on its potential utility as a biomarker of various diseases is presented, of all cardiovascular, respiratory, hepatobiliary, gastrointestinal, pancreatic, renal, gynecological, andrological, dental, oral, otorhinolaryngological, ophthalmological, dermatological, musculoskeletal, neurological, mental, splenic, thyroid conditions, as well as infections, autoimmune-supposed conditions and neoplasms, including other possible factors that have been linked with elevated concentrations of that protein. Moreover, data on molecular diagnostics on CRP are discussed, and possible etiologies of false test results are highlighted. Additionally, this review evaluates all current pieces of evidence on CRP and systemic inflammation, and highlights future goals. Finally, a novel diagnostic algorithm to carefully assess the CRP level for a precise diagnosis of a medical condition is illustrated.

Analysis of risk factors of neonatal hypoglycemia and its correlation with blood glucose control of gestational diabetes mellitus: A retrospective study

This study aimed to investigate the risk factors associated with neonatal hypoglycemia and its correlation with blood glucose control in patients with gestational diabetes mellitus (GDM). This study was a retrospective study. 880 pregnant women with GDM and their newborns were hospitalized from January 2018 to December 2022 in our hospital. The clinical information of GDM pregnant women and their newborns were reviewed and the hemoglobin A1c (HbA1c) values measured within 1 week before delivery were collected. According to the occurrence of neonatal hypoglycemia, which was divided into the control and observation groups. Logistic regression model was used to estimate the potential factors associated with neonatal hypoglycemia. The association between HbA1c of pregnant women before delivery and abnormal glucose metabolism in newborns was examined using spearman correlation analysis. A total of 104 cases of hypoglycemia occurred in neonates delivered by 880 GDM women and the incidence of neonatal hypoglycemia was 11.82%. There were significant differences in pre-pregnancy overweight or obesity, delivery mode, maternal blood sugar control effect and neonatal feeding standard between the 2 groups of GDM women (P < .05). Pre-pregnancy overweight or obesity, poor blood sugar control in GDM women, and improper neonatal feeding were risk factors for neonatal hypoglycemia. The results of logistic regression analysis showed that abnormal glucose metabolism in newborn (odds ratio [OR]: 2.43, 95% confidence interval [CI]: 1.12-4.73) and neonatal hypoglycemia (OR: 3.04, 95% CI: 1.33-5.79) were a risk factor. We also conducted the logistic analysis to evaluate the correlation between HbA1c before delivery and abnormal glucose metabolism in newborns of pregnant women with GDM through adjusting some potential factors. The results were still significant in the abnormal glucose metabolism in newborn (OR: 2.84, 95% CI: 1.23-6.63) and neonatal hypoglycemia (OR: 3.64, 95% CI: 1.46-8.18). Overweight or obesity of GDM parturient before pregnancy, poor blood glucose control of GDM parturient and improper feeding of newborns are all risk factors for neonatal hypoglycemia. HbA1c before delivery has a certain predictive value for abnormal glucose metabolism in newborns.

Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus

BACKGROUND

Numerous laboratory tests are used in the diagnosis and management of diabetes mellitus. The quality of the scientific evidence supporting the use of these assays varies substantially.

APPROACH

An expert committee compiled evidence-based recommendations for laboratory analysis in screening, diagnosis, or monitoring of diabetes. The overall quality of the evidence and the strength of the recommendations were evaluated. The draft consensus recommendations were evaluated by invited reviewers and presented for public comment. Suggestions were incorporated as deemed appropriate by the authors (see Acknowledgments). The guidelines were reviewed by the Evidence Based Laboratory Medicine Committee and the Board of Directors of the American Association of Clinical Chemistry and by the Professional Practice Committee of the American Diabetes Association.

CONTENT

Diabetes can be diagnosed by demonstrating increased concentrations of glucose in venous plasma or increased hemoglobin A1c (Hb A1c) in the blood. Glycemic control is monitored by the people with diabetes measuring their own blood glucose with meters and/or with continuous interstitial glucose monitoring (CGM) devices and also by laboratory analysis of Hb A1c. The potential roles of noninvasive glucose monitoring, genetic testing, and measurement of ketones, autoantibodies, urine albumin, insulin, proinsulin, and C-peptide are addressed.

SUMMARY

The guidelines provide specific recommendations based on published data or derived from expert consensus. Several analytes are found to have minimal clinical value at the present time, and measurement of them is not recommended.

Gestational Diabetes Is Characterized by Decreased Medium-Chain Acylcarnitines and Elevated Purine Degradation Metabolites across Pregnancy: A Case-Control Time-Course Analysis

Gestational Diabetes Mellitus (GDM) results in complications affecting both mothers and their offspring. Metabolomic analysis across pregnancy provides an opportunity to better understand GDM pathophysiology. The objective was to conduct a metabolomics analysis of first and third trimester plasma samples to identify metabolic differences associated with GDM development. Forty pregnant women with overweight/obesity from a multisite clinical trial of a lifestyle intervention were included. Participants who developed GDM (n = 20; GDM group) were matched with those who did not develop GDM (n = 20; Non-GDM group). Plasma samples collected at the first (10-16 weeks) and third (28-35 weeks) trimesters were analyzed with ultra-performance liquid chromatography-mass spectrometry (UPLC-MS). Cardiometabolic risk markers, dietary recalls, and physical activity metrics were also assessed. Four medium-chain acylcarnitines, lauroyl-, octanoyl-, decanoyl-, and decenoylcarnitine, significantly differed over the course of pregnancy in the GDM vs Non-GDM group in a group-by-time interaction (p < 0.05). Hypoxanthine and inosine monophosphate were elevated in the GDM group (p < 0.04). In both groups over time, bile acids and sorbitol increased while numerous acylcarnitines and α-hydroxybutyrate decreased (p < 0.05). Metabolites involved in fatty acid oxidation and purine degradation were altered across the first and third trimesters of GDM-affected pregnancies, providing insight into metabolites and metabolic pathways altered with GDM development.

Combining HbA1c and insulin resistance to assess the risk of gestational diabetes mellitus: a prospective cohort study

OBJECTIVE

To investigate the association of glycated hemoglobin (HbA1c) and homeostasis model assessment insulin resistance (HOMA-IR) with gestational diabetes mellitus (GDM) risk.

METHODS

Data for this study were from a prospective cohort in Hangzhou, China. We included pregnant women with HbA1c, fasting insulin, and fasting glucose (FG) measured at 15-20 weeks of gestation and underwent oral glucose tolerance test (OGTT) at 24-28 weeks. Based on HbA1c and HOMA-IR, participants were divided into four groups. We estimated the odds ratios (OR) with 95% confidence intervals (CI) to assess the associations of HbA1c and HOMA-IR with GDM occurrence. Finally, we the potential additive interaction between HbA1c and HOMA-IR by calculating relative excess risk due to interaction (RERI) and the attributable proportion due to interaction (AP).

RESULT

462 pregnant women were included, of whom 136 (29.44%) developed GDM. Based on HbA1c and HOMA-IR, the study population was divided into four groups, with the percentages of each group being 51.30%, 15.58%, 20.56%, and 12.55%, respectively. The incidence of GDM increased with the increase of HOMA-IR and HbA1c, respectively, and the risk of GDM was significantly increased when both HOMA-IR and HbA1c were elevated. However, no such risk was observed in pregnant women < 35 years. Finally, we found significantly higher FG at 24-28 weeks in the high HOMA-IR and HbA1c group among GDM-positive pregnant women.

CONCLUSIONS

The incidence of GDM increased with increasing HbA1c and HOMA-IR, and the risk of GDM was significantly increased when both HbA1c and HOMA-IR were elevated. This finding may help to identify high-risk women for GDM early in pregnancy and provide timely interventions.

Trends in Screening for Diabetes in Early Pregnancy in the United States

Objective: The aim of this study was to characterize current diabetes screening practices in the first trimester of pregnancy in the United States, evaluate patient characteristics and risk factors associated with early diabetes screening, and compare perinatal outcomes by early diabetes screening. Methods: This was a retrospective cohort study of US medical claims data of persons diagnosed with a viable intrauterine pregnancy and who presented for care with private insurance before 14 weeks of gestation, without pre-existing pregestational diabetes, from the IBM MarketScan® database for the period January 1, 2016, to December 31, 2018. Univariate and multivariate analyses were used to evaluate perinatal outcomes. Results: A total of 400,588 pregnancies were identified as eligible for inclusion, with 18.0% of persons receiving early screening for diabetes. Of those with laboratory order claims, 53.1% underwent hemoglobin A1c testing, 30.0% underwent fasting glucose testing, and 16.9% underwent oral glucose tolerance testing. Compared with those who did not undergo early diabetes screening, those who did were more likely to be older; obese; having a history of gestational diabetes, chronic hypertension, polycystic ovarian syndrome, or hyperlipidemia; and having a family history of diabetes. In adjusted logistic regression, history of gestational diabetes (adjusted odds ratio 3.99; 95% confidence interval 3.73-4.26) had the strongest association with early diabetes screening. Adverse perinatal outcomes, including a higher rate of cesarean delivery, preterm delivery, preeclampsia, and gestational diabetes, occurred more frequently among women who underwent early diabetes screening. Conclusions: First-trimester early diabetes screening was mostly commonly performed by hemoglobin A1c evaluation, and persons who underwent early diabetes screening were more likely to experience adverse perinatal outcomes.

Maternal plasma diacylglycerols and triacylglycerols in the prediction of gestational diabetes mellitus

OBJECTIVE

To define the lipidomic profile in plasma across pregnancy, and identify lipid biomarkers for gestational diabetes mellitus (GDM) prediction in early pregnancy.

DESIGN

Case-control study.

SETTING

Tertiary referral maternity unit.

POPULATION OR SAMPLE

Plasma samples from 100 GDM and 100 normal glucose tolerance (NGT) women, divided into a training set (GDM first trimester = 50, GDM second trimester = 40, NGT first trimester = 50, NGT second trimester = 50) and a validation set (GDM first trimester = 45, GDM second trimester = 34, NGT first trimester = 44, NGT second trimester = 40).

METHODS

Plasma samples were collected in the first (11⁺⁰ to 13⁺⁶ weeks), second (19⁺⁰ to 24⁺⁶ weeks), and third trimesters (30⁺⁰ to 34⁺⁶ weeks), and tested by ultra-high-performance liquid chromatography coupled with electrospray ionisation-quadrupole-time of flight-mass spectrometry; The GDM prediction model was established by the machine-learning method of random forest.

MAIN OUTCOME MEASURES

Gestational diabetes mellitus.

RESULTS

In both the GDM and NGT group, lyso-glycerophospholipids were down-regulated, whereas ceramides, sphingomyelins, cholesteryl ester, diacylglycerols (DGs) and triacylglycerols (TGs) and glucosylceramide were up-regulated across the three trimesters of pregnancy. In the training dataset, seven TGs and five DGs demonstrated good performance in the prediction of GDM in the first and second trimesters (area under the curve [AUC] = 0.96 with 95% confidence interval [CI] of 0.93-1 and AUC = 0.97 with 95% CI of 0.95-1, respectively), independent of maternal body mass index (BMI) and ethnicity. In the validation dataset, the predictive model achieved an AUC of 0.88 and 0.94 at the first and second trimesters, respectively.

CONCLUSIONS

Our results have proposed new lipid biomarkers for the first trimester prediction of GDM, independent of ethnicity and BMI.

Prediction model for gestational diabetes mellitus using the XG Boost machine learning algorithm

OBJECTIVE

To develop the extreme gradient boosting (XG Boost) machine learning (ML) model for predicting gestational diabetes mellitus (GDM) compared with a model using the traditional logistic regression (LR) method.

METHODS

A case-control study was carried out among pregnant women, who were assigned to either the training set (these women were recruited from August 2019 to November 2019) or the testing set (these women were recruited in August 2020). We applied the XG Boost ML model approach to identify the best set of predictors out of a set of 33 variables. The performance of the prediction model was determined by using the area under the receiver operating characteristic (ROC) curve (AUC) to assess discrimination, and the Hosmer-Lemeshow (HL) test and calibration plots to assess calibration. Decision curve analysis (DCA) was introduced to evaluate the clinical use of each of the models.

RESULTS

A total of 735 and 190 pregnant women were included in the training and testing sets, respectively. The XG Boost ML model, which included 20 predictors, resulted in an AUC of 0.946 and yielded a predictive accuracy of 0.875, whereas the model using a traditional LR included four predictors and presented an AUC of 0.752 and yielded a predictive accuracy of 0.786. The HL test and calibration plots show that the two models have good calibration. DCA indicated that treating only those women whom the XG Boost ML model predicts are at risk of GDM confers a net benefit compared with treating all women or treating none.

CONCLUSIONS

The established model using XG Boost ML showed better predictive ability than the traditional LR model in terms of discrimination. The calibration performance of both models was good.

Development and Validation of Risk Prediction Models for Gestational Diabetes Mellitus Using Four Different Methods

Gestational diabetes mellitus (GDM), a common perinatal disease, is related to increased risks of maternal and neonatal adverse perinatal outcomes. We aimed to establish GDM risk prediction models that can be widely used in the first trimester using four different methods, including a score-scaled model derived from a meta-analysis using 42 studies, a logistic regression model, and two machine learning models (decision tree and random forest algorithms). The score-scaled model (seven variables) was established via a meta-analysis and a stratified cohort of 1075 Chinese pregnant women from the Northwest Women's and Children's Hospital (NWCH) and showed an area under the curve (AUC) of 0.772. The logistic regression model (seven variables) was established and validated using the above cohort and showed AUCs of 0.799 and 0.834 for the training and validation sets, respectively. Another two models were established using the decision tree (DT) and random forest (RF) algorithms and showed corresponding AUCs of 0.825 and 0.823 for the training set, and 0.816 and 0.827 for the validation set. The validation of the developed models suggested good performance in a cohort derived from another period. The score-scaled GDM prediction model, the logistic regression GDM prediction model, and the two machine learning GDM prediction models could be employed to identify pregnant women with a high risk of GDM using common clinical indicators, and interventions can be sought promptly.

First trimester screening of serum advanced glycation end products levels of pregnant women who have risk factors for gestational diabetes and their obstetric outcomes: a preliminary case-control study

Advanced glycation end-products (AGE) are complex compounds formed by nonenzymatic glycosylation of proteins, nucleic acids, and lipids with glucose in the blood. We aimed to investigate whether there was a difference in first-trimester serum AGE levels of pregnant women with and without risk factors for gestational diabetes mellitus (GDM) and their obstetric outcomes. There were 44 women in study group who have risk factors for GDM and 44 as controls. Demographic features, serum AGE levels, adverse perinatal and neonatal outcomes were compared between groups. Five patients (11.4%) in the study group and one patient (2.3%) in the control group were diagnosed as GDM (p = .2). The serum AGE values were not statistically different between the study and control groups. There were no statistical differences between groups in terms of adverse perinatal and neonatal outcomes. However, in the group with adverse perinatal outcome (n = 25), AGE values were higher than the control group. The results of our preliminary study suggested that high-risk women for GDM did not have increased serum levels of AGE in the first trimester. Nevertheless, a high first-trimester serum AGE level was found to be associated with adverse perinatal outcomes. IMPACT STATEMENTWhat is already known on this subject? Advanced glycation end products (AGE) are markers that are associated with diabetes and its complications. For pregnant women, a high third trimester serum AGEs levels were found in women who had gestational diabetes.What do the results of this study add? The results of our study revealed that first trimester screening of serum AGE levels of women who had risk factors for gestational diabetes was not discriminate. Nevertheless, a high first trimester serum AGE levels was associated with adverse perinatal outcome.What are the implications of these findings for clinical practice and/or further research? Whether reducing exogenous sources of AGE (western-style diet, smoking) before pregnancy will be associated with better pregnancy outcomes should be investigated in future studies.

Parental Programming of Offspring Health: The Intricate Interplay between Diet, Environment, Reproduction and Development

As adults, our health can be influenced by a range of lifestyle and environmental factors, increasing the risk for developing a series of non-communicable diseases such as type 2 diabetes, heart disease and obesity. Over the past few decades, our understanding of how our adult health can be shaped by events occurring before birth has developed into a well-supported concept, the Developmental Origins of Health and Disease (DOHaD). Supported by epidemiological data and experimental studies, specific mechanisms have been defined linking environmental perturbations, disrupted fetal and neonatal development and adult ill-health. Originally, such studies focused on the significance of poor maternal health during pregnancy. However, the role of the father in directing the development and well-being of his offspring has come into recent focus. Whereas these studies identify the individual role of each parent in shaping the long-term health of their offspring, few studies have explored the combined influences of both parents on offspring well-being. Such understanding is necessary as parental influences on offspring development extend beyond the direct genetic contributions from the sperm and oocyte. This article reviews our current understanding of the parental contribution to offspring health, exploring some of the mechanisms linking parental well-being with gamete quality, embryo development and offspring health.

Predictive Metabolomic Markers in Early to Mid-pregnancy for Gestational Diabetes Mellitus: A Prospective Test and Validation Study

Gestational diabetes mellitus (GDM) predisposes pregnant individuals to perinatal complications and long-term diabetes and cardiovascular diseases. We developed and validated metabolomic markers for GDM in a prospective test-validation study. In a case-control sample within the PETALS cohort (GDM n = 91 and non-GDM n = 180; discovery set), a random PETALS subsample (GDM n = 42 and non-GDM n = 372; validation set 1), and a case-control sample within the GLOW trial (GDM n = 35 and non-GDM n = 70; validation set 2), fasting serum untargeted metabolomics were measured by gas chromatography/time-of-flight mass spectrometry. Multivariate enrichment analysis examined associations between metabolites and GDM. Ten-fold cross-validated LASSO regression identified predictive metabolomic markers at gestational weeks (GW) 10-13 and 16-19 for GDM. Purinone metabolites at GW 10-13 and 16-19 and amino acids, amino alcohols, hexoses, indoles, and pyrimidine metabolites at GW 16-19 were positively associated with GDM risk (false discovery rate <0.05). A 17-metabolite panel at GW 10-13 outperformed the model using conventional risk factors, including fasting glycemia (area under the curve: discovery 0.871 vs. 0.742, validation 1 0.869 vs. 0.731, and validation 2 0.972 vs. 0.742; P < 0.01). Similar results were observed with a 13-metabolite panel at GW 17-19. Dysmetabolism is present early in pregnancy among individuals progressing to GDM. Multimetabolite panels in early pregnancy can predict GDM risk beyond conventional risk factors.

Hepatitis B Virus Infection and Increased Risk of Gestational Diabetes Regardless of Liver Function Status: A Xiamen Area Population-Based Study

Background &amp; Aims: Hepatitis B virus (HBV) infection is a significant cause of liver function damage. However, previous studies on HBV mainly aimed at ordinary people, and there is a lack of consensus on the relationship between HBV infection and gestational diabetes mellitus (GDM) and whether HBV-infected pregnant women should undergo antiviral treatment. In addition, systematic studies on the impact of HBV infection on GDM have rarely been studied directly. Therefore, the overall goal of this study was to pursue the association between HBV infection, liver function, and GDM using Xiamen area gestational big data.

Methods: Using the Xiamen Primary Health Information System-maternal and child health information system, the data on participants (138,867 in total) expected confinement between 2008 and 2018 were included. Using univariate and multivariate logistic regressions, we constructed models to determine the role of HBV infection and liver function status in GDM. In addition, an analysis of variance tests was performed to study whether the relationship between HBsAg and GDM differed in the normal liver function and the abnormal liver function subgroups.

Results: HBsAg's positive status showed a substantial correlation with GDM onset in univariate and multivariate logistic regressions (p &lt; 0.001). Subgroup analysis among HBsAg, liver function, and GDM suggests that both HBsAg and liver function affect the onset of GDM and have the highest prevalence of both abnormalities. Furthermore, ANOVA was used to investigate the association of HBsAg positive (p &lt; 0.001), abnormal liver function (p &lt; 0.001), and their interaction (p = 0.302) on the onset of GDM. This result showed that HBsAg is an independent factor of GDM pathogenesis, regardless of liver function status.

Conclusion: HBsAg and liver function are independent factors in GDM. Therefore, regarding these results, while clinicians consider the traditional risk factors of GDM, it is necessary to consider the HBV infection status. Conducting a dietary intervention for HBsAg-positive pregnant women at the early stage of pregnancy is conducive to reducing the adverse effects.

Adiponectin-targeted SERS immunoassay biosensing platform for early detection of gestational diabetes mellitus

The anisotropic gold nanotriangles (AuNTs) were synthesized by a fast seedless growth process. The high-yield monodispersed AuNT colloids were obtained through a purification process based on depletion-induced interactions. AuNTs were modulated with a localized surface plasmon resonance (LSPR) peak of 638 nm wavelength coherent with the Raman excitation light. However, from finite element computation results, the AuNT clusters showed better performance for the 785 nm laser source due to a red shift in their LSPR properties, hence it was selected for the surface-enhanced Raman scattering (SERS) immunoassay. A self-assembly strategy using a thiol group and ON-OFF strategy in the heat map was performed to ensure the stability of SERS immunoassay platform. The sandwich SERS immunoassay biosensor platform for adiponectin detection demonstrated a wide assay range (10^-15-10^-6 g/mL), good reliability (R² = 0.994, clinically relevant range), femto-scale limit of detection (3.0 × 10^-16 g/mL), and excellent selectivity without interference from other biomarkers. This showed the possibility of effectively detecting adiponectin levels in the biofluids of pregnant women. Therefore, our technology is the first to quantitatively detect adiponectin based on SERS technology for early detection of gestational diabetes mellitus and has the potential to be used as a clinical biosensor capable of diagnosing various obstetric diseases during early pregnancy.

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.

Genomics and Epigenomics of Gestational Diabetes Mellitus: Understanding the Molecular Pathways of the Disease Pathogenesis

One of the most common complications during pregnancy is gestational diabetes mellitus (GDM), hyperglycemia that occurs for the first time during pregnancy. The condition is multifactorial, caused by an interaction between genetic, epigenetic, and environmental factors. However, the underlying mechanisms responsible for its pathogenesis remain elusive. Moreover, in contrast to several common metabolic disorders, molecular research in GDM is lagging. It is important to recognize that GDM is still commonly diagnosed during the second trimester of pregnancy using the oral glucose tolerance test (OGGT), at a time when both a fetal and maternal pathophysiology is already present, demonstrating the increased blood glucose levels associated with exacerbated insulin resistance. Therefore, early detection of metabolic changes and associated epigenetic and genetic factors that can lead to an improved prediction of adverse pregnancy outcomes and future cardio-metabolic pathologies in GDM women and their children is imperative. Several genomic and epigenetic approaches have been used to identify the genes, genetic variants, metabolic pathways, and epigenetic modifications involved in GDM to determine its etiology. In this article, we explore these factors as well as how their functional effects may contribute to immediate and future pathologies in women with GDM and their offspring from birth to adulthood. We also discuss how these approaches contribute to the changes in different molecular pathways that contribute to the GDM pathogenesis, with a special focus on the development of insulin resistance.

Relationship of anthropometric measurements with glycated hemoglobin and 1-h blood glucose after 50 g glucose challenge test in pregnant women: A longitudinal cohort study in Southern Thailand

AIMS

To assess correlations of anthropometric measurements with glycated hemoglobin (HbA1c) and 1-h blood glucose after a 50 g glucose challenge test during the first and late second trimesters and explore their relationships of anthropometric measurements with neonatal birth weight.

METHODS

A longitudinal study was conducted among pregnant Thai women with gestational age ≤14 weeks. Anthropometric measurements, using body mass index, body compositions, and circumferences, and skinfold thickness, were measured at four-time points: ≤14, 18-22, 24-28, and 30-34 weeks of gestation. HbA1c and 1-h blood glucose were examined at ≤14 and 24-28 weeks. Neonatal birth weight was recorded.

RESULTS

Of 312 women, HbA1c was more correlated with anthropometric measurements during pregnancy than 1-h blood glucose. At 24-28 weeks, women with high/very high body fat percentage were more likely to have higher HbA1c. Women with high subscapular skinfold thickness were more likely to have higher 1-h blood glucose at ≤14 and 24-28 weeks. High hip circumference significantly increased neonatal birth weights.

CONCLUSION

Anthropometric measurements were longitudinally correlated with HbA1c and 1-h blood glucose, higher in the late second than first trimesters, as well as neonatal birth weight. The mechanisms to explain the relationship of different anthropometric measurements are required to be further studied.

Prediction of gestational diabetes mellitus in the first trimester by fasting plasma glucose which cutoff is better?

OBJECTIVE

We aimed to predict subsequent gestational diabetes mellitus (GDM) by fasting plasma glucose (FPG) in the first trimester.

METHODS

Healthy pregnant women who were screened for GDM at 24-28 gestational weeks and had FPG levels calculated during their first antenatal visit and less than 14 gestational weeks were included in this study.

RESULTS

Of the 2605 women who were recruited for the study, 245 (9.4%) were diagnosed with GDM at weeks 24-28. The diagnostic accuracy for FPG predicting GDM was 66.5, 78.4, and 88.2 for the cutoff values of 87.5 mg/dl, 92 mg/dl, and 99.5 mg/dl, respectively.

CONCLUSIONS

FPG values which are within the normoglycaemic range constitute an independent risk factor for the development of GDM. The threshold for gestational diabetes diagnosis must be revised.

COVID-19 pandemic: Impact on gestational diabetes mellitus prevalence

AIM

Although an increased risk of gestational diabetes mellitus (GDM) has been noted in women exposed to stressful conditions and traumatic events, limited information is available about such risk in the context of the COVID-19 pandemic.

METHODS

The study was designed as a non-concurrent case-control study on the prevalence of GDM, defined according to IADPSG 2010, in women giving birth during the COVID-19 pandemic in the hot spot of Northeast Italy from March 9th to May 18th, 2020, with an antecedent puerperae-matched group whose women had given birth in 2019.

RESULTS

Analysis revealed that during the COVID-19 pandemic in 2020, GDM prevalence was significantly higher than in 2019 (GDM, 48/533, 9 vs 86/637, 13.5%, p = 0.01), as illustrated by a higher GDM prevalence in 5/6 months of the final semester of 2020. In addition, logistic regression analysisconfirmed a statistically significant temporal relationship between experiencing the lockdown during the first trimester of gestation and later GDM incidence (t = 2.765, P = 0.012), with an 34% increase in mean number of GDM diagnoses per month (antilog of the parameter = 1.34).

CONCLUSION

The COVID-19 pandemic negatively impacted GDM prevalence in 2020 compared to 2019, especially for pregnant women in the 1st trimester of gestation.

Early-pregnancy maternal heart rate is related to gestational diabetes mellitus (GDM)

OBJECTIVE

The study examined the association between resting heart rate (RHR) of early pregnancy and risk of gestational diabetes mellitus (GDM) in Chinese population.

METHODS

As retrospective study, medical data of 15,092 pregnant women gave birth in 2019 was collected and analyzed. The pregnant women's age, educational level, pre-pregnancy body weight, height, parity, family history of diabetes, lipid profile, blood pressure and RHR were recorded during 11 ∼ 13⁺⁶ weeks. Multivariate logistic regression analysis was used to estimate the association between maternal characteristics and RHR and GDM. And we further evaluated the predictive roll of RHR in different sub-groups defined by their body mass index (BMI), age, fasting plasma glucose (FPG), total cholesterol and triglyceride.

RESULTS

2313 women (15.33%) were diagnosed as GDM according to 75 g OGTT. According to the quartile value of RHR, the subjects were divided into four groups. Risk of GDM increased significantly as RHR increased. In the fully adjusted model, ORs(95%CI) for the lowest vs highest quartiles of heart rate were 1(as reference), 1.14(0.97 ∼ 1.33), 1.25(1.05 ∼ 1.40), 1.41(1.21 ∼ 1.62), respectively. In the subgroup's analysis, we found the relationship between RHR and risk of GDM was evident in women with low and normal BMI; with normal fasting plasma; and normal serum lipid level.

CONCLUSION

The current study shows early-pregnancy maternal RHR is associated with potential risk of developing GDM.

Early markers of gestational diabetes mellitus: what we know and which way forward?

Women’s metabolism during pregnancy undergoes numerous changes that can lead to gestational diabetes mellitus (GDM). The cause and pathogenesis of GDM, a heterogeneous disease, are not completely clear, but GDM is increasing in prevalence and is associated with the modern lifestyle. Most diagnoses of GDM are made via the guidelines from the International Association of Diabetes and Pregnancy Study Groups (IADSPG), which involve an oral glucose tolerance test (OGTT) between 24 and 28 weeks of pregnancy. Diagnosis in this stage of pregnancy can lead to short- and long-term implications for the mother and child. Therefore, there is an urgent need for earlier GDM markers in order to enable prevention and earlier treatment. Routine GDM biomarkers (plasma glucose, insulin, C-peptide, homeostatic model assessment of insulin resistance, and sex hormone-binding globulin) can differentiate between healthy pregnant women and those with GDM but are not suitable for early GDM diagnosis. In this article, we present an overview of the potential early biomarkers for GDM that have been investigated recently. We also present our view of future developments in the laboratory diagnosis of GDM.

The role of complement factor H in gestational diabetes mellitus and pregnancy

BACKGROUND

Complement factor H (CFH) has been found to be associated with insulin resistance. This study assessed the correlation between CFH and other clinical parameters, and determined whether CFH played a role in gestational diabetes mellitus (GDM) and adverse pregnancy outcomes.

METHODS

A total of 397 pregnant women were included for analysis in this nested case-control study. Clinical parameters and serum were collected within the 11-17th gestational age at the first prenatal visit. At 24-28 weeks of gestation, a 75 g oral glucose tolerance test was performed and subjects were divided into a GDM (n = 80) and a non-GDM control group (n = 317). The delivery data were also followed. The serum CFH level was assayed by ELISA.

RESULTS

CFH was higher in GDM than in non-GDM controls (280.02 [58.60] vs. 264.20 [68.77]; P = 0.014). CFH level was moderately associated with pre-pregnancy body mass index (BMI), BMI and total triglycerides (TG), and slightly associated with gestational age, low density lipoprotein cholesterol (LDL-C), total cholesterol (TC) in GDM and non-GDM (all P <  0.05). Moreover, CFH level was moderately correlated with alkaline phosphatase (ALP) and slightly correlated with age, uric acid (UA) and total bilirubin (TB) in non-GDM (all P <  0.05). After adjustment for clinical confounding factors, BMI, TG, gestational age, ALP, TB, age and UA were independent risk factors for log₁₀ CFH levels (all P <  0.05) in all subjects. In addition, overweight or obese pregnant women, women with hypertriglyceridemia and women in the second trimester had significantly higher CFH levels than normal weight and underweight group (P <  0.001), the non-hypertriglyceridemia group (P <  0.001) and women in the first trimester group (P < 0.05) in all pregnant women respectively. Following binary logistic regression, CFH was not independently associated with GDM and related pregnant outcomes.

CONCLUSIONS

The CFH in 11-17th weeks of gestation might be affected by many factors, including BMI, TG, gestational age, ALP, TB, age and UA. CFH was not an independent risk factor for GDM and avderse pregnancy outcomes.

Associations between Human Chorionic Gonadotropin, Maternal Free Thyroxine, and Gestational Diabetes Mellitus

Background: Human chorionic gonadotropin (hCG) is a marker of placental function, which also stimulates the maternal thyroid gland. Maternal thyroid function can be associated with the pathophysiology of gestational diabetes mellitus (GDM). We aimed to study whether there is an association of hCG concentrations in early pregnancy with GDM and whether it is mediated through maternal thyroid hormones. Methods: This study included 18,683 pregnant women presenting at a tertiary hospital in Shanghai, China, between January 2015 and December 2016. GDM was diagnosed using a 2-hour, 75-g, oral glucose tolerance test (OGTT) according to the American Diabetes Association guidelines. Multivariable logistic or linear regression models were used to identify associations, adjusting for maternal age, education level, family history of diabetes, parity, fetal sex, thyroperoxidase antibody (TPOAb) status, and prepregnancy body-mass index. Results: Higher hCG concentrations were associated with a lower plasma glucose level during the OGTT, but not with fasting plasma glucose or hemoglobin A1c concentrations tested during early pregnancy. hCG in early pregnancy was negatively associated with GDM risk (p = 0.027). Mediation analysis identified that an estimated 21.4% of the association of hCG-associated GDM risk was mediated through changes in free thyroxine (fT4) concentrations (p < 0.05). In the sensitivity analysis restricted to TPOAb-positive women, hCG was not associated with GDM (p = 0.452). Conclusions: Higher hCG levels in early pregnancy are associated with a lower risk of GDM. Maternal fT4 may act as an important mediator in this association.

An Update on Screening Strategies for Gestational Diabetes Mellitus: A Narrative Review

Gestational diabetes mellitus (GDM) is a frequent medical complication during pregnancy. Screening and diagnostic practices for GDM are inconsistent across the world. This narrative review includes data from 87 observational studies and randomized controlled trials (RCTs), and aims to give an overview of the current evidence on screening strategies and diagnostic criteria for GDM. Screening in early pregnancy remains controversial and studies show conflicting results on the benefit of screening and treatment of GDM in early pregnancy. Implementing the one-step "International Association of Diabetes and Pregnancy Study Groups" (IADPSG) screening strategy at 24-28 weeks often leads to a substantial increase in the prevalence of GDM, without conclusive evidence regarding the benefits on pregnancy outcomes compared to a two-step screening strategy with a glucose challenge test (GCT). In addition, RCTs are needed to investigate the impact of treatment of GDM diagnosed with IADPSG criteria on long-term maternal and childhood outcomes. Selective screening using a risk-factor-based approach could be helpful in simplifying the screening algorithm but carries the risk of missing significant proportions of GDM cases. A two-step screening method with a 50g GCT and subsequently a 75g oral glucose tolerance test (OGTT) with IADPSG could be an alternative to reduce the need for an OGTT. However, to have an acceptable sensitivity to screen for GDM with the IADPSG criteria, the threshold of the GCT should be lowered from 7.8 to 7.2 mmol/L. A pragmatic approach to screen for GDM can be implemented during the COVID-19 pandemic, using fasting plasma glucose (FPG), HbA1c or even random plasma glucose (RPG) to reduce the number of OGTTs needed. However, usual guidelines and care should be resumed as soon as the COVID pandemic is controlled.

Early pregnancy metabolites predict gestational diabetes mellitus: implications for fetal programming

BACKGROUND

Aberrant fetal programming in gestational diabetes mellitus seems to increase the risk of obesity, type 2 diabetes, and cardiovascular disease. The inability to accurately identify gestational diabetes mellitus in the first trimester of pregnancy has thwarted ascertaining whether early therapeutic interventions reduce the predisposition to these prevalent medical disorders.

OBJECTIVE

A metabolomics study was conducted to determine whether advanced analytical methods could identify accurate predictors of gestational diabetes mellitus in early pregnancy.

STUDY DESIGN

This nested observational case-control study was composed of 92 gravidas (46 in the gestational diabetes mellitus group and 46 in the control group) in early pregnancy, who were matched by maternal age, body mass index, and gestational age at urine collection. Gestational diabetes mellitus was diagnosed according to community standards. A comprehensive metabolomics platform measured 626 endogenous metabolites in randomly collected urine. Consensus multivariate criteria or the most important by 1 method identified low-molecular weight metabolites independently associated with gestational diabetes mellitus, and a classification tree selected a subset most predictive of gestational diabetes mellitus.

RESULTS

Urine for both groups was collected at a mean gestational age of 12 weeks (range, 6-19 weeks' gestation). Consensus multivariate analysis identified 11 metabolites independently linked to gestational diabetes mellitus. Classification tree analysis selected a 7-metabolite subset that predicted gestational diabetes mellitus with an accuracy of 96.7%, independent of maternal age, body mass index, and time of urine collection.

CONCLUSION

Validation of this high-accuracy model by a larger study is now needed to support future studies to determine whether therapeutic interventions in the first trimester of pregnancy for gestational diabetes mellitus reduce short- and long-term morbidity.

Longitudinal Assessment of Relationships Between Health Behaviors and IL-6 in Overweight and Obese Pregnancy

BACKGROUND

Inflammation is a common factor in adverse pregnancy outcomes (APOs). Behavioral factors influence inflammatory markers and APOs but rarely have been investigated simultaneously in pregnancy. Our purpose was to determine how diet, physical activity, and obesity are associated with interleukin (IL)-6 in early and late pregnancy.

METHODS

We conducted a secondary analysis of 49 overweight/obese pregnant women. Health behavior data, including diet quality using the Dietary Inflammatory Index (DII®); physical activity (Leisure Time Physical Activity scale); body mass index (BMI); and plasma IL-6 concentrations were collected at 13-16 weeks (early pregnancy) and 34-36 weeks (late pregnancy) gestation. Multiple linear regression analyses were used to determine the amount of variance explained in early and late pregnancy IL-6 concentrations by early and late pregnancy diet, physical activity, and BMI.

RESULTS

Early diet and early BMI were the strongest predictors of early IL-6 concentrations (R² = 0.43; p < .001) and late IL-6 concentrations (R² = 0.30; p < .001). Late BMI predicted late IL-6 (R² = .11; p = .02). Change in diet over pregnancy predicted late IL-6 (R² = 0.17; p = .03).

CONCLUSION

These findings suggest that maternal diet and BMI in early pregnancy, which likely reflects prepregnancy status, may have a greater impact on inflammatory processes than factors later in pregnancy. Future work should assess if behavioral factors before pregnancy produce similar relationships to those reported here, which may clarify the timing and type of lifestyle interventions to effectively reduce APOs.

Inflammatory and Adipokine Status from Early to Midpregnancy in Arab Women and Its Associations with Gestational Diabetes Mellitus

Objective

To examine differences in maternal serum levels of adipokines (adiponectin, leptin, and resistin) and inflammatory markers (tumor necrosis factor-alpha (TNF-α) and interlukin-6 (IL-6)) from early to midpregnancy among Arab women with or without gestational diabetes mellitus (GDM), along with their links to GDM risk.

Methods

This is a multicenter prospective study involving 232 Saudi women attending obstetric care. Both circulating adipokine and markers of inflammation were observed at the first (eight to 12 weeks) and second trimesters (24 to 28 weeks). GDM was screened at 24 to 28 weeks using the International Association of the Diabetes and Pregnancy Study Groups (IADPSG) criteria.

Results

Age and body mass index- (BMI-) matched circulating TNF-α was significantly higher in women with GDM in comparison to non-GDM women (p = 0.01). Adiponectin and resistin significantly decreased from the first to second trimester in women without GDM (p = 0.002 and 0.026, respectively). Leptin presented a significant rise from the first to second trimester in both groups, with a higher increase in women with GDM (p = 0.013). Multivariate logistic regression analysis revealed that TNF-α was significantly correlated with GDM (p = 0.03). However, significance was lost after adjustments for maternal and lifestyle risk factors (OR 23.58 (0.50 to 1119.98), p = 0.11).

Conclusion

Inflammatory and adipocytokine profiles are altered in Arab women with GDM, TNF-α in particular. Further studies are needed to establish whether maternal inflammatory and adipocytokine profile influence fetal levels in the same manner.

Predictive Value of First-Trimester Glycosylated Hemoglobin Levels in Gestational Diabetes Mellitus: A Chinese Population Cohort Study

This study was aimed at exploring the predictive value of first-trimester glycosylated hemoglobin (HbA1c) levels in the diagnosis of gestational diabetes mellitus (GDM). A total of 744 pregnant women registered at the Peking University International Hospital between March 2017 and March 2019 were included in this study. Data on personal characteristics and biochemical indicators of the pregnant women were collected during the first trimester. The International Association of Diabetes and Pregnancy Study Groups has adopted specific diagnostic criteria as the gold standard for the diagnosis of GDM. Receiver operating characteristic (ROC) curve statistics were used to assess the predictive value of first-trimester HbA1c levels in the diagnosis of GDM. HbA1c levels in the first trimester were significantly higher in the GDM group than in the non-GDM group (5.23% ± 0.29% vs. 5.06 ± 0.28%, P < 0.05). The first-trimester HbA1c level was an independent risk factor for gestational diabetes. The area under the ROC curve (AUC) of HbA1c for GDM was 0.655 (95% confidence interval 0.620-0.689, P < 0.001). The positive likelihood ratio was the highest at HbA1c = 5.9%, sensitivity was 2.78, and specificity was 99.83%. There was no statistical difference in AUC between fasting blood glucose and HbA1c (P = 0.407). First-trimester HbA1c levels can be used to predict GDM. The risk of GDM was significantly increased in pregnant women with first-trimester HbA1c levels > 5.9%. There was no statistical difference between first-trimester HbA1c and fasting blood glucose levels in predicting GDM.

CircRNA circVEGFC is Highly Expressed in Gestational Diabetes Mellitus (GDM) and It is Correlated with Multiple Adverse Events

BACKGROUND

Circular RNA vascular endothelial growth factor C (circVEGFC) is a novel regulator of glucose metabolism, while its role in gestational diabetes mellitus (GDM) is unclear. This study aimed to detect the expression of circVEGFC in GDM and explore its clinical values.

METHODS

This study enrolled 220 pregnant women (gestational age less than 5 weeks) with normal blood glucose level on the day of admission. The expression of circVEGFC in plasma samples of these participants was determined by RT-qPCR. The participants were divided into high and low circVEGFC level groups with the median expression level of plasma circVEGFC as the cutoff value. The development of GDM was monitored until delivery. Adverse events were also monitored.

RESULTS

Compared to low circVEGFC level group, GDM-free curve analysis revealed significantly higher incidence of GDM in high circVEGFC level group. In addition, plasma expression levels of circVEGFC were also higher in GDM patients than that in non-GDM patients on the day of admission and at 1 month before and after delivery. ROC curve analysis revealed that high expression levels of circVEGFC on the day of admission showed higher sensitivity and specificity in the early diagnosis of GDM. Moreover, high circVEGFC level group showed higher incidence rates of fetal malformation and hypertension.

CONCLUSION

Therefore, circVEGFC is highly expressed in GDM, and it is correlated with multiple adverse events.

The Role(s) of Eicosanoids and Exosomes in Human Parturition

The roles that eicosanoids play during pregnancy and parturition are crucial to a successful outcome. A better understanding of the regulation of eicosanoid production and the roles played by the various end products during pregnancy and parturition has led to our view that accurate measurements of a panel of those end products has exciting potential as diagnostics and prognostics of preterm labor and delivery. Exosomes and their contents represent an exciting new area for research of movement of key biological factors circulating between tissues and organs akin to a parallel endocrine system but involving key intracellular mediators. Eicosanoids and enzymes regulating their biosynthesis and metabolism as well as regulatory microRNAs have been identified within exosomes. In this review, the regulation of eicosanoid production, abundance and actions during pregnancy will be explored. Additionally, the functional significance of placental exosomes will be discussed.

First trimester secreted Frizzled-Related Protein 4 and other adipokine serum concentrations in women developing gestational diabetes mellitus

Background

The aim of this study was to evaluate whether soluble frizzled-related protein 4 (sFRP4) concentration in the first trimester of pregnancy is individually, or in combination with Leptin, Chemerin and/or Adiponectin, associated with the development of gestational diabetes (GDM).

Methods

In a nested case-control study, 50 women with GDM who spontaneously conceived and delivered a live-born infant were matched with a total of 100 uncomplicated singleton control pregnancies based on body mass index (± 2 kg/m²), gestational age at sampling (exact day) and maternal age (± 2 years). In serum samples, obtained between 70–90 days gestational age, sFRP4, Chemerin, Leptin and Adiponectin concentrations were determined by ELISA. Statistical comparisons were performed using univariate and multi-variate logistic regression analysis after logarithmic transformation of the concentrations. Discrimination of the models was assessed by the area under the curve (AUC).

Results

First trimester sFRP4 concentrations were significantly increased in GDM cases (2.04 vs 1.93 ng/ml; p<0.05), just as Chemerin (3.19 vs 3.15 ng/ml; p<0.05) and Leptin (1.44 vs 1.32 ng/ml; p<0.01). Adiponectin concentrations were significantly decreased (2.83 vs 2.94 ng/ml; p<0.01) in GDM cases. Further analysis only showed a weak, though significant, correlation of sFRP4 with Chemerin (R² = 0.124; p<0.001) and Leptin (R² = 0.145; p<0.001), and Chemerin with Leptin (R² = 0.282; p<0.001) in the control group. In a multivariate logistic regression model of these four markers, only Adiponectin showed to be significantly associated with GDM (odds ratio 0.12, 95%CI 0.02–0.68). The AUC of this model was 0.699 (95%CI 0.605–0.793).

Conclusion

In the first trimester of pregnancy, a multi-marker model with sFRP4, Leptin, Chemerin and Adiponectin is associated with the development of GDM. Therefore, this panel seems to be an interesting candidate to further evaluate for prediction of GDM in a prospective study.

Comparison of Effectiveness of Routine Antenatal Care with a Midwife-Managed Clinic Service in Prevention of Gestational Diabetes Mellitus in Early Pregnancy at a Hospital in China

Background

Women with normal fasting glucose (FPG) range (5.1 ≤FPG <6.09 mmol/L) in early pregnancy are at high risk of gestational diabetes mellitus (GDM). The aim of this study was to compare the effectiveness of routine antenatal care with a midwife-managed clinic service in the prevention of GDM in early pregnancy at a hospital in China.

Material/Methods

We designed a prospective observational clinical study among pregnancy women with normal fasting glucose (FPG) range (5.1 ≤FPG <6.09 mmol/L) in early pregnancy. Routine antenatal care was compared with a midwife-managed clinic service providing diet and exercise education before week 16. A 75-g OGTT was performed at weeks 24–28 for both groups. Results of OGTT and gestational weight gain were compared between the 2 groups.

Results

Of the 592 eligible women, 296 women received the antenatal nursing clinic service and 296 were enrolled in a control group. Thirty-three women were lost to follow-up during the study, leaving 279 in the intervention group and 280 in the control group. Baseline demographic characteristics were similar between the 2 groups. GDM was diagnosed in 115 participants (41.2%) in the intervention group and 141 (50.4%) in the control group. Subgroup analysis showed a significantly lower rate of GDM in the intervention group among the No-IVF population (37.8% vs. 49.0%, P=0.01%). For pre-pregnancy BMI, significant differences were found in the incidence of GDM and maternal hypertension between the different groups, showing that the overweight group benefited most from the midwife-managed antenatal clinic service.

Conclusions

The midwife-managed clinic service was feasible and effective in the prevention of GDM.

Prognostic models versus single risk factor approach in first-trimester selective screening for gestational diabetes mellitus: a prospective population-based multicentre cohort study

Objectives

To evaluate whether (1) first‐trimester prognostic models for gestational diabetes mellitus (GDM) outperform the currently used single risk factor approach, and (2) a first‐trimester random venous glucose measurement improves model performance.

Design

Prospective population‐based multicentre cohort.

Setting

Thirty‐one independent midwifery practices and six hospitals in the Netherlands.

Population

Women recruited before 14 weeks of gestation without pre‐existing diabetes.

Methods

The single risk factor approach (presence of at least one risk factor: BMI ≥30 kg/m², previous macrosomia, history of GDM, positive first‐degree family history of diabetes, non‐western ethnicity) was compared with the four best performing models in our previously published external validation study (Gabbay‐Benziv 2014, Nanda 2011, Teede 2011, van Leeuwen 2010) with and without the addition of glucose.

Main outcome measures

Discrimination was assessed by c‐statistics, calibration by calibration plots, added value of glucose by the likelihood ratio chi‐square test, net benefit by decision curve analysis and reclassification by reclassification plots.

Results

Of the 3723 women included, a total of 181 (4.9%) developed GDM. The c‐statistics of the prognostic models were higher, ranging from 0.74 to 0.78 without glucose and from 0.78 to 0.80 with glucose, compared with the single risk factor approach (0.72). Models showed adequate calibration, and yielded a higher net benefit than the single risk factor approach for most threshold probabilities. Teede 2011 performed best in the reclassification analysis.

Conclusions

First‐trimester prognostic models seem to outperform the currently used single risk factor approach in screening for GDM, particularly when glucose was added as a predictor.

Tweetable abstract

Prognostic models seem to outperform the currently used single risk factor approach in screening for gestational diabetes.

Prognostic models seem to outperform the currently used single risk factor approach in screening for gestational diabetes.

Hemoglobin A1c and Early Gestational Diabetes

Background: Screening for diabetes in early pregnancy is recommended for high-risk women, however, the optimal test for the diagnosis of early gestational diabetes mellitus (GDM) is unknown. Thus, the objective of this study was to evaluate hemoglobin A_1c (HbA_1c) as a diagnostic test for early GDM compared with two-step testing. Materials and Methods: Retrospective cohort of women with prior GDM or obesity who had HbA_1c and two-step testing <21 weeks' gestation. Early GDM was diagnosed by 1 hour, 50 g oral glucose challenge test (GCT) ≥135 mg/dL and ≥2 abnormal values on 3 hour, 100 g oral glucose tolerance test or GCT >200 mg/dL. The area under the receiver operating characteristic curve (AUC) evaluated HbA_1c for diagnosis of early GDM. Results: Of 243 women, 14 (5.8%) had early GDM by two-step testing. Median HbA_1c levels were higher among women with GDM versus those without GDM (5.8% vs. 5.3%, p < 0.001). The AUC for HbA_1c compared with two-step testing was 0.80 (95% CI 0.69-0.91). The optimal HbA_1c threshold was 5.6% (64% sensitivity, 84% specificity). Conclusions: HbA_1c is moderately predictive of early GDM compared with two-step testing, and a threshold lower than that used for diabetes diagnosis among nonpregnant adults is justified.

Placental Regulation of Energy Homeostasis During Human Pregnancy

Successful pregnancies rely on sufficient energy and nutrient supply, which require the mother to metabolically adapt to support fetal needs. The placenta has a critical role in this process, as this specialized organ produces hormones and peptides that regulate fetal and maternal metabolism. The ability for the mother to metabolically adapt to support the fetus depends on maternal prepregnancy health. Two-thirds of pregnancies in the United States involve obese or overweight women at the time of conception. This poses significant risks for the infant and mother by disrupting metabolic changes that would normally occur during pregnancy. Despite well characterized functions of placental hormones, there is scarce knowledge surrounding placental endocrine regulation of maternal metabolic trends in pathological pregnancies. In this review, we discuss current efforts to close this gap of knowledge and highlight areas where more research is needed. As the intrauterine environment predetermines the health and wellbeing of the offspring in later life, adequate metabolic control is essential for a successful pregnancy outcome. Understanding how placental hormones contribute to aberrant metabolic adaptations in pathological pregnancies may unveil disease mechanisms and provide methods for better identification and treatment. Studies discussed in this review were identified through PubMed searches between the years of 1966 to the present. We investigated studies of normal pregnancy and metabolic disorders in pregnancy that focused on energy requirements during pregnancy, endocrine regulation of glucose metabolism and insulin resistance, cholesterol and lipid metabolism, and placental hormone regulation.

Exosomes as Messengers Between Mother and Fetus in Pregnancy

The ability of exosomes to transport different molecular cargoes and their ability to influence various physiological factors is already well known. An exciting area of research explores the functions of exosomes in healthy and pathological pregnancies. Placenta-derived exosomes were identified in the maternal circulation during pregnancy and their contribution in the crosstalk between mother and fetus are now starting to become defined. In this review, we will try to summarize actual knowledge about this topic and to answer the question of how important exosomes are for a healthy pregnancy.

Placental exosomes isolated from urine of patients with gestational diabetes exhibit a differential profile expression of microRNAs across gestation

Placenta‑derived exosomes play an important role in cellular communication both in the mother and the fetus. Their concentration and composition are altered in several pregnancy disorders, such as gestational diabetes mellitus (GDM). The isolation and characterization of placental exosomes from serum, plasma and tissues from patients with GDM have been previously described; however, to the best of our knowledge, to date, there is no study available on placental exosomes isolated from urine of patients with GDM. In the present study, placental exosomes were purified from urine the 1st, 2nd and 3rd trimester of gestation. Placental exosomes were characterized by transmission electron microscopy in cryogenic mode and by western blot analysis, confirming the presence of exosomal vesicles. The expression profile of five microRNAs (miR‑516‑5p, miR‑517‑3p, miR‑518‑5p, miR‑222‑3p and miR‑16‑5p) was determined by RT‑qPCR. In healthy pregnant women, the expression of the miRNAs increased across gestation, apart from miR‑516‑5p, which was not expressed at the 2nd trimester. All the miRNAs examined were downregulated in patients with GDM at the 3rd trimester of gestation. The downregulated miRNAs affected several metabolic pathways closely associated with the pathophysiology of GDM. This provides further evidence of the regulatory role of miRNAs in the GDM. This also suggests that the of urinary exosomes may be an excellent source of biomarkers and therapeutic targets.

Elevated Glucose and Insulin Levels Decrease DHA Transfer across Human Trophoblasts via SIRT1-Dependent Mechanism

Gestational diabetes mellitus (GDM) results in reduced docosahexaenoic acid (DHA) transfer to the fetus, likely due to placental dysfunction. Sirtuin-1 (SIRT1) is a nutrient sensor and regulator of lipid metabolism. This study investigated whether the high glucose and insulin condition of GDM regulates DHA transfer and expression of fatty acid transporters and if this effect is related to SIRT1 expression and function. Syncytialized primary human trophoblasts were treated with and without glucose (25 mmol/L) and insulin (10^-7 mol/L) for 72 h to mimic the insulin-resistance conditions of GDM pregnancies. In control conditions, DHA transfer across trophoblasts increased in a time- and dose-dependent manner. Exposure to GDM conditions significantly decreased DHA transfer, but increased triglyceride accumulation and fatty acid transporter expression (CD36, FABP3, and FABP4). GDM conditions significantly suppressed SIRT1 mRNA and protein expression. The SIRT1 inhibitor decreased DHA transfer across control trophoblasts, and recombinant SIRT1 and SIRT1 activators restored the decreased DHA transport induced by GDM conditions. The results demonstrate a novel role of SIRT1 in the regulation of DHA transfer across trophoblasts. The suppressed SIRT1 expression and the resultant decrease in placental DHA transfer caused by high glucose and insulin levels suggest new insights of molecular mechanisms linking GDM to fetal DHA deficiency.

Gestational Diabetes Mellitus-Innovative Approach to Prediction, Diagnosis, Management, and Prevention of Future NCD-Mother and Offspring

Gestational diabetes mellitus (GDM) is the commonest medical complication of pregnancy. The association of GDM with immediate pregnancy complications including excess fetal growth and adiposity with subsequent risk of birth trauma and with hypertensive disorders of pregnancy is well recognized. However, the associations with wide ranges of longer-term health outcomes for mother and baby, including the lifetime risks of obesity, pre-diabetes, and diabetes and cardiovascular disease have received less attention and few health systems address these important issues in a systematic way. This article reviews historical and recent data regarding prediction of GDM using demographic, clinical, and biochemical parameters. We evaluate current and potential future diagnostic approaches designed to most effectively identify GDM and extend this analysis into a critical evaluation of lifestyle and nutritional/pharmacologic interventions designed to prevent the development of GDM. The general approach to management of GDM during pregnancy is then discussed and the major final focus of the article revolves around the importance of a GDM diagnosis as a future marker of the risk of non-communicable disease (NCD), in particular pre-diabetes, diabetes, and cardiovascular disease, both in mother and offspring.

Gestational Diabetes Mellitus Screening and Diagnosis

An ideal screening test for gestational diabetes should be capable of identifying not only women with the disease but also the women with a high risk of developing gestational diabetes mellitus (GDM). Screening and diagnosis are the main steps leading to the way of management. There is a lack of consensus among healthcare professionals regarding the screening methods worldwide. Different study groups advocate a variety of screening methods with the support of evidence-based comprehensive data. Some of the organizations suggest screening for high risk or all pregnant women, while others prefer to offer definitive testing without screening. Glycemic thresholds are also not standardized to decide GDM among different guidelines. Prevalence rates of GDM vary between populations and with the choice of glucose thresholds for both screening and definitive tests. One-step or two-step methods have been used for GDM diagnosis. However, screening includes selecting patients with historical risk factors, 50 g 1-h glucose challenge test, fasting plasma glucose, random plasma glucose, and hemoglobin A1c with different cutoffs. In this chapter, screening and diagnosis methods of GDM accepted by different study groups will be discussed which will be followed by the evaluation of different glycemic thresholds. Then the advantages and disadvantages of used methods will be explained and the chapter will finish with an evaluation of the current international guidelines.

Pregnancy-associated plasma protein-A2 levels are increased in early-pregnancy gestational diabetes: a novel biomarker for early risk estimation

AIM

To determine whether pregnancy-associated plasma protein-A2 levels are increased in early pregnancies complicated by gestational diabetes and whether gestation age influences levels. The possible use of pregnancy-associated plasma protein-A2 as a pre-screening biomarker to reduce the need for performing oral glucose tolerance tests in pregnant women was also investigated.

METHODS

Pregnant women were diagnosed with gestational diabetes in early pregnancy after a 2-hour 75 g oral glucose tolerance test in the catchment area of Skåne University Hospital, Lund, Sweden during 2011-2015 (n = 99). Age- and BMI-matched pregnant women without diabetes were recruited at similar gestational ages from maternal healthcare centres in the same geographical area during 2014-2015 to act as controls (n = 100). Circulating pregnancy-associated plasma protein-A2 was analysed in participant serum using commercially available enzyme-linked immunosorbent assay kits.

RESULTS

Circulating pregnancy-associated plasma protein-A2 was increased in women diagnosed with gestational diabetes [13.5 (9.58-18.8) ng/ml] compared with controls [8.11 (5.74-11.3) ng/ml; P < 0.001]. Pregnancy-associated plasma protein-A2 was associated with gestational diabetes independent of age, BMI, C-peptide and adiponectin (P < 0.001). Pregnancy-associated plasma protein-A2 as a pre-screening biomarker to identify women at a decreased risk of gestational diabetes resulted in a negative predictive value of 99.7%, with a sensitivity of 96% and a specificity of 30% at a cut-off level of 6 ng/ml.

CONCLUSIONS

This is the first study to show increased pregnancy-associated plasma protein-A2 levels in gestational diabetes. Pregnancy-associated plasma protein-A2 also shows promise as a pre-screening biomarker with the potential to reduce the need for performing oral glucose tolerance tests in early pregnancy. Future prospective cohort studies in a larger group of both high- and low-risk women are, however, needed to further confirm this observation.